Out-of-Memory Error in Multi-GPU Distributed with Torch and Hugging Face Trainer

username: enterthevoidf22
cooked: <h3><a name="p-460292-problem-description-1" class="anchor" href="#p-460292-problem-description-1"></a>Problem Description:</h3>
<p>I’m encountering an out-of-memory (OOM) error while running a PyTorch-based distributed training script with multi-GPU using <code>torch.distributed.launch</code>. The model is too large for a single GPU, so I use multi-GPU training to distribute the load. However, when computing gradients on specific inputs (e.g., <code>aux</code> tensor), the memory usage increases significantly, leading to OOM.</p>
<p>To isolate the issue, I created a reproducible example using a smaller model (BERT for sequence classification). While this toy example fits in memory even in distributed mode, my actual model doesn’t, despite seemingly similar setups. Moreover, if I disable gradient computation (<code>aux.requires_grad_(False)</code>), the real model works fine.</p>
<p>I suspect that either:</p>
<ol>
<li>Gradient computation is significantly increasing the memory footprint in distributed mode.</li>
<li>There might be inefficiencies in how gradients are handled across GPUs.</li>
</ol>
<p>Below is the reproducible example code and the steps to reproduce the issue.</p>
<hr>
<h3><a name="p-460292-code-to-reproduce-2" class="anchor" href="#p-460292-code-to-reproduce-2"></a>Code to Reproduce:</h3>
<p>python</p>
<p>Copy code</p>
<pre><code class="lang-auto">import torch
from torch import nn
from transformers import BertForSequenceClassification, BertTokenizer, Trainer, TrainingArguments
from torch.utils.data import Dataset, DataLoader
from tqdm import tqdm


class ExampleDataset(Dataset):
    def __init__(self, tokenizer, size=10000, max_length=128):
        self.tokenizer = tokenizer
        self.texts = [f"This is example text {i}" for i in range(size)]
        self.labels = torch.randint(0, 2, (size,))  # Binary classification
        self.max_length = max_length

    def __len__(self):
        return len(self.texts)

    def __getitem__(self, idx):
        tokenized = self.tokenizer(
            self.texts[idx],
            padding="max_length",
            truncation=True,
            max_length=self.max_length,
            return_tensors="pt",
        )
        input_ids = tokenized["input_ids"].squeeze(0)
        aux = torch.rand((5,)) * 100  # Random auxiliary input

        return {
            "aux": aux,
            "input_ids": input_ids,
            "attention_mask": tokenized["attention_mask"].squeeze(0),
            "labels": self.labels[idx],
        }


class CustomModel(nn.Module):
    def __init__(self, base_model_name="bert-base-uncased", num_labels=2):
        super(CustomModel, self).__init__()
        self.bert = BertForSequenceClassification.from_pretrained(base_model_name, num_labels=num_labels)
        self.embedding_dim = self.bert.config.hidden_size
        self.aux_linear = nn.Linear(5, self.embedding_dim)

    def forward(self, input_ids, attention_mask, aux, labels=None):
        aux_embedded = self.aux_linear(aux)  # Embed auxiliary input
        input_embeddings = self.bert.bert.embeddings(input_ids)
        input_embeddings[:, 0, :] += aux_embedded  # Modify embeddings

        outputs = self.bert(
            inputs_embeds=input_embeddings,
            attention_mask=attention_mask,
            labels=labels,
        )
        return outputs


def compute_saliency_maps(trainer, loader, device, repeat_factor=1000):
    model = trainer.model
    model.eval()

    for _ in range(repeat_factor):
        for batch in tqdm(loader, desc="Computing Saliency Maps"):
            batch = {key: value.to(device) if isinstance(value, torch.Tensor) else value for key, value in batch.items()}
            batch["aux"].requires_grad_(True)  # Enable gradients for aux

            outputs = model(
                input_ids=batch["input_ids"],
                attention_mask=batch["attention_mask"],
                aux=batch["aux"],
                labels=batch["labels"]
            )
            loss = outputs.loss
            loss.backward()  # Compute gradients
            grads = batch["aux"].grad
            print(f"Gradient norm: {grads.norm().item()}")


if __name__ == "__main__":
    tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
    model = CustomModel()

    dataset = ExampleDataset(tokenizer, size=200)
    loader = DataLoader(dataset, batch_size=1000)

    training_args = TrainingArguments(
        output_dir="./results",
        per_device_eval_batch_size=8,
        do_train=False,
        do_eval=True,
    )
    trainer = Trainer(
        model=model,
        args=training_args,
        eval_dataset=dataset,
    )

    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.to(device)
    compute_saliency_maps(trainer, loader, device, repeat_factor=3)
</code></pre>
<hr>
<h3><a name="p-460292-steps-to-reproduce-3" class="anchor" href="#p-460292-steps-to-reproduce-3"></a>Steps to Reproduce:</h3>
<ol>
<li>Run the script in single-GPU mode:</li>
</ol>
<p>bash</p>
<pre><code class="lang-auto">python debug_playground.py
</code></pre>
<p>This works as expected, but for my real model, it causes an OOM error.<br>
2. Run in multi-GPU mode with <code>torch.distributed.launch</code>:</p>
<p>bash</p>
<pre><code class="lang-auto">python -m torch.distributed.launch --nproc_per_node=8 debug_playground.py
</code></pre>
<ol start="3">
<li>Observe the GPU memory usage (<code>nvidia-smi</code>).</li>
</ol>
<hr>
<h3><a name="p-460292-observations-4" class="anchor" href="#p-460292-observations-4"></a>Observations:</h3>
<ul>
<li><strong>Single-GPU mode:</strong> OOM occurs with my real model, as expected for large parameter counts.</li>
<li><strong>Multi-GPU mode:</strong> The toy example works, but my real model still OOMs. Gradient computation for <code>aux</code> seems to cause a drastic increase in memory usage.</li>
<li><strong>Without gradients for <code>aux</code>:</strong> The real model runs without issues in multi-GPU mode.</li>
</ul>
<hr>
<h3><a name="p-460292-questions-5" class="anchor" href="#p-460292-questions-5"></a>Questions:</h3>
<ol>
<li>Why does enabling gradients for the <code>aux</code> tensor cause such a significant memory increase in distributed mode?</li>
<li>is there a way to distribute the gradients still in this setup?</li>
</ol>
<hr>
<p>Any insights or suggestions would be greatly appreciated!</p>