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clear cuda cache to help with memory leak/creep #1858

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merged 2 commits into from
Aug 26, 2024
Merged

clear cuda cache to help with memory leak/creep #1858

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e8d47e5
clear cuda cache to help with memory leak/creep
winglian Aug 23, 2024
3b612b1
reverse order of gc
winglian Aug 23, 2024
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12 changes: 11 additions & 1 deletion src/axolotl/core/trainer_builder.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,6 +4,7 @@
"""

import abc
import gc
import importlib
import importlib.util
import logging
Expand All @@ -15,11 +16,12 @@
from dataclasses import dataclass, field
from functools import wraps
from pathlib import Path
from typing import Dict, List, Literal, Optional, Type, Union
from typing import Any, Dict, List, Literal, Optional, Type, Union

import torch
import transformers
from datasets import Dataset
from torch import nn
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import BatchSampler, DataLoader, RandomSampler, SequentialSampler
from transformers import (
Expand Down Expand Up @@ -997,6 +999,14 @@ def tokenize_row(
res[key] = res[key][1:]
return res

def training_step(
self, model: nn.Module, inputs: Dict[str, Union[torch.Tensor, Any]]
) -> torch.Tensor:
loss: torch.Tensor = super().training_step(model, inputs)
torch.cuda.empty_cache()
gc.collect()
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Shouldn't gc.collect be first?
In my experience if there is a true leak (hanging references) empty cache cannot not get rid of leaks

If it helps, I can try and debug where the leak is given the training config

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Thanks. I reordered the calls and added some context from reproduction

return loss


class AxolotlORPOTrainer(SchedulerMixin, ORPOTrainer):
"""
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