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Reduce base_train batch size and set PYTORCH_HIP_ALLOC_CONF

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To address "HIP out of memory" errors on some AMD ROCm configurations (potentially due to memory fragmentation or limited per-device VRAM), this change:
1. Reduces the default `device_batch_size` from 32 to 16.
2. Explicitly sets `PYTORCH_HIP_ALLOC_CONF=expandable_segments:True` when ROCm is detected, which helps the allocator manage fragmented memory better than the default behavior.
This commit is contained in:
google-labs-jules[bot] 2025-11-23 16:03:02 +00:00
parent 41ba458c3b
commit bbc816dc77
1 changed files with 4 additions and 1 deletions
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scripts/base_train.py
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@ -19,6 +19,9 @@ import wandb
import torch
if torch.cuda.is_available() or (hasattr(torch.version, 'hip') and torch.version.hip):
os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True"
# Also set the HIP-specific env var if on ROCm, as suggested by OOM errors
if hasattr(torch.version, 'hip') and torch.version.hip:
os.environ["PYTORCH_HIP_ALLOC_CONF"] = "expandable_segments:True"
from nanochat.gpt import GPT, GPTConfig
from nanochat.dataloader import tokenizing_distributed_data_loader, tokenizing_distributed_data_loader_with_state
@ -43,7 +46,7 @@ num_iterations = -1 # explicit number of steps of the optimization (-1 = disable
target_flops = -1.0 # calculate num_iterations to reach target_flops. Useful for scaling laws experiments (-1 = disable)
target_param_data_ratio = 20 # calculate num_iterations to maintain fixed data:param ratio (Chinchilla=20) (-1 = disable)
# Optimization
device_batch_size = 32 # per-device batch size (set to not OOM)
device_batch_size = 16 # per-device batch size (reduced from 32 to avoid OOM on some GPUs)
total_batch_size = 524288 # total desired batch size, in #tokens
embedding_lr = 0.2 # learning rate for the embedding parameters (Adam)
unembedding_lr = 0.004 # learning rate for the unembedding parameters (Adam)