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f6e0440 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 | """
Fine-tuning training script for Qwen2.5 on a coding dataset.
Intentionally uses CUDA-specific APIs so ROCmPort AI has meaningful
patterns to detect and patch.
"""
import os
import torch
from torch.utils.data import DataLoader, TensorDataset
from transformers import AutoModelForCausalLM, AutoTokenizer
# ── CUDA-specific patterns that ROCmPort will flag ─────────────────────────
os.environ["CUDA_VISIBLE_DEVICES"] = "0" # should → HIP_VISIBLE_DEVICES
os.environ["CUDA_HOME"] = "/usr/local/cuda" # should be removed / replaced
MODEL_ID = "Qwen/Qwen2.5-0.5B-Instruct"
device = torch.device("cuda") # hardcoded CUDA device
print("CUDA available:", torch.cuda.is_available())
def get_dummy_batch(seq_len: int = 64, batch_size: int = 4):
ids = torch.randint(0, 1000, (batch_size, seq_len))
labels = ids.clone()
return ids, labels
def train(epochs: int = 3, lr: float = 2e-5):
tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
model = AutoModelForCausalLM.from_pretrained(MODEL_ID).cuda() # .cuda() call
optimizer = torch.optim.AdamW(model.parameters(), lr=lr)
ids, labels = get_dummy_batch()
ids = ids.to("cuda") # hardcoded "cuda" string
labels = labels.to("cuda") # hardcoded "cuda" string
dataset = TensorDataset(ids, labels)
loader = DataLoader(dataset, batch_size=2)
model.train()
for epoch in range(epochs):
for batch_ids, batch_labels in loader:
batch_ids = batch_ids.cuda() # another .cuda() call
batch_labels = batch_labels.cuda()
outputs = model(input_ids=batch_ids, labels=batch_labels)
loss = outputs.loss
loss.backward()
optimizer.step()
optimizer.zero_grad()
print(f"Epoch {epoch+1}/{epochs} loss={loss.item():.4f}")
model.save_pretrained("./qwen-finetuned")
tokenizer.save_pretrained("./qwen-finetuned")
print("Model saved to ./qwen-finetuned")
if __name__ == "__main__":
if not torch.cuda.is_available():
raise RuntimeError("CUDA GPU required for training")
train()
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