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# dependencies = [
# "trl",
# "peft",
# "datasets",
# "transformers",
# "accelerate",
# "torch",
# "deepspeed",
# "mpi4py"
# ]
# ///
import time
from transformers import TrainerCallback
class SpeedCallback(TrainerCallback):
def __init__(self):
self.last_time = None
def on_step_begin(self, args, state, control, **kwargs):
self.last_time = time.time()
def on_step_end(self, args, state, control, **kwargs):
if self.last_time is None:
return
elapsed = time.time() - self.last_time
remaining = max(0, state.max_steps - state.global_step)
eta_min = remaining * elapsed / 60
print(
f"[speed] step {state.global_step}/{state.max_steps} | "
f"{elapsed:.2f}s/step | ETA {eta_min:.1f} min",
flush=True,
)
import inspect
import datasets
import trl.experimental.gold as gold
from transformers import AutoTokenizer
# -----------------------------
# Models
# -----------------------------
STUDENT_MODEL = "Qwen/Qwen2.5-0.5B-Instruct"
TEACHER_MODEL = "Qwen/Qwen2.5-Coder-1.5B-Instruct"
OUTPUT_DIR = "gold-code-deepspeed-test"
# -----------------------------
#
# If ZeRO-3 is painfully slow, try this instead:
DS_CONFIG = {
"zero_optimization": {
"stage": 2,
"overlap_comm": True,
"contiguous_gradients": True,
},
"bf16": {
"enabled": True,
},
"train_micro_batch_size_per_gpu": "auto",
"gradient_accumulation_steps": "auto",
"gradient_clipping": "auto",
}
# -----------------------------
# Dataset
# -----------------------------
def to_messages(example):
description = str(example.get("description", "")).strip()
if not description:
description = str(example)
# Keep prompts short at first. code_contests descriptions can be long.
description = description[:1500]
return {
"messages": [
{
"role": "system",
"content": (
"You are a careful competitive programming assistant. "
"Return only the final correct solution code. "
"Do not include markdown or explanations."
),
},
{
"role": "user",
"content": (
"Solve this programming problem:\n\n"
f"{description}"
),
},
]
}
def main():
print("Loading tokenizer...")
tokenizer = AutoTokenizer.from_pretrained(
STUDENT_MODEL,
trust_remote_code=True,
)
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
if tokenizer.pad_token_id is None:
tokenizer.pad_token_id = tokenizer.eos_token_id
print("Loading dataset...")
raw = datasets.load_dataset(
"deepmind/code_contests",
split="train[:10000]",
)
print("Raw columns:", raw.column_names)
train_dataset = raw.map(
to_messages,
remove_columns=raw.column_names,
)
print("Processed example:")
print(train_dataset[0])
config = gold.GOLDConfig(
output_dir=OUTPUT_DIR,
# GOLD generation settings
temperature=0.8,
top_p=0.95,
max_length=512,
disable_tqdm=True,
# Training settings
max_steps=1000,
per_device_train_batch_size=1,
gradient_accumulation_steps=4,
learning_rate=5e-6,
model_init_kwargs={
"torch_dtype": "bfloat16",
"attn_implementation": "sdpa",
},
# Logging/saving
logging_steps=10,
save_steps=100,
report_to="none",
# Precision
bf16=True,
hub_model_id="moos124/gold-code-deepspeed-testV2",
push_to_hub=True,
# DeepSpeed
deepspeed=DS_CONFIG,
)
# TRL versions differ: some use processing_class, some older ones use tokenizer.
trainer_kwargs = {
"model": STUDENT_MODEL,
"teacher_model": TEACHER_MODEL,
"args": config,
"train_dataset": train_dataset,
}
signature = inspect.signature(gold.GOLDTrainer)
if "processing_class" in signature.parameters:
trainer_kwargs["processing_class"] = tokenizer
elif "tokenizer" in signature.parameters:
trainer_kwargs["tokenizer"] = tokenizer
else:
print("Warning: GOLDTrainer signature has no processing_class/tokenizer parameter.")
print("Building GOLDTrainer...")
trainer = gold.GOLDTrainer(**trainer_kwargs)
trainer.add_callback(SpeedCallback())
print("Training...")
trainer.train()
print("Saving...")
trainer.save_model(OUTPUT_DIR)
# Optional push
trainer.push_to_hub()
if __name__ == "__main__":
main() |