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fix checkpoint naming; add num_epoch parameter (#6277)

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YeAnbang 2025-04-26 14:00:28 +08:00 committed by GitHub
parent 26d859f68e
commit 38008858e4
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2 changed files with 3 additions and 2 deletions
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2
applications/ColossalChat/coati/distributed/consumer.py
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@ -130,7 +130,7 @@ class BaseConsumer:
if (step + 1) % self.save_interval == 0 or (step + 1) == self.num_update_per_episode:
if self.rank == 0:
print(f"Start saving policy model at step {step + 1}.")
save_path = os.path.join(self.save_dir, f"modeling-step-{step + 1}")
save_path = os.path.join(self.save_dir, f"modeling-episode-{episode}-step-{step + 1}")
self.booster.save_model(self.policy_model, save_path, shard=True)
if self.rank == 0:
print(f"Saved model checkpoint at step {step + 1} in folder {save_path}")

3
applications/ColossalChat/rl_example.py
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@ -10,6 +10,7 @@ if __name__ == "__main__":
parser.add_argument("-m", "--model", type=str, default="Qwen/Qwen2.5-7B")
parser.add_argument("-d", "--dataset", type=str, default="data.jsonl")
parser.add_argument("-p", "--project", type=str, default="GRPO", help="Project name.")
parser.add_argument("-e", "--num-episodes", type=int, default=1, help="Number of episodes to train.")
# Distributed training parameters
parser.add_argument("-t", "--num-trainers", type=int, default=2)
@ -192,7 +193,7 @@ if __name__ == "__main__":
num_producers=args.num_inferencer,
num_proc_per_producer=inference_model_config.get("tensor_parallel_size", 1),
num_consumer_procs=args.num_trainers,
num_episodes=1,
num_episodes=args.num_episodes,
inference_batch_size=args.inference_batch_size,
inference_microbatch_size=args.inference_microbatch_size,
train_batch_size=args.train_batch_size,