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50153005b4
* add microbatch forwarding * fix forward microbatch * fix producer OOM * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * change project name * fix temperature annealing * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * address conversation --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
100 lines
3.7 KiB
Python
100 lines
3.7 KiB
Python
import argparse
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import ray
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import torch
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from coati.distributed.launch import launch_distributed
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("-m", "--model", type=str, default="Qwen/Qwen2.5-7B")
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parser.add_argument("-d", "--dataset", type=str, default="data.jsonl")
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parser.add_argument("-t", "--num-trainers", type=int, default=2)
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parser.add_argument("-i", "--num-inferencer", type=int, default=2)
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parser.add_argument("-g", "--num-generations", type=int, default=8)
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parser.add_argument("-ibs", "--inference-batch-size", type=int, default=64)
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parser.add_argument("-imbs", "--inference-microbatch-size", type=int, default=8)
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parser.add_argument("-tbs", "--train-batch-size", type=int, default=32)
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parser.add_argument("-tMbs", "--train-minibatch-size", type=int, default=1)
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parser.add_argument("-tmbs", "--train-microbatch-size", type=int, default=2)
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parser.add_argument("-b", "--backend", type=str, default="transformers")
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parser.add_argument("-a", "--algo", type=str, default="GRPO", choices=["Simple", "GRPO", "EvalGRPO"])
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args = parser.parse_args()
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assert args.train_minibatch_size > 0, "Train mini batch size must be greater than 0"
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assert (
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args.train_minibatch_size * args.num_generations >= args.train_microbatch_size
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and args.train_microbatch_size > 0
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), "Train micro batch size must be greater than 0 less than train mini batch size * num generations"
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ray.init(address="local", namespace="ray-example")
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inference_model_config = dict(path=args.model)
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train_model_config = dict(
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path=args.model,
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# use_flash_attention_2=True,
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# use_cache=False
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)
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generate_config = dict(top_k=50, top_p=0.75, temperature=0.9)
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if args.backend == "transformers":
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inference_model_config.update(
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dict(
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use_flash_attention_2=True,
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torch_dtype=torch.bfloat16,
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)
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)
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generate_config.update(
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dict(
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max_length=1024 + 512,
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do_sample=True,
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max_new_tokens=None,
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early_stopping=False,
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stop_strings=["</answer>"],
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)
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)
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elif args.backend == "vllm":
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inference_model_config.update(dict(gpu_memory_utilization=0.7, enforce_eager=True, enable_chunked_prefill=True))
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generate_config.update(
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dict(
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max_tokens=2048,
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ignore_eos=True,
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include_stop_str_in_output=True,
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stop=["</answer>"],
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)
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)
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else:
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inference_model_config.update(
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dict(
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mem_fraction_static=0.6,
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)
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)
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generate_config.update(
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dict(
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max_new_tokens=256,
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ignore_eos=True,
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)
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)
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launch_distributed(
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num_producers=args.num_inferencer,
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num_proc_per_producer=1,
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num_consumer_procs=args.num_trainers,
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num_episodes=10,
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inference_batch_size=args.inference_batch_size,
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inference_microbatch_size=args.inference_microbatch_size,
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train_batch_size=args.train_batch_size,
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train_minibatch_size=args.train_minibatch_size,
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train_microbatch_size=args.train_microbatch_size,
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dataset_config={"path": args.dataset, "max_length": 300},
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dataloaders_config={},
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inference_model_config=inference_model_config,
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generate_config=generate_config,
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num_generations=args.num_generations,
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train_model_config=train_model_config,
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plugin_config={},
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inference_backend=args.backend,
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master_addr="localhost",
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master_port=29505,
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core_algo=args.algo,
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)
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