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import torch
import argparse
from lcb_runner.utils.scenarios import Scenario
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument(
"--model",
type=str,
default="gpt-3.5-turbo-0301",
help="Name of the model to use matching `lm_styles.py`, or a path if not in the store",
)
parser.add_argument(
"--nickname",
type=str,
default=None,
help="Short name used as model_repr when --model is not present in lm_styles.py",
)
parser.add_argument(
"--model_style",
type=str,
default="CodeQwenInstruct",
help="LMStyle to use when --model is not present in lm_styles.py (default: CodeQwenInstruct)",
)
parser.add_argument(
"--local_model_path",
type=str,
default=None,
help="If you have a local model, specify it here in conjunction with --model",
)
parser.add_argument(
"--trust_remote_code",
action="store_true",
help="trust_remote_code option used in huggingface models",
)
parser.add_argument(
"--scenario",
type=Scenario,
default=Scenario.codegeneration,
help="Type of scenario to run",
)
parser.add_argument(
"--not_fast",
action="store_true",
help="whether to use full set of tests (slower and more memory intensive evaluation)",
)
parser.add_argument(
"--release_version",
type=str,
default="release_latest",
help="whether to use full set of tests (slower and more memory intensive evaluation)",
)
parser.add_argument(
"--cot_code_execution",
action="store_true",
help="whether to use CoT in code execution scenario",
)
parser.add_argument(
"--n", type=int, default=10, help="Number of samples to generate"
)
parser.add_argument(
"--codegen_n",
type=int,
default=10,
help="Number of samples for which code generation was run (used to map the code generation file during self-repair)",
)
parser.add_argument(
"--temperature", type=float, default=0.2, help="Temperature for sampling"
)
parser.add_argument("--top_p", type=float, default=0.95, help="Top p for sampling")
parser.add_argument(
"--max_tokens", type=int, default=2000, help="Max tokens for sampling"
)
parser.add_argument(
"--multiprocess",
default=0,
type=int,
help="Number of processes to use for generation (vllm runs do not use this)",
)
parser.add_argument(
"--stop",
default="###",
type=str,
help="Stop token (use `,` to separate multiple tokens)",
)
parser.add_argument("--continue_existing", action="store_true")
parser.add_argument("--continue_existing_with_eval", action="store_true")
parser.add_argument(
"--use_cache", action="store_true", help="Use cache for generation"
)
parser.add_argument(
"--cache_batch_size", type=int, default=100, help="Batch size for caching"
)
parser.add_argument("--debug", action="store_true", help="Debug mode")
parser.add_argument("--evaluate", action="store_true", help="Evaluate the results")
parser.add_argument(
"--num_process_evaluate",
type=int,
default=12,
help="Number of processes to use for evaluation",
)
parser.add_argument("--timeout", type=int, default=6, help="Timeout for evaluation")
parser.add_argument(
"--openai_timeout", type=int, default=90, help="Timeout for requests to OpenAI"
)
parser.add_argument(
"--tensor_parallel_size",
type=int,
default=-1,
help="Tensor parallel size for vllm",
)
parser.add_argument(
"--enable_prefix_caching",
action="store_true",
help="Enable prefix caching for vllm",
)
parser.add_argument(
"--custom_output_file",
type=str,
default=None,
help="Path to the custom output file used in `custom_evaluator.py`",
)
parser.add_argument(
"--custom_output_save_name",
type=str,
default=None,
help="Folder name to save the custom output results (output file folder modified if None)",
)
parser.add_argument("--dtype", type=str, default="bfloat16", help="Dtype for vllm")
# Added to avoid running extra generations (it's slow for reasoning models)
parser.add_argument(
"--start_date",
type=str,
default=None,
help="Start date for the contest to filter the evaluation file (format - YYYY-MM-DD)",
)
parser.add_argument(
"--end_date",
type=str,
default=None,
help="End date for the contest to filter the evaluation file (format - YYYY-MM-DD)",
)
args = parser.parse_args()
args.stop = args.stop.split(",")
if args.tensor_parallel_size == -1:
args.tensor_parallel_size = torch.cuda.device_count()
if args.multiprocess == -1:
args.multiprocess = os.cpu_count()
return args
def test():
args = get_args()
print(args)
if __name__ == "__main__":
test()
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