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import argparse
import pandas as pd
from . import common
from .browsecomp_eval import BrowseCompEval
from .drop_eval import DropEval
from .gpqa_eval import GPQAEval
from .humaneval_eval import HumanEval
from .math_eval import MathEval
from .mgsm_eval import MGSMEval
from .mmlu_eval import MMLUEval
from .simpleqa_eval import SimpleQAEval
from .sampler.chat_completion_sampler import (
OPENAI_SYSTEM_MESSAGE_API,
OPENAI_SYSTEM_MESSAGE_CHATGPT,
ChatCompletionSampler,
)
from .sampler.o_chat_completion_sampler import OChatCompletionSampler
from .sampler.responses_sampler import ResponsesSampler
from .sampler.claude_sampler import ClaudeCompletionSampler, CLAUDE_SYSTEM_MESSAGE_LMSYS
from .sampler.opensource_sampler import LLaDASampler
from .sampler.llama_sampler import LLaMASampler
import torch
from torch.distributed import scatter_object_list, gather_object
import torch.distributed as dist
def init_distributed():
from datetime import datetime, timedelta
dist.init_process_group(
backend="nccl", timeout=timedelta(hours=24)
)
rank = dist.get_rank()
world = dist.get_world_size()
torch.cuda.set_device(rank)
return rank, world
def main():
parser = argparse.ArgumentParser(
description="Run sampling and evaluations using different samplers and evaluations."
)
parser.add_argument(
"--task", type=str, required=True, choices=["mmlu", "math", "gpqa", "humaneval"]
)
parser.add_argument(
"--list-models", action="store_true", help="List available models"
)
parser.add_argument("--model", type=str, help="Select a model by name")
parser.add_argument("--debug", action="store_true", help="Run in debug mode")
parser.add_argument(
"--examples", type=int, help="Number of examples to use (overrides default)"
)
# Generation Config
parser.add_argument(
"--remasking", type=str, default="low_confidence", help="Remasking strategy"
)
parser.add_argument(
"--steps", type=int, default=128, help="Number of sampling steps"
)
parser.add_argument(
"--max-len", type=int, default=128, help="Length of generated sequence"
)
parser.add_argument(
"--block", type=int, default=64, help="Block length for sampling"
)
# for Cache
parser.add_argument("--kv-cache-masked", action="store_true")
parser.add_argument("--kv-cache-decoded", action="store_true")
parser.add_argument("--cache-steps", type=int, default=None)
parser.add_argument("--window-size", type=int, default=0)
args = parser.parse_args()
if torch.cuda.device_count() > 1:
rank, world = init_distributed()
else:
world, rank = 1, 0
models = {
"llada": LLaDASampler(
model_name="GSAI-ML/LLaDA-8B-Instruct",
generation_kwargs={
'steps': args.steps,
'gen_length': args.max_len,
'block_length': args.block,
'temperature': 0.,
'cfg_scale': 0.,
'remasking': args.remasking,
'enable_cache': args.kv_cache_masked or args.kv_cache_decoded,
'cache_reloading_step': args.cache_steps,
'window_size': args.window_size
},
kv_cache_masked=args.kv_cache_masked,
kv_cache_decoded=args.kv_cache_decoded
),
"llama": LLaMASampler(
model_name = "meta-llama/Llama-3.1-8B-Instruct",
generation_kwargs={
'max_new_tokens': args.max_len
}
)
}
if args.list_models:
print("Available models:")
for model_name in models.keys():
print(f" - {model_name}")
return
if args.model:
if args.model not in models:
print(f"Error: Model '{args.model}' not found.")
return
models = {args.model: models[args.model]}
grading_sampler = ChatCompletionSampler(model="gpt-4o")
equality_checker = ChatCompletionSampler(model="gpt-4-turbo-preview")
# ^^^ used for fuzzy matching, just for math
def get_evals(eval_name, debug_mode):
num_examples = (
args.examples if args.examples is not None else (5 if debug_mode else None)
)
# Set num_examples = None to reproduce full evals
match eval_name:
case "mmlu":
return MMLUEval(num_examples=10 if debug_mode else num_examples)
case "math":
return MathEval(
equality_checker=equality_checker,
num_examples=num_examples,
n_repeats=1 if debug_mode else 10,
)
case "gpqa":
return GPQAEval(
n_repeats=1 if debug_mode else 1, num_examples=num_examples,
)
case "mgsm":
return MGSMEval(num_examples_per_lang=10 if debug_mode else 250)
case "drop":
return DropEval(
num_examples=10 if debug_mode else num_examples,
train_samples_per_prompt=3,
)
case "humaneval":
return HumanEval(num_examples=10 if debug_mode else num_examples)
case "simpleqa":
return SimpleQAEval(
grader_model=grading_sampler,
num_examples=10 if debug_mode else num_examples,
)
case "browsecomp":
return BrowseCompEval(
grader_model=grading_sampler,
num_examples=10 if debug_mode else num_examples,
)
case _:
raise Exception(f"Unrecognized eval type: {eval_name}")
evals = {
eval_name: get_evals(eval_name, args.debug)
for eval_name in [args.task]
}
debug_suffix = "_DEBUG" if args.debug else ""
print(debug_suffix)
mergekey2resultpath = {}
for model_name, sampler in models.items():
sampler.init_model()
for eval_name, eval_obj in evals.items():
result = eval_obj(sampler, rank = rank, world = world)
# ^^^ Gather from different gpus:
sampler._free_memory()
print("Start aggregate results")
if world > 1:
gathered = [None] * world
dist.all_gather_object(gathered, result)
flat = [o for sub in gathered for o in sub] # 展平
result = flat
result = common.aggregate_results(result)
# ^^^ how to use a sampler
if (world > 1 and rank ==0) or world == 1:
file_stem = f"{eval_name}_{model_name}"
file_stem += f"_{args.remasking}_steps_{args.steps}_len_{args.max_len}_block_{args.block}"
file_stem += f"_decoded_{args.kv_cache_decoded}_masked_{args.kv_cache_masked}_cache{args.cache_steps}_window{args.window_size}"
report_filename = f"simple_eval_results/{file_stem}{debug_suffix}.html"
print(f"Writing report to {report_filename}")
with open(report_filename, "w") as fh:
fh.write(common.make_report(result))
metrics = result.metrics | {"score": result.score}
print(metrics)
result_filename = f"simple_eval_results/{file_stem}{debug_suffix}.json"
with open(result_filename, "w") as f:
f.write(json.dumps(metrics, indent=2))
print(f"Writing results to {result_filename}")
mergekey2resultpath[f"{file_stem}"] = result_filename
if world > 1:
dist.barrier()
if (world > 1 and rank ==0) or world == 1:
merge_metrics = []
for eval_model_name, result_filename in mergekey2resultpath.items():
try:
result = json.load(open(result_filename, "r+"))
except Exception as e:
print(e, result_filename)
continue
result = result.get("f1_score", result.get("score", None))
eval_name = eval_model_name[: eval_model_name.find("_")]
model_name = eval_model_name[eval_model_name.find("_") + 1 :]
merge_metrics.append(
{"eval_name": eval_name, "model_name": model_name, "metric": result}
)
merge_metrics_df = pd.DataFrame(merge_metrics).pivot(
index=["model_name"], columns="eval_name"
)
print("\nAll results: ")
print(merge_metrics_df.to_markdown())
return merge_metrics
if __name__ == "__main__":
main()
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