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import argparse
import copy
import fnmatch
import math
import os.path as osp
import statistics
import time
from collections import Counter
from inspect import signature
from shutil import which
from typing import List, Optional
import mmengine
from mmengine.config import Config, ConfigDict
from mmengine.utils import mkdir_or_exist
from opencompass.registry import (ICL_EVALUATORS, MODELS, TASKS,
TEXT_POSTPROCESSORS)
from opencompass.tasks.base import BaseTask
from opencompass.utils import (build_dataset_from_cfg, dataset_abbr_from_cfg,
get_infer_output_path, get_logger,
task_abbr_from_cfg)
def extract_role_pred(s: str, begin_str: Optional[str],
end_str: Optional[str]) -> str:
"""Extract the role prediction from the full prediction string. The role
prediction may be the substring between the begin and end string.
Args:
s (str): Full prediction string.
begin_str (str): The beginning string of the role
end_str (str): The ending string of the role.
Returns:
str: The extracted role prediction.
"""
start = 0
end = len(s)
if begin_str:
begin_idx = s.find(begin_str)
if begin_idx != -1:
start = begin_idx + len(begin_str)
if end_str:
# TODO: Support calling tokenizer for the accurate eos token
# and avoid such hardcode
end_idx = s.find(end_str, start)
if end_idx != -1:
end = end_idx
return s[start:end]
@TASKS.register_module(force=(__name__ == '__main__')) # A hack for script run
class OpenICLEvalTask(BaseTask):
"""OpenICL Evaluation Task.
This task is used to evaluate the metric between predictions and
references.
"""
name_prefix = 'OpenICLEval'
log_subdir = 'logs/eval'
output_subdir = 'results'
def __init__(self, cfg: ConfigDict):
super().__init__(cfg)
self.logger = get_logger()
self.num_gpus = max(
c.get('eval_cfg', {}).get('num_gpus', 0)
for c in sum(self.dataset_cfgs, []))
self.dump_details = cfg.get('eval', {}).get('runner', {}).get(
'task', {}).get('dump_details', False)
def get_command(self, cfg_path, template):
script_path = __file__
python = 'python3' if which('python3') else 'python'
command = f'{python} {script_path} {cfg_path}'
return template.format(task_cmd=command)
def run(self):
for model_cfg, dataset_cfgs in zip(self.model_cfgs, self.dataset_cfgs):
for dataset_cfg in dataset_cfgs:
self.model_cfg = model_cfg
self.dataset_cfg = dataset_cfg
# Load Dataset
self.eval_cfg = self.dataset_cfg.get('eval_cfg')
self.output_column = dataset_cfg['reader_cfg']['output_column']
# overwrite postprocessor if the model has specified one
ds_abbr = dataset_abbr_from_cfg(self.dataset_cfg)
model_postprocessors = self.model_cfg.get(
'pred_postprocessor', {})
for pattern in model_postprocessors.keys():
if fnmatch.fnmatch(ds_abbr, pattern):
self.eval_cfg[
'pred_postprocessor'] = model_postprocessors[
pattern] # noqa
break
out_path = get_infer_output_path(
self.model_cfg, self.dataset_cfg,
osp.join(self.work_dir, 'results'))
if osp.exists(out_path):
continue
self._score()
def _score(self):
test_set = build_dataset_from_cfg(self.dataset_cfg).test
# Postprocess dataset if necessary
if 'dataset_postprocessor' in self.eval_cfg:
proc = self.eval_cfg['dataset_postprocessor']['type']
if isinstance(proc, str):
proc = TEXT_POSTPROCESSORS.get(proc)
def postprocess(sample):
s = sample[self.output_column]
sample[self.output_column] = proc(s)
return sample
test_set = test_set.map(postprocess)
# Load predictions
filename = get_infer_output_path(
self.model_cfg, self.dataset_cfg,
osp.join(self.work_dir, 'predictions'))
# in case the prediction is partial
root, ext = osp.splitext(filename)
partial_filename = root + '_0' + ext
# Get sc_size if use Self-Consistency
sc_size = self.eval_cfg.get('sc_size')
if not osp.exists(osp.realpath(filename)) and not osp.exists(
osp.realpath(partial_filename)):
result = {'error': 'No predictions found.'}
else:
if osp.exists(osp.realpath(filename)):
preds = mmengine.load(filename)
preds = [preds[str(i)] for i in range(len(preds))]
else:
filename = partial_filename
preds = []
i = 1
while osp.exists(osp.realpath(filename)):
sub_preds = mmengine.load(filename)
preds.extend(
[sub_preds[str(i)] for i in range(len(sub_preds))])
filename = root + f'_{i}' + ext
i += 1
pred_dicts = copy.deepcopy(preds)
preds = {k: [pred.get(k) for pred in preds] for k in preds[0]}
pred_strs = preds.pop('prediction', None)
pred_list_flag = pred_strs is not None and isinstance(
pred_strs[0], list)
if ('pred_role' in self.eval_cfg
and 'meta_template' in self.model_cfg
and not MODELS.get(self.model_cfg['type']).is_api):
# Create a prompt template for role config parsing
from opencompass.models.base import LMTemplateParser
parser = LMTemplateParser(self.model_cfg['meta_template'])
role = parser.roles[self.eval_cfg['pred_role']]
if sc_size is not None:
assert pred_list_flag, (
'The prediction for Self-Consistency'
'must be list.')
if pred_list_flag:
pred_strs = [[
extract_role_pred(_pred, role.get('begin', None),
role.get('end', None))
for _pred in pred
] for pred in pred_strs]
else:
pred_strs = [
extract_role_pred(pred, role.get('begin', None),
role.get('end', None))
for pred in pred_strs
]
# Postprocess predictions if necessary
if 'pred_postprocessor' in self.eval_cfg:
kwargs = self.eval_cfg['pred_postprocessor']
proc = kwargs.pop('type')
if isinstance(proc, str):
proc = TEXT_POSTPROCESSORS.get(proc)
if pred_list_flag:
pred_strs = [[proc(s, **kwargs) for s in preds]
for preds in pred_strs]
else:
pred_strs = [proc(s, **kwargs) for s in pred_strs]
# Get majority voting predictions if use self-consistency
if sc_size is not None:
pred_strs = [
Counter(s).most_common(1)[0][0] for s in pred_strs
]
icl_evaluator = ICL_EVALUATORS.build(self.eval_cfg['evaluator'])
# need results dir to save other files
out_path = get_infer_output_path(
self.model_cfg, self.dataset_cfg,
osp.join(self.work_dir, 'results'))
icl_evaluator._out_dir = osp.splitext(out_path)[
0] # strip extension
preds['predictions'] = pred_strs
preds['references'] = (test_set[self.output_column]
if self.output_column else None)
preds['test_set'] = test_set
preds = {
k: preds[k]
for k in signature(icl_evaluator.score).parameters
}
result = icl_evaluator.score(**preds)
if self.dump_details:
details = result.get('details', None)
try:
result['details'] = self.format_details(
pred_strs, test_set[self.output_column], details,
pred_dicts)
result['type'] = result['details'].pop('type', None)
if 'PPL' in str(
self.dataset_cfg.infer_cfg.inferencer.type):
result['correct_bpb'], result['incorrect_bpb'] = \
self.calculate_bpb(pred_dicts)
except Exception as e:
self.logger.warning(f'Skip dumping details due to: {e}.')
else:
result.pop('details', None)
if 'error' in result:
self.logger.error(
f'Task {task_abbr_from_cfg(self.cfg)}: {result["error"]}')
return
else:
result_wo_details = {
i: result[i]
for i in result if i != 'details'
}
self.logger.info(
f'Task {task_abbr_from_cfg(self.cfg)}: {result_wo_details}')
# Save result
out_path = get_infer_output_path(self.model_cfg, self.dataset_cfg,
osp.join(self.work_dir, 'results'))
mkdir_or_exist(osp.split(out_path)[0])
mmengine.dump(result, out_path, ensure_ascii=False, indent=4)
def format_details(self, predictions, references, details, pred_dicts):
"""This function is responsible for formatting prediction details.
Args:
predictions (list): The prediction list.
references (list): The reference list.
details (list): Contains the 'pred' 'answer' and 'correct' for each
sample. Such as `[{'pred': '光荣和ωforce',
'answers': ['光荣和ω-force', '光荣和ωforce'], 'correct': True}]`
pred_dicts (list): Contains a list of samples with the original
prompts. Such as
`[{'origin_prompt': '根据文章回答问题。你的答案应该尽可能3》…………',
'prediction': ' 光荣和ω-force\n', 'gold': ['光荣和ω-force']}]`
Returns:
list: The formatted prediction details.
"""
results = {}
for i in range(len(predictions)):
ppl_flag = False
result = {}
origin_prediction = copy.deepcopy(pred_dicts[i])
origin_prediction.pop('in-context examples', None)
origin_prediction.pop('prediction', None)
keys = copy.deepcopy(list(origin_prediction.keys()))
for key in keys:
if key.startswith('label:'):
ppl_flag = True
origin_prediction[key].pop('testing input', None)
new_key = key.replace('label: ', '')
origin_prediction[new_key] = origin_prediction.pop(key)
if ppl_flag:
results['type'] = 'PPL'
result['origin_prediction'] = origin_prediction
result['predictions'] = str(predictions[i])
result['references'] = str(references[i])
result['correct'] = str(predictions[i]) == str(references[i])
elif details is not None:
results['type'] = 'GEN'
result['prompt'] = origin_prediction['origin_prompt']
result['origin_prediction'] = pred_dicts[i]['prediction']
result['predictions'] = details[i]['pred']
result['references'] = details[i]['answer']
result['correct'] = details[i]['correct']
else:
results['type'] = 'GEN'
result['prompt'] = origin_prediction['origin_prompt']
result['origin_prediction'] = pred_dicts[i]['prediction']
result['predictions'] = str(predictions[i])
result['references'] = str(references[i])
results[str(i)] = result
return results
def calculate_bpb(self, pred_dicts: List):
"""This function is used to calculate the BPB (Bits Per Byte) for the
data. The correct BPB is obtained directly from the values in the
'predictions' file. The incorrect BPB is the average of the remaining
BPB values for each sample under different labels after subtracting the
correct BPB. The calculation of BPB (Bits Per Byte) is similar to PPL,
with the difference that it computes the additional bits needed on
average, in terms of character length, to encode the true sequence
based on the predictions. This calculation involves applying a
weighting factor based on the ratio of words to characters.
Args:
pred_dicts (list): Contains a list of samples with each options
and BPB scores.
Returns:
dict: Contains correct and incorrect bpb.
"""
incorrect_bpb_list = []
bpb_list = []
for pred_dict in pred_dicts:
preds = {
key: value
for key, value in pred_dict.items()
if key.startswith('label: ')
}
values = []
for item in preds.items():
values.append(item[1])
bpbs = [value['BPB'] for value in values]
incorrect_bpb_list.append(
(sum(bpbs) - min(bpbs)) / (len(bpbs) - 1))
bpb_list.append(min(bpbs))
def filters(origins):
targets = [target for target in origins if not math.isnan(target)]
return targets
mean_incorrect = statistics.mean(filters(incorrect_bpb_list))
mean_correct = statistics.mean(filters(bpb_list))
return 100 * mean_correct, 100 * mean_incorrect
def parse_args():
parser = argparse.ArgumentParser(description='Score Calculator')
parser.add_argument('config', help='Config file path')
args = parser.parse_args()
return args
if __name__ == '__main__':
args = parse_args()
cfg = Config.fromfile(args.config)
start_time = time.time()
inferencer = OpenICLEvalTask(cfg)
inferencer.run()
end_time = time.time()
get_logger().info(f'time elapsed: {end_time - start_time:.2f}s')
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