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adaptive_batch_size = None
if self.batch_size == 'auto':
print('Passed argument batch_size = auto. Detecting largest batch size')
batch_size = self._detect_batch_size()
print(f'Determined Largest batch size: {batch_size}')
adaptive_batch_size = batch_size
batch_size = self.batch_size if self.batch_size != 'auto' else adaptive_batch_size if adaptive_batch_size is not None else 0
batch_fn = self._batch_scheduler if self.batch_size == 'auto' and (not adaptive_batch_size) else None
re_ords = Collator([reg.args for reg in requests], sort_fn=_collate, group_by='gen_kwargs', group_fn=lambda x: x[1])
chunks = re_ords.get_batched(n=batch_size, batch_fn=batch_fn)
for chunk in chunks:
(contexts, all_gen_kwargs) = zip(*chunk)
gen_kwargs = all_gen_kwargs[0]
until = None
if isinstance(gen_kwargs, dict):
kwargs = copy.deepcopy(gen_kwargs)
if 'until' in kwargs.keys():
until = kwargs.pop('until')
if isinstance(until, str):
until = [until]
elif not isinstance(until, list):
raise ValueError(f"Expected `kwargs['until']` to be of type Union[str,list] but got {until}")
else:
raise ValueError(f'Expected `kwargs` to be of type `dict` but got {type(gen_kwargs)}')
eos = self.tok_decode(self.eot_token_id, skip_special_tokens=False)
if not until:
until = [eos]
else:
until.append(eos)
if 'max_gen_toks' in kwargs.keys():
max_gen_toks = kwargs.pop('max_gen_toks')
else:
max_gen_toks = self.max_gen_toks
if self.AUTO_MODEL_CLASS == transformers.AutoModelForCausalLM:
max_ctx_len = self.max_length - max_gen_toks
elif self.AUTO_MODEL_CLASS == transformers.AutoModelForSeq2SeqLM:
max_ctx_len = self.max_length
(context_enc, attn_masks) = self.tok_batch_encode(contexts, left_truncate_len=max_ctx_len, truncation=self.truncation)
context_enc = context_enc.to(self.device)
attn_masks = attn_masks.to(self.device)
if 'max_length' not in kwargs:
kwargs['max_length'] = context_enc.shape[1] + max_gen_toks
cont = self._model_generate(context=context_enc, attention_mask=attn_masks, stop=until, **kwargs)
cont_toks_list = cont.tolist()
for (cont_toks, context) in zip(cont_toks_list, contexts):
if self.AUTO_MODEL_CLASS == transformers.AutoModelForCausalLM:
cont_toks = cont_toks[context_enc.shape[1]:]
s = self.tok_decode(cont_toks)
for term in until:
if len(term) > 0:
s = s.split(term)[0]
res.append(s)
self.cache_hook.add_partial('generate_until', (context, gen_kwargs), s)
pbar.update(1)
res = re_ords.get_original(res)
pbar.close()
return res
def apply_chat_template(self, chat_history: List[Dict[str, str]]) -> str:
try:
chat_templated = self.tokenizer.apply_chat_template(chat_history, tokenize=False, add_generation_prompt=True)
except jinja2.exceptions.TemplateError:
eval_logger.warning('Failed to apply chat template. removing the system role in chat history.')
chat_history = [msg for msg in chat_history if msg['role'] != 'system']
chat_templated = self.tokenizer.apply_chat_template(chat_history, tokenize=False, add_generation_prompt=True)
return chat_templated
def get_model_info(self) -> dict:
def get_model_num_params(model) -> int:
if hasattr(model, 'num_parameters'):
return model.num_parameters()
if hasattr(model, 'parameters'):
return sum((p.numel() for p in model.parameters()))
else:
return -1
def get_model_dtype(model) -> str:
if hasattr(model, 'dtype'):
return model.dtype
else:
return ''
def get_model_sha(pretrained: str, revision: str) -> str:
try:
model_info = HfApi().model_info(repo_id=pretrained, revision=revision)
return model_info.sha
except Exception as e:
eval_logger.warn(f'Failed to get model SHA for {pretrained} at revision {revision}. Error: {e}')
return ''
model_info = {'model_num_parameters': get_model_num_params(self._model), 'model_dtype': get_model_dtype(self._model), 'model_revision': self.revision, 'model_sha': get_model_sha(self.pretrained, self.revision)}
if self.peft:
model_info['peft_sha'] = get_model_sha(self.peft, self.revision)
if self.delta:
model_info['delta_sha'] = get_model_sha(self.delta, self.revision)
return model_info
# File: lm-evaluation-harness-main/lm_eval/models/mamba_lm.py
from typing import Optional, Union
import torch