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from abc import abstractmethod
from copy import deepcopy
from typing import Dict, List, Optional, Tuple, Union
import torch
from mmengine import dist
from opencompass.utils.prompt import PromptList
PromptType = Union[PromptList, str]
class BaseModel:
"""Base class for model wrapper.
Args:
path (str): The path to the model.
max_seq_len (int): The maximum sequence length of the model. Defaults
to 2048.
tokenizer_only (bool): If True, only the tokenizer will be initialized.
Defaults to False.
meta_template (Dict, optional): The model's meta prompt
template if needed, in case the requirement of injecting or
wrapping of any meta instructions.
generation_kwargs (Dict, optional): The generation kwargs for the
model. Defaults to dict().
sync_rank (bool): Whether to sync inputs between ranks. Do not use this
if you are not familiar with this behavior. Check `sync_inputs`
function for more details. Defaults to False.
"""
is_api: bool = False
def __init__(self,
path: str,
max_seq_len: int = 2048,
tokenizer_only: bool = False,
meta_template: Optional[Dict] = None,
generation_kwargs: Optional[Dict] = dict(),
sync_rank: bool = False):
self.path = path
self.max_seq_len = max_seq_len
self.tokenizer_only = tokenizer_only
# meta template
self.template_parser = LMTemplateParser(meta_template)
self.eos_token_id = None
if meta_template and 'eos_token_id' in meta_template:
self.eos_token_id = meta_template['eos_token_id']
self.generation_kwargs = generation_kwargs
self.sync_rank = sync_rank
@abstractmethod
def generate(self, inputs: List[str], max_out_len: int) -> List[str]:
"""Generate results given a list of inputs.
Args:
inputs (List[str]): A list of strings.
max_out_len (int): The maximum length of the output.
Returns:
List[str]: A list of generated strings.
"""
raise NotImplementedError(f'{self.__class__.__name__} does not support'
' gen-based evaluation yet, try ppl-based '
'instead.')
@abstractmethod
def get_ppl(self,
inputs: List[str],
mask_length: Optional[List[int]] = None) -> List[float]:
"""Get perplexity scores given a list of inputs.
Args:
inputs (List[str]): A list of strings.
mask_length (Optional[List[int]]): A list of mask lengths. If
provided, the perplexity scores will be calculated with the
first mask_length[i] tokens masked out. It's okay to skip
its implementation if advanced features in PPLInfernecer is
not needed.
Returns:
List[float]: A list of perplexity scores.
"""
raise NotImplementedError(f'{self.__class__.__name__} does not support'
' ppl-based evaluation yet, try gen-based '
'instead.')
@abstractmethod
def encode(self, prompt: str) -> torch.Tensor:
"""Encode prompt to tokens. Not necessary for most cases.
Args:
prompt (str): Input string.
Returns:
torch.Tensor: Encoded tokens.
"""
raise NotImplementedError(
f'{self.__class__.__name__} does not implement'
'`encode` method.')
@abstractmethod
def decode(self, tokens: torch.Tensor) -> str:
"""Decode tokens to text. Not necessary for most cases.
Args:
tokens (torch.Tensor): Input tokens.
Returns:
str: Decoded text.
"""
raise NotImplementedError(
f'{self.__class__.__name__} does not implement'
'`decode` method.')
@abstractmethod
def get_token_len(self, prompt: str) -> int:
"""Get lengths of the tokenized strings.
Args:
prompt (str): Input string.
Returns:
int: Length of the input tokens
"""
def parse_template(self, prompt_template: PromptType, mode: str) -> str:
"""Parse a prompt template, and wrap it with meta template if
applicable.
Args:
prompt_template (List[str or PromptList]): A prompt
template (potentially before being wrapped by meta template).
mode (str): Parsing mode. Choices are 'ppl' and 'gen'.
Returns:
str: The final string.
"""
return self.template_parser.parse_template(prompt_template, mode)
def get_ppl_from_template(self,
templates: List[PromptType],
mask_length=None):
"""Get perplexity given a list of templates.
Args:
templates (List[PromptType]): A list of templates.
mask_length (List[int]): A list of mask lengths. If provided, the
perplexity will be calculated only on the unmasked tokens.
"""
inputs = self.parse_template(templates, mode='ppl')
return self.get_ppl(inputs, mask_length)
def generate_from_template(self, templates: List[PromptType],
max_out_len: int, **kwargs):
"""Generate completion from a list of templates.
Args:
templates (List[PromptType]): A list of templates.
max_out_len (int): The maximum length of the output.
"""
inputs = self.parse_template(templates, mode='gen')
if hasattr(self, 'sync_rank') and self.sync_rank:
inputs = self.sync_inputs(inputs)
return self.generate(inputs, max_out_len=max_out_len, **kwargs)
def get_token_len_from_template(
self,
templates: Union[PromptType, List[PromptType]],
mode: str = 'ppl') -> Union[List[int], int]:
"""Get lengths given a list of templates.
Args:
templates (Union[List[str], str]): Input template(s).
mode (str): Parsing mode. Choices are 'ppl' and 'gen'.
Returns:
Union[List[int], int]: Length(s) of the input tokens. If the input
is a list, a list of lengths will be returned. Otherwise, an int
will be returned.
"""
prompts = self.parse_template(templates, mode=mode)
assert isinstance(prompts, (list, str)), 'tokens must be list or str'
is_batched = isinstance(prompts,
list) and not isinstance(prompts, PromptList)
if not is_batched:
prompts = [prompts]
prompts = [str(prompt) for prompt in prompts]
token_lens = [self.get_token_len(prompt) for prompt in prompts]
return token_lens[0] if not is_batched else token_lens
def sync_inputs(self, inputs: str) -> str:
"""For some case, when it involves multiprocessing with multiple gpus,
there might be the chance that inputs are different among different
gpus. Therefore, we need to sync inputs for rank0.
Args:
inputs (str): Inputs for each rank.
"""
rank = dist.get_rank()
if rank == 0:
tokens = self.encode(inputs)
length = self.get_token_len(inputs)
if length > 2048:
from opencompass.utils import get_logger
get_logger().info(f'Large tokens nums: {length}')
size = torch.tensor([tokens.shape], dtype=torch.long)
else:
tokens = None
size = torch.empty(2, dtype=torch.long)
# broadcast data size
dist.broadcast(size, src=0)
if rank != 0:
tokens = torch.empty(size.tolist(), dtype=torch.long)
# broadcast tokens
dist.broadcast(tokens, src=0)
# the final input might be different from original input
# due to the max sequence limitation
return self.decode(tokens)
def to(self, device):
self.model.to(device)
class LMTemplateParser:
"""Intermidate prompt template parser, specifically for language models.
Args:
meta_template (Dict): The meta template for the model.
"""
def __init__(self, meta_template: Optional[Dict] = None):
self.meta_template = meta_template
if meta_template:
assert 'round' in meta_template, 'round is required in meta' \
' template'
assert isinstance(meta_template['round'], list)
keys_to_check = ['round']
if 'reserved_roles' in meta_template:
assert isinstance(meta_template['reserved_roles'], list)
keys_to_check.append('reserved_roles')
self.roles: Dict[str, dict] = dict() # maps role name to config
for meta_key in keys_to_check:
for item in meta_template[meta_key]:
assert isinstance(item, (str, dict))
if isinstance(item, dict):
assert item['role'] not in self.roles, \
'role in meta prompt must be unique!'
self.roles[item['role']] = item.copy()
# convert list of string and int into a raw string
# for the ease of future prompt processing
for key in ['begin', 'end']:
value = self.roles[item['role']].get(key, '')
if isinstance(value, list):
self.roles[item['role']][
key] = self._encode_speical_tokens(value)
def parse_template(self, prompt_template: PromptType, mode: str) -> str:
"""Parse a prompt template, and wrap it with meta template if
applicable.
Args:
prompt_template (List[str or PromptList]): A prompt
template (potentially before being wrapped by meta template).
mode (str): Parsing mode. Choices are 'ppl' and 'gen'.
Returns:
str: The final string.
"""
assert isinstance(prompt_template, (str, list, PromptList, tuple))
if not isinstance(prompt_template, (str, PromptList)):
return [self.parse_template(p, mode=mode) for p in prompt_template]
assert mode in ['ppl', 'gen']
if isinstance(prompt_template, str):
return prompt_template
if self.meta_template:
prompt = ''
# Whether to keep generating the prompt
generate = True
section_stack = [] # stores tuples: (section_name, start_idx)
for i, item in enumerate(prompt_template):
if not generate:
break
if isinstance(item, str):
prompt += item
elif isinstance(item, dict) and 'section' in item:
if item['pos'] == 'end':
section_name, start_idx = section_stack.pop(-1)
assert section_name == item['section']
if section_name in ['round', 'ice']:
dialogue = prompt_template[start_idx:i]
round_ranges = self._split_rounds(
dialogue, self.meta_template['round'])
# Consider inserting multiple round examples into
# template
for i in range(len(round_ranges) - 1):
start = round_ranges[i]
end = round_ranges[i + 1]
round_template = dialogue[start:end]
role_dict = self._update_role_dict(
round_template)
new_str, generate = self._prompt2str(
self.meta_template['round'],
role_dict,
# Start generating only when the mode is in
# generation and the template reaches the
# last round
for_gen=mode == 'gen'
and i == len(round_ranges) - 2
and section_name == 'round')
prompt += new_str
elif item['pos'] == 'begin':
assert item['section'] in [
'begin', 'round', 'end', 'ice'
]
section_stack.append((item['section'], i + 1))
else:
raise ValueError(f'Invalid pos {item["pos"]}')
# if in "begin" or "end" section
elif section_stack[-1][0] in ['begin', 'end']:
role_dict = self._update_role_dict(item)
new_str, generate = self._prompt2str(
item,
role_dict,
# never stop generation
for_gen=False)
prompt += new_str
prompt = self.meta_template.get('begin', '') + prompt
if generate:
prompt += self.meta_template.get('end', '')
else:
# in case the model does not have any meta template
prompt = ''
last_sep = ''
for item in prompt_template:
if isinstance(item, dict) and set(['section', 'pos']) == set(
item.keys()):
continue
if isinstance(item, str):
if item:
prompt += last_sep + item
elif item.get('prompt', ''): # it's a dict
prompt += last_sep + item.get('prompt', '')
last_sep = '\n'
return prompt
def _split_rounds(
self, prompt_template: List[Union[str, Dict]],
single_round_template: List[Union[str, Dict]]) -> List[int]:
"""Split the prompt template into rounds, based on single round
template.
Return the index ranges of each round. Specifically,
prompt_template[res[i]:res[i+1]] represents the i-th round in the
template.
"""
role_idxs = {
role_cfg['role']: i
for i, role_cfg in enumerate(single_round_template)
if not isinstance(role_cfg, str)
}
last_role_idx = -1
cutoff_idxs = [0]
for idx, template in enumerate(prompt_template):
if isinstance(template, str):
continue
role_idx = role_idxs[template['role']]
if role_idx <= last_role_idx:
cutoff_idxs.append(idx)
last_role_idx = role_idx
cutoff_idxs.append(len(prompt_template))
return cutoff_idxs
def _update_role_dict(self, prompt: Union[List, str,
Dict]) -> Dict[str, Dict]:
"""Update the default role dict with the given prompt(s)."""
assert isinstance(prompt, (str, list, dict))
role_dict = deepcopy(self.roles)
if isinstance(prompt, str):
return role_dict
if isinstance(prompt, dict):
prompt = [prompt]
for p in prompt:
if isinstance(p, dict):
role = p['role']
if role not in self.roles:
role = p.get('fallback_role', None)
if not role:
print(f'{p} neither has an appropriate role nor '
'a fallback role.')
role_dict[role].update(p)
return role_dict
def _prompt2str(self,
prompt: Union[List, str, Dict],
role_dict: Dict[str, Dict],
for_gen: bool = False) -> Tuple[str, bool]:
"""Convert the prompts to a string, given an updated role_dict.
Args:
prompts (Union[List, str, dict]): The prompt(s) to be converted.
role_dict (Dict[str, Dict]): The updated role dict.
for_gen (bool): If True, the prompts will be converted for
generation tasks. The conversion stops before the first
role whose "generate" is set to True.
Returns:
Tuple[str, bool]: The converted string, and whether the follow-up
conversion should be proceeded.
"""
assert isinstance(prompt, (list, str, dict))
if isinstance(prompt, str):
return prompt, True
if isinstance(prompt, dict):
return self._role2str(prompt, role_dict, for_gen)
res = ''
for p in prompt:
new_str, cont = self._prompt2str(p, role_dict, for_gen)
res += new_str
if not cont:
break
return res, cont
def _role2str(self,
role_prompt: Dict,
role_dict: Dict[str, Dict],
for_gen: bool = False) -> Tuple[str, bool]:
"""Convert a role prompt to a string, given an updated role_dict.
Args:
role_prompt (Dict): The role prompt to be converted.
role_dict (Dict[str, Dict]): The updated role dict.
for_gen (bool): If True, the prompts will be converted for
generation tasks. The conversion stops before the first
role whose "generate" is set to True.
Returns:
Tuple[str, bool]: The converted string, and whether the follow-up
conversion should be proceeded.
"""
merged_prompt = role_dict.get(
role_prompt['role'],
role_dict.get(role_prompt.get('fallback_role')))
res = merged_prompt.get('begin', '')
if for_gen and merged_prompt.get('generate', False):
return res, False
# res += merged_prompt.get('prompt', '') + merged_prompt.get('end', '')
res += merged_prompt.get('prompt', '') + merged_prompt.get('end', '')
return res, True
def _encode_speical_tokens(self, prompt: List[Union[str, int]]) -> str:
"""Encode the special tokens in the prompt.
Now this is left for the future work
"""
raise NotImplementedError('Using List[str|int] is as the begin or end'
'of a prompt is not supported yet.')
res = ''
for item in prompt:
if isinstance(item, str):
res += item
else:
res += f'<META_TOKEN_{item}>'
return res
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