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# Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
import re
import warnings
import torch
from transformers import PreTrainedTokenizerFast, StoppingCriteriaList
from transformers.generation.streamers import BaseStreamer
from xtuner.utils import StopWordStoppingCriteria
def get_base_model(model):
if hasattr(model, 'llm'):
model = model.llm
if 'PeftModel' in model.__class__.__name__:
model = model.base_model.model
return model
def get_streamer(model):
# TODO: deprecation, v0.3.0
warnings.warn(
('`get_streamer` is deprecated and will be removed in v0.3.0, '
"use `transformers`'s `TextStreamer` instead."), DeprecationWarning)
if model.__class__.__name__ == 'InferenceEngine':
model = model.module
base_model = get_base_model(model)
base_model_name = base_model.__class__.__name__.lower()
is_internlm = 'internlm' in base_model_name
is_qwen = 'qwen' in base_model_name
is_baichuan = 'baichuan' in base_model_name
is_chatglm = 'chatglm' in base_model_name
no_space = is_internlm or is_qwen or is_baichuan or is_chatglm
if no_space:
return NoSpaceStreamer
else:
return DecodeOutputStreamer
class DecodeOutputStreamer(BaseStreamer):
"""Default streamer for HuggingFace models."""
def __init__(self, tokenizer, skip_prompt=True) -> None:
super().__init__()
# TODO: deprecation, v0.3.0
warnings.warn(
'`DecodeOutputStreamer` is deprecated and will be '
'removed in v0.3.0.', DeprecationWarning)
self.tokenizer = tokenizer
self.skip_prompt = skip_prompt
self.gen_len = 0
if isinstance(tokenizer, PreTrainedTokenizerFast):
self.decode = self._decode_with_raw_id
self.hex_regex = re.compile(r'^<0x([0-9ABCDEF]+)>$')
else:
self.decode = self._decode_fallback
def _decode_with_raw_id(self, value):
"""Convert token ids to tokens and decode."""
tok = self.tokenizer._convert_id_to_token(value)
if tok.startswith('▁'): # sentencepiece
space = ' '
tok = tok[1:]
else:
space = ''
if res := self.hex_regex.match(tok):
tok = chr(int(res.group(1), 16))
if tok == '</s>':
tok = '\n'
return space + tok
def _decode_fallback(self, value):
"""Fallback decoder for non-fast tokenizer."""
tok = self.tokenizer.decode(
value,
skip_special_tokens=False,
clean_up_tokenization_spaces=False)
return tok + ' '
def put(self, value):
"""Callback function to decode token and output to stdout."""
if self.gen_len == 0 and self.skip_prompt:
pass
else:
tok = self.decode(value[0])
print(tok, end='', flush=True)
self.gen_len += 1
def end(self):
"""Callback function to finish generation."""
print('\n')
class NoSpaceStreamer(DecodeOutputStreamer):
def __init__(self, tokenizer, skip_prompt=True) -> None:
BaseStreamer().__init__()
# TODO: deprecation, v0.3.0
warnings.warn(
'`NoSpaceStreamer` is deprecated and will be '
'removed in v0.3.0.', DeprecationWarning)
self.tokenizer = tokenizer
self.skip_prompt = skip_prompt
self.gen_len = 0
self.hex_regex = re.compile(r'^<0x([0-9ABCDEF]+)>$')
def decode(self, value):
tok = self.tokenizer.decode(value)
if res := self.hex_regex.match(tok):
tok = chr(int(res.group(1), 16))
if tok == '</s>' or tok == '\r':
tok = '\n'
return tok
def get_stop_criteria(
tokenizer,
stop_words=[],
):
stop_criteria = StoppingCriteriaList()
for word in stop_words:
stop_criteria.append(StopWordStoppingCriteria(tokenizer, word))
return stop_criteria
def auto_dtype_of_deepspeed_config(ds_config):
if ds_config.get('fp16') and not ds_config.get('bf16'):
if ds_config.get('fp16').get('enabled') == 'auto':
ds_config['fp16']['enabled'] = torch.cuda.is_available()
elif not ds_config.get('fp16') and ds_config.get('bf16'):
if ds_config.get('bf16').get('enabled') == 'auto':
ds_config['bf16']['enabled'] = torch.cuda.is_bf16_supported()
elif ds_config.get('fp16') and ds_config.get('bf16'):
if ds_config.get('fp16').get('enabled') == 'auto':
ds_config['fp16']['enabled'] = torch.cuda.is_available()
if ds_config.get('bf16').get('enabled') == 'auto':
ds_config['bf16']['enabled'] = torch.cuda.is_bf16_supported()
if (ds_config['fp16']['enabled'] is True
and ds_config['bf16']['enabled'] is True):
ds_config['fp16']['enabled'] = False
ds_config['bf16']['enabled'] = True
return ds_config
def is_cn_string(s):
if re.search('[\u4e00-\u9fff]', s):
return True
return False
def get_seed_from_checkpoint(pth_model):
if osp.isfile(pth_model):
checkpoint = torch.load(pth_model, map_location='cpu')
elif osp.isdir(pth_model):
try:
from deepspeed.utils.zero_to_fp32 import get_model_state_files
except ImportError:
raise ImportError(
'The provided PTH model appears to be a DeepSpeed checkpoint. '
'However, DeepSpeed library is not detected in current '
'environment. This suggests that DeepSpeed may not be '
'installed or is incorrectly configured. Please verify your '
'setup.')
filename = get_model_state_files(pth_model)[0]
checkpoint = torch.load(filename, map_location='cpu')
else:
raise FileNotFoundError(f'Cannot find {pth_model}')
return checkpoint['meta']['seed']
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