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from __future__ import annotations
import configparser
import pathlib
import typing
import torch
import transformers
from torch.nn.utils.rnn import pad_sequence
from .config import BELLE_PARAM, LIB_SO_PATH
from .model import BelleModel
import os
class LyraBelle:
def __init__(self, model_path, model_name, dtype='fp16', int8_mode=0) -> None:
self.model_path = model_path
self.model_name = model_name
self.dtype = dtype
if dtype != 'int8':
int8_mode = 0
self.int8_mode = int8_mode
print(f'Loading model and tokenizer from {self.model_path}')
self.model, self.tokenizer = self.load_model_and_tokenizer()
print("Got model and tokenizer")
def load_model_and_tokenizer(self):
tokenizer = transformers.AutoTokenizer.from_pretrained(self.model_path)
checkpoint_path = pathlib.Path(self.model_path)
config_path = checkpoint_path / 'config.ini'
if config_path.exists():
# Read model params from config.
cfg = configparser.ConfigParser()
cfg.read(config_path)
model_name = 'belle'
inference_data_type = self.dtype
if inference_data_type == None:
inference_data_type = cfg.get(model_name, "weight_data_type")
model_args = dict(
head_num=cfg.getint(model_name, 'head_num'),
size_per_head=cfg.getint(model_name, "size_per_head"),
layer_num=cfg.getint(model_name, "num_layer"),
tensor_para_size=cfg.getint(model_name, "tensor_para_size"),
vocab_size=cfg.getint(model_name, "vocab_size"),
start_id=cfg.getint(model_name, "start_id"),
end_id=cfg.getint(model_name, "end_id"),
weights_data_type=cfg.get(model_name, "weight_data_type"),
layernorm_eps=cfg.getfloat(model_name, 'layernorm_eps'),
inference_data_type=inference_data_type)
else:
inference_data_type = self.dtype
if inference_data_type == None:
inference_data_type = BELLE_PARAM.weights_data_type
model_args = dict(head_num=BELLE_PARAM.num_heads,
size_per_head=BELLE_PARAM.size_per_head,
vocab_size=BELLE_PARAM.vocab_size,
start_id=BELLE_PARAM.start_id or tokenizer.bos_token_id,
end_id=BELLE_PARAM.end_id or tokenizer.eos_token_id,
layer_num=BELLE_PARAM.num_layers,
tensor_para_size=BELLE_PARAM.tensor_para_size,
weights_data_type=BELLE_PARAM.weights_data_type,
inference_data_type=inference_data_type)
# update common parameters
model_args.update(dict(
lib_path=LIB_SO_PATH,
pipeline_para_size=BELLE_PARAM.pipeline_para_size,
shared_contexts_ratio=BELLE_PARAM.shared_contexts_ratio,
int8_mode=self.int8_mode
))
print('[FT][INFO] Load Our FT Highly Optimized BELLE model')
for k, v in model_args.items():
print(f' - {k.ljust(25, ".")}: {v}')
# Check sanity and consistency between the model and tokenizer.
checklist = ['head_num', 'size_per_head', 'vocab_size', 'layer_num',
'tensor_para_size', 'tensor_para_size', 'weights_data_type']
if None in [model_args[k] for k in checklist]:
none_params = [p for p in checklist if model_args[p] is None]
print(f'[FT][WARNING] Found None parameters {none_params}. They must '
f'be provided either by config file or CLI arguments.')
if model_args['start_id'] != tokenizer.bos_token_id:
print('[FT][WARNING] Given start_id is not matched with the bos token '
'id of the pretrained tokenizer.')
if model_args['end_id'] not in (tokenizer.pad_token_id, tokenizer.eos_token_id):
print('[FT][WARNING] Given end_id is not matched with neither pad '
'token id nor eos token id of the pretrained tokenizer.')
model = BelleModel(**model_args)
if not model.load(ckpt_path=os.path.join(self.model_path, self.model_name)):
print('[FT][WARNING] Skip model loading since no checkpoints are found')
return model, tokenizer
def generate(self, prompts: typing.List[str] | str,
output_length: int = 512,
beam_width: int = 1,
top_k: typing.Optional[torch.IntTensor] = 1,
top_p: typing.Optional[torch.FloatTensor] = 1.0,
beam_search_diversity_rate: typing.Optional[torch.FloatTensor] = 0.0,
temperature: typing.Optional[torch.FloatTensor] = 1.0,
len_penalty: typing.Optional[torch.FloatTensor] = 0.0,
repetition_penalty: typing.Optional[torch.FloatTensor] = 1.0,
presence_penalty: typing.Optional[torch.FloatTensor] = None,
min_length: typing.Optional[torch.IntTensor] = None,
bad_words_list: typing.Optional[torch.IntTensor] = None,
do_sample: bool = False,
return_output_length: bool = False,
return_cum_log_probs: int = 0):
#
if isinstance(prompts, str):
prompts = [prompts, ]
inputs = ['Human: ' + prompt.strip() +
'\n\nAssistant:' for prompt in prompts]
batch_size = len(inputs)
ones_int = torch.ones(size=[batch_size], dtype=torch.int32)
ones_float = torch.ones(size=[batch_size], dtype=torch.float32)
# we must encode the raw prompt text one by one in order to compute the length of the original text.
input_token_ids = [self.tokenizer(text, return_tensors="pt").input_ids.int().squeeze() for text in inputs]
input_lengths = torch.IntTensor([len(ids) for ids in input_token_ids])
# after got the length of each input text tokens. we can batchfy the input list to a tensor. padding the right.
input_token_ids = pad_sequence(input_token_ids, batch_first=True, padding_value=self.tokenizer.eos_token_id)
random_seed = None
if do_sample:
random_seed = torch.randint(0, 262144, (batch_size,), dtype=torch.long)
outputs = self.model(start_ids=input_token_ids,
start_lengths=input_lengths,
output_len=output_length,
beam_width=beam_width,
top_k=top_k*ones_int,
top_p=top_p*ones_float,
beam_search_diversity_rate=beam_search_diversity_rate*ones_float,
temperature=temperature*ones_float,
len_penalty=len_penalty*ones_float,
repetition_penalty=repetition_penalty*ones_float,
presence_penalty=presence_penalty,
min_length=min_length,
random_seed=random_seed,
bad_words_list=bad_words_list,
return_output_length=return_output_length,
return_cum_log_probs=return_cum_log_probs)
if return_cum_log_probs > 0:
outputs = outputs[0] # output_token_ids.
# Slice the generated token ids of the 1st beam result.
# output = input tokens + generated tokens.
output_token_ids = [out[0, length:].cpu()
for out, length in zip(outputs, input_lengths)]
output_texts = self.tokenizer.batch_decode(
output_token_ids, skip_special_tokens=True)
return output_texts
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