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from __future__ import annotations
import configparser
import pathlib
import typing
import os
import torch
import transformers
from torch.nn.utils.rnn import pad_sequence
from .config import LYRA_XVERSE_PARAM
from .model import XVERSEModel
class lyraXVERSE:
def __init__(self, model_path, tokenizer_path=None, dtype='fp16', memopt_mode=1, arch='Ampere', cuda_version=12) -> None:
self.model_path = model_path
self.tokenizer_path = tokenizer_path
self.dtype = dtype
self.memopt_mode = memopt_mode
self.arch = arch
self.cuda_version = cuda_version
self.model, self.tokenizer = self.load_model_and_tokenizer()
print("Got model and tokenizer")
def load_model_and_tokenizer(self):
if self.tokenizer_path is None:
tokenizer_path = self.model_path
else:
tokenizer_path = self.tokenizer_path
print(f'Loading tokenizer from {tokenizer_path}')
tokenizer = transformers.AutoTokenizer.from_pretrained(tokenizer_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 = 'llama'
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"),
inter_size=cfg.getint(model_name, 'inter_size'),
layer_num=cfg.getint(model_name, "num_layer"),
rotary_embedding_dim=cfg.getint(model_name, 'rotary_embedding'),
layernorm_eps=cfg.getfloat(model_name, 'layernorm_eps'),
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"),
tensor_para_size=cfg.getint(model_name, "tensor_para_size"),
inference_data_type=inference_data_type)
else:
inference_data_type = self.dtype
if inference_data_type == None:
inference_data_type = LYRA_XVERSE_PARAM.weights_data_type
model_args = dict(head_num=LYRA_XVERSE_PARAM.num_heads,
size_per_head=LYRA_XVERSE_PARAM.size_per_head,
inter_size=LYRA_XVERSE_PARAM.inter_size,
layer_num=LYRA_XVERSE_PARAM.num_layers,
rotary_embedding_dim=LYRA_XVERSE_PARAM.rotary_embedding,
layernorm_eps=LYRA_XVERSE_PARAM.layernorm_eps,
vocab_size=LYRA_XVERSE_PARAM.vocab_size,
start_id=LYRA_XVERSE_PARAM.start_id or tokenizer.bos_token_id,
end_id=LYRA_XVERSE_PARAM.end_id or tokenizer.eos_token_id,
weights_data_type=LYRA_XVERSE_PARAM.weights_data_type,
tensor_para_size=LYRA_XVERSE_PARAM.tensor_para_size,
inference_data_type=inference_data_type)
# update common parameters
# Load the C++ model into Pytorch model.
sm = "sm80"
if self.arch == "Ampere":
sm = "sm80"
elif self.arch == "Volta":
sm = "sm70"
else:
raise Exception(f"unsupported arch: {self.arch}")
cu = 'cu11'
if self.cuda_version == 11:
cu = 'cu11'
elif self.cuda_version == 12:
cu = 'cu12'
else:
raise Exception(f"unsupported cuda version: {self.cuda_version}")
lib_path = pathlib.Path(__file__).parent / "ftlib" / f"libth_transformer_{sm}_{cu}.so"
model_args.update(dict(
lib_path=lib_path,
model_path=os.path.join(self.model_path, "1-gpu-fp16.bin"),
max_seq_len=0, # for position seq embedding
pipeline_para_size=LYRA_XVERSE_PARAM.pipeline_para_size,
use_gptj_residual=LYRA_XVERSE_PARAM.use_gptj_residual,
memopt_mode=self.memopt_mode
))
print('[FT][INFO] Load Our FT Highly Optimized XVERSE 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.')
print(f'Loading model from {self.model_path}')
model = XVERSEModel(**model_args)
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 = 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,
random_seed=random_seed,
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 |