def model_init(app):
ArgsSet = type('ArgsSet',(object,),{})
client = ArgsSet()
parser = ArgumentParser()
parser.add_argument("--model-config", type=str, default="openai-gpt",
help="Path, url or short name of the model")
parser.add_argument("--device", type=str, default="cuda" if torch.cuda.is_available()
else "cpu", help="Device (cuda or cpu)")
parser.add_argument("--outlens", type=int, default=30)
parser.add_argument("--beam", type=int, default=1)
parser.add_argument("--gpt-checkpoints", type=str)
parser.add_argument("--port", type=int, default=8866)
args = parser.parse_args()
args.load_model = True
args.fp32_embedding = False
args.fp32_layernorm = False
args.fp32_tokentypes = False
args.layernorm_epsilon = 1e-12
gpt = BertModel(None, args)
state_dict = convert_model(torch.load(args.gpt_checkpoints)['sd'])
gpt.load_state_dict(state_dict)
gpt.to(args.device)
gpt.eval()
tokenizer = BertWordPieceTokenizer("bert-base-chinese", cache_dir="temp_cache_dir")
print(" Load model from {}".format(args.gpt_checkpoints))
client.tokenizer = tokenizer
client.gpt =gpt
client.gpt_beam = SequenceGenerator(gpt, tokenizer, beam_size=args.beam, max_lens=args.outlens)
client.device = args.device
client.port = args.port
client.generator = sample_sequence
return client
def model_init(app):
ArgsSet = type('ArgsSet', (object, ), {})
client = ArgsSet()
parser = ArgumentParser()
parser.add_argument("--model-config",
type=str,
default="openai-gpt",
help="Path, url or short name of the model")
parser.add_argument("--device",
type=str,
default="cuda" if torch.cuda.is_available() else "cpu",
help="Device (cuda or cpu)")
parser.add_argument("--outlens", type=int, default=30)
parser.add_argument("--beam", type=int, default=1)
parser.add_argument("--fuse-checkpoints", type=str)
parser.add_argument("--gpt-checkpoints", type=str)
parser.add_argument("--qa-style-checkpoints", type=str)
parser.add_argument("--multi-task", type=str)
parser.add_argument("--split-sentence-with-task-embedding-checkpoints",
type=str)
parser.add_argument("--special-cls-checkpoints", type=str)
parser.add_argument("--port", type=int, default=8866)
args = parser.parse_args()
args.load_model = True
args.fp32_embedding = False
args.fp32_layernorm = False
args.fp32_tokentypes = False
args.layernorm_epsilon = 1e-12
fuse_model = BertModel(None, args)
state_dict = convert_model(torch.load(args.fuse_checkpoints)['sd'])
fuse_model.load_state_dict(state_dict)
fuse_model.to(args.device)
fuse_model.eval()
print("| Load model from {}".format(args.fuse_checkpoints))
gpt = BertModel(None, args)
state_dict = convert_model(torch.load(args.gpt_checkpoints)['sd'])
gpt.load_state_dict(state_dict)
gpt.to(args.device)
gpt.eval()
tokenizer = BertWordPieceTokenizer("bert-base-chinese",
cache_dir="temp_cache_dir")
print(" Load model from {}".format(args.gpt_checkpoints))
# Load bert checkpoints
args.load_model = False
args.fp32_embedding = False
args.fp32_layernorm = False
args.fp32_tokentypes = False
args.layernorm_epsilon = 1e-12
bert = BertModel(None, args)
bert.to(args.device)
bert.eval()
client.tokenizer = tokenizer
client.fuse_model = fuse_model
client.fuse_beam = SequenceGenerator(fuse_model,
tokenizer,
beam_size=args.beam,
max_lens=args.outlens)
client.gpt = gpt
client.gpt_beam = SequenceGenerator(gpt,
tokenizer,
beam_size=args.beam,
max_lens=args.outlens)
client.bert = bert
client.device = args.device
client.port = args.port
client.generator = sample_sequence
# multi task model
multi_task = BertModel(None, args)
state_dict = convert_model(torch.load(args.multi_task)['sd'])
print("| Load model from {}".format(args.multi_task))
multi_task.load_state_dict(state_dict)
multi_task.to(args.device)
multi_task.eval()
client.multi_task_model = multi_task
client.multi_task_beam = SequenceGenerator(multi_task,
tokenizer,
beam_size=args.beam,
max_lens=args.outlens)
# qa style model
qa_style = BertModel(None, args)
state_dict = convert_model(torch.load(args.qa_style_checkpoints)['sd'])
qa_style.load_state_dict(state_dict)
qa_style.to(args.device)
qa_style.eval()
print(" Load model from {}".format(args.qa_style_checkpoints))
client.qa_task_model = qa_style
# special cls tokens
special_cls_model = BertModel(None, args)
special_cls_model.eval()
state_dict = convert_model(torch.load(args.special_cls_checkpoints)['sd'])
special_cls_model.load_state_dict(state_dict)
special_cls_model.to(args.device)
special_cls_model.eval()
print(" Load model from {}".format(args.special_cls_checkpoints))
client.special_cls_model = special_cls_model
client.special_beam = SequenceGenerator(special_cls_model,
tokenizer,
beam_size=args.beam,
max_lens=args.outlens)
# split sentence model with task embedding
split_sentence_model = BertModel(None, args)
split_sentence_model.eval()
state_dict = convert_model(
torch.load(args.split_sentence_with_task_embedding_checkpoints)['sd'])
split_sentence_model.load_state_dict(state_dict)
split_sentence_model.to(args.device)
split_sentence_model.eval()
print(" Load model from {}".format(
args.split_sentence_with_task_embedding_checkpoints))
client.split_sentence_model = split_sentence_model
client.split_sentence_beam = SequenceGenerator(split_sentence_model,
tokenizer,
beam_size=args.beam,
max_lens=args.outlens)
return client
"""
import re
import torch
import sentencepiece as spm
from model import BertModel
sp = spm.SentencePieceProcessor()
sp.load('resource/sentencepiece.unigram.35000.model')
vocab_size = sp.get_piece_size()
n_embedding = 512
n_layer = 8
model = BertModel(vocab_size, n_embedding, n_layer)
model.eval()
model.load_state_dict(torch.load('resource/model.{}.{}.th'.format(n_embedding, n_layer),
map_location='cpu'))
# you should enable cuda if it is available
# model.cuda()
# if you are using a GPU that has tensor cores (nvidia volta, Turing architecture), you can enable half precision
# inference and training, we recommend to use the nvidia official apex to make everything as clean as possible from
# apex import amp [model] = amp.initialize([model], opt_level="O2")
device = model.embedding.weight.data.device
def clean_text(txt):
txt = txt.lower()
txt = re.sub('\s*', '', txt)
