def convert_tf_checkpoint_to_pytorch(tf_checkpoint_path, bert_config_file,
pytorch_dump_path):
# Initialise PyTorch model
config = AlbertConfig.from_pretrained(bert_config_file)
# print("Building PyTorch model from configuration: {}".format(str(config)))
model = AlbertForPreTraining(config)
# Load weights from tf checkpoint
load_tf_weights_in_albert(model, config, tf_checkpoint_path)
# Save pytorch-model
print("Save PyTorch model to {}".format(pytorch_dump_path))
torch.save(model.state_dict(), pytorch_dump_path)
def __init__(self,
model_path: str,
model_file: str,
vocab_file: str = "vocab.txt",
config_file: str = "config.json",
device: typing.Union[torch.device, int, str] = "cpu",
turns: int = 3,
uttr_len: int = 20,
resp_len: int = 20,
data_type: str = "uru",
add_special_tokens: bool = True):
"""
:param model_path: str型，模型的路径，需要包含训练好的模型，预训练模型pytorch_model.bin以及对应的词表和config文件
:param model_file: str型，训练好的模型的文件名
:param vocab_file: str型，词表的文件名
:param config_file: str型，预训练模型的配置文件
"""
super().__init__()
self.preprocessor = CNAlbertPreprocessorForMultiQA(
Path(model_path) / vocab_file,
uttr_len=uttr_len,
resp_len=resp_len,
add_special_tokens=add_special_tokens)
# 初始化模型
config = AlbertConfig.from_pretrained(Path(model_path) / config_file)
model = AlbertIMN(uttr_len,
resp_len,
turns,
config,
model_path,
data_type=data_type)
cls_task = tasks.Classification(num_classes=2,
losses=[nn.CrossEntropyLoss()])
cls_task.metrics = ['accuracy']
params = model.get_default_params()
params['task'] = cls_task
model.params = params
model.build()
model = model.float()
# 加载模型
self.predictor = Predictor(model,
save_dir=model_path,
checkpoint=model_file,
device=device)
self.device = self.predictor._device
self.turns = turns
self.uttr_len = uttr_len
self.resp_len = resp_len
self.data_type = data_type
from albert_pytorch.model.modeling_albert_bright import AlbertModel, AlbertConfig
from albert_pytorch.model.tokenization_bert import BertTokenizer
from snlp.tools.vector_similarity import cosine_similarity
albert_path = "/home/speech/models/albert_tiny_pytorch_489k"
vocab_file = "vocab.txt"
config_file = "config.json"
text_1 = "咱两谁最漂亮"
text_2 = "咱俩谁最漂亮"
text_3 = "你好"
tokenizer = BertTokenizer.from_pretrained(Path(albert_path) / vocab_file)
config = AlbertConfig.from_pretrained(Path(albert_path) / config_file)
model = AlbertModel.from_pretrained(Path(albert_path), config=config)
start = time.time()
input_ids_1 = tokenizer.encode_plus(text_1,
add_special_tokens=True)['input_ids']
input_ids_2 = tokenizer.encode_plus(text_2,
add_special_tokens=True)['input_ids']
input_ids_3 = tokenizer.encode_plus(text_3,
add_special_tokens=True)['input_ids']
input_ids_3.extend([0, 0, 0, 0])
# print(input_ids_1)
result = model(torch.tensor([input_ids_1, input_ids_2, input_ids_3]))[1]
result = result.detach().cpu().numpy()
def main():
parser = argparse.ArgumentParser()
## Required parameters
parser.add_argument(
"--data_dir",
default=None,
type=str,
required=True,
help=
"The input data dir. Should contain the .tsv files (or other data files) for the task."
)
parser.add_argument("--model_type",
default=None,
type=str,
required=True,
help="Model type selected in the list: ")
parser.add_argument(
"--model_name_or_path",
default=None,
type=str,
required=True,
help="Path to pre-trained model or shortcut name selected in the list")
parser.add_argument(
"--task_name",
default=None,
type=str,
required=True,
help="The name of the task to train selected in the list: " +
", ".join(processors.keys()))
parser.add_argument(
"--output_dir",
default=None,
type=str,
required=True,
help=
"The output directory where the model predictions and checkpoints will be written."
)
parser.add_argument("--vocab_file", default='', type=str)
parser.add_argument("--spm_model_file", default='', type=str)
## Other parameters
parser.add_argument(
"--config_name",
default="",
type=str,
help="Pretrained config name or path if not the same as model_name")
parser.add_argument(
"--tokenizer_name",
default="",
type=str,
help="Pretrained tokenizer name or path if not the same as model_name")
parser.add_argument(
"--cache_dir",
default="",
type=str,
help=
"Where do you want to store the pre-trained models downloaded from s3")
parser.add_argument(
"--max_seq_length",
default=512,
type=int,
help=
"The maximum total input sequence length after tokenization. Sequences longer "
"than this will be truncated, sequences shorter will be padded.")
parser.add_argument("--do_train",
action='store_true',
help="Whether to run training.")
parser.add_argument("--do_eval",
action='store_true',
help="Whether to run eval on the dev set.")
parser.add_argument(
"--do_predict",
action='store_true',
help="Whether to run the model in inference mode on the test set.")
parser.add_argument(
"--do_lower_case",
action='store_true',
help="Set this flag if you are using an uncased model.")
parser.add_argument("--per_gpu_train_batch_size",
default=8,
type=int,
help="Batch size per GPU/CPU for training.")
parser.add_argument("--per_gpu_eval_batch_size",
default=8,
type=int,
help="Batch size per GPU/CPU for evaluation.")
parser.add_argument(
'--gradient_accumulation_steps',
type=int,
default=1,
help=
"Number of updates steps to accumulate before performing a backward/update pass."
)
parser.add_argument("--learning_rate",
default=5e-5,
type=float,
help="The initial learning rate for Adam.")
parser.add_argument("--weight_decay",
default=0.0,
type=float,
help="Weight deay if we apply some.")
parser.add_argument("--adam_epsilon",
default=1e-6,
type=float,
help="Epsilon for Adam optimizer.")
parser.add_argument("--max_grad_norm",
default=1.0,
type=float,
help="Max gradient norm.")
parser.add_argument("--num_train_epochs",
default=3.0,
type=float,
help="Total number of training epochs to perform.")
parser.add_argument(
"--max_steps",
default=-1,
type=int,
help=
"If > 0: set total number of training steps to perform. Override num_train_epochs."
)
parser.add_argument(
"--warmup_proportion",
default=0.1,
type=float,
help=
"Proportion of training to perform linear learning rate warmup for,E.g., 0.1 = 10% of training."
)
parser.add_argument('--logging_steps',
type=int,
default=10,
help="Log every X updates steps.")
parser.add_argument('--save_steps',
type=int,
default=1000,
help="Save checkpoint every X updates steps.")
parser.add_argument(
"--eval_all_checkpoints",
action='store_true',
help=
"Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number"
)
parser.add_argument("--no_cuda",
action='store_true',
help="Avoid using CUDA when available")
parser.add_argument('--overwrite_output_dir',
action='store_true',
help="Overwrite the content of the output directory")
parser.add_argument(
'--overwrite_cache',
action='store_true',
help="Overwrite the cached training and evaluation sets")
parser.add_argument('--seed',
type=int,
default=42,
help="random seed for initialization")
parser.add_argument(
'--fp16',
action='store_true',
help=
"Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit"
)
parser.add_argument(
'--fp16_opt_level',
type=str,
default='O1',
help=
"For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
"See details at https://nvidia.github.io/apex/amp.html")
parser.add_argument("--local_rank",
type=int,
default=-1,
help="For distributed training: local_rank")
parser.add_argument('--server_ip',
type=str,
default='',
help="For distant debugging.")
parser.add_argument('--server_port',
type=str,
default='',
help="For distant debugging.")
os.environ['CUDA_VISIBLE_DEVICES'] = "0"
args = parser.parse_args()
if not os.path.exists(args.output_dir):
os.mkdir(args.output_dir)
args.output_dir = args.output_dir + '{}'.format(args.model_type)
if not os.path.exists(args.output_dir):
os.mkdir(args.output_dir)
init_logger(log_file=args.output_dir +
'/{}-{}.log'.format(args.model_type, args.task_name))
if os.path.exists(args.output_dir) and os.listdir(
args.output_dir
) and args.do_train and not args.overwrite_output_dir:
raise ValueError(
"Output directory ({}) already exists and is not empty. Use --overwrite_output_dir to overcome."
.format(args.output_dir))
# Setup distant debugging if needed
if args.server_ip and args.server_port:
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print("Waiting for debugger attach")
ptvsd.enable_attach(address=(args.server_ip, args.server_port),
redirect_output=True)
ptvsd.wait_for_attach()
# Setup CUDA, GPU & distributed training
if args.local_rank == -1 or args.no_cuda:
device = torch.device("cuda" if torch.cuda.is_available()
and not args.no_cuda else "cpu")
args.n_gpu = torch.cuda.device_count()
else: # Initializes the distributed backend which will take care of sychronizing nodes/GPUs
torch.cuda.set_device(args.local_rank)
device = torch.device("cuda", args.local_rank)
torch.distributed.init_process_group(backend='nccl')
args.n_gpu = 1
args.device = device
# Setup logging
logger.warning(
"Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s",
args.local_rank, device, args.n_gpu, bool(args.local_rank != -1),
args.fp16)
# Set seed
seed_everything(args.seed)
# Prepare GLUE task
args.task_name = args.task_name.lower()
if args.task_name not in processors:
raise ValueError("Task not found: %s" % (args.task_name))
processor = processors[args.task_name]()
args.output_mode = output_modes[args.task_name]
label_list = processor.get_labels()
num_labels = len(label_list)
# Load pretrained model and tokenizer
if args.local_rank not in [-1, 0]:
torch.distributed.barrier(
) # Make sure only the first process in distributed training will download model & vocab
args.model_type = args.model_type.lower()
config = AlbertConfig.from_pretrained(
args.config_name if args.config_name else args.model_name_or_path,
num_labels=num_labels,
finetuning_task=args.task_name)
tokenizer = tokenization_albert.FullTokenizer(
vocab_file=args.vocab_file,
do_lower_case=args.do_lower_case,
spm_model_file=args.spm_model_file)
model = AlbertForSentenceRanking.from_pretrained(
args.model_name_or_path,
from_tf=bool('.ckpt' in args.model_name_or_path),
config=config)
if args.local_rank == 0:
torch.distributed.barrier(
) # Make sure only the first process in distributed training will download model & vocab
model.to(args.device)
logger.info("Training/evaluation parameters %s", args)
# Training
if args.do_train:
train_dataset = load_and_cache_examples(args,
args.task_name,
tokenizer,
data_type='train')
global_step, tr_loss = train(args, train_dataset, model, tokenizer)
logger.info(" global_step = %s, average loss = %s", global_step,
tr_loss)
# Saving best-practices: if you use defaults names for the model, you can reload it using from_pretrained()
if args.do_train and (args.local_rank == -1
or torch.distributed.get_rank() == 0):
# Create output directory if needed
if not os.path.exists(args.output_dir) and args.local_rank in [-1, 0]:
os.makedirs(args.output_dir)
logger.info("Saving model checkpoint to %s", args.output_dir)
# Save a trained model, configuration and tokenizer using `save_pretrained()`.
# They can then be reloaded using `from_pretrained()`
model_to_save = model.module if hasattr(
model,
'module') else model # Take care of distributed/parallel training
model_to_save.save_pretrained(args.output_dir)
# Good practice: save your training arguments together with the trained model
torch.save(args, os.path.join(args.output_dir, 'training_args.bin'))
# Evaluation
results = []
if args.do_eval and args.local_rank in [-1, 0]:
tokenizer = tokenization_albert.FullTokenizer(
vocab_file=args.vocab_file,
do_lower_case=args.do_lower_case,
spm_model_file=args.spm_model_file)
checkpoints = [(0, args.output_dir)]
if args.eval_all_checkpoints:
checkpoints = list(
os.path.dirname(c) for c in sorted(
glob.glob(args.output_dir + '/**/' + WEIGHTS_NAME,
recursive=True)))
checkpoints = [(int(checkpoint.split('-')[-1]), checkpoint)
for checkpoint in checkpoints
if checkpoint.find('checkpoint') != -1]
checkpoints = sorted(checkpoints, key=lambda x: x[0])
logger.info("Evaluate the following checkpoints: %s", checkpoints)
for _, checkpoint in checkpoints:
global_step = checkpoint.split(
'-')[-1] if len(checkpoints) > 1 else ""
prefix = checkpoint.split(
'/')[-1] if checkpoint.find('checkpoint') != -1 else ""
model = AlbertForSentenceRanking.from_pretrained(checkpoint)
model.to(args.device)
result = evaluate(args, model, tokenizer, prefix=prefix)
results.extend([(k + '_{}'.format(global_step), v)
for k, v in result.items()])
output_eval_file = os.path.join(args.output_dir,
"checkpoint_eval_results.txt")
with open(output_eval_file, "w") as writer:
for key, value in results:
writer.write("%s = %s\n" % (key, str(value)))