def __init__(self, projection_dim=512, **kwargs):
super().__init__(**kwargs)
if "text_config" not in kwargs:
raise ValueError("`text_config` can not be `None`.")
if "vision_config" not in kwargs:
raise ValueError("`vision_config` can not be `None`.")
text_config = kwargs.pop("text_config")
vision_config = kwargs.pop("vision_config")
text_model_type = text_config.pop("model_type")
vision_model_type = vision_config.pop("model_type")
from transformers import AutoConfig
self.text_config = AutoConfig.for_model(text_model_type, **text_config)
if vision_model_type == "clip":
self.vision_config = AutoConfig.for_model(
vision_model_type, **vision_config).vision_config
elif vision_model_type == "clip_vision_model":
from transformers import CLIPVisionConfig
self.vision_config = CLIPVisionConfig(**vision_config)
else:
self.vision_config = AutoConfig.for_model(vision_model_type,
**vision_config)
self.projection_dim = projection_dim
self.initializer_factor = 1.0
def __init__(self, **kwargs):
super().__init__(**kwargs)
assert (
"encoder" in kwargs and "decoder" in kwargs
), "Config has to be initialized with encoder and decoder config"
encoder_config = kwargs.pop("encoder")
encoder_model_type = encoder_config.pop("model_type")
decoder_config = kwargs.pop("decoder")
decoder_model_type = decoder_config.pop("model_type")
from transformers import AutoConfig
self.encoder = AutoConfig.for_model(encoder_model_type, **encoder_config)
self.decoder = AutoConfig.for_model(decoder_model_type, **decoder_config)
self.is_encoder_decoder = True
def __init__(self, **kwargs):
super().__init__(**kwargs)
assert ("layout_lm" in kwargs
and "bert" in kwargs), "Layout Lm and Bert required."
layout_lm_config = kwargs.pop("layout_lm")
layout_lm_config_model_type = layout_lm_config.pop("model_type")
bert_config = kwargs.pop("bert")
bert_config_model_type = bert_config.pop("model_type")
from transformers import AutoConfig
self.layout_lm = AutoConfig.for_model(layout_lm_config_model_type,
**layout_lm_config)
self.bert = AutoConfig.for_model(bert_config_model_type, **bert_config)
def __init__(self, path: str = 'small', device=None, **kwargs):
if device is not None:
if isinstance(device, torch.device):
self.device = device
elif isinstance(device, str):
self.device = torch.device(device)
elif torch.cuda.is_available():
self.device = torch.device('cuda')
else:
self.device = torch.device('cpu')
if path in model_map or is_remote_url(path) or os.path.isfile(path):
proxies = kwargs.pop("proxies", None)
cache_dir = kwargs.pop("cache_dir", LTP_CACHE)
force_download = kwargs.pop("force_download", False)
resume_download = kwargs.pop("resume_download", False)
local_files_only = kwargs.pop("local_files_only", False)
path = cached_path(model_map.get(path, path),
cache_dir=cache_dir,
force_download=force_download,
proxies=proxies,
resume_download=resume_download,
local_files_only=local_files_only,
extract_compressed_file=True)
elif not os.path.isdir(path):
raise FileNotFoundError()
try:
ckpt = torch.load(os.path.join(path, "ltp.model"),
map_location=self.device)
except Exception as e:
fake_import_pytorch_lightning()
ckpt = torch.load(os.path.join(path, "ltp.model"),
map_location=self.device)
self.cache_dir = path
config = AutoConfig.for_model(**ckpt['transformer_config'])
self.model = Model(ckpt['model_config'], config=config).to(self.device)
self.model.load_state_dict(ckpt['model'], strict=False)
self.model.eval()
self.max_length = self.model.transformer.config.max_position_embeddings
self.seg_vocab = ckpt.get('seg', [WORD_MIDDLE, WORD_START])
self.pos_vocab = ckpt.get('pos', [])
self.ner_vocab = ckpt.get('ner', [])
self.dep_vocab = ckpt.get('dep', [])
self.sdp_vocab = ckpt.get('sdp', [])
self.srl_vocab = [
re.sub(r'ARG(\d)', r'A\1', tag.lstrip('ARGM-'))
for tag in ckpt.get('srl', [])
]
self.tokenizer = AutoTokenizer.from_pretrained(
path, config=self.model.transformer.config, use_fast=True)
self.trie = Trie()
def __init__(self, **kwargs):
super().__init__(**kwargs)
assert (
"image_encoder" in kwargs and "command_encoder" in kwargs
and "decoder" in kwargs
), "Config has to be initialized with image encoder, command encoder and decoder configs"
image_encoder_config = kwargs.pop("image_encoder")
image_encoder_model_type = image_encoder_config.pop("model_type")
command_encoder_config = kwargs.pop("command_encoder")
command_encoder_model_type = command_encoder_config.pop("model_type")
decoder_config = kwargs.pop("decoder")
decoder_model_type = decoder_config.pop("model_type")
self.image_encoder = AutoConfig.for_model(image_encoder_model_type,
**image_encoder_config)
self.command_encoder = AutoConfig.for_model(command_encoder_model_type,
**command_encoder_config)
self.decoder = AutoConfig.for_model(decoder_model_type,
**decoder_config)
self.is_encoder_decoder = True
def create_pretrained(self,
pretrained: str = None,
config: Union[str, dict, PretrainedConfig] = None,
freeze: bool = False):
if isinstance(pretrained, str): # 认为是 PATH 或 Huggingface 社区模型
pretrained = AutoModel.from_pretrained(pretrained)
elif isinstance(config, PretrainedConfig):
pretrained = AutoModel.from_config(config)
elif isinstance(config, str) or isinstance(
config, dict): # 认为是 PATH 或 Huggingface 社区模型
config = AutoConfig.for_model(**config)
pretrained = AutoModel.from_config(config)
else:
raise NotImplementedError()
if freeze:
for param in pretrained.parameters():
param.requires_grad = False
return pretrained
def main():
parser = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--train_data_file", default=None, type=str, required=True, help="The input training data file (a text file)."
)
parser.add_argument(
"--output_dir",
type=str,
required=True,
help="The output directory where the model predictions and checkpoints will be written.",
)
parser.add_argument(
"--model_type", type=str, required=True, help="The model architecture to be trained or fine-tuned.",
)
# Other parameters
parser.add_argument(
"--eval_data_file",
default=None,
type=str,
help="An optional input evaluation data file to evaluate the perplexity on (a text file).",
)
parser.add_argument(
"--line_by_line",
action="store_true",
help="Whether distinct lines of text in the dataset are to be handled as distinct sequences.",
)
parser.add_argument(
"--should_continue", action="store_true", help="Whether to continue from latest checkpoint in output_dir"
)
parser.add_argument(
"--model_name_or_path",
default=None,
type=str,
help="The model checkpoint for weights initialization. Leave None if you want to train a model from scratch.",
)
parser.add_argument(
"--mlm", action="store_true", help="Train with masked-language modeling loss instead of language modeling."
)
parser.add_argument(
"--mlm_probability", type=float, default=0.15, help="Ratio of tokens to mask for masked language modeling loss"
)
parser.add_argument(
"--config_name",
default=None,
type=str,
help="Optional pretrained config name or path if not the same as model_name_or_path. If both are None, initialize a new config.",
)
parser.add_argument(
"--tokenizer_name",
default=None,
type=str,
help="Optional pretrained tokenizer name or path if not the same as model_name_or_path. If both are None, initialize a new tokenizer.",
)
parser.add_argument(
"--tokenizer_class",
default="",
type=str,
help="Optional pre-trained tokenizer class"
)
parser.add_argument(
"--cache_dir",
default=None,
type=str,
help="Optional directory to store the pre-trained models downloaded from s3 (instead of the default one)",
)
parser.add_argument(
"--block_size",
default=-1,
type=int,
help="Optional input sequence length after tokenization."
"The training dataset will be truncated in block of this size for training."
"Default to the model max input length for single sentence inputs (take into account special tokens).",
)
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(
"--evaluate_during_training", action="store_true", help="Run evaluation during training at each logging step."
)
parser.add_argument("--per_gpu_train_batch_size", default=4, type=int, help="Batch size per GPU/CPU for training.")
parser.add_argument(
"--per_gpu_eval_batch_size", default=4, 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 decay if we apply some.")
parser.add_argument("--adam_epsilon", default=1e-8, 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=1.0, 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_steps", default=0, type=int, help="Linear warmup over warmup_steps.")
parser.add_argument("--logging_steps", type=int, default=500, help="Log every X updates steps.")
parser.add_argument("--save_steps", type=int, default=500, help="Save checkpoint every X updates steps.")
parser.add_argument(
"--save_total_limit",
type=int,
default=None,
help="Limit the total amount of checkpoints, delete the older checkpoints in the output_dir, does not delete by default",
)
parser.add_argument(
"--eval_all_checkpoints",
action="store_true",
help="Evaluate all checkpoints starting with the same prefix as model_name_or_path 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("--do_lower_case", action='store_true',
help="Set this flag if you are using an uncased model.")
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.")
args = parser.parse_args()
if args.model_type in ["bert", "roberta", "distilbert", "camembert"] and not args.mlm:
raise ValueError(
"BERT and RoBERTa-like models do not have LM heads but masked LM heads. They must be run using the --mlm "
"flag (masked language modeling)."
)
if args.eval_data_file is None and args.do_eval:
raise ValueError(
"Cannot do evaluation without an evaluation data file. Either supply a file to --eval_data_file "
"or remove the --do_eval argument."
)
if args.should_continue:
sorted_checkpoints = _sorted_checkpoints(args)
if len(sorted_checkpoints) == 0:
raise ValueError("Used --should_continue but no checkpoint was found in --output_dir.")
else:
args.model_name_or_path = sorted_checkpoints[-1]
if (
os.path.exists(args.output_dir)
and os.listdir(args.output_dir)
and args.do_train
and not args.overwrite_output_dir
and not args.should_continue
):
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 = 0 if args.no_cuda else 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
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN,
)
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
set_seed(args)
# Load pretrained model and tokenizer
if args.local_rank not in [-1, 0]:
torch.distributed.barrier() # Barrier to make sure only the first process in distributed training download model & vocab
if args.model_type:
config = AutoConfig.for_model(args.model_type)
elif args.config_name:
config = AutoConfig.from_pretrained(args.config_name, cache_dir=args.cache_dir)
elif args.model_name_or_path:
config = AutoConfig.from_pretrained(args.model_name_or_path, cache_dir=args.cache_dir)
else:
# When we release a pip version exposing CONFIG_MAPPING,
# we can do `config = CONFIG_MAPPING[args.model_type]()`.
raise ValueError(
"You are instantiating a new config instance from scratch. This is not supported, but you can do it from another script, save it,"
"and load it from here, using --config_name"
)
tokenizer_class = ''
if args.tokenizer_class:
tokenizer_class = globals()[args.tokenizer_class]
print("Tokenizer is %s" % tokenizer_class)
tokenizer = tokenizer_class.from_pretrained(
args.tokenizer_name if args.tokenizer_name else args.model_name_or_path, do_lower_case=args.do_lower_case)
if args.block_size <= 0:
args.block_size = tokenizer.max_len
# Our input block size will be the max possible for the model
else:
args.block_size = min(args.block_size, tokenizer.max_len)
if args.model_name_or_path:
model = AutoModelWithLMHead.from_pretrained(
args.model_name_or_path,
from_tf=bool(".ckpt" in args.model_name_or_path),
config=config,
cache_dir=args.cache_dir,
)
else:
logger.info("Training new model from scratch")
model = AutoModelWithLMHead.from_config(config)
model.to(args.device)
if args.local_rank == 0:
torch.distributed.barrier() # End of barrier to make sure only the first process in distributed training download model & vocab
logger.info("Training/evaluation parameters %s", args)
# Training
if args.do_train:
if args.local_rank not in [-1, 0]:
torch.distributed.barrier() # Barrier to make sure only the first process in distributed training process the dataset, and the others will use the cache
train_dataset = load_and_cache_examples(args, tokenizer, evaluate=False)
if args.local_rank == 0:
torch.distributed.barrier()
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 save_pretrained for the model and tokenizer, you can reload them using from_pretrained()
if args.do_train and (args.local_rank == -1 or torch.distributed.get_rank() == 0):
# Create output directory if needed
if args.local_rank in [-1, 0]:
os.makedirs(args.output_dir, exist_ok=True)
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)
tokenizer.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"))
# Load a trained model and vocabulary that you have fine-tuned
model = AutoModelWithLMHead.from_pretrained(args.output_dir)
tokenizer_class = ''
if args.tokenizer_class:
tokenizer_class = globals()[args.tokenizer_class]
print("Tokenizer is %s" % tokenizer_class)
tokenizer = tokenizer_class.from_pretrained(
args.tokenizer_name if args.tokenizer_name else args.model_name_or_path, do_lower_case=args.do_lower_case)
model.to(args.device)
# Evaluation
results = {}
if args.do_eval and args.local_rank in [-1, 0]:
checkpoints = [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))
)
logging.getLogger("transformers.modeling_utils").setLevel(logging.WARN) # Reduce logging
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 = AutoModelWithLMHead.from_pretrained(checkpoint)
model.to(args.device)
result = evaluate(args, model, tokenizer, prefix=prefix)
result = dict((k + "_{}".format(global_step), v) for k, v in result.items())
results.update(result)
return results
