# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. # Copyright 2018 Google AI, Google Brain and the HuggingFace Inc. team. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import importlib import io import json import os from collections import OrderedDict from huggingface_hub import hf_hub_download from paddlenlp import __version__ from paddlenlp.transformers import * # noqa from paddlenlp.transformers.configuration_utils import is_standard_config from paddlenlp.utils.downloader import ( COMMUNITY_MODEL_PREFIX, get_path_from_url, hf_file_exists, url_file_exists, ) from paddlenlp.utils.log import logger from ..utils import resolve_cache_dir __all__ = [ "AutoBackbone", "AutoModel", "AutoModelForPretraining", "AutoModelForSequenceClassification", "AutoModelForTokenClassification", "AutoModelForQuestionAnswering", "AutoModelForMultipleChoice", "AutoModelForMaskedLM", "AutoModelForCausalLM", "AutoEncoder", "AutoDecoder", "AutoGenerator", "AutoDiscriminator", "AutoModelForConditionalGeneration", "AutoModelForImageGeneration", ] MAPPING_NAMES = OrderedDict( [ # Base model mapping ("Albert", "albert"), ("BigBird", "bigbird"), ("BlenderbotSmall", "blenderbot_small"), ("Blenderbot", "blenderbot"), ("ChineseCLIP", "chineseclip"), ("ChineseBert", "chinesebert"), ("ConvBert", "convbert"), ("CTRL", "ctrl"), ("DistilBert", "distilbert"), ("DalleBart", "dallebart"), ("Electra", "electra"), ("ErnieViL", "ernie_vil"), ("ErnieCtm", "ernie_ctm"), ("ErnieDoc", "ernie_doc"), ("ErnieGen", "ernie_gen"), ("ErnieGram", "ernie_gram"), ("ErnieLayout", "ernie_layout"), ("ErnieM", "ernie_m"), ("Ernie", "ernie"), ("FNet", "fnet"), ("Funnel", "funnel"), ("LayoutXLM", "layoutxlm"), ("LayoutLMv2", "layoutlmv2"), ("LayoutLM", "layoutlm"), ("Luke", "luke"), ("MBart", "mbart"), ("MegatronBert", "megatronbert"), ("MobileBert", "mobilebert"), ("MPNet", "mpnet"), ("NeZha", "nezha"), ("Nystromformer", "nystromformer"), ("PPMiniLM", "ppminilm"), ("ProphetNet", "prophetnet"), ("Reformer", "reformer"), ("RemBert", "rembert"), ("Roberta", "roberta"), ("RoFormerv2", "roformerv2"), ("RoFormer", "roformer"), ("Skep", "skep"), ("SqueezeBert", "squeezebert"), ("TinyBert", "tinybert"), ("UnifiedTransformer", "unified_transformer"), ("UNIMO", "unimo"), ("XLNet", "xlnet"), ("XLM", "xlm"), ("GPT", "gpt"), ("MT5", "mt5"), ("T5", "t5"), ("Bert", "bert"), ("Bart", "bart"), ("GAUAlpha", "gau_alpha"), ("CodeGen", "codegen"), ("CLIPVision", "clip"), ("CLIPText", "clip"), ("CLIP", "clip"), ("ChineseCLIPVision", "chineseclip"), ("ChineseCLIPText", "chineseclip"), ("ChineseCLIP", "chineseclip"), ("Artist", "artist"), ("OPT", "opt"), ("Pegasus", "pegasus"), ("DPT", "dpt"), ("Bit", "bit"), ("BlipText", "blip"), ("BlipVision", "blip"), ("Blip", "blip"), ] ) MAPPING_TASKS = OrderedDict( [ ("Backbone", "AutoBackbone"), ("Model", "AutoModel"), ("ForPretraining", "AutoModelForPretraining"), ("ForSequenceClassification", "AutoModelForSequenceClassification"), ("ForTokenClassification", "AutoModelForTokenClassification"), ("ForQuestionAnswering", "AutoModelForQuestionAnswering"), ("ForMultipleChoice", "AutoModelForMultipleChoice"), ("ForMaskedLM", "AutoModelForMaskedLM"), ("ForCausalLM", "AutoModelForCausalLM"), ("Encoder", "AutoEncoder"), ("Decoder", "AutoDecoder"), ("Generator", "AutoGenerator"), ("Discriminator", "AutoDiscriminator"), ("ForConditionalGeneration", "AutoModelForConditionalGeneration"), ("ForImageGeneration", "AutoModelForImageGeneration"), ] ) def get_name_mapping(task="Model"): """ Task can be 'Backbone', 'Model', 'ForPretraining', 'ForSequenceClassification', 'ForTokenClassification', 'ForQuestionAnswering', 'ForMultipleChoice', 'ForMaskedLM', 'ForCausalLM', 'Encoder', 'Decoder', 'Generator', 'Discriminator', 'ForConditionalGeneration', 'ForImageGeneration'. """ NAME_MAPPING = OrderedDict() for key, value in MAPPING_NAMES.items(): import_class = key + task new_key = key + "Model_Import_Class" NAME_MAPPING[new_key] = import_class NAME_MAPPING[import_class] = value return NAME_MAPPING def get_task_name(model_class): for key, value in MAPPING_TASKS.items(): if model_class.endswith(key): return value return None def get_init_configurations(): CONFIGURATION_MODEL_MAPPING = OrderedDict() for key, class_name in MAPPING_NAMES.items(): import_class = importlib.import_module(f"paddlenlp.transformers.{class_name}.modeling") model_name = getattr(import_class, key + "Model") if key == "ErnieGen": name = tuple(model_name.ernie_gen_pretrained_init_configuration.keys()) else: name = tuple(model_name.pretrained_init_configuration.keys()) CONFIGURATION_MODEL_MAPPING[name] = key + "Model" return CONFIGURATION_MODEL_MAPPING class _BaseAutoModelClass: # Base class for auto models. _pretrained_model_dict = None _name_mapping = None _task_choice = False model_config_file = "config.json" legacy_model_config_file = "model_config.json" def __init__(self, *args, **kwargs): raise EnvironmentError( f"{self.__class__.__name__} is designed to be instantiated " f"using the `{self.__class__.__name__}.from_pretrained(pretrained_model_name_or_path).`" ) # TODO: Refactor into AutoConfig when available @classmethod def _get_model_class_from_config(cls, pretrained_model_name_or_path, config_file_path): with io.open(config_file_path, encoding="utf-8") as f: config = json.load(f) # Get class name corresponds to this configuration if is_standard_config(config): architectures = config["architectures"] init_class = architectures.pop() if len(architectures) > 0 else None else: init_class = config.pop("init_class", None) init_class = init_class[:-5] if init_class is not None and init_class.endswith("Model") else init_class model_name = None if init_class: for model_flag, name in MAPPING_NAMES.items(): if model_flag in init_class: model_name = model_flag + "Model" break else: # From pretrained_model_name_or_path for model_flag, name in MAPPING_NAMES.items(): if name in pretrained_model_name_or_path.lower(): model_name = model_flag + "Model" break if model_name is None: raise AttributeError( f"Unable to parse 'architectures' or 'init_class' from {config_file_path}. Also unable to infer model class from 'pretrained_model_name_or_path'" ) init_class = cls._name_mapping[model_name + "_Import_Class"] class_name = cls._name_mapping[init_class] import_class = importlib.import_module(f"paddlenlp.transformers.{class_name}.modeling") try: model_class = getattr(import_class, init_class) return model_class except AttributeError as err: logger.error(err) all_model_classes = import_class.__all__ all_tasks = {get_task_name(m) for m in all_model_classes if get_task_name(m) is not None} raise AttributeError( f"module '{import_class.__name__}' only supports the following classes: " + ", ".join(m for m in all_model_classes) + "\n" "Hint: you can use interface " + " or ".join(task + ".from_pretrained" for task in all_tasks) + f" to load '{pretrained_model_name_or_path}'\n" ) @classmethod def _from_pretrained( cls, pretrained_model_name_or_path, task=None, from_hf_hub=False, subfolder=None, *model_args, **kwargs ): if task: if cls._task_choice: cls._name_mapping = get_name_mapping(task) else: print("We only support task choice for AutoModel.") cache_dir = kwargs.get("cache_dir", None) cache_dir = resolve_cache_dir(pretrained_model_name_or_path, from_hf_hub, cache_dir) all_model_names = [] for pretrained_model_names, model_name in cls._pretrained_model_dict.items(): for name in pretrained_model_names: all_model_names.append(name) # From HF if from_hf_hub: if hf_file_exists(repo_id=pretrained_model_name_or_path, filename=cls.model_config_file): config_file = hf_hub_download( repo_id=pretrained_model_name_or_path, filename=cls.model_config_file, subfolder=subfolder, cache_dir=cache_dir, library_name="PaddleNLP", library_version=__version__, ) elif hf_file_exists(repo_id=pretrained_model_name_or_path, filename=cls.legacy_model_config_file): logger.info("Standard config do not exist, loading from legacy config") config_file = hf_hub_download( repo_id=pretrained_model_name_or_path, filename=cls.legacy_model_config_file, subfolder=subfolder, cache_dir=cache_dir, library_name="PaddleNLP", library_version=__version__, ) if os.path.exists(config_file): model_class = cls._get_model_class_from_config(pretrained_model_name_or_path, config_file) logger.info(f"We are using {model_class} to load '{pretrained_model_name_or_path}'.") return model_class.from_pretrained( pretrained_model_name_or_path, from_hf_hub=from_hf_hub, *model_args, **kwargs ) else: logger.warning(f"{config_file} is not a valid path to a model config file") # From built-in pretrained models elif pretrained_model_name_or_path in all_model_names: for pretrained_model_names, model_name in cls._pretrained_model_dict.items(): # From built-in pretrained models for pattern in pretrained_model_names: if pattern == pretrained_model_name_or_path: init_class = cls._name_mapping[model_name + "_Import_Class"] class_name = cls._name_mapping[init_class] import_class = importlib.import_module(f"paddlenlp.transformers.{class_name}.modeling") try: model_class = getattr(import_class, init_class) except AttributeError as err: logger.error(err) all_model_classes = import_class.__all__ all_tasks = {get_task_name(m) for m in all_model_classes if get_task_name(m) is not None} raise AttributeError( f"module '{import_class.__name__}' only supports the following classes: " + ", ".join(m for m in all_model_classes) + "\n" "Hint: you can use interface " + " or ".join(task + ".from_pretrained" for task in all_tasks) + f" to load '{pretrained_model_name_or_path}'\n" ) logger.info(f"We are using {model_class} to load '{pretrained_model_name_or_path}'.") return model_class.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) # From local dir path elif os.path.isdir(pretrained_model_name_or_path): config_file = os.path.join(pretrained_model_name_or_path, cls.model_config_file) legacy_config_file = os.path.join(pretrained_model_name_or_path, cls.legacy_model_config_file) if os.path.exists(config_file): model_class = cls._get_model_class_from_config(pretrained_model_name_or_path, config_file) logger.info(f"We are using {model_class} to load '{pretrained_model_name_or_path}'.") return model_class.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) elif os.path.exists(legacy_config_file): logger.info("Standard config do not exist, loading from legacy config") model_class = cls._get_model_class_from_config(pretrained_model_name_or_path, legacy_config_file) logger.info(f"We are using {model_class} to load '{pretrained_model_name_or_path}'.") return model_class.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) else: logger.warning(f"{config_file} is not a valid path to a model config file") # Assuming from community-contributed pretrained models else: standard_community_url = "/".join( [COMMUNITY_MODEL_PREFIX, pretrained_model_name_or_path, cls.model_config_file] ) legacy_community_url = "/".join( [COMMUNITY_MODEL_PREFIX, pretrained_model_name_or_path, cls.legacy_model_config_file] ) try: if url_file_exists(standard_community_url): resolved_vocab_file = get_path_from_url(standard_community_url, cache_dir) elif url_file_exists(legacy_community_url): logger.info("Standard config do not exist, loading from legacy config") resolved_vocab_file = get_path_from_url(legacy_community_url, cache_dir) else: raise RuntimeError("Neither 'config.json' nro 'model_config.json' exists") except RuntimeError as err: logger.error(err) raise RuntimeError( f"Can't load weights for '{pretrained_model_name_or_path}'.\n" f"Please make sure that '{pretrained_model_name_or_path}' is:\n" "- a correct model-identifier of built-in pretrained models,\n" "- or a correct model-identifier of community-contributed pretrained models,\n" "- or the correct path to a directory containing relevant modeling files(model_weights and model_config).\n" ) if os.path.exists(resolved_vocab_file): model_class = cls._get_model_class_from_config(pretrained_model_name_or_path, resolved_vocab_file) logger.info(f"We are using {model_class} to load '{pretrained_model_name_or_path}'.") return model_class.from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) else: logger.warning(f"{resolved_vocab_file} is not a valid path to a model config file") class AutoBackbone(_BaseAutoModelClass): """ AutoBackbone. """ CONFIGURATION_MODEL_MAPPING = get_init_configurations() _pretrained_model_dict = CONFIGURATION_MODEL_MAPPING _name_mapping = get_name_mapping("Backbone") @classmethod def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs): """ Creates an instance of `AutoBackbone`. Model weights are loaded by specifying name of a built-in pretrained model, or a community contributed model, or a local file directory path. Args: pretrained_model_name_or_path (str): See :class:`AutoModel`. *args (tuple): See :class:`AutoModel`. **kwargs (dict): See :class:`AutoModel`. Returns: PretrainedModel: An instance of `AutoBackbone`. Example: .. code-block:: from paddlenlp.transformers import AutoBackbone # Name of built-in pretrained model model = AutoBackbone.from_pretrained("google/bit-50") print(type(model)) # # Load from local directory path model = AutoBackbone.from_pretrained("./bit-50") print(type(model)) # """ return cls._from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) class AutoModel(_BaseAutoModelClass): """ AutoClass can help you automatically retrieve the relevant model given the provided pretrained weights/vocabulary. AutoModel is a generic model class that will be instantiated as one of the base model classes when created with the from_pretrained() classmethod. """ CONFIGURATION_MODEL_MAPPING = get_init_configurations() _pretrained_model_dict = CONFIGURATION_MODEL_MAPPING _name_mapping = get_name_mapping("Model") _task_choice = True @classmethod def from_pretrained(cls, pretrained_model_name_or_path, task=None, *model_args, **kwargs): """ Creates an instance of `AutoModel`. Model weights are loaded by specifying name of a built-in pretrained model, a pretrained model on HF, a community contributed model, or a local file directory path. Args: pretrained_model_name_or_path (str): Name of pretrained model or dir path to load from. The string can be: - Name of a built-in pretrained model - Name of a community-contributed pretrained model. - Local directory path which contains model weights file("model_state.pdparams") and model config file ("model_config.json"). task (str): Specify a downstream task. Task can be 'Model', 'ForPretraining', 'ForSequenceClassification', 'ForTokenClassification', 'ForQuestionAnswering', 'ForMultipleChoice', 'ForMaskedLM', 'ForCausalLM', 'Encoder', 'Decoder', 'Generator', 'Discriminator', 'ForConditionalGeneration'. We only support specify downstream tasks in AutoModel. Defaults to `None`. *args (tuple): Position arguments for model `__init__`. If provided, use these as position argument values for model initialization. **kwargs (dict): Keyword arguments for model `__init__`. If provided, use these to update pre-defined keyword argument values for model initialization. If the keyword is in `__init__` argument names of base model, update argument values of the base model; else update argument values of derived model. Returns: PretrainedModel: An instance of `AutoModel`. Example: .. code-block:: from paddlenlp.transformers import AutoModel # Name of built-in pretrained model model = AutoModel.from_pretrained('bert-base-uncased') print(type(model)) # # Name of community-contributed pretrained model model = AutoModel.from_pretrained('yingyibiao/bert-base-uncased-sst-2-finetuned') print(type(model)) # # Load from local directory path model = AutoModel.from_pretrained('./my_bert/') print(type(model)) # # choose task model = AutoModel.from_pretrained('bert-base-uncased', task='ForPretraining') print(type(model)) # """ return cls._from_pretrained(pretrained_model_name_or_path, task, *model_args, **kwargs) class AutoModelForPretraining(_BaseAutoModelClass): """ AutoModelForPretraining. """ CONFIGURATION_MODEL_MAPPING = get_init_configurations() _pretrained_model_dict = CONFIGURATION_MODEL_MAPPING _name_mapping = get_name_mapping("ForPretraining") @classmethod def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs): """ Creates an instance of `AutoModelForPretraining`. Model weights are loaded by specifying name of a built-in pretrained model, or a community contributed model, or a local file directory path. Args: pretrained_model_name_or_path (str): See :class:`AutoModel`. *args (tuple): See :class:`AutoModel`. **kwargs (dict): See :class:`AutoModel`. Returns: PretrainedModel: An instance of `AutoModelForPretraining`. Example: .. code-block:: from paddlenlp.transformers import AutoModelForPretraining # Name of built-in pretrained model model = AutoModelForPretraining.from_pretrained('bert-base-uncased') print(type(model)) # # Name of community-contributed pretrained model model = AutoModelForPretraining.from_pretrained('iverxin/bert-base-japanese') print(type(model)) # # Load from local directory path model = AutoModelForPretraining.from_pretrained('./my_bert/') print(type(model)) # """ return cls._from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) class AutoModelForSequenceClassification(_BaseAutoModelClass): """ AutoModelForSequenceClassification. """ CONFIGURATION_MODEL_MAPPING = get_init_configurations() _pretrained_model_dict = CONFIGURATION_MODEL_MAPPING _name_mapping = get_name_mapping("ForSequenceClassification") @classmethod def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs): """ Creates an instance of `AutoModelForSequenceClassification`. Model weights are loaded by specifying name of a built-in pretrained model, or a community contributed model, or a local file directory path. Args: pretrained_model_name_or_path (str): See :class:`AutoModel`. *args (tuple): See :class:`AutoModel`. **kwargs (dict): See :class:`AutoModel`. Returns: PretrainedModel: An instance of `AutoModelForSequenceClassification`. Example: .. code-block:: from paddlenlp.transformers import AutoModelForSequenceClassification # Name of built-in pretrained model model = AutoModelForSequenceClassification.from_pretrained('bert-base-uncased') print(type(model)) # # Name of community-contributed pretrained model model = AutoModelForSequenceClassification.from_pretrained('iverxin/bert-base-japanese') print(type(model)) # # Load from local directory path model = AutoModelForSequenceClassification.from_pretrained('./my_bert/') print(type(model)) # """ return cls._from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) class AutoModelForTokenClassification(_BaseAutoModelClass): """ AutoModelForTokenClassification. """ CONFIGURATION_MODEL_MAPPING = get_init_configurations() _pretrained_model_dict = CONFIGURATION_MODEL_MAPPING _name_mapping = get_name_mapping("ForTokenClassification") @classmethod def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs): """ Creates an instance of `AutoModelForTokenClassification`. Model weights are loaded by specifying name of a built-in pretrained model, or a community contributed model, or a local file directory path. Args: pretrained_model_name_or_path (str): See :class:`AutoModel`. *args (tuple): See :class:`AutoModel`. **kwargs (dict): See :class:`AutoModel`. Returns: PretrainedModel: An instance of `AutoModelForTokenClassification`. Example: .. code-block:: from paddlenlp.transformers import AutoModelForTokenClassification # Name of built-in pretrained model model = AutoModelForTokenClassification.from_pretrained('bert-base-uncased') print(type(model)) # # Name of community-contributed pretrained model model = AutoModelForTokenClassification.from_pretrained('iverxin/bert-base-japanese') print(type(model)) # # Load from local directory path model = AutoModelForTokenClassification.from_pretrained('./my_bert/') print(type(model)) # """ return cls._from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) class AutoModelForQuestionAnswering(_BaseAutoModelClass): """ AutoModelForQuestionAnswering. """ CONFIGURATION_MODEL_MAPPING = get_init_configurations() _pretrained_model_dict = CONFIGURATION_MODEL_MAPPING _name_mapping = get_name_mapping("ForQuestionAnswering") @classmethod def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs): """ Creates an instance of `AutoModelForQuestionAnswering`. Model weights are loaded by specifying name of a built-in pretrained model, or a community contributed model, or a local file directory path. Args: pretrained_model_name_or_path (str): See :class:`AutoModel`. *args (tuple): See :class:`AutoModel`. **kwargs (dict): See :class:`AutoModel`. Returns: PretrainedModel: An instance of `AutoModelForQuestionAnswering`. Example: .. code-block:: from paddlenlp.transformers import AutoModelForQuestionAnswering # Name of built-in pretrained model model = AutoModelForQuestionAnswering.from_pretrained('bert-base-uncased') print(type(model)) # # Name of community-contributed pretrained model model = AutoModelForQuestionAnswering.from_pretrained('iverxin/bert-base-japanese') print(type(model)) # # Load from local directory path model = AutoModelForQuestionAnswering.from_pretrained('./my_bert/') print(type(model)) # """ return cls._from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) class AutoModelForMultipleChoice(_BaseAutoModelClass): """ AutoModelForMultipleChoice. """ CONFIGURATION_MODEL_MAPPING = get_init_configurations() _pretrained_model_dict = CONFIGURATION_MODEL_MAPPING _name_mapping = get_name_mapping("ForMultipleChoice") @classmethod def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs): """ Creates an instance of `AutoModelForMultipleChoice`. Model weights are loaded by specifying name of a built-in pretrained model, or a community contributed model, or a local file directory path. Args: pretrained_model_name_or_path (str): See :class:`AutoModel`. *args (tuple): See :class:`AutoModel`. **kwargs (dict): See :class:`AutoModel`. Returns: PretrainedModel: An instance of `AutoModelForMultipleChoice`. Example: .. code-block:: from paddlenlp.transformers import AutoModelForMultipleChoice # Name of built-in pretrained model model = AutoModelForMultipleChoice.from_pretrained('bert-base-uncased') print(type(model)) # # Name of community-contributed pretrained model model = AutoModelForMultipleChoice.from_pretrained('iverxin/bert-base-japanese') print(type(model)) # # Load from local directory path model = AutoModelForMultipleChoice.from_pretrained('./my_bert/') print(type(model)) # """ return cls._from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) class AutoModelForMaskedLM(_BaseAutoModelClass): """ AutoModelForMaskedLM. """ CONFIGURATION_MODEL_MAPPING = get_init_configurations() _pretrained_model_dict = CONFIGURATION_MODEL_MAPPING _name_mapping = get_name_mapping("ForMaskedLM") @classmethod def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs): """ Creates an instance of `AutoModelForMaskedLM`. Model weights are loaded by specifying name of a built-in pretrained model, or a community contributed model, or a local file directory path. Args: pretrained_model_name_or_path (str): See :class:`AutoModel`. *args (tuple): See :class:`AutoModel`. **kwargs (dict): See :class:`AutoModel`. Returns: PretrainedModel: An instance of `AutoModelForMaskedLM`. Example: .. code-block:: from paddlenlp.transformers import AutoModelForMaskedLM # Name of built-in pretrained model model = AutoModelForMaskedLM.from_pretrained('bert-base-uncased') print(type(model)) # # Name of community-contributed pretrained model model = AutoModelForMaskedLM.from_pretrained('iverxin/bert-base-japanese') print(type(model)) # # Load from local directory path model = AutoModelForMaskedLM.from_pretrained('./my_bert/') print(type(model)) # """ return cls._from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) class AutoModelForCausalLM(_BaseAutoModelClass): """ AutoModelForCausalLM. """ CONFIGURATION_MODEL_MAPPING = get_init_configurations() _pretrained_model_dict = CONFIGURATION_MODEL_MAPPING _name_mapping = get_name_mapping("ForCausalLM") @classmethod def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs): """ Creates an instance of `AutoModelForCausalLM`. Model weights are loaded by specifying name of a built-in pretrained model, or a community contributed model, or a local file directory path. Args: pretrained_model_name_or_path (str): See :class:`AutoModel`. *args (tuple): See :class:`AutoModel`. **kwargs (dict): See :class:`AutoModel`. Returns: PretrainedModel: An instance of `AutoModelForCausalLM`. Example: .. code-block:: from paddlenlp.transformers import AutoModelForCausalLM # Name of built-in pretrained model model = AutoModelForCausalLM.from_pretrained('gpt2-en') print(type(model)) # # Name of community-contributed pretrained model model = AutoModelForCausalLM.from_pretrained('junnyu/distilgpt2') print(type(model)) # # Load from local directory path model = AutoModelForCausalLM.from_pretrained('./my_gpt/') print(type(model)) # """ return cls._from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) class AutoEncoder(_BaseAutoModelClass): """ AutoEncoder. """ CONFIGURATION_MODEL_MAPPING = get_init_configurations() _pretrained_model_dict = CONFIGURATION_MODEL_MAPPING _name_mapping = get_name_mapping("Encoder") @classmethod def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs): """ Creates an instance of `AutoEncoder`. Model weights are loaded by specifying name of a built-in pretrained model, or a community contributed model, or a local file directory path. Args: pretrained_model_name_or_path (str): See :class:`AutoModel`. *args (tuple): See :class:`AutoModel`. **kwargs (dict): See :class:`AutoModel`. Returns: PretrainedModel: An instance of `AutoEncoder`. Example: .. code-block:: from paddlenlp.transformers import AutoEncoder # Name of built-in pretrained model model = AutoEncoder.from_pretrained('bart-base',vocab_size=20000) print(type(model)) # # Load from local directory path model = AutoEncoder.from_pretrained('./my_bart/') print(type(model)) # """ return cls._from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) class AutoDecoder(_BaseAutoModelClass): """ AutoDecoder. """ CONFIGURATION_MODEL_MAPPING = get_init_configurations() _pretrained_model_dict = CONFIGURATION_MODEL_MAPPING _name_mapping = get_name_mapping("Decoder") @classmethod def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs): """ Creates an instance of `AutoDecoder`. Model weights are loaded by specifying name of a built-in pretrained model, or a community contributed model, or a local file directory path. Args: pretrained_model_name_or_path (str): See :class:`AutoModel`. *args (tuple): See :class:`AutoModel`. **kwargs (dict): See :class:`AutoModel`. Returns: PretrainedModel: An instance of `AutoDecoder`. Example: .. code-block:: from paddlenlp.transformers import AutoDecoder # Name of built-in pretrained model model = AutoDecoder.from_pretrained('bart-base', vocab_size=20000) print(type(model)) # # Load from local directory path model = AutoDecoder.from_pretrained('./my_bart/') print(type(model)) # """ return cls._from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) class AutoGenerator(_BaseAutoModelClass): """ AutoGenerator. """ CONFIGURATION_MODEL_MAPPING = get_init_configurations() _pretrained_model_dict = CONFIGURATION_MODEL_MAPPING _name_mapping = get_name_mapping("Generator") @classmethod def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs): """ Creates an instance of `AutoGenerator`. Model weights are loaded by specifying name of a built-in pretrained model, or a community contributed model, or a local file directory path. Args: pretrained_model_name_or_path (str): See :class:`AutoModel`. *args (tuple): See :class:`AutoModel`. **kwargs (dict): See :class:`AutoModel`. Returns: PretrainedModel: An instance of `AutoGenerator`. Example: .. code-block:: from paddlenlp.transformers import AutoGenerator # Name of built-in pretrained model model = AutoGenerator.from_pretrained('electra-small') print(type(model)) # # Name of community-contributed pretrained model model = AutoGenerator.from_pretrained('junnyu/hfl-chinese-legal-electra-small-generator') print(type(model)) # # Load from local directory path model = AutoGenerator.from_pretrained('./my_electra/') print(type(model)) # """ return cls._from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) class AutoDiscriminator(_BaseAutoModelClass): """ AutoDiscriminator. """ CONFIGURATION_MODEL_MAPPING = get_init_configurations() _pretrained_model_dict = CONFIGURATION_MODEL_MAPPING _name_mapping = get_name_mapping("Discriminator") @classmethod def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs): """ Creates an instance of `AutoDiscriminator`. Model weights are loaded by specifying name of a built-in pretrained model, or a community contributed model, or a local file directory path. Args: pretrained_model_name_or_path (str): See :class:`AutoModel`. *args (tuple): See :class:`AutoModel`. **kwargs (dict): See :class:`AutoModel`. Returns: PretrainedModel: An instance of `AutoDiscriminator`. Example: .. code-block:: from paddlenlp.transformers import AutoDiscriminator # Name of built-in pretrained model model = AutoDiscriminator.from_pretrained('electra-small') print(type(model)) # # Name of community-contributed pretrained model model = AutoDiscriminator.from_pretrained('junnyu/hfl-chinese-legal-electra-small-generator') print(type(model)) # # Load from local directory path model = AutoDiscriminator.from_pretrained('./my_electra/') print(type(model)) # """ return cls._from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) class AutoModelForConditionalGeneration(_BaseAutoModelClass): """ AutoModelForConditionalGeneration. """ CONFIGURATION_MODEL_MAPPING = get_init_configurations() _pretrained_model_dict = CONFIGURATION_MODEL_MAPPING _name_mapping = get_name_mapping("ForConditionalGeneration") @classmethod def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs): """ Creates an instance of `AutoModelForConditionalGeneration`. Model weights are loaded by specifying name of a built-in pretrained model, or a community contributed model, or a local file directory path. Args: pretrained_model_name_or_path (str): See :class:`AutoModel`. *args (tuple): See :class:`AutoModel`. **kwargs (dict): See :class:`AutoModel`. Returns: PretrainedModel: An instance of `AutoModelForConditionalGeneration`. Example: .. code-block:: from paddlenlp.transformers import AutoModelForConditionalGeneration # Name of built-in pretrained model model = AutoModelForConditionalGeneration.from_pretrained('bart-base') print(type(model)) # # Load from local directory path model = AutoModelForConditionalGeneration.from_pretrained('./my_bart/') print(type(model)) # """ return cls._from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs) class AutoModelForImageGeneration(_BaseAutoModelClass): """ AutoModelForImageGeneration. """ CONFIGURATION_MODEL_MAPPING = get_init_configurations() _pretrained_model_dict = CONFIGURATION_MODEL_MAPPING _name_mapping = get_name_mapping("ForImageGeneration") @classmethod def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs): """ Creates an instance of `AutoModelForImageGeneration`. Model weights are loaded by specifying name of a built-in pretrained model, or a community contributed model, or a local file directory path. Args: pretrained_model_name_or_path (str): See :class:`AutoModel`. *args (tuple): See :class:`AutoModel`. **kwargs (dict): See :class:`AutoModel`. Returns: PretrainedModel: An instance of `AutoModelForImageGeneration`. Example: .. code-block:: from paddlenlp.transformers import AutoModelForImageGeneration # Name of built-in pretrained model model = AutoModelForImageGeneration.from_pretrained('dalle-mini') print(type(model)) # # Load from local directory path model = AutoModelForImageGeneration.from_pretrained('./my_dalle_mini/') print(type(model)) # """ return cls._from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs)