paddlenlp.transformers.model_utils 源代码

# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
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import copy
import io
import json
import os
import six
import logging
import inspect

import paddle
import numpy as np
from paddle.nn import Layer
# TODO(fangzeyang) Temporary fix and replace by paddle framework downloader later
from paddlenlp.utils.downloader import get_path_from_url, download_check, COMMUNITY_MODEL_PREFIX
from paddlenlp.utils.env import MODEL_HOME
from paddlenlp.utils.log import logger

from .generation_utils import GenerationMixin
from .utils import InitTrackerMeta, fn_args_to_dict

__all__ = [
    'PretrainedModel',
    'register_base_model',
]


def unwrap_model(model, *args, **kwargs):
    raw_model = model._layers if isinstance(model,
                                            paddle.DataParallel) else model
    return raw_model


[文档]def register_base_model(cls): """ A decorator for `PretrainedModel` class. It first retrieves the parent class of the class being decorated, then sets the `base_model_class` attribute of that parent class to be the class being decorated. In summary, the decorator registers the decorated class as the base model class in all derived classes under the same architecture. Args: cls (PretrainedModel): The class (inherited from PretrainedModel) to be decorated . Returns: PretrainedModel: The input class `cls` after decorating. Example: .. code-block:: from paddlenlp.transformers import BertModel, register_base_model BertModel = register_base_model(BertModel) assert BertModel.base_model_class == BertModel """ base_cls = cls.__bases__[0] assert issubclass( base_cls, PretrainedModel ), "`register_base_model` should be used on subclasses of PretrainedModel." base_cls.base_model_class = cls return cls
[文档]@six.add_metaclass(InitTrackerMeta) class PretrainedModel(Layer, GenerationMixin): """ The base class for all pretrained models. It mainly provides common methods for loading (construction and loading) and saving pretrained models. Loading and saving also rely on the following class attributes which should be overridden by derived classes accordingly: - **model_config_file** (str): Represents the file name of model configuration for configuration saving and loading in local file system. The value is `model_config.json`. - **resource_files_names** (dict): Name of local file where the model configuration can be saved and loaded locally. Currently, resources only include the model state, thus the dict only includes `'model_state'` as key with corresponding value `'model_state.pdparams'` for model weights saving and loading. - **pretrained_init_configuration** (dict): Provides the model configurations of built-in pretrained models (contrasts to models in local file system). It has pretrained model names as keys (such as `bert-base-uncased`), and the values are dict preserving corresponding configuration for model initialization. - **pretrained_resource_files_map** (dict): Provides resource URLs of built-in pretrained models (contrasts to models in local file system). It has the same key as resource_files_names (that is "model_state"), and the corresponding value is a dict with specific model name to model weights URL mapping (such as "bert-base-uncased" -> "https://bj.bcebos.com/paddlenlp/models/transformers/bert-base-uncased.pdparams"). - **base_model_prefix** (str): Represents the attribute associated to the base model in derived classes of the same architecture adding layers on top of the base model. Note: A base model class is pretrained model class decorated by `register_base_model`, such as `BertModel`; A derived model class is a pretrained model class adding layers on top of the base model, and it has a base model as attribute, such as `BertForSequenceClassification`. Methods common to models for text generation are defined in `GenerationMixin` and also inherited here. Besides, metaclass `InitTrackerMeta` is used to create `PretrainedModel`, by which subclasses can track arguments for initialization automatically. """ model_config_file = "model_config.json" pretrained_init_configuration = {} # TODO: more flexible resource handle, namedtuple with fields as: # resource_name, saved_file, handle_name_for_load(None for used as __init__ # arguments), handle_name_for_save resource_files_names = {"model_state": "model_state.pdparams"} pretrained_resource_files_map = {} base_model_prefix = "" def _wrap_init(self, original_init, *args, **kwargs): """ It would be hooked after `__init__` to add a dict including arguments of `__init__` as a attribute named `config` of the pretrained model instance. """ init_dict = fn_args_to_dict(original_init, *((self, ) + args), **kwargs) self.config = init_dict @property def base_model(self): """ PretrainedModel: The body of the same model architecture. It is the base model itself for base model or the base model attribute for derived model. """ return getattr(self, self.base_model_prefix, self) @property def model_name_list(self): """ list: Contains all supported built-in pretrained model names of the current PretrainedModel class. """ # Todo: return all model name return list(self.pretrained_init_configuration.keys()) def get_input_embeddings(self): base_model = getattr(self, self.base_model_prefix, self) if base_model is not self: return base_model.get_input_embeddings() else: raise NotImplementedError def get_output_embeddings(self): return None # Overwrite for models with output embeddings
[文档] @classmethod def from_pretrained(cls, pretrained_model_name_or_path, *args, **kwargs): """ Creates an instance of `PretrainedModel`. 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): 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"). *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. load_state_as_np (bool, optional): The weights read in can be choosed to place on CPU or GPU though the model is on the default device. If `True`, load the model weights as `numpy.ndarray` on CPU. Otherwise, weights would be loaded as tensors on the default device. Note that if on GPU, the latter would creates extra temporary tensors in addition to the model weights, which doubles the memory usage . Thus it is suggested to use `True` for big models on GPU. Default to `False`. Returns: PretrainedModel: An instance of `PretrainedModel`. Example: .. code-block:: from paddlenlp.transformers import BertForSequenceClassification # Name of built-in pretrained model model = BertForSequenceClassification.from_pretrained('bert-base-uncased') # Name of community-contributed pretrained model model = BertForSequenceClassification.from_pretrained('yingyibiao/bert-base-uncased-sst-2-finetuned') # Load from local directory path model = BertForSequenceClassification.from_pretrained('./my_bert/') """ pretrained_models = list(cls.pretrained_init_configuration.keys()) resource_files = {} init_configuration = {} load_state_as_np = kwargs.pop("load_state_as_np", False) # From built-in pretrained models if pretrained_model_name_or_path in pretrained_models: for file_id, map_list in cls.pretrained_resource_files_map.items(): resource_files[file_id] = map_list[ pretrained_model_name_or_path] init_configuration = copy.deepcopy( cls.pretrained_init_configuration[ pretrained_model_name_or_path]) # From local dir path elif os.path.isdir(pretrained_model_name_or_path): for file_id, file_name in cls.resource_files_names.items(): full_file_name = os.path.join(pretrained_model_name_or_path, file_name) resource_files[file_id] = full_file_name resource_files["model_config_file"] = os.path.join( pretrained_model_name_or_path, cls.model_config_file) else: # Assuming from community-contributed pretrained models for file_id, file_name in cls.resource_files_names.items(): full_file_name = os.path.join(COMMUNITY_MODEL_PREFIX, pretrained_model_name_or_path, file_name) resource_files[file_id] = full_file_name resource_files["model_config_file"] = os.path.join( COMMUNITY_MODEL_PREFIX, pretrained_model_name_or_path, cls.model_config_file) default_root = os.path.join(MODEL_HOME, pretrained_model_name_or_path) resolved_resource_files = {} for file_id, file_path in resource_files.items(): if file_path is None or os.path.isfile(file_path): resolved_resource_files[file_id] = file_path continue path = os.path.join(default_root, file_path.split('/')[-1]) if os.path.exists(path): logger.info("Already cached %s" % path) resolved_resource_files[file_id] = path else: logger.info("Downloading %s and saved to %s" % (file_path, default_root)) try: resolved_resource_files[file_id] = get_path_from_url( file_path, default_root) 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" ) # Prepare model initialization kwargs # Did we saved some inputs and kwargs to reload ? model_config_file = resolved_resource_files.pop("model_config_file", None) if model_config_file is not None: with io.open(model_config_file, encoding="utf-8") as f: init_kwargs = json.load(f) else: init_kwargs = init_configuration # position args are stored in kwargs, maybe better not include init_args = init_kwargs.pop("init_args", ()) # class name corresponds to this configuration init_class = init_kwargs.pop("init_class", cls.base_model_class.__name__) # Check if the loaded config matches the current model class's __init__ # arguments. If not match, the loaded config is for the base model class. if init_class == cls.base_model_class.__name__: base_args = init_args base_kwargs = init_kwargs derived_args = () derived_kwargs = {} base_arg_index = None else: # extract config for base model derived_args = list(init_args) derived_kwargs = init_kwargs base_arg = None for i, arg in enumerate(init_args): if isinstance(arg, dict) and "init_class" in arg: assert arg.pop( "init_class") == cls.base_model_class.__name__, ( "pretrained base model should be {}" ).format(cls.base_model_class.__name__) base_arg_index = i base_arg = arg break for arg_name, arg in init_kwargs.items(): if isinstance(arg, dict) and "init_class" in arg: assert arg.pop( "init_class") == cls.base_model_class.__name__, ( "pretrained base model should be {}" ).format(cls.base_model_class.__name__) base_arg_index = arg_name base_arg = arg break base_args = base_arg.pop("init_args", ()) base_kwargs = base_arg if cls == cls.base_model_class: # Update with newly provided args and kwargs for base model base_args = base_args if not args else args base_kwargs.update(kwargs) model = cls(*base_args, **base_kwargs) else: # Update with newly provided args and kwargs for derived model base_parameters_dict = inspect.signature( cls.base_model_class.__init__).parameters for k, v in kwargs.items(): if k in base_parameters_dict: base_kwargs[k] = v base_model = cls.base_model_class(*base_args, **base_kwargs) if base_arg_index is not None: derived_args[base_arg_index] = base_model else: derived_args = (base_model, ) # assume at the first position derived_args = derived_args if not args else args derived_parameters_dict = inspect.signature(cls.__init__).parameters for k, v in kwargs.items(): if k in derived_parameters_dict: derived_kwargs[k] = v model = cls(*derived_args, **derived_kwargs) # Maybe need more ways to load resources. weight_path = resolved_resource_files["model_state"] assert weight_path.endswith( ".pdparams"), "suffix of weight must be .pdparams" # NOTE: Allow to load partial model for model parallel. # TODO(guosheng): To make model loading for the model parallel automatic, # maybe we should make rank 0 worker load weights of the full model on # CPU, then split weights into multiple parts and pickle separately. # The other workers wait util pickle finish and then load the corresponding # partial weights. Also we can directly use separate weight files for # simplicity. state_dict = paddle.load(weight_path, return_numpy=load_state_as_np) # Make sure we are able to load base models as well as derived models # (with heads) start_prefix = "" model_to_load = model state_to_load = state_dict unexpected_keys = [] missing_keys = [] if not hasattr(model, cls.base_model_prefix) and any( s.startswith(cls.base_model_prefix) for s in state_dict.keys()): # base model state_to_load = {} start_prefix = cls.base_model_prefix + "." for k, v in state_dict.items(): if k.startswith(cls.base_model_prefix): state_to_load[k[len(start_prefix):]] = v else: unexpected_keys.append(k) if hasattr(model, cls.base_model_prefix) and not any( s.startswith(cls.base_model_prefix) for s in state_dict.keys()): # derived model (base model with heads) model_to_load = getattr(model, cls.base_model_prefix) for k in model.state_dict().keys(): if not k.startswith(cls.base_model_prefix): missing_keys.append(k) if len(missing_keys) > 0: logger.info( "Weights of {} not initialized from pretrained model: {}". format(model.__class__.__name__, missing_keys)) if len(unexpected_keys) > 0: logger.info("Weights from pretrained model not used in {}: {}". format(model.__class__.__name__, unexpected_keys)) # Allow the float16 model to load float32 weights, which decreases memory # usage in model loading stage and is useful to big models. dtype_prefix_len = len("paddle.") # paddle.float16 for k, v in model_to_load.state_dict().items(): if not isinstance(v, np.ndarray): dtype = str(v.dtype)[dtype_prefix_len:] # TODO(guosheng): add warnings for unmatched dtypes if k in state_to_load: state_to_load[k] = state_to_load[k].astype(dtype) # Logging model download statistics download_check(pretrained_model_name_or_path, "from_pretrained") # For model parallel if FasterGeneration # To avoid recursive import temporarily. import paddlenlp.ops.faster_transformer.transformer.decoding as ft_decoding state_to_load = ft_decoding.get_ft_para_conf().fit_partial_model( model_to_load, state_to_load) if paddle.in_dynamic_mode(): model_to_load.set_state_dict(state_to_load) return model return model, state_to_load
[文档] def get_model_config(self): """Get model configuration. Returns: config: The config of the model. """ # If init_config contains a Layer, use the layer's init_config to save def get_config(model): model_config = model.init_config for key, value in model_config.items(): if key == "init_args": args = [] for arg in value: args.append( get_config(arg) if isinstance(arg, PretrainedModel) else arg) model_config[key] = tuple(args) elif isinstance(value, PretrainedModel): model_config[key] = value.init_config return model_config model_config = get_config(self) return model_config
[文档] def save_model_config(self, save_dir): """ Saves model configuration to a file named "model_config.json" under `save_dir`. Args: save_dir (str): Directory to save model_config file into. """ # Save model config model_config_file = os.path.join(save_dir, self.model_config_file) model_config = self.get_model_config() with io.open(model_config_file, "w", encoding="utf-8") as f: f.write(json.dumps(model_config, ensure_ascii=False, indent=2))
[文档] def save_pretrained(self, save_dir): """ Saves model configuration and related resources (model state) as files under `save_dir`. The model configuration would be saved into a file named "model_config.json", and model state would be saved into a file named "model_state.pdparams". The `save_dir` can be used in `from_pretrained` as argument value of `pretrained_model_name_or_path` to re-load the trained model. Args: save_dir (str): Directory to save files into. Example: .. code-block:: from paddlenlp.transformers import BertForSequenceClassification model = BertForSequenceClassification.from_pretrained('bert-base-uncased') model.save_pretrained('./trained_model/') # reload from save_directory model = BertForSequenceClassification.from_pretrained('./trained_model/') """ assert not os.path.isfile( save_dir ), "Saving directory ({}) should be a directory, not a file".format( save_dir) os.makedirs(save_dir, exist_ok=True) # Save model config self.save_model_config(save_dir) # Save model if paddle.in_dynamic_mode(): file_name = os.path.join( save_dir, list(self.resource_files_names.values())[0]) paddle.save(self.state_dict(), file_name) else: logger.warning( "Save pretrained model only supported dygraph mode for now!")