paddlenlp.transformers.model_utils 源代码

# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
from __future__ import annotations

import copy
import inspect
import io
import json
import os
import re
import shutil
import tempfile
from typing import Any, Dict, List, Optional, Tuple, Type

import numpy as np
import paddle
import paddle.nn as nn
import six
from huggingface_hub import (
    create_repo,
    get_hf_file_metadata,
    hf_hub_download,
    hf_hub_url,
    repo_type_and_id_from_hf_id,
    upload_folder,
)
from huggingface_hub.utils import EntryNotFoundError
from paddle import Tensor
from paddle.nn import Embedding, Layer

# TODO(fangzeyang) Temporary fix and replace by paddle framework downloader later
from paddle.utils.download import is_url

from paddlenlp import __version__
from paddlenlp.utils.downloader import (
    COMMUNITY_MODEL_PREFIX,
    download_check,
    get_path_from_url_with_filelock,
)
from paddlenlp.utils.env import HF_CACHE_HOME, MODEL_HOME
from paddlenlp.utils.log import logger

from .configuration_utils import PretrainedConfig
from .generation_utils import GenerationMixin
from .utils import (
    InitTrackerMeta,
    adapt_stale_fwd_patch,
    fn_args_to_dict,
    resolve_cache_dir,
)

__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


def get_parameter_dtype(parameter: nn.Layer) -> paddle.dtype:
    """get dtype of parameter which should be sub-class of nn.Layer

    Args:
        parameter (nn.Layer): the instance of layer

    Returns:
        paddle.dtype: the dtype of tensor
    """

    last_dtype = None
    for t in parameter.parameters():
        last_dtype = t.dtype
        if t.is_floating_point():
            return t.dtype

    # TODO(wj-Mcat): get dtype of model when it's in DataParallel Mode.
    return last_dtype


def _find_weight_file_path(
    cache_dir: str, model_class: Type[PretrainedModel], resource_uri: Optional[str] = None
) -> str:
    """find the target weight file under the cache dir, because there are some conflicts about weight file names.

    Args:
        cache_dir (str): the cache dir of pretrained weighted files
        model_class (Type[PretrainedModel]): the class of pretrained model
        resource_uri (Optional[str], optional): the weight file resource file uri to help find the target file name. Defaults to None.
    """
    # 1. if the weight file is the name of resource_uri, eg: 'bert-base-uncased.pdparams'
    if resource_uri is not None:
        resouce_uri_file_name = os.path.split(resource_uri)[-1]
        weight_file_path = os.path.join(cache_dir, resouce_uri_file_name)
        if os.path.isfile(weight_file_path):
            return weight_file_path

    # 2. find the target weight file name under the `resource_files_names` attribute of `PretrainedModel`
    resource_weight_file_name = model_class.resource_files_names.get("model_state", None)
    weight_file_path = os.path.join(cache_dir, resource_weight_file_name)
    if os.path.isfile(weight_file_path):
        return weight_file_path

    # 3. find the target weight file if there is only one weight file
    weight_file_names = [file for file in os.listdir(cache_dir) if file.endswith(".pdparams")]
    if len(weight_file_names) == 1:
        logger.warning(
            f"there is no <{resource_weight_file_name}> which is the expected weight file name "
            f"under dir<{cache_dir}>, but the file<{weight_file_names[0]}> is found, and it will "
            f"be used to init model weights. We suggest that you rename it to <{resource_weight_file_name}>"
        )
        return os.path.join(cache_dir, weight_file_names[0])

    raise ValueError(
        'can"t find the target weight files<%s> or <%s> under the cache_dir<%s>',
        resouce_uri_file_name,
        resource_weight_file_name,
        cache_dir,
    )


[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 = "" main_input_name = "input_ids" config_class = None # a list of `re` patterns of `state_dict` keys that should be removed from the list of missing # keys we find (keys inside the model but not in the checkpoint) and avoid unnecessary warnings. _keys_to_ignore_on_load_missing = None # a list of `re` patterns of `state_dict` keys that should be removed from the list of # unexpected keys we find (keys inside the checkpoint but not the model) and avoid unnecessary # warnings. _keys_to_ignore_on_load_unexpected = None # a list of `state_dict` keys to ignore when saving the model (useful for keys that aren't # trained, but which are either deterministic or tied variables) _keys_to_ignore_on_save = None def __init__(self, *args, **kwargs): super(PretrainedModel, self).__init__() if not self.constructed_from_pretrained_config(): return # extract config from args config = None for arg in args: if isinstance(arg, PretrainedConfig): config = arg break if config is not None: self.config: PretrainedConfig = config return # extract config from kwargs if "config" not in kwargs: raise ValueError( "PretarinedConfig instance not found in the arguments, you can set it as args or kwargs with config field" ) config = kwargs["config"] if not isinstance(config, PretrainedConfig): raise TypeError("config parameter should be the instance of PretraiendConfig") self.config: PretrainedConfig = kwargs["config"] self.warnings_issued = {} def _post_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. """ if not self.constructed_from_pretrained_config(): 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())
[文档] def get_input_embeddings(self) -> nn.Embedding: """get input embedding of model Returns: nn.Embedding: embedding of model """ base_model = getattr(self, self.base_model_prefix, self) if base_model is not self: return base_model.get_input_embeddings() raise NotImplementedError( f"model of {type(base_model)} has not implemented the `get_input_embeddings`" " or `set_input_embeddings` method" )
[文档] def set_input_embeddings(self, value: Embedding): """set new input embedding for model Args: value (Embedding): the new embedding of model Raises: NotImplementedError: Model has not implement `set_input_embeddings` method """ base_model = getattr(self, self.base_model_prefix, self) if base_model is not self: return base_model.set_input_embeddings(value) raise NotImplementedError( f"model of {type(base_model)} has not implemented the `get_input_embeddings`" " or `set_input_embeddings` method" )
[文档] def get_output_embeddings(self) -> Optional[Embedding]: """To be overwrited for models with output embeddings Returns: Optional[Embedding]: the otuput embedding of model """ return None
[文档] def resize_position_embeddings(self, new_num_position_embeddings: int): """resize position embedding, this method should be overrited overwrited by downstream models Args: new_num_position_embeddings (int): the new position size Raises: NotImplementedError: when called and not be implemented """ raise NotImplementedError( f"`resize_position_embeddings` is not implemented for {self.__class__}`. To implement it, you should " f"overwrite this method in the class {self.__class__} in `{self.__class__.__module__}.py`" )
[文档] @classmethod def constructed_from_pretrained_config(cls, init_func=None) -> bool: """check if the model is constructed from `PretrainedConfig` Returns: bool: if the model is constructed from `PretrainedConfig` """ return cls.config_class is not None and issubclass(cls.config_class, PretrainedConfig)
[文档] @classmethod def from_pretrained(cls, pretrained_model_name_or_path, *args, from_hf_hub=False, **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 pretrained model from HF hub - 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"). from_hf_hub (bool): whether to load from Huggingface Hub *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/') """ if cls.constructed_from_pretrained_config(): return cls.from_pretrained_v2(pretrained_model_name_or_path, from_hf_hub=from_hf_hub, *args, **kwargs) resource_files = {} init_configuration = {} load_state_as_np = kwargs.pop("load_state_as_np", False) track_download = True # From HF Hub if from_hf_hub: resource_files = cls.resource_files_names resource_files["model_config_file"] = cls.model_config_file # From built-in pretrained models elif pretrained_model_name_or_path in cls.pretrained_init_configuration: for file_id, map_list in cls.pretrained_resource_files_map.items(): if pretrained_model_name_or_path not in map_list: resource_files[file_id] = None else: 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): track_download = False 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 = "/".join([COMMUNITY_MODEL_PREFIX, pretrained_model_name_or_path, file_name]) resource_files[file_id] = full_file_name resource_files["model_config_file"] = "/".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 # If from_hf_hub, let HF Hub takes care of the cache and the download process if from_hf_hub: resolved_resource_files[file_id] = hf_hub_download( repo_id=pretrained_model_name_or_path, filename=file_path, cache_dir=HF_CACHE_HOME, library_name="PaddleNLP", library_version=__version__, ) else: 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_with_filelock(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) # save the model config file into cache dir model_config_file_path = os.path.join(default_root, cls.model_config_file) # check if there is model config file in cache directory if ( pretrained_model_name_or_path in cls.pretrained_init_configuration and init_kwargs is not None and not os.path.exists(model_config_file_path) ): model.save_model_config(default_root) # Maybe need more ways to load resources. weight_path = resolved_resource_files["model_state"] if weight_path is None: logger.warning( "No model weight found for %s, return with random initialization !!!" % pretrained_model_name_or_path ) return model 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: if paddle.in_dynamic_mode(): if isinstance(state_to_load[k], np.ndarray): state_to_load[k] = state_to_load[k].astype(dtype) else: state_to_load[k] = paddle.cast(state_to_load[k], dtype) else: # there are some latent error when case dtype in static-mode, so let's: # 1. convert fluid.*.Tensor -> numpy.ndarray # 2. cast the dtype with numpy tools # 3. paddle works well with ndarray state-dict state_to_load[k] = np.array(state_to_load[k]) state_to_load[k] = state_to_load[k].astype(dtype) # 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) if track_download: download_check(pretrained_model_name_or_path, "from_pretrained") return model if track_download: download_check(pretrained_model_name_or_path, "from_pretrained") 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: str): """ 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: str): """ 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/') """ if self.constructed_from_pretrained_config(): return self.save_pretrained_v2(save_dir) 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!")
[文档] def save_to_hf_hub( self, repo_id: str, private: Optional[bool] = None, commit_message: Optional[bool] = None, revision: Optional[str] = None, create_pr: bool = False, ): """ Uploads all elements of this model to a new HuggingFace Hub repository. Args: repo_id (str): Repository name for your model/tokenizer in the Hub. private (bool, optional): Whether the model/tokenizer is set to private commit_message (str, optional) — The summary / title / first line of the generated commit. Defaults to: f"Upload {path_in_repo} with huggingface_hub" revision (str, optional) — The git revision to commit from. Defaults to the head of the "main" branch. create_pr (boolean, optional) — Whether or not to create a Pull Request with that commit. Defaults to False. If revision is not set, PR is opened against the "main" branch. If revision is set and is a branch, PR is opened against this branch. If revision is set and is not a branch name (example: a commit oid), an RevisionNotFoundError is returned by the server. Returns: The url of the commit of your model in the given repository. """ repo_url = create_repo(repo_id, private=private, exist_ok=True) # Infer complete repo_id from repo_url # Can be different from the input `repo_id` if repo_owner was implicit _, repo_owner, repo_name = repo_type_and_id_from_hf_id(repo_url) repo_id = f"{repo_owner}/{repo_name}" # Check if README file already exist in repo try: get_hf_file_metadata(hf_hub_url(repo_id=repo_id, filename="README.md", revision=revision)) has_readme = True except EntryNotFoundError: has_readme = False with tempfile.TemporaryDirectory() as tmp_dir: # save model self.save_pretrained(tmp_dir) # Add readme if does not exist logger.info("README.md not found, adding the default README.md") if not has_readme: with open(os.path.join(tmp_dir, "README.md"), "w") as f: f.write(f"---\nlibrary_name: paddlenlp\n---\n# {repo_id}") # Upload model and return logger.info(f"Pushing to the {repo_id}. This might take a while") return upload_folder( repo_id=repo_id, repo_type="model", folder_path=tmp_dir, commit_message=commit_message, revision=revision, create_pr=create_pr, )
[文档] def resize_token_embeddings(self, new_num_tokens: Optional[int] = None) -> nn.Embedding: """ Resizes input token embeddings matrix of the model according to new_num_tokens. Args: new_num_tokens (Optional[int]): The number of new tokens in the embedding matrix. Increasing the size will add newly initialized vectors at the end. Reducing the size will remove vectors from the end. If not provided or None, just returns a pointer to the input tokens embedding module of the model without doing anything. Returns: paddle.nn.Embedding: The input tokens Embeddings Module of the model. """ old_embeddings: nn.Embedding = self.get_input_embeddings() if not new_num_tokens or new_num_tokens == old_embeddings.weight.shape[0]: return old_embeddings new_embeddings = self._get_resized_embeddings(old_embeddings, new_num_tokens) self.set_input_embeddings(new_embeddings) # 2. Update vocab_size self.base_model.config["vocab_size"] = new_num_tokens self.vocab_size = new_num_tokens # update init_config self._update_init_config(self.init_config, "vocab_size", new_num_tokens) # TODO(westfish@126.com): add tie_weight. # TODO(westfish) Add tie_weight to tie the weights between the input embeddings and the output embeddings if needed. return new_embeddings
def _update_init_config(self, init_config: dict, key: str, value: Any): """update init_config by <key, value> pair Args: init_config (dict): the init_config instance key (str): the key field value (Any): the new value of instance """ if key in init_config: init_config[key] = value return for arg in init_config.get("init_args", []): if not isinstance(arg, PretrainedModel): continue self._update_init_config(arg.init_config, key, value) def _get_resized_embeddings( self, old_embeddings: nn.Embedding, new_num_tokens: Optional[int] = None ) -> nn.Embedding: """ Build a resized Embedding Module from a provided token Embedding Module. Increasing the size will add newly initialized vectors at the end. Reducing the size will remove vectors from the end Args: old_embeddings (nn.Embedding): Old embeddings to be resized. new_num_tokens (Optional[int]): New number of tokens in the embedding matrix. Increasing the size will add newly initialized vectors at the end. Reducing the size will remove vectors from the end. Returns: paddle.nn.Embedding: The resized Embedding Module or the old Embedding Module if new_num_tokens is None. """ if new_num_tokens is None: return old_embeddings old_num_tokens, old_embedding_dim = old_embeddings.weight.shape if old_num_tokens == new_num_tokens: return old_embeddings if not isinstance(old_embeddings, nn.Embedding): raise TypeError( f"Old embeddings are of type {type(old_embeddings)}, which is not an instance of {nn.Embedding}. You" " should either use a different resize function or make sure that old_embeddings are an instance of" f" {nn.Embedding}." ) # Build new embeddings new_embeddings = nn.Embedding(new_num_tokens, old_embedding_dim) # numbers of tokens to copy n = min(old_num_tokens, new_num_tokens) with paddle.no_grad(): new_embeddings.weight[:n, :] = old_embeddings.weight[:n, :] return new_embeddings def __setattr__(self, name, value): value = adapt_stale_fwd_patch(self, name, value) return super(PretrainedModel, self).__setattr__(name, value) @classmethod def _resolve_model_file_path( cls: Type[PretrainedModel], pretrained_model_name_or_path: str, from_hf_hub: bool = False, cache_dir: Optional[str] = None, ) -> str: """resolve model target file path from `` and `cache_dir` 0. when it is file path: return the weight file 1. when it is model-name: 1.1 check default `MODEL_HOME` + `model-mame` + model_state.pdparams 1.2 get the url from `pretrained_resource_files_map`, and set it to `pretrained_model_name_or_path` 2. when it is url: fetch the resouce into the `cache_dir` (cache_dir or `MODEL_HOME` + `model-mame`) 3. when it is local dir: check whether the file<local_dir + weight_file> exist Args: cls (Type[PretrainedModel]): the inherited PretrainedModel class pretrained_model_name_or_path (str): the model-name/url/local_dir/local_dir cache_dir (Optional[str], optional): cache_dir is used when name_or_path is model-name/url. Defaults to None. Returns: str: the model weight file path """ # 0. when it is local file if os.path.isfile(pretrained_model_name_or_path): return pretrained_model_name_or_path # 1. when it's from HF if from_hf_hub: return hf_hub_download( repo_id=pretrained_model_name_or_path, filename=cls.resource_files_names["model_state"], cache_dir=HF_CACHE_HOME, library_name="PaddleNLP", library_version=__version__, ) # 2. when it is model-name if pretrained_model_name_or_path in cls.pretrained_init_configuration: # check the cache_dir: os.makedirs(cache_dir, exist_ok=True) # check the state_dict file weight_file_path = os.path.join(cache_dir, cls.resource_files_names["model_state"]) if os.path.exists(weight_file_path): return weight_file_path # fetch the weight url from the `pretrained_resource_files_map` pretrained_model_name_or_path = cls.pretrained_resource_files_map["model_state"][ pretrained_model_name_or_path ] # 3. when it is url if is_url(pretrained_model_name_or_path): weight_file_path = get_path_from_url_with_filelock(pretrained_model_name_or_path, cache_dir) # # check the downloaded weight file and registered weight file name # make sure that new_weight_file_path = os.path.join( os.path.split(weight_file_path)[0], cls.resource_files_names["model_state"] ) # if the weight file name of url is: `bert-base-uncased.pdparams`, the downloaded file is also of it. # and we should convert it to the new weitht file: `model_state.pdparams` if weight_file_path != new_weight_file_path: # move the `model-name.pdparams` to `model_state.pdparams` # get more details from: https://github.com/PaddlePaddle/PaddleNLP/pull/3843 shutil.move(weight_file_path, new_weight_file_path) weight_file_path = new_weight_file_path # find the weight file with the above two branch: `bert-base-uncased.pdparams`, `model_state.pdparams` weight_file_path = _find_weight_file_path( cache_dir=cache_dir, model_class=cls, resource_uri=pretrained_model_name_or_path ) return weight_file_path # 4. when it is local dir if os.path.isdir(pretrained_model_name_or_path): # in-order to compatible with old style: # file name in pretrained_resouce_file_maps is https://path/to/bert-base-uncased.pdparams, but the registered model-state file name in `resouce_file_maps` is `model_state.pdparams` weight_file_path = _find_weight_file_path(cache_dir=pretrained_model_name_or_path, model_class=cls) if os.path.exists(weight_file_path): return weight_file_path else: # assume that the community-based models, name format: community/model-name pretrained_model_name_or_path = os.path.join( COMMUNITY_MODEL_PREFIX, pretrained_model_name_or_path, cls.resource_files_names["model_state"] ) assert is_url(pretrained_model_name_or_path) return cls._resolve_model_file_path(pretrained_model_name_or_path, cache_dir=cache_dir) raise FileNotFoundError( "can't resolve the model_state file according to the <%s>", pretrained_model_name_or_path ) @classmethod def _load_pretrained_model( cls, model: PretrainedModel, state_dict: Dict[str, Tensor], loaded_keys: List[str], ignore_mismatched_sizes=False, dtype=None, ) -> Tuple[List[str]]: """load the state_dict into model, and do the following things: * check the Args: model (PretrainedModel): the pretrained model instance state_dict (Dict[str, Tensor]): the model state dict data loaded_keys (List[str]): ignore_mismatched_sizes (bool, optional): whether ignore error when tensor size mismatched. Defaults to False. dtype (_type_, optional): the dtype of model state dict. Defaults to None. Returns: Tuple[List[str]]: _description_ """ model_state_dict = model.state_dict() expected_keys = list(model_state_dict.keys()) prefix = model.base_model_prefix if len(prefix) > 0: has_prefix_module = any(s.startswith(prefix) for s in loaded_keys) expects_prefix_module = any(s.startswith(prefix) for s in expected_keys) else: has_prefix_module = False expects_prefix_module = False # key re-naming operations are never done on the keys # that are loaded, but always on the keys of the newly initialized model remove_prefix_from_model = not has_prefix_module and expects_prefix_module add_prefix_to_model = has_prefix_module and not expects_prefix_module if remove_prefix_from_model: expected_keys = [".".join(s.split(".")[1:]) if s.startswith(prefix) else s for s in expected_keys] elif add_prefix_to_model: expected_keys = [".".join([prefix, s]) for s in expected_keys] missing_keys = list(set(expected_keys) - set(loaded_keys)) unexpected_keys = list(set(loaded_keys) - set(expected_keys)) # Some models may have keys that are not in the state by design, removing them before needlessly warning # the user. if cls._keys_to_ignore_on_load_missing is not None: for pat in cls._keys_to_ignore_on_load_missing: missing_keys = [k for k in missing_keys if re.search(pat, k) is None] if cls._keys_to_ignore_on_load_unexpected is not None: for pat in cls._keys_to_ignore_on_load_unexpected: unexpected_keys = [k for k in unexpected_keys if re.search(pat, k) is None] # Make sure we are able to load base models as well as derived models (with heads) start_prefix = "" model_to_load = model if len(cls.base_model_prefix) > 0 and not hasattr(model, cls.base_model_prefix) and has_prefix_module: start_prefix = cls.base_model_prefix + "." def _find_mismatched_keys( state_dict, model_state_dict, loaded_keys, add_prefix_to_model, remove_prefix_from_model, ignore_mismatched_sizes, ): mismatched_keys = [] if ignore_mismatched_sizes: for checkpoint_key in loaded_keys: model_key = checkpoint_key if remove_prefix_from_model: # The model key starts with `prefix` but `checkpoint_key` doesn't so we add it. model_key = f"{prefix}.{checkpoint_key}" elif add_prefix_to_model: # The model key doesn't start with `prefix` but `checkpoint_key` does so we remove it. model_key = ".".join(checkpoint_key.split(".")[1:]) if ( model_key in model_state_dict and state_dict[checkpoint_key].shape != model_state_dict[model_key].shape ): mismatched_keys.append( (checkpoint_key, state_dict[checkpoint_key].shape, model_state_dict[model_key].shape) ) del state_dict[checkpoint_key] return mismatched_keys # Whole checkpoint mismatched_keys = _find_mismatched_keys( state_dict, model_state_dict, loaded_keys, add_prefix_to_model, remove_prefix_from_model, ignore_mismatched_sizes, ) start_prefix = prefix + "." # `add_prefix_to_model` and `remove_prefix_from_model` are for different situation, # you can check the following matrix, which means: # the value of cell: (add_prefix_to_model, remove_prefix_from_model) # the load/Init-Base is the state-dict which don't contain `prefix`. # the load/Init-DownStream is the state-dict which contain the `prefix` # # | | load-Base | load-DownStream | # |-----------------|-----------|-----------------| # | Init-Base | F,F | T,F | # | Init-DonwStream | F,T | F,F | # # the above value matrix will help you understand the following code. if add_prefix_to_model: for key in list(state_dict.keys()): if key.startswith(start_prefix): state_dict[key.replace(start_prefix, "")] = state_dict.pop(key) if remove_prefix_from_model: for key in list(state_dict.keys()): state_dict[start_prefix + key] = state_dict.pop(key) # convert the dtype of state dict if dtype is not None: if isinstance(dtype, paddle.dtype): dtype = str(dtype)[7:] if dtype not in ["float32", "float16"]: raise ValueError(f"the value of `dtype` should be one of [`float32`, `float16`], but received {dtype}") for key in state_dict.keys(): state_dict[key] = paddle.cast(state_dict[key], dtype=dtype) else: dtype_prefix_len = len("paddle.") for k, v in model_to_load.state_dict().items(): if not isinstance(v, np.ndarray): dtype = str(v.dtype)[dtype_prefix_len:] if k in state_dict: if paddle.in_dynamic_mode(): if isinstance(state_dict[k], np.ndarray): state_dict[k] = state_dict[k].astype(dtype) else: state_dict[k] = paddle.cast(state_dict[k], dtype) else: # there are some latent error when case dtype in static-mode, so let's: # 1. convert fluid.*.Tensor -> numpy.ndarray # 2. cast the dtype with numpy tools # 3. paddle works well with ndarray state-dict state_dict[k] = np.array(state_dict[k]) state_dict[k] = state_dict[k].astype(dtype) # 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_dict) if paddle.in_dynamic_mode(): model_to_load.set_state_dict(state_to_load) return model_to_load, missing_keys, unexpected_keys, mismatched_keys
[文档] @classmethod def from_pretrained_v2(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, a pretrained model from HF Hub, 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 pretrained model from HF Hub - 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 pretrained model from PaddleHub model = BertForSequenceClassification.from_pretrained('bert-base-uncased') # Name of community-contributed pretrained model model = BertForSequenceClassification.from_pretrained('yingyibiao/bert-base-uncased-sst-2-finetuned', num_labels=3) # Load from local directory path model = BertForSequenceClassification.from_pretrained('./my_bert/' """ load_state_as_np = kwargs.pop("load_state_as_np", False) config = kwargs.pop("config", None) force_download = kwargs.pop("force_download", False) from_hf_hub = kwargs.pop("from_hf_hub", False) ignore_mismatched_sizes = kwargs.pop("ignore_mismatched_sizes", None) dtype = kwargs.pop("dtype", None) cache_dir = kwargs.pop("cache_dir", None) cache_dir = resolve_cache_dir(pretrained_model_name_or_path, from_hf_hub, cache_dir) model_kwargs = kwargs # 1. get the PretrainedConfig to init model if not isinstance(config, PretrainedConfig): config_path = config if config is not None else pretrained_model_name_or_path config, model_kwargs = cls.config_class.from_pretrained( config_path, cache_dir=cache_dir, return_unused_kwargs=True, force_download=force_download, from_hf_hub=from_hf_hub, **kwargs, ) config.save_pretrained(cache_dir) # 2. init the model init_args = config["init_args"] or () model = cls(config, *init_args, **model_kwargs) # 3. resolve model_weight file model_weight_file = cls._resolve_model_file_path( pretrained_model_name_or_path, cache_dir=cache_dir, from_hf_hub=from_hf_hub ) # 4. loading the state dict model_state_dict = paddle.load(model_weight_file, return_numpy=load_state_as_np) loaded_state_dict_keys = list(model_state_dict.keys()) # TODO(wj-Mcat): load shard checkpoint weight file, refer to: https://github.com/huggingface/transformers/pull/16343 model, missing_keys, unexpected_keys, mismatched_keys = cls._load_pretrained_model( model=model, state_dict=model_state_dict, loaded_keys=loaded_state_dict_keys, ignore_mismatched_sizes=ignore_mismatched_sizes, dtype=dtype, ) if len(unexpected_keys) > 0: logger.warning( f"Some weights of the model checkpoint at {pretrained_model_name_or_path} were not used when" f" initializing {model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are" f" initializing {model.__class__.__name__} from the checkpoint of a model trained on another task or" " with another architecture (e.g. initializing a BertForSequenceClassification model from a" " BertForPreTraining model).\n- This IS NOT expected if you are initializing" f" {model.__class__.__name__} from the checkpoint of a model that you expect to be exactly identical" " (initializing a BertForSequenceClassification model from a BertForSequenceClassification model)." ) else: logger.info(f"All model checkpoint weights were used when initializing {model.__class__.__name__}.\n") if len(missing_keys) > 0: logger.warning( f"Some weights of {model.__class__.__name__} were not initialized from the model checkpoint at" f" {pretrained_model_name_or_path} and are newly initialized: {missing_keys}\nYou should probably" " TRAIN this model on a down-stream task to be able to use it for predictions and inference." ) elif len(mismatched_keys) == 0: logger.info( f"All the weights of {model.__class__.__name__} were initialized from the model checkpoint at" f" {pretrained_model_name_or_path}.\nIf your task is similar to the task the model of the checkpoint" f" was trained on, you can already use {model.__class__.__name__} for predictions without further" " training." ) if len(mismatched_keys) > 0: mismatched_warning = "\n".join( [ f"- {key}: found shape {shape1} in the checkpoint and {shape2} in the model instantiated" for key, shape1, shape2 in mismatched_keys ] ) logger.warning( f"Some weights of {model.__class__.__name__} were not initialized from the model checkpoint at" f" {pretrained_model_name_or_path} and are newly initialized because the shapes did not" f" match:\n{mismatched_warning}\nYou should probably TRAIN this model on a down-stream task to be able" " to use it for predictions and inference." ) if paddle.in_dynamic_mode(): return model return model, model_state_dict
[文档] def save_pretrained_v2(self, save_dir: str): """ 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) # 1. retrieve the model related config # save the string version of dtype to the config, e.g. convert paddle.float32 => "float32" # we currently don't use this setting automatically, but may start to use with v5 model_to_save = unwrap_model(self) dtype = get_parameter_dtype(model_to_save) model_to_save.config.dtype = str(dtype).split(".")[1] # Attach architecture to the config model_to_save.config.architectures = [model_to_save.__class__.__name__] model_to_save.config.save_pretrained(save_dir) # Save model if paddle.in_dynamic_mode(): file_name = os.path.join(save_dir, self.resource_files_names["model_state"]) paddle.save(self.state_dict(), file_name) else: logger.warning("Save pretrained model only supported dygraph mode for now!")