# 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. # FIXME(zhangxuefei): remove this file after paddlenlp is released. import copy import functools import inspect import io import json import os import six from typing import List, Tuple import paddle import paddle.nn as nn from paddle.dataset.common import DATA_HOME from paddle.utils.download import get_path_from_url from paddlehub.module.module import serving, RunModule, runnable from paddlehub.utils.log import logger __all__ = [ 'PretrainedModel', 'register_base_model', 'TransformerModule', ] def fn_args_to_dict(func, *args, **kwargs): """ Inspect function `func` and its arguments for running, and extract a dict mapping between argument names and keys. """ if hasattr(inspect, 'getfullargspec'): (spec_args, spec_varargs, spec_varkw, spec_defaults, _, _, _) = inspect.getfullargspec(func) else: (spec_args, spec_varargs, spec_varkw, spec_defaults) = inspect.getargspec(func) # add positional argument values init_dict = dict(zip(spec_args, args)) # add default argument values kwargs_dict = dict(zip(spec_args[-len(spec_defaults):], spec_defaults)) if spec_defaults else {} kwargs_dict.update(kwargs) init_dict.update(kwargs_dict) return init_dict class InitTrackerMeta(type(nn.Layer)): """ This metaclass wraps the `__init__` method of a class to add `init_config` attribute for instances of that class, and `init_config` use a dict to track the initial configuration. If the class has `_wrap_init` method, it would be hooked after `__init__` and called as `_wrap_init(self, init_fn, init_args)`. Since InitTrackerMeta would be used as metaclass for pretrained model classes, which always are Layer and `type(nn.Layer)` is not `type`, thus use `type(nn.Layer)` rather than `type` as base class for it to avoid inheritance metaclass conflicts. """ def __init__(cls, name, bases, attrs): init_func = cls.__init__ # If attrs has `__init__`, wrap it using accessable `_wrap_init`. # Otherwise, no need to wrap again since the super cls has been wraped. # TODO: remove reduplicated tracker if using super cls `__init__` help_func = getattr(cls, '_wrap_init', None) if '__init__' in attrs else None cls.__init__ = InitTrackerMeta.init_and_track_conf(init_func, help_func) super(InitTrackerMeta, cls).__init__(name, bases, attrs) @staticmethod def init_and_track_conf(init_func, help_func=None): """ wraps `init_func` which is `__init__` method of a class to add `init_config` attribute for instances of that class. Args: init_func (callable): It should be the `__init__` method of a class. help_func (callable, optional): If provided, it would be hooked after `init_func` and called as `_wrap_init(self, init_func, *init_args, **init_args)`. Default None. Returns: function: the wrapped function """ @functools.wraps(init_func) def __impl__(self, *args, **kwargs): # keep full configuration init_func(self, *args, **kwargs) # registed helper by `_wrap_init` if help_func: help_func(self, init_func, *args, **kwargs) self.init_config = kwargs if args: kwargs['init_args'] = args kwargs['init_class'] = self.__class__.__name__ return __impl__ def register_base_model(cls): """ Add a `base_model_class` attribute for the base class of decorated class, representing the base model class in derived classes of the same architecture. Args: cls (class): the name of the model """ 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 @six.add_metaclass(InitTrackerMeta) class PretrainedModel(nn.Layer): """ The base class for all pretrained models. It provides some attributes and common methods for all pretrained models, including attributes `init_config`, `config` for initialized arguments and methods for saving, loading. It also includes some class attributes (should be set by derived classes): - `model_config_file` (str): represents the file name for saving and loading model configuration, it's value is `model_config.json`. - `resource_files_names` (dict): use this to map resources to specific file names for saving and loading. - `pretrained_resource_files_map` (dict): The dict has the same keys as `resource_files_names`, the values are also dict mapping specific pretrained model name to URL linking to pretrained model. - `pretrained_init_configuration` (dict): The dict has pretrained model names as keys, and the values are also dict preserving corresponding configuration for model initialization. - `base_model_prefix` (str): represents the the attribute associated to the base model in derived classes of the same architecture adding layers on top of the base model. """ model_config_file = "model_config.json" pretrained_init_configuration = {} # TODO: more flexible resource handle, namedtuple with fileds 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 prtrained model instance. """ init_dict = fn_args_to_dict(original_init, *args, **kwargs) self.config = init_dict @property def base_model(self): return getattr(self, self.base_model_prefix, self) @property def model_name_list(self): 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 @classmethod def from_pretrained(cls, pretrained_model_name_or_path, *args, **kwargs): """ Instantiate an instance of `PretrainedModel` from a predefined model specified by name or path. Args: pretrained_model_name_or_path (str): A name of or a file path to a pretrained model. *args (tuple): position arguments for `__init__`. If provide, use this as position argument values for model initialization. **kwargs (dict): keyword arguments for `__init__`. If provide, use this to update pre-defined keyword argument values for model initialization. Returns: PretrainedModel: An instance of PretrainedModel. """ pretrained_models = list(cls.pretrained_init_configuration.keys()) resource_files = {} init_configuration = {} 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]) else: if 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: raise ValueError("Calling {}.from_pretrained() with a model identifier or the " "path to a directory instead. The supported model " "identifiers are as follows: {}".format(cls.__name__, cls.pretrained_init_configuration.keys())) # FIXME(chenzeyu01): We should use another data path for storing model default_root = os.path.join(DATA_HOME, pretrained_model_name_or_path) resolved_resource_files = {} for file_id, file_path in resource_files.items(): path = os.path.join(default_root, file_path.split('/')[-1]) if file_path is None or os.path.isfile(file_path): resolved_resource_files[file_id] = file_path elif 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)) resolved_resource_files[file_id] = get_path_from_url(file_path, default_root) # 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 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 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 break base_args = arg.pop("init_args", ()) base_kwargs = 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_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_kwargs.update(kwargs) model = cls(*derived_args, **derived_kwargs) # Maybe need more ways to load resources. weight_path = list(resolved_resource_files.values())[0] assert weight_path.endswith(".pdparams"), "suffix of weight must be .pdparams" state_dict = paddle.load(weight_path) # 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)) model_to_load.set_state_dict(state_to_load) if paddle.in_dynamic_mode(): return model return model, state_to_load def save_pretrained(self, save_directory): """ Save model configuration and related resources (model state) to files under `save_directory`. Args: save_directory (str): Directory to save files into. """ assert os.path.isdir(save_directory), "Saving directory ({}) should be a directory".format(save_directory) # save model config model_config_file = os.path.join(save_directory, self.model_config_file) model_config = self.init_config # If init_config contains a Layer, use the layer's init_config to save for key, value in model_config.items(): if key == "init_args": args = [] for arg in value: args.append(arg.init_config if isinstance(arg, PretrainedModel) else arg) model_config[key] = tuple(args) elif isinstance(value, PretrainedModel): model_config[key] = value.init_config with io.open(model_config_file, "w", encoding="utf-8") as f: f.write(json.dumps(model_config, ensure_ascii=False)) # save model file_name = os.path.join(save_directory, list(self.resource_files_names.values())[0]) paddle.save(self.state_dict(), file_name) class TextServing(object): """ A base class for text model which supports serving. """ @serving def predict_method( self, data: List[List[str]], max_seq_len: int = 128, batch_size: int = 1, use_gpu: bool = False ): """ Run predict method as a service. Serving as a task which is specified from serving config. Tasks supported: 1. seq-cls: sequence classification; 2. token-cls: sequence labeling; 3. None: embedding. Args: data (obj:`List(List(str))`): The processed data whose each element is the list of a single text or a pair of texts. max_seq_len (:obj:`int`, `optional`, defaults to 128): If set to a number, will limit the total sequence returned so that it has a maximum length. batch_size(obj:`int`, defaults to 1): The number of batch. use_gpu(obj:`bool`, defaults to `False`): Whether to use gpu to run or not. Returns: results(obj:`list`): All the predictions labels. """ if self.task in self._tasks_supported: # cls service if self.label_map: # compatible with json decoding label_map self.label_map = {int(k): v for k, v in self.label_map.items()} results = self.predict(data, max_seq_len, batch_size, use_gpu) if self.task == 'token-cls': # remove labels of [CLS] token and pad tokens results = [ token_labels[1:len(data[i][0])+1] for i, token_labels in enumerate(results) ] return results elif self.task is None: # embedding service results = self.get_embedding(data, use_gpu) return results else: # unknown service logger.error( f'Unknown task {self.task}, current tasks supported:\n' '1. seq-cls: sequence classification service;\n' '2. token-cls: sequence labeling service;\n' '3. None: embedding service' ) return class TransformerModule(RunModule, TextServing): """ The base class for Transformer models. """ _tasks_supported = [ 'seq-cls', 'token-cls', ] def _convert_text_to_input(self, tokenizer, text: List[str], max_seq_len: int): pad_to_max_seq_len = False if self.task is None else True if len(text) == 1: encoded_inputs = tokenizer.encode(text[0], text_pair=None, max_seq_len=max_seq_len, pad_to_max_seq_len=pad_to_max_seq_len) elif len(text) == 2: encoded_inputs = tokenizer.encode(text[0], text_pair=text[1], max_seq_len=max_seq_len, pad_to_max_seq_len=pad_to_max_seq_len) else: raise RuntimeError( 'The input text must have one or two sequence, but got %d. Please check your inputs.' % len(text)) return encoded_inputs def _batchify(self, data: List[List[str]], max_seq_len: int, batch_size: int): def _parse_batch(batch): input_ids = [entry[0] for entry in batch] segment_ids = [entry[1] for entry in batch] return input_ids, segment_ids tokenizer = self.get_tokenizer() examples = [] for text in data: encoded_inputs = self._convert_text_to_input(tokenizer, text, max_seq_len) examples.append((encoded_inputs['input_ids'], encoded_inputs['segment_ids'])) # Seperates data into some batches. one_batch = [] for example in examples: one_batch.append(example) if len(one_batch) == batch_size: yield _parse_batch(one_batch) one_batch = [] if one_batch: # The last batch whose size is less than the config batch_size setting. yield _parse_batch(one_batch) def training_step(self, batch: List[paddle.Tensor], batch_idx: int): """ One step for training, which should be called as forward computation. Args: batch(:obj:List[paddle.Tensor]): The one batch data, which contains the model needed, such as input_ids, sent_ids, pos_ids, input_mask and labels. batch_idx(int): The index of batch. Returns: results(:obj: Dict) : The model outputs, such as loss and metrics. """ if self.task == 'seq-cls': predictions, avg_loss, metric = self(input_ids=batch[0], token_type_ids=batch[1], labels=batch[2]) elif self.task == 'token-cls': predictions, avg_loss, metric = self(input_ids=batch[0], token_type_ids=batch[1], seq_lengths=batch[2], labels=batch[3]) return {'loss': avg_loss, 'metrics': metric} def validation_step(self, batch: List[paddle.Tensor], batch_idx: int): """ One step for validation, which should be called as forward computation. Args: batch(:obj:List[paddle.Tensor]): The one batch data, which contains the model needed, such as input_ids, sent_ids, pos_ids, input_mask and labels. batch_idx(int): The index of batch. Returns: results(:obj: Dict) : The model outputs, such as metrics. """ if self.task == 'seq-cls': predictions, avg_loss, metric = self(input_ids=batch[0], token_type_ids=batch[1], labels=batch[2]) elif self.task == 'token-cls': predictions, avg_loss, metric = self(input_ids=batch[0], token_type_ids=batch[1], seq_lengths=batch[2], labels=batch[3]) return {'metrics': metric} def get_embedding(self, data: List[List[str]], use_gpu=False): """ Get token level embeddings and sentence level embeddings from model. Args: data (obj:`List(List(str))`): The processed data whose each element is the list of a single text or a pair of texts. use_gpu(obj:`bool`, defaults to `False`): Whether to use gpu to run or not. Returns: results(obj:`list`): All the tokens and sentences embeddings. """ if self.task is not None: raise RuntimeError("The get_embedding method is only valid when task is None, but got task %s" % self.task) return self.predict( data=data, use_gpu=use_gpu ) def predict( self, data: List[List[str]], max_seq_len: int = 128, batch_size: int = 1, use_gpu: bool = False ): """ Predicts the data labels. Args: data (obj:`List(List(str))`): The processed data whose each element is the list of a single text or a pair of texts. max_seq_len (:obj:`int`, `optional`, defaults to :int:`None`): If set to a number, will limit the total sequence returned so that it has a maximum length. batch_size(obj:`int`, defaults to 1): The number of batch. use_gpu(obj:`bool`, defaults to `False`): Whether to use gpu to run or not. Returns: results(obj:`list`): All the predictions labels. """ if self.task not in self._tasks_supported \ and self.task is not None: # None for getting embedding raise RuntimeError( f'Unknown task {self.task}, current tasks supported:\n' '1. seq-cls: sequence classification;\n' '2. token-cls: sequence labeling;\n' '3. None: embedding' ) paddle.set_device('gpu') if use_gpu else paddle.set_device('cpu') batches = self._batchify(data, max_seq_len, batch_size) results = [] self.eval() for batch in batches: input_ids, segment_ids = batch input_ids = paddle.to_tensor(input_ids) segment_ids = paddle.to_tensor(segment_ids) if self.task == 'seq-cls': probs = self(input_ids, segment_ids) idx = paddle.argmax(probs, axis=1).numpy() idx = idx.tolist() labels = [self.label_map[i] for i in idx] results.extend(labels) elif self.task == 'token-cls': probs = self(input_ids, segment_ids) batch_ids = paddle.argmax(probs, axis=2).numpy() # (batch_size, max_seq_len) batch_ids = batch_ids.tolist() token_labels = [[self.label_map[i] for i in token_ids] for token_ids in batch_ids] results.extend(token_labels) elif self.task == None: sequence_output, pooled_output = self(input_ids, segment_ids) results.append([ pooled_output.squeeze(0).numpy().tolist(), sequence_output.squeeze(0).numpy().tolist() ]) return results