config.py

from typing import Union, Tuple, Dict, Any, Optional
import os
import json
from collections import OrderedDict
import torch
from utils import CONFIG_NAME, hf_bucket_url, cached_path, is_remote_url

class PretrainedConfig(object):
  model_type: str = ""
  is_composition: bool = False

  def __init__(self, **kwargs):
    # Attributes with defaults
    self.return_dict = kwargs.pop("return_dict", True)
    self.output_hidden_states = kwargs.pop("output_hidden_states", False)
    self.output_attentions = kwargs.pop("output_attentions", False)
    self.torchscript = kwargs.pop("torchscript", False)  # Only used by PyTorch models
    self.use_bfloat16 = kwargs.pop("use_bfloat16", False)
    self.pruned_heads = kwargs.pop("pruned_heads", {})
    self.tie_word_embeddings = kwargs.pop(
      "tie_word_embeddings", True
    )  # Whether input and output word embeddings should be tied for all MLM, LM and Seq2Seq models.

    # Is decoder is used in encoder-decoder models to differentiate encoder from decoder
    self.is_encoder_decoder = kwargs.pop("is_encoder_decoder", False)
    self.is_decoder = kwargs.pop("is_decoder", False)
    self.add_cross_attention = kwargs.pop("add_cross_attention", False)
    self.tie_encoder_decoder = kwargs.pop("tie_encoder_decoder", False)

    # Parameters for sequence generation
    self.max_length = kwargs.pop("max_length", 20)
    self.min_length = kwargs.pop("min_length", 0)
    self.do_sample = kwargs.pop("do_sample", False)
    self.early_stopping = kwargs.pop("early_stopping", False)
    self.num_beams = kwargs.pop("num_beams", 1)
    self.num_beam_groups = kwargs.pop("num_beam_groups", 1)
    self.diversity_penalty = kwargs.pop("diversity_penalty", 0.0)
    self.temperature = kwargs.pop("temperature", 1.0)
    self.top_k = kwargs.pop("top_k", 50)
    self.top_p = kwargs.pop("top_p", 1.0)
    self.repetition_penalty = kwargs.pop("repetition_penalty", 1.0)
    self.length_penalty = kwargs.pop("length_penalty", 1.0)
    self.no_repeat_ngram_size = kwargs.pop("no_repeat_ngram_size", 0)
    self.encoder_no_repeat_ngram_size = kwargs.pop("encoder_no_repeat_ngram_size", 0)
    self.bad_words_ids = kwargs.pop("bad_words_ids", None)
    self.num_return_sequences = kwargs.pop("num_return_sequences", 1)
    self.chunk_size_feed_forward = kwargs.pop("chunk_size_feed_forward", 0)
    self.output_scores = kwargs.pop("output_scores", False)
    self.return_dict_in_generate = kwargs.pop("return_dict_in_generate", False)
    self.forced_bos_token_id = kwargs.pop("forced_bos_token_id", None)
    self.forced_eos_token_id = kwargs.pop("forced_eos_token_id", None)

    # Fine-tuning task arguments
    self.architectures = kwargs.pop("architectures", None)
    self.finetuning_task = kwargs.pop("finetuning_task", None)
    self.id2label = kwargs.pop("id2label", None)
    self.label2id = kwargs.pop("label2id", None)
    if self.id2label is not None:
      kwargs.pop("num_labels", None)
      self.id2label = dict((int(key), value) for key, value in self.id2label.items())
      # Keys are always strings in JSON so convert ids to int here.
    else:
      self.num_labels = kwargs.pop("num_labels", 2)

    # Tokenizer arguments
    self.tokenizer_class = kwargs.pop("tokenizer_class", None)
    self.prefix = kwargs.pop("prefix", None)
    self.bos_token_id = kwargs.pop("bos_token_id", None)
    self.pad_token_id = kwargs.pop("pad_token_id", None)
    self.eos_token_id = kwargs.pop("eos_token_id", None)
    self.sep_token_id = kwargs.pop("sep_token_id", None)

    self.decoder_start_token_id = kwargs.pop("decoder_start_token_id", None)

    # task specific arguments
    self.task_specific_params = kwargs.pop("task_specific_params", None)

    # TPU arguments
    self.xla_device = kwargs.pop("xla_device", None)

    # Name or path to the pretrained checkpoint
    self._name_or_path = str(kwargs.pop("name_or_path", ""))

    # Drop the transformers version info
    kwargs.pop("transformers_version", None)

    # Additional attributes without default values
    for key, value in kwargs.items():
      try:
        setattr(self, key, value)
      except AttributeError as err:
        raise err

  @classmethod
  def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
    config_dict, kwargs = cls.get_config_dict(pretrained_model_name_or_path, **kwargs)
    return cls.from_dict(config_dict, **kwargs)

  @classmethod
  def _dict_from_json_file(cls, json_file: Union[str, os.PathLike]):
    with open(json_file, "r", encoding="utf-8") as reader:
      text = reader.read()
    return json.loads(text)

  @classmethod
  def from_dict(cls, config_dict: Dict[str, Any], **kwargs) -> "PretrainedConfig":
    return_unused_kwargs = kwargs.pop("return_unused_kwargs", False)

    config = cls(**config_dict)

    if hasattr(config, "pruned_heads"):
      config.pruned_heads = dict((int(key), value) for key, value in config.pruned_heads.items())

    # Update config with kwargs if needed
    to_remove = []
    for key, value in kwargs.items():
      if hasattr(config, key):
        setattr(config, key, value)
        to_remove.append(key)
    for key in to_remove:
      kwargs.pop(key, None)

    if return_unused_kwargs:
      return config, kwargs
    else:
      return config

  @classmethod
  def get_config_dict(
    cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs
  ) -> Tuple[Dict[str, Any], Dict[str, Any]]:
    cache_dir = kwargs.pop("cache_dir", None)
    force_download = kwargs.pop("force_download", False)
    resume_download = kwargs.pop("resume_download", False)
    proxies = kwargs.pop("proxies", None)
    use_auth_token = kwargs.pop("use_auth_token", None)
    local_files_only = kwargs.pop("local_files_only", False)
    revision = kwargs.pop("revision", None)

    pretrained_model_name_or_path = str(pretrained_model_name_or_path)
    if os.path.isdir(pretrained_model_name_or_path):
      config_file = os.path.join(pretrained_model_name_or_path, CONFIG_NAME)
    elif os.path.isfile(pretrained_model_name_or_path) or is_remote_url(pretrained_model_name_or_path):
      config_file = pretrained_model_name_or_path
    else:
      config_file = hf_bucket_url(
        pretrained_model_name_or_path, filename=CONFIG_NAME, revision=revision, mirror=None
      )

    try:
      # Load from URL or cache if already cached
      resolved_config_file = cached_path(
        config_file,
        cache_dir=cache_dir,
        force_download=force_download,
        proxies=proxies,
        resume_download=resume_download,
        local_files_only=local_files_only,
        use_auth_token=use_auth_token,
      )
      # Load config dict
      config_dict = cls._dict_from_json_file(resolved_config_file)

    except EnvironmentError as err:
      msg = (
        f"Can't load config for '{pretrained_model_name_or_path}'. Make sure that:\n\n"
        f"- '{pretrained_model_name_or_path}' is a correct model identifier listed on 'https://huggingface.co/models'\n\n"
        f"- or '{pretrained_model_name_or_path}' is the correct path to a directory containing a {CONFIG_NAME} file\n\n"
      )
      raise EnvironmentError(msg)

    except json.JSONDecodeError:
      msg = (
        "Couldn't reach server at '{}' to download configuration file or "
        "configuration file is not a valid JSON file. "
        "Please check network or file content here: {}.".format(config_file, resolved_config_file)
      )
      raise EnvironmentError(msg)

    return config_dict, kwargs


class BertConfig(PretrainedConfig):
  model_type = "bert"

  def __init__(
    self,
    vocab_size=30522,
    hidden_size=768,
    num_hidden_layers=12,
    num_attention_heads=12,
    intermediate_size=3072,
    hidden_act="gelu",
    hidden_dropout_prob=0.1,
    attention_probs_dropout_prob=0.1,
    max_position_embeddings=512,
    type_vocab_size=2,
    initializer_range=0.02,
    layer_norm_eps=1e-12,
    pad_token_id=0,
    gradient_checkpointing=False,
    position_embedding_type="absolute",
    use_cache=True,
    **kwargs
  ):
    super().__init__(pad_token_id=pad_token_id, **kwargs)

    self.vocab_size = vocab_size
    self.hidden_size = hidden_size
    self.num_hidden_layers = num_hidden_layers
    self.num_attention_heads = num_attention_heads
    self.hidden_act = hidden_act
    self.intermediate_size = intermediate_size
    self.hidden_dropout_prob = hidden_dropout_prob
    self.attention_probs_dropout_prob = attention_probs_dropout_prob
    self.max_position_embeddings = max_position_embeddings
    self.type_vocab_size = type_vocab_size
    self.initializer_range = initializer_range
    self.layer_norm_eps = layer_norm_eps
    self.gradient_checkpointing = gradient_checkpointing
    self.position_embedding_type = position_embedding_type
    self.use_cache = use_cache