# coding=utf-8 """ AraGPT2 configuration""" from collections import OrderedDict from typing import Any, List, Mapping, Optional from transformers import PreTrainedTokenizer, TensorType, is_torch_available from transformers.configuration_utils import PretrainedConfig from transformers.onnx import OnnxConfigWithPast, PatchingSpec from transformers.utils import logging logger = logging.get_logger(__name__) AraGPT2_PRETRAINED_CONFIG_ARCHIVE_MAP = { "aubmindlab/aragpt2-mega": "https://huggingface.co/aubmindlab/aragpt2-mega/resolve/main/config.json", } class AraGPT2Config(PretrainedConfig): """ This is the configuration class to store the configuration of a [`AraGPT2Model`] or a [`TFAraGPT2Model`]. It is used to instantiate a AraGPT2 model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the AraGPT2 [aubmindlab/aragpt2-mega](https://huggingface.co/aubmindlab/aragpt2-mega) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: vocab_size (`int`, *optional*, defaults to 64000): Vocabulary size of the AraGPT2 model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`AraGPT2Model`] or [`TFAraGPT2Model`]. n_positions (`int`, *optional*, defaults to 1024): The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048). n_embd (`int`, *optional*, defaults to 768): Dimensionality of the embeddings and hidden states. n_layer (`int`, *optional*, defaults to 12): Number of hidden layers in the Transformer encoder. n_head (`int`, *optional*, defaults to 12): Number of attention heads for each attention layer in the Transformer encoder. n_inner (`int`, *optional*): Dimensionality of the inner feed-forward layers. `None` will set it to 4 times n_embd activation_function (`str`, *optional*, defaults to `"gelu_new"`): Activation function, to be selected in the list `["relu", "silu", "gelu", "tanh", "gelu_new"]`. resid_pdrop (`float`, *optional*, defaults to 0.1): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. embd_pdrop (`float`, *optional*, defaults to 0.1): The dropout ratio for the embeddings. attn_pdrop (`float`, *optional*, defaults to 0.1): The dropout ratio for the attention. layer_norm_epsilon (`float`, *optional*, defaults to 1e-05): The epsilon to use in the layer normalization layers. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. summary_type (`string`, *optional*, defaults to `"cls_index"`): Argument used when doing sequence summary, used in the models [`GPT2DoubleHeadsModel`] and [`TFGPT2DoubleHeadsModel`]. Has to be one of the following options: - `"last"`: Take the last token hidden state (like XLNet). - `"first"`: Take the first token hidden state (like BERT). - `"mean"`: Take the mean of all tokens hidden states. - `"cls_index"`: Supply a Tensor of classification token position (like GPT/AraGPT2). - `"attn"`: Not implemented now, use multi-head attention. summary_use_proj (`bool`, *optional*, defaults to `True`): Argument used when doing sequence summary, used in the models [`GPT2DoubleHeadsModel`] and [`TFGPT2DoubleHeadsModel`]. Whether or not to add a projection after the vector extraction. summary_activation (`str`, *optional*): Argument used when doing sequence summary. Used in for the multiple choice head in [`GPT2DoubleHeadsModel`]. Pass `"tanh"` for a tanh activation to the output, any other value will result in no activation. summary_proj_to_labels (`bool`, *optional*, defaults to `True`): Argument used when doing sequence summary, used in the models [`GPT2DoubleHeadsModel`] and [`TFGPT2DoubleHeadsModel`]. Whether the projection outputs should have `config.num_labels` or `config.hidden_size` classes. summary_first_dropout (`float`, *optional*, defaults to 0.1): Argument used when doing sequence summary, used in the models [`GPT2DoubleHeadsModel`] and [`TFGPT2DoubleHeadsModel`]. The dropout ratio to be used after the projection and activation. scale_attn_weights (`bool`, *optional*, defaults to `True`): Scale attention weights by dividing by sqrt(hidden_size).. use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models). bos_token_id (`int`, *optional*, defaults to 50256): Id of the beginning of sentence token in the vocabulary. eos_token_id (`int`, *optional*, defaults to 50256): Id of the end of sentence token in the vocabulary. scale_attn_by_inverse_layer_idx (`bool`, *optional*, defaults to `False`): Whether to additionally scale attention weights by `1 / layer_idx + 1`. reorder_and_upcast_attn (`bool`, *optional*, defaults to `False`): Whether to scale keys (K) prior to computing attention (dot-product) and upcast attention dot-product/softmax to float() when training with mixed precision. Example: ```python >>> from transformers import AraGPT2Config, AraGPT2Model >>> # Initializing a AraGPT2 configuration >>> configuration = AraGPT2Config() >>> # Initializing a model (with random weights) from the configuration >>> model = AraGPT2Model(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "aragpt2" keys_to_ignore_at_inference = ["past_key_values"] attribute_map = { "hidden_size": "n_embd", "max_position_embeddings": "n_positions", "num_attention_heads": "n_head", "num_hidden_layers": "n_layer", } def __init__( self, vocab_size=64000, n_positions=1024, n_embd=768, n_layer=12, n_head=12, n_inner=None, activation_function="gelu_new", resid_pdrop=0.1, embd_pdrop=0.1, attn_pdrop=0.1, layer_norm_epsilon=1e-5, initializer_range=0.02, summary_type="cls_index", summary_use_proj=True, summary_activation=None, summary_proj_to_labels=True, summary_first_dropout=0.1, scale_attn_weights=True, use_cache=True, bos_token_id=0, eos_token_id=0, scale_attn_by_inverse_layer_idx=False, reorder_and_upcast_attn=False, **kwargs, ): self.vocab_size = vocab_size self.n_positions = n_positions self.n_embd = n_embd self.n_layer = n_layer self.n_head = n_head self.n_inner = n_inner self.activation_function = activation_function self.resid_pdrop = resid_pdrop self.embd_pdrop = embd_pdrop self.attn_pdrop = attn_pdrop self.layer_norm_epsilon = layer_norm_epsilon self.initializer_range = initializer_range self.summary_type = summary_type self.summary_use_proj = summary_use_proj self.summary_activation = summary_activation self.summary_first_dropout = summary_first_dropout self.summary_proj_to_labels = summary_proj_to_labels self.scale_attn_weights = scale_attn_weights self.use_cache = use_cache self.scale_attn_by_inverse_layer_idx = scale_attn_by_inverse_layer_idx self.reorder_and_upcast_attn = reorder_and_upcast_attn self.bos_token_id = bos_token_id self.eos_token_id = eos_token_id super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs) class AraGPT2OnnxConfig(OnnxConfigWithPast): def __init__( self, config: PretrainedConfig, task: str = "default", patching_specs: List[PatchingSpec] = None, use_past: bool = False, ): super().__init__( config, task=task, patching_specs=patching_specs, use_past=use_past ) if not getattr(self._config, "pad_token_id", None): # TODO: how to do that better? self._config.pad_token_id = 0 @property def inputs(self) -> Mapping[str, Mapping[int, str]]: common_inputs = OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}) if self.use_past: self.fill_with_past_key_values_(common_inputs, direction="inputs") common_inputs["attention_mask"] = { 0: "batch", 1: "past_sequence + sequence", } else: common_inputs["attention_mask"] = {0: "batch", 1: "sequence"} return common_inputs @property def num_layers(self) -> int: return self._config.n_layer @property def num_attention_heads(self) -> int: return self._config.n_head def generate_dummy_inputs( self, tokenizer: PreTrainedTokenizer, batch_size: int = -1, seq_length: int = -1, is_pair: bool = False, framework: Optional[TensorType] = None, ) -> Mapping[str, Any]: common_inputs = super(OnnxConfigWithPast, self).generate_dummy_inputs( tokenizer, batch_size=batch_size, seq_length=seq_length, is_pair=is_pair, framework=framework, ) # We need to order the input in the way they appears in the forward() ordered_inputs = OrderedDict({"input_ids": common_inputs["input_ids"]}) # Need to add the past_keys if self.use_past: if not is_torch_available(): raise ValueError( "Cannot generate dummy past_keys inputs without PyTorch installed." ) else: import torch batch, seqlen = common_inputs["input_ids"].shape # Not using the same length for past_key_values past_key_values_length = seqlen + 2 past_shape = ( batch, self.num_attention_heads, past_key_values_length, self._config.hidden_size // self.num_attention_heads, ) ordered_inputs["past_key_values"] = [ (torch.zeros(past_shape), torch.zeros(past_shape)) for _ in range(self.num_layers) ] ordered_inputs["attention_mask"] = common_inputs["attention_mask"] if self.use_past: mask_dtype = ordered_inputs["attention_mask"].dtype ordered_inputs["attention_mask"] = torch.cat( [ ordered_inputs["attention_mask"], torch.ones(batch, past_key_values_length, dtype=mask_dtype), ], dim=1, ) return ordered_inputs @property def default_onnx_opset(self) -> int: return 13