# coding=utf-8 # [Apache-2.0] Modified from https://github.com/OFA-Sys/OFA """ TiO model configuration""" import warnings from transformers import PretrainedConfig class TiOConfig(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`~TiOModel`]. It is used to instantiate an TiO 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 TiO. 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 50265): Vocabulary size of the TiO model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`~TiOModel`] or [`~TFTiOModel`]. d_model (`int`, *optional*, defaults to 1024): Dimension of the layers and the pooler layer. encoder_layers (`int`, *optional*, defaults to 12): Number of encoder layers. decoder_layers (`int`, *optional*, defaults to 12): Number of decoder layers. encoder_attention_heads (`int`, *optional*, defaults to 16): Number of attention heads for each attention layer in the Transformer encoder. decoder_attention_heads (`int`, *optional*, defaults to 16): Number of attention heads for each attention layer in the Transformer decoder. decoder_ffn_dim (`int`, *optional*, defaults to 4096): Dimension of the "intermediate" (often named feed-forward) layer in decoder. encoder_ffn_dim (`int`, *optional*, defaults to 4096): Dimension of the "intermediate" (often named feed-forward) layer in decoder. activation_function (`str` or `function`, *optional*, defaults to `"gelu"`): The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`, `"relu"`, `"silu"` and `"gelu_new"` are supported. dropout (`float`, *optional*, defaults to 0.1): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for the attention probabilities. activation_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for activations inside the fully connected layer. classifier_dropout (`float`, *optional*, defaults to 0.0): The dropout ratio for classifier. max_position_embeddings (`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). init_std (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. encoder_layerdrop: (`float`, *optional*, defaults to 0.0): The LayerDrop probability for the encoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556) for more details. decoder_layerdrop: (`float`, *optional*, defaults to 0.0): The LayerDrop probability for the decoder. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556) for more details. use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models). """ model_type = "tio" keys_to_ignore_at_inference = ["past_key_values"] attribute_map = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"} def __init__( self, vocab_size=59457, max_position_embeddings=1024, encoder_layers=4, encoder_ffn_dim=512 * 4, encoder_attention_heads=8, decoder_layers=4, decoder_ffn_dim=512 * 4, decoder_attention_heads=8, encoder_layerdrop=0.0, decoder_layerdrop=0.0, use_cache=True, is_encoder_decoder=True, activation_function="gelu", d_model=512, dropout=0.1, attention_dropout=0.0, activation_dropout=0.0, init_std=0.02, classifier_dropout=0.0, scale_embedding=False, pad_token_id=1, bos_token_id=0, decoder_start_token_id=0, eos_token_id=2, forced_eos_token_id=2, encoder_normalize_before=True, decoder_normalize_before=True, normformer=True, encoder_drop_path_rate=0.0, decoder_drop_path_rate=0.0, layernorm_embedding=True, patch_layernorm_embedding=True, resnet_type="resnet101", resnet_model_path=None, resnet_drop_path_rate=0.0, token_bucket_size=256, image_bucket_size=42, add_type_embedding=True, share_decoder_input_output_embed=True, attn_scale_factor=2.0, code_layernorm_embedding=True, code_image_size=128, entangle_position_embedding=False, label_smoothing=0.1, **kwargs ): self.vocab_size = vocab_size self.max_position_embeddings = max_position_embeddings self.d_model = d_model self.encoder_ffn_dim = encoder_ffn_dim self.encoder_layers = encoder_layers self.encoder_attention_heads = encoder_attention_heads self.decoder_ffn_dim = decoder_ffn_dim self.decoder_layers = decoder_layers self.decoder_attention_heads = decoder_attention_heads self.dropout = dropout self.attention_dropout = attention_dropout self.activation_dropout = activation_dropout self.activation_function = activation_function self.init_std = init_std self.encoder_layerdrop = encoder_layerdrop self.decoder_layerdrop = decoder_layerdrop self.classifier_dropout = classifier_dropout self.use_cache = use_cache self.num_hidden_layers = encoder_layers self.scale_embedding = scale_embedding # scale factor will be sqrt(d_model) if True self.encoder_normalize_before = encoder_normalize_before self.decoder_normalize_before = decoder_normalize_before self.normformer = normformer self.encoder_drop_path_rate = encoder_drop_path_rate self.decoder_drop_path_rate = decoder_drop_path_rate self.layernorm_embedding = layernorm_embedding self.patch_layernorm_embedding = patch_layernorm_embedding self.resnet_type = resnet_type self.resnet_model_path = resnet_model_path self.resnet_drop_path_rate = resnet_drop_path_rate self.token_bucket_size = token_bucket_size self.image_bucket_size = image_bucket_size self.add_type_embedding = add_type_embedding self.share_decoder_input_output_embed = share_decoder_input_output_embed self.attn_scale_factor = attn_scale_factor self.code_layernorm_embedding = code_layernorm_embedding self.code_image_size = code_image_size self.entangle_position_embedding = entangle_position_embedding self.label_smoothing = label_smoothing super().__init__( pad_token_id=pad_token_id, bos_token_id=bos_token_id, eos_token_id=eos_token_id, is_encoder_decoder=is_encoder_decoder, decoder_start_token_id=bos_token_id, forced_eos_token_id=forced_eos_token_id, **kwargs, ) # ensure backward compatibility for BART CNN models if self.forced_bos_token_id is None and kwargs.get("force_bos_token_to_be_generated", False): self.forced_bos_token_id = self.bos_token_id warnings.warn( f"Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. " "The config can simply be saved and uploaded again to be fixed." )