| |
| |
| """ 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, |
| **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 |
| 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 |
|
|
| 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, |
| ) |
|
|
| |
| 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." |
| ) |
|
|
