# coding=utf-8 # Copyright 2020, The T5 Authors and HuggingFace Inc. # # 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. """ VT5 model configuration""" import os from typing import Tuple, Union from transformers import AutoConfig from transformers.configuration_utils import PretrainedConfig from transformers.utils import logging logger = logging.get_logger(__name__) T5_PRETRAINED_CONFIG_ARCHIVE_MAP = { "t5-small": "https://huggingface.co/t5-small/resolve/main/config.json", "t5-base": "https://huggingface.co/t5-base/resolve/main/config.json", "t5-large": "https://huggingface.co/t5-large/resolve/main/config.json", "t5-3b": "https://huggingface.co/t5-3b/resolve/main/config.json", "t5-11b": "https://huggingface.co/t5-11b/resolve/main/config.json", } class VT5Config(PretrainedConfig): r""" This is the configuration class to store the configuration of a [`T5Model`] or a [`TFT5Model`]. It is used to instantiate a T5 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 T5 [t5-small](https://huggingface.co/t5-small) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. TODO: this doc is completely out of sync with the actual args Arguments: vocab_size (`int`, *optional*, defaults to 32128): Vocabulary size of the T5 model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`T5Model`] or [`TFT5Model`]. d_model (`int`, *optional*, defaults to 512): Size of the encoder layers and the pooler layer. d_kv (`int`, *optional*, defaults to 64): Size of the key, query, value projections per attention head. `d_kv` has to be equal to `d_model // num_heads`. d_ff (`int`, *optional*, defaults to 2048): Size of the intermediate feed forward layer in each `T5Block`. num_layers (`int`, *optional*, defaults to 6): Number of hidden layers in the Transformer encoder. num_decoder_layers (`int`, *optional*): Number of hidden layers in the Transformer decoder. Will use the same value as `num_layers` if not set. num_heads (`int`, *optional*, defaults to 8): Number of attention heads for each attention layer in the Transformer encoder. relative_attention_num_buckets (`int`, *optional*, defaults to 32): The number of buckets to use for each attention layer. relative_attention_max_distance (`int`, *optional*, defaults to 128): The maximum distance of the longer sequences for the bucket separation. dropout_rate (`float`, *optional*, defaults to 0.1): The ratio for all dropout layers. layer_norm_eps (`float`, *optional*, defaults to 1e-6): The epsilon used by the layer normalization layers. initializer_factor (`float`, *optional*, defaults to 1): A factor for initializing all weight matrices (should be kept to 1, used internally for initialization testing). feed_forward_proj (`string`, *optional*, defaults to `"relu"`): Type of feed forward layer to be used. Should be one of `"relu"` or `"gated-gelu"`. T5v1.1 uses the `"gated-gelu"` feed forward projection. Original T5 uses `"relu"`. use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models). additional_vocab_size (`int`, *optional`, defaults to 0): Additional vocabulary size of the model, typically for the special "" token. Additional vocab tokens are always trainable whereas regular vocab tokens can be frozen or not. alpha_initializer (`str`, *optional*, defaults to `"ones"`): Initialization type for the alphas. alphas_initializer_range (`float`, *optional*, defaults to 0.0): The standard deviation of the truncated_normal_initializer for initializing the alphas in the Gated Cross Attention. alpha_type (`str`, *optional*, defaults to `"vector"`): Whether the gating alphas should be vectors or single floats. """ model_type = "vt5" keys_to_ignore_at_inference = ["past_key_values"] attribute_map = {"hidden_size": "d_model", "num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"} def __init__( self, vocab_size=32128, d_model=512, d_kv=64, d_ff=2048, num_layers=6, num_decoder_layers=None, num_heads=8, relative_attention_num_buckets=32, relative_attention_max_distance=128, dropout_rate=0.1, layer_norm_epsilon=1e-6, initializer_factor=1.0, feed_forward_proj="relu", is_encoder_decoder=True, use_cache=True, pad_token_id=0, eos_token_id=1, additional_vocab_size=0, alpha_initializer="ones", alphas_initializer_range=0.0, alpha_type="vector", cross_layer_interval=1, tie_word_embeddings=False, freeze_text_layers=True, freeze_lm_head=False, freeze_vision_layers=True, vision_model_name="google/vit-base-patch16-224", vision_model_params="{}", vision_embed_dim=768, image_token_index=32128, **kwargs, ): self.vocab_size = vocab_size self.additional_vocab_size = additional_vocab_size self.d_model = d_model self.d_kv = d_kv self.d_ff = d_ff self.num_layers = num_layers self.num_decoder_layers = ( num_decoder_layers if num_decoder_layers is not None else self.num_layers ) # default = symmetry self.num_heads = num_heads self.relative_attention_num_buckets = relative_attention_num_buckets self.relative_attention_max_distance = relative_attention_max_distance self.dropout_rate = dropout_rate self.layer_norm_epsilon = layer_norm_epsilon self.initializer_factor = initializer_factor self.feed_forward_proj = feed_forward_proj self.use_cache = use_cache act_info = self.feed_forward_proj.split("-") self.dense_act_fn = act_info[-1] self.is_gated_act = act_info[0] == "gated" if len(act_info) > 1 and act_info[0] != "gated" or len(act_info) > 2: raise ValueError( f"`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer." "Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. " "'gated-gelu' or 'relu'" ) # for backwards compatibility if feed_forward_proj == "gated-gelu": self.dense_act_fn = "gelu_new" self.alpha_initializer = alpha_initializer self.alphas_initializer_range = alphas_initializer_range self.alpha_type = alpha_type self.cross_layer_interval = cross_layer_interval self.freeze_vision_layers = freeze_vision_layers self.vision_model_name = vision_model_name self.vision_model_params = vision_model_params self.tie_word_embeddings = tie_word_embeddings self.freeze_text_layers = freeze_text_layers self.freeze_lm_head = freeze_lm_head self.image_token_index = image_token_index self.vision_embed_dim = vision_embed_dim # IMPORTANT: Do not do any __init__ args-based checks in the constructor, since # PretrainedConfig.from_dict first instantiates the class with the config dict and only then # updates the config object with `kwargs` from from_pretrained, so during the instantiation # of this object many attributes have default values and haven't yet been overridden. # Do any required checks inside `from_pretrained` once the superclass' `from_pretrained` was run. super().__init__( pad_token_id=pad_token_id, eos_token_id=eos_token_id, is_encoder_decoder=is_encoder_decoder, tie_word_embeddings=tie_word_embeddings, **kwargs, ) def check_compatibilities(self): if self.tie_word_embeddings and (self.freeze_text_layers != self.freeze_lm_head): raise ValueError( "if `tie_word_embeddings` is True, then `freeze_lm_head` and `freeze_text_layers` must be equal." ) vision_model_params = eval(self.vision_model_params) config = AutoConfig.from_pretrained(self.vision_model_name, **vision_model_params) if hasattr(config, "vision_config"): vison_config = config.vision_config else: vison_config = config vision_embed_dim = vison_config.hidden_size if self.vision_embed_dim != vision_embed_dim: raise ValueError( f"vision_embed_dim ({self.vision_embed_dim}) must match the hidden size of the vision model" f" ({vision_embed_dim})" ) @classmethod def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig": outputs = super(VT5Config, cls).from_pretrained(pretrained_model_name_or_path, **kwargs) if isinstance(outputs, Tuple): # When called with return_unused_kwargs=True, the first item will be the config outputs[0].check_compatibilities() else: outputs.check_compatibilities() return outputs