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| # 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 "<img>" 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})" | |
| ) | |
| 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 | |