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179 lines
8.4 KiB
179 lines
8.4 KiB
# Copyright 2023 Adept AI and the HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Fuyu model configuration"""
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from ...configuration_utils import PreTrainedConfig
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from ...modeling_rope_utils import RopeParameters
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from ...utils import logging
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from ..auto import CONFIG_MAPPING, AutoConfig
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logger = logging.get_logger(__name__)
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class FuyuConfig(PreTrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`FuyuForCausalLM`]. It is used to instantiate an
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Fuyu model according to the specified arguments, defining the model architecture. Instantiating a configuration
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with the defaults will yield a similar configuration to that of the
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[adept/fuyu-8b](https://huggingface.co/adept/fuyu-8b).
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Configuration objects inherit from [`PreTrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PreTrainedConfig`] for more information.
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Args:
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vocab_size (`int`, *optional*, defaults to 262144):
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Vocabulary size of the Fuyu model. Defines the number of different tokens that can be represented by the
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`inputs_ids` passed when calling [`FuyuForCausalLM`]
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hidden_size (`int`, *optional*, defaults to 4096):
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Dimension of the hidden representations.
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intermediate_size (`int`, *optional*, defaults to 16384):
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Dimension of the MLP representations.
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num_hidden_layers (`int`, *optional*, defaults to 36):
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Number of hidden layers in the Transformer encoder.
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num_attention_heads (`int`, *optional*, defaults to 64):
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Number of attention heads for each attention layer in the Transformer encoder.
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hidden_act (`str` or `function`, *optional*, defaults to `"relu2"`):
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The non-linear activation function (function or string) in the decoder.
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max_position_embeddings (`int`, *optional*, defaults to 16384):
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The maximum sequence length that this model might ever be used with.
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image_size (`int`, *optional*, defaults to 300):
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The input image size.
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patch_size (`int`, *optional*, defaults to 30):
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The input vision transformer encoding patch size.
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num_channels (`int`, *optional*, defaults to 3):
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The input image number of channels.
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initializer_range (`float`, *optional*, defaults to 0.02):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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layer_norm_eps (`float`, *optional*, defaults to 1e-05):
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The epsilon used by the rms normalization layers.
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use_cache (`bool`, *optional*, defaults to `True`):
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Whether or not the model should return the last key/values attentions (not used by all models). Only
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relevant if `config.is_decoder=True`. Whether to tie weight embeddings
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tie_word_embeddings (`bool`, *optional*, defaults to `False`):
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Whether to tie input and output embeddings.
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rope_parameters (`RopeParameters`, *optional*):
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Dictionary containing the configuration parameters for the RoPE embeddings. The dictionary should contain
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a value for `rope_theta` and optionally parameters used for scaling in case you want to use RoPE
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with longer `max_position_embeddings`.
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qk_layernorm (`bool`, *optional*, defaults to `True`):
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Whether or not to normalize the Queries and Keys after projecting the hidden states
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hidden_dropout (`float`, *optional*, defaults to 0.0):
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The dropout ratio after applying the MLP to the hidden states.
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attention_dropout (`float`, *optional*, defaults to 0.0):
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The dropout ratio after computing the attention scores.
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pad_token_id (`int`, *optional*):
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The id of the *padding* token.
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bos_token_id (`int`, *optional*, defaults to 1):
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The id of the *beginning-of-sequence* token.
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eos_token_id (`Union[int, list[int]]`, *optional*, defaults to 2):
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The id of the *end-of-sequence* token. Optionally, use a list to set multiple *end-of-sequence* tokens.
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image_token_id (`int`, *optional*, defaults to 71011):
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The id of the image placeholder token.
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text_config (`dict`, *optional*):
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Dictionary of configuration options used to initialize the `language``[`Aut`].
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```python
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>>> from transformers import FuyuConfig
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>>> # Initializing a Fuyu fuyu-7b style configuration
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>>> configuration = FuyuConfig()
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```"""
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model_type = "fuyu"
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sub_configs = {"text_config": AutoConfig}
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keys_to_ignore_at_inference = ["past_key_values"]
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default_theta = 25000.0
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def __init__(
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self,
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vocab_size: int | None = 262144,
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hidden_size: int | None = 4096,
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intermediate_size: int | None = 16384,
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num_hidden_layers: int | None = 36,
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num_attention_heads: int | None = 64,
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hidden_act: str | None = "relu2",
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max_position_embeddings: int | None = 16384,
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image_size: int | None = 300,
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patch_size: int | None = 30,
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num_channels: int | None = 3,
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initializer_range: float | None = 0.02,
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layer_norm_eps: int | None = 1e-5,
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use_cache: bool | None = True,
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tie_word_embeddings: bool | None = False,
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rope_parameters: RopeParameters | dict[str, RopeParameters] | None = None,
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qk_layernorm: bool | None = True,
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hidden_dropout: float | None = 0.0,
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attention_dropout: float | None = 0.0,
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pad_token_id: int | None = None,
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bos_token_id: int | None = 1,
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eos_token_id: int | None = 2,
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image_token_id: int | None = 71011,
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text_config: dict | None = None,
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**kwargs,
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):
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if text_config is None:
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text_config = {
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"vocab_size": vocab_size,
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"max_position_embeddings": max_position_embeddings,
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"hidden_size": hidden_size,
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"intermediate_size": intermediate_size,
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"num_hidden_layers": num_hidden_layers,
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"num_attention_heads": num_attention_heads,
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"hidden_act": hidden_act,
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"initializer_range": initializer_range,
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"layer_norm_eps": layer_norm_eps,
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"use_cache": use_cache,
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"rope_parameters": rope_parameters,
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"qk_layernorm": qk_layernorm,
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"hidden_dropout": hidden_dropout,
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"attention_dropout": attention_dropout,
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"pad_token_id": pad_token_id,
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"bos_token_id": bos_token_id,
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"eos_token_id": eos_token_id,
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}
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logger.info("text_config is None. initializing the text model with default values.")
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text_model_type = text_config.get("model_type", "persimmon")
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self.text_config = CONFIG_MAPPING[text_model_type](**text_config)
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self._vocab_size = vocab_size
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self.max_position_embeddings = max_position_embeddings
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self.image_size = image_size
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self.patch_size = patch_size
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self.num_channels = num_channels
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self.hidden_size = hidden_size
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self.intermediate_size = intermediate_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.hidden_act = hidden_act
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self.initializer_range = initializer_range
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self.layer_norm_eps = layer_norm_eps
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self.use_cache = use_cache
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self.qk_layernorm = qk_layernorm
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self.hidden_dropout = hidden_dropout
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self.attention_dropout = attention_dropout
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self.image_token_id = image_token_id
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self.rope_parameters = rope_parameters
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kwargs.setdefault("partial_rotary_factor", 0.5) # assign default for BC
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self.tie_word_embeddings = tie_word_embeddings
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self.pad_token_id = pad_token_id
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self.bos_token_id = bos_token_id
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self.eos_token_id = eos_token_id
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super().__init__(**kwargs)
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__all__ = ["FuyuConfig"]
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