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# 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,
**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
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."
)
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