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# coding=utf-8
# Copyright 2021 The Fairseq Authors and The HuggingFace Inc. team. All rights reserved.
#
# 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.
""" DalleBart model configuration """
import warnings
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging
logger = logging.get_logger(__name__)
class DalleBartConfig(PretrainedConfig):
model_type = "dallebart"
keys_to_ignore_at_inference = ["past_key_values"]
attribute_map = {
"num_attention_heads": "encoder_attention_heads",
"hidden_size": "d_model",
}
def __init__(
self,
normalize_text=False,
encoder_vocab_size=50264,
image_vocab_size=16384, # encoded image token space
image_length=256, # number of encoded tokens
max_text_length=64, # max number of text tokens
encoder_layers=12,
encoder_ffn_dim=4096,
encoder_attention_heads=16,
decoder_layers=12,
decoder_ffn_dim=4096,
decoder_attention_heads=16,
encoder_layerdrop=0.0,
decoder_layerdrop=0.0,
activation_function="gelu",
d_model=1024,
dropout=0.1,
attention_dropout=0.0,
activation_dropout=0.0,
init_std=0.02,
classifier_dropout=0.0,
scale_embedding=False,
gradient_checkpointing=False,
use_cache=True,
is_encoder_decoder=True,
forced_eos_token_id=None,
tie_word_embeddings=False, # different modalities and sizes
**kwargs,
):
self.normalize_text = normalize_text
self.encoder_vocab_size = encoder_vocab_size
self.image_vocab_size = image_vocab_size
self.image_length = image_length
self.max_text_length = max_text_length
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.gradient_checkpointing = gradient_checkpointing
self.scale_embedding = (
scale_embedding # scale factor will be sqrt(d_model) if True
)
self.min_length = image_length + 1
self.max_length = image_length + 1
# remove inferred keys to prevent errors when loading config (passed as kwargs)
for k in [
"pad_token_id",
"bos_token_id",
"eos_token_id",
"decoder_start_token_id",
]:
kwargs.pop(k, None)
super().__init__(
pad_token_id=image_vocab_size
+ 1, # needed to avoid errors during generation (converted to jnp.array)
bos_token_id=image_vocab_size + 1, # set to unreachable values
eos_token_id=image_vocab_size + 1,
is_encoder_decoder=is_encoder_decoder,
decoder_start_token_id=image_vocab_size, # BOS appended to vocab
forced_eos_token_id=forced_eos_token_id,
tie_word_embeddings=tie_word_embeddings,
**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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