162 lines
7.6 KiB
Python
162 lines
7.6 KiB
Python
# coding=utf-8
|
|
# Copyright 2024 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.
|
|
""" UDOP model configuration"""
|
|
|
|
|
|
from ...configuration_utils import PretrainedConfig
|
|
from ...utils import logging
|
|
|
|
|
|
logger = logging.get_logger(__name__)
|
|
|
|
|
|
from ..deprecated._archive_maps import UDOP_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402
|
|
|
|
|
|
class UdopConfig(PretrainedConfig):
|
|
r"""
|
|
This is the configuration class to store the configuration of a [`UdopForConditionalGeneration`]. It is used to
|
|
instantiate a UDOP 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 UDOP
|
|
[microsoft/udop-large](https://huggingface.co/microsoft/udop-large) architecture.
|
|
|
|
Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
|
|
documentation from [`PretrainedConfig`] for more information.
|
|
|
|
Arguments:
|
|
vocab_size (`int`, *optional*, defaults to 33201):
|
|
Vocabulary size of the UDOP model. Defines the number of different tokens that can be represented by the
|
|
`inputs_ids` passed when calling [`UdopForConditionalGeneration`].
|
|
d_model (`int`, *optional*, defaults to 1024):
|
|
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. The `inner_dim` of the projection layer will
|
|
be defined as `num_heads * d_kv`.
|
|
d_ff (`int`, *optional*, defaults to 4096):
|
|
Size of the intermediate feed forward layer in each `UdopBlock`.
|
|
num_layers (`int`, *optional*, defaults to 24):
|
|
Number of hidden layers in the Transformer encoder and decoder.
|
|
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 16):
|
|
Number of attention heads for each attention layer in the Transformer encoder and decoder.
|
|
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.
|
|
relative_bias_args (`List[dict]`, *optional*, defaults to `[{'type': '1d'}, {'type': 'horizontal'}, {'type': 'vertical'}]`):
|
|
A list of dictionaries containing the arguments for the relative bias layers.
|
|
dropout_rate (`float`, *optional*, defaults to 0.1):
|
|
The ratio for all dropout layers.
|
|
layer_norm_epsilon (`float`, *optional*, defaults to 1e-06):
|
|
The epsilon used by the layer normalization layers.
|
|
initializer_factor (`float`, *optional*, defaults to 1.0):
|
|
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"`. Udopv1.1 uses the
|
|
`"gated-gelu"` feed forward projection. Original Udop uses `"relu"`.
|
|
is_encoder_decoder (`bool`, *optional*, defaults to `True`):
|
|
Whether the model should behave as an encoder/decoder or not.
|
|
use_cache (`bool`, *optional*, defaults to `True`):
|
|
Whether or not the model should return the last key/values attentions (not used by all models).
|
|
pad_token_id (`int`, *optional*, defaults to 0):
|
|
The id of the padding token in the vocabulary.
|
|
eos_token_id (`int`, *optional*, defaults to 1):
|
|
The id of the end-of-sequence token in the vocabulary.
|
|
max_2d_position_embeddings (`int`, *optional*, defaults to 1024):
|
|
The maximum absolute position embeddings for relative position encoding.
|
|
image_size (`int`, *optional*, defaults to 224):
|
|
The size of the input images.
|
|
patch_size (`int`, *optional*, defaults to 16):
|
|
The patch size used by the vision encoder.
|
|
num_channels (`int`, *optional*, defaults to 3):
|
|
The number of channels in the input images.
|
|
"""
|
|
|
|
model_type = "udop"
|
|
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=33201,
|
|
d_model=1024,
|
|
d_kv=64,
|
|
d_ff=4096,
|
|
num_layers=24,
|
|
num_decoder_layers=None,
|
|
num_heads=16,
|
|
relative_attention_num_buckets=32,
|
|
relative_attention_max_distance=128,
|
|
relative_bias_args=[{"type": "1d"}, {"type": "horizontal"}, {"type": "vertical"}],
|
|
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,
|
|
max_2d_position_embeddings=1024,
|
|
image_size=224,
|
|
patch_size=16,
|
|
num_channels=3,
|
|
**kwargs,
|
|
):
|
|
self.vocab_size = 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
|
|
|
|
# UDOP attributes
|
|
self.max_2d_position_embeddings = max_2d_position_embeddings
|
|
self.image_size = image_size
|
|
self.patch_size = patch_size
|
|
self.num_channels = num_channels
|
|
if not isinstance(relative_bias_args, list):
|
|
raise ValueError("`relative_bias_args` should be a list of dictionaries.")
|
|
self.relative_bias_args = relative_bias_args
|
|
|
|
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'"
|
|
)
|
|
|
|
super().__init__(
|
|
pad_token_id=pad_token_id,
|
|
eos_token_id=eos_token_id,
|
|
is_encoder_decoder=is_encoder_decoder,
|
|
**kwargs,
|
|
)
|