Spaces:
Runtime error
Runtime error
File size: 10,346 Bytes
217780a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 |
# 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})"
)
@classmethod
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
|