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# coding=utf-8
# Copyright 2021 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.
""" GPT Neo 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__)
GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"EleutherAI/gpt-neo-125M": "https://huggingface.co/EleutherAI/gpt-neo-125M/resolve/main/config.json",
"EleutherAI/gpt-neo-1.3B": "https://huggingface.co/EleutherAI/gpt-neo-1.3B/resolve/main/config.json",
# See all GPTNeo models at https://huggingface.co/models?filter=gpt_neo
}
class VGPTNeoConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`GPTNeoModel`]. It is used to instantiate a GPT
Neo 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 GPTNeo
[EleutherAI/gpt-neo-1.3B](https://huggingface.co/EleutherAI/gpt-neo-1.3B) 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
Args:
vocab_size (`int`, *optional*, defaults to 50257):
Vocabulary size of the GPT Neo model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`GPTNeoModel`]. Vocabulary size of the model. Defines the different
tokens that can be represented by the *inputs_ids* passed to the forward method of [`GPTNeoModel`].
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.
attention_types (`List`, *optional*, defaults to `[[["global", "local"], 12]]`):
The type of attention for each layer in a `List` of the following format `[[["attention_type"],
num_layerss]]` e.g. for a 24 layer model `[[["global"], 24]]` or `[[["global", "local"], 12]]` Choose the
value of `attention_type` from `["global", "local"]`
hidden_size (`int`, *optional*, defaults to 2048):
Dimensionality of the encoder layers and the pooler layer.
num_layers (`int`, *optional*, defaults to 24):
Number of hidden layers in the Transformer encoder.
num_heads (`int`, *optional*, defaults to 16):
Number of attention heads for each attention layer in the Transformer encoder.
intermediate_size (`int`, *optional*, defaults to 8192):
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
activation_function (`str` or `function`, *optional*, defaults to `"gelu_new"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"selu"` and `"gelu_new"` are supported.
embed_dropout (`float`, *optional*, defaults to 0.0):
The dropout probabilitiy 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.
max_position_embeddings (`int`, *optional*, defaults to 2048):
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).
type_vocab_size (`int`, *optional*, defaults to 2):
The vocabulary size of the `token_type_ids` passed when calling [`GPTNeoModel`].
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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.
layer_norm_epsilon (`float`, *optional*, defaults to 1e-5):
The epsilon used by the layer normalization layers.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models). Only
relevant if `config.is_decoder=True`.
cross_layer_interval (`int`, *optional*, default to 1)
Interval for cross attention (from text to image) layers.
Example:
```python
>>> from transformers import GPTNeoConfig, GPTNeoModel
>>> # Initializing a GPTNeo EleutherAI/gpt-neo-1.3B style configuration
>>> configuration = GPTNeoConfig()
>>> # Initializing a model (with random weights) from the EleutherAI/gpt-neo-1.3B style configuration
>>> model = GPTNeoModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "vgpt_neo"
keys_to_ignore_at_inference = ["past_key_values"]
attribute_map = {"num_attention_heads": "num_heads", "num_hidden_layers": "num_layers"}
def __init__(
self,
vocab_size=50257,
additional_vocab_size=0,
max_position_embeddings=2048,
hidden_size=2048,
num_layers=24,
attention_types=[[["global", "local"], 12]],
num_heads=16,
intermediate_size=None,
window_size=256,
activation_function="gelu_new",
resid_dropout=0.0,
embed_dropout=0.0,
attention_dropout=0.0,
layer_norm_epsilon=1e-5,
initializer_range=0.02,
alpha_initializer="ones",
alphas_initializer_range=0.0,
alpha_type="vector",
summary_type="cls_index",
summary_use_proj=True,
summary_activation=None,
summary_proj_to_labels=True,
summary_first_dropout=0.1,
use_cache=True,
bos_token_id=50256,
eos_token_id=50256,
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,
vision_image_size=224,
image_token_index=50257,
use_resampler=False,
resampler_n_latents=64,
resampler_depth=6,
resampler_n_heads=16,
resampler_head_dim=96,
**kwargs,
):
self.vocab_size = vocab_size
self.additional_vocab_size = additional_vocab_size
self.max_position_embeddings = max_position_embeddings
self.hidden_size = hidden_size
self.num_layers = num_layers
self.num_heads = num_heads
self.intermediate_size = intermediate_size
self.window_size = window_size
self.activation_function = activation_function
self.resid_dropout = resid_dropout
self.embed_dropout = embed_dropout
self.attention_dropout = attention_dropout
self.layer_norm_epsilon = layer_norm_epsilon
self.initializer_range = initializer_range
self.alpha_initializer = alpha_initializer
self.alphas_initializer_range = alphas_initializer_range
self.alpha_type = alpha_type
self.summary_type = summary_type
self.summary_use_proj = summary_use_proj
self.summary_activation = summary_activation
self.summary_first_dropout = summary_first_dropout
self.summary_proj_to_labels = summary_proj_to_labels
self.use_cache = use_cache
self.bos_token_id = bos_token_id
self.eos_token_id = eos_token_id
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.attention_types = attention_types
self.attention_layers = self.expand_attention_types_params(attention_types)
self.vision_embed_dim = vision_embed_dim
self.vision_image_size = vision_image_size
# Resampler params
self.use_resampler = use_resampler
self.resampler_n_latents = resampler_n_latents
self.resampler_depth = resampler_depth
self.resampler_n_heads = resampler_n_heads
self.resampler_head_dim = resampler_head_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__(
bos_token_id=bos_token_id, eos_token_id=eos_token_id, 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"):
vision_config = config.vision_config
else:
vision_config = config
vision_embed_dim = vision_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})"
)
vision_image_size = vision_config.image_size
if self.vision_image_size != vision_image_size:
raise ValueError(
f"vision_image_size ({self.vision_image_size}) must match the hidden size of the vision model"
f" ({vision_image_size})"
)
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path: Union[str, os.PathLike], **kwargs) -> "PretrainedConfig":
outputs = super(VGPTNeoConfig, 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
@staticmethod
def expand_attention_types_params(attention_types):
attentions = []
for item in attention_types:
for _ in range(item[1]):
attentions.extend(item[0])
return attentions
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