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configuration_imagegpt.py
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configuration_imagegpt.py
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# coding=utf-8
# Copyright 2021 The HuggingFace Inc. team.
#
# 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.
""" OpenAI ImageGPT configuration"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, TensorType
logger = logging.get_logger(__name__)
from ..deprecated._archive_maps import IMAGEGPT_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402
class ImageGPTConfig(PretrainedConfig):
"""
This is the configuration class to store the configuration of a [`ImageGPTModel`] or a [`TFImageGPTModel`]. It is
used to instantiate a GPT-2 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 ImageGPT
[openai/imagegpt-small](https://huggingface.co/openai/imagegpt-small) 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 512):
Vocabulary size of the GPT-2 model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`ImageGPTModel`] or [`TFImageGPTModel`].
n_positions (`int`, *optional*, defaults to 32*32):
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).
n_embd (`int`, *optional*, defaults to 512):
Dimensionality of the embeddings and hidden states.
n_layer (`int`, *optional*, defaults to 24):
Number of hidden layers in the Transformer encoder.
n_head (`int`, *optional*, defaults to 8):
Number of attention heads for each attention layer in the Transformer encoder.
n_inner (`int`, *optional*, defaults to None):
Dimensionality of the inner feed-forward layers. `None` will set it to 4 times n_embd
activation_function (`str`, *optional*, defaults to `"quick_gelu"`):
Activation function (can be one of the activation functions defined in src/transformers/activations.py).
Defaults to "quick_gelu".
resid_pdrop (`float`, *optional*, defaults to 0.1):
The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
embd_pdrop (`int`, *optional*, defaults to 0.1):
The dropout ratio for the embeddings.
attn_pdrop (`float`, *optional*, defaults to 0.1):
The dropout ratio for the attention.
layer_norm_epsilon (`float`, *optional*, defaults to 1e-5):
The epsilon to use in the layer normalization layers.
initializer_range (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
scale_attn_weights (`bool`, *optional*, defaults to `True`):
Scale attention weights by dividing by sqrt(hidden_size)..
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models).
scale_attn_by_inverse_layer_idx (`bool`, *optional*, defaults to `False`):
Whether to additionally scale attention weights by `1 / layer_idx + 1`.
reorder_and_upcast_attn (`bool`, *optional*, defaults to `False`):
Whether to scale keys (K) prior to computing attention (dot-product) and upcast attention
dot-product/softmax to float() when training with mixed precision.
Example:
```python
>>> from transformers import ImageGPTConfig, ImageGPTModel
>>> # Initializing a ImageGPT configuration
>>> configuration = ImageGPTConfig()
>>> # Initializing a model (with random weights) from the configuration
>>> model = ImageGPTModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "imagegpt"
keys_to_ignore_at_inference = ["past_key_values"]
attribute_map = {
"hidden_size": "n_embd",
"max_position_embeddings": "n_positions",
"num_attention_heads": "n_head",
"num_hidden_layers": "n_layer",
}
def __init__(
self,
vocab_size=512 + 1, # add one for start of sentence (sos) token
n_positions=32 * 32,
n_embd=512,
n_layer=24,
n_head=8,
n_inner=None,
activation_function="quick_gelu",
resid_pdrop=0.1,
embd_pdrop=0.1,
attn_pdrop=0.1,
layer_norm_epsilon=1e-5,
initializer_range=0.02,
scale_attn_weights=True,
use_cache=True,
tie_word_embeddings=False,
scale_attn_by_inverse_layer_idx=False,
reorder_and_upcast_attn=False,
**kwargs,
):
self.vocab_size = vocab_size
self.n_positions = n_positions
self.n_embd = n_embd
self.n_layer = n_layer
self.n_head = n_head
self.n_inner = n_inner
self.activation_function = activation_function
self.resid_pdrop = resid_pdrop
self.embd_pdrop = embd_pdrop
self.attn_pdrop = attn_pdrop
self.layer_norm_epsilon = layer_norm_epsilon
self.initializer_range = initializer_range
self.scale_attn_weights = scale_attn_weights
self.use_cache = use_cache
self.scale_attn_by_inverse_layer_idx = scale_attn_by_inverse_layer_idx
self.reorder_and_upcast_attn = reorder_and_upcast_attn
self.tie_word_embeddings = tie_word_embeddings
super().__init__(tie_word_embeddings=tie_word_embeddings, **kwargs)
class ImageGPTOnnxConfig(OnnxConfig):
@property
def inputs(self) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("input_ids", {0: "batch", 1: "sequence"}),
]
)
def generate_dummy_inputs(
self,
preprocessor: "FeatureExtractionMixin",
batch_size: int = 1,
seq_length: int = -1,
is_pair: bool = False,
framework: Optional["TensorType"] = None,
num_channels: int = 3,
image_width: int = 32,
image_height: int = 32,
) -> Mapping[str, Any]:
"""
Generate inputs to provide to the ONNX exporter for the specific framework
Args:
preprocessor ([`PreTrainedTokenizerBase`] or [`FeatureExtractionMixin`]):
The preprocessor associated with this model configuration.
batch_size (`int`, *optional*, defaults to -1):
The batch size to export the model for (-1 means dynamic axis).
num_choices (`int`, *optional*, defaults to -1):
The number of candidate answers provided for multiple choice task (-1 means dynamic axis).
seq_length (`int`, *optional*, defaults to -1):
The sequence length to export the model for (-1 means dynamic axis).
is_pair (`bool`, *optional*, defaults to `False`):
Indicate if the input is a pair (sentence 1, sentence 2)
framework (`TensorType`, *optional*, defaults to `None`):
The framework (PyTorch or TensorFlow) that the tokenizer will generate tensors for.
num_channels (`int`, *optional*, defaults to 3):
The number of channels of the generated images.
image_width (`int`, *optional*, defaults to 40):
The width of the generated images.
image_height (`int`, *optional*, defaults to 40):
The height of the generated images.
Returns:
Mapping[str, Tensor] holding the kwargs to provide to the model's forward function
"""
input_image = self._generate_dummy_images(batch_size, num_channels, image_height, image_width)
inputs = dict(preprocessor(images=input_image, return_tensors=framework))
return inputs