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147 lines
6.3 KiB
147 lines
6.3 KiB
# Copyright 2021 The HuggingFace Inc. team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""OpenAI ImageGPT configuration"""
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from ...configuration_utils import PreTrainedConfig
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from ...utils import logging
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logger = logging.get_logger(__name__)
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class ImageGPTConfig(PreTrainedConfig):
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"""
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This is the configuration class to store the configuration of a [`ImageGPTModel`]. It is
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used to instantiate a GPT-2 model according to the specified arguments, defining the model architecture.
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Instantiating a configuration with the defaults will yield a similar configuration to that of the ImageGPT
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[openai/imagegpt-small](https://huggingface.co/openai/imagegpt-small) architecture.
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Configuration objects inherit from [`PreTrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PreTrainedConfig`] for more information.
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Args:
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vocab_size (`int`, *optional*, defaults to 512):
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Vocabulary size of the GPT-2 model. Defines the number of different tokens that can be represented by the
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`inputs_ids` passed when calling [`ImageGPTModel`].
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n_positions (`int`, *optional*, defaults to 32*32):
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The maximum sequence length that this model might ever be used with. Typically set this to something large
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just in case (e.g., 512 or 1024 or 2048).
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n_embd (`int`, *optional*, defaults to 512):
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Dimensionality of the embeddings and hidden states.
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n_layer (`int`, *optional*, defaults to 24):
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Number of hidden layers in the Transformer encoder.
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n_head (`int`, *optional*, defaults to 8):
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Number of attention heads for each attention layer in the Transformer encoder.
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n_inner (`int`, *optional*, defaults to None):
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Dimensionality of the inner feed-forward layers. `None` will set it to 4 times n_embd
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activation_function (`str`, *optional*, defaults to `"quick_gelu"`):
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Activation function (can be one of the activation functions defined in src/transformers/activations.py).
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Defaults to "quick_gelu".
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resid_pdrop (`float`, *optional*, defaults to 0.1):
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The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
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embd_pdrop (`int`, *optional*, defaults to 0.1):
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The dropout ratio for the embeddings.
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attn_pdrop (`float`, *optional*, defaults to 0.1):
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The dropout ratio for the attention.
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layer_norm_epsilon (`float`, *optional*, defaults to 1e-5):
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The epsilon to use in the layer normalization layers.
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initializer_range (`float`, *optional*, defaults to 0.02):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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scale_attn_weights (`bool`, *optional*, defaults to `True`):
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Scale attention weights by dividing by sqrt(hidden_size)..
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use_cache (`bool`, *optional*, defaults to `True`):
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Whether or not the model should return the last key/values attentions (not used by all models).
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scale_attn_by_inverse_layer_idx (`bool`, *optional*, defaults to `False`):
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Whether to additionally scale attention weights by `1 / layer_idx + 1`.
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reorder_and_upcast_attn (`bool`, *optional*, defaults to `False`):
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Whether to scale keys (K) prior to computing attention (dot-product) and upcast attention
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dot-product/softmax to float() when training with mixed precision.
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Example:
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```python
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>>> from transformers import ImageGPTConfig, ImageGPTModel
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>>> # Initializing a ImageGPT configuration
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>>> configuration = ImageGPTConfig()
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>>> # Initializing a model (with random weights) from the configuration
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>>> model = ImageGPTModel(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```"""
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model_type = "imagegpt"
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keys_to_ignore_at_inference = ["past_key_values"]
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attribute_map = {
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"hidden_size": "n_embd",
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"max_position_embeddings": "n_positions",
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"num_attention_heads": "n_head",
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"num_hidden_layers": "n_layer",
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}
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def __init__(
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self,
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vocab_size=512 + 1, # add one for start of sentence (sos) token
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n_positions=32 * 32,
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n_embd=512,
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n_layer=24,
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n_head=8,
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n_inner=None,
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activation_function="quick_gelu",
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resid_pdrop=0.1,
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embd_pdrop=0.1,
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attn_pdrop=0.1,
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layer_norm_epsilon=1e-5,
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initializer_range=0.02,
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scale_attn_weights=True,
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use_cache=True,
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tie_word_embeddings=False,
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scale_attn_by_inverse_layer_idx=False,
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reorder_and_upcast_attn=False,
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add_cross_attention=False,
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pad_token_id=None,
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bos_token_id=None,
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eos_token_id=None,
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**kwargs,
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):
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self.add_cross_attention = add_cross_attention
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self.vocab_size = vocab_size
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self.n_positions = n_positions
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self.n_embd = n_embd
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self.n_layer = n_layer
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self.n_head = n_head
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self.n_inner = n_inner
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self.activation_function = activation_function
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self.resid_pdrop = resid_pdrop
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self.embd_pdrop = embd_pdrop
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self.attn_pdrop = attn_pdrop
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self.layer_norm_epsilon = layer_norm_epsilon
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self.initializer_range = initializer_range
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self.scale_attn_weights = scale_attn_weights
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self.use_cache = use_cache
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self.scale_attn_by_inverse_layer_idx = scale_attn_by_inverse_layer_idx
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self.reorder_and_upcast_attn = reorder_and_upcast_attn
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self.pad_token_id = pad_token_id
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self.bos_token_id = bos_token_id
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self.eos_token_id = eos_token_id
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self.tie_word_embeddings = tie_word_embeddings
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super().__init__(**kwargs)
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__all__ = ["ImageGPTConfig"]
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