117 lines
4.6 KiB
Python
117 lines
4.6 KiB
Python
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# coding=utf-8
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# Copyright 2018 Salesforce and HuggingFace Inc. team.
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# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
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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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""" Salesforce CTRL 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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from ..deprecated._archive_maps import CTRL_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402
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class CTRLConfig(PretrainedConfig):
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"""
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This is the configuration class to store the configuration of a [`CTRLModel`] or a [`TFCTRLModel`]. It is used to
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instantiate a CTRL model according to the specified arguments, defining the model architecture. Instantiating a
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configuration with the defaults will yield a similar configuration to that of the
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[Salesforce/ctrl](https://huggingface.co/Salesforce/ctrl) architecture from SalesForce.
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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 246534):
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Vocabulary size of the CTRL model. Defines the number of different tokens that can be represented by the
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`inputs_ids` passed when calling [`CTRLModel`] or [`TFCTRLModel`].
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n_positions (`int`, *optional*, defaults to 256):
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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 1280):
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Dimensionality of the embeddings and hidden states.
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dff (`int`, *optional*, defaults to 8192):
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Dimensionality of the inner dimension of the feed forward networks (FFN).
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n_layer (`int`, *optional*, defaults to 48):
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Number of hidden layers in the Transformer encoder.
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n_head (`int`, *optional*, defaults to 16):
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Number of attention heads for each attention layer in the Transformer encoder.
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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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layer_norm_epsilon (`float`, *optional*, defaults to 1e-06):
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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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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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Examples:
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```python
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>>> from transformers import CTRLConfig, CTRLModel
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>>> # Initializing a CTRL configuration
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>>> configuration = CTRLConfig()
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>>> # Initializing a model (with random weights) from the configuration
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>>> model = CTRLModel(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 = "ctrl"
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keys_to_ignore_at_inference = ["past_key_values"]
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attribute_map = {
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"max_position_embeddings": "n_positions",
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"hidden_size": "n_embd",
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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=246534,
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n_positions=256,
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n_embd=1280,
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dff=8192,
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n_layer=48,
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n_head=16,
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resid_pdrop=0.1,
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embd_pdrop=0.1,
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layer_norm_epsilon=1e-6,
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initializer_range=0.02,
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use_cache=True,
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**kwargs,
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):
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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.dff = dff
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self.resid_pdrop = resid_pdrop
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self.embd_pdrop = embd_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.use_cache = use_cache
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super().__init__(**kwargs)
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