/
configuration_opt.py
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configuration_opt.py
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# coding=utf-8
# Copyright 2022 The Metaseq Authors and 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.
""" OPT model configuration"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
logger = logging.get_logger(__name__)
class OPTConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`OPTModel`]. It is used to instantiate a OPT 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 OPT
[facebook/opt-350m](https://huggingface.co/facebook/opt-350m) 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 50272):
Vocabulary size of the OPT model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`OPTModel`]
hidden_size (`int`, *optional*, defaults to 768):
Dimensionality of the layers and the pooler layer.
num_hidden_layers (`int`, *optional*, defaults to 12):
Number of decoder layers.
ffn_dim (`int`, *optional*, defaults to 3072):
Dimensionality of the "intermediate" (often named feed-forward) layer in decoder.
num_attention_heads (`int`, *optional*, defaults to 12):
Number of attention heads for each attention layer in the Transformer decoder.
activation_function (`str` or `function`, *optional*, defaults to `"relu"`):
The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
`"relu"`, `"silu"` and `"gelu_new"` are supported.
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).
do_layer_norm_before (`bool`, *optional*, defaults to `True`):
Whether to perform layer normalization before the attention block.
word_embed_proj_dim (`int`, *optional*):
`word_embed_proj_dim` can be set to down-project word embeddings, *e.g.* `opt-350m`. Defaults to
`hidden_size`.
dropout (`float`, *optional*, defaults to 0.1):
The dropout probability 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.
layerdrop (`float`, *optional*, defaults to 0.0):
The LayerDrop probability. See the [LayerDrop paper](see https://arxiv.org/abs/1909.11556) for more
details.
init_std (`float`, *optional*, defaults to 0.02):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
use_cache (`bool`, *optional*, defaults to `True`):
Whether or not the model should return the last key/values attentions (not used by all models).
enable_bias (`bool`, *optional*, defaults to `True`):
Whether or not if the linear layers in the attention blocks should use the bias term.
layer_norm_elementwise_affine (`bool`, *optional*, defaults to `True`):
Whether or not if the layer norms should have learnable parameters.
Example:
```python
>>> from transformers import OPTConfig, OPTModel
>>> # Initializing a OPT facebook/opt-large style configuration
>>> configuration = OPTConfig()
>>> # Initializing a model (with random weights) from the facebook/opt-large style configuration
>>> model = OPTModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "opt"
keys_to_ignore_at_inference = ["past_key_values"]
def __init__(
self,
vocab_size=50272,
hidden_size=768,
num_hidden_layers=12,
ffn_dim=3072,
max_position_embeddings=2048,
do_layer_norm_before=True,
_remove_final_layer_norm=False,
word_embed_proj_dim=None,
dropout=0.1,
attention_dropout=0.0,
num_attention_heads=12,
activation_function="relu",
layerdrop=0.0,
init_std=0.02,
use_cache=True,
pad_token_id=1,
bos_token_id=2,
eos_token_id=2,
enable_bias=True,
layer_norm_elementwise_affine=True,
**kwargs,
):
super().__init__(
pad_token_id=pad_token_id,
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
**kwargs,
)
self.vocab_size = vocab_size
self.max_position_embeddings = max_position_embeddings
self.num_attention_heads = num_attention_heads
self.word_embed_proj_dim = word_embed_proj_dim if word_embed_proj_dim is not None else hidden_size
self.ffn_dim = ffn_dim
self.hidden_size = hidden_size
self.num_hidden_layers = num_hidden_layers
self.dropout = dropout
self.attention_dropout = attention_dropout
self.activation_function = activation_function
self.init_std = init_std
self.layerdrop = layerdrop
self.use_cache = use_cache
self.do_layer_norm_before = do_layer_norm_before
# We keep these variables at `True` for backward compatibility.
self.enable_bias = enable_bias
self.layer_norm_elementwise_affine = layer_norm_elementwise_affine
# Note that the only purpose of `_remove_final_layer_norm` is to keep backward compatibility
# with checkpoints that have been fine-tuned before transformers v4.20.1
# see https://github.com/facebookresearch/metaseq/pull/164
self._remove_final_layer_norm = _remove_final_layer_norm