184 lines
9.7 KiB
Python
184 lines
9.7 KiB
Python
|
# coding=utf-8
|
||
|
|
# Copyright 2021 The LG AI Research EXAONE Lab. 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.
|
||
|
|
"""EXAONE model configuration"""
|
||
|
|
|
||
|
|
from transformers.configuration_utils import PretrainedConfig
|
||
|
|
from transformers.utils import logging
|
||
|
|
|
||
|
|
|
||
|
|
logger = logging.get_logger(__name__)
|
||
|
|
|
||
|
|
EXAONE_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
|
||
|
|
|
||
|
|
|
||
|
|
class ExaoneConfig(PretrainedConfig):
|
||
|
|
r"""
|
||
|
|
This is the configuration class to store the configuration of a [`ExaoneModel`]. It is used to
|
||
|
|
instantiate a EXAONE 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 EXAONE-3.0-7.8B-Instruct [LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct](https://huggingface.co/LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct)
|
||
|
|
|
||
|
|
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 102400):
|
||
|
|
Vocabulary size of the EXAONE model. Defines the number of different tokens that can be represented by the
|
||
|
|
`inputs_ids` passed when calling [`ExaoneModel`]. Vocabulary size of the model.
|
||
|
|
Defines the different tokens that can be represented by the `inputs_ids` passed to the forward method of
|
||
|
|
[`ExaoneModel`].
|
||
|
|
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).
|
||
|
|
hidden_size (`int`, *optional*, defaults to 2048):
|
||
|
|
Dimensionality of the encoder layers and the pooler layer.
|
||
|
|
num_layers (`int`, *optional*, defaults to 32):
|
||
|
|
Number of hidden layers in the Transformer encoder.
|
||
|
|
num_attention_heads (`int`, *optional*, defaults to 32):
|
||
|
|
Number of attention heads for each attention layer in the Transformer decoder.
|
||
|
|
num_key_value_heads (`int`, *optional*):
|
||
|
|
This is the number of key_value heads that should be used to implement Grouped Query Attention. If
|
||
|
|
`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
|
||
|
|
`num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
|
||
|
|
converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
|
||
|
|
by meanpooling all the original heads within that group. For more details checkout [this
|
||
|
|
paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
|
||
|
|
`num_attention_heads`.
|
||
|
|
intermediate_size (`int`, *optional*, defaults to `hidden_size * 4`):
|
||
|
|
Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
|
||
|
|
activation_function (`str` or `function`, *optional*, defaults to `"silu"`):
|
||
|
|
The non-linear activation function (function or string) in the decoder.
|
||
|
|
rope_theta (`float`, *optional*, defaults to 10000.0):
|
||
|
|
The base period of the RoPE embeddings.
|
||
|
|
rope_scaling (`Dict`, *optional*):
|
||
|
|
Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type
|
||
|
|
and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value
|
||
|
|
accordingly.
|
||
|
|
Expected contents:
|
||
|
|
`rope_type` (`str`):
|
||
|
|
The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope',
|
||
|
|
'llama3'], with 'default' being the original RoPE implementation.
|
||
|
|
`factor` (`float`, *optional*):
|
||
|
|
Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In
|
||
|
|
most scaling types, a `factor` of x will enable the model to handle sequences of length x *
|
||
|
|
original maximum pre-trained length.
|
||
|
|
`original_max_position_embeddings` (`int`, *optional*):
|
||
|
|
Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during
|
||
|
|
pretraining.
|
||
|
|
`attention_factor` (`float`, *optional*):
|
||
|
|
Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention
|
||
|
|
computation. If unspecified, it defaults to value recommended by the implementation, using the
|
||
|
|
`factor` field to infer the suggested value.
|
||
|
|
`beta_fast` (`float`, *optional*):
|
||
|
|
Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear
|
||
|
|
ramp function. If unspecified, it defaults to 32.
|
||
|
|
`beta_slow` (`float`, *optional*):
|
||
|
|
Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear
|
||
|
|
ramp function. If unspecified, it defaults to 1.
|
||
|
|
`short_factor` (`List[float]`, *optional*):
|
||
|
|
Only used with 'longrope'. The scaling factor to be applied to short contexts (<
|
||
|
|
`original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
|
||
|
|
size divided by the number of attention heads divided by 2
|
||
|
|
`long_factor` (`List[float]`, *optional*):
|
||
|
|
Only used with 'longrope'. The scaling factor to be applied to long contexts (<
|
||
|
|
`original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
|
||
|
|
size divided by the number of attention heads divided by 2
|
||
|
|
`low_freq_factor` (`float`, *optional*):
|
||
|
|
Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE
|
||
|
|
`high_freq_factor` (`float`, *optional*):
|
||
|
|
Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE
|
||
|
|
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.
|
||
|
|
layer_norm_epsilon (`float`, *optional*, defaults to 1e-05):
|
||
|
|
The epsilon used by 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.
|
||
|
|
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``.
|
||
|
|
bos_token_id (`int`, *optional*, defaults to 0):
|
||
|
|
Beginning of stream token id.
|
||
|
|
eos_token_id (`int`, *optional*, defaults to 2):
|
||
|
|
End of stream token id.
|
||
|
|
|
||
|
|
Example:
|
||
|
|
|
||
|
|
```python
|
||
|
|
>>> from transformers import EXAONEModel, ExaoneConfig
|
||
|
|
|
||
|
|
>>> # Initializing a EXAONE configuration
|
||
|
|
>>> configuration = ExaoneConfig()
|
||
|
|
|
||
|
|
>>> # Initializing a model from configuration
|
||
|
|
>>> model = EXAONEModel(configuration)
|
||
|
|
|
||
|
|
>>> # Accessing the model configuration
|
||
|
|
>>> configuration = model.config
|
||
|
|
```"""
|
||
|
|
|
||
|
|
model_type = "exaone"
|
||
|
|
keys_to_ignore_at_inference = ["past_key_values"]
|
||
|
|
attribute_map = {"num_hidden_layers": "num_layers"}
|
||
|
|
|
||
|
|
def __init__(
|
||
|
|
self,
|
||
|
|
vocab_size=102400,
|
||
|
|
max_position_embeddings=2048,
|
||
|
|
hidden_size=2048,
|
||
|
|
num_layers=32,
|
||
|
|
num_attention_heads=32,
|
||
|
|
num_key_value_heads=None,
|
||
|
|
intermediate_size=None,
|
||
|
|
activation_function="silu",
|
||
|
|
rope_theta=10000.0,
|
||
|
|
rope_scaling=None,
|
||
|
|
embed_dropout=0.0,
|
||
|
|
attention_dropout=0.0,
|
||
|
|
layer_norm_epsilon=1e-5,
|
||
|
|
initializer_range=0.02,
|
||
|
|
use_cache=True,
|
||
|
|
bos_token_id=0,
|
||
|
|
eos_token_id=2,
|
||
|
|
**kwargs,
|
||
|
|
):
|
||
|
|
self.vocab_size = vocab_size
|
||
|
|
self.max_position_embeddings = max_position_embeddings
|
||
|
|
self.hidden_size = hidden_size
|
||
|
|
self.num_layers = num_layers
|
||
|
|
self.num_attention_heads = num_attention_heads
|
||
|
|
self.num_layers = num_layers
|
||
|
|
if num_key_value_heads is None:
|
||
|
|
num_key_value_heads = num_attention_heads
|
||
|
|
self.num_key_value_heads = num_key_value_heads
|
||
|
|
if intermediate_size:
|
||
|
|
self.intermediate_size = intermediate_size
|
||
|
|
else:
|
||
|
|
self.intermediate_size = hidden_size * 4
|
||
|
|
self.activation_function = activation_function
|
||
|
|
self.embed_dropout = embed_dropout
|
||
|
|
self.attention_dropout = attention_dropout
|
||
|
|
self.layer_norm_epsilon = layer_norm_epsilon
|
||
|
|
self.initializer_range = initializer_range
|
||
|
|
self.use_cache = use_cache
|
||
|
|
self.rope_theta = rope_theta
|
||
|
|
self.rope_scaling = rope_scaling
|
||
|
|
|
||
|
|
self.bos_token_id = bos_token_id
|
||
|
|
self.eos_token_id = eos_token_id
|
||
|
|
|
||
|
|
super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
|