119 lines
5.0 KiB
Python
119 lines
5.0 KiB
Python
# coding=utf-8
|
|
# Copyright 2023 The OpenAI Team Authors and HuggingFace Inc. team.
|
|
# Copyright (c) 2018, NVIDIA CORPORATION. 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.
|
|
""" RWKV configuration"""
|
|
|
|
from transformers.configuration_utils import PretrainedConfig
|
|
from transformers.utils import logging
|
|
|
|
|
|
logger = logging.get_logger(__name__)
|
|
|
|
RWKV5_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
|
|
|
|
|
|
class Rwkv5Config(PretrainedConfig):
|
|
"""
|
|
This is the configuration class to store the configuration of a [`Rwkv5Model`]. It is used to instantiate a RWKV5
|
|
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 RWVK-4
|
|
[RWKV/rwkv-5-world-1b5](https://huggingface.co/RWKV/rwkv-5-world-1b5) 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 65536):
|
|
Vocabulary size of the RWKV5 model. Defines the number of different tokens that can be represented by the
|
|
`inputs_ids` passed when calling [`Rwkv5Model`].
|
|
hidden_size (`int`, *optional*, defaults to 768):
|
|
Dimensionality of the embeddings and hidden states.
|
|
num_hidden_layers (`int`, *optional*, defaults to 24):
|
|
Number of hidden layers in the model.
|
|
attention_hidden_size (`int`, *optional*):
|
|
Dimensionality of the attention hidden states. Will default to `hidden_size` if unset.
|
|
num_attention_heads (`int`, *optional*, defaults to 64):
|
|
The attention heads to use in rwkv5 self_attention module.
|
|
head_size (`int`, *optional*, defaults to 64): head_size of rwkv5 self_attention module.
|
|
intermediate_size (`int`, *optional*):
|
|
Dimensionality of the inner feed-forward layers. Will default to 4 times `hidden_size` if unset.
|
|
layer_norm_epsilon (`float`, *optional*, defaults to 1e-05):
|
|
The epsilon to use in the layer normalization layers.
|
|
bos_token_id (`int`, *optional*, defaults to 0):
|
|
The id of the beginning of sentence token in the vocabulary. Defaults to 0.
|
|
eos_token_id (`int`, *optional*, defaults to 0):
|
|
The id of the end of sentence token in the vocabulary. Defaults to 0.
|
|
rescale_every (`int`, *optional*, defaults to 6):
|
|
At inference, the hidden states (and weights of the correponding output layers) are divided by 2 every
|
|
`rescale_every` layer. If set to 0 or a negative number, no rescale is done.
|
|
tie_word_embeddings (`bool`, *optional*, defaults to `False`):
|
|
Whether or not to tie the word embeddings with the input token embeddings.
|
|
use_cache (`bool`, *optional*, defaults to `True`):
|
|
Whether or not the model should return the last state.
|
|
|
|
|
|
Example:
|
|
|
|
```python
|
|
>>> from transformers import Rwkv5Config, Rwkv5Model
|
|
|
|
>>> # Initializing a Rwkv5 configuration
|
|
>>> configuration = Rwkv5Config()
|
|
|
|
>>> # Initializing a model (with random weights) from the configuration
|
|
>>> model = Rwkv5Model(configuration)
|
|
|
|
>>> # Accessing the model configuration
|
|
>>> configuration = model.config
|
|
```"""
|
|
|
|
model_type = "rwkv5"
|
|
|
|
def __init__(
|
|
self,
|
|
vocab_size=65536,
|
|
hidden_size=768,
|
|
num_hidden_layers=24,
|
|
attention_hidden_size=None,
|
|
head_size=64,
|
|
head_size_divisor=8,
|
|
intermediate_size=None,
|
|
layer_norm_epsilon=1e-5,
|
|
bos_token_id=0,
|
|
eos_token_id=0,
|
|
rescale_every=6,
|
|
tie_word_embeddings=False,
|
|
use_cache=True,
|
|
**kwargs,
|
|
):
|
|
self.vocab_size = vocab_size
|
|
self.hidden_size = hidden_size
|
|
self.num_hidden_layers = num_hidden_layers
|
|
self.attention_hidden_size = attention_hidden_size if attention_hidden_size is not None else hidden_size
|
|
self.head_size = head_size
|
|
self.head_size_divisor = head_size_divisor
|
|
self.intermediate_size = None
|
|
self.layer_norm_epsilon = layer_norm_epsilon
|
|
self.rescale_every = rescale_every
|
|
self.use_cache = use_cache
|
|
|
|
self.bos_token_id = bos_token_id
|
|
self.eos_token_id = eos_token_id
|
|
|
|
super().__init__(
|
|
tie_word_embeddings=tie_word_embeddings, bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs
|
|
)
|