File size: 5,081 Bytes
91af9d6 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 |
# 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__)
RWKV6_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
class Rwkv6Config(PretrainedConfig):
"""
This is the configuration class to store the configuration of a [`Rwkv6Model`]. It is used to instantiate a RWKV6
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 RWKV6 model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`Rwkv6Model`].
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 rwkv6 self_attention module.
head_size (`int`, *optional*, defaults to 64): head_size of rwkv6 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 Rwkv6Config, Rwkv6Model
>>> # Initializing a Rwkv6 configuration
>>> configuration = Rwkv6Config()
>>> # Initializing a model (with random weights) from the configuration
>>> model = Rwkv6Model(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "rwkv6"
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
)
|