File size: 7,163 Bytes
be54062 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 |
# coding=utf-8
# Copyright 2023 the Falcon authors and 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.
""" Falcon configuration"""
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging
logger = logging.get_logger(__name__)
FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP = {
"tiiuae/falcon-40b": "https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json",
"tiiuae/falcon-7b": "https://huggingface.co/tiiuae/falcon-7b/resolve/main/config.json",
}
class FalconConfig(PretrainedConfig):
r"""
This is the configuration class to store the configuration of a [`FalconModel`]. It is used to instantiate a Falcon
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
[tiiuae/falcon-7b](https://huggingface.co/tiiuae/falcon-7b) 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 65024):
Vocabulary size of the Falcon model. Defines the number of different tokens that can be represented by the
`inputs_ids` passed when calling [`FalconModel`]
hidden_size (`int`, *optional*, defaults to 4544):
Dimension of the hidden representations.
num_hidden_layers (`int`, *optional*, defaults to 32):
Number of hidden layers in the Transformer decoder.
num_attention_heads (`int`, *optional*, defaults to 71):
Number of attention heads for each attention layer in the Transformer encoder.
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 the model should return the last key/values attentions (not used by all models). Only relevant if
`config.is_decoder=True`.
layer_norm_epsilon (`float`, *optional*, defaults to 1e-5):
The epsilon used by the layer normalization layers.
hidden_dropout (`float`, *optional*, defaults to 0.0):
The dropout probability for MLP layers.
attention_dropout (`float`, *optional*, defaults to 0.0):
The dropout probability for attention layers.
num_kv_heads (`int`, *optional*):
Number of key-value heads to use per attention layer. If unset, defaults to the same value as
`num_attention_heads`.
alibi (`bool`, *optional*, defaults to `False`):
Whether to use ALiBi positional biases during self-attention.
new_decoder_architecture (`bool`, *optional*, defaults to `False`):
Whether to use the new (Falcon-40B) decoder architecture. If `True`, the `multi_query` and `parallel_attn`
arguments are ignored, as the new decoder always uses parallel attention.
multi_query (`bool`, *optional*, defaults to `True`):
Whether to use multi-query attention in the decoder. Ignored when `new_decoder_architecture` is `True`.
parallel_attn (`bool`, *optional*, defaults to `True`):
Whether to compute attention in parallel with the feedforward layer. If False, they are consecutive
instead, as in the original Transformer architecture. Ignored when `new_decoder_architecture` is `True`.
bias (`bool`, *optional*, defaults to `False`):
Whether to use bias on Linear layers.
bos_token_id (`int`, *optional*, defaults to 11):
The id of the "beginning-of-sequence" token.
eos_token_id (`int`, *optional*, defaults to 11):
The id of the "end-of-sequence" token.
Example:
```python
>>> from transformers import FalconModel, FalconConfig
>>> # Initializing a small (2-layer) Falcon configuration
>>> configuration = FalconConfig(num_hidden_layers=2)
>>> # Initializing a model from the small configuration
>>> model = FalconModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "falcon"
keys_to_ignore_at_inference = ["past_key_values"]
def __init__(
self,
vocab_size=65024,
hidden_size=4544,
num_hidden_layers=32,
num_attention_heads=71,
layer_norm_epsilon=1e-5,
initializer_range=0.02,
use_cache=True,
hidden_dropout=0.0,
attention_dropout=0.0,
num_kv_heads=None,
alibi=False,
new_decoder_architecture=False,
multi_query=True,
parallel_attn=True,
bias=False,
bos_token_id=11,
eos_token_id=11,
**kwargs,
):
logger.warning_once(
"\nWARNING: You are currently loading Falcon using legacy code contained in the model repository. Falcon has now been fully ported into the Hugging Face transformers library. "
"For the most up-to-date and high-performance version of the Falcon model code, please update to the latest version of transformers and then load the model "
"without the trust_remote_code=True argument.\n"
)
self.vocab_size = vocab_size
# Backward compatibility with n_embed kwarg
n_embed = kwargs.pop("n_embed", None)
self.hidden_size = hidden_size if n_embed is None else n_embed
self.num_hidden_layers = num_hidden_layers
self.num_attention_heads = num_attention_heads
self.layer_norm_epsilon = layer_norm_epsilon
self.initializer_range = initializer_range
self.use_cache = use_cache
self.hidden_dropout = hidden_dropout
self.attention_dropout = attention_dropout
self.bos_token_id = bos_token_id
self.eos_token_id = eos_token_id
self.num_kv_heads = num_attention_heads if num_kv_heads is None else num_kv_heads
self.alibi = alibi
self.new_decoder_architecture = new_decoder_architecture
self.multi_query = multi_query # Ignored when new_decoder_architecture is True
self.parallel_attn = parallel_attn
self.bias = bias
super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
@property
def head_dim(self):
return self.hidden_size // self.num_attention_heads
@property
def rotary(self):
return not self.alibi
|