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eafbe80 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 | # Copyright 2024 The HuggingFace Inc. team.
#
# 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.
import math
import warnings
from transformers.configuration_utils import PretrainedConfig
class MambaConfig(PretrainedConfig):
"""
This is the configuration class to store the configuration of a [`MambaModel`]. It is used to instantiate a MAMBA
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 MAMBA
[state-spaces/mamba-2.8b](https://huggingface.co/state-spaces/mamba-2.8b) 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*):
Vocabulary size of the Mamba model.
hidden_size (`int`, *optional*):
Dimensionality of the embeddings and hidden states. Default: 2048.
state_size (`int`, *optional*):
Shape of the state space latents. Default: 16.
num_hidden_layers (`int`, *optional*):
Number of hidden layers in the model. Default: 48.
norm_eps (`float`, *optional*):
The epsilon to use in the layer normalization layers. Default: 1e-5.
pad_token_id (`int`, *optional*):
Padding token id. Default: 0.
bos_token_id (`int`, *optional*):
The id of the beginning of sentence token in the vocabulary. Default: 0.
eos_token_id (`int`, *optional*):
The id of the end of sentence token in the vocabulary. Default: 0.
expand (`int`, *optional*):
Expanding factor used to determine the intermediate size. Default: 2.
conv_kernel (`int`, *optional*):
Size of the convolution kernel. Default: 4.
use_bias (`bool`, *optional*):
Whether or not to use bias in ["in_proj", "out_proj"] of the mixer block. Default: `False`.
use_conv_bias (`bool`, *optional*):
Whether or not to use bias in the convolution layer of the mixer block. Default: `True`.
hidden_act (`str`, *optional*):
The non-linear activation function (function or string) in the decoder. Default: `"silu"`.
initializer_range (`float`, *optional*):
The standard deviation of the truncated_normal_initializer for initializing all weight matrices. Default: 0.02.
residual_in_fp32 (`bool`, *optional*):
Whether or not residuals should be in `float32`.
If set to `False` residuals will keep the same `dtype` as the rest of the model. Default: `True`.
time_step_rank (`Union[int,str]`, *optional*):
Rank of the the discretization projection matrix.
`"auto"` means that it will default to `math.ceil(self.hidden_size / 16)`. Default: `"auto"`.
time_step_scale (`float`, *optional*):
Scale used used to scale `dt_proj.bias`. Default: 1.0.
time_step_min (`float`, *optional*):
Minimum `time_step` used to bound `dt_proj.bias`. Default: 0.001.
time_step_max (`float`, *optional*):
Maximum `time_step` used to bound `dt_proj.bias`. Default: 0.1.
time_step_init_scheme (`float`, *optional*):
Init scheme used for `dt_proj.weight`. Should be one of `["random","uniform"]`. Default: `"random"`.
time_step_floor (`float`, *optional*):
Minimum clamping value of the `dt_proj.bias` layer initialization. Default: 0.0001.
window_size (`int`, *optional*):
The window size used for sliding window attention. Default: 2048.
rescale_prenorm_residual (`bool`, *optional*):
Whether or not to rescale `out_proj` weights when initializing. Default: `False`.
use_cache (`bool`, *optional*):
Whether or not the cache should be used. Default: `True`.
Example:
```python
>>> from transformers import MambaConfig, MambaModel
>>> # Initializing a Mamba configuration
>>> configuration = MambaConfig()
>>> # Initializing a model (with random weights) from the configuration
>>> model = MambaModel(configuration)
>>> # Accessing the model configuration
>>> configuration = model.config
```"""
model_type = "mamba"
def __init__(
self,
vocab_size: int = 32000,
hidden_size: int = 2048,
state_size: int = 16,
num_hidden_layers: int = 48,
norm_eps=1e-5,
pad_token_id: int = 0,
bos_token_id: int = 1,
eos_token_id: int = 2,
expand: int = 2,
conv_kernel: int = 4,
use_bias: bool = False,
use_conv_bias: bool = True,
hidden_act: str = "silu",
initializer_range: float = 0.02,
residual_in_fp32: bool = False,
time_step_rank: str = "auto",
time_step_scale: float = 1.0,
time_step_min: float = 0.001,
time_step_max: float = 0.1,
time_step_init_scheme: str = "random",
time_step_floor: float = 1e-4,
rescale_prenorm_residual: bool = False,
use_cache: bool = True,
fuse_norm: bool = True,
fuse_cross_entropy: bool = True,
fuse_linear_cross_entropy: bool = False,
use_l2warp: bool = False,
tie_word_embeddings: bool = False,
**kwargs,
):
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.state_size = state_size
self.num_hidden_layers = num_hidden_layers
self.norm_eps = norm_eps
self.conv_kernel = conv_kernel
self.expand = expand
self.intermediate_size = int(expand * self.hidden_size)
self.bos_token_id = bos_token_id
self.eos_token_id = eos_token_id
self.pad_token_id = pad_token_id
self.use_bias = use_bias
self.use_conv_bias = use_conv_bias
self.hidden_act = hidden_act
self.initializer_range = initializer_range
self.time_step_rank = math.ceil(self.hidden_size / 16) if time_step_rank == "auto" else time_step_rank
self.time_step_scale = time_step_scale
self.time_step_min = time_step_min
self.time_step_max = time_step_max
self.time_step_init_scheme = time_step_init_scheme
self.time_step_floor = time_step_floor
self.rescale_prenorm_residual = rescale_prenorm_residual
self.residual_in_fp32 = residual_in_fp32
self.use_cache = use_cache
self.fuse_norm = fuse_norm
self.fuse_cross_entropy = fuse_cross_entropy
self.fuse_linear_cross_entropy = fuse_linear_cross_entropy
self.use_l2warp = use_l2warp
if fuse_cross_entropy and fuse_linear_cross_entropy:
raise ValueError(
"`fuse_cross_entropy` and `fuse_linear_cross_entropy` cannot be True at the same time.",
)
if fuse_linear_cross_entropy:
warnings.warn(
"`fuse_linear_cross_entropy` is enabled, which can improves memory efficiency "
"at the potential cost of reduced precision. "
"If you observe issues like loss divergence, consider disabling this setting.",
)
super().__init__(
bos_token_id=bos_token_id,
eos_token_id=eos_token_id,
pad_token_id=pad_token_id,
tie_word_embeddings=tie_word_embeddings,
**kwargs,
)
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