157 lines
6.9 KiB
Python
157 lines
6.9 KiB
Python
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# coding=utf-8
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# Copyright 2024 The HuggingFace Inc. team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""MAMBA configuration"""
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import math
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from ...configuration_utils import PretrainedConfig
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from ...utils import logging
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logger = logging.get_logger(__name__)
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from ..deprecated._archive_maps import MAMBA_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402
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class MambaConfig(PretrainedConfig):
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"""
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This is the configuration class to store the configuration of a [`MambaModel`]. It is used to instantiate a MAMBA
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model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
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defaults will yield a similar configuration to that of the MAMBA
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[state-spaces/mamba-2.8b](https://huggingface.co/state-spaces/mamba-2.8b) architecture.
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PretrainedConfig`] for more information.
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Args:
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vocab_size (`int`, *optional*, defaults to 50280):
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Vocabulary size of the MAMBA model. Defines the number of different tokens that can be represented by the
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`inputs_ids` passed when calling [`MambaModel`].
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hidden_size (`int`, *optional*, defaults to 768):
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Dimensionality of the embeddings and hidden states.
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state_size (`int`, *optional*, defaults to 16): shape of the state space latents.
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num_hidden_layers (`int`, *optional*, defaults to 32):
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Number of hidden layers in the model.
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layer_norm_epsilon (`float`, *optional*, defaults to 1e-05):
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The epsilon to use in the layer normalization layers.
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pad_token_id (`int`, *optional*, defaults to 0):
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Padding token id.
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bos_token_id (`int`, *optional*, defaults to 0):
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The id of the beginning of sentence token in the vocabulary.
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eos_token_id (`int`, *optional*, defaults to 0):
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The id of the end of sentence token in the vocabulary.
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expand (`int`, *optional*, defaults to 2): Expanding factor used to determine the intermediate size.
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conv_kernel (`int`, *optional*, defaults to 4): Size of the convolution kernel.
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use_bias (`bool`, *optional*, defaults to `False`):
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Whether or not to use bias in ["in_proj", "out_proj"] of the mixer block
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use_conv_bias (`bool`, *optional*, defaults to `True`):
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Whether or not to use bias in the convolution layer of the mixer block.
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hidden_act (`str`, *optional*, defaults to `"silu"`):
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The non-linear activation function (function or string) in the decoder.
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initializer_range (`float`, *optional*, defaults to 0.1):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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residual_in_fp32 (`bool`, *optional*, defaults to `True`):
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Whether or not residuals should be in `float32`. If set to `False` residuals will keep the same `dtype` as the rest of the model
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time_step_rank (`Union[int,str]`, *optional*, defaults to `"auto"`):
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Rank of the discretization projection matrix. `"auto"` means that it will default to `math.ceil(self.hidden_size / 16)`
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time_step_scale (`float`, *optional*, defaults to 1.0):
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Scale used used to scale `dt_proj.bias`.
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time_step_min (`float`, *optional*, defaults to 0.001):
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Minimum `time_step` used to bound `dt_proj.bias`.
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time_step_max (`float`, *optional*, defaults to 0.1):
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Maximum `time_step` used to bound `dt_proj.bias`.
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time_step_init_scheme (`float`, *optional*, defaults to `"random"`):
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Init scheme used for `dt_proj.weight`. Should be one of `["random","uniform"]`
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time_step_floor (`float`, *optional*, defaults to 0.0001):
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Minimum clamping value of the `dt_proj.bias` layer initialization.
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rescale_prenorm_residual (`bool`, *optional*, defaults to `False`):
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Whether or not to rescale `out_proj` weights when initializing.
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use_cache (`bool`, *optional*, defaults to `True`):
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Whether or not the cache should be used.
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Example:
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```python
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>>> from transformers import MambaConfig, MambaModel
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>>> # Initializing a Mamba configuration
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>>> configuration = MambaConfig()
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>>> # Initializing a model (with random weights) from the configuration
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>>> model = MambaModel(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```"""
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model_type = "mamba"
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def __init__(
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self,
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vocab_size=50280,
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hidden_size=768,
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state_size=16,
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num_hidden_layers=32,
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layer_norm_epsilon=1e-5,
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pad_token_id=0,
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bos_token_id=0,
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eos_token_id=0,
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expand=2,
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conv_kernel=4,
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use_bias=False,
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use_conv_bias=True,
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hidden_act="silu",
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initializer_range=0.1,
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residual_in_fp32=True,
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time_step_rank="auto",
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time_step_scale=1.0,
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time_step_min=0.001,
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time_step_max=0.1,
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time_step_init_scheme="random",
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time_step_floor=1e-4,
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rescale_prenorm_residual=False,
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use_cache=True,
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**kwargs,
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):
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self.vocab_size = vocab_size
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self.hidden_size = hidden_size
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self.state_size = state_size
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self.num_hidden_layers = num_hidden_layers
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self.layer_norm_epsilon = layer_norm_epsilon
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self.conv_kernel = conv_kernel
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self.expand = expand
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self.intermediate_size = int(expand * self.hidden_size)
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self.bos_token_id = bos_token_id
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self.eos_token_id = eos_token_id
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self.pad_token_id = pad_token_id
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self.use_bias = use_bias
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self.use_conv_bias = use_conv_bias
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self.hidden_act = hidden_act
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self.initializer_range = initializer_range
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self.time_step_rank = math.ceil(self.hidden_size / 16) if time_step_rank == "auto" else time_step_rank
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self.time_step_scale = time_step_scale
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self.time_step_min = time_step_min
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self.time_step_max = time_step_max
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self.time_step_init_scheme = time_step_init_scheme
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self.time_step_floor = time_step_floor
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self.rescale_prenorm_residual = rescale_prenorm_residual
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self.residual_in_fp32 = residual_in_fp32
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self.use_cache = use_cache
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super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, pad_token_id=pad_token_id, **kwargs)
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