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