easydel.modules.rwkv.rwkv_configuration

easydel.modules.rwkv.rwkv_configuration#

class easydel.modules.rwkv.rwkv_configuration.RwkvConfig(vocab_size=50277, context_length=1024, hidden_size=4096, num_hidden_layers=32, attention_hidden_size=None, intermediate_size=None, layer_norm_epsilon=1e-05, bos_token_id=0, eos_token_id=0, rescale_every=6, tie_word_embeddings=False, use_cache=True, bits: Optional[int] = None, gradient_checkpointing: EasyDeLGradientCheckPointers = EasyDeLGradientCheckPointers.NONE, **kwargs)[source]#

Bases: EasyDeLBaseConfig

Configuration objects inherit from [EasyDeLBaseConfig] and can be used to control the model outputs. Read the documentation from [EasyDeLBaseConfig] for more information.

Parameters

  • vocab_size (int, optional, defaults to 50277) – Vocabulary size of the RWKV model. Defines the number of different tokens that can be represented by the inputs_ids passed when calling [~easydel.modules.RwkvModel].

  • context_length (int, optional, defaults to 1024) – The maximum sequence length that this model might ever be used with.

  • hidden_size (int, optional, defaults to 4096) – Dimensionality of the encoder layers and the pooler layer.

  • num_hidden_layers (int, optional, defaults to 32) – Number of hidden layers in the Transformer encoder.

  • attention_hidden_size (int, optional) – Dimensionality of the query/key/value of the MultiHead Attention layer of the RWKV* model. If None, it is set to hidden_size.

  • intermediate_size (int, optional) – Dimensionality of the “intermediate” (often named feed-forward) layer in the Transformer encoder. If None, it is set to 4 * hidden_size.

  • layer_norm_epsilon (float, optional, defaults to 1e-5) – The epsilon used by the layer normalization layers.

  • rescale_every (int, optional, defaults to 6) – Interval of layers at which to rescale the attention scores.

  • use_cache (bool, optional, defaults to True) – Whether or not the model should return the last key/values attentions (not used by all models).

  • bos_token_id (int, optional, defaults to 0) – The id for the beginning of stream token.

  • eos_token_id (int, optional, defaults to 0) – The id for the end of stream token.

  • tie_word_embeddings (bool, optional, defaults to False) – Whether to tie the weights of the input embeddings and the output embeddings.

  • bits (int, optional) – The number of bits to quantize the model to. If None, the model is not quantized.

  • gradient_checkpointing (str, optional, defaults to “nothing_saveable”) – What to save during gradient checkpointing. Choose one of “nothing_saveable”, “first_half_saveable”, “full_saveable”.

attribute_map: Dict[str, str] = {'max_position_embeddings': 'context_length'}#
get_partition_rules(fully_sharded_data_parallel: bool = True)[source]#

Get the partition rules for the model.

Parameters

fully_sharded_data_parallel (bool, optional, defaults to True) – Whether to use fully sharded data parallelism.

Returns

The partition rules.

Return type

tp.Tuple[tp.Tuple[str, PartitionSpec]]

model_type: str = 'rwkv'#
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