easydel.modules.falcon.falcon_configuration

easydel.modules.falcon.falcon_configuration#

class easydel.modules.falcon.falcon_configuration.FalconConfig(vocab_size=65024, hidden_size=4544, num_hidden_layers=32, num_attention_heads=71, num_ln_in_parallel_attn=None, layer_norm_epsilon=1e-05, 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, max_position_embeddings=2048, rope_theta=10000.0, rope_scaling=None, bos_token_id=11, eos_token_id=11, ffn_hidden_size=None, ff_factor=None, activation='gelu', gradient_checkpointing: EasyDeLGradientCheckPointers = EasyDeLGradientCheckPointers.NONE, bits: int | None = 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 65024) – Vocabulary size of the Falcon model. Defines the number of different tokens that can be represented by the inputs_ids passed to the forward method.

  • hidden_size (int, optional, defaults to 4544) – 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.

  • num_attention_heads (int, optional, defaults to 71) – Number of attention heads for each attention layer in the Transformer encoder.

  • num_ln_in_parallel_attn (int, optional) – The number of layer norms in the parallel attention layer.

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

  • 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 or not the model should return the last key/values attentions (not used by all models). Only relevant if config.is_decoder=True.

  • hidden_dropout (float, optional, defaults to 0.0) – The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.

  • attention_dropout (float, optional, defaults to 0.0) – The dropout ratio for the attention probabilities.

  • num_kv_heads (int, optional) – Number of key and value heads for each attention layer in the Transformer encoder. Will default to num_attention_heads if not set.

  • alibi (bool, optional) – Whether to use alibi attention.

  • new_decoder_architecture (bool, optional) – Whether to use the new decoder architecture.

  • multi_query (bool, optional, defaults to True) – Whether to use multi-query attention.

  • parallel_attn (bool, optional, defaults to True) – Whether to use parallel attention.

  • bias (bool, optional, defaults to False) – Whether to use bias in the linear layers.

  • max_position_embeddings (int, optional, defaults to 2048) – The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 2048 or 4096).

  • rope_theta (float, optional, defaults to 10000.0) – The theta value to use for rotary position embeddings.

  • rope_scaling (tp.Dict[str, tp.Union[str, float]], optional) – The rope scaling configuration.

  • bos_token_id (int, optional, defaults to 11) – The index of the beginning of sequence token in the vocabulary.

  • eos_token_id (int, optional, defaults to 11) – The index of the end of sequence token in the vocabulary.

  • ffn_hidden_size (int, optional) – Dimensionality of the hidden layer in the FFN

  • ff_factor (int, optional) – The scaling factor of the FFN

  • activation (str, optional, defaults to “gelu”) – The non-linear activation function (function or string) to use in the encoder and pooler. If string, “gelu”, “relu”, “swish” and “gelu_new” are supported.

  • gradient_checkpointing (str, optional, defaults to “”) – The gradient checkpointing configuration.

  • bits (int, optional) – The number of bits to quantize the model to.

get_partition_rules(*args, **kwargs)[source]#

Get the partition rules for the model. :returns: The partition rules. :rtype: tp.Tuple[tp.Tuple[str, PartitionSpec]]

model_type: str = 'falcon'#
property rotary#
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