# Copyright 2023 the Falcon authors and HuggingFace Inc. team. All rights reserved. # # 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. """Falcon configuration""" from ...configuration_utils import PreTrainedConfig from ...modeling_rope_utils import RopeParameters from ...utils import logging logger = logging.get_logger(__name__) class FalconConfig(PreTrainedConfig): r""" This is the configuration class to store the configuration of a [`FalconModel`]. It is used to instantiate a Falcon 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 [tiiuae/falcon-7b](https://huggingface.co/tiiuae/falcon-7b) 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*, defaults to 65024): Vocabulary size of the Falcon model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`FalconModel`] hidden_size (`int`, *optional*, defaults to 4544): Dimension of the hidden representations. num_hidden_layers (`int`, *optional*, defaults to 32): Number of hidden layers in the Transformer decoder. 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*): Set to 2 if separate layer norms are to be used for the MLP and the attention output when using parallel attention, otherwise, 1. layer_norm_epsilon (`float`, *optional*, defaults to 1e-05): 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 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 MLP layers. attention_dropout (`float`, *optional*, defaults to 0.0): The dropout probability for attention layers. num_kv_heads (`int`, *optional*): Number of key-value heads to use per attention layer. If unset, defaults to the same value as `num_attention_heads`. alibi (`bool`, *optional*, defaults to `False`): Whether to use ALiBi positional biases during self-attention. new_decoder_architecture (`bool`, *optional*, defaults to `False`): Whether to use the new (Falcon-40B) decoder architecture. If `True`, the `multi_query` and `parallel_attn` arguments are ignored, as the new decoder always uses parallel attention. multi_query (`bool`, *optional*, defaults to `True`): Whether to use multi-query attention in the decoder. Ignored when `new_decoder_architecture` is `True`. parallel_attn (`bool`, *optional*, defaults to `True`): Whether to compute attention in parallel with the feedforward layer. If False, they are consecutive instead, as in the original Transformer architecture. Ignored when `new_decoder_architecture` is `True`. bias (`bool`, *optional*, defaults to `False`): Whether to use bias on Linear layers. max_position_embeddings (`int`, *optional*, defaults to 2048): The maximum sequence length that this model might ever be used with, when `alibi` is `False`. Pretrained Falcon models with RoPE support up to 2048 tokens. rope_parameters (`RopeParameters`, *optional*): Dictionary containing the configuration parameters for the RoPE embeddings. The dictionary should contain a value for `rope_theta` and optionally parameters used for scaling in case you want to use RoPE with longer `max_position_embeddings`. bos_token_id (`int`, *optional*, defaults to 11): The id of the "beginning-of-sequence" token. eos_token_id (`int`, *optional*, defaults to 11): The id of the "end-of-sequence" token. pad_token_id (`int`, *optional*): Padding token id. ffn_hidden_size (`int`, *optional*): The hidden size of the feedforward layer in the Transformer decoder. defaults to 4x hidden dim activation (`str`, *optional*, defaults to `"gelu"`): The activation function used in the feedforward layer. tie_word_embeddings (`bool`, *optional*, defaults to `True`): Whether to tie weight embeddings Example: ```python >>> from transformers import FalconModel, FalconConfig >>> # Initializing a small (2-layer) Falcon configuration >>> configuration = FalconConfig(num_hidden_layers=2) >>> # Initializing a model from the small configuration >>> model = FalconModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "falcon" keys_to_ignore_at_inference = ["past_key_values"] def __init__( self, vocab_size: int | None = 65024, hidden_size: int | None = 4544, num_hidden_layers: int | None = 32, num_attention_heads: int | None = 71, num_ln_in_parallel_attn: int | None = None, layer_norm_epsilon: int | None = 1e-5, initializer_range: float | None = 0.02, use_cache: bool | None = True, hidden_dropout: float | None = 0.0, attention_dropout: float | None = 0.0, num_kv_heads: int | None = None, alibi: bool | None = False, new_decoder_architecture: bool | None = False, multi_query: bool | None = True, parallel_attn: bool | None = True, bias: bool | None = False, max_position_embeddings: int | None = 2048, rope_parameters: RopeParameters | dict[str, RopeParameters] | None = None, bos_token_id: int | None = 11, eos_token_id: int | None = 11, pad_token_id: int | None = None, ffn_hidden_size: int | None = None, activation: str | None = "gelu", tie_word_embeddings: bool | None = True, **kwargs, ): self.vocab_size = vocab_size # Backward compatibility with n_embed kwarg n_embed = kwargs.pop("n_embed", None) self.hidden_size = hidden_size if n_embed is None else n_embed self.num_hidden_layers = num_hidden_layers self.num_attention_heads = num_attention_heads self.layer_norm_epsilon = layer_norm_epsilon self.initializer_range = initializer_range self.use_cache = use_cache self.hidden_dropout = hidden_dropout self.attention_dropout = attention_dropout self.bos_token_id = bos_token_id self.eos_token_id = eos_token_id self.pad_token_id = pad_token_id self.num_kv_heads = num_attention_heads if num_kv_heads is None else num_kv_heads self.alibi = alibi self.new_decoder_architecture = new_decoder_architecture self.multi_query = multi_query # Ignored when new_decoder_architecture is True self.parallel_attn = parallel_attn self.bias = bias self.num_ln_in_parallel_attn = num_ln_in_parallel_attn self.max_position_embeddings = max_position_embeddings self.activation = activation self.tie_word_embeddings = tie_word_embeddings if ffn_hidden_size is None: self.ffn_hidden_size = hidden_size * 4 else: self.ffn_hidden_size = ffn_hidden_size self.rope_parameters = rope_parameters super().__init__(**kwargs) @property def head_dim(self): return self.hidden_size // self.num_attention_heads @property def rotary(self): return not self.alibi __all__ = ["FalconConfig"]