# coding=utf-8 # 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 ...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_theta (`float`, *optional*, defaults to 10000.0): The base period of the RoPE embeddings. rope_scaling (`Dict`, *optional*): Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value accordingly. Expected contents: `rope_type` (`str`): The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope', 'llama3'], with 'default' being the original RoPE implementation. `factor` (`float`, *optional*): Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In most scaling types, a `factor` of x will enable the model to handle sequences of length x * original maximum pre-trained length. `original_max_position_embeddings` (`int`, *optional*): Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during pretraining. `attention_factor` (`float`, *optional*): Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention computation. If unspecified, it defaults to value recommended by the implementation, using the `factor` field to infer the suggested value. `beta_fast` (`float`, *optional*): Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear ramp function. If unspecified, it defaults to 32. `beta_slow` (`float`, *optional*): Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear ramp function. If unspecified, it defaults to 1. `short_factor` (`List[float]`, *optional*): Only used with 'longrope'. The scaling factor to be applied to short contexts (< `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden size divided by the number of attention heads divided by 2 `long_factor` (`List[float]`, *optional*): Only used with 'longrope'. The scaling factor to be applied to long contexts (< `original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden size divided by the number of attention heads divided by 2 `low_freq_factor` (`float`, *optional*): Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE `high_freq_factor` (`float`, *optional*): Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE 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. 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. 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=65024, hidden_size=4544, num_hidden_layers=32, num_attention_heads=71, num_ln_in_parallel_attn=None, layer_norm_epsilon=1e-5, 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, activation="gelu", **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.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.rope_theta = rope_theta self.rope_scaling = rope_scaling self.activation = activation if ffn_hidden_size is None: self.ffn_hidden_size = hidden_size * 4 else: self.ffn_hidden_size = ffn_hidden_size super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs) @property def head_dim(self): return self.hidden_size // self.num_attention_heads @property def rotary(self): return not self.alibi __all__ = ["FalconConfig"]