211 lines
11 KiB
Python
211 lines
11 KiB
Python
# coding=utf-8
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# Copyright 2023 the Falcon authors and HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""Falcon configuration"""
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from ...configuration_utils import PretrainedConfig
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from ...utils import logging
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logger = logging.get_logger(__name__)
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class FalconConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`FalconModel`]. It is used to instantiate a Falcon
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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
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[tiiuae/falcon-7b](https://huggingface.co/tiiuae/falcon-7b) 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 65024):
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Vocabulary size of the Falcon model. Defines the number of different tokens that can be represented by the
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`inputs_ids` passed when calling [`FalconModel`]
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hidden_size (`int`, *optional*, defaults to 4544):
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Dimension of the hidden representations.
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num_hidden_layers (`int`, *optional*, defaults to 32):
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Number of hidden layers in the Transformer decoder.
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num_attention_heads (`int`, *optional*, defaults to 71):
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Number of attention heads for each attention layer in the Transformer encoder.
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num_ln_in_parallel_attn (`int`, *optional*):
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Set to 2 if separate layer norms are to be used for the MLP and the attention output when using parallel
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attention, otherwise, 1.
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layer_norm_epsilon (`float`, *optional*, defaults to 1e-05):
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The epsilon used by the layer normalization layers.
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initializer_range (`float`, *optional*, defaults to 0.02):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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use_cache (`bool`, *optional*, defaults to `True`):
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Whether the model should return the last key/values attentions (not used by all models). Only relevant if
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`config.is_decoder=True`.
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hidden_dropout (`float`, *optional*, defaults to 0.0):
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The dropout probability for MLP layers.
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attention_dropout (`float`, *optional*, defaults to 0.0):
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The dropout probability for attention layers.
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num_kv_heads (`int`, *optional*):
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Number of key-value heads to use per attention layer. If unset, defaults to the same value as
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`num_attention_heads`.
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alibi (`bool`, *optional*, defaults to `False`):
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Whether to use ALiBi positional biases during self-attention.
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new_decoder_architecture (`bool`, *optional*, defaults to `False`):
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Whether to use the new (Falcon-40B) decoder architecture. If `True`, the `multi_query` and `parallel_attn`
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arguments are ignored, as the new decoder always uses parallel attention.
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multi_query (`bool`, *optional*, defaults to `True`):
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Whether to use multi-query attention in the decoder. Ignored when `new_decoder_architecture` is `True`.
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parallel_attn (`bool`, *optional*, defaults to `True`):
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Whether to compute attention in parallel with the feedforward layer. If False, they are consecutive
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instead, as in the original Transformer architecture. Ignored when `new_decoder_architecture` is `True`.
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bias (`bool`, *optional*, defaults to `False`):
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Whether to use bias on Linear layers.
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max_position_embeddings (`int`, *optional*, defaults to 2048):
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The maximum sequence length that this model might ever be used with, when `alibi` is `False`. Pretrained
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Falcon models with RoPE support up to 2048 tokens.
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rope_theta (`float`, *optional*, defaults to 10000.0):
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The base period of the RoPE embeddings.
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rope_scaling (`Dict`, *optional*):
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Dictionary containing the scaling configuration for the RoPE embeddings. NOTE: if you apply new rope type
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and you expect the model to work on longer `max_position_embeddings`, we recommend you to update this value
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accordingly.
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Expected contents:
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`rope_type` (`str`):
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The sub-variant of RoPE to use. Can be one of ['default', 'linear', 'dynamic', 'yarn', 'longrope',
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'llama3'], with 'default' being the original RoPE implementation.
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`factor` (`float`, *optional*):
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Used with all rope types except 'default'. The scaling factor to apply to the RoPE embeddings. In
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most scaling types, a `factor` of x will enable the model to handle sequences of length x *
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original maximum pre-trained length.
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`original_max_position_embeddings` (`int`, *optional*):
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Used with 'dynamic', 'longrope' and 'llama3'. The original max position embeddings used during
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pretraining.
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`attention_factor` (`float`, *optional*):
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Used with 'yarn' and 'longrope'. The scaling factor to be applied on the attention
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computation. If unspecified, it defaults to value recommended by the implementation, using the
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`factor` field to infer the suggested value.
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`beta_fast` (`float`, *optional*):
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Only used with 'yarn'. Parameter to set the boundary for extrapolation (only) in the linear
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ramp function. If unspecified, it defaults to 32.
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`beta_slow` (`float`, *optional*):
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Only used with 'yarn'. Parameter to set the boundary for interpolation (only) in the linear
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ramp function. If unspecified, it defaults to 1.
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`short_factor` (`list[float]`, *optional*):
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Only used with 'longrope'. The scaling factor to be applied to short contexts (<
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`original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
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size divided by the number of attention heads divided by 2
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`long_factor` (`list[float]`, *optional*):
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Only used with 'longrope'. The scaling factor to be applied to long contexts (<
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`original_max_position_embeddings`). Must be a list of numbers with the same length as the hidden
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size divided by the number of attention heads divided by 2
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`low_freq_factor` (`float`, *optional*):
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Only used with 'llama3'. Scaling factor applied to low frequency components of the RoPE
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`high_freq_factor` (`float`, *optional*):
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Only used with 'llama3'. Scaling factor applied to high frequency components of the RoPE
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bos_token_id (`int`, *optional*, defaults to 11):
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The id of the "beginning-of-sequence" token.
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eos_token_id (`int`, *optional*, defaults to 11):
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The id of the "end-of-sequence" token.
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ffn_hidden_size (`int`, *optional*):
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The hidden size of the feedforward layer in the Transformer decoder.
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defaults to 4x hidden dim
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activation (`str`, *optional*, defaults to `"gelu"`):
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The activation function used in the feedforward layer.
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Example:
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```python
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>>> from transformers import FalconModel, FalconConfig
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>>> # Initializing a small (2-layer) Falcon configuration
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>>> configuration = FalconConfig(num_hidden_layers=2)
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>>> # Initializing a model from the small configuration
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>>> model = FalconModel(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 = "falcon"
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keys_to_ignore_at_inference = ["past_key_values"]
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def __init__(
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self,
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vocab_size=65024,
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hidden_size=4544,
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num_hidden_layers=32,
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num_attention_heads=71,
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num_ln_in_parallel_attn=None,
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layer_norm_epsilon=1e-5,
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initializer_range=0.02,
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use_cache=True,
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hidden_dropout=0.0,
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attention_dropout=0.0,
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num_kv_heads=None,
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alibi=False,
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new_decoder_architecture=False,
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multi_query=True,
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parallel_attn=True,
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bias=False,
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max_position_embeddings=2048,
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rope_theta=10000.0,
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rope_scaling=None,
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bos_token_id=11,
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eos_token_id=11,
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ffn_hidden_size=None,
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activation="gelu",
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**kwargs,
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):
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self.vocab_size = vocab_size
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# Backward compatibility with n_embed kwarg
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n_embed = kwargs.pop("n_embed", None)
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self.hidden_size = hidden_size if n_embed is None else n_embed
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.layer_norm_epsilon = layer_norm_epsilon
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self.initializer_range = initializer_range
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self.use_cache = use_cache
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self.hidden_dropout = hidden_dropout
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self.attention_dropout = attention_dropout
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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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self.num_kv_heads = num_attention_heads if num_kv_heads is None else num_kv_heads
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self.alibi = alibi
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self.new_decoder_architecture = new_decoder_architecture
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self.multi_query = multi_query # Ignored when new_decoder_architecture is True
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self.parallel_attn = parallel_attn
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self.bias = bias
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self.num_ln_in_parallel_attn = num_ln_in_parallel_attn
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self.max_position_embeddings = max_position_embeddings
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self.rope_theta = rope_theta
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self.rope_scaling = rope_scaling
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self.activation = activation
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if ffn_hidden_size is None:
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self.ffn_hidden_size = hidden_size * 4
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else:
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self.ffn_hidden_size = ffn_hidden_size
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super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
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@property
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def head_dim(self):
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return self.hidden_size // self.num_attention_heads
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@property
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def rotary(self):
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return not self.alibi
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__all__ = ["FalconConfig"]
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