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""" Falcon configuration""" |
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from transformers.configuration_utils import PretrainedConfig |
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from transformers.utils import logging |
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logger = logging.get_logger(__name__) |
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FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP = { |
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"tiiuae/falcon-40b": "https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json", |
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"tiiuae/falcon-7b": "https://huggingface.co/tiiuae/falcon-7b/resolve/main/config.json", |
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} |
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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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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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layer_norm_epsilon (`float`, *optional*, defaults to 1e-5): |
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The epsilon used by the layer normalization layers. |
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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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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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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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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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bos_token_id=11, |
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eos_token_id=11, |
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**kwargs, |
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): |
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self.vocab_size = vocab_size |
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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 |
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self.parallel_attn = parallel_attn |
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self.bias = bias |
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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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