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| """ Falcon configuration""" |
| from transformers.configuration_utils import PretrainedConfig |
| from transformers.utils import logging |
|
|
|
|
| logger = logging.get_logger(__name__) |
|
|
| FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP = { |
| "tiiuae/falcon-40b": "https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json", |
| "tiiuae/falcon-7b": "https://huggingface.co/tiiuae/falcon-7b/resolve/main/config.json", |
| } |
|
|
|
|
| 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. |
| 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. Currently supports two scaling |
| strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is |
| `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update |
| `max_position_embeddings` to the expected new maximum. See the following thread for more information on how |
| these scaling strategies behave: |
| https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an |
| experimental feature, subject to breaking API changes in future versions. |
| 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. |
| |
| 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, |
| 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=8192, |
| rope_theta=10000.0, |
| rope_scaling=None, |
| bos_token_id=11, |
| eos_token_id=11, |
| **kwargs, |
| ): |
| self.vocab_size = vocab_size |
| |
| 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 |
| self.parallel_attn = parallel_attn |
| self.bias = bias |
| self.max_position_embeddings = max_position_embeddings |
| self.rope_theta = rope_theta |
| self.rope_scaling = rope_scaling |
| self._rope_scaling_validation() |
|
|
| 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 |
|
|
| def _rope_scaling_validation(self): |
| """ |
| Validate the `rope_scaling` configuration. |
| """ |
| if self.rope_scaling is None: |
| return |
|
|
| if self.alibi: |
| raise ValueError("`rope_scaling` is not supported when `alibi` is `True`.") |
|
|
| if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2: |
| raise ValueError( |
| "`rope_scaling` must be a dictionary with with two fields, `type` and `factor`, " |
| f"got {self.rope_scaling}" |
| ) |
| rope_scaling_type = self.rope_scaling.get("type", None) |
| rope_scaling_factor = self.rope_scaling.get("factor", None) |
| if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]: |
| raise ValueError( |
| f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}" |
| ) |
| if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0: |
| raise ValueError(f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}") |
