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| # 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 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. | |
| 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`. | |
| layer_norm_epsilon (`float`, *optional*, defaults to 1e-5): | |
| The epsilon used by the layer normalization layers. | |
| 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. | |
| 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, | |
| bos_token_id=11, | |
| eos_token_id=11, | |
| **kwargs, | |
| ): | |
| logger.warning_once( | |
| "\nWARNING: You are currently loading Falcon using legacy code contained in the model repository. Falcon has now been fully ported into the Hugging Face transformers library. " | |
| "For the most up-to-date and high-performance version of the Falcon model code, please update to the latest version of transformers and then load the model " | |
| "without the trust_remote_code=True argument.\n" | |
| ) | |
| 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 | |
| super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs) | |
| def head_dim(self): | |
| return self.hidden_size // self.num_attention_heads | |
| def rotary(self): | |
| return not self.alibi | |