164 lines
7.6 KiB
Python
164 lines
7.6 KiB
Python
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# coding=utf-8
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# Copyright 2023 Adept AI and the 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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""" Persimmon model 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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from ..deprecated._archive_maps import PERSIMMON_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402
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class PersimmonConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`PersimmonModel`]. It is used to instantiate an
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Persimmon model according to the specified arguments, defining the model architecture. Instantiating a
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configuration with the defaults will yield a similar configuration to that of the
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[adept/persimmon-8b-base](https://huggingface.co/adept/persimmon-8b-base).
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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 262144):
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Vocabulary size of the Persimmon model. Defines the number of different tokens that can be represented by
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the `inputs_ids` passed when calling [`PersimmonModel`]
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hidden_size (`int`, *optional*, defaults to 4096):
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Dimension of the hidden representations.
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intermediate_size (`int`, *optional*, defaults to 16384):
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Dimension of the MLP representations.
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num_hidden_layers (`int`, *optional*, defaults to 36):
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Number of hidden layers in the Transformer encoder.
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num_attention_heads (`int`, *optional*, defaults to 64):
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Number of attention heads for each attention layer in the Transformer encoder.
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hidden_act (`str` or `function`, *optional*, defaults to `"relu2"`):
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The non-linear activation function (function or string) in the decoder.
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max_position_embeddings (`int`, *optional*, defaults to 16384):
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The maximum sequence length that this model might ever be used with.
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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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layer_norm_eps (`float`, *optional*, defaults to 1e-5):
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The epsilon used by the rms normalization layers.
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use_cache (`bool`, *optional*, defaults to `True`):
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Whether or not the model should return the last key/values attentions (not used by all models). Only
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relevant if `config.is_decoder=True`.
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tie_word_embeddings(`bool`, *optional*, defaults to `False`):
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Whether to tie weight embeddings
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rope_theta (`float`, *optional*, defaults to 25000.0):
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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. Currently supports two scaling
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strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
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`{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
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`max_position_embeddings` to the expected new maximum. See the following thread for more information on how
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these scaling strategies behave:
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https://www.reddit.com/r/LocalPersimmon/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This
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is an experimental feature, subject to breaking API changes in future versions.
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qk_layernorm (`bool`, *optional*, default to `True`):
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Whether or not to normalize the Queries and Keys after projecting the hidden states
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hidden_dropout (`float`, *optional*, default to 0.0):
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The dropout ratio after applying the MLP to the hidden states.
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attention_dropout (`float`, *optional*, default to 0.0):
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The dropout ratio after computing the attention scores.
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partial_rotary_factor (`float`, *optional*, default to 0.5):
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Percentage of the query and keys which will have rotary embedding.
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Example:
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```python
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>>> from transformers import PersimmonModel, PersimmonConfig
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>>> # Initializing a Persimmon persimmon-7b style configuration
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>>> configuration = PersimmonConfig()
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```"""
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model_type = "persimmon"
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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=262144,
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hidden_size=4096,
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intermediate_size=16384,
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num_hidden_layers=36,
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num_attention_heads=64,
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hidden_act="relu2",
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max_position_embeddings=16384,
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initializer_range=0.02,
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layer_norm_eps=1e-5,
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use_cache=True,
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tie_word_embeddings=False,
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rope_theta=25000.0,
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rope_scaling=None,
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qk_layernorm=True,
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hidden_dropout=0.0,
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attention_dropout=0.0,
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partial_rotary_factor=0.5,
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pad_token_id=None,
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bos_token_id=1,
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eos_token_id=2,
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**kwargs,
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):
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self.vocab_size = vocab_size
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self.max_position_embeddings = max_position_embeddings
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self.hidden_size = hidden_size
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self.intermediate_size = intermediate_size
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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.hidden_act = hidden_act
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self.initializer_range = initializer_range
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self.layer_norm_eps = layer_norm_eps
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self.use_cache = use_cache
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self.rope_theta = rope_theta
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self.rope_scaling = rope_scaling
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self.qk_layernorm = qk_layernorm
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self.hidden_dropout = hidden_dropout
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self.attention_dropout = attention_dropout
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self.partial_rotary_factor = partial_rotary_factor
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self._rope_scaling_validation()
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super().__init__(
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pad_token_id=pad_token_id,
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bos_token_id=bos_token_id,
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eos_token_id=eos_token_id,
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tie_word_embeddings=tie_word_embeddings,
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**kwargs,
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)
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# Copied from transformers.models.llama.configuration_llama.LlamaConfig._rope_scaling_validation
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def _rope_scaling_validation(self):
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"""
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Validate the `rope_scaling` configuration.
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"""
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if self.rope_scaling is None:
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return
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if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
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raise ValueError(
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"`rope_scaling` must be a dictionary with two fields, `type` and `factor`, " f"got {self.rope_scaling}"
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)
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rope_scaling_type = self.rope_scaling.get("type", None)
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rope_scaling_factor = self.rope_scaling.get("factor", None)
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if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
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raise ValueError(
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f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
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)
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if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
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raise ValueError(f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}")
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