176 lines
8.9 KiB
Python
176 lines
8.9 KiB
Python
# 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 ...modeling_rope_utils import rope_config_validation
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from ...utils import logging
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logger = logging.get_logger(__name__)
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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. 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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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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# Validate the correctness of rotary position embeddings parameters
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# BC: if there is a 'type' field, move it to 'rope_type'.
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if self.rope_scaling is not None and "type" in self.rope_scaling:
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self.rope_scaling["rope_type"] = self.rope_scaling["type"]
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rope_config_validation(self)
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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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__all__ = ["PersimmonConfig"]
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