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129 lines
6.0 KiB
129 lines
6.0 KiB
# 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 RopeParameters
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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_parameters (`RopeParameters`, *optional*):
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Dictionary containing the configuration parameters for the RoPE embeddings. The dictionary should contain
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a value for `rope_theta` and optionally parameters used for scaling in case you want to use RoPE
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with longer `max_position_embeddings`.
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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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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: int | None = 262144,
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hidden_size: int | None = 4096,
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intermediate_size: int | None = 16384,
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num_hidden_layers: int | None = 36,
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num_attention_heads: int | None = 64,
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hidden_act: str | None = "relu2",
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max_position_embeddings: int | None = 16384,
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initializer_range: float | None = 0.02,
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layer_norm_eps: int | None = 1e-5,
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use_cache: bool | None = True,
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tie_word_embeddings: bool | None = False,
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rope_parameters: RopeParameters | dict[str, RopeParameters] | None = None,
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qk_layernorm: bool | None = True,
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hidden_dropout: float | None = 0.0,
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attention_dropout: float | None = 0.0,
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pad_token_id: int | None = None,
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bos_token_id: int | None = 1,
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eos_token_id: int | None = 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.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.rope_parameters = rope_parameters
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kwargs.setdefault("partial_rotary_factor", 0.5) # assign default for BC
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self.tie_word_embeddings = tie_word_embeddings
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self.pad_token_id = pad_token_id
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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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super().__init__(**kwargs)
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__all__ = ["PersimmonConfig"]
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