123 lines
5.1 KiB
Python
123 lines
5.1 KiB
Python
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# coding=utf-8
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# Copyright 2022 The OpenBMB Team 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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""" CPMAnt 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 CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402
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class CpmAntConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`CpmAntModel`]. It is used to instantiate an
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CPMAnt model according to the specified arguments, defining the model architecture. Instantiating a configuration
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with the defaults will yield a similar configuration to that of the CPMAnt
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[openbmb/cpm-ant-10b](https://huggingface.co/openbmb/cpm-ant-10b) architecture.
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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 30720):
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Vocabulary size of the CPMAnt model. Defines the number of different tokens that can be represented by the
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`input` passed when calling [`CpmAntModel`].
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hidden_size (`int`, *optional*, defaults to 4096):
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Dimension of the encoder layers.
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num_attention_heads (`int`, *optional*, defaults to 32):
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Number of attention heads in the Transformer encoder.
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dim_head (`int`, *optional*, defaults to 128):
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Dimension of attention heads for each attention layer in the Transformer encoder.
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dim_ff (`int`, *optional*, defaults to 10240):
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Dimension of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
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num_hidden_layers (`int`, *optional*, defaults to 48):
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Number of layers of the Transformer encoder.
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dropout_p (`float`, *optional*, defaults to 0.0):
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The dropout probability for all fully connected layers in the embeddings, encoder.
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position_bias_num_buckets (`int`, *optional*, defaults to 512):
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The number of position_bias buckets.
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position_bias_max_distance (`int`, *optional*, defaults to 2048):
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The maximum sequence length that this model might ever be used with. Typically set this to something large
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just in case (e.g., 512 or 1024 or 2048).
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eps (`float`, *optional*, defaults to 1e-06):
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The epsilon used by the layer normalization layers.
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init_std (`float`, *optional*, defaults to 1.0):
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Initialize parameters with std = init_std.
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prompt_types (`int`, *optional*, defaults to 32):
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The type of prompt.
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prompt_length (`int`, *optional*, defaults to 32):
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The length of prompt.
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segment_types (`int`, *optional*, defaults to 32):
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The type of segment.
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use_cache (`bool`, *optional*, defaults to `True`):
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Whether to use cache.
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Example:
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```python
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>>> from transformers import CpmAntModel, CpmAntConfig
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>>> # Initializing a CPMAnt cpm-ant-10b style configuration
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>>> configuration = CpmAntConfig()
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>>> # Initializing a model from the cpm-ant-10b style configuration
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>>> model = CpmAntModel(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```"""
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model_type = "cpmant"
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def __init__(
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self,
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vocab_size: int = 30720,
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hidden_size: int = 4096,
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num_attention_heads: int = 32,
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dim_head: int = 128,
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dim_ff: int = 10240,
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num_hidden_layers: int = 48,
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dropout_p: int = 0.0,
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position_bias_num_buckets: int = 512,
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position_bias_max_distance: int = 2048,
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eps: int = 1e-6,
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init_std: float = 1.0,
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prompt_types: int = 32,
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prompt_length: int = 32,
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segment_types: int = 32,
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use_cache: bool = True,
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**kwargs,
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):
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super().__init__(**kwargs)
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self.prompt_types = prompt_types
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self.prompt_length = prompt_length
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self.segment_types = segment_types
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self.hidden_size = hidden_size
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self.num_attention_heads = num_attention_heads
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self.dim_head = dim_head
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self.dim_ff = dim_ff
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self.num_hidden_layers = num_hidden_layers
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self.position_bias_num_buckets = position_bias_num_buckets
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self.position_bias_max_distance = position_bias_max_distance
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self.dropout_p = dropout_p
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self.eps = eps
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self.use_cache = use_cache
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self.vocab_size = vocab_size
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self.init_std = init_std
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