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