cpm-bee-2b / configuration_cpmbee.py

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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.
	""" CpmBee model configuration"""

	from typing import List, Optional, Tuple, Union

	from transformers.configuration_utils import PretrainedConfig
	from transformers.utils import logging


	logger = logging.get_logger(__name__)

	CPMBEE_PRETRAINED_CONFIG_ARCHIVE_MAP = {
	"openbmb/cpm-bee-10b": "https://huggingface.co/openbmb/cpm-bee-10b/resolve/main/config.json",
	"openbmb/cpm-bee-5b": "https://huggingface.co/openbmb/cpm-bee-5b/resolve/main/config.json",
	"openbmb/cpm-bee-2b": "https://huggingface.co/openbmb/cpm-bee-2b/resolve/main/config.json",
	"openbmb/cpm-bee-1b": "https://huggingface.co/openbmb/cpm-bee-1b/resolve/main/config.json",
	# See all CpmBee models at https://huggingface.co/models?filter=cpmbee
	}


	class CpmBeeConfig(PretrainedConfig):
	r"""
	This is the configuration class to store the configuration of a [`CpmBeeModel`]. It is used to instbeeiate an
	CPMBee 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 CPMBee
	[openbmb/cpm-bee-10b](https://huggingface.co/openbmb/cpm-bee-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 CPMBee model. Defines the number of different tokens that can be represented by the
	`input` passed when calling [`CpmBeeModel`].
	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 0.1):
	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_num_segment_buckets (`int`, optional, defaults to 32):
	The number of segment 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-6):
	The epsilon used by the layer normalization layers.
	init_std (`float`, optional, defaults to 1.0):
	Initialize parameters with std = init_std.
	use_cache (`bool`, optional, defaults to `True`):
	Whether to use cache.
	distance_scale (`float` or `int`, optional, defaults to 16):
	Scale the rotary embedding.
	mask_modules (`list` or `tuple`, optional, defaults to None):
	Decides which feedforward block or attention block is pruned.
	half (`bool`, optional, defaults to `False`):
	Decides the model parameters are half-precision or not.

	Example:

	```python
	>>> from transformers import CpmBeeModel, CpmBeeConfig

	>>> # Initializing a CPMBee cpm-bee-10b style configuration
	>>> configuration = CpmBeeConfig()

	>>> # Initializing a model from the cpm-bee-10b style configuration
	>>> model = CpmBeeModel(configuration)

	>>> # Accessing the model configuration
	>>> configuration = model.config
	```"""
	model_type = "cpmbee"

	def __init__(
	self,
	vocab_size: int = 30720,
	hidden_size: int = 4096,
	num_attention_heads: int = 64,
	dim_head: int = 64,
	dim_ff: int = 10240,
	num_hidden_layers: int = 32,
	dropout_p: int = 0.0,
	position_bias_num_buckets: int = 256,
	position_bias_num_segment_buckets: int = 32,
	position_bias_max_distance: int = 2048,
	eps: int = 1e-6,
	init_std: float = 1.0,
	use_cache: bool = True,
	distance_scale: Union[int, float] = 16,
	mask_modules: Optional[Union[List, Tuple]] = None,
	half: bool = False,
	**kwargs,
	):
	super().__init__(**kwargs)
	self.position_bias_num_segment_buckets = position_bias_num_segment_buckets
	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
	self.distance_scale = distance_scale
	self.half = half
	self.mask_modules = mask_modules