glm-large-chinese / configuration_glm.py

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	# coding=utf-8
	# Copyright 2022 shunxing1234 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.
	""" GLM model configuration """

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

	logger = logging.get_logger(__name__)

	GLM_PRETRAINED_CONFIG_ARCHIVE_MAP = {
	"shunxing1234/GLM": "https://huggingface.co/shunxing1234/GLM/resolve/main/config.json",
	# See all GLM models at https://huggingface.co/models?filter=glm
	}


	class GLMConfig(PretrainedConfig):
	r"""
	This is the configuration class to store the configuration of a [`~GLMModel`].
	It is used to instantiate an GLM 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 GLM [shunxing1234/GLM-base-cased](https://huggingface.co/shunxing1234/GLM-base-cased) 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 30522):
	Vocabulary size of the GLM model. Defines the number of different tokens that can be represented by the
	`inputs_ids` passed when calling [`~GLMModel`] or
	[`~TFGLMModel`].
	hidden_size (`int`, optional, defaults to 768):
	Dimension of the encoder layers and the pooler layer.
	num_hidden_layers (`int`, optional, defaults to 12):
	Number of hidden layers in the Transformer encoder.
	num_attention_heads (`int`, optional, defaults to 12):
	Number of attention heads for each attention layer in the Transformer encoder.
	intermediate_size (`int`, optional, defaults to 3072):
	Dimension of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
	hidden_act (`str` or `function`, optional, defaults to `"gelu"`):
	The non-linear activation function (function or string) in the encoder and pooler.
	If string, `"gelu"`, `"relu"`, `"selu"` and `"gelu_new"` are supported.
	hidden_dropout_prob (`float`, optional, defaults to 0.1):
	The dropout probabilitiy for all fully connected layers in the embeddings, encoder, and pooler.
	attention_probs_dropout_prob (`float`, optional, defaults to 0.1):
	The dropout ratio for the attention probabilities.
	max_position_embeddings (`int`, optional, defaults to 512):
	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).
	type_vocab_size (`int`, optional, defaults to 2):
	The vocabulary size of the `token_type_ids` passed when calling [`~GLMModel`] or
	[`~TFGLMModel`].
	initializer_range (`float`, optional, defaults to 0.02):
	The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
	layer_norm_eps (`float`, optional, defaults to 1e-12):
	The epsilon used by the layer normalization layers.
	use_cache (`bool`, optional, defaults to `True`):
	Whether or not the model should return the last key/values attentions (not used by all models). Only
	relevant if `config.is_decoder=True`.
	Example:

	```python
	>>> from transformers import GLMModel, GLMConfig

	>>> # Initializing a GLM shunxing1234/GLM-base-cased style configuration
	>>> configuration = GLMConfig()

	>>> # Initializing a model from the shunxing1234/GLM-base-cased style configuration
	>>> model = GLMModel(configuration)

	>>> # Accessing the model configuration
	>>> configuration = model.config
	```
	"""
	model_type = "glm"
	attribute_map = {
	"num_hidden_layers": "num_layers"
	}

	def __init__(
	self,
	num_layers=24,
	vocab_size=30592,
	hidden_size=1024,
	num_attention_heads=16,
	embedding_dropout_prob=0.1,
	attention_dropout_prob=0.1,
	output_dropout_prob=0.1,
	max_sequence_length=512,
	checkpoint_activations=False,
	checkpoint_num_layers=1,
	parallel_output=True,
	relative_encoding=False,
	block_position_encoding=True,
	output_predict=False,
	spell_length=None,
	spell_func="lstm",
	attention_scale=1.0,
	initializer_range=0.02,
	pool_token="cls",
	**kwargs
	):
	self.num_layers = num_layers
	self.vocab_size = vocab_size
	self.hidden_size = hidden_size
	self.num_attention_heads = num_attention_heads
	self.embedding_dropout_prob = embedding_dropout_prob
	self.attention_dropout_prob = attention_dropout_prob
	self.output_dropout_prob = output_dropout_prob
	self.max_sequence_length = max_sequence_length
	self.checkpoint_activations = checkpoint_activations
	self.checkpoint_num_layers = checkpoint_num_layers
	self.parallel_output = parallel_output
	self.relative_encoding = relative_encoding
	self.block_position_encoding = block_position_encoding
	self.output_predict = output_predict
	self.spell_length = spell_length
	self.spell_func = spell_func
	self.attention_scale = attention_scale
	self.initializer_range = initializer_range
	self.pool_token = pool_token

	super().__init__(**kwargs)