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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)
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