File size: 8,840 Bytes
e92022a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 |
# 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 typing import Dict
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`.
last_logits_l2_alpha ('float', *optional*, defaults to -1.0):
Whether use l2 norm for last output logits.
If < 0, will not compute last logits l2 norm,
elif == 0, will compute l2 norm but not plus in the loss,
while > 0, will plus this loss in the total loss.
rotary_type (`str` or `function`, *optional*, defaults to `"none"`):
The Rotary Embedding type to used in SelfAttention.
If string, `"none"`, `"1d"`, `"2d"` are supported.
unidirectional ('bool', *optional*, defaults to `False`):
Whether or not the model is train with prefix LM or causal LM.
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_experts=1,
expert_capacity=None,
moe_config: Dict = {},
num_attention_heads=16,
num_key_value_heads=0,
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",
max_memory_length=0,
bf16=True,
intermediate_size=None,
last_logits_l2_alpha=-1.0,
rotary_type='none',
use_rmsnorm=False,
use_atorch_rmsnorm=False,
use_swiglu=False,
rope_scaling=1.0,
use_cache=True,
focused_attention=False,
cache_in_memory=False,
attention_grouping=None,
output_hidden_states=False,
tie_word_embeddings=True,
unidirectional=False,
use_bias=True,
use_qkv_bias=False,
mlp_version='v1',
norm_softmax=False,
norm_head=False,
num_decoder_image_token=1024,
num_decoder_audio_token=512,
**kwargs,
):
self.num_layers = num_layers
self.vocab_size = vocab_size
self.hidden_size = hidden_size
self.num_experts = num_experts
self.expert_capacity = expert_capacity
self.moe_config = moe_config
self.num_attention_heads = num_attention_heads
self.num_key_value_heads = num_key_value_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
self.max_memory_length = max_memory_length
self.bf16 = bf16
self.intermediate_size = intermediate_size
self.last_logits_l2_alpha = last_logits_l2_alpha
self.rotary_type = rotary_type
self.use_rmsnorm = use_rmsnorm
self.use_atorch_rmsnorm = use_atorch_rmsnorm
self.use_swiglu = use_swiglu
self.rope_scaling = rope_scaling
self.use_cache = use_cache
self.focused_attention = focused_attention
self.cache_in_memory = cache_in_memory
self.attention_grouping = attention_grouping
self.unidirectional = unidirectional
self.use_bias = use_bias
self.use_qkv_bias = use_qkv_bias
self.mlp_version = mlp_version
self.norm_softmax = norm_softmax
self.norm_head = norm_head
self.num_decoder_image_token = num_decoder_image_token
self.num_decoder_audio_token = num_decoder_audio_token
super().__init__(output_hidden_states=output_hidden_states, tie_word_embeddings=tie_word_embeddings, **kwargs)
|