mirror of https://github.com/hpcaitech/ColossalAI
107 lines
4.3 KiB
Python
107 lines
4.3 KiB
Python
"""
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This code is copied from https://huggingface.co/THUDM/chatglm-6b/resolve/main/configuration_chatglm.py
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"""
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""" ChatGLM model configuration """
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from transformers.configuration_utils import PretrainedConfig
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from transformers.utils import logging
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logger = logging.get_logger(__name__)
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class ChatGLMConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`~ChatGLMModel`].
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It is used to instantiate an ChatGLM model according to the specified arguments, defining the model
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architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of
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the ChatGLM-6B [THUDM/ChatGLM-6B](https://huggingface.co/THUDM/chatglm-6b) architecture.
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Configuration objects inherit from [`PretrainedConfig`] and can be used
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to control the model outputs. Read the documentation from [`PretrainedConfig`]
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for more information.
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Args:
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vocab_size (`int`, *optional*, defaults to 150528):
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Vocabulary size of the ChatGLM-6B model. Defines the number of different tokens that can be represented by the
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`inputs_ids` passed when calling [`~ChatGLMModel`] or
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[`~TFChatGLMModel`].
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hidden_size (`int`, *optional*, defaults to 4096):
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Dimension of the encoder layers and the pooler layer.
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num_hidden_layers (`int`, *optional*, defaults to 28):
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Number of hidden layers in the Transformer encoder.
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num_attention_heads (`int`, *optional*, defaults to 32):
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Number of attention heads for each attention layer in the Transformer encoder.
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inner_hidden_size (`int`, *optional*, defaults to 16384):
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Dimension of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
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max_sequence_length (`int`, *optional*, defaults to 512):
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The maximum sequence length that this model might ever be used with.
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Typically set this to something large just in case (e.g., 512 or 1024 or 2048).
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layernorm_epsilon (`float`, *optional*, defaults to 1e-5):
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The epsilon used by the layer normalization layers.
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use_cache (`bool`, *optional*, defaults to `True`):
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Whether the model should return the last key/values attentions (not used by all models).
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Example:
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```python
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>>> from configuration_chatglm import ChatGLMConfig
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>>> from modeling_chatglm import ChatGLMModel
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>>> # Initializing a ChatGLM-6B THUDM/ChatGLM-6B style configuration
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>>> configuration = ChatGLMConfig()
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>>> # Initializing a model from the THUDM/ChatGLM-6B style configuration
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>>> model = ChatGLMModel(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```
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"""
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model_type = "chatglm"
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def __init__(
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self,
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vocab_size=130528,
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hidden_size=4096,
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num_layers=28,
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num_attention_heads=32,
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layernorm_epsilon=1e-5,
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use_cache=True,
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bos_token_id=130004,
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eos_token_id=130005,
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mask_token_id=130000,
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gmask_token_id=130001,
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pad_token_id=3,
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max_sequence_length=2048,
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inner_hidden_size=16384,
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position_encoding_2d=True,
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quantization_bit=0,
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pre_seq_len=None,
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prefix_projection=False,
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**kwargs
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):
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self.num_layers = num_layers
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self.vocab_size = vocab_size
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self.hidden_size = hidden_size
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self.num_attention_heads = num_attention_heads
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self.max_sequence_length = max_sequence_length
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self.layernorm_epsilon = layernorm_epsilon
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self.inner_hidden_size = inner_hidden_size
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self.use_cache = use_cache
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self.bos_token_id = bos_token_id
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self.eos_token_id = eos_token_id
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self.pad_token_id = pad_token_id
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self.mask_token_id = mask_token_id
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self.gmask_token_id = gmask_token_id
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self.position_encoding_2d = position_encoding_2d
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self.quantization_bit = quantization_bit
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self.pre_seq_len = pre_seq_len
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self.prefix_projection = prefix_projection
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super().__init__(
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pad_token_id=pad_token_id,
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bos_token_id=bos_token_id,
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eos_token_id=eos_token_id,
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**kwargs
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) |