Upload configuration_moss.py with huggingface_hub
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configuration_moss.py
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""" Moss model configuration"""
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from transformers.utils import logging
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from transformers.configuration_utils import PretrainedConfig
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logger = logging.get_logger(__name__)
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class MossConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`MossModel`]. It is used to instantiate a
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Moss model according to the specified arguments, defining the model architecture. Instantiating a configuration
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with the defaults will yield a similar configuration to that of the Moss
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[fnlp/moss-moon-003-base](https://huggingface.co/fnlp/moss-moon-003-base) architecture. Configuration objects
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inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from
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[`PretrainedConfig`] for more information.
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Args:
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vocab_size (`int`, *optional*, defaults to 107008):
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Vocabulary size of the Moss model. Defines the number of different tokens that can be represented by the
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`inputs_ids` passed when calling [`MossModel`].
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n_positions (`int`, *optional*, defaults to 2048):
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The maximum sequence length that this model might ever be used with. Typically set this to something large
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just in case (e.g., 512 or 1024 or 2048).
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n_embd (`int`, *optional*, defaults to 4096):
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Dimensionality of the embeddings and hidden states.
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n_layer (`int`, *optional*, defaults to 28):
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Number of hidden layers in the Transformer encoder.
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n_head (`int`, *optional*, defaults to 16):
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Number of attention heads for each attention layer in the Transformer encoder.
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rotary_dim (`int`, *optional*, defaults to 64):
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Number of dimensions in the embedding that Rotary Position Embedding is applied to.
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n_inner (`int`, *optional*, defaults to None):
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Dimensionality of the inner feed-forward layers. `None` will set it to 4 times n_embd
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activation_function (`str`, *optional*, defaults to `"gelu_new"`):
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Activation function, to be selected in the list `["relu", "silu", "gelu", "tanh", "gelu_new"]`.
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resid_pdrop (`float`, *optional*, defaults to 0.1):
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The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
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embd_pdrop (`int`, *optional*, defaults to 0.1):
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The dropout ratio for the embeddings.
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attn_pdrop (`float`, *optional*, defaults to 0.1):
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The dropout ratio for the attention.
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layer_norm_epsilon (`float`, *optional*, defaults to 1e-5):
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The epsilon to use in the layer normalization layers.
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initializer_range (`float`, *optional*, defaults to 0.02):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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use_cache (`bool`, *optional*, defaults to `True`):
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Whether or not 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 modeling_moss import MossModel
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>>> from configuration_moss import MossConfig
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>>> # Initializing a moss-moon-003-base configuration
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>>> configuration = MossConfig()
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>>> # Initializing a model (with random weights) from the configuration
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>>> model = MossModel(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```"""
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model_type = "moss"
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attribute_map = {
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"max_position_embeddings": "n_positions",
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"hidden_size": "n_embd",
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"num_attention_heads": "n_head",
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"num_hidden_layers": "n_layer",
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}
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def __init__(
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self,
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vocab_size=107008,
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n_positions=2048,
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n_ctx=2048,
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n_embd=4096,
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n_layer=28,
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n_head=16,
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rotary_dim=64,
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n_inner=None,
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activation_function="gelu_new",
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resid_pdrop=0.0,
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embd_pdrop=0.0,
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attn_pdrop=0.0,
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layer_norm_epsilon=1e-5,
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initializer_range=0.02,
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use_cache=True,
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bos_token_id=106028,
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eos_token_id=106068,
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tie_word_embeddings=False,
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wbits=32,
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groupsize=128,
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**kwargs,
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):
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self.vocab_size = vocab_size
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self.n_ctx = n_ctx
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self.n_positions = n_positions
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self.n_embd = n_embd
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self.n_layer = n_layer
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self.n_head = n_head
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self.n_inner = n_inner
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self.rotary_dim = rotary_dim
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self.activation_function = activation_function
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self.resid_pdrop = resid_pdrop
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self.embd_pdrop = embd_pdrop
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self.attn_pdrop = attn_pdrop
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self.layer_norm_epsilon = layer_norm_epsilon
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self.initializer_range = initializer_range
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self.use_cache = use_cache
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self.wbits = wbits
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self.groupsize = groupsize
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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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super().__init__(
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bos_token_id=bos_token_id, eos_token_id=eos_token_id, tie_word_embeddings=tie_word_embeddings, **kwargs
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)
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