# coding=utf-8 # Copyright 2023 WisdomShell Inc. 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. # This code is based on Bigcode's GPTBigCode configuration. It has been modified from # its original forms to accommodate minor architectural differences compared to # GPTBigCode Configuration that trained the model. # Copyright 2023 The BigCode team and HuggingFace Inc. team. # # 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. """ Shell configuration""" from transformers.configuration_utils import PretrainedConfig from transformers.utils import logging logger = logging.get_logger(__name__) class CodeShellConfig(PretrainedConfig): """ This is the configuration class to store the configuration of a [`CodeShellModel`]. It is used to instantiate a CodeShell model according to the specified arguments, defining the model 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 50257): Vocabulary size of the GPT-2 model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`ShellModel`]. n_positions (`int`, *optional*, defaults to 1024): 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). n_embd (`int`, *optional*, defaults to 768): Dimensionality of the embeddings and hidden states. n_layer (`int`, *optional*, defaults to 12): Number of hidden layers in the Transformer encoder. n_head (`int`, *optional*, defaults to 12): Number of attention heads for each attention layer in the Transformer encoder. n_inner (`int`, *optional*, defaults to None): Dimensionality of the inner feed-forward layers. `None` will set it to 4 times n_embd activation_function (`str`, *optional*, defaults to `"gelu_pytorch_tanh"`): Activation function, to be selected in the list `["relu", "silu", "gelu", "tanh", "gelu_new", "gelu_pytorch_tanh"]`. resid_pdrop (`float`, *optional*, defaults to 0.1): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. embd_pdrop (`float`, *optional*, defaults to 0.1): The dropout ratio for the embeddings. attn_pdrop (`float`, *optional*, defaults to 0.1): The dropout ratio for the attention. layer_norm_epsilon (`float`, *optional*, defaults to 1e-5): The epsilon to use in the layer normalization layers. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. scale_attn_weights (`bool`, *optional*, defaults to `True`): Scale attention weights by dividing by sqrt(hidden_size).. use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models). attention_softmax_in_fp32 (`bool`, *optional*, defaults to `True`): Whether to call the fused softmax in float32. scale_attention_softmax_in_fp32 (`bool`, *optional*, defaults to `True`): Whether to scale the attention softmax in float32. attention_type (`bool`, *optional*, defaults to `True`): Whether to use Multi-Query Attion (`True`) or Multi-Head Attention (`False`). Example: ```python >>> from configuration_codeshell import CodeShellConfig >>> from modeling_codeshell import CodeShellForCausalLM >>> # Initializing a CodeShell configuration >>> configuration = CodeShellConfig() >>> # Initializing a model (with random weights) from the configuration >>> model = CodeShellForCausalLM(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```""" model_type = "codeshell" keys_to_ignore_at_inference = ["past_key_values"] attribute_map = { "hidden_size": "n_embd", "max_position_embeddings": "n_positions", "num_attention_heads": "n_head", "num_hidden_layers": "n_layer", } def __init__( self, vocab_size=70144, n_positions=8192, n_embd=4096, n_layer=42, n_head=32, n_inner=None, activation_function="gelu_pytorch_tanh", resid_pdrop=0.1, embd_pdrop=0.1, attn_pdrop=0.1, layer_norm_epsilon=1e-5, initializer_range=0.02, scale_attn_weights=True, use_cache=True, bos_token_id=70000, eos_token_id=70000, attention_softmax_in_fp32=True, scale_attention_softmax_in_fp32=True, group_query_attention=True, num_query_groups=1, position_embedding_type="learned_absolute", rope_scaling=None, **kwargs, ): self.vocab_size = vocab_size self.n_positions = n_positions self.n_embd = n_embd self.n_layer = n_layer self.n_head = n_head self.n_inner = n_inner self.activation_function = activation_function self.resid_pdrop = resid_pdrop self.embd_pdrop = embd_pdrop self.attn_pdrop = attn_pdrop self.layer_norm_epsilon = layer_norm_epsilon self.initializer_range = initializer_range self.scale_attn_weights = scale_attn_weights self.use_cache = use_cache self.attention_softmax_in_fp32 = attention_softmax_in_fp32 self.scale_attention_softmax_in_fp32 = scale_attention_softmax_in_fp32 self.group_query_attention = group_query_attention self.num_query_groups = num_query_groups self.position_embedding_type = position_embedding_type self.rope_scaling = rope_scaling assert self.position_embedding_type in [ "learned_absolute", "rope" ], "position_embedding_type must be one of ['learned_absolute', 'rope']" self.bos_token_id = bos_token_id self.eos_token_id = eos_token_id super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)