# coding=utf-8 # Copyright 2021 The EleutherAI and HuggingFace Teams. 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. # Modified configuration implementation based on https://github.com/huggingface/transformers/blob/main/src/transformers/models/gptj/configuration_gptj.py from transformers.configuration_utils import PretrainedConfig from transformers.utils import logging logger = logging.get_logger(__name__) class CodeGenConfig(PretrainedConfig): model_type = "codegen" def __init__( self, vocab_size=50400, n_positions=2048, n_ctx=2048, n_embd=4096, n_layer=28, n_head=16, rotary_dim=64, n_inner=None, activation_function="gelu_new", resid_pdrop=0.0, embd_pdrop=0.0, attn_pdrop=0.0, layer_norm_epsilon=1e-5, initializer_range=0.02, scale_attn_weights=True, gradient_checkpointing=False, use_cache=True, bos_token_id=50256, eos_token_id=50256, **kwargs ): super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs) self.vocab_size = vocab_size self.n_ctx = n_ctx 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.rotary_dim = rotary_dim 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.gradient_checkpointing = gradient_checkpointing self.scale_attn_weights = scale_attn_weights self.use_cache = use_cache self.bos_token_id = bos_token_id self.eos_token_id = eos_token_id @property def max_position_embeddings(self): return self.n_positions @property def hidden_size(self): return self.n_embd @property def num_attention_heads(self): return self.n_head @property def num_hidden_layers(self): return self.n_layer