| from transformers import PretrainedConfig | |
| class ChatGLMConfig(PretrainedConfig): | |
| model_type = "chatglm" | |
| def __init__( | |
| self, | |
| num_layers=28, | |
| padded_vocab_size=65024, | |
| hidden_size=4096, | |
| ffn_hidden_size=13696, | |
| kv_channels=128, | |
| num_attention_heads=32, | |
| seq_length=2048, | |
| hidden_dropout=0.0, | |
| attention_dropout=0.0, | |
| layernorm_epsilon=1e-5, | |
| rmsnorm=True, | |
| apply_residual_connection_post_layernorm=False, | |
| post_layer_norm=True, | |
| add_bias_linear=False, | |
| add_qkv_bias=False, | |
| bias_dropout_fusion=True, | |
| multi_query_attention=False, | |
| multi_query_group_num=1, | |
| apply_query_key_layer_scaling=True, | |
| attention_softmax_in_fp32=True, | |
| fp32_residual_connection=False, | |
| quantization_bit=0, | |
| pre_seq_len=None, | |
| prefix_projection=False, | |
| **kwargs | |
| ): | |
| self.num_layers = num_layers | |
| self.vocab_size = padded_vocab_size | |
| self.padded_vocab_size = padded_vocab_size | |
| self.hidden_size = hidden_size | |
| self.ffn_hidden_size = ffn_hidden_size | |
| self.kv_channels = kv_channels | |
| self.num_attention_heads = num_attention_heads | |
| self.seq_length = seq_length | |
| self.hidden_dropout = hidden_dropout | |
| self.attention_dropout = attention_dropout | |
| self.layernorm_epsilon = layernorm_epsilon | |
| self.rmsnorm = rmsnorm | |
| self.apply_residual_connection_post_layernorm = apply_residual_connection_post_layernorm | |
| self.post_layer_norm = post_layer_norm | |
| self.add_bias_linear = add_bias_linear | |
| self.add_qkv_bias = add_qkv_bias | |
| self.bias_dropout_fusion = bias_dropout_fusion | |
| self.multi_query_attention = multi_query_attention | |
| self.multi_query_group_num = multi_query_group_num | |
| self.apply_query_key_layer_scaling = apply_query_key_layer_scaling | |
| self.attention_softmax_in_fp32 = attention_softmax_in_fp32 | |
| self.fp32_residual_connection = fp32_residual_connection | |
| self.quantization_bit = quantization_bit | |
| self.pre_seq_len = pre_seq_len | |
| self.prefix_projection = prefix_projection | |
| super().__init__(**kwargs) |