aphrodite-engine/aphrodite/transformers_utils/configs/chatglm.py

# coding=utf-8
# Adapted from
# https://github.com/THUDM/ChatGLM2-6B
from transformers import PretrainedConfig


class ChatGLMConfig(PretrainedConfig):
    model_type = "chatglm"
    attribute_map = {
        "num_hidden_layers": "num_layers",
        "n_head_kv": "multi_query_group_num",
    }

    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,
                 interleaved_qkv=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
        # It is to be compatible with long lora.
        self.max_position_embeddings = 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
        self.interleaved_qkv = interleaved_qkv
        super().__init__(**kwargs)