sgl-project--sglang/python/sglang/srt/configs/chatglm.py

# Adapted from
# https://github.com/THUDM/ChatGLM2-6B
# https://github.com/vllm-project/vllm/blob/main/vllm/transformers_utils/configs/chatglm.py

# ChatGLM2 and ChatGLM3 share the same config.
# ChatGLM4 is officially supported by Huggingface
# transformers >= 4.46.0 is required
# https://huggingface.co/docs/transformers/en/model_doc/glm
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)