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2e9a2227ec
Currently we need to call rotary embedding kernel for each LoRA, which makes it hard to serve multiple long context length LoRA. Add batched rotary embedding kernel and pipe it through. It replaces the rotary embedding layer to the one that is aware of multiple cos-sin-cache per scaling factors. Follow up of https://github.com/vllm-project/vllm/pull/3095/files
71 lines
2.8 KiB
Python
71 lines
2.8 KiB
Python
# coding=utf-8
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# Adapted from
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# https://github.com/THUDM/ChatGLM2-6B
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from transformers import PretrainedConfig
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class ChatGLMConfig(PretrainedConfig):
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model_type = "chatglm"
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attribute_map = {
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"num_hidden_layers": "num_layers",
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"n_head_kv": "multi_query_group_num",
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}
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def __init__(self,
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num_layers=28,
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padded_vocab_size=65024,
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hidden_size=4096,
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ffn_hidden_size=13696,
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kv_channels=128,
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num_attention_heads=32,
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seq_length=2048,
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hidden_dropout=0.0,
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attention_dropout=0.0,
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layernorm_epsilon=1e-5,
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rmsnorm=True,
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apply_residual_connection_post_layernorm=False,
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post_layer_norm=True,
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add_bias_linear=False,
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add_qkv_bias=False,
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interleaved_qkv=False,
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bias_dropout_fusion=True,
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multi_query_attention=False,
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multi_query_group_num=1,
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apply_query_key_layer_scaling=True,
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attention_softmax_in_fp32=True,
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fp32_residual_connection=False,
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quantization_bit=0,
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pre_seq_len=None,
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prefix_projection=False,
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**kwargs):
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self.num_layers = num_layers
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self.vocab_size = padded_vocab_size
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self.padded_vocab_size = padded_vocab_size
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self.hidden_size = hidden_size
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self.ffn_hidden_size = ffn_hidden_size
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self.kv_channels = kv_channels
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self.num_attention_heads = num_attention_heads
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self.seq_length = seq_length
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# It is to be compatible with long lora.
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self.max_position_embeddings = seq_length
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self.hidden_dropout = hidden_dropout
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self.attention_dropout = attention_dropout
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self.layernorm_epsilon = layernorm_epsilon
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self.rmsnorm = rmsnorm
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self.apply_residual_connection_post_layernorm = (
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apply_residual_connection_post_layernorm)
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self.post_layer_norm = post_layer_norm
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self.add_bias_linear = add_bias_linear
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self.add_qkv_bias = add_qkv_bias
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self.bias_dropout_fusion = bias_dropout_fusion
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self.multi_query_attention = multi_query_attention
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self.multi_query_group_num = multi_query_group_num
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self.apply_query_key_layer_scaling = apply_query_key_layer_scaling
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self.attention_softmax_in_fp32 = attention_softmax_in_fp32
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self.fp32_residual_connection = fp32_residual_connection
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self.quantization_bit = quantization_bit
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self.pre_seq_len = pre_seq_len
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self.prefix_projection = prefix_projection
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self.interleaved_qkv = interleaved_qkv
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super().__init__(**kwargs)
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