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from transformers import PretrainedConfig
class ProteinGLMConfig(PretrainedConfig):
model_type = "ProteinGLM"
def __init__(
self,
num_layers=36,
padded_vocab_size=128,
hidden_size=4096,
ffn_hidden_size=10922,
kv_channels=128,
num_attention_heads=32,
seq_length=1024,
hidden_dropout=0.0,
attention_dropout=0.0,
layernorm_epsilon=1e-5,
glu_activation='geglu',
rmsnorm=False,
deepnorm=True,
apply_residual_connection_post_layernorm=True,
post_layer_norm=True,
add_bias_linear=True,
add_qkv_bias=True,
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,
rotary_embedding_2d=False,
use_pytorch_sdpa=True,
is_causal=True,
use_cache=True,
initializer_range=0.02,
moe=False,
num_experts=0,
experts_per_token=0,
untie_head=False,
head_num=1,
**kwargs
):
if not deepnorm and apply_residual_connection_post_layernorm:
print(f"Warning: deepnorm is False and apply_residual_connection_post_layernorm is True")
if deepnorm:
apply_residual_connection_post_layernorm = True
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.glu_activation = glu_activation
self.rmsnorm = rmsnorm
self.deepnorm = deepnorm
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.rotary_embedding_2d = rotary_embedding_2d
self.is_causal = is_causal
self.use_cache = use_cache
self.initializer_range = initializer_range
self.use_pytorch_sdpa = use_pytorch_sdpa
self.moe = moe
self.num_experts = num_experts
self.experts_per_token = experts_per_token
self.untie_head = untie_head
self.head_num=head_num
super().__init__(**kwargs) |