| from transformers import PretrainedConfig | |
| class xTrimoPGLMConfig(PretrainedConfig): | |
| model_type = "xTrimoPGLM" | |
| def __init__( | |
| self, | |
| num_layers=36, | |
| padded_vocab_size=128, | |
| hidden_size=2560, | |
| ffn_hidden_size=6832, | |
| kv_channels=64, | |
| num_attention_heads=40, | |
| 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) |