Delete configuration_xtrimopglm.py

configuration_xtrimopglm.py

@@ -1,86 +0,0 @@
-from transformers import PretrainedConfig
-
-
-class xTrimoPGLMConfig(PretrainedConfig):
-    model_type = "xTrimoPGLM"
-    def __init__(
-        self,
-        num_layers=72,
-        padded_vocab_size=128,
-        hidden_size=10240,
-        ffn_hidden_size=31744,
-        kv_channels=128,
-        num_attention_heads=80,
-        seq_length=2048,
-        hidden_dropout=0.0,
-        attention_dropout=0.0,
-        layernorm_epsilon=1e-5,
-        initializer_range=0.02,
-        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=True,
-        use_pytorch_sdpa=True,
-        is_causal=False,
-        use_cache=True,
-        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.initializer_range = initializer_range
-        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.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)