Upload configuration_proteinglm.py

configuration_proteinglm.py

@@ -0,0 +1,86 @@
+from transformers import PretrainedConfig
+
+
+class ProteinGLMConfig(PretrainedConfig):
+    model_type = "ProteinGLM"
+    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)