Create modeling_dummy.py

modeling_dummy.py

@@ -0,0 +1,81 @@
+import torch
+from torch import nn
+from transformers import PreTrainedModel, PretrainedConfig
+
+class DummyConfig(PretrainedConfig):
+    model_type = "dummy"
+    
+    def __init__(
+        self,
+        vocab_size=32000,
+        hidden_size=32,
+        intermediate_size=64,
+        num_hidden_layers=1,
+        num_attention_heads=1,
+        max_position_embeddings=2048,
+        pad_token_id=0,
+        bos_token_id=1,
+        eos_token_id=2,
+        **kwargs
+    ):
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            **kwargs
+        )
+        self.vocab_size = vocab_size
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+        self.max_position_embeddings = max_position_embeddings
+
+class DummyForCausalLM(PreTrainedModel):
+    config_class = DummyConfig
+    _keys_to_ignore_on_load_missing = ["lm_head.weight"]
+    
+    def __init__(self, config):
+        super().__init__(config)
+        self.config = config
+        self.embed = nn.Embedding(config.vocab_size, config.hidden_size)
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+        
+        # 고정 응답용 토큰
+        self.fixed_response = "이것은 더미 모델의 고정 응답입니다. vLLM 서빙 테스트용으로 만들어졌습니다."
+        
+        # 가중치 초기화
+        self.post_init()
+        
+    def get_input_embeddings(self):
+        return self.embed
+        
+    def set_input_embeddings(self, value):
+        self.embed = value
+        
+    def get_output_embeddings(self):
+        return self.lm_head
+    
+    def forward(self, input_ids=None, attention_mask=None, **kwargs):
+        batch_size = input_ids.shape[0] if input_ids is not None else 1
+        seq_len = input_ids.shape[1] if input_ids is not None else 1
+        
+        # 매우 간단한 임베딩
+        dummy_hidden = torch.zeros((batch_size, seq_len, self.config.hidden_size), 
+                                    dtype=torch.float32, 
+                                    device=input_ids.device if input_ids is not None else "cpu")
+        
+        # 임의의 로짓 값 생성
+        logits = self.lm_head(dummy_hidden)
+        
+        # 항상 고정된 응답으로 예측되도록
+        return {"logits": logits}
+    
+    def prepare_inputs_for_generation(self, input_ids, past_key_values=None, **kwargs):
+        return {
+            "input_ids": input_ids,
+            "past_key_values": past_key_values
+        }
+    
+    def _reorder_cache(self, past_key_values, beam_idx):
+        return past_key_values