Fix a bug in tying OPT embeddings (#1)

cacheflow/models/model_utils.py

@@ -25,7 +25,8 @@ def get_model(
         torch_dtype = STR_DTYPE_TO_TORCH_DTYPE[dtype.lower()]
     else:
         torch_dtype = dtype
-    for model_class, model in MODEL_CLASSES.items():
+    for model_class, hf_model in MODEL_CLASSES.items():
         if model_class in model_name:
-            return model.from_pretrained(model_name, torch_dtype=torch_dtype)
+            model = hf_model.from_pretrained(model_name, torch_dtype=torch_dtype)
+            return model.eval()
     raise ValueError(f'Invalid model name: {model_name}')

cacheflow/models/opt.py

@@ -232,6 +232,28 @@ class OPTForCausalLM(OPTPreTrainedModel):
         # Initialize weights and apply final processing
         self.post_init()
 
+    # NOTE(woosuk): While the following methods are not called in the model code,
+    # they may be internally used by the transformers library.
+    # For example, tie_weights() does not work without these methods.
+    # Thus, do not delete these methods.
+    def get_input_embeddings(self):
+        return self.model.decoder.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.model.decoder.embed_tokens = value
+
+    def get_output_embeddings(self):
+        return self.lm_head
+
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head = new_embeddings
+
+    def set_decoder(self, decoder):
+        self.model.decoder = decoder
+
+    def get_decoder(self):
+        return self.model.decoder
+
     def forward(
         self,
         input_ids: torch.LongTensor,