make compilation optional, return None for empty attention graph

Author: sfluegel05

chebifier/prediction_models/electra_predictor.py

@@ -1,3 +1,5 @@
+from typing import Optional
+
 import numpy as np
 
 from .nn_predictor import NNPredictor
@@ -40,7 +42,7 @@ def __init__(self, model_name: str, ckpt_path: str, **kwargs):
             f"Initialised Electra model {self.model_name} (device: {self.predictor.device})"
         )
 
-    def explain_smiles(self, smiles) -> dict:
+    def explain_smiles(self, smiles) -> Optional[dict]:
         from chebai.preprocessing.reader import EMBEDDING_OFFSET
 
         # Add dummy labels because the collate function requires them.
@@ -69,4 +71,6 @@ def explain_smiles(self, smiles) -> dict:
             ]
             for a in result["attentions"]
         ]
+        if len(graphs) == 0:
+            return None
         return {"graphs": graphs}

chebifier/prediction_models/nn_predictor.py

@@ -20,8 +20,13 @@ def __init__(
     ):
         super().__init__(model_name, **kwargs)
         self.batch_size = kwargs.get("batch_size", None)
+        # compile_model will run the model in eager mode, which gives better performance, but does not return intermediate states
+        # such as attention weights. Therfore, ELECTRA attention graphs will only work with compile_model=False.
+        compile_model = kwargs.get("compile_model", True)
         # If batch_size is not provided, it will be set to default batch size used during training in Predictor
-        self.predictor: Predictor = Predictor(ckpt_path, self.batch_size)
+        self.predictor: Predictor = Predictor(
+            ckpt_path, self.batch_size, compile_model=compile_model
+        )
 
     @modelwise_smiles_lru_cache.batch_decorator
     def predict_smiles_list(self, smiles_list: list[str]) -> list:
@@ -51,4 +56,5 @@ def calculate_results(self, batch):
         dat = self.predictor._model._process_batch(
             collator(batch).to(self.predictor.device), 0
         )
+
         return self.predictor._model(dat, **dat["model_kwargs"])