feat: support gpu

research/neural_network_model.py

@@ -71,6 +71,8 @@ class NeuralNetworkModel(BaseModel):
         # Extract and prepare features (this will also initialize tokenizer)
         features_df = self.feature_extractor.extract_features(X)
         X_prepared = self.prepare_features(features_df)
+        # Sanitize any out-of-range indices to avoid embedding scatter errors
+        X_prepared = self._sanitize_sequences(X_prepared)
 
         # Encode labels
         if self.label_encoder is None:
@@ -113,6 +115,44 @@ class NeuralNetworkModel(BaseModel):
         self.is_fitted = True
         return self
 
+    def _sanitize_sequences(self, sequences: np.ndarray) -> np.ndarray:
+        """Clamp invalid token indices to OOV and ensure int32 dtype.
+
+        This prevents rare cases where malformed inputs or dtype issues introduce
+        large or negative indices which can trigger TensorScatterUpdate errors
+        during embedding updates on GPU.
+        """
+        try:
+            if sequences is None:
+                return sequences
+            arr = np.asarray(sequences)
+            # Ensure integer dtype for embedding lookups
+            if not np.issubdtype(arr.dtype, np.integer):
+                arr = arr.astype(np.int64, copy=False)
+
+            if self.tokenizer is not None and hasattr(self.tokenizer, "word_index"):
+                # Use the actual max index present in the tokenizer mapping
+                if self.tokenizer.word_index:
+                    max_idx = max(self.tokenizer.word_index.values())
+                else:
+                    max_idx = 0
+                # OOV token index if available, else fall back to 1
+                oov_index = self.tokenizer.word_index.get(
+                    getattr(self.tokenizer, "oov_token", "<OOV>"), 1
+                )
+                # Keep zeros (padding) untouched; clamp negatives and > max_idx to OOV
+                invalid_mask = (arr < 0) | (arr > max_idx)
+                # Avoid turning zeros into OOV
+                invalid_mask &= (arr != 0)
+                if invalid_mask.any():
+                    arr[invalid_mask] = oov_index
+
+            # Use int32 for TF embedding ops compatibility
+            return arr.astype(np.int32, copy=False)
+        except Exception as e:
+            logging.debug(f"Sequence sanitization skipped due to: {e}")
+            return sequences
+
     def _collect_text_corpus(self, X: pd.DataFrame) -> List[str]:
         """Combine configured textual features into one string per record."""
 
@@ -165,6 +205,7 @@ class NeuralNetworkModel(BaseModel):
             pass
         features_df = self.feature_extractor.extract_features(X)
         X_prepared = self.prepare_features(features_df)
+        X_prepared = self._sanitize_sequences(X_prepared)
         y_encoded = self.label_encoder.transform(y)
 
         cv = StratifiedKFold(n_splits=cv_folds, shuffle=True, random_state=self.config.random_seed)
@@ -264,6 +305,7 @@ class NeuralNetworkModel(BaseModel):
         # Prepare features and get vocabulary size
         features_df = self.feature_extractor.extract_features(X)
         X_prepared = self.prepare_features(features_df)
+        X_prepared = self._sanitize_sequences(X_prepared)
         y_encoded = self.label_encoder.transform(y)
 
         vocab_size = len(self.tokenizer.word_index) + 1 if self.tokenizer else 1000