refactor: replace torch.no_grad with torch.inference_mode (where possible)

haystack/document_stores/memory.py

@@ -237,7 +237,7 @@ def get_scores_torch(self, query_emb: np.ndarray, document_to_search: List[Docum
         while curr_pos < len(doc_embeds):
             doc_embeds_slice = doc_embeds[curr_pos : curr_pos + self.scoring_batch_size]
             doc_embeds_slice = doc_embeds_slice.to(self.main_device)
-            with torch.no_grad():
+            with torch.inference_mode():
                 slice_scores = torch.matmul(doc_embeds_slice, query_emb.T).cpu()
                 slice_scores = slice_scores.squeeze(dim=1)
                 slice_scores = slice_scores.numpy().tolist()

haystack/modeling/data_handler/data_silo.py

@@ -771,7 +771,7 @@ def _pass_batches(
         teacher_outputs: List[List[Tuple[torch.Tensor, ...]]],
         tensor_names: List[str],
     ):
-        with torch.no_grad():
+        with torch.inference_mode():
             batch_transposed = zip(*batch)  # transpose dimensions (from batch, features, ... to features, batch, ...)
             batch_transposed_list = [torch.stack(b) for b in batch_transposed]  # create tensors for each feature
             batch_dict = {

haystack/modeling/evaluation/eval.py

@@ -77,7 +77,7 @@ def eval(
             else:
                 module = model
 
-            with torch.no_grad():
+            with torch.inference_mode():
                 if isinstance(module, AdaptiveModel):
                     logits = model.forward(
                         input_ids=batch.get("input_ids", None),

haystack/modeling/infer.py

@@ -365,7 +365,7 @@ def _get_predictions(self, dataset: Dataset, tensor_names: List, baskets):
             batch_samples = samples[i * self.batch_size : (i + 1) * self.batch_size]
 
             # get logits
-            with torch.no_grad():
+            with torch.inference_mode():
                 logits = self.model.forward(**batch)
                 preds = self.model.formatted_preds(
                     logits=logits, samples=batch_samples, padding_mask=batch.get("padding_mask", None)
@@ -402,7 +402,7 @@ def _get_predictions_and_aggregate(self, dataset: Dataset, tensor_names: List, b
             batch = {key: batch[key].to(self.devices[0]) for key in batch}
 
             # get logits
-            with torch.no_grad():
+            with torch.inference_mode():
                 # Aggregation works on preds, not logits. We want as much processing happening in one batch + on GPU
                 # So we transform logits to preds here as well
                 logits = self.model.forward(

haystack/modeling/model/adaptive_model.py

@@ -778,7 +778,7 @@ def forward(self, **kwargs):
         :param kwargs: All arguments that need to be passed on to the model.
         :return: All logits as torch.tensor or multiple tensors.
         """
-        with torch.no_grad():
+        with torch.inference_mode():
             if self.language_model_class == "Bert":
                 input_to_onnx = {
                     "input_ids": numpy.ascontiguousarray(kwargs["input_ids"].cpu().numpy()),

haystack/nodes/ranker/sentence_transformers.py

@@ -129,7 +129,7 @@ def predict(self, query: str, documents: List[Document], top_k: Optional[int] =
         # 1. the logit as similarity score/answerable classification
         # 2. the logits as answerable classification  (no_answer / has_answer)
         # https://www.sbert.net/docs/pretrained-models/ce-msmarco.html#usage-with-transformers
-        with torch.no_grad():
+        with torch.inference_mode():
             similarity_scores = self.transformer_model(**features).logits
 
         logits_dim = similarity_scores.shape[1]  # [batch_size, logits_dim]
@@ -216,7 +216,7 @@ def predict_batch(
                 cur_queries, [doc.content for doc in cur_docs], padding=True, truncation=True, return_tensors="pt"
             ).to(self.devices[0])
 
-            with torch.no_grad():
+            with torch.inference_mode():
                 similarity_scores = self.transformer_model(**features).logits
                 preds.extend(similarity_scores)
             pb.update(len(cur_docs))

haystack/nodes/retriever/_embedding_encoder.py

@@ -304,7 +304,7 @@ def embed_queries(self, queries: List[str]) -> np.ndarray:
 
         for i, batch in enumerate(tqdm(dataloader, desc=f"Creating Embeddings", unit=" Batches", disable=disable_tqdm)):
             batch = {key: batch[key].to(self.embedding_model.device) for key in batch}
-            with torch.no_grad():
+            with torch.inference_mode():
                 q_reps = (
                     self.embedding_model.embed_questions(
                         input_ids=batch["input_ids"], attention_mask=batch["padding_mask"]
@@ -331,7 +331,7 @@ def embed_documents(self, docs: List[Document]) -> np.ndarray:
 
         for i, batch in enumerate(tqdm(dataloader, desc=f"Creating Embeddings", unit=" Batches", disable=disable_tqdm)):
             batch = {key: batch[key].to(self.embedding_model.device) for key in batch}
-            with torch.no_grad():
+            with torch.inference_mode():
                 q_reps = (
                     self.embedding_model.embed_answers(
                         input_ids=batch["input_ids"], attention_mask=batch["padding_mask"]

haystack/nodes/retriever/dense.py

@@ -528,7 +528,7 @@ def _get_predictions(self, dicts: List[Dict[str, Any]]) -> Dict[str, np.ndarray]
                 batch = {key: raw_batch[key].to(self.devices[0]) for key in raw_batch}
 
                 # get logits
-                with torch.no_grad():
+                with torch.inference_mode():
                     query_embeddings, passage_embeddings = self.model.forward(
                         query_input_ids=batch.get("query_input_ids", None),
                         query_segment_ids=batch.get("query_segment_ids", None),
@@ -1171,7 +1171,7 @@ def _get_predictions(self, dicts: List[Dict[str, Any]]) -> Dict[str, np.ndarray]
                 batch = {key: batch[key].to(self.devices[0]) for key in batch}
 
                 # get logits
-                with torch.no_grad():
+                with torch.inference_mode():
                     query_embeddings, passage_embeddings = self.model.forward(**batch)[0]
                     if query_embeddings is not None:
                         query_embeddings_batched.append(query_embeddings.cpu().numpy())

haystack/utils/augment_squad.py

@@ -77,7 +77,7 @@ def load_glove(
             id_word_mapping[i] = split[0]
             vector_list.append(torch.tensor([float(x) for x in split[1:]]))
     vectors = torch.stack(vector_list)
-    with torch.no_grad():
+    with torch.inference_mode():
         vectors = vectors.to(device)
         vectors = F.normalize(vectors, dim=1)
     return word_id_mapping, id_word_mapping, vectors
@@ -132,7 +132,7 @@ def get_replacements(
         inputs.append((input_ids_, subword_index))
 
     # doing batched forward pass
-    with torch.no_grad():
+    with torch.inference_mode():
         prediction_list = []
         while len(inputs) != 0:
             batch_list, token_indices = tuple(zip(*inputs[:batch_size]))
@@ -165,7 +165,7 @@ def get_replacements(
         elif word in glove_word_id_mapping:  # word was split into subwords so we use glove instead
             word_id = glove_word_id_mapping[word]
             glove_vector = glove_vectors[word_id]
-            with torch.no_grad():
+            with torch.inference_mode():
                 word_similarities = torch.mm(glove_vectors, glove_vector.unsqueeze(1)).squeeze(1)
                 ranking = torch.argsort(word_similarities, descending=True)[: word_possibilities + 1]
                 possible_words.append([glove_id_word_mapping[int(id_)] for id_ in ranking.cpu()])

test/modeling/test_dpr.py

@@ -829,7 +829,7 @@ def test_dpr_processor_save_load_non_bert_tokenizer(tmp_path: Path, query_and_pa
         batch = {key: batch[key].to(device) for key in batch}
 
         # get logits
-        with torch.no_grad():
+        with torch.inference_mode():
             query_embeddings, passage_embeddings = model.forward(
                 query_input_ids=batch.get("query_input_ids", None),
                 query_segment_ids=batch.get("query_segment_ids", None),
@@ -863,7 +863,7 @@ def test_dpr_processor_save_load_non_bert_tokenizer(tmp_path: Path, query_and_pa
         batch = {key: batch[key].to(device) for key in batch}
 
         # get logits
-        with torch.no_grad():
+        with torch.inference_mode():
             query_embeddings, passage_embeddings = loaded_model.forward(
                 query_input_ids=batch.get("query_input_ids", None),
                 query_segment_ids=batch.get("query_segment_ids", None),
@@ -952,7 +952,7 @@ def test_dpr_processor_save_load_non_bert_tokenizer(tmp_path: Path, query_and_pa
         batch = {key: batch[key].to(device) for key in batch}
 
         # get logits
-        with torch.no_grad():
+        with torch.inference_mode():
             query_embeddings, passage_embeddings = loaded_model.forward(
                 query_input_ids=batch.get("query_input_ids", None),
                 query_segment_ids=batch.get("query_segment_ids", None),