Reduce the number of concatenation for 10% inference time reduction

flair/data.py

@@ -237,7 +237,9 @@ def set_embedding(self, name: str, vector: torch.tensor):
         device = flair.device
         if len(self._embeddings.keys()) > 0:
             device = next(iter(self._embeddings.values())).device
-        self._embeddings[name] = vector.to(device)
+        if device != vector.device:
+            vector = vector.to(device)
+        self._embeddings[name] = vector
 
     def to(self, device: str, pin_memory: bool = False):
         for name, vector in self._embeddings.items():
@@ -257,6 +259,9 @@ def clear_embeddings(self, embedding_names: List[str] = None):
                 if name in self._embeddings.keys():
                     del self._embeddings[name]
 
+    def get_each_embedding(self) -> torch.tensor:
+        return [self._embeddings[embed] for embed in sorted(self._embeddings.keys())]
+
     def get_embedding(self) -> torch.tensor:
         embeddings = [
             self._embeddings[embed] for embed in sorted(self._embeddings.keys())
@@ -642,7 +647,9 @@ def set_embedding(self, name: str, vector: torch.tensor):
         device = flair.device
         if len(self._embeddings.keys()) > 0:
             device = next(iter(self._embeddings.values())).device
-        self._embeddings[name] = vector.to(device, non_blocking=True)
+        if device != vector.device:
+            vector = vector.to(device)
+        self._embeddings[name] = vector
 
     def get_embedding(self) -> torch.tensor:
         embeddings = []

flair/embeddings.py

@@ -2756,9 +2756,18 @@ def _add_embeddings_internal(self, sentences: Union[List[Sentence], Sentence]):
 
         for s_id, sentence in enumerate(sentences):
             # fill values with word embeddings
-            sentence_tensor[s_id][: len(sentence)] = torch.cat(
-                [token.get_embedding().unsqueeze(0) for token in sentence], 0
-            )
+            all_embs = list()
+
+            for index_token, token in enumerate(sentence):
+                embs = token.get_each_embedding()
+                if not all_embs:
+                    all_embs = [list() for _ in range(len(embs))]
+                for index_emb, emb in enumerate(embs):
+                    all_embs[index_emb].append(emb)
+
+            concat_word_emb = [torch.stack(embs) for embs in all_embs]
+            concat_sentence_emb = torch.cat(concat_word_emb, dim=1)
+            sentence_tensor[s_id][: len(sentence)] = concat_sentence_emb
 
         # --------------------------------------------------------------------
         # FF PART

flair/models/sequence_tagger_model.py

@@ -457,9 +457,18 @@ def forward(self, sentences: List[Sentence]):
         )
 
         for s_id, sentence in enumerate(sentences):
-            # fill values with word embeddings
-            token_embeddings = [token.get_embedding() for token in sentence]
-            sentence_tensor[s_id][: len(sentence)] = torch.stack(token_embeddings)
+            all_embs = list()
+
+            for index_token, token in enumerate(sentence):
+                embs = token.get_each_embedding()
+                if not all_embs:
+                    all_embs = [list() for _ in range(len(embs))]
+                for index_emb, emb in enumerate(embs):
+                    all_embs[index_emb].append(emb)
+
+            concat_word_emb = [torch.stack(embs) for embs in all_embs]
+            concat_sentence_emb = torch.cat(concat_word_emb, dim=1)
+            sentence_tensor[s_id][: len(sentence)] = concat_sentence_emb
 
         # --------------------------------------------------------------------
         # FF PART