From b9a6ebe31b5ab1f28c18b4a6ed973f317dd74bbb Mon Sep 17 00:00:00 2001
From: alanakbik <alan.akbik@gmail.com>
Date: Fri, 24 Jan 2020 14:45:04 +0100
Subject: [PATCH] GH-1377: fix torch 1.4.0 error

---
 flair/embeddings.py | 60 +++++++++++++++++++++++++--------------------
 1 file changed, 34 insertions(+), 26 deletions(-)

diff --git a/flair/embeddings.py b/flair/embeddings.py
index 289fca690c..c7fddd26d3 100644
--- a/flair/embeddings.py
+++ b/flair/embeddings.py
@@ -2930,32 +2930,6 @@ def _add_embeddings_internal(self, sentences: Union[List[Sentence], Sentence]):
         lengths: List[int] = [len(sentence.tokens) for sentence in sentences]
         longest_token_sequence_in_batch: int = max(lengths)
 
-        # initialize zero-padded word embeddings tensor
-        # sentence_tensor = torch.zeros(
-        #     [
-        #         len(sentences),
-        #         longest_token_sequence_in_batch,
-        #         self.embeddings.embedding_length,
-        #     ],
-        #     dtype=torch.float,
-        #     device=flair.device,
-        # )
-        #
-        # for s_id, sentence in enumerate(sentences):
-        #     # fill values with word 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
-
         pre_allocated_zero_tensor = torch.zeros(
             self.embeddings.embedding_length * longest_token_sequence_in_batch,
             dtype=torch.float,
@@ -3023,6 +2997,40 @@ def _add_embeddings_internal(self, sentences: Union[List[Sentence], Sentence]):
             sentence = sentences[sentence_no]
             sentence.set_embedding(self.name, embedding)
 
+    def _apply(self, fn):
+        major, minor, build, *_ = (int(info)
+                                for info in torch.__version__.split('.'))
+
+        # fixed RNN change format for torch 1.4.0
+        if major >= 1 and minor >= 4:
+            for child_module in self.children():
+                if isinstance(child_module, torch.nn.RNNBase):
+                    _flat_weights_names = []
+                    num_direction = None
+
+                    if child_module.__dict__["bidirectional"]:
+                        num_direction = 2
+                    else:
+                        num_direction = 1
+                    for layer in range(child_module.__dict__["num_layers"]):
+                        for direction in range(num_direction):
+                            suffix = "_reverse" if direction == 1 else ""
+                            param_names = ["weight_ih_l{}{}", "weight_hh_l{}{}"]
+                            if child_module.__dict__["bias"]:
+                                param_names += ["bias_ih_l{}{}", "bias_hh_l{}{}"]
+                            param_names = [
+                                x.format(layer, suffix) for x in param_names
+                            ]
+                            _flat_weights_names.extend(param_names)
+
+                    setattr(child_module, "_flat_weights_names",
+                            _flat_weights_names)
+
+                child_module._apply(fn)
+
+        else:
+            super()._apply(fn)
+
 
 @deprecated(
     version="0.4",