We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
Add torch.no_grad() to LanguageModel.generate_text()
The autograd engine is not required when using an LM to generate text.

So, as pointed out in #167, `torch.no_grad()` needs to be added to `LanguageModel.generate_text()` for better performance and to avoid out of memory issues.

--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `flair/models/language_model.py`
Content:
```
1 import torch.nn as nn
2 import torch
3 import math
4 from torch.autograd import Variable
5 from typing import List
6 from flair.data import Dictionary
7 
8 
9 class LanguageModel(nn.Module):
10     """Container module with an encoder, a recurrent module, and a decoder."""
11 
12     def __init__(self,
13                  dictionary: Dictionary,
14                  is_forward_lm: bool,
15                  hidden_size: int,
16                  nlayers: int,
17                  embedding_size: int = 100,
18                  nout=None,
19                  dropout=0.5):
20 
21         super(LanguageModel, self).__init__()
22 
23         self.dictionary = dictionary
24         self.is_forward_lm: bool = is_forward_lm
25 
26         self.dropout = dropout
27         self.hidden_size = hidden_size
28         self.embedding_size = embedding_size
29         self.nlayers = nlayers
30 
31         self.drop = nn.Dropout(dropout)
32         self.encoder = nn.Embedding(len(dictionary), embedding_size)
33 
34         if nlayers == 1:
35             self.rnn = nn.LSTM(embedding_size, hidden_size, nlayers)
36         else:
37             self.rnn = nn.LSTM(embedding_size, hidden_size, nlayers, dropout=dropout)
38 
39         self.hidden = None
40 
41         self.nout = nout
42         if nout is not None:
43             self.proj = nn.Linear(hidden_size, nout)
44             self.initialize(self.proj.weight)
45             self.decoder = nn.Linear(nout, len(dictionary))
46         else:
47             self.proj = None
48             self.decoder = nn.Linear(hidden_size, len(dictionary))
49 
50         self.init_weights()
51 
52         # auto-spawn on GPU if available
53         if torch.cuda.is_available():
54             self.cuda()
55 
56     def init_weights(self):
57         initrange = 0.1
58         self.encoder.weight.data.uniform_(-initrange, initrange)
59         self.decoder.bias.data.fill_(0)
60         self.decoder.weight.data.uniform_(-initrange, initrange)
61 
62     def set_hidden(self, hidden):
63         self.hidden = hidden
64 
65     def forward(self, input, hidden, ordered_sequence_lengths=None):
66         encoded = self.encoder(input)
67         emb = self.drop(encoded)
68 
69         self.rnn.flatten_parameters()
70 
71         output, hidden = self.rnn(emb, hidden)
72 
73         if self.proj is not None:
74             output = self.proj(output)
75 
76         output = self.drop(output)
77 
78         decoded = self.decoder(output.view(output.size(0) * output.size(1), output.size(2)))
79 
80         return decoded.view(output.size(0), output.size(1), decoded.size(1)), output, hidden
81 
82     def init_hidden(self, bsz):
83         weight = next(self.parameters()).data
84         return (Variable(weight.new(self.nlayers, bsz, self.hidden_size).zero_()),
85                 Variable(weight.new(self.nlayers, bsz, self.hidden_size).zero_()))
86 
87     def get_representation(self, strings: List[str], detach_from_lm=True):
88 
89         sequences_as_char_indices: List[List[int]] = []
90         for string in strings:
91             char_indices = [self.dictionary.get_idx_for_item(char) for char in string]
92             sequences_as_char_indices.append(char_indices)
93 
94         batch = Variable(torch.LongTensor(sequences_as_char_indices).transpose(0, 1))
95 
96         if torch.cuda.is_available():
97             batch = batch.cuda()
98 
99         hidden = self.init_hidden(len(strings))
100         prediction, rnn_output, hidden = self.forward(batch, hidden)
101 
102         if detach_from_lm: rnn_output = self.repackage_hidden(rnn_output)
103 
104         return rnn_output
105 
106     def repackage_hidden(self, h):
107         """Wraps hidden states in new Variables, to detach them from their history."""
108         if type(h) == torch.Tensor:
109             return Variable(h.data)
110         else:
111             return tuple(self.repackage_hidden(v) for v in h)
112 
113     def initialize(self, matrix):
114         in_, out_ = matrix.size()
115         stdv = math.sqrt(3. / (in_ + out_))
116         matrix.data.uniform_(-stdv, stdv)
117 
118     @classmethod
119     def load_language_model(cls, model_file):
120 
121         if not torch.cuda.is_available():
122             state = torch.load(model_file, map_location='cpu')
123         else:
124             state = torch.load(model_file)
125 
126         model = LanguageModel(state['dictionary'],
127                                              state['is_forward_lm'],
128                                              state['hidden_size'],
129                                              state['nlayers'],
130                                              state['embedding_size'],
131                                              state['nout'],
132                                              state['dropout'])
133         model.load_state_dict(state['state_dict'])
134         model.eval()
135         if torch.cuda.is_available():
136             model.cuda()
137         return model
138 
139     def save(self, file):
140         model_state = {
141             'state_dict': self.state_dict(),
142             'dictionary': self.dictionary,
143             'is_forward_lm': self.is_forward_lm,
144             'hidden_size': self.hidden_size,
145             'nlayers': self.nlayers,
146             'embedding_size': self.embedding_size,
147             'nout': self.nout,
148             'dropout': self.dropout
149         }
150         torch.save(model_state, file, pickle_protocol=4)
151 
152     def generate_text(self, number_of_characters=1000) -> str:
153         characters = []
154 
155         idx2item = self.dictionary.idx2item
156 
157         # initial hidden state
158         hidden = self.init_hidden(1)
159         input = torch.rand(1, 1).mul(len(idx2item)).long()
160         if torch.cuda.is_available():
161             input = input.cuda()
162 
163         for i in range(number_of_characters):
164             prediction, rnn_output, hidden = self.forward(input, hidden)
165             word_weights = prediction.squeeze().data.div(1.0).exp().cpu()
166             word_idx = torch.multinomial(word_weights, 1)[0]
167             input.data.fill_(word_idx)
168             word = idx2item[word_idx].decode('UTF-8')
169             characters.append(word)
170 
171         return ''.join(characters)
172
```
--- END FILES ---

Please first localize the bug based on the issue statement, and then generate a patch according to the `git diff` format fenced by three backticks.

Here is an example:

```diff
diff --git a/examples/server_async.py b/examples/server_async.py
--- a/examples/server_async.py
+++ b/examples/server_async.py
@@ -313,4 +313,4 @@
 
 
 if __name__ == "__main__":
-    asyncio.run(run_async_server("."), debug=True)
+    asyncio.run(run_async_server(), debug=True)
diff --git a/examples/server_sync.py b/examples/server_sync.py
--- a/examples/server_sync.py
+++ b/examples/server_sync.py
@@ -313,5 +313,5 @@
 
 
 if __name__ == "__main__":
-    server = run_sync_server(".")
+    server = run_sync_server()
     server.shutdown()

```

diff --git a/flair/models/language_model.py b/flair/models/language_model.py
--- a/flair/models/language_model.py
+++ b/flair/models/language_model.py
@@ -150,22 +150,23 @@
         torch.save(model_state, file, pickle_protocol=4)
 
     def generate_text(self, number_of_characters=1000) -> str:
-        characters = []
-
-        idx2item = self.dictionary.idx2item
-
-        # initial hidden state
-        hidden = self.init_hidden(1)
-        input = torch.rand(1, 1).mul(len(idx2item)).long()
-        if torch.cuda.is_available():
-            input = input.cuda()
-
-        for i in range(number_of_characters):
-            prediction, rnn_output, hidden = self.forward(input, hidden)
-            word_weights = prediction.squeeze().data.div(1.0).exp().cpu()
-            word_idx = torch.multinomial(word_weights, 1)[0]
-            input.data.fill_(word_idx)
-            word = idx2item[word_idx].decode('UTF-8')
-            characters.append(word)
-
-        return ''.join(characters)
+        with torch.no_grad():
+            characters = []
+
+            idx2item = self.dictionary.idx2item
+
+            # initial hidden state
+            hidden = self.init_hidden(1)
+            input = torch.rand(1, 1).mul(len(idx2item)).long()
+            if torch.cuda.is_available():
+                input = input.cuda()
+
+            for i in range(number_of_characters):
+                prediction, rnn_output, hidden = self.forward(input, hidden)
+                word_weights = prediction.squeeze().data.div(1.0).exp().cpu()
+                word_idx = torch.multinomial(word_weights, 1)[0]
+                input.data.fill_(word_idx)
+                word = idx2item[word_idx].decode('UTF-8')
+                characters.append(word)
+
+            return ''.join(characters)

{"golden_diff": "diff --git a/flair/models/language_model.py b/flair/models/language_model.py\n--- a/flair/models/language_model.py\n+++ b/flair/models/language_model.py\n@@ -150,22 +150,23 @@\n         torch.save(model_state, file, pickle_protocol=4)\n \n     def generate_text(self, number_of_characters=1000) -> str:\n-        characters = []\n-\n-        idx2item = self.dictionary.idx2item\n-\n-        # initial hidden state\n-        hidden = self.init_hidden(1)\n-        input = torch.rand(1, 1).mul(len(idx2item)).long()\n-        if torch.cuda.is_available():\n-            input = input.cuda()\n-\n-        for i in range(number_of_characters):\n-            prediction, rnn_output, hidden = self.forward(input, hidden)\n-            word_weights = prediction.squeeze().data.div(1.0).exp().cpu()\n-            word_idx = torch.multinomial(word_weights, 1)[0]\n-            input.data.fill_(word_idx)\n-            word = idx2item[word_idx].decode('UTF-8')\n-            characters.append(word)\n-\n-        return ''.join(characters)\n+        with torch.no_grad():\n+            characters = []\n+\n+            idx2item = self.dictionary.idx2item\n+\n+            # initial hidden state\n+            hidden = self.init_hidden(1)\n+            input = torch.rand(1, 1).mul(len(idx2item)).long()\n+            if torch.cuda.is_available():\n+                input = input.cuda()\n+\n+            for i in range(number_of_characters):\n+                prediction, rnn_output, hidden = self.forward(input, hidden)\n+                word_weights = prediction.squeeze().data.div(1.0).exp().cpu()\n+                word_idx = torch.multinomial(word_weights, 1)[0]\n+                input.data.fill_(word_idx)\n+                word = idx2item[word_idx].decode('UTF-8')\n+                characters.append(word)\n+\n+            return ''.join(characters)\n", "issue": "Add torch.no_grad() to LanguageModel.generate_text()\nThe autograd engine is not required when using an LM to generate text.\r\n\r\nSo, as pointed out in #167, `torch.no_grad()` needs to be added to `LanguageModel.generate_text()` for better performance and to avoid out of memory issues.\n", "before_files": [{"content": "import torch.nn as nn\nimport torch\nimport math\nfrom torch.autograd import Variable\nfrom typing import List\nfrom flair.data import Dictionary\n\n\nclass LanguageModel(nn.Module):\n    \"\"\"Container module with an encoder, a recurrent module, and a decoder.\"\"\"\n\n    def __init__(self,\n                 dictionary: Dictionary,\n                 is_forward_lm: bool,\n                 hidden_size: int,\n                 nlayers: int,\n                 embedding_size: int = 100,\n                 nout=None,\n                 dropout=0.5):\n\n        super(LanguageModel, self).__init__()\n\n        self.dictionary = dictionary\n        self.is_forward_lm: bool = is_forward_lm\n\n        self.dropout = dropout\n        self.hidden_size = hidden_size\n        self.embedding_size = embedding_size\n        self.nlayers = nlayers\n\n        self.drop = nn.Dropout(dropout)\n        self.encoder = nn.Embedding(len(dictionary), embedding_size)\n\n        if nlayers == 1:\n            self.rnn = nn.LSTM(embedding_size, hidden_size, nlayers)\n        else:\n            self.rnn = nn.LSTM(embedding_size, hidden_size, nlayers, dropout=dropout)\n\n        self.hidden = None\n\n        self.nout = nout\n        if nout is not None:\n            self.proj = nn.Linear(hidden_size, nout)\n            self.initialize(self.proj.weight)\n            self.decoder = nn.Linear(nout, len(dictionary))\n        else:\n            self.proj = None\n            self.decoder = nn.Linear(hidden_size, len(dictionary))\n\n        self.init_weights()\n\n        # auto-spawn on GPU if available\n        if torch.cuda.is_available():\n            self.cuda()\n\n    def init_weights(self):\n        initrange = 0.1\n        self.encoder.weight.data.uniform_(-initrange, initrange)\n        self.decoder.bias.data.fill_(0)\n        self.decoder.weight.data.uniform_(-initrange, initrange)\n\n    def set_hidden(self, hidden):\n        self.hidden = hidden\n\n    def forward(self, input, hidden, ordered_sequence_lengths=None):\n        encoded = self.encoder(input)\n        emb = self.drop(encoded)\n\n        self.rnn.flatten_parameters()\n\n        output, hidden = self.rnn(emb, hidden)\n\n        if self.proj is not None:\n            output = self.proj(output)\n\n        output = self.drop(output)\n\n        decoded = self.decoder(output.view(output.size(0) * output.size(1), output.size(2)))\n\n        return decoded.view(output.size(0), output.size(1), decoded.size(1)), output, hidden\n\n    def init_hidden(self, bsz):\n        weight = next(self.parameters()).data\n        return (Variable(weight.new(self.nlayers, bsz, self.hidden_size).zero_()),\n                Variable(weight.new(self.nlayers, bsz, self.hidden_size).zero_()))\n\n    def get_representation(self, strings: List[str], detach_from_lm=True):\n\n        sequences_as_char_indices: List[List[int]] = []\n        for string in strings:\n            char_indices = [self.dictionary.get_idx_for_item(char) for char in string]\n            sequences_as_char_indices.append(char_indices)\n\n        batch = Variable(torch.LongTensor(sequences_as_char_indices).transpose(0, 1))\n\n        if torch.cuda.is_available():\n            batch = batch.cuda()\n\n        hidden = self.init_hidden(len(strings))\n        prediction, rnn_output, hidden = self.forward(batch, hidden)\n\n        if detach_from_lm: rnn_output = self.repackage_hidden(rnn_output)\n\n        return rnn_output\n\n    def repackage_hidden(self, h):\n        \"\"\"Wraps hidden states in new Variables, to detach them from their history.\"\"\"\n        if type(h) == torch.Tensor:\n            return Variable(h.data)\n        else:\n            return tuple(self.repackage_hidden(v) for v in h)\n\n    def initialize(self, matrix):\n        in_, out_ = matrix.size()\n        stdv = math.sqrt(3. / (in_ + out_))\n        matrix.data.uniform_(-stdv, stdv)\n\n    @classmethod\n    def load_language_model(cls, model_file):\n\n        if not torch.cuda.is_available():\n            state = torch.load(model_file, map_location='cpu')\n        else:\n            state = torch.load(model_file)\n\n        model = LanguageModel(state['dictionary'],\n                                             state['is_forward_lm'],\n                                             state['hidden_size'],\n                                             state['nlayers'],\n                                             state['embedding_size'],\n                                             state['nout'],\n                                             state['dropout'])\n        model.load_state_dict(state['state_dict'])\n        model.eval()\n        if torch.cuda.is_available():\n            model.cuda()\n        return model\n\n    def save(self, file):\n        model_state = {\n            'state_dict': self.state_dict(),\n            'dictionary': self.dictionary,\n            'is_forward_lm': self.is_forward_lm,\n            'hidden_size': self.hidden_size,\n            'nlayers': self.nlayers,\n            'embedding_size': self.embedding_size,\n            'nout': self.nout,\n            'dropout': self.dropout\n        }\n        torch.save(model_state, file, pickle_protocol=4)\n\n    def generate_text(self, number_of_characters=1000) -> str:\n        characters = []\n\n        idx2item = self.dictionary.idx2item\n\n        # initial hidden state\n        hidden = self.init_hidden(1)\n        input = torch.rand(1, 1).mul(len(idx2item)).long()\n        if torch.cuda.is_available():\n            input = input.cuda()\n\n        for i in range(number_of_characters):\n            prediction, rnn_output, hidden = self.forward(input, hidden)\n            word_weights = prediction.squeeze().data.div(1.0).exp().cpu()\n            word_idx = torch.multinomial(word_weights, 1)[0]\n            input.data.fill_(word_idx)\n            word = idx2item[word_idx].decode('UTF-8')\n            characters.append(word)\n\n        return ''.join(characters)\n", "path": "flair/models/language_model.py"}], "after_files": [{"content": "import torch.nn as nn\nimport torch\nimport math\nfrom torch.autograd import Variable\nfrom typing import List\nfrom flair.data import Dictionary\n\n\nclass LanguageModel(nn.Module):\n    \"\"\"Container module with an encoder, a recurrent module, and a decoder.\"\"\"\n\n    def __init__(self,\n                 dictionary: Dictionary,\n                 is_forward_lm: bool,\n                 hidden_size: int,\n                 nlayers: int,\n                 embedding_size: int = 100,\n                 nout=None,\n                 dropout=0.5):\n\n        super(LanguageModel, self).__init__()\n\n        self.dictionary = dictionary\n        self.is_forward_lm: bool = is_forward_lm\n\n        self.dropout = dropout\n        self.hidden_size = hidden_size\n        self.embedding_size = embedding_size\n        self.nlayers = nlayers\n\n        self.drop = nn.Dropout(dropout)\n        self.encoder = nn.Embedding(len(dictionary), embedding_size)\n\n        if nlayers == 1:\n            self.rnn = nn.LSTM(embedding_size, hidden_size, nlayers)\n        else:\n            self.rnn = nn.LSTM(embedding_size, hidden_size, nlayers, dropout=dropout)\n\n        self.hidden = None\n\n        self.nout = nout\n        if nout is not None:\n            self.proj = nn.Linear(hidden_size, nout)\n            self.initialize(self.proj.weight)\n            self.decoder = nn.Linear(nout, len(dictionary))\n        else:\n            self.proj = None\n            self.decoder = nn.Linear(hidden_size, len(dictionary))\n\n        self.init_weights()\n\n        # auto-spawn on GPU if available\n        if torch.cuda.is_available():\n            self.cuda()\n\n    def init_weights(self):\n        initrange = 0.1\n        self.encoder.weight.data.uniform_(-initrange, initrange)\n        self.decoder.bias.data.fill_(0)\n        self.decoder.weight.data.uniform_(-initrange, initrange)\n\n    def set_hidden(self, hidden):\n        self.hidden = hidden\n\n    def forward(self, input, hidden, ordered_sequence_lengths=None):\n        encoded = self.encoder(input)\n        emb = self.drop(encoded)\n\n        self.rnn.flatten_parameters()\n\n        output, hidden = self.rnn(emb, hidden)\n\n        if self.proj is not None:\n            output = self.proj(output)\n\n        output = self.drop(output)\n\n        decoded = self.decoder(output.view(output.size(0) * output.size(1), output.size(2)))\n\n        return decoded.view(output.size(0), output.size(1), decoded.size(1)), output, hidden\n\n    def init_hidden(self, bsz):\n        weight = next(self.parameters()).data\n        return (Variable(weight.new(self.nlayers, bsz, self.hidden_size).zero_()),\n                Variable(weight.new(self.nlayers, bsz, self.hidden_size).zero_()))\n\n    def get_representation(self, strings: List[str], detach_from_lm=True):\n\n        sequences_as_char_indices: List[List[int]] = []\n        for string in strings:\n            char_indices = [self.dictionary.get_idx_for_item(char) for char in string]\n            sequences_as_char_indices.append(char_indices)\n\n        batch = Variable(torch.LongTensor(sequences_as_char_indices).transpose(0, 1))\n\n        if torch.cuda.is_available():\n            batch = batch.cuda()\n\n        hidden = self.init_hidden(len(strings))\n        prediction, rnn_output, hidden = self.forward(batch, hidden)\n\n        if detach_from_lm: rnn_output = self.repackage_hidden(rnn_output)\n\n        return rnn_output\n\n    def repackage_hidden(self, h):\n        \"\"\"Wraps hidden states in new Variables, to detach them from their history.\"\"\"\n        if type(h) == torch.Tensor:\n            return Variable(h.data)\n        else:\n            return tuple(self.repackage_hidden(v) for v in h)\n\n    def initialize(self, matrix):\n        in_, out_ = matrix.size()\n        stdv = math.sqrt(3. / (in_ + out_))\n        matrix.data.uniform_(-stdv, stdv)\n\n    @classmethod\n    def load_language_model(cls, model_file):\n\n        if not torch.cuda.is_available():\n            state = torch.load(model_file, map_location='cpu')\n        else:\n            state = torch.load(model_file)\n\n        model = LanguageModel(state['dictionary'],\n                                             state['is_forward_lm'],\n                                             state['hidden_size'],\n                                             state['nlayers'],\n                                             state['embedding_size'],\n                                             state['nout'],\n                                             state['dropout'])\n        model.load_state_dict(state['state_dict'])\n        model.eval()\n        if torch.cuda.is_available():\n            model.cuda()\n        return model\n\n    def save(self, file):\n        model_state = {\n            'state_dict': self.state_dict(),\n            'dictionary': self.dictionary,\n            'is_forward_lm': self.is_forward_lm,\n            'hidden_size': self.hidden_size,\n            'nlayers': self.nlayers,\n            'embedding_size': self.embedding_size,\n            'nout': self.nout,\n            'dropout': self.dropout\n        }\n        torch.save(model_state, file, pickle_protocol=4)\n\n    def generate_text(self, number_of_characters=1000) -> str:\n        with torch.no_grad():\n            characters = []\n\n            idx2item = self.dictionary.idx2item\n\n            # initial hidden state\n            hidden = self.init_hidden(1)\n            input = torch.rand(1, 1).mul(len(idx2item)).long()\n            if torch.cuda.is_available():\n                input = input.cuda()\n\n            for i in range(number_of_characters):\n                prediction, rnn_output, hidden = self.forward(input, hidden)\n                word_weights = prediction.squeeze().data.div(1.0).exp().cpu()\n                word_idx = torch.multinomial(word_weights, 1)[0]\n                input.data.fill_(word_idx)\n                word = idx2item[word_idx].decode('UTF-8')\n                characters.append(word)\n\n            return ''.join(characters)\n", "path": "flair/models/language_model.py"}]}