We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
Error when loading optimizer state
Thanks for this awesome product!!

When I try to load optimizer state on TPUs, I get an error, since self.device_placement is never initialized in AcceleratedOptimizer..!
https://github.com/huggingface/accelerate/blob/e0a420f7cb32124cadeeae690b56e463f8fc598f/src/accelerate/optimizer.py#L83-L86
Error when loading optimizer state
Thanks for this awesome product!!

When I try to load optimizer state on TPUs, I get an error, since self.device_placement is never initialized in AcceleratedOptimizer..!
https://github.com/huggingface/accelerate/blob/e0a420f7cb32124cadeeae690b56e463f8fc598f/src/accelerate/optimizer.py#L83-L86

--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `src/accelerate/optimizer.py`
Content:
```
1 # Copyright 2021 The HuggingFace Team. All rights reserved.
2 #
3 # Licensed under the Apache License, Version 2.0 (the "License");
4 # you may not use this file except in compliance with the License.
5 # You may obtain a copy of the License at
6 #
7 #     http://www.apache.org/licenses/LICENSE-2.0
8 #
9 # Unless required by applicable law or agreed to in writing, software
10 # distributed under the License is distributed on an "AS IS" BASIS,
11 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12 # See the License for the specific language governing permissions and
13 # limitations under the License.
14 
15 import torch
16 
17 from packaging import version
18 
19 from .state import AcceleratorState, DistributedType, is_tpu_available
20 from .utils import honor_type
21 
22 
23 if is_tpu_available():
24     import torch_xla.core.xla_model as xm
25 
26 
27 def move_to_device(state, device):
28     if isinstance(state, (list, tuple)):
29         return honor_type(state, (move_to_device(t, device) for t in state))
30     elif isinstance(state, dict):
31         return type(state)({k: move_to_device(v, device) for k, v in state.items()})
32     elif isinstance(state, torch.Tensor):
33         return state.to(device)
34     return state
35 
36 
37 class AcceleratedOptimizer(torch.optim.Optimizer):
38     """
39     Internal wrapper around a torch optimizer.
40 
41     Args:
42         optimizer (:obj:`torch.optim.optimizer.Optimizer`):
43             The optimizer to wrap.
44         device_placement (:obj:`bool`, `optional`, defaults to :obj:`True`):
45             Whether or not the optimizer should handle device placement. If so, it will place the state dictionary of
46             :obj:`optimizer` on the right device.
47         scaler (:obj:`torch.cuda.amp.grad_scaler.GradScaler`, `optional`):
48             The scaler to use in the step function if training with mixed precision.
49     """
50 
51     def __init__(self, optimizer, device_placement=True, scaler=None):
52         self.optimizer = optimizer
53         self.scaler = scaler
54         self.state = AcceleratorState()
55 
56         # Handle device placement
57         if device_placement:
58             state_dict = self.optimizer.state_dict()
59             if self.state.distributed_type == DistributedType.TPU:
60                 xm.send_cpu_data_to_device(state_dict, self.state.device)
61             else:
62                 state_dict = move_to_device(state_dict, self.state.device)
63             self.optimizer.load_state_dict(state_dict)
64 
65     @property
66     def param_groups(self):
67         return self.optimizer.param_groups
68 
69     @param_groups.setter
70     def param_groups(self, param_groups):
71         self.optimizer.param_groups = param_groups
72 
73     @property
74     def defaults(self):
75         return self.optimizer.defaults
76 
77     @defaults.setter
78     def defaults(self, defaults):
79         self.optimizer.defaults = defaults
80 
81     def add_param_group(self, param_group):
82         self.optimizer.add_param_group(param_group)
83 
84     def load_state_dict(self, state_dict):
85         if self.state.distributed_type == DistributedType.TPU and self.device_placement:
86             xm.send_cpu_data_to_device(state_dict, self.state.device)
87         self.optimizer.load_state_dict(state_dict)
88 
89     def state_dict(self):
90         return self.optimizer.state_dict()
91 
92     def zero_grad(self, set_to_none=None):
93         if version.parse(torch.__version__) < version.parse("1.7.0"):
94             if set_to_none is not None:
95                 raise ValueError(
96                     "`set_to_none` for Optimizer.zero_grad` was introduced in PyTorch 1.7.0 and can't be used for "
97                     f"earlier versions (found version {torch.__version__})."
98                 )
99             self.optimizer.zero_grad()
100         else:
101             if set_to_none is not None:
102                 set_to_none = False
103             self.optimizer.zero_grad(set_to_none=set_to_none)
104 
105     def step(self, closure=None):
106         if self.state.distributed_type == DistributedType.TPU:
107             optimizer_args = {"closure": closure} if closure is not None else {}
108             xm.optimizer_step(self.optimizer, optimizer_args=optimizer_args)
109         elif self.scaler is not None:
110             self.scaler.step(self.optimizer, closure)
111             self.scaler.update()
112         else:
113             self.optimizer.step(closure)
114 
115     def _switch_parameters(self, parameters_map):
116         for param_group in self.optimizer.param_groups:
117             param_group["params"] = [parameters_map.get(p, p) for p in param_group["params"]]
118 
119     @property
120     def is_overflow(self):
121         """This needs to be implemented at the end"""
122         return False  # TODO: implement it
123
```
--- 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/src/accelerate/optimizer.py b/src/accelerate/optimizer.py
--- a/src/accelerate/optimizer.py
+++ b/src/accelerate/optimizer.py
@@ -52,6 +52,7 @@
         self.optimizer = optimizer
         self.scaler = scaler
         self.state = AcceleratorState()
+        self.device_placement = device_placement
 
         # Handle device placement
         if device_placement:

{"golden_diff": "diff --git a/src/accelerate/optimizer.py b/src/accelerate/optimizer.py\n--- a/src/accelerate/optimizer.py\n+++ b/src/accelerate/optimizer.py\n@@ -52,6 +52,7 @@\n         self.optimizer = optimizer\n         self.scaler = scaler\n         self.state = AcceleratorState()\n+        self.device_placement = device_placement\n \n         # Handle device placement\n         if device_placement:\n", "issue": "Error when loading optimizer state\nThanks for this awesome product!!\r\n\r\nWhen I try to load optimizer state on TPUs, I get an error, since self.device_placement is never initialized in AcceleratedOptimizer..!\r\nhttps://github.com/huggingface/accelerate/blob/e0a420f7cb32124cadeeae690b56e463f8fc598f/src/accelerate/optimizer.py#L83-L86\nError when loading optimizer state\nThanks for this awesome product!!\r\n\r\nWhen I try to load optimizer state on TPUs, I get an error, since self.device_placement is never initialized in AcceleratedOptimizer..!\r\nhttps://github.com/huggingface/accelerate/blob/e0a420f7cb32124cadeeae690b56e463f8fc598f/src/accelerate/optimizer.py#L83-L86\n", "before_files": [{"content": "# Copyright 2021 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#     http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport torch\n\nfrom packaging import version\n\nfrom .state import AcceleratorState, DistributedType, is_tpu_available\nfrom .utils import honor_type\n\n\nif is_tpu_available():\n    import torch_xla.core.xla_model as xm\n\n\ndef move_to_device(state, device):\n    if isinstance(state, (list, tuple)):\n        return honor_type(state, (move_to_device(t, device) for t in state))\n    elif isinstance(state, dict):\n        return type(state)({k: move_to_device(v, device) for k, v in state.items()})\n    elif isinstance(state, torch.Tensor):\n        return state.to(device)\n    return state\n\n\nclass AcceleratedOptimizer(torch.optim.Optimizer):\n    \"\"\"\n    Internal wrapper around a torch optimizer.\n\n    Args:\n        optimizer (:obj:`torch.optim.optimizer.Optimizer`):\n            The optimizer to wrap.\n        device_placement (:obj:`bool`, `optional`, defaults to :obj:`True`):\n            Whether or not the optimizer should handle device placement. If so, it will place the state dictionary of\n            :obj:`optimizer` on the right device.\n        scaler (:obj:`torch.cuda.amp.grad_scaler.GradScaler`, `optional`):\n            The scaler to use in the step function if training with mixed precision.\n    \"\"\"\n\n    def __init__(self, optimizer, device_placement=True, scaler=None):\n        self.optimizer = optimizer\n        self.scaler = scaler\n        self.state = AcceleratorState()\n\n        # Handle device placement\n        if device_placement:\n            state_dict = self.optimizer.state_dict()\n            if self.state.distributed_type == DistributedType.TPU:\n                xm.send_cpu_data_to_device(state_dict, self.state.device)\n            else:\n                state_dict = move_to_device(state_dict, self.state.device)\n            self.optimizer.load_state_dict(state_dict)\n\n    @property\n    def param_groups(self):\n        return self.optimizer.param_groups\n\n    @param_groups.setter\n    def param_groups(self, param_groups):\n        self.optimizer.param_groups = param_groups\n\n    @property\n    def defaults(self):\n        return self.optimizer.defaults\n\n    @defaults.setter\n    def defaults(self, defaults):\n        self.optimizer.defaults = defaults\n\n    def add_param_group(self, param_group):\n        self.optimizer.add_param_group(param_group)\n\n    def load_state_dict(self, state_dict):\n        if self.state.distributed_type == DistributedType.TPU and self.device_placement:\n            xm.send_cpu_data_to_device(state_dict, self.state.device)\n        self.optimizer.load_state_dict(state_dict)\n\n    def state_dict(self):\n        return self.optimizer.state_dict()\n\n    def zero_grad(self, set_to_none=None):\n        if version.parse(torch.__version__) < version.parse(\"1.7.0\"):\n            if set_to_none is not None:\n                raise ValueError(\n                    \"`set_to_none` for Optimizer.zero_grad` was introduced in PyTorch 1.7.0 and can't be used for \"\n                    f\"earlier versions (found version {torch.__version__}).\"\n                )\n            self.optimizer.zero_grad()\n        else:\n            if set_to_none is not None:\n                set_to_none = False\n            self.optimizer.zero_grad(set_to_none=set_to_none)\n\n    def step(self, closure=None):\n        if self.state.distributed_type == DistributedType.TPU:\n            optimizer_args = {\"closure\": closure} if closure is not None else {}\n            xm.optimizer_step(self.optimizer, optimizer_args=optimizer_args)\n        elif self.scaler is not None:\n            self.scaler.step(self.optimizer, closure)\n            self.scaler.update()\n        else:\n            self.optimizer.step(closure)\n\n    def _switch_parameters(self, parameters_map):\n        for param_group in self.optimizer.param_groups:\n            param_group[\"params\"] = [parameters_map.get(p, p) for p in param_group[\"params\"]]\n\n    @property\n    def is_overflow(self):\n        \"\"\"This needs to be implemented at the end\"\"\"\n        return False  # TODO: implement it\n", "path": "src/accelerate/optimizer.py"}], "after_files": [{"content": "# Copyright 2021 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#     http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport torch\n\nfrom packaging import version\n\nfrom .state import AcceleratorState, DistributedType, is_tpu_available\nfrom .utils import honor_type\n\n\nif is_tpu_available():\n    import torch_xla.core.xla_model as xm\n\n\ndef move_to_device(state, device):\n    if isinstance(state, (list, tuple)):\n        return honor_type(state, (move_to_device(t, device) for t in state))\n    elif isinstance(state, dict):\n        return type(state)({k: move_to_device(v, device) for k, v in state.items()})\n    elif isinstance(state, torch.Tensor):\n        return state.to(device)\n    return state\n\n\nclass AcceleratedOptimizer(torch.optim.Optimizer):\n    \"\"\"\n    Internal wrapper around a torch optimizer.\n\n    Args:\n        optimizer (:obj:`torch.optim.optimizer.Optimizer`):\n            The optimizer to wrap.\n        device_placement (:obj:`bool`, `optional`, defaults to :obj:`True`):\n            Whether or not the optimizer should handle device placement. If so, it will place the state dictionary of\n            :obj:`optimizer` on the right device.\n        scaler (:obj:`torch.cuda.amp.grad_scaler.GradScaler`, `optional`):\n            The scaler to use in the step function if training with mixed precision.\n    \"\"\"\n\n    def __init__(self, optimizer, device_placement=True, scaler=None):\n        self.optimizer = optimizer\n        self.scaler = scaler\n        self.state = AcceleratorState()\n        self.device_placement = device_placement\n\n        # Handle device placement\n        if device_placement:\n            state_dict = self.optimizer.state_dict()\n            if self.state.distributed_type == DistributedType.TPU:\n                xm.send_cpu_data_to_device(state_dict, self.state.device)\n            else:\n                state_dict = move_to_device(state_dict, self.state.device)\n            self.optimizer.load_state_dict(state_dict)\n\n    @property\n    def param_groups(self):\n        return self.optimizer.param_groups\n\n    @param_groups.setter\n    def param_groups(self, param_groups):\n        self.optimizer.param_groups = param_groups\n\n    @property\n    def defaults(self):\n        return self.optimizer.defaults\n\n    @defaults.setter\n    def defaults(self, defaults):\n        self.optimizer.defaults = defaults\n\n    def add_param_group(self, param_group):\n        self.optimizer.add_param_group(param_group)\n\n    def load_state_dict(self, state_dict):\n        if self.state.distributed_type == DistributedType.TPU and self.device_placement:\n            xm.send_cpu_data_to_device(state_dict, self.state.device)\n        self.optimizer.load_state_dict(state_dict)\n\n    def state_dict(self):\n        return self.optimizer.state_dict()\n\n    def zero_grad(self, set_to_none=None):\n        if version.parse(torch.__version__) < version.parse(\"1.7.0\"):\n            if set_to_none is not None:\n                raise ValueError(\n                    \"`set_to_none` for Optimizer.zero_grad` was introduced in PyTorch 1.7.0 and can't be used for \"\n                    f\"earlier versions (found version {torch.__version__}).\"\n                )\n            self.optimizer.zero_grad()\n        else:\n            if set_to_none is not None:\n                set_to_none = False\n            self.optimizer.zero_grad(set_to_none=set_to_none)\n\n    def step(self, closure=None):\n        if self.state.distributed_type == DistributedType.TPU:\n            optimizer_args = {\"closure\": closure} if closure is not None else {}\n            xm.optimizer_step(self.optimizer, optimizer_args=optimizer_args)\n        elif self.scaler is not None:\n            self.scaler.step(self.optimizer, closure)\n            self.scaler.update()\n        else:\n            self.optimizer.step(closure)\n\n    def _switch_parameters(self, parameters_map):\n        for param_group in self.optimizer.param_groups:\n            param_group[\"params\"] = [parameters_map.get(p, p) for p in param_group[\"params\"]]\n\n    @property\n    def is_overflow(self):\n        \"\"\"This needs to be implemented at the end\"\"\"\n        return False  # TODO: implement it\n", "path": "src/accelerate/optimizer.py"}]}