We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
Failing optim_wrapper due to missing Scaler argument
When creating an optimizer and wrapping it via ``amp_handle.wrap_optimizer(optim)``, the handle [`calls the OptimWrapper`](https://github.com/NVIDIA/apex/blob/master/apex/amp/handle.py#L148), who wraps the optimizer and tries to instantiate a loss scaler per loss. 

The `OptimWrapper` tries to [instantiate the loss-scaler without an argument](https://github.com/NVIDIA/apex/blob/master/apex/amp/opt.py#L16), but the loss-scaler [needs an argument `loss_scale`](https://github.com/NVIDIA/apex/blob/master/apex/amp/scaler.py#L28), which causes the whole wrapping process to fail.

--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `apex/amp/opt.py`
Content:
```
1 import contextlib
2 import logging
3 import warnings
4 
5 from .scaler import LossScaler, master_params
6 
7 import numpy as np
8 
9 class OptimWrapper(object):
10     def __init__(self, optimizer, amp_handle, num_loss):
11         self._optimizer = optimizer
12         self._amp_handle = amp_handle
13         self._num_loss = num_loss
14         self._loss_idx = 0
15         self._skip_next = [False] * num_loss
16         self._loss_scaler = [LossScaler() for _ in range(num_loss)]
17 
18     @contextlib.contextmanager
19     def scale_loss(self, loss):
20         if not self._amp_handle.is_active():
21             yield loss
22             return
23 
24         # When there are multiple losses per-optimizer, we need
25         # to save out current grad accumulation, since we won't be
26         # able to unscale this particulare loss once the grads are
27         # all mixed together.
28         cached_grads = []
29         if self._loss_idx > 0:
30             for p in master_params(self._optimizer):
31                 if p.grad is not None:
32                     cached_grads.append(p.grad.data.detach().clone())
33                 else:
34                     cached_grads.append(None)
35             self._optimizer.zero_grad()
36 
37         loss_scale = self._cur_loss_scaler().loss_scale()
38         yield loss * loss_scale
39 
40         self._cur_loss_scaler().clear_overflow_state()
41         self._cur_loss_scaler().unscale(
42             master_params(self._optimizer),
43             master_params(self._optimizer),
44             loss_scale)
45         self._skip_next[self._loss_idx] = self._cur_loss_scaler().update_scale()
46         self._loss_idx += 1
47 
48         if len(cached_grads) > 0:
49             for p, cached_grad in zip(master_params(self._optimizer),
50                                       cached_grads):
51                 if cached_grad is not None:
52                     p.grad.data.add_(cached_grad)
53             cached_grads = []
54 
55     def _cur_loss_scaler(self):
56         assert 0 <= self._loss_idx < self._num_loss
57         return self._loss_scaler[self._loss_idx]
58 
59     def step(self, closure=None):
60         if not self._amp_handle.is_active():
61             return self._optimizer.step(closure=closure)
62 
63         self._loss_idx = 0
64 
65         for group in self._optimizer.param_groups:
66             for p in group['params']:
67                 self._amp_handle.remove_cache(p)
68 
69         if closure is not None:
70             raise NotImplementedError(
71                 'The `closure` argument is unsupported by the amp ' +
72                 'optimizer wrapper.')
73         if any(self._skip_next):
74             logger = logging.getLogger('apex.amp')
75             logger.info('Gradient overflow, skipping update')
76             self._skip_next = [False] * self._num_loss
77         else:
78             return self._optimizer.step(closure=closure)
79 
80     # Forward any attribute lookups
81     def __getattr__(self, attr):
82         return getattr(self._optimizer, attr)
83 
84     # Forward all torch.optim.Optimizer methods
85     def __getstate__(self):
86         return self._optimizer.__getstate__()
87 
88     def __setstate__(self):
89         return self._optimizer.__setstate__()
90 
91     def __repr__(self):
92         return self._optimizer.__repr__()
93 
94     def state_dict(self):
95         return self._optimizer.state_dict()
96 
97     def load_state_dict(self, state_dict):
98         return self._optimizer.load_state_dict(state_dict)
99 
100     def zero_grad(self):
101         return self._optimizer.zero_grad()
102 
103     def add_param_group(self, param_group):
104         return self._optimizer.add_param_group(param_group)
105
```
--- 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/apex/amp/opt.py b/apex/amp/opt.py
--- a/apex/amp/opt.py
+++ b/apex/amp/opt.py
@@ -13,7 +13,7 @@
         self._num_loss = num_loss
         self._loss_idx = 0
         self._skip_next = [False] * num_loss
-        self._loss_scaler = [LossScaler() for _ in range(num_loss)]
+        self._loss_scaler = [LossScaler('dynamic') for _ in range(num_loss)]
 
     @contextlib.contextmanager
     def scale_loss(self, loss):

{"golden_diff": "diff --git a/apex/amp/opt.py b/apex/amp/opt.py\n--- a/apex/amp/opt.py\n+++ b/apex/amp/opt.py\n@@ -13,7 +13,7 @@\n         self._num_loss = num_loss\n         self._loss_idx = 0\n         self._skip_next = [False] * num_loss\n-        self._loss_scaler = [LossScaler() for _ in range(num_loss)]\n+        self._loss_scaler = [LossScaler('dynamic') for _ in range(num_loss)]\n \n     @contextlib.contextmanager\n     def scale_loss(self, loss):\n", "issue": "Failing optim_wrapper due to missing Scaler argument\nWhen creating an optimizer and wrapping it via ``amp_handle.wrap_optimizer(optim)``, the handle [`calls the OptimWrapper`](https://github.com/NVIDIA/apex/blob/master/apex/amp/handle.py#L148), who wraps the optimizer and tries to instantiate a loss scaler per loss. \r\n\r\nThe `OptimWrapper` tries to [instantiate the loss-scaler without an argument](https://github.com/NVIDIA/apex/blob/master/apex/amp/opt.py#L16), but the loss-scaler [needs an argument `loss_scale`](https://github.com/NVIDIA/apex/blob/master/apex/amp/scaler.py#L28), which causes the whole wrapping process to fail.\n", "before_files": [{"content": "import contextlib\nimport logging\nimport warnings\n\nfrom .scaler import LossScaler, master_params\n\nimport numpy as np\n\nclass OptimWrapper(object):\n    def __init__(self, optimizer, amp_handle, num_loss):\n        self._optimizer = optimizer\n        self._amp_handle = amp_handle\n        self._num_loss = num_loss\n        self._loss_idx = 0\n        self._skip_next = [False] * num_loss\n        self._loss_scaler = [LossScaler() for _ in range(num_loss)]\n\n    @contextlib.contextmanager\n    def scale_loss(self, loss):\n        if not self._amp_handle.is_active():\n            yield loss\n            return\n\n        # When there are multiple losses per-optimizer, we need\n        # to save out current grad accumulation, since we won't be\n        # able to unscale this particulare loss once the grads are\n        # all mixed together.\n        cached_grads = []\n        if self._loss_idx > 0:\n            for p in master_params(self._optimizer):\n                if p.grad is not None:\n                    cached_grads.append(p.grad.data.detach().clone())\n                else:\n                    cached_grads.append(None)\n            self._optimizer.zero_grad()\n\n        loss_scale = self._cur_loss_scaler().loss_scale()\n        yield loss * loss_scale\n\n        self._cur_loss_scaler().clear_overflow_state()\n        self._cur_loss_scaler().unscale(\n            master_params(self._optimizer),\n            master_params(self._optimizer),\n            loss_scale)\n        self._skip_next[self._loss_idx] = self._cur_loss_scaler().update_scale()\n        self._loss_idx += 1\n\n        if len(cached_grads) > 0:\n            for p, cached_grad in zip(master_params(self._optimizer),\n                                      cached_grads):\n                if cached_grad is not None:\n                    p.grad.data.add_(cached_grad)\n            cached_grads = []\n\n    def _cur_loss_scaler(self):\n        assert 0 <= self._loss_idx < self._num_loss\n        return self._loss_scaler[self._loss_idx]\n\n    def step(self, closure=None):\n        if not self._amp_handle.is_active():\n            return self._optimizer.step(closure=closure)\n\n        self._loss_idx = 0\n\n        for group in self._optimizer.param_groups:\n            for p in group['params']:\n                self._amp_handle.remove_cache(p)\n\n        if closure is not None:\n            raise NotImplementedError(\n                'The `closure` argument is unsupported by the amp ' +\n                'optimizer wrapper.')\n        if any(self._skip_next):\n            logger = logging.getLogger('apex.amp')\n            logger.info('Gradient overflow, skipping update')\n            self._skip_next = [False] * self._num_loss\n        else:\n            return self._optimizer.step(closure=closure)\n\n    # Forward any attribute lookups\n    def __getattr__(self, attr):\n        return getattr(self._optimizer, attr)\n\n    # Forward all torch.optim.Optimizer methods\n    def __getstate__(self):\n        return self._optimizer.__getstate__()\n\n    def __setstate__(self):\n        return self._optimizer.__setstate__()\n\n    def __repr__(self):\n        return self._optimizer.__repr__()\n\n    def state_dict(self):\n        return self._optimizer.state_dict()\n\n    def load_state_dict(self, state_dict):\n        return self._optimizer.load_state_dict(state_dict)\n\n    def zero_grad(self):\n        return self._optimizer.zero_grad()\n\n    def add_param_group(self, param_group):\n        return self._optimizer.add_param_group(param_group)\n", "path": "apex/amp/opt.py"}], "after_files": [{"content": "import contextlib\nimport logging\nimport warnings\n\nfrom .scaler import LossScaler, master_params\n\nimport numpy as np\n\nclass OptimWrapper(object):\n    def __init__(self, optimizer, amp_handle, num_loss):\n        self._optimizer = optimizer\n        self._amp_handle = amp_handle\n        self._num_loss = num_loss\n        self._loss_idx = 0\n        self._skip_next = [False] * num_loss\n        self._loss_scaler = [LossScaler('dynamic') for _ in range(num_loss)]\n\n    @contextlib.contextmanager\n    def scale_loss(self, loss):\n        if not self._amp_handle.is_active():\n            yield loss\n            return\n\n        # When there are multiple losses per-optimizer, we need\n        # to save out current grad accumulation, since we won't be\n        # able to unscale this particulare loss once the grads are\n        # all mixed together.\n        cached_grads = []\n        if self._loss_idx > 0:\n            for p in master_params(self._optimizer):\n                if p.grad is not None:\n                    cached_grads.append(p.grad.data.detach().clone())\n                else:\n                    cached_grads.append(None)\n            self._optimizer.zero_grad()\n\n        loss_scale = self._cur_loss_scaler().loss_scale()\n        yield loss * loss_scale\n\n        self._cur_loss_scaler().clear_overflow_state()\n        self._cur_loss_scaler().unscale(\n            master_params(self._optimizer),\n            master_params(self._optimizer),\n            loss_scale)\n        self._skip_next[self._loss_idx] = self._cur_loss_scaler().update_scale()\n        self._loss_idx += 1\n\n        if len(cached_grads) > 0:\n            for p, cached_grad in zip(master_params(self._optimizer),\n                                      cached_grads):\n                if cached_grad is not None:\n                    p.grad.data.add_(cached_grad)\n            cached_grads = []\n\n    def _cur_loss_scaler(self):\n        assert 0 <= self._loss_idx < self._num_loss\n        return self._loss_scaler[self._loss_idx]\n\n    def step(self, closure=None):\n        if not self._amp_handle.is_active():\n            return self._optimizer.step(closure=closure)\n\n        self._loss_idx = 0\n\n        for group in self._optimizer.param_groups:\n            for p in group['params']:\n                self._amp_handle.remove_cache(p)\n\n        if closure is not None:\n            raise NotImplementedError(\n                'The `closure` argument is unsupported by the amp ' +\n                'optimizer wrapper.')\n        if any(self._skip_next):\n            logger = logging.getLogger('apex.amp')\n            logger.info('Gradient overflow, skipping update')\n            self._skip_next = [False] * self._num_loss\n        else:\n            return self._optimizer.step(closure=closure)\n\n    # Forward any attribute lookups\n    def __getattr__(self, attr):\n        return getattr(self._optimizer, attr)\n\n    # Forward all torch.optim.Optimizer methods\n    def __getstate__(self):\n        return self._optimizer.__getstate__()\n\n    def __setstate__(self):\n        return self._optimizer.__setstate__()\n\n    def __repr__(self):\n        return self._optimizer.__repr__()\n\n    def state_dict(self):\n        return self._optimizer.state_dict()\n\n    def load_state_dict(self, state_dict):\n        return self._optimizer.load_state_dict(state_dict)\n\n    def zero_grad(self):\n        return self._optimizer.zero_grad()\n\n    def add_param_group(self, param_group):\n        return self._optimizer.add_param_group(param_group)\n", "path": "apex/amp/opt.py"}]}