prompt: You will be provided with a partial code base and an issue statement explaining a problem to resolve.
  <issue>
  Improve R2Score metric for DDP
  ## 🚀 Feature
  
  Current implementation of `R2Score` metric is not compatible with ddp. The idea is to improve this metric to work in parallel.
  
  Related to #1284
  
  </issue>
  <code>
  [start of ignite/contrib/metrics/regression/r2_score.py]
  1 import torch
  2 
  3 from ignite.contrib.metrics.regression._base import _BaseRegression
  4 from ignite.exceptions import NotComputableError
  5 
  6 
  7 class R2Score(_BaseRegression):
  8     r"""
  9         Calculates the R-Squared, the
  10         `coefficient of determination <https://en.wikipedia.org/wiki/Coefficient_of_determination>`_:
  11 
  12         :math:`R^2 = 1 - \frac{\sum_{j=1}^n(A_j - P_j)^2}{\sum_{j=1}^n(A_j - \bar{A})^2}`,
  13 
  14         where :math:`A_j` is the ground truth, :math:`P_j` is the predicted value and
  15         :math:`\bar{A}` is the mean of the ground truth.
  16 
  17         - ``update`` must receive output of the form ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``.
  18         - `y` and `y_pred` must be of same shape `(N, )` or `(N, 1)` and of type `float32`.
  19     """
  20 
  21     def reset(self):
  22         self._num_examples = 0
  23         self._sum_of_errors = 0
  24         self._y_sq_sum = 0
  25         self._y_sum = 0
  26 
  27     def _update(self, output):
  28         y_pred, y = output
  29         self._num_examples += y.shape[0]
  30         self._sum_of_errors += torch.sum(torch.pow(y_pred - y, 2)).item()
  31 
  32         self._y_sum += torch.sum(y).item()
  33         self._y_sq_sum += torch.sum(torch.pow(y, 2)).item()
  34 
  35     def compute(self):
  36         if self._num_examples == 0:
  37             raise NotComputableError("R2Score must have at least one example before it can be computed.")
  38         return 1 - self._sum_of_errors / (self._y_sq_sum - (self._y_sum ** 2) / self._num_examples)
  39
  [end of ignite/contrib/metrics/regression/r2_score.py]
  </code>
  
  I need you to solve this issue by generating a single patch file that I can apply directly to this repository using git apply. Please respond with a single patch file in the following format.
  <patch>
  diff --git a/file.py b/file.py
  --- a/file.py
  +++ b/file.py
  @@ -1,27 +1,35 @@
   def euclidean(a, b):
  -    while b:
  -        a, b = b, a % b
  -    return a
  +    if b == 0:
  +        return a
  +    return euclidean(b, a % b)
   
   
   def bresenham(x0, y0, x1, y1):
       points = []
       dx = abs(x1 - x0)
       dy = abs(y1 - y0)
  -    sx = 1 if x0 < x1 else -1
  -    sy = 1 if y0 < y1 else -1
  -    err = dx - dy
  +    x, y = x0, y0
  +    sx = -1 if x0 > x1 else 1
  +    sy = -1 if y0 > y1 else 1
   
  -    while True:
  -        points.append((x0, y0))
  -        if x0 == x1 and y0 == y1:
  -            break
  -        e2 = 2 * err
  -        if e2 > -dy:
  -            err -= dy
  -            x0 += sx
  -        if e2 < dx:
  -            err += dx
  -            y0 += sy
  +    if dx > dy:
  +        err = dx / 2.0
  +        while x != x1:
  +            points.append((x, y))
  +            err -= dy
  +            if err < 0:
  +                y += sy
  +                err += dx
  +            x += sx
  +    else:
  +        err = dy / 2.0
  +        while y != y1:
  +            points.append((x, y))
  +            err -= dx
  +            if err < 0:
  +                x += sx
  +                err += dy
  +            y += sy
  +
  +    points.append((x, y))
       return points
  
  </patch>
golden_diff: diff --git a/ignite/contrib/metrics/regression/r2_score.py b/ignite/contrib/metrics/regression/r2_score.py
  --- a/ignite/contrib/metrics/regression/r2_score.py
  +++ b/ignite/contrib/metrics/regression/r2_score.py
  @@ -1,7 +1,10 @@
  +from typing import Callable, Union
  +
   import torch
   
   from ignite.contrib.metrics.regression._base import _BaseRegression
   from ignite.exceptions import NotComputableError
  +from ignite.metrics.metric import reinit__is_reduced, sync_all_reduce
   
   
   class R2Score(_BaseRegression):
  @@ -18,21 +21,32 @@
           - `y` and `y_pred` must be of same shape `(N, )` or `(N, 1)` and of type `float32`.
       """
   
  +    def __init__(
  +        self, output_transform: Callable = lambda x: x, device: Union[str, torch.device] = torch.device("cpu")
  +    ):
  +        self._num_examples = None
  +        self._sum_of_errors = None
  +        self._y_sq_sum = None
  +        self._y_sum = None
  +        super(R2Score, self).__init__(output_transform, device)
  +
  +    @reinit__is_reduced
       def reset(self):
           self._num_examples = 0
  -        self._sum_of_errors = 0
  -        self._y_sq_sum = 0
  -        self._y_sum = 0
  +        self._sum_of_errors = torch.tensor(0.0, device=self._device)
  +        self._y_sq_sum = torch.tensor(0.0, device=self._device)
  +        self._y_sum = torch.tensor(0.0, device=self._device)
   
       def _update(self, output):
           y_pred, y = output
           self._num_examples += y.shape[0]
  -        self._sum_of_errors += torch.sum(torch.pow(y_pred - y, 2)).item()
  +        self._sum_of_errors += torch.sum(torch.pow(y_pred - y, 2)).to(self._device)
   
  -        self._y_sum += torch.sum(y).item()
  -        self._y_sq_sum += torch.sum(torch.pow(y, 2)).item()
  +        self._y_sum += torch.sum(y).to(self._device)
  +        self._y_sq_sum += torch.sum(torch.pow(y, 2)).to(self._device)
   
  +    @sync_all_reduce("_num_examples", "_sum_of_errors", "_y_sq_sum", "_y_sum")
       def compute(self):
           if self._num_examples == 0:
               raise NotComputableError("R2Score must have at least one example before it can be computed.")
  -        return 1 - self._sum_of_errors / (self._y_sq_sum - (self._y_sum ** 2) / self._num_examples)
  +        return 1 - self._sum_of_errors.item() / (self._y_sq_sum.item() - (self._y_sum.item() ** 2) / self._num_examples)
  
verification_info: {"golden_diff": "diff --git a/ignite/contrib/metrics/regression/r2_score.py b/ignite/contrib/metrics/regression/r2_score.py\n--- a/ignite/contrib/metrics/regression/r2_score.py\n+++ b/ignite/contrib/metrics/regression/r2_score.py\n@@ -1,7 +1,10 @@\n+from typing import Callable, Union\n+\n import torch\n \n from ignite.contrib.metrics.regression._base import _BaseRegression\n from ignite.exceptions import NotComputableError\n+from ignite.metrics.metric import reinit__is_reduced, sync_all_reduce\n \n \n class R2Score(_BaseRegression):\n@@ -18,21 +21,32 @@\n         - `y` and `y_pred` must be of same shape `(N, )` or `(N, 1)` and of type `float32`.\n     \"\"\"\n \n+    def __init__(\n+        self, output_transform: Callable = lambda x: x, device: Union[str, torch.device] = torch.device(\"cpu\")\n+    ):\n+        self._num_examples = None\n+        self._sum_of_errors = None\n+        self._y_sq_sum = None\n+        self._y_sum = None\n+        super(R2Score, self).__init__(output_transform, device)\n+\n+    @reinit__is_reduced\n     def reset(self):\n         self._num_examples = 0\n-        self._sum_of_errors = 0\n-        self._y_sq_sum = 0\n-        self._y_sum = 0\n+        self._sum_of_errors = torch.tensor(0.0, device=self._device)\n+        self._y_sq_sum = torch.tensor(0.0, device=self._device)\n+        self._y_sum = torch.tensor(0.0, device=self._device)\n \n     def _update(self, output):\n         y_pred, y = output\n         self._num_examples += y.shape[0]\n-        self._sum_of_errors += torch.sum(torch.pow(y_pred - y, 2)).item()\n+        self._sum_of_errors += torch.sum(torch.pow(y_pred - y, 2)).to(self._device)\n \n-        self._y_sum += torch.sum(y).item()\n-        self._y_sq_sum += torch.sum(torch.pow(y, 2)).item()\n+        self._y_sum += torch.sum(y).to(self._device)\n+        self._y_sq_sum += torch.sum(torch.pow(y, 2)).to(self._device)\n \n+    @sync_all_reduce(\"_num_examples\", \"_sum_of_errors\", \"_y_sq_sum\", \"_y_sum\")\n     def compute(self):\n         if self._num_examples == 0:\n             raise NotComputableError(\"R2Score must have at least one example before it can be computed.\")\n-        return 1 - self._sum_of_errors / (self._y_sq_sum - (self._y_sum ** 2) / self._num_examples)\n+        return 1 - self._sum_of_errors.item() / (self._y_sq_sum.item() - (self._y_sum.item() ** 2) / self._num_examples)\n", "issue": "Improve R2Score metric for DDP\n## \ud83d\ude80 Feature\r\n\r\nCurrent implementation of `R2Score` metric is not compatible with ddp. The idea is to improve this metric to work in parallel.\r\n\r\nRelated to #1284\n", "before_files": [{"content": "import torch\n\nfrom ignite.contrib.metrics.regression._base import _BaseRegression\nfrom ignite.exceptions import NotComputableError\n\n\nclass R2Score(_BaseRegression):\n    r\"\"\"\n        Calculates the R-Squared, the\n        `coefficient of determination <https://en.wikipedia.org/wiki/Coefficient_of_determination>`_:\n\n        :math:`R^2 = 1 - \\frac{\\sum_{j=1}^n(A_j - P_j)^2}{\\sum_{j=1}^n(A_j - \\bar{A})^2}`,\n\n        where :math:`A_j` is the ground truth, :math:`P_j` is the predicted value and\n        :math:`\\bar{A}` is the mean of the ground truth.\n\n        - ``update`` must receive output of the form ``(y_pred, y)`` or ``{'y_pred': y_pred, 'y': y}``.\n        - `y` and `y_pred` must be of same shape `(N, )` or `(N, 1)` and of type `float32`.\n    \"\"\"\n\n    def reset(self):\n        self._num_examples = 0\n        self._sum_of_errors = 0\n        self._y_sq_sum = 0\n        self._y_sum = 0\n\n    def _update(self, output):\n        y_pred, y = output\n        self._num_examples += y.shape[0]\n        self._sum_of_errors += torch.sum(torch.pow(y_pred - y, 2)).item()\n\n        self._y_sum += torch.sum(y).item()\n        self._y_sq_sum += torch.sum(torch.pow(y, 2)).item()\n\n    def compute(self):\n        if self._num_examples == 0:\n            raise NotComputableError(\"R2Score must have at least one example before it can be computed.\")\n        return 1 - self._sum_of_errors / (self._y_sq_sum - (self._y_sum ** 2) / self._num_examples)\n", "path": "ignite/contrib/metrics/regression/r2_score.py"}]}