We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
[master]Use different RPC for reporting gradient and task result.


--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `elasticdl/master/servicer.py`
Content:
```
1 import threading
2 import numpy as np
3 
4 import tensorflow as tf
5 assert tf.executing_eagerly()
6 
7 from proto import master_pb2
8 from proto import master_pb2_grpc
9 from util.ndarray import ndarray_to_tensor, tensor_to_ndarray
10 
11 
12 class MasterServicer(master_pb2_grpc.MasterServicer):
13     """Master service implementation"""
14 
15     def __init__(self, logger, grads_to_wait, optimizer):
16         self.logger = logger
17         self._opt = optimizer
18         self._lock = threading.Lock()
19         # TODO: random initialization
20         # A <string, tf.ResourceVariable> map. We use tf.ResourceVariable
21         # instead ndarray to avoid copying and conversion when calling
22         # optimizer's apply_gradients() function.
23         self._model = {}
24         self._version = 0
25         self._gradient_sum = {}
26         self._grad_to_wait = grads_to_wait
27         self._grad_n = 0
28 
29     def _set_model_var(self, name, value):
30         """Add or set model variable. Value should be a float32 ndarray"""
31         if value.dtype != np.float32:
32             raise ValueError("Value should be a float32 numpy array")
33         self._model[name] = tf.Variable(value, name=name)
34 
35     def GetTask(self, request, context):
36         # TODO: implent task queues. Return an empty task for now.
37         res = master_pb2.Task()
38         res.shard_file_name = ""
39         res.model_version = self._version
40         return res
41 
42     def GetModel(self, request, context):
43         if request.min_version > self._version:
44             err_msg = (
45                 "Requested version %d not available yet, current version: %d"
46                 % (request.min_version, self._version)
47             )
48             self.logger.warning(err_msg)
49             raise ValueError(err_msg)
50 
51         res = master_pb2.Model()
52         with self._lock:
53             res.version = self._version
54             for k, v in self._model.items():
55                 res.param[k].CopyFrom(ndarray_to_tensor(v.numpy()))
56         return res
57 
58     def _update_model(self):
59         assert self._lock.locked()
60         grad_var = []
61         for k in self._gradient_sum:
62             self._gradient_sum[k] = self._gradient_sum[k] / self._grad_to_wait
63             grad_var.append((self._gradient_sum[k], self._model[k]))
64         self._opt.apply_gradients(grad_var)
65         self._version += 1
66         self._gradient_sum.clear()
67         self._grad_n = 0
68 
69     def ReportTaskResult(self, request, context):
70         if request.model_version > self._version:
71             err_msg = "Model version %d out of range, current version: %d" % (
72                 request.model_version,
73                 self._version,
74             )
75             self.logger.warning(err_msg)
76             raise ValueError(err_msg)
77 
78         res = master_pb2.ReportTaskResultReply()
79         if request.model_version < self._version:
80             self.logger.warning(
81                 "Task result for outdated version %d dropped",
82                 request.model_version,
83             )
84             res.accepted = False
85             res.model_version = self._version
86             return res
87 
88         if request.err_message:
89             self.logger.warning("Worker error: %s" % request.err_message)
90             res.accepted = False
91             res.model_version = self._version
92             return res
93 
94         # TODO: Update task queue with task_id
95         with self._lock:
96             tmp = {}
97             # Do sanity check before accumulating gradients.
98             for k, v in request.gradient.items():
99                 if k not in self._model:
100                     raise ValueError(
101                         "Gradient key: %s is not part of model", k
102                     )
103                 arr = tensor_to_ndarray(v)
104                 if arr.shape != self._model[k].numpy().shape:
105                     raise ValueError(
106                         "Gradient key: %s has incompatible dimension", k
107                     )
108                 tmp[k] = arr
109 
110             for k, v in tmp.items():
111                 if k in self._gradient_sum:
112                     self._gradient_sum[k] = self._gradient_sum[k] + v
113                 else:
114                     self._gradient_sum[k] = v
115 
116             self._grad_n += 1
117             if self._grad_n >= self._grad_to_wait:
118                 self._update_model()
119         res.accepted = True
120         res.model_version = self._version
121         return res
122
```
--- 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/elasticdl/master/servicer.py b/elasticdl/master/servicer.py
--- a/elasticdl/master/servicer.py
+++ b/elasticdl/master/servicer.py
@@ -66,7 +66,7 @@
         self._gradient_sum.clear()
         self._grad_n = 0
 
-    def ReportTaskResult(self, request, context):
+    def ReportGradient(self, request, context):
         if request.model_version > self._version:
             err_msg = "Model version %d out of range, current version: %d" % (
                 request.model_version,
@@ -75,7 +75,7 @@
             self.logger.warning(err_msg)
             raise ValueError(err_msg)
 
-        res = master_pb2.ReportTaskResultReply()
+        res = master_pb2.ReportGradientReply()
         if request.model_version < self._version:
             self.logger.warning(
                 "Task result for outdated version %d dropped",
@@ -85,12 +85,6 @@
             res.model_version = self._version
             return res
 
-        if request.err_message:
-            self.logger.warning("Worker error: %s" % request.err_message)
-            res.accepted = False
-            res.model_version = self._version
-            return res
-
         # TODO: Update task queue with task_id
         with self._lock:
             tmp = {}

{"golden_diff": "diff --git a/elasticdl/master/servicer.py b/elasticdl/master/servicer.py\n--- a/elasticdl/master/servicer.py\n+++ b/elasticdl/master/servicer.py\n@@ -66,7 +66,7 @@\n         self._gradient_sum.clear()\n         self._grad_n = 0\n \n-    def ReportTaskResult(self, request, context):\n+    def ReportGradient(self, request, context):\n         if request.model_version > self._version:\n             err_msg = \"Model version %d out of range, current version: %d\" % (\n                 request.model_version,\n@@ -75,7 +75,7 @@\n             self.logger.warning(err_msg)\n             raise ValueError(err_msg)\n \n-        res = master_pb2.ReportTaskResultReply()\n+        res = master_pb2.ReportGradientReply()\n         if request.model_version < self._version:\n             self.logger.warning(\n                 \"Task result for outdated version %d dropped\",\n@@ -85,12 +85,6 @@\n             res.model_version = self._version\n             return res\n \n-        if request.err_message:\n-            self.logger.warning(\"Worker error: %s\" % request.err_message)\n-            res.accepted = False\n-            res.model_version = self._version\n-            return res\n-\n         # TODO: Update task queue with task_id\n         with self._lock:\n             tmp = {}\n", "issue": "[master]Use different RPC for reporting gradient and task result.\n\n", "before_files": [{"content": "import threading\nimport numpy as np\n\nimport tensorflow as tf\nassert tf.executing_eagerly()\n\nfrom proto import master_pb2\nfrom proto import master_pb2_grpc\nfrom util.ndarray import ndarray_to_tensor, tensor_to_ndarray\n\n\nclass MasterServicer(master_pb2_grpc.MasterServicer):\n    \"\"\"Master service implementation\"\"\"\n\n    def __init__(self, logger, grads_to_wait, optimizer):\n        self.logger = logger\n        self._opt = optimizer\n        self._lock = threading.Lock()\n        # TODO: random initialization\n        # A <string, tf.ResourceVariable> map. We use tf.ResourceVariable\n        # instead ndarray to avoid copying and conversion when calling\n        # optimizer's apply_gradients() function.\n        self._model = {}\n        self._version = 0\n        self._gradient_sum = {}\n        self._grad_to_wait = grads_to_wait\n        self._grad_n = 0\n\n    def _set_model_var(self, name, value):\n        \"\"\"Add or set model variable. Value should be a float32 ndarray\"\"\"\n        if value.dtype != np.float32:\n            raise ValueError(\"Value should be a float32 numpy array\")\n        self._model[name] = tf.Variable(value, name=name)\n\n    def GetTask(self, request, context):\n        # TODO: implent task queues. Return an empty task for now.\n        res = master_pb2.Task()\n        res.shard_file_name = \"\"\n        res.model_version = self._version\n        return res\n\n    def GetModel(self, request, context):\n        if request.min_version > self._version:\n            err_msg = (\n                \"Requested version %d not available yet, current version: %d\"\n                % (request.min_version, self._version)\n            )\n            self.logger.warning(err_msg)\n            raise ValueError(err_msg)\n\n        res = master_pb2.Model()\n        with self._lock:\n            res.version = self._version\n            for k, v in self._model.items():\n                res.param[k].CopyFrom(ndarray_to_tensor(v.numpy()))\n        return res\n\n    def _update_model(self):\n        assert self._lock.locked()\n        grad_var = []\n        for k in self._gradient_sum:\n            self._gradient_sum[k] = self._gradient_sum[k] / self._grad_to_wait\n            grad_var.append((self._gradient_sum[k], self._model[k]))\n        self._opt.apply_gradients(grad_var)\n        self._version += 1\n        self._gradient_sum.clear()\n        self._grad_n = 0\n\n    def ReportTaskResult(self, request, context):\n        if request.model_version > self._version:\n            err_msg = \"Model version %d out of range, current version: %d\" % (\n                request.model_version,\n                self._version,\n            )\n            self.logger.warning(err_msg)\n            raise ValueError(err_msg)\n\n        res = master_pb2.ReportTaskResultReply()\n        if request.model_version < self._version:\n            self.logger.warning(\n                \"Task result for outdated version %d dropped\",\n                request.model_version,\n            )\n            res.accepted = False\n            res.model_version = self._version\n            return res\n\n        if request.err_message:\n            self.logger.warning(\"Worker error: %s\" % request.err_message)\n            res.accepted = False\n            res.model_version = self._version\n            return res\n\n        # TODO: Update task queue with task_id\n        with self._lock:\n            tmp = {}\n            # Do sanity check before accumulating gradients.\n            for k, v in request.gradient.items():\n                if k not in self._model:\n                    raise ValueError(\n                        \"Gradient key: %s is not part of model\", k\n                    )\n                arr = tensor_to_ndarray(v)\n                if arr.shape != self._model[k].numpy().shape:\n                    raise ValueError(\n                        \"Gradient key: %s has incompatible dimension\", k\n                    )\n                tmp[k] = arr\n\n            for k, v in tmp.items():\n                if k in self._gradient_sum:\n                    self._gradient_sum[k] = self._gradient_sum[k] + v\n                else:\n                    self._gradient_sum[k] = v\n\n            self._grad_n += 1\n            if self._grad_n >= self._grad_to_wait:\n                self._update_model()\n        res.accepted = True\n        res.model_version = self._version\n        return res\n", "path": "elasticdl/master/servicer.py"}], "after_files": [{"content": "import threading\nimport numpy as np\n\nimport tensorflow as tf\nassert tf.executing_eagerly()\n\nfrom proto import master_pb2\nfrom proto import master_pb2_grpc\nfrom util.ndarray import ndarray_to_tensor, tensor_to_ndarray\n\n\nclass MasterServicer(master_pb2_grpc.MasterServicer):\n    \"\"\"Master service implementation\"\"\"\n\n    def __init__(self, logger, grads_to_wait, optimizer):\n        self.logger = logger\n        self._opt = optimizer\n        self._lock = threading.Lock()\n        # TODO: random initialization\n        # A <string, tf.ResourceVariable> map. We use tf.ResourceVariable\n        # instead ndarray to avoid copying and conversion when calling\n        # optimizer's apply_gradients() function.\n        self._model = {}\n        self._version = 0\n        self._gradient_sum = {}\n        self._grad_to_wait = grads_to_wait\n        self._grad_n = 0\n\n    def _set_model_var(self, name, value):\n        \"\"\"Add or set model variable. Value should be a float32 ndarray\"\"\"\n        if value.dtype != np.float32:\n            raise ValueError(\"Value should be a float32 numpy array\")\n        self._model[name] = tf.Variable(value, name=name)\n\n    def GetTask(self, request, context):\n        # TODO: implent task queues. Return an empty task for now.\n        res = master_pb2.Task()\n        res.shard_file_name = \"\"\n        res.model_version = self._version\n        return res\n\n    def GetModel(self, request, context):\n        if request.min_version > self._version:\n            err_msg = (\n                \"Requested version %d not available yet, current version: %d\"\n                % (request.min_version, self._version)\n            )\n            self.logger.warning(err_msg)\n            raise ValueError(err_msg)\n\n        res = master_pb2.Model()\n        with self._lock:\n            res.version = self._version\n            for k, v in self._model.items():\n                res.param[k].CopyFrom(ndarray_to_tensor(v.numpy()))\n        return res\n\n    def _update_model(self):\n        assert self._lock.locked()\n        grad_var = []\n        for k in self._gradient_sum:\n            self._gradient_sum[k] = self._gradient_sum[k] / self._grad_to_wait\n            grad_var.append((self._gradient_sum[k], self._model[k]))\n        self._opt.apply_gradients(grad_var)\n        self._version += 1\n        self._gradient_sum.clear()\n        self._grad_n = 0\n\n    def ReportGradient(self, request, context):\n        if request.model_version > self._version:\n            err_msg = \"Model version %d out of range, current version: %d\" % (\n                request.model_version,\n                self._version,\n            )\n            self.logger.warning(err_msg)\n            raise ValueError(err_msg)\n\n        res = master_pb2.ReportGradientReply()\n        if request.model_version < self._version:\n            self.logger.warning(\n                \"Task result for outdated version %d dropped\",\n                request.model_version,\n            )\n            res.accepted = False\n            res.model_version = self._version\n            return res\n\n        # TODO: Update task queue with task_id\n        with self._lock:\n            tmp = {}\n            # Do sanity check before accumulating gradients.\n            for k, v in request.gradient.items():\n                if k not in self._model:\n                    raise ValueError(\n                        \"Gradient key: %s is not part of model\", k\n                    )\n                arr = tensor_to_ndarray(v)\n                if arr.shape != self._model[k].numpy().shape:\n                    raise ValueError(\n                        \"Gradient key: %s has incompatible dimension\", k\n                    )\n                tmp[k] = arr\n\n            for k, v in tmp.items():\n                if k in self._gradient_sum:\n                    self._gradient_sum[k] = self._gradient_sum[k] + v\n                else:\n                    self._gradient_sum[k] = v\n\n            self._grad_n += 1\n            if self._grad_n >= self._grad_to_wait:\n                self._update_model()\n        res.accepted = True\n        res.model_version = self._version\n        return res\n", "path": "elasticdl/master/servicer.py"}]}