prompt: 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()
  
  ```
  
golden_diff: 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 = {}
  
verification_info: {"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"}]}