!16912 Optimize ps doc

From: @zpac
Reviewed-by: @limingqi107,@cristoval
Signed-off-by: @cristoval

mindspore/ccsrc/pipeline/jit/init.cc

@@ -359,8 +359,6 @@ PYBIND11_MODULE(_c_expression, m) {
     .def("set_client_batch_size", &PSContext::set_client_batch_size, "Set federated learning client batch size.")
     .def("set_client_learning_rate", &PSContext::set_client_learning_rate,
          "Set federated learning client learning rate.")
-    .def("set_secure_aggregation", &PSContext::set_secure_aggregation,
-         "Set federated learning client using secure aggregation.")
     .def("set_enable_ssl", &PSContext::enable_ssl, "Set PS SSL mode enabled or disabled.");
 
   (void)py::class_<OpInfoLoaderPy, std::shared_ptr<OpInfoLoaderPy>>(m, "OpInfoLoaderPy")

mindspore/ccsrc/ps/ps_context.cc

@@ -304,10 +304,6 @@ void PSContext::set_client_learning_rate(float client_learning_rate) { client_le
 
 float PSContext::client_learning_rate() const { return client_learning_rate_; }
 
-void PSContext::set_secure_aggregation(bool secure_aggregation) { secure_aggregation_ = secure_aggregation; }
-
-bool PSContext::secure_aggregation() const { return secure_aggregation_; }
-
 bool PSContext::enable_ssl() const { return enable_ssl_; }
 
 void PSContext::set_enable_ssl(bool enabled) { enable_ssl_ = enabled; }

mindspore/ccsrc/ps/ps_context.h

@@ -144,10 +144,6 @@ class PSContext {
   void set_client_learning_rate(float client_learning_rate);
   float client_learning_rate() const;
 
-  // Set true if using secure aggregation for federated learning.
-  void set_secure_aggregation(bool secure_aggregation);
-  bool secure_aggregation() const;
-
   core::ClusterConfig &cluster_config();
 
  private:

mindspore/context.py

@@ -740,15 +740,14 @@ def set_ps_context(**kwargs):
         Some other environment variables should also be set for parameter server training mode.
         These environment variables are listed below:
 
-        MS_SERVER_NUM  # Server number
-        MS_WORKER_NUM  # Worker number
-        MS_SCHED_HOST  # Scheduler IP address
-        MS_SCHED_PORT  # Scheduler port
-        MS_ROLE        # The role of this process:
-        MS_SCHED       #represents the scheduler,
-        MS_WORKER      #represents the worker,
-        MS_PSERVER     #represents the Server
-
+    - MS_SERVER_NUM: Server number
+    - MS_WORKER_NUM: Worker number
+    - MS_SCHED_HOST: Scheduler IP address
+    - MS_SCHED_PORT: Scheduler port
+    - MS_ROLE: The role of this process:
+    - MS_SCHED: represents the scheduler,
+    - MS_WORKER: represents the worker,
+    - MS_PSERVER: represents the Server
 
     Args:
         enable_ps (bool): Whether to enable parameter server training mode.
@@ -771,6 +770,8 @@ def get_ps_context(attr_key):
     Args:
         attr_key (str): The key of the attribute.
 
+        - "enable_ps": Whether to enable parameter server training mode.
+
     Returns:
         Returns attribute value according to the key.
 
@@ -818,7 +819,6 @@ def set_fl_context(**kwargs):
         client_epoch_num (int): Client training epoch number. Default: 25.
         client_batch_size (int): Client training data batch size. Default: 32.
         client_learning_rate (float): Client training learning rate. Default: 0.001.
-        secure_aggregation (bool): Whether to use secure aggregation algorithm. Default: False.
 
     Raises:
         ValueError: If input key is not the attribute in federated learning mode context.
@@ -835,11 +835,15 @@ def get_fl_context(attr_key):
 
     Args:
         attr_key (str): The key of the attribute.
+                        Please refer to `set_fl_context`'s parameters to decide which key should passed.
 
     Returns:
         Returns attribute value according to the key.
 
     Raises:
         ValueError: If input key is not attribute in federated learning mode context.
+
+    Examples:
+        >>> context.get_fl_context("server_mode")
     """
     return _get_ps_context(attr_key)

mindspore/parallel/_ps_context.py

@@ -52,7 +52,6 @@ _set_ps_context_func_map = {
     "client_epoch_num": ps_context().set_client_epoch_num,
     "client_batch_size": ps_context().set_client_batch_size,
     "client_learning_rate": ps_context().set_client_learning_rate,
-    "secure_aggregation": ps_context().set_secure_aggregation,
     "enable_ps_ssl": ps_context().set_enable_ssl
 }
 

tests/st/fl/mobile/run_mobile_server.py

@@ -13,7 +13,6 @@
 # limitations under the License.
 # ============================================================================
 
-import ast
 import argparse
 import subprocess
 
@@ -34,7 +33,6 @@ parser.add_argument("--fl_iteration_num", type=int, default=25)
 parser.add_argument("--client_epoch_num", type=int, default=20)
 parser.add_argument("--client_batch_size", type=int, default=32)
 parser.add_argument("--client_learning_rate", type=float, default=0.1)
-parser.add_argument("--secure_aggregation", type=ast.literal_eval, default=False)
 parser.add_argument("--local_server_num", type=int, default=-1)
 
 if __name__ == "__main__":
@@ -55,7 +53,6 @@ if __name__ == "__main__":
     client_epoch_num = args.client_epoch_num
     client_batch_size = args.client_batch_size
     client_learning_rate = args.client_learning_rate
-    secure_aggregation = args.secure_aggregation
     local_server_num = args.local_server_num
 
     if local_server_num == -1:
@@ -86,7 +83,6 @@ if __name__ == "__main__":
         cmd_server += " --client_epoch_num=" + str(client_epoch_num)
         cmd_server += " --client_batch_size=" + str(client_batch_size)
         cmd_server += " --client_learning_rate=" + str(client_learning_rate)
-        cmd_server += " --secure_aggregation=" + str(secure_aggregation)
         cmd_server += " > server.log 2>&1 &"
 
         import time

tests/st/fl/mobile/test_mobile_lenet.py

@@ -13,7 +13,6 @@
 # limitations under the License.
 # ============================================================================
 
-import ast
 import argparse
 import numpy as np
 
@@ -42,7 +41,6 @@ parser.add_argument("--fl_iteration_num", type=int, default=25)
 parser.add_argument("--client_epoch_num", type=int, default=20)
 parser.add_argument("--client_batch_size", type=int, default=32)
 parser.add_argument("--client_learning_rate", type=float, default=0.1)
-parser.add_argument("--secure_aggregation", type=ast.literal_eval, default=False)
 
 args, _ = parser.parse_known_args()
 device_target = args.device_target
@@ -62,7 +60,6 @@ fl_iteration_num = args.fl_iteration_num
 client_epoch_num = args.client_epoch_num
 client_batch_size = args.client_batch_size
 client_learning_rate = args.client_learning_rate
-secure_aggregation = args.secure_aggregation
 
 ctx = {
     "enable_fl": True,
@@ -82,7 +79,6 @@ ctx = {
     "client_epoch_num": client_epoch_num,
     "client_batch_size": client_batch_size,
     "client_learning_rate": client_learning_rate,
-    "secure_aggregation": secure_aggregation
 }
 
 context.set_context(mode=context.GRAPH_MODE, device_target=device_target, save_graphs=False)