fix distribute doc test=develop (#17318)

* fix distribute doc

paddle/fluid/API.spec

@@ -26,7 +26,7 @@ paddle.fluid.DistributeTranspiler.get_pserver_program (ArgSpec(args=['self', 'en
 paddle.fluid.DistributeTranspiler.get_pserver_programs (ArgSpec(args=['self', 'endpoint'], varargs=None, keywords=None, defaults=None), ('document', '78f4949aedf317666a89ca74b3748ba8'))
 paddle.fluid.DistributeTranspiler.get_startup_program (ArgSpec(args=['self', 'endpoint', 'pserver_program', 'startup_program'], varargs=None, keywords=None, defaults=(None, None)), ('document', 'd796fc0c8d51503b556fcf6dc15c4f0c'))
 paddle.fluid.DistributeTranspiler.get_trainer_program (ArgSpec(args=['self', 'wait_port'], varargs=None, keywords=None, defaults=(True,)), ('document', '736330e31a7a54abccc0c7fd9119d9ff'))
-paddle.fluid.DistributeTranspiler.transpile (ArgSpec(args=['self', 'trainer_id', 'program', 'pservers', 'trainers', 'sync_mode', 'startup_program', 'current_endpoint'], varargs=None, keywords=None, defaults=(None, '127.0.0.1:6174', 1, True, None, '127.0.0.1:6174')), ('document', '06ce55338dfe96311ad1078235ab3bf4'))
+paddle.fluid.DistributeTranspiler.transpile (ArgSpec(args=['self', 'trainer_id', 'program', 'pservers', 'trainers', 'sync_mode', 'startup_program', 'current_endpoint'], varargs=None, keywords=None, defaults=(None, '127.0.0.1:6174', 1, True, None, '127.0.0.1:6174')), ('document', '418c7e8b268e9be4104f2809e654c2f7'))
 paddle.fluid.memory_optimize (ArgSpec(args=['input_program', 'skip_opt_set', 'print_log', 'level', 'skip_grads'], varargs=None, keywords=None, defaults=(None, False, 0, False)), ('document', '97fdf61cf1ed4fb8f6a4b58aebce26a2'))
 paddle.fluid.release_memory (ArgSpec(args=['input_program', 'skip_opt_set'], varargs=None, keywords=None, defaults=(None,)), ('document', 'b524b73e10f9ccdfa6d189e2e535fc17'))
 paddle.fluid.DistributeTranspilerConfig.__init__ 
@@ -427,7 +427,7 @@ paddle.fluid.transpiler.DistributeTranspiler.get_pserver_program (ArgSpec(args=[
 paddle.fluid.transpiler.DistributeTranspiler.get_pserver_programs (ArgSpec(args=['self', 'endpoint'], varargs=None, keywords=None, defaults=None), ('document', '78f4949aedf317666a89ca74b3748ba8'))
 paddle.fluid.transpiler.DistributeTranspiler.get_startup_program (ArgSpec(args=['self', 'endpoint', 'pserver_program', 'startup_program'], varargs=None, keywords=None, defaults=(None, None)), ('document', 'd796fc0c8d51503b556fcf6dc15c4f0c'))
 paddle.fluid.transpiler.DistributeTranspiler.get_trainer_program (ArgSpec(args=['self', 'wait_port'], varargs=None, keywords=None, defaults=(True,)), ('document', '736330e31a7a54abccc0c7fd9119d9ff'))
-paddle.fluid.transpiler.DistributeTranspiler.transpile (ArgSpec(args=['self', 'trainer_id', 'program', 'pservers', 'trainers', 'sync_mode', 'startup_program', 'current_endpoint'], varargs=None, keywords=None, defaults=(None, '127.0.0.1:6174', 1, True, None, '127.0.0.1:6174')), ('document', '06ce55338dfe96311ad1078235ab3bf4'))
+paddle.fluid.transpiler.DistributeTranspiler.transpile (ArgSpec(args=['self', 'trainer_id', 'program', 'pservers', 'trainers', 'sync_mode', 'startup_program', 'current_endpoint'], varargs=None, keywords=None, defaults=(None, '127.0.0.1:6174', 1, True, None, '127.0.0.1:6174')), ('document', '418c7e8b268e9be4104f2809e654c2f7'))
 paddle.fluid.transpiler.memory_optimize (ArgSpec(args=['input_program', 'skip_opt_set', 'print_log', 'level', 'skip_grads'], varargs=None, keywords=None, defaults=(None, False, 0, False)), ('document', '97fdf61cf1ed4fb8f6a4b58aebce26a2'))
 paddle.fluid.transpiler.release_memory (ArgSpec(args=['input_program', 'skip_opt_set'], varargs=None, keywords=None, defaults=(None,)), ('document', 'b524b73e10f9ccdfa6d189e2e535fc17'))
 paddle.fluid.transpiler.HashName.__init__ (ArgSpec(args=['self', 'pserver_endpoints'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))

paddle/fluid/pybind/pybind.cc

@@ -1209,15 +1209,23 @@ All parameter, weight, gradient are variables in Paddle.
     Examples:
         .. code-block:: python
 
+          x = fluid.layers.data(name='x', shape=[13], dtype='float32')
+          y = fluid.layers.data(name='y', shape=[1], dtype='float32')
+          y_predict = fluid.layers.fc(input=x, size=1, act=None)
+
+          cost = fluid.layers.square_error_cost(input=y_predict, label=y)
+          avg_loss = fluid.layers.mean(cost)
+
+          sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001)
+          sgd_optimizer.minimize(avg_loss)
+
           exec_strategy = fluid.ExecutionStrategy()
           exec_strategy.num_threads = 4
 
-          train_exe = fluid.ParallelExecutor(use_cuda=True,
-                                             loss_name=loss.name,
+          train_exe = fluid.ParallelExecutor(use_cuda=False,
+                                             loss_name=avg_loss.name,
                                              exec_strategy=exec_strategy)
 
-          train_loss, = train_exe.run([loss.name], feed=feed_dict)
-
         )DOC");
 
   exec_strategy.def(py::init())

python/paddle/fluid/initializer.py

@@ -824,6 +824,7 @@ class NumpyArrayInitializer(Initializer):
     Examples:
         .. code-block:: python
 
+            x = fluid.layers.data(name="x", shape=[5], dtype='float32')
             fc = fluid.layers.fc(input=x, size=10,
                 param_attr=fluid.initializer.NumpyArrayInitializer(numpy.array([1,2])))
     """

python/paddle/fluid/transpiler/distribute_transpiler.py

@@ -146,6 +146,11 @@ class DistributeTranspilerConfig(object):
           We can use bandwidth effiently when data size is larger than 2MB.If you
           want to change it, please be sure you have read the slice_variable function.
 
+    Examples:
+        .. code-block:: python
+
+            config = fluid.DistributeTranspilerConfig()
+            config.slice_var_up = True
     """
 
     slice_var_up = True
@@ -181,13 +186,23 @@ class DistributeTranspiler(object):
     Examples:
         .. code-block:: python
 
+            x = fluid.layers.data(name='x', shape=[13], dtype='float32')
+            y = fluid.layers.data(name='y', shape=[1], dtype='float32')
+            y_predict = fluid.layers.fc(input=x, size=1, act=None)
+
+            cost = fluid.layers.square_error_cost(input=y_predict, label=y)
+            avg_loss = fluid.layers.mean(cost)
+
+            sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001)
+            sgd_optimizer.minimize(avg_loss)
+
             # for pserver mode
             pserver_endpoints = "192.168.0.1:6174,192.168.0.2:6174"
             trainer_endpoints = "192.168.0.1:6174,192.168.0.2:6174"
             current_endpoint = "192.168.0.1:6174"
             trainer_id = 0
             trainers = 4
-            role = os.getenv("PADDLE_TRAINING_ROLE")
+            role = "PSERVER"
             t = fluid.DistributeTranspiler()
             t.transpile(
                  trainer_id, pservers=pserver_endpoints, trainers=trainers)
@@ -199,14 +214,17 @@ class DistributeTranspiler(object):
                  trainer_program = t.get_trainer_program()
 
             # for nccl2 mode
+            trainer_num = 2
+            trainer_id = 0
             config = fluid.DistributeTranspilerConfig()
             config.mode = "nccl2"
+            trainer_endpoints = "192.168.0.1:6174,192.168.0.2:6174"
             t = fluid.DistributeTranspiler(config=config)
-            t.transpile(trainer_id, workers=workers, current_endpoint=curr_ep)
+            t.transpile(trainer_id=trainer_id, trainers=trainer_endpoints, current_endpoint="192.168.0.1:6174")
             exe = fluid.ParallelExecutor(
-                use_cuda,
-                loss_name=loss_var.name,
-                num_trainers=len(trainers.split(",)),
+                use_cuda=True,
+                loss_name=avg_loss.name,
+                num_trainers=trainer_num,
                 trainer_id=trainer_id
             )
     """
@@ -289,7 +307,7 @@ class DistributeTranspiler(object):
                   startup_program=None,
                   current_endpoint="127.0.0.1:6174"):
         """
-        Run the transpiler.
+        Run the transpiler. Transpile the input program.
 
         Args:
             trainer_id (int): id for current trainer worker, if you have
@@ -309,6 +327,17 @@ class DistributeTranspiler(object):
             current_endpoint (str): need pass current endpoint when
                 transpile as nccl2 distributed mode. In pserver mode
                 this argument is not used.
+
+        Examples:
+            .. code-block:: python
+
+                transpiler = fluid.DistributeTranspiler()
+                t.transpile(
+                    trainer_id=0,
+                    pservers="127.0.0.1:7000,127.0.0.1:7001",
+                    trainers=2,
+                    sync_mode=False,
+                    current_endpoint="127.0.0.1:7000")
         """
         if program is None:
             program = default_main_program()