update for simple dist train

paddle/operators/send_op.cc

@@ -43,13 +43,14 @@ class SendOp : public framework::OperatorBase {
   }
   void Run(const framework::Scope &scope,
            const platform::DeviceContext &dev_ctx) const override {
-    auto iname = Input("X");
-    auto oname = Output("Out");
+    auto ins = Inputs("X");
     // TODO(typhoonzero): currently it's non-blocking,
     // should block until server responds.
-    bool ret = client_->SendVariable(scope, iname, oname);
-    if (!ret) {
-      LOG(ERROR) << "send variable error";
+    for (auto in : ins) {
+      bool ret = client_->SendVariable(scope, in, in);
+      if (!ret) {
+        LOG(ERROR) << "send variable error";
+      }
     }
   }
 
@@ -61,8 +62,7 @@ class SendOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
   SendOpMaker(framework::OpProto *proto, framework::OpAttrChecker *op_checker)
       : OpProtoAndCheckerMaker(proto, op_checker) {
-    AddInput("X", "(Tensor) Input tensor to be saved");
-    AddOutput("Out", "(Tensor) Output fetched from server");
+    AddInput("X", "(Tensor) Input tensor to be send").AsDuplicable();
     AddComment(R"DOC(
 Recv operator
 

python/paddle/v2/fluid/distribute_planner.py

@@ -30,7 +30,7 @@ def hash_name_to_server(params_grads, pserver_endpoints):
 
 
 def round_robin(parameters, pserver_endpoints):
-    assert (len(parameters) < len(pserver_endpoints))
+    assert (len(parameters) > len(pserver_endpoints))
 
     param_grad_map = dict()
     pserver_idx = 0
@@ -44,6 +44,6 @@ def round_robin(parameters, pserver_endpoints):
             param_grad_map[server_for_param]["grads"].append(param)
 
             pserver_idx += 1
-            if pserver_idx > len(pserver_endpoints):
+            if pserver_idx >= len(pserver_endpoints):
                 pserver_idx = 0
     return param_grad_map

python/paddle/v2/fluid/executor.py

@@ -50,7 +50,7 @@ class Executor(object):
         self.executor = core.Executor(act_places)
         self.places = places
 
-    def optimize(self, optimize_ops, program=None, **kwargs):
+    def optimize(self, optimize_ops, params_grads, program=None, **kwargs):
         """
             optimize the program for different runtime environment
 
@@ -67,7 +67,8 @@ class Executor(object):
             program = default_main_program()
 
         if kwargs.has_key("pservers"):
-            return self._optimize_distributed(optimize_ops, program, **kwargs)
+            return self._optimize_distributed(optimize_ops, program,
+                                              params_grads, **kwargs)
 
     def _optimize_distributed(self, optimize_ops, program, params_and_grads,
                               **kwargs):
@@ -92,7 +93,7 @@ class Executor(object):
                 type="send",
                 inputs={"X": self.param_grad_map[ep]["params"]
                         },  # inputs is a list of tensors to be send
-                outputs={"Out": self.param_grad_map[ep]["params"]},
+                outputs={},
                 attrs={"endpoint": ep})
         # -------------- generate optimize sub program --------------
         self.optimize_sub_program = Program()

python/paddle/v2/fluid/framework.py

@@ -304,7 +304,8 @@ class Operator(object):
         self.desc.check_attrs()
         no_kernel_op_set = {
             'feed', 'fetch', 'save', 'load', 'recurrent',
-            'rnn_memory_helper_grad', 'conditional_block', 'while'
+            'rnn_memory_helper_grad', 'conditional_block', 'while', 'send',
+            'recv'
         }
         if type not in no_kernel_op_set:
             self.desc.infer_var_type(self.block.desc)

python/paddle/v2/fluid/optimizer.py

@@ -202,7 +202,7 @@ class Optimizer(object):
         params_grads = append_regularization_ops(params_grads)
         optimize_ops = self.create_optimization_pass(params_grads, loss,
                                                      startup_program)
-        return optimize_ops
+        return optimize_ops, params_grads
 
 
 class SGDOptimizer(Optimizer):

python/paddle/v2/fluid/tests/book/test_recognize_digits_conv_dist.py

@@ -2,6 +2,7 @@ from __future__ import print_function
 import numpy as np
 import paddle.v2 as paddle
 import paddle.v2.fluid as fluid
+import os
 
 images = fluid.layers.data(name='pixel', shape=[1, 28, 28], dtype='float32')
 label = fluid.layers.data(name='label', shape=[1], dtype='int64')
@@ -24,7 +25,7 @@ predict = fluid.layers.fc(input=conv_pool_2, size=10, act="softmax")
 cost = fluid.layers.cross_entropy(input=predict, label=label)
 avg_cost = fluid.layers.mean(x=cost)
 optimizer = fluid.optimizer.Adam(learning_rate=0.01)
-optimizer.minimize(avg_cost)
+optimize_ops, params_grads = optimizer.minimize(avg_cost)
 
 accuracy = fluid.evaluator.Accuracy(input=predict, label=label)
 
@@ -38,10 +39,10 @@ train_reader = paddle.batch(
 place = fluid.CPUPlace()
 exe = fluid.Executor(place)
 
-exe.optimize(pservers="127.0.0.1:6174", trainers=1)
+exe.optimize(optimize_ops, params_grads, pservers="127.0.0.1:6174", trainers=1)
 
 pserver_endpoint = os.getenv("PSERVER")
-if is_pserver:
+if pserver_endpoint:
     pserver_prog = exe.get_pserver_program(pserver_endpoint)
     exe.run(fluid.default_startup_program())
     exe.run(pserver_prog)