add fetch_barrier_op

paddle/fluid/framework/executor.cc

@@ -353,7 +353,7 @@ void Executor::RunPreparedContext(ExecutorPrepareContext* ctx, Scope* scope,
     scope->DeleteScope(local_scope);
   } else {
     // Delete the local scopes created in operators.
-    scope->DropKids();
+    // scope->DropKids();
   }
   if (FLAGS_benchmark) {
     VLOG(2) << "-------------------------------------------------------";

paddle/fluid/operators/CMakeLists.txt

@@ -199,11 +199,13 @@ if(WITH_DISTRIBUTE)
     op_library(send_vars_op DEPS ${DISTRIBUTE_DEPS})
     set_source_files_properties(send_vars_op.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS})
     op_library(send_barrier_op DEPS ${DISTRIBUTE_DEPS})
+    op_library(fetch_barrier_op DEPS ${DISTRIBUTE_DEPS})
     set_source_files_properties(send_barrier_op.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS})
+    set_source_files_properties(fetch_barrier_op.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS})
     set_source_files_properties(send_recv_op_test.cc PROPERTIES COMPILE_FLAGS ${DISTRIBUTE_COMPILE_FLAGS})
     cc_test(test_send_recv SRCS send_recv_op_test.cc DEPS prefetch_op send_op listen_and_serv_op sum_op executor)
 else()
-    set(DEPS_OPS ${DEPS_OPS} send_op prefetch_op recv_op listen_and_serv_op send_vars_op send_barrier_op)
+    set(DEPS_OPS ${DEPS_OPS} send_op prefetch_op recv_op listen_and_serv_op send_vars_op send_barrier_op fetch_barrier_op)
 endif()
 
 op_library(cross_entropy_op DEPS cross_entropy)

paddle/fluid/operators/fetch_barrier_op.cc

@@ -0,0 +1,101 @@
+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License. */
+
+#include <future>  // NOLINT
+#include <ostream>
+
+#include "paddle/fluid/framework/data_type.h"
+#include "paddle/fluid/framework/framework.pb.h"
+#include "paddle/fluid/framework/lod_tensor.h"
+#include "paddle/fluid/framework/op_registry.h"
+
+#include "paddle/fluid/operators/detail/grpc_client.h"
+
+namespace paddle {
+namespace operators {
+
+class FetchBarrierOp : public framework::OperatorBase {
+ public:
+  FetchBarrierOp(const std::string& type,
+                 const framework::VariableNameMap& inputs,
+                 const framework::VariableNameMap& outputs,
+                 const framework::AttributeMap& attrs)
+      : OperatorBase(type, inputs, outputs, attrs) {}
+
+  void RunImpl(const framework::Scope& scope,
+               const platform::Place& place) const override {
+    std::vector<std::string> eps = Attr<std::vector<std::string>>("endpoints");
+
+    auto client_var_name = Output("RPCClient");
+    PADDLE_ENFORCE_NOT_NULL(scope.FindVar(client_var_name),
+                            "Can not find variable '%s' in the scope.",
+                            client_var_name);
+    auto* client_var = scope.FindVar(client_var_name);
+    detail::RPCClient* rpc_client = client_var->GetMutable<detail::RPCClient>();
+
+    PADDLE_ENFORCE(rpc_client->Wait());
+
+    for (auto& ep : eps) {
+      VLOG(3) << "fetch barrier, ep: " << ep;
+      rpc_client->AsyncSendFetchBarrier(ep);
+    }
+    PADDLE_ENFORCE(rpc_client->Wait());
+  }
+};
+
+class FetchBarrierOpMaker : public framework::OpProtoAndCheckerMaker {
+ public:
+  void Make() {
+    AddOutput("RPCClient",
+              "(RPCClient) The RPC client object which is"
+              "initialized at most once.");
+    AddComment(R"DOC(
+SendBarrier operator
+
+This operator will send a send barrier signal to list_and_serv op, so that
+the Parameter Server would knew all variables have been sent.
+)DOC");
+
+    AddAttr<std::vector<std::string>>("endpoints",
+                                      "(string vector, default 127.0.0.1:6164)"
+                                      "Server endpoints to send variables to.")
+        .SetDefault({"127.0.0.1:6164"});
+  }
+};
+
+class FetchBarrierOpVarTypeInference : public framework::VarTypeInference {
+ public:
+  void operator()(const framework::OpDesc& op_desc,
+                  framework::BlockDesc* block) const override {
+    auto out_var_name = op_desc.Output("RPCClient").front();
+    auto& out_var = block->FindRecursiveOrCreateVar(out_var_name);
+    auto var_type = framework::proto::VarType::RAW;
+    out_var.SetType(var_type);
+  }
+};
+
+class FetchBarrierOpShapeInference : public framework::InferShapeBase {
+ public:
+  void operator()(framework::InferShapeContext* ctx) const override {}
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+
+REGISTER_OPERATOR(fetch_barrier, ops::FetchBarrierOp,
+                  paddle::framework::EmptyGradOpMaker, ops::FetchBarrierOpMaker,
+                  ops::FetchBarrierOpVarTypeInference,
+                  ops::FetchBarrierOpShapeInference);

python/paddle/fluid/transpiler/distribute_transpiler.py

@@ -315,12 +315,22 @@ class DistributeTranspiler:
         # step 3.1: insert send op to send gradient vars to parameter servers
         ps_dispatcher.reset()
         send_vars = []
-        for varname, splited_vars in grad_var_mapping.items():
+        for orig_varname, splited_vars in grad_var_mapping.items():
-            index = find_op_by_output_arg(program.global_block(), varname)
             eplist = ps_dispatcher.dispatch(splited_vars)
-            if len(splited_vars) > 1:
+            if len(splited_vars) == 1:
-                self._insert_split_op(program, varname, splited_vars)
+                orig_varname = splited_vars[0].name
+                index = find_op_by_output_arg(program.global_block(),
+                                              orig_varname)
+            elif len(splited_vars) > 1:
+                orig_var = program.global_block().vars[orig_varname]
+                index = find_op_by_output_arg(program.global_block(),
+                                              orig_varname)
+                self._insert_split_op(program, orig_var, index, splited_vars)
                 index += 1
+            else:
+                AssertionError("Can not insert the send op by original "
+                               "variable name :", orig_varname)
+
             program.global_block().insert_op(
                 index=index + 1,
                 type="send_vars",
@@ -351,6 +361,12 @@ class DistributeTranspiler:
                      "RPCClient": rpc_client_var},
             attrs={"epmap": eplist})
 
+        program.global_block().append_op(
+            type="fetch_barrier",
+            inputs={},
+            outputs={"RPCClient": rpc_client_var},
+            attrs={"endpoints": pserver_endpoints})
+
         for i, ep in enumerate(eplist):
             self.param_grad_ep_mapping[ep]["params"].append(recv_vars[i])
             self.param_grad_ep_mapping[ep]["grads"].append(send_vars[i])
@@ -859,9 +875,7 @@ class DistributeTranspiler:
             lod_level=var.lod_level,
             persistable=persistable)
 
-    def _insert_split_op(self, program, orig_varname, splited_vars):
+    def _insert_split_op(self, program, orig_var, index, splited_vars):
-        orig_var = program.global_block().vars[orig_varname]
-        index = find_op_by_output_arg(program.global_block(), orig_varname)
         if orig_var.type == core.VarDesc.VarType.SELECTED_ROWS:
             height_sections = []
             for v in splited_vars:
@@ -887,45 +901,6 @@ class DistributeTranspiler:
             AssertionError("Variable type should be in set "
                            "[LOD_TENSOR, SELECTED_ROWS]")
 
-    def _append_split_op(self, program, gradblocks):
-        # Split variables that need to be split and append respective ops
-        add_suffix = False
-        if self.trainer_num > 1:
-            add_suffix = True
-        var_mapping = self._create_vars_from_blocklist(
-            program, gradblocks, add_trainer_suffix=add_suffix)
-        for varname, splited_vars in var_mapping.iteritems():
-            # variable that don't need to split have empty splited_vars
-            if len(splited_vars) <= 1:
-                continue
-            orig_var = program.global_block().vars[varname]
-            index = find_op_by_output_arg(program.global_block(), orig_var.name)
-            if orig_var.type == core.VarDesc.VarType.SELECTED_ROWS:
-                height_sections = []
-                for v in splited_vars:
-                    height_sections.append(v.shape[0])
-                program.global_block().insert_op(
-                    index=index + 1,
-                    type="split_selected_rows",
-                    inputs={"X": orig_var},
-                    outputs={"Out": splited_vars},
-                    attrs={"height_sections": height_sections})
-            elif orig_var.type == core.VarDesc.VarType.LOD_TENSOR:
-                sections = []
-                for v in splited_vars:
-                    sections.append(v.shape[0])
-                program.global_block().insert_op(
-                    index=index + 1,
-                    type="split_byref",
-                    inputs={"X": orig_var},
-                    outputs={"Out": splited_vars},
-                    attrs={"sections": sections}  # assume split evenly
-                )
-            else:
-                AssertionError("Variable type should be in set "
-                               "[LOD_TENSOR, SELECTED_ROWS]")
-        return var_mapping
-
     def _get_optimizer_input_shape(self, op_type, varkey, orig_shape,
                                    param_shape):
         """