Refine dist rpc deps (#12899)

* refine dist train RPC deps

* clean up

* clean up

* fix ut

* remove input for fetch_barrier

* follow comments

paddle/fluid/framework/details/multi_devices_graph_pass.cc

@@ -754,17 +754,26 @@ void MultiDevSSAGraphBuilder::CreateDistTrainOp(ir::Graph *result,
                  node->Op()->Type());
 
   CreateComputationalOp(result, node, op_dev_id);
-  if (node->Op()->Type() == "concat") {
-    ConnectOp(result, result->Get<GraphOps>(kGraphOps).back().get(),
-              "fetch_barrier");
+}
+
+void SetOpInputsAllPlaces(ir::Graph *result, ir::Node *node, int num_places) {
+  auto *op_handle = result->Get<GraphOps>(kGraphOps).back().get();
+  for (ir::Node *input : node->inputs) {
+    VarHandle *var = nullptr;
+    for (int place_offset = 0; place_offset < num_places; ++place_offset) {
+      auto &var_holders = result->Get<GraphVars>(kGraphVars)[place_offset];
+      auto &var_holder = var_holders[input->Name()];
+      if (!var_holder.empty()) {
+        var = var_holder.rbegin()->get();
+        op_handle->AddInput(var);
+      }
+    }
   }
 }
 
 // Create RPC related op handles that connects its in ops and out ops.
 void MultiDevSSAGraphBuilder::CreateRPCOp(ir::Graph *result,
                                           ir::Node *node) const {
-  // FIXME(typhoonzero): Cleanup this deps for both sync mode and async mode
-  //                     put them into transpiler.
   int op_dev_id = -1;
   if (node->Op()->Type() == "send") {
     // TODO(paddle-dev): getting the first var is not safe.
@@ -799,8 +808,6 @@ void MultiDevSSAGraphBuilder::CreateRPCOp(ir::Graph *result,
     }
     auto recv_param_grad = boost::get<std::vector<std::string>>(
         node->Op()->GetAttr(OpProtoAndCheckerMaker::OpRoleVarAttrName()));
-    // FIXME(typhoonzero): assume each recv op output one param
-    // Use the same place as send.
     if (recv_param_grad.size() == 2U) {
       op_dev_id = GetVarDeviceID(*result, recv_param_grad[1]);
       VLOG(10) << "recv param " << recv_param_grad[0]
@@ -814,34 +821,44 @@ void MultiDevSSAGraphBuilder::CreateRPCOp(ir::Graph *result,
           .emplace(varname, op_dev_id);
     }
   } else {
-    // send_barrier and fetch_barrier op can be scheduled on device 0
+    // send_barrier, fetch_barrier will run on place 0;
     op_dev_id = 0;
   }
 
   PADDLE_ENFORCE(op_dev_id != -1, "can not find the right place for rpc op: %s",
                  node->Op()->Type());
-
   result->Get<GraphOps>(kGraphOps).emplace_back(new RPCOpHandle(
       result->CreateOpNode(node->Op()), *node->Op(), local_scopes_[op_dev_id],
       node->Op()->Type(), places_[op_dev_id]));
 
-  // TODO(panyx0718): This might not be needed anymore.
-  if (node->Op()->Type() == "send_barrier") {
-    ConnectOp(result, result->Get<GraphOps>(kGraphOps).back().get(), "send");
-  } else if (node->Op()->Type() == "recv") {
-    ConnectOp(result, result->Get<GraphOps>(kGraphOps).back().get(),
-              "send_barrier");
-  } else if (node->Op()->Type() == "fetch_barrier") {
-    ConnectOp(result, result->Get<GraphOps>(kGraphOps).back().get(), "recv");
-  } else if (node->Op()->Type() == "send") {
-    // do nothing
+  if (node->Op()->Type() == "send") {
+    CreateOpHandleIOs(result, node, op_dev_id);
   } else {
-    PADDLE_THROW(
-        "rpc op should be in ["
-        "send, send_barrier. recv, fetch_barrier]");
-  }
+    // send_barrier, recv, fetch_barrier's inputs are deps var, get them from
+    // all places
+    auto p = places_[op_dev_id];
+    auto *op_handle = result->Get<GraphOps>(kGraphOps).back().get();
+    op_handle->SetDeviceContext(p,
+                                platform::DeviceContextPool::Instance().Get(p));
 
-  CreateOpHandleIOs(result, node, op_dev_id);
+    SetOpInputsAllPlaces(result, node, places_.size());
+    for (ir::Node *output : node->outputs) {
+      int outvar_dev_id = op_dev_id;
+      if (node->Op()->Type() == "fetch_barrier") {
+        outvar_dev_id = GetVarDeviceID(*result, output->Name());
+        PADDLE_ENFORCE_NE(outvar_dev_id, -1);
+      }
+      p = places_[outvar_dev_id];
+      ir::Node *new_node = nullptr;
+      if (output->Var()) {
+        new_node = result->CreateVarNode(output->Var());
+      } else {
+        new_node =
+            result->CreateEmptyNode(output->Name(), ir::Node::Type::kVariable);
+      }
+      CreateOpOutput(result, op_handle, new_node, p, outvar_dev_id);
+    }
+  }
 }
 
 bool MultiDevSSAGraphBuilder::IsScaleLossOp(ir::Node *node) const {

paddle/fluid/framework/ir/graph.cc

@@ -132,63 +132,6 @@ Graph::Graph(const ProgramDesc &program) : program_(program) {
     }
   }
 
-  std::vector<ir::Node *> send_ops;
-  ir::Node *send_bar = nullptr;
-  std::vector<ir::Node *> recv_ops;
-  ir::Node *fetch_bar = nullptr;
-  for (ir::Node *node : Nodes()) {
-    if (node->Name() == "send") {
-      send_ops.push_back(node);
-    } else if (node->Name() == "send_barrier") {
-      PADDLE_ENFORCE(!send_bar, "only has one send barrier");
-      send_bar = node;
-    } else if (node->Name() == "recv") {
-      recv_ops.push_back(node);
-    } else if (node->Name() == "fetch_barrier") {
-      PADDLE_ENFORCE(!fetch_bar, "only has one fetch barrier");
-      fetch_bar = node;
-    }
-  }
-  if (send_bar) {
-    for (ir::Node *send : send_ops) {
-      ir::Node *dep_var = CreateControlDepVar();
-      send->outputs.push_back(dep_var);
-      dep_var->inputs.push_back(send);
-      send_bar->inputs.push_back(dep_var);
-      dep_var->outputs.push_back(send_bar);
-    }
-    for (ir::Node *recv : recv_ops) {
-      ir::Node *dep_var = CreateControlDepVar();
-      recv->inputs.push_back(dep_var);
-      dep_var->outputs.push_back(recv);
-      send_bar->outputs.push_back(dep_var);
-      dep_var->inputs.push_back(send_bar);
-    }
-  }
-  if (fetch_bar) {
-    for (ir::Node *recv : recv_ops) {
-      ir::Node *dep_var = CreateControlDepVar();
-      recv->outputs.push_back(dep_var);
-      dep_var->inputs.push_back(recv);
-      fetch_bar->inputs.push_back(dep_var);
-      dep_var->outputs.push_back(fetch_bar);
-    }
-  }
-
-  std::vector<std::string> send_vars = FindDistTrainSendVars(send_ops);
-  std::vector<std::string> recv_vars = FindDistTrainRecvVars(recv_ops);
-  for (ir::Node *node : Nodes()) {
-    if (IsDistTrainOp(node, send_vars, recv_vars)) {
-      if (fetch_bar && node->Name() == "concat") {
-        ir::Node *dep_var = CreateControlDepVar();
-        fetch_bar->outputs.push_back(dep_var);
-        dep_var->inputs.push_back(fetch_bar);
-        node->inputs.push_back(dep_var);
-        dep_var->outputs.push_back(node);
-      }
-    }
-  }
-
   /**
    * We should handle write after read(WAR) and write after write(WAW) here.
    * Because some of the operators of the program can be executed parallelly.

paddle/fluid/operators/fetch_barrier_op.cc

@@ -52,6 +52,8 @@ class FetchBarrierOp : public framework::OperatorBase {
 class FetchBarrierOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
   void Make() {
+    AddOutput("Out", "(Any) Dummy outputs, used for control dependency")
+        .AsDuplicable();
     AddComment(R"DOC(
 SendBarrier operator
 

paddle/fluid/operators/send_barrier_op.cc

@@ -56,6 +56,10 @@ class SendBarrierOp : public framework::OperatorBase {
 class SendBarrierOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
   void Make() {
+    AddInput("X", "(Any) Dummy inputs, used for control dependency")
+        .AsDuplicable();
+    AddOutput("Out", "(Any) Dummy outputs, used for control dependency")
+        .AsDuplicable();
     AddComment(R"DOC(
 SendBarrier operator
 

python/paddle/fluid/layers/io.py

@@ -246,7 +246,11 @@ def Send(endpoints, send_vars, dummy_output=None, sync=True):
             rpc_op_role_name: core.op_proto_and_checker_maker.OpRole.RPC
         })
     if sync:
-        helper.append_op(type="send_barrier", attrs={"endpoints": endpoints})
+        helper.append_op(
+            type="send_barrier",
+            inputs={"X": dummy_output},
+            outputs={"Out": []},
+            attrs={"endpoints": endpoints})
 
 
 def Recv(endpoints, get_vars, dummy_input=None, sync=True):
@@ -282,7 +286,10 @@ def Recv(endpoints, get_vars, dummy_input=None, sync=True):
         attrs={"endpoints": endpoints,
                "epmap": epmap})
     if sync:
-        helper.append_op(type="fetch_barrier", attrs={"endpoints": endpoints})
+        helper.append_op(
+            type="fetch_barrier",
+            outputs={"Out": get_vars},
+            attrs={"endpoints": endpoints})
     return get_vars
 
 

python/paddle/fluid/tests/unittests/dist_se_resnext.py

@@ -134,7 +134,7 @@ class SE_ResNeXt():
             size=class_dim,
             act='softmax',
             param_attr=fluid.ParamAttr(
-                initializer=fluid.initializer.Constant(value=0.2)))
+                initializer=fluid.initializer.Constant(value=0.05)))
         return out
 
     def shortcut(self, input, ch_out, stride):
@@ -184,7 +184,7 @@ class SE_ResNeXt():
             act=None,
             # avoid pserver CPU init differs from GPU
             param_attr=fluid.ParamAttr(
-                initializer=fluid.initializer.Constant(value=0.2)),
+                initializer=fluid.initializer.Constant(value=0.05)),
             bias_attr=False)
         return fluid.layers.batch_norm(input=conv, act=act)
 
@@ -192,12 +192,18 @@ class SE_ResNeXt():
         pool = fluid.layers.pool2d(
             input=input, pool_size=0, pool_type='avg', global_pooling=True)
         stdv = 1.0 / math.sqrt(pool.shape[1] * 1.0)
-        squeeze = fluid.layers.fc(input=pool,
+        squeeze = fluid.layers.fc(
+            input=pool,
             size=num_channels // reduction_ratio,
+            param_attr=fluid.ParamAttr(
+                initializer=fluid.initializer.Constant(value=0.05)),
             act='relu')
         stdv = 1.0 / math.sqrt(squeeze.shape[1] * 1.0)
-        excitation = fluid.layers.fc(input=squeeze,
+        excitation = fluid.layers.fc(
+            input=squeeze,
             size=num_channels,
+            param_attr=fluid.ParamAttr(
+                initializer=fluid.initializer.Constant(value=0.05)),
             act='sigmoid')
         scale = fluid.layers.elementwise_mul(x=input, y=excitation, axis=0)
         return scale

python/paddle/fluid/tests/unittests/dist_word2vec.py

@@ -49,28 +49,32 @@ class TestDistWord2vec2x2(TestDistRunnerBase):
                 dtype='float32',
                 is_sparse=IS_SPARSE,
                 param_attr=fluid.ParamAttr(
-                    name='shared_w', initializer=fluid.initializer.Constant()))
+                    name='shared_w',
+                    initializer=fluid.initializer.Constant(value=0.1)))
             embed_second = fluid.layers.embedding(
                 input=words[1],
                 size=[dict_size, EMBED_SIZE],
                 dtype='float32',
                 is_sparse=IS_SPARSE,
                 param_attr=fluid.ParamAttr(
-                    name='shared_w', initializer=fluid.initializer.Constant()))
+                    name='shared_w',
+                    initializer=fluid.initializer.Constant(value=0.1)))
             embed_third = fluid.layers.embedding(
                 input=words[2],
                 size=[dict_size, EMBED_SIZE],
                 dtype='float32',
                 is_sparse=IS_SPARSE,
                 param_attr=fluid.ParamAttr(
-                    name='shared_w', initializer=fluid.initializer.Constant()))
+                    name='shared_w',
+                    initializer=fluid.initializer.Constant(value=0.1)))
             embed_forth = fluid.layers.embedding(
                 input=words[3],
                 size=[dict_size, EMBED_SIZE],
                 dtype='float32',
                 is_sparse=IS_SPARSE,
                 param_attr=fluid.ParamAttr(
-                    name='shared_w', initializer=fluid.initializer.Constant()))
+                    name='shared_w',
+                    initializer=fluid.initializer.Constant(value=0.1)))
 
             concat_embed = fluid.layers.concat(
                 input=[embed_first, embed_second, embed_third, embed_forth],
@@ -80,13 +84,13 @@ class TestDistWord2vec2x2(TestDistRunnerBase):
                 size=HIDDEN_SIZE,
                 act='sigmoid',
                 param_attr=fluid.ParamAttr(
-                    initializer=fluid.initializer.Constant()))
+                    initializer=fluid.initializer.Constant(value=0.1)))
             predict_word = fluid.layers.fc(
                 input=hidden1,
                 size=dict_size,
                 act='softmax',
                 param_attr=fluid.ParamAttr(
-                    initializer=fluid.initializer.Constant()))
+                    initializer=fluid.initializer.Constant(value=0.1)))
             cost = fluid.layers.cross_entropy(
                 input=predict_word, label=words[4])
             avg_cost = fluid.layers.mean(cost)

python/paddle/fluid/tests/unittests/test_dist_train.py

@@ -100,7 +100,7 @@ class TestSendOp(unittest.TestCase):
             main.global_block().append_op(
                 type="fetch_barrier",
                 inputs={},
-                outputs={},
+                outputs={"Out": []},
                 attrs={
                     "endpoints": ["127.0.0.1:{0}".format(port)],
                     RPC_OP_ROLE_ATTR_NAME: RPC_OP_ROLE_ATTR_VALUE

python/paddle/fluid/tests/unittests/test_dist_word2vec.py

@@ -22,7 +22,7 @@ class TestDistSeResneXt2x2(TestDistBase):
         self._sync_mode = True
 
     def test_se_resnext(self):
-        self.check_with_place("dist_word2vec.py", delta=1e-7)
+        self.check_with_place("dist_word2vec.py", delta=1e-4)
 
 
 class TestDistSeResneXt2x2Async(TestDistBase):

python/paddle/fluid/transpiler/distribute_transpiler.py

@@ -283,10 +283,13 @@ class DistributeTranspiler(object):
                 send_vars.append(var)
 
         if self.sync_mode:
+            send_barrier_out = program.global_block().create_var(
+                name=framework.generate_control_dev_var_name())
+            input_deps = grad_name_to_send_dummy_out.values()
             program.global_block().append_op(
                 type="send_barrier",
-                inputs={},
-                outputs={},
+                inputs={"X": input_deps},
+                outputs={"Out": send_barrier_out},
                 attrs={
                     "endpoints": pserver_endpoints,
                     RPC_OP_ROLE_ATTR_NAME: RPC_OP_ROLE_ATTR_VALUE
@@ -304,16 +307,22 @@ class DistributeTranspiler(object):
             self.param_grad_ep_mapping[ep]["grads"].append(send_vars[i])
 
         # step4: Concat the parameters splits together after recv.
+        all_recv_outputs = []
         for param_varname, splited_var in six.iteritems(self.param_var_mapping):
             eps = []
             for var in splited_var:
                 index = [v.name for v in recv_vars].index(var.name)
                 eps.append(eplist[index])
-            grad_send_dummy_out = grad_name_to_send_dummy_out[
+            if self.sync_mode:
+                recv_dep_in = send_barrier_out
+            else:
+                # connect deps to send op in async mode
+                recv_dep_in = grad_name_to_send_dummy_out[
                     self.param_name_to_grad_name[param_varname]]
+            all_recv_outputs.extend(splited_var)
             program.global_block().append_op(
                 type="recv",
-                inputs={"X": [grad_send_dummy_out]},
+                inputs={"X": [recv_dep_in]},
                 outputs={"Out": splited_var},
                 attrs={
                     "epmap": eps,
@@ -326,10 +335,11 @@ class DistributeTranspiler(object):
                 })
 
         if self.sync_mode:
+            # form a WAW dependency
             program.global_block().append_op(
                 type="fetch_barrier",
                 inputs={},
-                outputs={},
+                outputs={"Out": all_recv_outputs},
                 attrs={
                     "endpoints": pserver_endpoints,
                     RPC_OP_ROLE_ATTR_NAME: RPC_OP_ROLE_ATTR_VALUE
@@ -413,10 +423,12 @@ class DistributeTranspiler(object):
                     RPC_OP_ROLE_ATTR_NAME: RPC_OP_ROLE_ATTR_VALUE
                 })
 
+        fetch_barrier_out = startup_program.global_block().create_var(
+            name=framework.generate_control_dev_var_name())
         startup_program.global_block().append_op(
             type="fetch_barrier",
             inputs={},
-            outputs={},
+            outputs={"Out": fetch_barrier_out},
             attrs={
                 "endpoints": self.pserver_endpoints,
                 RPC_OP_ROLE_ATTR_NAME: RPC_OP_ROLE_ATTR_VALUE