Merge pull request #13102 from typhoonzero/merge_dist_deps_fixes

Cherrypick dist fixes

paddle/fluid/API.spec

@@ -55,9 +55,10 @@ paddle.fluid.Inferencer.__init__ ArgSpec(args=['self', 'infer_func', 'param_path
 paddle.fluid.Inferencer.infer ArgSpec(args=['self', 'inputs', 'return_numpy'], varargs=None, keywords=None, defaults=(True,))
 paddle.fluid.DistributeTranspiler.__init__ ArgSpec(args=['self', 'config'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.DistributeTranspiler.get_pserver_program ArgSpec(args=['self', 'endpoint'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.DistributeTranspiler.get_startup_program ArgSpec(args=['self', 'endpoint', 'pserver_program', 'startup_program'], varargs=None, keywords=None, defaults=(None,))
+paddle.fluid.DistributeTranspiler.get_pserver_programs ArgSpec(args=['self', 'endpoint'], varargs=None, keywords=None, defaults=None)
+paddle.fluid.DistributeTranspiler.get_startup_program ArgSpec(args=['self', 'endpoint', 'pserver_program', 'startup_program'], varargs=None, keywords=None, defaults=(None, None))
 paddle.fluid.DistributeTranspiler.get_trainer_program ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.DistributeTranspiler.transpile ArgSpec(args=['self', 'trainer_id', 'program', 'pservers', 'trainers', 'sync_mode'], varargs=None, keywords=None, defaults=(None, '127.0.0.1:6174', 1, True))
+paddle.fluid.DistributeTranspiler.transpile ArgSpec(args=['self', 'trainer_id', 'program', 'pservers', 'trainers', 'sync_mode', 'startup_program'], varargs=None, keywords=None, defaults=(None, '127.0.0.1:6174', 1, True, None))
 paddle.fluid.InferenceTranspiler.__init__ 
 paddle.fluid.InferenceTranspiler.transpile ArgSpec(args=['self', 'program', 'place', 'scope'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.memory_optimize ArgSpec(args=['input_program', 'skip_opt_set', 'print_log', 'level'], varargs=None, keywords=None, defaults=(None, False, 0))
@@ -329,9 +330,10 @@ paddle.fluid.contrib.BeamSearchDecoder.update_array ArgSpec(args=['self', 'array
 paddle.fluid.contrib.memory_usage ArgSpec(args=['program', 'batch_size'], varargs=None, keywords=None, defaults=None)
 paddle.fluid.transpiler.DistributeTranspiler.__init__ ArgSpec(args=['self', 'config'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.transpiler.DistributeTranspiler.get_pserver_program ArgSpec(args=['self', 'endpoint'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.transpiler.DistributeTranspiler.get_startup_program ArgSpec(args=['self', 'endpoint', 'pserver_program', 'startup_program'], varargs=None, keywords=None, defaults=(None,))
+paddle.fluid.transpiler.DistributeTranspiler.get_pserver_programs ArgSpec(args=['self', 'endpoint'], varargs=None, keywords=None, defaults=None)
+paddle.fluid.transpiler.DistributeTranspiler.get_startup_program ArgSpec(args=['self', 'endpoint', 'pserver_program', 'startup_program'], varargs=None, keywords=None, defaults=(None, None))
 paddle.fluid.transpiler.DistributeTranspiler.get_trainer_program ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None)
-paddle.fluid.transpiler.DistributeTranspiler.transpile ArgSpec(args=['self', 'trainer_id', 'program', 'pservers', 'trainers', 'sync_mode'], varargs=None, keywords=None, defaults=(None, '127.0.0.1:6174', 1, True))
+paddle.fluid.transpiler.DistributeTranspiler.transpile ArgSpec(args=['self', 'trainer_id', 'program', 'pservers', 'trainers', 'sync_mode', 'startup_program'], varargs=None, keywords=None, defaults=(None, '127.0.0.1:6174', 1, True, None))
 paddle.fluid.transpiler.InferenceTranspiler.__init__ 
 paddle.fluid.transpiler.InferenceTranspiler.transpile ArgSpec(args=['self', 'program', 'place', 'scope'], varargs=None, keywords=None, defaults=(None,))
 paddle.fluid.transpiler.memory_optimize ArgSpec(args=['input_program', 'skip_opt_set', 'print_log', 'level'], varargs=None, keywords=None, defaults=(None, False, 0))

paddle/fluid/framework/details/multi_devices_graph_pass.cc

@@ -736,7 +736,7 @@ void MultiDevSSAGraphBuilder::CreateDistTrainOp(ir::Graph *result,
           .emplace(varname, op_dev_id);
     }
   } else {
-    PADDLE_ENFORCE(
+    PADDLE_THROW(
         "the distribute training related op should be in [split_byref, "
         "concat].");
   }
@@ -746,17 +746,26 @@ 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.
@@ -791,8 +800,6 @@ 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]
@@ -806,34 +813,44 @@ 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

@@ -130,7 +130,12 @@ class SE_ResNeXt():
             input=conv, pool_size=7, pool_type='avg', global_pooling=True)
         drop = fluid.layers.dropout(x=pool, dropout_prob=0.2)
         stdv = 1.0 / math.sqrt(drop.shape[1] * 1.0)
-        out = fluid.layers.fc(input=drop, size=class_dim, act='softmax')
+        out = fluid.layers.fc(
+            input=drop,
+            size=class_dim,
+            act='softmax',
+            param_attr=fluid.ParamAttr(
+                initializer=fluid.initializer.Constant(value=0.05)))
         return out
 
     def shortcut(self, input, ch_out, stride):
@@ -180,7 +185,7 @@ class SE_ResNeXt():
             act=None,
             # avoid pserver CPU init differs from GPU
             param_attr=fluid.ParamAttr(
-                initializer=fluid.initializer.Constant()),
+                initializer=fluid.initializer.Constant(value=0.05)),
             bias_attr=False)
         return fluid.layers.batch_norm(input=conv, act=act)
 
@@ -188,13 +193,19 @@ 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,
-                                  size=num_channels // reduction_ratio,
-                                  act='relu')
+        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,
-                                     size=num_channels,
-                                     act='sigmoid')
+        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_base.py

@@ -21,7 +21,7 @@ import sys
 import six
 import signal
 import subprocess
-import six
+import argparse
 
 
 class TestDistRunnerBase(object):
@@ -30,7 +30,7 @@ class TestDistRunnerBase(object):
             "get_model should be implemented by child classes.")
 
     def get_transpiler(self, trainer_id, main_program, pserver_endpoints,
-                       trainers):
+                       trainers, sync_mode):
         # NOTE: import fluid until runtime, or else forking processes will cause error.
         import paddle
         import paddle.fluid as fluid
@@ -39,33 +39,35 @@ class TestDistRunnerBase(object):
             trainer_id=trainer_id,
             program=main_program,
             pservers=pserver_endpoints,
-            trainers=trainers)
+            trainers=trainers,
+            sync_mode=sync_mode)
         return t
 
-    def run_pserver(self, pserver_endpoints, trainers, current_endpoint,
-                    trainer_id):
+    def run_pserver(self, args):
         import paddle
         import paddle.fluid as fluid
         self.get_model(batch_size=2)
-        t = self.get_transpiler(trainer_id,
-                                fluid.default_main_program(), pserver_endpoints,
-                                trainers)
-        pserver_prog = t.get_pserver_program(current_endpoint)
-        startup_prog = t.get_startup_program(current_endpoint, pserver_prog)
+        t = self.get_transpiler(args.trainer_id,
+                                fluid.default_main_program(), args.endpoints,
+                                args.trainers, args.sync_mode)
+        pserver_prog = t.get_pserver_program(args.current_endpoint)
+        startup_prog = t.get_startup_program(args.current_endpoint,
+                                             pserver_prog)
         place = fluid.CPUPlace()
         exe = fluid.Executor(place)
         exe.run(startup_prog)
         exe.run(pserver_prog)
 
-    def run_trainer(self, place, endpoints, trainer_id, trainers, is_dist=True):
+    def run_trainer(self, place, args):
         import paddle
         import paddle.fluid as fluid
         test_program, avg_cost, train_reader, test_reader, batch_acc, predict = \
         self.get_model(batch_size=2)
-        if is_dist:
-            t = self.get_transpiler(trainer_id,
-                                    fluid.default_main_program(), endpoints,
-                                    trainers)
+        if args.is_dist:
+            t = self.get_transpiler(args.trainer_id,
+                                    fluid.default_main_program(),
+                                    args.endpoints, args.trainers,
+                                    args.sync_mode)
             trainer_prog = t.get_trainer_program()
         else:
             trainer_prog = fluid.default_main_program()
@@ -76,8 +78,18 @@ class TestDistRunnerBase(object):
         strategy = fluid.ExecutionStrategy()
         strategy.num_threads = 1
         strategy.allow_op_delay = False
+        build_stra = fluid.BuildStrategy()
+
+        if args.use_reduce:
+            build_stra.reduce_strategy = fluid.BuildStrategy.ReduceStrategy.Reduce
+        else:
+            build_stra.reduce_strategy = fluid.BuildStrategy.ReduceStrategy.AllReduce
+
         exe = fluid.ParallelExecutor(
-            True, loss_name=avg_cost.name, exec_strategy=strategy)
+            True,
+            loss_name=avg_cost.name,
+            exec_strategy=strategy,
+            build_strategy=build_stra)
 
         feed_var_list = [
             var for var in trainer_prog.global_block().vars.values()
@@ -106,45 +118,64 @@ def runtime_main(test_class):
     import paddle.fluid as fluid
     import paddle.fluid.core as core
 
-    if len(sys.argv) != 7:
-        print(
-            "Usage: python dist_se_resnext.py [pserver/trainer] [endpoints] [trainer_id] [current_endpoint] [trainers] [is_dist]"
-        )
-    role = sys.argv[1]
-    endpoints = sys.argv[2]
-    trainer_id = int(sys.argv[3])
-    current_endpoint = sys.argv[4]
-    trainers = int(sys.argv[5])
-    is_dist = True if sys.argv[6] == "TRUE" else False
+    parser = argparse.ArgumentParser(description='Run dist test.')
+    parser.add_argument(
+        '--role', type=str, required=True, choices=['pserver', 'trainer'])
+    parser.add_argument('--endpoints', type=str, required=False, default="")
+    parser.add_argument('--is_dist', action='store_true')
+    parser.add_argument('--trainer_id', type=int, required=False, default=0)
+    parser.add_argument('--trainers', type=int, required=False, default=1)
+    parser.add_argument(
+        '--current_endpoint', type=str, required=False, default="")
+    parser.add_argument('--sync_mode', action='store_true')
+    parser.add_argument('--mem_opt', action='store_true')
+    parser.add_argument('--use_reduce', action='store_true')
+
+    args = parser.parse_args()
 
     model = test_class()
-    if role == "pserver":
-        model.run_pserver(endpoints, trainers, current_endpoint, trainer_id)
+    if args.role == "pserver":
+        model.run_pserver(args)
     else:
         p = fluid.CUDAPlace(0) if core.is_compiled_with_cuda(
         ) else fluid.CPUPlace()
-        model.run_trainer(p, endpoints, trainer_id, trainers, is_dist)
+        model.run_trainer(p, args)
 
 
 import paddle.compat as cpt
 
 
 class TestDistBase(unittest.TestCase):
+    def _setup_config(self):
+        raise NotImplementedError("tests should have _setup_config implemented")
+
     def setUp(self):
         self._trainers = 2
         self._pservers = 2
         self._ps_endpoints = "127.0.0.1:9123,127.0.0.1:9124"
         self._python_interp = "python"
+        self._sync_mode = True
+        self._mem_opt = False
+        self._use_reduce = False
+        self._setup_config()
 
     def start_pserver(self, model_file, check_error_log):
         ps0_ep, ps1_ep = self._ps_endpoints.split(",")
-        ps0_cmd = "%s %s pserver %s 0 %s %d TRUE" % \
+        ps_cmd = "%s %s --role pserver --endpoints %s --trainer_id 0 --current_endpoint %s --trainers %d --is_dist"
+        ps0_cmd = ps_cmd % \
             (self._python_interp, model_file, self._ps_endpoints, ps0_ep,
              self._trainers)
-        ps1_cmd = "%s %s pserver %s 0 %s %d TRUE" % \
+        ps1_cmd = ps_cmd % \
             (self._python_interp, model_file, self._ps_endpoints, ps1_ep,
              self._trainers)
 
+        if self._sync_mode:
+            ps0_cmd += " --sync_mode"
+            ps1_cmd += " --sync_mode"
+        if self._mem_opt:
+            ps0_cmd += " --mem_opt"
+            ps1_cmd += " --mem_opt"
+
         ps0_pipe = subprocess.PIPE
         ps1_pipe = subprocess.PIPE
         if check_error_log:
@@ -195,9 +226,7 @@ class TestDistBase(unittest.TestCase):
         # Run local to get a base line
         env_local = {"CUDA_VISIBLE_DEVICES": "0"}
         env_local.update(required_envs)
-        local_cmd = "%s %s trainer %s 0 %s %d FLASE" % \
-            (self._python_interp, model_file,
-             "127.0.0.1:1234", "127.0.0.1:1234", 1)
+        local_cmd = "%s %s --role trainer" % (self._python_interp, model_file)
         if not check_error_log:
             local_proc = subprocess.Popen(
                 local_cmd.split(" "),
@@ -226,12 +255,23 @@ class TestDistBase(unittest.TestCase):
         self._wait_ps_ready(ps1.pid)
 
         ps0_ep, ps1_ep = self._ps_endpoints.split(",")
-        tr0_cmd = "%s %s trainer %s 0 %s %d TRUE" % \
-            (self._python_interp, model_file, self._ps_endpoints, ps0_ep,
-             self._trainers)
-        tr1_cmd = "%s %s trainer %s 1 %s %d TRUE" % \
-            (self._python_interp, model_file, self._ps_endpoints, ps1_ep,
-             self._trainers)
+        tr_cmd = "%s %s --role trainer --endpoints %s --trainer_id %d --current_endpoint %s --trainers %d --is_dist"
+        tr0_cmd = tr_cmd % \
+            (self._python_interp, model_file, self._ps_endpoints,
+             0, ps0_ep, self._trainers)
+        tr1_cmd = tr_cmd % \
+            (self._python_interp, model_file, self._ps_endpoints,
+             1, ps1_ep, self._trainers)
+
+        if self._sync_mode:
+            tr0_cmd += " --sync_mode"
+            tr1_cmd += " --sync_mode"
+        if self._mem_opt:
+            tr0_cmd += " --mem_opt"
+            tr1_cmd += " --mem_opt"
+        if self._use_reduce:
+            tr0_cmd += " --use_reduce"
+            tr1_cmd += " --use_reduce"
 
         env0 = {"CUDA_VISIBLE_DEVICES": "0"}
         env1 = {"CUDA_VISIBLE_DEVICES": "1"}
@@ -282,6 +322,10 @@ class TestDistBase(unittest.TestCase):
         # FIXME: use terminate() instead of sigkill.
         os.kill(ps0.pid, signal.SIGKILL)
         os.kill(ps1.pid, signal.SIGKILL)
+        ps0.terminate()
+        ps1.terminate()
+        ps0.wait()
+        ps1.wait()
         FNULL.close()
 
         self.assertAlmostEqual(local_first_loss, dist_first_loss, delta=delta)

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

@@ -17,10 +17,51 @@ import unittest
 from test_dist_base import TestDistBase
 
 
-class TestDistSeResneXt2x2(TestDistBase):
+class TestDistMnist2x2(TestDistBase):
+    def _setup_config(self):
+        self._sync_mode = True
+        self._use_reduce = False
+
+    def test_se_resnext(self):
+        self.check_with_place("dist_mnist.py", delta=1e-7)
+
+
+class TestDistMnist2x2WithMemopt(TestDistBase):
+    def _setup_config(self):
+        self._sync_mode = True
+        self._mem_opt = True
+
     def test_se_resnext(self):
         self.check_with_place("dist_mnist.py", delta=1e-7)
 
 
+class TestDistMnistAsync(TestDistBase):
+    def _setup_config(self):
+        self._sync_mode = False
+        self._use_reduce = False
+
+    def test_se_resnext(self):
+        self.check_with_place("dist_mnist.py", delta=200)
+
+
+# FIXME(typhoonzero): enable these tests once we have 4
+# 4 GPUs on CI machine, and the base class should be updated.
+#
+# class TestDistMnist2x2ReduceMode(TestDistBase):
+#     def _setup_config(self):
+#         self._sync_mode = True
+#         self._use_reduce = True
+
+#     def test_se_resnext(self):
+#         self.check_with_place("dist_mnist.py", delta=1e-7)
+
+# class TestDistMnistAsyncReduceMode(TestDistBase):
+#     def _setup_config(self):
+#         self._sync_mode = False
+#         self._use_reduce = True
+
+#     def test_se_resnext(self):
+#         self.check_with_place("dist_mnist.py", delta=200)
+
 if __name__ == "__main__":
     unittest.main()

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

@@ -18,9 +18,20 @@ from test_dist_base import TestDistBase
 
 
 class TestDistSeResneXt2x2(TestDistBase):
+    def _setup_config(self):
+        self._sync_mode = True
+
     def test_se_resnext(self):
         self.check_with_place("dist_se_resnext.py", delta=1e-7)
 
 
+class TestDistSeResneXt2x2Async(TestDistBase):
+    def _setup_config(self):
+        self._sync_mode = False
+
+    def test_se_resnext(self):
+        self.check_with_place("dist_se_resnext.py", delta=100)
+
+
 if __name__ == "__main__":
     unittest.main()

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_transformer.py

@@ -15,14 +15,55 @@
 from __future__ import print_function
 
 import unittest
+import paddle
 from test_dist_base import TestDistBase
 
 
-class TestDistTransformer2x2(TestDistBase):
+def download_files():
+    url_prefix = 'http://paddle-unittest-data.cdn.bcebos.com/dist_transformer/'
+    vocab_url = url_prefix + 'vocab.bpe.32000'
+    vocab_md5 = 'a86d345ca6e27f6591d0dccb1b9be853'
+    paddle.dataset.common.download(vocab_url, 'test_dist_transformer',
+                                   vocab_md5)
+
+    local_train_url = url_prefix + 'train.tok.clean.bpe.32000.en-de'
+    local_train_md5 = '033eb02b9449e6dd823f050782ac8914'
+    paddle.dataset.common.download(local_train_url, 'test_dist_transformer',
+                                   local_train_md5)
+
+    train0_url = url_prefix + 'train.tok.clean.bpe.32000.en-de.train_0'
+    train0_md5 = 'ddce7f602f352a0405267285379a38b1'
+    paddle.dataset.common.download(train0_url, 'test_dist_transformer',
+                                   train0_md5)
+
+    train1_url = url_prefix + 'train.tok.clean.bpe.32000.en-de.train_1'
+    train1_md5 = '8757798200180285b1a619cd7f408747'
+    paddle.dataset.common.download(train1_url, 'test_dist_transformer',
+                                   train1_md5)
+
+    test_url = url_prefix + 'newstest2013.tok.bpe.32000.en-de'
+    test_md5 = '9dd74a266dbdb25314183899f269b4a2'
+    paddle.dataset.common.download(test_url, 'test_dist_transformer', test_md5)
+
+
+class TestDistTransformer2x2Sync(TestDistBase):
+    def _setup_config(self):
+        self._sync_mode = True
+
+    def test_transformer(self):
+        download_files()
+        #Note: loss on test dataset of the first 5 batch are:
+        # 10.518872, 10.518871, 10.518868, 10.518862, 10.518855
+        self.check_with_place("dist_transformer.py", delta=1e-7)
+
+
+class TestDistTransformer2x2Async(TestDistBase):
+    def _setup_config(self):
+        self._sync_mode = False
+
     def test_transformer(self):
-        # TODO(paddle-dev): check if the delta is OK.
-        # Usually start around ~8000 and converge to ~5000
-        self.check_with_place("dist_transformer.py", delta=400)
+        download_files()
+        self.check_with_place("dist_transformer.py", delta=1.0)
 
 
 if __name__ == "__main__":

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

@@ -18,8 +18,19 @@ from test_dist_base import TestDistBase
 
 
 class TestDistSeResneXt2x2(TestDistBase):
+    def _setup_config(self):
+        self._sync_mode = True
+
+    def test_se_resnext(self):
+        self.check_with_place("dist_word2vec.py", delta=1e-4)
+
+
+class TestDistSeResneXt2x2Async(TestDistBase):
+    def _setup_config(self):
+        self._sync_mode = False
+
     def test_se_resnext(self):
-        self.check_with_place("dist_word2vec.py", delta=1e-7)
+        self.check_with_place("dist_word2vec.py", delta=1)
 
 
 if __name__ == "__main__":

python/paddle/fluid/transpiler/details/program_utils.py

@@ -39,3 +39,136 @@ def find_op_by_output_arg(block, arg_name):
         if arg_name in op.output_arg_names:
             return index
     return -1
+
+
+def get_indent_space(indent, space_num=4):
+    ret = ""
+    for i in range(0, indent * space_num):
+        ret += " "
+
+    return ret
+
+
+def variable_to_code(var):
+    """
+    Get readable codes of fluid variable.
+    Args:
+        var: A fluid operator.
+    Returns:
+        string: The formatted string.
+    """
+    if var.type == core.VarDesc.VarType.SELECTED_ROWS or var.type == core.VarDesc.VarType.LOD_TENSOR:
+        var_str = "{name} : fluid.{type}.shape{shape}.astype({dtype})".\
+            format(i="{", e="}", name=var.name, type=var.type, shape=var.shape, dtype=var.dtype)
+    else:
+        var_str = "{name} : fluid.{type})".\
+            format(i="{", e="}", name=var.name, type=var.type)
+
+    if type(var) == paddle.fluid.framework.Parameter:
+        if var.trainable:
+            var_str = "trainable parameter " + var_str
+        else:
+            var_str = "parameter " + var_str
+    else:
+        var_str = "var " + var_str
+
+    if var.persistable:
+        var_str = "persist " + var_str
+
+    return var_str
+
+
+def op_to_code(op):
+    """
+    Get readable codes of fluid operator.
+    Args:
+        op: A fluid operator.
+    Returns:
+        string: The foramtted string.
+    """
+
+    outputs_str = "{"
+    for i in range(0, len(op.output_names)):
+        outputs_str += "{name}=".format(name=op.output_names[i])
+        o = op.output(op.output_names[i])
+        outputs_str += "{value}".format(value=o)
+        if i != len(op.output_names) - 1:
+            outputs_str += ", "
+    outputs_str += "}"
+
+    inputs_str = "{"
+    for i in range(0, len(op.input_names)):
+        inputs_str += "{name}=".format(name=op.input_names[i])
+        o = op.input(op.input_names[i])
+        inputs_str += "{value}".format(value=o)
+
+        if i != len(op.input_names) - 1:
+            inputs_str += ", "
+    inputs_str += "}"
+
+    attrs_str = ""
+    for i in range(0, len(op.attr_names)):
+        name = op.attr_names[i]
+
+        attr_type = op.desc.attr_type(name)
+        if attr_type == core.AttrType.BLOCK:
+            a = "{name} = block[{value}]".format(
+                name=name, type=attr_type, value=op.block_attr_id(name))
+            attrs_str += a
+            continue
+
+        if attr_type == core.AttrType.BLOCKS:
+            a = "{name} = blocks{value}".format(
+                name=name, type=attr_type, value=op.blocks_attr_ids(name))
+            attrs_str += a
+            continue
+
+        a = "{name} = {value}".format(
+            name=name, type=attr_type, value=op.desc.attr(name))
+        attrs_str += a
+        if i != len(op.attr_names) - 1:
+            attrs_str += ", "
+
+    if outputs_str != "{}":
+        op_str = "{outputs} = {op_type}(inputs={inputs}, {attrs})".\
+            format(outputs = outputs_str, op_type=op.type, inputs=inputs_str, attrs=attrs_str)
+    else:
+        op_str = "{op_type}(inputs={inputs}, {attrs})".\
+            format(op_type=op.type, inputs=inputs_str, attrs=attrs_str)
+    return op_str
+
+
+def block_to_code(block, block_idx):
+    indent = 0
+
+    print("{0}{1} // block {2}".format(
+        get_indent_space(indent), '{', block_idx))
+
+    indent += 1
+    # sort all vars
+    all_vars = sorted(block.vars.iteritems(), key=lambda x: x[0])
+    for var in all_vars:
+        print("{}{}".format(get_indent_space(indent), variable_to_code(var[1])))
+
+    if len(all_vars) > 0:
+        print("")
+
+    for op in block.ops:
+        print("{}{}".format(get_indent_space(indent), op_to_code(op)))
+    indent -= 1
+
+    print("{0}{1}".format(get_indent_space(indent), '}'))
+
+
+def program_to_code(prog):
+    """
+    Print readable codes of fluid program.
+    Args:
+        prog : A fluid program.
+    An example result like bellow:
+    https://github.com/PaddlePaddle/Paddle/pull/12673
+    """
+    block_idx = 0
+    for block in prog.blocks:
+        block_to_code(block, block_idx)
+        block_idx += 1

python/paddle/fluid/transpiler/distribute_transpiler.py

@@ -31,9 +31,10 @@ Steps to transpile pserver:
 """
 
 import math
-import random
+import sys
 import numpy as np
 import collections
+import random
 
 from .ps_dispatcher import RoundRobin, HashName, PSDispatcher
 from .. import core, framework
@@ -181,7 +182,8 @@ class DistributeTranspiler(object):
                   program=None,
                   pservers="127.0.0.1:6174",
                   trainers=1,
-                  sync_mode=True):
+                  sync_mode=True,
+                  startup_program=None):
         """
         Run the transpiler.
 
@@ -194,13 +196,17 @@ class DistributeTranspiler(object):
                 list.
             trainers (int): number of trainers in the distributed job.
             sync_mode (bool): Do sync training or not, default is True.
+            startup_program (Program|None): startup_program to transpile,
+                default is fluid.default_main_program().
         """
         if program is None:
             program = default_main_program()
+        if startup_program is None:
+            startup_program = default_startup_program()
         self.origin_program = program
-        self.origin_startup_program = default_startup_program().clone()
+        self.startup_program = startup_program
+        self.origin_startup_program = self.startup_program.clone()
 
-        self.startup_program = default_startup_program()
         self.trainer_num = trainers
         self.sync_mode = sync_mode
         self.trainer_id = trainer_id
@@ -260,6 +266,10 @@ class DistributeTranspiler(object):
                 name=framework.generate_control_dev_var_name())
             grad_name_to_send_dummy_out[grad_varname] = dummy_output
 
+            # get send op_role_var, if not splited, the grad should have .trainer suffix
+            # if splited, grad should be the original grad var name (split_by_ref and send
+            # will be on the same place). ParallelExecutor
+            # will use op_role_var to get expected device place to run this op.
             program.global_block()._insert_op(
                 index=index + 1,
                 type="send",
@@ -268,18 +278,23 @@ class DistributeTranspiler(object):
                 attrs={
                     "epmap": eplist,
                     RPC_OP_ROLE_ATTR_NAME: RPC_OP_ROLE_ATTR_VALUE,
-                    OP_ROLE_VAR_ATTR_NAME:
-                    [self.grad_name_to_param_name[grad_varname], grad_varname],
+                    OP_ROLE_VAR_ATTR_NAME: [
+                        self.grad_name_to_param_name[grad_varname],
+                        splited_grad_varname
+                    ],
                     "sync_mode": not self.sync_mode,
                 })
             for _, var in enumerate(splited_vars):
                 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
@@ -297,32 +312,46 @@ 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[
-                self.param_name_to_grad_name[param_varname]]
+            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)
+            # get recv op_role_var, if not splited, the grad should have .trainer suffix
+            # if splited, grad should be the original grad var name. ParallelExecutor
+            # will use op_role_var to get expected device place to run this op.
+            orig_grad_name = self.param_name_to_grad_name[param_varname]
+            recv_op_role_var_name = orig_grad_name
+            splited_trainer_grad = self.grad_var_mapping[orig_grad_name]
+            if len(splited_trainer_grad) == 1:
+                recv_op_role_var_name = splited_trainer_grad[0].name
+
             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,
                     RPC_OP_ROLE_ATTR_NAME: RPC_OP_ROLE_ATTR_VALUE,
-                    OP_ROLE_VAR_ATTR_NAME: [
-                        param_varname,
-                        self.param_name_to_grad_name[param_varname]
-                    ],
+                    OP_ROLE_VAR_ATTR_NAME:
+                    [param_varname, recv_op_role_var_name],
                     "sync_mode": not self.sync_mode
                 })
 
         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
@@ -359,21 +388,18 @@ class DistributeTranspiler(object):
 
         return self.origin_program
 
-    def _get_trainer_startup_program(self,
-                                     recv_vars,
-                                     eplist,
-                                     startup_program=None):
+    def _get_trainer_startup_program(self, recv_vars, eplist):
         """
         Get transpiled trainer side startup program.
 
         Args:
-            startup_program(Program): Startup program.
+            recv_vars (list): Variable list to recv for current trainer_id
+            eplist (list): A list of strings indicating 
 
         Returns:
             Program: trainer side startup program.
         """
-        if startup_program is None:
-            startup_program = self.startup_program
+        startup_program = self.startup_program
 
         # FIXME(gongwb): delete not need ops.
         # note that: some parameter is not trainable and those ops can't be deleted.
@@ -406,10 +432,12 @@ 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
@@ -419,7 +447,18 @@ class DistributeTranspiler(object):
             #add concat ops to merge splited parameters received from parameter servers.
             if len(splited_var) <= 1:
                 continue
-            orig_param = startup_program.global_block().vars[varname]
+            # NOTE: if enable memory optimization, origin vars maybe removed.
+            if startup_program.global_block().vars.has_key(varname):
+                orig_param = startup_program.global_block().vars[varname]
+            else:
+                origin_param_var = self.origin_program.global_block().vars[
+                    varname]
+                orig_param = startup_program.global_block().create_var(
+                    name=varname,
+                    persistable=origin_param_var.persistable,
+                    type=origin_param_var.type,
+                    dtype=origin_param_var.dtype,
+                    shape=origin_param_var.shape)
             startup_program.global_block().append_op(
                 type="concat",
                 inputs={"X": splited_var},
@@ -442,7 +481,9 @@ class DistributeTranspiler(object):
         # NOTE: assume blocks of the same variable is not distributed
         # on the same pserver, only change param/grad varnames for
         # trainers to fetch.
-
+        sys.stderr.write("get_pserver_program() is deprecated, call\
+            get_pserver_programs() to get pserver main and startup\
+            in a single call.")
         # step1
         pserver_program = Program()
         pserver_program.random_seed = self.origin_program.random_seed
@@ -626,32 +667,58 @@ class DistributeTranspiler(object):
             attrs=attrs)
 
         pserver_program._sync_with_cpp()
+        # save pserver program to generate pserver side startup relatively.
+        self.pserver_program = pserver_program
         return pserver_program
 
+    def get_pserver_programs(self, endpoint):
+        """
+        Get pserver side main program and startup program for distributed training.
+
+        Args:
+            endpoint (str): current pserver endpoint.
+        
+        Returns:
+            tuple: (main_program, startup_program), of type "Program"
+        """
+        pserver_prog = self.get_pserver_program(endpoint)
+        pserver_startup = self.get_startup_program(endpoint)
+        return pserver_prog, pserver_startup
+
     def get_startup_program(self,
                             endpoint,
-                            pserver_program,
+                            pserver_program=None,
                             startup_program=None):
         """
+        **Deprecated**
+
         Get startup program for current parameter server.
         Modify operator input variables if there are variables that
         were split to several blocks.
 
         Args:
             endpoint (str): current pserver endpoint.
-            pserver_program (Program): call get_pserver_program first and
-                pass the result here.
-            startup_program (Program): if pass None, will use
-                default_startup_program
+            pserver_program (Program): deprecated, call get_pserver_program first.
+            startup_program (Program): deprecated, should pass startup_program
+                when initalizing 
 
         Returns:
             Program: parameter server side startup program.
         """
+        sys.stderr.write("get_startup_program() is deprecated, call\
+            get_pserver_programs() to get pserver main and startup\
+            in a single call.")
+        if pserver_program != None:
+            sys.stderr.write("passing pserver_program to get_startup_program()\
+                is deprecated, you can use new API get_pserver_programs() to\
+                get both pserver main program and startup program.")
+        if startup_program != None:
+            sys.stderr.write("passing startup_program to get_startup_program()\
+                is deprecated, use fluid.program_guard() or pass this argument\
+                to transpile() call.")
+
         s_prog = Program()
-        if not startup_program:
-            orig_s_prog = default_startup_program()
-        else:
-            orig_s_prog = startup_program
+        orig_s_prog = self.startup_program
         s_prog.random_seed = orig_s_prog.random_seed
         params = self.param_grad_ep_mapping[endpoint]["params"]