Merge pull request #12907 from jacquesqiao/cherry-pick-add-dependency-to-send-recv

Cherry pick add dependency to send recv

paddle/fluid/framework/details/multi_devices_graph_pass.cc

@@ -763,6 +763,8 @@ void MultiDevSSAGraphBuilder::CreateDistTrainOp(ir::Graph *result,
 // 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.
@@ -771,26 +773,42 @@ void MultiDevSSAGraphBuilder::CreateRPCOp(ir::Graph *result,
                    "This hack no longer holds, please fix.");
     // the variable name which contains .block means it was splited by
     // split_byref op
-    // so that we can balance the variable blocks to all the pserver
-    // instances.
     if (strategy_.reduce_ == BuildStrategy::ReduceStrategy::kAllReduce &&
         node->inputs[0]->Name().find(".block") == std::string::npos) {
       std::vector<std::string> input_var_names;
       for (ir::Node *n : node->inputs) {
         input_var_names.push_back(n->Name());
       }
-      op_dev_id = GetAppropriateDeviceID(input_var_names);
+      auto send_param_grad = boost::get<std::vector<std::string>>(
+          node->Op()->GetAttr(OpProtoAndCheckerMaker::OpRoleVarAttrName()));
+      PADDLE_ENFORCE_EQ(send_param_grad.size(), 2U);
+      op_dev_id = GetAppropriateDeviceID({send_param_grad[1]});
+      VLOG(10) << "send grad " << input_var_names[0] << " origin "
+               << send_param_grad[1] << " place: " << op_dev_id;
       for (auto &varname : input_var_names) {
         result->Get<ShardedVarDevice>(kShardedVarDevice)
             .emplace(varname, op_dev_id);
       }
+      result->Get<ShardedVarDevice>(kShardedVarDevice)
+          .emplace(send_param_grad[1], op_dev_id);
     }
   } else if (node->Op()->Type() == "recv") {
     std::vector<std::string> output_var_names;
     for (ir::Node *n : node->outputs) {
       output_var_names.push_back(n->Name());
     }
-    op_dev_id = GetAppropriateDeviceID(output_var_names);
+    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]
+               << " get grad place: " << recv_param_grad[1]
+               << " place: " << op_dev_id;
+    } else {
+      op_dev_id = GetAppropriateDeviceID(output_var_names);
+    }
     for (auto &varname : output_var_names) {
       result->Get<ShardedVarDevice>(kShardedVarDevice)
           .emplace(varname, op_dev_id);

paddle/fluid/framework/ir/node.cc

@@ -17,7 +17,7 @@ limitations under the License. */
 namespace paddle {
 namespace framework {
 namespace ir {
-const char Node::kControlDepVarName[] = "__control_var";
+constexpr char Node::kControlDepVarName[];
 }  // namespace ir
 }  // namespace framework
 }  // namespace paddle

paddle/fluid/framework/ir/node.h

@@ -27,7 +27,7 @@ namespace ir {
 class Node {
  public:
   enum class Type { kOperation, kVariable };
-  static const char kControlDepVarName[];
+  static constexpr char kControlDepVarName[] = "__control_var";
 
   explicit Node(const std::string& name, Type type)
       : name_(name), var_desc_(nullptr), op_desc_(nullptr), type_(type) {}

paddle/fluid/operators/recv_op.cc

@@ -57,6 +57,8 @@ class RecvOp : public framework::OperatorBase {
 class RecvOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
   void Make() {
+    AddInput("X", "(Any) Dummy inputs, used for control dependency")
+        .AsDuplicable();
     AddOutput("Out", "(Tensor) Variables to get from server.").AsDuplicable();
     AddComment(R"DOC(
 Recv operator

paddle/fluid/operators/send_barrier_op.cc

@@ -37,22 +37,19 @@ class SendBarrierOp : public framework::OperatorBase {
   void RunImpl(const framework::Scope& scope,
                const platform::Place& place) const override {
     std::vector<std::string> eps = Attr<std::vector<std::string>>("endpoints");
-    bool sync_mode = Attr<bool>("sync_mode");
 
     distributed::RPCClient* rpc_client =
         distributed::RPCClient::GetInstance<RPCCLIENT_T>();
 
-    VLOG(3) << "SendBarrierOp sync_mode:" << sync_mode;
+    VLOG(3) << "SendBarrierOp sync";
 
     // need to wait before sending send_barrier message
     PADDLE_ENFORCE(rpc_client->Wait(), "internal error in RPCClient");
-    if (sync_mode) {
-      for (auto& ep : eps) {
-        VLOG(3) << "send barrier, ep: " << ep;
-        rpc_client->AsyncSendBatchBarrier(ep);
-      }
-      PADDLE_ENFORCE(rpc_client->Wait(), "internal error in RPCClient");
+    for (auto& ep : eps) {
+      VLOG(3) << "send barrier, ep: " << ep;
+      rpc_client->AsyncSendBatchBarrier(ep);
     }
+    PADDLE_ENFORCE(rpc_client->Wait(), "internal error in RPCClient");
   }
 };
 
@@ -70,7 +67,6 @@ the Parameter Server would knew all variables have been sent.
                                       "(string vector, default 127.0.0.1:6164)"
                                       "Server endpoints to send variables to.")
         .SetDefault({"127.0.0.1:6164"});
-    AddAttr<bool>("sync_mode", "work in sync_mode or not").SetDefault(true);
   }
 };
 

paddle/fluid/operators/send_op.cc

@@ -66,6 +66,8 @@ class SendOpMaker : public framework::OpProtoAndCheckerMaker {
   void Make() {
     AddInput("X", "(Tensor, SelectedRows) Input variables to be sent")
         .AsDuplicable();
+    AddOutput("Out", "(Any) Dummy outputs, used for control dependency")
+        .AsDuplicable();
     AddComment(R"DOC(
 Send operator
 

paddle/fluid/pybind/const_value.cc

@@ -13,7 +13,8 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/fluid/pybind/const_value.h"
-#include <paddle/fluid/framework/op_proto_maker.h>
+#include "paddle/fluid/framework/ir/node.h"
+#include "paddle/fluid/framework/op_proto_maker.h"
 #include "paddle/fluid/framework/operator.h"
 
 namespace paddle {
@@ -24,6 +25,8 @@ void BindConstValue(pybind11::module* m) {
   m->def("kTempVarName", [] { return framework::kTempVarName; });
   m->def("kGradVarSuffix", [] { return framework::kGradVarSuffix; });
   m->def("kZeroVarSuffix", [] { return framework::kZeroVarSuffix; });
+  m->def("kControlDepVarName",
+         [] { return framework::ir::Node::kControlDepVarName; });
 
   auto op_proto_and_checker_maker =
       m->def_submodule("op_proto_and_checker_maker");

python/paddle/fluid/framework.py

@@ -49,6 +49,12 @@ EMPTY_VAR_NAME = core.kEmptyVarName()
 TEMP_VAR_NAME = core.kTempVarName()
 GRAD_VAR_SUFFIX = core.kGradVarSuffix()
 ZERO_VAR_SUFFIX = core.kZeroVarSuffix()
+CONTROL_DEP_VAR_PREFIX = core.kControlDepVarName()
+
+
+def generate_control_dev_var_name():
+    import random
+    return CONTROL_DEP_VAR_PREFIX + "@" + str(random.random())
 
 
 def grad_var_name(var_name):

python/paddle/fluid/layers/io.py

@@ -24,7 +24,7 @@ from .layer_function_generator import templatedoc
 from .. import core
 from ..executor import global_scope
 from ..framework import convert_np_dtype_to_dtype_, default_main_program, \
-    default_startup_program, program_guard, Program
+    default_startup_program, program_guard, Program, Variable
 from ..layer_helper import LayerHelper
 from ..unique_name import generate as unique_name
 
@@ -209,7 +209,7 @@ class ListenAndServ(object):
             })
 
 
-def Send(endpoints, send_vars, sync=True):
+def Send(endpoints, send_vars, dummy_output=None, sync=True):
     """
     Send variables to the server side, and get vars from server
     side when server have finished running server side program.
@@ -223,6 +223,13 @@ def Send(endpoints, send_vars, sync=True):
     """
     assert (type(send_vars) == list)
 
+    if dummy_output is None:
+        dummy_output = []
+    elif isinstance(dummy_output, Variable):
+        dummy_output = [dummy_output]
+
+    assert (type(dummy_output) == list)
+
     epmap = endpoints.split(",")
     endpoints = list(set(epmap))
 
@@ -232,6 +239,7 @@ def Send(endpoints, send_vars, sync=True):
     helper.append_op(
         type="send",
         inputs={"X": send_vars},
+        outputs={"Out": dummy_output},
         attrs={
             "endpoints": endpoints,
             "epmap": epmap,
@@ -241,7 +249,7 @@ def Send(endpoints, send_vars, sync=True):
         helper.append_op(type="send_barrier", attrs={"endpoints": endpoints})
 
 
-def Recv(endpoints, get_vars, sync=True):
+def Recv(endpoints, get_vars, dummy_input=None, sync=True):
     """
     Receive variables from server side
 
@@ -256,13 +264,20 @@ def Recv(endpoints, get_vars, sync=True):
     """
     assert (type(get_vars) == list)
 
+    if dummy_input is None:
+        dummy_input = []
+    elif isinstance(dummy_input, Variable):
+        dummy_input = [dummy_input]
+
+    assert (type(dummy_input) == list)
+
     epmap = endpoints.split(",")
     endpoints = list(set(epmap))
 
     helper = LayerHelper("Recv", **locals())
     helper.append_op(
         type="recv",
-        inputs={"X": get_vars},
+        inputs={"X": dummy_input},
         outputs={"Out": get_vars},
         attrs={"endpoints": endpoints,
                "epmap": epmap})

python/paddle/fluid/transpiler/distribute_transpiler.py

@@ -210,6 +210,11 @@ class DistributeTranspiler(object):
 
         ps_dispatcher = self.config.split_method(self.pserver_endpoints)
         self.has_distributed_lookup_table = self._has_distributed_lookup_table()
+        self.param_name_to_grad_name = dict()
+        self.grad_name_to_param_name = dict()
+        for param_var, grad_var in self.params_grads:
+            self.param_name_to_grad_name[param_var.name] = grad_var.name
+            self.grad_name_to_param_name[grad_var.name] = param_var.name
 
         # step 1: split and create vars, then put splited vars in dicts for later use.
         self._init_splited_vars()
@@ -229,34 +234,43 @@ class DistributeTranspiler(object):
             random.seed(self.origin_program.random_seed)
             random.shuffle(grad_var_mapping_items)
 
-        for orig_varname, splited_vars in grad_var_mapping_items:
+        grad_name_to_send_dummy_out = dict()
+        for grad_varname, splited_vars in grad_var_mapping_items:
             eplist = ps_dispatcher.dispatch(splited_vars)
 
             if not self.config.slice_var_up:
                 assert (len(splited_vars) == 1)
 
+            splited_grad_varname = grad_varname
             if len(splited_vars) == 1:
-                orig_varname = splited_vars[0].name
+                splited_grad_varname = splited_vars[0].name
                 index = find_op_by_output_arg(program.global_block(),
-                                              orig_varname)
+                                              splited_grad_varname)
             elif len(splited_vars) > 1:
-                orig_var = program.global_block().vars[orig_varname]
+                orig_var = program.global_block().vars[splited_grad_varname]
                 index = find_op_by_output_arg(program.global_block(),
-                                              orig_varname)
+                                              splited_grad_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)
+                               "variable name :", splited_grad_varname)
+
+            dummy_output = program.global_block().create_var(
+                name=framework.generate_control_dev_var_name())
+            grad_name_to_send_dummy_out[grad_varname] = dummy_output
 
             program.global_block()._insert_op(
                 index=index + 1,
                 type="send",
                 inputs={"X": splited_vars},
-                outputs={},
+                outputs={"Out": dummy_output},
                 attrs={
                     "epmap": eplist,
-                    RPC_OP_ROLE_ATTR_NAME: RPC_OP_ROLE_ATTR_VALUE
+                    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],
+                    "sync_mode": not self.sync_mode,
                 })
             for _, var in enumerate(splited_vars):
                 send_vars.append(var)
@@ -268,7 +282,6 @@ class DistributeTranspiler(object):
                 outputs={},
                 attrs={
                     "endpoints": pserver_endpoints,
-                    "sync_mode": self.sync_mode,
                     RPC_OP_ROLE_ATTR_NAME: RPC_OP_ROLE_ATTR_VALUE
                 })
 
@@ -284,19 +297,25 @@ class DistributeTranspiler(object):
             self.param_grad_ep_mapping[ep]["grads"].append(send_vars[i])
 
         # step4: Concat the parameters splits together after recv.
-        for varname, splited_var in six.iteritems(self.param_var_mapping):
+        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]]
             program.global_block().append_op(
                 type="recv",
-                inputs={},
+                inputs={"X": [grad_send_dummy_out]},
                 outputs={"Out": splited_var},
                 attrs={
                     "epmap": eps,
-                    RPC_OP_ROLE_ATTR_NAME: RPC_OP_ROLE_ATTR_VALUE
+                    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]
+                    ],
+                    "sync_mode": not self.sync_mode
                 })
 
         if self.sync_mode:
@@ -309,10 +328,10 @@ class DistributeTranspiler(object):
                     RPC_OP_ROLE_ATTR_NAME: RPC_OP_ROLE_ATTR_VALUE
                 })
 
-        for varname, splited_var in six.iteritems(self.param_var_mapping):
+        for param_varname, splited_var in six.iteritems(self.param_var_mapping):
             if len(splited_var) <= 1:
                 continue
-            orig_param = program.global_block().vars[varname]
+            orig_param = program.global_block().vars[param_varname]
             program.global_block().append_op(
                 type="concat",
                 inputs={"X": splited_var},
@@ -380,7 +399,7 @@ class DistributeTranspiler(object):
 
             op = startup_program.global_block().append_op(
                 type="recv",
-                inputs={},
+                inputs={"X": []},
                 outputs={"Out": splited_var},
                 attrs={
                     "epmap": eps,
@@ -786,19 +805,21 @@ class DistributeTranspiler(object):
                                           self.config.min_block_size)
         assert (len(grad_blocks) == len(param_blocks))
 
-        # origin_varname -> [splited_var]
+        # origin_param_name -> [splited_param_vars]
         self.param_var_mapping = self._create_vars_from_blocklist(
             self.origin_program, param_blocks)
+        # origin_grad_name -> [splited_grad_vars]
         self.grad_var_mapping = self._create_vars_from_blocklist(
             self.origin_program,
             grad_blocks,
             add_trainer_suffix=self.trainer_num > 1)
+        # dict(grad_splited_var -> param_splited_var)
         self.grad_param_mapping = collections.OrderedDict()
         for g, p in zip(grad_blocks, param_blocks):
             g_name, g_bid, _ = g.split(":")
             p_name, p_bid, _ = p.split(":")
             self.grad_param_mapping[self.grad_var_mapping[g_name][int(g_bid)]] =  \
-                    self.param_var_mapping[p_name][int(p_bid)]
+                self.param_var_mapping[p_name][int(p_bid)]
 
         # create mapping of endpoint -> split var to create pserver side program
         self.param_grad_ep_mapping = collections.OrderedDict()
@@ -919,11 +940,15 @@ class DistributeTranspiler(object):
                     index=op_index + 2,
                     type="send",
                     inputs={'X': self.trainer_side_table_grad_list},
-                    outputs={},
+                    outputs={'Out': []},
                     attrs={
                         "sync_mode": True,
                         "epmap": pserver_endpoints,
-                        RPC_OP_ROLE_ATTR_NAME: RPC_OP_ROLE_ATTR_VALUE
+                        RPC_OP_ROLE_ATTR_NAME: RPC_OP_ROLE_ATTR_VALUE,
+                        OP_ROLE_VAR_ATTR_NAME: [
+                            self.grad_name_to_param_name[table_grad_name],
+                            table_grad_name
+                        ]
                     })
                 break