Merge branch 'develop' into feature/fix_get_empty_lod

paddle/fluid/framework/mixed_vector.h

@@ -110,6 +110,10 @@ class Vector {
 
   T* end() { return size() == 0 ? &EmptyDummy() : &this->operator[](size()); }
 
+  const T* cbegin() const { return begin(); }
+
+  const T* cend() const { return end(); }
+
   T& front() { return *begin(); }
 
   T& back() {
@@ -249,9 +253,9 @@ class Vector {
 
   bool operator==(const Vector<T>& other) const {
     if (size() != other.size()) return false;
-    auto it1 = begin();
-    auto it2 = other.begin();
-    for (; it1 < end(); ++it1, ++it2) {
+    auto it1 = cbegin();
+    auto it2 = other.cbegin();
+    for (; it1 < cend(); ++it1, ++it2) {
       if (*it1 != *it2) {
         return false;
       }

paddle/fluid/operators/listen_and_serv_op.cc

@@ -101,6 +101,9 @@ class ListenAndServOp : public framework::OperatorBase {
 
     // TODO(typhoonzero): change this to a while_op for every cluster-batch.
     bool exit_flag = false;
+    // Record received sparse variables, so that
+    // we could reset those after execute optimize program
+    std::vector<framework::Variable *> sparse_vars;
     while (!exit_flag) {
       // Get from multiple trainers, we don't care about the order in which
       // the gradients arrives, just add suffix 0~n and merge the gradient.
@@ -143,6 +146,9 @@ class ListenAndServOp : public framework::OperatorBase {
             PADDLE_THROW("Can not find server side var");
           }
           detail::DeserializeFromMessage(v.second, dev_ctx, var);
+          if (var->IsType<framework::SelectedRows>()) {
+            sparse_vars.push_back(var);
+          }
         }
       }
       VLOG(3) << "recv " << recv_var_cnt << " parmeters for one barrier.";
@@ -156,9 +162,19 @@ class ListenAndServOp : public framework::OperatorBase {
       } catch (std::exception &e) {
         LOG(ERROR) << "run sub program error " << e.what();
       }
+
+      // Reset the received sparse variables, the sum operator would not
+      // sum the input sparse variables which rows is empty at the next
+      // mini-batch.
+      // TOOD(Yancey1989): move the reset action into an operator, we couldn't
+      // have any hide logic in the operator.
+      for (auto &var : sparse_vars) {
+        var->GetMutable<framework::SelectedRows>()->mutable_rows()->clear();
+      }
       rpc_service_->SetCond(1);
       rpc_service_->WaitClientGet(update_param_cnt);
       grads_counter_.clear();
+      sparse_vars.clear();
     }  // while(true)
   }
 

paddle/fluid/operators/send_op.cc

@@ -24,6 +24,22 @@ limitations under the License. */
 
 namespace paddle {
 namespace operators {
+static bool IsVariableInitialized(const framework::Scope& scope,
+                                  const std::string& varname) {
+  auto* var = scope.FindVar(varname);
+  PADDLE_ENFORCE_NOT_NULL(var, "Can not find variable '%s' in the send side.",
+                          varname);
+  if (var->IsType<framework::LoDTensor>()) {
+    return var->Get<framework::LoDTensor>().IsInitialized();
+  } else if (var->IsType<framework::SelectedRows>()) {
+    return var->Get<framework::SelectedRows>().value().IsInitialized();
+  } else {
+    PADDLE_THROW(
+        "Variable type in send side should be in "
+        "[LodTensor, SelectedRows]");
+  }
+  return false;
+}
 
 class SendOp : public framework::OperatorBase {
  public:
@@ -51,8 +67,12 @@ class SendOp : public framework::OperatorBase {
     detail::RPCClient* rpc_client = client_var->GetMutable<detail::RPCClient>();
 
     for (size_t i = 0; i < ins.size(); i++) {
-      VLOG(3) << "sending " << ins[i] << " to " << epmap[i];
-      rpc_client->AsyncSendVariable(epmap[i], ctx, scope, ins[i]);
+      if (IsVariableInitialized(scope, ins[i])) {
+        VLOG(3) << "sending " << ins[i] << " to " << epmap[i];
+        rpc_client->AsyncSendVariable(epmap[i], ctx, scope, ins[i]);
+      } else {
+        VLOG(3) << "don't send no-initialied variable: " << ins[i];
+      }
     }
     PADDLE_ENFORCE(rpc_client->Wait());
 

paddle/fluid/operators/split_selected_rows_op.cc

@@ -22,7 +22,7 @@ class SplitSelectedRowsOpMaker : public framework::OpProtoAndCheckerMaker {
   SplitSelectedRowsOpMaker(OpProto *proto, OpAttrChecker *op_checker)
       : OpProtoAndCheckerMaker(proto, op_checker) {
     AddInput("X", "The input SelectedRows.");
-    AddOutput("Out", "The outputs of input SelectedRows.").AsDuplicable();
+    AddOutput("Out", "The outputs of the input SelectedRows.").AsDuplicable();
     AddAttr<std::vector<int>>("height_sections",
                               "Height for each output SelectedRows.")
         .SetDefault(std::vector<int>({}));
@@ -56,27 +56,6 @@ class SplitSelectedRowsOp : public framework::OperatorWithKernel {
     PADDLE_ENFORCE(ctx->HasInput("X"), "SplitSelectedRowsOp must has input X.");
     PADDLE_ENFORCE(ctx->HasOutputs("Out"),
                    "SplitSelectedRowsOp must has output Out.");
-
-    std::vector<int> height_sections =
-        ctx->Attrs().Get<std::vector<int>>("height_sections");
-    int64_t n = ctx->Outputs("Out").size();
-
-    std::vector<framework::DDim> outs_dims;
-    outs_dims.reserve(n);
-
-    // make output dims
-    for (int64_t i = 0; i < n; ++i) {
-      auto dims = ctx->GetInputDim("X");
-      if (height_sections.size()) {
-        PADDLE_ENFORCE_EQ(
-            height_sections.size(), static_cast<size_t>(n),
-            "The size of height section should be the same with height"
-            " section size.");
-        dims[0] = height_sections[i];
-      }
-      outs_dims.push_back(dims);
-    }
-    ctx->SetOutputsDim("Out", outs_dims);
   }
 };
 

paddle/fluid/operators/split_selected_rows_op.h

@@ -55,6 +55,7 @@ class SplitSelectedRowsOpKernel : public framework::OpKernel<T> {
 
     for (size_t i = 0; i < outs_rows_idx.size(); ++i) {
       auto rows_idx = outs_rows_idx[i];
+      outs[i]->set_height(height_sections[i]);
       if (rows_idx.size() > 0) {
         auto dims = x->GetCompleteDims();
         dims[0] = rows_idx.size();

paddle/fluid/operators/sum_op.h

@@ -116,7 +116,9 @@ class SumKernel : public framework::OpKernel<T> {
       int64_t offset = 0;
       for (int i = 0; i < N; i++) {
         auto &sel_row = get_selected_row(i);
-
+        if (!sel_row.value().IsInitialized() || sel_row.rows().size() == 0) {
+          continue;
+        }
         PADDLE_ENFORCE_EQ(out->height(), sel_row.height());
         functor(context.template device_context<DeviceContext>(), sel_row,
                 offset, out);

python/paddle/v2/fluid/distribute_transpiler.py

@@ -191,6 +191,7 @@ class DistributeTranspiler:
         for b in param_blocks:
             varname, block_id, _ = b.split(":")
             send_outputs.append(param_var_mapping[varname][int(block_id)])
+
         # let send_op know which endpoint to send which var to, eplist has the same
         # order as send_inputs.
         eplist = split_method(send_inputs, pserver_endpoints)
@@ -274,6 +275,7 @@ class DistributeTranspiler:
                     name="%s.block%d" % (varname, i),
                     psersistable=False,
                     dtype=orig_var.dtype,
+                    type=orig_var.type,
                     shape=splited_shape)  # flattend splited var
                 var_mapping[varname].append(var)
         return var_mapping
@@ -335,6 +337,7 @@ class DistributeTranspiler:
                 name="%s.trainer_%d" % (var.name, i),
                 psersistable=var.persistable,
                 dtype=var.dtype,
+                type=var.type,
                 shape=var.shape)
             var_list.append(var_each)
         return var_list
@@ -561,6 +564,7 @@ class DistributeTranspiler:
                     persistable=True,
                     dtype=v.dtype,
                     shape=v.shape)
+
         # step6
         optimize_block = pserver_program.create_block(0)
         # step 6.1