Merge branch develop

paddle/fluid/framework/block_desc.h

@@ -92,7 +92,7 @@ class BlockDesc {
 
   /*
    * Remove Op and its input/output variables.
-   * Note that for either input or ouput variable, if it is also an input or
+   * Note that for either input or output variable, if it is also an input or
    * output variable of other ops, we should remain it.
    */
   void RemoveOp(size_t s, size_t e);

paddle/fluid/framework/details/computation_op_handle.cc

@@ -14,6 +14,8 @@
 
 #include "paddle/fluid/framework/details/computation_op_handle.h"
 
+#include <string>
+
 namespace paddle {
 namespace framework {
 namespace details {
@@ -33,7 +35,7 @@ void ComputationOpHandle::RunImpl() {
     }
   }
 
-  op_->Run(*scope_->FindVar("@TMP_SCOPE@")->Get<Scope *>(), place_);
+  op_->Run(*scope_->FindVar(kLocalExecScopeName)->Get<Scope *>(), place_);
 }
 
 std::string ComputationOpHandle::Name() const { return op_->Type(); }

paddle/fluid/framework/details/fetch_op_handle.cc

@@ -14,6 +14,9 @@
 
 #include "paddle/fluid/framework/details/fetch_op_handle.h"
 
+#include <string>
+#include <vector>
+
 namespace paddle {
 namespace framework {
 namespace details {
@@ -57,7 +60,10 @@ void FetchOpHandle::RunImpl() {
 
   for (size_t i = 0; i < scopes.size(); ++i) {
     auto &scope = scopes[i];
-    auto &t = scope->FindVar(var_name)->Get<framework::LoDTensor>();
+    auto &t = scope->FindVar(kLocalExecScopeName)
+                  ->Get<Scope *>()
+                  ->FindVar(var_name)
+                  ->Get<framework::LoDTensor>();
     if (platform::is_gpu_place(var->place_)) {
 #ifdef PADDLE_WITH_CUDA
       TensorCopy(t, cpu, *dev_ctxes_[t.place()], &tensors_[i]);

paddle/fluid/framework/details/op_handle_base.h

@@ -24,6 +24,8 @@ namespace paddle {
 namespace framework {
 namespace details {
 
+constexpr char kLocalExecScopeName[] = "@LCOAL_SCOPE@";
+
 class OpHandleBase {
  private:
   DISABLE_COPY_AND_ASSIGN(OpHandleBase);

paddle/fluid/framework/details/ssa_graph_executor.h

@@ -15,13 +15,15 @@
 #pragma once
 
 #include <memory>
+#include <string>
+#include <vector>
+
 #include "paddle/fluid/framework/details/ssa_graph.h"
 #include "paddle/fluid/framework/feed_fetch_type.h"
 
 namespace paddle {
 namespace framework {
 namespace details {
-
 class SSAGraphExecutor {
   DISABLE_COPY_AND_ASSIGN(SSAGraphExecutor);
 

paddle/fluid/framework/details/threaded_ssa_graph_executor.cc

@@ -136,12 +136,6 @@ FeedFetchList ThreadedSSAGraphExecutor::Run(
     ready_ops.clear();
   };
 
-  // Create local scopes.
-  for (auto &scope : local_scopes_) {
-    auto &local_scope = scope->NewScope();
-    *scope->Var("@TMP_SCOPE@")->GetMutable<Scope *>() = &local_scope;
-  }
-
   // Step 3. Execution
   while (!pending_vars.empty() || !ready_ops.empty() || !delayed_ops.empty()) {
     // 1. Run All Ready ops
@@ -189,34 +183,10 @@ FeedFetchList ThreadedSSAGraphExecutor::Run(
   PADDLE_ENFORCE(ready_ops.empty());
   PADDLE_ENFORCE(delayed_ops.empty());
   PADDLE_ENFORCE(blocked_by_delayed_ops.empty());
-  ++computation_count_;
-
-  auto sync_computation = [&] {
-    computation_count_ = 0;
-    // Wait All computational streams
-    for (auto p : this->places_) {
-      platform::DeviceContextPool::Instance().Get(p)->Wait();
-    }
-    for (auto &scope : local_scopes_) {
-      scope->DropKids();
-    }
-  };
 
   // Wait FetchOps.
   if (!fetch_ops.empty()) {
     fetch_ops.clear();
-    sync_computation();
-  }
-
-  if (computation_count_ == max_async_computation) {
-    sync_computation();
-  }
-
-  // NOTE: the temp scope can be dropped lazily if needed.
-  // Drop tmp scopes;
-  for (auto &scope : local_scopes_) {
-    auto &kid = *scope->Var("@TMP_SCOPE@")->GetMutable<Scope *>();
-    kid = nullptr;
   }
 
   return fetch_data;

paddle/fluid/framework/details/threaded_ssa_graph_executor.h

@@ -99,9 +99,6 @@ class ThreadedSSAGraphExecutor : public SSAGraphExecutor {
   std::unique_ptr<platform::EnforceNotMet> exception_;
   std::atomic<int> running_ops_;
   bool allow_op_delay_;
-
-  size_t computation_count_{0};
-  size_t max_async_computation{100};
 };
 
 }  // namespace details

paddle/fluid/framework/operator.cc

@@ -46,7 +46,8 @@ proto::VarType::Type GetDataTypeOfVar(const Variable* var) {
   }
 }
 
-static DDim GetDims(const Scope& scope, const std::string& name) {
+static DDim GetDims(const Scope& scope, const std::string& name,
+                    bool get_actual_dim = false) {
   Variable* var = scope.FindVar(name);
   if (var == nullptr) {
     return DDim({-1});
@@ -55,7 +56,11 @@ static DDim GetDims(const Scope& scope, const std::string& name) {
   if (var->IsType<LoDTensor>()) {
     return var->Get<LoDTensor>().dims();
   } else if (var->IsType<SelectedRows>()) {
-    return var->Get<SelectedRows>().GetCompleteDims();
+    if (get_actual_dim) {
+      return var->Get<SelectedRows>().value().dims();
+    } else {
+      return var->Get<SelectedRows>().GetCompleteDims();
+    }
   } else {
     return DDim({-1});
   }
@@ -129,7 +134,7 @@ std::string OperatorBase::DebugStringEx(const Scope* scope) const {
     for (size_t i = 0; i < input.second.size(); ++i) {
       ss << input.second[i];
       if (scope) {
-        ss << "[" << GetDims(*scope, input.second[i]) << "]";
+        ss << "[" << GetDims(*scope, input.second[i], true) << "]";
         ss << "(" << GetLoD(*scope, input.second[i]) << ")";
       }
       if (i != input.second.size() - 1) {
@@ -149,7 +154,7 @@ std::string OperatorBase::DebugStringEx(const Scope* scope) const {
     for (size_t i = 0; i < output.second.size(); ++i) {
       ss << output.second[i];
       if (scope) {
-        ss << "[" << GetDims(*scope, output.second[i]) << "]";
+        ss << "[" << GetDims(*scope, output.second[i], true) << "]";
         ss << "(" << GetLoD(*scope, output.second[i]) << ")";
       }
       if (i != output.second.size() - 1) {

paddle/fluid/framework/parallel_executor.cc

@@ -15,6 +15,7 @@ limitations under the License. */
 #include "paddle/fluid/framework/parallel_executor.h"
 
 #include <string>
+#include <tuple>
 #include <vector>
 
 #ifdef PADDLE_WITH_CUDA
@@ -41,6 +42,8 @@ class ParallelExecutorPrivate {
 #ifdef PADDLE_WITH_CUDA
   std::unique_ptr<platform::NCCLContextMap> nccl_ctxs_;
 #endif
+
+  std::vector<std::tuple<std::string, proto::VarType::Type, bool>> var_types_;
 };
 
 std::vector<Scope *> &ParallelExecutor::GetLocalScopes() {
@@ -97,14 +100,9 @@ ParallelExecutor::ParallelExecutor(
       allow_op_delay));
 
   // Step 3. Create vars in each scope;
-  for (auto *scope : member_->local_scopes_) {
-    for (auto *var : main_program.Block(0).AllVars()) {
-      if (scope->FindVar(var->Name()) != nullptr) {
-        continue;
-      }
-
-      InitializeVariable(scope->Var(var->Name()), var->GetType());
-    }
+  for (auto *var : main_program.Block(0).AllVars()) {
+    member_->var_types_.emplace_back(var->Name(), var->GetType(),
+                                     var->Persistable());
   }
 }
 
@@ -163,9 +161,42 @@ void ParallelExecutor::Run(
     const std::unordered_map<std::string, LoDTensor> &feed_tensors) {
   platform::RecordBlock b(0);
   SplitTensorToPlaces(feed_tensors);
+
+  // Create local scopes.
+  for (auto &scope : member_->local_scopes_) {
+    Scope &local_scope = scope->NewScope();
+    *scope->Var(details::kLocalExecScopeName)->GetMutable<Scope *>() =
+        &local_scope;
+
+    for (auto &name_type_pair : member_->var_types_) {
+      if (scope->FindVar(std::get<0>(name_type_pair)) != nullptr) {
+        continue;
+      }
+
+      if (std::get<2>(name_type_pair)) {  // Persistable
+        InitializeVariable(scope->Var(std::get<0>(name_type_pair)),
+                           std::get<1>(name_type_pair));
+      } else {
+        InitializeVariable(scope->Var(std::get<0>(name_type_pair)),
+                           std::get<1>(name_type_pair));
+      }
+    }
+  }
+
   auto fetch_data = member_->executor_->Run(fetch_tensors);
   *member_->global_scope_->Var(fetched_var_name)->GetMutable<FeedFetchList>() =
       fetch_data;
+
+  // Wait All computational streams
+  for (auto p : member_->places_) {
+    platform::DeviceContextPool::Instance().Get(p)->Wait();
+  }
+  for (auto &scope : member_->local_scopes_) {
+    auto &local_scope =
+        *scope->Var(details::kLocalExecScopeName)->GetMutable<Scope *>();
+    scope->DeleteScope(local_scope);
+    local_scope = nullptr;
+  }
 }
 
 void ParallelExecutor::SplitTensorToPlaces(

paddle/fluid/operators/concat_op.cc

@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/fluid/operators/concat_op.h"
+
 #include <string>
 #include <vector>
 
@@ -34,7 +35,10 @@ class ConcatOp : public framework::OperatorWithKernel {
     size_t axis = static_cast<size_t>(ctx->Attrs().Get<int>("axis"));
     const size_t n = ins.size();
 
-    PADDLE_ENFORCE_GT(n, 1, "Input tensors count should > 1.");
+    PADDLE_ENFORCE_GT(n, 0, "Input tensors count should > 0.");
+    if (n == 1) {
+      VLOG(3) << "Warning: concat op have only one input, may waste memory";
+    }
 
     auto out_dims = ins[0];
     size_t in_zero_dims_size = out_dims.size();

paddle/fluid/operators/detail/grpc_server.cc

@@ -161,6 +161,7 @@ class RequestPrefetch final : public RequestBase {
     ::grpc::ByteBuffer reply;
 
     std::string var_name = request_->OutVarname();
+    VLOG(3) << "prefetch var " << var_name;
     auto var_desc = program_->Block(0).FindVar(var_name);
     framework::Scope* local_scope = &scope_->NewScope();
     auto* var = local_scope->FindVar(var_name);

paddle/fluid/operators/listen_and_serv_op.cc

@@ -13,7 +13,8 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include <ostream>
-#include <thread>
+#include <thread>  // NOLINT
+#include <vector>
 
 #include "paddle/fluid/operators/listen_and_serv_op.h"
 
@@ -88,8 +89,9 @@ void ListenAndServOp::RunImpl(const framework::Scope &scope,
 
   auto ins = Inputs("X");
   auto fan_in = Attr<int>("Fanin");
-  auto *block = Attr<framework::BlockDesc *>(kOptimizeBlock);
-  auto *program = block->Program();
+  auto *optimize_block = Attr<framework::BlockDesc *>(kOptimizeBlock);
+  auto *prefetch_block = Attr<framework::BlockDesc *>(kPrefetchBlock);
+  auto *program = optimize_block->Program();
   size_t num_blocks = program->Size();
   PADDLE_ENFORCE_GE(num_blocks, 2,
                     "server program should have at least 2 blocks");
@@ -97,18 +99,25 @@ void ListenAndServOp::RunImpl(const framework::Scope &scope,
   framework::Executor executor(dev_place);
   std::vector<int> block_list;
   for (size_t blkid = 1; blkid < num_blocks; ++blkid) {
-    block_list.push_back(blkid);
+    if (blkid != prefetch_block->ID()) {
+      block_list.push_back(blkid);
+    }
   }
-  auto prepared = executor.Prepare(*program, block_list);
+  auto optimize_prepared = executor.Prepare(*program, block_list);
   // Insert placeholder for block0 which holds current op itself.
-  prepared.insert(prepared.begin(),
-                  std::shared_ptr<framework::ExecutorPrepareContext>(nullptr));
+  optimize_prepared.insert(
+      optimize_prepared.begin(),
+      std::shared_ptr<framework::ExecutorPrepareContext>(nullptr));
 
   rpc_service_->SetScope(&recv_scope);
   rpc_service_->SetDevCtx(&dev_ctx);
   // TODO(qiao) set proper fields for table lookup and update
   rpc_service_->SetExecutor(&executor);
-  rpc_service_->SetPrefetchBlkdId(0);
+  VLOG(3) << "prefetch block id is " << prefetch_block->ID();
+  auto prefetch_prepared = executor.Prepare(*program, prefetch_block->ID());
+  rpc_service_->SetPrefetchBlkdId(prefetch_block->ID());
+  rpc_service_->SetPrefetchPreparedCtx(prefetch_prepared.get());
+  prefetch_prepared.release();
   rpc_service_->SetProgram(program);
   // start the server listening after all member initialized.
   server_thread_.reset(new std::thread(RunServer, rpc_service_));
@@ -166,16 +175,18 @@ void ListenAndServOp::RunImpl(const framework::Scope &scope,
     parallel_blkids.push_back(1);
     double ts = detail::GetTimestamp();
     for (size_t blkid = 2; blkid < num_blocks; ++blkid) {
-      if (program->Block(blkid).Parent() != last_parent_blkid) {
-        ParallelExecuteBlocks(parallel_blkids, &executor, prepared, program,
-                              &recv_scope);
-        parallel_blkids.clear();
-        last_parent_blkid = program->Block(blkid).Parent();
+      if (blkid != prefetch_block->ID()) {
+        if (program->Block(blkid).Parent() != last_parent_blkid) {
+          ParallelExecuteBlocks(parallel_blkids, &executor, optimize_prepared,
+                                program, &recv_scope);
+          parallel_blkids.clear();
+          last_parent_blkid = program->Block(blkid).Parent();
+        }
+        parallel_blkids.push_back(blkid);
       }
-      parallel_blkids.push_back(blkid);
     }
-    ParallelExecuteBlocks(parallel_blkids, &executor, prepared, program,
-                          &recv_scope);
+    ParallelExecuteBlocks(parallel_blkids, &executor, optimize_prepared,
+                          program, &recv_scope);
     VLOG(2) << "run all blocks spent " << detail::GetTimestamp() - ts << "(ms)";
 
     // Reset the received sparse variables, the sum operator would not
@@ -211,6 +222,8 @@ from send_op and send back variables to recv_op.
         .AddCustomChecker([](const std::string &ip) { return !ip.empty(); });
     AddAttr<framework::BlockDesc *>(kOptimizeBlock,
                                     "BlockID to run on server side.");
+    AddAttr<framework::BlockDesc *>(kPrefetchBlock,
+                                    "prefetch block to run on server side.");
     AddAttr<int>("Fanin", "How many clients send to this server.")
         .SetDefault(1);
   }

paddle/fluid/operators/listen_and_serv_op.h

@@ -16,6 +16,7 @@ limitations under the License. */
 
 #include <stdint.h>
 #include <ostream>
+#include <string>
 
 #include "paddle/fluid/framework/executor.h"
 #include "paddle/fluid/framework/lod_tensor.h"
@@ -27,6 +28,7 @@ namespace paddle {
 namespace operators {
 
 constexpr char kOptimizeBlock[] = "OptimizeBlock";
+constexpr char kPrefetchBlock[] = "PrefetchBlock";
 
 void RunServer(std::shared_ptr<detail::AsyncGRPCServer> service);
 

paddle/fluid/operators/lookup_table_op.cc

@@ -78,6 +78,9 @@ class LookupTableOpMaker : public framework::OpProtoAndCheckerMaker {
                   "(boolean, default false) "
                   "Sparse update.")
         .SetDefault(false);
+    AddAttr<bool>("is_distributed",
+                  "(boolean, default false) distributed lookup table.")
+        .SetDefault(false);
     AddAttr<int64_t>("padding_idx",
                      "(int64, default -1) "
                      "If the value is -1, it makes no effect to lookup. "

paddle/fluid/operators/prefetch_op.cc

@@ -12,7 +12,7 @@ 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>
+#include <future>  // NOLINT
 #include <ostream>
 
 #include "paddle/fluid/framework/data_type.h"
@@ -50,8 +50,8 @@ class PrefetchOp : public framework::OperatorBase {
 
     for (size_t i = 0; i < ins.size(); i++) {
       if (NeedSend(scope, ins[i])) {
-        VLOG(3) << "sending " << ins[i] << " to " << epmap[i] << "to get "
-                << outs[i] << "back";
+        VLOG(3) << "sending " << ins[i] << " to " << epmap[i] << " to get "
+                << outs[i] << " back";
         rpc_client->AsyncPrefetchVariable(epmap[i], ctx, scope, ins[i],
                                           outs[i]);
       } else {
@@ -71,7 +71,7 @@ class PrefetchOpMaker : public framework::OpProtoAndCheckerMaker {
               "(RPCClient) The RPC client object which will be"
               "initialized at most once.");
     AddOutput("Out",
-              "(SelectedRows) result "
+              "(LoDTensor) result "
               "to be fetched from parameter server")
         .AsDuplicable();
     AddAttr<std::vector<std::string>>(

paddle/fluid/operators/send_recv_op_test.cc

@@ -14,7 +14,7 @@ limitations under the License. */
 
 #include <unistd.h>
 #include <string>
-#include <thread>
+#include <thread>  // NOLINT
 
 #include "gtest/gtest.h"
 #include "paddle/fluid/framework/op_registry.h"
@@ -37,11 +37,11 @@ namespace m = paddle::operators::math;
 std::unique_ptr<f::OperatorBase> listen_and_serv_op;
 int selected_port;
 
-void InitTensorsInScope(f::Scope &scope, p::CPUPlace &place) {
+void InitTensorsInScope(const p::CPUPlace &place, f::Scope *scope) {
   p::CPUDeviceContext ctx(place);
   for (int i = 0; i < 2; ++i) {
     auto var_name = paddle::string::Sprintf("x%d", i);
-    auto var = scope.Var(var_name);
+    auto var = scope->Var(var_name);
     auto tensor = var->GetMutable<f::LoDTensor>();
     tensor->Resize({10, 10});
     float *expect = tensor->mutable_data<float>(place);
@@ -50,20 +50,20 @@ void InitTensorsInScope(f::Scope &scope, p::CPUPlace &place) {
     }
   }
 
-  auto out_var = scope.Var("Out");
+  auto out_var = scope->Var("Out");
   auto out_tensor = out_var->GetMutable<f::LoDTensor>();
   out_tensor->Resize({10, 10});
   out_tensor->mutable_data<float>(place);  // allocate
 }
 
-void InitSelectedRowsInScope(f::Scope &scope, p::CPUPlace &place) {
+void InitSelectedRowsInScope(const p::CPUPlace &place, f::Scope *scope) {
   p::CPUDeviceContext ctx(place);
   int64_t height = 10;
   int64_t row_numel = 10;
   m::SetConstant<p::CPUDeviceContext, float> set_one;
   // init x0
   std::vector<int64_t> rows0{0, 4, 7};
-  auto x0_var = scope.Var("x0");
+  auto x0_var = scope->Var("x0");
   auto x0 = x0_var->GetMutable<f::SelectedRows>();
   x0->set_rows(rows0);
   x0->set_height(height);
@@ -74,7 +74,7 @@ void InitSelectedRowsInScope(f::Scope &scope, p::CPUPlace &place) {
 
   // init x1
   std::vector<int64_t> rows1{2, 9};
-  auto x1_var = scope.Var("x1");
+  auto x1_var = scope->Var("x1");
   auto x1 = x1_var->GetMutable<f::SelectedRows>();
   x1->set_rows(rows1);
   x1->set_height(height);
@@ -83,7 +83,7 @@ void InitSelectedRowsInScope(f::Scope &scope, p::CPUPlace &place) {
       f::make_ddim({static_cast<int64_t>(rows1.size()), row_numel}), place);
   set_one(ctx, x1_value, 1.0);
 
-  auto out_var = scope.Var("Out");
+  auto out_var = scope->Var("Out");
   auto out = out_var->GetMutable<f::SelectedRows>();
   auto out_value = out->mutable_value();
   out->set_height(height);
@@ -117,15 +117,16 @@ void StartServerNet(bool is_sparse) {
   f::Scope scope;
   p::CPUPlace place;
   if (is_sparse) {
-    InitSelectedRowsInScope(scope, place);
+    InitSelectedRowsInScope(place, &scope);
   } else {
-    InitTensorsInScope(scope, place);
+    InitTensorsInScope(place, &scope);
   }
 
   // sub program run in listen_and_serv_op, for simple test we use sum
   f::ProgramDesc program;
   const auto &root_block = program.Block(0);
   auto *optimize_block = program.AppendBlock(root_block);
+  auto *prefetch_block = program.AppendBlock(root_block);
   // X for server side tensors, RX for received tensers, must be of same shape.
   AddOp("sum", {{"X", {"x0", "x1"}}}, {{"Out", {"Out"}}}, {}, optimize_block);
 
@@ -135,6 +136,7 @@ void StartServerNet(bool is_sparse) {
   attrs.insert({"ParamList", std::vector<std::string>({"Out"})});
   attrs.insert({"GradList", std::vector<std::string>({"x1"})});
   attrs.insert({"OptimizeBlock", optimize_block});
+  attrs.insert({"PrefetchBlock", prefetch_block});
   listen_and_serv_op =
       f::OpRegistry::CreateOp("listen_and_serv", {{"X", {"x1"}}}, {}, attrs);
   LOG(INFO) << "selected port before run " << selected_port;
@@ -148,7 +150,7 @@ TEST(SendRecvOp, CPUDense) {
   // local net
   f::Scope scope;
   p::CPUPlace place;
-  InitTensorsInScope(scope, place);
+  InitTensorsInScope(place, &scope);
   // create rpc client var
   scope.Var("RPC_CLIENT_VAR");
 
@@ -191,7 +193,7 @@ TEST(SendRecvOp, CPUSparse) {
   f::Scope scope;
   p::CPUPlace place;
   p::CPUDeviceContext ctx(place);
-  InitSelectedRowsInScope(scope, place);
+  InitSelectedRowsInScope(place, &scope);
   scope.Var("RPC_CLIENT_VAR");
   f::AttributeMap attrs;
   selected_port = static_cast<paddle::operators::ListenAndServOp *>(

paddle/fluid/operators/send_vars_op.cc

@@ -12,7 +12,7 @@ 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>
+#include <future>  // NOLINT
 #include <ostream>
 
 #include "paddle/fluid/framework/data_type.h"
@@ -36,7 +36,7 @@ class SendVarsOp : public framework::OperatorBase {
     auto ins = Inputs("X");
 
     std::vector<std::string> epmap = Attr<std::vector<std::string>>("epmap");
-    int sync_send = Attr<int>("sync_sent");
+    int sync_send = Attr<int>("sync_send");
 
     platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
     auto& ctx = *pool.Get(place);

paddle/fluid/operators/sgd_op.cc

@@ -35,8 +35,8 @@ class SGDOp : public framework::OperatorWithKernel {
     PADDLE_ENFORCE_EQ(framework::product(lr_dims), 1,
                       "Learning rate should have 1 element");
     auto param_dim = ctx->GetInputDim("Param");
-    // TODO(qijun): check dimensions of Param and Grad at complie
-    // and run time.
+    // TODO(qijun): check dimensions of Param and Grad at compile
+    // and runtime.
     ctx->SetOutputDim("ParamOut", param_dim);
   }
 

paddle/fluid/operators/split_ids_op.cc

@@ -48,11 +48,11 @@ class SplitIdsOp : public framework::OperatorWithKernel {
     PADDLE_ENFORCE(ctx->HasOutputs("Out"), "SplitIdsOp must has output Out.");
 
     auto ids_var_type = ctx->GetInputsVarType("Ids").front();
-    PADDLE_ENFORCE_EQ(ids_var_type, framework::proto::VarType::LOD_TENSOR);
-
     auto ids_dims = ctx->GetInputDim("Ids");
-    PADDLE_ENFORCE_EQ(ids_dims.size(), 2);
-    PADDLE_ENFORCE_EQ(ids_dims[1], 1);
+    if (ids_var_type == framework::proto::VarType::LOD_TENSOR) {
+      PADDLE_ENFORCE_EQ(ids_dims.size(), 2);
+      PADDLE_ENFORCE_EQ(ids_dims[1], 1);
+    }
   }
 };
 
@@ -60,8 +60,9 @@ class SplitIdsOpInferVarType : public framework::VarTypeInference {
  public:
   void operator()(const framework::OpDesc &op_desc,
                   framework::BlockDesc *block) const override {
+    auto *input_var = block->Var(op_desc.Input("Ids")[0]);
     for (auto &out_var : op_desc.Output("Out")) {
-      block->Var(out_var)->SetType(framework::proto::VarType::LOD_TENSOR);
+      block->Var(out_var)->SetType(input_var->GetType());
     }
   }
 };
@@ -73,4 +74,5 @@ namespace ops = paddle::operators;
 REGISTER_OPERATOR(split_ids, ops::SplitIdsOp, ops::SplitIdsOpMaker,
                   ops::SplitIdsOpInferVarType);
 REGISTER_OP_CPU_KERNEL(
-    split_ids, ops::SplitIdsOpKernel<paddle::platform::CPUPlace, int64_t>);
+    split_ids, ops::SplitIdsOpKernel<paddle::platform::CPUPlace, int64_t>,
+    ops::SplitIdsOpKernel<paddle::platform::CPUPlace, float>);

paddle/fluid/operators/split_ids_op.h

@@ -24,35 +24,63 @@ namespace operators {
 template <typename DeviceContext, typename T>
 class SplitIdsOpKernel : public framework::OpKernel<T> {
  public:
-  void Compute(const framework::ExecutionContext& ctx) const override {
+  void Compute(const framework::ExecutionContext &ctx) const override {
     auto place = ctx.GetPlace();
     if (!platform::is_cpu_place(place)) {
       PADDLE_THROW("SplitIds do not support GPU kernel");
     }
 
-    auto& ids_dims = ctx.Input<framework::LoDTensor>("Ids")->dims();
-    const T* ids = ctx.Input<framework::LoDTensor>("Ids")->data<T>();
-    auto outs = ctx.MultiOutput<framework::LoDTensor>("Out");
-    const size_t shard_num = outs.size();
+    const auto *ids_var = ctx.InputVar("Ids");
+    if (ids_var->IsType<framework::LoDTensor>()) {
+      const auto &ids_dims = ctx.Input<framework::LoDTensor>("Ids")->dims();
+      const T *ids = ctx.Input<framework::LoDTensor>("Ids")->data<T>();
+      auto outs = ctx.MultiOutput<framework::LoDTensor>("Out");
+      const size_t shard_num = outs.size();
 
-    std::vector<std::vector<T>> out_ids;
-    out_ids.resize(outs.size());
+      std::vector<std::vector<T>> out_ids;
+      out_ids.resize(outs.size());
 
-    // split id by their shard_num.
-    for (int i = 0; i < ids_dims[0]; ++i) {
-      T id = ids[i];
-      size_t shard_id = static_cast<size_t>(id) % shard_num;
-      out_ids[shard_id].push_back(id);
-    }
+      // split id by their shard_num.
+      for (int i = 0; i < ids_dims[0]; ++i) {
+        T id = ids[i];
+        size_t shard_id = static_cast<size_t>(id) % shard_num;
+        out_ids[shard_id].push_back(id);
+      }
+
+      // create tensor for each shard and send to parameter server
+      for (size_t i = 0; i < out_ids.size(); ++i) {
+        auto *shard_t = outs[i];
+        std::vector<T> ids = out_ids[i];
+        auto *shard_data = shard_t->mutable_data<T>(
+            framework::make_ddim({static_cast<int64_t>(ids.size()), 1}), place);
+        for (size_t i = 0; i < ids.size(); ++i) {
+          shard_data[i] = ids[i];
+        }
+      }
+    } else if (ids_var->IsType<framework::SelectedRows>()) {
+      const auto *ids_selected_rows = ctx.Input<framework::SelectedRows>("Ids");
+      auto &ids_dims = ids_selected_rows->value().dims();
+      PADDLE_ENFORCE_EQ(ids_dims[0], ids_selected_rows->rows().size(), "");
+      const T *ids = ids_selected_rows->value().data<T>();
+      const auto &ids_rows = ids_selected_rows->rows();
+      auto outs = ctx.MultiOutput<framework::SelectedRows>("Out");
+      const size_t shard_num = outs.size();
+      // get rows for outputs
+      for (auto &id : ids_rows) {
+        size_t shard_id = static_cast<size_t>(id) % shard_num;
+        outs[shard_id]->mutable_rows()->push_back(id);
+      }
 
-    // create tensor for each shard and send to parameter server
-    for (size_t i = 0; i < out_ids.size(); ++i) {
-      auto* shard_t = outs[i];
-      std::vector<T> ids = out_ids[i];
-      auto* shard_data = shard_t->mutable_data<T>(
-          framework::make_ddim({static_cast<int64_t>(ids.size()), 1}), place);
-      for (size_t i = 0; i < ids.size(); ++i) {
-        shard_data[i] = ids[i];
+      int64_t row_width = ids_dims[1];
+      for (auto &out : outs) {
+        out->set_height(ids_selected_rows->height());
+        framework::DDim ddim = framework::make_ddim(
+            {static_cast<int64_t>(out->rows().size()), row_width});
+        T *output = out->mutable_value()->mutable_data<T>(ddim, place);
+        for (size_t i = 0; i < ddim[0]; ++i) {
+          memcpy(output + i * row_width, ids + out->rows()[i] * row_width,
+                 row_width * sizeof(T));
+        }
       }
     }
   }

paddle/fluid/operators/sum_op.cc

@@ -10,9 +10,11 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/fluid/operators/sum_op.h"
+
 #include <algorithm>
 #include <string>
 #include <vector>
+
 #include "paddle/fluid/framework/var_type_inference.h"
 #include "paddle/fluid/operators/detail/safe_ref.h"
 
@@ -37,7 +39,10 @@ class SumOp : public framework::OperatorWithKernel {
 
     auto x_dims = ctx->GetInputsDim("X");
     size_t N = x_dims.size();
-    PADDLE_ENFORCE_GT(N, 1, "Input tensors count should > 1.");
+    PADDLE_ENFORCE_GT(N, 0, "Input tensors count should > 0.");
+    if (N == 1) {
+      VLOG(3) << "Warning: sum have only one input, may waste memory";
+    }
 
     framework::DDim in_dim({0});
     for (auto& x_dim : x_dims) {

python/paddle/fluid/distribute_transpiler.py

@@ -13,14 +13,17 @@
 # limitations under the License.
 
 from __future__ import print_function
-import framework
-from framework import Program, default_main_program, default_startup_program, Parameter, Variable
-import optimizer
-from layer_helper import LayerHelper
-import distributed_splitter as splitter
+
 import math
+
+import distributed_splitter as splitter
+import framework
+from framework import Program, default_main_program, Variable
 from . import core
-import debuger
+
+LOOKUP_TABLE_TYPE = "lookup_table"
+LOOKUP_TABLE_GRAD_TYPE = "lookup_table_grad"
+RPC_CLIENT_VAR_NAME = "RPC_CLIENT_VAR"
 
 
 class VarBlock:
@@ -35,9 +38,9 @@ class VarBlock:
 
 
 class UnionFind(object):
-    """ Union-find data struct.
+    """ Union-find data structure.
 
-    Union-find is a data struct that keeps track of a set of elements partitioned
+    Union-find is a data structure that keeps track of a set of elements partitioned
     into a number of disjoint (non-overlapping) subsets.
 
     Reference:
@@ -185,19 +188,66 @@ class DistributeTranspiler:
         assert (callable(split_method))
         if program is None:
             program = default_main_program()
-        self.program = program
-        self.trainers = trainers
+        self.origin_program = program
+        self.trainer_num = trainers
         self.optimize_ops = optimize_ops
         # TODO(typhoonzero): currently trainer_id is fetched from cluster system
         # like Kubernetes, we should port this to use etcd later when developing
         # fluid distributed training with fault-tolerance.
         self.trainer_id = trainer_id
         pserver_endpoints = pservers.split(",")
+        self.pserver_endpoints = pserver_endpoints
+
+        # process lookup_table_op
+        # 1. check all lookup_table_op is distributed
+        # 2. check all lookup_table_op share the same table.
+        distributed_lookup_table_ops = []
+        # support only one distributed_lookup_table now
+        self.table_name = None
+        for op in program.global_block().ops:
+            if op.type == LOOKUP_TABLE_TYPE:
+                if op.attrs['is_distributed'] is True:
+                    if self.table_name is None:
+                        self.table_name = op.input("W")[0]
+                    if self.table_name != op.input("W")[0]:
+                        raise RuntimeError("all distributed lookup_table_ops"
+                                           " should have only one table")
+                    distributed_lookup_table_ops.append(op)
+                else:
+                    if self.table_name is not None:
+                        assert op.input("W")[0] != self.table_name
+
+        self.has_distributed_lookup_table = len(
+            distributed_lookup_table_ops) > 0
 
         # step1: For large parameters and gradients, split them into smaller
         # blocks.
         param_list = [pg[0] for pg in params_grads]
         grad_list = [pg[1] for pg in params_grads]
+
+        if self.has_distributed_lookup_table:
+            param_list = [
+                param for param in param_list if param.name != self.table_name
+            ]
+            grad_list = [
+                grad for grad in grad_list
+                if grad.name != framework.grad_var_name(self.table_name)
+            ]
+            self.table_param_grad = [
+                param_grad for param_grad in params_grads
+                if param_grad[0].name == self.table_name
+            ][0]
+            table_grad_var = self.table_param_grad[1]
+            self.table_grad_list = [
+                program.global_block().create_var(
+                    name="%s.trainer_%d.pserver_%d" %
+                    (table_grad_var.name, trainer_id, index),
+                    type=table_grad_var.type,
+                    shape=table_grad_var.shape,
+                    dtype=table_grad_var.dtype)
+                for index in range(len(self.pserver_endpoints))
+            ]
+
         grad_blocks = split_dense_variable(grad_list, len(pserver_endpoints))
         param_blocks = split_dense_variable(param_list, len(pserver_endpoints))
         # step2: Create new vars for the parameters and gradients blocks and
@@ -229,7 +279,7 @@ class DistributeTranspiler:
             self.param_grad_ep_mapping[ep]["grads"].append(grad)
 
         rpc_client_var = program.global_block().create_var(
-            name="RPC_CLIENT_VAR",
+            name=RPC_CLIENT_VAR_NAME,
             persistable=True,
             type=core.VarDesc.VarType.RAW)
 
@@ -252,13 +302,19 @@ class DistributeTranspiler:
                 outputs={"Out": [orig_param]},
                 attrs={"axis": 0})
 
+        if self.has_distributed_lookup_table:
+            self._replace_lookup_table_op_with_prefetch(program, rpc_client_var,
+                                                        eplist)
+            self._split_table_grad_and_add_send_vars(program, rpc_client_var,
+                                                     pserver_endpoints)
+
     def get_trainer_program(self):
         # remove optimize ops and add a send op to main_program
-        self.program.global_block().delete_ops(self.optimize_ops)
-        self.program.sync_with_cpp()
+        self.origin_program.global_block().delete_ops(self.optimize_ops)
+        self.origin_program.sync_with_cpp()
         # FIXME(typhoonzero): serialize once will fix error occurs when clone.
-        self.program.__str__()
-        return self.program
+        self.origin_program.__str__()
+        return self.origin_program
 
     def get_pserver_program(self, endpoint):
         """
@@ -294,8 +350,8 @@ class DistributeTranspiler:
                     type=v.type,
                     dtype=v.dtype,
                     shape=v.shape)
-            if self.trainers > 1:
-                for trainer_id in xrange(self.trainers):
+            if self.trainer_num > 1:
+                for trainer_id in xrange(self.trainer_num):
                     var = pserver_program.global_block().create_var(
                         name="%s.trainer_%d" % (orig_var_name, trainer_id),
                         persistable=False,
@@ -309,7 +365,7 @@ class DistributeTranspiler:
         # step3
         optimize_block = pserver_program.create_block(0)
         # step 4
-        # Create a union-find data struct from optimize ops,
+        # Create a union-find data structure from optimize ops,
         # If two ops are connected, we could add these two ops
         # into one set.
         ufind = self._create_ufind(self.optimize_ops)
@@ -384,6 +440,23 @@ class DistributeTranspiler:
         #             __append_optimize_op__(glb_op, optimize_block)
         #             break
 
+        # process distributed lookup_table
+        prefetch_block = None
+        if self.has_distributed_lookup_table:
+            pserver_index = self.pserver_endpoints.index(endpoint)
+            self._create_table_optimize_block(pserver_index, pserver_program,
+                                              append_block)
+            prefetch_block = self._create_prefetch_block(
+                pserver_index, pserver_program, optimize_block)
+
+        # NOTE: if has_distributed_lookup_table is False, then prefetch_block will
+        # not be executed, so it's safe to use optimize_block to hold the place
+        if self.has_distributed_lookup_table:
+            assert prefetch_block is not None
+        else:
+            assert prefetch_block is None
+            prefetch_block = pserver_program.global_block()
+
         # step5 append the listen_and_serv op
         pserver_program.global_block().append_op(
             type="listen_and_serv",
@@ -392,8 +465,10 @@ class DistributeTranspiler:
             attrs={
                 "OptimizeBlock": optimize_block,
                 "endpoint": endpoint,
-                "Fanin": self.trainers
+                "Fanin": self.trainer_num,
+                "PrefetchBlock": prefetch_block
             })
+
         pserver_program.sync_with_cpp()
         return pserver_program
 
@@ -451,6 +526,197 @@ class DistributeTranspiler:
                     attrs=op.attrs)
         return s_prog
 
+    # transpiler function for dis lookup_table
+    def _replace_lookup_table_op_with_prefetch(self, program, rpc_client_var,
+                                               eplist):
+        # 1. replace lookup_table_op with split_ids_op -> prefetch_op -> sum_op
+        self.prefetch_input_vars = None
+        self.prefetch_output_vars = None
+
+        continue_search_lookup_table_op = True
+        while continue_search_lookup_table_op:
+            continue_search_lookup_table_op = False
+            all_ops = program.global_block().ops
+            for op in all_ops:
+                if op.type == LOOKUP_TABLE_TYPE:
+                    continue_search_lookup_table_op = True
+
+                    op_index = list(all_ops).index(op)
+                    ids_name = op.input("Ids")
+                    out_name = op.output("Out")
+
+                    if self.prefetch_input_vars is None:
+                        ids_var = program.global_block().vars[ids_name[0]]
+                        self.prefetch_input_vars = self.create_splited_vars(
+                            source_var=ids_var,
+                            block=program.global_block(),
+                            tag="_prefetch_in_")
+                    if self.prefetch_output_vars is None:
+                        out_var = program.global_block().vars[out_name[0]]
+                        self.prefetch_output_vars = self.create_splited_vars(
+                            source_var=out_var,
+                            block=program.global_block(),
+                            tag="_prefetch_out_")
+
+                    # insert split_ids_op
+                    program.global_block().insert_op(
+                        index=op_index,
+                        type="split_ids",
+                        inputs={
+                            'Ids': [
+                                program.global_block().vars[varname]
+                                for varname in ids_name
+                            ]
+                        },
+                        outputs={"Out": self.prefetch_input_vars})
+
+                    # insert prefetch_op
+                    program.global_block().insert_op(
+                        index=op_index + 1,
+                        type="prefetch",
+                        inputs={'X': self.prefetch_input_vars},
+                        outputs={
+                            "Out": self.prefetch_output_vars,
+                            "RPCClient": rpc_client_var
+                        },
+                        attrs={"epmap": eplist})
+
+                    # insert concat_op
+                    program.global_block().insert_op(
+                        index=op_index + 2,
+                        type="concat",
+                        inputs={'X': self.prefetch_output_vars},
+                        outputs={
+                            "Out": [
+                                program.global_block().vars[varname]
+                                for varname in out_name
+                            ]
+                        },
+                        attrs={"axis": 0})
+
+                    # delete lookup_table_op
+                    program.global_block().delete_ops([op])
+                    program.sync_with_cpp()
+                    # break for loop
+                    break
+
+    def _split_table_grad_and_add_send_vars(self, program, rpc_client_var,
+                                            pserver_endpoints):
+        # 2. add split_ids_op and send_vars_op to send gradient to pservers
+        # there should only be one table_name
+        all_ops = program.global_block().ops
+        table_grad_name = framework.grad_var_name(self.table_name)
+        for op in all_ops:
+            if table_grad_name in op.output_arg_names:
+                op_index = list(all_ops).index(op)
+                # insert split_ids_op
+                program.global_block().insert_op(
+                    index=op_index + 1,
+                    type="split_ids",
+                    inputs={
+                        'Ids': [program.global_block().vars[table_grad_name]]
+                    },
+                    outputs={"Out": self.table_grad_list})
+                program.global_block().insert_op(
+                    index=op_index + 2,
+                    type="send_vars",
+                    inputs={'X': self.table_grad_list},
+                    outputs={"RPCClient": rpc_client_var},
+                    attrs={"sync_send": True,
+                           "epmap": pserver_endpoints})
+                break
+
+    def _create_prefetch_block(self, pserver_index, pserver_program,
+                               optimize_block):
+        # STEP: create prefetch block
+        table_var = pserver_program.global_block().vars[self.table_name]
+        prefetch_block = pserver_program.create_block(optimize_block.idx)
+        trainer_ids = self.prefetch_input_vars[pserver_index]
+        pserver_ids = pserver_program.global_block().create_var(
+            name=trainer_ids.name,
+            type=trainer_ids.type,
+            shape=trainer_ids.shape,
+            dtype=trainer_ids.dtype)
+        trainer_out = self.prefetch_output_vars[pserver_index]
+        pserver_out = pserver_program.global_block().create_var(
+            name=trainer_out.name,
+            type=trainer_out.type,
+            shape=trainer_out.shape,
+            dtype=trainer_out.dtype)
+        prefetch_block.append_op(
+            type=LOOKUP_TABLE_TYPE,
+            inputs={'Ids': pserver_ids,
+                    "W": table_var},
+            outputs={"Out": pserver_out},
+            attrs={
+                "is_sparse": True,  # has no effect on lookup_table op
+                "is_distributed": True,
+                "padding_idx": -1
+            })
+        return prefetch_block
+
+    def _create_table_optimize_block(self, pserver_index, pserver_program,
+                                     append_block):
+        def _clone_var(block, var, persistable=True):
+            assert isinstance(var, Variable)
+            return block.create_var(
+                name=var.name,
+                shape=var.shape,
+                dtype=var.dtype,
+                type=var.type,
+                persistable=persistable)
+
+        # STEP: create table optimize block
+        # create table param and grad var in pserver program
+        param_var = _clone_var(
+            pserver_program.global_block(),
+            self.origin_program.global_block().vars[self.table_name])
+        grad_var = _clone_var(
+            pserver_program.global_block(),
+            self.origin_program.global_block().vars[framework.grad_var_name(
+                self.table_name)],
+            persistable=False)
+
+        # create grad vars in pserver program
+        table_grad_var = self.table_param_grad[1]
+        table_grad_list = [
+            pserver_program.global_block().create_var(
+                name="%s.trainer_%d.pserver_%d" %
+                (table_grad_var.name, index, pserver_index),
+                type=table_grad_var.type,
+                shape=table_grad_var.shape,
+                dtype=table_grad_var.dtype) for index in range(self.trainer_num)
+        ]
+
+        # create table optimize block in pserver program
+        table_opt_op = [
+            op for op in self.optimize_ops
+            if op.input("Param")[0] == self.table_name
+        ][0]
+        table_opt_block = pserver_program.create_block(append_block.idx)
+        # only support sgd now
+        assert table_opt_op.type == "sgd"
+
+        # append sum op for table_grad_list
+        table_opt_block.append_op(
+            type="sum",
+            inputs={"X": table_grad_list},
+            outputs={"Out": [grad_var]})
+
+        lr_var = pserver_program.global_block().vars[table_opt_op.input(
+            "LearningRate")[0]]
+        inputs = {
+            "Param": [param_var],
+            "Grad": [grad_var],
+            "LearningRate": [lr_var]
+        }
+        outputs = {"ParamOut": [param_var]}
+        table_opt_block.append_op(
+            type=table_opt_op.type,
+            inputs=inputs,
+            outputs=outputs,
+            attrs=table_opt_op.attrs)
+
     # ====================== private transpiler functions =====================
     def _create_vars_from_blocklist(self,
                                     program,
@@ -512,7 +778,17 @@ class DistributeTranspiler:
             program.global_block().sync_with_cpp()
         return var_mapping
 
-    def _clone_var(self, block, var):
+    def create_splited_vars(self, source_var, block, tag):
+        return [
+            block.create_var(
+                name=str(source_var.name + tag + str(index)),
+                type=source_var.type,
+                shape=source_var.shape,
+                dtype=source_var.dtype)
+            for index in range(len(self.pserver_endpoints))
+        ]
+
+    def _clone_var(self, block, var, persistable=True):
         assert isinstance(var, Variable)
         return block.create_var(
             name=var.name,
@@ -520,12 +796,12 @@ class DistributeTranspiler:
             dtype=var.dtype,
             type=var.type,
             lod_level=var.lod_level,
-            persistable=True)
+            persistable=persistable)
 
     def _append_split_op(self, program, gradblocks):
         # Split variables that need to be split and append respective ops
         add_suffix = False
-        if self.trainers > 1:
+        if self.trainer_num > 1:
             add_suffix = True
         var_mapping = self._create_vars_from_blocklist(
             program, gradblocks, add_trainer_suffix=add_suffix)
@@ -616,9 +892,9 @@ class DistributeTranspiler:
                     return
                 merged_var = \
                     pserver_block.vars[self._orig_varname(grad_block.name)]
-                if self.trainers > 1:
+                if self.trainer_num > 1:
                     vars2merge = []
-                    for i in xrange(self.trainers):
+                    for i in xrange(self.trainer_num):
                         per_trainer_name = "%s.trainer_%d" % \
                         (self._orig_varname(grad_block.name), i)
                         vars2merge.append(pserver_block.vars[per_trainer_name])
@@ -633,7 +909,7 @@ class DistributeTranspiler:
                             type="scale",
                             inputs={"X": merged_var},
                             outputs={"Out": merged_var},
-                            attrs={"scale": 1.0 / float(self.trainers)})
+                            attrs={"scale": 1.0 / float(self.trainer_num)})
                 new_inputs[key] = merged_var
             elif key == "Param":
                 # param is already created on global program
@@ -669,7 +945,7 @@ class DistributeTranspiler:
             new_shape = None
             if key in ["Param", "Grad", "LearningRate"]:
                 continue
-            var = self.program.global_block().vars[opt_op.input(key)[0]]
+            var = self.origin_program.global_block().vars[opt_op.input(key)[0]]
             # update accumulator variable shape
             param_shape = new_inputs["Param"].shape
             new_shape = self._get_optimizer_input_shape(opt_op.type, key,
@@ -682,8 +958,8 @@ class DistributeTranspiler:
             new_inputs[key] = tmpvar
 
         # change output's ParamOut variable
-        outputs = self._get_output_map_from_op(self.program.global_block().vars,
-                                               opt_op)
+        outputs = self._get_output_map_from_op(
+            self.origin_program.global_block().vars, opt_op)
         outputs["ParamOut"] = new_inputs["Param"]
 
         optimize_block.append_op(
@@ -695,8 +971,8 @@ class DistributeTranspiler:
     def _append_pserver_non_opt_ops(self, optimize_block, opt_op):
         program = optimize_block.program
         # Append the ops for parameters that do not need to be optimized/updated
-        inputs = self._get_input_map_from_op(self.program.global_block().vars,
-                                             opt_op)
+        inputs = self._get_input_map_from_op(
+            self.origin_program.global_block().vars, opt_op)
         for varlist in inputs.itervalues():
             if not isinstance(varlist, list):
                 varlist = [varlist]
@@ -709,8 +985,8 @@ class DistributeTranspiler:
                         dtype=var.dtype,
                         shape=var.shape)
 
-        outputs = self._get_output_map_from_op(self.program.global_block().vars,
-                                               opt_op)
+        outputs = self._get_output_map_from_op(
+            self.origin_program.global_block().vars, opt_op)
 
         for varlist in outputs.itervalues():
             if not isinstance(varlist, list):
@@ -783,7 +1059,6 @@ class DistributeTranspiler:
                 if same_or_split_var(n, param) and n != param:
                     return True
             return False
-        return False
 
     def _get_input_map_from_op(self, varmap, op):
         """Returns a dict from op input name to the vars in varmap."""
@@ -821,7 +1096,7 @@ class DistributeTranspiler:
 
         find_ops = []
         # find ops which output is lr var
-        block = self.program.global_block()
+        block = self.origin_program.global_block()
         for op in block.ops:
             if set(op.output_arg_names) & lr_vars:
                 find_ops.append(op)

python/paddle/fluid/layers/nn.py

@@ -218,6 +218,7 @@ def fc(input,
 def embedding(input,
               size,
               is_sparse=False,
+              is_distributed=False,
               padding_idx=None,
               param_attr=None,
               dtype='float32'):
@@ -268,8 +269,11 @@ def embedding(input,
         inputs={'Ids': input,
                 'W': w},
         outputs={'Out': tmp},
-        attrs={'is_sparse': is_sparse,
-               'padding_idx': padding_idx})
+        attrs={
+            'is_sparse': is_sparse,
+            'is_distributed': is_distributed,
+            'padding_idx': padding_idx
+        })
     return tmp