add fix op run order pass (#34427)

* add fix op run order pass

* add ut for fix_op_run_order

* fix ci error

* improve coverage

* improve coverge again and fix cpu test case

* follow some comments

paddle/fluid/framework/details/CMakeLists.txt

@@ -134,7 +134,8 @@ set(IR_PASS_DEPS graph_viz_pass multi_devices_graph_pass
     modify_op_lock_and_record_event_pass
     coalesce_grad_tensor_pass fuse_all_reduce_op_pass backward_optimizer_op_deps_pass
     fuse_adam_op_pass fuse_sgd_op_pass fuse_momentum_op_pass
-    sync_batch_norm_pass runtime_context_cache_pass graph_to_program_pass)
+    sync_batch_norm_pass runtime_context_cache_pass graph_to_program_pass
+    fix_op_run_order_pass)
 if(NOT APPLE AND NOT WIN32 AND (WITH_GPU OR WITH_ROCM))
   set(IR_PASS_DEPS ${IR_PASS_DEPS} fusion_group_pass)
 endif()

paddle/fluid/framework/details/build_strategy.h

@@ -100,6 +100,9 @@ struct BuildStrategy {
   // while running.
   bool cache_runtime_context_{false};
 
+  // Fix the op run order.
+  bool fix_op_run_order_{false};
+
   // Operator fusion
   // TODO(dev-paddle): fuse_elewise_add_act_ops may cause some models have
   // cycle.

paddle/fluid/framework/details/eager_deletion_op_handle.cc

@@ -19,6 +19,7 @@
 #if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
 #include "paddle/fluid/platform/cuda_device_guard.h"
 #endif
+#include <algorithm>
 
 namespace paddle {
 namespace framework {
@@ -177,6 +178,16 @@ void EagerDeletionOpHandle::ClearGarbages(
 #endif
 }
 
+std::vector<std::string> EagerDeletionOpHandle::VarsToDelete() const {
+  std::vector<std::string> var_names;
+  var_names.reserve(var_infos_.size());
+  for (auto &info : var_infos_) {
+    var_names.emplace_back(info->Name());
+  }
+  std::sort(var_names.begin(), var_names.end());
+  return var_names;
+}
+
 }  // namespace details
 }  // namespace framework
 }  // namespace paddle

paddle/fluid/framework/details/eager_deletion_op_handle.h

@@ -64,6 +64,8 @@ class EagerDeletionOpHandle : public OpHandleBase {
 
   size_t GetScopeIdx() const { return scope_idx_; }
 
+  std::vector<std::string> VarsToDelete() const;
+
  protected:
   void RunImpl() override;
 

paddle/fluid/framework/details/fast_threaded_ssa_graph_executor.cc

@@ -40,9 +40,14 @@ FastThreadedSSAGraphExecutor::FastThreadedSSAGraphExecutor(
       places_(places),
       graph_(graph),
       fetch_ctxs_(places),
-      pool_(strategy.num_threads_),
       // add one more thread for generate op_deps
       prepare_pool_(1) {
+  if (ir::IsTopologySortOperationsUnique(*graph_)) {
+    VLOG(10)
+        << "Change thread number to 1 because the toposort order is unique";
+    strategy_.num_threads_ = 1;
+  }
+  pool_.reset(new ::ThreadPool(strategy.num_threads_));
   for (auto &op : ir::FilterByNodeWrapper<OpHandleBase>(*graph_)) {
     int dep = static_cast<int>(op->NotReadyInputSize());
     op_deps_.emplace(op, dep);
@@ -223,7 +228,7 @@ void FastThreadedSSAGraphExecutor::RunOpAsync(
     OpHandleBase *op,
     const std::shared_ptr<BlockingQueue<size_t>> &complete_q) {
   ++remaining_;
-  this->pool_.enqueue([=] {
+  this->pool_->enqueue([=] {
     std::deque<OpHandleBase *> op_queue;
     op_queue.push_front(op);
 

paddle/fluid/framework/details/fast_threaded_ssa_graph_executor.h

@@ -60,7 +60,7 @@ class FastThreadedSSAGraphExecutor : public SSAGraphExecutor {
       atomic_op_deps_;
   ExceptionHolder exception_;
 
-  ::ThreadPool pool_;
+  std::unique_ptr<::ThreadPool> pool_;
   ::ThreadPool prepare_pool_;
 
   std::vector<OpHandleBase *> traced_ops_;

paddle/fluid/framework/distributed_strategy.proto

@@ -111,6 +111,7 @@ message BuildStrategy {
   optional bool fuse_bn_add_act_ops = 10 [ default = true ];
   optional bool enable_auto_fusion = 11 [ default = false ];
   optional bool enable_addto = 12 [ default = false ];
+  optional bool fix_op_run_order = 13 [ default = false ];
 }
 
 message ExecutionStrategy {

paddle/fluid/framework/ir/coalesce_grad_tensor_pass.cc

@@ -13,6 +13,7 @@
 // limitations under the License.
 
 #include "paddle/fluid/framework/ir/coalesce_grad_tensor_pass.h"
+#include <algorithm>
 #include <string>
 #include "paddle/fluid/framework/details/multi_devices_helper.h"
 #include "paddle/fluid/framework/ir/graph_helper.h"
@@ -254,8 +255,15 @@ class CoalesceGradTensorPass : public ir::Pass {
       const std::unordered_map<std::string, std::vector<ir::Node *>> &vars_info,
       const details::ParamsAndGrads &params_grads,
       details::GroupParamsAndGrads *group_params_grads) const {
+    if (GetFuseParameterMemorySize() == 0) {
+      group_params_grads->resize(1);
+      auto &result_param_grads = (*group_params_grads)[0];
+      result_param_grads = params_grads;
+      std::sort(result_param_grads.begin(), result_param_grads.end());
+    } else {
       SetGroupAccordingToLayers(vars_info, params_grads, group_params_grads);
       SetGroupAccordingToMemorySize(vars_info, group_params_grads);
+    }
     if (!IsUnifiedDtype(params_grads, vars_info)) {
       ReGroupByDtype(vars_info, group_params_grads);
     }

paddle/fluid/framework/ir/graph_helper.cc

@@ -143,6 +143,32 @@ std::vector<ir::Node *> TopologySortOperations(const Graph &graph) {
   return ret;
 }
 
+bool IsTopologySortOperationsUnique(const Graph &graph) {
+  auto nodes = TopologySortOperations(graph);
+  size_t n = nodes.size();
+  for (size_t i = 1; i < n; ++i) {
+    auto *prev_op = nodes[i - 1];
+    auto *cur_op = nodes[i];
+
+    std::unordered_set<Node *> prev_op_outputs;
+    for (auto *output : prev_op->outputs) {
+      prev_op_outputs.insert(output);
+    }
+
+    bool found = false;
+    for (auto *input : cur_op->inputs) {
+      if (prev_op_outputs.count(input) > 0) {
+        found = true;
+        break;
+      }
+    }
+    if (!found) {
+      return false;
+    }
+  }
+  return true;
+}
+
 // Build operator inlink edge table.
 std::map<ir::Node *, std::set<ir::Node *, ir::NodeComp>, ir::NodeComp>
 BuildOperationAdjList(const Graph &graph) {

paddle/fluid/framework/ir/graph_helper.h

@@ -57,6 +57,9 @@ size_t GraphNum(const Graph &graph);
 // `graph` cannot contain circle.
 std::vector<ir::Node *> TopologySortOperations(const Graph &graph);
 
+// Check whether the topological order of graph ops is unique
+bool IsTopologySortOperationsUnique(const Graph &graph);
+
 // Topological sort, but try to DFS.
 std::vector<ir::Node *> TopologyDfsSortOperations(const Graph &graph);
 

paddle/fluid/framework/ir/multi_devices_graph_pass/CMakeLists.txt

@@ -18,3 +18,4 @@ cc_library(fuse_all_reduce_op_pass SRCS fuse_all_reduce_op_pass.cc DEPS graph gr
 cc_library(all_reduce_deps_pass SRCS all_reduce_deps_pass.cc DEPS all_reduce_op_handle graph graph_helper pass)
 cc_library(backward_optimizer_op_deps_pass SRCS backward_optimizer_op_deps_pass.cc DEPS graph graph_helper pass)
 cc_library(add_reader_dependency_pass SRCS add_reader_dependency_pass.cc DEPS graph graph_helper pass)
+cc_library(fix_op_run_order_pass SRCS fix_op_run_order_pass DEPS graph graph_helper multi_devices_helper pass op_handle_base eager_deletion_op_handle)   

paddle/fluid/framework/ir/multi_devices_graph_pass/fix_op_run_order_pass.cc

+// Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// Unless required by applicable law or agreed to in writing, software
+// distributed under the License is distributed on an "AS IS" BASIS,
+// 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 <algorithm>
+#include "paddle/fluid/framework/details/eager_deletion_op_handle.h"
+#include "paddle/fluid/framework/details/multi_devices_helper.h"
+#include "paddle/fluid/framework/details/op_handle_base.h"
+#include "paddle/fluid/framework/details/var_handle.h"
+#include "paddle/fluid/framework/ir/graph.h"
+#include "paddle/fluid/framework/ir/graph_helper.h"
+#include "paddle/fluid/framework/ir/memory_optimize_pass/op_graph_view.h"
+#include "paddle/fluid/framework/ir/pass.h"
+#include "paddle/fluid/string/string_helper.h"
+
+namespace paddle {
+namespace framework {
+namespace ir {
+
+static std::string kSep(1, static_cast<char>(1));  // NOLINT
+
+// NOTE: VariableNameMap is sorted!
+static std::string VarNameMapToString(const VariableNameMap &var_map) {
+  std::vector<std::string> tmp_strs;
+  tmp_strs.reserve(var_map.size());
+  for (auto &pair : var_map) {
+    auto str = pair.first + kSep + string::join_strings(pair.second, kSep);
+    tmp_strs.emplace_back(std::move(str));
+  }
+  return string::join_strings(tmp_strs, kSep);
+}
+
+static std::string OpDescToString(const OpDesc &op) {
+  return "OpDesc" + kSep + op.Type() + kSep + VarNameMapToString(op.Inputs()) +
+         kSep + VarNameMapToString(op.Outputs());
+}
+
+static std::string VarHandleListToString(
+    const std::vector<details::VarHandleBase *> &vars) {
+  std::vector<std::string> valid_vars;
+  valid_vars.reserve(vars.size());
+  for (auto *v : vars) {
+    auto *valid_var = dynamic_cast<details::VarHandle *>(v);
+    if (valid_var != nullptr) {
+      valid_vars.emplace_back(valid_var->Name());
+    }
+  }
+  std::sort(valid_vars.begin(), valid_vars.end());
+  return string::join_strings(valid_vars, kSep);
+}
+
+static std::string EagerDeletionOpHandleToString(
+    const details::EagerDeletionOpHandle &op);
+static std::string OpHandleToString(const details::OpHandleBase &op);
+
+static std::string EagerDeletionOpHandleToString(
+    const details::EagerDeletionOpHandle &op) {
+  auto vars_to_delete = op.VarsToDelete();
+  std::unordered_set<details::OpHandleBase *> prev_ops;
+  std::vector<std::string> prev_op_strs;
+  prev_op_strs.reserve(op.Inputs().size());
+  for (auto *var : op.Inputs()) {
+    auto *prev_op = var->GeneratedOp();
+    if (prev_op == nullptr) continue;
+    prev_op_strs.push_back(OpHandleToString(*prev_op));
+  }
+  std::sort(prev_op_strs.begin(), prev_op_strs.end());
+  // NOTE: gc op does not have any valid input/output vars
+  return "OpHandleBase" + kSep + op.Name() + kSep +
+         string::join_strings(vars_to_delete, kSep) + kSep +
+         string::join_strings(prev_op_strs, kSep);
+}
+
+static std::string OpHandleToString(const details::OpHandleBase &op) {
+  // NOTE: gc op does not have any valid input/output vars
+  auto gc_op = dynamic_cast<const details::EagerDeletionOpHandle *>(&op);
+  if (gc_op) {
+    return EagerDeletionOpHandleToString(*gc_op);
+  }
+  return "OpHandleBase" + kSep + op.Name() + kSep +
+         VarHandleListToString(op.Inputs()) + kSep +
+         VarHandleListToString(op.Outputs());
+}
+
+static void AddSequentialDepsForSortedOps(
+    Graph *graph, const std::vector<details::OpHandleBase *> &sorted_ops) {
+  size_t n = sorted_ops.size();
+  for (size_t i = 1; i < n; ++i) {
+    auto *prev_op = sorted_ops[i - 1];
+    auto *cur_op = sorted_ops[i];
+    auto *dep_var = new details::DummyVarHandle(graph->CreateControlDepVar());
+    graph->Get<details::GraphDepVars>(details::kGraphDepVars).emplace(dep_var);
+    prev_op->AddOutput(dep_var);
+    cur_op->AddInput(dep_var);
+  }
+}
+
+class FixOpRunOrderPass : public Pass {
+ protected:
+  void ApplyImpl(Graph *graph) const override {
+    const auto &program = graph->OriginProgram();
+    std::unordered_map<std::string, size_t> op_to_idx;
+    size_t i = 0;
+    for (auto *op_desc : program.Block(0).AllOps()) {
+      auto op_desc_str = OpDescToString(*op_desc);
+      PADDLE_ENFORCE_EQ(
+          op_to_idx.emplace(op_desc_str, i).second, true,
+          platform::errors::PermissionDenied(
+              "FixOpRunOrderPass cannot handle OpDesc with same "
+              "type, inputs and outputs yet, error string repr: %s",
+              op_desc_str));
+      ++i;
+    }
+
+    // a map to record: "Node" -> "Node Index"
+    std::unordered_map<Node *, size_t> node_to_idx;
+    // a map to record found "Node Index"
+    std::unordered_set<size_t> found_node_indices;
+    // a map to record the new OpDesc created by other Passes. These ops does
+    // not exist in the origin program
+    std::map<std::string, Node *> new_op_desc_nodes;
+    // a map to record the new OpHandle created by other Passes. These ops does
+    // not have OpDesc and does not exist in the origin program
+    std::map<std::string, Node *> new_op_handle_nodes;
+
+    // Step 1: handle the unchanged OpDesc, and record new OpDesc/OpHandle
+    auto op_handles = FilterByNodeWrapper<details::OpHandleBase>(*graph);
+    for (auto *op_handle : op_handles) {
+      auto *node = op_handle->Node();
+      if (node->Op() == nullptr) {
+        auto node_str = OpHandleToString(*op_handle);
+        PADDLE_ENFORCE_EQ(new_op_handle_nodes.emplace(node_str, node).second,
+                          true,
+                          platform::errors::PermissionDenied(
+                              "FixOpRunOrderPass cannot OpHandle with same "
+                              "inputs and outputs yet, error repr: %s",
+                              node_str));
+        continue;
+      }
+
+      auto node_str = OpDescToString(*(node->Op()));
+      auto iter = op_to_idx.find(node_str);
+      if (iter != op_to_idx.end()) {
+        size_t idx = iter->second;
+        PADDLE_ENFORCE_EQ(
+            found_node_indices.count(idx), 0,
+            platform::errors::PermissionDenied(
+                "FixOpRunOrderPass cannot handle OpDesc with same "
+                "type, inputs and outputs yet, error repr: %s",
+                node_str));
+        found_node_indices.insert(idx);
+        node_to_idx[node] = idx;
+      } else {
+        PADDLE_ENFORCE_EQ(
+            new_op_desc_nodes.emplace(node_str, node).second, true,
+            platform::errors::PermissionDenied(
+                "FixOpRunOrderPass cannot handle OpDesc with same "
+                "type, inputs and outputs yet, error repr: %s",
+                node_str));
+      }
+    }
+
+    VLOG(10) << "Found unchanged OpDesc " << node_to_idx.size()
+             << ", new OpDesc " << new_op_desc_nodes.size() << ", new OpHandle "
+             << new_op_handle_nodes.size();
+
+    // Step 2: assign node index to new OpDesc
+    size_t node_id_offset = op_to_idx.size();
+    for (auto &pair : new_op_desc_nodes) {
+      node_to_idx[pair.second] = node_id_offset;
+      ++node_id_offset;
+    }
+
+    // Step 3: assign node index to new OpHandle
+    for (auto &pair : new_op_handle_nodes) {
+      node_to_idx[pair.second] = node_id_offset;
+      ++node_id_offset;
+    }
+
+    // Step 4: sort unchanged OpDesc/new OpDesc/new OpHandle by topological
+    // order and node index
+    OpGraphView graph_view(op_handles);
+    auto comp = [&node_to_idx](details::OpHandleBase *op1,
+                               details::OpHandleBase *op2) {
+      auto priority1 = static_cast<int>(op1->GetPriority());
+      auto priority2 = static_cast<int>(op2->GetPriority());
+      if (priority1 != priority2) {
+        return priority1 < priority2;
+      }
+      return node_to_idx.at(op1->Node()) < node_to_idx.at(op2->Node());
+    };
+
+    std::vector<details::OpHandleBase *> sorted_ops;
+    sorted_ops.reserve(op_handles.size());
+    std::queue<details::OpHandleBase *> q;
+    std::vector<details::OpHandleBase *> tmp_ops;
+    auto op_deps = graph_view.GetPrecedingDepNum();
+    // Get ready ops first
+    for (auto iter = op_deps.begin(); iter != op_deps.end();) {
+      if (iter->second != 0) {
+        ++iter;
+        continue;
+      }
+      tmp_ops.push_back(iter->first);
+      op_deps.erase(iter++);
+    }
+    // Sort ready ops by node index
+    std::sort(tmp_ops.begin(), tmp_ops.end(), comp);
+    for (auto *op : tmp_ops) {
+      q.push(op);
+    }
+    while (!q.empty()) {
+      auto *cur_op = q.front();
+      q.pop();
+      sorted_ops.push_back(cur_op);
+
+      auto &pending_ops = graph_view.PendingOps(cur_op);
+      tmp_ops.clear();
+      for (auto *pending_op : pending_ops) {
+        if (--op_deps.at(pending_op) == 0) {
+          op_deps.erase(pending_op);
+          tmp_ops.push_back(pending_op);
+        }
+      }
+      // sort next ready ops by node index
+      std::sort(tmp_ops.begin(), tmp_ops.end(), comp);
+      for (auto *op : tmp_ops) {
+        q.push(op);
+      }
+    }
+
+    PADDLE_ENFORCE_EQ(
+        sorted_ops.size(), op_handles.size(),
+        platform::errors::PermissionDenied("There are unvisited ops"));
+    if (VLOG_IS_ON(10)) {
+      // print op order to debug
+      std::vector<size_t> sorted_ops_indices;
+      sorted_ops_indices.reserve(sorted_ops.size());
+      for (auto *op : sorted_ops) {
+        sorted_ops_indices.push_back(node_to_idx.at(op->Node()));
+      }
+      VLOG(10) << "Fix op order: "
+               << string::join_strings(sorted_ops_indices, ',');
+    }
+
+    // Step 5: add sequential deps for ops to guarantee there is only one
+    // toposort order
+    AddSequentialDepsForSortedOps(graph, sorted_ops);
+    PADDLE_ENFORCE_EQ(IsTopologySortOperationsUnique(*graph), true,
+                      platform::errors::PermissionDenied(
+                          "The topological order must be unique "
+                          "after FixOpRunOrderPass is applied"));
+  }
+};
+
+}  // namespace ir
+}  // namespace framework
+}  // namespace paddle
+
+REGISTER_PASS(fix_op_run_order_pass, paddle::framework::ir::FixOpRunOrderPass);

paddle/fluid/framework/parallel_executor.cc

@@ -104,6 +104,13 @@ class ParallelExecutorPrivate {
 
   inline bool HasGarbageCollectors() const { return !gcs_.empty(); }
 
+  void ApplyFixOpRunOrderPass(ir::Graph *graph) {
+    if (build_strategy_.fix_op_run_order_) {
+      auto pass = ir::PassRegistry::Instance().Get("fix_op_run_order_pass");
+      pass->Apply(graph);
+    }
+  }
+
   /**
    * NOTE(zengjinle): the fed variables of users should not be reused,
    * because users may feed them into another network. Changing the fed
@@ -1462,6 +1469,10 @@ std::vector<ir::Graph *> ParallelExecutor::CreateSSAGraphExecutor(
     auto possible_inference_graphs =
         details::TrySeparateToMultipleSingleDeviceGraphs(graph);
     if (!possible_inference_graphs.empty()) {
+      for (auto &g : possible_inference_graphs) {
+        member_->ApplyFixOpRunOrderPass(g.get());
+      }
+
       VLOG(5) << "Use ParallelSSAGraphExecutor in inference phase";
       auto *pg_exe = new details::ParallelSSAGraphExecutor(
           exec_strategy, member_->local_scopes_, member_->local_exec_scopes_,
@@ -1474,6 +1485,9 @@ std::vector<ir::Graph *> ParallelExecutor::CreateSSAGraphExecutor(
       member_->executor_.reset(pg_exe);
       member_->inference_executor_ = pg_exe;
     } else {
+      if (member_->places_.size() == 1) {
+        member_->ApplyFixOpRunOrderPass(graph);
+      }
       LOG_IF(WARNING, details::HasKeepLastReadOp(*graph))
           << "drop_last=False for DataLoader is not supported in training "
              "network. It is automatically turned to drop_last=True.";
@@ -1560,3 +1574,4 @@ USE_PASS(eager_deletion_pass);
 USE_PASS(buffer_shared_inplace_pass);
 USE_PASS(buffer_shared_cross_op_memory_reuse_pass);
 USE_PASS(inplace_addto_op_pass);
+USE_PASS(fix_op_run_order_pass);

paddle/fluid/platform/dynload/nccl.cc

@@ -29,6 +29,10 @@ NCCL_RAND_ROUTINE_EACH(DEFINE_WRAP);
 NCCL_RAND_ROUTINE_EACH_AFTER_2212(DEFINE_WRAP)
 #endif
 
+#if NCCL_VERSION_CODE >= 2304
+NCCL_RAND_ROUTINE_EACH_AFTER_2304(DEFINE_WRAP)
+#endif
+
 #if NCCL_VERSION_CODE >= 2703
 NCCL_RAND_ROUTINE_EACH_AFTER_2703(DEFINE_WRAP)
 #endif

paddle/fluid/platform/dynload/nccl.h

@@ -64,6 +64,11 @@ NCCL_RAND_ROUTINE_EACH(DECLARE_DYNAMIC_LOAD_NCCL_WRAP)
 NCCL_RAND_ROUTINE_EACH_AFTER_2212(DECLARE_DYNAMIC_LOAD_NCCL_WRAP)
 #endif
 
+#if NCCL_VERSION_CODE >= 2304
+#define NCCL_RAND_ROUTINE_EACH_AFTER_2304(__macro) __macro(ncclGetVersion);
+NCCL_RAND_ROUTINE_EACH_AFTER_2304(DECLARE_DYNAMIC_LOAD_NCCL_WRAP)
+#endif
+
 #if NCCL_VERSION_CODE >= 2703
 #define NCCL_RAND_ROUTINE_EACH_AFTER_2703(__macro) \
   __macro(ncclSend);                               \

paddle/fluid/pybind/pybind.cc

@@ -467,6 +467,19 @@ static void AssertStaticGraphAndDygraphGradMakerNoDiff() {
                         string::join_strings(ops, ',')));
 }
 
+#ifdef PADDLE_WITH_NCCL
+static int GetNCCLVersion() {
+#if NCCL_VERSION_CODE >= 2304
+  int ver;
+  PADDLE_ENFORCE_CUDA_SUCCESS(platform::dynload::ncclGetVersion(&ver));
+  return ver;
+#else
+  PADDLE_THROW(platform::errors::External(
+      "Cannot get NCCL version successfully when nccl version < 2.3.4"));
+#endif
+}
+#endif
+
 #ifdef PADDLE_WITH_AVX
 PYBIND11_MODULE(core_avx, m) {
 #else
@@ -496,6 +509,14 @@ PYBIND11_MODULE(core_noavx, m) {
   m.def("cudnn_version", &platform::CudnnVersion);
 #endif
 
+#ifdef PADDLE_WITH_NCCL
+  m.def("nccl_version", &GetNCCLVersion);
+#endif
+
+  m.def("wait_device", [](const platform::Place &place) {
+    platform::DeviceContextPool::Instance().Get(place)->Wait();
+  });
+
   m.def("from_dlpack", [](py::capsule *dltensor) {
     DLManagedTensor *dmt = reinterpret_cast<DLManagedTensor *>(
         PyCapsule_GetPointer(dltensor->ptr(), "dltensor"));
@@ -1796,17 +1817,17 @@ All parameter, weight, gradient are variables in Paddle.
       .def("__str__", string::to_string<const platform::Place &>);
 
   py::class_<OperatorBase>(m, "Operator")
-      .def_static(
-          "create",
+      .def_static("create",
                   [](py::bytes protobin) {
                     proto::OpDesc desc;
-            PADDLE_ENFORCE_EQ(desc.ParsePartialFromString(protobin), true,
+                    PADDLE_ENFORCE_EQ(desc.ParsePartialFromString(protobin),
+                                      true,
                                       platform::errors::InvalidArgument(
                                           "Cannot parse user input to OpDesc"));
-            PADDLE_ENFORCE_EQ(
-                desc.IsInitialized(), true,
+                    PADDLE_ENFORCE_EQ(desc.IsInitialized(), true,
                                       platform::errors::InvalidArgument(
-                    "The provided OpDesc is not initialized, the reason is: %s",
+                                          "The provided OpDesc is not "
+                                          "initialized, the reason is: %s",
                                           desc.InitializationErrorString()));
                     return OpRegistry::CreateOp(desc);
                   })
@@ -2928,8 +2949,8 @@ All parameter, weight, gradient are variables in Paddle.
               self.memory_optimize_ = (py_obj == Py_True);
             } else {
               PADDLE_THROW(platform::errors::InvalidArgument(
-                  "BuildStrategy.memory_optimize must be set to None, False or "
-                  "True"));
+                  "BuildStrategy.memory_optimize must be set to None, False "
+                  "or True"));
             }
           },
           R"DOC((bool, optional): memory opitimize aims to save total memory
@@ -3003,6 +3024,12 @@ All parameter, weight, gradient are variables in Paddle.
              const std::unordered_set<std::string> &mkldnn_enabled_op_types) {
             self.mkldnn_enabled_op_types_ = mkldnn_enabled_op_types;
           })
+      .def_property(
+          "fix_op_run_order",
+          [](const BuildStrategy &self) { return self.fix_op_run_order_; },
+          [](BuildStrategy &self, bool fix_op_run_order) {
+            self.fix_op_run_order_ = fix_op_run_order;
+          })
       .def("_finalize_strategy_and_create_passes",
            [](BuildStrategy &self) -> std::shared_ptr<ir::PassBuilder> {
              return self.CreatePassesFromStrategy(true);

paddle/fluid/string/string_helper.h

@@ -38,7 +38,8 @@ void format_string_append(std::string& str, const char* fmt,  // NOLINT
   CHECK_GE(len, 0);
   size_t oldlen = str.length();
   str.resize(oldlen + len + 1);
-  CHECK(snprintf(&str[oldlen], (size_t)len + 1, fmt, args...) == len);
+  CHECK(snprintf(&str[oldlen], (size_t)len + 1, fmt, args...) ==  // NOLINT
+        len);
   str.resize(oldlen + len);
 }
 
@@ -127,7 +128,24 @@ template <class Container>
 std::string join_strings(const Container& strs, char delim) {
   std::string str;
 
-  int i = 0;
+  size_t i = 0;
+  for (auto& elem : strs) {
+    if (i > 0) {
+      str += delim;
+    }
+
+    str += boost::lexical_cast<std::string>(elem);
+    ++i;
+  }
+
+  return str;
+}
+
+template <class Container>
+std::string join_strings(const Container& strs, const std::string& delim) {
+  std::string str;
+
+  size_t i = 0;
   for (auto& elem : strs) {
     if (i > 0) {
       str += delim;

python/paddle/fluid/tests/unittests/CMakeLists.txt

@@ -688,6 +688,7 @@ add_subdirectory(ir)
 
 if (WITH_TESTING)
     set_property(TEST test_parallel_executor_mnist PROPERTY ENVIRONMENT GLOG_vmodule=all_reduce_deps_pass=10)
+    set_property(TEST test_parallel_executor_fix_op_run_order PROPERTY ENVIRONMENT GLOG_vmodule=fix_op_run_order_pass=10)
 endif()
 
 set_tests_properties(test_parallel_executor_test_while_train test_parallel_executor_mnist

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

+# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+# 
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+# 
+#     http://www.apache.org/licenses/LICENSE-2.0
+# 
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# 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.
+
+import paddle
+import paddle.fluid as fluid
+import unittest
+import numpy as np
+from paddle.vision.models import resnet50
+from paddle.nn import CrossEntropyLoss
+
+
+class TestFixOpRunOrder(unittest.TestCase):
+    def setUp(self):
+        paddle.enable_static()
+        paddle.seed(1)
+        paddle.framework.random._manual_program_seed(1)
+        if paddle.is_compiled_with_cuda():
+            fluid.set_flags({'FLAGS_cudnn_deterministic': 1})
+
+    def get_place(self):
+        return paddle.CUDAPlace(0) if paddle.is_compiled_with_cuda(
+        ) else paddle.CPUPlace()
+
+    def get_feed(self):
+        batch_size = 32
+        image = np.random.random([batch_size, 3, 224, 224]).astype('float32')
+        label = np.random.randint(0, 1000, [batch_size, 1]).astype('int64')
+        return {"image": image, "label": label}
+
+    def create_model(self, fix_op_run_order):
+        main_prog = paddle.static.Program()
+        startup_prog = paddle.static.Program()
+        scope = paddle.static.Scope()
+        with paddle.static.program_guard(main_prog, startup_prog):
+            image = paddle.static.data(
+                name="image", shape=[None, 3, 224, 224], dtype="float32")
+            label = paddle.static.data(
+                name="label", shape=[None, 1], dtype="int64")
+            model = resnet50()
+            pred = model(image)
+            loss_fn = CrossEntropyLoss()
+            loss = loss_fn(pred, label)
+            optimizer = paddle.optimizer.SGD(learning_rate=1e-3)
+            optimizer.minimize(loss)
+
+        build_strategy = paddle.static.BuildStrategy()
+        build_strategy.fix_op_run_order = fix_op_run_order
+        build_strategy.fuse_bn_act_ops = True
+        build_strategy.fuse_bn_add_act_ops = True
+        main_prog = paddle.static.CompiledProgram(main_prog).with_data_parallel(
+            loss_name=loss.name,
+            build_strategy=build_strategy,
+            places=[self.get_place()])
+
+        exe = paddle.static.Executor(self.get_place())
+        with paddle.static.scope_guard(scope):
+            exe.run(startup_prog)
+
+        return main_prog, scope, loss
+
+    def run_and_fetch_loss(self, main_prog, scope, loss, feed):
+        with paddle.static.scope_guard(scope):
+            exe = paddle.static.Executor(self.get_place())
+            loss_value = exe.run(main_prog, feed=feed, fetch_list=[loss])[0]
+            return loss_value
+
+    def test_main(self):
+        if not paddle.is_compiled_with_cuda():
+            return
+
+        main1, scope1, loss1 = self.create_model(True)
+        main2, scope2, loss2 = self.create_model(False)
+        for i in range(10):
+            feed = self.get_feed()
+            loss_val1 = self.run_and_fetch_loss(main1, scope1, loss1, feed)
+            loss_val2 = self.run_and_fetch_loss(main2, scope2, loss2, feed)
+            self.assertEqual(loss_val1, loss_val2)
+
+
+if __name__ == "__main__":
+    unittest.main()