Enable fused_all_reduce_op_handle support GPU and CPU Gradients (#19418)

* Enable fused_all_reduce_op_handle support GPU and CPU Gradients

paddle/fluid/framework/details/all_reduce_op_handle.cc

@@ -40,11 +40,124 @@ AllReduceOpHandle::AllReduceOpHandle(ir::Node *node,
 AllReduceOpHandle::AllReduceOpHandle(ir::Node *node,
                                      const std::vector<Scope *> &local_scopes,
                                      const std::vector<platform::Place> &places)
-    : OpHandleBase(node), local_scopes_(local_scopes), places_(places) {}
+    : OpHandleBase(node), local_scopes_(local_scopes), places_(places) {
+  PADDLE_ENFORCE_EQ(places_.size(), local_scopes_.size());
+}
 #endif
 
+void AllReduceOpHandle::RunImpl() {
+  platform::RecordEvent record_event(Name());
+
+  WaitInputVarGenerated();
+  std::vector<VarHandleBase *> inputs = this->Inputs();
+  std::vector<VarHandleBase *> outputs = this->Outputs();
+  auto in_var_handles = DynamicCast<VarHandle>(inputs);
+  auto out_var_handles = DynamicCast<VarHandle>(outputs);
+  AllReduceImpl(in_var_handles, out_var_handles);
+}
+
+void AllReduceOpHandle::AllReduceImpl(
+    const std::vector<VarHandle *> &in_var_handles,
+    const std::vector<VarHandle *> &out_var_handles) {
+  size_t num_places = places_.size();
+  PADDLE_ENFORCE_EQ(
+      in_var_handles.size(), num_places,
+      "The NoDummyInputSize should be equal to the number of places.");
+  PADDLE_ENFORCE_EQ(
+      in_var_handles.size(), out_var_handles.size(),
+      "The NoDummyInputSize and NoDummyOutputSize should be equal.");
+  PADDLE_ENFORCE_EQ(local_exec_scopes_.size(), num_places);
+
+  std::vector<const void *> lod_tensor_data;
+  std::vector<platform::Place> places;
+  lod_tensor_data.reserve(num_places);
+  places.reserve(num_places);
+  int64_t numel = -1;
+  bool is_gpu_place = false;
+  auto dtype = static_cast<framework::proto::VarType::Type>(0);
+  for (size_t i = 0; i < local_exec_scopes_.size(); ++i) {
+    auto &local_scope = local_exec_scopes_[i];
+    auto var = local_scope->FindVar(in_var_handles[i]->name());
+    PADDLE_ENFORCE_NOT_NULL(var, "%s is not found int scope.",
+                            in_var_handles[i]->name());
+    auto &lod_tensor = var->Get<LoDTensor>();
+
+    if (i == 0) {
+      numel = static_cast<int64_t>(lod_tensor.numel());
+      dtype = lod_tensor.type();
+      is_gpu_place = platform::is_gpu_place(lod_tensor.place());
+    }
+    PADDLE_ENFORCE_EQ(numel, static_cast<int64_t>(lod_tensor.numel()));
+    PADDLE_ENFORCE_EQ(dtype, lod_tensor.type());
+    PADDLE_ENFORCE_EQ(is_gpu_place, platform::is_gpu_place(lod_tensor.place()));
+
+    lod_tensor_data.emplace_back(lod_tensor.data<void>());
+    places.emplace_back(lod_tensor.place());
+
+    VLOG(10) << "place:" << i << ", input_name:" << in_var_handles[i]->name()
+             << ", out_name:" << out_var_handles[i]->name();
+
+    PADDLE_ENFORCE_EQ(in_var_handles[i]->name(), out_var_handles[i]->name(),
+                      "The name of input and output should be equal.");
+  }
+
+  std::vector<std::string> grad_var_names;
+  grad_var_names.reserve(num_places);
+  for (auto &out_var : out_var_handles) {
+    grad_var_names.emplace_back(out_var->Name());
+  }
+
+  AllReduceFunc(lod_tensor_data, dtype, numel, places, grad_var_names);
+}
+
+void AllReduceOpHandle::AllReduceFunc(
+    std::vector<const void *> lod_tensor_data,
+    const framework::proto::VarType::Type &dtype, int64_t numel,
+    const std::vector<platform::Place> &places,
+    const std::vector<std::string> &out_var_names) {
+  if (is_gpu_place(places[0])) {
 #if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
-void AllReduceOpHandle::RunAllReduceFuncs(
+    PADDLE_ENFORCE_NOT_NULL(nccl_ctxs_, "nccl_ctxs should not be nullptr.");
+    ncclDataType_t nccl_dtype = platform::ToNCCLDataType(dtype);
+    std::vector<std::function<void()>> all_reduce_calls;
+    for (size_t i = 0; i < local_exec_scopes_.size(); ++i) {
+      auto &p = places[i];
+      void *buffer = const_cast<void *>(lod_tensor_data.at(i));
+      all_reduce_calls.emplace_back([=] {
+        NCCLAllReduce(p, buffer, buffer, numel, nccl_dtype, ncclSum);
+      });
+    }
+    NCCLAllReduceFunc(all_reduce_calls);
+#else
+    PADDLE_THROW("Not compiled with CUDA.");
+#endif
+  } else {  // Special handle CPU only Operator's gradient. Like CRF
+    auto &trg = *local_exec_scopes_[0]
+                     ->FindVar(out_var_names[0])
+                     ->GetMutable<LoDTensor>();
+
+    // Reduce All Tensor to trg in CPU
+    ReduceBufferData func(lod_tensor_data, trg.data<void>(), numel);
+    VisitDataType(trg.type(), func);
+
+    for (size_t i = 1; i < local_exec_scopes_.size(); ++i) {
+      auto &scope = local_exec_scopes_[i];
+      auto &p = places[i];
+      auto *var = scope->FindVar(out_var_names[i]);
+
+      size_t size = numel * SizeOfType(trg.type());
+      RunAndRecordEvent(p, [&trg, var, p, size] {
+        auto dst_ptr = var->GetMutable<framework::LoDTensor>()->data<void>();
+        platform::CPUPlace cpu_place;
+        memory::Copy(cpu_place, dst_ptr, cpu_place, trg.data<void>(), size);
+      });
+    }
+  }
+  VLOG(10) << Name() << " size:" << numel * SizeOfType(dtype);
+}
+
+#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
+void AllReduceOpHandle::NCCLAllReduceFunc(
     const std::vector<std::function<void()>> &all_reduce_calls) {
   this->RunAndRecordEvent([&] {
     if (all_reduce_calls.size() == 1UL) {
@@ -80,85 +193,6 @@ void AllReduceOpHandle::RunAllReduceFuncs(
 }
 #endif
 
-void AllReduceOpHandle::RunImpl() {
-  platform::RecordEvent record_event(Name());
-
-  WaitInputVarGenerated();
-
-  auto in_var_handles = DynamicCast<VarHandle>(this->Inputs());
-  auto out_var_handles = DynamicCast<VarHandle>(this->Outputs());
-  PADDLE_ENFORCE_EQ(
-      in_var_handles.size(), places_.size(),
-      "The NoDummyInputSize should be equal to the number of places.");
-  PADDLE_ENFORCE_EQ(
-      in_var_handles.size(), out_var_handles.size(),
-      "The NoDummyInputSize and NoDummyOutputSize should be equal.");
-
-  std::vector<const LoDTensor *> lod_tensors;
-  for (size_t i = 0; i < local_scopes_.size(); ++i) {
-    auto &local_scope = local_exec_scopes_[i];
-    auto &lod_tensor =
-        local_scope->FindVar(in_var_handles[i]->name())->Get<LoDTensor>();
-    lod_tensors.emplace_back(&lod_tensor);
-    VLOG(10) << "place:" << i << ", input_name:" << in_var_handles[i]->name()
-             << ", out_name:" << out_var_handles[i]->name();
-    PADDLE_ENFORCE_EQ(in_var_handles[i]->name(), out_var_handles[i]->name(),
-                      "The name of input and output should be equal.");
-  }
-
-  if (platform::is_gpu_place(lod_tensors[0]->place())) {
-#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
-    PADDLE_ENFORCE(nccl_ctxs_, "nccl_ctxs should not be nullptr.");
-    int dtype = -1;
-    size_t numel = 0;
-    std::vector<std::function<void()>> all_reduce_calls;
-    for (size_t i = 0; i < local_scopes_.size(); ++i) {
-      auto &p = places_[i];
-      auto &lod_tensor = *lod_tensors[i];
-      void *buffer = const_cast<void *>(lod_tensor.data<void>());
-
-      if (dtype == -1) {
-        dtype = platform::ToNCCLDataType(lod_tensor.type());
-      }
-
-      if (numel == 0) {
-        numel = static_cast<size_t>(lod_tensor.numel());
-      }
-
-      all_reduce_calls.emplace_back([=] {
-        NCCLAllReduce(p, buffer, buffer, numel,
-                      static_cast<ncclDataType_t>(dtype), ncclSum);
-      });
-    }
-    VLOG(10) << "allreduce size:" << numel * SizeOfType(lod_tensors[0]->type());
-    RunAllReduceFuncs(all_reduce_calls);
-#else
-    PADDLE_THROW("Not compiled with CUDA");
-#endif
-  } else {  // Special handle CPU only Operator's gradient. Like CRF
-    auto &trg = *this->local_exec_scopes_[0]
-                     ->FindVar(out_var_handles[0]->name())
-                     ->GetMutable<framework::LoDTensor>();
-
-    // Reduce All Tensor to trg in CPU
-    ReduceLoDTensor func(lod_tensors, &trg);
-    VisitDataType(lod_tensors[0]->type(), func);
-
-    for (size_t i = 1; i < local_scopes_.size(); ++i) {
-      auto &scope = local_exec_scopes_[i];
-      auto &p = places_[i];
-      auto *var = scope->FindVar(out_var_handles[i]->name());
-      auto *dev_ctx = dev_ctxes_.at(p);
-
-      RunAndRecordEvent(p, [&trg, var, dev_ctx, p] {
-        auto &tensor_gpu = *var->GetMutable<framework::LoDTensor>();
-        auto &tensor_cpu = trg;
-        TensorCopy(tensor_cpu, p, *dev_ctx, &tensor_gpu);
-      });
-    }
-  }
-}
-
 std::string AllReduceOpHandle::Name() const { return "all_reduce"; }
 }  // namespace details
 }  // namespace framework

paddle/fluid/framework/details/all_reduce_op_handle.h

@@ -61,9 +61,17 @@ class AllReduceOpHandle : public OpHandleBase {
 #endif
 
 #if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
-  void RunAllReduceFuncs(
+  void NCCLAllReduceFunc(
       const std::vector<std::function<void()>> &all_reduce_calls);
 #endif
+
+  void AllReduceImpl(const std::vector<VarHandle *> &in_var_handles,
+                     const std::vector<VarHandle *> &out_var_handles);
+
+  void AllReduceFunc(std::vector<const void *> lod_tensor_data,
+                     const framework::proto::VarType::Type &dtype,
+                     int64_t numel, const std::vector<platform::Place> &places,
+                     const std::vector<std::string> &out_var_handles);
 };
 
 }  // namespace details

paddle/fluid/framework/details/build_strategy.cc

@@ -83,12 +83,20 @@ class ParallelExecutorPassBuilder : public ir::PassBuilder {
           << "Currently, fuse_all_optimizer_ops doesn't work under "
              "parallel_graph.";
       strategy_.fuse_all_optimizer_ops_ = false;
+      VLOG_IF(3, strategy_.fuse_all_reduce_ops_)
+          << "fuse_all_reduce_ops doesn't work under "
+             "parallel_graph.";
+      strategy_.fuse_all_reduce_ops_ = false;
     }
     if (strategy_.is_distribution_) {
       VLOG_IF(3, strategy_.fuse_all_optimizer_ops_)
           << "Currently, fuse_all_optimizer_ops only works under "
              "Non-distributed mode.";
       strategy_.fuse_all_optimizer_ops_ = false;
+      VLOG_IF(3, strategy_.fuse_all_reduce_ops_)
+          << "Currently, fuse_all_reduce_ops_ only works under "
+             "Non-distributed mode.";
+      strategy_.fuse_all_reduce_ops_ = false;
     }
     if (strategy_.reduce_ == BuildStrategy::ReduceStrategy::kReduce) {
       VLOG_IF(3, strategy_.fuse_all_optimizer_ops_)
@@ -284,8 +292,8 @@ ir::Graph *BuildStrategy::Apply(ir::Graph *graph,
       pass->Erase(kLocalScopes);
       pass->SetNotOwned<const std::vector<Scope *>>(kLocalScopes,
                                                     &local_scopes);
-      pass->Erase(ir::kNRanks);
+      pass->Erase(kNRanks);
-      pass->Set<size_t>(ir::kNRanks, new size_t(nranks));
+      pass->Set<size_t>(kNRanks, new size_t(nranks));
 
 #if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
       platform::NCCLCommunicator *nctx = use_cuda ? nccl_ctxs : nullptr;
@@ -293,6 +301,8 @@ ir::Graph *BuildStrategy::Apply(ir::Graph *graph,
       pass->SetNotOwned<platform::NCCLCommunicator>(kNCCLCtxs, nctx);
 #endif
     } else if (pass->Type() == "fuse_all_reduce_op_pass") {
+      pass->Erase(kNRanks);
+      pass->Set<size_t>(kNRanks, new size_t(nranks));
       pass->Erase(kPlaces);
       pass->SetNotOwned<const std::vector<platform::Place>>(kPlaces, &places);
       pass->Erase(kLocalScopes);
@@ -307,11 +317,8 @@ ir::Graph *BuildStrategy::Apply(ir::Graph *graph,
                       new bool(use_hierarchical_allreduce_));
 #endif
     } else if (pass->Type() == "coalesce_grad_tensor_pass") {
-      pass->Erase(kPlaces);
+      pass->Erase(kNRanks);
-      pass->SetNotOwned<const std::vector<platform::Place>>(kPlaces, &places);
+      pass->Set<size_t>(kNRanks, new size_t(nranks));
-      pass->Erase(kLocalScopes);
-      pass->SetNotOwned<const std::vector<Scope *>>(kLocalScopes,
-                                                    &local_scopes);
     } else if (pass->Type() == "sequential_execution_pass") {
       LOG(INFO) << "set enable_sequential_execution:"
                 << enable_sequential_execution_;

paddle/fluid/framework/details/fused_all_reduce_op_handle.cc

@@ -33,28 +33,18 @@ FusedAllReduceOpHandle::FusedAllReduceOpHandle(
     ir::Node *node, const std::vector<Scope *> &local_scopes,
     const std::vector<platform::Place> &places, const size_t num_of_all_reduce,
     const platform::NCCLCommunicator *ctxs)
-    : NCCLOpHandleBase(node, places, ctxs),
+    : AllReduceOpHandle(node, local_scopes, places, ctxs),
-      local_scopes_(local_scopes),
+      num_of_all_reduce_(num_of_all_reduce) {}
-      num_of_all_reduce_(num_of_all_reduce) {
-  PADDLE_ENFORCE_EQ(places_.size(), local_scopes_.size());
-}
 #else
-
 FusedAllReduceOpHandle::FusedAllReduceOpHandle(
     ir::Node *node, const std::vector<Scope *> &local_scopes,
     const std::vector<platform::Place> &places, const size_t num_of_all_reduce)
-    : OpHandleBase(node),
+    : AllReduceOpHandle(node, local_scopes, places),
-      local_scopes_(local_scopes),
+      num_of_all_reduce_(num_of_all_reduce) {}
-      places_(places),
-      num_of_all_reduce_(num_of_all_reduce) {
-  PADDLE_ENFORCE_EQ(places_.size(), local_scopes_.size());
-}
-
 #endif
 
 void FusedAllReduceOpHandle::RunImpl() {
   platform::RecordEvent record_event(Name());
-
   VLOG(4) << this->DebugString();
 
   WaitInputVarGenerated();
@@ -71,6 +61,30 @@ void FusedAllReduceOpHandle::RunImpl() {
       in_var_handles.size(), out_var_handles.size(),
       "The NoDummyInputSize and NoDummyOutputSize should be equal.");
 
+  // Note: some gradient op doesn't have CUDAKernel, so the gradients of
+  // those op are in CPUPlace, in this case, the all reduce should not be fused.
+  if (InputIsInDifferentPlace(in_var_handles)) {
+    for (size_t j = 0; j < num_of_all_reduce_; ++j) {
+      std::vector<VarHandle *> dev_inputs;
+      std::vector<VarHandle *> dev_outputs;
+      dev_inputs.reserve(place_num);
+      dev_outputs.reserve(place_num);
+      for (size_t idx = 0; idx < place_num; ++idx) {
+        dev_inputs.emplace_back(in_var_handles.at(j * place_num + idx));
+        dev_outputs.emplace_back(out_var_handles.at(j * place_num + idx));
+      }
+      AllReduceImpl(dev_inputs, dev_outputs);
+    }
+  } else {
+    FusedAllReduceFunc(in_var_handles, out_var_handles);
+  }
+}
+
+void FusedAllReduceOpHandle::FusedAllReduceFunc(
+    const std::vector<VarHandle *> &in_var_handles,
+    const std::vector<VarHandle *> &out_var_handles) {
+  size_t place_num = places_.size();
+
   GradientAndLoDTensor grads_tensor;
   grads_tensor.resize(place_num);
 
@@ -87,14 +101,11 @@ void FusedAllReduceOpHandle::RunImpl() {
         static_cast<framework::proto::VarType::Type>(0);
     GetDTypeAndNumel(g_tensor, &ele_dtype, &element_num);
 
-    if (numel == -1) {
+    if (scope_idx == 0) {
       numel = element_num;
-    }
-    if (dtype == static_cast<framework::proto::VarType::Type>(0)) {
       dtype = ele_dtype;
-      PADDLE_ENFORCE_NE(ele_dtype,
-                        static_cast<framework::proto::VarType::Type>(0));
     }
+
     PADDLE_ENFORCE_EQ(ele_dtype, dtype);
 
     // Check whether the address space is contiguous.
@@ -134,66 +145,36 @@ void FusedAllReduceOpHandle::RunImpl() {
   }
 
   std::vector<const void *> lod_tensor_data;
+  lod_tensor_data.reserve(place_num);
   for (size_t scope_idx = 0; scope_idx < place_num; ++scope_idx) {
     auto data = grads_tensor.at(scope_idx).at(0).second->data<void>();
     lod_tensor_data.emplace_back(data);
   }
+  std::vector<std::string> grad_var_names;
+  grad_var_names.reserve(place_num);
+  for (auto &grad_t : grads_tensor) {
+    grad_var_names.emplace_back(grad_t.at(0).first);
+  }
 
-  if (platform::is_gpu_place(places_[0])) {
+  AllReduceFunc(lod_tensor_data, dtype, numel, this->places_, grad_var_names);
-#if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
+}
-    PADDLE_ENFORCE(nccl_ctxs_, "nccl_ctxs should not be nullptr.");
-    int nccl_dtype = platform::ToNCCLDataType(dtype);
-    std::vector<std::function<void()>> all_reduce_calls;
-    for (size_t i = 0; i < local_scopes_.size(); ++i) {
-      auto &p = places_[i];
-      void *buffer = const_cast<void *>(lod_tensor_data.at(i));
-
-      all_reduce_calls.emplace_back([=] {
-        NCCLAllReduce(p, buffer, buffer, numel,
-                      static_cast<ncclDataType_t>(nccl_dtype), ncclSum);
-      });
-    }
 
-    VLOG(10) << "fusedallreduce size:" << numel * SizeOfType(dtype);
+bool FusedAllReduceOpHandle::InputIsInDifferentPlace(
-
+    const std::vector<VarHandle *> &in_var_handles) const {
-    this->RunAndRecordEvent([&] {
+  for (size_t scope_idx = 0; scope_idx < local_scopes_.size(); ++scope_idx) {
-      if (all_reduce_calls.size() == 1UL) {
+    auto *local_scope = local_exec_scopes_[scope_idx];
-        // Do not use NCCLGroup when manage NCCL by per thread per device
+    size_t place_num = places_.size();
-        all_reduce_calls[0]();
+    for (size_t j = 0; j < in_var_handles.size(); j += place_num) {
-      } else {
+      auto var_name = in_var_handles[j]->name();
-        platform::NCCLGroupGuard guard;
+      auto var = local_scope->FindVar(var_name);
-        for (auto &call : all_reduce_calls) {
+      PADDLE_ENFORCE_NOT_NULL(var, "%s is not found in local scope.", var_name);
-          call();
+      auto &lod_tensor = var->Get<LoDTensor>();
-        }
+      if (!is_same_place(lod_tensor.place(), places_.at(scope_idx))) {
+        return true;
       }
-    });
-#else
-    PADDLE_THROW("Not compiled with CUDA");
-#endif
-  } else {
-    // Special handle CPU only Operator's gradient. Like CRF
-    auto grad_name = grads_tensor.at(0).at(0).first;
-    auto &trg = *this->local_exec_scopes_[0]
-                     ->FindVar(grad_name)
-                     ->GetMutable<framework::LoDTensor>();
-
-    // Reduce All data to trg in CPU
-    ReduceBufferData func(lod_tensor_data, trg.data<void>(), numel);
-    VisitDataType(trg.type(), func);
-
-    for (size_t i = 1; i < local_exec_scopes_.size(); ++i) {
-      auto &scope = *local_exec_scopes_[i];
-      auto &p = places_[i];
-      auto *var = scope.FindVar(grad_name);
-      auto *dev_ctx = dev_ctxes_.at(p);
-      size_t size = numel * SizeOfType(trg.type());
-      RunAndRecordEvent(p, [&trg, var, dev_ctx, p, size] {
-        auto dst_ptr = var->GetMutable<framework::LoDTensor>()->data<void>();
-        platform::CPUPlace cpu_place;
-        memory::Copy(cpu_place, dst_ptr, cpu_place, trg.data<void>(), size);
-      });
     }
   }
+  return false;
 }
 
 void FusedAllReduceOpHandle::GetGradLoDTensor(
@@ -202,12 +183,14 @@ void FusedAllReduceOpHandle::GetGradLoDTensor(
     std::vector<std::pair<std::string, const LoDTensor *>> *grad_tensor) const {
   auto *local_scope = local_exec_scopes_[scope_idx];
   size_t place_num = places_.size();
-
   for (size_t j = 0; j < in_var_handles.size(); j += place_num) {
     auto var_name = in_var_handles[j]->name();
     PADDLE_ENFORCE_EQ(var_name, out_var_handles[j]->name());
-    auto &lod_tensor = local_scope->FindVar(var_name)->Get<LoDTensor>();
+    auto var = local_scope->FindVar(var_name);
-    PADDLE_ENFORCE_EQ(lod_tensor.place(), places_.at(scope_idx));
+    PADDLE_ENFORCE_NOT_NULL(var, "%s is not found in local scope.", var_name);
+    auto &lod_tensor = var->Get<LoDTensor>();
+    PADDLE_ENFORCE_EQ(lod_tensor.place(), places_.at(scope_idx),
+                      "%s(%d) is not in the right place.", var_name, scope_idx);
     grad_tensor->emplace_back(std::make_pair(var_name, &lod_tensor));
   }
 }

paddle/fluid/framework/details/fused_all_reduce_op_handle.h

@@ -17,6 +17,7 @@
 #include <string>
 #include <utility>
 #include <vector>
+#include "paddle/fluid/framework/details/all_reduce_op_handle.h"
 #include "paddle/fluid/framework/details/op_handle_base.h"
 #include "paddle/fluid/framework/lod_tensor.h"
 #include "paddle/fluid/framework/scope.h"
@@ -30,14 +31,14 @@ namespace framework {
 namespace details {
 
 #if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
-struct FusedAllReduceOpHandle : public NCCLOpHandleBase {
+struct FusedAllReduceOpHandle : public AllReduceOpHandle {
   FusedAllReduceOpHandle(ir::Node *node,
                          const std::vector<Scope *> &local_scopes,
                          const std::vector<platform::Place> &places,
                          const size_t num_of_all_reduce,
                          const platform::NCCLCommunicator *ctxs);
 #else
-struct FusedAllReduceOpHandle : public OpHandleBase {
+struct FusedAllReduceOpHandle : public AllReduceOpHandle {
   FusedAllReduceOpHandle(ir::Node *node,
                          const std::vector<Scope *> &local_scopes,
                          const std::vector<platform::Place> &places,
@@ -45,22 +46,10 @@ struct FusedAllReduceOpHandle : public OpHandleBase {
 #endif
   std::string Name() const override;
 
-  // Delay and buffer nccl_all_reduce together can significantly increase
-  // performance. Disable this feature by returning false.
-  bool IsMultiDeviceTransfer() override { return true; };
-
  protected:
   void RunImpl() override;
 
-  std::vector<Scope *> GetLocalScopes() override { return local_scopes_; }
-
  private:
-  std::vector<Scope *> local_scopes_;
-#if !(defined(PADDLE_WITH_CUDA) && !defined(_WIN32))
-  // NCCLOpHandleBase already have these attributes.
-  // Will polish it by class inheritance framework.
-  std::vector<platform::Place> places_;
-#endif
   size_t num_of_all_reduce_;
 
   // Check the dtype of the input
@@ -74,6 +63,12 @@ struct FusedAllReduceOpHandle : public OpHandleBase {
                         const std::vector<VarHandle *> &out_var_handles,
                         std::vector<std::pair<std::string, const LoDTensor *>>
                             *grad_tensor) const;
+
+  bool InputIsInDifferentPlace(
+      const std::vector<VarHandle *> &in_var_handles) const;
+
+  void FusedAllReduceFunc(const std::vector<VarHandle *> &in_var_handles,
+                          const std::vector<VarHandle *> &out_var_handles);
 };
 
 }  // namespace details

paddle/fluid/framework/details/multi_devices_helper.h

@@ -42,6 +42,8 @@ typedef std::vector<std::unordered_map<std::string, std::vector<VarHandle *>>>
     GraphVars;
 constexpr char kGraphVars[] = "vars";
 
+constexpr char kNRanks[] = "nranks";
+
 constexpr char kPlaces[] = "places";
 constexpr char kLocalScopes[] = "local_scopes";
 constexpr char kNCCLCtxs[] = "nccl_ctxs";
@@ -68,6 +70,9 @@ constexpr char kParamsAndSparseGrads[] = "params_and_sparse_grads";
 typedef std::vector<ProgramDesc> ProgramDescs;
 constexpr char kProgramDescs[] = "program_descs";
 
+typedef std::unordered_set<std::string> PinnedVars;
+constexpr char kPinnedVars[] = "pinned_vars";
+
 typedef std::vector<std::vector<std::pair<std::string, std::string>>>
     GroupParamsAndGrads;
 constexpr char kGroupParamsAndDenseGrads[] = "group_params_dense_grads";

paddle/fluid/framework/details/sparse_all_reduce_op_handle.cc

@@ -126,7 +126,7 @@ void SparseAllReduceOpHandle::RunImplEncoded() {
     });
   }
 
-  RunAllReduceFuncs(all_reduce_calls);
+  NCCLAllReduceFunc(all_reduce_calls);
 }
 
 int SparseAllReduceOpHandle::GetKValue(const std::string &grad_name) {

paddle/fluid/framework/ir/coalesce_grad_tensor_pass.cc

@@ -65,28 +65,33 @@ double GetFuseParameterMemorySize() { return FLAGS_fuse_parameter_memory_size; }
 class CoalesceGradTensorPass : public ir::Pass {
  protected:
   void ApplyImpl(ir::Graph *graph) const {
+    if (Get<size_t>(details::kNRanks) <= 1) {
+      VLOG(6) << "The number of place is" << Get<size_t>(details::kNRanks)
+              << ", there doesn't need apply FuseAllReduceOpPass.";
+      return;
+    }
     ir::Graph &result = *graph;
-
     details::ParamsAndGrads params_grads;
     RecordParamsAndGrads(result, &params_grads);
 
-    VLOG(10) << "The number of params and grads is:" << params_grads.size();
-    if (params_grads.size() == 0) {
-      return;
-    }
-
-    auto vars_info = GetVarInfo(result);
     ResetAttribute<details::ParamsAndGrads>(details::kParamsAndDenseGrads,
                                             &result);
     ResetAttribute<details::ParamsAndGrads>(details::kParamsAndSparseGrads,
                                             &result);
     ResetAttribute<details::GroupParamsAndGrads>(
         details::kGroupParamsAndDenseGrads, &result);
+
+    VLOG(10) << "The number of params and grads is:" << params_grads.size();
+    if (params_grads.size() == 0) {
+      return;
+    }
+
     auto &p_g_dense_grad =
         result.Get<details::ParamsAndGrads>(details::kParamsAndDenseGrads);
     auto &p_g_sparse_grad =
         result.Get<details::ParamsAndGrads>(details::kParamsAndSparseGrads);
 
+    auto vars_info = GetVarInfo(result);
     for (auto &param_grad : params_grads) {
       if (IsLoDTensorType(GetTypeOfVar(vars_info, param_grad.second))) {
         p_g_dense_grad.emplace_back(param_grad);
@@ -118,33 +123,37 @@ class CoalesceGradTensorPass : public ir::Pass {
         p_g_dense_grad.size(), num_of_p_g_dense_grad,
         "The number of p_g_dense_grad is not consistent with before.");
 
+    auto &pinned_var_set =
+        graph->GetOrInit<details::PinnedVars>(details::kPinnedVars);
     if (IsUnifiedDtype(p_g_dense_grad, vars_info)) {
-      SetGradientPersistable(p_g_dense_grad, vars_info);
+      RecordGradients(p_g_dense_grad, vars_info, &pinned_var_set);
       CoalesceTensors(vars_info, p_g_dense_grad, &result);
     } else {
       for (auto &sub_param_grad : group_params_grads) {
-        SetGradientPersistable(p_g_dense_grad, vars_info);
+        RecordGradients(p_g_dense_grad, vars_info, &pinned_var_set);
-        PADDLE_ENFORCE(IsUnifiedDtype(sub_param_grad, vars_info),
+        PADDLE_ENFORCE_EQ(IsUnifiedDtype(sub_param_grad, vars_info), true,
-                       "The data type of the same group is not consistent.");
+                          "The data type of the same group is not consistent.");
         CoalesceTensors(vars_info, sub_param_grad, &result);
       }
     }
   }
 
-  void SetGradientPersistable(
+  void RecordGradients(
       const std::vector<std::pair<std::string, std::string>> &sub_param_grad,
-      const std::unordered_map<std::string, std::vector<ir::Node *>> &vars_info)
+      const std::unordered_map<std::string, std::vector<ir::Node *>> &vars_info,
-      const {
+      std::unordered_set<std::string> *pinned_var_set) const {
+    // The Gradients should not be reused during memory optimization.
     for (auto &p_g : sub_param_grad) {
       auto iter = vars_info.find(p_g.second);
-      PADDLE_ENFORCE(iter != vars_info.end(), "%s is not found.", p_g.second);
+      PADDLE_ENFORCE_EQ(iter != vars_info.end(), true, "%s is not found.",
-      PADDLE_ENFORCE(!iter->second.empty());
+                        p_g.second);
-      // Set persistable
+      PADDLE_ENFORCE_EQ(!iter->second.empty(), true);
       for (auto it : iter->second) {
         PADDLE_ENFORCE_NOT_NULL(it->Var());
-        it->Var()->SetPersistable(true);
+        pinned_var_set->insert(it->Var()->Name());
       }
-      PADDLE_ENFORCE(IsLoDTensorType(GetTypeOfVar(vars_info, p_g.second)));
+      PADDLE_ENFORCE_EQ(IsLoDTensorType(GetTypeOfVar(vars_info, p_g.second)),
+                        true);
     }
   }
 
@@ -411,8 +420,10 @@ class CoalesceGradTensorPass : public ir::Pass {
       const std::unordered_map<std::string, std::vector<Node *>> &vars_info,
       const std::string &var_name) const {
     auto grad_iter = vars_info.find(var_name);
-    PADDLE_ENFORCE(grad_iter != vars_info.end(), "%s is not found.", var_name);
+    PADDLE_ENFORCE_EQ(grad_iter != vars_info.end(), true, "%s is not found.",
-    PADDLE_ENFORCE(!grad_iter->second.empty());
+                      var_name);
+    PADDLE_ENFORCE_EQ(!grad_iter->second.empty(), true, "%s is not found.",
+                      var_name);
     PADDLE_ENFORCE_NOT_NULL(grad_iter->second.front()->Var());
     return grad_iter->second.front()->Var();
   }
@@ -483,4 +494,5 @@ class CoalesceGradTensorPass : public ir::Pass {
 }  // namespace paddle
 
 REGISTER_PASS(coalesce_grad_tensor_pass,
-              paddle::framework::ir::CoalesceGradTensorPass);
+              paddle::framework::ir::CoalesceGradTensorPass)
+    .RequirePassAttr(paddle::framework::details::kNRanks);

paddle/fluid/framework/ir/fuse_optimizer_ops_pass/fuse_optimizer_op_pass.cc

@@ -106,6 +106,7 @@ void FuseOptimizerOpPass::ApplyImpl(ir::Graph *graph) const {
     PADDLE_ENFORCE_LE(
         params_and_dense_grads.size(), aux_var_set.at(kGrad).size(),
         "The number of dense gradients should be little than optimizer ops.");
+
     std::unordered_set<std::string> opt_grad_set(aux_var_set.at(kGrad).size());
     for (auto &p_g : params_and_dense_grads) {
       opt_grad_set.insert(p_g.second);
@@ -138,7 +139,8 @@ void FuseOptimizerOpPass::ApplyImpl(ir::Graph *graph) const {
       auto &fused_vars = result.Get<details::FusedVars>(details::kFusedVars);
       auto iter =
           std::find(fused_vars.begin(), fused_vars.end(), fused_grad.front());
-      PADDLE_ENFORCE(iter != fused_vars.end(), "Not find the fused_grad.");
+      PADDLE_ENFORCE_EQ(iter != fused_vars.end(), true,
+                        "Not find the fused_grad.");
       fused_vars_name[kGrad] = fused_grad.front();
 
       // Sort the parameters and auxiliary variables according
@@ -246,18 +248,24 @@ void FuseOptimizerOpPass::InitFusedGradsAndAllocSpaceForGrads(
     const std::vector<std::string> &params,
     const std::vector<std::string> &grads, const std::string &fused_grad_name,
     ir::Graph *result) const {
+  auto &pinned_var_set =
+      result->GetOrInit<details::PinnedVars>(details::kPinnedVars);
+
   auto vars_info = GetVarInfo(*result);
-  // Set Gradients as Persistable to prevent this var becoming reusable.
+  // The Gradients should not be reused during memory optimization.
   for (auto &grad_var_name : grads) {
     auto iter = vars_info.find(grad_var_name);
-    PADDLE_ENFORCE(iter != vars_info.end());
+    PADDLE_ENFORCE_EQ(iter != vars_info.end(), true, "%s is not found.",
-    PADDLE_ENFORCE(!iter->second.empty());
+                      grad_var_name);
+    PADDLE_ENFORCE_EQ(!iter->second.empty(), true, "%s is not found.",
+                      grad_var_name);
     PADDLE_ENFORCE_NOT_NULL(iter->second.front()->Var());
-    PADDLE_ENFORCE(IsLoDTensorType(iter->second.front()->Var()->GetType()),
+    PADDLE_ENFORCE_EQ(
-                   "Currently the gradient type only should be LoDTensor when "
+        IsLoDTensorType(iter->second.front()->Var()->GetType()), true,
-                   "fusing optimizer ops.");
+        "Currently the gradient type only should be LoDTensor when "
+        "fusing optimizer ops.");
     for (auto var : iter->second) {
-      var->Var()->SetPersistable(true);
+      pinned_var_set.insert(var->Var()->Name());
     }
   }
 
@@ -293,8 +301,9 @@ proto::VarType::Type FuseOptimizerOpPass::GetTypeOfVar(
     const std::unordered_map<std::string, std::vector<Node *>> &var_nodes,
     const std::string &name) const {
   auto grad_iter = var_nodes.find(name);
-  PADDLE_ENFORCE(grad_iter != var_nodes.end());
+  PADDLE_ENFORCE_EQ(grad_iter != var_nodes.end(), true, "%s is not found.",
-  PADDLE_ENFORCE(grad_iter->second.size() > 0);
+                    name);
+  PADDLE_ENFORCE_GT(grad_iter->second.size(), 0);
   PADDLE_ENFORCE_NOT_NULL(grad_iter->second.front()->Var());
   return grad_iter->second.front()->Var()->GetType();
 }
@@ -321,24 +330,25 @@ void FuseOptimizerOpPass::SortParametersAndAuxVars(
     const std::vector<std::pair<std::string, std::string>> &params_grads,
     std::unordered_map<std::string, std::vector<std::string>> *aux_vars_set,
     std::vector<ir::Node *> *ops) const {
-  PADDLE_ENFORCE_NE(aux_vars_set->count(kParam), static_cast<size_t>(0));
+  PADDLE_ENFORCE_NE(aux_vars_set->count(kGrad), static_cast<size_t>(0));
-  auto &param_vec = aux_vars_set->at(kParam);
+  auto &grad_vec = aux_vars_set->at(kGrad);
 
-  std::vector<size_t> param_sort_idx;
+  std::vector<size_t> grad_sort_idx;
-  param_sort_idx.reserve(param_vec.size());
+  grad_sort_idx.reserve(grad_vec.size());
 
   for (auto &p_g : params_grads) {
-    auto iter = std::find(param_vec.begin(), param_vec.end(), p_g.first);
+    auto iter = std::find(grad_vec.begin(), grad_vec.end(), p_g.second);
-    PADDLE_ENFORCE(iter != param_vec.end());
+    PADDLE_ENFORCE_EQ(iter != grad_vec.end(), true,
-    auto idx = std::distance(param_vec.begin(), iter);
+                      "%s is not found in grad_vec", p_g.second);
-    param_sort_idx.emplace_back(idx);
+    auto idx = std::distance(grad_vec.begin(), iter);
+    grad_sort_idx.emplace_back(idx);
   }
 
   for (auto &aux_vars : *aux_vars_set) {
     std::vector<std::string> sorted_vars;
     sorted_vars.reserve(aux_vars.second.size());
     for (size_t i = 0; i < aux_vars.second.size(); ++i) {
-      sorted_vars.emplace_back(aux_vars.second.at(param_sort_idx[i]));
+      sorted_vars.emplace_back(aux_vars.second.at(grad_sort_idx[i]));
     }
     std::swap(aux_vars.second, sorted_vars);
 
@@ -354,7 +364,7 @@ void FuseOptimizerOpPass::SortParametersAndAuxVars(
   std::vector<ir::Node *> sorted_ops;
   sorted_ops.reserve(ops->size());
   for (size_t i = 0; i < ops->size(); ++i) {
-    sorted_ops.emplace_back(ops->at(param_sort_idx[i]));
+    sorted_ops.emplace_back(ops->at(grad_sort_idx[i]));
   }
   std::swap(*ops, sorted_ops);
 }

paddle/fluid/framework/ir/graph.h

@@ -85,10 +85,18 @@ class Graph {
     return attrs_.count(attr_name) > 0;
   }
 
+  template <typename AttrType>
+  AttrType &GetOrInit(const std::string &attr_name) {
+    if (!Has(attr_name)) {
+      Set(attr_name, new AttrType);
+    }
+    return Get<AttrType>(attr_name);
+  }
+
   template <typename AttrType>
   AttrType &Get(const std::string &attr_name) const {
-    PADDLE_ENFORCE(Has(attr_name), "%s attr not registered for graph.",
+    PADDLE_ENFORCE_EQ(Has(attr_name), true, "%s attr not registered for graph.",
-                   attr_name);
+                      attr_name);
     try {
       return *boost::any_cast<AttrType *>(attrs_.at(attr_name));
     } catch (boost::bad_any_cast &) {
@@ -101,8 +109,8 @@ class Graph {
 
   template <typename AttrType>
   void Set(const std::string &attr_name, AttrType *attr) {
-    PADDLE_ENFORCE(attrs_.count(attr_name) == 0, "%s already set in the graph",
+    PADDLE_ENFORCE_EQ(attrs_.count(attr_name), 0, "%s already set in the graph",
-                   attr_name);
+                      attr_name);
     attrs_[attr_name] = attr;
     attr_dels_[attr_name] = [attr, attr_name]() {
       VLOG(3) << "deleting " << attr_name;
@@ -112,15 +120,15 @@ class Graph {
 
   template <typename AttrType>
   void SetNotOwned(const std::string &attr_name, AttrType *attr) {
-    PADDLE_ENFORCE(attrs_.count(attr_name) == 0, "%s already set in the graph",
+    PADDLE_ENFORCE_EQ(attrs_.count(attr_name), 0, "%s already set in the graph",
-                   attr_name);
+                      attr_name);
     attrs_[attr_name] = attr;
     attr_dels_[attr_name] = []() {};
   }
 
   void Erase(const std::string &attr_name) {
-    PADDLE_ENFORCE(attrs_.count(attr_name) != 0, "%s not set in the graph",
+    PADDLE_ENFORCE_NE(attrs_.count(attr_name), 0, "%s not set in the graph",
-                   attr_name);
+                      attr_name);
     attr_dels_[attr_name]();
     attrs_.erase(attr_name);
     attr_dels_.erase(attr_name);
@@ -130,7 +138,7 @@ class Graph {
 
   // Create a normal variable with non-null VarDesc.
   ir::Node *CreateVarNode(VarDesc *var_desc) {
-    PADDLE_ENFORCE(var_desc);
+    PADDLE_ENFORCE_NOT_NULL(var_desc);
     auto *x = AddNode(new ir::Node(var_desc));
     x->SetId(num_node_created_++);
     return x;
@@ -138,7 +146,7 @@ class Graph {
 
   // Create a normal runnable operator with OpDesc.
   ir::Node *CreateOpNode(OpDesc *op_desc) {
-    PADDLE_ENFORCE(op_desc);
+    PADDLE_ENFORCE_NOT_NULL(op_desc);
     auto *x = AddNode(new ir::Node(op_desc));
     x->SetId(num_node_created_++);
     return x;
@@ -178,7 +186,7 @@ class Graph {
   }
 
   std::unique_ptr<ir::Node> RemoveNode(ir::Node *node) {
-    PADDLE_ENFORCE(node_set_.find(node) != node_set_.end());
+    PADDLE_ENFORCE_EQ(node_set_.find(node) != node_set_.end(), true);
     std::unique_ptr<ir::Node> ret;
     ret.reset(nodes_.at(node).release());
     nodes_.erase(node);
@@ -204,7 +212,7 @@ class Graph {
 
   // This method takes ownership of `node`.
   ir::Node *AddNode(ir::Node *node) {
-    PADDLE_ENFORCE(node_set_.find(node) == node_set_.end());
+    PADDLE_ENFORCE_EQ(node_set_.find(node) == node_set_.end(), true);
     nodes_[node].reset(node);
     node_set_.insert(node);
     return node;

paddle/fluid/framework/ir/graph_test.cc

@@ -206,5 +206,51 @@ TEST(GraphTest, WriteAfterWrite) {
   ASSERT_NE(control_dep2, nullptr);
   ASSERT_EQ(control_dep1, control_dep2);
 }
+
+TEST(GraphTest, TestException) {
+  ProgramDesc prog;
+  std::unique_ptr<ir::Graph> g(new ir::Graph(prog));
+
+  bool not_met_exception = false;
+  try {
+    g->Erase("no_attr");
+  } catch (const platform::EnforceNotMet &e) {
+    not_met_exception = true;
+  }
+  ASSERT_TRUE(not_met_exception);
+
+  not_met_exception = false;
+  try {
+    g->CreateVarNode(nullptr);
+  } catch (const platform::EnforceNotMet &e) {
+    not_met_exception = true;
+  }
+  ASSERT_TRUE(not_met_exception);
+
+  not_met_exception = false;
+  try {
+    g->CreateOpNode(nullptr);
+  } catch (const platform::EnforceNotMet &e) {
+    not_met_exception = true;
+  }
+  ASSERT_TRUE(not_met_exception);
+
+  not_met_exception = false;
+  try {
+    g->RemoveNode(nullptr);
+  } catch (const platform::EnforceNotMet &e) {
+    not_met_exception = true;
+  }
+  ASSERT_TRUE(not_met_exception);
+
+  not_met_exception = false;
+  try {
+    g->AddNode(nullptr);
+    g->AddNode(nullptr);
+  } catch (const platform::EnforceNotMet &e) {
+    not_met_exception = true;
+  }
+  ASSERT_TRUE(not_met_exception);
+}
 }  // namespace framework
 }  // namespace paddle

paddle/fluid/framework/ir/memory_optimize_pass/memory_reuse_pass.cc

@@ -36,6 +36,11 @@ void MemoryReusePass::ApplyImpl(Graph *graph) const {
   reused_out_var_names_.resize(all_vars_->size());
   var_descs_.resize(all_vars_->size());
 
+  pinned_var_set_ = nullptr;
+  if (graph->Has(details::kPinnedVars)) {
+    pinned_var_set_ = &graph->Get<details::PinnedVars>(details::kPinnedVars);
+  }
+
   // Collect the existing ShareTensorBufferOpHandles.
   // This is because (1) we want to reuse the existing
   // ShareTensorBufferOpHandles to avoid inserting too many ops;
@@ -195,7 +200,7 @@ bool MemoryReusePass::IsInVarReusable(const details::VarHandle &in_var) const {
 
   const VarDesc *in_var_desc = GetVarDesc(in_var);
 
-  if (in_var_desc->Persistable()) {
+  if (IsPinnedVar(*in_var_desc)) {
     return false;
   }
 
@@ -244,7 +249,7 @@ bool MemoryReusePass::IsOutVarReusable(
   }
 
   const VarDesc *out_var_desc = GetVarDesc(out_var);
-  if (out_var_desc->Persistable()) {
+  if (IsPinnedVar(*out_var_desc)) {
     return false;
   }
 
@@ -261,6 +266,11 @@ bool MemoryReusePass::IsOutVarReusable(
   return true;
 }
 
+bool MemoryReusePass::IsPinnedVar(const VarDesc &var_desc) const {
+  return var_desc.Persistable() ||
+         (pinned_var_set_ && pinned_var_set_->count(var_desc.Name()));
+}
+
 /**
  * Input-Output pair can be reused only when:
  *  - they are not the same var.

paddle/fluid/framework/ir/memory_optimize_pass/memory_reuse_pass.h

@@ -133,6 +133,9 @@ class MemoryReusePass : public Pass {
   mutable std::vector<std::unordered_set<std::string>> reused_out_var_names_;
 
   mutable std::vector<std::unordered_map<std::string, VarDesc *>> var_descs_;
+  mutable details::PinnedVars *pinned_var_set_;
+
+  bool IsPinnedVar(const VarDesc &out_var_desc) const;
 };
 
 }  // namespace ir

paddle/fluid/framework/ir/memory_optimize_pass/reference_count_pass.cc

@@ -312,13 +312,22 @@ void ReferenceCountPass::ApplyImpl(ir::Graph *graph) const {
   ShrinkDepsOpFunctor shrink_func(
       ir::FilterByNodeWrapper<details::OpHandleBase>(*graph));
 
+  details::PinnedVars *pinned_var_set = nullptr;
+  if (graph->Has(details::kPinnedVars)) {
+    pinned_var_set = &graph->Get<details::PinnedVars>(details::kPinnedVars);
+  }
+  auto is_pinned_var = [&pinned_var_set](const VarDesc &var_desc) {
+    return var_desc.Persistable() ||
+           (pinned_var_set && pinned_var_set->count(var_desc.Name()));
+  };
+
   VLOG(1) << "Place number: " << vars.size();
   for (size_t i = 0; i < vars.size(); ++i) {
     for (auto &name_var_pair : vars[i]) {
       // Whether this variable can be reused or deleted? If not, we do not
       // compute reference counts and dependencies.
       VarDesc *var_desc = TryGetLatestVarDesc(name_var_pair.second);
-      if (var_desc == nullptr || var_desc->Persistable()) {
+      if (var_desc == nullptr || is_pinned_var(*var_desc)) {
         continue;
       }
 

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

@@ -29,14 +29,21 @@ namespace ir {
 class FuseAllReduceOpPass : public ir::Pass {
  protected:
   void ApplyImpl(ir::Graph *graph) const override {
-    ir::Graph &result = *graph;
+    if (Get<size_t>(details::kNRanks) <= 1) {
+      VLOG(6) << "The number of place is" << Get<size_t>(details::kNRanks)
+              << ", there doesn't need apply FuseAllReduceOpPass.";
+      return;
+    }
+
     auto &places = Get<const std::vector<platform::Place>>(details::kPlaces);
     auto &local_scopes = Get<const std::vector<Scope *>>(details::kLocalScopes);
+
 #if defined(PADDLE_WITH_CUDA) && !defined(_WIN32)
     auto *multi_nccl_ctxs =
         &Get<platform::NCCLCommunicator>(details::kNCCLCtxs);
 #endif
 
+    ir::Graph &result = *graph;
     auto &params_grads =
         result.Get<details::ParamsAndGrads>(details::kParamsAndDenseGrads);
     size_t num_of_all_reduce = params_grads.size();
@@ -203,4 +210,5 @@ class FuseAllReduceOpPass : public ir::Pass {
 }  // namespace paddle
 
 REGISTER_PASS(fuse_all_reduce_op_pass,
-              paddle::framework::ir::FuseAllReduceOpPass);
+              paddle::framework::ir::FuseAllReduceOpPass)
+    .RequirePassAttr(paddle::framework::details::kNRanks);

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

@@ -205,7 +205,7 @@ void MultiDevSSAGraphBuilderBase::ApplyImpl(ir::Graph *graph) const {
       }
 
       // Insert collective ops if nranks > 1
-      if (!is_forwarding && Get<size_t>(kNRanks) > 1) {
+      if (!is_forwarding && Get<size_t>(details::kNRanks) > 1) {
         try {
           bool is_bk_op =
               static_cast<bool>(boost::get<int>(node->Op()->GetAttr(
@@ -273,7 +273,7 @@ void MultiDevSSAGraphBuilderBase::InsertScaleLossGradOp(
       loss_scale = 1;
       break;
     case details::BuildStrategy::GradientScaleStrategy::kCoeffNumDevice:
-      loss_scale = Get<size_t>(kNRanks);
+      loss_scale = Get<size_t>(details::kNRanks);
       break;
     case details::BuildStrategy::GradientScaleStrategy::kCustomized:
       loss_scale = 0;
@@ -1106,7 +1106,7 @@ static int MultiDevSSAGraphBuilderRegister(const std::string &builder_mode) {
       .RequirePassAttr(paddle::framework::details::kPlaces)               \
       .RequirePassAttr(paddle::framework::details::kLocalScopes)          \
       .RequirePassAttr(paddle::framework::ir::kStrategy)                  \
-      .RequirePassAttr(paddle::framework::ir::kNRanks)
+      .RequirePassAttr(paddle::framework::details::kNRanks)
 
 REGISTER_MULTI_DEVICES_PASS(reduce_mode_multi_devices_pass,
                             paddle::framework::ir::ReduceSSAGraphBuilder);

paddle/fluid/framework/ir/multi_devices_graph_pass/multi_devices_graph_pass.h

@@ -35,7 +35,6 @@ namespace ir {
 
 constexpr char kLossVarName[] = "loss_var_name";
 constexpr char kStrategy[] = "strategy";
-constexpr char kNRanks[] = "nranks";
 
 class MultiDevSSAGraphBuilderBase : public ir::Pass {
  protected: