fix conflict

cmake/cuda.cmake

@@ -107,6 +107,9 @@ function(select_nvcc_arch_flags out_variable)
   elseif(${CUDA_ARCH_NAME} STREQUAL "Maxwell")
     set(cuda_arch_bin "50")
   elseif(${CUDA_ARCH_NAME} STREQUAL "Pascal")
+    if (NOT ${CMAKE_CUDA_COMPILER_VERSION} LESS 10.0)
+      add_definitions("-DSUPPORTS_CUDA_FP16")
+    endif()
     set(cuda_arch_bin "60 61")
   elseif(${CUDA_ARCH_NAME} STREQUAL "Volta")
     if (NOT ${CMAKE_CUDA_COMPILER_VERSION} LESS 10.0)

paddle/fluid/framework/data_feed.cc

@@ -527,6 +527,8 @@ bool MultiSlotDataFeed::CheckFile(const char* filename) {
         VLOG(0) << "error: the number of ids is a negative number: " << num;
         VLOG(0) << "please check line<" << instance_cout << "> in file<"
                 << filename << ">";
+        VLOG(0) << "Error occured when parsing " << i
+                << " th slot with total slots number: " << all_slots_.size();
         return false;
       } else if (num == 0) {
         VLOG(0)
@@ -536,42 +538,66 @@ bool MultiSlotDataFeed::CheckFile(const char* filename) {
                "characters.";
         VLOG(0) << "please check line<" << instance_cout << "> in file<"
                 << filename << ">";
+        VLOG(0) << "Error occured when parsing " << i
+                << " th slot with total slots number: " << all_slots_.size();
         return false;
       } else if (errno == ERANGE || num > INT_MAX) {
         VLOG(0) << "error: the number of ids greater than INT_MAX";
         VLOG(0) << "please check line<" << instance_cout << "> in file<"
                 << filename << ">";
+        VLOG(0) << "Error occured when parsing " << i
+                << " th slot with total slots number: " << all_slots_.size();
         return false;
       }
       if (all_slots_type_[i] == "float") {
-        for (int i = 0; i < num; ++i) {
+        for (int j = 0; j < num; ++j) {
           strtof(endptr, &endptr);
           if (errno == ERANGE) {
             VLOG(0) << "error: the value is out of the range of "
                        "representable values for float";
             VLOG(0) << "please check line<" << instance_cout << "> in file<"
                     << filename << ">";
+            VLOG(0) << "Error occured when parsing " << i
+                    << " th slot with total slots number: "
+                    << all_slots_.size();
+            VLOG(0) << "and in this slot: " << j
+                    << " th id with total id number: " << num;
             return false;
           }
-          if (i + 1 != num && endptr - str == len) {
+          if (j + 1 != num && endptr - str == len) {
             VLOG(0) << "error: there is a wrong with the number of ids.";
+            VLOG(0) << "Error occured when parsing " << i
+                    << " th slot with total slots number: "
+                    << all_slots_.size();
+            VLOG(0) << "and in this slot: " << j
+                    << " th id with total id number: " << num;
             VLOG(0) << "please check line<" << instance_cout << "> in file<"
                     << filename << ">";
             return false;
           }
         }
       } else if (all_slots_type_[i] == "uint64") {
-        for (int i = 0; i < num; ++i) {
+        for (int j = 0; j < num; ++j) {
           strtoull(endptr, &endptr, 10);
           if (errno == ERANGE) {
             VLOG(0) << "error: the value is out of the range of "
                        "representable values for uint64_t";
+            VLOG(0) << "Error occured when parsing " << i
+                    << " th slot with total slots number: "
+                    << all_slots_.size();
+            VLOG(0) << "and in this slot: " << j
+                    << " th id with total id number: " << num;
             VLOG(0) << "please check line<" << instance_cout << "> in file<"
                     << filename << ">";
             return false;
           }
-          if (i + 1 != num && endptr - str == len) {
+          if (j + 1 != num && endptr - str == len) {
             VLOG(0) << "error: there is a wrong with the number of ids.";
+            VLOG(0) << "Error occured when parsing " << i
+                    << " th slot with total slots number: "
+                    << all_slots_.size();
+            VLOG(0) << "and in this slot: " << j
+                    << " th id with total id number: " << num;
             VLOG(0) << "please check line<" << instance_cout << "> in file<"
                     << filename << ">";
             return false;
@@ -632,8 +658,13 @@ bool MultiSlotDataFeed::ParseOneInstanceFromPipe(
               "The number of ids can not be zero, you need padding "
               "it in data generator; or if there is something wrong with "
               "the data, please check if the data contains unresolvable "
-              "characters.\nplease check this error line: %s",
-              str));
+              "characters.\nplease check this error line: %s, \n Specifically, "
+              "something wrong happened(the length of this slot's feasign is 0)"
+              "when we parse the %d th slots."
+              "Maybe something wrong around this slot",
+              "\nWe detect the feasign number of this slot is %d, "
+              "which is illegal.",
+              str, i, num));
       if (idx != -1) {
         (*instance)[idx].Init(all_slots_type_[i]);
         if ((*instance)[idx].GetType()[0] == 'f') {  // float
@@ -683,8 +714,13 @@ bool MultiSlotDataFeed::ParseOneInstance(std::vector<MultiSlotType>* instance) {
               "The number of ids can not be zero, you need padding "
               "it in data generator; or if there is something wrong with "
               "the data, please check if the data contains unresolvable "
-              "characters.\nplease check this error line: %s.",
-              str));
+              "characters.\nplease check this error line: %s, \n Specifically, "
+              "something wrong happened(the length of this slot's feasign is 0)"
+              "when we parse the %d th slots."
+              "Maybe something wrong around this slot",
+              "\nWe detect the feasign number of this slot is %d, "
+              "which is illegal.",
+              str, i, num));
 
       if (idx != -1) {
         (*instance)[idx].Init(all_slots_type_[i]);
@@ -916,8 +952,13 @@ bool MultiSlotInMemoryDataFeed::ParseOneInstanceFromPipe(Record* instance) {
               "The number of ids can not be zero, you need padding "
               "it in data generator; or if there is something wrong with "
               "the data, please check if the data contains unresolvable "
-              "characters.\nplease check this error line: %s.",
-              str));
+              "characters.\nplease check this error line: %s, \n Specifically, "
+              "something wrong happened(the length of this slot's feasign is 0)"
+              "when we parse the %d th slots."
+              "Maybe something wrong around this slot",
+              "\nWe detect the feasign number of this slot is %d, "
+              "which is illegal.",
+              str, i, num));
       if (idx != -1) {
         if (all_slots_type_[i][0] == 'f') {  // float
           for (int j = 0; j < num; ++j) {
@@ -982,8 +1023,13 @@ bool MultiSlotInMemoryDataFeed::ParseOneInstance(Record* instance) {
               "The number of ids can not be zero, you need padding "
               "it in data generator; or if there is something wrong with "
               "the data, please check if the data contains unresolvable "
-              "characters.\nplease check this error line: %s.",
-              str));
+              "characters.\nplease check this error line: %s, \n Specifically, "
+              "something wrong happened(the length of this slot's feasign is 0)"
+              "when we parse the %d th slots."
+              "Maybe something wrong around this slot",
+              "\nWe detect the feasign number of this slot is %d, "
+              "which is illegal.",
+              str, i, num));
 
       if (idx != -1) {
         if (all_slots_type_[i][0] == 'f') {  // float

paddle/fluid/framework/details/CMakeLists.txt

@@ -74,6 +74,7 @@ set(SSA_GRAPH_EXECUTOR_DEPS graph framework_proto
     eager_deletion_pass
     buffer_shared_inplace_op_pass
     buffer_shared_cross_op_memory_reuse_pass
+    inplace_addto_op_pass
     set_reader_device_info_utils
     add_reader_dependency_pass)
 cc_library(ssa_graph_executor SRCS ssa_graph_executor.cc DEPS ${SSA_GRAPH_EXECUTOR_DEPS})

paddle/fluid/framework/details/all_reduce_op_handle.cc

@@ -12,7 +12,9 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 #include "paddle/fluid/framework/details/all_reduce_op_handle.h"
+
 #include <algorithm>
+
 #include "paddle/fluid/framework/details/container_cast.h"
 #include "paddle/fluid/framework/details/reduce_and_gather.h"
 #include "paddle/fluid/framework/details/variable_visitor.h"
@@ -34,14 +36,24 @@ AllReduceOpHandle::AllReduceOpHandle(ir::Node *node,
                                      const std::vector<platform::Place> &places,
                                      const platform::NCCLCommunicator *ctxs)
     : NCCLOpHandleBase(node, places, ctxs), local_scopes_(local_scopes) {
-  PADDLE_ENFORCE_EQ(places_.size(), local_scopes_.size());
+  PADDLE_ENFORCE_EQ(places_.size(), local_scopes_.size(),
+                    platform::errors::InvalidArgument(
+                        "The number of places and the number of local scopes "
+                        "should be equal, but got number of places is %d and "
+                        "number of local scopes is %d.",
+                        places_.size(), local_scopes_.size()));
 }
 #else
 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) {
-  PADDLE_ENFORCE_EQ(places_.size(), local_scopes_.size());
+  PADDLE_ENFORCE_EQ(places_.size(), local_scopes_.size(),
+                    platform::errors::InvalidArgument(
+                        "The number of places and the number of local scopes "
+                        "should be equal, but got number of places is %d and "
+                        "number of local scopes is %d.",
+                        places_.size(), local_scopes_.size()));
 }
 #endif
 
@@ -60,13 +72,25 @@ 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(), num_places,
+                    platform::errors::InvalidArgument(
+                        "The NoDummyInputSize should be equal "
+                        "to the number of places, but got NoDummyInputSize is "
+                        "%d and the number of place is %d.",
+                        in_var_handles.size(), num_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);
+      platform::errors::InvalidArgument(
+          "The NoDummyInputSize and NoDummyOutputSize should be "
+          "equal, but got NoDummyInputSize is %d and NoDummyOutputSize is %d.",
+          in_var_handles.size(), out_var_handles.size()));
+  PADDLE_ENFORCE_EQ(
+      local_exec_scopes_.size(), num_places,
+      platform::errors::InvalidArgument(
+          "The number of local scopes should be equal "
+          "to the number of places, but got the number of local scopes is "
+          "%d and the number of place is %d.",
+          in_var_handles.size(), num_places));
 
   std::vector<const void *> lod_tensor_data;
   std::vector<platform::Place> places;
@@ -78,23 +102,36 @@ void AllReduceOpHandle::AllReduceImpl(
   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());
+    PADDLE_ENFORCE_NOT_NULL(var, platform::errors::NotFound(
+                                     "Variable %s is not found in local scope.",
+                                     in_var_handles[i]->name()));
     auto &lod_tensor = var->Get<LoDTensor>();
 
     if (i == 0) {
       numel = static_cast<int64_t>(lod_tensor.numel());
       // only enforce place0, we will enforce other palce numel == place0 numel
       PADDLE_ENFORCE_GT(
-          numel, 0, platform::errors::InvalidArgument(
-                        "The numel of tensos=[%s] must > 0. But now numel=[%d]",
-                        in_var_handles[i]->name(), numel));
+          numel, 0,
+          platform::errors::PreconditionNotMet(
+              "The numel of tensor %s should be > 0, but got numel is %d.",
+              in_var_handles[i]->name(), 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()));
+    PADDLE_ENFORCE_EQ(
+        numel, static_cast<int64_t>(lod_tensor.numel()),
+        platform::errors::PreconditionNotMet(
+            "The size of tensors of the same variable in different local "
+            "scopes should be equal."));
+    PADDLE_ENFORCE_EQ(
+        dtype, lod_tensor.type(),
+        platform::errors::PreconditionNotMet(
+            "The dtype of tensors of the same variable in different local "
+            "scopes should be equal."));
+    PADDLE_ENFORCE_EQ(is_gpu_place, platform::is_gpu_place(lod_tensor.place()),
+                      platform::errors::PreconditionNotMet(
+                          "The place type of tensors of the same variable "
+                          "in different local scopes should be equal."));
 
     lod_tensor_data.emplace_back(lod_tensor.data<void>());
     places.emplace_back(lod_tensor.place());
@@ -102,8 +139,12 @@ void AllReduceOpHandle::AllReduceImpl(
     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.");
+    PADDLE_ENFORCE_EQ(
+        in_var_handles[i]->name(), out_var_handles[i]->name(),
+        platform::errors::InvalidArgument(
+            "The name of input and output of all_reduce op should be equal, "
+            "but got input is %s and output is %s.",
+            in_var_handles[i]->name(), out_var_handles[i]->name()));
   }
 
   std::vector<std::string> grad_var_names;
@@ -122,7 +163,9 @@ void AllReduceOpHandle::AllReduceFunc(
     const std::vector<std::string> &out_var_names) {
   if (is_gpu_place(places[0])) {
 #if defined(PADDLE_WITH_NCCL)
-    PADDLE_ENFORCE_NOT_NULL(nccl_ctxs_, "nccl_ctxs should not be nullptr.");
+    PADDLE_ENFORCE_NOT_NULL(nccl_ctxs_,
+                            platform::errors::InvalidArgument(
+                                "The nccl context should not be NULL."));
     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) {
@@ -134,7 +177,8 @@ void AllReduceOpHandle::AllReduceFunc(
     }
     NCCLAllReduceFunc(all_reduce_calls);
 #else
-    PADDLE_THROW("Not compiled with CUDA.");
+    PADDLE_THROW(
+        platform::errors::PreconditionNotMet("Not compiled with CUDA."));
 #endif
   } else {  // Special handle CPU only Operator's gradient. Like CRF
     auto &trg = *local_exec_scopes_[0]

paddle/fluid/framework/details/async_ssa_graph_executor.cc

@@ -89,8 +89,19 @@ AsyncSSAGraphExecutor::AsyncSSAGraphExecutor(
       places_(std::move(places)),
       graphs_(std::move(graphs)) {
   VLOG(3) << "build AsyncSSAGraphExecutor";
-  PADDLE_ENFORCE_EQ(places_.size(), local_scopes_.size());
-  PADDLE_ENFORCE_EQ(local_scopes_.size(), local_exec_scopes_.size());
+  PADDLE_ENFORCE_EQ(places_.size(), local_scopes_.size(),
+                    platform::errors::InvalidArgument(
+                        "The number of places and the number of local scopes "
+                        "should be equal, but got number of places is %d and "
+                        "number of local scopes is %d.",
+                        places_.size(), local_scopes_.size()));
+  PADDLE_ENFORCE_EQ(
+      local_scopes_.size(), local_exec_scopes_.size(),
+      platform::errors::InvalidArgument(
+          "The number of local scopes and the number of local execution scopes "
+          "should be equal, but got number of local scopes is %d and "
+          "number of local execution scopes is %d.",
+          local_scopes_.size(), local_exec_scopes_.size()));
 
   // set the correct size of thread pool to each device.
   strategy_.num_threads_ = strategy_.num_threads_ < places_.size()

paddle/fluid/framework/details/build_strategy.h

@@ -19,6 +19,7 @@
 #include <unordered_set>
 #include <utility>
 #include <vector>
+
 #include "boost/optional.hpp"
 #include "paddle/fluid/framework/ir/pass_builder.h"
 #include "paddle/fluid/framework/program_desc.h"
@@ -119,6 +120,9 @@ struct BuildStrategy {
   // Turn on inplace by default.
   bool enable_inplace_{true};
 
+  // Turn off inplace addto by default.
+  bool enable_addto_{false};
+
   // FIXME(zcd): is_distribution_ is a temporary field, because in pserver mode,
   // num_trainers is 1, so the current fields of build_strategy doesn't tell if
   // it's distributed model.

paddle/fluid/framework/details/fast_threaded_ssa_graph_executor.cc

@@ -12,12 +12,14 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 #include "paddle/fluid/framework/details/fast_threaded_ssa_graph_executor.h"
+
 #include <deque>
 #include <memory>
 #include <string>
 #include <unordered_map>
 #include <unordered_set>
 #include <vector>
+
 #include "paddle/fluid/framework/details/computation_op_handle.h"
 #include "paddle/fluid/framework/details/fetch_async_op_handle.h"
 #include "paddle/fluid/framework/details/multi_devices_helper.h"
@@ -48,7 +50,9 @@ FastThreadedSSAGraphExecutor::FastThreadedSSAGraphExecutor(
       bootstrap_ops_.emplace_back(op);
     }
   }
-  PADDLE_ENFORCE_GT(op_deps_.size(), 0, "The graph doesn't have operators.");
+  PADDLE_ENFORCE_GT(op_deps_.size(), 0,
+                    platform::errors::PreconditionNotMet(
+                        "The graph doesn't have operators."));
   PrepareAtomicOpDeps();
 }
 

paddle/fluid/framework/details/fetch_op_handle.cc

@@ -13,9 +13,11 @@
 // limitations under the License.
 
 #include "paddle/fluid/framework/details/fetch_op_handle.h"
+
 #include <string>
 #include <utility>
 #include <vector>
+
 #include "paddle/fluid/platform/profiler.h"
 
 namespace paddle {
@@ -138,8 +140,10 @@ void FetchOpHandle::RunImpl() {
     auto *var_handle = static_cast<VarHandle *>(inputs_[i]);
     auto &scope = scopes.at(var_handle->scope_idx());
     auto *var = scope->FindVar(var_handle->name());
-    PADDLE_ENFORCE_NOT_NULL(var, "Cannot find variable %s in execution scope",
-                            var_handle->name());
+    PADDLE_ENFORCE_NOT_NULL(
+        var,
+        platform::errors::NotFound(
+            "Cannot find variable %s in execution scope.", var_handle->name()));
 
     if (var->IsType<LoDTensor>()) {
       auto &t = var->Get<framework::LoDTensor>();

paddle/fluid/framework/details/op_handle_base.cc

@@ -12,6 +12,7 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 #include "paddle/fluid/framework/details/op_handle_base.h"
+
 #include <map>
 #include <unordered_set>
 
@@ -88,6 +89,12 @@ void OpHandleBase::Run(bool use_cuda) {
   PADDLE_ENFORCE(!use_cuda);
 #endif
 
+  // skip running current op, used with inplace_addto_op_pass
+  if (skip_running_) {
+    VLOG(4) << "skip running: " << Name();
+    return;
+  }
+
   RunImpl();
 }
 

paddle/fluid/framework/details/op_handle_base.h

@@ -18,6 +18,7 @@
 #include <unordered_map>
 #include <unordered_set>
 #include <vector>
+
 #include "paddle/fluid/framework/details/var_handle.h"
 #include "paddle/fluid/framework/ir/node.h"
 #include "paddle/fluid/platform/device_context.h"
@@ -52,6 +53,10 @@ class OpHandleBase {
 
   virtual Priority GetPriority() const { return kNormal; }
 
+  virtual bool GetSkipRunning() const { return skip_running_; }
+
+  virtual void SetSkipRunning(bool skip_runing) { skip_running_ = skip_runing; }
+
   virtual std::string Name() const = 0;
 
   void Run(bool use_cuda);
@@ -131,6 +136,7 @@ class OpHandleBase {
   std::map<platform::Place, platform::DeviceContext *> dev_ctxes_;
 
   std::vector<Scope *> local_exec_scopes_;
+  bool skip_running_ = false;
 
 #ifdef PADDLE_WITH_CUDA
   std::unordered_map<int, cudaEvent_t> events_;

paddle/fluid/framework/details/parallel_ssa_graph_executor.cc

@@ -13,9 +13,11 @@
 // limitations under the License.
 
 #include "paddle/fluid/framework/details/parallel_ssa_graph_executor.h"
+
 #include <algorithm>
 #include <memory>
 #include <utility>
+
 #include "paddle/fluid/framework/ir/graph_helper.h"
 
 namespace paddle {
@@ -104,7 +106,12 @@ ParallelSSAGraphExecutor::ParallelSSAGraphExecutor(
       places_(places),
       graphs_(std::move(graphs)),
       feed_status_(places.size(), FeedStatus::kNone) {
-  PADDLE_ENFORCE_EQ(places_.size(), local_scopes_.size());
+  PADDLE_ENFORCE_EQ(places_.size(), local_scopes_.size(),
+                    platform::errors::InvalidArgument(
+                        "The number of places and the number of local scopes "
+                        "should be equal, but got number of places is %d and "
+                        "number of local scopes is %d.",
+                        places_.size(), local_scopes_.size()));
 
   PADDLE_ENFORCE_EQ(places_.size(), graphs_.size(),
                     platform::errors::InvalidArgument(

paddle/fluid/framework/details/scope_buffered_ssa_graph_executor.cc

@@ -13,10 +13,12 @@
 // limitations under the License.
 
 #include "paddle/fluid/framework/details/scope_buffered_ssa_graph_executor.h"
+
 #include <stdexcept>
 #include <string>
 #include <utility>
 #include <vector>
+
 #include "paddle/fluid/framework/details/multi_devices_helper.h"
 #include "paddle/fluid/framework/op_registry.h"
 #include "paddle/fluid/framework/variable_helper.h"
@@ -37,7 +39,13 @@ ScopeBufferedSSAGraphExecutor::ScopeBufferedSSAGraphExecutor(
       var_infos_(std::move(var_infos)),
       places_(std::move(places)),
       scope_monitor_(places_, local_exec_scopes_) {
-  PADDLE_ENFORCE_EQ(local_scopes_.size(), local_exec_scopes_.size());
+  PADDLE_ENFORCE_EQ(
+      local_scopes_.size(), local_exec_scopes_.size(),
+      platform::errors::InvalidArgument(
+          "The number of local scopes and the number of local execution scopes "
+          "should be equal, but got number of local scopes is %d and "
+          "number of local execution scopes is %d.",
+          local_scopes_.size(), local_exec_scopes_.size()));
   PrepareLocalExeScopes();
 }
 

paddle/fluid/framework/details/share_tensor_buffer_functor.cc

@@ -13,9 +13,11 @@
 // limitations under the License.
 
 #include "paddle/fluid/framework/details/share_tensor_buffer_functor.h"
+
 #include <string>
 #include <unordered_map>
 #include <unordered_set>
+
 #include "paddle/fluid/framework/lod_tensor.h"
 #include "paddle/fluid/framework/selected_rows.h"
 #include "paddle/fluid/platform/enforce.h"
@@ -29,7 +31,8 @@ static inline const Tensor &GetTensorFromVar(const Variable *var) {
   if (var->IsType<LoDTensor>()) {
     return var->Get<LoDTensor>();
   } else {
-    PADDLE_THROW("Variable must be type of LoDTensor");
+    PADDLE_THROW(platform::errors::InvalidArgument(
+        "Variable must be type of LoDTensor."));
   }
 }
 
@@ -37,20 +40,27 @@ static inline Tensor *GetMutableTensorFromVar(Variable *var) {
   if (var->IsType<LoDTensor>()) {
     return var->GetMutable<LoDTensor>();
   } else {
-    PADDLE_THROW("Variable must be type of LoDTensor");
+    PADDLE_THROW(platform::errors::InvalidArgument(
+        "Variable must be type of LoDTensor."));
   }
 }
 
 ShareTensorBufferFunctor::ShareTensorBufferFunctor(
     Scope *scope, size_t scope_idx, const std::string &op_type,
     const std::vector<const ir::MemOptVarInfo *> &in_var_infos,
-    const std::vector<std::string> &out_var_names)
+    const std::vector<std::string> &out_var_names, bool share_dims)
     : scope_(scope),
       scope_idx_(scope_idx),
       op_type_(op_type),
       in_var_infos_(in_var_infos),
-      out_var_names_(out_var_names) {
-  PADDLE_ENFORCE_EQ(in_var_infos_.size(), out_var_names_.size());
+      out_var_names_(out_var_names),
+      share_dims_(share_dims) {
+  PADDLE_ENFORCE_EQ(in_var_infos_.size(), out_var_names_.size(),
+                    platform::errors::PreconditionNotMet(
+                        "The number of input variables and output variables "
+                        "should be equal, but got number of input variables is "
+                        "%d and number of output variables is %d.",
+                        in_var_infos_.size(), out_var_names_.size()));
   for (size_t i = 0; i < in_var_infos_.size(); ++i) {
     AddReuseVarPair(in_var_infos_[i], out_var_names_[i]);
   }
@@ -67,32 +77,59 @@ ShareTensorBufferFunctor::ReusedVars() const {
 
 void ShareTensorBufferFunctor::AddReuseVarPair(
     const ir::MemOptVarInfo *in_var_info, const std::string &out_var_name) {
-  PADDLE_ENFORCE_NOT_NULL(in_var_info, "in_var_info cannot be nullptr");
+  PADDLE_ENFORCE_NOT_NULL(
+      in_var_info,
+      platform::errors::InvalidArgument(
+          "The input variables to be inplaced should not be NULL."));
   PADDLE_ENFORCE_NE(in_var_info->Name(), out_var_name,
-                    "in/out cannot have same name: %s", out_var_name);
+                    platform::errors::InvalidArgument(
+                        "The input variable and output variable to be inplaced "
+                        "cannot have the same name: %s.",
+                        out_var_name));
   in_var_infos_.emplace_back(in_var_info);
   out_var_names_.emplace_back(out_var_name);
 }
 
 void ShareTensorBufferFunctor::CallOnce() {
-  PADDLE_ENFORCE(in_out_vars_.empty(), "in_out_vars_ must be initialized here");
+  PADDLE_ENFORCE(in_out_vars_.empty(),
+                 platform::errors::InvalidArgument(
+                     "The input-output variable pairs to be "
+                     "inplaced should be initialized here."));
   for (size_t i = 0; i < in_var_infos_.size(); ++i) {
     auto *in_var = exec_scope_->FindVar(in_var_infos_[i]->Name());
     auto *out_var = exec_scope_->FindVar(out_var_names_[i]);
-    PADDLE_ENFORCE_NOT_NULL(in_var);
-    PADDLE_ENFORCE_NOT_NULL(out_var);
-    PADDLE_ENFORCE_NE(in_var, out_var);
+    PADDLE_ENFORCE_NOT_NULL(
+        in_var, platform::errors::NotFound(
+                    "The input variable(%s)to be inplaced should not be NULL.",
+                    in_var_infos_[i]->Name()));
+    PADDLE_ENFORCE_NOT_NULL(
+        out_var,
+        platform::errors::NotFound(
+            "The output variable(%s) to be inplaced should not be NULL.",
+            out_var_names_[i]));
+    PADDLE_ENFORCE_NE(
+        in_var, out_var,
+        platform::errors::PreconditionNotMet(
+            "The input variable and output variable to be inplaced "
+            "cannot be the same variable(%s).",
+            out_var_names_[i]));
     in_out_vars_.emplace_back(in_var, out_var);
   }
 }
 
 void ShareTensorBufferFunctor::operator()(Scope *exec_scope) {
   if (!exec_scope_) {
-    PADDLE_ENFORCE_NOT_NULL(exec_scope);
+    PADDLE_ENFORCE_NOT_NULL(exec_scope,
+                            platform::errors::InvalidArgument(
+                                "The given execution scope should not be NULL "
+                                "if the cached scope is NULL."));
     exec_scope_ = exec_scope;
     CallOnce();
   } else {
-    PADDLE_ENFORCE(exec_scope_ == exec_scope, "Scope must be the same");
+    PADDLE_ENFORCE_EQ(exec_scope_, exec_scope,
+                      platform::errors::InvalidArgument(
+                          "The given execution scope and the cached execution "
+                          "scope should be the same."));
   }
 
   for (size_t i = 0; i < in_var_infos_.size(); ++i) {
@@ -115,6 +152,13 @@ void ShareTensorBufferFunctor::operator()(Scope *exec_scope) {
     } else {
       out_tensor->ShareBufferWith(in_tensor);
 
+      // NOTE(zhiqiu): In the case of inplace addto, if the operator of
+      // the in_out_vars is skipped during running, we should set the dims of
+      // output as the same as input.
+      if (share_dims_) {
+        out_tensor->Resize(in_tensor.dims());
+      }
+
       VLOG(2) << "Share tensor buffer when running " << op_type_ << " : "
               << in_var_info->Name() << " -> " << out_var_names_[i];
     }

paddle/fluid/framework/details/share_tensor_buffer_functor.h

@@ -19,6 +19,7 @@
 #include <unordered_set>
 #include <utility>
 #include <vector>
+
 #include "paddle/fluid/framework/details/op_handle_base.h"
 #include "paddle/fluid/framework/ir/memory_optimize_pass/memory_optimization_var_info.h"
 #include "paddle/fluid/framework/scope.h"
@@ -40,11 +41,13 @@ class ShareTensorBufferFunctor {
   ShareTensorBufferFunctor(
       Scope *scope, size_t scope_idx, const std::string &op_type,
       const std::vector<const ir::MemOptVarInfo *> &in_var_infos,
-      const std::vector<std::string> &out_var_names);
+      const std::vector<std::string> &out_var_names, bool share_dims = false);
 
   void AddReuseVarPair(const ir::MemOptVarInfo *in_var_info,
                        const std::string &out_var_name);
 
+  void SetShareDims(bool share_dims) { share_dims_ = share_dims; }
+
   void operator()(Scope *exec_scope);
 
   std::unordered_map<std::string, std::string> ReusedVars() const;
@@ -66,6 +69,11 @@ class ShareTensorBufferFunctor {
   std::vector<std::string> out_var_names_;
 
   std::vector<std::pair<const Variable *, Variable *>> in_out_vars_;
+
+  // NOTE(zhiqiu): In the case of inplace addto, if the operator of
+  // the in_out_vars is skipped during running, we should set the dims of output
+  // as the same as input.
+  bool share_dims_{false};
 };
 
 }  // namespace details

paddle/fluid/framework/details/share_tensor_buffer_op_handle.cc

@@ -13,8 +13,10 @@
 // limitations under the License.
 
 #include "paddle/fluid/framework/details/share_tensor_buffer_op_handle.h"
+
 #include <string>
 #include <unordered_set>
+
 #include "paddle/fluid/framework/ir/memory_optimize_pass/memory_optimization_var_info.h"
 #include "paddle/fluid/framework/lod_tensor.h"
 #include "paddle/fluid/framework/scope.h"
@@ -32,26 +34,35 @@ ComputationOpHandle *GetUniquePendingComputationOpHandle(
     for (ir::Node *pending_op : out_var->outputs) {
       auto &op = pending_op->Wrapper<OpHandleBase>();
       auto *compute_op = dynamic_cast<ComputationOpHandle *>(&op);
-      PADDLE_ENFORCE_NOT_NULL(compute_op);
+      PADDLE_ENFORCE_NOT_NULL(
+          compute_op,
+          platform::errors::PreconditionNotMet(
+              "The pending OpHandle should be ComputationOpHandle."));
 
       if (result_op == nullptr) {
         result_op = compute_op;
       } else {
-        PADDLE_ENFORCE_EQ(result_op, compute_op);
+        PADDLE_ENFORCE_EQ(
+            result_op, compute_op,
+            platform::errors::PreconditionNotMet(
+                "The pending OpHandle should be the unique one."));
       }
     }
   }
 
-  PADDLE_ENFORCE_NOT_NULL(result_op);
+  PADDLE_ENFORCE_NOT_NULL(result_op,
+                          platform::errors::PreconditionNotMet(
+                              "The pending OpHandle should not be NULL."));
   return result_op;
 }
 
 ShareTensorBufferOpHandle::ShareTensorBufferOpHandle(
     ir::Node *node, Scope *scope, size_t scope_idx, const std::string &op_type,
     const std::vector<const ir::MemOptVarInfo *> &in_var_infos,
-    const std::vector<std::string> &out_var_names)
+    const std::vector<std::string> &out_var_names, bool share_dims)
     : OpHandleBase(node),
-      functor_(scope, scope_idx, op_type, in_var_infos, out_var_names) {}
+      functor_(scope, scope_idx, op_type, in_var_infos, out_var_names,
+               share_dims) {}
 
 std::unordered_map<std::string, std::string>
 ShareTensorBufferOpHandle::ReusedVars() const {
@@ -63,6 +74,10 @@ void ShareTensorBufferOpHandle::AddReuseVarPair(
   functor_.AddReuseVarPair(in_var_info, out_var_name);
 }
 
+void ShareTensorBufferOpHandle::SetShareDims(bool share_dims) {
+  functor_.SetShareDims(share_dims);
+}
+
 void ShareTensorBufferOpHandle::InitCUDA() {
 #ifdef PADDLE_WITH_CUDA
   int dev_id =

paddle/fluid/framework/details/share_tensor_buffer_op_handle.h

@@ -17,6 +17,7 @@
 #include <unordered_map>
 #include <utility>
 #include <vector>
+
 #include "paddle/fluid/framework/details/computation_op_handle.h"
 #include "paddle/fluid/framework/details/op_handle_base.h"
 #include "paddle/fluid/framework/details/share_tensor_buffer_functor.h"
@@ -31,7 +32,7 @@ class ShareTensorBufferOpHandle : public OpHandleBase {
       ir::Node *node, Scope *scope, size_t scope_idx,
       const std::string &op_type,
       const std::vector<const ir::MemOptVarInfo *> &in_vars_infos,
-      const std::vector<std::string> &out_var_names);
+      const std::vector<std::string> &out_var_names, bool share_dims = false);
 
   std::unordered_map<std::string, std::string> ReusedVars() const;
 
@@ -42,6 +43,8 @@ class ShareTensorBufferOpHandle : public OpHandleBase {
   void AddReuseVarPair(const ir::MemOptVarInfo *in_var_info,
                        const std::string &out_var_name);
 
+  void SetShareDims(bool share_dims);
+
   const ShareTensorBufferFunctor &Functor() const { return functor_; }
 
  protected:

paddle/fluid/framework/details/ssa_graph_executor.cc

@@ -13,6 +13,7 @@
 // limitations under the License.
 
 #include "paddle/fluid/framework/details/ssa_graph_executor.h"
+
 #include "paddle/fluid/framework/details/fetch_async_op_handle.h"
 
 namespace paddle {
@@ -27,8 +28,9 @@ void ClearFetchOp(ir::Graph* graph, std::vector<OpHandleBase*>* fetch_ops) {
     PADDLE_ENFORCE_EQ(dynamic_cast<FetchOpHandle*>(op) != nullptr ||
                           dynamic_cast<FetchAsyncOpHandle*>(op) != nullptr,
                       true,
-                      "The input ops of ClearFetchOp function should be "
-                      "FetchOpHandle or FetchAsyncOpHandle.");
+                      platform::errors::PreconditionNotMet(
+                          "The input ops of ClearFetchOp function should be "
+                          "FetchOpHandle or FetchAsyncOpHandle."));
     for (auto& out_var : op->Node()->outputs) {
       graph->RemoveNode(out_var);
     }

paddle/fluid/framework/details/threaded_ssa_graph_executor.cc

@@ -13,6 +13,7 @@
 // limitations under the License.
 
 #include "paddle/fluid/framework/details/threaded_ssa_graph_executor.h"
+
 #include "paddle/fluid/framework/ir/graph_helper.h"
 #include "paddle/fluid/platform/profiler.h"
 
@@ -138,7 +139,10 @@ inline FetchResultType ThreadedSSAGraphExecutor::RunImpl(
         }
       }
     }
-    PADDLE_ENFORCE(ready_ops.empty());
+    PADDLE_ENFORCE_EQ(
+        ready_ops.empty(), true,
+        platform::errors::Fatal("After the execution of computation graph, "
+                                "there are unexecuted operators left."));
   }
 
   // Wait FetchOps.
@@ -165,9 +169,8 @@ void ThreadedSSAGraphExecutor::InsertFetchOps(
     FetchResultType *fetch_data, bool return_merged) {
   std::unordered_map<std::string, std::vector<VarHandleBase *>> fetched_vars;
   std::unordered_set<VarHandleBase *> local_ready_vars;
-  std::unordered_set<std::string> fetch_tensor_set(fetch_tensors.begin(),
-                                                   fetch_tensors.end());
-  for (auto &fetch_var_name : fetch_tensor_set) {
+
+  for (auto &fetch_var_name : fetch_tensors) {
     for (auto &var_map : graph_->Get<details::GraphVars>(details::kGraphVars)) {
       auto it = var_map.find(fetch_var_name);
       if (it != var_map.end()) {
@@ -231,7 +234,11 @@ void ThreadedSSAGraphExecutor::InsertFetchOps(
       ready_ops->insert(static_cast<OpHandleBase *>(op));
     }
   }
-  PADDLE_ENFORCE_EQ(local_ready_vars.size(), 0);
+  PADDLE_ENFORCE_EQ(
+      local_ready_vars.size(), 0,
+      platform::errors::Fatal(
+          "The number of ready variables should be 0, but got %d.",
+          local_ready_vars.size()));
 }
 
 void ThreadedSSAGraphExecutor::InsertPendingOp(
@@ -277,7 +284,9 @@ void ThreadedSSAGraphExecutor::PrepareOpDeps() {
     }
   }
   op_deps_->num_ops_ = ready_ops.size() + pending_ops.size();
-  PADDLE_ENFORCE_GT(op_deps_->num_ops_, 0, "The graph doesn't have operators.");
+  PADDLE_ENFORCE_GT(
+      op_deps_->num_ops_, 0,
+      platform::errors::InvalidArgument("The graph doesn't have operators."));
 
   for (auto ready_var : ready_vars) {
     pending_vars.erase(ready_var);

paddle/fluid/framework/details/threaded_ssa_graph_executor.h

@@ -14,6 +14,8 @@
 
 #pragma once
 
+#include <ThreadPool.h>  // ThreadPool in thrird party
+
 #include <deque>
 #include <functional>
 #include <list>
@@ -24,8 +26,6 @@
 #include <utility>
 #include <vector>
 
-#include <ThreadPool.h>  // ThreadPool in thrird party
-
 #include "paddle/fluid/framework/blocking_queue.h"
 #include "paddle/fluid/framework/details/exception_holder.h"
 #include "paddle/fluid/framework/details/execution_strategy.h"

paddle/fluid/framework/details/var_handle.h

@@ -54,8 +54,10 @@ struct VarHandleBase {
 
   void AddOutput(OpHandleBase* out, ir::Node* node) {
     if (pending_ops_.find(out) == pending_ops_.end()) {
-      PADDLE_ENFORCE(out != nullptr, "The output of %s should not be nullptr",
-                     this->Node()->Name());
+      PADDLE_ENFORCE_NOT_NULL(out,
+                              platform::errors::InvalidArgument(
+                                  "The output added to VarHandle %s is NULL.",
+                                  this->Node()->Name()));
       pending_ops_.insert(out);
       node_->outputs.push_back(node);
     }
@@ -120,7 +122,10 @@ struct VarHandle : public VarHandleBase {
   bool HasEvent() { return has_event_; }
 
   const cudaEvent_t& GetEvent() {
-    PADDLE_ENFORCE(HasEvent(), "The event is not set.");
+    PADDLE_ENFORCE_EQ(
+        HasEvent(), true,
+        platform::errors::PreconditionNotMet(
+            "The cuda event is not set, maybe InitCUDA() is not called."));
     return event_;
   }
 

paddle/fluid/framework/details/variable_visitor.cc

@@ -13,6 +13,7 @@
 // limitations under the License.
 
 #include "paddle/fluid/framework/details/variable_visitor.h"
+
 #include "paddle/fluid/framework/selected_rows.h"
 namespace paddle {
 namespace framework {
@@ -24,7 +25,9 @@ static void VisitVariable(Variable* var, Func* func) {
   } else if (var->IsType<SelectedRows>()) {
     (*func)(var->GetMutable<SelectedRows>());
   } else {
-    PADDLE_THROW("Not supported type %s", ToTypeName(var->Type()));
+    PADDLE_THROW(platform::errors::Unimplemented(
+        "VisitVariable is not supported for type %s.",
+        ToTypeName(var->Type())));
   }
 }
 
@@ -35,7 +38,8 @@ static void VisitVariable(const Variable& var, Func* func) {
   } else if (var.IsType<SelectedRows>()) {
     (*func)(var.Get<SelectedRows>());
   } else {
-    PADDLE_THROW("Not supported type %s", ToTypeName(var.Type()));
+    PADDLE_THROW(platform::errors::Unimplemented(
+        "VisitVariable is not supported for type %s.", ToTypeName(var.Type())));
   }
 }
 
@@ -50,7 +54,8 @@ struct TensorVisitor {
 
   template <typename T>
   void operator()() {
-    PADDLE_THROW("Not Support to get LoDTensor from %s", typeid(T).name());
+    PADDLE_THROW(platform::errors::Unimplemented(
+        "Getting tensor from type %s is not supported.", typeid(T).name()));
   }
 };
 
@@ -78,8 +83,8 @@ struct ShareDimsAndLoDVisitor {
 
   template <typename T>
   void operator()(const T&) {
-    PADDLE_ENFORCE("ShareDimsAndLoD is not supported by type %s",
-                   typeid(T).name());
+    PADDLE_THROW(platform::errors::Unimplemented(
+        "ShareDimsAndLoD is not supported for type %s.", typeid(T).name()));
   }
 };
 
@@ -89,42 +94,54 @@ void VariableVisitor::ShareDimsAndLoD(const Variable& src, Variable* trg) {
 }
 
 struct EnforceShapeAndDTypeEQVisitor {
-  const Variable* trg_;
+  const Variable* dst_;
 
   void operator()(const LoDTensor& src) {
-    auto& tensor = trg_->Get<LoDTensor>();
-    PADDLE_ENFORCE_EQ(
-        src.place().which(), tensor.place().which(),
-        "The Places of the two Variable must be all on CPU or all on GPU.");
+    auto& tensor = dst_->Get<LoDTensor>();
+    PADDLE_ENFORCE_EQ(src.place().which(), tensor.place().which(),
+                      platform::errors::PreconditionNotMet(
+                          "The place type of the two variables is not equal."));
     PADDLE_ENFORCE_EQ(src.type(), tensor.type(),
-                      "The dtype of the two Variable is not equal.");
-    PADDLE_ENFORCE_EQ(src.dims(), tensor.dims(),
-                      "The dims of the two Variable is not equal.");
+                      platform::errors::PreconditionNotMet(
+                          "The dtype of the two variables is not equal."));
+    PADDLE_ENFORCE_EQ(
+        src.dims(), tensor.dims(),
+        platform::errors::PreconditionNotMet(
+            "The layout of the two variables' tensors is not equal."));
     PADDLE_ENFORCE_EQ(src.lod(), tensor.lod(),
-                      "The lod of the two Variable is not equal.");
-    PADDLE_ENFORCE_EQ(src.layout(), tensor.layout(),
-                      "The layout of the two Variable's tensor is not equal.");
+                      platform::errors::PreconditionNotMet(
+                          "The lod of the two variable is not equal."));
+    PADDLE_ENFORCE_EQ(
+        src.layout(), tensor.layout(),
+        platform::errors::PreconditionNotMet(
+            "The layout of the two variables' tensors tensor is not equal."));
   }
 
   void operator()(const SelectedRows& src) {
-    auto& selected_rows = trg_->Get<SelectedRows>();
-    PADDLE_ENFORCE_EQ(
-        src.place().which(), selected_rows.place().which(),
-        "The Places of the two Variable must be all on CPU or all on GPU.");
+    auto& selected_rows = dst_->Get<SelectedRows>();
+    PADDLE_ENFORCE_EQ(src.place().which(), selected_rows.place().which(),
+                      platform::errors::PreconditionNotMet(
+                          "The place type of the two variables is not equal."));
     PADDLE_ENFORCE_EQ(src.value().type(), selected_rows.value().type(),
-                      "The dtype of the two Variable is not equal.");
-    PADDLE_ENFORCE_EQ(src.value().layout(), selected_rows.value().layout(),
-                      "The layout of the two Variable's tensor is not equal.");
+                      platform::errors::PreconditionNotMet(
+                          "The dtype of the two variables is not equal."));
+    PADDLE_ENFORCE_EQ(
+        src.value().layout(), selected_rows.value().layout(),
+        platform::errors::PreconditionNotMet(
+            "The layout of the two variables' tensors is not equal."));
     PADDLE_ENFORCE_EQ(src.height(), selected_rows.height(),
-                      "The height of the two Variable is not equal.");
+                      platform::errors::PreconditionNotMet(
+                          "The height of the two variables is not equal."));
     PADDLE_ENFORCE_EQ(src.GetCompleteDims(), selected_rows.GetCompleteDims(),
-                      "The dims of the two Variable is not equal.");
+                      platform::errors::PreconditionNotMet(
+                          "The dims of the two variables is not equal."));
   }
 
   template <typename T>
   void operator()(const T&) {
-    PADDLE_ENFORCE("EnforceShapeAndDTypeEQ is not supported by type %s",
-                   typeid(T).name());
+    PADDLE_THROW(platform::errors::Unimplemented(
+        "EnforceShapeAndDTypeEQ is not supported for type %s.",
+        typeid(T).name()));
   }
 };
 

paddle/fluid/framework/fleet/gloo_wrapper.cc

@@ -19,6 +19,8 @@ limitations under the License. */
 namespace gloo {
 namespace rendezvous {
 
+constexpr int kNodeSize = 136;
+
 HdfsStore::HdfsStore(const std::string& path) {
   path_ = path;
   wait_sleep_ms_ = 10000;
@@ -213,12 +215,14 @@ void ParallelConnectContext::connectFullMesh(
   storeKey << rank;
   store.set(storeKey.str(), allBytes);
 
+  auto total_add_size = kNodeSize * (size - 1);
+
   std::vector<std::shared_ptr<std::thread>> connect_threads(thread_num_);
   // Connect every pair
   for (uint32_t i = 0; i < connect_threads.size(); ++i) {
     connect_threads[i].reset(new std::thread(
-        [&store, &transportContext, this](size_t thread_idx,
-                                          size_t thread_num) -> void {
+        [&store, &transportContext, total_add_size, this](
+            size_t thread_idx, size_t thread_num) -> void {
           for (int i = thread_idx; i < size; i += thread_num) {
             if (i == rank) {
               continue;
@@ -226,8 +230,23 @@ void ParallelConnectContext::connectFullMesh(
             // Wait for address of other side of this pair to become available
             std::string key = std::to_string(i);
             store.wait({key}, getTimeout());
+
+            std::vector<char> allAddrs;
+            auto max_retry_times = 5;
             // Connect to other side of this pair
-            auto allAddrs = store.get(key);
+
+            while (max_retry_times > 0) {
+              allAddrs = store.get(key);
+
+              VLOG(3) << "store get all address size: " << allAddrs.size()
+                      << " except: " << total_add_size;
+              if (allAddrs.size() == static_cast<size_t>(total_add_size)) {
+                break;
+              }
+
+              --max_retry_times;
+            }
+
             auto addr = extractAddress(allAddrs, i);
             transportContext->getPair(i)->connect(addr);
           }

paddle/fluid/framework/ir/conv_affine_channel_fuse_pass.cc

@@ -18,6 +18,7 @@
 #include <string>
 #include <vector>
 #include "paddle/fluid/framework/lod_tensor.h"
+#include "paddle/fluid/framework/op_version_registry.h"
 #include "paddle/fluid/operators/math/cpu_vec.h"
 #include "paddle/fluid/platform/enforce.h"
 
@@ -225,3 +226,14 @@ REGISTER_PASS(conv_affine_channel_fuse_pass,
               paddle::framework::ir::ConvAffineChannelFusePass);
 REGISTER_PASS(conv_eltwiseadd_affine_channel_fuse_pass,
               paddle::framework::ir::ConvEltwiseAddAffineChannelFusePass);
+REGISTER_PASS_CAPABILITY(conv_affine_channel_fuse_pass)
+    .AddCombination(
+        paddle::framework::compatible::OpVersionComparatorCombination()
+            .EQ("conv2d", 0)
+            .EQ("affine_channel", 0));
+REGISTER_PASS_CAPABILITY(conv_eltwiseadd_affine_channel_fuse_pass)
+    .AddCombination(
+        paddle::framework::compatible::OpVersionComparatorCombination()
+            .EQ("conv2d", 0)
+            .EQ("elementwise_add", 0)
+            .EQ("affine_channel", 0));

paddle/fluid/framework/ir/conv_bn_fuse_pass.cc

@@ -18,6 +18,7 @@
 #include <string>
 #include <vector>
 #include "paddle/fluid/framework/lod_tensor.h"
+#include "paddle/fluid/framework/op_version_registry.h"
 #include "paddle/fluid/operators/math/cpu_vec.h"
 #include "paddle/fluid/platform/enforce.h"
 
@@ -372,3 +373,14 @@ REGISTER_PASS(depthwise_conv_bn_fuse_pass,
               paddle::framework::ir::DepthwiseConvBNFusePass);
 REGISTER_PASS(depthwise_conv_eltwiseadd_bn_fuse_pass,
               paddle::framework::ir::DepthwiseConvEltwiseAddBNFusePass);
+REGISTER_PASS_CAPABILITY(conv_bn_fuse_pass)
+    .AddCombination(
+        paddle::framework::compatible::OpVersionComparatorCombination()
+            .EQ("conv2d", 0)
+            .EQ("batch_norm", 0));
+REGISTER_PASS_CAPABILITY(conv_eltwiseadd_bn_fuse_pass)
+    .AddCombination(
+        paddle::framework::compatible::OpVersionComparatorCombination()
+            .EQ("conv2d", 0)
+            .EQ("elementwise_add", 0)
+            .EQ("batch_norm", 0));

paddle/fluid/framework/ir/conv_elementwise_add2_act_fuse_pass.cc

@@ -11,9 +11,9 @@
 // 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 "paddle/fluid/framework/ir/conv_elementwise_add2_act_fuse_pass.h"
 #include <string>
+#include "paddle/fluid/framework/op_version_registry.h"
 
 namespace paddle {
 namespace framework {
@@ -116,3 +116,10 @@ void ConvElementwiseAdd2ActFusePass::ApplyImpl(ir::Graph* graph) const {
 
 REGISTER_PASS(conv_elementwise_add2_act_fuse_pass,
               paddle::framework::ir::ConvElementwiseAdd2ActFusePass);
+REGISTER_PASS_CAPABILITY(conv_elementwise_add2_act_fuse_pass)
+    .AddCombination(
+        paddle::framework::compatible::OpVersionComparatorCombination()
+            .EQ("conv2d", 0)
+            .EQ("elementwise_add", 0)
+            .EQ("relu", 0)
+            .EQ("identity", 0));

paddle/fluid/framework/ir/conv_elementwise_add_act_fuse_pass.cc

@@ -15,6 +15,7 @@
 #include "paddle/fluid/framework/ir/conv_elementwise_add_act_fuse_pass.h"
 #include <string>
 #include "paddle/fluid/framework/ir/graph_viz_pass.h"
+#include "paddle/fluid/framework/op_version_registry.h"
 
 namespace paddle {
 namespace framework {
@@ -102,3 +103,10 @@ void ConvElementwiseAddActFusePass::ApplyImpl(ir::Graph* graph) const {
 
 REGISTER_PASS(conv_elementwise_add_act_fuse_pass,
               paddle::framework::ir::ConvElementwiseAddActFusePass);
+REGISTER_PASS_CAPABILITY(conv_elementwise_add_act_fuse_pass)
+    .AddCombination(
+        paddle::framework::compatible::OpVersionComparatorCombination()
+            .EQ("conv2d", 0)
+            .EQ("elementwise_add", 0)
+            .EQ("relu", 0)
+            .EQ("identity", 0));

paddle/fluid/framework/ir/conv_elementwise_add_fuse_pass.cc

@@ -12,10 +12,10 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-#include <string>
-
 #include "paddle/fluid/framework/ir/conv_elementwise_add_fuse_pass.h"
+#include <string>
 #include "paddle/fluid/framework/ir/graph_viz_pass.h"
+#include "paddle/fluid/framework/op_version_registry.h"
 
 namespace paddle {
 namespace framework {
@@ -89,3 +89,8 @@ void ConvElementwiseAddFusePass::ApplyImpl(ir::Graph* graph) const {
 
 REGISTER_PASS(conv_elementwise_add_fuse_pass,
               paddle::framework::ir::ConvElementwiseAddFusePass);
+REGISTER_PASS_CAPABILITY(conv_elementwise_add_fuse_pass)
+    .AddCombination(
+        paddle::framework::compatible::OpVersionComparatorCombination()
+            .EQ("conv2d", 0)
+            .EQ("elementwise_add", 0));

paddle/fluid/framework/ir/embedding_fc_lstm_fuse_pass.cc

@@ -23,6 +23,8 @@
 #include "paddle/fluid/operators/math/cpu_vec.h"
 #include "paddle/fluid/platform/cpu_info.h"
 
+#include "paddle/fluid/framework/op_version_registry.h"
+
 namespace paddle {
 namespace framework {
 namespace ir {
@@ -34,7 +36,7 @@ static int BuildFusion(Graph* graph, const std::string& name_scope,
 
   // Build pattern
   PDNode* x = pattern->NewNode(patterns::PDNodeName(name_scope, "x"))
-                  ->assert_is_op_input("lookup_table")
+                  ->assert_is_op_input("lookup_table_v2")
                   ->assert_var_not_persistable();
   patterns::Embedding embedding_pattern(pattern, name_scope);
   // TODO(jczaja): Intermediate can only be for val that are not used anywhere
@@ -256,3 +258,11 @@ void EmbeddingFCLSTMFusePass::ApplyImpl(ir::Graph* graph) const {
 
 REGISTER_PASS(embedding_fc_lstm_fuse_pass,
               paddle::framework::ir::EmbeddingFCLSTMFusePass);
+REGISTER_PASS_CAPABILITY(embedding_fc_lstm_fuse_pass)
+    .AddCombination(
+        paddle::framework::compatible::OpVersionComparatorCombination()
+            .EQ("lookup_table_v2", 0)
+            .EQ("mul", 0)
+            .EQ("elementwise_add", 0)
+            .EQ("lstm", 0)
+            .EQ("fused_embedding_fc_lstm", 0));

paddle/fluid/framework/ir/fc_fuse_pass.cc

@@ -18,6 +18,7 @@
 #include <unordered_set>
 #include <vector>
 #include "paddle/fluid/framework/ir/graph_helper.h"
+#include "paddle/fluid/framework/op_version_registry.h"
 #include "paddle/fluid/platform/enforce.h"
 
 namespace paddle {
@@ -182,3 +183,10 @@ int FCFusePass::ApplyFCPattern(Graph* graph, bool with_relu) const {
 
 REGISTER_PASS(fc_fuse_pass, paddle::framework::ir::FCFusePass)
     .RequirePassAttr("use_gpu");
+REGISTER_PASS_CAPABILITY(fc_fuse_pass)
+    .AddCombination(
+        paddle::framework::compatible::OpVersionComparatorCombination()
+            .EQ("mul", 0)
+            .EQ("elementwise_add", 0)
+            .EQ("relu", 0)
+            .EQ("fc", 0));

paddle/fluid/framework/ir/fc_gru_fuse_pass.cc

@@ -16,6 +16,7 @@
 #include <string>
 #include <unordered_set>
 #include "paddle/fluid/framework/lod_tensor.h"
+#include "paddle/fluid/framework/op_version_registry.h"
 
 namespace paddle {
 namespace framework {
@@ -125,7 +126,6 @@ static int BuildFusion(Graph* graph, const std::string& name_scope,
     auto* x_n = subgraph.at(x);
     GET_IR_NODE_FROM_SUBGRAPH(w, w, fc_pattern);
     GET_IR_NODE_FROM_SUBGRAPH(mul, mul, fc_pattern);
-    GET_IR_NODE_FROM_SUBGRAPH(fc_out, elementwise_add_out, fc_pattern);
     GET_IR_NODE_FROM_SUBGRAPH(Weight, Weight, gru_pattern);
     GET_IR_NODE_FROM_SUBGRAPH(gru, gru, gru_pattern);
     GET_IR_NODE_FROM_SUBGRAPH(Bias, Bias, gru_pattern);
@@ -136,10 +136,17 @@ static int BuildFusion(Graph* graph, const std::string& name_scope,
                               gru_pattern);
     GET_IR_NODE_FROM_SUBGRAPH(BatchHidden, BatchHidden, gru_pattern);
 
+    // TODO(wilber): Support origin_mode=True.
+    if (gru->Op()->GetAttrIfExists<bool>("origin_mode") == true) {
+      LOG(INFO) << "fc_gru_fuse_pass not supported when origin_mode=True.";
+      return;
+    }
+
     if (with_fc_bias) {
       GET_IR_NODE_FROM_SUBGRAPH(mul_out, mul_out, fc_pattern);
       GET_IR_NODE_FROM_SUBGRAPH(fc_bias, bias, fc_pattern);
       GET_IR_NODE_FROM_SUBGRAPH(elementwise_add, elementwise_add, fc_pattern);
+      GET_IR_NODE_FROM_SUBGRAPH(fc_out, elementwise_add_out, fc_pattern);
 
       gru_creater(gru, x_n, w, Weight, Bias, Hidden, fc_bias);
       // Remove unneeded nodes.
@@ -188,3 +195,16 @@ void FCGRUFusePass::ApplyImpl(ir::Graph* graph) const {
 
 REGISTER_PASS(mul_gru_fuse_pass, paddle::framework::ir::MulGRUFusePass);
 REGISTER_PASS(fc_gru_fuse_pass, paddle::framework::ir::FCGRUFusePass);
+REGISTER_PASS_CAPABILITY(mul_gru_fuse_pass)
+    .AddCombination(
+        paddle::framework::compatible::OpVersionComparatorCombination()
+            .EQ("mul", 0)
+            .EQ("gru", 0)
+            .EQ("fusion_gru", 0));
+REGISTER_PASS_CAPABILITY(fc_gru_fuse_pass)
+    .AddCombination(
+        paddle::framework::compatible::OpVersionComparatorCombination()
+            .EQ("mul", 0)
+            .EQ("elementwise_add", 0)
+            .EQ("gru", 0)
+            .EQ("fusion_gru", 0));

paddle/fluid/framework/ir/fc_lstm_fuse_pass.cc

@@ -16,6 +16,7 @@
 #include <string>
 #include <unordered_set>
 #include "paddle/fluid/framework/lod_tensor.h"
+#include "paddle/fluid/framework/op_version_registry.h"
 
 namespace paddle {
 namespace framework {
@@ -196,3 +197,17 @@ void FCLstmFusePass::ApplyImpl(ir::Graph* graph) const {
 
 REGISTER_PASS(mul_lstm_fuse_pass, paddle::framework::ir::MulLstmFusePass);
 REGISTER_PASS(fc_lstm_fuse_pass, paddle::framework::ir::FCLstmFusePass);
+
+REGISTER_PASS_CAPABILITY(fc_lstm_fuse_pass)
+    .AddCombination(
+        paddle::framework::compatible::OpVersionComparatorCombination()
+            .EQ("mul", 0)
+            .EQ("elementwise_add", 0)
+            .EQ("lstm", 0)
+            .EQ("fusion_lstm", 0));
+REGISTER_PASS_CAPABILITY(mul_lstm_fuse_pass)
+    .AddCombination(
+        paddle::framework::compatible::OpVersionComparatorCombination()
+            .EQ("mul", 0)
+            .EQ("lstm", 0)
+            .EQ("fusion_lstm", 0));

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

@@ -13,4 +13,6 @@ cc_library(memory_reuse_pass SRCS memory_reuse_pass.cc DEPS computation_op_handl
 cc_library(buffer_shared_inplace_op_pass SRCS buffer_shared_inplace_op_pass.cc DEPS memory_reuse_pass)
 cc_library(buffer_shared_cross_op_memory_reuse_pass SRCS buffer_shared_cross_op_memory_reuse_pass.cc DEPS memory_reuse_pass) 
 
+cc_library(inplace_addto_op_pass SRCS inplace_addto_op_pass.cc DEPS memory_reuse_pass)
+
 cc_test(test_reference_count_pass_last_lived_ops SRCS test_reference_count_pass_last_lived_ops.cc DEPS parallel_executor elementwise_mul_op elementwise_add_op scale_op)

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

@@ -16,6 +16,7 @@
 #include <unordered_map>
 #include <unordered_set>
 #include <vector>
+
 #include "paddle/fluid/framework/details/computation_op_handle.h"
 #include "paddle/fluid/framework/details/multi_devices_helper.h"
 #include "paddle/fluid/framework/details/share_tensor_buffer_op_handle.h"
@@ -141,11 +142,12 @@ void BufferSharedInplaceOpPass::Run(Graph *graph) const {
         VLOG(4) << "Inplace performed in op " << op_type << ": "
                 << in_var_handle_ptr->Name() << " -> "
                 << out_var_handle_ptr->Name()
-                << ". Debug String is: " << op->GetOp()->DebugString();
+                << ". Debug String is: " << op->GetOp()->DebugString()
+                << ". ReuseType: " << ReuseType();
       } else {
         VLOG(3) << "Inplace failed in op " << op_type << ": "
                 << in_var_handle_ptr->Name() << " -> "
-                << out_var_handle_ptr->Name();
+                << out_var_handle_ptr->Name() << ". ReuseType: " << ReuseType();
       }
     }
   }

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

+// Copyright (c) 2020 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 <string>
+#include <unordered_map>
+#include <unordered_set>
+#include <vector>
+
+#include "paddle/fluid/framework/details/computation_op_handle.h"
+#include "paddle/fluid/framework/details/multi_devices_helper.h"
+#include "paddle/fluid/framework/details/share_tensor_buffer_op_handle.h"
+#include "paddle/fluid/framework/ir/memory_optimize_pass/memory_optimization_var_info.h"
+#include "paddle/fluid/framework/ir/memory_optimize_pass/memory_reuse_pass.h"
+#include "paddle/fluid/framework/ir/memory_optimize_pass/reference_count_pass_helper.h"
+#include "paddle/fluid/framework/ir/pass.h"
+
+namespace paddle {
+namespace framework {
+namespace ir {
+
+class InplaceAddToOpPass : public MemoryReusePass {
+ protected:
+  std::string ReuseType() const override { return "inplace_addto"; }
+
+  void Run(Graph *graph) const override;
+
+ private:
+  // 1. Add last living op of in_var, add any last living op of out_var
+  // 2. Set reference count of in_var to be 2
+  void UpdateLastLiveOpOfVar(details::ComputationOpHandle *op,
+                             details::VarHandle *in_var,
+                             details::VarHandle *out_var) const override {
+    size_t scope_idx = op->GetScopeIdx();
+    auto *last_live_ops_of_vars_ =
+        &Get<std::vector<LastLiveOpsOfVars>>(kLastLiveOpsOfVars);
+    auto *var_infos_ = &(Get<MemOptVarInfoMapList>(kMemOptVarInfoMapList));
+    auto out_var_op_iter =
+        (*last_live_ops_of_vars_)[scope_idx].find(out_var->Name());
+
+    // In Reduce mode, some output variable(gradient of parameter) does not have
+    // last live ops
+    details::ComputationOpHandle *last_live_op_of_in_var = nullptr;
+    if (out_var_op_iter == (*last_live_ops_of_vars_)[scope_idx].end()) {
+      last_live_op_of_in_var = op;
+    } else {
+      PADDLE_ENFORCE_EQ(
+          out_var_op_iter->second.ops().empty(), false,
+          platform::errors::InvalidArgument(
+              "Var(%s)'s last live op should not empty.", out_var->Name()));
+      last_live_op_of_in_var = *(out_var_op_iter->second.ops().begin());
+    }
+
+    auto *last_live_ops_of_in_var =
+        (*last_live_ops_of_vars_)[scope_idx][in_var->Name()].mutable_ops();
+    // last_live_ops_of_in_var->clear();
+    last_live_ops_of_in_var->insert(last_live_op_of_in_var);
+
+    auto in_var_info_iter = (*var_infos_)[scope_idx].find(in_var->Name());
+    PADDLE_ENFORCE_NE(
+        in_var_info_iter, (*var_infos_)[scope_idx].end(),
+        platform::errors::NotFound("Cannot find variable %s.", in_var->Name()));
+
+    in_var_info_iter->second->SetRefCnt(2);  // before inplace, it is 1
+  }
+};
+
+void InplaceAddToOpPass::Run(Graph *graph) const {
+  const auto &last_live_ops =
+      Get<std::vector<LastLiveOpsOfVars>>(kLastLiveOpsOfVars);
+
+  bool use_cuda = Get<bool>(kUseCuda);
+
+  // Currently, only perform InplaceAddToOpPass on cuda place
+  if (!use_cuda) {
+    return;
+  }
+
+  // Step 1: Build a reverse map of last_live_ops
+  // i.e.: op -> vars
+  std::unordered_map<details::ComputationOpHandle *,
+                     std::unordered_map<std::string, ir::Node *>>
+      candidate_ops;
+  for (auto &each_scope_ops : last_live_ops) {
+    for (auto &pair : each_scope_ops) {
+      // If variable has more than 1 last lived ops, this variable cannot
+      // be inplaced.
+      if (pair.second.ops().size() != 1) {
+        continue;
+      }
+
+      auto *op = *(pair.second.ops().begin());
+      const std::string &op_type = op->GetOp()->Type();
+      const framework::OpDesc *op_desc = op->Node()->Op();
+      PADDLE_ENFORCE_NOT_NULL(
+          op_desc, platform::errors::NotFound("Op(%s) can not find opdesc.",
+                                              op->Name()));
+
+      // only grad op should be processed.
+      if (op_type != "grad_add") {
+        continue;
+      }
+
+      const std::string &var_name = pair.first;
+      auto in_nodes = this->FindNodesByName(var_name, op->Node()->inputs);
+      if (in_nodes.size() == 1) {
+        candidate_ops[op][var_name] = *in_nodes.begin();
+      }
+      VLOG(4) << "Find op " << op_type << " with input(" << var_name
+              << ") that can do inplace add to";
+    }
+  }
+
+  // Step 2: Check which vars can be inplaced indeed
+  for (auto &op_vars_pair : candidate_ops) {
+    auto *op = op_vars_pair.first;
+
+    // The original gradient accumulation is g = sum(g_0, g_1,..., g_n), and it
+    // could be changed as follws if inplace addto is enabled:
+    // g_sum_0 = g_0
+    // g_sum_1 = grad_add(g_sum_0, g_1)
+    // g_sum_2 = grad_add(g_sum_1, g_2)
+    // ...
+    // g_sum_n = grad_add(g_sum_n-1, g_n)
+
+    // here we will add inplace for each grad_add, for example, for the first
+    // grad_add, g_sum_0 -> g1, g_sum_1 -> g1, and set grad_add as skipped.
+
+    const std::string &op_type = op->GetOp()->Type();
+
+    PADDLE_ENFORCE_EQ(op->Node()->inputs.size(), 2,
+                      platform::errors::InvalidArgument(
+                          "The size of inputs of %s should be 2, but got %d",
+                          op_type, op->Node()->inputs.size()));
+
+    PADDLE_ENFORCE_EQ(op->Node()->outputs.size(), 1,
+                      platform::errors::InvalidArgument(
+                          "The size of outputs of %s should be 1, but got %d",
+                          op_type, op->Node()->outputs.size()));
+
+    auto *left_var_ptr = dynamic_cast<details::VarHandle *>(
+        &(op->Node()->inputs[0]->Wrapper<details::VarHandleBase>()));
+    auto *right_var_ptr = dynamic_cast<details::VarHandle *>(
+        &(op->Node()->inputs[1]->Wrapper<details::VarHandleBase>()));
+    auto *out_var_ptr = dynamic_cast<details::VarHandle *>(
+        &(op->Node()->outputs[0]->Wrapper<details::VarHandleBase>()));
+
+    if (left_var_ptr == nullptr || right_var_ptr == nullptr ||
+        out_var_ptr == nullptr) {
+      continue;
+    }
+
+    // auto *left_generated_op = dynamic_cast<details::ComputationOpHandle *>(
+    //     left_var_ptr->GeneratedOp());
+
+    auto *right_generated_op = dynamic_cast<details::ComputationOpHandle *>(
+        right_var_ptr->GeneratedOp());
+
+    auto *out_generated_op = dynamic_cast<details::ComputationOpHandle *>(
+        out_var_ptr->GeneratedOp());
+
+    // NOTE(zhiqiu): currently, only conv2d_grad supports addto strategy
+    if (right_generated_op->Name() != "conv2d_grad") {
+      continue;
+    }
+
+    // NOTE(zhiqiu): Normally, if we inplace a->b, we should let a generated
+    // before b. However, in the situation of inplace addto, we do not care
+    // the order, since a+b is equal to b+a. Is there any exception for that?
+
+    // AddDependencyVar(right_generated_op, left_generated_op);
+    // no need, as discussed above.
+
+    // step (a): inplace right_var->left_var of grad_add
+
+    this->AddReuseVar(right_generated_op, left_var_ptr, right_var_ptr);
+    UpdateLastLiveOpOfVar(right_generated_op, left_var_ptr, right_var_ptr);
+    VLOG(4) << "Inplace performed in op " << right_generated_op->GetOp()->Type()
+            << ": " << left_var_ptr->Name() << " -> " << right_var_ptr->Name()
+            << ". Debug String is: "
+            << right_generated_op->GetOp()->DebugString()
+            << ". ReuseType: " << ReuseType();
+
+    // step (b): inplace out -> right_var of grad_add
+
+    this->AddReuseVar(out_generated_op, right_var_ptr, out_var_ptr, true);
+
+    VLOG(4) << "Inplace performed in op " << op_type << ": "
+            << left_var_ptr->Name() << " -> " << out_var_ptr->Name()
+            << ". Debug String is: " << op->GetOp()->DebugString()
+            << ". ReuseType: " << ReuseType();
+
+    // step (c): make right_var cannot inplace afterwards. canbe done
+    // aotomatically since CollectReusedVars is called before any reuse.
+
+    // step (d): make right_var's generated op use addto
+    right_generated_op->GetOp()->SetAttr("use_addto", true);
+
+    // step (e): make grad_add skip running
+    op->SetSkipRunning(true);
+  }
+}
+
+}  // namespace ir
+}  // namespace framework
+}  // namespace paddle
+
+REGISTER_PASS(inplace_addto_op_pass, paddle::framework::ir::InplaceAddToOpPass)
+    .RequirePassAttr(paddle::framework::ir::kMemOptVarInfoMapList)
+    .RequirePassAttr(paddle::framework::ir::kLastLiveOpsOfVars)
+    .RequirePassAttr(paddle::framework::ir::kUseCuda);

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

@@ -13,6 +13,7 @@
 // limitations under the License.
 
 #include "paddle/fluid/framework/ir/memory_optimize_pass/memory_reuse_pass.h"
+
 #include <functional>
 #include <map>
 #include <string>
@@ -73,6 +74,7 @@ bool MemoryReusePass::TryReuseVar(details::VarHandle *in_var,
           out_var->Name()));
   if (IsVarPairReusable(*in_var, *out_var)) {
     AddReuseVar(op, in_var, out_var);
+    UpdateLastLiveOpOfVar(op, in_var, out_var);
     return true;
   } else {
     return false;
@@ -324,7 +326,8 @@ bool MemoryReusePass::IsVarPairReusable(
 
 void MemoryReusePass::AddReuseVar(details::ComputationOpHandle *op,
                                   details::VarHandle *in_var,
-                                  details::VarHandle *out_var) const {
+                                  details::VarHandle *out_var,
+                                  bool share_dims) const {
   PADDLE_ENFORCE_GT(
       (*var_infos_)[op->GetScopeIdx()].count(in_var->Name()), 0,
       platform::errors::NotFound("Var(%s) does not in mem opt var infos.",
@@ -344,13 +347,15 @@ void MemoryReusePass::AddReuseVar(details::ComputationOpHandle *op,
     share_buffer_op->AddInput(in_var);
   }
 
+  if (share_dims) {
+    share_buffer_op->SetShareDims(true);
+  }
+
   share_buffer_op->AddReuseVarPair(
       (*var_infos_)[op->GetScopeIdx()].at(in_var->Name()).get(),
       out_var->Name());
   reused_in_var_names_[op->GetScopeIdx()].insert(in_var->Name());
   reused_out_var_names_[op->GetScopeIdx()].insert(out_var->Name());
-
-  UpdateLastLiveOpOfVar(op, in_var, out_var);
 }
 
 // 1. Set last living op of in_var to be any last living op of out_var

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

@@ -18,6 +18,7 @@
 #include <unordered_map>
 #include <unordered_set>
 #include <vector>
+
 #include "paddle/fluid/framework/details/computation_op_handle.h"
 #include "paddle/fluid/framework/details/multi_devices_helper.h"
 #include "paddle/fluid/framework/details/share_tensor_buffer_op_handle.h"
@@ -92,6 +93,12 @@ class MemoryReusePass : public Pass {
 
   int64_t GetMemorySize(const details::VarHandle &var) const;
 
+  void AddReuseVar(details::ComputationOpHandle *op, details::VarHandle *in_var,
+                   details::VarHandle *out_var, bool share_dims = false) const;
+  virtual void UpdateLastLiveOpOfVar(details::ComputationOpHandle *op,
+                                     details::VarHandle *in_var,
+                                     details::VarHandle *out_var) const;
+
  private:
   VarDesc *GetVarDesc(const details::VarHandle &var) const;
 
@@ -109,13 +116,6 @@ class MemoryReusePass : public Pass {
 
   void CollectReusedVars() const;
 
-  void AddReuseVar(details::ComputationOpHandle *op, details::VarHandle *in_var,
-                   details::VarHandle *out_var) const;
-
-  void UpdateLastLiveOpOfVar(details::ComputationOpHandle *op,
-                             details::VarHandle *in_var,
-                             details::VarHandle *out_var) const;
-
  private:
   mutable Graph *graph_;
   mutable bool use_cuda_;

paddle/fluid/framework/ir/repeated_fc_relu_fuse_pass.cc

@@ -18,6 +18,7 @@ limitations under the License. */
 #include <unordered_set>
 #include <vector>
 #include "paddle/fluid/framework/lod_tensor.h"
+#include "paddle/fluid/framework/op_version_registry.h"
 
 #define MAX_NUM_FC 10
 
@@ -174,6 +175,10 @@ void BuildRepeatedFCReluPattern(PDPattern* pattern,
             if (x->outputs.size() <= 0 || x->inputs.size() <= 0U) {
               return false;
             }
+            if (x->IsVar() && x->Var() && x->Var()->GetShape().size() > 2) {
+              LOG(WARNING) << "repeated fc relu only supports input dims = 2";
+              return false;
+            }
             int fc_idx = FindFCIdx(x);
             if (fc_idx < 0) {
               return false;
@@ -384,3 +389,8 @@ void RepeatedFCReluFusePass::ApplyImpl(ir::Graph* graph) const {
 
 REGISTER_PASS(repeated_fc_relu_fuse_pass,
               paddle::framework::ir::RepeatedFCReluFusePass);
+REGISTER_PASS_CAPABILITY(repeated_fc_relu_fuse_pass)
+    .AddCombination(
+        paddle::framework::compatible::OpVersionComparatorCombination()
+            .EQ("fc", 0)
+            .EQ("relu", 0));

paddle/fluid/framework/ir/shuffle_channel_detect_pass.cc

@@ -16,6 +16,7 @@
 
 #include "paddle/fluid/framework/ir/graph_viz_pass.h"
 #include "paddle/fluid/framework/ir/shuffle_channel_detect_pass.h"
+#include "paddle/fluid/framework/op_version_registry.h"
 
 namespace paddle {
 namespace framework {
@@ -34,6 +35,8 @@ void ShuffleChannelDetectPass::ApplyImpl(ir::Graph* graph) const {
   const std::string pattern_name = "shufflechannel_pattern";
   FusePassBase::Init(pattern_name, graph);
 
+  LOG(WARNING) << "There is fluid.layers.shuffle_channel API already, you can "
+                  "use it instead of (reshape + transpose +reshape)";
   GraphPatternDetector gpd;
   auto* x = gpd.mutable_pattern()
                 ->NewNode("x")
@@ -93,3 +96,8 @@ void ShuffleChannelDetectPass::ApplyImpl(ir::Graph* graph) const {
 
 REGISTER_PASS(shuffle_channel_detect_pass,
               paddle::framework::ir::ShuffleChannelDetectPass);
+REGISTER_PASS_CAPABILITY(shuffle_channel_detect_pass)
+    .AddCombination(
+        paddle::framework::compatible::OpVersionComparatorCombination()
+            .EQ("reshape2", 0)
+            .EQ("transpose2", 0));

paddle/fluid/framework/ir/squared_mat_sub_fuse_pass.cc

@@ -17,6 +17,7 @@
 #include <unordered_set>
 #include <vector>
 #include "paddle/fluid/framework/lod_tensor.h"
+#include "paddle/fluid/framework/op_version_registry.h"
 
 namespace paddle {
 namespace framework {
@@ -77,7 +78,8 @@ PDNode* BuildSquaredMatSubPattern(PDPattern* pattern,
   };
 
   auto is_fusion_input_var = [=](Node* x, const std::string& arg_name) {
-    bool basic = var_is_op_input(x, "matmul", arg_name) &&
+    bool basic = (var_is_op_input(x, "matmul_v2", arg_name) ||
+                  var_is_op_input(x, "matmul", arg_name)) &&
                  var_is_op_input(x, "square", "X");
     if (!basic) {
       return false;
@@ -88,7 +90,8 @@ PDNode* BuildSquaredMatSubPattern(PDPattern* pattern,
     }
     auto* squared_x = squared_x_op->outputs[0];
     bool next_is_matmul_from_arg =
-        var_is_op_input(squared_x, "matmul", arg_name) &&
+        (var_is_op_input(squared_x, "matmul_v2", arg_name) ||
+         var_is_op_input(squared_x, "matmul", arg_name)) &&
         squared_x->outputs.size() == 1 &&
         squared_x->outputs[0]->outputs.size() == 1;
     if (!next_is_matmul_from_arg) {
@@ -103,7 +106,8 @@ PDNode* BuildSquaredMatSubPattern(PDPattern* pattern,
   auto is_fusion_first_mul_out = [=](Node* x) -> bool {
     bool input_is_matmul_op = x && x->inputs.size() == 1 &&
                               x->inputs[0]->IsOp() &&
-                              x->inputs[0]->Op()->Type() == "matmul";
+                              (x->inputs[0]->Op()->Type() == "matmul_v2" ||
+                               x->inputs[0]->Op()->Type() == "matmul");
     if (!input_is_matmul_op) {
       return false;
     }
@@ -167,7 +171,8 @@ PDNode* BuildSquaredMatSubPattern(PDPattern* pattern,
 
   auto* matmul_xy_op = pattern->NewNode(
       [=](Node* x) {
-        return x && x->IsOp() && x->Op()->Type() == "matmul" &&
+        return x && x->IsOp() && (x->Op()->Type() == "matmul_v2" ||
+                                  x->Op()->Type() == "matmul") &&
                is_fusion_first_mul_out(x->outputs[0]);
       },
       name_scope + "/matmul_xy_op");
@@ -189,7 +194,9 @@ PDNode* BuildSquaredMatSubPattern(PDPattern* pattern,
 
   auto is_fusion_mat_squared_x_y_op_out = [=](Node* x) -> bool {
     bool basic = x && x->IsVar() && x->inputs.size() == 1 &&
-                 x->inputs[0]->IsOp() && x->inputs[0]->Op()->Type() == "matmul";
+                 x->inputs[0]->IsOp() &&
+                 (x->inputs[0]->Op()->Type() == "matmul_v2" ||
+                  x->inputs[0]->Op()->Type() == "matmul");
     if (!basic) {
       return false;
     }
@@ -206,7 +213,8 @@ PDNode* BuildSquaredMatSubPattern(PDPattern* pattern,
 
   auto* matmul_squared_x_y_op = pattern->NewNode(
       [=](Node* x) {
-        return x && x->IsOp() && x->Op()->Type() == "matmul" &&
+        return x && x->IsOp() && (x->Op()->Type() == "matmul_v2" ||
+                                  x->Op()->Type() == "matmul") &&
                is_fusion_mat_squared_x_y_op_out(x->outputs[0]);
       },
       name_scope + "/matmul_squared_x_y_op");
@@ -378,3 +386,13 @@ void SquaredMatSubFusePass::ApplyImpl(ir::Graph* graph) const {
 
 REGISTER_PASS(squared_mat_sub_fuse_pass,
               paddle::framework::ir::SquaredMatSubFusePass);
+REGISTER_PASS_CAPABILITY(squared_mat_sub_fuse_pass)
+    .AddCombination(
+        paddle::framework::compatible::OpVersionComparatorCombination()
+            .EQ("matmul", 0)
+            .EQ("matmul_v2", 0)
+            .EQ("square", 0)
+            .EQ("elementwise_mul", 0)
+            .EQ("elementwise_sub", 0)
+            .EQ("fill_constant", 0)
+            .EQ("fusion_squared_mat_sub", 0));

paddle/fluid/framework/ir/squared_mat_sub_fuse_pass.h

@@ -24,7 +24,7 @@ namespace framework {
 namespace ir {
 
 /**
- * Fuse ( (A.^2 * B.^2) - (A * B).^2 ) .* scalar
+ * Fuse ( (A * B).^2 - (A.^2 * B.^2) ) .* scalar
  */
 class SquaredMatSubFusePass : public FusePassBase {
  public:

paddle/fluid/framework/operator.h

@@ -157,6 +157,14 @@ class OperatorBase {
         platform::errors::NotFound("(%s) is not found in AttributeMap.", name));
     return BOOST_GET_CONST(T, attrs_.at(name));
   }
+  void SetAttr(const std::string& name, const Attribute& v) {
+    PADDLE_ENFORCE_EQ(
+        HasAttr(name), true,
+        platform::errors::NotFound(
+            "The attribute %s is not found in operator %s", name, Type()));
+
+    attrs_[name] = v;
+  }
   const AttributeMap& Attrs() const { return attrs_; }
 
   const VariableNameMap& Inputs() const { return inputs_; }

paddle/fluid/framework/parallel_executor.cc

@@ -13,12 +13,14 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/fluid/framework/parallel_executor.h"
+
 #include <algorithm>
 #include <memory>
 #include <string>
 #include <tuple>
 #include <utility>
 #include <vector>
+
 #include "paddle/fluid/framework/details/async_ssa_graph_executor.h"
 #include "paddle/fluid/framework/details/fast_threaded_ssa_graph_executor.h"
 #include "paddle/fluid/framework/details/multi_devices_helper.h"
@@ -108,6 +110,11 @@ class ParallelExecutorPrivate {
    *                                       them.
    */
   inline void SetSkipMemoryReuse(size_t scope_idx, const std::string &name) {
+    if (mem_opt_var_infos_.size() == 0) {
+      VLOG(4) << "The mem_opt_var_infos_ is empty, maybe no memory "
+                 "optimization strategy is enabled";
+      return;
+    }
     auto iter = mem_opt_var_infos_[scope_idx].find(name);
     if (iter != mem_opt_var_infos_[scope_idx].end()) {
       iter->second->SetSkipMemoryReuse(true);
@@ -308,6 +315,7 @@ ir::Graph *ParallelExecutorPrivate::ApplyMemoryOptimizePass(ir::Graph *graph) {
   }
 
   bool need_mem_opt = build_strategy_.enable_inplace_ ||
+                      build_strategy_.enable_addto_ ||
                       build_strategy_.memory_optimize_.get() || is_gc_enabled;
 
   if (!need_mem_opt) return graph;
@@ -320,6 +328,16 @@ ir::Graph *ParallelExecutorPrivate::ApplyMemoryOptimizePass(ir::Graph *graph) {
   graph = ref_cnt_pass->Apply(graph);
   VLOG(10) << "ReferenceCountPass Applied";
 
+  if (build_strategy_.enable_addto_) {
+    auto addto_pass = ir::PassRegistry::Instance().Get("inplace_addto_op_pass");
+    addto_pass->SetNotOwned(ir::kMemOptVarInfoMapList, &mem_opt_var_infos_);
+    addto_pass->SetNotOwned(ir::kLastLiveOpsOfVars, &last_live_ops_of_vars);
+    addto_pass->SetNotOwned(ir::kUseCuda, &use_cuda_);
+    VLOG(10) << "Start to apply inplace_addto_op_pass";
+    graph = addto_pass->Apply(graph);
+    VLOG(10) << "inplace_addto_op_pass Applied";
+  }
+
   if (build_strategy_.enable_inplace_) {
     auto inplace_pass =
         ir::PassRegistry::Instance().Get("buffer_shared_inplace_pass");
@@ -1068,3 +1086,4 @@ USE_PASS(reference_count_pass);
 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);

paddle/fluid/inference/api/paddle_pass_builder.cc

@@ -156,7 +156,8 @@ CpuPassStrategy::CpuPassStrategy() : PassStrategy({}) {
                   // "seqpool_concat_fuse_pass",    //
                   "seqpool_cvm_concat_fuse_pass",  //
                   // "embedding_fc_lstm_fuse_pass", //
-                  "fc_lstm_fuse_pass",                       //
+                  // TODO(wilber): fix correctness problem.
+                  // "fc_lstm_fuse_pass",                       //
                   "mul_lstm_fuse_pass",                      //
                   "fc_gru_fuse_pass",                        //
                   "mul_gru_fuse_pass",                       //

paddle/fluid/inference/tensorrt/convert/emb_eltwise_layernorm.cc

@@ -80,10 +80,10 @@ class EmbEltwiseLayerNormOpConverter : public OpConverter {
     nvinfer1::ILayer* layer = nullptr;
 
     if (engine_->with_dynamic_shape()) {
-      plugin::DynamicPluginTensorRT* plugin = nullptr;
-      plugin = new plugin::EmbEltwiseLayernormPluginDynamic<float>(
+      auto use_fp16 = engine_->WithFp16();
+      auto plugin = new plugin::EmbEltwiseLayernormPluginDynamic(
           input_embs, bias, scale, emb_sizes, bias_size, scale_size, hidden,
-          eps);
+          eps, use_fp16);
       layer = engine_->AddPluginV2(input_ids.data(), input_num, plugin);
     } else {
       PADDLE_THROW(platform::errors::Fatal(

paddle/fluid/inference/tensorrt/plugin/emb_eltwise_layernorm_plugin.cu

@@ -32,13 +32,34 @@ namespace plugin {
 #if IS_TRT_VERSION_GE(6000)
 
 template <typename T>
-int EmbEltwiseLayernormPluginDynamic<T>::initialize() {
+EmbEltwiseLayernormPluginDynamicImpl<
+    T>::~EmbEltwiseLayernormPluginDynamicImpl() {
+  this->terminate();
+}
+
+inline half fp32tofp16(float x) { return static_cast<half>(x); }
+
+template <typename T>
+int EmbEltwiseLayernormPluginDynamicImpl<T>::initialize() {
   embs_gpu_.resize(embs_.size());
   for (int i = 0; i < embs_.size(); i++) {
     if (embs_[i]) {
-      cudaMalloc(&embs_gpu_[i], sizeof(float) * emb_sizes_[i]);
-      cudaMemcpy(embs_gpu_[i], embs_[i], emb_sizes_[i] * sizeof(float),
+      T *host_ptr;
+      auto size = emb_sizes_[i];
+
+      if (std::is_same<T, half>::value) {
+        host_ptr = new T[size];
+        std::transform(embs_[i], (embs_[i] + size), host_ptr, fp32tofp16);
+      } else {
+        host_ptr = reinterpret_cast<T *>(embs_[i]);
+      }
+
+      cudaMalloc(&embs_gpu_[i], sizeof(T) * size);
+      cudaMemcpy(embs_gpu_[i], host_ptr, size * sizeof(T),
                  cudaMemcpyHostToDevice);
+      if (std::is_same<T, half>::value) {
+        delete[] host_ptr;
+      }
     }
   }
 
@@ -53,11 +74,105 @@ int EmbEltwiseLayernormPluginDynamic<T>::initialize() {
                cudaMemcpyHostToDevice);
   }
 
+  int input_num = embs_.size();
+  in_ptr_tensor_.Resize({input_num});
+  emb_ptr_tensor_.Resize({input_num});
+
+  cudaGetDevice(&device_id_);
+  auto emb_ptr_gpu_d =
+      emb_ptr_tensor_.mutable_data<int64_t>(platform::CUDAPlace(device_id_));
+  cudaMemcpy(emb_ptr_gpu_d, embs_gpu_.data(), sizeof(uintptr_t) * input_num,
+             cudaMemcpyHostToDevice);
+
   return 0;
 }
 
 template <typename T>
-nvinfer1::DimsExprs EmbEltwiseLayernormPluginDynamic<T>::getOutputDimensions(
+void EmbEltwiseLayernormPluginDynamicImpl<T>::terminate() {
+  for (int i = 0; i < embs_gpu_.size(); ++i) {
+    if (embs_gpu_[i]) {
+      cudaFree(embs_gpu_[i]);
+      embs_gpu_[i] = nullptr;
+    }
+  }
+
+  if (bias_gpu_) {
+    cudaFree(bias_gpu_);
+    bias_gpu_ = nullptr;
+  }
+
+  if (scale_gpu_) {
+    cudaFree(scale_gpu_);
+    scale_gpu_ = nullptr;
+  }
+}
+
+template <typename T>
+int EmbEltwiseLayernormPluginDynamicImpl<T>::enqueue(
+    const nvinfer1::PluginTensorDesc *input_desc,
+    const nvinfer1::PluginTensorDesc *output_desc, const void *const *inputs,
+    void *const *outputs, void *workspace, cudaStream_t stream) {
+  auto id_dims = input_desc[0].dims;
+  int batch = id_dims.d[0];
+  int seq_len = id_dims.d[1];
+  int input_num = embs_.size();
+
+  auto in_ptr_gpu_d =
+      in_ptr_tensor_.mutable_data<int64_t>(platform::CUDAPlace(device_id_));
+  auto emb_ptr_gpu_d =
+      emb_ptr_tensor_.mutable_data<int64_t>(platform::CUDAPlace(device_id_));
+
+  auto new_input_ptr = reinterpret_cast<uintptr_t>(inputs[0]);
+
+  if (old_input_ptr_ != new_input_ptr) {
+    old_input_ptr_ = new_input_ptr;
+
+    cudaMemcpyAsync(in_ptr_gpu_d, reinterpret_cast<const void *>(inputs),
+                    sizeof(uintptr_t) * input_num, cudaMemcpyHostToDevice,
+                    stream);
+  }
+
+  auto out_type = output_desc[0].type;
+
+  if (std::is_same<T, float>::value) {
+    PADDLE_ENFORCE_EQ(
+        out_type == nvinfer1::DataType::kFLOAT, true,
+        platform::errors::InvalidArgument(
+            "The EmbEltwiseLayernorm Plugin only support fp32 input."));
+  } else if (std::is_same<T, half>::value) {
+    PADDLE_ENFORCE_EQ(
+        out_type == nvinfer1::DataType::kHALF, true,
+        platform::errors::InvalidArgument(
+            "The EmbEltwiseLayernorm Plugin only support fp16 input."));
+  } else {
+    PADDLE_THROW(platform::errors::Fatal(
+        "Unsupport data type, the out type of EmbEltwiseLayernorm should be "
+        "float or half."));
+  }
+
+  auto *output_d = reinterpret_cast<T *>(outputs[0]);
+
+  operators::math::EmbEltwiseLayerNormFunctor<T> emb_eltwise_layernorm_func;
+  emb_eltwise_layernorm_func(batch, seq_len, hidden_size_, in_ptr_gpu_d,
+                             scale_gpu_, bias_gpu_, emb_ptr_gpu_d, output_d,
+                             eps_, input_num, stream);
+  return cudaGetLastError() != cudaSuccess;
+}
+
+template class EmbEltwiseLayernormPluginDynamicImpl<float>;
+#ifdef SUPPORTS_CUDA_FP16
+template class EmbEltwiseLayernormPluginDynamicImpl<half>;
+#endif  // SUPPORTS_CUDA_FP16
+
+int EmbEltwiseLayernormPluginDynamic::initialize() {
+  impl_->initialize();
+
+  return 0;
+}
+
+void EmbEltwiseLayernormPluginDynamic::terminate() { impl_->terminate(); }
+
+nvinfer1::DimsExprs EmbEltwiseLayernormPluginDynamic::getOutputDimensions(
     int output_index, const nvinfer1::DimsExprs *inputs, int nb_inputs,
     nvinfer1::IExprBuilder &expr_builder) {  // NOLINT
   PADDLE_ENFORCE_EQ(output_index, 0,
@@ -76,18 +191,7 @@ nvinfer1::DimsExprs EmbEltwiseLayernormPluginDynamic<T>::getOutputDimensions(
   return ret;
 }
 
-template <typename T>
-void EmbEltwiseLayernormPluginDynamic<T>::terminate() {
-  for (auto ptr : embs_gpu_) {
-    if (ptr) cudaFree(ptr);
-  }
-
-  if (bias_gpu_) cudaFree(bias_gpu_);
-  if (scale_gpu_) cudaFree(scale_gpu_);
-}
-
-template <typename T>
-bool EmbEltwiseLayernormPluginDynamic<T>::supportsFormatCombination(
+bool EmbEltwiseLayernormPluginDynamic::supportsFormatCombination(
     int pos, const nvinfer1::PluginTensorDesc *in_out, int nb_inputs,
     int nb_outputs) {
   PADDLE_ENFORCE_NOT_NULL(
@@ -98,6 +202,11 @@ bool EmbEltwiseLayernormPluginDynamic<T>::supportsFormatCombination(
                         "The EmbEltwiseLayerNorm's output should be one"
                         "but it's (%d) outputs.",
                         nb_outputs));
+  PADDLE_ENFORCE_EQ(nb_outputs, 1,
+                    platform::errors::InvalidArgument(
+                        "The EmbEltwiseLayerNorm's output should be one"
+                        "but it's (%d) outputs.",
+                        nb_outputs));
   PADDLE_ENFORCE_LT(
       pos, nb_inputs + nb_outputs,
       platform::errors::InvalidArgument("The pos(%d) should be less than the "
@@ -122,7 +231,7 @@ bool EmbEltwiseLayernormPluginDynamic<T>::supportsFormatCombination(
   }
 
   if (pos == all_nums - 1) {
-    if (sizeof(T) == sizeof(float)) {
+    if (with_fp16_ == false) {
       return desc.type == nvinfer1::DataType::kFLOAT;
     } else {
       return desc.type == nvinfer1::DataType::kHALF;
@@ -131,84 +240,27 @@ bool EmbEltwiseLayernormPluginDynamic<T>::supportsFormatCombination(
   return false;
 }
 
-template <typename T>
-nvinfer1::DataType EmbEltwiseLayernormPluginDynamic<T>::getOutputDataType(
+nvinfer1::DataType EmbEltwiseLayernormPluginDynamic::getOutputDataType(
     int index, const nvinfer1::DataType *input_types, int nb_inputs) const {
   PADDLE_ENFORCE_EQ(
       index, 0, platform::errors::InvalidArgument(
                     "The EmbEltwiseLayernorm Plugin only has one input, so the "
                     "index value should be 0, but get %d.",
                     index));
-  return nvinfer1::DataType::kFLOAT;
+  if (with_fp16_)
+    return nvinfer1::DataType::kHALF;
+  else
+    return nvinfer1::DataType::kFLOAT;
 }
 
-template <typename T>
-int EmbEltwiseLayernormPluginDynamic<T>::enqueue(
+int EmbEltwiseLayernormPluginDynamic::enqueue(
     const nvinfer1::PluginTensorDesc *input_desc,
     const nvinfer1::PluginTensorDesc *output_desc, const void *const *inputs,
     void *const *outputs, void *workspace, cudaStream_t stream) {
-  auto id_dims = input_desc[0].dims;
-  int batch = id_dims.d[0];
-  int seq_len = id_dims.d[1];
-  int input_num = embs_.size();
-
-  framework::Tensor in_ptr_tensor, emb_ptr_tensor;
-  int device_id;
-  cudaGetDevice(&device_id);
-
-  in_ptr_tensor.Resize({input_num});
-  emb_ptr_tensor.Resize({input_num});
-  int64_t *in_ptr_gpu_d =
-      in_ptr_tensor.mutable_data<int64_t>(platform::CUDAPlace(device_id));
-  int64_t *emb_ptr_gpu_d =
-      emb_ptr_tensor.mutable_data<int64_t>(platform::CUDAPlace(device_id));
-
-  std::vector<uintptr_t> in_ptr, emb_ptr;
-  for (int i = 0; i < input_num; i++) {
-    in_ptr.push_back(reinterpret_cast<uintptr_t>(inputs[i]));
-    emb_ptr.push_back(reinterpret_cast<uintptr_t>(embs_gpu_[i]));
-  }
-
-  cudaMemcpyAsync(in_ptr_gpu_d, in_ptr.data(), sizeof(int64_t) * input_num,
-                  cudaMemcpyHostToDevice, stream);
-  cudaMemcpyAsync(emb_ptr_gpu_d, emb_ptr.data(), sizeof(int64_t) * input_num,
-                  cudaMemcpyHostToDevice, stream);
-
-  auto out_type = output_desc[0].type;
-
-  const unsigned tpb = 256;
-  const dim3 grid(seq_len, batch, 1);
-  const dim3 block(tpb, 1, 1);
-  if (sizeof(T) == sizeof(float)) {
-    PADDLE_ENFORCE_EQ(
-        out_type == nvinfer1::DataType::kFLOAT, true,
-        platform::errors::InvalidArgument(
-            "The EmbEltwiseLayernorm Plugin only support fp32 input."));
-  } else if (sizeof(T) == sizeof(int16_t)) {
-    PADDLE_ENFORCE_EQ(
-        out_type == nvinfer1::DataType::kHALF, true,
-        platform::errors::InvalidArgument(
-            "The EmbEltwiseLayernorm Plugin only support fp16 input."));
-  } else {
-    PADDLE_THROW(platform::errors::Fatal(
-        "Unsupport data type, the out type of EmbEltwiseLayernorm should be "
-        "float or half."));
-  }
-
-  T *output_d = static_cast<T *>(outputs[0]);
-
-  operators::math::EmbEltwiseLayerNormFunctor<T> emb_eltwise_layernorm_func;
-  emb_eltwise_layernorm_func(batch, seq_len, hidden_size_, in_ptr_gpu_d,
-                             scale_gpu_, bias_gpu_, emb_ptr_gpu_d, output_d,
-                             eps_, input_num, stream);
+  impl_->enqueue(input_desc, output_desc, inputs, outputs, workspace, stream);
   return cudaGetLastError() != cudaSuccess;
 }
 
-template class EmbEltwiseLayernormPluginDynamic<float>;
-#ifdef SUPPORTS_CUDA_FP16
-template class EmbEltwiseLayernormPluginDynamic<half>;
-#endif  // SUPPORTS_CUDA_FP16
-
 #endif
 
 }  // namespace plugin

paddle/fluid/inference/tensorrt/plugin/emb_eltwise_layernorm_plugin.h

@@ -27,14 +27,76 @@ namespace tensorrt {
 namespace plugin {
 
 #if IS_TRT_VERSION_GE(6000)
+
+class EmbEltwiseLayernormPluginDynamicImplBase {
+ public:
+  EmbEltwiseLayernormPluginDynamicImplBase() {}
+  virtual ~EmbEltwiseLayernormPluginDynamicImplBase() {}
+
+  virtual int initialize() = 0;
+  virtual void terminate() = 0;
+  virtual int enqueue(const nvinfer1::PluginTensorDesc* inputDesc,
+                      const nvinfer1::PluginTensorDesc* outputDesc,
+                      const void* const* inputs, void* const* outputs,
+                      void* workspace, cudaStream_t stream) = 0;
+};
+
 template <typename T>
+class EmbEltwiseLayernormPluginDynamicImpl
+    : public EmbEltwiseLayernormPluginDynamicImplBase {
+ public:
+  explicit EmbEltwiseLayernormPluginDynamicImpl(std::vector<float*> input_embs,
+                                                float* bias, float* scale,
+                                                std::vector<int> emb_sizes,
+                                                int bias_size, int scale_size,
+                                                int hidden_size, float eps)
+      : embs_(input_embs),
+        bias_(bias),
+        scale_(scale),
+        emb_sizes_(emb_sizes),
+        bias_size_(bias_size),
+        scale_size_(scale_size),
+        hidden_size_(hidden_size),
+        eps_(eps) {}
+
+  ~EmbEltwiseLayernormPluginDynamicImpl();
+
+  int initialize();
+  void terminate();
+  int enqueue(const nvinfer1::PluginTensorDesc* inputDesc,
+              const nvinfer1::PluginTensorDesc* outputDesc,
+              const void* const* inputs, void* const* outputs, void* workspace,
+              cudaStream_t stream);
+
+ private:
+  std::vector<float*> embs_;
+  float* bias_{nullptr};
+  float* scale_{nullptr};
+
+  // data on devices
+  float* bias_gpu_{nullptr};
+  float* scale_gpu_{nullptr};
+  std::vector<T*> embs_gpu_;
+
+  std::vector<int> emb_sizes_;
+  int bias_size_;
+  int scale_size_;
+  int hidden_size_;
+  float eps_;
+
+  framework::Tensor in_ptr_tensor_, emb_ptr_tensor_;
+  int device_id_{0};
+  uintptr_t old_input_ptr_{0};
+};
+
 class EmbEltwiseLayernormPluginDynamic : public DynamicPluginTensorRT {
  public:
   explicit EmbEltwiseLayernormPluginDynamic(std::vector<float*> input_embs,
                                             float* bias, float* scale,
                                             std::vector<int> emb_sizes,
                                             int bias_size, int scale_size,
-                                            int hidden_size, float eps)
+                                            int hidden_size, float eps,
+                                            bool with_fp16)
       : embs_(input_embs),
         bias_(bias),
         scale_(scale),
@@ -42,51 +104,81 @@ class EmbEltwiseLayernormPluginDynamic : public DynamicPluginTensorRT {
         bias_size_(bias_size),
         scale_size_(scale_size),
         hidden_size_(hidden_size),
-        eps_(eps) {}
+        eps_(eps),
+        with_fp16_(with_fp16),
+        own_host_buff_(false) {
+    if (with_fp16) {
+#ifdef SUPPORTS_CUDA_FP16
+      impl_ = new EmbEltwiseLayernormPluginDynamicImpl<half>(
+          embs_, bias_, scale_, emb_sizes_, bias_size_, scale_size_,
+          hidden_size_, eps_);
+#else
+      PADDLE_THROW(platform::errors::Fatal(
+          "Unsupported data type, current GPU doesn't support half."));
+#endif  // SUPPORTS_CUDA_FP16
+    } else {
+      impl_ = new EmbEltwiseLayernormPluginDynamicImpl<float>(
+          embs_, bias_, scale_, emb_sizes_, bias_size_, scale_size_,
+          hidden_size_, eps_);
+    }
+  }
 
   EmbEltwiseLayernormPluginDynamic(void const* serial_data,
-                                   size_t serial_length) {
+                                   size_t serial_length)
+      : own_host_buff_(true) {
     DeserializeValue(&serial_data, &serial_length, &emb_sizes_);
 
-    embs_gpu_.resize(emb_sizes_.size());
     embs_.resize(emb_sizes_.size());
     for (size_t i = 0; i < emb_sizes_.size(); i++) {
-      cudaMalloc(&embs_gpu_[i], sizeof(float) * emb_sizes_[i]);
-      cudaMemcpy(embs_gpu_[i], serial_data, emb_sizes_[i] * sizeof(float),
-                 cudaMemcpyHostToDevice);
+      auto size = emb_sizes_[i];
+      auto ptr = new float[size];
+      memcpy(ptr, serial_data, sizeof(float) * size);
+      embs_[i] = ptr;
       reinterpret_cast<char const*&>(serial_data) +=
           emb_sizes_[i] * sizeof(float);
       serial_length -= emb_sizes_[i] * sizeof(float);
-      embs_[i] = nullptr;
     }
     DeserializeValue(&serial_data, &serial_length, &bias_size_);
     DeserializeValue(&serial_data, &serial_length, &scale_size_);
 
-    cudaMalloc(&bias_gpu_, sizeof(float) * bias_size_);
-    cudaMemcpy(bias_gpu_, serial_data, bias_size_ * sizeof(float),
-               cudaMemcpyHostToDevice);
-    bias_ = nullptr;
+    if (bias_size_) {
+      bias_ = new float[bias_size_];
+      memcpy(bias_, serial_data, sizeof(float) * bias_size_);
+    }
     reinterpret_cast<char const*&>(serial_data) += bias_size_ * sizeof(float);
     serial_length -= bias_size_ * sizeof(float);
 
-    cudaMalloc(&scale_gpu_, sizeof(float) * scale_size_);
-    cudaMemcpy(scale_gpu_, serial_data, scale_size_ * sizeof(float),
-               cudaMemcpyHostToDevice);
-    scale_ = nullptr;
+    if (scale_size_) {
+      scale_ = new float[scale_size_];
+      memcpy(scale_, serial_data, sizeof(float) * scale_size_);
+    }
     reinterpret_cast<char const*&>(serial_data) += scale_size_ * sizeof(float);
     serial_length -= scale_size_ * sizeof(float);
 
     DeserializeValue(&serial_data, &serial_length, &hidden_size_);
     DeserializeValue(&serial_data, &serial_length, &eps_);
+    DeserializeValue(&serial_data, &serial_length, &with_fp16_);
+
+    if (with_fp16_) {
+#ifdef SUPPORTS_CUDA_FP16
+      impl_ = new EmbEltwiseLayernormPluginDynamicImpl<half>(
+          embs_, bias_, scale_, emb_sizes_, bias_size_, scale_size_,
+          hidden_size_, eps_);
+#else
+      PADDLE_THROW(platform::errors::Fatal(
+          "Unsupported data type, current GPU doesn't support half."));
+#endif  // SUPPORTS_CUDA_FP16
+    } else {
+      impl_ = new EmbEltwiseLayernormPluginDynamicImpl<float>(
+          embs_, bias_, scale_, emb_sizes_, bias_size_, scale_size_,
+          hidden_size_, eps_);
+    }
   }
 
   nvinfer1::IPluginV2DynamicExt* clone() const override {
     auto ptr = new EmbEltwiseLayernormPluginDynamic(
         embs_, bias_, scale_, emb_sizes_, bias_size_, scale_size_, hidden_size_,
-        eps_);
-    ptr->embs_gpu_ = embs_gpu_;
-    ptr->bias_gpu_ = bias_gpu_;
-    ptr->scale_gpu_ = scale_gpu_;
+        eps_, with_fp16_);
     return ptr;
   }
 
@@ -95,6 +187,7 @@ class EmbEltwiseLayernormPluginDynamic : public DynamicPluginTensorRT {
   }
   int getNbOutputs() const override { return 1; }
   int initialize() override;
+  void terminate() override;
 
   size_t getSerializationSize() const override {
     int sum_num = 0;
@@ -110,24 +203,32 @@ class EmbEltwiseLayernormPluginDynamic : public DynamicPluginTensorRT {
     sum_num += (bias_size_ + scale_size_) * sizeof(float);
     sum_num += SerializedSize(hidden_size_);
     sum_num += SerializedSize(eps_);
-    // sum_num += SerializedSize(with_fp16_);
+    sum_num += SerializedSize(with_fp16_);
 
     return sum_num;
   }
 
-  void terminate() override;
   void serialize(void* buffer) const override {
-    // SerializeValue(&buffer, with_fp16_);
     SerializeValue(&buffer, emb_sizes_);
     for (size_t i = 0; i < emb_sizes_.size(); i++) {
-      SerializeCudaPointer(&buffer, embs_gpu_[i], emb_sizes_[i]);
+      auto size = emb_sizes_[i];
+      for (int j = 0; j < size; ++j) {
+        SerializeValue(&buffer, embs_[i][j]);
+      }
     }
     SerializeValue(&buffer, bias_size_);
     SerializeValue(&buffer, scale_size_);
-    SerializeCudaPointer(&buffer, bias_gpu_, bias_size_);
-    SerializeCudaPointer(&buffer, scale_gpu_, scale_size_);
+    for (int i = 0; i < bias_size_; ++i) {
+      SerializeValue(&buffer, bias_[i]);
+    }
+
+    for (int i = 0; i < scale_size_; ++i) {
+      SerializeValue(&buffer, scale_[i]);
+    }
+
     SerializeValue(&buffer, hidden_size_);
     SerializeValue(&buffer, eps_);
+    SerializeValue(&buffer, with_fp16_);
   }
 
   nvinfer1::DimsExprs getOutputDimensions(
@@ -158,23 +259,33 @@ class EmbEltwiseLayernormPluginDynamic : public DynamicPluginTensorRT {
                                        const nvinfer1::DataType* input_types,
                                        int nb_inputs) const override;
 
-  void destroy() override { delete this; }
+  void destroy() override {
+    if (own_host_buff_) {
+      for (auto ptr : embs_) {
+        delete[] ptr;
+      }
+      delete[] bias_;
+      delete[] scale_;
+    }
+
+    delete impl_;
+    delete this;
+  }
 
  private:
   std::vector<float*> embs_;
   float* bias_;
   float* scale_;
 
-  // data on devices
-  float* bias_gpu_;
-  float* scale_gpu_;
-  std::vector<float*> embs_gpu_;
-
   std::vector<int> emb_sizes_;
   int bias_size_;
   int scale_size_;
   int hidden_size_;
   float eps_;
+
+  bool with_fp16_;
+  bool own_host_buff_{false};
+  EmbEltwiseLayernormPluginDynamicImplBase* impl_{nullptr};
 };
 
 class EmbEltwiseLayernormPluginV2Creator : public nvinfer1::IPluginCreator {
@@ -198,8 +309,7 @@ class EmbEltwiseLayernormPluginV2Creator : public nvinfer1::IPluginCreator {
   nvinfer1::IPluginV2* deserializePlugin(const char* name,
                                          const void* serial_data,
                                          size_t serial_length) override {
-    return new EmbEltwiseLayernormPluginDynamic<float>(serial_data,
-                                                       serial_length);
+    return new EmbEltwiseLayernormPluginDynamic(serial_data, serial_length);
   }
 
   void setPluginNamespace(const char* lib_namespace) override {

paddle/fluid/inference/tests/api/trt_dynamic_shape_ernie_deserialize_test.cc

@@ -151,7 +151,7 @@ void trt_ernie(bool with_fp16, std::vector<float> result) {
   run(config, &out_data);         // serialize
   run(*config_deser, &out_data);  // deserialize
   for (size_t i = 0; i < out_data.size(); i++) {
-    EXPECT_NEAR(result[i], out_data[i], 1e-6);
+    EXPECT_NEAR(result[i], out_data[i], 1e-2);
   }
 }
 
@@ -159,13 +159,11 @@ TEST(AnalysisPredictor, no_fp16) {
   std::vector<float> result = {0.597841, 0.219972, 0.182187};
   trt_ernie(false, result);
 }
-
-TEST(AnalysisPredictor, fp16) {
 #ifdef SUPPORTS_CUDA_FP16
-  std::vector<float> result = {0.598336, 0.219558, 0.182106};
+TEST(AnalysisPredictor, fp16) {
+  std::vector<float> result = {0.59923654, 0.21923761, 0.18152587};
   trt_ernie(true, result);
-#endif
 }
-
+#endif  // SUPPORTS_CUDA_FP16
 }  // namespace inference
 }  // namespace paddle

paddle/fluid/operators/conv_cudnn_op.cu

@@ -14,6 +14,7 @@ limitations under the License. */
 
 #include <utility>
 #include <vector>
+
 #include "paddle/fluid/framework/eigen.h"
 #include "paddle/fluid/framework/op_registry.h"
 #include "paddle/fluid/framework/tensor.h"
@@ -287,7 +288,9 @@ class CUDNNConvOpKernel : public framework::OpKernel<T> {
 #endif
 
     // ------------------- cudnn conv forward ---------------------
-    ScalingParamType<T> alpha = 1.0f, beta = 0.0f;
+    ScalingParamType<T> alpha = 1.0f;
+    ScalingParamType<T> beta = ctx.Attr<bool>("use_addto") ? 1.0f : 0.0f;
+    VLOG(4) << "Conv: use_addto = " << ctx.Attr<bool>("use_addto");
     for (int i = 0; i < groups; i++) {
       workspace_handle.RunFunc(
           [&](void* workspace_ptr) {
@@ -609,9 +612,13 @@ class CUDNNConvGradOpKernel : public framework::OpKernel<T> {
     }
 
     // ------------------- cudnn conv backward data ---------------------
-    ScalingParamType<T> alpha = 1.0f, beta = 0.0f;
+    ScalingParamType<T> alpha = 1.0f;
+    ScalingParamType<T> beta = ctx.Attr<bool>("use_addto") ? 1.0f : 0.0f;
+    VLOG(4) << "Conv_grad: use_addto = " << ctx.Attr<bool>("use_addto");
+
     if (input_grad) {
-      // Because beta is zero, it is unnecessary to reset input_grad.
+      // When beta is 0, it is unnecessary to reset input_grad.
+      // When beta is 1, the output cannot be reset since addt strategy used.
       for (int i = 0; i < groups; i++) {
         workspace_handle.RunFunc(
             [&](void* cudnn_workspace_ptr) {
@@ -653,6 +660,9 @@ class CUDNNConvGradOpKernel : public framework::OpKernel<T> {
             ctx, &transformed_input_grad_channel, input_grad);
       }
     }
+
+    // filter_grad do not use inplace addto.
+    ScalingParamType<T> beta_filter = 0.0f;
     // ------------------- cudnn conv backward filter ---------------------
     if (filter_grad) {
       // Because beta is zero, it is unnecessary to reset filter_grad.
@@ -665,7 +675,7 @@ class CUDNNConvGradOpKernel : public framework::OpKernel<T> {
                       input_data + i * group_offset_in, args2.odesc.desc(),
                       output_grad_data + i * group_offset_out,
                       args2.cdesc.desc(), filter_algo, cudnn_workspace_ptr,
-                      workspace_size, &beta, args2.wdesc.desc(),
+                      workspace_size, &beta_filter, args2.wdesc.desc(),
                       filter_grad_data + i * group_offset_filter));
             },
             workspace_size);
@@ -1017,7 +1027,14 @@ class CUDNNConvDoubleGradOpKernel : public framework::OpKernel<T> {
     int group_offset_out = o_c / groups * o_h * o_w * o_d;
     int group_offset_filter = W->numel() / groups;
 
-    ScalingParamType<T> alpha = 1.0f, beta = 0.0f;
+    ScalingParamType<T> alpha = 1.0f;
+    ScalingParamType<T> beta = 0.0f;
+
+    // NOTE(zhiqiu): inplace addto is not supportted in double grad yet.
+    // ScalingParamType<T> beta = ctx.Attr<bool>("use_addto") ? 1.0f :
+    // 0.0f;
+    // VLOG(4) << "Conv_grad_grad: use_addto = " << ctx.Attr<bool>("use_addto");
+
     auto wkspace_handle = dev_ctx.cudnn_workspace_handle();
 
     if (ddO) {

paddle/fluid/operators/conv_op.cc

@@ -305,6 +305,11 @@ void Conv2DOpMaker::Make() {
       .SetDefault(0.0f);
   AddAttr<float>("fuse_beta", "(float, default 0.0) Only used in mkldnn kernel")
       .SetDefault(0.0f);
+  AddAttr<bool>(
+      "use_addto",
+      "(bool, default false) If use addto strategy or not, only used in "
+      "cudnn kernel")
+      .SetDefault(false);
   AddAttr<bool>("fuse_residual_connection",
                 "(bool, default false) Only used in mkldnn kernel. Used "
                 "whenever convolution output is as an input to residual "
@@ -460,6 +465,11 @@ void Conv3DOpMaker::Make() {
       .SetDefault(0.0f);
   AddAttr<float>("fuse_beta", "(float, default 0.0) Only used in mkldnn kernel")
       .SetDefault(0.0f);
+  AddAttr<bool>(
+      "use_addto",
+      "(bool, default false) If use addto strategy or not, only used in "
+      "cudnn kernel")
+      .SetDefault(false);
   AddAttr<bool>("fuse_residual_connection",
                 "(bool, default false) Only used in mkldnn kernel. Used "
                 "whenever convolution output is as an input to residual "

paddle/fluid/operators/cudnn_lstm_cache.h

@@ -54,6 +54,8 @@ class ScopedRNNBase {
       x_descs_.emplace_back(x_desc_.descriptor<T>(dims_x, strides_x));
       y_descs_.emplace_back(y_desc_.descriptor<T>(dims_y, strides_y));
     }
+
+#if CUDNN_VERSION >= 7201
     if (!sequence_length.empty()) {
       x_seq_desc_.descriptor<T>(seq_length_, batch_size_, input_size_, true,
                                 sequence_length);
@@ -61,6 +63,7 @@ class ScopedRNNBase {
                                 hidden_size_ * numDirections, true,
                                 sequence_length);
     }
+#endif
 
     // ------------------- cudnn hx, hy, cx, cy descriptors----------
     std::vector<int> dims_hx = {num_layers_ * numDirections, batch_size_,
@@ -96,10 +99,13 @@ class ScopedRNNBase {
         is_bidirec_ ? CUDNN_BIDIRECTIONAL : CUDNN_UNIDIRECTIONAL, CUDNN_LSTM,
         cudnn_type));
 #endif
+
+#if CUDNN_VERSION >= 7201
     if (!sequence_length.empty()) {
       PADDLE_ENFORCE_CUDA_SUCCESS(platform::dynload::cudnnSetRNNPaddingMode(
           rnn_desc_.desc(), CUDNN_RNN_PADDED_IO_ENABLED));
     }
+#endif
 
     // ------------------- cudnn weights_size ---------------------
     size_t weights_size_;
@@ -125,8 +131,10 @@ class ScopedRNNBase {
   }
   cudnnTensorDescriptor_t* x_descs() { return x_descs_.data(); }
   cudnnTensorDescriptor_t* y_descs() { return y_descs_.data(); }
+#if CUDNN_VERSION >= 7201
   cudnnRNNDataDescriptor_t x_seq_desc() { return x_seq_desc_.desc(); }
   cudnnRNNDataDescriptor_t y_seq_desc() { return y_seq_desc_.desc(); }
+#endif
   cudnnTensorDescriptor_t init_h_desc() { return init_h_desc_.desc(); }
   cudnnTensorDescriptor_t init_c_desc() { return init_c_desc_.desc(); }
   cudnnTensorDescriptor_t last_h_desc() { return last_h_desc_.desc(); }
@@ -151,8 +159,10 @@ class ScopedRNNBase {
 
   platform::ScopedTensorDescriptor x_desc_;
   platform::ScopedTensorDescriptor y_desc_;
+#if CUDNN_VERSION >= 7201
   platform::ScopedRNNTensorDescriptor x_seq_desc_;
   platform::ScopedRNNTensorDescriptor y_seq_desc_;
+#endif
   platform::ScopedTensorDescriptor init_h_desc_;
   platform::ScopedTensorDescriptor init_c_desc_;
   platform::ScopedTensorDescriptor last_h_desc_;

paddle/fluid/operators/elementwise/elementwise_add_op.cc

@@ -13,8 +13,11 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/fluid/operators/elementwise/elementwise_add_op.h"
+
 #include <memory>
 #include <string>
+
+#include "paddle/fluid/framework/op_version_registry.h"
 #include "paddle/fluid/operators/elementwise/elementwise_op.h"
 
 namespace paddle {
@@ -129,3 +132,18 @@ REGISTER_OP_CPU_KERNEL(
                                         int>,
     ops::ElementwiseAddDoubleGradKernel<paddle::platform::CPUDeviceContext,
                                         int64_t>);
+
+// A specialization elementwise_add operator, used in gradient accumulation with
+// inplace addto.
+REGISTER_OPERATOR(
+    grad_add, paddle::operators::ElementwiseOp,
+    paddle::operators::ElementwiseAddOpMaker,
+    paddle::framework::EmptyGradOpMaker<paddle::framework::OpDesc>,
+    paddle::framework::EmptyGradOpMaker<paddle::imperative::OpBase>);
+
+REGISTER_OP_CPU_KERNEL(
+    grad_add,
+    ops::ElementwiseAddKernel<paddle::platform::CPUDeviceContext, float>,
+    ops::ElementwiseAddKernel<paddle::platform::CPUDeviceContext, double>,
+    ops::ElementwiseAddKernel<paddle::platform::CPUDeviceContext, int>,
+    ops::ElementwiseAddKernel<paddle::platform::CPUDeviceContext, int64_t>);

paddle/fluid/operators/elementwise/elementwise_add_op.cu

@@ -111,3 +111,10 @@ REGISTER_OP_CUDA_KERNEL(
     ops::ElementwiseAddDoubleGradKernel<plat::CUDADeviceContext, int64_t>,
     ops::ElementwiseAddDoubleGradKernel<plat::CUDADeviceContext,
                                         plat::float16>);
+
+REGISTER_OP_CUDA_KERNEL(
+    grad_add, ops::ElementwiseAddKernel<plat::CUDADeviceContext, float>,
+    ops::ElementwiseAddKernel<plat::CUDADeviceContext, double>,
+    ops::ElementwiseAddKernel<plat::CUDADeviceContext, int>,
+    ops::ElementwiseAddKernel<plat::CUDADeviceContext, int64_t>,
+    ops::ElementwiseAddKernel<plat::CUDADeviceContext, plat::float16>);

paddle/fluid/operators/fake_quantize_op.cc

@@ -174,7 +174,64 @@ struct ChannelClipAndFakeQuantFunctor<platform::CPUDeviceContext, T> {
 
 template struct ChannelClipAndFakeQuantFunctor<platform::CPUDeviceContext,
                                                float>;
+template <typename T>
+struct ChannelClipFakeQuantDequantFunctor<platform::CPUDeviceContext, T> {
+  void operator()(const platform::CPUDeviceContext& ctx,
+                  const framework::Tensor& in, const framework::Tensor& scale,
+                  const int bin_cnt, const int quant_axis,
+                  framework::Tensor* out) {
+    PADDLE_ENFORCE_EQ(
+        quant_axis == 0 || quant_axis == 1, true,
+        platform::errors::InvalidArgument("'quant_axis' should be 0 or 1, but "
+                                          "the received is %d",
+                                          quant_axis));
 
+    auto* scale_data = scale.data<T>();
+    auto* in_data = in.data<T>();
+    auto* out_data = out->mutable_data<T>(ctx.GetPlace());
+    auto in_dims = in.dims();
+    const int64_t channel = in_dims[quant_axis];
+    platform::Transform<platform::CPUDeviceContext> trans;
+    if (quant_axis == 0) {
+      const int64_t channel_size = in.numel() / channel;
+      for (int i = 0; i < channel; i++) {
+        T s = scale_data[i];
+        auto* start = in_data + i * channel_size;
+        auto* end = in_data + (i + 1) * channel_size;
+        trans(ctx, start, end, out_data + i * channel_size,
+              ClipFunctor<T>(-s, s));
+      }
+      for (int i = 0; i < channel; i++) {
+        T s = scale_data[i];
+        T inv_s = inverse(s);
+        framework::Tensor one_channel_out = out->Slice(i, i + 1);
+        auto out_e = framework::EigenVector<T>::Flatten(one_channel_out);
+        out_e.device(*ctx.eigen_device()) =
+            (bin_cnt * inv_s * out_e).round() * s / static_cast<T>(bin_cnt);
+      }
+    } else if (quant_axis == 1) {
+      const int64_t step_i = in.numel() / in_dims[0];
+      const int64_t step_j = in.numel() / (in_dims[0] * in_dims[1]);
+      for (int i = 0; i < in_dims[0]; i++) {
+        for (int j = 0; j < in_dims[1]; j++) {
+          T s = scale_data[j];
+          T inv_s = inverse(s);
+          auto* start = in_data + i * step_i + j * step_j;
+          auto* end = in_data + i * step_i + (j + 1) * step_j;
+          auto* cur_out_data = out_data + i * step_i + j * step_j;
+          trans(ctx, start, end, cur_out_data, ClipFunctor<T>(-s, s));
+          for (int k = 0; k < step_j; k++) {
+            cur_out_data[k] = std::round(bin_cnt * inv_s * cur_out_data[k]) *
+                              s / static_cast<T>(bin_cnt);
+          }
+        }
+      }
+    }
+  }
+};
+
+template struct ChannelClipFakeQuantDequantFunctor<platform::CPUDeviceContext,
+                                                   float>;
 template <typename T>
 struct FindRangeAbsMaxFunctor<platform::CPUDeviceContext, T> {
   void operator()(const platform::CPUDeviceContext& ctx,
@@ -360,6 +417,75 @@ $$0 \leq c \lt \ the\ channel\ number\ of\ X$$
   }
 };
 
+class FakeChannelWiseQuantizeDequantizeAbsMaxOp
+    : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+  void InferShape(framework::InferShapeContext* ctx) const override {
+    OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X",
+                   "FakeChannelWiseQuantizeDequantizeAbsMax");
+    OP_INOUT_CHECK(ctx->HasOutput("Out"), "Output", "Out",
+                   "FakeChannelWiseQuantizeDequantizeAbsMax");
+    OP_INOUT_CHECK(ctx->HasOutput("OutScale"), "Output", "OutScale",
+                   "FakeChannelWiseQuantizeDequantizeAbsMax");
+    int quant_axis = ctx->Attrs().Get<int>("quant_axis");
+    ctx->SetOutputDim("Out", ctx->GetInputDim("X"));
+    ctx->SetOutputDim("OutScale", {ctx->GetInputDim("X")[quant_axis]});
+    ctx->ShareLoD("X", /*->*/ "Out");
+  }
+
+ protected:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext& ctx) const override {
+    return framework::OpKernelType(
+        OperatorWithKernel::IndicateVarDataType(ctx, "X"), ctx.GetPlace());
+  }
+};
+
+class FakeChannelWiseQuantizeDequantizeAbsMaxOpMaker
+    : public framework::OpProtoAndCheckerMaker {
+ public:
+  void Make() override {
+    AddInput("X", "(Tensor) Input is float data type.");
+    AddOutput("Out",
+              "(Tensor) Output of quantized and dequantized low level tensor, "
+              "saved as float data type.");
+    AddOutput("OutScale", "(Tensor) Current channel wise scale");
+    AddAttr<int>("quant_axis",
+                 "(int, default 0) The axis for quantization. "
+                 "For conv2d, depthwise_conv2d, conv2d_transpose "
+                 "and mul, the quant_axis is equal to the cout axis.")
+        .SetDefault(0)
+        .AddCustomChecker([](const int& quant_axis) {
+          PADDLE_ENFORCE_EQ(quant_axis == 0 || quant_axis == 1, true,
+                            platform::errors::InvalidArgument(
+                                "'quant_axis' should be 0 or 1, but "
+                                "the received is %d",
+                                quant_axis));
+        });
+    AddAttr<int>("bit_length", "(int, default 8)")
+        .SetDefault(8)
+        .AddCustomChecker([](const int& bit_length) {
+          PADDLE_ENFORCE_EQ(bit_length >= 1 && bit_length <= 16, true,
+                            platform::errors::InvalidArgument(
+                                "'bit_length' should be between 1 and 16, but "
+                                "the received is %d",
+                                bit_length));
+        });
+    AddComment(R"DOC(
+The scale of FakeChannelWiseQuantize operator is a vector.
+In detail, each channel of the input X has a scale value.
+
+$$scale_c = max(abs(X_c))$$
+$$range = 2^{bit\_length - 1} - 1$$
+$$Out_c = round(\frac{X_c * range} {scale_c}) * \frac{scale_c} {range}$$
+In above three formulas, the range value of c is as follow:
+$$0 \leq c \lt \ the\ channel\ number\ of\ X$$
+)DOC");
+  }
+};
+
 class FakeQuantizeRangeAbsMaxOp : public framework::OperatorWithKernel {
  public:
   FakeQuantizeRangeAbsMaxOp(const std::string& type,
@@ -666,3 +792,12 @@ REGISTER_OP_CPU_KERNEL(moving_average_abs_max_scale,
 REGISTER_OPERATOR(fake_quantize_dequantize_grad, ops::FakeQuantDequantGradOp);
 REGISTER_OP_CPU_KERNEL(fake_quantize_dequantize_grad,
                        ops::FakeQuantDequantGradKernel<CPU, float>);
+
+REGISTER_OPERATOR(fake_channel_wise_quantize_dequantize_abs_max,
+                  ops::FakeChannelWiseQuantizeDequantizeAbsMaxOp,
+                  ops::FakeChannelWiseQuantizeDequantizeAbsMaxOpMaker,
+                  ops::FakeQuantDequantGradMaker<paddle::framework::OpDesc>,
+                  ops::FakeQuantDequantGradMaker<paddle::imperative::OpBase>);
+REGISTER_OP_CPU_KERNEL(
+    fake_channel_wise_quantize_dequantize_abs_max,
+    ops::FakeChannelWiseQuantizeDequantizeAbsMaxKernel<CPU, float>);

paddle/fluid/operators/fake_quantize_op.cu

@@ -417,8 +417,90 @@ struct FindMovingAverageAbsMaxFunctor<platform::CUDADeviceContext, T> {
   }
 };
 
-template struct FindMovingAverageAbsMaxFunctor<platform::CUDADeviceContext,
-                                               float>;
+// ChannelClipAndQuantDequantKernel for quant_axis is 0
+template <typename T>
+__global__ void ChannelClipAndQuantDequantKernelQuantAxis0(
+    const T* in, const T* scale, const int bin_cnt, const int n, const int c,
+    T* out) {
+  int tid = threadIdx.x;
+
+  int channel_size = n / c;
+  const T* in_c = in + blockIdx.x * channel_size;
+  T* out_c = out + blockIdx.x * channel_size;
+
+  T s = scale[blockIdx.x];
+  T inv_s = inverse(s);
+
+  for (int i = tid; i < channel_size; i += blockDim.x) {
+    T x = in_c[i];
+    T v = x > s ? s : x;
+    v = v < -s ? -s : v;
+    v = bin_cnt * inv_s * v;
+    out_c[i] = round(v) * s / bin_cnt;
+  }
+}
+
+// ChannelClipAndQuantDequantKernel for quant_axis is 1
+template <typename T>
+__global__ void ChannelClipAndQuantDequantKernelQuantAxis1(
+    const T* in, const T* scale, const int bin_cnt, const int n, const int cin,
+    const int cout, T* out) {
+  T s = scale[blockIdx.x % cout];
+  T inv_s = inverse(s);
+
+  int wh_size = n / (cin * cout);
+  const T* in_c = in + blockIdx.x * wh_size;
+  T* out_c = out + blockIdx.x * wh_size;
+
+  for (int i = threadIdx.x; i < wh_size; i += blockDim.x) {
+    T x = in_c[i];
+    T v = x > s ? s : x;
+    v = v < -s ? -s : v;
+    v = bin_cnt * inv_s * v;
+    out_c[i] = round(v) * s / bin_cnt;
+  }
+}
+
+template <typename T>
+struct ChannelClipFakeQuantDequantFunctor<platform::CUDADeviceContext, T> {
+  void operator()(const platform::CUDADeviceContext& ctx,
+                  const framework::Tensor& in, const framework::Tensor& scale,
+                  const int bin_cnt, const int quant_axis,
+                  framework::Tensor* out) {
+    // At present, channelwise quantization supports conv2d, depthwise_conv2d
+    // conv2d_transpose and mul
+    PADDLE_ENFORCE_EQ(
+        quant_axis == 0 || quant_axis == 1, true,
+        platform::errors::InvalidArgument("'quant_axis' should be 0 or 1, but "
+                                          "the received is %d",
+                                          quant_axis));
+
+    int num = in.numel();
+    auto in_dims = in.dims();
+
+    const T* in_data = in.data<T>();
+    const T* scale_data = scale.data<T>();
+    T* out_data = out->mutable_data<T>(ctx.GetPlace());
+
+    if (quant_axis == 0) {
+      int grid = in_dims[0];
+      int block = 1024;
+      ChannelClipAndQuantDequantKernelQuantAxis0<
+          T><<<grid, block, 0, ctx.stream()>>>(in_data, scale_data, bin_cnt,
+                                               num, in_dims[0], out_data);
+    } else if (quant_axis == 1) {
+      int grid = in_dims[0] * in_dims[1];
+      int block = 1024;
+
+      ChannelClipAndQuantDequantKernelQuantAxis1<
+          T><<<grid, block, 0, ctx.stream()>>>(
+          in_data, scale_data, bin_cnt, num, in_dims[0], in_dims[1], out_data);
+    }
+  }
+};
+
+template struct ChannelClipFakeQuantDequantFunctor<platform::CUDADeviceContext,
+                                                   float>;
 
 }  // namespace operators
 }  // namespace paddle
@@ -443,3 +525,6 @@ REGISTER_OP_CUDA_KERNEL(
     ops::FakeQuantizeDequantizeMovingAverageAbsMaxKernel<CUDA, float>);
 REGISTER_OP_CUDA_KERNEL(fake_quantize_dequantize_grad,
                         ops::FakeQuantDequantGradKernel<CUDA, float>);
+REGISTER_OP_CUDA_KERNEL(
+    fake_channel_wise_quantize_dequantize_abs_max,
+    ops::FakeChannelWiseQuantizeDequantizeAbsMaxKernel<CUDA, float>);

paddle/fluid/operators/fake_quantize_op.h

@@ -72,6 +72,13 @@ struct ChannelClipAndFakeQuantFunctor {
                   const int quant_axis, framework::Tensor* out);
 };
 
+template <typename DeviceContext, typename T>
+struct ChannelClipFakeQuantDequantFunctor {
+  void operator()(const DeviceContext& ctx, const framework::Tensor& in,
+                  const framework::Tensor& scale, const int bin_cnt,
+                  const int quant_axis, framework::Tensor* out);
+};
+
 template <typename DeviceContext, typename T>
 struct FindMovingAverageAbsMaxFunctor {
   void operator()(const DeviceContext& ctx, const framework::Tensor& in_accum,
@@ -154,6 +161,30 @@ class FakeChannelWiseQuantizeAbsMaxKernel : public framework::OpKernel<T> {
   }
 };
 
+template <typename DeviceContext, typename T>
+class FakeChannelWiseQuantizeDequantizeAbsMaxKernel
+    : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& context) const override {
+    auto* in = context.Input<framework::Tensor>("X");
+    auto* out = context.Output<framework::Tensor>("Out");
+    auto* out_scale = context.Output<framework::Tensor>("OutScale");
+    T* out_scale_data = out_scale->mutable_data<T>(context.GetPlace());
+    auto& dev_ctx = context.template device_context<DeviceContext>();
+    out->mutable_data<T>(dev_ctx.GetPlace());
+
+    int bit_length = context.Attr<int>("bit_length");
+    int bin_cnt = std::pow(2, bit_length - 1) - 1;
+    int quant_axis = context.Attr<int>("quant_axis");
+
+    FindChannelAbsMaxFunctor<DeviceContext, T>()(dev_ctx, *in, quant_axis,
+                                                 out_scale_data);
+
+    ChannelClipFakeQuantDequantFunctor<DeviceContext, T>()(
+        dev_ctx, *in, *out_scale, bin_cnt, quant_axis, out);
+  }
+};
+
 template <typename DeviceContext, typename T>
 class FakeQuantizeRangeAbsMaxKernel : public framework::OpKernel<T> {
  public:

paddle/fluid/operators/fused/fusion_gru_op.cc

@@ -15,6 +15,7 @@ limitations under the License. */
 #include "paddle/fluid/operators/fused/fusion_gru_op.h"
 #include <cstring>  // for memcpy
 #include <string>
+#include <vector>
 #include "paddle/fluid/operators/jit/kernels.h"
 #include "paddle/fluid/operators/math/blas.h"
 #include "paddle/fluid/operators/math/fc.h"

paddle/fluid/operators/optimizers/rmsprop_op.cc

@@ -143,4 +143,5 @@ http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf)
 namespace ops = paddle::operators;
 REGISTER_OP_WITHOUT_GRADIENT(rmsprop, ops::RmspropOp, ops::RmspropOpMaker);
 REGISTER_OP_CPU_KERNEL(
-    rmsprop, ops::RmspropOpKernel<paddle::platform::CPUDeviceContext, float>);
+    rmsprop, ops::RmspropOpKernel<paddle::platform::CPUDeviceContext, float>,
+    ops::RmspropOpKernel<paddle::platform::CPUDeviceContext, double>);

paddle/fluid/operators/optimizers/rmsprop_op.cu

@@ -15,4 +15,5 @@ limitations under the License. */
 
 namespace ops = paddle::operators;
 REGISTER_OP_CUDA_KERNEL(
-    rmsprop, ops::RmspropOpKernel<paddle::platform::CUDADeviceContext, float>);
+    rmsprop, ops::RmspropOpKernel<paddle::platform::CUDADeviceContext, float>,
+    ops::RmspropOpKernel<paddle::platform::CUDADeviceContext, double>);

paddle/fluid/operators/top_k_v2_op.cc

@@ -32,7 +32,6 @@ class TopkV2Op : public framework::OperatorWithKernel {
 
     auto input_dims = ctx->GetInputDim("X");
     const int& dim_size = input_dims.size();
-    const int k = static_cast<int>(ctx->Attrs().Get<int>("k"));
     int axis = static_cast<int>(ctx->Attrs().Get<int>("axis"));
     PADDLE_ENFORCE_EQ((axis < dim_size) && (axis >= (-1 * dim_size)), true,
                       "the axis of topk"
@@ -41,8 +40,18 @@ class TopkV2Op : public framework::OperatorWithKernel {
 
     if (axis < 0) axis += dim_size;
 
-    PADDLE_ENFORCE_GE(
-        k, 1, "the attribute of k in the topk must >= 1, but received %d .", k);
+    int k;
+    auto k_is_tensor = ctx->HasInput("K");
+    if (k_is_tensor) {
+      k = -1;
+    } else {
+      k = static_cast<int>(ctx->Attrs().Get<int>("k"));
+      PADDLE_ENFORCE_EQ(k >= 1, true,
+                        "the attribute of k in the topk must >= 1 or be a "
+                        "Tensor, but received %d .",
+                        k);
+    }
+
     PADDLE_ENFORCE_GE(input_dims.size(), 1,
                       "input of topk must have >= 1d shape");
 

paddle/fluid/platform/cudnn_helper.h

@@ -294,6 +294,7 @@ class ScopedTensorDescriptor {
   DISABLE_COPY_AND_ASSIGN(ScopedTensorDescriptor);
 };
 
+#if CUDNN_VERSION >= 7201
 class ScopedRNNTensorDescriptor {
  public:
   ScopedRNNTensorDescriptor() {
@@ -337,6 +338,7 @@ class ScopedRNNTensorDescriptor {
   cudnnRNNDataDescriptor_t desc_;
   DISABLE_COPY_AND_ASSIGN(ScopedRNNTensorDescriptor);
 };
+#endif
 
 class ScopedDropoutDescriptor {
  public:

paddle/fluid/platform/dynload/cudnn.cc

@@ -46,6 +46,10 @@ CUDNN_DNN_ROUTINE_EACH_R6(DEFINE_WRAP);
 CUDNN_DNN_ROUTINE_EACH_R7(DEFINE_WRAP);
 #endif
 
+#ifdef CUDNN_DNN_ROUTINE_EACH_AFTER_TWO_R7
+CUDNN_DNN_ROUTINE_EACH_AFTER_TWO_R7(DEFINE_WRAP);
+#endif
+
 #ifdef CUDNN_DNN_ROUTINE_EACH_AFTER_R7
 CUDNN_DNN_ROUTINE_EACH_AFTER_R7(DEFINE_WRAP);
 #endif

paddle/fluid/platform/dynload/cudnn.h

@@ -101,9 +101,6 @@ extern void EnforceCUDNNLoaded(const char* fn_name);
   __macro(cudnnDropoutGetStatesSize);                     \
   __macro(cudnnSetDropoutDescriptor);                     \
   __macro(cudnnRestoreDropoutDescriptor);                 \
-  __macro(cudnnCreateRNNDataDescriptor);                  \
-  __macro(cudnnDestroyRNNDataDescriptor);                 \
-  __macro(cudnnSetRNNDataDescriptor);                     \
   __macro(cudnnCreateRNNDescriptor);                      \
   __macro(cudnnGetRNNParamsSize);                         \
   __macro(cudnnGetRNNWorkspaceSize);                      \
@@ -112,11 +109,6 @@ extern void EnforceCUDNNLoaded(const char* fn_name);
   __macro(cudnnRNNBackwardData);                          \
   __macro(cudnnRNNBackwardWeights);                       \
   __macro(cudnnRNNForwardInference);                      \
-  __macro(cudnnRNNForwardTrainingEx);                     \
-  __macro(cudnnSetRNNPaddingMode);                        \
-  __macro(cudnnRNNBackwardDataEx);                        \
-  __macro(cudnnRNNBackwardWeightsEx);                     \
-  __macro(cudnnRNNForwardInferenceEx);                    \
   __macro(cudnnDestroyDropoutDescriptor);                 \
   __macro(cudnnDestroyRNNDescriptor);                     \
   __macro(cudnnSetTensorNdDescriptorEx);
@@ -188,6 +180,19 @@ CUDNN_DNN_ROUTINE_EACH_R6(DECLARE_DYNAMIC_LOAD_CUDNN_WRAP)
 CUDNN_DNN_ROUTINE_EACH_R7(DECLARE_DYNAMIC_LOAD_CUDNN_WRAP)
 #endif
 
+#if CUDNN_VERSION >= 7201
+#define CUDNN_DNN_ROUTINE_EACH_AFTER_TWO_R7(__macro) \
+  __macro(cudnnCreateRNNDataDescriptor);             \
+  __macro(cudnnDestroyRNNDataDescriptor);            \
+  __macro(cudnnSetRNNDataDescriptor);                \
+  __macro(cudnnSetRNNPaddingMode);                   \
+  __macro(cudnnRNNForwardTrainingEx);                \
+  __macro(cudnnRNNBackwardDataEx);                   \
+  __macro(cudnnRNNBackwardWeightsEx);                \
+  __macro(cudnnRNNForwardInferenceEx);
+CUDNN_DNN_ROUTINE_EACH_AFTER_TWO_R7(DECLARE_DYNAMIC_LOAD_CUDNN_WRAP)
+#endif
+
 #if CUDNN_VERSION >= 7401
 #define CUDNN_DNN_ROUTINE_EACH_AFTER_R7(__macro)                     \
   __macro(cudnnGetBatchNormalizationForwardTrainingExWorkspaceSize); \

paddle/fluid/platform/flags.cc

@@ -521,3 +521,18 @@ DEFINE_int32(
 DEFINE_bool(sort_sum_gradient, false,
             "Sum gradients by the reverse order of "
             "the forward execution sequence.");
+
+/**
+ * Performance related FLAG
+ * Name: max_inplace_grad_add
+ * Since Version: 2.0.0
+ * Value Range: int32, default=0
+ * Example:
+ * Note: The maximum number of inplace grad_add.
+ */
+DEFINE_int32(
+    max_inplace_grad_add, 0,
+    "The maximum number of inplace grad_add. When doing "
+    "gradient accumulation, if the number of gradients need to that "
+    "less FLAGS_max_inplace_grad_add, than it will be use several grad_add"
+    "instead of sum. Default is 0.");

paddle/fluid/pybind/global_value_getter_setter.cc

@@ -62,6 +62,7 @@ DECLARE_bool(use_system_allocator);
 // others
 DECLARE_bool(benchmark);
 DECLARE_int32(inner_op_parallelism);
+DECLARE_int32(max_inplace_grad_add);
 DECLARE_string(tracer_profile_fname);
 #ifdef PADDLE_WITH_CUDA
 // cudnn
@@ -348,7 +349,7 @@ static void RegisterGlobalVarGetterSetter() {
       FLAGS_init_allocated_mem, FLAGS_initial_cpu_memory_in_mb,
       FLAGS_memory_fraction_of_eager_deletion, FLAGS_use_pinned_memory,
       FLAGS_benchmark, FLAGS_inner_op_parallelism, FLAGS_tracer_profile_fname,
-      FLAGS_paddle_num_threads, FLAGS_use_mkldnn);
+      FLAGS_paddle_num_threads, FLAGS_use_mkldnn, FLAGS_max_inplace_grad_add);
 
 #ifdef PADDLE_WITH_CUDA
   REGISTER_PUBLIC_GLOBAL_VAR(

paddle/fluid/pybind/op_function_generator.cc

@@ -111,6 +111,7 @@ std::map<std::string, std::set<std::string>> op_passing_outs_map = {
     {"fake_quantize_dequantize_moving_average_abs_max",
      {"Out", "OutScale", "OutAccum", "OutState"}},
     {"fake_quantize_dequantize_abs_max", {"Out", "OutScale"}},
+    {"fake_channel_wise_quantize_dequantize_abs_max", {"Out", "OutScale"}},
     {"check_finite_and_unscale", {"Out", "FoundInfinite"}},
     {"update_loss_scaling",
      {"Out", "LossScaling", "OutGoodSteps", "OutBadSteps"}},

paddle/fluid/pybind/pybind.cc

@@ -12,6 +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 <Python.h>
+
 #include <algorithm>
 #include <cstdlib>
 #include <map>
@@ -22,6 +23,7 @@ limitations under the License. */
 #include <unordered_set>
 #include <utility>
 #include <vector>
+
 #include "paddle/fluid/framework/executor.h"
 #include "paddle/fluid/framework/feed_fetch_method.h"
 #include "paddle/fluid/framework/feed_fetch_type.h"
@@ -2528,6 +2530,10 @@ All parameter, weight, gradient are variables in Paddle.
           "enable_inplace",
           [](const BuildStrategy &self) { return self.enable_inplace_; },
           [](BuildStrategy &self, bool b) { self.enable_inplace_ = b; })
+      .def_property(
+          "enable_addto",
+          [](const BuildStrategy &self) { return self.enable_addto_; },
+          [](BuildStrategy &self, bool b) { self.enable_addto_ = b; })
       .def_property(
           "fuse_all_reduce_ops",
           [](const BuildStrategy &self) {

paddle/scripts/paddle_build.sh

@@ -121,6 +121,18 @@ function cmake_base() {
             else
                 exit 1
             fi
+        elif [ "$1" == "cp38-cp38" ]; then
+            if [ -d "/Library/Frameworks/Python.framework/Versions/3.8" ]; then
+                export LD_LIBRARY_PATH=/Library/Frameworks/Python.framework/Versions/3.8/lib/
+                export DYLD_LIBRARY_PATH=/Library/Frameworks/Python.framework/Versions/3.8/lib/
+                export PATH=/Library/Frameworks/Python.framework/Versions/3.8/bin/:${PATH}
+                PYTHON_FLAGS="-DPYTHON_EXECUTABLE:FILEPATH=/Library/Frameworks/Python.framework/Versions/3.8/bin/python3
+            -DPYTHON_INCLUDE_DIR:PATH=/Library/Frameworks/Python.framework/Versions/3.8/include/python3.8/
+            -DPYTHON_LIBRARY:FILEPATH=/Library/Frameworks/Python.framework/Versions/3.8/lib/libpython3.8.dylib"
+                pip3.8 install --user -r ${PADDLE_ROOT}/python/requirements.txt
+            else
+                exit 1
+            fi
         fi
         # delete `gym` to avoid modifying requirements.txt in *.whl
         sed -i .bak "/^gym$/d" ${PADDLE_ROOT}/python/requirements.txt
@@ -176,6 +188,13 @@ function cmake_base() {
             -DPYTHON_INCLUDE_DIR:PATH=/opt/_internal/cpython-3.7.0/include/python3.7m
             -DPYTHON_LIBRARIES:FILEPATH=/opt/_internal/cpython-3.7.0/lib/libpython3.so"
                 pip3.7 install -r ${PADDLE_ROOT}/python/requirements.txt
+            elif [ "$1" == "cp38-cp38" ]; then
+                export LD_LIBRARY_PATH=/opt/_internal/cpython-3.8.0/lib/:${LD_LIBRARY_PATH}
+                export PATH=/opt/_internal/cpython-3.8.0/bin/:${PATH}
+                export PYTHON_FLAGS="-DPYTHON_EXECUTABLE:FILEPATH=/opt/_internal/cpython-3.8.0/bin/python3.8
+            -DPYTHON_INCLUDE_DIR:PATH=/opt/_internal/cpython-3.8.0/include/python3.8
+            -DPYTHON_LIBRARIES:FILEPATH=/opt/_internal/cpython-3.8.0/lib/libpython3.so"
+                pip3.8 install -r ${PADDLE_ROOT}/python/requirements.txt
            fi
         else
             pip install -r ${PADDLE_ROOT}/python/requirements.txt
@@ -514,6 +533,8 @@ EOF
             pip3.6 uninstall -y paddlepaddle
         elif [ "$1" == "cp37-cp37m" ]; then
             pip3.7 uninstall -y paddlepaddle
+        elif [ "$1" == "cp38-cp38" ]; then
+            pip3.8 uninstall -y paddlepaddle
         fi
         set -ex
 
@@ -527,6 +548,8 @@ EOF
             pip3.6 install --user ${INSTALL_PREFIX:-/paddle/build}/opt/paddle/share/wheels/*.whl
         elif [ "$1" == "cp37-cp37m" ]; then
             pip3.7 install --user ${INSTALL_PREFIX:-/paddle/build}/opt/paddle/share/wheels/*.whl
+        elif [ "$1" == "cp38-cp38" ]; then
+            pip3.8 install --user ${INSTALL_PREFIX:-/paddle/build}/opt/paddle/share/wheels/*.whl
         fi
         tmpfile_rand=`date +%s%N`
         tmpfile=$tmp_dir/$tmpfile_rand
@@ -666,7 +689,7 @@ function generate_api_spec() {
 
     awk -F '(' '{print $NF}' $spec_path >${spec_path}.doc
     awk -F '(' '{$NF="";print $0}' $spec_path >${spec_path}.api
-    if [ "$1" == "cp35-cp35m" ] || [ "$1" == "cp36-cp36m" ] || [ "$1" == "cp37-cp37m" ]; then 
+    if [ "$1" == "cp35-cp35m" ] || [ "$1" == "cp36-cp36m" ] || [ "$1" == "cp37-cp37m" ] || [ "$1" == "cp38-cp38" ]; then
         # Use sed to make python2 and python3 sepc keeps the same
         sed -i 's/arg0: str/arg0: unicode/g' $spec_path
         sed -i "s/\(.*Transpiler.*\).__init__ (ArgSpec(args=\['self'].*/\1.__init__ /g" $spec_path
@@ -1244,21 +1267,25 @@ EOF
     ref_paddle35=paddlepaddle${install_gpu}-${PADDLE_BRANCH}-cp35-cp35m-linux_x86_64.whl
     ref_paddle36=paddlepaddle${install_gpu}-${PADDLE_BRANCH}-cp36-cp36m-linux_x86_64.whl
     ref_paddle37=paddlepaddle${install_gpu}-${PADDLE_BRANCH}-cp37-cp37m-linux_x86_64.whl
+    ref_paddle38=paddlepaddle${install_gpu}-${PADDLE_BRANCH}-cp38-cp38-linux_x86_64.whl
 
     ref_paddle2_whl=paddlepaddle${install_gpu}-${PADDLE_BRANCH}-cp27-cp27mu-linux_x86_64.whl
     ref_paddle35_whl=paddlepaddle${install_gpu}-${PADDLE_BRANCH}-cp35-cp35m-linux_x86_64.whl
     ref_paddle36_whl=paddlepaddle${install_gpu}-${PADDLE_BRANCH}-cp36-cp36m-linux_x86_64.whl
     ref_paddle37_whl=paddlepaddle${install_gpu}-${PADDLE_BRANCH}-cp37-cp37m-linux_x86_64.whl
+    ref_paddle38_whl=paddlepaddle${install_gpu}-${PADDLE_BRANCH}-cp38-cp38-linux_x86_64.whl
 
     if [[ ${PADDLE_BRANCH} != "0.0.0" && ${WITH_MKL} == "ON" && ${WITH_GPU} == "ON" ]]; then
         ref_paddle2=paddlepaddle${install_gpu}-${PADDLE_BRANCH}.post${ref_CUDA_MAJOR}${CUDNN_MAJOR}-cp27-cp27mu-linux_x86_64.whl
         ref_paddle35=paddlepaddle${install_gpu}-${PADDLE_BRANCH}.post${ref_CUDA_MAJOR}${CUDNN_MAJOR}-cp35-cp35m-linux_x86_64.whl
         ref_paddle36=paddlepaddle${install_gpu}-${PADDLE_BRANCH}.post${ref_CUDA_MAJOR}${CUDNN_MAJOR}-cp36-cp36m-linux_x86_64.whl
         ref_paddle37=paddlepaddle${install_gpu}-${PADDLE_BRANCH}.post${ref_CUDA_MAJOR}${CUDNN_MAJOR}-cp37-cp37m-linux_x86_64.whl
+        ref_paddle38=paddlepaddle${install_gpu}-${PADDLE_BRANCH}.post${ref_CUDA_MAJOR}${CUDNN_MAJOR}-cp38-cp38-linux_x86_64.whl
         ref_paddle2_whl=paddlepaddle${install_gpu}-${PADDLE_BRANCH}.post${ref_CUDA_MAJOR}${CUDNN_MAJOR}-cp27-cp27mu-linux_x86_64.whl
         ref_paddle35_whl=paddlepaddle${install_gpu}-${PADDLE_BRANCH}.post${ref_CUDA_MAJOR}${CUDNN_MAJOR}-cp35-cp35m-linux_x86_64.whl
         ref_paddle36_whl=paddlepaddle${install_gpu}-${PADDLE_BRANCH}.post${ref_CUDA_MAJOR}${CUDNN_MAJOR}-cp36-cp36m-linux_x86_64.whl
         ref_paddle37_whl=paddlepaddle${install_gpu}-${PADDLE_BRANCH}.post${ref_CUDA_MAJOR}${CUDNN_MAJOR}-cp37-cp37m-linux_x86_64.whl
+        ref_paddle38_whl=paddlepaddle${install_gpu}-${PADDLE_BRANCH}.post${ref_CUDA_MAJOR}${CUDNN_MAJOR}-cp38-cp38-linux_x86_64.whl
     fi
 
     #ref_paddle2_mv1=""
@@ -1363,6 +1390,22 @@ EOF
         apt-get clean -y && \
         rm -f ${ref_paddle37} && \
         ldconfig
+EOF
+    cat >> ${PADDLE_ROOT}/build/Dockerfile <<EOF
+    # run paddle version to install python packages first
+    RUN apt-get update && ${NCCL_DEPS}
+    RUN apt-get install -y make build-essential libssl-dev zlib1g-dev libbz2-dev \
+        libreadline-dev libsqlite3-dev wget curl llvm libncurses5-dev libncursesw5-dev \
+        xz-utils tk-dev libffi-dev liblzma-dev
+    RUN wget -q https://www.python.org/ftp/python/3.8.0/Python-3.8.0.tgz && \
+        tar -xzf Python-3.8.0.tgz && cd Python-3.8.0 && \
+        CFLAGS="-Wformat" ./configure --prefix=/usr/local/ --enable-shared > /dev/null && \
+        make -j8 > /dev/null && make altinstall > /dev/null && cd ../ && rm Python-3.8.0.tgz
+    RUN apt-get install -y libgtk2.0-dev dmidecode python3-tk && ldconfig && \
+        pip3.8 install opencv-python && wget ${ref_web}/${ref_paddle38} && pip3.8 install ${ref_paddle38_whl}; apt-get install -f -y && \
+        apt-get clean -y && \
+        rm -f ${ref_paddle38} && \
+        ldconfig
 EOF
     cat >> ${PADDLE_ROOT}/build/Dockerfile <<EOF
     # run paddle version to install python packages first

python/paddle/distributed/fleet/__init__.py

@@ -42,6 +42,7 @@ server_num = fleet.server_num
 server_index = fleet.server_index
 server_endpoints = fleet.server_endpoints
 is_server = fleet.is_server
+set_util = fleet.set_util
 util = fleet.util
 barrier_worker = fleet.barrier_worker
 init_worker = fleet.init_worker

python/paddle/distributed/fleet/base/fleet_base.py

@@ -180,6 +180,8 @@ class Fleet(object):
                 raise ValueError(
                     "`role_maker` should be subclass of `RoleMakerBase`, but got {}".
                     format(type(role_maker)))
+        self._role_maker._generate_role()
+
         self.strategy_compiler = StrategyCompiler()
         if paddle.fluid.framework.in_dygraph_mode():
             if parallel_helper._is_parallel_ctx_initialized():
@@ -187,7 +189,6 @@ class Fleet(object):
                     "The dygraph parallel environment has been initialized.")
             else:
                 paddle.distributed.init_parallel_env()
-        return None
 
     def is_first_worker(self):
         """
@@ -206,7 +207,7 @@ class Fleet(object):
                 fleet.is_first_worker()
 
         """
-        return self._role_maker.is_first_worker()
+        return self._role_maker._is_first_worker()
 
     def worker_index(self):
         """
@@ -223,7 +224,7 @@ class Fleet(object):
                 fleet.worker_index()
 
         """
-        return self._role_maker.worker_index()
+        return self._role_maker._worker_index()
 
     def worker_num(self):
         """
@@ -240,7 +241,7 @@ class Fleet(object):
                 fleet.worker_num()
 
         """
-        return self._role_maker.worker_num()
+        return self._role_maker._worker_num()
 
     def is_worker(self):
         """
@@ -258,7 +259,7 @@ class Fleet(object):
                 fleet.is_worker()
 
         """
-        return self._role_maker.is_worker()
+        return self._role_maker._is_worker()
 
     def worker_endpoints(self, to_string=False):
         """
@@ -275,13 +276,10 @@ class Fleet(object):
                 fleet.worker_endpoints()
 
         """
-        '''
         if to_string:
-            return ",".join(self._role_maker.get_trainer_endpoints())
+            return ",".join(self._role_maker._get_trainer_endpoints())
         else:
-            return self._role_maker.get_trainer_endpoints()
-        '''
-        return ["127.0.0.1:1001", "127.0.0.1:1002"]
+            return self._role_maker._get_trainer_endpoints()
 
     def server_num(self):
         """
@@ -296,7 +294,7 @@ class Fleet(object):
             fleet.init()
             fleet.server_num()
         """
-        return len(self._role_maker.get_pserver_endpoints())
+        return len(self._role_maker._get_pserver_endpoints())
 
     def server_index(self):
         """
@@ -313,7 +311,7 @@ class Fleet(object):
                 fleet.server_index()
 
         """
-        return self._role_maker.server_index()
+        return self._role_maker._server_index()
 
     def server_endpoints(self, to_string=False):
         """
@@ -332,9 +330,9 @@ class Fleet(object):
         """
 
         if to_string:
-            return ",".join(self._role_maker.get_pserver_endpoints())
+            return ",".join(self._role_maker._get_pserver_endpoints())
         else:
-            return self._role_maker.get_pserver_endpoints()
+            return self._role_maker._get_pserver_endpoints()
 
     def is_server(self):
         """
@@ -352,10 +350,12 @@ class Fleet(object):
                 fleet.is_server()
 
         """
-        return self._role_maker.is_server(
+        return self._role_maker._is_server(
         ) or self._role_maker._is_heter_worker()
 
-    @property
+    def set_util(self, util):
+        self._util = util
+
     def util(self):
         """
         Utility functions that can be used under certain runtime
@@ -376,16 +376,6 @@ class Fleet(object):
         """
         return self._util
 
-    @util.setter
-    def util(self, util):
-        """
-        Set Utility functions for userd-defined runtime
-
-        Returns:
-            None
-        """
-        self._util = util
-
     def barrier_worker(self):
         """
         barrier all workers
@@ -393,7 +383,7 @@ class Fleet(object):
         Returns:
             None
         """
-        self._role_maker.barrier_worker()
+        self._role_maker._barrier("worker")
 
     @is_non_distributed_check
     @inited_runtime_handler

python/paddle/distributed/fleet/base/role_maker.py

@@ -13,18 +13,332 @@
 # limitations under the License.
 """Defination of Role Makers."""
 import os
+import time
 import numpy as np
 import warnings
 from multiprocessing import Process, Manager
-import paddle.fluid as fluid
 
-#__all__ = ['UserDefinedRoleMaker', 'PaddleCloudRoleMaker']
+import paddle.fluid as fluid
 
 
 class Role:
     WORKER = 1
     SERVER = 2
     HETER_WORKER = 3
+    ALL = 4
+
+
+class Gloo(object):
+    """
+    Gloo is a universal class for barrier and collective communication
+    """
+
+    class RENDEZVOUS:
+        HDFS = 1
+        FILE = 2
+        HTTP = 3
+
+    def __init__(self):
+        self._worker_comm = None
+        self._server_comm = None
+        self._nodes_comm = None
+
+        self._comm_world = ["worker", "server", "all"]
+        self._err_init = "gloo is not initialized, will not communicator with other nodes"
+        self._err_type = "gloo initialized error, please check arguments"
+        self._err_world = "argument error, comm_world must in {}".format(
+            self._comm_world)
+
+        self._is_initialized = False
+        self._init_timeout_seconds = 3600
+        self._run_timeout_seconds = 9999999
+
+        self._rendezvous = None
+        self._role = None
+        self._iface = None
+
+        self._role_id = -1
+        self._worker_num = -1
+        self._server_num = -1
+        self._need_init_all = False
+
+    def init(self,
+             rendezvous,
+             role,
+             role_id,
+             worker_num,
+             server_num,
+             need_init_all=False,
+             kwargs=None):
+
+        self._rendezvous = rendezvous
+        self._role = role
+        self._role_id = role_id
+        self._worker_num = worker_num
+        self._server_num = server_num
+        self._need_init_all = need_init_all
+        self._iface = self.__get_default_iface()
+        self._prefix = kwargs.get("store.prefix", "")
+
+        if self._rendezvous == Gloo.RENDEZVOUS.HDFS:
+            dfs_name = kwargs.get("dfs.name", "")
+            dfs_ugi = kwargs.get("dfs.ugi", "")
+            dfs_path = kwargs.get("dfs.path", "")
+
+            if not dfs_name or not dfs_ugi or not dfs_path:
+                raise ValueError(self._err_type)
+            self._init_dfs(dfs_name, dfs_ugi, dfs_path, self._prefix)
+
+        elif self._rendezvous == Gloo.RENDEZVOUS.FILE:
+            fs_path = kwargs.get("dfs.path", "")
+
+            if not fs_path:
+                raise ValueError(self._err_type)
+            self._init_fs(fs_path, self._prefix)
+
+        elif self._rendezvous == Gloo.RENDEZVOUS.HTTP:
+            ip = kwargs.get("http.host", "")
+            port = kwargs.get("http.port", "")
+
+            if not ip or not port:
+                raise ValueError(self._err_type)
+            self._init_http(ip, port, self._prefix)
+
+        else:
+            raise ValueError(self._err_type)
+
+        self._is_initialized = True
+
+    def _init_fs(self, fs_path, prefix):
+        def init(rank, nodes, role):
+            gloo = fluid.core.Gloo()
+            gloo.set_rank(rank)
+            gloo.set_size(nodes)
+            gloo.set_prefix(prefix)
+            gloo.set_iface(self._iface)
+            gloo.set_timeout_seconds(self._init_timeout_seconds,
+                                     self._run_timeout_seconds)
+            gloo.set_hdfs_store(os.path.join(fs_path, role), "", "")
+            gloo.init()
+            return gloo
+
+        if self._role == Role.WORKER:
+            rank, nodes = self._get_rank_nodes(Role.WORKER)
+            gloo = init(rank, nodes, "WORKER")
+            self._worker_comm = gloo
+        else:
+            rank, nodes = self._get_rank_nodes(Role.SERVER)
+            gloo = init(rank, nodes, "SERVER")
+            self._server_comm = gloo
+
+        if self._need_init_all:
+            rank, nodes = self._get_rank_nodes(Role.ALL)
+            gloo = init(rank, nodes, "ALL")
+            self._nodes_comm = gloo
+
+    def _init_dfs(self, dfs_name, dfs_ugi, dfs_path, prefix):
+        def init(rank, nodes, role):
+            gloo = fluid.core.Gloo()
+            gloo.set_rank(rank)
+            gloo.set_size(nodes)
+            gloo.set_prefix(prefix)
+            gloo.set_iface(self._iface)
+            gloo.set_timeout_seconds(self._init_timeout_seconds,
+                                     self._run_timeout_seconds)
+            gloo.set_hdfs_store(os.path.join(dfs_path, role), dfs_name, dfs_ugi)
+            gloo.init()
+            return gloo
+
+        if self._role == Role.WORKER:
+            rank, nodes = self._get_rank_nodes(Role.WORKER)
+            gloo = init(rank, nodes, "WORKER")
+            self._worker_comm = gloo
+        else:
+            rank, nodes = self._get_rank_nodes(Role.SERVER)
+            gloo = init(rank, nodes, "SERVER")
+            self._server_comm = gloo
+
+        if self._need_init_all:
+            rank, nodes = self._get_rank_nodes(Role.ALL)
+            gloo = init(rank, nodes, "ALL")
+            self._nodes_comm = gloo
+
+    def _init_http(self, ip, port, prefix):
+        def __start_kv_server(http_server_d, size_d):
+            from paddle.distributed.fleet.utils.http_server import KVServer
+            http_server = KVServer(port, size_d)
+            http_server.start()
+            wait_seconds = 5
+            while http_server_d.get("running",
+                                    False) and not http_server.shoud_stop():
+                time.sleep(wait_seconds)
+            http_server.stop()
+
+        def init_kv_server():
+            size_d = {
+                "trainer": self._worker_num,
+                "pserver": self._server_num,
+                "all": self._worker_num + self._server_num
+            }
+
+            _http_server_d = {"running": True}
+            # child process for http server
+            _http_server = Process(
+                target=__start_kv_server, args=(_http_server_d, size_d))
+            _http_server.daemon = True
+            # set running status to True
+            # start child process
+            _http_server.start()
+
+        def init(rank, nodes, role):
+            gloo = fluid.core.Gloo()
+            gloo.set_rank(rank)
+            gloo.set_size(nodes)
+            gloo.set_prefix(prefix)
+            gloo.set_iface(self._iface)
+            gloo.set_timeout_seconds(self._init_timeout_seconds,
+                                     self._run_timeout_seconds)
+            gloo.set_http_store(ip, port, role)
+            return gloo
+
+        port = int(port)
+
+        if self._role == Role.SERVER and self._role_id == 0:
+            init_kv_server()
+
+        if self._role == Role.WORKER:
+            rank, nodes = self._get_rank_nodes(Role.WORKER)
+            gloo = init(rank, nodes, "WORKER")
+            self._worker_comm = gloo
+        else:
+            rank, nodes = self._get_rank_nodes(Role.SERVER)
+            gloo = init(rank, nodes, "SERVER")
+            self._server_comm = gloo
+
+        if self._need_init_all:
+            rank, nodes = self._get_rank_nodes(Role.ALL)
+            gloo = init(rank, nodes, "ALL")
+            self._nodes_comm = gloo
+
+    def _get_rank_nodes(self, role):
+        nodes = 0
+        rank = -1
+
+        if role == Role.WORKER:
+            nodes = self._worker_num
+            rank = self._role_id
+        elif role == Role.SERVER:
+            nodes = self._server_num
+            rank = self._role_id
+        elif role == Role.ALL:
+            nodes = self._worker_num + self._server_num
+
+            if self._role == Role.WORKER:
+                rank = self._role_id
+            else:
+                rank = self._worker_num + self._role_id
+        else:
+            ValueError(self._err_type)
+
+        return rank, nodes
+
+    def __get_default_iface(self):
+        """
+        get default physical interface
+        """
+        default1 = self.__get_default_iface_from_gateway()
+        default2 = self.__get_default_iface_from_interfaces()
+        return default2 if default1 == "lo" else default1
+
+    def __get_default_iface_from_gateway(self):
+        """
+        get default physical interface
+        """
+        import netifaces
+        gateways = netifaces.gateways()
+        if gateways.get(netifaces.AF_INET) != None:
+            gateway = gateways[netifaces.AF_INET]
+            if len(gateway) > 0 and len(gateway[0]) > 1:
+                return gateway[0][1]
+        return "lo"
+
+    def __get_default_iface_from_interfaces(self):
+        """
+        get default physical interface
+        """
+        import netifaces
+        for intf_name in netifaces.interfaces():
+            addresses = netifaces.ifaddresses(intf_name)
+            if netifaces.AF_INET in addresses:
+                ipv4_addresses = addresses[netifaces.AF_INET]
+                for ipv4_address in ipv4_addresses:
+                    if 'broadcast' in ipv4_address:
+                        return intf_name
+        return "lo"
+
+    def barrier(self, comm_world):
+        """
+        dummy barrier, do nothing
+        """
+        if not self._is_initialized:
+            warnings.warn(self._err_init)
+            return
+
+        if comm_world not in self._comm_world:
+            raise ValueError(self._err_world)
+
+        if comm_world == "worker":
+            self._worker_comm.barrier()
+        elif comm_world == "server":
+            self._server_comm.barrier()
+        else:
+            self._nodes_comm.barrier()
+
+    def all_reduce(self, input, mode="sum", comm_world="worker"):
+        if not self._is_initialized:
+            warnings.warn(self._err_init)
+            return input
+
+        if comm_world not in self._comm_world:
+            raise ValueError(self._err_world)
+
+        input = np.array(input)
+        input_shape = input.shape
+        input_list = input.reshape(-1).tolist()
+
+        self.barrier(comm_world)
+
+        if comm_world == "worker":
+            ans = self._worker_comm.all_reduce(input_list, mode)
+        elif comm_world == "server":
+            ans = self._server_comm.all_reduce(input_list, mode)
+        else:
+            ans = self._nodes_comm.all_reduce(input_list, mode)
+
+        output = np.array(ans).reshape(input_shape)
+        return output
+
+    def all_gather(self, input, comm_world="worker"):
+        """
+        dummy all gather, do nothing
+        Args:
+            obj(any): obj to do all gather
+        """
+        if not self._is_initialized:
+            warnings.warn(self._err_init)
+            return input
+
+        if comm_world not in self._comm_world:
+            raise ValueError(self._err_world)
+
+        if comm_world == "worker":
+            output = self._worker_comm.all_gather(input)
+        elif comm_world == "server":
+            output = self._server_comm.all_gather(input)
+        else:
+            output = self._nodes_comm.all_gather(input)
+
+        return output
 
 
 class RoleMakerBase(object):
@@ -47,23 +361,19 @@ class RoleMakerBase(object):
         self._heter_trainer_device = "CPU"
         self._is_heter_parameter_server_mode = False
 
-        self._node_type = None
-        self._node_type_comm = None
-        self._all_comm = None
-
-    def is_worker(self):
+    def _is_worker(self):
         """
         return is_worker() of current process
         """
         raise NotImplementedError("Please implement this method in child class")
 
-    def is_server(self):
+    def _is_server(self):
         """
         return is_server() of current process
         """
         raise NotImplementedError("Please implement this method in child class")
 
-    def is_first_worker(self):
+    def _is_first_worker(self):
         """
         Check whether the node is the first instance of worker.
         Returns:
@@ -72,7 +382,7 @@ class RoleMakerBase(object):
         """
         raise NotImplementedError("Please implement this method in child class")
 
-    def worker_num(self):
+    def _worker_num(self):
         """
         Get current total worker number.
 
@@ -81,7 +391,7 @@ class RoleMakerBase(object):
         """
         raise NotImplementedError("Please implement this method in child class")
 
-    def server_num(self):
+    def _server_num(self):
         """
         Get current total server number.
 
@@ -90,7 +400,7 @@ class RoleMakerBase(object):
         """
         raise NotImplementedError("Please implement this method in child class")
 
-    def worker_index(self):
+    def _worker_index(self):
         """
         Get current worker id.
 
@@ -99,7 +409,7 @@ class RoleMakerBase(object):
         """
         raise NotImplementedError("Please implement this method in child class")
 
-    def server_index(self):
+    def _server_index(self):
         """
         Get current server id.
 
@@ -108,7 +418,7 @@ class RoleMakerBase(object):
         """
         raise NotImplementedError("Please implement this method in child class")
 
-    def role_id(self):
+    def _role_id(self):
         """
         Get current id.
 
@@ -117,7 +427,7 @@ class RoleMakerBase(object):
         """
         raise NotImplementedError("Please implement this method in child class")
 
-    def node_num(self):
+    def _node_num(self):
         """
         Get the training node number
         Returns:
@@ -125,13 +435,13 @@ class RoleMakerBase(object):
         """
         raise NotImplementedError("Please implement this method in child class")
 
-    def get_trainer_endpoints(self):
+    def _get_trainer_endpoints(self):
         """
         return trainer endpoints
         """
         return self._worker_endpoints
 
-    def get_pserver_endpoints(self):
+    def _get_pserver_endpoints(self):
         """
         return pserver endpoints
         """
@@ -142,19 +452,11 @@ class RoleMakerBase(object):
             self._role, self._current_id, self._worker_endpoints,
             self._server_endpoints)
 
-    def _all_gather(self, comm_world, input):
-        """
-
-        Args:
-            input(int|float): input value
-
-        Returns:
-            return a list of values
-        """
-        print("warning: RoleMakerBase does not have all gather.")
+    def _all_gather(self, input, comm_world="worker"):
+        print("warning: RoleMakerBase does not have all gather worker.")
         return None
 
-    def _all_reduce(self, comm_world, input, mode="sum"):
+    def _all_reduce(self, input, mode="sum", comm_world="worker"):
         """
         Args:
             input(list/numpy.array): array of one dim
@@ -221,158 +523,112 @@ class PaddleCloudRoleMaker(RoleMakerBase):
     def __init__(self, is_collective=False, **kwargs):
         super(PaddleCloudRoleMaker, self).__init__()
         self._is_collective = is_collective
-        self._init_gloo = False  # default no init gloo
-        self._kwargs = kwargs
 
+        self._non_distributed = False
+
+        self._kwargs = kwargs
         self._role_is_generated = False
 
         self._server_endpoints = None
         self._worker_endpoints = None
 
-        self._node_type_comm = None
-        self._all_comm = None
-
-        self._non_distributed = False
-
-        if not self._is_collective:
-            self._hdfs_name = kwargs.get("hdfs_name", "")
-            self._hdfs_ugi = kwargs.get("hdfs_ugi", "")
-            self._hdfs_path = kwargs.get("path", "").rstrip("/")
-            self._init_timeout_seconds = kwargs.get("init_timeout_seconds",
-                                                    3600)
-            self._run_timeout_seconds = kwargs.get("run_timeout_seconds",
-                                                   9999999)
-            ip_port = kwargs.get("http_ip_port", "")
-            self._http_ip_port = []
-            self._http_server = None
-            # if ip_port is not empty, it will use http instead of hdfs
-            if ip_port != "":
-                self._http_ip_port = ip_port.split(":")
-                # it's for communication between processes
-                self._manager = Manager()
-                # global dict to store status
-                self._http_server_d = self._manager.dict()
-                # set running status of http server
-                self._http_server_d["running"] = False
-            self._iface = self.__get_default_iface()
-            # this environment variable can be empty
-            self._prefix = os.getenv("SYS_JOB_ID", "")
+        self._gloo = Gloo()  # gloo instance
 
     def _barrier(self, comm_world):
-        if isinstance(comm_world, fluid.core.Gloo):
-            comm_world.barrier()
-        else:
-            print("warning: must init Gloo before using _barrier() function")
+        self._gloo.barrier(comm_world)
 
-    def _all_gather(self, comm_world, input):
-        if isinstance(comm_world, fluid.core.Gloo):
-            self._barrier(comm_world)
-            output = comm_world.all_gather(input)
-            return output
-        else:
-            print("warning: must init Gloo before using _all_gather() function")
-            return None
+    def _all_gather(self, input, comm_world="worker"):
+        return self._gloo.all_gather(input, comm_world)
 
-    def _all_reduce(self, comm_world, input, mode="sum"):
-        if isinstance(comm_world, fluid.core.Gloo):
+    def _all_reduce(self, input, mode="sum", comm_world="worker"):
+        return self._gloo.all_reduce(input, mode, comm_world)
 
-            input = np.array(input)
-
-            input_shape = input.shape
-            input_list = input.reshape(-1).tolist()
-
-            self._barrier(comm_world)
-            ans = comm_world.all_reduce(input_list, mode)
-            output = np.array(ans).reshape(input_shape)
-            return output
-        else:
-            print("warning: must init Gloo before using _all_reduce() function")
-            return None
-
-    def is_worker(self):
+    def _is_worker(self):
         """
         whether current process is worker
         """
         if not self._role_is_generated:
-            self.generate_role()
+            self._generate_role()
         return self._role == Role.WORKER
 
-    def is_server(self):
+    def _is_server(self):
         """
         whether current process is server
         """
         if not self._role_is_generated:
-            self.generate_role()
+            self._generate_role()
         return self._role == Role.SERVER
 
-    def is_first_worker(self):
+    def _is_first_worker(self):
         """
         whether current process is worker of rank 0
         """
         if not self._role_is_generated:
-            self.generate_role()
+            self._generate_role()
         return self._role == Role.WORKER and self._current_id == 0
 
-    def worker_index(self):
+    def _worker_index(self):
         """
         get index of current worker
         """
         if not self._role_is_generated:
-            self.generate_role()
+            self._generate_role()
         return self._current_id
 
-    def server_index(self):
+    def _server_index(self):
         """
         get index of current server
         """
         if not self._role_is_generated:
-            self.generate_role()
+            self._generate_role()
         return self._current_id
 
-    def role_id(self):
+    def _role_id(self):
         """
         get index of current node
         """
+        if not self._role_is_generated:
+            self._generate_role()
         return self._current_id
 
-    def worker_num(self):
+    def _worker_num(self):
         """
         retrun the current number of worker
         """
         if not self._role_is_generated:
-            self.generate_role()
+            self._generate_role()
         return self._trainers_num
 
-    def server_num(self):
+    def _server_num(self):
         """
         return the current number of server
         """
         if not self._role_is_generated:
-            self.generate_role()
-        return self._trainers_num
+            self._generate_role()
+        return len(self._get_pserver_endpoints())
 
-    def node_num(self):
+    def _node_num(self):
         """
         return the training node number
         """
         if not self._role_is_generated:
-            self.generate_role()
-        return self._node_num
+            self._generate_role()
+        return self._nodes_num
 
-    def get_trainer_endpoints(self):
+    def _get_trainer_endpoints(self):
         """
         get endpoint of all trainers
         """
         if not self._role_is_generated:
-            self.generate_role()
+            self._generate_role()
         return self._worker_endpoints
 
-    def get_pserver_endpoints(self):
+    def _get_pserver_endpoints(self):
         """
         get endpoint of all pservers
         """
         if not self._role_is_generated:
-            self.generate_role()
+            self._generate_role()
         return self._server_endpoints
 
     def _is_non_distributed(self):
@@ -381,7 +637,7 @@ class PaddleCloudRoleMaker(RoleMakerBase):
         (use python-run to launch fleet-code directly)
         """
         if not self._role_is_generated:
-            self.generate_role()
+            self._generate_role()
         return self._non_distributed
 
     def _heter_worker_num(self):
@@ -389,7 +645,7 @@ class PaddleCloudRoleMaker(RoleMakerBase):
         get heter worker nums
         """
         if not self._role_is_generated:
-            self.generate_role()
+            self._generate_role()
         return self._heter_trainers_num
 
     def _is_heter_worker(self):
@@ -397,45 +653,35 @@ class PaddleCloudRoleMaker(RoleMakerBase):
         whether current process is heter worker
         """
         if not self._role_is_generated:
-            self.generate_role()
+            self._generate_role()
         return self._role == Role.HETER_WORKER
 
-    def _get_rank(self):
-        """
-        get current rank in all workers and pservers
-        """
-        if not self._role_is_generated:
-            self.generate_role()
-        return self._rank
-
-    def _get_size(self):
-        """
-        get total num of all workers and pservers
-        """
-        if not self._role_is_generated:
-            self.generate_role()
-        return self._size
-
     def _ps_env(self):
         try:
             # Environment variable PADDLE_PSERVERS_IP_PORT_LIST must be set
             # format: string(ip:port,ip:port), eg. 127.0.0.1:6001,127.0.0.1:6002
             self._server_endpoints = os.getenv("PADDLE_PSERVERS_IP_PORT_LIST")
-            self._worker_endpoints = os.getenv("PADDLE_TRAINER_ENDPOINTS",
-                                               "").split(",")
+
             if self._server_endpoints is None:
                 # back to non_distributed execution.
                 self._server_endpoints = ""
                 self._trainers_num = 1
                 self._role = Role.WORKER
                 self._current_id = 0
-                self._node_num = 1
+                self._nodes_num = 1
                 self._heter_trainers_num = 0
                 self._heter_trainer_endpoints = None
                 self._non_distributed = True
                 return
 
             self._server_endpoints = self._server_endpoints.split(",")
+
+            self._worker_endpoints = os.getenv("PADDLE_TRAINER_ENDPOINTS")
+            if self._worker_endpoints:
+                self._worker_endpoints = self._worker_endpoints.split(",")
+            else:
+                self._worker_endpoints = []
+
             trainers_num = int(os.environ["PADDLE_TRAINERS_NUM"])
             training_role = os.environ["TRAINING_ROLE"]
 
@@ -497,7 +743,7 @@ class PaddleCloudRoleMaker(RoleMakerBase):
         self._trainers_num = trainers_num
         self._role = role
         self._current_id = current_id
-        self._node_num = len(
+        self._nodes_num = len(
             set([x.split(':')[0] for x in self._worker_endpoints]))
         self._heter_trainers_num = heter_trainers_num
         self._heter_trainer_endpoints = heter_trainer_eplist
@@ -506,6 +752,7 @@ class PaddleCloudRoleMaker(RoleMakerBase):
         self._current_id = int(os.getenv("PADDLE_TRAINER_ID", "0"))
         self._training_role = os.getenv("PADDLE_TRAINING_ROLE", "TRAINER")
         assert (self._training_role == "TRAINER")
+        self._role = Role.WORKER
         self._worker_endpoints = os.getenv("PADDLE_TRAINER_ENDPOINTS")
         self._cur_endpoint = os.getenv("PADDLE_CURRENT_ENDPOINT")
         if self._worker_endpoints is None:
@@ -515,136 +762,79 @@ class PaddleCloudRoleMaker(RoleMakerBase):
             self._non_distributed = True
         self._worker_endpoints = self._worker_endpoints.split(",")
         self._trainers_num = len(self._worker_endpoints)
-        self._node_num = len(
+        self._nodes_num = len(
             set([x.split(':')[0] for x in self._worker_endpoints]))
 
-    def _init_gloo_env(self):
-        def init_gloo_instance(role="trainer"):
-            role = role.lower()
-            assert role in ["trainer", "pserver", "all"]
-            if role == "trainer":
-                all_list = self._worker_endpoints
-                rank = self._current_id
-            elif role == "pserver":
-                all_list = self._server_endpoints
-                rank = self._current_id
-            else:
-                all_list = self._worker_endpoints + self._server_endpoints
-                rank = all_list.index(self._cur_endpoint)
-            gloo = fluid.core.Gloo()
-            gloo.set_rank(rank)
-            gloo.set_size(len(all_list))
-            gloo.set_prefix(self._prefix)
-            gloo.set_iface(self._iface)
-            gloo.set_timeout_seconds(self._init_timeout_seconds,
-                                     self._run_timeout_seconds)
-            if len(self._http_ip_port) != 0:
-                gloo.set_http_store(self._http_ip_port[0],
-                                    int(self._http_ip_port[1]), role)
-            else:
-                gloo.set_hdfs_store(self._hdfs_path + "/" + role,
-                                    self._hdfs_name, self._hdfs_ugi)
-            gloo.init()
-            return gloo
-
-        # paddlecloud support gloo
-        if self._role == Role.WORKER:
-            if self._current_id == 0 and len(self._http_ip_port) != 0:
-                size_d = {
-                    "trainer": len(self._worker_endpoints),
-                    "pserver": len(self._server_endpoints),
-                    "all":
-                    len(self._worker_endpoints) + len(self._server_endpoints)
-                }
-                # child process for http server
-                self._http_server = Process(
-                    target=self.__start_kv_server,
-                    args=(self._http_server_d, size_d))
-                self._http_server.daemon = True
-                # set running status to True
-                self._http_server_d["running"] = True
-                # start child process
-                self._http_server.start()
-            self._node_type = 1
-            gloo = init_gloo_instance("trainer")
-            self._node_type_comm = gloo
+    def _gloo_init(self):
+        # PADDLE_WITH_GLOO 1: trainer barrier, 2: all barrier
+        use_gloo = int(os.getenv("PADDLE_WITH_GLOO", "0"))
+        if use_gloo not in [1, 2]:
+            return
+
+        # PADDLE_GLOO_RENDEZVOUS 1: HDFS 2: FILE 3: HTTP
+        rendezvous_type = int(os.getenv("PADDLE_GLOO_RENDEZVOUS", "0"))
+        prefix = os.getenv("SYS_JOB_ID", "")
+        if rendezvous_type not in [
+                Gloo.RENDEZVOUS.HDFS, Gloo.RENDEZVOUS.HTTP, Gloo.RENDEZVOUS.FILE
+        ]:
+            raise ValueError(self._gloo._err_type)
+
+        need_init_all = True if use_gloo == 2 else False
+
+        if rendezvous_type == Gloo.RENDEZVOUS.HDFS:
+            dfs_name = os.getenv("PADDLE_GLOO_FS_NAME", "")
+            dfs_ugi = os.getenv("PADDLE_GLOO_FS_UGI", "")
+            dfs_path = os.getenv("PADDLE_GLOO_FS_PATH", "")
+            kwargs = {
+                "dfs.name": dfs_name,
+                "dfs.ugi": dfs_ugi,
+                "dfs.path": dfs_path,
+                "store.prefix": prefix,
+            }
+        elif rendezvous_type == Gloo.RENDEZVOUS.HTTP:
+            ip = os.getenv("PADDLE_GLOO_HTTP_HOST", "")
+            port = os.getenv("PADDLE_GLOO_HTTP_PORT", "")
+            kwargs = {
+                "http.host": ip,
+                "http.port": port,
+                "store.prefix": prefix,
+            }
         else:
-            assert self._role == Role.SERVER
-            self._node_type = 0
-            gloo = init_gloo_instance("pserver")
-            self._node_type_comm = gloo
-
-        all_list = self._worker_endpoints + self._server_endpoints
-        self._rank = all_list.index(self._cur_endpoint)
-        self._size = len(all_list)
-
-        gloo = init_gloo_instance("all")
-        self._all_comm = gloo
+            dfs_path = os.getenv("PADDLE_GLOO_FS_PATH", "")
+            kwargs = {
+                "dfs.path": dfs_path,
+                "store.prefix": prefix,
+            }
+
+        if rendezvous_type == Gloo.RENDEZVOUS.HDFS:
+            type = "HDFS"
+        elif rendezvous_type == Gloo.RENDEZVOUS.HTTP:
+            type = "HTTP"
+        else:
+            type = "FILE"
+        print("Gloo init with {}: need_init_all: {}, args: {}".format(
+            type, need_init_all, kwargs))
 
-        if self._http_server is not None:
-            # set running status to False
-            self._http_server_d["running"] = False
-            # wait until child process exits
-            self._http_server.join()
+        self._gloo.init(
+            rendezvous=rendezvous_type,
+            role=self._role,
+            role_id=self._role_id(),
+            worker_num=self._worker_num(),
+            server_num=self._server_num(),
+            need_init_all=need_init_all,
+            kwargs=kwargs)
 
-    def generate_role(self):
+    def _generate_role(self):
         """
         generate role for role maker
         """
         if not self._role_is_generated:
             if not self._is_collective:
                 self._ps_env()
-                if "PADDLE_WITH_GLOO" in os.environ:
-                    self._init_gloo = bool(os.environ["PADDLE_WITH_GLOO"])
-                if self._init_gloo:
-                    self._init_gloo_env()
             else:
                 self._collective_env()
             self._role_is_generated = True
-
-    def __get_default_iface(self):
-        """
-        get default physical interface
-        """
-        default1 = self.__get_default_iface_from_gateway()
-        default2 = self.__get_default_iface_from_interfaces()
-        return default2 if default1 == "lo" else default1
-
-    def __get_default_iface_from_gateway(self):
-        """
-        get default physical interface
-        """
-        import netifaces
-        gateways = netifaces.gateways()
-        if gateways.get(netifaces.AF_INET) != None:
-            gateway = gateways[netifaces.AF_INET]
-            if len(gateway) > 0 and len(gateway[0]) > 1:
-                return gateway[0][1]
-        return "lo"
-
-    def __get_default_iface_from_interfaces(self):
-        """
-        get default physical interface
-        """
-        import netifaces
-        for intf_name in netifaces.interfaces():
-            addresses = netifaces.ifaddresses(intf_name)
-            if netifaces.AF_INET in addresses:
-                ipv4_addresses = addresses[netifaces.AF_INET]
-                for ipv4_address in ipv4_addresses:
-                    if 'broadcast' in ipv4_address:
-                        return intf_name
-        return "lo"
-
-    def __start_kv_server(self, http_server_d, size_d):
-        from paddle.distributed.fleet.utils.http_server import KVServer
-        http_server = KVServer(int(self._http_ip_port[1]), size_d)
-        http_server.start()
-        wait_seconds = 5
-        while http_server_d.get("running",
-                                False) and not http_server.shoud_stop():
-            time.sleep(wait_seconds)
-        http_server.stop()
+            self._gloo_init()
 
 
 class UserDefinedRoleMaker(PaddleCloudRoleMaker):
@@ -670,26 +860,24 @@ class UserDefinedRoleMaker(PaddleCloudRoleMaker):
             self._cur_endpoint = self._worker_endpoints[self._current_id]
         elif self._role == Role.SERVER:
             self._cur_endpoint = self._server_endpoints[self._current_id]
-        self._node_num = len(
+        self._nodes_num = len(
             set([x.split(':')[0] for x in self._worker_endpoints]))
 
     def _user_defined_collective_env(self):
         self._worker_endpoints = self._kwargs.get("worker_endpoints")
         self._current_id = self._kwargs.get("current_id")
         self._trainers_num = len(self._worker_endpoints)
-        self._training_role = Role.Worker
-        self._node_num = len(
+        self._training_role = Role.WORKER
+        self._nodes_num = len(
             set([x.split(':')[0] for x in self._worker_endpoints]))
 
-    def generate_role(self):
+    def _generate_role(self):
         """
         generate role for role maker
         """
         if not self._role_is_generated:
             if not self._is_collective:
                 self._user_defined_ps_env()
-                if self._init_gloo:
-                    self._init_gloo_env()
             else:
                 self._user_defined_collective_env()
             self._role_is_generated = True

python/paddle/distributed/fleet/base/util_factory.py

@@ -57,34 +57,7 @@ class UtilBase(object):
         ), "fs_client must be the instance of paddle.distributed.fleet.utils.FS"
         self.fs_client = fs_client
 
-    def __check_comm_world(self, comm_world="worker"):
-        if not self.role_maker._role_is_generated:
-            self.role_maker.generate_role()
-
-        _comm_world = None
-        comm_world_upper = comm_world.upper()
-        if comm_world_upper == "WORKER":
-            if not self.role_maker.is_worker():
-                print(
-                    "warning: current role is not worker in collective_func(comm_world=\"worker\")"
-                )
-            _comm_world = self.role_maker._node_type_comm
-        elif comm_world_upper == "SERVER":
-            if not self.role_maker.is_server():
-                print(
-                    "warning: current role is not server in collective_func(comm_world=\"server\")"
-                )
-            _comm_world = self.role_maker._node_type_comm
-        elif comm_world_upper == "ALL":
-            _comm_world = self.role_maker._all_comm
-        else:
-            raise ValueError(
-                "not support comm_world, please choose one from [worker, server, all]"
-            )
-
-        return _comm_world
-
-    def all_reduce(self, input, mode, comm_world="worker"):
+    def all_reduce(self, input, mode="sum", comm_world="worker"):
         """
         All reduce `input` between specified collection. This is a distributed API.
 
@@ -130,8 +103,7 @@ class UtilBase(object):
                 if __name__ == "__main__":
                     train()
         """
-        _comm_world = self.__check_comm_world(comm_world)
-        return self.role_maker._all_reduce(_comm_world, input, mode)
+        return self.role_maker._all_reduce(input, mode, comm_world)
 
     def barrier(self, comm_world="worker"):
         """
@@ -170,8 +142,7 @@ class UtilBase(object):
                 if __name__ == "__main__":
                     train()
         """
-        _comm_world = self.__check_comm_world(comm_world)
-        self.role_maker._barrier(_comm_world)
+        self.role_maker._barrier(comm_world)
 
     def all_gather(self, input, comm_world="worker"):
         """
@@ -219,8 +190,8 @@ class UtilBase(object):
                 if __name__ == "__main__":
                     train()
         """
-        _comm_world = self.__check_comm_world(comm_world)
-        return self.role_maker._all_gather(_comm_world, input)
+
+        return self.role_maker._all_gather(input, comm_world)
 
     def _broadcast(self):
         pass
@@ -266,8 +237,8 @@ class UtilBase(object):
         if not isinstance(files, list):
             raise TypeError("files should be a list of file need to be read.")
 
-        trainer_id = self.role_maker.worker_index()
-        trainers = self.role_maker.worker_num()
+        trainer_id = self.role_maker._worker_index()
+        trainers = self.role_maker._worker_num()
 
         remainder = len(files) % trainers
         blocksize = int(len(files) / trainers)
@@ -309,7 +280,7 @@ class UtilBase(object):
                 fleet_util._set_role_maker(role)
                 fleet_util.print_on_rank("I'm worker 0", 0)
         """
-        if self.role_maker.worker_index() != rank_id:
+        if self.role_maker._worker_index() != rank_id:
             return
         print(message)
 

python/paddle/distributed/fleet/launch.py

@@ -55,7 +55,10 @@ launch a process on each of the given gpu card or cpu machine.
 """
 
 from __future__ import print_function
+
+import shutil
 import sys
+import tempfile
 from sys import version
 import subprocess
 import os
@@ -213,12 +216,20 @@ def launch_collective(args):
         cluster, pod = get_cluster_from_args(args, gpus)
         logger.debug("get cluster from args:{}".format(cluster))
 
+    global_envs = copy.copy(os.environ.copy())
+    gloo_rendezvous_dir = tempfile.mkdtemp()
+    # add gloo env
+    global_envs["PADDLE_WITH_GLOO"] = "1"
+    global_envs["PADDLE_GLOO_RENDEZVOUS"] = "2"
+    global_envs["PADDLE_GLOO_FS_PATH"] = gloo_rendezvous_dir
+
     procs = start_local_trainers(
         cluster,
         pod,
         training_script=args.training_script,
         training_script_args=args.training_script_args,
-        log_dir=args.log_dir)
+        log_dir=args.log_dir,
+        envs=global_envs)
 
     while True:
         alive = watch_local_trainers(procs, cluster.trainers_nranks())
@@ -230,6 +241,9 @@ def launch_collective(args):
 
         time.sleep(3)
 
+    if os.path.exists(gloo_rendezvous_dir):
+        shutil.rmtree(gloo_rendezvous_dir)
+
 
 def launch_ps(args):
     ports = None
@@ -315,6 +329,13 @@ def launch_ps(args):
 
     default_env = os.environ.copy()
     current_env = copy.copy(default_env)
+
+    gloo_rendezvous_dir = tempfile.mkdtemp()
+    # add gloo env
+    current_env["PADDLE_WITH_GLOO"] = "1"
+    current_env["PADDLE_GLOO_RENDEZVOUS"] = "2"
+    current_env["PADDLE_GLOO_FS_PATH"] = gloo_rendezvous_dir
+
     current_env.pop("http_proxy", None)
     current_env.pop("https_proxy", None)
     procs = []
@@ -419,6 +440,9 @@ def launch_ps(args):
         procs[i].proc.terminate()
     print("all parameter server are killed", file=sys.stderr)
 
+    if os.path.exists(gloo_rendezvous_dir):
+        shutil.rmtree(gloo_rendezvous_dir)
+
 
 def launch():
     args = _parse_args()

python/paddle/distributed/fleet/launch_utils.py

@@ -398,8 +398,14 @@ def start_local_trainers(cluster,
                          pod,
                          training_script,
                          training_script_args,
-                         log_dir=None):
-    current_env = copy.copy(os.environ.copy())
+                         log_dir=None,
+                         envs=None):
+
+    if envs is None:
+        current_env = copy.copy(os.environ.copy())
+    else:
+        current_env = copy.copy(envs)
+
     #paddle broadcast ncclUniqueId use socket, and
     #proxy maybe make trainers unreachable, so delete them.
     #if we set them to "", grpc will log error message "bad uri"

python/paddle/distributed/fleet/meta_optimizers/common.py

@@ -57,12 +57,12 @@ class CollectiveHelper(object):
         if startup_program is None:
             self.startup_program = fluid.default_startup_program()
 
-        endpoints = self.role_maker.get_trainer_endpoints()
-        current_endpoint = endpoints[self.role_maker.worker_index()]
+        endpoints = self.role_maker._get_trainer_endpoints()
+        current_endpoint = endpoints[self.role_maker._worker_index()]
         for ring_id in range(self.nrings):
             self._init_communicator(
                 self.startup_program, current_endpoint, endpoints,
-                self.role_maker.worker_index(), ring_id, self.wait_port)
+                self.role_maker._worker_index(), ring_id, self.wait_port)
         self._broadcast_params()
 
     def _init_communicator(self, program, current_endpoint, endpoints, rank,

python/paddle/distributed/fleet/meta_optimizers/dgc_optimizer.py

@@ -47,7 +47,7 @@ class DGCOptimizer(MetaOptimizerBase):
             sparsity=configs['sparsity'],
             parameter_list=opt._parameter_list,
             use_nesterov=opt._use_nesterov,
-            num_trainers=self.role_maker.worker_num(),
+            num_trainers=self.role_maker._worker_num(),
             regularization=opt.regularization,
             grad_clip=opt._grad_clip,
             name=opt._name)
@@ -60,7 +60,7 @@ class DGCOptimizer(MetaOptimizerBase):
             if not isinstance(self.inner_opt, Momentum):
                 logging.warn("dgc only works on Momentum optimizer")
                 return False
-            if self.role_maker.worker_num() <= 1:
+            if self.role_maker._worker_num() <= 1:
                 logging.warn("dgc only works on multi cards")
                 return False
 

python/paddle/distributed/fleet/meta_optimizers/graph_execution_optimizer.py

@@ -50,12 +50,12 @@ class GraphExecutionOptimizer(MetaOptimizerBase):
 
     # should fix the variable
     def _setup_nccl_op(self, startup_program, main_program, build_strategy):
-        trainer_endpoints = self.role_maker.get_trainer_endpoints()
+        trainer_endpoints = self.role_maker._get_trainer_endpoints()
         trainers = trainer_endpoints
-        trainer_id = self.role_maker.worker_index()
-        current_endpoint = self.role_maker.get_trainer_endpoints()[trainer_id]
+        trainer_id = self.role_maker._worker_index()
+        current_endpoint = self.role_maker._get_trainer_endpoints()[trainer_id]
         trainer_endpoints_env = ",".join(trainer_endpoints)
-        trainers_num = self.role_maker.worker_num()
+        trainers_num = self.role_maker._worker_num()
         nccl_id_var = startup_program.global_block().create_var(
             name="NCCLID", persistable=True, type=core.VarDesc.VarType.RAW)
         for i in range(1, build_strategy.nccl_comm_num):
@@ -127,8 +127,8 @@ class GraphExecutionOptimizer(MetaOptimizerBase):
             local_build_strategy.enable_sequential_execution = True
 
         exe_strategy = self.user_defined_strategy.execution_strategy
-        worker_num = self.role_maker.worker_num()
-        node_num = self.role_maker.node_num()
+        worker_num = self.role_maker._worker_num()
+        node_num = self.role_maker._node_num()
 
         if self.role_maker._is_collective:
             assert worker_num >= 1, "nccl2 worker_num must >= 1, now:{}" % worker_num
@@ -170,9 +170,9 @@ class GraphExecutionOptimizer(MetaOptimizerBase):
         # TODO(guru4elephant): should be an independent optimizer
         self._setup_nccl_op(startup_program, main_program, local_build_strategy)
 
-        local_build_strategy.num_trainers = self.role_maker.worker_num()
-        local_build_strategy.trainer_id = self.role_maker.worker_index()
-        local_build_strategy.trainers_endpoints = self.role_maker.get_trainer_endpoints(
+        local_build_strategy.num_trainers = self.role_maker._worker_num()
+        local_build_strategy.trainer_id = self.role_maker._worker_index()
+        local_build_strategy.trainers_endpoints = self.role_maker._get_trainer_endpoints(
         )
         local_build_strategy.enable_backward_optimizer_op_deps = True
 

python/paddle/distributed/fleet/meta_optimizers/localsgd_optimizer.py

@@ -38,7 +38,7 @@ class LocalSGDOptimizer(MetaOptimizerBase):
         if not self.user_defined_strategy.localsgd:
             return False
 
-        if self.role_maker.worker_num() <= 1:
+        if self.role_maker._worker_num() <= 1:
             return False
 
         return isinstance(self.inner_opt, paddle.optimizer.momentum.Momentum) \
@@ -168,7 +168,7 @@ class LocalSGDOptimizer(MetaOptimizerBase):
                         inputs={'X': [param]},
                         outputs={'Out': [param]},
                         attrs={
-                            'scale': 1.0 / self.role_maker.worker_num(),
+                            'scale': 1.0 / self.role_maker._worker_num(),
                             OP_ROLE_KEY: OpRole.Optimize
                         })
                     sub_block.append_op(
@@ -208,7 +208,7 @@ class AdaptiveLocalSGDOptimizer(MetaOptimizerBase):
         if not self.user_defined_strategy.adaptive_localsgd:
             return False
 
-        if self.role_maker.worker_num() <= 1:
+        if self.role_maker._worker_num() <= 1:
             return False
 
         return isinstance(self.inner_opt, paddle.optimizer.momentum.Momentum) \
@@ -275,7 +275,7 @@ class AdaptiveLocalSGDOptimizer(MetaOptimizerBase):
             inputs={'X': [avg_loss]},
             outputs={'Out': [avg_loss]},
             attrs={
-                'scale': 1.0 / self.role_maker.worker_num(),
+                'scale': 1.0 / self.role_maker._worker_num(),
                 OP_ROLE_KEY: OpRole.Optimize
             })
 
@@ -398,7 +398,7 @@ class AdaptiveLocalSGDOptimizer(MetaOptimizerBase):
                         inputs={'X': [param]},
                         outputs={'Out': [param]},
                         attrs={
-                            'scale': 1.0 / self.role_maker.worker_num(),
+                            'scale': 1.0 / self.role_maker._worker_num(),
                             OP_ROLE_KEY: OpRole.Optimize
                         })
                     sub_block.append_op(

python/paddle/distributed/fleet/meta_optimizers/parameter_server_graph_optimizer.py

@@ -31,7 +31,7 @@ class ParameterServerGraphOptimizer(ParameterServerOptimizer):
         if k_steps < 0:
             return False
 
-        if self.role_maker.is_server():
+        if self.role_maker._is_server():
             return False
 
         if self.role_maker._is_heter_parameter_server_mode:

python/paddle/distributed/fleet/meta_optimizers/parameter_server_optimizer.py

@@ -239,10 +239,10 @@ class ParameterServerOptimizer(MetaOptimizerBase):
                                                      strategy, self.role_maker)
         compiled_config.strategy = strategy
 
-        if self.role_maker.is_worker() or self.role_maker._is_heter_worker():
+        if self.role_maker._is_worker() or self.role_maker._is_heter_worker():
             main_program, startup_program = self._build_trainer_programs(
                 compiled_config)
-        elif self.role_maker.is_server():
+        elif self.role_maker._is_server():
             main_program, startup_program = self._build_pserver_programs(
                 compiled_config)
 

python/paddle/distributed/fleet/meta_optimizers/pipeline_optimizer.py

@@ -126,11 +126,11 @@ class PipelineOptimizer(MetaOptimizerBase):
         optimize_ops, params_grads, prog_list = \
             self.wrapped_opt.minimize(loss, startup_program,
                                       parameter_list, no_grad_set)
-        if self.role_maker.worker_num() == 1:
+        if self.role_maker._worker_num() == 1:
             return optimize_ops, params_grads
 
-        endpoints = self.role_maker.get_trainer_endpoints()
-        current_endpoint = endpoints[self.role_maker.worker_index()]
+        endpoints = self.role_maker._get_trainer_endpoints()
+        current_endpoint = endpoints[self.role_maker._worker_index()]
         self.startup_program = startup_program
         if startup_program is None:
             self.startup_program = fluid.default_startup_program()
@@ -142,7 +142,7 @@ class PipelineOptimizer(MetaOptimizerBase):
         self.nranks = nranks
         self.nrings = len(self.main_program_list)
 
-        self.rank = self.role_maker.worker_index()
+        self.rank = self.role_maker._worker_index()
         self.endpoints = endpoints
         self.current_endpoint = current_endpoint
 

python/paddle/distributed/fleet/runtime/parameter_server_runtime.py

@@ -104,9 +104,9 @@ class ParameterServerRuntime(RuntimeBase):
     def _init_worker(self):
         def sync_strategy_envs():
             kwargs = {}
-            kwargs["pserver_endpoints"] = self.role_maker.get_pserver_endpoints(
-            )
-            kwargs["trainer_id"] = self.role_maker.worker_index()
+            kwargs[
+                "pserver_endpoints"] = self.role_maker._get_pserver_endpoints()
+            kwargs["trainer_id"] = self.role_maker._worker_index()
             return kwargs
 
         def geo_strategy_envs():
@@ -150,7 +150,7 @@ class ParameterServerRuntime(RuntimeBase):
                 return "#".join(init_attrs)
 
             kwargs = {}
-            kwargs["trainers"] = self.role_maker.worker_num()
+            kwargs["trainers"] = self.role_maker._worker_num()
             kwargs["sparse_attrs"] = get_sparse_attrs()
             return kwargs
 
@@ -338,7 +338,7 @@ class ParameterServerRuntime(RuntimeBase):
                 block.append_op(
                     type='recv_save',
                     attrs={
-                        "trainer_id": self.role_maker.worker_index(),
+                        "trainer_id": self.role_maker._worker_index(),
                         "shape": var.shape,
                         "slice_shapes":
                         [",".join([str(i) for i in var.shape])],
@@ -378,14 +378,15 @@ class ParameterServerRuntime(RuntimeBase):
             block.append_op(
                 type='recv_save',
                 attrs={
-                    "trainer_id": self.role_maker.worker_index(),
+                    "trainer_id": self.role_maker._worker_index(),
                     "shape": var.shape,
                     "slice_shapes": slice_shapes,
                     "slice_varnames": var_ctx.split_varnames(),
                     "remote_varnames": var_ctx.split_varnames(),
                     "is_sparse": True,
                     "endpoints": var_ctx.split_endpoints(),
-                    "pserver_num": len(self.role_maker.get_pserver_endpoints()),
+                    "pserver_num":
+                    len(self.role_maker._get_pserver_endpoints()),
                     "file_path": os.path.join(dirname, var.name)
                 })
 
@@ -403,7 +404,7 @@ class ParameterServerRuntime(RuntimeBase):
                 block.append_op(
                     type='recv_save',
                     attrs={
-                        "trainer_id": self.role_maker.worker_index(),
+                        "trainer_id": self.role_maker._worker_index(),
                         "shape": var.shape,
                         "slice_shapes": slice_shapes,
                         "slice_varnames": slice_varnames,
@@ -411,7 +412,7 @@ class ParameterServerRuntime(RuntimeBase):
                         "is_sparse": True,
                         "endpoints": var_ctx.split_endpoints(),
                         "pserver_num":
-                        len(self.role_maker.get_pserver_endpoints()),
+                        len(self.role_maker._get_pserver_endpoints()),
                         "file_path": os.path.join(dirname, var.name)
                     })
 
@@ -422,7 +423,7 @@ class ParameterServerRuntime(RuntimeBase):
                 block.append_op(
                     type='recv_save',
                     attrs={
-                        "trainer_id": self.role_maker.worker_index(),
+                        "trainer_id": self.role_maker._worker_index(),
                         "shape": var.shape,
                         "slice_shapes":
                         [",".join([str(i) for i in var.shape])],

python/paddle/fluid/__init__.py

@@ -197,6 +197,7 @@ def __bootstrap__():
         'free_when_no_cache_hit',
         'call_stack_level',
         'sort_sum_gradient',
+        'max_inplace_grad_add',
     ]
     if 'Darwin' not in sysstr:
         read_env_flags.append('use_pinned_memory')

python/paddle/fluid/backward.py

@@ -251,12 +251,19 @@ def _rename_arg_(op_descs, old_name, new_name, begin_idx=None, end_idx=None):
         begin_idx = 0
     if end_idx is None:
         end_idx = len(op_descs)
-    for i in range(begin_idx, end_idx):
-        op_desc = op_descs[i]
-        if isinstance(op_desc, tuple):
-            op_desc = op_desc[0]
-        op_desc._rename_input(old_name, new_name)
-        op_desc._rename_output(old_name, new_name)
+    if isinstance(op_descs, (list, tuple)):
+        for i in range(begin_idx, end_idx):
+            op_desc = op_descs[i]
+            if isinstance(op_desc, tuple):
+                op_desc = op_desc[0]
+            op_desc._rename_input(old_name, new_name)
+            op_desc._rename_output(old_name, new_name)
+    if isinstance(op_descs, collections.OrderedDict):
+        for key, value in op_descs.items():
+            if isinstance(value, (list, tuple)):
+                for op_desc in value:
+                    op_desc._rename_input(old_name, new_name)
+                    op_desc._rename_output(old_name, new_name)
 
 
 def _create_op_desc_(op_type, inputs, outputs, attrs):
@@ -369,6 +376,41 @@ def _append_grad_suffix_(name):
     return cpt.to_text(name) + core.grad_var_suffix()
 
 
+def _accumulate_gradients_by_sum_op_(var_name, renamed_vars, pending_sum_ops,
+                                     op_idx):
+    """
+    Use sum op to accumulate_gradients, the gradients are stored in renamed_vars.
+    """
+    if op_idx not in pending_sum_ops.keys():
+        pending_sum_ops[op_idx] = []
+    pending_sum_ops[op_idx].append(
+        _create_op_desc_("sum", {"X": renamed_vars[var_name]},
+                         {"Out": [var_name]}, {"use_mkldnn": False}))
+    renamed_vars[var_name] = [var_name]
+
+
+def _accumulate_gradients_by_add_ops_(var_name, renamed_vars, pending_sum_ops,
+                                      op_idx):
+    """
+    Use several inplace add op to accumulate_gradients, the gradients are stored in renamed_vars.
+    """
+    if op_idx not in pending_sum_ops.keys():
+        pending_sum_ops[op_idx] = []
+    out_name = renamed_vars[var_name][0]
+    for i in range(1, len(renamed_vars[var_name])):
+        x_name = out_name
+        y_name = renamed_vars[var_name][i]
+        if i != len(renamed_vars[var_name]) - 1:
+            out_name = var_name + '@ADD@' + str(i)
+        else:
+            out_name = var_name
+        pending_sum_ops[op_idx].append(
+            _create_op_desc_("grad_add", {"X": [x_name],
+                                          "Y": [y_name]}, {"Out": [out_name]},
+                             {"use_mkldnn": False}))
+    renamed_vars[var_name] = [var_name]
+
+
 def _addup_repetitive_outputs_(op_descs, block_idx):
     """
     In backward part, an variable may be the output of more than one ops.
@@ -376,7 +418,9 @@ def _addup_repetitive_outputs_(op_descs, block_idx):
     In these cases, the variable should be the accumulation of all the outputs.
     `sum_op`s are added to implement the accumulate.
     """
-    pending_sum_ops = []
+    _MAX_ADD_NUM_ = core.globals()['FLAGS_max_inplace_grad_add']
+    #pending_sum_ops = []
+    pending_sum_ops = collections.OrderedDict()
     var_rename_count = collections.defaultdict(int)
     renamed_vars = collections.defaultdict(list)
     renamed_var_start_idx = collections.defaultdict(list)
@@ -385,10 +429,13 @@ def _addup_repetitive_outputs_(op_descs, block_idx):
             if "@GRAD" not in var_name:
                 continue
             if len(renamed_vars[var_name]) > 1:
-                pending_sum_ops.append((_create_op_desc_(
-                    "sum", {"X": renamed_vars[var_name]}, {"Out": [var_name]},
-                    {"use_mkldnn": False}), idx))
-                renamed_vars[var_name] = [var_name]
+                if len(renamed_vars[var_name]) > _MAX_ADD_NUM_:
+                    _accumulate_gradients_by_sum_op_(var_name, renamed_vars,
+                                                     pending_sum_ops, idx)
+                else:
+                    _accumulate_gradients_by_add_ops_(var_name, renamed_vars,
+                                                      pending_sum_ops, idx)
+
         for param_idx, param_name in enumerate(op_desc.output_names()):
             arg_names = op_desc.output(param_name)
             for arg_idx, var_name in enumerate(arg_names):
@@ -440,13 +487,26 @@ def _addup_repetitive_outputs_(op_descs, block_idx):
                     renamed_vars[var_name].append(new_name)
 
     for var_name, inputs in six.iteritems(renamed_vars):
-        if len(inputs) > 1:
-            pending_sum_ops.append(
-                (_create_op_desc_("sum", {"X": inputs}, {"Out": [var_name]},
-                                  {"use_mkldnn": False}), len(op_descs)))
+        if len(renamed_vars[var_name]) > 1:
+            if len(renamed_vars[var_name]) > _MAX_ADD_NUM_:
+                _accumulate_gradients_by_sum_op_(var_name, renamed_vars,
+                                                 pending_sum_ops, len(op_descs))
+            else:
+                _accumulate_gradients_by_add_ops_(var_name, renamed_vars,
+                                                  pending_sum_ops,
+                                                  len(op_descs))
+
     # sum_op descs are sorted according to their insert position
-    for p in reversed(pending_sum_ops):
-        op_descs.insert(p[1], p[0])
+    for key, value in collections.OrderedDict(
+            reversed(list(pending_sum_ops.items()))).items():
+
+        # NOTE(zhiqiu): Since reversed, the idx of op_descs to be inserted will remains correct.
+        # For example, [0, 1, 2], and we want to insert 'a' at idx 1, 'b' at idx 2, and the expected result is [0, 1, 'a', 2, 'b'].
+        # If reversed, we first insert 'b' at idx 2, it becomes [0, 1, 2, 'b'], and then insert 'a' at idx 1, it becomes [0, 1, 'a', 2, 'b'].
+        # If not reverse, we first insert 'a' at idx 1, it becomes [0, 1, 'a', 2], and then insert 'b' at idx 2, it becomes [0, 1, 'a', 'b', 2].
+        idx = key
+        for i, op in enumerate(value):
+            op_descs.insert(idx + i, op)
 
     return op_descs
 

python/paddle/fluid/contrib/slim/quantization/imperative/qat.py

@@ -99,7 +99,12 @@ class ImperativeQuantAware(object):
         self._activation_bits = activation_bits
         self._moving_rate = moving_rate
 
-        quant_type = {'abs_max', 'moving_average_abs_max'}
+        quant_type = {
+            'abs_max', 'moving_average_abs_max', 'channel_wise_abs_max'
+        }
+
+        assert activation_quantize_type != 'channel_wise_abs_max', \
+            "The activation quantization type does not support 'channel_wise_abs_max'."
         if activation_quantize_type not in quant_type:
             raise ValueError(
                 "Unknown activation_quantize_type : '%s'. It can only be "
@@ -108,8 +113,8 @@ class ImperativeQuantAware(object):
         if weight_quantize_type not in quant_type:
             raise ValueError(
                 "Unknown weight_quantize_type: '%s'. It can only be "
-                "'abs_max' or 'moving_average_abs_max' now." %
-                (str(weight_quantize_type)))
+                "'abs_max' or 'moving_average_abs_max' or 'channel_wise_abs_max' now."
+                % (str(weight_quantize_type)))
         self._activation_quantize_type = activation_quantize_type
         self._weight_quantize_type = weight_quantize_type
 

python/paddle/fluid/contrib/slim/quantization/imperative/quant_nn.py

@@ -24,7 +24,7 @@ from paddle.fluid.data_feeder import check_variable_and_dtype
 
 __all__ = [
     'FakeQuantMovingAverage', 'FakeQuantAbsMax', 'QuantizedConv2D',
-    'QuantizedLinear'
+    'QuantizedLinear', 'FakeChannelWiseQuantDequantAbsMax'
 ]
 
 
@@ -209,6 +209,89 @@ class FakeQuantAbsMax(layers.Layer):
         return quant_out
 
 
+class FakeChannelWiseQuantDequantAbsMax(layers.Layer):
+    def __init__(self,
+                 name=None,
+                 channel_num=None,
+                 quant_bits=8,
+                 quant_axis=0,
+                 dtype='float32',
+                 quant_on_weight=False):
+        assert quant_on_weight == True, "Channel_wise only can be used on weight quantization."
+        super(FakeChannelWiseQuantDequantAbsMax, self).__init__()
+        self._quant_bits = quant_bits
+        self._quant_axis = quant_axis
+        self._dtype = dtype
+        self._name = name
+        self._channel_num = channel_num
+        scale_prefix = "{}.scale".format(
+            name) if name else 'quant_dequant.scale'
+        self._scale_name = unique_name.generate(scale_prefix)
+        if quant_on_weight:
+            scale_attr = ParamAttr(
+                name=self._scale_name,
+                initializer=Constant(0.0),
+                trainable=False)
+            self._scale = self.create_parameter(
+                shape=[self._channel_num], attr=scale_attr, dtype=self._dtype)
+            self._scale.stop_gradient = True
+        else:
+            self._scale = None
+
+    def forward(self, input):
+        if in_dygraph_mode():
+            attrs = ('bit_length', self._quant_bits, 'quant_axis',
+                     self._quant_axis)
+            quant_out = _varbase_creator(
+                type=input.type,
+                name="{}.quantized.dequantized".format(input.name),
+                shape=input.shape,
+                dtype=input.dtype,
+                persistable=False)
+
+            out_scale = self._scale
+            if out_scale is None:
+                out_scale = _varbase_creator(
+                    type=core.VarDesc.VarType.LOD_TENSOR,
+                    name=self._scale_name,
+                    shape=[self._channel_num],
+                    dtype=self._dtype,
+                    persistable=False)
+                out_scale.stop_gradient = True
+
+            out, _, = core.ops.fake_channel_wise_quantize_dequantize_abs_max(
+                input, quant_out, out_scale, *attrs)
+            return out
+
+        check_variable_and_dtype(input, 'input', ['float32'],
+                                 "FakeChannelWiseQuantDequantAbsMax")
+        attrs = {'bit_length': self._quant_bits, 'quant_axis': self._quant_axis}
+        inputs = {"X": [input]}
+        quant_out = self._helper.create_variable(
+            name="{}.quantized.dequantized".format(input.name),
+            dtype=input.dtype,
+            type=core.VarDesc.VarType.LOD_TENSOR,
+            persistable=False,
+            stop_gradient=False)
+        out_scale = self._scale
+        if not out_scale:
+            out_scale = self._helper.create_variable(
+                name=self._scale_name,
+                dtype=self._dtype,
+                type=core.VarDesc.VarType.LOD_TENSOR,
+                persistable=False,
+                stop_gradient=True)
+        outputs = {"Out": [quant_out], "OutScale": [out_scale]}
+
+        self._helper.append_op(
+            type="fake_channel_wise_quantize_dequantize_abs_max",
+            inputs=inputs,
+            outputs=outputs,
+            attrs=attrs)
+
+        return quant_out
+
+
 def _get_fake_quant_type(quant_type, **kwargs):
     call_args = {
         "name": kwargs.get("name", None),
@@ -220,10 +303,17 @@ def _get_fake_quant_type(quant_type, **kwargs):
         call_args["quant_on_weight"] = kwargs.get("quant_on_weight", False)
     elif quant_type == 'moving_average_abs_max':
         call_args["moving_rate"] = kwargs.get("moving_rate", 0.9)
-
+    elif quant_type == 'channel_wise_abs_max':
+        call_args["quant_on_weight"] = kwargs.get("quant_on_weight", False)
+        call_args["channel_num"] = kwargs.get("channel_num", None)
+        call_args["quant_axis"] = kwargs.get("quant_axis", 0)
+        assert call_args["channel_num"] is not None, (
+            "You need to input channel_num"
+            "when you use channel_wise_abs_max strategy.")
     fake_quant_map = {
         'abs_max': FakeQuantAbsMax,
-        'moving_average_abs_max': FakeQuantMovingAverage
+        'moving_average_abs_max': FakeQuantMovingAverage,
+        'channel_wise_abs_max': FakeChannelWiseQuantDequantAbsMax
     }
 
     return fake_quant_map[quant_type](**call_args)
@@ -255,19 +345,23 @@ class QuantizedConv2D(layers.Layer):
         self.weight = getattr(layer, 'weight')
         self.bias = getattr(layer, 'bias')
         # For FakeQuant
+        self._conv2d_quant_axis = 0
         self._fake_quant_weight = _get_fake_quant_type(
             weight_quantize_type,
             name=self.weight.name,
             moving_rate=moving_rate,
             quant_bits=weight_bits,
             dtype=self._dtype,
-            quant_on_weight=True)
+            quant_on_weight=True,
+            channel_num=self.weight.shape[self._conv2d_quant_axis],
+            quant_axis=self._conv2d_quant_axis)
         self._fake_quant_input = _get_fake_quant_type(
             activation_quantize_type,
             name=layer.full_name(),
             moving_rate=moving_rate,
             quant_bits=activation_bits,
-            dtype=self._dtype)
+            dtype=self._dtype,
+            quant_on_weight=False)
 
     def forward(self, input):
         quant_input = self._fake_quant_input(input)
@@ -341,19 +435,23 @@ class QuantizedLinear(layers.Layer):
         self.weight = getattr(layer, 'weight')
         self.bias = getattr(layer, 'bias')
         # For FakeQuant
+        self._linear_quant_axis = 1
         self._fake_quant_weight = _get_fake_quant_type(
             weight_quantize_type,
             name=self.weight.name,
             moving_rate=moving_rate,
             quant_bits=weight_bits,
             dtype=self._dtype,
-            quant_on_weight=True)
+            quant_on_weight=True,
+            channel_num=self.weight.shape[self._linear_quant_axis],
+            quant_axis=self._linear_quant_axis)
         self._fake_quant_input = _get_fake_quant_type(
             activation_quantize_type,
             name=layer.full_name(),
             moving_rate=moving_rate,
             quant_bits=activation_bits,
-            dtype=self._dtype)
+            dtype=self._dtype,
+            quant_on_weight=False)
 
     def forward(self, input):
         quant_input = self._fake_quant_input(input)

python/paddle/fluid/contrib/slim/tests/test_imperative_qat.py

@@ -181,7 +181,6 @@ class TestImperativeQat(unittest.TestCase):
 
                     img = fluid.dygraph.to_variable(x_data)
                     label = fluid.dygraph.to_variable(y_data)
-
                     out = lenet(img)
                     acc = fluid.layers.accuracy(out, label)
                     loss = fluid.layers.cross_entropy(out, label)

python/paddle/fluid/contrib/slim/tests/test_imperative_qat_channelwise.py

+#   copyright (c) 2018 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.
+
+from __future__ import print_function
+
+import os
+import numpy as np
+import random
+import unittest
+import logging
+import paddle
+import paddle.fluid as fluid
+from paddle.fluid import core
+from paddle.fluid.optimizer import AdamOptimizer
+from paddle.fluid.framework import IrGraph
+from paddle.fluid.contrib.slim.quantization import ImperativeQuantAware
+from paddle.fluid.contrib.slim.quantization import QuantizationTransformPass
+from paddle.fluid.dygraph.container import Sequential
+from paddle.fluid.dygraph.nn import Conv2D
+from paddle.fluid.dygraph.nn import Pool2D
+from paddle.fluid.dygraph.nn import Linear
+from paddle.fluid.log_helper import get_logger
+
+os.environ["CPU_NUM"] = "1"
+if core.is_compiled_with_cuda():
+    fluid.set_flags({"FLAGS_cudnn_deterministic": True})
+
+_logger = get_logger(
+    __name__, logging.INFO, fmt='%(asctime)s-%(levelname)s: %(message)s')
+
+
+def StaticLenet(data, num_classes=10, classifier_activation='softmax'):
+    conv2d_w1_attr = fluid.ParamAttr(name="conv2d_w_1")
+    conv2d_w2_attr = fluid.ParamAttr(name="conv2d_w_2")
+    fc_w1_attr = fluid.ParamAttr(name="fc_w_1")
+    fc_w2_attr = fluid.ParamAttr(name="fc_w_2")
+    fc_w3_attr = fluid.ParamAttr(name="fc_w_3")
+    conv2d_b1_attr = fluid.ParamAttr(name="conv2d_b_1")
+    conv2d_b2_attr = fluid.ParamAttr(name="conv2d_b_2")
+    fc_b1_attr = fluid.ParamAttr(name="fc_b_1")
+    fc_b2_attr = fluid.ParamAttr(name="fc_b_2")
+    fc_b3_attr = fluid.ParamAttr(name="fc_b_3")
+    conv1 = fluid.layers.conv2d(
+        data,
+        num_filters=6,
+        filter_size=3,
+        stride=1,
+        padding=1,
+        param_attr=conv2d_w1_attr,
+        bias_attr=conv2d_b1_attr)
+    pool1 = fluid.layers.pool2d(
+        conv1, pool_size=2, pool_type='max', pool_stride=2)
+    conv2 = fluid.layers.conv2d(
+        pool1,
+        num_filters=16,
+        filter_size=5,
+        stride=1,
+        padding=0,
+        param_attr=conv2d_w2_attr,
+        bias_attr=conv2d_b2_attr)
+    pool2 = fluid.layers.pool2d(
+        conv2, pool_size=2, pool_type='max', pool_stride=2)
+
+    fc1 = fluid.layers.fc(input=pool2,
+                          size=120,
+                          param_attr=fc_w1_attr,
+                          bias_attr=fc_b1_attr)
+    fc2 = fluid.layers.fc(input=fc1,
+                          size=84,
+                          param_attr=fc_w2_attr,
+                          bias_attr=fc_b2_attr)
+    fc3 = fluid.layers.fc(input=fc2,
+                          size=num_classes,
+                          act=classifier_activation,
+                          param_attr=fc_w3_attr,
+                          bias_attr=fc_b3_attr)
+
+    return fc3
+
+
+class ImperativeLenet(fluid.dygraph.Layer):
+    def __init__(self, num_classes=10, classifier_activation='softmax'):
+        super(ImperativeLenet, self).__init__()
+        conv2d_w1_attr = fluid.ParamAttr(name="conv2d_w_1")
+        conv2d_w2_attr = fluid.ParamAttr(name="conv2d_w_2")
+        fc_w1_attr = fluid.ParamAttr(name="fc_w_1")
+        fc_w2_attr = fluid.ParamAttr(name="fc_w_2")
+        fc_w3_attr = fluid.ParamAttr(name="fc_w_3")
+        conv2d_b1_attr = fluid.ParamAttr(name="conv2d_b_1")
+        conv2d_b2_attr = fluid.ParamAttr(name="conv2d_b_2")
+        fc_b1_attr = fluid.ParamAttr(name="fc_b_1")
+        fc_b2_attr = fluid.ParamAttr(name="fc_b_2")
+        fc_b3_attr = fluid.ParamAttr(name="fc_b_3")
+        self.features = Sequential(
+            Conv2D(
+                num_channels=1,
+                num_filters=6,
+                filter_size=3,
+                stride=1,
+                padding=1,
+                param_attr=conv2d_w1_attr,
+                bias_attr=conv2d_b1_attr),
+            Pool2D(
+                pool_size=2, pool_type='max', pool_stride=2),
+            Conv2D(
+                num_channels=6,
+                num_filters=16,
+                filter_size=5,
+                stride=1,
+                padding=0,
+                param_attr=conv2d_w2_attr,
+                bias_attr=conv2d_b2_attr),
+            Pool2D(
+                pool_size=2, pool_type='max', pool_stride=2))
+
+        self.fc = Sequential(
+            Linear(
+                input_dim=400,
+                output_dim=120,
+                param_attr=fc_w1_attr,
+                bias_attr=fc_b1_attr),
+            Linear(
+                input_dim=120,
+                output_dim=84,
+                param_attr=fc_w2_attr,
+                bias_attr=fc_b2_attr),
+            Linear(
+                input_dim=84,
+                output_dim=num_classes,
+                act=classifier_activation,
+                param_attr=fc_w3_attr,
+                bias_attr=fc_b3_attr))
+
+    def forward(self, inputs):
+        x = self.features(inputs)
+
+        x = fluid.layers.flatten(x, 1)
+        x = self.fc(x)
+        return x
+
+
+class TestImperativeQat(unittest.TestCase):
+    """
+    QAT = quantization-aware training
+    """
+
+    def test_qat_save(self):
+        imperative_qat = ImperativeQuantAware(
+            weight_quantize_type='channel_wise_abs_max',
+            activation_quantize_type='moving_average_abs_max')
+
+        with fluid.dygraph.guard():
+            lenet = ImperativeLenet()
+            imperative_qat.quantize(lenet)
+            adam = AdamOptimizer(
+                learning_rate=0.001, parameter_list=lenet.parameters())
+            train_reader = paddle.batch(
+                paddle.dataset.mnist.train(), batch_size=32, drop_last=True)
+            test_reader = paddle.batch(
+                paddle.dataset.mnist.test(), batch_size=32)
+
+            epoch_num = 1
+            for epoch in range(epoch_num):
+                lenet.train()
+                for batch_id, data in enumerate(train_reader()):
+                    x_data = np.array([x[0].reshape(1, 28, 28)
+                                       for x in data]).astype('float32')
+                    y_data = np.array(
+                        [x[1] for x in data]).astype('int64').reshape(-1, 1)
+
+                    img = fluid.dygraph.to_variable(x_data)
+                    label = fluid.dygraph.to_variable(y_data)
+                    out = lenet(img)
+                    acc = fluid.layers.accuracy(out, label)
+                    loss = fluid.layers.cross_entropy(out, label)
+                    avg_loss = fluid.layers.mean(loss)
+                    avg_loss.backward()
+                    adam.minimize(avg_loss)
+                    lenet.clear_gradients()
+                    if batch_id % 100 == 0:
+                        _logger.info(
+                            "Train | At epoch {} step {}: loss = {:}, acc= {:}".
+                            format(epoch, batch_id,
+                                   avg_loss.numpy(), acc.numpy()))
+
+                lenet.eval()
+                for batch_id, data in enumerate(test_reader()):
+                    x_data = np.array([x[0].reshape(1, 28, 28)
+                                       for x in data]).astype('float32')
+                    y_data = np.array(
+                        [x[1] for x in data]).astype('int64').reshape(-1, 1)
+
+                    img = fluid.dygraph.to_variable(x_data)
+                    label = fluid.dygraph.to_variable(y_data)
+
+                    out = lenet(img)
+                    acc_top1 = fluid.layers.accuracy(
+                        input=out, label=label, k=1)
+                    acc_top5 = fluid.layers.accuracy(
+                        input=out, label=label, k=5)
+
+                    if batch_id % 100 == 0:
+                        _logger.info(
+                            "Test | At epoch {} step {}: acc1 = {:}, acc5 = {:}".
+                            format(epoch, batch_id,
+                                   acc_top1.numpy(), acc_top5.numpy()))
+
+            # save weights
+            model_dict = lenet.state_dict()
+            fluid.save_dygraph(model_dict, "save_temp")
+
+            # test the correctness of `paddle.jit.save`
+            data = next(test_reader())
+            test_data = np.array([x[0].reshape(1, 28, 28)
+                                  for x in data]).astype('float32')
+            test_img = fluid.dygraph.to_variable(test_data)
+            lenet.eval()
+            before_save = lenet(test_img)
+
+        # save inference quantized model
+        path = "./mnist_infer_model"
+        paddle.jit.save(
+            layer=lenet,
+            model_path=path,
+            input_spec=[
+                paddle.static.InputSpec(
+                    shape=[None, 1, 28, 28], dtype='float32')
+            ])
+
+        if core.is_compiled_with_cuda():
+            place = core.CUDAPlace(0)
+        else:
+            place = core.CPUPlace()
+        exe = fluid.Executor(place)
+        [inference_program, feed_target_names, fetch_targets] = (
+            fluid.io.load_inference_model(
+                dirname=path,
+                executor=exe,
+                model_filename="__model__",
+                params_filename="__variables__"))
+        after_save, = exe.run(inference_program,
+                              feed={feed_target_names[0]: test_data},
+                              fetch_list=fetch_targets)
+
+        self.assertTrue(
+            np.allclose(after_save, before_save.numpy()),
+            msg='Failed to save the inference quantized model.')
+
+    def test_qat_acc(self):
+        def _build_static_lenet(main, startup, is_test=False, seed=1000):
+            with fluid.unique_name.guard():
+                with fluid.program_guard(main, startup):
+                    main.random_seed = seed
+                    startup.random_seed = seed
+                    img = fluid.layers.data(
+                        name='image', shape=[1, 28, 28], dtype='float32')
+                    label = fluid.layers.data(
+                        name='label', shape=[1], dtype='int64')
+                    prediction = StaticLenet(img)
+                    if not is_test:
+                        loss = fluid.layers.cross_entropy(
+                            input=prediction, label=label)
+                        avg_loss = fluid.layers.mean(loss)
+                    else:
+                        avg_loss = prediction
+            return img, label, avg_loss
+
+        reader = paddle.batch(
+            paddle.dataset.mnist.test(), batch_size=32, drop_last=True)
+        weight_quantize_type = 'channel_wise_abs_max'
+        activation_quant_type = 'moving_average_abs_max'
+        param_init_map = {}
+        seed = 1000
+        lr = 0.1
+
+        # imperative train
+        _logger.info(
+            "--------------------------dynamic graph qat--------------------------"
+        )
+        imperative_qat = ImperativeQuantAware(
+            weight_quantize_type=weight_quantize_type,
+            activation_quantize_type=activation_quant_type)
+
+        with fluid.dygraph.guard():
+            np.random.seed(seed)
+            fluid.default_main_program().random_seed = seed
+            fluid.default_startup_program().random_seed = seed
+            lenet = ImperativeLenet()
+            fixed_state = {}
+            for name, param in lenet.named_parameters():
+                p_shape = param.numpy().shape
+                p_value = param.numpy()
+                if name.endswith("bias"):
+                    value = np.zeros_like(p_value).astype('float32')
+                else:
+                    value = np.random.normal(
+                        loc=0.0, scale=0.01, size=np.product(p_shape)).reshape(
+                            p_shape).astype('float32')
+                fixed_state[name] = value
+                param_init_map[param.name] = value
+            lenet.set_dict(fixed_state)
+
+            imperative_qat.quantize(lenet)
+            adam = AdamOptimizer(
+                learning_rate=lr, parameter_list=lenet.parameters())
+            dynamic_loss_rec = []
+            lenet.train()
+            for batch_id, data in enumerate(reader()):
+                x_data = np.array([x[0].reshape(1, 28, 28)
+                                   for x in data]).astype('float32')
+                y_data = np.array(
+                    [x[1] for x in data]).astype('int64').reshape(-1, 1)
+
+                img = fluid.dygraph.to_variable(x_data)
+                label = fluid.dygraph.to_variable(y_data)
+
+                out = lenet(img)
+                loss = fluid.layers.cross_entropy(out, label)
+                avg_loss = fluid.layers.mean(loss)
+                avg_loss.backward()
+                adam.minimize(avg_loss)
+                lenet.clear_gradients()
+                dynamic_loss_rec.append(avg_loss.numpy()[0])
+                if batch_id % 100 == 0:
+                    _logger.info('{}: {}'.format('loss', avg_loss.numpy()))
+
+        paddle.jit.save(
+            layer=lenet,
+            model_path="./dynamic_mnist",
+            input_spec=[
+                paddle.static.InputSpec(
+                    shape=[None, 1, 28, 28], dtype='float32')
+            ])
+
+        # static graph train
+        _logger.info(
+            "--------------------------static graph qat--------------------------"
+        )
+        static_loss_rec = []
+        if core.is_compiled_with_cuda():
+            place = core.CUDAPlace(0)
+        else:
+            place = core.CPUPlace()
+        exe = fluid.Executor(place)
+
+        main = fluid.Program()
+        infer = fluid.Program()
+        startup = fluid.Program()
+        static_img, static_label, static_loss = _build_static_lenet(
+            main, startup, False, seed)
+        infer_img, _, infer_pre = _build_static_lenet(infer, startup, True,
+                                                      seed)
+        with fluid.unique_name.guard():
+            with fluid.program_guard(main, startup):
+                opt = AdamOptimizer(learning_rate=lr)
+                opt.minimize(static_loss)
+
+        scope = core.Scope()
+        with fluid.scope_guard(scope):
+            exe.run(startup)
+        for param in main.all_parameters():
+            param_tensor = scope.var(param.name).get_tensor()
+            param_tensor.set(param_init_map[param.name], place)
+
+        main_graph = IrGraph(core.Graph(main.desc), for_test=False)
+        infer_graph = IrGraph(core.Graph(infer.desc), for_test=True)
+        transform_pass = QuantizationTransformPass(
+            scope=scope,
+            place=place,
+            activation_quantize_type=activation_quant_type,
+            weight_quantize_type=weight_quantize_type,
+            quantizable_op_type=['conv2d', 'depthwise_conv2d', 'mul'])
+        transform_pass.apply(main_graph)
+        transform_pass.apply(infer_graph)
+        build_strategy = fluid.BuildStrategy()
+        build_strategy.fuse_all_reduce_ops = False
+        binary = fluid.CompiledProgram(main_graph.graph).with_data_parallel(
+            loss_name=static_loss.name, build_strategy=build_strategy)
+
+        feeder = fluid.DataFeeder(
+            feed_list=[static_img, static_label], place=place)
+        with fluid.scope_guard(scope):
+            for batch_id, data in enumerate(reader()):
+                loss_v, = exe.run(binary,
+                                  feed=feeder.feed(data),
+                                  fetch_list=[static_loss])
+                static_loss_rec.append(loss_v[0])
+                if batch_id % 100 == 0:
+                    _logger.info('{}: {}'.format('loss', loss_v))
+
+        save_program = infer_graph.to_program()
+        with fluid.scope_guard(scope):
+            fluid.io.save_inference_model("./static_mnist", [infer_img.name],
+                                          [infer_pre], exe, save_program)
+        rtol = 1e-05
+        atol = 1e-08
+        for i, (loss_d,
+                loss_s) in enumerate(zip(dynamic_loss_rec, static_loss_rec)):
+            diff = np.abs(loss_d - loss_s)
+            if diff > (atol + rtol * np.abs(loss_s)):
+                _logger.info(
+                    "diff({}) at {}, dynamic loss = {}, static loss = {}".
+                    format(diff, i, loss_d, loss_s))
+                break
+
+        self.assertTrue(
+            np.allclose(
+                np.array(dynamic_loss_rec),
+                np.array(static_loss_rec),
+                rtol=rtol,
+                atol=atol,
+                equal_nan=True),
+            msg='Failed to do the imperative qat.')
+
+
+if __name__ == '__main__':
+    unittest.main()

python/paddle/fluid/incubate/fleet/parameter_server/ir/public.py

@@ -170,22 +170,40 @@ class CompileTimeStrategy(object):
         return trainer.mode == DistributedMode.ASYNC
 
     def get_role_id(self):
-        return self.role_maker.role_id()
+        try:
+            return self.role_maker._role_id()
+        except Exception:
+            return self.role_maker.role_id()
 
     def get_trainers(self):
-        return self.role_maker.worker_num()
+        try:
+            return self.role_maker._worker_num()
+        except Exception:
+            return self.role_maker.worker_num()
 
     def get_ps_endpoint(self):
-        return self.role_maker.get_pserver_endpoints()[self.get_role_id()]
+        try:
+            return self.role_maker._get_pserver_endpoints()[self.get_role_id()]
+        except Exception:
+            return self.role_maker.get_pserver_endpoints()[self.get_role_id()]
 
     def get_ps_endpoints(self):
-        return self.role_maker.get_pserver_endpoints()
+        try:
+            return self.role_maker._get_pserver_endpoints()
+        except Exception:
+            return self.role_maker.get_pserver_endpoints()
 
     def get_heter_worker_endpoints(self):
-        return self.role_maker._get_heter_worker_endpoints()
+        try:
+            return self.role_maker._get_heter_worker_endpoints()
+        except Exception:
+            return self.role_maker.get_heter_worker_endpoints()
 
     def get_heter_worker_endpoint(self):
-        return self.role_maker._get_heter_worker_endpoint()
+        try:
+            return self.role_maker._get_heter_worker_endpoint()
+        except Exception:
+            return self.role_maker.get_heter_worker_endpoint()
 
     def get_origin_programs(self):
         return self.origin_main_program, self.origin_startup_program

python/paddle/fluid/layers/tensor.py

@@ -680,8 +680,10 @@ def fill_constant(shape, dtype, value, force_cpu=False, out=None, name=None):
     if not isinstance(value, Variable):
         if dtype in ['int64', 'int32']:
             attrs['str_value'] = str(int(value))
+            attrs['value'] = int(value)
         else:
             attrs['str_value'] = str(float(value))
+            attrs['value'] = float(value)
 
     if in_dygraph_mode():
         shape = utils.convert_shape_to_list(shape)

python/paddle/fluid/tests/unittests/ir/inference/test_conv_affine_channel_fuse_pass.py

+# Copyright (c) 2020 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 unittest
+import numpy as np
+from inference_pass_test import InferencePassTest
+import paddle.fluid as fluid
+import paddle.fluid.core as core
+from paddle.fluid.core import PassVersionChecker
+
+
+class ConvAffineChannelFusePassExplicitPaddingTest(InferencePassTest):
+    def setUp(self):
+        with fluid.program_guard(self.main_program, self.startup_program):
+            data = fluid.data(
+                name="data", shape=[-1, 3, 64, 64], dtype="float32")
+            conv_out = fluid.layers.conv2d(
+                input=data,
+                num_filters=3,
+                filter_size=3,
+                groups=3,
+                padding=[1, 1, 1, 1],
+                bias_attr=False,
+                act=None)
+            input_scale = fluid.layers.create_parameter(
+                shape=[3], dtype="float32")
+            input_bias = fluid.layers.create_parameter(
+                shape=[3], dtype="float32")
+            ac_out = fluid.layers.affine_channel(
+                x=conv_out, scale=input_scale, bias=input_bias)
+
+        self.feeds = {
+            "data": np.random.random([1, 3, 64, 64]).astype("float32"),
+        }
+        self.fetch_list = [ac_out]
+
+    def test_check_output(self):
+        self.check_output()
+
+        self.assertTrue(
+            PassVersionChecker.IsCompatible('conv_affine_channel_fuse_pass'))
+
+
+class ConvAffineChannelFusePassValidPaddingTest(InferencePassTest):
+    def setUp(self):
+        with fluid.program_guard(self.main_program, self.startup_program):
+            data = fluid.data(
+                name="data", shape=[-1, 3, 64, 64], dtype="float32")
+            conv_out = fluid.layers.conv2d(
+                input=data,
+                num_filters=3,
+                filter_size=3,
+                groups=3,
+                padding='VALID',
+                bias_attr=False,
+                act=None)
+            input_scale = fluid.layers.create_parameter(
+                shape=[3], dtype="float32")
+            input_bias = fluid.layers.create_parameter(
+                shape=[3], dtype="float32")
+            ac_out = fluid.layers.affine_channel(
+                x=conv_out, scale=input_scale, bias=input_bias)
+
+        self.feeds = {
+            "data": np.random.random([1, 3, 64, 64]).astype("float32"),
+        }
+        self.fetch_list = [ac_out]
+
+    def test_check_output(self):
+        self.check_output()
+
+        self.assertTrue(
+            PassVersionChecker.IsCompatible('conv_affine_channel_fuse_pass'))
+
+
+class ConvAffineChannelFusePassSamePaddingTest(InferencePassTest):
+    def setUp(self):
+        with fluid.program_guard(self.main_program, self.startup_program):
+            data = fluid.data(
+                name="data", shape=[-1, 3, 64, 64], dtype="float32")
+            conv_out = fluid.layers.conv2d(
+                input=data,
+                num_filters=3,
+                filter_size=3,
+                groups=3,
+                padding='SAME',
+                bias_attr=False,
+                act=None)
+            input_scale = fluid.layers.create_parameter(
+                shape=[3], dtype="float32")
+            input_bias = fluid.layers.create_parameter(
+                shape=[3], dtype="float32")
+            ac_out = fluid.layers.affine_channel(
+                x=conv_out, scale=input_scale, bias=input_bias)
+
+        self.feeds = {
+            "data": np.random.random([1, 3, 64, 64]).astype("float32"),
+        }
+        self.fetch_list = [ac_out]
+
+    def test_check_output(self):
+        self.check_output()
+
+        self.assertTrue(
+            PassVersionChecker.IsCompatible('conv_affine_channel_fuse_pass'))
+
+
+class ConvEltwiseAddAffineChannelFusePassExplicitPaddingTest(InferencePassTest):
+    def setUp(self):
+        with fluid.program_guard(self.main_program, self.startup_program):
+            data = fluid.data(
+                name="data", shape=[-1, 3, 64, 64], dtype="float32")
+            param_attr = fluid.ParamAttr(
+                initializer=fluid.initializer.Xavier(uniform=False),
+                learning_rate=0.001)
+            conv_out = fluid.layers.conv2d(
+                input=data,
+                num_filters=3,
+                filter_size=3,
+                groups=3,
+                padding=[1, 1, 1, 1],
+                bias_attr=param_attr,
+                act=None)
+            input_scale = fluid.layers.create_parameter(
+                shape=[3], dtype="float32")
+            input_bias = fluid.layers.create_parameter(
+                shape=[3], dtype="float32")
+            ac_out = fluid.layers.affine_channel(
+                x=conv_out, scale=input_scale, bias=input_bias)
+
+        self.feeds = {
+            "data": np.random.random([1, 3, 64, 64]).astype("float32"),
+        }
+        self.fetch_list = [ac_out]
+
+    def test_check_output(self):
+        self.check_output()
+
+        self.assertTrue(
+            PassVersionChecker.IsCompatible(
+                'conv_eltwiseadd_affine_channel_fuse_pass'))
+
+
+class ConvEltwiseAddAffineChannelFusePassValidPaddingTest(InferencePassTest):
+    def setUp(self):
+        with fluid.program_guard(self.main_program, self.startup_program):
+            data = fluid.data(
+                name="data", shape=[-1, 3, 64, 64], dtype="float32")
+            param_attr = fluid.ParamAttr(
+                initializer=fluid.initializer.Xavier(uniform=False),
+                learning_rate=0.001)
+            conv_out = fluid.layers.conv2d(
+                input=data,
+                num_filters=3,
+                filter_size=3,
+                groups=3,
+                padding='VALID',
+                bias_attr=param_attr,
+                act=None)
+            input_scale = fluid.layers.create_parameter(
+                shape=[3], dtype="float32")
+            input_bias = fluid.layers.create_parameter(
+                shape=[3], dtype="float32")
+            ac_out = fluid.layers.affine_channel(
+                x=conv_out, scale=input_scale, bias=input_bias)
+
+        self.feeds = {
+            "data": np.random.random([1, 3, 64, 64]).astype("float32"),
+        }
+        self.fetch_list = [ac_out]
+
+    def test_check_output(self):
+        self.check_output()
+
+        self.assertTrue(
+            PassVersionChecker.IsCompatible(
+                'conv_eltwiseadd_affine_channel_fuse_pass'))
+
+
+class ConvEltwiseAddAffineChannelFusePassSamePaddingTest(InferencePassTest):
+    def setUp(self):
+        with fluid.program_guard(self.main_program, self.startup_program):
+            data = fluid.data(
+                name="data", shape=[-1, 3, 64, 64], dtype="float32")
+            param_attr = fluid.ParamAttr(
+                initializer=fluid.initializer.Xavier(uniform=False),
+                learning_rate=0.001)
+            conv_out = fluid.layers.conv2d(
+                input=data,
+                num_filters=3,
+                filter_size=3,
+                groups=3,
+                padding='Same',
+                bias_attr=param_attr,
+                act=None)
+            input_scale = fluid.layers.create_parameter(
+                shape=[3], dtype="float32")
+            input_bias = fluid.layers.create_parameter(
+                shape=[3], dtype="float32")
+            ac_out = fluid.layers.affine_channel(
+                x=conv_out, scale=input_scale, bias=input_bias)
+
+        self.feeds = {
+            "data": np.random.random([1, 3, 64, 64]).astype("float32"),
+        }
+        self.fetch_list = [ac_out]
+
+    def test_check_output(self):
+        self.check_output()
+
+        self.assertTrue(
+            PassVersionChecker.IsCompatible(
+                'conv_eltwiseadd_affine_channel_fuse_pass'))
+
+
+if __name__ == "__main__":
+    unittest.main()

python/paddle/fluid/tests/unittests/ir/inference/test_conv_bn_fuse_pass.py

+# Copyright (c) 2020 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 unittest
+import numpy as np
+from inference_pass_test import InferencePassTest
+import paddle.fluid as fluid
+import paddle.fluid.core as core
+from paddle.fluid.core import PassVersionChecker
+
+
+class ConvBnFusePassExplicitPaddingTest(InferencePassTest):
+    def setUp(self):
+        with fluid.program_guard(self.main_program, self.startup_program):
+            data = fluid.data(
+                name="data", shape=[-1, 3, 64, 64], dtype="float32")
+            conv_out = fluid.layers.conv2d(
+                input=data,
+                num_filters=6,
+                filter_size=6,
+                groups=3,
+                padding=[1, 1, 1, 1],
+                bias_attr=False,
+                act=None)
+            bn_out = fluid.layers.batch_norm(conv_out, is_test=True)
+
+        self.feeds = {
+            "data": np.random.random([1, 3, 64, 64]).astype("float32"),
+        }
+        self.fetch_list = [bn_out]
+
+    def test_check_output(self):
+        self.check_output()
+        self.assertTrue(PassVersionChecker.IsCompatible('conv_bn_fuse_pass'))
+
+
+class ConvBnFusePassValidPaddingTest(InferencePassTest):
+    def setUp(self):
+        with fluid.program_guard(self.main_program, self.startup_program):
+            data = fluid.data(
+                name="data", shape=[-1, 3, 64, 64], dtype="float32")
+            conv_out = fluid.layers.conv2d(
+                input=data,
+                num_filters=6,
+                filter_size=6,
+                groups=3,
+                padding='VALID',
+                bias_attr=False,
+                act=None)
+            bn_out = fluid.layers.batch_norm(conv_out, is_test=True)
+
+        self.feeds = {
+            "data": np.random.random([1, 3, 64, 64]).astype("float32"),
+        }
+        self.fetch_list = [bn_out]
+
+    def test_check_output(self):
+        self.check_output()
+        self.assertTrue(PassVersionChecker.IsCompatible('conv_bn_fuse_pass'))
+
+
+class ConvBnFusePassSamePaddingTest(InferencePassTest):
+    def setUp(self):
+        with fluid.program_guard(self.main_program, self.startup_program):
+            data = fluid.data(
+                name="data", shape=[-1, 3, 64, 64], dtype="float32")
+            conv_out = fluid.layers.conv2d(
+                input=data,
+                num_filters=6,
+                filter_size=6,
+                groups=3,
+                padding='SAME',
+                bias_attr=False,
+                act=None)
+            bn_out = fluid.layers.batch_norm(conv_out, is_test=True)
+
+        self.feeds = {
+            "data": np.random.random([1, 3, 64, 64]).astype("float32"),
+        }
+        self.fetch_list = [bn_out]
+
+    def test_check_output(self):
+        self.check_output()
+        self.assertTrue(PassVersionChecker.IsCompatible('conv_bn_fuse_pass'))
+
+
+class ConvEltwiseAddBnFuseExplicitPaddingPass(InferencePassTest):
+    def setUp(self):
+        with fluid.program_guard(self.main_program, self.startup_program):
+            data = fluid.data(
+                name="data", shape=[-1, 3, 64, 64], dtype="float32")
+            conv_out = fluid.layers.conv2d(
+                input=data,
+                num_filters=6,
+                filter_size=6,
+                groups=3,
+                padding=[1, 1, 1, 1],
+                bias_attr=None,
+                act=None)
+            bn_out = fluid.layers.batch_norm(conv_out, is_test=True)
+
+        self.feeds = {
+            "data": np.random.random([1, 3, 64, 64]).astype("float32"),
+        }
+        self.fetch_list = [bn_out]
+
+    def test_check_output(self):
+        self.check_output()
+        self.assertTrue(
+            PassVersionChecker.IsCompatible('conv_eltwiseadd_bn_fuse_pass'))
+
+
+class ConvEltwiseAddBnFuseValidPaddingPass(InferencePassTest):
+    def setUp(self):
+        with fluid.program_guard(self.main_program, self.startup_program):
+            data = fluid.data(
+                name="data", shape=[-1, 3, 64, 64], dtype="float32")
+            conv_out = fluid.layers.conv2d(
+                input=data,
+                num_filters=6,
+                filter_size=6,
+                groups=3,
+                padding='VALID',
+                bias_attr=None,
+                act=None)
+            bn_out = fluid.layers.batch_norm(conv_out, is_test=True)
+
+        self.feeds = {
+            "data": np.random.random([1, 3, 64, 64]).astype("float32"),
+        }
+        self.fetch_list = [bn_out]
+
+    def test_check_output(self):
+        self.check_output()
+        self.assertTrue(
+            PassVersionChecker.IsCompatible('conv_eltwiseadd_bn_fuse_pass'))
+
+
+class ConvEltwiseAddBnFuseSamePaddingPass(InferencePassTest):
+    def setUp(self):
+        with fluid.program_guard(self.main_program, self.startup_program):
+            data = fluid.data(
+                name="data", shape=[-1, 3, 64, 64], dtype="float32")
+            conv_out = fluid.layers.conv2d(
+                input=data,
+                num_filters=6,
+                filter_size=6,
+                groups=3,
+                padding='SAME',
+                bias_attr=None,
+                act=None)
+            bn_out = fluid.layers.batch_norm(conv_out, is_test=True)
+
+        self.feeds = {
+            "data": np.random.random([1, 3, 64, 64]).astype("float32"),
+        }
+        self.fetch_list = [bn_out]
+
+    def test_check_output(self):
+        self.check_output()
+        self.assertTrue(
+            PassVersionChecker.IsCompatible('conv_eltwiseadd_bn_fuse_pass'))
+
+
+if __name__ == "__main__":
+    unittest.main()

python/paddle/fluid/tests/unittests/ir/inference/test_conv_elementwise_add2_act_fuse_pass.py

@@ -19,6 +19,7 @@ import numpy as np
 from inference_pass_test import InferencePassTest
 import paddle.fluid as fluid
 import paddle.fluid.core as core
+from paddle.fluid.core import PassVersionChecker
 from paddle.fluid.core import AnalysisConfig
 """Test for fusion of conv, elementwise_add and 2 act."""
 
@@ -46,6 +47,9 @@ class ConvElementwiseAdd2ActFusePassTest(InferencePassTest):
         if core.is_compiled_with_cuda():
             use_gpu = True
             self.check_output_with_option(use_gpu)
+        self.assertTrue(
+            PassVersionChecker.IsCompatible(
+                'conv_elementwise_add2_act_fuse_pass'))
 
 
 if __name__ == "__main__":

python/paddle/fluid/tests/unittests/ir/inference/test_conv_elementwise_add_act_fuse_pass.py

@@ -19,6 +19,7 @@ import numpy as np
 from inference_pass_test import InferencePassTest
 import paddle.fluid as fluid
 import paddle.fluid.core as core
+from paddle.fluid.core import PassVersionChecker
 from paddle.fluid.core import AnalysisConfig
 """Test for fusion of conv, elementwise_add and act."""
 
@@ -48,6 +49,9 @@ class ConvElementwiseAddActFusePassTest(InferencePassTest):
         if core.is_compiled_with_cuda():
             use_gpu = True
             self.check_output_with_option(use_gpu)
+        self.assertTrue(
+            PassVersionChecker.IsCompatible(
+                'conv_elementwise_add_act_fuse_pass'))
 
 
 if __name__ == "__main__":

python/paddle/fluid/tests/unittests/ir/inference/test_conv_elementwise_add_fuse_pass.py

@@ -19,6 +19,7 @@ import numpy as np
 from inference_pass_test import InferencePassTest
 import paddle.fluid as fluid
 import paddle.fluid.core as core
+from paddle.fluid.core import PassVersionChecker
 from paddle.fluid.core import AnalysisConfig
 """Test for fusion of conv and elementwise_add."""
 
@@ -44,6 +45,8 @@ class ConvElementwiseAddFusePassTest(InferencePassTest):
         if core.is_compiled_with_cuda():
             use_gpu = True
             self.check_output_with_option(use_gpu)
+        self.assertTrue(
+            PassVersionChecker.IsCompatible('conv_elementwise_add_fuse_pass'))
 
 
 if __name__ == "__main__":

python/paddle/fluid/tests/unittests/ir/inference/test_fc_fuse_pass.py

+# Copyright (c) 2020 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.
+
+from __future__ import print_function
+
+import unittest
+import numpy as np
+from inference_pass_test import InferencePassTest
+import paddle.fluid as fluid
+import paddle.fluid.core as core
+from paddle.fluid.core import AnalysisConfig
+from paddle.fluid.core import PassVersionChecker
+
+
+class FcFusePassTest(InferencePassTest):
+    def setUp(self):
+        with fluid.program_guard(self.main_program, self.startup_program):
+            data = fluid.data(
+                name="data", shape=[-1, 128, 768], dtype="float32")
+            data_y = fluid.data(name="y", shape=[-1, 128, 768], dtype="float32")
+            fc_out1 = fluid.layers.fc(input=data,
+                                      size=3072,
+                                      num_flatten_dims=2,
+                                      act="relu")
+            fc_out2 = fluid.layers.fc(input=fc_out1,
+                                      size=768,
+                                      num_flatten_dims=2)
+
+        self.feeds = {"data": np.random.random((4, 128, 768)).astype("float32")}
+        self.fetch_list = [fc_out2]
+
+    def test_check_output(self):
+        use_gpu = [False]
+        if core.is_compiled_with_cuda():
+            use_gpu.append(True)
+        for i in range(len(use_gpu)):
+            self.check_output_with_option(use_gpu[i])
+
+        self.assertTrue(PassVersionChecker.IsCompatible('fc_fuse_pass'))
+
+
+if __name__ == "__main__":
+    unittest.main()

python/paddle/fluid/tests/unittests/ir/inference/test_fc_gru_fuse_pass.py

+# Copyright (c) 2020 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 unittest
+import numpy as np
+from inference_pass_test import InferencePassTest
+import paddle.fluid as fluid
+import paddle.fluid.core as core
+from paddle.fluid.core import PassVersionChecker
+
+
+class FcGruFusePassTest(InferencePassTest):
+    def setUp(self):
+        with fluid.program_guard(self.main_program, self.startup_program):
+            dict_dim, emb_dim = 128, 64
+            data = fluid.data(
+                name='step_data', shape=[None], dtype='int64', lod_level=1)
+            emb = fluid.embedding(input=data, size=[dict_dim, emb_dim])
+            hidden_dim = 512
+            x = fluid.layers.fc(input=emb, size=hidden_dim * 3)
+            hidden = fluid.layers.dynamic_gru(
+                input=x,
+                size=hidden_dim,
+                bias_attr=True,
+                origin_mode=False,
+                is_reverse=True)
+
+        batch = 16
+        lod_tensor = fluid.LoDTensor()
+        lod_tensor.set(np.random.randint(
+            0, dict_dim, size=[batch]).astype("int64"),
+                       fluid.CPUPlace())
+        lod_tensor.set_lod([[0, batch]])
+        self.feeds = {"step_data": lod_tensor}
+        self.fetch_list = [hidden]
+
+    def test_check_output(self):
+        use_gpu = False
+        self.check_output_with_option(use_gpu)
+        self.assertTrue(PassVersionChecker.IsCompatible('fc_gru_fuse_pass'))
+
+
+class MulGruFusePassTest(InferencePassTest):
+    def setUp(self):
+        with fluid.program_guard(self.main_program, self.startup_program):
+            dict_dim, emb_dim = 128, 64
+            data = fluid.data(
+                name='step_data', shape=[None], dtype='int64', lod_level=1)
+            emb = fluid.embedding(input=data, size=[dict_dim, emb_dim])
+            hidden_dim = 512
+            x = fluid.layers.fc(input=emb, size=hidden_dim * 3, bias_attr=False)
+            hidden = fluid.layers.dynamic_gru(
+                input=x,
+                size=hidden_dim,
+                bias_attr=True,
+                origin_mode=False,
+                is_reverse=True)
+
+        batch = 16
+        lod_tensor = fluid.LoDTensor()
+        lod_tensor.set(np.random.randint(
+            0, dict_dim, size=[batch]).astype("int64"),
+                       fluid.CPUPlace())
+        lod_tensor.set_lod([[0, batch]])
+        self.feeds = {"step_data": lod_tensor}
+        self.fetch_list = [hidden]
+
+    def test_check_output(self):
+        use_gpu = False
+        self.check_output_with_option(use_gpu)
+        self.assertTrue(PassVersionChecker.IsCompatible('mul_gru_fuse_pass'))
+
+
+if __name__ == "__main__":
+    unittest.main()

python/paddle/fluid/tests/unittests/ir/inference/test_fc_lstm_fuse_pass.py

+# Copyright (c) 2020 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 unittest
+import numpy as np
+from inference_pass_test import InferencePassTest
+import paddle.fluid as fluid
+import paddle.fluid.core as core
+from paddle.fluid.core import PassVersionChecker
+
+
+class MulLstmFusePassTest(InferencePassTest):
+    def setUp(self):
+        with fluid.program_guard(self.main_program, self.startup_program):
+            dict_dim, emb_dim = 128, 64
+            hidden_dim = 512
+
+            data = fluid.data(
+                name='data', shape=[1], dtype='int64', lod_level=1)
+            emb = fluid.embedding(input=data, size=[dict_dim, emb_dim])
+            x = fluid.layers.fc(input=emb, size=hidden_dim * 4, bias_attr=False)
+            forward, cell = fluid.layers.dynamic_lstm(
+                input=x, size=hidden_dim * 4)
+
+        batch = 16
+        lod_tensor = fluid.LoDTensor()
+        lod_tensor.set(np.random.randint(
+            0, dict_dim, size=[batch]).astype("int64"),
+                       fluid.CPUPlace())
+        lod_tensor.set_lod([[0, batch]])
+        self.feeds = {"data": lod_tensor}
+        self.fetch_list = [forward, cell]
+
+    def test_check_output(self):
+        use_gpu = False
+        self.check_output_with_option(use_gpu)
+        self.assertTrue(PassVersionChecker.IsCompatible('mul_lstm_fuse_pass'))
+
+
+if __name__ == "__main__":
+    unittest.main()

python/paddle/fluid/tests/unittests/ir/inference/test_repeated_fc_relu_fuse_pass.py

+# Copyright (c) 2020 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 unittest
+import numpy as np
+from inference_pass_test import InferencePassTest
+import paddle.fluid as fluid
+import paddle.fluid.core as core
+from paddle.fluid.core import PassVersionChecker
+
+
+class RepeatedFcReluFusePass3Test(InferencePassTest):
+    def setUp(self):
+        fc_num = 3
+        with fluid.program_guard(self.main_program, self.startup_program):
+            data = fluid.data(
+                name="data", shape=[-1, 3, 64, 64], dtype="float32")
+            param_attr = fluid.ParamAttr(
+                initializer=fluid.initializer.Xavier(uniform=False),
+                learning_rate=0.001)
+            conv_out = fluid.layers.conv2d(
+                input=data,
+                num_filters=3,
+                filter_size=3,
+                bias_attr=param_attr,
+                act=None)
+            fc_outs = []
+            fc_outs.append(
+                fluid.layers.fc(input=[conv_out], act="relu", size=1000))
+            for i in range(1, fc_num):
+                fc_outs.append(
+                    fluid.layers.fc(
+                        input=[fc_outs[i - 1]], act="relu", size=1000))
+        self.feeds = {
+            "data": np.random.random([1, 3, 64, 64]).astype("float32"),
+        }
+        self.fetch_list = [fc_outs[fc_num - 1]]
+
+    def test_check_output(self):
+        use_gpu = False
+        self.check_output_with_option(use_gpu)
+
+        self.assertTrue(
+            PassVersionChecker.IsCompatible('repeated_fc_relu_fuse_pass'))
+
+
+class RepeatedFcReluFusePass9Test(InferencePassTest):
+    def setUp(self):
+        fc_num = 9
+        with fluid.program_guard(self.main_program, self.startup_program):
+            data = fluid.data(
+                name="data", shape=[-1, 3, 64, 64], dtype="float32")
+            param_attr = fluid.ParamAttr(
+                initializer=fluid.initializer.Xavier(uniform=False),
+                learning_rate=0.001)
+            conv_out = fluid.layers.conv2d(
+                input=data,
+                num_filters=3,
+                filter_size=3,
+                bias_attr=param_attr,
+                act=None)
+            fc_outs = []
+            fc_outs.append(
+                fluid.layers.fc(input=[conv_out], act="relu", size=1000))
+            for i in range(1, fc_num):
+                fc_outs.append(
+                    fluid.layers.fc(
+                        input=[fc_outs[i - 1]], act="relu", size=1000))
+        self.feeds = {
+            "data": np.random.random([1, 3, 64, 64]).astype("float32"),
+        }
+        self.fetch_list = [fc_outs[fc_num - 1]]
+
+    def test_check_output(self):
+        use_gpu = False
+        self.check_output_with_option(use_gpu)
+
+        self.assertTrue(
+            PassVersionChecker.IsCompatible('repeated_fc_relu_fuse_pass'))
+
+
+if __name__ == "__main__":
+    unittest.main()

python/paddle/fluid/tests/unittests/ir/inference/test_squared_mat_sub_fuse_pass.py

+# Copyright (c) 2020 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.
+
+from __future__ import print_function
+
+import unittest
+import numpy as np
+from inference_pass_test import InferencePassTest
+import paddle
+import paddle.fluid as fluid
+import paddle.fluid.core as core
+from paddle.fluid.core import AnalysisConfig
+from paddle.fluid.core import PassVersionChecker
+
+
+class SquaredMatSubFusePassTest(InferencePassTest):
+    def setUp(self):
+        with fluid.program_guard(self.main_program, self.startup_program):
+            data_a = fluid.data(name="data_a", shape=[128, 1], dtype="float32")
+            data_b = fluid.data(name="data_b", shape=[256, 1], dtype="float32")
+
+            fc_a = fluid.layers.fc(data_a, size=256)
+            fc_b = fluid.layers.fc(data_b, size=64)
+
+            data_a_square = paddle.square(fc_a)
+            data_b_square = paddle.square(fc_b)
+
+            matmul_ab = paddle.matmul(fc_a, fc_b)
+            matmul_ab_square = paddle.square(matmul_ab)
+            matmul_square_ab = paddle.matmul(data_a_square, data_b_square)
+
+            scale = paddle.fill_constant(shape=[1], value=0.5, dtype='float32')
+
+            sub_val = paddle.elementwise_sub(matmul_ab_square, matmul_square_ab)
+            squared_mat_sub_out = fluid.layers.elementwise_mul(sub_val, scale)
+
+        self.feeds = {
+            "data_a": np.random.random((128, 1)).astype("float32"),
+            "data_b": np.random.random((256, 1)).astype("float32")
+        }
+        self.fetch_list = [squared_mat_sub_out]
+
+    def test_check_output(self):
+        use_gpu = False
+        self.check_output_with_option(use_gpu)
+
+        self.assertTrue(
+            PassVersionChecker.IsCompatible('squared_mat_sub_fuse_pass'))
+
+
+if __name__ == "__main__":
+    unittest.main()

python/paddle/fluid/tests/unittests/ir/inference/test_transpose_flatten_concat_fuse_pass.py

@@ -75,7 +75,9 @@ class TransposeFlattenConcatFusePassWithAxisTest(InferencePassTest):
             use_gpu = True
             self.check_output_with_option(use_gpu)
 
-        PassVersionChecker.IsCompatible('transpose_flatten_concat_fuse_pass')
+        self.assertTrue(
+            PassVersionChecker.IsCompatible(
+                'transpose_flatten_concat_fuse_pass'))
 
 
 if __name__ == "__main__":

python/paddle/fluid/tests/unittests/ir/inference/test_trt_shuffle_channel_detect_pass.py

+# Copyright (c) 2020 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 unittest
+import numpy as np
+from inference_pass_test import InferencePassTest
+import paddle.fluid as fluid
+import paddle.fluid.core as core
+from paddle.fluid.core import PassVersionChecker
+from paddle.fluid.core import AnalysisConfig
+
+
+class ShuffleChannelFuseTRTPassTest(InferencePassTest):
+    def setUp(self):
+        with fluid.program_guard(self.main_program, self.startup_program):
+            data = fluid.data(
+                name="data", shape=[-1, 6, 64, 64], dtype="float32")
+            reshape1 = fluid.layers.reshape(x=data, shape=[-1, 2, 3, 64, 64])
+            trans = fluid.layers.transpose(x=reshape1, perm=[0, 2, 1, 3, 4])
+            reshape2 = fluid.layers.reshape(x=trans, shape=[-1, 6, 64, 64])
+            out = fluid.layers.batch_norm(reshape2, is_test=True)
+
+        self.feeds = {
+            "data": np.random.random([1, 6, 64, 64]).astype("float32"),
+        }
+        self.enable_trt = True
+        self.trt_parameters = ShuffleChannelFuseTRTPassTest.TensorRTParam(
+            1 << 30, 32, 1, AnalysisConfig.Precision.Float32, False, False)
+        self.fetch_list = [out]
+
+    def test_check_output(self):
+
+        self.check_output()
+
+        self.assertTrue(
+            PassVersionChecker.IsCompatible('shuffle_channel_detect_pass'))
+
+
+if __name__ == "__main__":
+    unittest.main()

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

@@ -306,5 +306,70 @@ class TestFakeQuantDequantAbsOp(OpTest):
         self.check_grad(["X"], "Out", user_defined_grads=gradient)
 
 
+class TestChannelWiseFakeQuantDequantOp(OpTest):
+    def setUp(self):
+        self.set_arg()
+        assert self.quant_axis in [0, 1], "quant_axis should be 0 or 1."
+
+        self.op_type = "fake_channel_wise_quantize_dequantize_abs_max"
+        self.attrs = {'bit_length': 8, 'quant_axis': self.quant_axis}
+
+        scales = []
+        outputs = self.inputs['X'].copy()
+        range_v = (1 << (self.attrs['bit_length'] - 1)) - 1
+        if self.quant_axis == 0:
+            for i in range(self.inputs['X'].shape[0]):
+                scale_v = np.max(np.abs(self.inputs['X'][i])).astype("float32")
+                scales.append(scale_v)
+                outputs[i] = np.round(outputs[i] * range_v /
+                                      scale_v) * scale_v / range_v
+        elif self.quant_axis == 1:
+            for i in range(self.inputs['X'].shape[1]):
+                scale_v = np.max(np.abs(self.inputs['X'][:, i])).astype(
+                    "float32")
+                scales.append(scale_v)
+                outputs[:, i] = np.round(outputs[:, i] * range_v /
+                                         scale_v) * scale_v / range_v
+
+        self.outputs = {
+            'Out': outputs,
+            'OutScale': np.array(scales).astype("float32"),
+        }
+
+    def set_arg(self):
+        self.quant_axis = 0
+        self.inputs = {
+            'X': np.random.random((3, 4, 64, 64)).astype("float32"),
+        }
+
+    def test_check_output(self):
+        self.check_output()
+
+    def test_check_grad(self):
+        x = self.inputs["X"]
+        gradient = [np.ones(x.shape) / np.product(x.shape)]
+        self.check_grad(["X"], "Out", user_defined_grads=gradient)
+
+
+class TestChannelWiseFakeQuantDequantOp1(TestChannelWiseFakeQuantDequantOp):
+    def set_arg(self):
+        self.quant_axis = 1
+        self.inputs = {
+            'X': np.random.random((15, 20, 5, 5)).astype("float32"),
+        }
+
+
+class TestChannelWiseFakeQuantDequantOp2(TestChannelWiseFakeQuantDequantOp):
+    def set_arg(self):
+        self.quant_axis = 0
+        self.inputs = {'X': np.random.random((30, 15)).astype("float32"), }
+
+
+class TestChannelWiseFakeQuantDequantOp3(TestChannelWiseFakeQuantDequantOp):
+    def set_arg(self):
+        self.quant_axis = 1
+        self.inputs = {'X': np.random.random((30, 15)).astype("float32"), }
+
+
 if __name__ == "__main__":
     unittest.main()

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

@@ -24,10 +24,10 @@ import numpy as np
 class TestFleetBase(unittest.TestCase):
     def setUp(self):
         os.environ["POD_IP"] = "127.0.0.1"
-        os.environ["PADDLE_TRAINER_ENDPOINTS"] = "127.0.0.1:36001"
+        os.environ["PADDLE_TRAINER_ENDPOINTS"] = "127.0.0.1:36000"
         os.environ["PADDLE_TRAINERS_NUM"] = "2"
         os.environ["PADDLE_PSERVERS_IP_PORT_LIST"] = \
-                       "127.0.0.1:36001,127.0.0.2:36001"
+            "127.0.0.1:36001,127.0.0.2:36002"
 
     def test_init(self):
         role = role_maker.PaddleCloudRoleMaker(is_collective=True)
@@ -58,37 +58,56 @@ class TestFleetBase(unittest.TestCase):
     def test_worker_endpoints(self):
         role = role_maker.PaddleCloudRoleMaker(is_collective=True)
         fleet.init(role)
-        print(fleet.worker_endpoints(to_string=True))
+        self.assertEqual(
+            "127.0.0.1:36000", fleet.worker_endpoints(to_string=True))
+        self.assertEqual(["127.0.0.1:36000"], fleet.worker_endpoints())
 
     def test_server_num(self):
-        role = role_maker.PaddleCloudRoleMaker(is_collective=True)
+        os.environ["TRAINING_ROLE"] = "PSERVER"
+        os.environ["PADDLE_PORT"] = "36001"
+        os.environ["POD_IP"] = "127.0.0.1"
+
+        role = role_maker.PaddleCloudRoleMaker()
         fleet.init(role)
-        if fleet.is_server():
-            print("fleet server num: {}".format(fleet.server_num()))
+        os.environ["PADDLE_TRAINERS_NUM"] = "2"
+        self.assertEqual(2, fleet.server_num())
 
     def test_server_index(self):
-        role = role_maker.PaddleCloudRoleMaker(is_collective=True)
+        os.environ["TRAINING_ROLE"] = "PSERVER"
+        os.environ["PADDLE_PORT"] = "36001"
+        os.environ["POD_IP"] = "127.0.0.1"
+
+        role = role_maker.PaddleCloudRoleMaker()
         fleet.init(role)
-        if fleet.is_server():
-            print("fleet server index: {}".format(fleet.server_index()))
+        self.assertEqual(0, fleet.server_index())
 
     def test_server_endpoints(self):
-        role = role_maker.PaddleCloudRoleMaker(is_collective=True)
+        os.environ["TRAINING_ROLE"] = "PSERVER"
+        os.environ["PADDLE_PORT"] = "36001"
+        os.environ["POD_IP"] = "127.0.0.1"
+
+        role = role_maker.PaddleCloudRoleMaker()
         fleet.init(role)
         if fleet.is_server():
-            print("fleet server index: {}".format(
-                fleet.server_endpoints(to_string=True)))
+            self.assertEqual(
+                "127.0.0.1:36001,127.0.0.2:36002",
+                fleet.server_endpoints(to_string=True))
+            self.assertEqual(["127.0.0.1:36001", "127.0.0.2:36002"],
+                             fleet.server_endpoints())
 
     def test_is_server(self):
-        role = role_maker.PaddleCloudRoleMaker(is_collective=True)
+        os.environ["TRAINING_ROLE"] = "PSERVER"
+        os.environ["PADDLE_PORT"] = "36001"
+        os.environ["POD_IP"] = "127.0.0.1"
+
+        role = role_maker.PaddleCloudRoleMaker()
         fleet.init(role)
-        if fleet.is_server():
-            print("test fleet is server")
+        self.assertTrue(fleet.is_server())
 
     def test_util(self):
         role = role_maker.PaddleCloudRoleMaker(is_collective=True)
         fleet.init(role)
-        self.assertEqual(fleet.util, None)
+        self.assertEqual(fleet.util(), None)
 
     def test_barrier_worker(self):
         role = role_maker.PaddleCloudRoleMaker(is_collective=True)
@@ -99,20 +118,17 @@ class TestFleetBase(unittest.TestCase):
     def test_init_worker(self):
         role = role_maker.PaddleCloudRoleMaker(is_collective=True)
         fleet.init(role)
-        if fleet.is_worker():
-            fleet.init_worker()
 
-    def test_run_server(self):
-        role = role_maker.PaddleCloudRoleMaker(is_collective=True)
-        fleet.init(role)
-        if fleet.is_worker():
-            fleet.run_worker()
+        with self.assertRaises(ValueError):
+            if fleet.is_worker():
+                fleet.init_worker()
 
     def test_stop_worker(self):
         role = role_maker.PaddleCloudRoleMaker(is_collective=True)
         fleet.init(role)
-        if fleet.is_worker():
-            fleet.stop_worker()
+        with self.assertRaises(ValueError):
+            if fleet.is_worker():
+                fleet.stop_worker()
 
     def test_distributed_optimizer(self):
         role = role_maker.PaddleCloudRoleMaker(is_collective=True)

python/paddle/fluid/tests/unittests/test_fleet_rolemaker_2.py

@@ -87,7 +87,7 @@ class TestCloudRoleMaker2(unittest.TestCase):
         role2._all_gather(1)
         role2._all_gather(1)
         role2._barrier_server()
-        role2.all_gather(1)
+        role2._all_gather(1)
         role3 = GeneralRoleMaker(path="./test_gloo_3")
         role3._worker_gather(1)
         role3._worker_gather(1)

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

@@ -15,7 +15,11 @@
 
 from __future__ import print_function
 import os
+import platform
+import shutil
+import tempfile
 import unittest
+import paddle
 import paddle.distributed.fleet.base.role_maker as role_maker
 
 
@@ -26,25 +30,25 @@ class TestRoleMakerBase(unittest.TestCase):
 
     def test_rolemaker_base(self):
         role = role_maker.RoleMakerBase()
-        self.assertRaises(Exception, role.is_worker)
-        self.assertRaises(Exception, role.is_server)
-        self.assertRaises(Exception, role.is_first_worker)
-        self.assertRaises(Exception, role.worker_num)
-        self.assertRaises(Exception, role.server_num)
-        self.assertRaises(Exception, role.worker_index)
-        self.assertRaises(Exception, role.server_index)
-        self.assertRaises(Exception, role.role_id)
-        self.assertRaises(Exception, role.node_num)
-
-        trainer_endpoints = role.get_trainer_endpoints()
+        self.assertRaises(Exception, role._is_worker)
+        self.assertRaises(Exception, role._is_server)
+        self.assertRaises(Exception, role._is_first_worker)
+        self.assertRaises(Exception, role._worker_num)
+        self.assertRaises(Exception, role._server_num)
+        self.assertRaises(Exception, role._worker_index)
+        self.assertRaises(Exception, role._server_index)
+        self.assertRaises(Exception, role._role_id)
+        self.assertRaises(Exception, role._node_num)
+
+        trainer_endpoints = role._get_trainer_endpoints()
         self.assertTrue(len(trainer_endpoints) == 0)
-        pserver_endpoints = role.get_pserver_endpoints()
+        pserver_endpoints = role._get_pserver_endpoints()
         self.assertTrue(len(pserver_endpoints) == 0)
 
         print(role.to_string())
-        self.assertTrue(role._all_gather(role._node_type_comm, 1) is None)
-        self.assertTrue(role._all_reduce(role._node_type_comm, 1) is None)
-        role._barrier(role._node_type_comm)
+        self.assertTrue(role._all_gather(1, "worker") is None)
+        self.assertTrue(role._all_reduce(1, "sum", "worker") is None)
+        role._barrier("worker")
 
 
 class TestCloudRoleMaker(unittest.TestCase):
@@ -72,21 +76,33 @@ class TestCloudRoleMaker(unittest.TestCase):
             print("warning: no netifaces, skip test_tr_rolemaker")
             return
 
-        ro = role_maker.PaddleCloudRoleMaker(
-            is_collective=False, init_gloo=False)
-        self.assertTrue(ro.is_worker())
-        self.assertFalse(ro.is_server())
-        self.assertEqual(ro.worker_num(), 2)
-        self.assertTrue(ro.is_first_worker())
-        worker_endpoints = ro.get_trainer_endpoints()
+        ro = role_maker.PaddleCloudRoleMaker(is_collective=False)
+        self.assertTrue(ro._is_worker())
+        ro = role_maker.PaddleCloudRoleMaker(is_collective=False)
+        self.assertFalse(ro._is_server())
+        ro = role_maker.PaddleCloudRoleMaker(is_collective=False)
+        self.assertEqual(ro._worker_num(), 2)
+        ro = role_maker.PaddleCloudRoleMaker(is_collective=False)
+        self.assertTrue(ro._is_first_worker())
+        ro = role_maker.PaddleCloudRoleMaker(is_collective=False)
+        worker_endpoints = ro._get_trainer_endpoints()
         self.assertEqual(worker_endpoints[0], '127.0.0.1:36001')
-        self.assertEqual(ro.role_id(), 0)
-        self.assertEqual(ro.node_num(), 2)
+        ro = role_maker.PaddleCloudRoleMaker(is_collective=False)
+        self.assertEqual(ro._role_id(), 0)
+        ro = role_maker.PaddleCloudRoleMaker(is_collective=False)
+        self.assertEqual(ro._node_num(), 2)
+        ro = role_maker.PaddleCloudRoleMaker(is_collective=False)
+        self.assertFalse(ro._is_non_distributed())
+        ro = role_maker.PaddleCloudRoleMaker(is_collective=False)
+        self.assertEqual(ro._heter_worker_num(), 0)
+        ro = role_maker.PaddleCloudRoleMaker(is_collective=False)
+        self.assertFalse(ro._is_heter_worker())
 
     def test_tr_rolemaker_collective(self):
         ro = role_maker.PaddleCloudRoleMaker(is_collective=True)
-        self.assertEqual(ro.worker_num(), 2)
-        self.assertEqual(ro.node_num(), 2)
+        self.assertEqual(ro._worker_num(), 2)
+        ro = role_maker.PaddleCloudRoleMaker(is_collective=True)
+        self.assertEqual(ro._node_num(), 2)
 
     def test_ps_rolemaker(self):
         """Test ps rolemaker."""
@@ -102,14 +118,15 @@ class TestCloudRoleMaker(unittest.TestCase):
 
         ro = role_maker.PaddleCloudRoleMaker(
             is_collective=False, init_gloo=False)
-        self.assertEqual(ro.server_index(), 0)
-        self.assertFalse(ro.is_worker())
-        self.assertTrue(ro.is_server())
-        self.assertEqual(ro.server_num(), 2)
-        pserver_endpoints = ro.get_pserver_endpoints()
+        self.assertEqual(ro._server_index(), 0)
+        self.assertFalse(ro._is_worker())
+        self.assertTrue(ro._is_server())
+        self.assertEqual(ro._server_num(), 2)
+        pserver_endpoints = ro._get_pserver_endpoints()
         self.assertEqual(pserver_endpoints[0], '127.0.0.1:36001')
-        self.assertTrue(ro._all_gather(ro._all_comm, 1) is None)
-        self.assertTrue(ro._all_reduce(ro._all_comm, 1) is None)
+
+        self.assertEqual(ro._all_gather(1, "worker"), 1)
+        self.assertEqual(ro._all_reduce(1, "sum", "worker"), 1)
 
     def test_traing_role(self):
         """Test training role."""
@@ -121,7 +138,7 @@ class TestCloudRoleMaker(unittest.TestCase):
             return
 
         ro = role_maker.PaddleCloudRoleMaker(is_collective=False)
-        self.assertRaises(ValueError, ro.generate_role)
+        self.assertRaises(ValueError, ro._generate_role)
 
 
 class TestUserDefinedRoleMaker(unittest.TestCase):
@@ -142,14 +159,14 @@ class TestUserDefinedRoleMaker(unittest.TestCase):
         ro = role_maker.UserDefinedRoleMaker(
             is_collective=False,
             init_gloo=False,
-            server_endpoints="127.0.0.1:36001,127.0.0.1:36001",
+            server_endpoints=["127.0.0.1:36001", "127.0.0.1:36001"],
             role=role_maker.Role.SERVER,
             current_id=0,
             worker_num=2)
-        self.assertEqual(ro.server_num(), 2)
-        ro.generate_role()
-        self.assertTrue(ro.is_server())
-        self.assertEqual(ro.role_id(), 0)
+        self.assertEqual(ro._server_num(), 2)
+        ro._generate_role()
+        self.assertTrue(ro._is_server())
+        self.assertEqual(ro._role_id(), 0)
 
     def test_tr_rolemaker(self):
         try:
@@ -161,13 +178,589 @@ class TestUserDefinedRoleMaker(unittest.TestCase):
         ro = role_maker.UserDefinedRoleMaker(
             is_collective=False,
             init_gloo=False,
-            server_endpoints="127.0.0.1:36001,127.0.0.1:36001",
+            server_endpoints=["127.0.0.1:36001", "127.0.0.1:36001"],
             role=role_maker.Role.WORKER,
             current_id=0,
             worker_num=2)
-        self.assertIn("127.0.0.1:36001", ro.get_pserver_endpoints())
-        self.assertTrue(ro.is_worker())
-        self.assertEqual(ro.role_id(), 0)
+
+        self.assertIn("127.0.0.1:36001", ro._get_pserver_endpoints())
+        self.assertTrue(ro._is_worker())
+        self.assertEqual(ro._role_id(), 0)
+
+
+class TestGlooWithCloudRoleMaker(unittest.TestCase):
+    def setUp(self):
+        os.environ["PADDLE_TRAINERS_NUM"] = "1"
+        os.environ["PADDLE_PSERVERS_IP_PORT_LIST"] = "127.0.0.1:36001"
+        os.environ["PADDLE_TRAINER_ENDPOINTS"] = "127.0.0.1:36001"
+        os.environ["POD_IP"] = "127.0.0.1"
+        os.environ["PADDLE_TRAINER_ID"] = "0"
+
+    def case(self, role, comm_world):
+        role._barrier(comm_world)
+
+        gather = role._all_gather(1, comm_world)
+        self.assertEqual(gather[0], 1)
+
+        all_reduce = role._all_reduce(1, "sum", comm_world)
+        self.assertEqual(1, all_reduce)
+
+    def mkdir(self):
+        tmp = tempfile.mkdtemp()
+        return tmp
+
+    def clean(self, tmp):
+        shutil.rmtree(tmp)
+
+    def test_hdfs_gloo(self):
+        plats = platform.platform()
+        if 'Linux' not in plats:
+            print("skip gloo UT on MacOS/Win")
+            return
+
+        tmp = self.mkdir()
+        os.environ["TRAINING_ROLE"] = "TRAINER"
+        os.environ["SYS_JOB_ID"] = "gloo_for_cluster"
+        os.environ["PADDLE_WITH_GLOO"] = "1"
+        os.environ["PADDLE_GLOO_RENDEZVOUS"] = "1"
+        os.environ["PADDLE_GLOO_FS_NAME"] = "NULL"
+        os.environ["PADDLE_GLOO_FS_UGI"] = "NULL"
+        os.environ["PADDLE_GLOO_FS_PATH"] = tmp
+
+        role = role_maker.PaddleCloudRoleMaker()
+        role._generate_role()
+        self.case(role, "worker")
+        self.clean(tmp)
+
+    def test_fs_gloo(self):
+        plats = platform.platform()
+        if 'Linux' not in plats:
+            print("skip gloo UT on MacOS/Win")
+            return
+
+        tmp = self.mkdir()
+        os.environ["TRAINING_ROLE"] = "TRAINER"
+        os.environ["SYS_JOB_ID"] = "gloo_for_cluster"
+        os.environ["PADDLE_WITH_GLOO"] = "1"
+        os.environ["PADDLE_GLOO_RENDEZVOUS"] = "2"
+        os.environ["PADDLE_GLOO_FS_PATH"] = tmp
+
+        role = role_maker.PaddleCloudRoleMaker()
+        role._generate_role()
+        self.case(role, "worker")
+        self.clean(tmp)
+
+    def test_fs_gloo2(self):
+        plats = platform.platform()
+        if 'Linux' not in plats:
+            print("skip gloo UT on MacOS/Win")
+            return
+
+        tmp = self.mkdir()
+        os.environ["TRAINING_ROLE"] = "PSERVER"
+        os.environ["PADDLE_PSERVERS_IP_PORT_LIST"] = "127.0.0.1:36001"
+        os.environ["POD_IP"] = "127.0.0.1"
+        os.environ["PADDLE_PORT"] = "36001"
+
+        os.environ["SYS_JOB_ID"] = "gloo_for_cluster"
+        os.environ["PADDLE_WITH_GLOO"] = "1"
+        os.environ["PADDLE_GLOO_RENDEZVOUS"] = "2"
+        os.environ["PADDLE_GLOO_FS_PATH"] = tmp
+
+        role = role_maker.PaddleCloudRoleMaker()
+        role._generate_role()
+        self.case(role, "server")
+        self.clean(tmp)
+
+    def test_fs_gloo3(self):
+        plats = platform.platform()
+        if 'Linux' not in plats:
+            print("skip gloo UT on MacOS/Win")
+            return
+
+        tmp = self.mkdir()
+        os.environ["TRAINING_ROLE"] = "PSERVER"
+        os.environ["PADDLE_PSERVERS_IP_PORT_LIST"] = "127.0.0.1:36001"
+        os.environ["POD_IP"] = "127.0.0.1"
+        os.environ["PADDLE_PORT"] = "36001"
+
+        os.environ["SYS_JOB_ID"] = "gloo_for_cluster"
+        os.environ["PADDLE_WITH_GLOO"] = "1"
+        os.environ["PADDLE_GLOO_RENDEZVOUS"] = "1"
+        os.environ["PADDLE_GLOO_FS_NAME"] = "NULL"
+        os.environ["PADDLE_GLOO_FS_UGI"] = "NULL"
+        os.environ["PADDLE_GLOO_FS_PATH"] = tmp
+
+        role = role_maker.PaddleCloudRoleMaker()
+        role._generate_role()
+        self.case(role, "server")
+        self.clean(tmp)
+
+    def test_fs_gloo4(self):
+        plats = platform.platform()
+        if 'Linux' not in plats:
+            print("skip gloo UT on MacOS/Win")
+            return
+
+        os.environ["TRAINING_ROLE"] = "PSERVER"
+        os.environ["PADDLE_PSERVERS_IP_PORT_LIST"] = "127.0.0.1:36001"
+        os.environ["POD_IP"] = "127.0.0.1"
+        os.environ["PADDLE_PORT"] = "36001"
+
+        os.environ["SYS_JOB_ID"] = "gloo_for_cluster"
+        os.environ["PADDLE_WITH_GLOO"] = "1"
+        os.environ["PADDLE_GLOO_RENDEZVOUS"] = "3"
+        os.environ["PADDLE_GLOO_HTTP_HOST"] = "127.0.0.1"
+        os.environ["PADDLE_GLOO_HTTP_PORT"] = "30019"
+
+        role = role_maker.PaddleCloudRoleMaker()
+        role._generate_role()
+        import time
+        time.sleep(3)
+
+    def test_fs_gloo5(self):
+        plats = platform.platform()
+        if 'Linux' not in plats:
+            print("skip gloo UT on MacOS/Win")
+            return
+
+        tmp = self.mkdir()
+
+        os.environ["TRAINING_ROLE"] = "PSERVER"
+        os.environ["PADDLE_PSERVERS_IP_PORT_LIST"] = "127.0.0.1:36001"
+        os.environ["POD_IP"] = "127.0.0.1"
+        os.environ["PADDLE_PORT"] = "36001"
+        os.environ["PADDLE_TRAINERS_NUM"] = "0"
+
+        os.environ["SYS_JOB_ID"] = "gloo_for_cluster"
+        os.environ["PADDLE_WITH_GLOO"] = "2"
+        os.environ["PADDLE_GLOO_RENDEZVOUS"] = "2"
+        os.environ["PADDLE_GLOO_FS_PATH"] = tmp
+
+        role = role_maker.PaddleCloudRoleMaker()
+        role._generate_role()
+        self.case(role, "server")
+        self.case(role, "all")
+        self.clean(tmp)
+
+    def test_fs_gloo6(self):
+        plats = platform.platform()
+        if 'Linux' not in plats:
+            print("skip gloo UT on MacOS/Win")
+            return
+
+        tmp = self.mkdir()
+
+        os.environ["TRAINING_ROLE"] = "PSERVER"
+        os.environ["PADDLE_PSERVERS_IP_PORT_LIST"] = "127.0.0.1:36001"
+        os.environ["POD_IP"] = "127.0.0.1"
+        os.environ["PADDLE_PORT"] = "36001"
+        os.environ["PADDLE_TRAINERS_NUM"] = "0"
+
+        os.environ["SYS_JOB_ID"] = "gloo_for_cluster"
+
+        os.environ["PADDLE_WITH_GLOO"] = "2"
+        os.environ["PADDLE_GLOO_RENDEZVOUS"] = "1"
+        os.environ["PADDLE_GLOO_FS_NAME"] = "NULL"
+        os.environ["PADDLE_GLOO_FS_UGI"] = "NULL"
+        os.environ["PADDLE_GLOO_FS_PATH"] = tmp
+
+        role = role_maker.PaddleCloudRoleMaker()
+        role._generate_role()
+        self.case(role, "server")
+        self.case(role, "all")
+        self.clean(tmp)
+
+    def test_fs_gloo7(self):
+        plats = platform.platform()
+        if 'Linux' not in plats:
+            print("skip gloo UT on MacOS/Win")
+            return
+
+        os.environ["TRAINING_ROLE"] = "PSERVER"
+        os.environ["PADDLE_PSERVERS_IP_PORT_LIST"] = "127.0.0.1:36001"
+        os.environ["POD_IP"] = "127.0.0.1"
+        os.environ["PADDLE_PORT"] = "36001"
+        os.environ["PADDLE_TRAINERS_NUM"] = "0"
+
+        os.environ["SYS_JOB_ID"] = "gloo_for_cluster"
+
+        os.environ["PADDLE_WITH_GLOO"] = "1"
+        os.environ["PADDLE_GLOO_RENDEZVOUS"] = "5"
+
+        role = role_maker.PaddleCloudRoleMaker()
+        self.assertRaises(ValueError, role._generate_role)
+
+    def test_fs_gloo8(self):
+        plats = platform.platform()
+        if 'Linux' not in plats:
+            print("skip gloo UT on MacOS/Win")
+            return
+
+        tmp = self.mkdir()
+
+        os.environ["TRAINING_ROLE"] = "PSERVER"
+        os.environ["PADDLE_PSERVERS_IP_PORT_LIST"] = "127.0.0.1:36001"
+        os.environ["POD_IP"] = "127.0.0.1"
+        os.environ["PADDLE_PORT"] = "36001"
+        os.environ["PADDLE_TRAINERS_NUM"] = "0"
+
+        os.environ["SYS_JOB_ID"] = "gloo_for_cluster"
+
+        os.environ["PADDLE_WITH_GLOO"] = "2"
+        os.environ["PADDLE_GLOO_RENDEZVOUS"] = "1"
+        os.environ["PADDLE_GLOO_FS_NAME"] = "NULL"
+        os.environ["PADDLE_GLOO_FS_UGI"] = "NULL"
+        os.environ["PADDLE_GLOO_FS_PATH"] = tmp
+
+        def net():
+            x = paddle.fluid.layers.data(name='x', shape=[13], dtype='float32')
+            y_predict = paddle.fluid.layers.fc(input=x, size=1, act=None)
+            y = paddle.fluid.layers.data(name='y', shape=[1], dtype='float32')
+            cost = paddle.fluid.layers.square_error_cost(
+                input=y_predict, label=y)
+            avg_cost = paddle.fluid.layers.mean(cost)
+            return avg_cost
+
+        from paddle.distributed import fleet
+
+        role = role_maker.PaddleCloudRoleMaker()
+        fleet.init(role)
+        avg_cost = net()
+
+        strategy = paddle.distributed.fleet.DistributedStrategy()
+        strategy.a_sync = False
+
+        optimizer = paddle.optimizer.SGD(0.01)
+        optimizer = fleet.distributed_optimizer(optimizer, strategy)
+        optimizer.minimize(avg_cost)
+
+        comm_world = "server"
+        fleet.util().barrier(comm_world)
+
+        gather = fleet.util().all_gather(1, comm_world)
+        self.assertEqual(gather[0], 1)
+
+        all_reduce = fleet.util().all_reduce(1, "sum", comm_world)
+        self.assertEqual(1, all_reduce)
+
+        self.clean(tmp)
+
+
+class TestGlooWithCloudRoleMaker(unittest.TestCase):
+    def setUp(self):
+        os.environ["PADDLE_TRAINERS_NUM"] = "1"
+        os.environ["PADDLE_PSERVERS_IP_PORT_LIST"] = "127.0.0.1:36001"
+        os.environ["PADDLE_TRAINER_ENDPOINTS"] = "127.0.0.1:36001"
+        os.environ["POD_IP"] = "127.0.0.1"
+        os.environ["PADDLE_TRAINER_ID"] = "0"
+
+    def case(self, role, comm_world):
+        role._barrier(comm_world)
+
+        gather = role._all_gather(1, comm_world)
+        self.assertEqual(gather[0], 1)
+
+        all_reduce = role._all_reduce(1, "sum", comm_world)
+        self.assertEqual(1, all_reduce)
+
+    def mkdir(self):
+        tmp = tempfile.mkdtemp()
+        return tmp
+
+    def clean(self, tmp):
+        shutil.rmtree(tmp)
+
+    def test_hdfs_gloo(self):
+        plats = platform.platform()
+        if 'Linux' not in plats:
+            print("skip gloo UT on MacOS/Win")
+            return
+
+        tmp = self.mkdir()
+        os.environ["TRAINING_ROLE"] = "TRAINER"
+        os.environ["SYS_JOB_ID"] = "gloo_for_cluster"
+        os.environ["PADDLE_WITH_GLOO"] = "1"
+        os.environ["PADDLE_GLOO_RENDEZVOUS"] = "1"
+        os.environ["PADDLE_GLOO_FS_NAME"] = "NULL"
+        os.environ["PADDLE_GLOO_FS_UGI"] = "NULL"
+        os.environ["PADDLE_GLOO_FS_PATH"] = tmp
+
+        role = role_maker.PaddleCloudRoleMaker()
+        role._generate_role()
+        self.case(role, "worker")
+        self.clean(tmp)
+
+    def test_fs_gloo(self):
+        plats = platform.platform()
+        if 'Linux' not in plats:
+            print("skip gloo UT on MacOS/Win")
+            return
+
+        tmp = self.mkdir()
+        os.environ["TRAINING_ROLE"] = "TRAINER"
+        os.environ["SYS_JOB_ID"] = "gloo_for_cluster"
+        os.environ["PADDLE_WITH_GLOO"] = "1"
+        os.environ["PADDLE_GLOO_RENDEZVOUS"] = "2"
+        os.environ["PADDLE_GLOO_FS_PATH"] = tmp
+
+        role = role_maker.PaddleCloudRoleMaker()
+        role._generate_role()
+        self.case(role, "worker")
+        self.clean(tmp)
+
+    def test_fs_gloo2(self):
+        plats = platform.platform()
+        if 'Linux' not in plats:
+            print("skip gloo UT on MacOS/Win")
+            return
+
+        tmp = self.mkdir()
+        os.environ["TRAINING_ROLE"] = "PSERVER"
+        os.environ["PADDLE_PSERVERS_IP_PORT_LIST"] = "127.0.0.1:36001"
+        os.environ["POD_IP"] = "127.0.0.1"
+        os.environ["PADDLE_PORT"] = "36001"
+
+        os.environ["SYS_JOB_ID"] = "gloo_for_cluster"
+        os.environ["PADDLE_WITH_GLOO"] = "1"
+        os.environ["PADDLE_GLOO_RENDEZVOUS"] = "2"
+        os.environ["PADDLE_GLOO_FS_PATH"] = tmp
+
+        role = role_maker.PaddleCloudRoleMaker()
+        role._generate_role()
+        self.case(role, "server")
+        self.clean(tmp)
+
+    def test_fs_gloo3(self):
+        plats = platform.platform()
+        if 'Linux' not in plats:
+            print("skip gloo UT on MacOS/Win")
+            return
+
+        tmp = self.mkdir()
+        os.environ["TRAINING_ROLE"] = "PSERVER"
+        os.environ["PADDLE_PSERVERS_IP_PORT_LIST"] = "127.0.0.1:36001"
+        os.environ["POD_IP"] = "127.0.0.1"
+        os.environ["PADDLE_PORT"] = "36001"
+
+        os.environ["SYS_JOB_ID"] = "gloo_for_cluster"
+        os.environ["PADDLE_WITH_GLOO"] = "1"
+        os.environ["PADDLE_GLOO_RENDEZVOUS"] = "1"
+        os.environ["PADDLE_GLOO_FS_NAME"] = "NULL"
+        os.environ["PADDLE_GLOO_FS_UGI"] = "NULL"
+        os.environ["PADDLE_GLOO_FS_PATH"] = tmp
+
+        role = role_maker.PaddleCloudRoleMaker()
+        role._generate_role()
+        self.case(role, "server")
+        self.clean(tmp)
+
+    def test_fs_gloo4(self):
+        plats = platform.platform()
+        if 'Linux' not in plats:
+            print("skip gloo UT on MacOS/Win")
+            return
+
+        os.environ["TRAINING_ROLE"] = "PSERVER"
+        os.environ["PADDLE_PSERVERS_IP_PORT_LIST"] = "127.0.0.1:36001"
+        os.environ["POD_IP"] = "127.0.0.1"
+        os.environ["PADDLE_PORT"] = "36001"
+
+        os.environ["SYS_JOB_ID"] = "gloo_for_cluster"
+        os.environ["PADDLE_WITH_GLOO"] = "1"
+        os.environ["PADDLE_GLOO_RENDEZVOUS"] = "3"
+        os.environ["PADDLE_GLOO_HTTP_HOST"] = "127.0.0.1"
+        os.environ["PADDLE_GLOO_HTTP_PORT"] = "30019"
+
+        role = role_maker.PaddleCloudRoleMaker()
+        role._generate_role()
+        import time
+        time.sleep(3)
+
+    def test_fs_gloo5(self):
+        plats = platform.platform()
+        if 'Linux' not in plats:
+            print("skip gloo UT on MacOS/Win")
+            return
+
+        tmp = self.mkdir()
+
+        os.environ["TRAINING_ROLE"] = "PSERVER"
+        os.environ["PADDLE_PSERVERS_IP_PORT_LIST"] = "127.0.0.1:36001"
+        os.environ["POD_IP"] = "127.0.0.1"
+        os.environ["PADDLE_PORT"] = "36001"
+        os.environ["PADDLE_TRAINERS_NUM"] = "0"
+
+        os.environ["SYS_JOB_ID"] = "gloo_for_cluster"
+        os.environ["PADDLE_WITH_GLOO"] = "2"
+        os.environ["PADDLE_GLOO_RENDEZVOUS"] = "2"
+        os.environ["PADDLE_GLOO_FS_PATH"] = tmp
+
+        role = role_maker.PaddleCloudRoleMaker()
+        role._generate_role()
+        self.case(role, "server")
+        self.case(role, "all")
+        self.clean(tmp)
+
+    def test_fs_gloo6(self):
+        plats = platform.platform()
+        if 'Linux' not in plats:
+            print("skip gloo UT on MacOS/Win")
+            return
+
+        tmp = self.mkdir()
+
+        os.environ["TRAINING_ROLE"] = "PSERVER"
+        os.environ["PADDLE_PSERVERS_IP_PORT_LIST"] = "127.0.0.1:36001"
+        os.environ["POD_IP"] = "127.0.0.1"
+        os.environ["PADDLE_PORT"] = "36001"
+        os.environ["PADDLE_TRAINERS_NUM"] = "0"
+
+        os.environ["SYS_JOB_ID"] = "gloo_for_cluster"
+
+        os.environ["PADDLE_WITH_GLOO"] = "2"
+        os.environ["PADDLE_GLOO_RENDEZVOUS"] = "1"
+        os.environ["PADDLE_GLOO_FS_NAME"] = "NULL"
+        os.environ["PADDLE_GLOO_FS_UGI"] = "NULL"
+        os.environ["PADDLE_GLOO_FS_PATH"] = tmp
+
+        role = role_maker.PaddleCloudRoleMaker()
+        role._generate_role()
+        self.case(role, "server")
+        self.case(role, "all")
+        self.clean(tmp)
+
+    def test_fs_gloo7(self):
+        plats = platform.platform()
+        if 'Linux' not in plats:
+            print("skip gloo UT on MacOS/Win")
+            return
+
+        os.environ["TRAINING_ROLE"] = "PSERVER"
+        os.environ["PADDLE_PSERVERS_IP_PORT_LIST"] = "127.0.0.1:36001"
+        os.environ["POD_IP"] = "127.0.0.1"
+        os.environ["PADDLE_PORT"] = "36001"
+        os.environ["PADDLE_TRAINERS_NUM"] = "0"
+
+        os.environ["SYS_JOB_ID"] = "gloo_for_cluster"
+
+        os.environ["PADDLE_WITH_GLOO"] = "1"
+        os.environ["PADDLE_GLOO_RENDEZVOUS"] = "5"
+
+        role = role_maker.PaddleCloudRoleMaker()
+        self.assertRaises(ValueError, role._generate_role)
+
+    def test_hdfs_gloo_v2(self):
+        plats = platform.platform()
+        if 'Linux' not in plats:
+            print("skip gloo UT on MacOS/Win")
+            return
+
+        os.environ["TRAINING_ROLE"] = "TRAINER"
+        os.environ["SYS_JOB_ID"] = "gloo_for_cluster"
+        os.environ["PADDLE_WITH_GLOO"] = "1"
+        os.environ["PADDLE_GLOO_RENDEZVOUS"] = "1"
+        os.environ["PADDLE_GLOO_FS_NAME"] = ""
+        os.environ["PADDLE_GLOO_FS_UGI"] = ""
+        os.environ["PADDLE_GLOO_FS_PATH"] = ""
+
+        role = role_maker.PaddleCloudRoleMaker()
+        self.assertRaises(ValueError, role._generate_role)
+
+    def test_fs_gloo_v2(self):
+        plats = platform.platform()
+        if 'Linux' not in plats:
+            print("skip gloo UT on MacOS/Win")
+            return
+
+        os.environ["TRAINING_ROLE"] = "PSERVER"
+        os.environ["PADDLE_PSERVERS_IP_PORT_LIST"] = "127.0.0.1:36001"
+        os.environ["POD_IP"] = "127.0.0.1"
+        os.environ["PADDLE_PORT"] = "36001"
+        os.environ["PADDLE_TRAINERS_NUM"] = "0"
+
+        os.environ["SYS_JOB_ID"] = "gloo_for_cluster"
+        os.environ["PADDLE_WITH_GLOO"] = "1"
+        os.environ["PADDLE_GLOO_RENDEZVOUS"] = "2"
+        os.environ["PADDLE_GLOO_FS_PATH"] = ""
+
+        role = role_maker.PaddleCloudRoleMaker()
+        self.assertRaises(ValueError, role._generate_role)
+
+    def test_http_gloo_v2(self):
+        plats = platform.platform()
+        if 'Linux' not in plats:
+            print("skip gloo UT on MacOS/Win")
+            return
+
+        os.environ["TRAINING_ROLE"] = "PSERVER"
+        os.environ["PADDLE_PSERVERS_IP_PORT_LIST"] = "127.0.0.1:36001"
+        os.environ["POD_IP"] = "127.0.0.1"
+        os.environ["PADDLE_PORT"] = "36001"
+
+        os.environ["SYS_JOB_ID"] = "gloo_for_cluster"
+        os.environ["PADDLE_WITH_GLOO"] = "1"
+        os.environ["PADDLE_GLOO_RENDEZVOUS"] = "3"
+        os.environ["PADDLE_GLOO_HTTP_HOST"] = ""
+        os.environ["PADDLE_GLOO_HTTP_PORT"] = ""
+
+        role = role_maker.PaddleCloudRoleMaker()
+        self.assertRaises(ValueError, role._generate_role)
+
+    def test_fs_gloo8(self):
+        plats = platform.platform()
+        if 'Linux' not in plats:
+            print("skip gloo UT on MacOS/Win")
+            return
+
+        tmp = self.mkdir()
+
+        os.environ["TRAINING_ROLE"] = "PSERVER"
+        os.environ["PADDLE_PSERVERS_IP_PORT_LIST"] = "127.0.0.1:36001"
+        os.environ["POD_IP"] = "127.0.0.1"
+        os.environ["PADDLE_PORT"] = "36001"
+        os.environ["PADDLE_TRAINERS_NUM"] = "0"
+
+        os.environ["SYS_JOB_ID"] = "gloo_for_cluster"
+
+        os.environ["PADDLE_WITH_GLOO"] = "2"
+        os.environ["PADDLE_GLOO_RENDEZVOUS"] = "1"
+        os.environ["PADDLE_GLOO_FS_NAME"] = "NULL"
+        os.environ["PADDLE_GLOO_FS_UGI"] = "NULL"
+        os.environ["PADDLE_GLOO_FS_PATH"] = tmp
+
+        def net():
+            x = paddle.fluid.layers.data(name='x', shape=[13], dtype='float32')
+            y_predict = paddle.fluid.layers.fc(input=x, size=1, act=None)
+            y = paddle.fluid.layers.data(name='y', shape=[1], dtype='float32')
+            cost = paddle.fluid.layers.square_error_cost(
+                input=y_predict, label=y)
+            avg_cost = paddle.fluid.layers.mean(cost)
+            return avg_cost
+
+        from paddle.distributed import fleet
+
+        role = role_maker.PaddleCloudRoleMaker()
+        fleet.init(role)
+        avg_cost = net()
+
+        strategy = paddle.distributed.fleet.DistributedStrategy()
+        strategy.a_sync = False
+
+        optimizer = paddle.optimizer.SGD(0.01)
+        optimizer = fleet.distributed_optimizer(optimizer, strategy)
+        optimizer.minimize(avg_cost)
+
+        comm_world = "server"
+        fleet.util().barrier(comm_world)
+
+        gather = fleet.util().all_gather(1, comm_world)
+        self.assertEqual(gather[0], 1)
+
+        all_reduce = fleet.util().all_reduce(1, "sum", comm_world)
+        self.assertEqual(1, all_reduce)
+
+        self.clean(tmp)
 
 
 if __name__ == "__main__":

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

@@ -59,7 +59,7 @@ class TestFleetUtil(unittest.TestCase):
         import paddle.distributed.fleet.base.role_maker as role_maker
         role = role_maker.PaddleCloudRoleMaker(is_collective=True)
         fleet.init(role)
-        default_util = fleet.util
+        default_util = fleet.util()
         self.assertEqual(default_util, None)
 
     def test_set_user_defined_util(self):
@@ -76,8 +76,8 @@ class TestFleetUtil(unittest.TestCase):
         role = role_maker.PaddleCloudRoleMaker(is_collective=True)
         fleet.init(role)
         my_util = UserDefinedUtil()
-        fleet.util = my_util
-        user_id = fleet.util.get_user_id()
+        fleet.set_util(my_util)
+        user_id = fleet.util().get_user_id()
         self.assertEqual(user_id, 10)
 
     def test_fs(self):
@@ -88,97 +88,6 @@ class TestFleetUtil(unittest.TestCase):
         self.assertFalse(fs.need_upload_download())
         fleet_util._set_file_system(fs)
 
-    def test_barrier(self):
-        try:
-            import netifaces
-        except:
-            print("warning: no netifaces, skip test_barrier")
-            return
-
-        gloo = fluid.core.Gloo()
-        gloo.set_rank(0)
-        gloo.set_size(1)
-        gloo.set_prefix("123")
-        gloo.set_iface("lo")
-        gloo.set_hdfs_store("./tmp_test_fleet_barrier", "", "")
-        gloo.init()
-
-        role = role_maker.UserDefinedRoleMaker(
-            is_collective=False,
-            init_gloo=False,
-            current_id=0,
-            role=role_maker.Role.SERVER,
-            worker_endpoints=["127.0.0.1:6003"],
-            server_endpoints=["127.0.0.1:6001"])
-        role._node_type_comm = gloo
-        role._role_is_generated = True
-        fleet_util._set_role_maker(role)
-
-        fleet_util.barrier("worker")
-
-    def test_all_reduce(self):
-        try:
-            import netifaces
-        except:
-            print("warning: no netifaces, skip test_all_reduce")
-            return
-
-        gloo = fluid.core.Gloo()
-        gloo.set_rank(0)
-        gloo.set_size(1)
-        gloo.set_prefix("123")
-        gloo.set_iface("lo")
-        gloo.set_hdfs_store("./tmp_test_fleet_reduce", "", "")
-        gloo.init()
-
-        role = role_maker.UserDefinedRoleMaker(
-            is_collective=False,
-            init_gloo=False,
-            current_id=0,
-            role=role_maker.Role.WORKER,
-            worker_endpoints=["127.0.0.1:6003"],
-            server_endpoints=["127.0.0.1:6001"])
-        role._node_type_comm = gloo
-        role._role_is_generated = True
-        fleet_util._set_role_maker(role)
-
-        output = fleet_util.all_reduce(1, "sum", comm_world="server")
-        print(output)
-
-    # self.assertEqual(output, 1)
-
-    def test_all_gather(self):
-        try:
-            import netifaces
-        except:
-            print("warning: no netifaces, skip test_all_gather")
-            return
-
-        gloo = fluid.core.Gloo()
-        gloo.set_rank(0)
-        gloo.set_size(1)
-        gloo.set_prefix("123")
-        gloo.set_iface("lo")
-        gloo.set_hdfs_store("./tmp_test_fleet_reduce", "", "")
-        gloo.init()
-
-        role = role_maker.UserDefinedRoleMaker(
-            is_collective=False,
-            init_gloo=False,
-            current_id=0,
-            role=role_maker.Role.SERVER,
-            worker_endpoints=["127.0.0.1:6003"],
-            server_endpoints=["127.0.0.1:6001"])
-        role._node_type_comm = gloo
-        role._all_comm = gloo
-        role._role_is_generated = True
-        fleet_util._set_role_maker(role)
-
-        output = fleet_util.all_gather(1, comm_world="all")
-        print(output)
-        # self.assertTrue(len(output) == 1 and output[0] == 1)
-        self.assertRaises(Exception, fleet_util.all_gather, 1, "test")
-
     def download_files(self):
         path = download(self.proto_data_url, self.module_name,
                         self.proto_data_md5)

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

+#   Copyright (c) 2020 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.
+
+from __future__ import print_function
+
+import unittest
+
+import paddle
+import paddle.fluid as fluid
+import paddle.fluid.layers as layers
+from paddle.fluid.backward import calc_gradient
+import numpy as np
+
+
+class ConvBNLayer(fluid.Layer):
+    def __init__(self,
+                 num_channels,
+                 num_filters,
+                 filter_size,
+                 stride=1,
+                 groups=1,
+                 act=None,
+                 use_cudnn=False):
+        super(ConvBNLayer, self).__init__()
+
+        self._conv = fluid.dygraph.Conv2D(
+            num_channels=num_channels,
+            num_filters=num_filters,
+            filter_size=filter_size,
+            stride=stride,
+            padding=(filter_size - 1) // 2,
+            groups=groups,
+            act=None,
+            bias_attr=False,
+            use_cudnn=use_cudnn)
+
+        self._batch_norm = fluid.dygraph.BatchNorm(num_filters, act=act)
+
+    def forward(self, inputs):
+        y = self._conv(inputs)
+        y = self._batch_norm(y)
+        return y
+
+
+def create_program():
+    main = fluid.Program()
+    startup = fluid.Program()
+    with fluid.program_guard(main, startup):
+        x = fluid.data(name='img', shape=[-1, 3, 224, 224])
+        x.stop_gradient = False
+        x = fluid.layers.prelu(x, mode="channel")
+        conv = ConvBNLayer(
+            num_channels=3,
+            num_filters=3,
+            filter_size=1,
+            act='relu',
+            use_cudnn=True)
+        y = conv(x) + x
+
+        loss = fluid.layers.reduce_sum(y)
+
+        sgd = fluid.optimizer.SGD(learning_rate=0.01)
+        sgd.minimize(loss)
+
+    return loss, main, startup, conv._conv.weight
+
+
+class TestInplaceAddto(unittest.TestCase):
+    def test_result(self):
+        def run_program(enable_addto):
+            np.random.seed(10)
+            paddle.manual_seed(10)
+            paddle.framework.random._manual_program_seed(10)
+            if fluid.core.is_compiled_with_cuda():
+                fluid.set_flags({"FLAGS_cudnn_deterministic": True})
+            fluid.set_flags({"FLAGS_max_inplace_grad_add": 2})
+            loss, main, startup, w = create_program()
+            place = fluid.CUDAPlace(0) if fluid.core.is_compiled_with_cuda(
+            ) else fluid.CPUPlace()
+            exe = fluid.Executor(place)
+
+            strategy = fluid.BuildStrategy()
+            strategy.enable_addto = enable_addto
+            compiled = fluid.CompiledProgram(main).with_data_parallel(
+                loss_name=loss.name, build_strategy=strategy)
+
+            exe.run(startup)
+            img = np.random.uniform(-128, 128,
+                                    [8, 3, 224, 224]).astype(np.float32)
+            for i in range(2):
+                res = exe.run(compiled,
+                              feed={'img': img},
+                              fetch_list=[loss.name, w.name])
+            return res
+
+        res1, w1 = run_program(True)
+        res2, w2 = run_program(False)
+        print(res1, res2)
+        self.assertTrue(np.array_equal(res1, res2))
+
+
+if __name__ == "__main__":
+    unittest.main()

python/paddle/fluid/tests/unittests/test_top_k_v2_op.py

@@ -63,28 +63,28 @@ class TestTopkOp(OpTest):
         self.check_grad(set(['X']), 'Out')
 
 
-class TestTopOp1(TestTopkOp):
+class TestTopkOp1(TestTopkOp):
     def init_args(self):
         self.k = 3
         self.axis = 0
         self.largest = True
 
 
-class TestTopOp2(TestTopkOp):
+class TestTopkOp2(TestTopkOp):
     def init_args(self):
         self.k = 3
         self.axis = 0
         self.largest = False
 
 
-class TestTopOp3(TestTopkOp):
+class TestTopkOp3(TestTopkOp):
     def init_args(self):
         self.k = 4
         self.axis = 0
         self.largest = False
 
 
-class TestTopOp4(TestTopkOp):
+class TestTopkOp4(TestTopkOp):
     def init_args(self):
         self.k = 4
         self.axis = 0
@@ -189,6 +189,8 @@ class TestTopKAPI(unittest.TestCase):
             result1 = paddle.topk(input_tensor, k=2)
             result2 = paddle.topk(input_tensor, k=2, axis=-1)
             result3 = paddle.topk(input_tensor, k=k_tensor, axis=1)
+            self.assertEqual(result3[0].shape, (6, -1, 8))
+            self.assertEqual(result3[1].shape, (6, -1, 8))
             result4 = paddle.topk(input_tensor, k=2, axis=1, largest=False)
             result5 = paddle.topk(input_tensor, k=2, axis=-1, largest=False)
             result6 = paddle.topk(large_input_tensor, k=1, axis=-1)
@@ -239,6 +241,15 @@ class TestTopKAPI(unittest.TestCase):
             self.run_dygraph(place)
             self.run_static(place)
 
+    def test_errors(self):
+        paddle.disable_static()
+        x = paddle.to_tensor([1, 2, 3])
+        with self.assertRaises(BaseException):
+            paddle.topk(x, k=-1)
+
+        with self.assertRaises(BaseException):
+            paddle.topk(x, k=0)
+
 
 if __name__ == "__main__":
     unittest.main()

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

@@ -474,6 +474,141 @@ class TestTransformer(unittest.TestCase):
             trans_output = transformer(src, tgt, src_mask, tgt_mask,
                                        memory_mask)
 
+    def test_transformer_attr_1(self):
+        batch_size, d_model, n_head, dim_feedforward, dropout, _, _, source_length, target_length = generate_basic_params(
+            mode="decoder_layer")
+
+        # batch_size, source_length, target_length, d_model, n_head = 4, 8, 8, 64, 8
+        with fluid.dygraph.guard(fluid.CPUPlace()):
+            transformer = Transformer(
+                d_model,
+                n_head,
+                dim_feedforward=dim_feedforward,
+                dropout=dropout,
+                weight_attr=[None],
+                bias_attr=[False])
+            src = paddle.to_variable(
+                np.random.rand(batch_size, source_length, d_model).astype(
+                    "float32"))
+            tgt = paddle.to_variable(
+                np.random.rand(batch_size, target_length, d_model).astype(
+                    "float32"))
+            src_mask = np.zeros((batch_size, n_head, source_length,
+                                 source_length)).astype("float32")
+            src_mask[0][0][0][0] = -np.inf
+            src_mask = paddle.to_variable(src_mask)
+            tgt_mask = np.zeros((batch_size, n_head, target_length,
+                                 target_length)).astype("float32")
+            tgt_mask[0][0][0][0] = -1e9
+            memory_mask = np.zeros((batch_size, n_head, target_length,
+                                    source_length)).astype("float32")
+            memory_mask[0][0][0][0] = -1e9
+            tgt_mask, memory_mask = paddle.to_variable(
+                tgt_mask), paddle.to_variable(memory_mask)
+            trans_output = transformer(src, tgt, src_mask, tgt_mask,
+                                       memory_mask)
+
+    def test_transformer_attr_2(self):
+        batch_size, d_model, n_head, dim_feedforward, dropout, _, _, source_length, target_length = generate_basic_params(
+            mode="decoder_layer")
+
+        # batch_size, source_length, target_length, d_model, n_head = 4, 8, 8, 64, 8
+        with fluid.dygraph.guard(fluid.CPUPlace()):
+            transformer = Transformer(
+                d_model,
+                n_head,
+                dim_feedforward=dim_feedforward,
+                dropout=dropout,
+                weight_attr=[None, None],
+                bias_attr=[False, False])
+            src = paddle.to_variable(
+                np.random.rand(batch_size, source_length, d_model).astype(
+                    "float32"))
+            tgt = paddle.to_variable(
+                np.random.rand(batch_size, target_length, d_model).astype(
+                    "float32"))
+            src_mask = np.zeros((batch_size, n_head, source_length,
+                                 source_length)).astype("float32")
+            src_mask[0][0][0][0] = -np.inf
+            src_mask = paddle.to_variable(src_mask)
+            tgt_mask = np.zeros((batch_size, n_head, target_length,
+                                 target_length)).astype("float32")
+            tgt_mask[0][0][0][0] = -1e9
+            memory_mask = np.zeros((batch_size, n_head, target_length,
+                                    source_length)).astype("float32")
+            memory_mask[0][0][0][0] = -1e9
+            tgt_mask, memory_mask = paddle.to_variable(
+                tgt_mask), paddle.to_variable(memory_mask)
+            trans_output = transformer(src, tgt, src_mask, tgt_mask,
+                                       memory_mask)
+
+    def test_transformer_attr_3(self):
+        batch_size, d_model, n_head, dim_feedforward, dropout, _, _, source_length, target_length = generate_basic_params(
+            mode="decoder_layer")
+
+        # batch_size, source_length, target_length, d_model, n_head = 4, 8, 8, 64, 8
+        with fluid.dygraph.guard(fluid.CPUPlace()):
+            transformer = Transformer(
+                d_model,
+                n_head,
+                dim_feedforward=dim_feedforward,
+                dropout=dropout,
+                weight_attr=[None, None, None],
+                bias_attr=[False, False, True])
+            src = paddle.to_variable(
+                np.random.rand(batch_size, source_length, d_model).astype(
+                    "float32"))
+            tgt = paddle.to_variable(
+                np.random.rand(batch_size, target_length, d_model).astype(
+                    "float32"))
+            src_mask = np.zeros((batch_size, n_head, source_length,
+                                 source_length)).astype("float32")
+            src_mask[0][0][0][0] = -np.inf
+            src_mask = paddle.to_variable(src_mask)
+            tgt_mask = np.zeros((batch_size, n_head, target_length,
+                                 target_length)).astype("float32")
+            tgt_mask[0][0][0][0] = -1e9
+            memory_mask = np.zeros((batch_size, n_head, target_length,
+                                    source_length)).astype("float32")
+            memory_mask[0][0][0][0] = -1e9
+            tgt_mask, memory_mask = paddle.to_variable(
+                tgt_mask), paddle.to_variable(memory_mask)
+            trans_output = transformer(src, tgt, src_mask, tgt_mask,
+                                       memory_mask)
+
+    def test_transformer_attr_boolean(self):
+        batch_size, d_model, n_head, dim_feedforward, dropout, _, _, source_length, target_length = generate_basic_params(
+            mode="decoder_layer")
+
+        # batch_size, source_length, target_length, d_model, n_head = 4, 8, 8, 64, 8
+        with fluid.dygraph.guard(fluid.CPUPlace()):
+            transformer = Transformer(
+                d_model,
+                n_head,
+                dim_feedforward=dim_feedforward,
+                dropout=dropout,
+                bias_attr=False)
+            src = paddle.to_variable(
+                np.random.rand(batch_size, source_length, d_model).astype(
+                    "float32"))
+            tgt = paddle.to_variable(
+                np.random.rand(batch_size, target_length, d_model).astype(
+                    "float32"))
+            src_mask = np.zeros((batch_size, n_head, source_length,
+                                 source_length)).astype("float32")
+            src_mask[0][0][0][0] = -np.inf
+            src_mask = paddle.to_variable(src_mask)
+            tgt_mask = np.zeros((batch_size, n_head, target_length,
+                                 target_length)).astype("float32")
+            tgt_mask[0][0][0][0] = -1e9
+            memory_mask = np.zeros((batch_size, n_head, target_length,
+                                    source_length)).astype("float32")
+            memory_mask[0][0][0][0] = -1e9
+            tgt_mask, memory_mask = paddle.to_variable(
+                tgt_mask), paddle.to_variable(memory_mask)
+            trans_output = transformer(src, tgt, src_mask, tgt_mask,
+                                       memory_mask)
+
 
 if __name__ == "__main__":
     unittest.main()

python/paddle/nn/layer/transformer.py

@@ -53,7 +53,22 @@ def _convert_param_attr_to_list(param_attr, n):
     if isinstance(param_attr, (list, tuple)):
         assert len(param_attr) == n, (
             "length of param_attr should be %d when it is a list/tuple" % n)
-        param_attrs = [ParamAttr._to_attr(attr) for attr in param_attr]
+        param_attrs = []
+        for attr in param_attr:
+            if isinstance(attr, bool):
+                if attr:
+                    param_attrs.append(ParamAttr._to_attr(None))
+                else:
+                    param_attrs.append(False)
+            else:
+                param_attrs.append(ParamAttr._to_attr(attr))
+        # param_attrs = [ParamAttr._to_attr(attr) for attr in param_attr]
+    elif isinstance(param_attr, bool):
+        param_attrs = []
+        if param_attr:
+            param_attrs = [ParamAttr._to_attr(None) for i in range(n)]
+        else:
+            param_attrs = [False] * n
     else:
         param_attrs = []
         attr = ParamAttr._to_attr(param_attr)
@@ -417,7 +432,7 @@ class TransformerEncoderLayer(Layer):
             Otherwise, MHA and FFN both use it as `weight_attr` to create parameters.
             Default: None, which means the default weight parameter property is used.
             See usage for details in :code:`ParamAttr` . 
-        bias_attr (ParamAttr|tuple, optional): To specify the bias parameter property.
+        bias_attr (ParamAttr|tuple|bool, optional): To specify the bias parameter property.
             If it is a tuple, `bias_attr[0]` would be used as `bias_attr` for
             MHA, and `bias_attr[1]` would be used as `bias_attr` for linear in FFN.
             Otherwise, MHA and FFN both use it as `bias_attr` to create parameters.
@@ -986,22 +1001,31 @@ class Transformer(Layer):
             Otherwise, no pre-process and post-precess includes dropout, residual
             connection, layer normalization. Default False
         weight_attr(ParamAttr|tuple, optional): To specify the weight parameter property.
-            If it is a tuple, `weight_attr[0]` would be used as `weight_attr` for
-            self attention, `weight_attr[1]` would be used as `weight_attr` for
-            cross attention, and `weight_attr[2]` would be used as `weight_attr`
-            for linear in FFN. Otherwise, the three sub-layers all uses it as
-            `weight_attr` to create parameters. Default: None, which means the
-            default weight parameter property is used. See usage for details
+            If it is a tuple, the length of `weight_attr` could be 1, 2 or 3. If it is 3, 
+            `weight_attr[0]` would be used as `weight_attr` for self attention, `weight_attr[1]` 
+            would be used as `weight_attr` for cross attention of `TransformerDecoder`, 
+            and `weight_attr[2]` would be used as `weight_attr` for linear in FFN. 
+            If it is 2, `weight_attr[0]` would be used as `weight_attr` both for self attention 
+            and cross attntion and `weight_attr[1]` would be used as `weight_attr` for 
+            linear in FFN. If it is 1, `weight_attr[0]` would be used as `weight_attr` 
+            for self attention, cross attention and linear in FFN. Otherwise, 
+            the three sub-layers all uses it as `weight_attr` to create parameters. 
+            Default: None, which means the default weight parameter property is used. 
+            See usage for details
             in :code:`ParamAttr` . 
         bias_attr (ParamAttr|tuple, optional): To specify the bias parameter property.
-            If it is a tuple, `bias_attr[0]` would be used as `bias_attr` for
-            self attention, `bias_attr[1]` would be used as `bias_attr` for
-            cross attention, and `bias_attr[2]` would be used as `bias_attr`
-            for linear in FFN. Otherwise, the three sub-layers all uses it as
-            `bias_attr` to create parameters. The `False` value means the
-            corresponding layer would not have trainable bias parameter. See
-            usage for details in :code:`ParamAttr` . Default: None,which means
-            the default bias parameter property is used.
+            If it is a tuple, the length of `bias_attr` could be 1, 2 or 3. If it is 3, 
+            `bias_attr[0]` would be used as `bias_attr` for self attention, `bias_attr[1]` 
+            would be used as `bias_attr` for cross attention of `TransformerDecoder`, 
+            and `bias_attr[2]` would be used as `bias_attr` for linear in FFN. 
+            If it is 2, `bias_attr[0]` would be used as `bias_attr` both for self attention 
+            and cross attntion and `bias_attr[1]` would be used as `bias_attr` for 
+            linear in FFN. If it is 1, `bias_attr[0]` would be used as `bias_attr` 
+            for self attention, cross attention and linear in FFN. Otherwise, 
+            the three sub-layers all uses it as `bias_attr` to create parameters. 
+            The `False` value means the corresponding layer would not have trainable 
+            bias parameter. See usage for details in :code:`ParamAttr` . 
+            Default: None,which means the default bias parameter property is used.
         custom_encoder (Layer): If custom encoder is provided, use it as the encoder.
             Default None
         custom_decoder (Layer): If custom decoder is provided, use it as the decoder.
@@ -1049,13 +1073,51 @@ class Transformer(Layer):
                  custom_decoder=None):
         super(Transformer, self).__init__()
 
+        if isinstance(bias_attr, (list, tuple)):
+            if len(bias_attr) == 1:
+                encoder_bias_attr = [bias_attr[0]] * 2
+                decoder_bias_attr = [bias_attr[0]] * 3
+            elif len(bias_attr) == 2:
+                encoder_bias_attr = bias_attr
+                decoder_bias_attr = [bias_attr[0], bias_attr[0], bias_attr[-1]]
+            elif len(bias_attr) == 3:
+                encoder_bias_attr = [bias_attr[0], bias_attr[-1]]
+                decoder_bias_attr = bias_attr
+            else:
+                assert False, (
+                    "length of bias_attr should be 1 or 2 or 3 when it is a list/tuple"
+                )
+        else:
+            encoder_bias_attr = bias_attr
+            decoder_bias_attr = bias_attr
+
+        if isinstance(weight_attr, (list, tuple)):
+            if len(weight_attr) == 1:
+                encoder_weight_attr = [weight_attr[0]] * 2
+                decoder_weight_attr = [weight_attr[0]] * 3
+            elif len(weight_attr) == 2:
+                encoder_weight_attr = weight_attr
+                decoder_weight_attr = [
+                    weight_attr[0], weight_attr[0], weight_attr[-1]
+                ]
+            elif len(weight_attr) == 3:
+                encoder_weight_attr = [weight_attr[0], weight_attr[-1]]
+                decoder_weight_attr = weight_attr
+            else:
+                assert False, (
+                    "length of weight_attr should be 1 or 2 or 3 when it is a list/tuple"
+                )
+        else:
+            encoder_weight_attr = weight_attr
+            decoder_weight_attr = weight_attr
+
         if custom_encoder is not None:
             self.encoder = custom_encoder
         else:
             encoder_layer = TransformerEncoderLayer(
                 d_model, nhead, dim_feedforward, dropout, activation,
-                attn_dropout, act_dropout, normalize_before, weight_attr,
-                bias_attr)
+                attn_dropout, act_dropout, normalize_before,
+                encoder_weight_attr, encoder_bias_attr)
             encoder_norm = LayerNorm(d_model)
             self.encoder = TransformerEncoder(encoder_layer, num_encoder_layers,
                                               encoder_norm)
@@ -1065,8 +1127,8 @@ class Transformer(Layer):
         else:
             decoder_layer = TransformerDecoderLayer(
                 d_model, nhead, dim_feedforward, dropout, activation,
-                attn_dropout, act_dropout, normalize_before, weight_attr,
-                bias_attr)
+                attn_dropout, act_dropout, normalize_before,
+                decoder_weight_attr, decoder_bias_attr)
             decoder_norm = LayerNorm(d_model)
             self.decoder = TransformerDecoder(decoder_layer, num_decoder_layers,
                                               decoder_norm)

python/paddle/optimizer/adam.py

@@ -282,14 +282,13 @@ class Adam(Optimizer):
         for param in self._parameter_list:
             if not param.trainable:
                 continue
-            if hasattr(
-                    param, "_is_sparse"
-            ) and param._is_sparse and self.regularization is not None:
-                raise RuntimeError(
-                    "Adam don't support weight_decay with sparse parameters, please set it to None."
-                )
             if param._grad_ivar() is not None:
                 grad_var = param._grad_ivar()
+                if hasattr(grad_var, "_is_sparse") and grad_var._is_sparse(
+                ) and self.regularization is not None:
+                    raise RuntimeError(
+                        "Adam don't support weight_decay with sparse parameters, please set it to None."
+                    )
                 params_grads.append((param, grad_var))
 
         optimize_ops = self._apply_optimize(

python/paddle/reader/decorator.py

@@ -42,7 +42,7 @@ import paddle.compat as cpt
 # For more details, please refer to
 # https://docs.python.org/3/library/multiprocessing.html#contexts-and-start-methods
 # https://bugs.python.org/issue33725
-if sys.version_info >= (3, 8):
+if sys.version_info >= (3, 8) and sys.platform == 'darwin':
     fork_context = multiprocessing.get_context('fork')
 else:
     fork_context = multiprocessing

python/paddle/tests/test_dataset_cifar.py

@@ -27,8 +27,10 @@ class TestCifar10Train(unittest.TestCase):
         # long time, randomly check 1 sample
         idx = np.random.randint(0, 50000)
         data, label = cifar[idx]
-        self.assertTrue(len(data.shape) == 1)
-        self.assertTrue(data.shape[0] == 3072)
+        self.assertTrue(len(data.shape) == 3)
+        self.assertTrue(data.shape[0] == 3)
+        self.assertTrue(data.shape[1] == 32)
+        self.assertTrue(data.shape[2] == 32)
         self.assertTrue(0 <= int(label) <= 9)
 
 
@@ -41,8 +43,10 @@ class TestCifar10Test(unittest.TestCase):
         # long time, randomly check 1 sample
         idx = np.random.randint(0, 10000)
         data, label = cifar[idx]
-        self.assertTrue(len(data.shape) == 1)
-        self.assertTrue(data.shape[0] == 3072)
+        self.assertTrue(len(data.shape) == 3)
+        self.assertTrue(data.shape[0] == 3)
+        self.assertTrue(data.shape[1] == 32)
+        self.assertTrue(data.shape[2] == 32)
         self.assertTrue(0 <= int(label) <= 9)
 
 
@@ -55,8 +59,10 @@ class TestCifar100Train(unittest.TestCase):
         # long time, randomly check 1 sample
         idx = np.random.randint(0, 50000)
         data, label = cifar[idx]
-        self.assertTrue(len(data.shape) == 1)
-        self.assertTrue(data.shape[0] == 3072)
+        self.assertTrue(len(data.shape) == 3)
+        self.assertTrue(data.shape[0] == 3)
+        self.assertTrue(data.shape[1] == 32)
+        self.assertTrue(data.shape[2] == 32)
         self.assertTrue(0 <= int(label) <= 99)
 
 
@@ -69,8 +75,10 @@ class TestCifar100Test(unittest.TestCase):
         # long time, randomly check 1 sample
         idx = np.random.randint(0, 10000)
         data, label = cifar[idx]
-        self.assertTrue(len(data.shape) == 1)
-        self.assertTrue(data.shape[0] == 3072)
+        self.assertTrue(len(data.shape) == 3)
+        self.assertTrue(data.shape[0] == 3)
+        self.assertTrue(data.shape[1] == 32)
+        self.assertTrue(data.shape[2] == 32)
         self.assertTrue(0 <= int(label) <= 99)
 
 

python/paddle/tests/test_datasets.py

@@ -103,12 +103,14 @@ class TestMNISTTest(unittest.TestCase):
 
 class TestMNISTTrain(unittest.TestCase):
     def test_main(self):
-        mnist = MNIST(mode='train', chw_format=False)
+        mnist = MNIST(mode='train')
         self.assertTrue(len(mnist) == 60000)
 
         for i in range(len(mnist)):
             image, label = mnist[i]
-            self.assertTrue(image.shape[0] == 784)
+            self.assertTrue(image.shape[0] == 1)
+            self.assertTrue(image.shape[1] == 28)
+            self.assertTrue(image.shape[2] == 28)
             self.assertTrue(label.shape[0] == 1)
             self.assertTrue(0 <= int(label) <= 9)
 

python/paddle/text/datasets/uci_housing.py

@@ -17,6 +17,7 @@ from __future__ import print_function
 import six
 import numpy as np
 
+import paddle
 from paddle.io import Dataset
 from paddle.dataset.common import _check_exists_and_download
 
@@ -88,6 +89,8 @@ class UCIHousing(Dataset):
         # read dataset into memory
         self._load_data()
 
+        self.dtype = paddle.get_default_dtype()
+
     def _load_data(self, feature_num=14, ratio=0.8):
         data = np.fromfile(self.data_file, sep=' ')
         data = data.reshape(data.shape[0] // feature_num, feature_num)
@@ -103,7 +106,8 @@ class UCIHousing(Dataset):
 
     def __getitem__(self, idx):
         data = self.data[idx]
-        return np.array(data[:-1]), np.array(data[-1:])
+        return np.array(data[:-1]).astype(self.dtype), \
+                np.array(data[-1:]).astype(self.dtype)
 
     def __len__(self):
         return len(self.data)

python/paddle/utils/__init__.py

@@ -16,6 +16,7 @@ from .profiler import ProfilerOptions
 from .profiler import Profiler
 from .profiler import get_profiler
 from .deprecated import deprecated
+from .lazy_import import try_import
 from ..fluid.framework import unique_name
 from ..fluid.framework import load_op_library
 from ..fluid.framework import require_version

python/paddle/utils/lazy_import.py

+# Copyright (c) 2018 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.
+"""Lazy imports for heavy dependencies."""
+
+import importlib
+
+
+def try_import(module_name):
+    """Try importing a module, with an informative error message on failure."""
+    install_name = module_name
+    if module_name == 'cv2':
+        install_name = 'opencv-python'
+
+    try:
+        mod = importlib.import_module(module_name)
+        return mod
+    except ImportError:
+        err_msg = (
+            "Failed importing {}. This likely means that some paddle modules "
+            "requires additional dependencies that have to be "
+            "manually installed (usually with `pip install {}`). ").format(
+                module_name, install_name)
+        raise ImportError(err_msg)

python/paddle/vision/datasets/cifar.py

@@ -139,6 +139,7 @@ class Cifar10(Dataset):
 
     def __getitem__(self, idx):
         image, label = self.data[idx]
+        image = np.reshape(image, [3, 32, 32])
         if self.transform is not None:
             image = self.transform(image)
         return image, label

python/paddle/vision/datasets/folder.py

@@ -14,9 +14,9 @@
 
 import os
 import sys
-import cv2
 
 from paddle.io import Dataset
+from paddle.utils import try_import
 
 __all__ = ["DatasetFolder", "ImageFolder"]
 
@@ -191,6 +191,7 @@ IMG_EXTENSIONS = ('.jpg', '.jpeg', '.png', '.ppm', '.bmp', '.pgm', '.tif',
 
 
 def cv2_loader(path):
+    cv2 = try_import('cv2')
     return cv2.imread(path)
 
 

python/paddle/vision/datasets/mnist.py

@@ -44,8 +44,6 @@ class MNIST(Dataset):
             :attr:`download` is True. Default None
         label_path(str): path to label file, can be set None if
             :attr:`download` is True. Default None
-        chw_format(bool): If set True, the output shape is [1, 28, 28],
-            otherwise, output shape is [1, 784]. Default True.
         mode(str): 'train' or 'test' mode. Default 'train'.
         download(bool): whether to download dataset automatically if
             :attr:`image_path` :attr:`label_path` is not set. Default True
@@ -70,14 +68,12 @@ class MNIST(Dataset):
     def __init__(self,
                  image_path=None,
                  label_path=None,
-                 chw_format=True,
                  mode='train',
                  transform=None,
                  download=True):
         assert mode.lower() in ['train', 'test'], \
                 "mode should be 'train' or 'test', but got {}".format(mode)
         self.mode = mode.lower()
-        self.chw_format = chw_format
         self.image_path = image_path
         if self.image_path is None:
             assert download, "image_path is not set and downloading automatically is disabled"
@@ -139,10 +135,6 @@ class MNIST(Dataset):
                                                       cols)).astype('float32')
                     offset_img += struct.calcsize(fmt_images)
 
-                    images = images / 255.0
-                    images = images * 2.0
-                    images = images - 1.0
-
                     for i in range(buffer_size):
                         self.images.append(images[i, :])
                         self.labels.append(
@@ -150,8 +142,7 @@ class MNIST(Dataset):
 
     def __getitem__(self, idx):
         image, label = self.images[idx], self.labels[idx]
-        if self.chw_format:
-            image = np.reshape(image, [1, 28, 28])
+        image = np.reshape(image, [1, 28, 28])
         if self.transform is not None:
             image = self.transform(image)
         return image, label

python/paddle/vision/transforms/functional.py

@@ -18,10 +18,11 @@ import random
 import math
 import functools
 
-import cv2
 import numbers
 import numpy as np
 
+from paddle.utils import try_import
+
 if sys.version_info < (3, 3):
     Sequence = collections.Sequence
     Iterable = collections.Iterable
@@ -54,8 +55,8 @@ def flip(image, code):
     Accordding to the code (the type of flip), flip the input image
 
     Args:
-        image: Input image, with (H, W, C) shape
-        code: Code that indicates the type of flip.
+        image (np.ndarray): Input image, with (H, W, C) shape
+        code (int): Code that indicates the type of flip.
             -1 : Flip horizontally and vertically
             0 : Flip vertically
             1 : Flip horizontally
@@ -77,18 +78,28 @@ def flip(image, code):
             # flip horizontally
             F.flip(fake_img, 1)
     """
+    cv2 = try_import('cv2')
     return cv2.flip(image, flipCode=code)
 
 
 @keepdims
-def resize(img, size, interpolation=cv2.INTER_LINEAR):
+def resize(img, size, interpolation=1):
     """
     resize the input data to given size
 
     Args:
-        input: Input data, could be image or masks, with (H, W, C) shape
-        size: Target size of input data, with (height, width) shape.
-        interpolation: Interpolation method.
+        input (np.ndarray): Input data, could be image or masks, with (H, W, C) shape
+        size (int|list|tuple): Target size of input data, with (height, width) shape.
+        interpolation (int, optional): Interpolation method.
+            0 : cv2.INTER_NEAREST 
+            1 : cv2.INTER_LINEAR 
+            2 : cv2.INTER_CUBIC 
+            3 : cv2.INTER_AREA 
+            4 : cv2.INTER_LANCZOS4 
+            5 : cv2.INTER_LINEAR_EXACT
+            7 : cv2.INTER_MAX 
+            8 : cv2.WARP_FILL_OUTLIERS 
+            16: cv2.WARP_INVERSE_MAP 
 
     Examples:
         .. code-block:: python
@@ -102,7 +113,7 @@ def resize(img, size, interpolation=cv2.INTER_LINEAR):
 
             F.resize(fake_img, (200, 150))
     """
-
+    cv2 = try_import('cv2')
     if isinstance(interpolation, Sequence):
         interpolation = random.choice(interpolation)
 
@@ -179,6 +190,8 @@ def pad(img, padding, fill=(0, 0, 0), padding_mode='constant'):
     assert padding_mode in ['constant', 'edge', 'reflect', 'symmetric'], \
         'Expected padding mode be either constant, edge, reflect or symmetric, but got {}'.format(padding_mode)
 
+    cv2 = try_import('cv2')
+
     PAD_MOD = {
         'constant': cv2.BORDER_CONSTANT,
         'edge': cv2.BORDER_REPLICATE,
@@ -214,18 +227,22 @@ def pad(img, padding, fill=(0, 0, 0), padding_mode='constant'):
 
 
 @keepdims
-def rotate(img,
-           angle,
-           interpolation=cv2.INTER_LINEAR,
-           expand=False,
-           center=None):
+def rotate(img, angle, interpolation=1, expand=False, center=None):
     """Rotates the image by angle.
 
     Args:
         img (numpy.ndarray): Image to be rotated.
         angle (float|int): In degrees clockwise order.
-        interpolation (int, optional):
-            interpolation: Interpolation method.
+        interpolation (int, optional): Interpolation method. Default: 1.
+            0 : cv2.INTER_NEAREST 
+            1 : cv2.INTER_LINEAR 
+            2 : cv2.INTER_CUBIC 
+            3 : cv2.INTER_AREA 
+            4 : cv2.INTER_LANCZOS4 
+            5 : cv2.INTER_LINEAR_EXACT
+            7 : cv2.INTER_MAX 
+            8 : cv2.WARP_FILL_OUTLIERS 
+            16: cv2.WARP_INVERSE_MAP 
         expand (bool|optional): Optional expansion flag.
             If true, expands the output image to make it large enough to hold the entire rotated image.
             If false or omitted, make the output image the same size as the input image.
@@ -250,8 +267,9 @@ def rotate(img,
             fake_img = rotate(fake_img, 10)
             print(fake_img.shape)
     """
-    dtype = img.dtype
+    cv2 = try_import('cv2')
 
+    dtype = img.dtype
     h, w, _ = img.shape
     point = center or (w / 2, h / 2)
     M = cv2.getRotationMatrix2D(point, angle=-angle, scale=1)
@@ -312,6 +330,7 @@ def to_grayscale(img, num_output_channels=1):
             fake_img = to_grayscale(fake_img)
             print(fake_img.shape)
     """
+    cv2 = try_import('cv2')
 
     if num_output_channels == 1:
         img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)

python/paddle/vision/transforms/transforms.py

@@ -17,7 +17,6 @@ from __future__ import division
 import math
 import sys
 import random
-import cv2
 
 import numpy as np
 import numbers
@@ -26,6 +25,7 @@ import collections
 import warnings
 import traceback
 
+from paddle.utils import try_import
 from . import functional as F
 
 if sys.version_info < (3, 3):
@@ -214,7 +214,16 @@ class Resize(object):
             smaller edge of the image will be matched to this number.
             i.e, if height > width, then image will be rescaled to
             (size * height / width, size)
-        interpolation (int): Interpolation mode of resize. Default: cv2.INTER_LINEAR.
+        interpolation (int, optional): Interpolation mode of resize. Default: 1.
+            0 : cv2.INTER_NEAREST 
+            1 : cv2.INTER_LINEAR 
+            2 : cv2.INTER_CUBIC 
+            3 : cv2.INTER_AREA 
+            4 : cv2.INTER_LANCZOS4 
+            5 : cv2.INTER_LINEAR_EXACT
+            7 : cv2.INTER_MAX 
+            8 : cv2.WARP_FILL_OUTLIERS 
+            16: cv2.WARP_INVERSE_MAP 
 
     Examples:
     
@@ -232,7 +241,7 @@ class Resize(object):
             print(fake_img.shape)
     """
 
-    def __init__(self, size, interpolation=cv2.INTER_LINEAR):
+    def __init__(self, size, interpolation=1):
         assert isinstance(size, int) or (isinstance(size, Iterable) and
                                          len(size) == 2)
         self.size = size
@@ -252,6 +261,16 @@ class RandomResizedCrop(object):
         output_size (int|list|tuple): Target size of output image, with (height, width) shape.
         scale (list|tuple): Range of size of the origin size cropped. Default: (0.08, 1.0)
         ratio (list|tuple): Range of aspect ratio of the origin aspect ratio cropped. Default: (0.75, 1.33)
+        interpolation (int, optional): Interpolation mode of resize. Default: 1.
+            0 : cv2.INTER_NEAREST 
+            1 : cv2.INTER_LINEAR 
+            2 : cv2.INTER_CUBIC 
+            3 : cv2.INTER_AREA 
+            4 : cv2.INTER_LANCZOS4 
+            5 : cv2.INTER_LINEAR_EXACT
+            7 : cv2.INTER_MAX 
+            8 : cv2.WARP_FILL_OUTLIERS 
+            16: cv2.WARP_INVERSE_MAP 
 
     Examples:
     
@@ -273,7 +292,7 @@ class RandomResizedCrop(object):
                  output_size,
                  scale=(0.08, 1.0),
                  ratio=(3. / 4, 4. / 3),
-                 interpolation=cv2.INTER_LINEAR):
+                 interpolation=1):
         if isinstance(output_size, int):
             self.output_size = (output_size, output_size)
         else:
@@ -328,7 +347,16 @@ class CenterCropResize(object):
     Args:
         size (int|list|tuple): Target size of output image, with (height, width) shape.
         crop_padding (int): Center crop with the padding. Default: 32.
-        interpolation (int): Interpolation mode of resize. Default: cv2.INTER_LINEAR.
+        interpolation (int, optional): Interpolation mode of resize. Default: 1.
+            0 : cv2.INTER_NEAREST 
+            1 : cv2.INTER_LINEAR 
+            2 : cv2.INTER_CUBIC 
+            3 : cv2.INTER_AREA 
+            4 : cv2.INTER_LANCZOS4 
+            5 : cv2.INTER_LINEAR_EXACT
+            7 : cv2.INTER_MAX 
+            8 : cv2.WARP_FILL_OUTLIERS 
+            16: cv2.WARP_INVERSE_MAP 
 
     Examples:
     
@@ -346,7 +374,7 @@ class CenterCropResize(object):
             print(fake_img.shape)
     """
 
-    def __init__(self, size, crop_padding=32, interpolation=cv2.INTER_LINEAR):
+    def __init__(self, size, crop_padding=32, interpolation=1):
         if isinstance(size, int):
             self.size = (size, size)
         else:
@@ -661,6 +689,7 @@ class ContrastTransform(object):
         if self.value == 0:
             return img
 
+        cv2 = try_import('cv2')
         dtype = img.dtype
         img = img.astype(np.float32)
         alpha = np.random.uniform(max(0, 1 - self.value), 1 + self.value)
@@ -701,6 +730,8 @@ class SaturationTransform(object):
         if self.value == 0:
             return img
 
+        cv2 = try_import('cv2')
+
         dtype = img.dtype
         img = img.astype(np.float32)
         alpha = np.random.uniform(max(0, 1 - self.value), 1 + self.value)
@@ -742,6 +773,7 @@ class HueTransform(object):
         if self.value == 0:
             return img
 
+        cv2 = try_import('cv2')
         dtype = img.dtype
         img = img.astype(np.uint8)
         hsv_img = cv2.cvtColor(img, cv2.COLOR_BGR2HSV_FULL)
@@ -1036,7 +1068,16 @@ class RandomRotate(object):
         degrees (sequence or float or int): Range of degrees to select from.
             If degrees is a number instead of sequence like (min, max), the range of degrees
             will be (-degrees, +degrees) clockwise order.
-        interpolation (int|optional): Interpolation mode of resize. Default: cv2.INTER_LINEAR.
+        interpolation (int, optional): Interpolation mode of resize. Default: 1.
+            0 : cv2.INTER_NEAREST 
+            1 : cv2.INTER_LINEAR 
+            2 : cv2.INTER_CUBIC 
+            3 : cv2.INTER_AREA 
+            4 : cv2.INTER_LANCZOS4 
+            5 : cv2.INTER_LINEAR_EXACT
+            7 : cv2.INTER_MAX 
+            8 : cv2.WARP_FILL_OUTLIERS 
+            16: cv2.WARP_INVERSE_MAP 
         expand (bool|optional): Optional expansion flag. Default: False.
             If true, expands the output to make it large enough to hold the entire rotated image.
             If false or omitted, make the output image the same size as the input image.
@@ -1061,11 +1102,7 @@ class RandomRotate(object):
             print(fake_img.shape)
     """
 
-    def __init__(self,
-                 degrees,
-                 interpolation=cv2.INTER_LINEAR,
-                 expand=False,
-                 center=None):
+    def __init__(self, degrees, interpolation=1, expand=False, center=None):
         if isinstance(degrees, numbers.Number):
             if degrees < 0:
                 raise ValueError(

python/requirements.txt

-opencv-python<=4.2.0.32
 requests>=2.20.0
 numpy>=1.13, <=1.16.4 ; python_version<"3.5"
 numpy>=1.13 ; python_version>="3.5"

python/setup.py.in

@@ -238,9 +238,6 @@ if sys.version_info >= (3,7):
         setup_requires_tmp+=[setup_requires_i]
     setup_requires = setup_requires_tmp
 
-if '${CMAKE_SYSTEM_PROCESSOR}' not in ['arm', 'armv7-a', 'aarch64']:
-    setup_requires+=['opencv-python']
-
 # the prefix is sys.prefix which should always be usr
 paddle_bins = ''
 

tools/check_api_approvals.sh

@@ -39,9 +39,9 @@ fi
 
 api_spec_diff=`python ${PADDLE_ROOT}/tools/check_api_source_without_core_ops.py ${PADDLE_ROOT}/paddle/fluid/API_DEV.source.md5  ${PADDLE_ROOT}/paddle/fluid/API_PR.source.md5` 
 if [ "$api_spec_diff" != "" ]; then
+    echo_line="${echo_line}Related APIs: ${api_spec_diff}\n"
     echo_line="You must have one RD (zhiqiu (Recommend) or phlrain) approval for the api change for the opreator-related api without 'core.ops'.\n"
     echo_line="${echo_line}For more details, please click [https://github.com/PaddlePaddle/Paddle/wiki/paddle_api_development_manual.md]\n"
-    echo_line="${echo_line}Related APIs: ${api_spec_diff}\n"
     check_approval 1 6888866 43953930
 fi