merge develop

fix conflict
test=develop

CMakeLists.txt

@@ -208,10 +208,10 @@ include(external/xxhash)    # download xxhash
 include(external/dlpack)
 include(external/snappy)    # download snappy
 include(external/snappystream) # download snappystream
+include(external/warpctc)   # download, build, install warpctc
 
 if (NOT WIN32)
-# there is no official support of warpctc, nccl, cupti in windows
-include(external/warpctc)   # download, build, install warpctc
+# there is no official support of nccl, cupti in windows
 include(cupti)
 include(external/gzstream)
 endif (NOT WIN32)

cmake/external/warpctc.cmake

@@ -26,25 +26,33 @@ SET(WARPCTC_INCLUDE_DIR "${WARPCTC_INSTALL_DIR}/include"
 # Used in unit test test_WarpCTCLayer
 SET(WARPCTC_LIB_DIR "${WARPCTC_INSTALL_DIR}/lib"
     CACHE PATH "Warp-ctc Library Directory" FORCE)
-SET(WARPCTC_LIBRARIES "${WARPCTC_INSTALL_DIR}/lib/libwarpctc${CMAKE_SHARED_LIBRARY_SUFFIX}"
-    CACHE FILEPATH "Warp-ctc Library" FORCE)
 
-IF(CMAKE_CXX_COMPILER_ID STREQUAL "Clang" OR CMAKE_CXX_COMPILER_ID STREQUAL "AppleClang" )
+IF(CMAKE_CXX_COMPILER_ID STREQUAL "Clang" OR CMAKE_CXX_COMPILER_ID STREQUAL "AppleClang" OR WIN32)
     SET(USE_OMP OFF)
 ELSE()
     SET(USE_OMP ON)
 ENDIF()
 
+IF(WIN32)
+    SET(WARPCTC_REPOSITORY "https://github.com/wopeizl/warp-ctc.git")
+ELSE()
+    SET(WARPCTC_REPOSITORY "https://github.com/dzhwinter/warp-ctc.git")
+ENDIF()
+
 ExternalProject_Add(
     extern_warpctc
     ${EXTERNAL_PROJECT_LOG_ARGS}
-    GIT_REPOSITORY  "https://github.com/dzhwinter/warp-ctc.git"
+    GIT_REPOSITORY ${WARPCTC_REPOSITORY}
     PREFIX          ${WARPCTC_SOURCES_DIR}
     UPDATE_COMMAND  ""
     CMAKE_ARGS      -DCMAKE_CXX_COMPILER=${CMAKE_CXX_COMPILER}
                     -DCMAKE_C_COMPILER=${CMAKE_C_COMPILER}
-                    -DCMAKE_CXX_FLAGS=${CMAKE_CXX_FLAGS}
                     -DCMAKE_C_FLAGS=${CMAKE_C_FLAGS}
+                    -DCMAKE_C_FLAGS_DEBUG=${CMAKE_C_FLAGS_DEBUG}
+                    -DCMAKE_C_FLAGS_RELEASE=${CMAKE_C_FLAGS_RELEASE}
+                    -DCMAKE_CXX_FLAGS=${CMAKE_CXX_FLAGS}
+                    -DCMAKE_CXX_FLAGS_RELEASE=${CMAKE_CXX_FLAGS_RELEASE}
+                    -DCMAKE_CXX_FLAGS_DEBUG=${CMAKE_CXX_FLAGS_DEBUG}
                     -DCMAKE_INSTALL_PREFIX=${WARPCTC_INSTALL_DIR}
                     -DWITH_GPU=${WITH_GPU}
                     -DWITH_OMP=${USE_OMP}
@@ -59,6 +67,18 @@ ExternalProject_Add(
                      -DCMAKE_POSITION_INDEPENDENT_CODE:BOOL=ON
                      -DCMAKE_INSTALL_PREFIX:PATH=${WARPCTC_INSTALL_DIR}
 )
+IF(WIN32)
+    IF(NOT EXISTS "${WARPCTC_INSTALL_DIR}/lib/warpctc${CMAKE_SHARED_LIBRARY_SUFFIX}")
+        add_custom_command(TARGET extern_warpctc POST_BUILD
+                COMMAND cmake -E copy ${WARPCTC_INSTALL_DIR}/bin/warpctc${CMAKE_SHARED_LIBRARY_SUFFIX} ${WARPCTC_INSTALL_DIR}/lib/warpctc${CMAKE_SHARED_LIBRARY_SUFFIX}
+                )
+    ENDIF()
+    SET(WARPCTC_LIBRARIES "${WARPCTC_INSTALL_DIR}/lib/warpctc${CMAKE_SHARED_LIBRARY_SUFFIX}"
+            CACHE FILEPATH "Warp-ctc Library" FORCE)
+else(WIN32)
+    SET(WARPCTC_LIBRARIES "${WARPCTC_INSTALL_DIR}/lib/libwarpctc${CMAKE_SHARED_LIBRARY_SUFFIX}"
+            CACHE FILEPATH "Warp-ctc Library" FORCE)
+ENDIF(WIN32)
 
 MESSAGE(STATUS "warp-ctc library: ${WARPCTC_LIBRARIES}")
 INCLUDE_DIRECTORIES(${WARPCTC_INCLUDE_DIR}) # For warpctc code to include its headers.

cmake/operators.cmake

@@ -84,7 +84,7 @@ function(op_library TARGET)
     endif()
     if (WIN32)
     # remove windows unsupported op, because windows has no nccl, no warpctc such ops.
-    foreach(windows_unsupport_op "nccl_op" "gen_nccl_id_op" "warpctc_op")
+    foreach(windows_unsupport_op "nccl_op" "gen_nccl_id_op")
         if ("${TARGET}" STREQUAL "${windows_unsupport_op}")
           return()
         endif()

paddle/fluid/API.spec

@@ -376,7 +376,7 @@ paddle.fluid.optimizer.MomentumOptimizer.__init__ ArgSpec(args=['self', 'learnin
 paddle.fluid.optimizer.MomentumOptimizer.minimize ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None))
 paddle.fluid.optimizer.AdagradOptimizer.__init__ ArgSpec(args=['self', 'learning_rate', 'epsilon', 'regularization', 'name'], varargs=None, keywords=None, defaults=(1e-06, None, None))
 paddle.fluid.optimizer.AdagradOptimizer.minimize ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None))
-paddle.fluid.optimizer.AdamOptimizer.__init__ ArgSpec(args=['self', 'learning_rate', 'beta1', 'beta2', 'epsilon', 'regularization', 'name'], varargs=None, keywords=None, defaults=(0.001, 0.9, 0.999, 1e-08, None, None))
+paddle.fluid.optimizer.AdamOptimizer.__init__ ArgSpec(args=['self', 'learning_rate', 'beta1', 'beta2', 'epsilon', 'regularization', 'name', 'lazy_mode'], varargs=None, keywords=None, defaults=(0.001, 0.9, 0.999, 1e-08, None, None, False))
 paddle.fluid.optimizer.AdamOptimizer.minimize ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None))
 paddle.fluid.optimizer.AdamaxOptimizer.__init__ ArgSpec(args=['self', 'learning_rate', 'beta1', 'beta2', 'epsilon', 'regularization', 'name'], varargs=None, keywords=None, defaults=(0.001, 0.9, 0.999, 1e-08, None, None))
 paddle.fluid.optimizer.AdamaxOptimizer.minimize ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None))

paddle/fluid/framework/ngraph_bridge.cc

@@ -16,100 +16,25 @@ limitations under the License. */
 #include <functional>
 #include <vector>
 
+#include "ngraph/ngraph.hpp"
 #include "paddle/fluid/framework/ngraph_bridge.h"
 #include "paddle/fluid/framework/operator.h"
+#include "paddle/fluid/operators/ngraph/ngraph_ops.h"
 #include "paddle/fluid/platform/enforce.h"
-
-#include "ngraph/ngraph.hpp"
+#include "paddle/fluid/platform/ngraph_helper.h"
 
 namespace paddle {
 namespace framework {
 
-static std::shared_ptr<ngraph::Node> GetNode(
-    const std::shared_ptr<OperatorBase>& op, const std::string name,
-    const VariableNameMap& var_map,
-    std::shared_ptr<
-        std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
-        ngb_node_map) {
-  auto& var_names = var_map.at(name);
-  PADDLE_ENFORCE_EQ(var_names.size(), 1,
-                    "op %s name %s expects one associated var", op->Type(),
-                    name);
-  if (ngb_node_map->find(var_names[0]) != ngb_node_map->end()) {
-    return (*ngb_node_map)[var_names[0]];
-  } else {
-    return nullptr;
-  }
-}
-
-static std::shared_ptr<ngraph::Node> GetInputNode(
-    const std::shared_ptr<OperatorBase>& op, const std::string name,
-    std::shared_ptr<
-        std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
-        ngb_node_map) {
-  return GetNode(op, name, op->Inputs(), ngb_node_map);
-}
-
-static std::shared_ptr<ngraph::Node> GetOutputNode(
-    const std::shared_ptr<OperatorBase>& op, const std::string name,
-    std::shared_ptr<
-        std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
-        ngb_node_map) {
-  return GetNode(op, name, op->Outputs(), ngb_node_map);
-}
-
-static void SetOutputNode(
-    const std::shared_ptr<OperatorBase>& op, const std::string name,
-    std::shared_ptr<ngraph::Node> node,
-    std::shared_ptr<
-        std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
-        ngb_node_map) {
-  auto& var_names = op->Outputs().at(name);
-  if (var_names.size() == 1) {
-    (*ngb_node_map)[var_names[0]] = node;
-  } else if (var_names.size() == 0) {
-    (*ngb_node_map)[""] = node;
-  } else {
-    PADDLE_THROW("name %s has more than 1 var_names.", name);
-  }
-}
-
-static bool HasOutput(const std::shared_ptr<OperatorBase>& op,
-                      const std::string name) {
-  auto& outputs = op->Outputs();
-  if (outputs.find(name) == outputs.end()) return false;
-  return outputs.at(name).size() > 0;
-}
-
-template <typename T>
-static void BuildBinaryNode(
-    const std::shared_ptr<OperatorBase>& op,
-    std::shared_ptr<
-        std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
-        ngb_node_map) {
-  auto x = GetInputNode(op, "X", ngb_node_map);
-  auto y = GetInputNode(op, "Y", ngb_node_map);
-  auto out = std::make_shared<T>(x, y);
-  SetOutputNode(op, "Out", out, ngb_node_map);
-}
-
-template <typename T>
-static void BuildUnaryNode(
-    const std::shared_ptr<OperatorBase>& op,
-    std::shared_ptr<
-        std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
-        ngb_node_map) {
-  auto input = GetInputNode(op, "X", ngb_node_map);
-  auto out = std::make_shared<T>(input);
-  SetOutputNode(op, "Out", out, ngb_node_map);
-}
-
 std::map<std::string,
          std::function<void(const std::shared_ptr<OperatorBase>&,
                             std::shared_ptr<std::unordered_map<
                                 std::string, std::shared_ptr<ngraph::Node>>>)>>
-    NgraphBridge::NG_NODE_MAP = {{"relu", BuildUnaryNode<ngraph::op::Relu>},
-                                 {"tanh", BuildUnaryNode<ngraph::op::Tanh>}};
+    NgraphBridge::NG_NODE_MAP = {
+        {"mul", paddle::operators::ngraphs::BuildMulNode},
+        {"mul_grad", paddle::operators::ngraphs::BuildMulGradNode},
+        {"relu", paddle::operators::ngraphs::BuildUnaryNode<ngraph::op::Relu>},
+        {"tanh", paddle::operators::ngraphs::BuildUnaryNode<ngraph::op::Tanh>}};
 
 void NgraphBridge::BuildNgNode(const std::shared_ptr<OperatorBase>& op) {
   auto& op_type = op->Type();

paddle/fluid/framework/ngraph_operator.cc

@@ -278,7 +278,8 @@ std::shared_ptr<ngraph::runtime::Backend> NgraphEngine::backend_ =
     ngraph::runtime::Backend::create("CPU");
 
 void NgraphEngine::GetNgInputShape(std::shared_ptr<OperatorBase> op) {
-  op->RuntimeInferShape(scope_, place_);
+  RuntimeContext ctx(op->Inputs(), op->Outputs(), scope_);
+  op->RuntimeInferShape(scope_, place_, ctx);
   for (auto& var_name_item : op->Inputs()) {
     for (auto& var_name : var_name_item.second) {
       auto* var = scope_.FindVar(var_name);

paddle/fluid/framework/operator.cc

@@ -137,6 +137,23 @@ static LoD GetLoD(const Scope& scope, const std::string& name) {
   }
 }
 
+RuntimeContext::RuntimeContext(const VariableNameMap& innames,
+                               const VariableNameMap& outnames,
+                               const Scope& scope) {
+  for (auto& var_name_item : innames) {
+    std::vector<Variable*>& input_vars = inputs[var_name_item.first];
+    for (auto& var_name : var_name_item.second) {
+      input_vars.push_back(scope.FindVar(var_name));
+    }
+  }
+  for (auto& var_name_item : outnames) {
+    std::vector<Variable*>& output_vars = outputs[var_name_item.first];
+    for (auto& var_name : var_name_item.second) {
+      output_vars.push_back(scope.FindVar(var_name));
+    }
+  }
+}
+
 void OperatorBase::Run(const Scope& scope, const platform::Place& place) {
   VLOG(4) << place << " " << DebugStringEx(&scope);
   if (platform::is_gpu_place(place)) {
@@ -412,11 +429,48 @@ bool ExecutionContext::HasOutput(const std::string& name) const {
   return var != nullptr;
 }
 
+const Variable* ExecutionContext::InputVar(const std::string& name) const {
+  auto it = ctx_.inputs.find(name);
+  if (it == ctx_.inputs.end()) return nullptr;
+
+  PADDLE_ENFORCE_LE(it->second.size(), 1UL,
+                    "Operator %s's input %s should contain only one variable.",
+                    op_.Type(), name);
+  return it->second.empty() ? nullptr : it->second[0];
+}
+
+const Variable* ExecutionContext::LegacyInputVar(
+    const std::string& name) const {
+  auto ipt = op_.Input(name);
+  return ipt == kEmptyVarName ? nullptr : scope_.FindVar(ipt);
+}
+
+Variable* ExecutionContext::OutputVar(const std::string& name) const {
+  auto it = ctx_.outputs.find(name);
+  if (it == ctx_.outputs.end()) return nullptr;
+
+  PADDLE_ENFORCE_LE(it->second.size(), 1UL,
+                    "Operator %s's output %s should contain only one variable.",
+                    op_.Type(), name);
+  return it->second.empty() ? nullptr : it->second[0];
+}
+
+Variable* ExecutionContext::LegacyOutputVar(const std::string& name) const {
+  auto opt = op_.Output(name);
+  return opt == kEmptyVarName ? nullptr : scope_.FindVar(opt);
+}
+
 template <>
 const Tensor* ExecutionContext::Input<Tensor>(const std::string& name) const {
   return Input<LoDTensor>(name);
 }
 
+template <>
+const Tensor* ExecutionContext::LegacyInput<Tensor>(
+    const std::string& name) const {
+  return LegacyInput<LoDTensor>(name);
+}
+
 template <>
 const std::vector<const Tensor*> ExecutionContext::MultiInput<Tensor>(
     const std::string& name) const {
@@ -441,6 +495,11 @@ Tensor* ExecutionContext::Output<Tensor>(const std::string& name) const {
   return Output<LoDTensor>(name);
 }
 
+template <>
+Tensor* ExecutionContext::LegacyOutput<Tensor>(const std::string& name) const {
+  return LegacyOutput<LoDTensor>(name);
+}
+
 template <>
 std::vector<Tensor*> ExecutionContext::MultiOutput<Tensor>(
     const std::string& name) const {
@@ -477,23 +536,22 @@ bool OpSupportGPU(const std::string& op_type) {
 
 class RuntimeInferShapeContext : public InferShapeContext {
  public:
-  RuntimeInferShapeContext(const OperatorBase& op, const Scope& scope)
-      : op_(op), scope_(scope) {}
+  RuntimeInferShapeContext(const OperatorBase& op, const Scope& scope,
+                           const RuntimeContext& ctx)
+      : op_(op), scope_(scope), ctx_(ctx) {}
 
   bool HasInput(const std::string& name) const override {
     // has only one input
-    const auto& ins = op_.Inputs();
+    const auto& ins = ctx_.inputs;
     auto it = ins.find(name);
     if (it == ins.end()) {
       return false;
     }
     const auto& in = it->second;
-    if (in.size() == 0 || in[0] == kEmptyVarName) {
-      return false;
-    }
+    if (in.size() == 0) return false;
     PADDLE_ENFORCE_EQ(in.size(), 1UL,
                       "Input %s should not have more than one inputs", name);
-    return scope_.FindVar(in[0]) != nullptr;
+    return in[0] != nullptr;
   }
 
   bool HasOutput(const std::string& name) const override {
@@ -678,6 +736,7 @@ class RuntimeInferShapeContext : public InferShapeContext {
  private:
   const OperatorBase& op_;
   const Scope& scope_;
+  const RuntimeContext& ctx_;
 };
 
 static void CheckTensorNANOrInf(const std::string& name,
@@ -696,15 +755,15 @@ static void CheckTensorNANOrInf(const std::string& name,
 }
 
 void OperatorWithKernel::RuntimeInferShape(const Scope& scope,
-                                           const platform::Place& place) const {
-  RuntimeInferShapeContext infer_shape_ctx(*this, scope);
+                                           const platform::Place& place,
+                                           const RuntimeContext& ctx) const {
+  RuntimeInferShapeContext infer_shape_ctx(*this, scope, ctx);
   this->InferShape(&infer_shape_ctx);
 }
 
 void OperatorWithKernel::RunImpl(const Scope& scope,
                                  const platform::Place& place) const {
-  RuntimeInferShapeContext infer_shape_ctx(*this, scope);
-  this->InferShape(&infer_shape_ctx);
+  RuntimeContext ctx(Inputs(), Outputs(), scope);
   platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
   auto* dev_ctx = pool.Get(place);
 
@@ -718,15 +777,8 @@ void OperatorWithKernel::RunImpl(const Scope& scope,
 
   OpKernelMap& kernels = kernels_iter->second;
 
-  // TODO(dzhwinter) : kernel fallback mechanism will be added when all the
-  // transform functions are ready.
-
-  // for (auto& candidate : kKernelPriority) {
-  //   Do selection
-  // }
-
-  auto expected_kernel_key =
-      this->GetExpectedKernelType(ExecutionContext(*this, scope, *dev_ctx));
+  auto expected_kernel_key = this->GetExpectedKernelType(
+      ExecutionContext(*this, scope, *dev_ctx, ctx));
   VLOG(3) << "expected_kernel_key:" << expected_kernel_key;
 
   auto kernel_iter = kernels.find(expected_kernel_key);
@@ -748,7 +800,7 @@ void OperatorWithKernel::RunImpl(const Scope& scope,
   // do data transformScope &transfer_scope;
   std::vector<std::string> transfered_inplace_vars;
   auto* transfer_scope =
-      TryTransferData(scope, expected_kernel_key, &transfered_inplace_vars);
+      PrepareData(scope, expected_kernel_key, &transfered_inplace_vars, &ctx);
 
   // exec scope is the scope that kernel actually executed on.
   const Scope& exec_scope =
@@ -758,7 +810,11 @@ void OperatorWithKernel::RunImpl(const Scope& scope,
     dev_ctx = pool.Get(expected_kernel_key.place_);
   }
 
-  kernel_iter->second(ExecutionContext(*this, exec_scope, *dev_ctx));
+  RuntimeInferShapeContext infer_shape_ctx(*this, exec_scope, ctx);
+  this->InferShape(&infer_shape_ctx);
+  // TODO(panyx0718): ExecutionContext should only depend on RuntimeContext
+  // not Scope. Imperative mode only pass inputs and get outputs.
+  kernel_iter->second(ExecutionContext(*this, exec_scope, *dev_ctx, ctx));
 
   if (!transfered_inplace_vars.empty()) {
     // there is inplace variable has been transfered.
@@ -782,6 +838,7 @@ void OperatorWithKernel::RunImpl(const Scope& scope,
     }
   }
 }
+
 void OperatorWithKernel::TransferInplaceVarsBack(
     const Scope& scope, const std::vector<std::string>& inplace_vars,
     const Scope& transfer_scope) const {
@@ -797,13 +854,19 @@ void OperatorWithKernel::TransferInplaceVarsBack(
   }
 }
 
-Scope* OperatorWithKernel::TryTransferData(
+Scope* OperatorWithKernel::PrepareData(
     const Scope& scope, const OpKernelType& expected_kernel_key,
-    std::vector<std::string>* transfered_inplace_vars) const {
+    std::vector<std::string>* transfered_inplace_vars,
+    RuntimeContext* ctx) const {
   Scope* new_scope = nullptr;
   for (auto& var_name_item : Inputs()) {
-    for (auto& var_name : var_name_item.second) {
+    std::vector<Variable*>& input_vars = ctx->inputs[var_name_item.first];
+
+    for (size_t i = 0; i < var_name_item.second.size(); ++i) {
+      auto& var_name = var_name_item.second[i];
       auto* var = scope.FindVar(var_name);
+      input_vars[i] = var;
+
       // Only tensor can be tranfer to another device.
       if (var == nullptr || !VarIsTensor(*var)) {
         continue;
@@ -851,6 +914,7 @@ Scope* OperatorWithKernel::TryTransferData(
       }
 
       auto* trans_var = new_scope->Var(var_name);
+      input_vars[i] = trans_var;
 
       Tensor out;
       TransformData(expected_kernel_key, kernel_type_for_var, *tensor_in, &out);

paddle/fluid/framework/operator.h

@@ -70,6 +70,15 @@ Tensor* GetMutableLoDTensorOrSelectedRowsValueFromVar(Variable* var);
 class OperatorBase;
 class ExecutionContext;
 
+class RuntimeContext {
+ public:
+  RuntimeContext(const VariableNameMap& innames,
+                 const VariableNameMap& outnames, const Scope& scope);
+
+  VariableValueMap inputs;
+  VariableValueMap outputs;
+};
+
 /**
  * OperatorBase has the basic elements that Net will call to do computation.
  * Only CreateOperator from OpRegistry will new Operator directly. User
@@ -129,7 +138,8 @@ class OperatorBase {
 
   void SetIsCalledByExecutor(bool x) { run_by_executor_ = x; }
   virtual void RuntimeInferShape(const Scope& scope,
-                                 const platform::Place& place) const {}
+                                 const platform::Place& place,
+                                 const RuntimeContext& ctx) const {}
 
  protected:
   std::string type_;
@@ -156,8 +166,9 @@ class OperatorBase {
 class ExecutionContext {
  public:
   ExecutionContext(const OperatorBase& op, const Scope& scope,
-                   const platform::DeviceContext& device_context)
-      : op_(op), scope_(scope), device_context_(device_context) {}
+                   const platform::DeviceContext& device_context,
+                   const RuntimeContext& ctx)
+      : op_(op), scope_(scope), device_context_(device_context), ctx_(ctx) {}
 
   const OperatorBase& op() const { return op_; }
 
@@ -180,15 +191,9 @@ class ExecutionContext {
     return op_.Outputs(name).size();
   }
 
-  const Variable* InputVar(const std::string& name) const {
-    auto ipt = op_.Input(name);
-    return ipt == kEmptyVarName ? nullptr : scope_.FindVar(ipt);
-  }
+  const Variable* InputVar(const std::string& name) const;
 
-  Variable* OutputVar(const std::string& name) const {
-    auto opt = op_.Output(name);
-    return opt == kEmptyVarName ? nullptr : scope_.FindVar(opt);
-  }
+  Variable* OutputVar(const std::string& name) const;
 
   const std::vector<const Variable*> MultiInputVar(
       const std::string& name) const {
@@ -227,6 +232,22 @@ class ExecutionContext {
     return var == nullptr ? nullptr : var->GetMutable<T>();
   }
 
+  template <typename T>
+  const T* LegacyInput(const std::string& name) const {
+    auto* var = LegacyInputVar(name);
+    return var == nullptr ? nullptr : &var->Get<T>();
+  }
+
+  template <typename T>
+  T* LegacyOutput(const std::string& name) const {
+    auto var = LegacyOutputVar(name);
+    return var == nullptr ? nullptr : var->GetMutable<T>();
+  }
+
+  const Variable* LegacyInputVar(const std::string& name) const;
+
+  Variable* LegacyOutputVar(const std::string& name) const;
+
   template <typename T>
   const std::vector<const T*> MultiInput(const std::string& name) const {
     auto names = op_.Inputs(name);
@@ -286,6 +307,7 @@ class ExecutionContext {
   const OperatorBase& op_;
   const Scope& scope_;
   const platform::DeviceContext& device_context_;
+  const RuntimeContext& ctx_;
 };
 
 inline bool CanCUDNNBeUsed(const framework::ExecutionContext& ctx) {
@@ -303,6 +325,10 @@ inline bool CanCUDNNBeUsed(const framework::ExecutionContext& ctx) {
 template <>
 const Tensor* ExecutionContext::Input<Tensor>(const std::string& name) const;
 
+template <>
+const Tensor* ExecutionContext::LegacyInput<Tensor>(
+    const std::string& name) const;
+
 template <>
 const std::vector<const Tensor*> ExecutionContext::MultiInput<Tensor>(
     const std::string& name) const;
@@ -310,6 +336,9 @@ const std::vector<const Tensor*> ExecutionContext::MultiInput<Tensor>(
 template <>
 Tensor* ExecutionContext::Output<Tensor>(const std::string& name) const;
 
+template <>
+Tensor* ExecutionContext::LegacyOutput<Tensor>(const std::string& name) const;
+
 template <>
 std::vector<Tensor*> ExecutionContext::MultiOutput<Tensor>(
     const std::string& name) const;
@@ -362,8 +391,8 @@ class OperatorWithKernel : public OperatorBase {
     OpInfoMap::Instance().Get(Type()).infer_shape_(ctx);
   }
 
-  void RuntimeInferShape(const Scope& scope,
-                         const platform::Place& place) const override;
+  void RuntimeInferShape(const Scope& scope, const platform::Place& place,
+                         const RuntimeContext& ctx) const override;
 
  protected:
   virtual OpKernelType GetExpectedKernelType(const ExecutionContext& ctx) const;
@@ -383,9 +412,10 @@ class OperatorWithKernel : public OperatorBase {
    *
    * * transfered_inplace_vars is a output vector.
    */
-  Scope* TryTransferData(
-      const Scope& scope, const OpKernelType& expected_kernel_key,
-      std::vector<std::string>* transfered_inplace_vars) const;
+  Scope* PrepareData(const Scope& scope,
+                     const OpKernelType& expected_kernel_key,
+                     std::vector<std::string>* transfered_inplace_vars,
+                     RuntimeContext* ctx) const;
 
   void TransferInplaceVarsBack(const Scope& scope,
                                const std::vector<std::string>& inplace_vars,

paddle/fluid/framework/type_defs.h

@@ -28,8 +28,11 @@ class OperatorBase;
 class OpDesc;
 class InferShapeContext;
 class BlockDesc;
+class Variable;
 
 using VariableNameMap = std::map<std::string, std::vector<std::string>>;
+// TODO(panyx0718): Replace vector with something like gtl::Vector.
+using VariableValueMap = std::map<std::string, std::vector<Variable*>>;
 
 // The order should be as same as framework.proto
 using Attribute =

paddle/fluid/operators/CMakeLists.txt

@@ -64,9 +64,7 @@ endif()
 set(COMMON_OP_DEPS ${OP_HEADER_DEPS})
 
 set(COMMON_OP_DEPS ${COMMON_OP_DEPS} selected_rows_functor selected_rows lod_tensor maxouting unpooling pooling lod_rank_table context_project sequence_pooling executor)
-if (NOT WIN32)
-    set(COMMON_OP_DEPS ${COMMON_OP_DEPS} dynload_warpctc)
-endif()
+set(COMMON_OP_DEPS ${COMMON_OP_DEPS} dynload_warpctc)
 set(COMMON_OP_DEPS ${COMMON_OP_DEPS} sequence_padding sequence_scale cos_sim_functor memory jit_kernel concat_and_split cross_entropy softmax vol2col im2col sampler)
 set(COMMON_OP_DEPS ${COMMON_OP_DEPS} sequence2batch lstm_compute matrix_bit_code gru_compute activation_functions)
 if (WITH_GPU)

paddle/fluid/operators/beam_search_decode_op.cc

@@ -122,7 +122,8 @@ class BeamSearchDecodeOp : public framework::OperatorBase {
     platform::DeviceContextPool& pool = platform::DeviceContextPool::Instance();
     auto& dev_ctx = *pool.Get(dev_place);
 
-    framework::ExecutionContext ctx(*this, scope, dev_ctx);
+    framework::RuntimeContext run_ctx(Inputs(), Outputs(), scope);
+    framework::ExecutionContext ctx(*this, scope, dev_ctx, run_ctx);
 
     const LoDTensorArray* ids = ctx.Input<LoDTensorArray>("Ids");
     const LoDTensorArray* scores = ctx.Input<LoDTensorArray>("Scores");

paddle/fluid/operators/controlflow/parallel_do_op.cc

-/* Copyright (c) 2016 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 <vector>
-
-#include "paddle/fluid/framework/executor.h"
-#include "paddle/fluid/framework/op_registry.h"
-#include "paddle/fluid/framework/threadpool.h"
-#include "paddle/fluid/operators/detail/safe_ref.h"
-
-namespace paddle {
-namespace operators {
-
-static constexpr char kInputs[] = "inputs";
-static constexpr char kParameters[] = "parameters";
-static constexpr char kPlaces[] = "places";
-
-static constexpr char kOutputs[] = "outputs";
-static constexpr char kParallelScopes[] = "parallel_scopes";
-
-static constexpr char kParallelBlock[] = "sub_block";
-static constexpr char kUseNCCL[] = "use_nccl";
-
-using LoDTensor = framework::LoDTensor;
-using SelectedRows = framework::SelectedRows;
-
-static void SplitTensorAndMoveTensorToScopes(
-    const framework::Scope &scope, std::vector<framework::Scope *> *sub_scopes,
-    const std::vector<platform::Place> &places,
-    const std::vector<std::string> &names) {
-  size_t num_sub_scopes = 0;
-  for (auto &argu : names) {
-    const auto &tensor =
-        detail::Ref(scope.FindVar(argu),
-                    "Cannot find variable %s in the parent scope", argu)
-            .Get<LoDTensor>();
-    auto lod_tensors = tensor.SplitLoDTensor(places);
-
-    for (auto &lod : lod_tensors) {
-      VLOG(3) << lod.dims();
-    }
-    if (num_sub_scopes == 0) {
-      num_sub_scopes = lod_tensors.size();
-    } else {
-      PADDLE_ENFORCE_EQ(num_sub_scopes, lod_tensors.size());
-    }
-    PADDLE_ENFORCE_NE(num_sub_scopes, 0);
-    if (sub_scopes->size() == 0) {
-      sub_scopes->reserve(num_sub_scopes);
-      for (size_t i = 0; i < num_sub_scopes; ++i) {
-        sub_scopes->emplace_back(&scope.NewScope());
-      }
-    }
-
-    for (size_t i = 0; i < lod_tensors.size(); ++i) {
-      *detail::Ref(sub_scopes->at(i)->Var(argu),
-                   "Cannot find variable in the sub-scope", argu)
-           .GetMutable<LoDTensor>() = lod_tensors[i];
-    }
-  }
-}
-
-inline void CopyOrShare(const framework::Variable &src,
-                        const platform::Place &dst_place,
-                        framework::Variable *dst) {
-  if (src.IsType<LoDTensor>()) {
-    if (src.Get<LoDTensor>().place() == dst_place) {
-      dst->GetMutable<LoDTensor>()->ShareDataWith(src.Get<LoDTensor>());
-      dst->GetMutable<LoDTensor>()->set_lod(src.Get<LoDTensor>().lod());
-    } else {
-      TensorCopy(src.Get<LoDTensor>(), dst_place, dst->GetMutable<LoDTensor>());
-    }
-  } else if (src.IsType<SelectedRows>()) {
-    auto &src_sr = src.Get<SelectedRows>();
-    auto *dst_sr = dst->GetMutable<SelectedRows>();
-    dst_sr->set_height(src_sr.height());
-    if (src_sr.value().place() == dst_place) {
-      dst_sr->mutable_value()->ShareDataWith(src_sr.value());
-      dst_sr->set_rows(src_sr.rows());
-    } else {
-      TensorCopy(src_sr.value(), dst_place, dst_sr->mutable_value());
-    }
-  } else {
-    PADDLE_THROW("Expect LoDTensor/SelectedRows, get %s",
-                 framework::ToTypeName(src.Type()));
-  }
-}
-
-void WaitOnPlace(const platform::Place place) {
-  platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
-  auto &dev_ctx = *pool.Get(place);
-  dev_ctx.Wait();
-}
-
-void WaitOnPlaces(const std::vector<platform::Place> places) {
-  platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
-
-  for (auto &place : places) {
-    auto &dev_ctx = *pool.Get(place);
-    dev_ctx.Wait();
-  }
-}
-
-class ParallelDoOp : public framework::OperatorBase {
- public:
-  ParallelDoOp(const std::string &type,
-               const framework::VariableNameMap &inputs,
-               const framework::VariableNameMap &outputs,
-               const framework::AttributeMap &attrs)
-      : framework::OperatorBase(type, inputs, outputs, attrs) {}
-
- private:
-  void RunImpl(const framework::Scope &scope,
-               const platform::Place &place) const override {
-    // get device context from pool
-    platform::DeviceContextPool &pool = platform::DeviceContextPool::Instance();
-    auto &dev_ctx = *pool.Get(place);
-
-    auto *block = Attr<framework::BlockDesc *>(kParallelBlock);
-    auto *program = block->Program();
-
-    auto &places = scope.FindVar(Input(kPlaces))->Get<platform::PlaceList>();
-
-    auto &sub_scopes = *scope.FindVar(Output(kParallelScopes))
-                            ->GetMutable<std::vector<framework::Scope *>>();
-
-    // split input
-    SplitTensorAndMoveTensorToScopes(scope, &sub_scopes, places,
-                                     Inputs(kInputs));
-
-    // copy parameter
-    for (auto &param : Inputs(kParameters)) {
-      PADDLE_ENFORCE(scope.FindVar(param)->IsType<LoDTensor>(),
-                     "Only support parameter type as LoDTensor");
-      auto &src = scope.FindVar(param)->Get<LoDTensor>();
-
-      auto *sub_scope0 = sub_scopes[0];
-      auto *dst0 = sub_scope0->Var(param)->GetMutable<LoDTensor>();
-      dst0->ShareDataWith(src);
-
-      for (size_t i = 1; i < sub_scopes.size(); ++i) {
-        auto &place = places[i];
-        auto *sub_scope = sub_scopes[i];
-        auto *dst = sub_scope->Var(param)->GetMutable<LoDTensor>();
-        framework::TensorCopy(src, place, dst);
-      }
-    }
-    WaitOnPlaces(places);
-
-    std::vector<std::future<void>> workers;
-    workers.reserve(places.size());
-    for (size_t place_idx = 0; place_idx < sub_scopes.size(); ++place_idx) {
-      auto &place = places[place_idx];
-      auto *cur_scope = sub_scopes[place_idx];
-
-      workers.emplace_back(framework::Async([program, cur_scope, place, block] {
-        framework::Executor executor(place);
-        executor.Run(*program, cur_scope, block->ID(),
-                     false /*create_local_scope*/);
-      }));
-    }
-    for (auto &worker : workers) {
-      worker.wait();
-    }
-    WaitOnPlaces(places);
-
-    // merge output
-    for (auto &o_name : Outputs(kOutputs)) {
-      std::vector<const framework::LoDTensor *> lod_tensors;
-      lod_tensors.reserve(sub_scopes.size());
-      for (auto *sub_scope : sub_scopes) {
-        lod_tensors.emplace_back(&sub_scope->FindVar(o_name)->Get<LoDTensor>());
-      }
-
-      auto *lod_tensor_to_be_merged =
-          scope.FindVar(o_name)->GetMutable<LoDTensor>();
-      lod_tensor_to_be_merged->MergeLoDTensor(lod_tensors, dev_ctx.GetPlace());
-    }
-    WaitOnPlaces(places);
-  }
-};
-
-class ParallelDoOpProtoMaker : public framework::OpProtoAndCheckerMaker {
- public:
-  void Make() override {
-    AddInput(kInputs, "").AsDuplicable();
-    AddInput(kParameters, "").AsDuplicable();
-    AddInput(kPlaces, "");
-    AddOutput(kOutputs, "").AsDuplicable();
-    AddOutput(kParallelScopes, "");
-    AddAttr<framework::BlockDesc *>(kParallelBlock, "");
-    AddAttr<bool>(kUseNCCL, "true if we use nccl on backward")
-        .SetDefault(false);
-    AddComment(R"DOC(
-ParallelDo Operator.
-)DOC");
-  }
-};
-
-class ParallelDoGradOp : public framework::OperatorBase {
- public:
-  ParallelDoGradOp(const std::string &type,
-                   const framework::VariableNameMap &inputs,
-                   const framework::VariableNameMap &outputs,
-                   const framework::AttributeMap &attrs)
-      : framework::OperatorBase(type, inputs, outputs, attrs) {}
-
- private:
-  void RunImpl(const framework::Scope &scope,
-               const platform::Place &place) const override {
-    auto *block = Attr<framework::BlockDesc *>(kParallelBlock);
-    auto *program = block->Program();
-
-    auto &sub_scopes = scope.FindVar(Input(kParallelScopes))
-                           ->Get<std::vector<framework::Scope *>>();
-    auto &places = scope.FindVar(Input(kPlaces))->Get<platform::PlaceList>();
-
-    // feed output@grad
-    SplitTensorAndMoveTensorToScopes(
-        scope, const_cast<std::vector<framework::Scope *> *>(&sub_scopes),
-        places, Inputs(framework::GradVarName(kOutputs)));
-    WaitOnPlaces(places);
-
-    // exe run
-    std::vector<std::future<void>> workers;
-    for (size_t i = 0; i < sub_scopes.size(); ++i) {
-      auto &place = places[i];
-      auto *cur_scope = sub_scopes[i];
-
-      // execute
-      workers.emplace_back(framework::Async([program, cur_scope, place, block] {
-        framework::Executor executor(place);
-        executor.Run(*program, cur_scope, block->ID(),
-                     false /*create_local_scope*/);
-      }));
-    }
-    for (auto &worker : workers) {
-      worker.wait();
-    }
-    WaitOnPlaces(places);
-
-    // NCCL allreduce op will be added by backward,
-    // so no need to explicitly accumulate grad
-    if (!(Attr<bool>(kUseNCCL))) {
-      AccumulateGrad(scope, place, sub_scopes, places);
-    } else {
-      for (auto &place : places) {
-        PADDLE_ENFORCE(platform::is_gpu_place(place),
-                       "NCCL only supports cuda place");
-      }
-    }
-    for (auto &s : Outputs(framework::GradVarName(kParameters))) {
-      if (s == framework::kEmptyVarName) {
-        continue;
-      }
-      VLOG(3) << "Moving " << s;
-      CopyOrShare(*sub_scopes[0]->FindVar(s), place, scope.FindVar(s));
-    }
-    WaitOnPlaces(places);
-  }
-
-  void AccumulateGrad(const framework::Scope &scope,
-                      const platform::Place &place,
-                      const std::vector<framework::Scope *> &sub_scopes,
-                      const platform::PlaceList &places) const {
-    for (auto &s : Outputs(framework::GradVarName(kParameters))) {
-      if (s == framework::kEmptyVarName) {
-        continue;
-      }
-      VLOG(3) << "Accumulating " << s;
-      if (s == framework::kEmptyVarName) continue;
-      std::string tmp_name;
-      auto *tmp = sub_scopes[0]->Var(&tmp_name);
-
-      for (size_t i = 1; i < sub_scopes.size(); ++i) {
-        CopyOrShare(*sub_scopes[i]->FindVar(s), places[0], tmp);
-        WaitOnPlaces(places);
-
-        auto sum_op = framework::OpRegistry::CreateOp(
-            "sum", {{"X", {s, tmp_name}}}, {{"Out", {s}}},
-            framework::AttributeMap{{"use_mkldnn", {false}}});
-        VLOG(10) << sum_op->DebugStringEx(sub_scopes[0]);
-        sum_op->Run(*sub_scopes[0], places[0]);
-        WaitOnPlace(places[0]);
-      }
-
-      CopyOrShare(*sub_scopes[0]->FindVar(s), place, scope.FindVar(s));
-    }
-    WaitOnPlaces(places);
-  }
-};
-
-std::ostream &operator<<(std::ostream &sout,
-                         const std::vector<std::string> &strs) {
-  std::copy(strs.begin(), strs.end(),
-            std::ostream_iterator<std::string>(sout, ","));
-  return sout;
-}
-
-class ParallelDoGradOpDescMaker : public framework::SingleGradOpDescMaker {
- public:
-  using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
-
- protected:
-  virtual std::unique_ptr<framework::OpDesc> Apply() const {
-    auto *grad = new framework::OpDesc();
-    grad->SetType("parallel_do_grad");
-    for (auto &input_param : this->InputNames()) {
-      VLOG(3) << input_param;
-      grad->SetInput(input_param, this->Input(input_param));
-      if (input_param != kPlaces) {
-        grad->SetOutput(framework::GradVarName(input_param),
-                        this->InputGrad(input_param, false));
-      }
-    }
-    auto *g_block = this->grad_block_[0];
-
-    // All variable name that needed by gradient operators
-    std::unordered_set<std::string> all_inputs_in_grad_blocks;
-
-    for (size_t i = 0; i < g_block->OpSize(); ++i) {
-      auto *op = g_block->Op(i);
-      for (auto &var_name : op->InputArgumentNames()) {
-        all_inputs_in_grad_blocks.insert(var_name);
-      }
-    }
-
-    for (auto &output_param : this->OutputNames()) {
-      if (output_param == kParallelScopes) {
-        grad->SetInput(output_param, this->Output(output_param));
-        grad->SetInput(framework::GradVarName(output_param),
-                       this->Output(output_param));
-      } else {
-        grad->SetInput(output_param, this->Output(output_param));
-        std::vector<std::string> og_names;
-        for (auto &og_name : this->OutputGrad(output_param)) {
-          if (all_inputs_in_grad_blocks.count(og_name) != 0) {
-            // there are some gradient operators who need the OG. So make this
-            // OG as an input of parallel.do
-            og_names.push_back(og_name);
-          }
-          // else, there is no operator who need the OG. Do not use this OG as
-          // an input
-        }
-        grad->SetInput(framework::GradVarName(output_param), og_names);
-      }
-    }
-    grad->SetInput("Communicator", {"nccl_com__do_not_change_"});
-    grad->SetAttrMap(this->Attrs());
-    grad->SetBlockAttr(kParallelBlock, grad_block_[0]);
-
-    return std::unique_ptr<framework::OpDesc>(grad);
-  }
-};
-
-class ParallelDoGradOpShapeInference : public framework::InferShapeBase {
- public:
-  void operator()(framework::InferShapeContext *ctx) const override {
-    PADDLE_ENFORCE(ctx->HasInputs(kParameters));
-    PADDLE_ENFORCE(ctx->HasInputs(kInputs));
-    PADDLE_ENFORCE(ctx->HasInputs(kOutputs));
-
-    ctx->SetOutputsDim(framework::GradVarName(kParameters),
-                       ctx->GetInputsDim(kParameters));
-
-    auto i_dims = ctx->GetInputsDim(kInputs);
-    auto ig_names = ctx->Outputs(framework::GradVarName(kInputs));
-
-    for (size_t i = 0; i < ig_names.size(); ++i) {
-      auto &ig_name = ig_names[i];
-      if (ig_name == framework::kEmptyVarName) {
-        continue;
-      }
-
-      ctx->SetDims({ig_name}, {i_dims[i]});
-    }
-
-    auto p_dims = ctx->GetInputsDim(kParameters);
-    auto pg_names = ctx->Outputs(framework::GradVarName(kParameters));
-    for (size_t i = 0; i < pg_names.size(); ++i) {
-      auto &pg_name = pg_names[i];
-      if (pg_name == framework::kEmptyVarName) {
-        continue;
-      }
-      ctx->SetDims({pg_name}, {p_dims[i]});
-    }
-  }
-};
-
-class ParallelDoGradOpVarTypeInference : public framework::VarTypeInference {
- public:
-  void operator()(const framework::OpDesc &op_desc,
-                  framework::BlockDesc *block) const override {
-    framework::BlockDesc *sub_block =
-        boost::get<framework::BlockDesc *>(op_desc.GetAttr(kParallelBlock));
-    for (auto &out_vars : op_desc.Outputs()) {
-      for (auto &out_var : out_vars.second) {
-        auto &var = block->FindRecursiveOrCreateVar(out_var);
-        auto sub_var = sub_block->FindRecursiveOrCreateVar(out_var);
-        if (sub_var.GetType() != var.GetType()) {
-          var.SetType(sub_var.GetType());
-        }
-      }
-    }
-  }
-};
-
-}  // namespace operators
-}  // namespace paddle
-
-REGISTER_OPERATOR(parallel_do, paddle::operators::ParallelDoOp,
-                  paddle::operators::ParallelDoOpProtoMaker,
-                  paddle::operators::ParallelDoGradOpDescMaker);
-REGISTER_OPERATOR(parallel_do_grad, paddle::operators::ParallelDoGradOp,
-                  paddle::operators::ParallelDoGradOpShapeInference,
-                  paddle::operators::ParallelDoGradOpVarTypeInference);

paddle/fluid/operators/distributed/brpc_sendrecvop_utils.cc

@@ -16,6 +16,7 @@ limitations under the License. */
 #include <nccl.h>
 #endif
 #include <sys/time.h>
+#include <limits>
 #include <thread>  // NOLINT
 
 #include "paddle/fluid/framework/data_type.h"
@@ -31,7 +32,12 @@ namespace distributed {
 
 class IOBufWriter {
  public:
-  static void Append(butil::IOBuf* iobuf, int k, const char* v, int64_t vlen) {
+  static void Append(const std::string& varname, butil::IOBuf* iobuf, int k,
+                     const char* v, int64_t vlen) {
+    if (vlen >= std::numeric_limits<int>::max() || vlen < 0) {
+      LOG(FATAL) << "AppendZeroCopy varname:" << varname << ", vlen:" << vlen;
+    }
+
     iobuf->append(reinterpret_cast<char*>(&k), 4);
     iobuf->append(reinterpret_cast<char*>(&vlen), 8);
     iobuf->append(v, vlen);
@@ -87,6 +93,10 @@ class IOBufWriter {
                              int k, const char* v, int64_t vlen,
                              bool in_cuda_pinned, void (*destroy)(void*),
                              void* user_data) {
+    if (vlen >= std::numeric_limits<int>::max() || vlen < 0) {
+      LOG(FATAL) << "AppendZeroCopy varname:" << varname << ", vlen:" << vlen;
+    }
+
 #ifdef PADDLE_WITH_BRPC_RDMA
     IOBufWriter::AppendRdmaZeroCopy(varname, iobuf, k, v, vlen, in_cuda_pinned,
                                     destroy, user_data);
@@ -134,7 +144,7 @@ void SerializeToIOBuf(const std::string& name, framework::Variable* var,
     request->set_type(::sendrecv::NCCL_ID);
     const ncclUniqueId& uid = var->Get<ncclUniqueId>();
     // TODO(gongwb): use append_zero to avoid data copy.
-    IOBufWriter::Append(iobuf,
+    IOBufWriter::Append(name, iobuf,
                         sendrecv::VariableMessage::kSerializedFieldNumber,
                         uid.internal, NCCL_UNIQUE_ID_BYTES);
     return;
@@ -149,7 +159,7 @@ void SerializeToIOBuf(const std::string& name, framework::Variable* var,
   // FIXME(gongwb): it seems that can use zero copy.
   if (var_is_not_stable) {
     IOBufWriter::Append(
-        iobuf, ::sendrecv::VariableMessage::kSerializedFieldNumber,
+        name, iobuf, ::sendrecv::VariableMessage::kSerializedFieldNumber,
         static_cast<const char*>(payload->ptr()), payload->memory_size());
   } else {
     if (platform::is_gpu_place(ctx.GetPlace())) {
@@ -171,9 +181,11 @@ void SerializeToIOBuf(const std::string& name, framework::Variable* var,
 
   if (var->IsType<framework::SelectedRows>()) {
     auto* slr = var->GetMutable<framework::SelectedRows>();
+    PADDLE_ENFORCE(VectorElemName(slr->rows()) == typeid(int64_t).name());
     size_t rows_memory_size = slr->rows().size() * sizeof(int64_t);
 
-    IOBufWriter::Append(iobuf, ::sendrecv::VariableMessage::kRowsFieldNumber,
+    IOBufWriter::Append(name, iobuf,
+                        ::sendrecv::VariableMessage::kRowsFieldNumber,
                         reinterpret_cast<const char*>(slr->rows().data()),
                         static_cast<int64_t>(rows_memory_size));
   }

paddle/fluid/operators/distributed/grpc_serde.cc

@@ -15,6 +15,7 @@ limitations under the License. */
 #ifdef PADDLE_WITH_CUDA
 #include <nccl.h>
 #endif
+#include <limits>
 #include <thread>  // NOLINT
 
 #include "google/protobuf/io/coded_stream.h"
@@ -102,6 +103,10 @@ void SerializeToByteBuffer(const std::string& name, framework::Variable* var,
 
   e.WriteVarlengthBeginning(VarMsg::kSerializedFieldNumber,
                             payload->memory_size());
+  if (payload->memory_size() >= std::numeric_limits<int>::max()) {
+    LOG(FATAL) << "AppendZeroCopy varname:" << name
+               << ", vlen:" << payload->memory_size();
+  }
   // steal reference of tensor data
   ::grpc::Slice slices[4];  // metadata, tensor, rows meta, rows
   int num_slices = 2;       // only SelectedRows have rows buffer
@@ -115,7 +120,10 @@ void SerializeToByteBuffer(const std::string& name, framework::Variable* var,
   if (var->IsType<framework::SelectedRows>()) {
     auto* slr = var->GetMutable<framework::SelectedRows>();
     ProtoEncodeHelper e2(static_cast<char*>(buf), 128);
+
+    PADDLE_ENFORCE(VectorElemName(slr->rows()) == typeid(int64_t).name());
     size_t rows_memory_size = slr->rows().size() * sizeof(int64_t);
+
     e2.WriteVarlengthBeginning(VarMsg::kRowsFieldNumber, rows_memory_size);
     slices[2] = ::grpc::Slice(e2.size());
     memcpy(const_cast<uint8_t*>(slices[2].begin()), e2.data(), e2.size());

paddle/fluid/operators/distributed/sendrecvop_utils.h

@@ -15,6 +15,7 @@ limitations under the License. */
 #pragma once
 #include <iostream>
 #include <string>
+#include <typeindex>
 #include <vector>
 
 #include "paddle/fluid/framework/data_type.h"
@@ -23,9 +24,8 @@ limitations under the License. */
 #include "paddle/fluid/framework/selected_rows.h"
 #include "paddle/fluid/framework/tensor_util.h"
 #include "paddle/fluid/framework/var_type.h"
-#include "paddle/fluid/platform/port.h"
-
 #include "paddle/fluid/operators/distributed/send_recv.pb.h"
+#include "paddle/fluid/platform/port.h"
 
 namespace paddle {
 namespace operators {
@@ -83,6 +83,11 @@ inline framework::proto::VarType::Type ToVarType(
   }
 }
 
+template <template <typename> class T, typename Elem>
+std::string VectorElemName(const T<Elem>& arg) {
+  return typeid(Elem).name();
+}
+
 }  // namespace distributed
 }  // namespace operators
 }  // namespace paddle

paddle/fluid/operators/distributed/variable_response.cc

@@ -118,7 +118,7 @@ bool VariableResponse::CopyLodTensorData(
 
   VLOG(6) << "Tensor.memory_size = " << tensor->memory_size()
           << ", Buffer Size = " << length;
-  PADDLE_ENFORCE_EQ(tensor->memory_size(), length);
+  PADDLE_ENFORCE_EQ(tensor->memory_size(), static_cast<unsigned int>(length));
   return ReadRaw(input, ctx, tensor->place(), tensor_data, length);
 }
 

paddle/fluid/operators/ngraph/ngraph_ops.h

+/* 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. */
+
+/*
+ * This file contains the list of the ngraph operators for Paddle.
+ *
+ * ATTENTION: It requires some C++11 features, for lower version C++ or C, we
+ * might release another API.
+ */
+
+#pragma once
+
+#include "ops/binary_unnary_op.h"
+#include "ops/mul_op.h"

paddle/fluid/operators/ngraph/ops/binary_unnary_op.h

+/* 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. */
+
+#ifdef PADDLE_WITH_NGRAPH
+#pragma once
+
+#include <string>
+#include "ngraph/ngraph.hpp"
+#include "paddle/fluid/platform/ngraph_helper.h"
+
+namespace paddle {
+namespace operators {
+namespace ngraphs {
+
+template <typename T>
+static void BuildBinaryNode(
+    const std::shared_ptr<paddle::framework::OperatorBase>& op,
+    std::shared_ptr<
+        std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
+        ngb_node_map) {
+  auto x = paddle::platform::GetInputNode(op, "X", ngb_node_map);
+  auto y = paddle::platform::GetInputNode(op, "Y", ngb_node_map);
+  auto out = std::make_shared<T>(x, y);
+  paddle::platform::SetOutputNode(op, "Out", out, ngb_node_map);
+}
+
+template <typename T>
+static void BuildUnaryNode(
+    const std::shared_ptr<paddle::framework::OperatorBase>& op,
+    std::shared_ptr<
+        std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
+        ngb_node_map) {
+  auto input = paddle::platform::GetInputNode(op, "X", ngb_node_map);
+  auto out = std::make_shared<T>(input);
+  paddle::platform::SetOutputNode(op, "Out", out, ngb_node_map);
+}
+
+}  // namespace ngraphs
+}  // namespace operators
+}  // namespace paddle
+#endif

paddle/fluid/operators/ngraph/ops/mul_op.h

+/*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. */
+
+#ifdef PADDLE_WITH_NGRAPH
+#pragma once
+
+#include <string>
+#include "ngraph/ngraph.hpp"
+#include "paddle/fluid/platform/ngraph_helper.h"
+
+namespace paddle {
+namespace operators {
+namespace ngraphs {
+
+static void BuildMulNode(
+    const std::shared_ptr<paddle::framework::OperatorBase>& op,
+    std::shared_ptr<
+        std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
+        ngb_node_map) {
+  auto op_attrs = paddle::framework::AttrReader(op->Attrs());
+  int x_num_col_dims = op_attrs.Get<int>("x_num_col_dims");
+  int y_num_col_dims = op_attrs.Get<int>("y_num_col_dims");
+  auto x = paddle::platform::GetInputNode(op, "X", ngb_node_map);
+  auto y = paddle::platform::GetInputNode(op, "Y", ngb_node_map);
+
+  auto x_reshape = x;
+  auto y_reshape = y;
+
+  if (x->get_shape().size() > 2) {
+    auto x_2d = paddle::platform::FlattenTo2d(x->get_shape(), x_num_col_dims);
+    x_reshape = paddle::platform::NgReshaper(x, x_2d);
+  }
+
+  if (y->get_shape().size() > 2) {
+    auto y_2d = paddle::platform::FlattenTo2d(y->get_shape(), y_num_col_dims);
+    y_reshape = paddle::platform::NgReshaper(y, y_2d);
+  }
+
+  std::shared_ptr<ngraph::Node> out =
+      std::make_shared<ngraph::op::Dot>(x_reshape, y_reshape);
+
+  auto dummy_out = paddle::platform::GetOutputNode(op, "Out", ngb_node_map);
+  if (dummy_out && dummy_out->get_shape() != out->get_shape()) {
+    out = paddle::platform::NgReshaper(out, dummy_out->get_shape());
+  }
+  paddle::platform::SetOutputNode(op, "Out", out, ngb_node_map);
+}
+
+static void BuildMulGradNode(
+    const std::shared_ptr<paddle::framework::OperatorBase>& op,
+    std::shared_ptr<
+        std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
+        ngb_node_map) {
+  auto op_attrs = paddle::framework::AttrReader(op->Attrs());
+  int x_num_col_dims = op_attrs.Get<int>("x_num_col_dims");
+  int y_num_col_dims = op_attrs.Get<int>("y_num_col_dims");
+  auto x = paddle::platform::GetInputNode(op, "X", ngb_node_map);
+  auto y = paddle::platform::GetInputNode(op, "Y", ngb_node_map);
+  auto dout = paddle::platform::GetInputNode(op, "Out@GRAD", ngb_node_map);
+
+  bool is_dx = paddle::platform::HasOutput(op, "X@GRAD") ? true : false;
+  bool is_dy = paddle::platform::HasOutput(op, "Y@GRAD") ? true : false;
+
+  auto x_shape = x->get_shape();
+  auto y_shape = y->get_shape();
+
+  auto x_reshape = x;
+  auto y_reshape = y;
+
+  if (x_shape.size() > 2) {
+    auto x_2d_shape = paddle::platform::FlattenTo2d(x_shape, x_num_col_dims);
+    x_reshape = paddle::platform::NgReshaper(x, x_2d_shape);
+  }
+
+  if (y_shape.size() > 2) {
+    auto y_2d_shape = paddle::platform::FlattenTo2d(y_shape, y_num_col_dims);
+    y_reshape = paddle::platform::NgReshaper(y, y_2d_shape);
+  }
+
+  auto x_reshape_shape = x_reshape->get_shape();
+  std::reverse(x_reshape_shape.begin(), x_reshape_shape.end());
+  auto x_transpose = std::make_shared<ngraph::op::Reshape>(
+      x_reshape, ngraph::AxisVector{1, 0}, x_reshape_shape);
+
+  auto y_reshape_shape = y_reshape->get_shape();
+  std::reverse(y_reshape_shape.begin(), y_reshape_shape.end());
+  auto y_transpose = std::make_shared<ngraph::op::Reshape>(
+      y_reshape, ngraph::AxisVector{1, 0}, y_reshape_shape);
+
+  if (is_dx) {
+    if (dout->get_shape().size() > 2) {
+      auto dout_2d_shape = paddle::platform::FlattenTo2d(dout->get_shape(), 2);
+      dout = paddle::platform::NgReshaper(dout, dout_2d_shape);
+    }
+    auto dx = std::make_shared<ngraph::op::Dot>(dout, y_transpose);
+
+    if (dx->get_shape() == x_shape) {
+      paddle::platform::SetOutputNode(op, "X@GRAD", dx, ngb_node_map);
+    } else {
+      auto dx_reshape = paddle::platform::NgReshaper(dx, x_shape);
+      paddle::platform::SetOutputNode(op, "X@GRAD", dx_reshape, ngb_node_map);
+    }
+  }
+
+  if (is_dy) {
+    if (dout->get_shape().size() > 2) {
+      auto dout_2d_shape = paddle::platform::FlattenTo2d(dout->get_shape(), 2);
+      dout = paddle::platform::NgReshaper(dout, dout_2d_shape);
+    }
+    auto dy = std::make_shared<ngraph::op::Dot>(x_transpose, dout);
+
+    if (dy->get_shape() == y_shape) {
+      paddle::platform::SetOutputNode(op, "Y@GRAD", dy, ngb_node_map);
+    } else {
+      auto dy_reshape = paddle::platform::NgReshaper(dy, y_shape);
+      paddle::platform::SetOutputNode(op, "Y@GRAD", dy_reshape, ngb_node_map);
+    }
+  }
+}
+}  // namespace ngraphs
+}  // namespace operators
+}  // namespace paddle
+#endif

paddle/fluid/operators/optimizers/adam_op.cc

@@ -109,6 +109,11 @@ class AdamOpMaker : public framework::OpProtoAndCheckerMaker {
                    "(float, default 1.0e-8) "
                    "Constant for numerical stability")
         .SetDefault(1.0e-8f);
+    AddAttr<bool>(
+        "lazy_mode",
+        "(bool, default false) "
+        "only update the parameter that has gradient in sparse update")
+        .SetDefault(false);
 
     AddComment(R"DOC(
 Adam Optimizer.

paddle/fluid/operators/optimizers/adam_op.h

@@ -177,12 +177,13 @@ struct SparseAdamFunctor {
   const int64_t* rows_;
   int64_t row_numel_;
   int64_t row_count_;
+  bool lazy_mode_;
 
   SparseAdamFunctor(T beta1, T beta2, T epsilon, const T* beta1_pow,
                     const T* beta2_pow, const T* mom1, T* mom1_out,
                     const T* mom2, T* mom2_out, const T* lr, const T* grad,
                     const T* param, T* param_out, const int64_t* rows,
-                    int64_t row_numel, int64_t row_count)
+                    int64_t row_numel, int64_t row_count, bool lazy_mode)
       : beta1_(beta1),
         beta2_(beta2),
         epsilon_(epsilon),
@@ -198,13 +199,10 @@ struct SparseAdamFunctor {
         param_out_(param_out),
         rows_(rows),
         row_numel_(row_numel),
-        row_count_(row_count) {}
-
-  inline HOSTDEVICE void operator()(size_t i) const {
-    auto row_idx =
-        math::BinarySearch<int64_t>(rows_, row_count_, i / row_numel_);
-    T g = row_idx >= 0 ? grad_[row_idx * row_numel_ + i % row_numel_] : 0;
+        row_count_(row_count),
+        lazy_mode_(lazy_mode) {}
 
+  inline HOSTDEVICE void adam_update(size_t i, T g) const {
     // The following code is the same as dense
     T mom1 = moment1_[i];
     T mom2 = moment2_[i];
@@ -225,6 +223,17 @@ struct SparseAdamFunctor {
     moment2_out_[i] = mom2;
     param_out_[i] = p;
   }
+
+  inline HOSTDEVICE void operator()(size_t i) const {
+    auto row_idx =
+        math::BinarySearch<int64_t>(rows_, row_count_, i / row_numel_);
+    if (lazy_mode_ && row_idx < 0) {
+      return;
+    } else {
+      T g = row_idx >= 0 ? grad_[row_idx * row_numel_ + i % row_numel_] : 0;
+      adam_update(i, g);
+    }
+  }
 };
 
 template <typename DeviceContext, typename T>
@@ -241,6 +250,7 @@ class AdamOpKernel : public framework::OpKernel<T> {
     using paddle::framework::LoDTensor;
     using paddle::operators::detail::Ref;
 
+    bool lazy_mode = ctx.Attr<bool>("lazy_mode");
     T beta1 = static_cast<T>(ctx.Attr<float>("beta1"));
     T beta2 = static_cast<T>(ctx.Attr<float>("beta2"));
     T epsilon = static_cast<T>(ctx.Attr<float>("epsilon"));
@@ -352,11 +362,23 @@ class AdamOpKernel : public framework::OpKernel<T> {
           mom2_out.template mutable_data<T>(ctx.GetPlace()),
           lr.template data<T>(), grad_data, param.template data<T>(),
           param_out.template mutable_data<T>(ctx.GetPlace()), rows, row_numel,
-          grad_merge.rows().size());
-      platform::ForRange<DeviceContext> for_range(
-          static_cast<const DeviceContext&>(ctx.device_context()),
-          param.numel());
-      for_range(functor);
+          grad_merge.rows().size(), lazy_mode);
+      VLOG(3) << "lazy_mode :" << lazy_mode;
+      if (lazy_mode && platform::is_cpu_place(ctx.GetPlace())) {
+        size_t row_count = grad_merge.rows().size();
+        std::vector<int64_t> cpu_rows(grad_merge.rows());
+        for (size_t row_index = 0; row_index < row_count; ++row_index) {
+          for (size_t offset = 0; offset < row_numel; ++offset) {
+            size_t i = cpu_rows[row_index] * row_numel + offset;
+            functor.adam_update(i, grad_data[row_index * row_numel + offset]);
+          }
+        }
+      } else {
+        platform::ForRange<DeviceContext> for_range(
+            static_cast<const DeviceContext&>(ctx.device_context()),
+            param.numel());
+        for_range(functor);
+      }
     } else {
       PADDLE_THROW("Variable type not supported by adam_op");
     }

paddle/fluid/operators/transpose_mkldnn_op.cc

+/* 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. */
+
+#include "paddle/fluid/framework/data_layout_transform.h"
+#include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/memory/malloc.h"
+#include "paddle/fluid/platform/mkldnn_reuse.h"
+
+namespace paddle {
+namespace operators {
+
+using Tensor = framework::Tensor;
+using framework::DataLayout;
+
+template <typename T>
+class TransposeMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
+ public:
+  void Compute(const paddle::framework::ExecutionContext& ctx) const override {
+    PADDLE_ENFORCE(paddle::platform::is_cpu_place(ctx.GetPlace()),
+                   "It must use CPUPlace.");
+    const bool is_test = ctx.Attr<bool>("is_test");
+    PADDLE_ENFORCE(
+        is_test == true,
+        "ConvTransposeMKLDNN works only for inference!. Set is_test = True");
+    auto& dev_ctx =
+        ctx.template device_context<paddle::platform::MKLDNNDeviceContext>();
+    const auto& mkldnn_engine = dev_ctx.GetEngine();
+    std::vector<int> axis = ctx.Attr<std::vector<int>>("axis");
+    int ndims = axis.size();
+    auto* input = ctx.Input<Tensor>("X");
+    auto* output = ctx.Output<Tensor>("Out");
+    const T* input_data = input->data<T>();
+
+    if (ndims == 1) {
+      output->ShareDataWith(*input);
+      return;
+    }
+
+    std::vector<int> nchw_axis(ndims, 0);
+    for (size_t i = 0; i < nchw_axis.size(); ++i) {
+      nchw_axis[i] = i;
+    }
+
+    std::vector<int> nchw_tz = paddle::framework::vectorize2int(input->dims());
+    std::string data_format = ctx.Attr<std::string>("data_format");
+
+    auto src_md =
+        input->format() != mkldnn::memory::format::nchw
+            ? platform::MKLDNNMemDesc(nchw_tz, platform::MKLDNNGetDataType<T>(),
+                                      input->format())
+            : Axis2MemoryDesc(nchw_tz, nchw_axis);
+
+    this->TransposeKernel(ctx.GetPlace(), Axis2MemoryDesc(nchw_tz, axis),
+                          src_md, output, input_data, nchw_tz, mkldnn_engine);
+  }
+
+ protected:
+  mkldnn::memory::desc Axis2MemoryDesc(std::vector<int>& nchw_tz,
+                                       std::vector<int>& axis) const {
+    mkldnn_memory_desc_t mem_fmt;
+
+    mem_fmt.primitive_kind = mkldnn_memory;
+    mem_fmt.ndims = axis.size();
+    for (unsigned int i = 0; i < nchw_tz.size(); ++i) {
+      mem_fmt.dims[i] = nchw_tz[i];  // logical dimensions (nchw format,
+                                     // regardless physical layout)
+    }
+    mem_fmt.data_type = mkldnn_f32;
+    mem_fmt.format = mkldnn_blocked;
+
+    unsigned int total_stride = 1;
+    for (int i = nchw_tz.size() - 1; i >= 0; --i) {
+      mem_fmt.layout_desc.blocking.padding_dims[i] =
+          nchw_tz[i];  // logical dimensions (nchw format, regardless physical
+                       // layout)
+      mem_fmt.layout_desc.blocking.block_dims[i] = 1;
+      mem_fmt.layout_desc.blocking.offset_padding_to_data[i] = 0;  // no offset
+      mem_fmt.layout_desc.blocking.strides[0][axis[i]] = total_stride;
+      mem_fmt.layout_desc.blocking.strides[1][axis[i]] = 1;
+      total_stride *= nchw_tz[axis[i]];
+    }
+    mem_fmt.layout_desc.blocking.offset_padding = 0;  // no initial offset
+    return mem_fmt;
+  }
+
+  void TransposeKernel(platform::Place place, mkldnn::memory::desc md_o,
+                       mkldnn::memory::desc md_i, Tensor* output,
+                       const T* data_i, std::vector<int>& nchw_dims,
+                       const mkldnn::engine& eng) const {
+    // Make Memory primitive descriptors
+    auto mpd_o = mkldnn::memory::primitive_desc(md_o, eng);
+    auto mpd_i = mkldnn::memory::primitive_desc(md_i, eng);
+
+    auto data_o = output->mutable_data<T>(
+        place, paddle::memory::Allocator::kDefault, mpd_o.get_size());
+
+    auto src = mkldnn::memory(mpd_i, (T*)(data_i));
+    auto dst = mkldnn::memory(mpd_o, data_o);
+
+    auto r = mkldnn::reorder(src, dst);
+    mkldnn::stream(mkldnn::stream::kind::eager).submit({r}).wait();
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+
+REGISTER_OP_KERNEL(transpose2, MKLDNN, ::paddle::platform::CPUPlace,
+                   ops::TransposeMKLDNNOpKernel<float>);
+REGISTER_OP_KERNEL(transpose, MKLDNN, ::paddle::platform::CPUPlace,
+                   ops::TransposeMKLDNNOpKernel<float>);

paddle/fluid/operators/transpose_op.cc

@@ -16,6 +16,10 @@ limitations under the License. */
 #include <string>
 #include <vector>
 
+#ifdef PADDLE_WITH_MKLDNN
+#include "paddle/fluid/platform/mkldnn_helper.h"
+#endif
+
 namespace paddle {
 namespace operators {
 
@@ -53,11 +57,32 @@ class TransposeOp : public framework::OperatorWithKernel {
     }
     ctx->SetOutputDim("Out", out_dims);
   }
+
+ protected:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext &ctx) const override {
+    framework::LibraryType library_{framework::LibraryType::kPlain};
+    std::string data_format = ctx.Attr<std::string>("data_format");
+    framework::DataLayout layout_ = framework::StringToDataLayout(data_format);
+#ifdef PADDLE_WITH_MKLDNN
+    if (library_ == framework::LibraryType::kPlain &&
+        platform::CanMKLDNNBeUsed(ctx)) {
+      library_ = framework::LibraryType::kMKLDNN;
+      layout_ = framework::DataLayout::kMKLDNN;
+    }
+#endif
+    return framework::OpKernelType(ctx.Input<Tensor>("X")->type(),
+                                   ctx.GetPlace(), layout_, library_);
+  }
 };
 
 class TransposeOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
   void Make() override {
+    AddAttr<bool>("is_test",
+                  "(bool, default false) Set to true for inference only, false "
+                  "for training. Some layers may run faster when this is true.")
+        .SetDefault(false);
     AddInput(
         "X",
         "(Tensor) The input tensor, tensors with rank up to 6 are supported.");
@@ -67,6 +92,16 @@ class TransposeOpMaker : public framework::OpProtoAndCheckerMaker {
         "(vector<int>) A list of values, and the size of the list should be "
         "the same with the input tensor rank. This operator permutes the input "
         "tensor's axes according to the values given.");
+    AddAttr<bool>("use_mkldnn",
+                  "(bool, default false) Only used in mkldnn kernel")
+        .SetDefault(false);
+    AddAttr<std::string>(
+        "data_format",
+        "(string, default NCHW) Only used in "
+        "An optional string from: \"NHWC\", \"NCHW\". "
+        "Defaults to \"NHWC\". Specify the data format of the output data, "
+        "the input will be transformed automatically. ")
+        .SetDefault("AnyLayout");
     AddComment(R"DOC(
 Transpose Operator.
 
@@ -144,8 +179,18 @@ class Transpose2Op : public TransposeOp {
  protected:
   framework::OpKernelType GetExpectedKernelType(
       const framework::ExecutionContext &ctx) const override {
-    return framework::OpKernelType(ctx.Input<framework::LoDTensor>("X")->type(),
-                                   ctx.device_context());
+    framework::LibraryType library_{framework::LibraryType::kPlain};
+    std::string data_format = ctx.Attr<std::string>("data_format");
+    framework::DataLayout layout_ = framework::StringToDataLayout(data_format);
+#ifdef PADDLE_WITH_MKLDNN
+    if (library_ == framework::LibraryType::kPlain &&
+        platform::CanMKLDNNBeUsed(ctx)) {
+      library_ = framework::LibraryType::kMKLDNN;
+      layout_ = framework::DataLayout::kMKLDNN;
+    }
+#endif
+    return framework::OpKernelType(ctx.Input<Tensor>("X")->type(),
+                                   ctx.GetPlace(), layout_, library_);
   }
 };
 

paddle/fluid/platform/dynload/CMakeLists.txt

@@ -16,9 +16,7 @@ if (CUPTI_FOUND)
     list(APPEND CUDA_SRCS cupti.cc)
 endif(CUPTI_FOUND)
 nv_library(dynload_cuda SRCS ${CUDA_SRCS} DEPS dynamic_loader)
-if (NOT WIN32)
 cc_library(dynload_warpctc SRCS warpctc.cc DEPS dynamic_loader warpctc)
-endif(NOT WIN32)
 if (WITH_MKLML)
     cc_library(dynload_mklml SRCS mklml.cc DEPS dynamic_loader mklml)
 endif()

paddle/fluid/platform/dynload/cudnn.h

@@ -34,7 +34,7 @@ extern void EnforceCUDNNLoaded(const char* fn_name);
 #define DECLARE_DYNAMIC_LOAD_CUDNN_WRAP(__name)                            \
   struct DynLoad__##__name {                                               \
     template <typename... Args>                                            \
-    auto operator()(Args... args) -> decltype(__name(args...)) {           \
+    auto operator()(Args... args) -> DECLARE_TYPE(__name, args...) {       \
       using cudnn_func = decltype(&::__name);                              \
       std::call_once(cudnn_dso_flag, []() {                                \
         cudnn_dso_handle = paddle::platform::dynload::GetCUDNNDsoHandle(); \

paddle/fluid/platform/dynload/dynamic_loader.cc

@@ -201,6 +201,8 @@ void* GetCurandDsoHandle() {
 void* GetWarpCTCDsoHandle() {
 #if defined(__APPLE__) || defined(__OSX__)
   return GetDsoHandleFromSearchPath(FLAGS_warpctc_dir, "libwarpctc.dylib");
+#elif defined(_WIN32)
+  return GetDsoHandleFromSearchPath(FLAGS_warpctc_dir, "warpctc.dll");
 #else
   return GetDsoHandleFromSearchPath(FLAGS_warpctc_dir, "libwarpctc.so");
 #endif

paddle/fluid/platform/dynload/dynamic_loader.h

@@ -18,6 +18,12 @@ namespace paddle {
 namespace platform {
 namespace dynload {
 
+#ifndef _WIN32
+#define DECLARE_TYPE(__name, ...) decltype(__name(__VA_ARGS__))
+#else
+#define DECLARE_TYPE(__name, ...) decltype(auto)
+#endif
+
 void* GetCublasDsoHandle();
 void* GetCUDNNDsoHandle();
 void* GetCUPTIDsoHandle();

paddle/fluid/platform/dynload/mklml.h

@@ -34,7 +34,7 @@ extern void* mklml_dso_handle;
 #define DYNAMIC_LOAD_MKLML_WRAP(__name)                                    \
   struct DynLoad__##__name {                                               \
     template <typename... Args>                                            \
-    auto operator()(Args... args) -> decltype(__name(args...)) {           \
+    auto operator()(Args... args) -> DECLARE_TYPE(__name, args...) {       \
       using mklmlFunc = decltype(&::__name);                               \
       std::call_once(mklml_dso_flag, []() {                                \
         mklml_dso_handle = paddle::platform::dynload::GetMKLMLDsoHandle(); \

paddle/fluid/platform/dynload/tensorrt.h

@@ -33,7 +33,7 @@ extern void* tensorrt_dso_handle;
 #define DECLARE_DYNAMIC_LOAD_TENSORRT_WRAP(__name)                      \
   struct DynLoad__##__name {                                            \
     template <typename... Args>                                         \
-    auto operator()(Args... args) -> decltype(__name(args...)) {        \
+    auto operator()(Args... args) -> DECLARE_TYPE(__name, args...) {    \
       using tensorrt_func = decltype(__name(args...)) (*)(Args...);     \
       std::call_once(tensorrt_dso_flag, []() {                          \
         tensorrt_dso_handle =                                           \

paddle/fluid/platform/dynload/warpctc.h

@@ -34,7 +34,7 @@ extern void* warpctc_dso_handle;
 #define DYNAMIC_LOAD_WARPCTC_WRAP(__name)                                      \
   struct DynLoad__##__name {                                                   \
     template <typename... Args>                                                \
-    auto operator()(Args... args) -> decltype(__name(args...)) {               \
+    auto operator()(Args... args) -> DECLARE_TYPE(__name, args...) {           \
       using warpctcFunc = decltype(&::__name);                                 \
       std::call_once(warpctc_dso_flag, []() {                                  \
         warpctc_dso_handle = paddle::platform::dynload::GetWarpCTCDsoHandle(); \

paddle/fluid/platform/ngraph_helper.h

+/* 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. */
+
+#ifdef PADDLE_WITH_NGRAPH
+#pragma once
+
+#include <functional>
+#include <string>
+#include <vector>
+#include "ngraph/ngraph.hpp"
+
+namespace paddle {
+namespace platform {
+
+static ngraph::Shape FlattenTo2d(ngraph::Shape sh, int num) {
+  auto x1 = std::accumulate(std::begin(sh), std::begin(sh) + num, 1,
+                            std::multiplies<size_t>());
+  auto x2 = std::accumulate(std::begin(sh) + num, std::end(sh), 1,
+                            std::multiplies<size_t>());
+  size_t x1_l = static_cast<size_t>(x1);
+  size_t x2_l = static_cast<size_t>(x2);
+  return ngraph::Shape{x1_l, x2_l};
+}
+
+static std::shared_ptr<ngraph::Node> NgReshaper(
+    std::shared_ptr<ngraph::Node> input, ngraph::Shape shape) {
+  std::vector<size_t> input_order(input->get_shape().size());
+  std::iota(std::begin(input_order), std::end(input_order), 0);
+  return std::make_shared<ngraph::op::Reshape>(
+      input, ngraph::AxisVector(input_order), shape);
+}
+
+static std::shared_ptr<ngraph::Node> GetNode(
+    const std::shared_ptr<paddle::framework::OperatorBase>& op,
+    const std::string prm, const paddle::framework::VariableNameMap& var_map,
+    std::shared_ptr<
+        std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
+        ngb_node_map) {
+  auto& var_names = var_map.at(prm);
+  PADDLE_ENFORCE_EQ(var_names.size(), 1,
+                    "op %s prm %s expects one associated var", op->Type(), prm);
+  if (ngb_node_map->find(var_names[0]) != ngb_node_map->end()) {
+    return (*ngb_node_map)[var_names[0]];
+  } else {
+    return nullptr;
+  }
+}
+
+static std::shared_ptr<ngraph::Node> GetInputNode(
+    const std::shared_ptr<paddle::framework::OperatorBase>& op,
+    const std::string prm,
+    std::shared_ptr<
+        std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
+        ngb_node_map) {
+  return GetNode(op, prm, op->Inputs(), ngb_node_map);
+}
+
+static std::shared_ptr<ngraph::Node> GetOutputNode(
+    const std::shared_ptr<paddle::framework::OperatorBase>& op,
+    const std::string prm,
+    std::shared_ptr<
+        std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
+        ngb_node_map) {
+  return GetNode(op, prm, op->Outputs(), ngb_node_map);
+}
+
+static void SetOutputNode(
+    const std::shared_ptr<paddle::framework::OperatorBase>& op,
+    const std::string prm, std::shared_ptr<ngraph::Node> node,
+    std::shared_ptr<
+        std::unordered_map<std::string, std::shared_ptr<ngraph::Node>>>
+        ngb_node_map) {
+  auto& var_names = op->Outputs().at(prm);
+  if (var_names.size() == 1) {
+    (*ngb_node_map)[var_names[0]] = node;
+  } else if (var_names.size() == 0) {
+    (*ngb_node_map)[""] = node;
+  } else {
+    PADDLE_THROW("prm %s has more than 1 var_names.", prm);
+  }
+}
+
+static bool HasOutput(
+    const std::shared_ptr<paddle::framework::OperatorBase>& op,
+    const std::string prm) {
+  auto& outputs = op->Outputs();
+  if (outputs.find(prm) == outputs.end()) return false;
+  return outputs.at(prm).size() > 0;
+}
+
+}  // namespace platform
+}  // namespace paddle
+
+#endif

paddle/fluid/platform/port.h

@@ -55,7 +55,6 @@ static void *dlsym(void *handle, const char *symbol_name) {
 
 static void *dlopen(const char *filename, int flag) {
   std::string file_name(filename);
-  file_name.replace(0, file_name.size() - 1, '/', '\\');
   HMODULE hModule = LoadLibrary(file_name.c_str());
   if (!hModule) {
     throw std::runtime_error(file_name + " not found.");

paddle/scripts/paddle_build.sh

@@ -509,11 +509,11 @@ function assert_api_spec_approvals() {
       if [ ${API_CHANGE} ] && [ "${GIT_PR_ID}" != "" ]; then
           # NOTE: per_page=10000 should be ok for all cases, a PR review > 10000 is not human readable.
           APPROVALS=`curl -H "Authorization: token ${GITHUB_API_TOKEN}" https://api.github.com/repos/PaddlePaddle/Paddle/pulls/${GIT_PR_ID}/reviews?per_page=10000 | \
-          python ${PADDLE_ROOT}/tools/check_pr_approval.py 2 7845005 2887803 728699 13348433`
+          python ${PADDLE_ROOT}/tools/check_pr_approval.py 1 2887803`
           echo "current pr ${GIT_PR_ID} got approvals: ${APPROVALS}"
           if [ "${APPROVALS}" == "FALSE" ]; then
-              echo "You must have at least 2 approvals for the api change! ${API_FILE}"
-          exit 1
+              echo "You must have panyx0718 approval for the api change! ${API_FILE}"
+              exit 1
           fi
       fi
     done
@@ -521,11 +521,11 @@ function assert_api_spec_approvals() {
     HAS_CONST_CAST=`git diff -U0 upstream/$BRANCH |grep -o -m 1 "const_cast" || true`
     if [ ${HAS_CONST_CAST} ] && [ "${GIT_PR_ID}" != "" ]; then
         APPROVALS=`curl -H "Authorization: token ${GITHUB_API_TOKEN}" https://api.github.com/repos/PaddlePaddle/Paddle/pulls/${GIT_PR_ID}/reviews?per_page=10000 | \
-        python ${PADDLE_ROOT}/tools/check_pr_approval.py 2 7845005 2887803 728699 13348433`
+        python ${PADDLE_ROOT}/tools/check_pr_approval.py 1 2887803`
         echo "current pr ${GIT_PR_ID} got approvals: ${APPROVALS}"
         if [ "${APPROVALS}" == "FALSE" ]; then
-            echo "You must have at least 2 approvals for the const_cast"
-        exit 1
+            echo "You must have panyx0718 approval for the const_cast"
+            exit 1
         fi
     fi
 

python/paddle/fluid/__init__.py

@@ -102,6 +102,13 @@ def __bootstrap__():
     import sys
     import os
     import platform
+
+    if os.name == 'nt':
+        third_lib_path = os.path.abspath(os.path.dirname(
+            __file__)) + os.sep + '..' + os.sep + 'libs'
+        os.environ['path'] += ';' + third_lib_path
+        sys.path.append(third_lib_path)
+
     from . import core
 
     in_test = 'unittest' in sys.modules
@@ -128,13 +135,12 @@ def __bootstrap__():
         'free_idle_memory', 'paddle_num_threads', "dist_threadpool_size",
         'eager_delete_tensor_gb', 'fast_eager_deletion_mode',
         'allocator_strategy', 'reader_queue_speed_test_mode',
-        'print_sub_graph_dir', 'pe_profile_fname'
+        'print_sub_graph_dir', 'pe_profile_fname', 'warpctc_dir'
     ]
     if 'Darwin' not in sysstr:
         read_env_flags.append('use_pinned_memory')
 
     if os.name != 'nt':
-        read_env_flags.append('warpctc_dir')
         read_env_flags.append('cpu_deterministic')
 
     if core.is_compiled_with_dist():

python/paddle/fluid/backward.py

@@ -249,69 +249,6 @@ def serialize_op_decs(op_desc):
     return proto.__str__()
 
 
-def _callback_lookup_(op):
-    """
-    Only used in _append_backward_ops_
-    Build and returns a callback function for certain op. For example
-
-    parallel_do:           AllReduce
-
-    :param op:
-    :return: callback function
-    """
-    if op.type == 'parallel_do' and op.attr('use_nccl'):
-        all_vars = op.block.vars
-        param_names = set(op.input('parameters'))
-        param_names = [
-            name for name in param_names
-            if all_vars[name].stop_gradient is False
-        ]
-        param_grad_names = [n + "@GRAD" for n in param_names]
-
-        class ParallelDoCallBack(object):
-            def __init__(self, param_grad_names, parallel_scopes_name):
-                self.has_inserted_nccl_init = False
-                self.param_grad_names = param_grad_names
-                self.parallel_scopes_name = parallel_scopes_name
-
-            def __call__(self, block, context):
-                if not self.has_inserted_nccl_init:
-                    op_desc = _create_op_desc_(
-                        "ncclInit",
-                        {"parallel_scopes": self.parallel_scopes_name},
-                        {"Communicator": ['nccl_com__do_not_change_']}, {})
-                    block.program.global_block().desc.append_op().copy_from(
-                        op_desc)
-                    self.has_inserted_nccl_init = True
-
-                current_op_desc = context["__current_op_desc__"]
-                for o_param in current_op_desc.output_names():
-                    for o_argu in current_op_desc.output(o_param):
-                        if o_argu in self.param_grad_names:
-                            allreduce_out_name = o_argu + "__nccl_all_reduce__"
-                            op_desc = _create_op_desc_(
-                                "ncclReduce",
-                                {
-                                    "X": [o_argu],
-                                    "Communicator":
-                                    ['nccl_com__do_not_change_']
-                                },
-                                {"Out": [allreduce_out_name]},
-                                {"reduction": "ncclSum",
-                                 "root": 0}, )
-                            block.desc.append_op().copy_from(op_desc)
-
-                            op_desc = _create_op_desc_(
-                                "assign", {"X": [allreduce_out_name]},
-                                {"Out": [o_argu]}, {})
-                            block.desc.append_op().copy_from(op_desc)
-
-        return ParallelDoCallBack(param_grad_names,
-                                  op.output("parallel_scopes"))
-    else:
-        return None
-
-
 def _append_backward_ops_(block,
                           ops,
                           target_block,
@@ -349,17 +286,8 @@ def _append_backward_ops_(block,
             sub_block = program.block(op._block_attr_id("sub_block"))
             grad_sub_block = program._create_block()
             grad_sub_block._set_forward_block_idx(sub_block.idx)
-            cb = _callback_lookup_(op)
-            if cb is not None:
-                if callbacks is None:
-                    new_callbacks = [cb]
-                else:
-                    new_callbacks = callbacks + [_callback_lookup_(op)]
-                _append_backward_ops_(sub_block, sub_block.ops, grad_sub_block,
-                                      no_grad_dict, grad_to_var, new_callbacks)
-            else:
-                _append_backward_ops_(sub_block, sub_block.ops, grad_sub_block,
-                                      no_grad_dict, grad_to_var, callbacks)
+            _append_backward_ops_(sub_block, sub_block.ops, grad_sub_block,
+                                  no_grad_dict, grad_to_var, callbacks)
 
             program._rollback()
             grad_sub_block_list.append(grad_sub_block.desc)
@@ -424,9 +352,6 @@ def _append_backward_vars_(block, start_op_idx, grad_to_var, grad_info_map):
         # infer_shape and infer_type
         op_desc.infer_var_type(block.desc)
         op_desc.infer_shape(block.desc)
-        # ncclInit dones't need to set data_type
-        if op_desc.type() == 'ncclInit':
-            continue
         for arg in op_desc.output_arg_names():
             if arg in new_vars:
                 _infer_var_data_type_(arg, block)

python/paddle/fluid/framework.py

@@ -16,6 +16,7 @@ from __future__ import print_function
 
 import collections
 import contextlib
+import os
 import re
 import six
 import sys
@@ -27,11 +28,18 @@ from .proto import framework_pb2
 try:
     from . import core
 except ImportError as e:
-    raise ImportError(
-        """NOTE: You may need to run \"export LD_LIBRARY_PATH=/usr/local/lib:$LD_LIBRARY_PATH\"
-    if you encounters \"libmkldnn.so not found\" errors. If you have python
-    installed in other directory, replace \"/usr/local/lib\" with your own
-    directory. The original error is: \n""" + cpt.get_exception_message(e))
+    if os.name == 'nt':
+        raise ImportError(
+            """NOTE: You may need to run \"set PATH=c:\python27\lib:%PATH%\"
+        if you encounters \"mkldnn.dll not found\" errors. If you have python
+        installed in other directory, replace \"c:\python27\lib" with your own
+        directory. The original error is: \n""" + cpt.get_exception_message(e))
+    else:
+        raise ImportError(
+            """NOTE: You may need to run \"export LD_LIBRARY_PATH=/usr/local/lib:$LD_LIBRARY_PATH\"
+        if you encounters \"libmkldnn.so not found\" errors. If you have python
+        installed in other directory, replace \"/usr/local/lib\" with your own
+        directory. The original error is: \n""" + cpt.get_exception_message(e))
 except Exception as e:
     raise e
 from . import unique_name
@@ -563,8 +571,8 @@ class Operator(object):
     OP_WITHOUT_KERNEL_SET = {
         'feed', 'fetch', 'save', 'load', 'recurrent', 'go',
         'rnn_memory_helper_grad', 'conditional_block', 'while', 'send', 'recv',
-        'listen_and_serv', 'parallel_do', 'save_combine', 'load_combine',
-        'ncclInit', 'select', 'checkpoint_notify', 'gen_nccl_id'
+        'listen_and_serv', 'save_combine', 'load_combine', 'ncclInit', 'select',
+        'checkpoint_notify', 'gen_nccl_id'
     }
 
     def __init__(self,

python/paddle/fluid/layers/control_flow.py

@@ -226,156 +226,6 @@ class BlockGuard(object):
         return True
 
 
-class ParallelDo(object):
-    """
-    ParallelDo is used to represent multi-thread data parallel processing.
-
-    Its vanilla implementation can be shown as the following (:math:`|` means
-    single thread and :math:`||||` means multiple threads)
-
-    .. code-block:: text
-
-      In the forward pass
-        |      Split input onto different devices
-        |      Copy parameter onto different devices
-        ||||   Compute forward pass in parallel
-        |      Merge output from different devices
-
-      In the backward pass
-        |      Split output@grad onto different devices
-        ||||   Compute backward pass in parallel
-        |      accumulate param@grad from different devices to the first device
-        |      Merge input@grad from different devices
-        |      Copy param@grad to the place of parallel_do_op
-
-    Examples:
-
-    .. code-block:: python
-
-      images = fluid.layers.data(name='pixel', shape=[1, 28, 28], dtype=DTYPE)
-      label = fluid.layers.data(name='label', shape=[1], dtype='int64')
-
-      # ParallelDo version & Single-thread version
-      if thread_num > 1:
-          places = fluid.layers.get_places(thread_num)
-          pd = fluid.layers.control_flow.ParallelDo(places)
-          with pd.do():
-              images = pd.read_input(images)
-              label = pd.read_input(label)
-              predict = cnn_model(images)
-              cost = fluid.layers.cross_entropy(input=predict, label=label)
-
-              avg_cost = fluid.layers.mean(x=cost)
-              pd.write_output(avg_cost)
-
-          avg_cost = pd()
-          avg_cost = fluid.layers.mean(avg_cost)
-      else:
-          predict = cnn_model(images)
-          cost = fluid.layers.cross_entropy(input=predict, label=label)
-          avg_cost = fluid.layers.mean(x=cost)
-
-    .. warning::
-
-       It will be soon deprecated, please use ParallelExecutor instead.
-    """
-
-    def __init__(self, places, use_nccl=False, name=None):
-        warnings.warn(
-            "API ParallelDo is deprecated since 0.15.0. Please use ParallelExecutor instead.",
-            Warning)
-        self.helper = LayerHelper("parallel_do", name=name)
-        self.inputs = []
-        self.places = places
-        self.outputs = []
-        self.status = StaticRNN.BEFORE_RNN_BLOCK
-        self.use_nccl = use_nccl
-
-    def do(self):
-        return BlockGuardWithCompletion(self)
-
-    def parent_block(self):
-        prog = self.helper.main_program
-        parent_idx = prog.current_block().parent_idx
-        assert parent_idx >= 0
-        parent_block = prog.block(parent_idx)
-        return parent_block
-
-    def __call__(self, *args, **kwargs):
-        if self.status != StaticRNN.AFTER_RNN_BLOCK:
-            raise ValueError("RNN output can only be retrieved after rnn block")
-        if len(self.outputs) == 0:
-            raise ValueError("RNN has no output")
-        elif len(self.outputs) == 1:
-            return self.outputs[0]
-        else:
-            return self.outputs
-
-    def read_input(self, var):
-        self.inputs.append(var)
-        return var
-
-    def write_output(self, var):
-        self.outputs.append(var)
-
-    def get_parameters(self):
-        main_program = self.helper.main_program
-        current_block = main_program.current_block()
-        parent_block = self.parent_block()
-
-        local_inputs = set()
-        params = list()
-        for var in self.inputs:
-            local_inputs.add(var.name)
-
-        for op in current_block.ops:
-            for iname in op.input_names:
-                for in_var_name in op.input(iname):
-                    if in_var_name not in local_inputs:
-                        params.append(in_var_name)
-
-            for oname in op.output_names:
-                for out_var_name in op.output(oname):
-                    local_inputs.add(out_var_name)
-
-        params = list(set(params))
-
-        return [parent_block.var(name) for name in params]
-
-    def _complete_op(self):
-        main_program = self.helper.main_program
-        current_block = main_program.current_block()
-        parent_block = self.parent_block()
-
-        step_scope = parent_block.create_var(
-            type=core.VarDesc.VarType.STEP_SCOPES)
-
-        self.outputs = [
-            parent_block.create_var(
-                name=o.name,
-                shape=o.shape,
-                dtype=o.dtype,
-                lod_level=o.lod_level,
-                persistable=o.persistable,
-                stop_gradient=o.stop_gradient) for o in self.outputs
-        ]
-
-        inputs = [parent_block.var(i.name) for i in self.inputs]
-        outputs = [parent_block.var(o.name) for o in self.outputs]
-
-        parent_block.append_op(
-            type='parallel_do',
-            inputs={
-                'inputs': inputs,
-                'parameters': self.get_parameters(),
-                'places': self.places
-            },
-            outputs={'outputs': outputs,
-                     'parallel_scopes': [step_scope]},
-            attrs={'sub_block': current_block,
-                   'use_nccl': self.use_nccl})
-
-
 class BlockGuardWithCompletion(BlockGuard):
     """
     BlockGuardWithCompletion class.
@@ -384,9 +234,8 @@ class BlockGuardWithCompletion(BlockGuard):
     """
 
     def __init__(self, rnn):
-        if not (isinstance(rnn, StaticRNN) or isinstance(rnn, ParallelDo)):
-            raise TypeError(
-                "BlockGuardWithCompletion takes a StaticRNN or ParallelDo")
+        if not isinstance(rnn, StaticRNN):
+            raise TypeError("BlockGuardWithCompletion takes a StaticRNN")
         super(BlockGuardWithCompletion, self).__init__(rnn.helper.main_program)
         self.rnn = rnn
 

python/paddle/fluid/optimizer.py

@@ -641,9 +641,14 @@ class AdamOptimizer(Optimizer):
         beta1 (float): The exponential decay rate for the 1st moment estimates.
         beta2 (float): The exponential decay rate for the 2nd moment estimates.
         epsilon (float): a small float value for numerical stability.
-        regularization: A Regularizer, such as
-                        fluid.regularizer.L2DecayRegularizer.
+        regularization: A Regularizer, such as fluid.regularizer.L2DecayRegularizer.
         name: A optional name prefix.
+        lazy_mode(bool: false): The official Adam algorithm has two moving-average accumulators
+        the accumulators are updated at every step. Every element of the two moving-average is updated
+        in both dense mode and sparse mode. If the size of parameter is very large, then the update
+        may be very slow. The lazy mode only update the element that has gradient is the current
+        mini-batch, so it will be much more faster. But this mode has different semantics with the
+        original Adam algorithm and may lead to different result.
 
     Examples:
         .. code-block:: python
@@ -663,7 +668,8 @@ class AdamOptimizer(Optimizer):
                  beta2=0.999,
                  epsilon=1e-8,
                  regularization=None,
-                 name=None):
+                 name=None,
+                 lazy_mode=False):
         assert learning_rate is not None
         assert beta1 is not None
         assert beta2 is not None
@@ -676,6 +682,7 @@ class AdamOptimizer(Optimizer):
         self._beta1 = beta1
         self._beta2 = beta2
         self._epsilon = epsilon
+        self._lazy_mode = lazy_mode
 
     def _create_accumulators(self, block, parameters):
         assert isinstance(block, framework.Block)
@@ -729,7 +736,8 @@ class AdamOptimizer(Optimizer):
             attrs={
                 "beta1": self._beta1,
                 "beta2": self._beta2,
-                "epsilon": self._epsilon
+                "epsilon": self._epsilon,
+                "lazy_mode": self._lazy_mode
             })
 
         return adam_op

python/paddle/fluid/tests/book/notest_understand_sentiment.py

@@ -15,7 +15,6 @@
 from __future__ import print_function
 
 from paddle.fluid.layers.device import get_places
-from paddle.fluid.layers.control_flow import ParallelDo
 import unittest
 import paddle.fluid as fluid
 import paddle
@@ -147,22 +146,7 @@ def train(word_dict,
         cost, acc_out, prediction = net_method(
             data, label, input_dim=dict_dim, class_dim=class_dim)
     else:
-        places = get_places()
-        pd = ParallelDo(places)
-        with pd.do():
-            cost, acc, _ = net_method(
-                pd.read_input(data),
-                pd.read_input(label),
-                input_dim=dict_dim,
-                class_dim=class_dim)
-            pd.write_output(cost)
-            pd.write_output(acc)
-
-        cost, acc = pd()
-        cost = fluid.layers.mean(cost)
-        acc_out = fluid.layers.mean(acc)
-        prediction = None
-        assert save_dirname is None
+        raise NotImplementedError()
 
     adagrad = fluid.optimizer.Adagrad(learning_rate=0.002)
     adagrad.minimize(cost)

python/paddle/fluid/tests/book/test_recognize_digits.py

@@ -25,7 +25,6 @@ import numpy
 import paddle
 import paddle.fluid as fluid
 from paddle.fluid.layers.device import get_places
-from paddle.fluid.layers.control_flow import ParallelDo
 
 BATCH_SIZE = 64
 
@@ -82,19 +81,7 @@ def train(nn_type,
         net_conf = conv_net
 
     if parallel:
-        places = get_places()
-        pd = ParallelDo(places)
-        with pd.do():
-            img_ = pd.read_input(img)
-            label_ = pd.read_input(label)
-            prediction, avg_loss, acc = net_conf(img_, label_)
-            for o in [avg_loss, acc]:
-                pd.write_output(o)
-
-        avg_loss, acc = pd()
-        # get mean loss and acc through every devices.
-        avg_loss = fluid.layers.mean(avg_loss)
-        acc = fluid.layers.mean(acc)
+        raise NotImplementedError()
     else:
         prediction, avg_loss, acc = net_conf(img, label)
 
@@ -273,7 +260,7 @@ def inject_all_tests():
     for use_cuda in (False, True):
         if use_cuda and not core.is_compiled_with_cuda():
             continue
-        for parallel in (False, True):
+        for parallel in (False, ):
             for nn_type in ('mlp', 'conv'):
                 inject_test_method(use_cuda, parallel, nn_type, True)
 

python/paddle/fluid/tests/book/test_word2vec.py

@@ -17,7 +17,6 @@ from __future__ import print_function
 import paddle
 import paddle.fluid as fluid
 from paddle.fluid.layers.device import get_places
-from paddle.fluid.layers.control_flow import ParallelDo
 import unittest
 import os
 import numpy as np
@@ -84,18 +83,7 @@ def train(use_cuda, is_sparse, is_parallel, save_dirname, is_local=True):
         avg_cost, predict_word = __network__(
             [first_word, second_word, third_word, forth_word, next_word])
     else:
-        places = get_places()
-        pd = ParallelDo(places)
-        with pd.do():
-            avg_cost, predict_word = __network__(
-                list(
-                    map(pd.read_input, [
-                        first_word, second_word, third_word, forth_word,
-                        next_word
-                    ])))
-            pd.write_output(avg_cost)
-
-        avg_cost = fluid.layers.mean(pd())
+        raise NotImplementedError()
 
     sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001)
     sgd_optimizer.minimize(avg_cost)
@@ -262,7 +250,7 @@ def inject_test_method(use_cuda, is_sparse, is_parallel):
 
 for use_cuda in (False, True):
     for is_sparse in (False, True):
-        for is_parallel in (False, True):
+        for is_parallel in (False, ):
             inject_test_method(use_cuda, is_sparse, is_parallel)
 
 if __name__ == '__main__':

python/paddle/fluid/tests/book_memory_optimization/test_memopt_fit_a_line.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 math
-import sys
-
-import paddle
-import paddle.fluid as fluid
-from paddle.fluid.layers.device import get_places
-from paddle.fluid.layers.control_flow import ParallelDo
-
-# need to fix random seed and training data to compare the loss
-# value accurately calculated by the default and the memory optimization
-# version.
-fluid.default_startup_program().random_seed = 111
-
-x = fluid.layers.data(name='x', shape=[13], dtype='float32')
-y = fluid.layers.data(name='y', shape=[1], dtype='float32')
-
-device_type = 'CPU'
-use_nccl = False
-place = fluid.CPUPlace()
-if fluid.core.is_compiled_with_cuda():
-    device_type = 'CUDA'
-    use_nccl = False
-    place = fluid.CUDAPlace(0)
-
-places = get_places(device_count=0, device_type=device_type)
-pd = ParallelDo(places, use_nccl=use_nccl)
-with pd.do():
-    x_ = pd.read_input(x)
-    y_ = pd.read_input(y)
-    y_predict = fluid.layers.fc(input=x_, size=1, act=None)
-    cost = fluid.layers.square_error_cost(input=y_predict, label=y_)
-    avg_cost = fluid.layers.mean(x=cost)
-    pd.write_output(avg_cost)
-
-cost = pd()
-avg_cost = fluid.layers.mean(x=cost)
-sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.01)
-sgd_optimizer.minimize(avg_cost)
-
-fluid.memory_optimize(fluid.default_main_program(), print_log=True)
-# fluid.release_memory(fluid.default_main_program())
-
-BATCH_SIZE = 200
-
-# fix the order of training data
-train_reader = paddle.batch(
-    paddle.dataset.uci_housing.train(), batch_size=BATCH_SIZE, drop_last=False)
-
-# train_reader = paddle.batch(
-#     paddle.reader.shuffle(
-#         paddle.dataset.uci_housing.train(), buf_size=500),
-#     batch_size=BATCH_SIZE)
-
-feeder = fluid.DataFeeder(place=place, feed_list=[x, y])
-exe = fluid.Executor(place)
-
-exe.run(fluid.default_startup_program())
-
-PASS_NUM = 100
-for pass_id in range(PASS_NUM):
-    for data in train_reader():
-        avg_loss_value, = exe.run(fluid.default_main_program(),
-                                  feed=feeder.feed(data),
-                                  fetch_list=[avg_cost])
-
-        if avg_loss_value[0] < 10.0:
-            exit(0)  # if avg cost less than 10.0, we think our code is good.
-        print(avg_loss_value[0])
-        if math.isnan(float(avg_loss_value)):
-            sys.exit("got NaN loss, training failed.")
-exit(1)

python/paddle/fluid/tests/unittests/ngraph/test_activation_ngraph_op.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 unittest
+import numpy as np
+import paddle.fluid.core as core
+from paddle.fluid.tests.unittests.op_test import OpTest
+from paddle.fluid.tests.unittests.test_activation_op import TestRelu, TestTanh
+
+
+class TestNGRAPHReluDim2(TestRelu):
+    def setUp(self):
+        super(TestNGRAPHReluDim2, self).setUp()
+
+
+class TestNGRAPHTanhDim2(TestTanh):
+    def setUp(self):
+        super(TestNGRAPHTanhDim2, self).setUp()
+
+
+class TestNGRAPHReluDim4(TestRelu):
+    def setUp(self):
+        super(TestNGRAPHReluDim4, self).setUp()
+
+        x = np.random.uniform(-1, 1, [2, 4, 3, 5]).astype("float32")
+        # The same reason with TestAbs
+        x[np.abs(x) < 0.005] = 0.02
+        out = np.maximum(x, 0)
+
+        self.inputs = {'X': OpTest.np_dtype_to_fluid_dtype(x)}
+        self.outputs = {'Out': out}
+
+
+class TestNGRAPHTanhDim4(TestTanh):
+    def setUp(self):
+        super(TestNGRAPHTanhDim4, self).setUp()
+
+        self.inputs = {
+            'X': np.random.uniform(0.1, 1, [2, 4, 3, 5]).astype("float32")
+        }
+        self.outputs = {'Out': np.tanh(self.inputs['X'])}
+
+
+if __name__ == '__main__':
+    unittest.main()

python/paddle/fluid/tests/unittests/ngraph/test_mul_ngraph_op.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 unittest
+from paddle.fluid.tests.unittests.test_mul_op import TestMulOp, TestMulOp2, TestFP16MulOp1, TestFP16MulOp2
+
+
+class TestNGRAPHMulOp(TestMulOp):
+    def init_dtype_type(self):
+        pass
+
+
+class TestNGRAPHMulOp2(TestMulOp2):
+    def init_dtype_type(self):
+        pass
+
+
+class TestNGRAPHFP16MulOp1(TestFP16MulOp1):
+    def init_dtype_type(self):
+        pass
+
+
+class TestNGRAPHFP16MulOp2(TestFP16MulOp2):
+    def init_dtype_type(self):
+        pass
+
+
+if __name__ == "__main__":
+    unittest.main()

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

@@ -194,7 +194,8 @@ def adam_step(inputs, attributes):
     return param_out, moment1_out, moment2_out
 
 
-def adam_step_sparse(inputs, attributes, height, rows, row_numel, np_grad):
+def adam_step_sparse(inputs, attributes, height, rows, row_numel, np_grad,
+                     lazy_mode):
     '''
     Simulate one step of the adam optimizer
     :param inputs: dict of inputs
@@ -218,19 +219,30 @@ def adam_step_sparse(inputs, attributes, height, rows, row_numel, np_grad):
     moment2_out = np.zeros(shape=[height, row_numel])
     param_out = np.zeros(shape=[height, row_numel])
 
-    for idx, row_id in enumerate(rows):
+    def update_row(row_id, update_value):
         moment1_out[row_id] = beta1 * moment1[row_id] + (1 - beta1
-                                                         ) * np_grad[idx]
+                                                         ) * update_value
         moment2_out[row_id] = beta2 * moment2[row_id] + (
-            1 - beta2) * np.square(np_grad[idx])
+            1 - beta2) * np.square(update_value)
         lr_t = lr * np.sqrt(1 - beta2_pow) / (1 - beta1_pow)
         param_out[row_id] = param[row_id] - lr_t * (moment1_out[row_id] / (
             np.sqrt(moment2_out[row_id]) + epsilon))
+
+    if lazy_mode:
+        for idx, row_id in enumerate(rows):
+            update_row(row_id, np_grad[idx])
+    else:
+        for row_id in range(param_out.shape[0]):
+            update_value = np.zeros(np_grad[0].shape).astype("float32")
+            if row_id in rows:
+                update_value = np_grad[rows.index(row_id)]
+            update_row(row_id, update_value)
+
     return param_out, moment1_out, moment2_out
 
 
 class TestSparseAdamOp(unittest.TestCase):
-    def setup(self, scope, place):
+    def setup(self, scope, place, lazy_mode):
         beta1 = 0.78
         beta2 = 0.836
         epsilon = 1e-4
@@ -248,6 +260,7 @@ class TestSparseAdamOp(unittest.TestCase):
             'Beta2Pow': np.array([beta2**10]).astype("float32"),
             "LearningRate": np.full((1), 2.0).astype("float32")
         }
+        self.init_output = np.full((height, row_numel), 0.0).astype("float32")
         self.attrs = {'epsilon': epsilon, 'beta1': beta1, 'beta2': beta2}
 
         grad_selected_rows = scope.var('Grad').get_selected_rows()
@@ -262,19 +275,21 @@ class TestSparseAdamOp(unittest.TestCase):
 
         self.sparse_inputs = ["Grad"]
 
-        param_out, mom1, mom2 = adam_step_sparse(
-            self.dense_inputs, self.attrs, height, rows, row_numel, np_array)
+        param_out, mom1, mom2 = adam_step_sparse(self.dense_inputs, self.attrs,
+                                                 height, rows, row_numel,
+                                                 np_array, lazy_mode)
         self.outputs = {
             "ParamOut": param_out,
             "Moment1Out": mom1,
             "Moment2Out": mom2
         }
 
-    def check_with_place(self, place):
+    def check_with_place(self, place, lazy_mode):
         scope = core.Scope()
-        self.setup(scope, place)
+        self.setup(scope, place, lazy_mode)
 
         op_args = dict()
+        op_args['lazy_mode'] = lazy_mode
         for key, np_array in self.dense_inputs.items():
             var = scope.var(key).get_tensor()
             var.set(np_array, place)
@@ -283,7 +298,7 @@ class TestSparseAdamOp(unittest.TestCase):
             op_args[s] = s
         for s in self.outputs:
             var = scope.var(s).get_tensor()
-            var.set(self.outputs[s], place)
+            var.set(self.init_output, place)
             op_args[s] = s
         for k in self.attrs:
             op_args[k] = self.attrs[k]
@@ -297,20 +312,17 @@ class TestSparseAdamOp(unittest.TestCase):
             actual = np.array(out_var)
             actual = actual.reshape([actual.size])
             np_array = np_array.reshape([np_array.size])
-            for idx, row_id in enumerate(self.rows):
-                j = 0
-                while j < self.row_numel:
-                    pos = row_id * self.row_numel + j
-                    self.assertLess((actual[pos] - np_array[pos]) / actual[pos],
-                                    0.00001)
-                    j += 1
-
-    def test_sparse_sgd(self):
+
+            for i in range(np_array.size):
+                self.assertLess((actual[i] - np_array[i]), 0.00001)
+
+    def test_sparse_adam(self):
         places = [core.CPUPlace()]
         if core.is_compiled_with_cuda():
             places.append(core.CUDAPlace(0))
         for place in places:
-            self.check_with_place(place)
+            for lazy_mode in (True, False):
+                self.check_with_place(place, lazy_mode)
 
 
 if __name__ == "__main__":

python/paddle/fluid/tests/unittests/test_parallel_op.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 unittest
-
-import paddle.fluid as fluid
-from paddle.fluid.layers.device import get_places
-from paddle.fluid.layers.control_flow import ParallelDo
-import paddle.fluid.profiler as profiler
-import numpy
-import six
-
-
-class BaseParallelForTest(unittest.TestCase):
-    def run_test(self, callback, feed, fetch):
-        """
-        Run the unittest for parallel.for
-        Args:
-            callback(callable): A callable function returns a generator. There
-                are two yields in the generator function. The first yield
-                returns the data layers, and the second yield returns the loss.
-                The modified data variables will be sent back during the first
-                yield.
-
-            feed(dict): The executor feeding dictionary.
-            fetch(list|basestr): The fetch name lists.
-
-        Returns:
-            None
-
-        Raises:
-            AssertionError when the computation of cpu, parallel.for in cpu,
-                gpu, parallel.for in gpu are different.
-
-        """
-        cpu = fluid.CPUPlace()
-        result_cpu = self._run_test_impl_(
-            callback=callback,
-            feed=feed,
-            fetch=fetch,
-            place=cpu,
-            use_parallel=False)
-        result_cpu_parallel = self._run_test_impl_(
-            callback=callback,
-            feed=feed,
-            fetch=fetch,
-            place=cpu,
-            use_parallel=True)
-        if fluid.core.is_compiled_with_cuda():
-            gpu = fluid.CUDAPlace(0)
-            result_gpu = self._run_test_impl_(
-                callback=callback,
-                feed=feed,
-                fetch=fetch,
-                place=gpu,
-                use_parallel=False,
-                use_gpu=True)
-            result_gpu_parallel = self._run_test_impl_(
-                callback=callback,
-                feed=feed,
-                fetch=fetch,
-                place=gpu,
-                use_parallel=True,
-                use_gpu=True)
-            result_gpu_nccl = self._run_test_impl_(
-                callback=callback,
-                feed=feed,
-                fetch=fetch,
-                place=gpu,
-                use_parallel=True,
-                use_nccl=True,
-                use_gpu=True)
-            self._assert_same_(fetch, result_cpu, result_cpu_parallel,
-                               result_gpu, result_gpu_parallel, result_gpu_nccl)
-        else:
-            self._assert_same_(fetch, result_cpu, result_cpu_parallel)
-
-    def _run_test_impl_(self,
-                        callback,
-                        feed,
-                        fetch,
-                        place,
-                        use_parallel=False,
-                        use_nccl=False,
-                        use_gpu=False):
-        """
-        Run a single test, returns the fetch values
-        Args:
-            place(Place): the computation place.
-            use_parallel(bool): Whether use parallel.for or not.
-
-        Returns:
-            Fetched numpy arrays.
-
-        """
-        if isinstance(fetch, six.string_types):
-            fetch = [fetch]
-        main = fluid.Program()
-        startup = fluid.Program()
-        # Fix seed
-        main.random_seed = 10
-        startup.random_seed = 10
-
-        with fluid.program_guard(main, startup):
-            generator = callback()
-            # Automatically insert parallel do if use_parallel = True
-            if use_parallel:
-                thread_num = fluid.core.get_cuda_device_count(
-                ) if use_gpu else 8
-                places = get_places(thread_num)
-                pd = ParallelDo(places, use_nccl=use_nccl)
-                data = next(generator)
-
-                if isinstance(data, fluid.framework.Variable):
-                    data = [data]
-
-                with pd.do():
-                    ins = list(map(pd.read_input, data))
-                    if len(ins) == 1:
-                        ins = ins[0]
-                    loss = generator.send(ins)  # patch input
-                    pd.write_output(loss)
-
-                loss = pd()
-            else:
-                data = next(generator)
-                loss = generator.send(data)
-            self.assertIsNotNone(loss)
-            avg_loss = fluid.layers.mean(loss)
-            fluid.backward.append_backward(loss=avg_loss)
-
-        exe = fluid.Executor(place)
-        exe.run(startup)
-        if use_gpu:
-            profile_type = 'GPU'
-        else:
-            profile_type = 'CPU'
-        with profiler.profiler(profile_type, 'total', '/tmp/profiler'):
-            return exe.run(main, feed=feed, fetch_list=fetch)
-
-    def _assert_same_(self, fetch, *args):
-        """
-        Assert the return values of `run_test` are same.
-        Args:
-            fetch: Fetch list. Used for print error message
-            *args: The fetch result lists of each situations.
-
-        Returns:
-            None
-
-        Raises:
-            AssertionError
-
-        """
-
-        def _impl_(a, b, fetch_id, item_id):
-            item_str = [
-                'CPU', 'ParallelCPU', 'GPU', 'ParallelGPU', 'ParallelGPUNCCL'
-            ]
-            flag = numpy.allclose(a, b, rtol=0.1, atol=1e-3)
-            self.assertTrue(flag,
-                            "The {0} are different in {1}, {2} vs {3}".format(
-                                fetch[fetch_id], item_str[item_id], a, b))
-
-        for i, items in enumerate(zip(*args)):
-            self.assertGreater(len(items), 0)
-            for j in range(1, len(items)):
-                _impl_(items[0], items[j], fetch_id=i, item_id=j)
-
-
-class ParallelOpTest(BaseParallelForTest):
-    @staticmethod
-    def __network__():
-        x = fluid.layers.data(shape=[784], dtype='float32', name='img')
-        x = yield x
-        hidden = fluid.layers.fc(input=x, size=200, param_attr='fc1.w')
-        hidden = fluid.layers.batch_norm(input=hidden)
-        loss = fluid.layers.mean(hidden)
-        yield loss
-
-    def test_simple_fc(self):
-        self.run_test(
-            callback=self.__network__,
-            feed={
-                'img': numpy.random.random(size=(51, 784)).astype('float32')
-            },
-            fetch=['fc1.w@GRAD'])
-
-    def test_fc_with_tiny_data(self):
-        self.run_test(
-            callback=self.__network__,
-            feed={'img': numpy.random.random(size=(1, 784)).astype('float32')},
-            fetch=['fc1.w@GRAD'])
-
-
-class ParallelOpTestMultipleInput(BaseParallelForTest):
-    @staticmethod
-    def __network__():
-        x = fluid.layers.data(
-            shape=[784], dtype='float32', name='img1', stop_gradient=False)
-        y = fluid.layers.data(
-            shape=[784], dtype='float32', name='img2', stop_gradient=False)
-        yield [x, y]
-        x = x + y
-        hidden1 = fluid.layers.fc(input=x, size=200, param_attr='fc1.w')
-        hidden2 = fluid.layers.fc(input=hidden1, size=200, param_attr='fc2.w')
-        hidden3 = fluid.layers.fc(input=hidden2, size=200, param_attr='fc3.w')
-        loss = fluid.layers.mean(hidden3)
-        yield loss
-
-    def test_simple_fc(self):
-        self.run_test(
-            callback=self.__network__,
-            feed={
-                'img1': numpy.random.random(size=(51, 784)).astype('float32'),
-                'img2': numpy.random.random(size=(51, 784)).astype('float32')
-            },
-            fetch=['fc1.w@GRAD', 'fc2.w@GRAD', 'fc3.w@GRAD'])
-
-
-if __name__ == '__main__':
-    unittest.main()

python/paddle/fluid/tests/unittests/test_transpose_mkldnn_op.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 unittest
+
+from test_transpose_op import TestTransposeOp
+
+
+class TestTransposeMKLDNN(TestTransposeOp):
+    def init_op_type(self):
+        self.op_type = "transpose2"
+        self.use_mkldnn = True
+        self.is_test = True
+        return
+
+    def test_check_grad(self):
+        return
+
+    def test_check_grad_no_input(self):
+        return
+
+    def test_check_grad_no_filter(self):
+        return
+
+
+class TestCase0MKLDNN(TestTransposeMKLDNN):
+    def initTestCase(self):
+        self.shape = (3, )
+        self.axis = (0, )
+
+
+class TestCase1a(TestTransposeMKLDNN):
+    def initTestCase(self):
+        self.shape = (3, 4, 5)
+        self.axis = (0, 2, 1)
+
+
+class TestCase1b(TestTransposeMKLDNN):
+    def initTestCase(self):
+        self.shape = (3, 4, 5)
+        self.axis = (2, 1, 0)
+
+
+class TestCase2(TestTransposeMKLDNN):
+    def initTestCase(self):
+        self.shape = (2, 3, 4, 5)
+        self.axis = (0, 2, 3, 1)
+
+
+class TestCase3(TestTransposeMKLDNN):
+    def initTestCase(self):
+        self.shape = (2, 3, 4, 5, 6)
+        self.axis = (4, 2, 3, 1, 0)
+
+
+class TestCase4(TestTransposeMKLDNN):
+    def initTestCase(self):
+        self.shape = (2, 3, 4, 5, 6, 1)
+        self.axis = (4, 2, 3, 1, 0, 5)
+
+
+if __name__ == '__main__':
+    unittest.main()

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

@@ -21,15 +21,24 @@ from op_test import OpTest
 
 class TestTransposeOp(OpTest):
     def setUp(self):
+        self.init_op_type()
         self.initTestCase()
-        self.op_type = "transpose2"
         self.inputs = {'X': np.random.random(self.shape).astype("float32")}
-        self.attrs = {'axis': list(self.axis)}
+        self.attrs = {
+            'axis': list(self.axis),
+            'use_mkldnn': self.use_mkldnn,
+            'is_test': self.is_test,
+        }
         self.outputs = {
             'XShape': np.random.random(self.shape).astype("float32"),
             'Out': self.inputs['X'].transpose(self.axis)
         }
 
+    def init_op_type(self):
+        self.op_type = "transpose2"
+        self.use_mkldnn = False
+        self.is_test = False
+
     def test_check_output(self):
         self.check_output(no_check_set=['XShape'])
 

python/paddle/fluid/transpiler/memory_optimization_transpiler.py

@@ -35,11 +35,10 @@ dtype_to_size = {
 }
 
 SUB_BLOCK_OPS = [
-    "while", "while_grad", "parallel_do", "parallel_do_grad",
-    "conditional_block", "conditional_block_grad"
+    "while", "while_grad", "conditional_block", "conditional_block_grad"
 ]
 
-SUB_BLOCK_PAIR = [("while", "while_grad"), ("parallel_do", "parallel_do_grad"),
+SUB_BLOCK_PAIR = [("while", "while_grad"),
                   ("conditional_block", "conditional_block_grad")]
 
 PRINT_LOG = False

python/setup.py.in

@@ -160,10 +160,11 @@ if '${WITH_FLUID_ONLY}'== 'OFF':
 
 # put all thirdparty libraries in paddle.libs
 libs_path='${PADDLE_BINARY_DIR}/python/paddle/libs'
-if os.name != 'nt':
-    package_data['paddle.libs']= []
-    package_data['paddle.libs']=['libwarpctc' + ext_name]
-    shutil.copy('${WARPCTC_LIBRARIES}', libs_path)
+
+package_data['paddle.libs']= []
+package_data['paddle.libs']=[('libwarpctc' if os.name != 'nt' else 'warpctc') + ext_name]
+shutil.copy('${WARPCTC_LIBRARIES}', libs_path)
+
 if '${WITH_MKL}' == 'ON':
     shutil.copy('${MKLML_LIB}', libs_path)
     shutil.copy('${MKLML_IOMP_LIB}', libs_path)