Add check for opt op (#13840)

* add check for opt op

* fix opt op
test=develop

* fix test fail
test=develop

* fix optimization doc
test=develop

* test=develop

paddle/fluid/operators/adadelta_op.cc

@@ -18,6 +18,7 @@ namespace paddle {
 namespace operators {
 
 using Tensor = framework::Tensor;
+
 class AdadeltaOp : public framework::OperatorWithKernel {
  public:
   using framework::OperatorWithKernel::OperatorWithKernel;
@@ -31,6 +32,16 @@ class AdadeltaOp : public framework::OperatorWithKernel {
                    "Input(AvgSquaredGrad) of AdadeltaOp should not be null.");
     PADDLE_ENFORCE(ctx->HasInput("AvgSquaredUpdate"),
                    "Input(AvgSquaredUpdate) of AdadeltaOp should not be null.");
+    PADDLE_ENFORCE(
+        ctx->GetInputsVarType("Param").front() ==
+            framework::proto::VarType::LOD_TENSOR,
+        "The input var's type should be LoDTensor, but the received is %s",
+        ctx->Inputs("Param").front(), ctx->GetInputsVarType("Param").front());
+    PADDLE_ENFORCE(
+        ctx->GetInputsVarType("Grad").front() ==
+            framework::proto::VarType::LOD_TENSOR,
+        "The input var's type should be LoDTensor, but the received is %s",
+        ctx->Inputs("Grad").front(), ctx->GetInputsVarType("Grad").front());
 
     PADDLE_ENFORCE(ctx->HasOutput("ParamOut"),
                    "Output(ParamOut) of AdadeltaOp should not be null.");
@@ -56,6 +67,7 @@ class AdadeltaOp : public framework::OperatorWithKernel {
     ctx->SetOutputDim("AvgSquaredGradOut", param_dim);
     ctx->SetOutputDim("AvgSquaredUpdateOut", param_dim);
   }
+
   framework::OpKernelType GetExpectedKernelType(
       const framework::ExecutionContext &ctx) const override {
     auto input_data_type =

paddle/fluid/operators/adadelta_op.h

@@ -23,6 +23,17 @@ template <typename DeviceContext, typename T>
 class AdadeltaOpKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
+    const auto* param_var = ctx.InputVar("Param");
+    PADDLE_ENFORCE(param_var->IsType<framework::LoDTensor>(),
+                   "The Var(%s)'s type should be LoDTensor, "
+                   "but the received is %s",
+                   ctx.Inputs("Param").front(), param_var->Type().name());
+    const auto* grad_var = ctx.InputVar("Grad");
+    PADDLE_ENFORCE(grad_var->IsType<framework::LoDTensor>(),
+                   "The Var(%s)'s type should be LoDTensor, "
+                   "but the received is %s",
+                   ctx.Inputs("Grad").front(), grad_var->Type().name());
+
     auto param_out_tensor = ctx.Output<framework::Tensor>("ParamOut");
     auto avg_squared_grad_out_tensor =
         ctx.Output<framework::Tensor>("AvgSquaredGradOut");

paddle/fluid/operators/adagrad_op.h

@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #pragma once
+
 #include "paddle/fluid/framework/eigen.h"
 #include "paddle/fluid/framework/op_registry.h"
 
@@ -21,25 +22,31 @@ namespace operators {
 
 template <typename DeviceContext, typename T>
 struct SparseAdagradFunctor {
-  void operator()(const DeviceContext& context,
-                  const framework::SelectedRows& grad,
-                  const framework::Tensor& learning_rate, T epsilon,
-                  framework::Tensor* moment, framework::Tensor* param);
+  void operator()(const DeviceContext &context,
+                  const framework::SelectedRows &grad,
+                  const framework::Tensor &learning_rate, T epsilon,
+                  framework::Tensor *moment, framework::Tensor *param);
 };
 
 template <typename DeviceContext, typename T>
 class AdagradOpKernel : public framework::OpKernel<T> {
  public:
-  void Compute(const framework::ExecutionContext& ctx) const override {
-    auto* param_out_tensor = ctx.Output<framework::Tensor>("ParamOut");
-    auto* moment_out_tensor = ctx.Output<framework::Tensor>("MomentOut");
+  void Compute(const framework::ExecutionContext &ctx) const override {
+    const auto *param_var = ctx.InputVar("Param");
+    PADDLE_ENFORCE(param_var->IsType<framework::LoDTensor>(),
+                   "The Var(%s)'s type should be LoDTensor, "
+                   "but the received is %s",
+                   ctx.Inputs("Param").front(), param_var->Type().name());
+
+    auto *param_out_tensor = ctx.Output<framework::Tensor>("ParamOut");
+    auto *moment_out_tensor = ctx.Output<framework::Tensor>("MomentOut");
 
     param_out_tensor->mutable_data<T>(ctx.GetPlace());
     moment_out_tensor->mutable_data<T>(ctx.GetPlace());
 
     T epsilon = static_cast<T>(ctx.Attr<float>("epsilon"));
 
-    auto* grad_var = ctx.InputVar("Grad");
+    auto *grad_var = ctx.InputVar("Grad");
     if (grad_var->IsType<framework::LoDTensor>()) {
       auto param = framework::EigenVector<T>::Flatten(
           *ctx.Input<framework::Tensor>("Param"));
@@ -47,16 +54,16 @@ class AdagradOpKernel : public framework::OpKernel<T> {
           *ctx.Input<framework::Tensor>("Grad"));
       auto moment = framework::EigenVector<T>::Flatten(
           *ctx.Input<framework::Tensor>("Moment"));
-      auto* learning_rate = ctx.Input<framework::Tensor>("LearningRate");
+      auto *learning_rate = ctx.Input<framework::Tensor>("LearningRate");
 
       auto param_out = framework::EigenVector<T>::Flatten(*param_out_tensor);
       auto moment_out = framework::EigenVector<T>::Flatten(*moment_out_tensor);
-      auto* place = ctx.template device_context<DeviceContext>().eigen_device();
+      auto *place = ctx.template device_context<DeviceContext>().eigen_device();
 
       moment_out.device(*place) = moment + grad * grad;
       Eigen::DSizes<int, 1> m_dsize(moment_out_tensor->numel());
       if (platform::is_cpu_place(ctx.GetPlace())) {
-        auto* lr = learning_rate->data<T>();
+        auto *lr = learning_rate->data<T>();
         param_out.device(*place) =
             param - lr[0] * grad / (moment_out.sqrt() + epsilon);
       } else {
@@ -66,10 +73,10 @@ class AdagradOpKernel : public framework::OpKernel<T> {
             lr.broadcast(m_dsize) * grad / (moment_out.sqrt() + epsilon);
       }
     } else if (grad_var->IsType<framework::SelectedRows>()) {
-      auto* param_tensor = ctx.Input<framework::Tensor>("Param");
+      auto *param_tensor = ctx.Input<framework::Tensor>("Param");
       PADDLE_ENFORCE_EQ(param_tensor, param_out_tensor);
 
-      auto* moment_tensor = ctx.Input<framework::Tensor>("Moment");
+      auto *moment_tensor = ctx.Input<framework::Tensor>("Moment");
       PADDLE_ENFORCE_EQ(moment_tensor, moment_out_tensor);
 
       SparseAdagradFunctor<DeviceContext, T> functor;

paddle/fluid/operators/adam_op.h

@@ -244,6 +244,12 @@ template <typename DeviceContext, typename T>
 class AdamOpKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
+    const auto* param_var = ctx.InputVar("Param");
+    PADDLE_ENFORCE(param_var->IsType<framework::LoDTensor>(),
+                   "The Var(%s)'s type should be LoDTensor, "
+                   "but the received is %s",
+                   ctx.Inputs("Param").front(), param_var->Type().name());
+
     using paddle::framework::LoDTensor;
     using paddle::operators::detail::Ref;
 

paddle/fluid/operators/adamax_op.cc

@@ -35,6 +35,16 @@ class AdamaxOp : public framework::OperatorWithKernel {
                    "Input(LearningRate) of AdamaxOp should not be null.");
     PADDLE_ENFORCE(ctx->HasInput("Beta1Pow"),
                    "Input(Beta1Pow) of AdamaxOp should not be null.");
+    PADDLE_ENFORCE(
+        ctx->GetInputsVarType("Param").front() ==
+            framework::proto::VarType::LOD_TENSOR,
+        "The input var's type should be LoDTensor, but the received is %s",
+        ctx->Inputs("Param").front(), ctx->GetInputsVarType("Param").front());
+    PADDLE_ENFORCE(
+        ctx->GetInputsVarType("Grad").front() ==
+            framework::proto::VarType::LOD_TENSOR,
+        "The input var's type should be LoDTensor, but the received is %s",
+        ctx->Inputs("Grad").front(), ctx->GetInputsVarType("Grad").front());
 
     PADDLE_ENFORCE(ctx->HasOutput("ParamOut"),
                    "Output(ParamOut) of AdamaxOp should not be null.");

paddle/fluid/operators/adamax_op.h

@@ -23,6 +23,17 @@ template <typename DeviceContext, typename T>
 class AdamaxOpKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
+    const auto* param_var = ctx.InputVar("Param");
+    PADDLE_ENFORCE(param_var->IsType<framework::LoDTensor>(),
+                   "The Var(%s)'s type should be LoDTensor, "
+                   "but the received is %s",
+                   ctx.Inputs("Param").front(), param_var->Type().name());
+    const auto* grad_var = ctx.InputVar("Grad");
+    PADDLE_ENFORCE(grad_var->IsType<framework::LoDTensor>(),
+                   "The Var(%s)'s type should be LoDTensor, "
+                   "but the received is %s",
+                   ctx.Inputs("Grad").front(), grad_var->Type().name());
+
     auto param_out_tensor = ctx.Output<framework::Tensor>("ParamOut");
     auto moment_out_tensor = ctx.Output<framework::Tensor>("MomentOut");
     auto inf_norm_out_tensor = ctx.Output<framework::Tensor>("InfNormOut");

paddle/fluid/operators/decayed_adagrad_op.cc

@@ -32,6 +32,16 @@ class DecayedAdagradOp : public framework::OperatorWithKernel {
     PADDLE_ENFORCE(
         ctx->HasInput("LearningRate"),
         "Input(LearningRate) of DecayedAdagradOp should not be null.");
+    PADDLE_ENFORCE(
+        ctx->GetInputsVarType("Param").front() ==
+            framework::proto::VarType::LOD_TENSOR,
+        "The input var's type should be LoDTensor, but the received is %s",
+        ctx->Inputs("Param").front(), ctx->GetInputsVarType("Param").front());
+    PADDLE_ENFORCE(
+        ctx->GetInputsVarType("Grad").front() ==
+            framework::proto::VarType::LOD_TENSOR,
+        "The input var's type should be LoDTensor, but the received is %s",
+        ctx->Inputs("Grad").front(), ctx->GetInputsVarType("Grad").front());
 
     PADDLE_ENFORCE(ctx->HasOutput("ParamOut"),
                    "Output(ParamOut) of DecayedAdagradOp should not be null.");

paddle/fluid/operators/decayed_adagrad_op.h

@@ -23,6 +23,17 @@ template <typename DeviceContext, typename T>
 class DecayedAdagradOpKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
+    const auto* param_var = ctx.InputVar("Param");
+    PADDLE_ENFORCE(param_var->IsType<framework::LoDTensor>(),
+                   "The Var(%s)'s type should be LoDTensor, "
+                   "but the received is %s",
+                   ctx.Inputs("Param").front(), param_var->Type().name());
+    const auto* grad_var = ctx.InputVar("Grad");
+    PADDLE_ENFORCE(grad_var->IsType<framework::LoDTensor>(),
+                   "The Var(%s)'s type should be LoDTensor, "
+                   "but the received is %s",
+                   ctx.Inputs("Grad").front(), grad_var->Type().name());
+
     auto param_out_tensor = ctx.Output<framework::Tensor>("ParamOut");
     auto moment_out_tensor = ctx.Output<framework::Tensor>("MomentOut");
 

paddle/fluid/operators/ftrl_op.cc

@@ -34,6 +34,16 @@ class FTRLOp : public framework::OperatorWithKernel {
                    "Input(Grad) of FTRL should not be null.");
     PADDLE_ENFORCE(ctx->HasInput("LearningRate"),
                    "Input(LearningRate) of FTRL should not be null.");
+    PADDLE_ENFORCE(
+        ctx->GetInputsVarType("Param").front() ==
+            framework::proto::VarType::LOD_TENSOR,
+        "The input var's type should be LoDTensor, but the received is %s",
+        ctx->Inputs("Param").front(), ctx->GetInputsVarType("Param").front());
+    PADDLE_ENFORCE(
+        ctx->GetInputsVarType("Grad").front() ==
+            framework::proto::VarType::LOD_TENSOR,
+        "The input var's type should be LoDTensor, but the received is %s",
+        ctx->Inputs("Grad").front(), ctx->GetInputsVarType("Grad").front());
 
     PADDLE_ENFORCE(ctx->HasOutput("ParamOut"),
                    "Output(ParamOut) of FTRL should not be null.");

paddle/fluid/operators/ftrl_op.h

@@ -28,6 +28,17 @@ template <typename DeviceContext, typename T>
 class FTRLOpKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
+    const auto* param_var = ctx.InputVar("Param");
+    PADDLE_ENFORCE(param_var->IsType<framework::LoDTensor>(),
+                   "The Var(%s)'s type should be LoDTensor, "
+                   "but the received is %s",
+                   ctx.Inputs("Param").front(), param_var->Type().name());
+    const auto* grad_var = ctx.InputVar("Grad");
+    PADDLE_ENFORCE(grad_var->IsType<framework::LoDTensor>(),
+                   "The Var(%s)'s type should be LoDTensor, "
+                   "but the received is %s",
+                   ctx.Inputs("Grad").front(), grad_var->Type().name());
+
     auto* param_out = ctx.Output<Tensor>("ParamOut");
     auto* sq_accum_out = ctx.Output<Tensor>("SquaredAccumOut");
     auto* lin_accum_out = ctx.Output<Tensor>("LinearAccumOut");

paddle/fluid/operators/momentum_op.cc

@@ -33,6 +33,11 @@ class MomentumOp : public framework::OperatorWithKernel {
                    "Input(velocity) of Momentum should not be null.");
     PADDLE_ENFORCE(ctx->HasInput("LearningRate"),
                    "Input(LearningRate) of Momentum should not be null.");
+    PADDLE_ENFORCE(
+        ctx->GetInputsVarType("Param").front() ==
+            framework::proto::VarType::LOD_TENSOR,
+        "The input var's type should be LoDTensor, but the received is %s",
+        ctx->Inputs("Param").front(), ctx->GetInputsVarType("Param").front());
 
     PADDLE_ENFORCE(ctx->HasOutput("ParamOut"),
                    "Output(ParamOut) of Momentum should not be null.");

paddle/fluid/operators/momentum_op.cu

@@ -46,6 +46,17 @@ template <typename T>
 class MomentumOpCUDAKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
+    const auto* param_var = ctx.InputVar("Param");
+    PADDLE_ENFORCE(param_var->IsType<framework::LoDTensor>(),
+                   "The Var(%s)'s type should be LoDTensor, "
+                   "but the received is %s",
+                   ctx.Inputs("Param").front(), param_var->Type().name());
+    const auto* grad_var = ctx.InputVar("Grad");
+    PADDLE_ENFORCE(grad_var->IsType<framework::LoDTensor>(),
+                   "The Var(%s)'s type should be LoDTensor, "
+                   "but the received is %s",
+                   ctx.Inputs("Grad").front(), grad_var->Type().name());
+
     auto param_out = ctx.Output<framework::Tensor>("ParamOut");
     auto velocity_out = ctx.Output<framework::Tensor>("VelocityOut");
     auto param = ctx.Input<framework::Tensor>("Param");

paddle/fluid/operators/momentum_op.h

@@ -23,6 +23,12 @@ template <typename T>
 class MomentumOpKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
+    const auto* param_var = ctx.InputVar("Param");
+    PADDLE_ENFORCE(param_var->IsType<framework::LoDTensor>(),
+                   "The Var(%s)'s type should be LoDTensor, "
+                   "but the received is %s",
+                   ctx.Inputs("Param").front(), param_var->Type().name());
+
     auto param_out = ctx.Output<framework::Tensor>("ParamOut");
     auto velocity_out = ctx.Output<framework::Tensor>("VelocityOut");
     auto param = ctx.Input<framework::Tensor>("Param");

paddle/fluid/operators/rmsprop_op.cc

@@ -32,6 +32,11 @@ class RmspropOp : public framework::OperatorWithKernel {
                    "Input(Grad) of RmspropOp should not be null.");
     PADDLE_ENFORCE(ctx->HasInput("Moment"),
                    "Input(Moment) of RmspropOp should not be null.");
+    PADDLE_ENFORCE(
+        ctx->GetInputsVarType("Param").front() ==
+            framework::proto::VarType::LOD_TENSOR,
+        "The input var's type should be LoDTensor, but the received is %s",
+        ctx->Inputs("Param").front(), ctx->GetInputsVarType("Param").front());
 
     PADDLE_ENFORCE(ctx->HasOutput("ParamOut"),
                    "Output(param_out) of RmspropOp should not be null.");

paddle/fluid/operators/rmsprop_op.h

@@ -28,6 +28,12 @@ template <typename DeviceContext, typename T>
 class RmspropOpKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
+    const auto* param_var = ctx.InputVar("Param");
+    PADDLE_ENFORCE(param_var->IsType<framework::LoDTensor>(),
+                   "The Var(%s)'s type should be LoDTensor, "
+                   "but the received is %s",
+                   ctx.Inputs("Param").front(), param_var->Type().name());
+
     auto* param_out = ctx.Output<Tensor>("ParamOut");
     auto* moment_out = ctx.Output<Tensor>("MomentOut");
     auto* mean_square_out = ctx.Output<Tensor>("MeanSquareOut");

paddle/fluid/operators/sgd_op.cc

@@ -21,7 +21,7 @@ class SGDOp : public framework::OperatorWithKernel {
  public:
   using framework::OperatorWithKernel::OperatorWithKernel;
 
-  void InferShape(framework::InferShapeContext* ctx) const override {
+  void InferShape(framework::InferShapeContext *ctx) const override {
     PADDLE_ENFORCE(ctx->HasInput("Param"),
                    "Input(Param) of SGDOp should not be null.");
     PADDLE_ENFORCE(ctx->HasInput("Grad"),
@@ -42,7 +42,7 @@ class SGDOp : public framework::OperatorWithKernel {
 
  protected:
   framework::OpKernelType GetExpectedKernelType(
-      const framework::ExecutionContext& ctx) const override {
+      const framework::ExecutionContext &ctx) const override {
     auto data_type = framework::GetDataTypeOfVar(ctx.InputVar("Param"));
     return framework::OpKernelType(data_type, ctx.device_context());
   }
@@ -50,17 +50,20 @@ class SGDOp : public framework::OperatorWithKernel {
 
 class SGDOpInferVarType : public framework::VarTypeInference {
  public:
-  void operator()(const framework::OpDesc& op_desc,
-                  framework::BlockDesc* block) const override {
-    auto input_var = op_desc.Input("Param")[0];
-    for (auto& out_var : op_desc.Output("ParamOut")) {
-      if (block->FindRecursiveOrCreateVar(input_var).GetType() ==
-          framework::proto::VarType::SELECTED_ROWS) {
-        block->FindRecursiveOrCreateVar(out_var).SetType(
-            framework::proto::VarType::SELECTED_ROWS);
-      } else {
-        block->FindRecursiveOrCreateVar(out_var).SetType(
-            framework::proto::VarType::LOD_TENSOR);
+  void operator()(const framework::OpDesc &op_desc,
+                  framework::BlockDesc *block) const override {
+    auto input_var_n = op_desc.Input("Param")[0];
+    auto in_var_type = block->FindRecursiveOrCreateVar(input_var_n).GetType();
+    PADDLE_ENFORCE(in_var_type == framework::proto::VarType::SELECTED_ROWS ||
+                       in_var_type == framework::proto::VarType::LOD_TENSOR,
+                   "The input Var's type should be LoDtensor or SelectedRows,"
+                   " but the received var(%s)'s type is %s",
+                   input_var_n, in_var_type);
+
+    for (auto &out_var_n : op_desc.Output("ParamOut")) {
+      auto &out_var = block->FindRecursiveOrCreateVar(out_var_n);
+      if (out_var.GetType() != in_var_type) {
+        out_var.SetType(in_var_type);
       }
     }
   }

paddle/fluid/operators/sgd_op.cu

@@ -56,6 +56,12 @@ template <typename T>
 class SGDOpCUDAKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
+    const auto* param_var = ctx.InputVar("Param");
+    PADDLE_ENFORCE(param_var->IsType<framework::LoDTensor>(),
+                   "The Var(%s)'s type should be LoDTensor, "
+                   "but the received is %s",
+                   ctx.Inputs("Param").front(), param_var->Type().name());
+
     auto* param = ctx.Input<framework::Tensor>("Param");
     auto* param_out = ctx.Output<framework::Tensor>("ParamOut");
     auto* learning_rate = ctx.Input<framework::Tensor>("LearningRate");

python/paddle/fluid/optimizer.py

@@ -659,6 +659,9 @@ class AdamaxOptimizer(Optimizer):
 
             optimizer = fluid.optimizer.Adamax(learning_rate=0.2)
             optimizer.minimize(cost)
+
+    Notes:
+       Currently, AdamaxOptimizer doesn't support sparse parameter optimization.
     """
     _moment_acc_str = "moment"
     _inf_norm_acc_str = "inf_norm"
@@ -778,6 +781,9 @@ class DecayedAdagradOptimizer(Optimizer):
 
             optimizer = fluid.optimizer.DecayedAdagrad(learning_rate=0.2)
             optimizer.minimize(cost)
+
+    Notes:
+       Currently, DecayedAdagradOptimizer doesn't support sparse parameter optimization.
     """
     _moment_acc_str = "moment"
 
@@ -858,6 +864,9 @@ class AdadeltaOptimizer(Optimizer):
             optimizer = fluid.optimizer.Adadelta(
                 learning_rate=0.0003, epsilon=1.0e-6, rho=0.95)
             _, params_grads = optimizer.minimize(cost)
+
+    Notes:
+       Currently, AdadeltaOptimizer doesn't support sparse parameter optimization.
     """
 
     _avg_squared_grad_acc_str = "_avg_squared_grad"
@@ -1126,6 +1135,9 @@ class FtrlOptimizer(Optimizer):
 
               optimizer = fluid.optimizer.Ftrl(0.0001)
               _, params_grads = optimizer.minimize(cost)
+
+    Notes:
+       Currently, FtrlOptimizer doesn't support sparse parameter optimization.
     """
 
     _squared_acc_str = "squared"