Implement infer var type context

paddle/fluid/framework/details/graph_test_base.h

@@ -68,11 +68,11 @@ class SplitOpMaker : public OpProtoAndCheckerMaker {
 
 class DummyVarTypeInference : public VarTypeInference {
  public:
-  void operator()(const OpDesc& op_desc, BlockDesc* block) const override {
+  void operator()(framework::InferVarTypeContext& ctx) const override {
-    auto& inputs = op_desc.Input("X");
+    auto& inputs = ctx.Input("X");
-    auto type = block->Var(inputs.front())->GetType();
+    auto type = ctx.GetType(inputs.front());
-    auto out_var_name = op_desc.Output("Out").front();
+    auto out_var_name = ctx.Output("Out").front();
-    block->Var(out_var_name)->SetType(type);
+    ctx.SetType(out_var_name, type);
   }
 };
 

paddle/fluid/framework/details/op_registry.h

@@ -127,9 +127,9 @@ struct OpInfoFiller<T, kGradOpDescMaker> {
 template <typename T>
 struct OpInfoFiller<T, kVarTypeInference> {
   void operator()(const char* op_type, OpInfo* info) const {
-    info->infer_var_type_ = [](const OpDesc& fwd_op, BlockDesc* block) {
+    info->infer_var_type_ = [](InferVarTypeContext& context) {
       T inference;
-      inference(fwd_op, block);
+      inference(context);
     };
   }
 };

paddle/fluid/framework/ir/graph_test.cc

@@ -43,20 +43,20 @@ class SumOpMaker : public OpProtoAndCheckerMaker {
 
 class SumOpVarTypeInference : public VarTypeInference {
  public:
-  void operator()(const OpDesc &op_desc, BlockDesc *block) const override {
+  void operator()(InferVarTypeContext &ctx) const override {
-    auto &inputs = op_desc.Input("X");
+    auto &inputs = ctx.Input("X");
     auto default_var_type = proto::VarType::SELECTED_ROWS;
 
     bool any_input_is_lod_tensor = std::any_of(
-        inputs.begin(), inputs.end(), [block](const std::string &name) {
+        inputs.begin(), inputs.end(), [ctx](const std::string &name) {
-          return block->Var(name)->GetType() == proto::VarType::LOD_TENSOR;
+          return ctx.GetType(name) == proto::VarType::LOD_TENSOR;
         });
     if (any_input_is_lod_tensor) {
       default_var_type = proto::VarType::LOD_TENSOR;
     }
 
-    auto out_var_name = op_desc.Output("Out").front();
+    auto out_var_name = ctx.Output("Out").front();
-    block->Var(out_var_name)->SetType(default_var_type);
+    ctx.SetType(out_var_name, default_var_type);
   }
 };
 
@@ -71,7 +71,7 @@ class DummyOpMaker : public OpProtoAndCheckerMaker {
 
 class DummyOpVarTypeInference : public VarTypeInference {
  public:
-  void operator()(const OpDesc &op_desc, BlockDesc *block) const override {}
+  void operator()(framework::InferVarTypeContext &ctx) const override {}
 };
 }  // namespace framework
 }  // namespace paddle

paddle/fluid/framework/op_desc.cc

@@ -24,6 +24,7 @@ limitations under the License. */
 #include "paddle/fluid/framework/operator.h"
 #include "paddle/fluid/framework/program_desc.h"
 #include "paddle/fluid/framework/shape_inference.h"
+#include "paddle/fluid/framework/var_type_inference.h"
 
 namespace paddle {
 namespace framework {
@@ -677,7 +678,8 @@ void OpDesc::InferVarType(BlockDesc *block) const {
   // var type inference. Hence, we don't do any "default" setting here.
   auto &info = OpInfoMap::Instance().Get(this->Type());
   if (info.infer_var_type_) {
-    info.infer_var_type_(*this, block);
+    InferVarTypeContext context(this, block);
+    info.infer_var_type_(context);
   }
 }
 

paddle/fluid/framework/type_defs.h

@@ -27,6 +27,7 @@ namespace framework {
 class OperatorBase;
 class OpDesc;
 class InferShapeContext;
+class InferVarTypeContext;
 class BlockDesc;
 class Variable;
 
@@ -53,7 +54,7 @@ using GradOpMakerFN = std::function<std::vector<std::unique_ptr<OpDesc>>(
     const std::vector<BlockDesc*>& grad_block)>;
 
 using InferVarTypeFN =
-    std::function<void(const OpDesc& /*op_desc*/, BlockDesc* /*block*/)>;
+    std::function<void(framework::InferVarTypeContext& /*context*/)>;
 
 using InferShapeFN = std::function<void(InferShapeContext*)>;
 

paddle/fluid/framework/var_type_inference.h

@@ -14,6 +14,7 @@ limitations under the License. */
 
 #pragma once
 #include <string>
+#include <vector>
 #include "paddle/fluid/framework/block_desc.h"
 #include "paddle/fluid/framework/op_desc.h"
 #include "paddle/fluid/framework/type_defs.h"
@@ -21,26 +22,113 @@ limitations under the License. */
 namespace paddle {
 namespace framework {
 
+class OpDesc;
+class BlockDesc;
+// default infer var type context
+class InferVarTypeContext {
+ public:
+  InferVarTypeContext(const OpDesc* op, BlockDesc* block)
+      : op_(op), block_(block) {}
+
+  Attribute GetAttr(const std::string& name) const {
+    PADDLE_ENFORCE_NOT_NULL(op_);
+    return op_->GetAttr(name);
+  }
+
+  inline bool HasVar(const std::string& name) const {
+    PADDLE_ENFORCE_NOT_NULL(block_);
+    return block_->FindVarRecursive(name) != nullptr;
+  }
+
+  inline bool HasInput(const std::string& name) const {
+    PADDLE_ENFORCE_NOT_NULL(op_);
+    return op_->Inputs().count(name) > 0;
+  }
+
+  inline bool HasOutput(const std::string& name) const {
+    PADDLE_ENFORCE_NOT_NULL(op_);
+    return op_->Outputs().count(name) > 0;
+  }
+
+  inline const std::vector<std::string>& Input(const std::string& name) const {
+    PADDLE_ENFORCE_NOT_NULL(op_);
+    return op_->Input(name);
+  }
+
+  inline const std::vector<std::string>& Output(const std::string& name) const {
+    PADDLE_ENFORCE_NOT_NULL(op_);
+    return op_->Output(name);
+  }
+
+  inline proto::VarType::Type GetType(const std::string& name) const {
+    PADDLE_ENFORCE_NOT_NULL(block_);
+    return block_->FindRecursiveOrCreateVar(name).GetType();
+  }
+
+  inline void SetType(const std::string& name, proto::VarType::Type type) {
+    PADDLE_ENFORCE_NOT_NULL(block_);
+    block_->FindRecursiveOrCreateVar(name).SetType(type);
+  }
+
+  inline proto::VarType::Type GetDataType(const std::string& name) const {
+    PADDLE_ENFORCE_NOT_NULL(block_);
+    return block_->FindRecursiveOrCreateVar(name).GetDataType();
+  }
+
+  inline void SetDataType(const std::string& name, proto::VarType::Type type) {
+    PADDLE_ENFORCE_NOT_NULL(block_);
+    block_->FindRecursiveOrCreateVar(name).SetDataType(type);
+  }
+
+  inline std::vector<int64_t> GetShape(const std::string& name) const {
+    PADDLE_ENFORCE_NOT_NULL(block_);
+    return block_->FindRecursiveOrCreateVar(name).GetShape();
+  }
+
+  inline void SetShape(const std::string& name,
+                       const std::vector<int64_t>& dims) {
+    PADDLE_ENFORCE_NOT_NULL(block_);
+    block_->FindRecursiveOrCreateVar(name).SetShape(dims);
+  }
+
+  inline int32_t GetLoDLevel(const std::string& name) const {
+    PADDLE_ENFORCE_NOT_NULL(block_);
+    return block_->FindRecursiveOrCreateVar(name).GetLoDLevel();
+  }
+
+  inline void SetLoDLevel(const std::string& name, int32_t lod_level) {
+    PADDLE_ENFORCE_NOT_NULL(block_);
+    block_->FindRecursiveOrCreateVar(name).SetLoDLevel(lod_level);
+  }
+
+ private:
+  const OpDesc* op_;
+  BlockDesc* block_;
+};
+
+// infer var type context for imperative mode
+class RuntimeInferVarTypeContext : public InferVarTypeContext {
+ public:
+  RuntimeInferVarTypeContext() : InferVarTypeContext(nullptr, nullptr) {}
+};
+
 class VarTypeInference {
  public:
   virtual ~VarTypeInference() {}
-  virtual void operator()(const OpDesc& op_desc, BlockDesc* block) const = 0;
+  virtual void operator()(InferVarTypeContext& context) const = 0;  // NOLINT
 };
 
 class PassInDtypeAndVarTypeToOutput : public framework::VarTypeInference {
  public:
-  void operator()(const framework::OpDesc& op_desc,
+  void operator()(framework::InferVarTypeContext& ctx) const final {  // NOLINT
-                  framework::BlockDesc* block) const final {
     auto in_out_var_names = this->GetInputOutputWithSameType();
 
     for (auto& i_o_n : in_out_var_names) {
-      auto& x_name = op_desc.Input(i_o_n.first).at(0);
+      auto& x_name = ctx.Input(i_o_n.first).at(0);
-      auto& out_name = op_desc.Output(i_o_n.second).at(0);
+      auto& out_name = ctx.Output(i_o_n.second).at(0);
 
-      auto& x = block->FindRecursiveOrCreateVar(x_name);
+      ctx.SetType(out_name, ctx.GetType(x_name));
-      auto& out = block->FindRecursiveOrCreateVar(out_name);
+      ctx.SetDataType(out_name, ctx.GetDataType(x_name));
-      out.SetType(x.GetType());
-      out.SetDataType(x.GetDataType());
     }
   }
 

paddle/fluid/framework/var_type_inference_test.cc

@@ -44,20 +44,20 @@ class SumOpMaker : public OpProtoAndCheckerMaker {
 
 class SumOpVarTypeInference : public VarTypeInference {
  public:
-  void operator()(const OpDesc &op_desc, BlockDesc *block) const override {
+  void operator()(framework::InferVarTypeContext &ctx) const override {
-    auto &inputs = op_desc.Input("X");
+    auto &inputs = ctx.Input("X");
     auto default_var_type = proto::VarType::SELECTED_ROWS;
 
     bool any_input_is_lod_tensor = std::any_of(
-        inputs.begin(), inputs.end(), [block](const std::string &name) {
+        inputs.begin(), inputs.end(), [ctx](const std::string &name) {
-          return block->Var(name)->GetType() == proto::VarType::LOD_TENSOR;
+          return ctx.GetType(name) == proto::VarType::LOD_TENSOR;
         });
     if (any_input_is_lod_tensor) {
       default_var_type = proto::VarType::LOD_TENSOR;
     }
 
-    auto out_var_name = op_desc.Output("Out").front();
+    auto out_var_name = ctx.Output("Out").front();
-    block->Var(out_var_name)->SetType(default_var_type);
+    ctx.SetType(out_var_name, default_var_type);
   }
 };
 }  // namespace framework

paddle/fluid/operators/beam_search_decode_op.cc

@@ -203,15 +203,12 @@ class BeamSearchDecodeInferShape : public framework::InferShapeBase {
 
 class BeamSearchDecodeInferVarType : public framework::VarTypeInference {
  public:
-  void operator()(const framework::OpDesc& op_desc,
+  void operator()(framework::InferVarTypeContext& ctx) const override {
-                  framework::BlockDesc* block) const override {
+    for (auto& o : ctx.Output("SentenceIds")) {
-    for (auto& o : op_desc.Output("SentenceIds")) {
+      ctx.SetType(o, framework::proto::VarType::LOD_TENSOR);
-      auto& sentence_ids = block->FindRecursiveOrCreateVar(o);
+    }
-      sentence_ids.SetType(framework::proto::VarType::LOD_TENSOR);
+    for (auto& o : ctx.Output("SentenceScores")) {
-    }
+      ctx.SetType(o, framework::proto::VarType::LOD_TENSOR);
-    for (auto& o : op_desc.Output("SentenceScores")) {
-      auto& sentence_scores = block->FindRecursiveOrCreateVar(o);
-      sentence_scores.SetType(framework::proto::VarType::LOD_TENSOR);
     }
   }
 };

paddle/fluid/operators/beam_search_op.cc

@@ -120,15 +120,12 @@ class BeamSearchOp : public framework::OperatorWithKernel {
 
 class BeamSearchInferVarType : public framework::VarTypeInference {
  public:
-  void operator()(const framework::OpDesc &op_desc,
+  void operator()(framework::InferVarTypeContext &ctx) const override {
-                  framework::BlockDesc *block) const override {
+    for (auto &o : ctx.Output("selected_ids")) {
-    for (auto &o : op_desc.Output("selected_ids")) {
+      ctx.SetType(o, framework::proto::VarType::LOD_TENSOR);
-      auto &selected_ids = block->FindRecursiveOrCreateVar(o);
-      selected_ids.SetType(framework::proto::VarType::LOD_TENSOR);
     }
-    for (auto &o : op_desc.Output("selected_scores")) {
+    for (auto &o : ctx.Output("selected_scores")) {
-      auto &selected_scores = block->FindRecursiveOrCreateVar(o);
+      ctx.SetType(o, framework::proto::VarType::LOD_TENSOR);
-      selected_scores.SetType(framework::proto::VarType::LOD_TENSOR);
     }
   }
 };

paddle/fluid/operators/controlflow/get_places_op.cc

@@ -93,11 +93,9 @@ execution.
 
 class GetPlacesInferVarType : public framework::VarTypeInference {
  public:
-  void operator()(const framework::OpDesc &op_desc,
+  void operator()(framework::InferVarTypeContext &ctx) const override {
-                  framework::BlockDesc *block) const override {
+    for (auto &o_name : ctx.Output("Out")) {
-    for (auto &o_name : op_desc.Output("Out")) {
+      ctx.SetType(o_name, framework::proto::VarType::PLACE_LIST);
-      block->FindRecursiveOrCreateVar(o_name).SetType(
-          framework::proto::VarType::PLACE_LIST);
     }
   }
 };

paddle/fluid/operators/controlflow/tensor_array_read_write_op.cc

@@ -100,16 +100,13 @@ class WriteToArrayInferShape : public framework::InferShapeBase {
 
 class WriteToArrayInferVarType : public framework::VarTypeInference {
  public:
-  void operator()(const framework::OpDesc &op_desc,
+  void operator()(framework::InferVarTypeContext &ctx) const override {
-                  framework::BlockDesc *block) const override {
+    auto x_name = ctx.Input("X")[0];
-    auto x_name = op_desc.Input("X")[0];
+    auto out_name = ctx.Output("Out")[0];
-    auto out_name = op_desc.Output("Out")[0];
     VLOG(10) << "Set Variable " << out_name << " as LOD_TENSOR_ARRAY";
-    auto &out = block->FindRecursiveOrCreateVar(out_name);
+    ctx.SetType(out_name, framework::proto::VarType::LOD_TENSOR_ARRAY);
-    out.SetType(framework::proto::VarType::LOD_TENSOR_ARRAY);
+    if (ctx.HasVar(x_name)) {
-    auto *x = block->FindVarRecursive(x_name);
+      ctx.SetDataType(out_name, ctx.GetDataType(x_name));
-    if (x != nullptr) {
-      out.SetDataType(x->GetDataType());
     }
   }
 };

paddle/fluid/operators/distributed_ops/merge_ids_op.cc

@@ -114,11 +114,10 @@ class MergeIdsOp : public framework::OperatorWithKernel {
 
 class MergeIdsOpInferVarType : public framework::VarTypeInference {
  public:
-  void operator()(const framework::OpDesc &op_desc,
+  void operator()(framework::InferVarTypeContext &ctx) const override {
-                  framework::BlockDesc *block) const override {
+    auto input_type = ctx.GetType(ctx.Input("Ids")[0]);
-    auto *input_var = block->Var(op_desc.Input("Ids")[0]);
+    for (auto &out_var : ctx.Output("Out")) {
-    for (auto &out_var : op_desc.Output("Out")) {
+      ctx.SetType(out_var, input_type);
-      block->Var(out_var)->SetType(input_var->GetType());
     }
   }
 };

paddle/fluid/operators/distributed_ops/split_ids_op.cc

@@ -71,11 +71,10 @@ class SplitIdsOp : public framework::OperatorWithKernel {
 
 class SplitIdsOpInferVarType : public framework::VarTypeInference {
  public:
-  void operator()(const framework::OpDesc &op_desc,
+  void operator()(framework::InferVarTypeContext &ctx) const override {
-                  framework::BlockDesc *block) const override {
+    auto input_type = ctx.GetType(ctx.Input("Ids")[0]);
-    auto *input_var = block->Var(op_desc.Input("Ids")[0]);
+    for (auto &out_var : ctx.Output("Out")) {
-    for (auto &out_var : op_desc.Output("Out")) {
+      ctx.SetType(out_var, input_type);
-      block->Var(out_var)->SetType(input_var->GetType());
     }
   }
 };

paddle/fluid/operators/fill_constant_op.cc

@@ -39,12 +39,11 @@ class FillConstantOp : public framework::OperatorWithKernel {
 
 class FillConstantOpVarTypeInference : public framework::VarTypeInference {
  public:
-  void operator()(const framework::OpDesc& op_desc,
+  void operator()(framework::InferVarTypeContext& ctx) const override {
-                  framework::BlockDesc* block) const override {
     auto data_type = static_cast<framework::proto::VarType::Type>(
-        boost::get<int>(op_desc.GetAttr("dtype")));
+        boost::get<int>(ctx.GetAttr("dtype")));
-    auto& out_var_name = op_desc.Output("Out").front();
+    auto& out_var_name = ctx.Output("Out").front();
-    block->Var(out_var_name)->SetDataType(data_type);
+    ctx.SetDataType(out_var_name, data_type);
   }
 };
 

paddle/fluid/operators/fused/fused_embedding_seq_pool_op.cc

@@ -137,22 +137,20 @@ class FusedEmbeddingSeqPoolOpGrad : public framework::OperatorWithKernel {
 class FusedEmbeddingSeqPoolOpGradVarTypeInference
     : public framework::VarTypeInference {
  public:
-  void operator()(const framework::OpDesc& op_desc,
+  void operator()(framework::InferVarTypeContext& ctx) const override {
-                  framework::BlockDesc* block) const override {
+    auto out_var_name = ctx.Output(framework::GradVarName("W")).front();
-    auto out_var_name = op_desc.Output(framework::GradVarName("W")).front();
+    auto attr = ctx.GetAttr("is_sparse");
-    auto attr = op_desc.GetAttr("is_sparse");
     bool is_sparse = boost::get<bool>(attr);
     if (is_sparse) {
       VLOG(3) << "fused_embedding_seq_pool_grad op "
               << framework::GradVarName("W") << " is set to SelectedRows";
-      block->Var(out_var_name)
+      ctx.SetType(out_var_name, framework::proto::VarType::SELECTED_ROWS);
-          ->SetType(framework::proto::VarType::SELECTED_ROWS);
     } else {
       VLOG(3) << "fused_embedding_seq_pool_grad op "
               << framework::GradVarName("W") << " is set to LoDTensor";
-      block->Var(out_var_name)->SetType(framework::proto::VarType::LOD_TENSOR);
+      ctx.SetType(out_var_name, framework::proto::VarType::LOD_TENSOR);
     }
-    block->Var(out_var_name)->SetDataType(block->Var("W")->GetDataType());
+    ctx.SetDataType(out_var_name, ctx.GetDataType(ctx.Input("W")[0]));
   }
 };
 

paddle/fluid/operators/get_tensor_from_selected_rows_op.cc

@@ -81,15 +81,12 @@ GetTensorFromSelectedRows is used to get the tensor from SelectedRows.
 class GetTensorFromSelectedRowsOpVarTypeInference
     : public framework::VarTypeInference {
  public:
-  void operator()(const framework::OpDesc &op_desc,
+  void operator()(framework::InferVarTypeContext &ctx) const {  // NOLINT
-                  framework::BlockDesc *block) const final {
+    auto out_var_name = ctx.Output("Out").front();
-    auto out_var_name = op_desc.Output("Out").front();
+    auto in_var_name = ctx.Input("X").front();
-    auto in_var_name = op_desc.Input("X").front();
+
-
+    ctx.SetType(out_var_name, framework::proto::VarType::LOD_TENSOR);
-    auto out_var = block->FindRecursiveOrCreateVar(out_var_name);
+    ctx.SetDataType(out_var_name, ctx.GetDataType(in_var_name));
-    auto in_var = block->FindRecursiveOrCreateVar(in_var_name);
-    out_var.SetType(framework::proto::VarType::LOD_TENSOR);
-    out_var.SetDataType(in_var.GetDataType());
   }
 };
 

paddle/fluid/operators/hierarchical_sigmoid_op.cc

@@ -197,38 +197,32 @@ class HierarchicalSigmoidGradOp : public framework::OperatorWithKernel {
 class HierarchicalSigmoidGradOpGradVarTypeInference
     : public framework::VarTypeInference {
  public:
-  void operator()(const framework::OpDesc& op_desc,
+  void operator()(framework::InferVarTypeContext& ctx) const override {
-                  framework::BlockDesc* block) const override {
+    auto w_grad_var_name = ctx.Output(framework::GradVarName("W")).front();
-    auto w_grad_var_name = op_desc.Output(framework::GradVarName("W")).front();
+    auto bias_grad_var_name_vec = ctx.Output(framework::GradVarName("Bias"));
-    auto bias_grad_var_name_vec =
-        op_desc.Output(framework::GradVarName("Bias"));
     std::string bias_grad_var_name;
     bool hasBias = false;
     if (bias_grad_var_name_vec.size()) {
       hasBias = true;
-      bias_grad_var_name =
+      bias_grad_var_name = ctx.Output(framework::GradVarName("Bias")).front();
-          op_desc.Output(framework::GradVarName("Bias")).front();
     }
-    auto attr = op_desc.GetAttr("is_sparse");
+    auto attr = ctx.GetAttr("is_sparse");
     bool is_sparse = boost::get<bool>(attr);
     if (is_sparse) {
       VLOG(30) << "hierarchical_sigmoid_grad op " << framework::GradVarName("W")
                << " is set to SelectedRows";
-      block->Var(w_grad_var_name)
+      ctx.SetType(w_grad_var_name, framework::proto::VarType::SELECTED_ROWS);
-          ->SetType(framework::proto::VarType::SELECTED_ROWS);
     } else {
       VLOG(30) << "hierarchical_sigmoid_grad op " << framework::GradVarName("W")
                << " is set to LoDTensor";
-      block->Var(w_grad_var_name)
+      ctx.SetType(w_grad_var_name, framework::proto::VarType::LOD_TENSOR);
-          ->SetType(framework::proto::VarType::LOD_TENSOR);
     }
     if (hasBias) {
       VLOG(30) << "hierarchical_sigmoid_grad op "
                << framework::GradVarName("Bias") << " is set to LoDTensor";
-      block->Var(bias_grad_var_name)
+      ctx.SetType(bias_grad_var_name, framework::proto::VarType::LOD_TENSOR);
-          ->SetType(framework::proto::VarType::LOD_TENSOR);
     }
-    block->Var(w_grad_var_name)->SetDataType(block->Var("W")->GetDataType());
+    ctx.SetDataType(w_grad_var_name, ctx.GetDataType(ctx.Input("W")[0]));
   }
 };
 

paddle/fluid/operators/lod_rank_table_op.cc

@@ -64,11 +64,9 @@ class LoDRankTableInferShape : public framework::InferShapeBase {
 
 class LoDRankTableInferVarType : public framework::VarTypeInference {
  public:
-  void operator()(const framework::OpDesc &op_desc,
+  void operator()(framework::InferVarTypeContext &ctx) const override {
-                  framework::BlockDesc *block) const override {
+    for (auto &o : ctx.Output("Out")) {
-    for (auto &o : op_desc.Output("Out")) {
+      ctx.SetType(o, framework::proto::VarType::LOD_RANK_TABLE);
-      block->FindRecursiveOrCreateVar(o).SetType(
-          framework::proto::VarType::LOD_RANK_TABLE);
     }
   }
 };

paddle/fluid/operators/lod_tensor_to_array_op.cc

@@ -201,10 +201,9 @@ class LoDTensorToArrayInferShape : public framework::InferShapeBase {
 
 class LoDTensorToArrayInferVarType : public framework::VarTypeInference {
  public:
-  void operator()(const framework::OpDesc &op_desc,
+  void operator()(framework::InferVarTypeContext &ctx) const override {
-                  framework::BlockDesc *block) const override {
+    for (auto &out_var : ctx.Output("Out")) {
-    for (auto &out_var : op_desc.Output("Out")) {
+      ctx.SetType(out_var, framework::proto::VarType::LOD_TENSOR_ARRAY);
-      block->Var(out_var)->SetType(framework::proto::VarType::LOD_TENSOR_ARRAY);
     }
   }
 };

paddle/fluid/operators/lookup_table_op.cc

@@ -147,22 +147,20 @@ class LookupTableOpGrad : public framework::OperatorWithKernel {
 
 class LookupTableOpGradVarTypeInference : public framework::VarTypeInference {
  public:
-  void operator()(const framework::OpDesc& op_desc,
+  void operator()(framework::InferVarTypeContext& ctx) const override {
-                  framework::BlockDesc* block) const override {
+    auto out_var_name = ctx.Output(framework::GradVarName("W")).front();
-    auto out_var_name = op_desc.Output(framework::GradVarName("W")).front();
+    auto attr = ctx.GetAttr("is_sparse");
-    auto attr = op_desc.GetAttr("is_sparse");
     bool is_sparse = boost::get<bool>(attr);
     if (is_sparse) {
       VLOG(3) << "lookup_table_grad op " << framework::GradVarName("W")
               << " is set to SelectedRows";
-      block->Var(out_var_name)
+      ctx.SetType(out_var_name, framework::proto::VarType::SELECTED_ROWS);
-          ->SetType(framework::proto::VarType::SELECTED_ROWS);
     } else {
       VLOG(3) << "lookup_table_grad op " << framework::GradVarName("W")
               << " is set to LoDTensor";
-      block->Var(out_var_name)->SetType(framework::proto::VarType::LOD_TENSOR);
+      ctx.SetType(out_var_name, framework::proto::VarType::LOD_TENSOR);
     }
-    block->Var(out_var_name)->SetDataType(block->Var("W")->GetDataType());
+    ctx.SetDataType(out_var_name, ctx.GetDataType(ctx.Input("W")[0]));
   }
 };
 

paddle/fluid/operators/nce_op.cc

@@ -237,23 +237,21 @@ class NCEOpGrad : public framework::OperatorWithKernel {
 
 class NCEOpGradVarTypeInference : public framework::VarTypeInference {
  public:
-  void operator()(const framework::OpDesc &op_desc,
+  void operator()(framework::InferVarTypeContext &ctx) const override {
-                  framework::BlockDesc *block) const override {
+    auto weight_grad = ctx.Output(framework::GradVarName("Weight")).front();
-    auto weight_grad = op_desc.Output(framework::GradVarName("Weight")).front();
 
-    auto attr = op_desc.GetAttr("is_sparse");
+    auto attr = ctx.GetAttr("is_sparse");
     bool is_sparse = boost::get<bool>(attr);
     if (is_sparse) {
       VLOG(3) << "nce_op_grad op " << weight_grad << " and "
               << " is set to SelectedRows";
-      block->Var(weight_grad)
+      ctx.SetType(weight_grad, framework::proto::VarType::SELECTED_ROWS);
-          ->SetType(framework::proto::VarType::SELECTED_ROWS);
     } else {
       VLOG(3) << "nce_op_grad op " << weight_grad << " and "
               << " is set to LoDTensor";
-      block->Var(weight_grad)->SetType(framework::proto::VarType::LOD_TENSOR);
+      ctx.SetType(weight_grad, framework::proto::VarType::LOD_TENSOR);
     }
-    block->Var(weight_grad)->SetDataType(block->Var("Input")->GetDataType());
+    ctx.SetDataType(weight_grad, ctx.GetDataType(ctx.Input("Input")[0]));
   }
 };
 

paddle/fluid/operators/ngraph/ngraph_engine_op.cc

@@ -37,8 +37,7 @@ class NgraphEngineOpMaker : public framework::OpProtoAndCheckerMaker {
 
 class NgraphEngineInferVarType : public framework::VarTypeInference {
  public:
-  void operator()(const framework::OpDesc &op_desc,
+  void operator()(framework::InferVarTypeContext &ctx) const override {}
-                  framework::BlockDesc *block) const override {}
 };
 
 }  // namespace operators

paddle/fluid/operators/optimizers/lars_momentum_op.cc

@@ -72,8 +72,7 @@ use L2 regularizers in case of using LARS.
 
 class LarsMomentumOpVarTypeInference : public framework::VarTypeInference {
  public:
-  void operator()(const framework::OpDesc &op_desc,
+  void operator()(framework::InferVarTypeContext &ctx) const override {}
-                  framework::BlockDesc *block) const override {}
 };
 }  // namespace operators
 }  // namespace paddle

paddle/fluid/operators/optimizers/momentum_op.cc

@@ -21,18 +21,14 @@ using Tensor = framework::Tensor;
 
 class MomentumOpInferVarType : public framework::VarTypeInference {
  public:
-  void operator()(const framework::OpDesc& op_desc,
+  void operator()(framework::InferVarTypeContext& ctx) const override {
-                  framework::BlockDesc* block) const override {
+    auto& input_var = ctx.Input("Param")[0];
-    auto input_var = op_desc.Input("Param")[0];
+    for (auto& out_var : ctx.Output("ParamOut")) {
-    for (auto& out_var : op_desc.Output("ParamOut")) {
+      if (ctx.GetType(input_var) == framework::proto::VarType::SELECTED_ROWS) {
-      if (block->FindRecursiveOrCreateVar(input_var).GetType() ==
+        ctx.SetType(out_var, framework::proto::VarType::SELECTED_ROWS);
-          framework::proto::VarType::SELECTED_ROWS) {
+      } else if (ctx.GetType(input_var) ==
-        block->FindRecursiveOrCreateVar(out_var).SetType(
-            framework::proto::VarType::SELECTED_ROWS);
-      } else if (block->FindRecursiveOrCreateVar(input_var).GetType() ==
                  framework::proto::VarType::LOD_TENSOR) {
-        block->FindRecursiveOrCreateVar(out_var).SetType(
+        ctx.SetType(out_var, framework::proto::VarType::LOD_TENSOR);
-            framework::proto::VarType::LOD_TENSOR);
       } else {
         PADDLE_THROW(
             "Only support LodTensor and SelectedRows, Unexpected Input Type.");

paddle/fluid/operators/optimizers/sgd_op.cc

@@ -50,20 +50,18 @@ class SGDOp : public framework::OperatorWithKernel {
 
 class SGDOpInferVarType : public framework::VarTypeInference {
  public:
-  void operator()(const framework::OpDesc &op_desc,
+  void operator()(framework::InferVarTypeContext &ctx) const override {
-                  framework::BlockDesc *block) const override {
+    auto &input_var_n = ctx.Input("Param")[0];
-    auto input_var_n = op_desc.Input("Param")[0];
+    auto in_var_type = ctx.GetType(input_var_n);
-    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")) {
+    for (auto &out_var_n : ctx.Output("ParamOut")) {
-      auto &out_var = block->FindRecursiveOrCreateVar(out_var_n);
+      if (ctx.GetType(out_var_n) != in_var_type) {
-      if (out_var.GetType() != in_var_type) {
+        ctx.SetType(out_var_n, in_var_type);
-        out_var.SetType(in_var_type);
       }
     }
   }