Cherry-pick lod_reset fix to 1.4 (#16939)

test=release/1.4

Author: Yibing Liu

paddle/fluid/framework/var_type_inference.h

@@ -45,12 +45,16 @@ class InferVarTypeContext {
 
   virtual bool HasInput(const std::string& name) const {
     PADDLE_ENFORCE_NOT_NULL(op_);
-    return op_->Inputs().count(name) > 0;
+    auto& inputs = op_->Inputs();
+    auto input = inputs.find(name);
+    return input != inputs.end() && !input->second.empty();
   }
 
   virtual bool HasOutput(const std::string& name) const {
     PADDLE_ENFORCE_NOT_NULL(op_);
-    return op_->Outputs().count(name) > 0;
+    auto& outputs = op_->Outputs();
+    auto output = outputs.find(name);
+    return output != outputs.end() && !output->second.empty();
   }
 
   virtual const std::vector<std::string>& Input(const std::string& name) const {

paddle/fluid/operators/lod_reset_op.cc

@@ -30,10 +30,10 @@ class LoDResetOp : public framework::OperatorWithKernel {
 
     if (!ctx->HasInput("Y")) {
       auto level0 = ctx->Attrs().Get<std::vector<int>>("target_lod");
-      PADDLE_ENFORCE_GT(level0.size(), 1,
+      PADDLE_ENFORCE_GT(level0.size(), 0,
                         "If Input(Y) not provided, the target lod should be "
                         "specified by attribute `target_lod`.");
-    } else {
+    } else if (ctx->IsRuntime()) {
       ctx->ShareLoD("Y", "Out");
     }
 
@@ -48,6 +48,23 @@ class LoDResetOp : public framework::OperatorWithKernel {
   }
 };
 
+class LoDResetOpVarTypeInference : public framework::VarTypeInference {
+ public:
+  void operator()(framework::InferVarTypeContext *ctx) const override {
+    auto x_var_name = ctx->Input("X").front();
+    auto out_var_name = ctx->Output("Out").front();
+    if (ctx->HasInput("Y")) {
+      auto y_var_name = ctx->Input("Y").front();
+      auto y_lod_level = std::max(ctx->GetLoDLevel(y_var_name), 1);
+      ctx->SetLoDLevel(out_var_name, y_lod_level);
+    } else {
+      ctx->SetLoDLevel(out_var_name, 1);
+    }
+    ctx->SetDataType(out_var_name, ctx->GetDataType(x_var_name));
+    ctx->SetType(out_var_name, paddle::framework::proto::VarType::LOD_TENSOR);
+  }
+};
+
 class LoDResetOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
   void Make() override {
@@ -177,9 +194,10 @@ DECLARE_NO_NEED_BUFFER_VARS_INFERENCE(LoDResetGradNoNeedBufferVarInference,
 
 namespace ops = paddle::operators;
 REGISTER_OPERATOR(lod_reset, ops::LoDResetOp, ops::LoDResetOpMaker,
-                  ops::LoDResetGradDescMaker);
+                  ops::LoDResetGradDescMaker, ops::LoDResetOpVarTypeInference);
 REGISTER_OPERATOR(lod_reset_grad, ops::LoDResetGradOp,
                   ops::LoDResetGradNoNeedBufferVarInference);
+
 REGISTER_OP_CPU_KERNEL(
     lod_reset, ops::LoDResetKernel<paddle::platform::CPUPlace, float>,
     ops::LoDResetKernel<paddle::platform::CPUPlace, double>,

paddle/fluid/operators/lod_reset_op.h

@@ -63,7 +63,7 @@ class LoDResetKernel : public framework::OpKernel<T> {
                       "Target LoD should be a vector end with the "
                       "first dimension of Input(X).");
     for (size_t i = 0; i < level0.size() - 1; ++i) {
-      PADDLE_ENFORCE(level0[i + 1] > level0[i],
+      PADDLE_ENFORCE(level0[i + 1] >= level0[i],
                      "Target LoD should be an ascending vector.");
     }
 

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

@@ -1333,7 +1333,15 @@ def test_lod_reset(self):
             x = layers.data(name='x', shape=[10], dtype='float32')
             y = layers.data(
                 name='y', shape=[10, 20], dtype='float32', lod_level=2)
-            print(layers.lod_reset(x=x, y=y))
+            z = layers.lod_reset(x=x, y=y)
+            self.assertTrue(z.lod_level == 2)
+            # case 2
+            lod_tensor_in = layers.data(name='lod_in', shape=[1], dtype='int64')
+            z = layers.lod_reset(x=x, y=lod_tensor_in)
+            self.assertTrue(z.lod_level == 1)
+            # case 3
+            z = layers.lod_reset(x=x, target_lod=[1, 2, 3])
+            self.assertTrue(z.lod_level == 1)
         print(str(program))
 
     def test_label_smooth(self):