diff --git a/paddle/pten/api/lib/creation.cc b/paddle/pten/api/lib/creation.cc
index 088ff919596258fc990af4422a3809cd63282869..de44f96a99392f95015975e51e02e696407d2ab8 100644
--- a/paddle/pten/api/lib/creation.cc
+++ b/paddle/pten/api/lib/creation.cc
@@ -52,8 +52,8 @@ PD_DLL_DECL Tensor full(const ScalarArray& shape,
   kernel_context.EmplaceBackAttr(pten::ScalarArray(shape));
   kernel_context.EmplaceBackAttr(pten::Scalar(value));
 
-  // 4. InferShape
-  auto out_meta = pten::FullInferShape(shape, dtype, layout);
+  // 4. InferMeta
+  auto out_meta = pten::FullInferMeta(shape, dtype, layout);
 
   // 5. Prepare outputs
   const auto allocator =
@@ -97,8 +97,8 @@ PD_DLL_DECL Tensor full_like(const Tensor& x,
   auto dense_x = std::dynamic_pointer_cast<pten::DenseTensor>(x.impl());
   kernel_context.EmplaceBackAttr(pten::Scalar(value));
 
-  // 4. InferShape
-  auto out_meta = FullLikeInferShape(dense_x->meta(), dtype, layout);
+  // 4. InferMeta
+  auto out_meta = FullLikeInferMeta(dense_x->meta(), dtype, layout);
 
   // 5. Prepare outputs
   Tensor out;
diff --git a/paddle/pten/api/lib/linalg.cc b/paddle/pten/api/lib/linalg.cc
index 3b7d4994ab75ab4655835b69349ec737958877fa..3255b3b1b5eaa3013d2f83c3e4704640b1523f78 100644
--- a/paddle/pten/api/lib/linalg.cc
+++ b/paddle/pten/api/lib/linalg.cc
@@ -55,8 +55,8 @@ PD_DLL_DECL Tensor dot(const Tensor& x, const Tensor& y) {
   kernel_context.EmplaceBackInput(dense_y);
   // TODO(chenweihang): add transform impl
 
-  // 4. InferShape
-  auto out_meta = DotInferShape(dense_x->meta(), dense_y->meta());
+  // 4. InferMeta
+  auto out_meta = DotInferMeta(dense_x->meta(), dense_y->meta());
 
   // 5. Prepare outputs
   Tensor out;
@@ -95,8 +95,8 @@ PD_DLL_DECL Tensor matmul(const Tensor& x,
   kernel_context.EmplaceBackAttr(transpose_y);
   // TODO(chenweihang): add transform impl
 
-  // 4. InferShape
-  auto out_meta = MatmulInferShape(
+  // 4. InferMeta
+  auto out_meta = MatmulInferMeta(
       dense_x->meta(), dense_y->meta(), transpose_x, transpose_y);
 
   // 5. Prepare outputs
diff --git a/paddle/pten/api/lib/manipulation.cc b/paddle/pten/api/lib/manipulation.cc
index d06602d721d712a91443d7c6926ed09f4ef9b256..51a7702d9fc6e75417f8fcc2b96730b5e43d18e5 100644
--- a/paddle/pten/api/lib/manipulation.cc
+++ b/paddle/pten/api/lib/manipulation.cc
@@ -50,8 +50,8 @@ PD_DLL_DECL Tensor flatten(const Tensor& x, int start_axis, int stop_axis) {
   kernel_context.EmplaceBackAttr(start_axis);
   kernel_context.EmplaceBackAttr(stop_axis);
 
-  // 4. InferShape
-  auto out_meta = FlattenInferShape(dense_x->meta(), start_axis, stop_axis);
+  // 4. InferMeta
+  auto out_meta = FlattenInferMeta(dense_x->meta(), start_axis, stop_axis);
 
   // 5. Prepare outputs
   Tensor out;
@@ -84,7 +84,7 @@ PD_DLL_DECL Tensor cast(const Tensor& x, DataType out_dtype) {
   kernel_context.EmplaceBackAttr(out_dtype);
   kernel_context.EmplaceBackAttr(dense_x->meta().dtype);
 
-  // 4. InferShape
+  // 4. InferMeta
   auto out_meta = CastInferMeta(dense_x->meta(), out_dtype);
 
   // 5. Prepare outputs
@@ -117,8 +117,8 @@ PD_DLL_DECL Tensor reshape(const Tensor& x, const std::vector<int64_t>& shape) {
   kernel_context.EmplaceBackInput(dense_x);
   kernel_context.EmplaceBackAttr(shape);
 
-  // 4. InferShape
-  auto out_meta = InferShapeFromVecValue(dense_x->meta(), shape);
+  // 4. InferMeta
+  auto out_meta = InferMetaFromVecValue(dense_x->meta(), shape);
 
   // 5. Prepare outputs
   Tensor out;
diff --git a/paddle/pten/api/lib/math.cc b/paddle/pten/api/lib/math.cc
index 56a2cddae2a06cbd07a50c354f1430c1343729f2..cbae7dcfc8abb437bc8012a54dc527f5cab58420 100644
--- a/paddle/pten/api/lib/math.cc
+++ b/paddle/pten/api/lib/math.cc
@@ -50,8 +50,8 @@ PD_DLL_DECL Tensor mean(const Tensor& x) {
   auto dense_x = std::dynamic_pointer_cast<pten::DenseTensor>(x.impl());
   kernel_context.EmplaceBackInput(dense_x);
 
-  // 4. InferShape
-  auto out_meta = ReductionInferShape(dense_x->meta());
+  // 4. InferMeta
+  auto out_meta = ReductionInferMeta(dense_x->meta());
 
   // 5. Prepare outputs
   Tensor out;
@@ -86,8 +86,8 @@ PD_DLL_DECL Tensor add(const Tensor& x, const Tensor& y) {
   kernel_context.EmplaceBackInput(dense_y);
   kernel_context.EmplaceBackAttr(-1);
 
-  // 4. InferShape
-  auto out_meta = ElementwiseInferShape(dense_x->meta(), dense_y->meta(), -1);
+  // 4. InferMeta
+  auto out_meta = ElementwiseInferMeta(dense_x->meta(), dense_y->meta(), -1);
 
   // 5. Prepare outputs
   Tensor out;
@@ -121,8 +121,8 @@ PD_DLL_DECL Tensor subtract(const Tensor& x, const Tensor& y) {
   kernel_context.EmplaceBackInput(dense_y);
   kernel_context.EmplaceBackAttr(-1);
 
-  // 4. InferShape
-  auto out_meta = ElementwiseInferShape(dense_x->meta(), dense_y->meta(), -1);
+  // 4. InferMeta
+  auto out_meta = ElementwiseInferMeta(dense_x->meta(), dense_y->meta(), -1);
 
   // 5. Prepare outputs
   Tensor out;
@@ -156,8 +156,8 @@ PD_DLL_DECL Tensor divide(const Tensor& x, const Tensor& y) {
   kernel_context.EmplaceBackInput(dense_y);
   kernel_context.EmplaceBackAttr(-1);
 
-  // 4. InferShape
-  auto out_meta = ElementwiseInferShape(dense_x->meta(), dense_y->meta(), -1);
+  // 4. InferMeta
+  auto out_meta = ElementwiseInferMeta(dense_x->meta(), dense_y->meta(), -1);
 
   // 5. Prepare outputs
   Tensor out;
@@ -191,8 +191,8 @@ PD_DLL_DECL Tensor multiply(const Tensor& x, const Tensor& y) {
   kernel_context.EmplaceBackInput(dense_y);
   kernel_context.EmplaceBackAttr(-1);
 
-  // 4. InferShape
-  auto out_meta = ElementwiseInferShape(dense_x->meta(), dense_y->meta(), -1);
+  // 4. InferMeta
+  auto out_meta = ElementwiseInferMeta(dense_x->meta(), dense_y->meta(), -1);
 
   // 5. Prepare outputs
   Tensor out;
diff --git a/paddle/pten/api/lib/utils.cc b/paddle/pten/api/lib/utils.cc
index 90ea1c357ffcdf8fc24c45f8a91c306b56809286..c17e2e33a6d2d03a8ca1fdaf66ab8558b6396d4b 100644
--- a/paddle/pten/api/lib/utils.cc
+++ b/paddle/pten/api/lib/utils.cc
@@ -55,7 +55,7 @@ PD_DLL_DECL Tensor copy_to(const Tensor& x, Backend backend, bool blocking) {
   kernel_context.EmplaceBackAttr(blocking);
 
   // 4. InferMeta
-  auto out_meta = UnchangedInferShape(dense_x->meta());
+  auto out_meta = UnchangedInferMeta(dense_x->meta());
 
   // 5. Prepare outputs
   const auto allocator =
diff --git a/paddle/pten/include/creation.h b/paddle/pten/include/creation.h
index 9ddc9b30f6b2005f351799949a1fa8c53417efab..ee6b9c11063019c7f1a774f86a46eefb2368aa66 100644
--- a/paddle/pten/include/creation.h
+++ b/paddle/pten/include/creation.h
@@ -31,7 +31,7 @@ DenseTensor FillAnyLike(
     DataType dtype = DataType::UNDEFINED,
     Backend backend = Backend::UNDEFINED,  // Is backend needed here?
     DataLayout layout = DataLayout::UNDEFINED) {
-  auto out_meta = FullLikeInferShape(x.meta(), dtype, layout);
+  auto out_meta = FullLikeInferMeta(x.meta(), dtype, layout);
   const auto allocator =
       std::make_shared<paddle::experimental::DefaultAllocator>(
           dev_ctx.GetPlace());
diff --git a/paddle/pten/include/linalg.h b/paddle/pten/include/linalg.h
index 70eebac5b684154e5dd9d8242aed562391562c38..6d5b51675e815a4c8a1dfb74d41edaf3a7779f8c 100644
--- a/paddle/pten/include/linalg.h
+++ b/paddle/pten/include/linalg.h
@@ -26,7 +26,7 @@ template <typename T, typename ContextT>
 DenseTensor Dot(const ContextT& dev_ctx,
                 const DenseTensor& x,
                 const DenseTensor& y) {
-  auto out_meta = DotInferShape(x.meta(), y.meta());
+  auto out_meta = DotInferMeta(x.meta(), y.meta());
   const auto allocator =
       std::make_shared<paddle::experimental::DefaultAllocator>(
           dev_ctx.GetPlace());
diff --git a/paddle/pten/include/manipulation.h b/paddle/pten/include/manipulation.h
index f6a7fcd3882f015fe1b8d7ddac8151cf970951fa..7a7ecb589842b3f7c7d1e0e2fefa24d37bddcdd5 100644
--- a/paddle/pten/include/manipulation.h
+++ b/paddle/pten/include/manipulation.h
@@ -28,7 +28,7 @@ DenseTensor Flatten(const ContextT& dev_ctx,
                     const DenseTensor& x,
                     int start_axis,
                     int stop_axis) {
-  auto out_meta = FlattenInferShape(x.meta(), start_axis, stop_axis);
+  auto out_meta = FlattenInferMeta(x.meta(), start_axis, stop_axis);
   const auto allocator =
       std::make_shared<paddle::experimental::DefaultAllocator>(
           dev_ctx.GetPlace());
@@ -55,7 +55,7 @@ template <typename T, typename ContextT>
 DenseTensor Reshape(const ContextT& dev_ctx,
                     const DenseTensor& x,
                     const std::vector<int64_t>& shape) {
-  auto out_meta = InferShapeFromVecValue(x.meta(), shape);
+  auto out_meta = InferMetaFromVecValue(x.meta(), shape);
   const auto allocator =
       std::make_shared<paddle::experimental::DefaultAllocator>(
           dev_ctx.GetPlace());
diff --git a/paddle/pten/include/math.h b/paddle/pten/include/math.h
index cc4c4f33f74c929fdde0e17fe3c718219961f104..8fc4e27ee758f1f941c318a0b4575731a41efdc5 100644
--- a/paddle/pten/include/math.h
+++ b/paddle/pten/include/math.h
@@ -24,7 +24,7 @@ namespace pten {
 
 template <typename T, typename ContextT>
 DenseTensor Sign(const ContextT& dev_ctx, const DenseTensor& x) {
-  auto out_meta = UnchangedInferShape(x.meta());
+  auto out_meta = UnchangedInferMeta(x.meta());
   const auto allocator =
       std::make_shared<paddle::experimental::DefaultAllocator>(
           dev_ctx.GetPlace());
@@ -35,7 +35,7 @@ DenseTensor Sign(const ContextT& dev_ctx, const DenseTensor& x) {
 
 template <typename T, typename ContextT>
 DenseTensor Mean(const ContextT& dev_ctx, const DenseTensor& x) {
-  auto out_meta = ReductionInferShape(x.meta());
+  auto out_meta = ReductionInferMeta(x.meta());
   const auto allocator =
       std::make_shared<paddle::experimental::DefaultAllocator>(
           dev_ctx.GetPlace());
@@ -50,7 +50,7 @@ DenseTensor Scale(const ContextT& dev_ctx,
                   float scale,
                   float bias,
                   bool bias_after_scale) {
-  auto out_meta = UnchangedInferShape(x.meta());
+  auto out_meta = UnchangedInferMeta(x.meta());
   const auto allocator =
       std::make_shared<paddle::experimental::DefaultAllocator>(
           dev_ctx.GetPlace());
@@ -65,7 +65,7 @@ DenseTensor Scale(const ContextT& dev_ctx,
                   const DenseTensor& scale,
                   float bias,
                   bool bias_after_scale) {
-  auto out_meta = UnchangedInferShape(x.meta());
+  auto out_meta = UnchangedInferMeta(x.meta());
   const auto allocator =
       std::make_shared<paddle::experimental::DefaultAllocator>(
           dev_ctx.GetPlace());
@@ -79,7 +79,7 @@ DenseTensor Add(const ContextT& dev_ctx,
                 const DenseTensor& x,
                 const DenseTensor& y,
                 int axis) {
-  auto out_meta = ElementwiseInferShape(x.meta(), y.meta(), axis);
+  auto out_meta = ElementwiseInferMeta(x.meta(), y.meta(), axis);
   const auto allocator =
       std::make_shared<paddle::experimental::DefaultAllocator>(
           dev_ctx.GetPlace());
@@ -93,7 +93,7 @@ DenseTensor Subtract(const ContextT& dev_ctx,
                      const DenseTensor& x,
                      const DenseTensor& y,
                      int axis) {
-  auto out_meta = ElementwiseInferShape(x.meta(), y.meta(), axis);
+  auto out_meta = ElementwiseInferMeta(x.meta(), y.meta(), axis);
   const auto allocator =
       std::make_shared<paddle::experimental::DefaultAllocator>(
           dev_ctx.GetPlace());
@@ -107,7 +107,7 @@ DenseTensor Divide(const ContextT& dev_ctx,
                    const DenseTensor& x,
                    const DenseTensor& y,
                    int axis) {
-  auto out_meta = ElementwiseInferShape(x.meta(), y.meta(), axis);
+  auto out_meta = ElementwiseInferMeta(x.meta(), y.meta(), axis);
   const auto allocator =
       std::make_shared<paddle::experimental::DefaultAllocator>(
           dev_ctx.GetPlace());
@@ -121,7 +121,7 @@ DenseTensor Multiply(const ContextT& dev_ctx,
                      const DenseTensor& x,
                      const DenseTensor& y,
                      int axis) {
-  auto out_meta = ElementwiseInferShape(x.meta(), y.meta(), axis);
+  auto out_meta = ElementwiseInferMeta(x.meta(), y.meta(), axis);
   const auto allocator =
       std::make_shared<paddle::experimental::DefaultAllocator>(
           dev_ctx.GetPlace());
diff --git a/paddle/pten/infermeta/binary.cc b/paddle/pten/infermeta/binary.cc
index 838e450007fcd458ae97ca0e00bb10dbe2e6c55f..a0d4cba90dae7b13b978a3d9c28d5968bde56cc4 100644
--- a/paddle/pten/infermeta/binary.cc
+++ b/paddle/pten/infermeta/binary.cc
@@ -18,8 +18,8 @@ limitations under the License. */
 
 namespace pten {
 
-DenseTensorMeta DotInferShape(const DenseTensorMeta& x_meta,
-                              const DenseTensorMeta& y_meta) {
+DenseTensorMeta DotInferMeta(const DenseTensorMeta& x_meta,
+                             const DenseTensorMeta& y_meta) {
   auto x_dims = x_meta.dims;
   auto x_rank = static_cast<size_t>(x_dims.size());
   PADDLE_ENFORCE_EQ(true,
@@ -60,10 +60,10 @@ DenseTensorMeta DotInferShape(const DenseTensorMeta& x_meta,
   return return_meta;
 }
 
-DenseTensorMeta MatmulInferShape(const DenseTensorMeta& x_meta,
-                                 const DenseTensorMeta& y_meta,
-                                 bool trans_x,
-                                 bool trans_y) {
+DenseTensorMeta MatmulInferMeta(const DenseTensorMeta& x_meta,
+                                const DenseTensorMeta& y_meta,
+                                bool trans_x,
+                                bool trans_y) {
   std::vector<int64_t> dims_x = paddle::framework::vectorize(x_meta.dims);
   std::vector<int64_t> dims_y = paddle::framework::vectorize(y_meta.dims);
   auto ndims_x = dims_x.size();
@@ -130,9 +130,9 @@ DenseTensorMeta MatmulInferShape(const DenseTensorMeta& x_meta,
   return {x_meta.dtype, ddim_out, x_meta.layout};
 }
 
-DenseTensorMeta ElementwiseInferShape(const DenseTensorMeta& x_meta,
-                                      const DenseTensorMeta& y_meta,
-                                      int axis) {
+DenseTensorMeta ElementwiseInferMeta(const DenseTensorMeta& x_meta,
+                                     const DenseTensorMeta& y_meta,
+                                     int axis) {
   DenseTensorMeta return_meta(x_meta.dtype, x_meta.dims, x_meta.layout);
   if (x_meta.dims != y_meta.dims) {
     auto x_dims = x_meta.dims;
diff --git a/paddle/pten/infermeta/binary.h b/paddle/pten/infermeta/binary.h
index 35749ab61b3661a5fbdfde227eed76b71ef657db..c86fc12a20abef6db422b93c1aa258e008688e0c 100644
--- a/paddle/pten/infermeta/binary.h
+++ b/paddle/pten/infermeta/binary.h
@@ -19,29 +19,29 @@ limitations under the License. */
 
 namespace pten {
 
-// Common InferShape Functions for binary operators, The format like:
+// Common InferMeta Functions for binary operators, The format like:
 //
-//   1. DenseTensorMeta [OpName]InferShape(const DenseTensorMeta& x_meta, ...)
+//   1. DenseTensorMeta [OpName]InferMeta(const DenseTensorMeta& x_meta, ...)
 //   {}
-//   2. std::pair<DenseTensorMeta, DenseTensorMeta> [OpName]InferShape(const
+//   2. std::pair<DenseTensorMeta, DenseTensorMeta> [OpName]InferMeta(const
 //   DenseTensorMeta&
 //   x_meta, ...) {}
 //   3. std::tuple<DenseTensorMeta, DenseTensorMeta, DenseTensorMeta>
-//   [OpName]InferShape(const
+//   [OpName]InferMeta(const
 //   DenseTensorMeta& x_meta, ...)
-//  NOTE: The name "InferShape" may be not appropriate. "InferMeta" may be good.
+//  NOTE: The name "InferMeta" may be not appropriate. "InferMeta" may be good.
 //  Because functions in this file
 //  not only can infer shape, but alse need infer lod or other useful data.
 
-DenseTensorMeta DotInferShape(const DenseTensorMeta& x_meta,
-                              const DenseTensorMeta& y_meta);
+DenseTensorMeta DotInferMeta(const DenseTensorMeta& x_meta,
+                             const DenseTensorMeta& y_meta);
 
-DenseTensorMeta MatmulInferShape(const DenseTensorMeta& x_meta,
-                                 const DenseTensorMeta& y_meta,
-                                 bool trans_x,
-                                 bool trans_y);
+DenseTensorMeta MatmulInferMeta(const DenseTensorMeta& x_meta,
+                                const DenseTensorMeta& y_meta,
+                                bool trans_x,
+                                bool trans_y);
 
-DenseTensorMeta ElementwiseInferShape(const DenseTensorMeta& x_meta,
-                                      const DenseTensorMeta& y_meta,
-                                      int axis);
+DenseTensorMeta ElementwiseInferMeta(const DenseTensorMeta& x_meta,
+                                     const DenseTensorMeta& y_meta,
+                                     int axis);
 }  // namespace pten
diff --git a/paddle/pten/infermeta/nary.cc b/paddle/pten/infermeta/nary.cc
index d79945a384ac84686ee73d2faba323887bab93c6..8b12a88f10fc011b7faa373f896ba4acdc1398da 100644
--- a/paddle/pten/infermeta/nary.cc
+++ b/paddle/pten/infermeta/nary.cc
@@ -17,16 +17,16 @@ limitations under the License. */
 
 namespace pten {
 
-DenseTensorMeta FullInferShape(const std::vector<int64_t>& shape,
-                               DataType dtype,
-                               DataLayout layout) {
+DenseTensorMeta FullInferMeta(const std::vector<int64_t>& shape,
+                              DataType dtype,
+                              DataLayout layout) {
   const auto& out_dims = paddle::framework::make_ddim(shape);
   return {dtype, out_dims, layout};
 }
 
-DenseTensorMeta FullInferShape(const ScalarArray& shape,
-                               DataType dtype,
-                               DataLayout layout) {
+DenseTensorMeta FullInferMeta(const ScalarArray& shape,
+                              DataType dtype,
+                              DataLayout layout) {
   const auto& out_dims = paddle::framework::make_ddim(shape.GetData());
   return {dtype, out_dims, layout};
 }
diff --git a/paddle/pten/infermeta/nary.h b/paddle/pten/infermeta/nary.h
index c526583d7ba75ca887e1c1b6ca59c64e6515dd47..010accd2e79e54a223122526df71d6cef0c98b3d 100644
--- a/paddle/pten/infermeta/nary.h
+++ b/paddle/pten/infermeta/nary.h
@@ -19,20 +19,20 @@ limitations under the License. */
 
 namespace pten {
 
-// Common InferShape Functions for 0-nary operators(no input tensor), The format
+// Common InferMeta Functions for 0-nary operators(no input tensor), The format
 // like:
 //
-//   1. DenseTensorMeta [OpName]InferShape( ...)
-//  NOTE: The name "InferShape" may be not appropriate. "InferMeta" may be good.
+//   1. DenseTensorMeta [OpName]InferMeta( ...)
+//  NOTE: The name "InferMeta" may be not appropriate. "InferMeta" may be good.
 //  Because functions in this file
 //  not only can infer shape, but alse need infer lod or other useful data.
 
-DenseTensorMeta FullInferShape(const std::vector<int64_t>& shape,
-                               DataType dtype,
-                               DataLayout layout);
+DenseTensorMeta FullInferMeta(const std::vector<int64_t>& shape,
+                              DataType dtype,
+                              DataLayout layout);
 
-DenseTensorMeta FullInferShape(const ScalarArray& shape,
-                               DataType dtype,
-                               DataLayout layout);
+DenseTensorMeta FullInferMeta(const ScalarArray& shape,
+                              DataType dtype,
+                              DataLayout layout);
 
 }  // namespace pten
diff --git a/paddle/pten/infermeta/unary.cc b/paddle/pten/infermeta/unary.cc
index 945a0b4e23f0eb7ac6493111a39018f5baac6da3..1ffdd1fed2815159a3888309df01a1b6ed39aa60 100644
--- a/paddle/pten/infermeta/unary.cc
+++ b/paddle/pten/infermeta/unary.cc
@@ -17,19 +17,19 @@ limitations under the License. */
 
 namespace pten {
 
-DenseTensorMeta UnchangedInferShape(const DenseTensorMeta& x_meta) {
+DenseTensorMeta UnchangedInferMeta(const DenseTensorMeta& x_meta) {
   return x_meta;
 }
 
-DenseTensorMeta ReductionInferShape(const DenseTensorMeta& x_meta) {
+DenseTensorMeta ReductionInferMeta(const DenseTensorMeta& x_meta) {
   const auto& out_dims = paddle::framework::make_ddim({1});
   DenseTensorMeta return_meta(x_meta.dtype, out_dims, x_meta.layout);
   return return_meta;
 }
 
-DenseTensorMeta FlattenInferShape(const DenseTensorMeta& x_meta,
-                                  int start_axis,
-                                  int stop_axis) {
+DenseTensorMeta FlattenInferMeta(const DenseTensorMeta& x_meta,
+                                 int start_axis,
+                                 int stop_axis) {
   auto& x_dims = x_meta.dims;
   int in_dims_size = x_dims.size();
   if (start_axis < 0) {
@@ -80,9 +80,9 @@ DenseTensorMeta CastInferMeta(const DenseTensorMeta& x_meta,
   return out_meta;
 }
 
-DenseTensorMeta FullLikeInferShape(const DenseTensorMeta& x_meta,
-                                   DataType dtype,
-                                   DataLayout layout) {
+DenseTensorMeta FullLikeInferMeta(const DenseTensorMeta& x_meta,
+                                  DataType dtype,
+                                  DataLayout layout) {
   return {dtype == DataType::UNDEFINED ? x_meta.dtype : dtype,
           x_meta.dims,
           layout == DataLayout::UNDEFINED ? x_meta.layout : layout};
@@ -208,8 +208,8 @@ static paddle::framework::DDim ValidateShape(
   return paddle::framework::make_ddim(output_shape);
 }
 
-DenseTensorMeta InferShapeFromVecValue(const DenseTensorMeta& x_meta,
-                                       const std::vector<int64_t>& shape) {
+DenseTensorMeta InferMetaFromVecValue(const DenseTensorMeta& x_meta,
+                                      const std::vector<int64_t>& shape) {
   PADDLE_ENFORCE_EQ(!shape.empty(),
                     true,
                     paddle::platform::errors::InvalidArgument(
diff --git a/paddle/pten/infermeta/unary.h b/paddle/pten/infermeta/unary.h
index 92c14d43ea94b4673cea0f915f5265a38ced3421..696e2b06df5f83b2b9e819a47374fd1021bf02f5 100644
--- a/paddle/pten/infermeta/unary.h
+++ b/paddle/pten/infermeta/unary.h
@@ -19,34 +19,34 @@ limitations under the License. */
 
 namespace pten {
 
-// Common InferShape Functions for unary operators, The format like:
+// Common InferMeta Functions for unary operators, The format like:
 //
-//   1. DenseTensorMeta [OpName]InferShape(const DenseTensorMeta& x_meta, ...)
+//   1. DenseTensorMeta [OpName]InferMeta(const DenseTensorMeta& x_meta, ...)
 //   {}
-//   2. std::pair<DenseTensorMeta, DenseTensorMeta> [OpName]InferShape(const
+//   2. std::pair<DenseTensorMeta, DenseTensorMeta> [OpName]InferMeta(const
 //   DenseTensorMeta&
 //   x_meta, ...) {}
 //   3. std::tuple<DenseTensorMeta, DenseTensorMeta, DenseTensorMeta>
-//   [OpName]InferShape(const
+//   [OpName]InferMeta(const
 //   DenseTensorMeta& x_meta, ...)
-//  NOTE: The name "InferShape" may be not appropriate. "InferMeta" may be good.
+//  NOTE: The name "InferMeta" may be not appropriate. "InferMeta" may be good.
 //  Because functions in this file
 //  not only can infer shape, but alse need infer lod or other useful data.
 
-DenseTensorMeta UnchangedInferShape(const DenseTensorMeta& x_meta);
+DenseTensorMeta UnchangedInferMeta(const DenseTensorMeta& x_meta);
 
-DenseTensorMeta ReductionInferShape(const DenseTensorMeta& x_meta);
+DenseTensorMeta ReductionInferMeta(const DenseTensorMeta& x_meta);
 
-DenseTensorMeta FlattenInferShape(const DenseTensorMeta& x_meta,
-                                  int start_axis,
-                                  int stop_axis);
+DenseTensorMeta FlattenInferMeta(const DenseTensorMeta& x_meta,
+                                 int start_axis,
+                                 int stop_axis);
 DenseTensorMeta CastInferMeta(const DenseTensorMeta& x_meta,
                               const DataType out_dtype);
 
-DenseTensorMeta FullLikeInferShape(const DenseTensorMeta& x_meta,
-                                   DataType dtype,
-                                   DataLayout layout);
+DenseTensorMeta FullLikeInferMeta(const DenseTensorMeta& x_meta,
+                                  DataType dtype,
+                                  DataLayout layout);
 
-DenseTensorMeta InferShapeFromVecValue(const DenseTensorMeta& x_meta,
-                                       const std::vector<int64_t>& shape);
+DenseTensorMeta InferMetaFromVecValue(const DenseTensorMeta& x_meta,
+                                      const std::vector<int64_t>& shape);
 }  // namespace pten
diff --git a/paddle/pten/kernels/cpu/manipulation.cc b/paddle/pten/kernels/cpu/manipulation.cc
index c7027e487b0e384084ebad33f9de6b0f15e82354..8f559b01b3bcb37275a4c0f24146b0fa323c35f1 100644
--- a/paddle/pten/kernels/cpu/manipulation.cc
+++ b/paddle/pten/kernels/cpu/manipulation.cc
@@ -50,7 +50,7 @@ void ReshapeFromVectorVal(const CPUContext& dev_ctx,
                           const DenseTensor& x,
                           const std::vector<int64_t>& shape,
                           DenseTensor* out) {
-  auto out_meta = InferShapeFromVecValue(x.meta(), shape);
+  auto out_meta = InferMetaFromVecValue(x.meta(), shape);
   if (&x == out) {
     out->Resize(out_meta.dims);
     return;
diff --git a/paddle/pten/kernels/cuda/manipulation.cu b/paddle/pten/kernels/cuda/manipulation.cu
index 9b8f18dab4ee6825032cec07b59b4d4ad765ec3e..f4bf9322047b442b4a78a1d67faa51cfabadfb56 100644
--- a/paddle/pten/kernels/cuda/manipulation.cu
+++ b/paddle/pten/kernels/cuda/manipulation.cu
@@ -50,7 +50,7 @@ void ReshapeFromVectorVal(const CUDAContext& dev_ctx,
                           const DenseTensor& x,
                           const std::vector<int64_t>& shape,
                           DenseTensor* out) {
-  auto out_meta = InferShapeFromVecValue(x.meta(), shape);
+  auto out_meta = InferMetaFromVecValue(x.meta(), shape);
   if (&x == out) {
     out->Resize(out_meta.dims);
     return;
diff --git a/paddle/pten/kernels/xpu/manipulation.cc b/paddle/pten/kernels/xpu/manipulation.cc
index d55def8b8a7b43193df5254dc783c2a2c9ba6369..352d21e6e9bf94a96bca544e12a73dbaf8dfc14f 100644
--- a/paddle/pten/kernels/xpu/manipulation.cc
+++ b/paddle/pten/kernels/xpu/manipulation.cc
@@ -55,7 +55,7 @@ void ReshapeFromVectorVal(const XPUContext& dev_ctx,
                           const DenseTensor& x,
                           const std::vector<int64_t>& shape,
                           DenseTensor* out) {
-  auto out_meta = InferShapeFromVecValue(x.meta(), shape);
+  auto out_meta = InferMetaFromVecValue(x.meta(), shape);
   if (&x == out) {
     out->Resize(out_meta.dims);
     return;