infershape func to infermeta (#37524)

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;

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

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;

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;

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 =

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());

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());

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());

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());

paddle/pten/infermeta/binary.cc

@@ -18,7 +18,7 @@ limitations under the License. */
 
 namespace pten {
 
-DenseTensorMeta DotInferShape(const DenseTensorMeta& x_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());
@@ -60,7 +60,7 @@ DenseTensorMeta DotInferShape(const DenseTensorMeta& x_meta,
   return return_meta;
 }
 
-DenseTensorMeta MatmulInferShape(const DenseTensorMeta& x_meta,
+DenseTensorMeta MatmulInferMeta(const DenseTensorMeta& x_meta,
                                 const DenseTensorMeta& y_meta,
                                 bool trans_x,
                                 bool trans_y) {
@@ -130,7 +130,7 @@ DenseTensorMeta MatmulInferShape(const DenseTensorMeta& x_meta,
   return {x_meta.dtype, ddim_out, x_meta.layout};
 }
 
-DenseTensorMeta ElementwiseInferShape(const DenseTensorMeta& x_meta,
+DenseTensorMeta ElementwiseInferMeta(const DenseTensorMeta& x_meta,
                                      const DenseTensorMeta& y_meta,
                                      int axis) {
   DenseTensorMeta return_meta(x_meta.dtype, x_meta.dims, x_meta.layout);

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,
+DenseTensorMeta DotInferMeta(const DenseTensorMeta& x_meta,
                              const DenseTensorMeta& y_meta);
 
-DenseTensorMeta MatmulInferShape(const DenseTensorMeta& x_meta,
+DenseTensorMeta MatmulInferMeta(const DenseTensorMeta& x_meta,
                                 const DenseTensorMeta& y_meta,
                                 bool trans_x,
                                 bool trans_y);
 
-DenseTensorMeta ElementwiseInferShape(const DenseTensorMeta& x_meta,
+DenseTensorMeta ElementwiseInferMeta(const DenseTensorMeta& x_meta,
                                      const DenseTensorMeta& y_meta,
                                      int axis);
 }  // namespace pten

paddle/pten/infermeta/nary.cc

@@ -17,14 +17,14 @@ limitations under the License. */
 
 namespace pten {
 
-DenseTensorMeta FullInferShape(const std::vector<int64_t>& shape,
+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,
+DenseTensorMeta FullInferMeta(const ScalarArray& shape,
                               DataType dtype,
                               DataLayout layout) {
   const auto& out_dims = paddle::framework::make_ddim(shape.GetData());

paddle/pten/infermeta/nary.h

@@ -19,19 +19,19 @@ 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,
+DenseTensorMeta FullInferMeta(const std::vector<int64_t>& shape,
                               DataType dtype,
                               DataLayout layout);
 
-DenseTensorMeta FullInferShape(const ScalarArray& shape,
+DenseTensorMeta FullInferMeta(const ScalarArray& shape,
                               DataType dtype,
                               DataLayout layout);
 

paddle/pten/infermeta/unary.cc

@@ -17,17 +17,17 @@ 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,
+DenseTensorMeta FlattenInferMeta(const DenseTensorMeta& x_meta,
                                  int start_axis,
                                  int stop_axis) {
   auto& x_dims = x_meta.dims;
@@ -80,7 +80,7 @@ DenseTensorMeta CastInferMeta(const DenseTensorMeta& x_meta,
   return out_meta;
 }
 
-DenseTensorMeta FullLikeInferShape(const DenseTensorMeta& x_meta,
+DenseTensorMeta FullLikeInferMeta(const DenseTensorMeta& x_meta,
                                   DataType dtype,
                                   DataLayout layout) {
   return {dtype == DataType::UNDEFINED ? x_meta.dtype : dtype,
@@ -208,7 +208,7 @@ static paddle::framework::DDim ValidateShape(
   return paddle::framework::make_ddim(output_shape);
 }
 
-DenseTensorMeta InferShapeFromVecValue(const DenseTensorMeta& x_meta,
+DenseTensorMeta InferMetaFromVecValue(const DenseTensorMeta& x_meta,
                                       const std::vector<int64_t>& shape) {
   PADDLE_ENFORCE_EQ(!shape.empty(),
                     true,

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,
+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,
+DenseTensorMeta FullLikeInferMeta(const DenseTensorMeta& x_meta,
                                   DataType dtype,
                                   DataLayout layout);
 
-DenseTensorMeta InferShapeFromVecValue(const DenseTensorMeta& x_meta,
+DenseTensorMeta InferMetaFromVecValue(const DenseTensorMeta& x_meta,
                                       const std::vector<int64_t>& shape);
 }  // namespace pten

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;

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;

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;