PaddlePaddle
diff --git a/‎python/paddle/sparse/unary.py
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+60-21Lines changed: 60 additions & 21 deletions b/‎python/paddle/sparse/unary.py
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+60-21Lines changed: 60 additions & 21 deletions
@@ -24,7 +24,6 @@
 from paddle.base.framework import (
     convert_np_dtype_to_dtype_,
     core,
-    dygraph_only,
     in_dynamic_or_pir_mode,
 )
 from paddle.common_ops_import import Variable
@@ -46,7 +45,6 @@
 ]
 
 
-@dygraph_only
 def sin(x: Tensor, name: str | None = None) -> Tensor:
     """
     Calculate elementwise sin of SparseTensor, requiring x to be a SparseCooTensor or SparseCsrTensor.
@@ -76,10 +74,12 @@ def sin(x: Tensor, name: str | None = None) -> Tensor:
                 indices=[[0, 2]],
                 values=[-0.90929741,  0.84147102])
     """
+    assert (
+        in_dynamic_or_pir_mode()
+    ), "Currently, Sparse API only support dynamic mode or pir mode."
     return _C_ops.sparse_sin(x)
 
 
-@dygraph_only
 def tan(x: Tensor, name: str | None = None) -> Tensor:
     """
     Calculate elementwise tan of SparseTensor, requiring x to be a SparseCooTensor or SparseCsrTensor.
@@ -109,10 +109,12 @@ def tan(x: Tensor, name: str | None = None) -> Tensor:
                 indices=[[0, 2]],
                 values=[2.18503976, 1.55740774])
     """
+    assert (
+        in_dynamic_or_pir_mode()
+    ), "Currently, Sparse API only support dynamic mode or pir mode."
     return _C_ops.sparse_tan(x)
 
 
-@dygraph_only
 def asin(x: Tensor, name: str | None = None) -> Tensor:
     """
     Calculate elementwise asin of SparseTensor, requiring x to be a SparseCooTensor or SparseCsrTensor.
@@ -142,10 +144,12 @@ def asin(x: Tensor, name: str | None = None) -> Tensor:
                 indices=[[0, 2]],
                 values=[nan       , 1.57079625])
     """
+    assert (
+        in_dynamic_or_pir_mode()
+    ), "Currently, Sparse API only support dynamic mode or pir mode."
     return _C_ops.sparse_asin(x)
 
 
-@dygraph_only
 def transpose(
     x: Tensor, perm: Sequence[int], name: str | None = None
 ) -> Tensor:
@@ -182,6 +186,9 @@ def transpose(
                         [ 1.,  2.]])
 
     """
+    assert (
+        in_dynamic_or_pir_mode()
+    ), "Currently, Sparse API only support dynamic mode or pir mode."
     return _C_ops.sparse_transpose(x, perm)
 
 
@@ -293,7 +300,6 @@ def sum(
         return out
 
 
-@dygraph_only
 def atan(x: Tensor, name: str | None = None) -> Tensor:
     """
     Calculate elementwise atan of SparseTensor, requiring x to be a SparseCooTensor or SparseCsrTensor.
@@ -323,10 +329,12 @@ def atan(x: Tensor, name: str | None = None) -> Tensor:
                 indices=[[0, 2]],
                 values=[-1.10714877,  0.78539819])
     """
+    assert (
+        in_dynamic_or_pir_mode()
+    ), "Currently, Sparse API only support dynamic mode or pir mode."
     return _C_ops.sparse_atan(x)
 
 
-@dygraph_only
 def sinh(x: Tensor, name: str | None = None) -> Tensor:
     """
     Calculate elementwise sinh of SparseTensor, requiring x to be a SparseCooTensor or SparseCsrTensor.
@@ -356,10 +364,12 @@ def sinh(x: Tensor, name: str | None = None) -> Tensor:
                 indices=[[0, 2]],
                 values=[-3.62686038,  1.17520118])
     """
+    assert (
+        in_dynamic_or_pir_mode()
+    ), "Currently, Sparse API only support dynamic mode or pir mode."
     return _C_ops.sparse_sinh(x)
 
 
-@dygraph_only
 def asinh(x: Tensor, name: str | None = None) -> Tensor:
     """
     Calculate elementwise asinh of SparseTensor, requiring x to be a SparseCooTensor or SparseCsrTensor.
@@ -389,10 +399,12 @@ def asinh(x: Tensor, name: str | None = None) -> Tensor:
                 indices=[[0, 2]],
                 values=[-1.44363546,  0.88137358])
     """
+    assert (
+        in_dynamic_or_pir_mode()
+    ), "Currently, Sparse API only support dynamic mode or pir mode."
     return _C_ops.sparse_asinh(x)
 
 
-@dygraph_only
 def atanh(x: Tensor, name: str | None = None) -> Tensor:
     """
     Calculate elementwise atanh of SparseTensor, requiring x to be a SparseCooTensor or SparseCsrTensor.
@@ -422,10 +434,12 @@ def atanh(x: Tensor, name: str | None = None) -> Tensor:
                 indices=[[0, 2]],
                 values=[nan , inf.])
     """
+    assert (
+        in_dynamic_or_pir_mode()
+    ), "Currently, Sparse API only support dynamic mode or pir mode."
     return _C_ops.sparse_atanh(x)
 
 
-@dygraph_only
 def tanh(x: Tensor, name: str | None = None) -> Tensor:
     """
     Calculate elementwise tanh of SparseTensor, requiring x to be a SparseCooTensor or SparseCsrTensor.
@@ -455,10 +469,12 @@ def tanh(x: Tensor, name: str | None = None) -> Tensor:
                 indices=[[0, 2]],
                 values=[-0.96402758,  0.76159418])
     """
+    assert (
+        in_dynamic_or_pir_mode()
+    ), "Currently, Sparse API only support dynamic mode or pir mode."
     return _C_ops.sparse_tanh(x)
 
 
-@dygraph_only
 def square(x: Tensor, name: str | None = None) -> Tensor:
     """
     Calculate elementwise square of SparseTensor, requiring x to be a SparseCooTensor or SparseCsrTensor.
@@ -488,10 +504,12 @@ def square(x: Tensor, name: str | None = None) -> Tensor:
                 indices=[[0, 2]],
                 values=[4., 1.])
     """
+    assert (
+        in_dynamic_or_pir_mode()
+    ), "Currently, Sparse API only support dynamic mode or pir mode."
     return _C_ops.sparse_square(x)
 
 
-@dygraph_only
 def sqrt(x: Tensor, name: str | None = None) -> Tensor:
     """
     Calculate elementwise sqrt of SparseTensor, requiring x to be a SparseCooTensor or SparseCsrTensor.
@@ -521,10 +539,12 @@ def sqrt(x: Tensor, name: str | None = None) -> Tensor:
                 indices=[[0, 2]],
                 values=[nan, 1. ])
     """
+    assert (
+        in_dynamic_or_pir_mode()
+    ), "Currently, Sparse API only support dynamic mode or pir mode."
     return _C_ops.sparse_sqrt(x)
 
 
-@dygraph_only
 def log1p(x: Tensor, name: str | None = None) -> Tensor:
     """
     Calculate the natural log of (1+x), requiring x to be a SparseCooTensor or SparseCsrTensor.
@@ -554,10 +574,12 @@ def log1p(x: Tensor, name: str | None = None) -> Tensor:
                 indices=[[0, 2]],
                 values=[nan       , 0.69314718])
     """
+    assert (
+        in_dynamic_or_pir_mode()
+    ), "Currently, Sparse API only support dynamic mode or pir mode."
     return _C_ops.sparse_log1p(x)
 
 
-@dygraph_only
 def cast(
     x: Tensor,
     index_dtype: DTypeLike | None = None,
@@ -593,14 +615,16 @@ def cast(
                 indices=[[0, 2]],
                 values=[-2.,  1.])
     """
+    assert (
+        in_dynamic_or_pir_mode()
+    ), "Currently, Sparse API only support dynamic mode or pir mode."
     if index_dtype and not isinstance(index_dtype, core.VarDesc.VarType):
         index_dtype = convert_np_dtype_to_dtype_(index_dtype)
     if value_dtype and not isinstance(value_dtype, core.VarDesc.VarType):
         value_dtype = convert_np_dtype_to_dtype_(value_dtype)
     return _C_ops.sparse_cast(x, index_dtype, value_dtype)
 
 
-@dygraph_only
 def pow(x: Tensor, factor: float, name: str | None = None) -> Tensor:
     """
     Calculate elementwise pow of x, requiring x to be a SparseCooTensor or SparseCsrTensor.
@@ -631,10 +655,12 @@ def pow(x: Tensor, factor: float, name: str | None = None) -> Tensor:
                 indices=[[0, 2]],
                 values=[4., 9.])
     """
+    assert (
+        in_dynamic_or_pir_mode()
+    ), "Currently, Sparse API only support dynamic mode or pir mode."
     return _C_ops.sparse_pow(x, float(factor))
 
 
-@dygraph_only
 def neg(x: Tensor, name: str | None = None) -> Tensor:
     """
     Calculate elementwise negative of x, requiring x to be a SparseCooTensor or SparseCsrTensor.
@@ -664,10 +690,12 @@ def neg(x: Tensor, name: str | None = None) -> Tensor:
                 indices=[[0, 2]],
                 values=[ 2., -3.])
     """
+    assert (
+        in_dynamic_or_pir_mode()
+    ), "Currently, Sparse API only support dynamic mode or pir mode."
     return _C_ops.sparse_scale(x, -1.0, 0.0, True)
 
 
-@dygraph_only
 def abs(x: Tensor, name: str | None = None) -> Tensor:
     """
     Calculate elementwise absolute value of x, requiring x to be a SparseCooTensor or SparseCsrTensor.
@@ -697,10 +725,12 @@ def abs(x: Tensor, name: str | None = None) -> Tensor:
                 indices=[[0, 2]],
                 values=[2., 3.])
     """
+    assert (
+        in_dynamic_or_pir_mode()
+    ), "Currently, Sparse API only support dynamic mode or pir mode."
     return _C_ops.sparse_abs(x)
 
 
-@dygraph_only
 def coalesce(x: Tensor, name: str | None = None) -> Tensor:
     r"""
     the coalesced operator include sorted and merge, after coalesced, the indices of x is sorted and unique.
@@ -730,10 +760,12 @@ def coalesce(x: Tensor, name: str | None = None) -> Tensor:
             Tensor(shape=[2], dtype=float32, place=Place(cpu), stop_gradient=True,
             [3., 3.])
     """
+    assert (
+        in_dynamic_or_pir_mode()
+    ), "Currently, Sparse API only support dynamic mode or pir mode."
     return _C_ops.sparse_coalesce(x)
 
 
-@dygraph_only
 def rad2deg(x: Tensor, name: str | None = None) -> Tensor:
     r"""
     Convert each of the elements of input x from radian to degree,
@@ -764,12 +796,14 @@ def rad2deg(x: Tensor, name: str | None = None) -> Tensor:
                 indices=[[0, 2]],
                 values=[ 180.02334595, -180.02334595])
     """
+    assert (
+        in_dynamic_or_pir_mode()
+    ), "Currently, Sparse API only support dynamic mode or pir mode."
     if x.dtype in _int_dtype_:
         x = _C_ops.sparse_cast(x, None, core.VarDesc.VarType.FP32)
     return _C_ops.sparse_scale(x, 180.0 / np.pi, 0.0, True)
 
 
-@dygraph_only
 def deg2rad(x: Tensor, name: str | None = None) -> Tensor:
     r"""
     Convert each of the elements of input x from degree to radian,
@@ -800,12 +834,14 @@ def deg2rad(x: Tensor, name: str | None = None) -> Tensor:
                 indices=[[0, 2]],
                 values=[-3.14159274,  3.14159274])
     """
+    assert (
+        in_dynamic_or_pir_mode()
+    ), "Currently, Sparse API only support dynamic mode or pir mode."
     if x.dtype in _int_dtype_:
         x = _C_ops.sparse_cast(x, None, core.VarDesc.VarType.FP32)
     return _C_ops.sparse_scale(x, np.pi / 180.0, 0.0, True)
 
 
-@dygraph_only
 def expm1(x: Tensor, name: str | None = None) -> Tensor:
     """
     Calculate elementwise `exp(x)-1` , requiring x to be a SparseCooTensor or SparseCsrTensor.
@@ -835,6 +871,9 @@ def expm1(x: Tensor, name: str | None = None) -> Tensor:
                 indices=[[0, 2]],
                 values=[-0.86466473,  1.71828187])
     """
+    assert (
+        in_dynamic_or_pir_mode()
+    ), "Currently, Sparse API only support dynamic mode or pir mode."
     return _C_ops.sparse_expm1(x)