fix to_sparse_coo, to_sparse_csr

paddle/fluid/pybind/eager_method.cc

@@ -2883,6 +2883,7 @@ PyDoc_STRVAR(tensor_to_sparse_csr__doc__,  // NOLINT
 Convert input Tensor to SparseCsrTensor.
 
 When input is SparseCooTensor, will convert `COO` to `CSR` . When input is DenseTensor, will convert `Dense` to `CSR` .
+When input is SparseCsrTensor, the function will directly return the input itself without performing any conversion.
 
 Returns:
     SparseCsrTensor
@@ -2909,6 +2910,10 @@ static PyObject* tensor_method_to_sparse_csr(TensorObject* self,
                                              PyObject* args,
                                              PyObject* kwargs) {
   EAGER_TRY
+  if (self->tensor.is_sparse_csr_tensor()) {
+    Py_INCREF(self);
+    return reinterpret_cast<PyObject*>(self);
+  }
   auto csr_tensor = self->tensor.to_sparse_csr();
   egr::EagerUtils::autograd_meta(&csr_tensor)
       ->SetStopGradient(

python/paddle/base/dygraph/tensor_patch_methods.py

@@ -1256,7 +1256,9 @@ def to_sparse_coo(self: Tensor, sparse_dim: int) -> Tensor:
         **Notes**:
             **This API is ONLY available in Dygraph mode**
 
-        Convert the current DenseTensor to SparseTensor in COO format.
+        Convert the current DenseTensor to SparseTensor in COO format. When the input is already a SparseCooTensor, this function will directly return
+        the input itself without performing any conversion.
+
 
         Returns:
             Tensor: A SparseCooTensor
@@ -1274,6 +1276,8 @@ def to_sparse_coo(self: Tensor, sparse_dim: int) -> Tensor:
                                 [1, 3, 2, 3]],
                        values=[1., 2., 3., 4.])
         """
+        if self.is_sparse_coo():
+            return self
 
         return _C_ops.sparse_to_sparse_coo(self, sparse_dim)
 

test/legacy_test/test_sparse_utils_op.py

@@ -180,6 +180,29 @@ def test_to_sparse_coo(self):
             dense_x.grad.numpy(), out_grad.to_dense().numpy()
         )
 
+    def test_coo_to_coo(self):
+        indices = [[0, 0, 1, 2, 2], [1, 3, 2, 0, 1]]
+        values = [1.0, 2.0, 3.0, 4.0, 5.0]
+        sparse_x = paddle.sparse.sparse_coo_tensor(
+            paddle.to_tensor(indices),
+            paddle.to_tensor(values),
+            shape=[3, 4],
+            stop_gradient=False,
+        )
+        sparse_x_ = sparse_x.to_sparse_coo(2)
+        assert sparse_x is sparse_x_
+
+    def test_csr_to_csr(self):
+        crows = [0, 2, 3, 5]
+        cols = [1, 3, 2, 0, 1]
+        values = [1.0, 2.0, 3.0, 4.0, 5.0]
+        crows = paddle.to_tensor(crows, dtype='int32')
+        cols = paddle.to_tensor(cols, dtype='int32')
+        values = paddle.to_tensor(values, dtype='float32')
+        sparse_x = paddle.sparse.sparse_csr_tensor(crows, cols, values)
+        sparse_x_ = sparse_x.to_sparse_csr()
+        assert sparse_x is sparse_x_
+
     def test_coo_to_dense(self):
         indices = [[0, 0, 1, 2, 2], [1, 3, 2, 0, 1]]
         values = [1.0, 2.0, 3.0, 4.0, 5.0]