paddle frontend cholesky_solve added

ivy/functional/frontends/paddle/tensor/linalg.py

@@ -122,6 +122,16 @@ def solve(x1, x2, name=None):
     return ivy.solve(x1, x2)
 
 
+# cholesky_solve
+@with_supported_dtypes({"2.5.1 and below": ("float32", "float64")}, "paddle")
+@to_ivy_arrays_and_back
+def cholesky_solve(x, y, /, *, upper=False, name=None):
+    if upper:
+        y = ivy.matrix_transpose(y)
+    Y = ivy.solve(y, x)
+    return ivy.solve(ivy.matrix_transpose(y), Y)
+
+
 # cholesky
 @with_supported_dtypes({"2.5.1 and below": ("float32", "float64")}, "paddle")
 @to_ivy_arrays_and_back

ivy_tests/test_ivy/test_frontends/test_paddle/test_tensor/test_linalg.py

@@ -7,7 +7,10 @@
 import ivy_tests.test_ivy.helpers as helpers
 from ivy_tests.test_ivy.helpers import assert_all_close
 from ivy_tests.test_ivy.helpers import handle_frontend_test, matrix_is_stable
-
+from ivy_tests.test_ivy.test_frontends.test_tensorflow.test_linalg import (
+    _get_second_matrix,
+    _get_cholesky_matrix,
+)
 
 # Helpers #
 # ------ #
@@ -528,6 +531,49 @@ def test_paddle_solve(
     )
 
 
+# cholesky_solve
+@st.composite
+def _get_paddle_cholesky_matrix(draw):
+    input_dtype, spd_chol = draw(_get_cholesky_matrix())
+    probability = draw(st.floats(min_value=0, max_value=1))
+    if probability > 0.5:
+        spd_chol = spd_chol.T  # randomly transpose the matrix
+    return input_dtype, spd_chol
+
+
+@handle_frontend_test(
+    fn_tree="paddle.tensor.linalg.cholesky_solve",
+    x=_get_second_matrix(),
+    y=_get_paddle_cholesky_matrix(),
+    test_with_out=st.just(False),
+)
+def test_paddle_cholesky_solve(
+    *,
+    x,
+    y,
+    frontend,
+    backend_fw,
+    test_flags,
+    fn_tree,
+    on_device,
+):
+    input_dtype1, x1 = x
+    input_dtype2, x2 = y
+    helpers.test_frontend_function(
+        input_dtypes=[input_dtype1, input_dtype2],
+        frontend=frontend,
+        backend_to_test=backend_fw,
+        test_flags=test_flags,
+        fn_tree=fn_tree,
+        on_device=on_device,
+        rtol=1e-3,
+        atol=1e-3,
+        x=x1,
+        y=x2,
+        upper=np.array_equal(x2, np.triu(x2)),  # check whether the matrix is upper
+    )
+
+
 # cholesky
 @handle_frontend_test(
     fn_tree="paddle.tensor.linalg.cholesky",