Adding superset "adjoint" to solve()

ivy/array/linear_algebra.py

@@ -714,9 +714,10 @@ def solve(
         x2: Union[ivy.Array, ivy.NativeArray],
         /,
         *,
+        adjoint: bool = False,
         out: Optional[ivy.Array] = None,
     ) -> ivy.Array:
-        return ivy.solve(self._data, x2, out=out)
+        return ivy.solve(self._data, x2, adjoint=adjoint, out=out)
 
     def svd(
         self: ivy.Array,

ivy/container/linear_algebra.py

@@ -2099,6 +2099,7 @@ def static_solve(
         x2: Union[ivy.Array, ivy.NativeArray, ivy.Container],
         /,
         *,
+        adjoint: bool = False,
         key_chains: Optional[Union[List[str], Dict[str, str]]] = None,
         to_apply: bool = True,
         prune_unapplied: bool = False,
@@ -2109,6 +2110,7 @@ def static_solve(
             "solve",
             x1,
             x2,
+            adjoint=adjoint,
             key_chains=key_chains,
             to_apply=to_apply,
             prune_unapplied=prune_unapplied,
@@ -2121,6 +2123,7 @@ def solve(
         x2: Union[ivy.Container, ivy.Array, ivy.NativeArray],
         /,
         *,
+        adjoint: bool = False,
         key_chains: Optional[Union[List[str], Dict[str, str]]] = None,
         to_apply: bool = True,
         prune_unapplied: bool = False,
@@ -2130,6 +2133,7 @@ def solve(
         return self.static_solve(
             self,
             x2,
+            adjoint=adjoint,
             key_chains=key_chains,
             to_apply=to_apply,
             prune_unapplied=prune_unapplied,

ivy/functional/backends/jax/linear_algebra.py

@@ -172,13 +172,13 @@ def matmul(
     adjoint_b: bool = False,
     out: Optional[JaxArray] = None,
 ) -> JaxArray:
-    if transpose_a is True:
+    if transpose_a:
         x1 = jnp.transpose(x1)
-    if transpose_b is True:
+    if transpose_b:
         x2 = jnp.transpose(x2)
-    if adjoint_a is True:
+    if adjoint_a:
         x1 = jnp.transpose(jnp.conjugate(x1))
-    if adjoint_b is True:
+    if adjoint_b:
         x2 = jnp.transpose(jnp.conjugate(x2))
     return jnp.matmul(x1, x2)
 
@@ -346,7 +346,16 @@ def slogdet(
     {"0.3.14 and below": ("bfloat16", "float16", "complex")},
     backend_version,
 )
-def solve(x1: JaxArray, x2: JaxArray, /, *, out: Optional[JaxArray] = None) -> JaxArray:
+def solve(
+    x1: JaxArray,
+    x2: JaxArray,
+    /,
+    *,
+    adjoint: bool = False,
+    out: Optional[JaxArray] = None
+) -> JaxArray:
+    if adjoint:
+        x1 = jnp.transpose(jnp.conjugate(x1))
     expanded_last = False
     x1, x2 = ivy.promote_types_of_inputs(x1, x2)
     if len(x2.shape) <= 1:

ivy/functional/backends/numpy/linear_algebra.py

@@ -125,13 +125,13 @@ def matmul(
     adjoint_b: bool = False,
     out: Optional[np.ndarray] = None,
 ) -> np.ndarray:
-    if transpose_a is True:
+    if transpose_a:
         x1 = np.transpose(x1)
-    if transpose_b is True:
+    if transpose_b:
         x2 = np.transpose(x2)
-    if adjoint_a is True:
+    if adjoint_a:
         x1 = np.transpose(np.conjugate(x1))
-    if adjoint_b is True:
+    if adjoint_b:
         x2 = np.transpose(np.conjugate(x2))
     ret = np.matmul(x1, x2, out=out)
     if len(x1.shape) == len(x2.shape) == 1:
@@ -302,8 +302,15 @@ def slogdet(
 
 @with_unsupported_dtypes({"1.23.0 and below": ("float16",)}, backend_version)
 def solve(
-    x1: np.ndarray, x2: np.ndarray, /, *, out: Optional[np.ndarray] = None
+    x1: np.ndarray,
+    x2: np.ndarray,
+    /,
+    *,
+    adjoint: bool = False,
+    out: Optional[np.ndarray] = None
 ) -> np.ndarray:
+    if adjoint:
+        x1 = np.transpose(np.conjugate(x1))
     expanded_last = False
     x1, x2 = ivy.promote_types_of_inputs(x1, x2)
     if len(x2.shape) <= 1:

ivy/functional/backends/tensorflow/linear_algebra.py

@@ -221,14 +221,14 @@ def matmul(
     x1, x2 = ivy.promote_types_of_inputs(x1, x2)
     dtype_from = tf.as_dtype(x1.dtype)
 
-    if transpose_a is True:
+    if transpose_a:
         x1 = tf.transpose(x1)
-    if transpose_b is True:
+    if transpose_b:
         x2 = tf.transpose(x2)
 
-    if adjoint_a is True:
+    if adjoint_a:
         x1 = tf.linalg.adjoint(x1)
-    if adjoint_b is True:
+    if adjoint_b:
         x2 = tf.linalg.adjoint(x2)
 
     if dtype_from.is_unsigned or dtype_from == tf.int8 or dtype_from == tf.int16:
@@ -554,8 +554,11 @@ def solve(
     x2: Union[tf.Tensor, tf.Variable],
     /,
     *,
+    adjoint: bool = False,
     out: Optional[Union[tf.Tensor, tf.Variable]] = None,
 ) -> Union[tf.Tensor, tf.Variable]:
+    if adjoint:
+        x1 = tf.linalg.adjoint(x1)
     x1, x2 = ivy.promote_types_of_inputs(x1, x2)
     expanded_last = False
     if len(x2.shape) <= 1:

ivy/functional/backends/torch/linear_algebra.py

@@ -169,13 +169,13 @@ def matmul(
     out: Optional[torch.Tensor] = None,
 ) -> torch.Tensor:
 
-    if transpose_a is True:
+    if transpose_a:
         x1 = torch.t(x1)
-    if transpose_b is True:
+    if transpose_b:
         x2 = torch.t(x2)
-    if adjoint_a is True:
+    if adjoint_a:
         x1 = torch.adjoint(x1)
-    if adjoint_b is True:
+    if adjoint_b:
         x2 = torch.adjoint(x2)
     x1, x2 = ivy.promote_types_of_inputs(x1, x2)
     return torch.matmul(x1, x2, out=out)
@@ -331,8 +331,11 @@ def solve(
     x2: torch.Tensor,
     /,
     *,
+    adjoint: bool = False,
     out: Optional[torch.Tensor] = None,
 ) -> torch.Tensor:
+    if adjoint:
+        x1 = torch.adjoint(x1)
     x1, x2 = ivy.promote_types_of_inputs(x1, x2)
     expanded_last = False
     if len(x2.shape) <= 1:

ivy_tests/test_ivy/test_functional/test_core/test_linalg.py

@@ -669,7 +669,7 @@ def test_slogdet(
 
 # solve
 @st.composite
-def _get_first_matrix(draw):
+def _get_first_matrix(draw, adjoint=True):
     # batch_shape, random_size, shared
 
     # float16 causes a crash when filtering out matrices
@@ -685,14 +685,19 @@ def _get_first_matrix(draw):
     shared_size = draw(
         st.shared(helpers.ints(min_value=2, max_value=4), key="shared_size")
     )
-    return input_dtype, draw(
+    matrix = draw(
         helpers.array_values(
             dtype=input_dtype,
             shape=tuple([shared_size, shared_size]),
             min_value=2,
             max_value=5,
         ).filter(lambda x: np.linalg.cond(x) < 1 / sys.float_info.epsilon)
     )
+    if adjoint:
+        adjoint = draw(st.booleans())
+        if adjoint:
+            matrix = np.transpose(np.conjugate(matrix))
+    return input_dtype, matrix, adjoint
 
 
 @st.composite
@@ -720,7 +725,7 @@ def _get_second_matrix(draw):
 
 @handle_test(
     fn_tree="functional.ivy.solve",
-    x=_get_first_matrix(),
+    x=_get_first_matrix(adjoint=True),
     y=_get_second_matrix(),
 )
 def test_solve(
@@ -733,7 +738,7 @@ def test_solve(
     on_device,
     ground_truth_backend,
 ):
-    input_dtype1, x1 = x
+    input_dtype1, x1, adjoint = x
     input_dtype2, x2 = y
     helpers.test_function(
         ground_truth_backend=ground_truth_backend,
@@ -746,6 +751,7 @@ def test_solve(
         atol_=1e-1,
         x1=x1,
         x2=x2,
+        adjoint=adjoint,
     )