Merge branch 'unifyai:main' into main

ivy/functional/backends/tensorflow/creation.py

@@ -93,6 +93,9 @@ def asarray(
     try:
         ret = tf.convert_to_tensor(obj, dtype)
     except (TypeError, ValueError):
+        obj = (
+            obj if isinstance(obj, tf.Tensor) else tf.convert_to_tensor(obj, tf.float64)
+        )
         ret = tf.cast(obj, dtype)
     return tf.identity(ret) if copy else ret
 

ivy/functional/frontends/numpy/mathematical_functions/sums_products_differences.py

@@ -8,6 +8,7 @@
     from_zero_dim_arrays_to_scalar,
     handle_numpy_out,
 )
+import ivy.functional.frontends.numpy as np_frontend
 
 
 @handle_numpy_out
@@ -114,9 +115,10 @@ def prod(
     initial=None,
     where=True,
 ):
-    if ivy.is_array(where):
+    if where is not True:
         x = ivy.where(where, x, ivy.default(out, ivy.ones_like(x)), out=out)
     if initial is not None:
+        initial = np_frontend.array(initial, dtype=dtype).tolist()
         if axis is not None:
             s = ivy.to_list(ivy.shape(x, as_array=True))
             s[axis] = 1

ivy/functional/frontends/torch/linalg.py

@@ -76,6 +76,14 @@ def eigh(a, /, UPLO="L", out=None):
     return ivy.eigh(a, UPLO=UPLO, out=out)
 
 
+@with_supported_dtypes(
+    {"2.0.1 and below": ("float32", "float64", "complex32", "complex64", "complex128")},
+    "torch",
+)
+def eigh(A, UPLO="L", *, out=None):
+    return ivy.eigh(A, UPLO=UPLO, out=out)
+
+
 @to_ivy_arrays_and_back
 @with_supported_dtypes(
     {"2.0.1 and below": ("float32", "float64", "complex32", "complex64")}, "torch"
@@ -390,9 +398,3 @@ def vector_norm(input, ord=2, dim=None, keepdim=False, *, dtype=None, out=None):
     return ivy.vector_norm(
         input, axis=dim, keepdims=keepdim, ord=ord, out=out, dtype=dtype
     )
-
-@with_supported_dtypes(
-    {"2.0.1 and below": ("float32", "float64", "complex32", "complex64", "complex128")}, "torch", 
-)
-def eigh(A, UPLO="L", *, out=None):
-    return ivy.eigh(A, UPLO=UPLO, out=out)

ivy_tests/test_ivy/test_frontends/test_torch/test_linalg.py

@@ -516,6 +516,50 @@ def test_torch_eigh(
     )
 
 
+@handle_frontend_test(
+    fn_tree="torch.linalg.eigh",
+    dtype_and_x=_get_dtype_and_matrix(dtype="valid", square=True, invertible=True),
+    UPLO=st.sampled_from(("L", "U")),
+)
+def test_torch_eigh(
+    *,
+    dtype_and_x,
+    UPLO,
+    on_device,
+    fn_tree,
+    frontend,
+    test_flags,
+    backend_fw,
+):
+    dtype, x = dtype_and_x
+    x = np.array(x[0], dtype=dtype[0])
+    # make symmetric positive-definite beforehand
+    x = np.matmul(x.T, x) + np.identity(x.shape[0]) * 1e-3
+
+    ret, frontend_ret = helpers.test_frontend_function(
+        input_dtypes=dtype,
+        backend_to_test=backend_fw,
+        frontend=frontend,
+        test_flags=test_flags,
+        fn_tree=fn_tree,
+        on_device=on_device,
+        test_values=False,
+        a=x,
+        UPLO=UPLO,
+    )
+    ret = [ivy.to_numpy(x) for x in ret]
+    frontend_ret = [np.asarray(x) for x in frontend_ret]
+
+    L, Q = ret
+    frontend_L, frontend_Q = frontend_ret
+
+    assert_all_close(
+        ret_np=Q @ np.diag(L) @ Q.T,
+        ret_from_gt_np=frontend_Q @ np.diag(frontend_L) @ frontend_Q.T,
+        atol=1e-02,
+    )
+
+
 # eigvals
 @handle_frontend_test(
     fn_tree="torch.linalg.eigvals",
@@ -1364,48 +1408,3 @@ def test_torch_vector_norm(
         keepdim=kd,
         dtype=dtype[0],
     )
-
-
-@handle_frontend_test(
-    fn_tree="torch.linalg.eigh",
-    dtype_and_x=_get_dtype_and_matrix(dtype="valid", square=True, invertible=True),
-    UPLO=st.sampled_from(("L", "U")),
-)
-def test_torch_eigh(
-    *,
-    dtype_and_x,
-    UPLO,
-    on_device,
-    fn_tree,
-    frontend,
-    test_flags,
-    backend_fw,
-):
-    dtype, x = dtype_and_x
-    x = np.array(x[0], dtype=dtype[0])
-    # make symmetric positive-definite beforehand
-    x = np.matmul(x.T, x) + np.identity(x.shape[0]) * 1e-3
-
-    ret, frontend_ret = helpers.test_frontend_function(
-        input_dtypes=dtype,
-        backend_to_test=backend_fw,
-        frontend=frontend,
-        test_flags=test_flags,
-        fn_tree=fn_tree,
-        on_device=on_device,
-        test_values=False,
-        a=x,
-        UPLO=UPLO,
-    )
-    ret = [ivy.to_numpy(x) for x in ret]
-    frontend_ret = [np.asarray(x) for x in frontend_ret]
-
-    L, Q = ret
-    frontend_L, frontend_Q = frontend_ret
-
-    assert_all_close(
-        ret_np=Q @ np.diag(L) @ Q.T,
-        ret_from_gt_np=frontend_Q @ np.diag(frontend_L) @ frontend_Q.T,
-        atol=1e-02,
-
-    ) 

requirements/requirements.txt

@@ -11,3 +11,4 @@ requests
 pyvis
 dill
 astunparse
+patchelf