Fix dpnp.linalg.solve() hang on CPU (#1778)

* Add wait() for each ht_lapack_ev(gesv) in loop on CPU

* Add wait() for each b_ht_copy_ev in the loop

dpnp/linalg/dpnp_utils_linalg.py

@@ -1824,6 +1824,7 @@ def dpnp_solve(a, b):
         return dpnp.empty_like(b, dtype=res_type, usm_type=res_usm_type)
 
     if a.ndim > 2:
+        is_cpu_device = exec_q.sycl_device.has_aspect_cpu
         reshape = False
         orig_shape_b = b_shape
         if a.ndim > 3:
@@ -1850,22 +1851,27 @@ def dpnp_solve(a, b):
         for i in range(batch_size):
             # oneMKL LAPACK assumes fortran-like array as input, so
             # allocate a memory with 'F' order for dpnp array of coefficient matrix
-            # and multiple dependent variables array
             coeff_vecs[i] = dpnp.empty_like(
                 a[i], order="F", dtype=res_type, usm_type=res_usm_type
             )
-            val_vecs[i] = dpnp.empty_like(
-                b[i], order="F", dtype=res_type, usm_type=res_usm_type
-            )
 
             # use DPCTL tensor function to fill the coefficient matrix array
-            # and the array of multiple dependent variables with content
-            # from the input arrays
+            # with content from the input array
             a_ht_copy_ev[i], a_copy_ev = ti._copy_usm_ndarray_into_usm_ndarray(
                 src=a_usm_arr[i],
                 dst=coeff_vecs[i].get_array(),
                 sycl_queue=a.sycl_queue,
             )
+
+            # oneMKL LAPACK assumes fortran-like array as input, so
+            # allocate a memory with 'F' order for dpnp array of multiple
+            # dependent variables array
+            val_vecs[i] = dpnp.empty_like(
+                b[i], order="F", dtype=res_type, usm_type=res_usm_type
+            )
+
+            # use DPCTL tensor function to fill the array of multiple dependent
+            # variables with content from the input arrays
             b_ht_copy_ev[i], b_copy_ev = ti._copy_usm_ndarray_into_usm_ndarray(
                 src=b_usm_arr[i],
                 dst=val_vecs[i].get_array(),
@@ -1882,6 +1888,15 @@ def dpnp_solve(a, b):
                 depends=[a_copy_ev, b_copy_ev],
             )
 
+            # TODO: Remove this w/a when MKLD-17201 is solved.
+            # Waiting for a host task executing an OneMKL LAPACK gesv call
+            # on CPU causes deadlock due to serialization of all host tasks
+            # in the queue.
+            # We need to wait for each host tasks before calling _gesv to avoid deadlock.
+            if is_cpu_device:
+                ht_lapack_ev[i].wait()
+                b_ht_copy_ev[i].wait()
+
         for i in range(batch_size):
             ht_lapack_ev[i].wait()
             b_ht_copy_ev[i].wait()

tests/test_usm_type.py

@@ -899,9 +899,6 @@ def test_eigenvalue(func, shape, usm_type):
 )
 def test_solve(matrix, vector, usm_type_matrix, usm_type_vector):
     x = dp.array(matrix, usm_type=usm_type_matrix)
-    if x.ndim > 2 and x.device.sycl_device.is_cpu:
-        pytest.skip("SAT-6842: reported hanging in public CI")
-
     y = dp.array(vector, usm_type=usm_type_vector)
     z = dp.linalg.solve(x, y)
 

tests/third_party/cupy/linalg_tests/test_solve.py

@@ -47,7 +47,6 @@ def check_x(self, a_shape, b_shape, xp, dtype):
         testing.assert_array_equal(b_copy, b)
         return result
 
-    @pytest.mark.skipif(is_cpu_device(), reason="SAT-6842")
     def test_solve(self):
         self.check_x((4, 4), (4,))
         self.check_x((5, 5), (5, 2))