Update linalg tests

dpnp/tests/test_linalg.py

@@ -1935,7 +1935,7 @@ def test_matrix_rank(self, data, dtype):
 
         np_rank = numpy.linalg.matrix_rank(a)
         dp_rank = dpnp.linalg.matrix_rank(a_dp)
-        assert np_rank == dp_rank
+        assert dp_rank.asnumpy() == np_rank
 
     @pytest.mark.parametrize("dtype", get_all_dtypes())
     @pytest.mark.parametrize(
@@ -1953,7 +1953,7 @@ def test_matrix_rank_hermitian(self, data, dtype):
 
         np_rank = numpy.linalg.matrix_rank(a, hermitian=True)
         dp_rank = dpnp.linalg.matrix_rank(a_dp, hermitian=True)
-        assert np_rank == dp_rank
+        assert dp_rank.asnumpy() == np_rank
 
     @pytest.mark.parametrize(
         "high_tol, low_tol",
@@ -1986,15 +1986,15 @@ def test_matrix_rank_tolerance(self, high_tol, low_tol):
         dp_rank_high_tol = dpnp.linalg.matrix_rank(
             a_dp, hermitian=True, tol=dp_high_tol
         )
-        assert np_rank_high_tol == dp_rank_high_tol
+        assert dp_rank_high_tol.asnumpy() == np_rank_high_tol
 
         np_rank_low_tol = numpy.linalg.matrix_rank(
             a, hermitian=True, tol=low_tol
         )
         dp_rank_low_tol = dpnp.linalg.matrix_rank(
             a_dp, hermitian=True, tol=dp_low_tol
         )
-        assert np_rank_low_tol == dp_rank_low_tol
+        assert dp_rank_low_tol.asnumpy() == np_rank_low_tol
 
     # rtol kwarg was added in numpy 2.0
     @testing.with_requires("numpy>=2.0")
@@ -2789,15 +2789,14 @@ def check_decomposition(
             for i in range(min(dp_a.shape[-2], dp_a.shape[-1])):
                 dpnp_diag_s[..., i, i] = dp_s[..., i]
                 reconstructed = dpnp.dot(dp_u, dpnp.dot(dpnp_diag_s, dp_vt))
-            # TODO: use assert dpnp.allclose() inside check_decomposition()
-            # when it will support complex dtypes
-            assert_allclose(dp_a, reconstructed, rtol=tol, atol=1e-4)
+
+            assert dpnp.allclose(dp_a, reconstructed, rtol=tol, atol=1e-4)
 
         assert_allclose(dp_s, np_s, rtol=tol, atol=1e-03)
 
         if compute_vt:
             for i in range(min(dp_a.shape[-2], dp_a.shape[-1])):
-                if np_u[..., 0, i] * dp_u[..., 0, i] < 0:
+                if np_u[..., 0, i] * dpnp.asnumpy(dp_u[..., 0, i]) < 0:
                     np_u[..., :, i] = -np_u[..., :, i]
                     np_vt[..., i, :] = -np_vt[..., i, :]
             for i in range(numpy.count_nonzero(np_s > tol)):