Fix random test failures in CPU-only runs (#1025)

* Anticipate some test failures when cuRand is not available * Ensure input to unstable sort algorithms contains no duplicates * Add mergesort to stable sort methods
nv-legate · Aug 14, 2023 · 06cd534 · 06cd534
1 parent 9017514
commit 06cd534
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Showing 3 changed files with 27 additions and 18 deletions.
diff --git a/cunumeric/random/legacy.py b/cunumeric/random/legacy.py
@@ -111,7 +111,6 @@ def randint(
     --------
     Multiple GPUs, Multiple CPUs
     """
-
     if not isinstance(low, int):
         raise NotImplementedError("'low' must be an integer")
     if high is not None and not isinstance(high, int):
@@ -126,7 +125,7 @@ def randint(
         dtype = np.dtype(np.int64)
     # TODO: randint must support unsigned integer dtypes as well
     if dtype.kind != "i":
-        raise TypeError(
+        raise NotImplementedError(
             "cunumeric.random.randint must be given an integer dtype"
         )
     if isinstance(size, int):

diff --git a/tests/integration/test_argsort.py b/tests/integration/test_argsort.py
@@ -58,8 +58,9 @@
     (DIM, DIM, DIM),
 ]
 
-SORT_TYPES = ["quicksort", "mergesort", "stable"]
-UNSTABLE_SORT_TYPE = ["heapsort"]
+STABLE_SORT_TYPES = ["stable", "mergesort"]
+UNSTABLE_SORT_TYPES = ["heapsort", "quicksort"]
+SORT_TYPES = STABLE_SORT_TYPES + UNSTABLE_SORT_TYPES
 
 
 class TestArgSort(object):
@@ -137,7 +138,7 @@ def test_basic_axis(self, size):
             assert np.array_equal(res_num, res_np)
 
     @pytest.mark.parametrize("size", SIZES)
-    @pytest.mark.parametrize("sort_type", SORT_TYPES)
+    @pytest.mark.parametrize("sort_type", STABLE_SORT_TYPES)
     def test_basic_axis_sort_type(self, size, sort_type):
         arr_np = np.random.randint(-100, 100, size)
         arr_num = num.array(arr_np)
@@ -146,13 +147,14 @@ def test_basic_axis_sort_type(self, size, sort_type):
             res_num = num.argsort(arr_num, axis=axis, kind=sort_type)
             assert np.array_equal(res_num, res_np)
 
-    @pytest.mark.xfail
     @pytest.mark.parametrize("size", SIZES)
-    @pytest.mark.parametrize("sort_type", UNSTABLE_SORT_TYPE)
+    @pytest.mark.parametrize("sort_type", UNSTABLE_SORT_TYPES)
     def test_basic_axis_sort_type_unstable(self, size, sort_type):
-        # intermittent failed due to
-        # https://github.com/nv-legate/cunumeric/issues/782
-        arr_np = np.random.randint(-100, 100, size)
+        # have to guarantee unique values in input
+        # see https://github.com/nv-legate/cunumeric/issues/782
+        arr_np = np.arange(np.prod(size))
+        np.random.shuffle(arr_np)
+        arr_np = arr_np.reshape(size)
         arr_num = num.array(arr_np)
         for axis in range(-arr_np.ndim + 1, arr_np.ndim):
             res_np = np.argsort(arr_np, axis=axis, kind=sort_type)
@@ -171,7 +173,7 @@ def test_arr_basic_axis(self, size):
             assert np.array_equal(arr_np_copy, arr_num_copy)
 
     @pytest.mark.parametrize("size", SIZES)
-    @pytest.mark.parametrize("sort_type", SORT_TYPES)
+    @pytest.mark.parametrize("sort_type", STABLE_SORT_TYPES)
     def test_arr_basic_axis_sort(self, size, sort_type):
         arr_np = np.random.randint(-100, 100, size)
         arr_num = num.array(arr_np)
@@ -182,13 +184,14 @@ def test_arr_basic_axis_sort(self, size, sort_type):
             arr_num_copy.argsort(axis=axis, kind=sort_type)
             assert np.array_equal(arr_np_copy, arr_num_copy)
 
-    @pytest.mark.xfail
     @pytest.mark.parametrize("size", SIZES)
-    @pytest.mark.parametrize("sort_type", UNSTABLE_SORT_TYPE)
+    @pytest.mark.parametrize("sort_type", UNSTABLE_SORT_TYPES)
     def test_arr_basic_axis_sort_unstable(self, size, sort_type):
-        # intermittent failed due to
-        # https://github.com/nv-legate/cunumeric/issues/782
-        arr_np = np.random.randint(-100, 100, size)
+        # have to guarantee unique values in input
+        # see https://github.com/nv-legate/cunumeric/issues/782
+        arr_np = np.arange(np.prod(size))
+        np.random.shuffle(arr_np)
+        arr_np = arr_np.reshape(size)
         arr_num = num.array(arr_np)
         for axis in range(-arr_num.ndim + 1, arr_num.ndim):
             arr_np_copy = arr_np

diff --git a/tests/integration/test_random_creation.py b/tests/integration/test_random_creation.py
@@ -246,12 +246,15 @@ def test_randint_float_range(low, high):
 
 
 @pytest.mark.xfail(
-    not EAGER_TEST, reason="cuNumeric raises NotImplementedError"
+    not num.runtime.has_curand or not EAGER_TEST,
+    reason="cuNumeric raises NotImplementedError",
 )
 @pytest.mark.parametrize("size", ALL_RNG_SIZES, ids=str)
 @pytest.mark.parametrize("low, high", [(1000, 65535), (0, 1024)], ids=str)
 @pytest.mark.parametrize("dtype", UINT_DTYPES, ids=str)
 def test_randint_uint(low, high, dtype, size):
+    # NotImplementedError: cunumeric.random.randint must be given an integer
+    # dtype
     # NotImplementedError: type for random.integers has to be int64 or int32
     # or int16
     arr_np, arr_num = gen_random_from_both(
@@ -287,7 +290,8 @@ def test_randint_distribution(low, high, size, dtype):
 
 
 @pytest.mark.xfail(
-    not EAGER_TEST, reason="cuNumeric raises NotImplementedError"
+    not num.runtime.has_curand or not EAGER_TEST,
+    reason="cuNumeric raises NotImplementedError",
 )
 @pytest.mark.parametrize("size", (1024, 1025))
 def test_randint_bool(size):
@@ -297,6 +301,9 @@ def test_randint_bool(size):
         arr_num, np.mean(arr_np), np.std(arr_np), mean_tol=0.05
     )
     # NumPy pass
+    # cuNumeric not num.runtime.has_curand:
+    # NotImplementedError: cunumeric.random.randint must be given an integer
+    # dtype
     # cuNumeric LEGATE_TEST=1 or size > 1024:
     # NotImplementedError: type for random.integers has to be int64 or int32
     # or int16