Use condition numbers to filter out ill-conditioned value testing for…

… sum/cumulative_sum/prod

This isn't completely rigorous (I haven't tweaked the tolerances used in
isclose from the generous ones we were using before), but I haven't gotten
hypothesis to find any bad corner cases for this yet. If any crop up we can
easily tweak the values.

Fixes data-apis#168

array_api_tests/test_statistical_functions.py

@@ -17,6 +17,7 @@
 
 
 @pytest.mark.min_version("2023.12")
+@pytest.mark.unvectorized
 @given(
     x=hh.arrays(
         dtype=hh.numeric_dtypes,
@@ -80,10 +81,15 @@ def test_cumulative_sum(x, data):
             if dh.is_int_dtype(out.dtype):
                 m, M = dh.dtype_ranges[out.dtype]
                 assume(m <= expected <= M)
-            ph.assert_scalar_equals("cumulative_sum", type_=scalar_type,
-                                    idx=out_idx.raw, out=out_val,
-                                    expected=expected)
-
+                ph.assert_scalar_equals("cumulative_sum", type_=scalar_type,
+                                        idx=out_idx.raw, out=out_val,
+                                        expected=expected)
+            else:
+                condition_number = _sum_condition_number(elements)
+                assume(condition_number < 1e6)
+                ph.assert_scalar_isclose("cumulative_sum", type_=scalar_type,
+                                         idx=out_idx.raw, out=out_val,
+                                         expected=expected)
 
 def kwarg_dtypes(dtype: DataType) -> st.SearchStrategy[Optional[DataType]]:
     dtypes = [d2 for d1, d2 in dh.promotion_table if d1 == dtype]
@@ -176,6 +182,16 @@ def test_min(x, data):
         ph.assert_scalar_equals("min", type_=scalar_type, idx=out_idx, out=min_, expected=expected)
 
 
+def _prod_condition_number(elements):
+    # Relative condition number using the infinity norm
+    abs_max = max([abs(i) for i in elements])
+    abs_min = min([abs(i) for i in elements])
+
+    if abs_min == 0:
+        return float('inf')
+
+    return abs_max / abs_min
+
 @pytest.mark.unvectorized
 @given(
     x=hh.arrays(
@@ -225,7 +241,13 @@ def test_prod(x, data):
         if dh.is_int_dtype(out.dtype):
             m, M = dh.dtype_ranges[out.dtype]
             assume(m <= expected <= M)
-        ph.assert_scalar_equals("prod", type_=scalar_type, idx=out_idx, out=prod, expected=expected)
+            ph.assert_scalar_equals("prod", type_=scalar_type, idx=out_idx,
+                                    out=prod, expected=expected)
+        else:
+            condition_number = _prod_condition_number(elements)
+            assume(condition_number < 1e15)
+            ph.assert_scalar_isclose("prod", type_=scalar_type, idx=out_idx,
+                                     out=prod, expected=expected)
 
 
 @pytest.mark.skip(reason="flaky")  # TODO: fix!
@@ -264,8 +286,16 @@ def test_std(x, data):
     )
     # We can't easily test the result(s) as standard deviation methods vary a lot
 
+def _sum_condition_number(elements):
+    sum_abs = sum([abs(i) for i in elements])
+    abs_sum = abs(sum(elements))
 
-@pytest.mark.unvectorized
+    if abs_sum == 0:
+        return float('inf')
+
+    return sum_abs / abs_sum
+
+# @pytest.mark.unvectorized
 @given(
     x=hh.arrays(
         dtype=hh.numeric_dtypes,
@@ -314,7 +344,15 @@ def test_sum(x, data):
         if dh.is_int_dtype(out.dtype):
             m, M = dh.dtype_ranges[out.dtype]
             assume(m <= expected <= M)
-        ph.assert_scalar_equals("sum", type_=scalar_type, idx=out_idx, out=sum_, expected=expected)
+            ph.assert_scalar_equals("sum", type_=scalar_type, idx=out_idx,
+                                    out=sum_, expected=expected)
+        else:
+            # Avoid value testing for ill conditioned summations. See
+            # https://en.wikipedia.org/wiki/Kahan_summation_algorithm#Accuracy and
+            # https://en.wikipedia.org/wiki/Condition_number.
+            condition_number = _sum_condition_number(elements)
+            assume(condition_number < 1e6)
+            ph.assert_scalar_isclose("sum", type_=scalar_type, idx=out_idx, out=sum_, expected=expected)
 
 
 @pytest.mark.unvectorized