Merge pull request #172 from jakevdp:divmod

PiperOrigin-RevId: 666881270

CHANGELOG.md

@@ -25,6 +25,7 @@ To release a new version (e.g. from `1.0.0` -> `2.0.0`):
 
 * Added new 8-bit float type following IEEE 754 convention:
   `ml_dtypes.float8_e4m3`.
+* Fix outputs of float `divmod` and `floor_divide` when denominator is zero.
 
 ## [0.4.0] - 2024-04-1
 

ml_dtypes/_src/ufuncs.h

@@ -168,7 +168,13 @@ struct TrueDivide {
 inline std::pair<float, float> divmod(float a, float b) {
   if (b == 0.0f) {
     float nan = std::numeric_limits<float>::quiet_NaN();
-    return {nan, nan};
+    float inf = std::numeric_limits<float>::infinity();
+
+    if (std::isnan(a) || (a == 0.0f)) {
+      return {nan, nan};
+    } else {
+      return {std::signbit(a) == std::signbit(b) ? inf : -inf, nan};
+    }
   }
   float mod = std::fmod(a, b);
   float div = (a - mod) / b;

ml_dtypes/tests/custom_float_test.py

@@ -830,6 +830,47 @@ def testDivmod(self, float_type):
         float_type=float_type,
     )
 
+  @ignore_warning(category=RuntimeWarning, message="invalid value encountered")
+  @ignore_warning(category=RuntimeWarning, message="divide by zero encountered")
+  def testDivmodCornerCases(self, float_type):
+    x = np.array(
+        [-np.nan, -np.inf, -1.0, -0.0, 0.0, 1.0, np.inf, np.nan],
+        dtype=float_type,
+    )
+    xf32 = x.astype("float32")
+    out = np.divmod.outer(x, x)
+    expected = np.divmod.outer(xf32, xf32)
+    numpy_assert_allclose(
+        out[0],
+        truncate(expected[0], float_type=float_type),
+        rtol=0.0,
+        float_type=float_type,
+    )
+    numpy_assert_allclose(
+        out[1],
+        truncate(expected[1], float_type=float_type),
+        rtol=0.0,
+        float_type=float_type,
+    )
+
+  @ignore_warning(category=RuntimeWarning, message="invalid value encountered")
+  @ignore_warning(category=RuntimeWarning, message="divide by zero encountered")
+  def testFloordivCornerCases(self, float_type):
+    # Regression test for https://github.com/jax-ml/ml_dtypes/issues/170
+    x = np.array(
+        [-np.nan, -np.inf, -1.0, -0.0, 0.0, 1.0, np.inf, np.nan],
+        dtype=float_type,
+    )
+    xf32 = x.astype("float32")
+    out = np.floor_divide.outer(x, x)
+    expected = np.floor_divide.outer(xf32, xf32)
+    numpy_assert_allclose(
+        out,
+        truncate(expected, float_type=float_type),
+        rtol=0.0,
+        float_type=float_type,
+    )
+
   def testModf(self, float_type):
     rng = np.random.RandomState(seed=42)
     x = rng.randn(3, 7).astype(float_type)