Make complex_arr.astype(bool) follow NumPy's semantics

Author: jakevdp

CHANGELOG.md

@@ -13,6 +13,8 @@ Remember to align the itemized text with the first line of an item within a list
     now use {class}`jax.Array` instead of {class}`np.ndarray`. You can recover
     the old behavior by transforming the arguments via
     `jax.tree.map(np.asarray, args)` before passing them to the callback.
+  * `complex_arr.astype(bool)` now follows the same semantics as NumPy, returning
+    False where `complex_arr` is equal to `0 + 0j`, and True otherwise.
 
 * Deprecations & Removals
   * Pallas now exclusively uses XLA for compiling kernels on GPU. The old

jax/_src/numpy/lax_numpy.py

@@ -2263,11 +2263,17 @@ def _convert_to_array_if_dtype_fails(x: ArrayLike) -> ArrayLike:
 implementation dependent.
 """)
 def astype(x: ArrayLike, dtype: DTypeLike | None, /, *, copy: bool = True) -> Array:
+  util.check_arraylike("astype", x)
+  x_arr = asarray(x)
   del copy  # unused in JAX
   if dtype is None:
     dtype = dtypes.canonicalize_dtype(float_)
   dtypes.check_user_dtype_supported(dtype, "astype")
-  return lax.convert_element_type(x, dtype)
+  if dtype == np.dtype('bool') and issubdtype(x_arr.dtype, complexfloating):
+    # Complex convert_element_type has the wrong semantics for boolean conversion
+    return (x_arr != _lax_const(x_arr, 0))
+  else:
+    return lax.convert_element_type(x_arr, dtype)
 
 
 @util.implements(np.asarray, lax_description=_ARRAY_DOC)

tests/lax_numpy_test.py

@@ -3822,6 +3822,24 @@ def testAstype(self, from_dtype, to_dtype, use_method):
     self._CheckAgainstNumpy(np_op, jnp_op, args_maker)
     self._CompileAndCheck(jnp_op, args_maker)
 
+  @jtu.sample_product(
+    from_dtype=['int32', 'float32', 'complex64'],
+    use_method=[True, False],
+  )
+  def testAstypeBool(self, from_dtype, use_method, to_dtype='bool'):
+    rng = self.rng()
+    args_maker = lambda: [rng.randn(3, 4).astype(from_dtype)]
+    if (not use_method) and hasattr(np, "astype"):  # Added in numpy 2.0
+      np_op = lambda x: np.astype(x, to_dtype)
+    else:
+      np_op = lambda x: np.asarray(x).astype(to_dtype)
+    if use_method:
+      jnp_op = lambda x: jnp.asarray(x).astype(to_dtype)
+    else:
+      jnp_op = lambda x: jnp.astype(x, to_dtype)
+    self._CheckAgainstNumpy(np_op, jnp_op, args_maker)
+    self._CompileAndCheck(jnp_op, args_maker)
+
   def testAstypeInt4(self):
     # Test converting from int4 to int8
     x = np.array([1, -2, -3, 4, -8, 7], dtype=jnp.int4)