Make jnp.negative a ufunc & add unary ufunc tests

jax/_src/numpy/array_methods.py

@@ -909,15 +909,15 @@ def max(self, values, *, indices_are_sorted=False, unique_indices=False,
   "setitem": _unimplemented_setitem,
   "copy": _copy,
   "deepcopy": _deepcopy,
-  "neg": ufuncs.negative,
-  "pos": ufuncs.positive,
+  "neg": lambda self: ufuncs.negative(self),
+  "pos": lambda self: ufuncs.positive(self),
   "eq": _defer_to_unrecognized_arg("==", ufuncs.equal),
   "ne": _defer_to_unrecognized_arg("!=", ufuncs.not_equal),
   "lt": _defer_to_unrecognized_arg("<", ufuncs.less),
   "le": _defer_to_unrecognized_arg("<=", ufuncs.less_equal),
   "gt": _defer_to_unrecognized_arg(">", ufuncs.greater),
   "ge": _defer_to_unrecognized_arg(">=", ufuncs.greater_equal),
-  "abs": ufuncs.abs,
+  "abs": lambda self: ufuncs.abs(self),
   "add": _defer_to_unrecognized_arg("+", ufuncs.add),
   "radd": _defer_to_unrecognized_arg("+", ufuncs.add, swap=True),
   "sub": _defer_to_unrecognized_arg("-", ufuncs.subtract),
@@ -944,7 +944,7 @@ def max(self, values, *, indices_are_sorted=False, unique_indices=False,
   "ror": _defer_to_unrecognized_arg("|", ufuncs.bitwise_or, swap=True),
   "xor": _defer_to_unrecognized_arg("^", ufuncs.bitwise_xor),
   "rxor": _defer_to_unrecognized_arg("^", ufuncs.bitwise_xor, swap=True),
-  "invert": ufuncs.bitwise_not,
+  "invert": lambda self: ufuncs.bitwise_not(self),
   "lshift": _defer_to_unrecognized_arg("<<", ufuncs.left_shift),
   "rshift": _defer_to_unrecognized_arg(">>", ufuncs.right_shift),
   "rlshift": _defer_to_unrecognized_arg("<<", ufuncs.left_shift, swap=True),

jax/_src/numpy/ufuncs.py

@@ -116,7 +116,7 @@ def invert(x: ArrayLike, /) -> Array:
 
 
 @partial(jit, inline=True)
-def negative(x: ArrayLike, /) -> Array:
+def _negative(x: ArrayLike, /) -> Array:
   """Return element-wise negative values of the input.
 
   JAX implementation of :obj:`numpy.negative`.
@@ -2221,3 +2221,4 @@ def _logical_or_reduce(a: ArrayLike, axis: int = 0, dtype: DTypeLike | None = No
 logical_and = ufunc(_logical_and, name="logical_and", nin=2, nout=1, identity=True, call=_logical_and, reduce=_logical_and_reduce)
 logical_or = ufunc(_logical_or, name="logical_or", nin=2, nout=1, identity=False, call=_logical_or, reduce=_logical_or_reduce)
 logical_xor = ufunc(_logical_xor, name="logical_xor", nin=2, nout=1, identity=False, call=_logical_xor)
+negative = ufunc(_negative, name="negative", nin=1, nout=1, call=_negative)

tests/lax_numpy_ufuncs_test.py

@@ -58,18 +58,35 @@ def _jnp_ufunc_props(name):
   jnp_func = getattr(jnp, name)
   assert isinstance(jnp_func, jnp.ufunc)
   np_func = getattr(np, name)
-  dtypes = [np.dtype(c) for c in "Ffi?" if f"{c}{c}->{c}" in np_func.types]
+  dtypes = [np.dtype(c) for c in "Ffi?" if f"{c}{c}->{c}" in np_func.types or f"{c}->{c}" in np_func.types]
   return [dict(name=name, dtype=dtype) for dtype in dtypes]
 
 
 JAX_NUMPY_UFUNCS = [
   name for name in dir(jnp) if isinstance(getattr(jnp, name), jnp.ufunc)
 ]
 
+BINARY_UFUNCS = [
+  name for name in JAX_NUMPY_UFUNCS if getattr(jnp, name).nin == 2
+]
+
+UNARY_UFUNCS = [
+  name for name in JAX_NUMPY_UFUNCS if getattr(jnp, name).nin == 1
+]
+
 JAX_NUMPY_UFUNCS_WITH_DTYPES = list(itertools.chain.from_iterable(
   _jnp_ufunc_props(name) for name in JAX_NUMPY_UFUNCS
 ))
 
+BINARY_UFUNCS_WITH_DTYPES = list(itertools.chain.from_iterable(
+  _jnp_ufunc_props(name) for name in BINARY_UFUNCS
+))
+
+UNARY_UFUNCS_WITH_DTYPES = list(itertools.chain.from_iterable(
+  _jnp_ufunc_props(name) for name in UNARY_UFUNCS
+))
+
+
 broadcast_compatible_shapes = [(), (1,), (3,), (1, 3), (4, 1), (4, 3)]
 nonscalar_shapes = [(3,), (4,), (4, 3)]
 
@@ -144,12 +161,25 @@ def test_frompyfunc_call(self, func, nin, nout, identity, lhs_shape, rhs_shape,
     self._CompileAndCheck(jnp_fun, args_maker)
 
   @jtu.sample_product(
-      JAX_NUMPY_UFUNCS_WITH_DTYPES,
+      UNARY_UFUNCS_WITH_DTYPES,
+      shape=broadcast_compatible_shapes,
+  )
+  def test_unary_ufunc_call(self, name, dtype, shape):
+    jnp_fun = getattr(jnp, name)
+    np_fun = getattr(np, name)
+    rng = jtu.rand_default(self.rng())
+    args_maker = lambda: [rng(shape, dtype)]
+
+    self._CheckAgainstNumpy(jnp_fun, np_fun, args_maker)
+    self._CompileAndCheck(jnp_fun, args_maker)
+
+  @jtu.sample_product(
+      BINARY_UFUNCS_WITH_DTYPES,
       lhs_shape=broadcast_compatible_shapes,
       rhs_shape=broadcast_compatible_shapes,
   )
   @jax.numpy_rank_promotion('allow')  # This test explicitly exercises implicit rank promotion.
-  def test_ufunc_call(self, name, dtype, lhs_shape, rhs_shape):
+  def test_bimary_ufunc_call(self, name, dtype, lhs_shape, rhs_shape):
     jnp_fun = getattr(jnp, name)
     np_fun = getattr(np, name)
     rng = jtu.rand_default(self.rng())
@@ -177,15 +207,13 @@ def test_frompyfunc_outer(self, func, nin, nout, identity, lhs_shape, rhs_shape,
     self._CompileAndCheck(jnp_fun, args_maker)
 
   @jtu.sample_product(
-      JAX_NUMPY_UFUNCS_WITH_DTYPES,
+      BINARY_UFUNCS_WITH_DTYPES,
       lhs_shape=broadcast_compatible_shapes,
       rhs_shape=broadcast_compatible_shapes,
   )
-  def test_ufunc_outer(self, name, lhs_shape, rhs_shape, dtype):
+  def test_binary_ufunc_outer(self, name, lhs_shape, rhs_shape, dtype):
     jnp_fun = getattr(jnp, name)
     np_fun = getattr(np, name)
-    if (jnp_fun.nin, jnp_fun.nout) != (2, 1):
-      self.skipTest(f"outer requires (nin, nout)=(2, 1); got {(jnp_fun.nin, jnp_fun.nout)=}")
 
     rng = jtu.rand_default(self.rng())
     args_maker = lambda: [rng(lhs_shape, dtype), rng(rhs_shape, dtype)]
@@ -213,16 +241,15 @@ def test_frompyfunc_reduce(self, func, nin, nout, identity, shape, axis, dtype):
     self._CompileAndCheck(jnp_fun, args_maker)
 
   @jtu.sample_product(
-      JAX_NUMPY_UFUNCS_WITH_DTYPES,
+      BINARY_UFUNCS_WITH_DTYPES,
       [{'shape': shape, 'axis': axis}
        for shape in nonscalar_shapes
        for axis in [None, *range(-len(shape), len(shape))]],
   )
-  def test_ufunc_reduce(self, name, shape, axis, dtype):
+  def test_binary_ufunc_reduce(self, name, shape, axis, dtype):
     jnp_fun = getattr(jnp, name)
     np_fun = getattr(np, name)
-    if (jnp_fun.nin, jnp_fun.nout) != (2, 1):
-      self.skipTest(f"reduce requires (nin, nout)=(2, 1); got {(jnp_fun.nin, jnp_fun.nout)=}")
+
     jnp_fun_reduce = partial(jnp_fun.reduce, axis=axis)
     np_fun_reduce = partial(np_fun.reduce, axis=axis)
 
@@ -266,16 +293,15 @@ def np_fun(arr, where):
     self._CompileAndCheck(jnp_fun, args_maker)
 
   @jtu.sample_product(
-      JAX_NUMPY_UFUNCS_WITH_DTYPES,
+      BINARY_UFUNCS_WITH_DTYPES,
       [{'shape': shape, 'axis': axis}
        for shape in nonscalar_shapes
        for axis in [None, *range(-len(shape), len(shape))]],
   )
-  def test_ufunc_reduce_where(self, name, shape, axis, dtype):
+  def test_binary_ufunc_reduce_where(self, name, shape, axis, dtype):
     jnp_fun = getattr(jnp, name)
     np_fun = getattr(np, name)
-    if (jnp_fun.nin, jnp_fun.nout) != (2, 1):
-      self.skipTest(f"reduce requires (nin, nout)=(2, 1); got {(jnp_fun.nin, jnp_fun.nout)=}")
+
     if jnp_fun.identity is None:
       self.skipTest("reduce with where requires identity")
 
@@ -309,16 +335,14 @@ def test_frompyfunc_accumulate(self, func, nin, nout, identity, shape, axis, dty
     self._CompileAndCheck(jnp_fun, args_maker)
 
   @jtu.sample_product(
-      JAX_NUMPY_UFUNCS_WITH_DTYPES,
+      BINARY_UFUNCS_WITH_DTYPES,
       [{'shape': shape, 'axis': axis}
        for shape in nonscalar_shapes
        for axis in range(-len(shape), len(shape))]
   )
-  def test_ufunc_accumulate(self, name, shape, axis, dtype):
+  def test_binary_ufunc_accumulate(self, name, shape, axis, dtype):
     jnp_fun = getattr(jnp, name)
     np_fun = getattr(np, name)
-    if (jnp_fun.nin, jnp_fun.nout) != (2, 1):
-      self.skipTest(f"accumulate requires (nin, nout)=(2, 1); got {(jnp_fun.nin, jnp_fun.nout)=}")
 
     rng = jtu.rand_default(self.rng())
     args_maker = lambda: [rng(shape, dtype)]
@@ -355,15 +379,35 @@ def np_fun(x, idx, y):
     self._CompileAndCheck(jnp_fun, args_maker)
 
   @jtu.sample_product(
-      JAX_NUMPY_UFUNCS_WITH_DTYPES,
+      UNARY_UFUNCS_WITH_DTYPES,
       shape=nonscalar_shapes,
       idx_shape=[(), (2,)],
   )
-  def test_ufunc_at(self, name, shape, idx_shape, dtype):
+  def test_unary_ufunc_at(self, name, shape, idx_shape, dtype):
+    jnp_fun = getattr(jnp, name)
+    np_fun = getattr(np, name)
+
+    rng = jtu.rand_default(self.rng())
+    idx_rng = jtu.rand_int(self.rng(), low=-shape[0], high=shape[0])
+    args_maker = lambda: [rng(shape, dtype), idx_rng(idx_shape, 'int32')]
+
+    jnp_fun_at = partial(jnp_fun.at, inplace=False)
+    def np_fun_at(x, idx):
+      x_copy = x.copy()
+      np_fun.at(x_copy, idx)
+      return x_copy
+
+    self._CheckAgainstNumpy(jnp_fun_at, np_fun_at, args_maker)
+    self._CompileAndCheck(jnp_fun_at, args_maker)
+
+  @jtu.sample_product(
+      BINARY_UFUNCS_WITH_DTYPES,
+      shape=nonscalar_shapes,
+      idx_shape=[(), (2,)],
+  )
+  def test_binary_ufunc_at(self, name, shape, idx_shape, dtype):
     jnp_fun = getattr(jnp, name)
     np_fun = getattr(np, name)
-    if (jnp_fun.nin, jnp_fun.nout) != (2, 1):
-      self.skipTest(f"accumulate requires (nin, nout)=(2, 1); got {(jnp_fun.nin, jnp_fun.nout)=}")
 
     rng = jtu.rand_default(self.rng())
     idx_rng = jtu.rand_int(self.rng(), low=-shape[0], high=shape[0])
@@ -413,13 +457,13 @@ def test_frompyfunc_reduceat(self, func, nin, nout, identity, shape, axis, idx_s
     self._CompileAndCheck(jnp_fun, args_maker)
 
   @jtu.sample_product(
-      JAX_NUMPY_UFUNCS_WITH_DTYPES,
+      BINARY_UFUNCS_WITH_DTYPES,
       [{'shape': shape, 'axis': axis}
        for shape in nonscalar_shapes
        for axis in [*range(-len(shape), len(shape))]],
       idx_shape=[(0,), (3,), (5,)],
   )
-  def test_ufunc_reduceat(self, name, shape, axis, idx_shape, dtype):
+  def test_binary_ufunc_reduceat(self, name, shape, axis, idx_shape, dtype):
     jnp_fun = getattr(jnp, name)
     np_fun = getattr(np, name)
     if (jnp_fun.nin, jnp_fun.nout) != (2, 1):