Add new cumulative_sum function to numpy and array_api

CHANGELOG.md

@@ -9,8 +9,9 @@ Remember to align the itemized text with the first line of an item within a list
 ## jax 0.4.27
 
 * New Functionality
-  * Added {func}`jax.numpy.unstack`, following the addition of this function in
-    the array API 2023 standard, soon to be adopted by NumPy.
+  * Added {func}`jax.numpy.unstack` and {func}`jax.numpy.cumulative_sum`,
+    following their addition in the array API 2023 standard, soon to be
+    adopted by NumPy.
 
 * Changes
   * {func}`jax.pure_callback` and {func}`jax.experimental.io_callback`

docs/jax.numpy.rst

@@ -138,6 +138,7 @@ namespace; they are listed below.
     csingle
     cumprod
     cumsum
+    cumulative_sum
     deg2rad
     degrees
     delete

jax/_src/numpy/reductions.py

@@ -26,7 +26,7 @@
 
 from jax import lax
 from jax._src import api
-from jax._src import core
+from jax._src import core, config
 from jax._src import dtypes
 from jax._src.numpy import ufuncs
 from jax._src.numpy.util import (
@@ -708,6 +708,40 @@ def _cumulative_reduction(a: ArrayLike, axis: Axis = None,
 nancumprod = _make_cumulative_reduction(np.nancumprod, lax.cumprod,
                                         fill_nan=True, fill_value=1)
 
+@implements(getattr(np, 'cumulative_sum', None))
+def cumulative_sum(
+    x: ArrayLike, /, *, axis: int | None = None,
+    dtype: DTypeLike | None = None,
+    include_initial: bool = False) -> Array:
+  check_arraylike("cumulative_sum", x)
+  x = lax_internal.asarray(x)
+  if x.ndim == 0:
+    raise ValueError(
+      "The input must be non-scalar to take a cumulative sum, however a "
+      "scalar value or scalar array was given."
+    )
+  if axis is None and x.ndim > 1:
+    raise ValueError(
+      f"The input array has rank {x.ndim}, however axis was not set to an "
+      "explicit value. The axis argument is only optional for one-dimensional "
+      "arrays.")
+  axis = axis or 0
+  axis = _canonicalize_axis(axis, x.ndim)
+  dtypes.check_user_dtype_supported(dtype)
+  kind = x.dtype.kind
+  if (dtype is None and kind in {'i', 'u'}
+      and x.dtype.itemsize*8 < int(config.default_dtype_bits.value)):
+    dtype = dtypes.canonicalize_dtype(dtypes._default_types[kind])
+  x = x.astype(dtype=dtype or x.dtype)
+  out = cumsum(x, axis=axis)
+  if include_initial:
+    zeros_shape = list(x.shape)
+    zeros_shape[axis] = 1
+    out = lax_internal.concatenate(
+      [lax_internal.full(zeros_shape, 0, dtype=out.dtype), out],
+      dimension=axis)
+  return out
+
 # Quantiles
 @implements(np.quantile, skip_params=['out', 'overwrite_input'])
 @partial(api.jit, static_argnames=('axis', 'overwrite_input', 'interpolation',

jax/experimental/array_api/__init__.py

@@ -204,6 +204,7 @@
 )
 
 from jax.experimental.array_api._statistical_functions import (
+    cumulative_sum as cumulative_sum,
     max as max,
     mean as mean,
     min as min,

jax/experimental/array_api/_statistical_functions.py

@@ -18,6 +18,10 @@
 )
 
 
+def cumulative_sum(x, /, *, axis=None, dtype=None, include_initial=False):
+  """Calculates the cumulative sum of elements in the input array x."""
+  return jax.numpy.cumulative_sum(x, axis=axis, dtype=dtype, include_initial=include_initial)
+
 def max(x, /, *, axis=None, keepdims=False):
   """Calculates the maximum value of the input array x."""
   return jax.numpy.max(x, axis=axis, keepdims=keepdims)

jax/numpy/__init__.py

@@ -296,6 +296,7 @@
     count_nonzero as count_nonzero,
     cumsum as cumsum,
     cumprod as cumprod,
+    cumulative_sum as cumulative_sum,
     max as max,
     mean as mean,
     median as median,

jax/numpy/__init__.pyi

@@ -241,6 +241,9 @@ def cumprod(a: ArrayLike, axis: _Axis = ..., dtype: DTypeLike = ...,
 cumproduct = cumprod
 def cumsum(a: ArrayLike, axis: _Axis = ..., dtype: DTypeLike = ...,
            out: None = ...) -> Array: ...
+def cumulative_sum(x: ArrayLike, /, *, axis: int | None = ...,
+                   dtype: DTypeLike | None = ...,
+                   include_initial: bool = ...) -> Array: ...
 
 def deg2rad(x: ArrayLike, /) -> Array: ...
 degrees = rad2deg

tests/array_api_test.py

@@ -68,6 +68,7 @@
   'copysign',
   'cos',
   'cosh',
+  'cumulative_sum',
   'divide',
   'e',
   'empty',

tests/lax_numpy_reducers_test.py

@@ -770,5 +770,69 @@ def test_f16_mean(self, dtype):
     self.assertAllClose(expected, actual, atol=0)
 
 
+  @jtu.sample_product(
+    [dict(shape=shape, axis=axis)
+      for shape in all_shapes
+      for axis in list(
+        range(-len(shape), len(shape))
+      ) + ([None] if len(shape) == 1 else [])],
+    dtype=all_dtypes,
+    out_dtype=all_dtypes,
+    include_initial=[False, True],
+  )
+  @jtu.ignore_warning(category=NumpyComplexWarning)
+  @jax.numpy_dtype_promotion('standard')  # This test explicitly exercises mixed type promotion
+  def testCumulativeSum(self, shape, axis, dtype, out_dtype, include_initial):
+    rng = jtu.rand_some_zero(self.rng())
+
+    def np_mock_op(x, axis=None, dtype=None, include_initial=False):
+      kind = x.dtype.kind
+      if (dtype is None and kind in {'i', 'u'}
+          and x.dtype.itemsize*8 < int(config.default_dtype_bits.value)):
+        dtype = dtypes.canonicalize_dtype(dtypes._default_types[kind])
+      axis = axis or 0
+      x = x.astype(dtype=dtype or x.dtype)
+      out = jnp.cumsum(x, axis=axis)
+      if include_initial:
+        zeros_shape = list(x.shape)
+        zeros_shape[axis] = 1
+        out = jnp.concat([jnp.zeros(zeros_shape, dtype=out.dtype), out], axis=axis)
+      return out
+
+
+    # We currently "cheat" to ensure we have JAX arrays, not NumPy arrays as
+    # input because we rely on JAX-specific casting behavior
+    args_maker = lambda: [jnp.array(rng(shape, dtype))]
+    np_op = getattr(np, "cumulative_sum", np_mock_op)
+    kwargs = dict(axis=axis, include_initial=include_initial)
+
+    np_fun = lambda x: np_op(x, dtype=out_dtype, **kwargs)
+    jnp_fun = lambda x: jnp.cumulative_sum(x, dtype=out_dtype, **kwargs)
+    self._CheckAgainstNumpy(np_fun, jnp_fun, args_maker)
+    self._CompileAndCheck(jnp_fun, args_maker)
+
+    np_fun = lambda x: np_op(x, dtype=None, **kwargs)
+    jnp_fun = lambda x: jnp.cumulative_sum(x, dtype=None, **kwargs)
+    self._CheckAgainstNumpy(np_fun, jnp_fun, args_maker)
+    self._CompileAndCheck(jnp_fun, args_maker)
+
+
+  @jtu.sample_product(
+      shape=filter(lambda x: len(x) != 1, all_shapes), dtype=all_dtypes,
+      include_initial=[False, True])
+  def testCumulativeSumErrors(self, shape, dtype, include_initial):
+    rng = jtu.rand_some_zero(self.rng())
+    x = rng(shape, dtype)
+    rank = jnp.asarray(x).ndim
+    if rank == 0:
+      msg = r"The input must be non-scalar to take"
+      with self.assertRaisesRegex(ValueError, msg):
+        jnp.cumulative_sum(x, include_initial=include_initial)
+    elif rank > 1:
+      msg = r"The input array has rank \d*, however"
+      with self.assertRaisesRegex(ValueError, msg):
+        jnp.cumulative_sum(x, include_initial=include_initial)
+
+
 if __name__ == "__main__":
   absltest.main(testLoader=jtu.JaxTestLoader())