Add implementation of np.searchsorted

docs/jax.numpy.rst

@@ -223,6 +223,7 @@ Not every function in NumPy is implemented; contributions are welcome!
     rot90
     round
     row_stack
+    searchsorted
     select
     sign
     signbit

jax/numpy/lax_numpy.py

@@ -29,6 +29,7 @@
 import collections
 from collections.abc import Sequence
 import itertools
+import operator
 import os
 import re
 import string
@@ -40,6 +41,7 @@
 import opt_einsum
 
 from jax import jit, device_put, custom_jvp
+from .vectorize import vectorize
 from ._util import _wraps
 from .. import core
 from .. import dtypes
@@ -3753,6 +3755,39 @@ def _quantile(a, q, axis, interpolation, keepdims):
   return lax.convert_element_type(result, a.dtype)
 
 
+@partial(jit, static_argnums=2)
+@partial(vectorize, excluded={0, 2})
+def _searchsorted(a, v, side):
+  op = operator.le if side == 'left' else operator.lt
+
+  def cond_fun(state):
+    start, stop = state
+    return stop - start > 1
+
+  def body_fun(state):
+    start, stop = state
+    mid = (start + stop) // 2
+    go_left = op(v, a[mid])
+    return where(go_left, start, mid), where(go_left, mid, stop)
+
+  result = lax.while_loop(cond_fun, body_fun, (0, a.shape[0]))
+  return where(op(v, a[0]), 0, result[1])
+
+
+@_wraps(onp.searchsorted)
+def searchsorted(a, v, side='left', sorter=None):
+  assert side in ['left', 'right']
+  if sorter is not None:
+    raise NotImplementedError("sorter is not implemented")
+  a = asarray(a)
+  v = asarray(v)
+  if ndim(a) != 1:
+    raise ValueError("a should be 1-dimensional")
+  if size(a) == 0:
+    return zeros_like(v, dtype=int)
+  return _searchsorted(a, v, side)
+
+
 @_wraps(onp.percentile)
 def percentile(a, q, axis=None, out=None, overwrite_input=False,
                interpolation="linear", keepdims=False):

tests/lax_numpy_test.py

@@ -1411,6 +1411,26 @@ def onp_fun(arg):
     self._CheckAgainstNumpy(onp_fun, jnp_fun, args_maker, check_dtypes=True)
     self._CompileAndCheck(jnp_fun, args_maker, check_dtypes=True)
 
+  @parameterized.named_parameters(jtu.cases_from_list(
+    {"testcase_name": "_a={}_v={}_side={}".format(
+      jtu.format_shape_dtype_string(ashape, dtype),
+      jtu.format_shape_dtype_string(vshape, dtype),
+      side), "ashape": ashape, "vshape": vshape, "side": side,
+     "dtype": dtype, "rng_factory": rng_factory}
+    for ashape in [(20,)]
+    for vshape in [(), (5,), (5, 5)]
+    for side in ['left', 'right']
+    for dtype in default_dtypes
+    for rng_factory in [jtu.rand_default]
+  ))
+  def testSearchsorted(self, ashape, vshape, side, dtype, rng_factory):
+    rng = rng_factory(self.rng())
+    args_maker = lambda: [jnp.sort(rng(ashape, dtype)), rng(vshape, dtype)]
+    onp_fun = lambda a, v: onp.searchsorted(a, v, side=side)
+    jnp_fun = lambda a, v: jnp.searchsorted(a, v, side=side)
+    self._CheckAgainstNumpy(onp_fun, jnp_fun, args_maker, check_dtypes=True)
+    self._CompileAndCheck(jnp_fun, args_maker, check_dtypes=True)
+
   @parameterized.named_parameters(jtu.cases_from_list(
       {"testcase_name": "_{}_axis={}".format(
           jtu.format_test_name_suffix("", [shape] * len(dtypes), dtypes), axis),