From 65a58d622ce42fcc1321adf0ab237fc05dfd5301 Mon Sep 17 00:00:00 2001
From: carlosgmartin <cgmartin@andrew.cmu.edu>
Date: Mon, 30 Sep 2024 18:27:39 -0400
Subject: [PATCH] Edit implementation of jax.numpy.ldexp to get correct
 gradient.

---
 CHANGELOG.md             |  1 +
 jax/_src/numpy/ufuncs.py | 42 ++++------------------------------------
 tests/lax_numpy_test.py  |  9 +++++++++
 3 files changed, 14 insertions(+), 38 deletions(-)

diff --git a/CHANGELOG.md b/CHANGELOG.md
index 079e055aa994..e8f8d7c6d96d 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -60,6 +60,7 @@ When releasing, please add the new-release-boilerplate to docs/pallas/CHANGELOG.
 * Bug fixes
   * Fixed a bug where {func}`jax.numpy.cumsum` would produce incorrect outputs
     if a non-boolean input was provided and `dtype=bool` was specified.
+  * Edit implementation of {func}`jax.numpy.ldexp` to get correct gradient.
 
 ## jax 0.4.33 (September 16, 2024)
 
diff --git a/jax/_src/numpy/ufuncs.py b/jax/_src/numpy/ufuncs.py
index 8c05671dc429..1ae7b6052b39 100644
--- a/jax/_src/numpy/ufuncs.py
+++ b/jax/_src/numpy/ufuncs.py
@@ -2441,44 +2441,10 @@ def ldexp(x1: ArrayLike, x2: ArrayLike, /) -> Array:
   if (dtypes.issubdtype(x1_dtype, np.complexfloating)
       or dtypes.issubdtype(x2_dtype, np.inexact)):
     raise ValueError(f"ldexp not supported for input types {(x1_dtype, x2_dtype)}")
-
-  x1, x2 = promote_shapes("ldexp", x1, x2)
-
-  dtype = dtypes.canonicalize_dtype(dtypes.to_inexact_dtype(x1_dtype))
-  info = dtypes.finfo(dtype)
-  int_type = _INT_DTYPES[info.bits]
-
-  x1 = lax.convert_element_type(x1, dtype)
-  x2 = lax.convert_element_type(x2, int_type)
-
-  mask = (1 << info.nexp) - 1
-  bias = 1 - info.minexp
-  x, e = _normalize_float(x1)
-  x2 += e + ((x >> info.nmant) & mask) - bias
-
-  # find underflow/overflow before denormalization
-  underflow_cond = less(x2, -(bias + info.nmant))
-  overflow_cond = greater(x2, bias)
-
-  m = lax.full_like(x, 1, dtype=dtype)
-
-  # denormals
-  cond = less(x2, -bias + 1)
-  x2 = _where(cond, x2 + info.nmant, x2)
-  m = _where(cond, m / (1 << info.nmant), m)
-
-  x2 = lax.convert_element_type(x2, np.int32)
-  x &= ~(mask << info.nmant)
-  x |= ((lax.convert_element_type(x2, int_type) + bias) << info.nmant)
-
-  x = lax.convert_element_type(m, dtype) * lax.bitcast_convert_type(x, dtype)
-
-  # underflow
-  x = _where(underflow_cond, lax.full_like(x, 0, dtype=dtype), x)
-  # overflow
-  x = _where(overflow_cond, lax.sign(x1) * lax.full_like(x, np.inf), x)
-  # ldexp(x1, x2) = x1 for x1 = inf, -inf, nan, 0
-  return _where(isinf(x1) | isnan(x1) | (x1 == 0), x1, x)
+  x1, = promote_args_inexact("ldexp", x1)
+  x2 = lax.convert_element_type(x2, dtypes.dtype(x1))
+  x = x1 * (2 ** x2)
+  return _where(isinf(x1) | (x1 == 0), x1, x)
 
 
 @implements(np.frexp, module='numpy')
diff --git a/tests/lax_numpy_test.py b/tests/lax_numpy_test.py
index 4e81a35003f4..c38ceeb4f803 100644
--- a/tests/lax_numpy_test.py
+++ b/tests/lax_numpy_test.py
@@ -6142,6 +6142,15 @@ def testGradLogaddexp2Complex(self, shapes, dtype):
       tol = 3e-2
     check_grads(jnp.logaddexp2, args, 1, ["fwd", "rev"], tol, tol)
 
+  @jtu.sample_product(
+    n=range(-4, 5),
+    dtype=[jnp.float32, jnp.float64],
+  )
+  def testGradLdexp(self, n, dtype):
+    rng = jtu.rand_default(self.rng())
+    x = rng((), dtype)
+    check_grads(lambda x: jnp.ldexp(x, n), (x,), 1)
+
 
 class NumpySignaturesTest(jtu.JaxTestCase):