fix(math/compat): gelu int input, unflatten negative dim, segment_mean Array

brainpy/math/activations.py

@@ -169,12 +169,21 @@ def gelu(x, approximate=True):
       whether to use the approximate or exact formulation.
     """
     x = x.value if isinstance(x, Array) else x
+    # Promote integer / boolean inputs to a floating dtype before computing.
+    # Without this the ``sqrt(2/pi)`` and ``0.044715`` constants are truncated to
+    # zero (approximate branch) or the float result is cast back to integer
+    # (exact branch), silently producing wrong values. This mirrors the
+    # ``promote_args_inexact`` step in ``jax.nn.gelu``.
+    x = jnp.asarray(x)
+    if not jnp.issubdtype(x.dtype, jnp.floating):
+        x = x.astype(jnp.promote_types(x.dtype, jnp.float32))
     if approximate:
         sqrt_2_over_pi = np.sqrt(2 / np.pi).astype(x.dtype)
         cdf = 0.5 * (1.0 + jnp.tanh(sqrt_2_over_pi * (x + 0.044715 * (x ** 3))))
         y = x * cdf
     else:
-        y = jnp.array(x * (jax.lax.erf(x / np.sqrt(2)) + 1) / 2, dtype=x.dtype)
+        sqrt_2 = np.sqrt(2).astype(x.dtype)
+        y = jnp.array(x * (jax.scipy.special.erf(x / sqrt_2) + 1) / 2, dtype=x.dtype)
     return y
 
 

brainpy/math/compat_pytorch.py

@@ -117,11 +117,18 @@ def unflatten(x: Union[jax.Array, Array], dim: int, sizes: Sequence[int]) -> Arr
       The returned tensor has one more dimension than the input tensor.
       The returned tensor shares the same underlying data with this tensor.
     """
-    assert x.ndim > dim, ('The dimension to be unflattened should be less than the tensor dimension. '
-                          f'Got {dim} and {x.ndim}.')
     x = _as_jax_array_(x)
+    ndim = x.ndim
+    # Normalise a negative ``dim`` to PyTorch semantics, where ``dim`` indexes
+    # into ``x.shape`` and may be in ``[-ndim, ndim)``.
+    canon_dim = dim + ndim if dim < 0 else dim
+    if not 0 <= canon_dim < ndim:
+        raise ValueError(
+            f'Dimension out of range (expected to be in range of [{-ndim}, {ndim - 1}], '
+            f'but got {dim}).'
+        )
     shape = x.shape
-    new_shape = shape[:dim] + tuple(sizes) + shape[dim + 1:]
+    new_shape = shape[:canon_dim] + tuple(sizes) + shape[canon_dim + 1:]
     r = jnp.reshape(x, new_shape)
     return _return(r)
 

brainpy/math/compat_tensorflow.py

@@ -137,12 +137,10 @@ def segment_mean(data, segment_ids):
     See https://tensorflow.google.cn/api_docs/python/tf/math/segment_mean
 
     """
-    r = jax.ops.segment_sum(_as_jax_array_(data),
-                            _as_jax_array_(segment_ids),
-                            indices_are_sorted=False)
-    d = jax.ops.segment_sum(jnp.ones_like(data),
-                            _as_jax_array_(segment_ids),
-                            indices_are_sorted=False)
+    data = _as_jax_array_(data)
+    segment_ids = _as_jax_array_(segment_ids)
+    r = jax.ops.segment_sum(data, segment_ids, indices_are_sorted=False)
+    d = jax.ops.segment_sum(jnp.ones_like(data), segment_ids, indices_are_sorted=False)
     return _return(jnp.nan_to_num(r / d))
 
 
@@ -204,12 +202,12 @@ def unsorted_segment_sqrt_n(data, segment_ids, num_segments):
     See https://tensorflow.google.cn/api_docs/python/tf/math/unsorted_segment_sqrt_n
 
     """
-    r = jax.ops.segment_sum(_as_jax_array_(data),
-                            _as_jax_array_(segment_ids),
+    data = _as_jax_array_(data)
+    segment_ids = _as_jax_array_(segment_ids)
+    r = jax.ops.segment_sum(data, segment_ids,
                             num_segments=num_segments,
                             indices_are_sorted=False)
-    d = jax.ops.segment_sum(jnp.ones_like(data),
-                            _as_jax_array_(segment_ids),
+    d = jax.ops.segment_sum(jnp.ones_like(data), segment_ids,
                             num_segments=num_segments,
                             indices_are_sorted=False)
     return _return(jnp.nan_to_num(r / jnp.sqrt(d)))
@@ -221,12 +219,12 @@ def unsorted_segment_mean(data, segment_ids, num_segments):
     See https://tensorflow.google.cn/api_docs/python/tf/math/unsorted_segment_mean
 
     """
-    r = jax.ops.segment_sum(_as_jax_array_(data),
-                            _as_jax_array_(segment_ids),
+    data = _as_jax_array_(data)
+    segment_ids = _as_jax_array_(segment_ids)
+    r = jax.ops.segment_sum(data, segment_ids,
                             num_segments=num_segments,
                             indices_are_sorted=False)
-    d = jax.ops.segment_sum(jnp.ones_like(data),
-                            _as_jax_array_(segment_ids),
+    d = jax.ops.segment_sum(jnp.ones_like(data), segment_ids,
                             num_segments=num_segments,
                             indices_are_sorted=False)
     return _return(jnp.nan_to_num(r / d))

brainpy/math/math_compat_p3_fixes_test.py

@@ -0,0 +1,161 @@
+# -*- coding: utf-8 -*-
+"""Regression tests for the 2026-06-19 ``math-compat`` audit (P3-* findings).
+
+Covered findings (see ``docs/issues-found-20260619-math-compat.md``):
+
+* P3-H1 (``activations.py``)      -- ``gelu`` must promote integer inputs to a
+  floating dtype before computing; both the approximate and exact branches were
+  silently wrong on integer input.
+* P3-H2 (``compat_pytorch.py``)   -- ``unflatten`` must honour a negative ``dim``
+  (PyTorch semantics) and reject out-of-range dims.
+* P3-M1 (``compat_tensorflow.py``) -- ``segment_mean`` / ``unsorted_segment_mean``
+  / ``unsorted_segment_sqrt_n`` must convert ``data`` to a jax array before
+  ``jnp.ones_like`` (do not rely on the deprecated implicit ``__jax_array__``).
+"""
+
+import jax
+import jax.nn as jnn
+import jax.numpy as jnp
+import numpy as np
+import pytest
+
+import brainpy.math as bm
+from brainpy.math import (
+    activations as act,
+    compat_pytorch as cpt,
+    compat_tensorflow as ctf,
+)
+
+
+def _j(x):
+    return bm.as_jax(x)
+
+
+def _finite(x):
+    return bool(jnp.all(jnp.isfinite(_j(x))))
+
+
+# ---------------------------------------------------------------------------
+# P3-H1: gelu integer-input promotion
+# ---------------------------------------------------------------------------
+
+def test_gelu_integer_input_matches_float_approximate():
+    """P3-H1: approximate gelu on int input must equal the float computation."""
+    xi = jnp.array([1, 2, 3], dtype=jnp.int32)
+    xf = jnp.array([1., 2., 3.])
+    ri = np.asarray(_j(act.gelu(xi, approximate=True)))
+    rf = np.asarray(_j(act.gelu(xf, approximate=True)))
+    np.testing.assert_allclose(ri, rf, atol=1e-6)
+    # and it must agree with jax.nn.gelu (the reference implementation)
+    np.testing.assert_allclose(ri, np.asarray(jnn.gelu(xi, approximate=True)), atol=1e-6)
+    # specifically: NOT the truncated x/2 result the bug produced
+    assert not np.allclose(ri, np.asarray(xf) / 2.0)
+
+
+def test_gelu_integer_input_matches_float_exact():
+    """P3-H1: exact gelu on int input must not be truncated back to int."""
+    xi = jnp.array([1, 2, 3], dtype=jnp.int32)
+    xf = jnp.array([1., 2., 3.])
+    ri = act.gelu(xi, approximate=False)
+    assert jnp.issubdtype(_j(ri).dtype, jnp.floating)
+    np.testing.assert_allclose(
+        np.asarray(_j(ri)),
+        np.asarray(_j(act.gelu(xf, approximate=False))),
+        atol=1e-6,
+    )
+    # reference parity
+    np.testing.assert_allclose(
+        np.asarray(_j(ri)), np.asarray(jnn.gelu(xi, approximate=False)), atol=1e-5)
+
+
+def test_gelu_float_unchanged():
+    """The float path must be unchanged by the promotion fix."""
+    x = bm.asarray([-1., 0., 1., 2.])
+    for approx in (True, False):
+        np.testing.assert_allclose(
+            np.asarray(_j(act.gelu(x, approximate=approx))),
+            np.asarray(jnn.gelu(_j(x), approximate=approx)),
+            atol=1e-5,
+        )
+
+
+def test_gelu_accepts_brainpy_array_and_is_finite():
+    x = bm.asarray([-3., -1., 0., 1., 3.])
+    assert _finite(act.gelu(x, approximate=True))
+    assert _finite(act.gelu(x, approximate=False))
+
+
+# ---------------------------------------------------------------------------
+# P3-H2: unflatten negative dim
+# ---------------------------------------------------------------------------
+
+def test_unflatten_negative_dim():
+    """P3-H2: negative dim must be normalised like torch.unflatten."""
+    x = bm.asarray(jnp.arange(6.))
+    r = cpt.unflatten(x, -1, (2, 3))
+    assert _j(r).shape == (2, 3)
+    # equivalent to the positive-dim call
+    np.testing.assert_allclose(
+        np.asarray(_j(r)), np.asarray(_j(cpt.unflatten(x, 0, (2, 3)))))
+
+
+def test_unflatten_negative_dim_higher_rank():
+    x = bm.asarray(jnp.arange(24.).reshape(2, 12))
+    r = cpt.unflatten(x, -1, (3, 4))
+    assert _j(r).shape == (2, 3, 4)
+    r2 = cpt.unflatten(x, -2, (1, 2))
+    assert _j(r2).shape == (1, 2, 12)
+
+
+def test_unflatten_positive_dim_still_works():
+    x = bm.asarray(jnp.arange(6.))
+    assert _j(cpt.unflatten(x, 0, (2, 3))).shape == (2, 3)
+    assert _j(cpt.unflatten(x, 0, (-1, 3))).shape == (2, 3)
+
+
+def test_unflatten_dim_out_of_range():
+    x = bm.asarray(jnp.arange(6.))
+    with pytest.raises((ValueError, AssertionError, IndexError)):
+        cpt.unflatten(x, 5, (2, 3))
+    with pytest.raises((ValueError, AssertionError, IndexError)):
+        cpt.unflatten(x, -5, (2, 3))
+
+
+# ---------------------------------------------------------------------------
+# P3-M1: TF segment helpers must not lean on implicit __jax_array__
+# ---------------------------------------------------------------------------
+
+def test_segment_mean_array_input():
+    data = bm.asarray([1., 2., 3., 4.])
+    seg = bm.asarray([0, 0, 1, 1])
+    np.testing.assert_allclose(np.asarray(_j(ctf.segment_mean(data, seg))), [1.5, 3.5])
+
+
+def test_unsorted_segment_mean_array_input():
+    data = bm.asarray([1., 2., 3., 4.])
+    seg = bm.asarray([0, 0, 1, 1])
+    np.testing.assert_allclose(
+        np.asarray(_j(ctf.unsorted_segment_mean(data, seg, 2))), [1.5, 3.5])
+
+
+def test_unsorted_segment_sqrt_n_array_input():
+    data = bm.asarray([1., 1., 1., 1.])
+    seg = bm.asarray([0, 0, 1, 1])
+    # sum over 2-element segments divided by sqrt(2)
+    np.testing.assert_allclose(
+        np.asarray(_j(ctf.unsorted_segment_sqrt_n(data, seg, 2))),
+        [2.0 / np.sqrt(2.0), 2.0 / np.sqrt(2.0)],
+        atol=1e-6,
+    )
+
+
+def test_unsorted_segment_mean_under_jit():
+    """The denominator (``jnp.ones_like``) must trace cleanly under jit.
+
+    ``unsorted_segment_mean`` takes a static ``num_segments`` so it is
+    jit-compatible (unlike ``segment_mean`` which infers it from the data).
+    """
+    data = jnp.array([1., 2., 3., 4.])
+    seg = jnp.array([0, 0, 1, 1])
+    f = jax.jit(lambda d: bm.as_jax(ctf.unsorted_segment_mean(bm.asarray(d), bm.asarray(seg), 2)))
+    np.testing.assert_allclose(np.asarray(f(data)), [1.5, 3.5])