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Merged
n-gao merged 6 commits into from
Sep 25, 2025
Merged

shard_map compatibility #36

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307a472
pvary I
inailuig Sep 1, 2025
4a40f3e
workaround to fix sparse version (jax>=0.7.2)
inailuig Sep 1, 2025
3d1ebef
restore compatibilty with older versions of jax
inailuig Sep 5, 2025
2934513
add a test
inailuig Sep 5, 2025
46202a3
fmt
inailuig Sep 25, 2025
288ee5e
fix test
inailuig Sep 25, 2025
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7 changes: 4 additions & 3 deletions folx/ad.py
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Original file line number Diff line number Diff line change
Expand Up @@ -71,9 +71,10 @@ def flat_f(x):

out = flat_f(flat_primals)

result = jax.vmap(vjp(flat_f, flat_primals))(
jnp.eye(out.size, dtype=out.dtype)
)[0]
eye = jnp.eye(out.size, dtype=out.dtype)
if hasattr(jax.lax, 'pvary'):
eye = jax.lax.pvary(eye, tuple(jax.typeof(out).vma))
result = jax.vmap(vjp(flat_f, flat_primals))(eye)[0]
result = jax.vmap(unravel, out_axes=0)(result)
if len(primals) == 1:
return result[0]
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13 changes: 12 additions & 1 deletion folx/api.py
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Expand Up @@ -131,7 +131,18 @@ def get_indices(mask, out_mask):

if isinstance(outputs, np.ndarray):
with jax.ensure_compile_time_eval():
result = np.asarray(get_indices(flat_mask, flat_outputs), dtype=int).T
if hasattr(jax.sharding, 'use_abstract_mesh'): # jax>=0.7.2
# see https://github.com/jax-ml/jax/discussions/31461
with jax.sharding.use_abstract_mesh(
jax.sharding.AbstractMesh((), ())
):
result = np.asarray(
get_indices(flat_mask, flat_outputs), dtype=int
).T
else:
result = np.asarray(
get_indices(flat_mask, flat_outputs), dtype=int
).T
else:
result = get_indices(flat_mask, flat_outputs).T
return result.reshape(mask.shape)
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4 changes: 4 additions & 0 deletions folx/wrapped_functions.py
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Original file line number Diff line number Diff line change
Expand Up @@ -227,6 +227,10 @@ def custom_jvp(jacobian, tangent, sign):
log_det_jvp = jac_dot_tangent.real
else:
sign_jvp = jnp.zeros((), dtype=jac_dot_tangent.dtype)
if hasattr(jax.lax, 'pvary'):
sign_jvp = jax.lax.pvary(
sign_jvp, tuple(jax.typeof(jac_dot_tangent).vma)
)
log_det_jvp = jac_dot_tangent
return (sign_jvp, log_det_jvp)

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74 changes: 57 additions & 17 deletions test/test_layers.py
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Original file line number Diff line number Diff line change
@@ -1,10 +1,12 @@
import functools
from functools import partial

import jax
import jax.numpy as jnp
import jax.tree_util as jtu
import numpy as np
from laplacian_testcase import LaplacianTestCase
from packaging.version import Version
from parameterized import parameterized

from folx import (
Expand Down Expand Up @@ -174,28 +176,66 @@ def test_slogdet(self, test_complex: bool):
w = w + 1j * np.random.normal(size=w.shape)

@jax.jit
def f(x):
def _f(w, x):
return jnp.linalg.slogdet(jnp.tanh((x @ w).reshape(16, 16)))

f = partial(_f, w)

for sparsity in [0, x.size]:
with self.subTest(sparsity=sparsity):
sign_y, log_y = jax.jit(forward_laplacian(f, sparsity))(x)
self.assertEqual(log_y.x.shape, f(x)[1].shape)
self.assert_allclose(log_y.x, f(x)[1])
self.assert_allclose(
log_y.jacobian.dense_array, self.jacobian(f, x)[1].T
)
self.assert_allclose(log_y.laplacian, self.laplacian(f, x)[1])

self.assertEqual(sign_y.shape, log_y.x.shape)
if test_complex:
self.assertIsInstance(sign_y, FwdLaplArray)
for use_shard_map in [False, True]:
with self.subTest(sparsity=sparsity, use_shard_map=use_shard_map):
if use_shard_map and (
Version(jax.__version__) < Version('0.7.1')
or (
sparsity != 0
and Version(jax.__version__) < Version('0.7.2')
)
):
self.skipTest('jax version too old')
if use_shard_map:
mesh = jax.sharding.Mesh(
jax.devices()[:1],
'i',
axis_types=jax.sharding.AxisType.Explicit,
)

@jax.jit
@partial(
jax.shard_map,
in_specs=(jax.P(), jax.P('i')),
out_specs=jax.P('i'),
)
@partial(jax.vmap, in_axes=(None, 0))
def forward_laplacian_sh(w, x):
return forward_laplacian(partial(_f, w), sparsity)(x)

with jax.set_mesh(mesh):
x_sh = jax.sharding.reshard(x[None], jax.P('i'))
w_sh = jax.sharding.reshard(w, jax.P())
sign_y, log_y = jax.tree.map(
lambda x: x[0], forward_laplacian_sh(w_sh, x_sh)
)
else:
sign_y, log_y = jax.jit(forward_laplacian(f, sparsity))(x)

self.assertEqual(log_y.x.shape, f(x)[1].shape)
self.assert_allclose(log_y.x, f(x)[1])
self.assert_allclose(
sign_y.jacobian.dense_array, self.jacobian(f, x)[0].T
log_y.jacobian.dense_array, self.jacobian(f, x)[1].T
)
self.assert_allclose(sign_y.laplacian, self.laplacian(f, x)[0])
else:
self.assertIsInstance(sign_y, jax.Array)
self.assert_allclose(log_y.laplacian, self.laplacian(f, x)[1])

self.assertEqual(sign_y.shape, log_y.x.shape)
if test_complex:
self.assertIsInstance(sign_y, FwdLaplArray)
self.assert_allclose(
sign_y.jacobian.dense_array, self.jacobian(f, x)[0].T
)
self.assert_allclose(sign_y.laplacian, self.laplacian(f, x)[0])
else:
self.assertIsInstance(sign_y, jax.Array)
del sign_y
del log_y

def test_custom_hessian(self):
x = np.random.normal(size=(16,))
Expand Down