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Fix sharding error (#37)
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* Use cosmo as arg for the ODE function

* Update examples

* format

* notebook update

* fix tests

* add correct annotations for weights in painting and warning for cic_paint in distributed pm

* update test_against_fpm

* update distributed tests and add jacfwd jacrev and vmap tests

* format

* add Caveats to notebook readme

* final touches

* update Growth.py to allow using FastPM solver

* fix 2D painting when input is (X , Y , 2) shape

* update cic read halo size and notebooks examples

* Allow env variable control of caching in growth

* Format

* update test jax version

* update notebooks/03-MultiGPU_PM_Halo.ipynb

* update numpy install in wf

* update tolerance :)

* reorganize install in test workflow

* update tests

* add mpi4py

* update tests.yml

* update tests

* update wf

* format

* make normal_field signature consistent with jax.random.normal

* update by default normal_field dtype to match JAX

* format

* debug test workflow

* format

* debug test workflow

* updating tests

* fix accuracy

* fixed tolerance

* adding caching

* Update conftest.py

* Update tolerance and precision settings in distributed PM tests

* revererting back changes to growth.py

---------

Co-authored-by: Francois Lanusse <fr.eiffel@gmail.com>
Co-authored-by: Francois Lanusse <EiffL@users.noreply.github.com>
This commit is contained in:
Wassim KABALAN 2025-06-28 23:07:31 +02:00 • committed by GitHub
parent cb2a7ab17f
commit 6693e5c725
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17 changed files with 675 additions and 298 deletions
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8
jaxpm/distributed.py
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@ -166,11 +166,11 @@ def uniform_particles(mesh_shape, sharding=None):
axis=-1)
def normal_field(mesh_shape, seed, sharding=None):
def normal_field(seed, shape, sharding=None, dtype=float):
"""Generate a Gaussian random field with the given power spectrum."""
gpu_mesh = sharding.mesh if sharding is not None else None
if gpu_mesh is not None and not (gpu_mesh.empty):
local_mesh_shape = get_local_shape(mesh_shape, sharding)
local_mesh_shape = get_local_shape(shape, sharding)
size = jax.device_count()
# rank = jax.process_index()
@ -190,9 +190,9 @@ def normal_field(mesh_shape, seed, sharding=None):
return jax.random.normal(key=keys[idx], shape=shape, dtype=dtype)
return shard_map(
partial(normal, shape=local_mesh_shape, dtype='float32'),
partial(normal, shape=local_mesh_shape, dtype=dtype),
mesh=gpu_mesh,
in_specs=P(None),
out_specs=spec)(keys) # yapf: disable
else:
return jax.random.normal(shape=mesh_shape, key=seed)
return jax.random.normal(shape=shape, key=seed, dtype=dtype)