JaxPM_highres/tests/conftest.py
Wassim KABALAN df8602b318 jaxdecomp proto (#21)
* adding example of distributed solution

* put back old functgion

* update formatting

* add halo exchange and slice pad

* apply formatting

* implement distributed optimized cic_paint

* Use new cic_paint with halo

* Fix seed for distributed normal

* Wrap interpolation function to avoid all gather

* Return normal order frequencies for single GPU

* add example

* format

* add optimised bench script

* times in ms

* add lpt2

* update benchmark and add slurm

* Visualize only final field

* Update scripts/distributed_pm.py

Co-authored-by: Francois Lanusse <EiffL@users.noreply.github.com>

* Adjust pencil type for frequencies

* fix painting issue with slabs

* Shared operation in fourrier space now take inverted sharding axis for
slabs

* add assert to make pyright happy

* adjust test for hpc-plotter

* add PMWD test

* bench

* format

* added github workflow

* fix formatting from main

* Update for jaxDecomp pure JAX

* revert single halo extent change

* update for latest jaxDecomp

* remove fourrier_space in autoshmap

* make normal_field work with single controller

* format

* make distributed pm work in single controller

* merge bench_pm

* update to leapfrog

* add a strict dependency on jaxdecomp

* global mesh no longer needed

* kernels.py no longer uses global mesh

* quick fix in distributed

* pm.py no longer uses global mesh

* painting.py no longer uses global mesh

* update demo script

* quick fix in kernels

* quick fix in distributed

* update demo

* merge hugos LPT2 code

* format

* Small fix

* format

* remove duplicate get_ode_fn

* update visualizer

* update compensate CIC

* By default check_rep is false for shard_map

* remove experimental distributed code

* update PGDCorrection and neural ode to use new fft3d

* jaxDecomp pfft3d promotes to complex automatically

* remove deprecated stuff

* fix painting issue with read_cic

* use jnp interp instead of jc interp

* delete old slurms

* add notebook examples

* apply formatting

* add distributed zeros

* fix code in LPT2

* jit cic_paint

* update notebooks

* apply formating

* get local shape and zeros can be used by users

* add a user facing function to create uniform particle grid

* use jax interp instead of jax_cosmo

* use float64 for enmeshing

* Allow applying weights with relative cic paint

* Weights can be traced

* remove script folder

* update example notebooks

* delete outdated design file

* add readme for tutorials

* update readme

* fix small error

* forgot particles in multi host

* clarifying why cic_paint_dx is slower

* clarifying the halo size dependence on the box size

* ability to choose snapshots number with MultiHost script

* Adding animation notebook

* Put plotting in package

* Add finite difference laplace kernel + powerspec functions from Hugo

Co-authored-by: Hugo Simonfroy <hugo.simonfroy@gmail.com>

* Put plotting utils in package

* By default use absoulute painting with

* update code

* update notebooks

* add tests

* Upgrade setup.py to pyproject

* Format

* format tests

* update test dependencies

* add test workflow

* fix deprecated FftType in jaxpm.kernels

* Add aboucaud comments

* JAX version is 0.4.35 until Diffrax new release

* add numpy explicitly as dependency for tests

* fix install order for tests

* add numpy to be installed

* enforce no build isolation for fastpm

* pip install jaxpm test without build isolation

* bump jaxdecomp version

* revert test workflow

* remove outdated tests

---------

Co-authored-by: EiffL <fr.eiffel@gmail.com>
Co-authored-by: Francois Lanusse <EiffL@users.noreply.github.com>
Co-authored-by: Wassim KABALAN <wassim@apc.in2p3.fr>
Co-authored-by: Hugo Simonfroy <hugo.simonfroy@gmail.com>
Former-commit-id: 8c2e823d4669eac712089bf7f85ffb7912e8232d
2024-12-20 05:44:02 -05:00

175 lines
4.7 KiB
Python

# Parameterized fixture for mesh_shape
import os
import pytest
os.environ["EQX_ON_ERROR"] = "nan"
setup_done = False
on_cluster = False
def is_on_cluster():
global on_cluster
return on_cluster
def initialize_distributed():
global setup_done
global on_cluster
if not setup_done:
if "SLURM_JOB_ID" in os.environ:
on_cluster = True
print("Running on cluster")
import jax
jax.distributed.initialize()
setup_done = True
on_cluster = True
else:
print("Running locally")
setup_done = True
on_cluster = False
os.environ["JAX_PLATFORM_NAME"] = "cpu"
os.environ[
"XLA_FLAGS"] = "--xla_force_host_platform_device_count=8"
import jax
@pytest.fixture(
scope="session",
params=[
((32, 32, 32), (256., 256., 256.)), # BOX
((32, 32, 64), (256., 256., 512.)), # RECTANGULAR
])
def simulation_config(request):
return request.param
@pytest.fixture(scope="session", params=[0.1, 0.5, 0.8])
def lpt_scale_factor(request):
return request.param
@pytest.fixture(scope="session")
def cosmo():
from functools import partial
from jax_cosmo import Cosmology
Planck18 = partial(
Cosmology,
# Omega_m = 0.3111
Omega_c=0.2607,
Omega_b=0.0490,
Omega_k=0.0,
h=0.6766,
n_s=0.9665,
sigma8=0.8102,
w0=-1.0,
wa=0.0,
)
return Planck18()
@pytest.fixture(scope="session")
def particle_mesh(simulation_config):
from pmesh.pm import ParticleMesh
mesh_shape, box_shape = simulation_config
return ParticleMesh(BoxSize=box_shape, Nmesh=mesh_shape, dtype='f4')
@pytest.fixture(scope="session")
def fpm_initial_conditions(cosmo, particle_mesh):
import jax_cosmo as jc
import numpy as np
from jax import numpy as jnp
# Generate initial particle positions
grid = particle_mesh.generate_uniform_particle_grid(shift=0).astype(
np.float32)
# Interpolate with linear_matter spectrum to get initial density field
k = jnp.logspace(-4, 1, 128)
pk = jc.power.linear_matter_power(cosmo, k)
def pk_fn(x):
return jnp.interp(x.reshape([-1]), k, pk).reshape(x.shape)
whitec = particle_mesh.generate_whitenoise(42,
type='complex',
unitary=False)
lineark = whitec.apply(lambda k, v: pk_fn(sum(ki**2 for ki in k)**0.5)**0.5
* v * (1 / v.BoxSize).prod()**0.5)
init_mesh = lineark.c2r().value # XXX
return lineark, grid, init_mesh
@pytest.fixture(scope="session")
def initial_conditions(fpm_initial_conditions):
_, _, init_mesh = fpm_initial_conditions
return init_mesh
@pytest.fixture(scope="session")
def solver(cosmo, particle_mesh):
from fastpm.core import Cosmology as FastPMCosmology
from fastpm.core import Solver
ref_cosmo = FastPMCosmology(cosmo)
return Solver(particle_mesh, ref_cosmo, B=1)
@pytest.fixture(scope="session")
def fpm_lpt1(solver, fpm_initial_conditions, lpt_scale_factor):
lineark, grid, _ = fpm_initial_conditions
statelpt = solver.lpt(lineark, grid, lpt_scale_factor, order=1)
return statelpt
@pytest.fixture(scope="session")
def fpm_lpt1_field(fpm_lpt1, particle_mesh):
return particle_mesh.paint(fpm_lpt1.X).value
@pytest.fixture(scope="session")
def fpm_lpt2(solver, fpm_initial_conditions, lpt_scale_factor):
lineark, grid, _ = fpm_initial_conditions
statelpt = solver.lpt(lineark, grid, lpt_scale_factor, order=2)
return statelpt
@pytest.fixture(scope="session")
def fpm_lpt2_field(fpm_lpt2, particle_mesh):
return particle_mesh.paint(fpm_lpt2.X).value
@pytest.fixture(scope="session")
def nbody_from_lpt1(solver, fpm_lpt1, particle_mesh, lpt_scale_factor):
import numpy as np
from fastpm.core import leapfrog
if lpt_scale_factor == 0.8:
pytest.skip("Do not run nbody simulation from scale factor 0.8")
stages = np.linspace(lpt_scale_factor, 1.0, 10, endpoint=True)
finalstate = solver.nbody(fpm_lpt1, leapfrog(stages))
fpm_mesh = particle_mesh.paint(finalstate.X).value
return fpm_mesh
@pytest.fixture(scope="session")
def nbody_from_lpt2(solver, fpm_lpt2, particle_mesh, lpt_scale_factor):
import numpy as np
from fastpm.core import leapfrog
if lpt_scale_factor == 0.8:
pytest.skip("Do not run nbody simulation from scale factor 0.8")
stages = np.linspace(lpt_scale_factor, 1.0, 10, endpoint=True)
finalstate = solver.nbody(fpm_lpt2, leapfrog(stages))
fpm_mesh = particle_mesh.paint(finalstate.X).value
return fpm_mesh