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This commit is contained in:
Wassim KABALAN 2024-10-27 03:50:34 +01:00
parent c93894f561
commit 19011d0712
5 changed files with 22 additions and 15 deletions
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7
jaxpm/distributed.py
View file

@ -117,10 +117,12 @@ def get_local_shape(mesh_shape, sharding):
else: else:
pdims = gpu_mesh.devices.shape pdims = gpu_mesh.devices.shape
return [ return [
mesh_shape[0] // pdims[0], mesh_shape[1] // pdims[1], *mesh_shape[2:] mesh_shape[0] // pdims[0], mesh_shape[1] // pdims[1],
*mesh_shape[2:]
] ]
def zeros(mesh_shape , sharding):
def zeros(mesh_shape, sharding):
gpu_mesh = sharding.mesh if sharding is not None else None gpu_mesh = sharding.mesh if sharding is not None else None
if not gpu_mesh is None and not (gpu_mesh.empty): if not gpu_mesh is None and not (gpu_mesh.empty):
local_mesh_shape = get_local_shape(mesh_shape, sharding) local_mesh_shape = get_local_shape(mesh_shape, sharding)
@ -132,6 +134,7 @@ def zeros(mesh_shape , sharding):
else: else:
return jnp.zeros(mesh_shape) return jnp.zeros(mesh_shape)
def normal_field(mesh_shape, seed, sharding): def normal_field(mesh_shape, seed, sharding):
"""Generate a Gaussian random field with the given power spectrum.""" """Generate a Gaussian random field with the given power spectrum."""
gpu_mesh = sharding.mesh if sharding is not None else None gpu_mesh = sharding.mesh if sharding is not None else None

3
jaxpm/growth.py
View file

@ -588,4 +588,5 @@ def dGf2a(cosmo, a):
f2p = cache['h2'] / cache['a'] * cache['g2'] f2p = cache['h2'] / cache['a'] * cache['g2']
f2p = interp(np.log(a), np.log(cache['a']), f2p) f2p = interp(np.log(a), np.log(cache['a']), f2p)
E_a = E(cosmo, a) E_a = E(cosmo, a)
return (f2p * a**3 * E_a + D2f * a**3 * dEa(cosmo, a) + 3 * a**2 * E_a * D2f) return (f2p * a**3 * E_a + D2f * a**3 * dEa(cosmo, a) +
3 * a**2 * E_a * D2f)

8
jaxpm/pm.py
View file

@ -5,7 +5,7 @@ import jax_cosmo as jc
from jax.sharding import PartitionSpec as P from jax.sharding import PartitionSpec as P
from jaxpm.distributed import (autoshmap, fft3d, get_local_shape, ifft3d, from jaxpm.distributed import (autoshmap, fft3d, get_local_shape, ifft3d,
normal_field,zeros) normal_field, zeros)
from jaxpm.growth import (dGf2a, dGfa, growth_factor, growth_factor_second, from jaxpm.growth import (dGf2a, dGfa, growth_factor, growth_factor_second,
growth_rate, growth_rate_second) growth_rate, growth_rate_second)
from jaxpm.kernels import (PGD_kernel, fftk, gradient_kernel, from jaxpm.kernels import (PGD_kernel, fftk, gradient_kernel,
@ -29,8 +29,10 @@ def pm_forces(positions,
mesh_shape = delta.shape mesh_shape = delta.shape
if paint_particles: if paint_particles:
paint_fn = lambda x: cic_paint( paint_fn = lambda x: cic_paint(zeros(mesh_shape, sharding),
zeros(mesh_shape,sharding), x , halo_size=halo_size, sharding=sharding) x,
halo_size=halo_size,
sharding=sharding)
read_fn = lambda x: cic_read( read_fn = lambda x: cic_read(
x, positions, halo_size=halo_size, sharding=sharding) x, positions, halo_size=halo_size, sharding=sharding)
else: else:

11
notebooks/03-MultiHost_PM.py
View file

@ -33,7 +33,8 @@ sharding = NamedSharding(mesh, P('x', 'y'))
mesh_shape = [512, 512, 512] mesh_shape = [512, 512, 512]
box_size = [500., 500., 1000.] box_size = [500., 500., 1000.]
halo_size = 64 halo_size = 64
snapshots = jnp.linspace(0.1,1.,2) snapshots = jnp.linspace(0.1, 1., 2)
@jax.jit @jax.jit
def run_simulation(omega_c, sigma8): def run_simulation(omega_c, sigma8):
@ -89,9 +90,11 @@ print(f"[{rank}] Solver stats: {solver_stats}")
# Gather the results # Gather the results
pm_dict = {"initial_conditions": all_gather(initial_conditions), pm_dict = {
"lpt_displacements": all_gather(lpt_displacements), "initial_conditions": all_gather(initial_conditions),
"solver_stats": solver_stats} "lpt_displacements": all_gather(lpt_displacements),
"solver_stats": solver_stats
}
for i in range(len(ode_solutions)): for i in range(len(ode_solutions)):
sol = ode_solutions[i] sol = ode_solutions[i]

8
notebooks/visualize.py
View file

@ -62,11 +62,9 @@ def plot_fields_single_projection(fields_dict, sum_over=None):
slicing = tuple(slicing) slicing = tuple(slicing)
# Sum projection over axis 0 and plot # Sum projection over axis 0 and plot
axes[i].imshow( axes[i].imshow(field[slicing].sum(axis=0) + 1,
field[slicing].sum(axis=0) + 1, cmap='magma',
cmap='magma', extent=[0, field.shape[1], 0, field.shape[2]])
extent=[0, field.shape[1], 0, field.shape[2]]
)
axes[i].set_xlabel('Mpc/h') axes[i].set_xlabel('Mpc/h')
axes[i].set_ylabel('Mpc/h') axes[i].set_ylabel('Mpc/h')
axes[i].set_title(f"{name} projection 0") axes[i].set_title(f"{name} projection 0")