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https://github.com/DifferentiableUniverseInitiative/JaxPM.git
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105 lines
3.2 KiB
Python
105 lines
3.2 KiB
Python
import os
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os.environ["EQX_ON_ERROR"] = "nan" # avoid an allgather caused by diffrax
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import jax
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jax.distributed.initialize()
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rank = jax.process_index()
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size = jax.process_count()
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import jax.numpy as jnp
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import jax_cosmo as jc
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import numpy as np
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from diffrax import Dopri5, ODETerm, PIDController, SaveAt, diffeqsolve
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from jax.experimental import mesh_utils
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from jax.sharding import Mesh, NamedSharding
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from jax.sharding import PartitionSpec as P
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from jaxpm.kernels import interpolate_power_spectrum
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from jaxpm.painting import cic_paint_dx
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from jaxpm.pm import linear_field, lpt, make_ode_fn
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size = 256
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mesh_shape = [size] * 3
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box_size = [float(size)] * 3
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snapshots = jnp.linspace(0.1, 1., 4)
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halo_size = 64
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if jax.device_count() > 1:
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pdims = (4, 2)
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devices = mesh_utils.create_device_mesh(pdims)
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mesh = Mesh(devices.T, axis_names=('x', 'y'))
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sharding = NamedSharding(mesh, P('x', 'y'))
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@jax.jit
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def run_simulation(omega_c, sigma8):
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# Create a small function to generate the matter power spectrum
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k = jnp.logspace(-4, 1, 128)
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pk = jc.power.linear_matter_power(
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jc.Planck15(Omega_c=omega_c, sigma8=sigma8), k)
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pk_fn = lambda x: interpolate_power_spectrum(x, k, pk)
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# Create initial conditions
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initial_conditions = linear_field(mesh_shape,
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box_size,
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pk_fn,
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seed=jax.random.PRNGKey(0))
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# Create particles
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particles = jnp.stack(jnp.meshgrid(*[jnp.arange(s) for s in mesh_shape]),
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axis=-1).reshape([-1, 3])
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cosmo = jc.Planck15(Omega_c=omega_c, sigma8=sigma8)
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# Initial displacement
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dx, p, _ = lpt(cosmo, initial_conditions, 0.1, halo_size=halo_size)
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# Evolve the simulation forward
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ode_fn = make_ode_fn(mesh_shape, halo_size=halo_size)
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term = ODETerm(
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lambda t, state, args: jnp.stack(ode_fn(state, t, args), axis=0))
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solver = Dopri5()
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stepsize_controller = PIDController(rtol=1e-4, atol=1e-4)
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res = diffeqsolve(term,
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solver,
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t0=0.1,
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t1=1.,
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dt0=0.01,
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y0=jnp.stack([dx, p], axis=0),
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args=cosmo,
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saveat=SaveAt(ts=snapshots),
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stepsize_controller=stepsize_controller)
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# Return the simulation volume at requested
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states = res.ys
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field = cic_paint_dx(dx, halo_size=halo_size)
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final_fields = [
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cic_paint_dx(state[0], halo_size=halo_size) for state in states
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]
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return initial_conditions, field, final_fields, res.stats
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# Run the simulation
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if jax.device_count() > 1:
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with mesh:
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init, field, final_fields, stats = run_simulation(0.32, 0.8)
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else:
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init, field, final_fields, stats = run_simulation(0.32, 0.8)
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# # Print the statistics
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print(stats)
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# # save the final state
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np.save(f'initial_conditions_{rank}.npy', init.addressable_data(0))
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np.save(f'field_{rank}.npy', field.addressable_data(0))
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if final_fields is not None:
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for i, final_field in enumerate(final_fields):
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np.save(f'final_field_{i}_{rank}.npy', final_field.addressable_data(0))
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print(f"Finished!!")
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