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https://github.com/DifferentiableUniverseInitiative/JaxPM.git
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123 lines
4.2 KiB
Python
123 lines
4.2 KiB
Python
import os
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from distributed_utils import initialize_distributed, is_on_cluster
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os.environ["EQX_ON_ERROR"] = "nan" # avoid an allgather caused by diffrax
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initialize_distributed()
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import jax
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size = jax.device_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 (ConstantStepSize, Dopri5, LeapfrogMidpoint, ODETerm,
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SaveAt, diffeqsolve)
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from jax.experimental import mesh_utils
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from jax.experimental.multihost_utils import process_allgather
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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 = 64
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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 = 4
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pdims = (1, 1)
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mesh = None
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sharding = None
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if jax.device_count() > 1:
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pdims = (2, 4)
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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, sharding)
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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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sharding=sharding,
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seed=jax.random.PRNGKey(0))
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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,
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initial_conditions,
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0.1,
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halo_size=halo_size,
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sharding=sharding)
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# return initial_conditions, cic_paint_dx(dx,
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# halo_size=halo_size,
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# sharding=sharding), None, None
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# Evolve the simulation forward
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ode_fn = make_ode_fn(mesh_shape, halo_size=halo_size, sharding=sharding)
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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 = LeapfrogMidpoint()
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stepsize_controller = ConstantStepSize()
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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, sharding=sharding)
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final_fields = [
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cic_paint_dx(state[0], halo_size=halo_size, sharding=sharding)
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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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distributed_str = "distributed" if mesh is not None else "single device"
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print(f"running {distributed_str} simulation")
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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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print(f"done now saving")
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if is_on_cluster():
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rank = jax.process_index()
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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',
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final_field.addressable_data(0))
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else:
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gathered_init = process_allgather(init, tiled=True)
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gathered_field = process_allgather(field, tiled=True)
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np.save(f'initial_conditions.npy', gathered_init)
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np.save(f'field.npy', gathered_field)
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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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gathered_final_field = process_allgather(final_field, tiled=True)
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np.save(f'final_field_{i}.npy', gathered_final_field)
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print(f"Finished!!")
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