forked from Aquila-Consortium/JaxPM_highres
88 lines
3.1 KiB
Python
88 lines
3.1 KiB
Python
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import jax
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import pytest
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from diffrax import (BacksolveAdjoint, Dopri5, ODETerm, PIDController,
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RecursiveCheckpointAdjoint, SaveAt, diffeqsolve)
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from helpers import MSE
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from jax import numpy as jnp
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from jaxpm.distributed import uniform_particles
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from jaxpm.painting import cic_paint, cic_paint_dx
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from jaxpm.pm import lpt, make_diffrax_ode
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@pytest.mark.single_device
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@pytest.mark.parametrize("order", [1, 2])
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@pytest.mark.parametrize("absolute_painting", [True, False])
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@pytest.mark.parametrize("adjoint", ['DTO', 'OTD'])
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def test_nbody_grad(simulation_config, initial_conditions, lpt_scale_factor,
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nbody_from_lpt1, nbody_from_lpt2, cosmo, order,
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absolute_painting, adjoint):
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mesh_shape, _ = simulation_config
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cosmo._workspace = {}
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if adjoint == 'OTD':
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pytest.skip("OTD adjoint not implemented yet (needs PFFT3D JVP)")
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adjoint = RecursiveCheckpointAdjoint(
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) if adjoint == 'DTO' else BacksolveAdjoint(solver=Dopri5())
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@jax.jit
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@jax.grad
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def forward_model(initial_conditions, cosmo):
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# Initial displacement
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if absolute_painting:
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particles = uniform_particles(mesh_shape)
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dx, p, _ = lpt(cosmo,
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initial_conditions,
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particles,
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a=lpt_scale_factor,
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order=order)
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ode_fn = ODETerm(make_diffrax_ode(cosmo, mesh_shape))
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y0 = jnp.stack([particles + dx, p])
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else:
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dx, p, _ = lpt(cosmo,
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initial_conditions,
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a=lpt_scale_factor,
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order=order)
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ode_fn = ODETerm(
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make_diffrax_ode(cosmo, mesh_shape, paint_absolute_pos=False))
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y0 = jnp.stack([dx, p])
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solver = Dopri5()
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controller = PIDController(rtol=1e-7,
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atol=1e-7,
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pcoeff=0.4,
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icoeff=1,
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dcoeff=0)
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saveat = SaveAt(t1=True)
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solutions = diffeqsolve(ode_fn,
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solver,
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t0=lpt_scale_factor,
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t1=1.0,
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dt0=None,
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y0=y0,
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adjoint=adjoint,
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stepsize_controller=controller,
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saveat=saveat)
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if absolute_painting:
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final_field = cic_paint(jnp.zeros(mesh_shape), solutions.ys[-1, 0])
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else:
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final_field = cic_paint_dx(solutions.ys[-1, 0])
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return MSE(final_field,
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nbody_from_lpt1 if order == 1 else nbody_from_lpt2)
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bad_initial_conditions = initial_conditions + jax.random.normal(
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jax.random.PRNGKey(0), initial_conditions.shape) * 0.5
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best_ic = forward_model(initial_conditions, cosmo)
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bad_ic = forward_model(bad_initial_conditions, cosmo)
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assert jnp.max(best_ic) < 1e-5
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assert jnp.max(bad_ic) > 1e-5
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