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https://github.com/DifferentiableUniverseInitiative/JaxPM.git
synced 2025-04-07 12:20:54 +00:00
update formatting
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parent
6408aff1de
commit
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5 changed files with 113 additions and 96 deletions
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@ -1,4 +1,5 @@
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import argparse
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import jax
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import numpy as np
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@ -9,15 +10,17 @@ 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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from jaxpm.pm import linear_field, lpt
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from jaxpm.painting import cic_paint
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from jax.experimental import mesh_utils
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from jax.sharding import Mesh
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mesh_shape= [256, 256, 256]
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box_size = [256.,256.,256.]
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from jaxpm.painting import cic_paint
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from jaxpm.pm import linear_field, lpt
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mesh_shape = [256, 256, 256]
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box_size = [256., 256., 256.]
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snapshots = jnp.linspace(0.1, 1., 2)
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@jax.jit
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def run_simulation(omega_c, sigma8, seed):
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# Create a cosmology
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@ -25,38 +28,42 @@ def run_simulation(omega_c, sigma8, seed):
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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(jc.Planck15(Omega_c=omega_c, sigma8=sigma8), k)
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pk_fn = lambda x: jc.scipy.interpolate.interp(x.reshape([-1]), k, pk).reshape(x.shape)
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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: jc.scipy.interpolate.interp(x.reshape([-1]), k, pk
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).reshape(x.shape)
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# Create initial conditions
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initial_conditions = linear_field(mesh_shape, box_size, pk_fn, seed=seed)
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# Initialize particle displacements
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# Initialize particle displacements
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dx, p, f = lpt(cosmo, initial_conditions, 1.0)
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field = cic_paint(jnp.zeros_like(initial_conditions), dx)
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return field
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def main(args):
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# Setting up distributed random numbers
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master_key = jax.random.PRNGKey(42)
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key = jax.random.split(master_key, size)[rank]
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# Setting up distributed random numbers
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master_key = jax.random.PRNGKey(42)
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key = jax.random.split(master_key, size)[rank]
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# Create computing mesh and sharding information
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devices = mesh_utils.create_device_mesh((2,2))
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mesh = Mesh(devices.T, axis_names=('x', 'y'))
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# Create computing mesh and sharding information
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devices = mesh_utils.create_device_mesh((2, 2))
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mesh = Mesh(devices.T, axis_names=('x', 'y'))
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# Run the simulation on the compute mesh
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with mesh:
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field = run_simulation(0.32, 0.8, key)
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# Run the simulation on the compute mesh
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with mesh:
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field = run_simulation(0.32, 0.8, key)
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print('done')
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np.save(f'field_{rank}.npy', field.addressable_data(0))
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# Closing distributed jax
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jax.distributed.shutdown()
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print('done')
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np.save(f'field_{rank}.npy', field.addressable_data(0))
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# Closing distributed jax
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jax.distributed.shutdown()
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if __name__ == '__main__':
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parser = argparse.ArgumentParser("Distributed LPT N-body simulation.")
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args = parser.parse_args()
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main(args)
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parser = argparse.ArgumentParser("Distributed LPT N-body simulation.")
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args = parser.parse_args()
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main(args)
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@ -16,11 +16,11 @@ from jax.experimental.shard_map import shard_map
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def autoshmap(f: Callable,
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in_specs: Specs,
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out_specs: Specs,
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check_rep: bool = True,
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auto: frozenset[AxisName] = frozenset()):
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"""Helper function to wrap the provided function in a shard map if
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in_specs: Specs,
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out_specs: Specs,
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check_rep: bool = True,
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auto: frozenset[AxisName] = frozenset()):
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"""Helper function to wrap the provided function in a shard map if
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the code is being executed in a mesh context."""
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mesh = mesh_lib.thread_resources.env.physical_mesh
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if mesh.empty:
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@ -28,23 +28,28 @@ def autoshmap(f: Callable,
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else:
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return shard_map(f, mesh, in_specs, out_specs, check_rep, auto)
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def fft3d(x):
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if distributed and not(mesh_lib.thread_resources.env.physical_mesh.empty):
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if distributed and not (mesh_lib.thread_resources.env.physical_mesh.empty):
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return jaxdecomp.pfft3d(x.astype(jnp.complex64))
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else:
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return jnp.fft.rfftn(x)
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def ifft3d(x):
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if distributed and not(mesh_lib.thread_resources.env.physical_mesh.empty):
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if distributed and not (mesh_lib.thread_resources.env.physical_mesh.empty):
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return jaxdecomp.pifft3d(x).real
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else:
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return jnp.fft.irfftn(x)
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def get_local_shape(mesh_shape):
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""" Helper function to get the local size of a mesh given the global size.
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""" Helper function to get the local size of a mesh given the global size.
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"""
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if mesh_lib.thread_resources.env.physical_mesh.empty:
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return mesh_shape
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else:
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pdims = mesh_lib.thread_resources.env.physical_mesh.devices.shape
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return [mesh_shape[0] // pdims[0], mesh_shape[1] // pdims[1], mesh_shape[2]]
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if mesh_lib.thread_resources.env.physical_mesh.empty:
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return mesh_shape
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else:
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pdims = mesh_lib.thread_resources.env.physical_mesh.devices.shape
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return [
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mesh_shape[0] // pdims[0], mesh_shape[1] // pdims[1], mesh_shape[2]
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]
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@ -1,12 +1,14 @@
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from jaxpm.distributed import autoshmap
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from jax.sharding import PartitionSpec as P
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from functools import partial
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import jax.numpy as jnp
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import numpy as np
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from jax.sharding import PartitionSpec as P
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from jaxpm.distributed import autoshmap
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def fftk(shape, dtype=np.float32):
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"""
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"""
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Generate Fourier transform wave numbers for a given mesh.
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Args:
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@ -16,18 +18,19 @@ def fftk(shape, dtype=np.float32):
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list: List of wave number arrays for each dimension in
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the order [kx, ky, kz].
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"""
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kx, ky, kz = [jnp.fft.fftfreq(s, dtype=dtype) * 2 * np.pi for s in shape]
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@partial(
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autoshmap,
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in_specs=(P('x'), P('y'), P(None)),
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out_specs=(P('x'), P(None, 'y'), P(None)))
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def get_kvec(ky, kz, kx):
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return (ky.reshape([-1, 1, 1]),
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kz.reshape([1, -1, 1]),
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kx.reshape([1, 1, -1])) # yapf: disable
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ky, kz, kx = get_kvec(ky, kz, kx) # The order corresponds
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# to the order of dimensions in the transposed FFT
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return kx, ky, kz
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kx, ky, kz = [jnp.fft.fftfreq(s, dtype=dtype) * 2 * np.pi for s in shape]
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@partial(autoshmap,
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in_specs=(P('x'), P('y'), P(None)),
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out_specs=(P('x'), P(None, 'y'), P(None)))
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def get_kvec(ky, kz, kx):
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return (ky.reshape([-1, 1, 1]),
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kz.reshape([1, -1, 1]),
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kx.reshape([1, 1, -1])) # yapf: disable
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ky, kz, kx = get_kvec(ky, kz, kx) # The order corresponds
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# to the order of dimensions in the transposed FFT
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return kx, ky, kz
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def gradient_kernel(kvec, direction, order=1):
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"""
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@ -1,26 +1,28 @@
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from functools import partial
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import jax
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import jax.lax as lax
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import jax.numpy as jnp
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from jaxpm.kernels import cic_compensation, fftk
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from jax.sharding import PartitionSpec as P
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from functools import partial
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from jaxpm.distributed import autoshmap
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@partial(autoshmap,
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in_specs=(P('x', 'y'), P('x','y'), P('x','y')),
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out_specs=P('x', 'y'))
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from jaxpm.distributed import autoshmap
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from jaxpm.kernels import cic_compensation, fftk
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@partial(autoshmap,
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in_specs=(P('x', 'y'), P('x', 'y'), P('x', 'y')),
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out_specs=P('x', 'y'))
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def cic_paint(mesh, displacement, weight=None):
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""" Paints positions onto mesh
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mesh: [nx, ny, nz]
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displacement field: [nx, ny, nz, 3]
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"""
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part_shape = displacement.shape
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positions = jnp.stack(jnp.meshgrid(
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jnp.arange(part_shape[0]),
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jnp.arange(part_shape[1]),
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jnp.arange(part_shape[2]),
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indexing='ij'), axis=-1) + displacement
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positions = jnp.stack(jnp.meshgrid(jnp.arange(part_shape[0]),
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jnp.arange(part_shape[1]),
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jnp.arange(part_shape[2]),
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indexing='ij'),
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axis=-1) + displacement
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positions = positions.reshape([-1, 3])
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positions = jnp.expand_dims(positions, 1)
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floor = jnp.floor(positions)
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return mesh
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@partial(autoshmap,
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in_specs=(P('x', 'y'), P('x','y')),
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out_specs=P('x', 'y'))
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@partial(autoshmap, in_specs=(P('x', 'y'), P('x', 'y')), out_specs=P('x', 'y'))
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def cic_read(mesh, displacement):
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""" Paints positions onto mesh
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mesh: [nx, ny, nz]
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@ -56,11 +56,11 @@ def cic_read(mesh, displacement):
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"""
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# Compute the position of the particles on a regular grid
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part_shape = displacement.shape
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positions = jnp.stack(jnp.meshgrid(
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jnp.arange(part_shape[0]),
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jnp.arange(part_shape[1]),
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jnp.arange(part_shape[2]),
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indexing='ij'), axis=-1) + displacement
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positions = jnp.stack(jnp.meshgrid(jnp.arange(part_shape[0]),
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jnp.arange(part_shape[1]),
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jnp.arange(part_shape[2]),
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indexing='ij'),
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axis=-1) + displacement
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positions = positions.reshape([-1, 3])
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positions = jnp.expand_dims(positions, 1)
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floor = jnp.floor(positions)
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jnp.array(mesh.shape))
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return (mesh[neighboor_coords[..., 0], neighboor_coords[..., 1],
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neighboor_coords[..., 3]] * kernel).sum(axis=-1).reshape(displacement.shape[:-1])
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neighboor_coords[..., 3]] * kernel).sum(axis=-1).reshape(
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displacement.shape[:-1])
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def cic_paint_2d(mesh, positions, weight):
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45
jaxpm/pm.py
45
jaxpm/pm.py
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from functools import partial
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import jax
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import jax.numpy as jnp
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import jax_cosmo as jc
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from jax.sharding import PartitionSpec as P
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from jaxpm.distributed import autoshmap, fft3d, get_local_shape, ifft3d
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from jaxpm.growth import dGfa, growth_factor, growth_rate
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from jaxpm.kernels import (PGD_kernel, fftk, gradient_kernel, laplace_kernel,
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longrange_kernel)
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from jaxpm.painting import cic_paint, cic_read
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from jaxpm.distributed import fft3d, ifft3d, autoshmap, get_local_shape
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from functools import partial
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def pm_forces(positions, mesh_shape=None, delta=None, r_split=0):
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"""
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return nbody_ode
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def pgd_correction(pos, params):
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"""
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improve the short-range interactions of PM-Nbody simulations with potential gradient descent method, based on https://arxiv.org/abs/1804.00671
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args:
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pos: particle positions [npart, 3]
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params: [alpha, kl, ks] pgd parameters
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"""
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kvec = fftk(mesh_shape)
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def pgd_correction(pos, params):
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"""
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improve the short-range interactions of PM-Nbody simulations with potential gradient descent method, based on https://arxiv.org/abs/1804.00671
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args:
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pos: particle positions [npart, 3]
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params: [alpha, kl, ks] pgd parameters
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"""
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kvec = fftk(mesh_shape)
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delta = cic_paint(jnp.zeros(mesh_shape), pos)
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alpha, kl, ks = params
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delta_k = jnp.fft.rfftn(delta)
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PGD_range = PGD_kernel(kvec, kl, ks)
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delta = cic_paint(jnp.zeros(mesh_shape), pos)
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alpha, kl, ks = params
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delta_k = jnp.fft.rfftn(delta)
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PGD_range = PGD_kernel(kvec, kl, ks)
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pot_k_pgd = (delta_k * laplace_kernel(kvec)) * PGD_range
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pot_k_pgd = (delta_k * laplace_kernel(kvec)) * PGD_range
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forces_pgd = jnp.stack([
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cic_read(jnp.fft.irfftn(gradient_kernel(kvec, i) * pot_k_pgd), pos)
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for i in range(3)
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],
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axis=-1)
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forces_pgd = jnp.stack([
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cic_read(jnp.fft.irfftn(gradient_kernel(kvec, i) * pot_k_pgd), pos)
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for i in range(3)
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],
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axis=-1)
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dpos_pgd = forces_pgd * alpha
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dpos_pgd = forces_pgd * alpha
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return dpos_pgd
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return dpos_pgd
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