adding example of distributed solution

dev/jaxdecomp.py

@@ -0,0 +1,62 @@
+import argparse
+import jax
+import numpy as np
+
+# Setting up distributed jax
+jax.distributed.initialize()
+rank = jax.process_index()
+size = jax.process_count()
+
+import jax.numpy as jnp
+import jax_cosmo as jc
+from jaxpm.pm import linear_field, lpt
+from jaxpm.painting import cic_paint
+from jax.experimental import mesh_utils
+from jax.sharding import Mesh
+
+mesh_shape= [256, 256, 256]
+box_size  = [256.,256.,256.]
+snapshots = jnp.linspace(0.1, 1., 2)
+
+@jax.jit
+def run_simulation(omega_c, sigma8, seed):
+    # Create a cosmology
+    cosmo = jc.Planck15(Omega_c=omega_c, sigma8=sigma8)
+
+    # Create a small function to generate the matter power spectrum
+    k = jnp.logspace(-4, 1, 128)
+    pk = jc.power.linear_matter_power(jc.Planck15(Omega_c=omega_c, sigma8=sigma8), k)
+    pk_fn = lambda x: jc.scipy.interpolate.interp(x.reshape([-1]), k, pk).reshape(x.shape)
+
+    # Create initial conditions
+    initial_conditions = linear_field(mesh_shape, box_size, pk_fn, seed=seed)
+    
+    # Initialize particle displacements 
+    dx, p, f = lpt(cosmo, initial_conditions, 1.0)
+
+    field = cic_paint(jnp.zeros_like(initial_conditions), dx)
+    return field
+
+def main(args):
+  # Setting up distributed random numbers
+  master_key = jax.random.PRNGKey(42)
+  key = jax.random.split(master_key, size)[rank]
+
+  # Create computing mesh and sharding information
+  devices = mesh_utils.create_device_mesh((2,2))
+  mesh = Mesh(devices.T, axis_names=('x', 'y'))
+
+  # Run the simulation on the compute mesh
+  with mesh:
+    field = run_simulation(0.32, 0.8, key)
+
+  print('done')
+  np.save(f'field_{rank}.npy', field.addressable_data(0))
+  
+  # Closing distributed jax
+  jax.distributed.shutdown()
+
+if __name__ == '__main__':
+  parser = argparse.ArgumentParser("Distributed LPT N-body simulation.")
+  args = parser.parse_args()
+  main(args)

jaxpm/distributed.py

@@ -0,0 +1,50 @@
+from typing import Any, Callable, Hashable
+
+Specs = Any
+AxisName = Hashable
+
+try:
+    import jaxdecomp
+    distributed = True
+except ImportError:
+    print("jaxdecomp not installed. Distributed functions will not work.")
+    distributed = False
+
+import jax.numpy as jnp
+from jax._src import mesh as mesh_lib
+from jax.experimental.shard_map import shard_map
+
+
+def autoshmap(f: Callable,
+          in_specs: Specs,
+          out_specs: Specs,
+          check_rep: bool = True,
+          auto: frozenset[AxisName] = frozenset()):
+    """Helper function to wrap the provided function in a shard map if 
+    the code is being executed in a mesh context."""
+    mesh = mesh_lib.thread_resources.env.physical_mesh
+    if mesh.empty:
+        return f
+    else:
+        return shard_map(f, mesh, in_specs, out_specs, check_rep, auto)
+
+def fft3d(x):
+    if distributed and not(mesh_lib.thread_resources.env.physical_mesh.empty):
+        return jaxdecomp.pfft3d(x.astype(jnp.complex64))
+    else:
+        return jnp.fft.rfftn(x)
+
+def ifft3d(x):
+    if distributed and not(mesh_lib.thread_resources.env.physical_mesh.empty):
+        return jaxdecomp.pifft3d(x).real
+    else:
+        return jnp.fft.irfftn(x)
+
+def get_local_shape(mesh_shape):
+  """ Helper function to get the local size of a mesh given the global size.
+  """
+  if mesh_lib.thread_resources.env.physical_mesh.empty:
+    return mesh_shape
+  else:
+    pdims = mesh_lib.thread_resources.env.physical_mesh.devices.shape
+    return [mesh_shape[0] // pdims[0], mesh_shape[1] // pdims[1], mesh_shape[2]]

jaxpm/kernels.py

@@ -1,24 +1,33 @@
+from jaxpm.distributed import autoshmap
+from jax.sharding import PartitionSpec as P
+from functools import partial
 import jax.numpy as jnp
 import numpy as np
 
 
-def fftk(shape, symmetric=True, finite=False, dtype=np.float32):
-    """ Return k_vector given a shape (nc, nc, nc) and box_size
+def fftk(shape, dtype=np.float32):
   """
-    k = []
-    for d in range(len(shape)):
-        kd = np.fft.fftfreq(shape[d])
-        kd *= 2 * np.pi
-        kdshape = np.ones(len(shape), dtype='int')
-        if symmetric and d == len(shape) - 1:
-            kd = kd[:shape[d] // 2 + 1]
-        kdshape[d] = len(kd)
-        kd = kd.reshape(kdshape)
+    Generate Fourier transform wave numbers for a given mesh.
 
-        k.append(kd.astype(dtype))
-    del kd, kdshape
-    return k
+    Args:
+        nc (int): Shape of the mesh grid.
 
+    Returns:
+        list: List of wave number arrays for each dimension in
+        the order [kx, ky, kz].
+  """
+  kx, ky, kz = [jnp.fft.fftfreq(s, dtype=dtype) * 2 * np.pi for s in shape]
+  @partial(
+      autoshmap,
+      in_specs=(P('x'), P('y'), P(None)),
+      out_specs=(P('x'), P(None, 'y'), P(None)))
+  def get_kvec(ky, kz, kx):
+    return (ky.reshape([-1, 1, 1]),
+            kz.reshape([1, -1, 1]),
+            kx.reshape([1, 1, -1])) # yapf: disable
+  ky, kz, kx = get_kvec(ky, kz, kx)  # The order corresponds 
+  # to the order of dimensions in the transposed FFT
+  return kx, ky, kz
 
 def gradient_kernel(kvec, direction, order=1):
     """
@@ -60,11 +69,7 @@ def laplace_kernel(kvec):
     Complex kernel
   """
     kk = sum(ki**2 for ki in kvec)
-    mask = (kk == 0).nonzero()
-    kk[mask] = 1
-    wts = 1. / kk
-    imask = (~(kk == 0)).astype(int)
-    wts *= imask
+    wts = jnp.where(kk == 0, 1., 1. / kk)
     return wts
 
 

jaxpm/painting.py

@@ -3,13 +3,25 @@ import jax.lax as lax
 import jax.numpy as jnp
 
 from jaxpm.kernels import cic_compensation, fftk
+from jax.sharding import PartitionSpec as P
+from functools import partial
+from jaxpm.distributed import autoshmap
 
-
-def cic_paint(mesh, positions, weight=None):
+@partial(autoshmap, 
+        in_specs=(P('x', 'y'), P('x','y'), P('x','y')), 
+        out_specs=P('x', 'y'))
+def cic_paint(mesh, displacement, weight=None):
     """ Paints positions onto mesh
-  mesh: [nx, ny, nz]
-  positions: [npart, 3]
-  """
+    mesh: [nx, ny, nz]
+    displacement field: [nx, ny, nz, 3]
+    """
+    part_shape = displacement.shape
+    positions = jnp.stack(jnp.meshgrid(
+        jnp.arange(part_shape[0]),
+        jnp.arange(part_shape[1]),
+        jnp.arange(part_shape[2]),
+        indexing='ij'), axis=-1) + displacement
+    positions = positions.reshape([-1, 3])
     positions = jnp.expand_dims(positions, 1)
     floor = jnp.floor(positions)
     connection = jnp.array([[[0, 0, 0], [1., 0, 0], [0., 1, 0], [0., 0, 1],
@@ -34,11 +46,22 @@ def cic_paint(mesh, positions, weight=None):
     return mesh
 
 
-def cic_read(mesh, positions):
+@partial(autoshmap, 
+        in_specs=(P('x', 'y'), P('x','y')), 
+        out_specs=P('x', 'y'))
+def cic_read(mesh, displacement):
     """ Paints positions onto mesh
   mesh: [nx, ny, nz]
-  positions: [npart, 3]
+  displacement: [nx,ny,nz, 3]
   """
+    # Compute the position of the particles on a regular grid
+    part_shape = displacement.shape
+    positions = jnp.stack(jnp.meshgrid(
+        jnp.arange(part_shape[0]),
+        jnp.arange(part_shape[1]),
+        jnp.arange(part_shape[2]),
+        indexing='ij'), axis=-1) + displacement
+    positions = positions.reshape([-1, 3])
     positions = jnp.expand_dims(positions, 1)
     floor = jnp.floor(positions)
     connection = jnp.array([[[0, 0, 0], [1., 0, 0], [0., 1, 0], [0., 0, 1],
@@ -52,7 +75,7 @@ def cic_read(mesh, positions):
                                jnp.array(mesh.shape))
 
     return (mesh[neighboor_coords[..., 0], neighboor_coords[..., 1],
-                 neighboor_coords[..., 3]] * kernel).sum(axis=-1)
+                 neighboor_coords[..., 3]] * kernel).sum(axis=-1).reshape(displacement.shape[:-1])
 
 
 def cic_paint_2d(mesh, positions, weight):

jaxpm/pm.py

@@ -1,12 +1,15 @@
 import jax
 import jax.numpy as jnp
 import jax_cosmo as jc
+from jax.sharding import PartitionSpec as P
 
 from jaxpm.growth import dGfa, growth_factor, growth_rate
 from jaxpm.kernels import (PGD_kernel, fftk, gradient_kernel, laplace_kernel,
                            longrange_kernel)
 from jaxpm.painting import cic_paint, cic_read
+from jaxpm.distributed import fft3d, ifft3d, autoshmap, get_local_shape
 
+from functools import partial
 
 def pm_forces(positions, mesh_shape=None, delta=None, r_split=0):
     """
@@ -17,26 +20,34 @@ def pm_forces(positions, mesh_shape=None, delta=None, r_split=0):
     kvec = fftk(mesh_shape)
 
     if delta is None:
-        delta_k = jnp.fft.rfftn(cic_paint(jnp.zeros(mesh_shape), positions))
+        delta_k = fft3d(cic_paint(jnp.zeros(mesh_shape), positions))
     else:
-        delta_k = jnp.fft.rfftn(delta)
+        delta_k = fft3d(delta)
 
     # Computes gravitational potential
     pot_k = delta_k * laplace_kernel(kvec) * longrange_kernel(kvec,
                                                               r_split=r_split)
     # Computes gravitational forces
     return jnp.stack([
-        cic_read(jnp.fft.irfftn(gradient_kernel(kvec, i) * pot_k), positions)
+        cic_read(ifft3d(gradient_kernel(kvec, i) * pot_k), positions)
         for i in range(3)
     ],
                      axis=-1)
 
 
-def lpt(cosmo, initial_conditions, positions, a):
+def lpt(cosmo, initial_conditions, a, particles_shape=None):
     """
     Computes first order LPT displacement
     """
-    initial_force = pm_forces(positions, delta=initial_conditions)
+    if particles_shape is None:
+        particles_shape = initial_conditions.shape
+    local_mesh_shape = get_local_shape(particles_shape)
+    displacement = autoshmap(
+      partial(jnp.zeros, shape=local_mesh_shape+[3], dtype='float32'),
+      in_specs=(),
+      out_specs=P('x', 'y'))()  # yapf: disable
+
+    initial_force = pm_forces(displacement, delta=initial_conditions)
     a = jnp.atleast_1d(a)
     dx = growth_factor(cosmo, a) * initial_force
     p = a**2 * growth_rate(cosmo, a) * jnp.sqrt(jc.background.Esqr(cosmo,
@@ -56,9 +67,15 @@ def linear_field(mesh_shape, box_size, pk, seed):
     pkmesh = pk(kmesh) * (mesh_shape[0] * mesh_shape[1] * mesh_shape[2]) / (
         box_size[0] * box_size[1] * box_size[2])
 
-    field = jax.random.normal(seed, mesh_shape)
-    field = jnp.fft.rfftn(field) * pkmesh**0.5
-    field = jnp.fft.irfftn(field)
+    # Initialize a random field with one slice on each gpu
+    local_mesh_shape = get_local_shape(mesh_shape)
+    field = autoshmap(
+      partial(jax.random.normal, shape=local_mesh_shape, dtype='float32'),
+      in_specs=P(None),
+      out_specs=P('x', 'y'))(seed)  # yapf: disable
+
+    field = fft3d(field) * pkmesh**0.5
+    field = ifft3d(field)
     return field
 
 
@@ -81,30 +98,3 @@ def make_ode_fn(mesh_shape):
         return dpos, dvel
 
     return nbody_ode
-
-
-def pgd_correction(pos, params):
-    """
-    improve the short-range interactions of PM-Nbody simulations with potential gradient descent method, based on https://arxiv.org/abs/1804.00671
-    args:
-      pos: particle positions [npart, 3]
-      params: [alpha, kl, ks] pgd parameters
-    """
-    kvec = fftk(mesh_shape)
-
-    delta = cic_paint(jnp.zeros(mesh_shape), pos)
-    alpha, kl, ks = params
-    delta_k = jnp.fft.rfftn(delta)
-    PGD_range = PGD_kernel(kvec, kl, ks)
-
-    pot_k_pgd = (delta_k * laplace_kernel(kvec)) * PGD_range
-
-    forces_pgd = jnp.stack([
-        cic_read(jnp.fft.irfftn(gradient_kernel(kvec, i) * pot_k_pgd), pos)
-        for i in range(3)
-    ],
-                           axis=-1)
-
-    dpos_pgd = forces_pgd * alpha
-
-    return dpos_pgd