implement distributed optimized cic_paint

jaxpm/painting.py

@@ -5,14 +5,13 @@ import jax.lax as lax
 import jax.numpy as jnp
 from jax.sharding import PartitionSpec as P
 
-from jaxpm.distributed import autoshmap
+from jaxpm.distributed import (autoshmap, get_halo_size, halo_exchange,
+                               slice_pad, slice_unpad)
 from jaxpm.kernels import cic_compensation, fftk
+from jaxpm.painting_utils import gather, scatter
 
 
-@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):
+def cic_paint_impl(mesh, displacement, weight=None):
     """ Paints positions onto mesh
     mesh: [nx, ny, nz]
     displacement field: [nx, ny, nz, 3]
@@ -48,8 +47,22 @@ def cic_paint(mesh, displacement, weight=None):
     return mesh
 
 
-@partial(autoshmap, in_specs=(P('x', 'y'), P('x', 'y')), out_specs=P('x', 'y'))
-def cic_read(mesh, displacement):
+@partial(jax.jit, static_argnums=(2, ))
+def cic_paint(mesh, positions, halo_size=0, weight=None):
+
+    halo_size, halo_extents = get_halo_size(halo_size)
+    mesh = slice_pad(mesh, halo_size)
+    mesh = autoshmap(cic_paint_impl,
+                     in_specs=(P('x', 'y'), P('x', 'y'), P()),
+                     out_specs=P('x', 'y'))(mesh, positions, weight)
+    mesh = halo_exchange(mesh,
+                         halo_extents=halo_extents,
+                         halo_periods=(True, True, True))
+    mesh = slice_unpad(mesh, halo_size)
+    return mesh
+
+
+def cic_read_impl(mesh, displacement):
     """ Paints positions onto mesh
   mesh: [nx, ny, nz]
   displacement: [nx,ny,nz, 3]
@@ -79,6 +92,21 @@ def cic_read(mesh, displacement):
                      displacement.shape[:-1])
 
 
+@partial(jax.jit, static_argnums=(2, ))
+def cic_read(mesh, displacement, halo_size=0):
+
+    halo_size, halo_extents = get_halo_size(halo_size)
+    mesh = slice_pad(mesh, halo_size)
+    mesh = halo_exchange(mesh,
+                         halo_extents=halo_extents,
+                         halo_periods=(True, True, True))
+    displacement = autoshmap(cic_read_impl,
+                             in_specs=(P('x', 'y'), P('x', 'y')),
+                             out_specs=P('x', 'y'))(mesh, displacement)
+
+    return displacement
+
+
 def cic_paint_2d(mesh, positions, weight):
     """ Paints positions onto a 2d mesh
   mesh: [nx, ny]
@@ -108,6 +136,72 @@ def cic_paint_2d(mesh, positions, weight):
     return mesh
 
 
+def cic_paint_dx_impl(displacements, halo_size):
+
+    halo_x, _ = halo_size[0]
+    halo_y, _ = halo_size[1]
+
+    original_shape = displacements.shape
+    particle_mesh = jnp.zeros(original_shape[:-1], dtype='float32')
+
+    # Padding is forced to be zero in a single gpu run
+
+    a, b, c = jnp.meshgrid(jnp.arange(particle_mesh.shape[0]),
+                           jnp.arange(particle_mesh.shape[1]),
+                           jnp.arange(particle_mesh.shape[2]),
+                           indexing='ij')
+
+    particle_mesh = jnp.pad(particle_mesh, halo_size)
+
+    pmid = jnp.stack([a + halo_x, b + halo_y, c], axis=-1)
+    pmid = pmid.reshape([-1, 3])
+    return scatter(pmid, displacements.reshape([-1, 3]), particle_mesh)
+
+
+@partial(jax.jit, static_argnums=(1, ))
+def cic_paint_dx(displacements, halo_size=0):
+
+    halo_size, halo_extents = get_halo_size(halo_size)
+
+    mesh = autoshmap(partial(cic_paint_dx_impl, halo_size=halo_size),
+                     in_specs=(P('x', 'y')),
+                     out_specs=P('x', 'y'))(displacements)
+    mesh = halo_exchange(mesh,
+                         halo_extents=halo_extents,
+                         halo_periods=(True, True, True))
+    mesh = slice_unpad(mesh, halo_size)
+    return mesh
+
+
+def cic_read_dx_impl(mesh):
+
+    original_shape = mesh.shape
+
+    a, b, c = jnp.meshgrid(jnp.arange(original_shape[0]),
+                           jnp.arange(original_shape[1]),
+                           jnp.arange(original_shape[2]),
+                           indexing='ij')
+
+    pmid = jnp.stack([a, b, c], axis=-1)
+    pmid = pmid.reshape([-1, 3])
+
+    return gather(pmid, jnp.zeros_like(pmid), mesh).reshape(original_shape)
+
+
+@partial(jax.jit, static_argnums=(1, ))
+def cic_read_dx(mesh, halo_size=0):
+
+    halo_size, halo_extents = get_halo_size(halo_size)
+    mesh = slice_pad(mesh, halo_size)
+    mesh = halo_exchange(mesh,
+                         halo_extents=halo_extents,
+                         halo_periods=(True, True, True))
+    displacements = autoshmap(cic_read_dx_impl,
+                              in_specs=(P('x', 'y')),
+                              out_specs=P('x', 'y'))(mesh)
+    return displacements
+
+
 def compensate_cic(field):
     """
   Compensate for CiC painting

jaxpm/painting_utils.py

@@ -0,0 +1,185 @@
+import jax
+import jax.numpy as jnp
+from jax.lax import scan
+
+
+def _chunk_split(ptcl_num, chunk_size, *arrays):
+    """Split and reshape particle arrays into chunks and remainders, with the remainders
+    preceding the chunks. 0D ones are duplicated as full arrays in the chunks."""
+    chunk_size = ptcl_num if chunk_size is None else min(chunk_size, ptcl_num)
+    remainder_size = ptcl_num % chunk_size
+    chunk_num = ptcl_num // chunk_size
+
+    remainder = None
+    chunks = arrays
+    if remainder_size:
+        remainder = [x[:remainder_size] if x.ndim != 0 else x for x in arrays]
+        chunks = [x[remainder_size:] if x.ndim != 0 else x for x in arrays]
+
+    # `scan` triggers errors in scatter and gather without the `full`
+    chunks = [
+        x.reshape(chunk_num, chunk_size, *x.shape[1:])
+        if x.ndim != 0 else jnp.full(chunk_num, x) for x in chunks
+    ]
+
+    return remainder, chunks
+
+
+def enmesh(i1, d1, a1, s1, b12, a2, s2):
+    """Multilinear enmeshing."""
+    i1 = jnp.asarray(i1)
+    d1 = jnp.asarray(d1)
+    a1 = jnp.float64(a1) if a2 is not None else jnp.array(a1, dtype=d1.dtype)
+    if s1 is not None:
+        s1 = jnp.array(s1, dtype=i1.dtype)
+    b12 = jnp.float64(b12)
+    if a2 is not None:
+        a2 = jnp.float64(a2)
+    if s2 is not None:
+        s2 = jnp.array(s2, dtype=i1.dtype)
+
+    dim = i1.shape[1]
+    neighbors = (jnp.arange(2**dim, dtype=i1.dtype)[:, jnp.newaxis] >>
+                 jnp.arange(dim, dtype=i1.dtype)) & 1
+
+    if a2 is not None:
+        P = i1 * a1 + d1 - b12
+        P = P[:, jnp.newaxis]  # insert neighbor axis
+        i2 = P + neighbors * a2  # multilinear
+
+        if s1 is not None:
+            L = s1 * a1
+            i2 %= L
+
+        i2 //= a2
+        d2 = P - i2 * a2
+
+        if s1 is not None:
+            d2 -= jnp.rint(d2 / L) * L  # also abs(d2) < a2 is expected
+
+        i2 = i2.astype(i1.dtype)
+        d2 = d2.astype(d1.dtype)
+        a2 = a2.astype(d1.dtype)
+
+        d2 /= a2
+    else:
+        i12, d12 = jnp.divmod(b12, a1)
+        i1 -= i12.astype(i1.dtype)
+        d1 -= d12.astype(d1.dtype)
+
+        # insert neighbor axis
+        i1 = i1[:, jnp.newaxis]
+        d1 = d1[:, jnp.newaxis]
+
+        # multilinear
+        d1 /= a1
+        i2 = jnp.floor(d1).astype(i1.dtype)
+        i2 += neighbors
+        d2 = d1 - i2
+        i2 += i1
+
+        if s1 is not None:
+            i2 %= s1
+
+    f2 = 1 - jnp.abs(d2)
+
+    if s1 is None and s2 is not None:  # all i2 >= 0 if s1 is not None
+        i2 = jnp.where(i2 < 0, s2, i2)
+
+    f2 = f2.prod(axis=-1)
+
+    return i2, f2
+
+
+def _scatter_chunk(carry, chunk):
+    mesh, offset, cell_size, mesh_shape = carry
+    pmid, disp, val = chunk
+    spatial_ndim = pmid.shape[1]
+    spatial_shape = mesh.shape
+
+    # multilinear mesh indices and fractions
+    ind, frac = enmesh(pmid, disp, cell_size, mesh_shape, offset, cell_size,
+                       spatial_shape)
+    # scatter
+    ind = tuple(ind[..., i] for i in range(spatial_ndim))
+    mesh = mesh.at[ind].add(val * frac)
+
+    carry = mesh, offset, cell_size, mesh_shape
+    return carry, None
+
+
+def scatter(pmid,
+            disp,
+            mesh,
+            chunk_size=2**24,
+            val=1.,
+            offset=0,
+            cell_size=1.):
+
+    ptcl_num, spatial_ndim = pmid.shape
+    val = jnp.asarray(val)
+    mesh = jnp.asarray(mesh)
+
+    remainder, chunks = _chunk_split(ptcl_num, chunk_size, pmid, disp, val)
+    carry = mesh, offset, cell_size, mesh.shape
+    if remainder is not None:
+        carry = _scatter_chunk(carry, remainder)[0]
+    carry = scan(_scatter_chunk, carry, chunks)[0]
+    mesh = carry[0]
+    return mesh
+
+
+def _chunk_cat(remainder_array, chunked_array):
+    """Reshape and concatenate one remainder and one chunked particle arrays."""
+    array = chunked_array.reshape(-1, *chunked_array.shape[2:])
+
+    if remainder_array is not None:
+        array = jnp.concatenate((remainder_array, array), axis=0)
+
+    return array
+
+
+def gather(pmid, disp, mesh, chunk_size=2**24, val=1, offset=0, cell_size=1.):
+    ptcl_num, spatial_ndim = pmid.shape
+
+    mesh = jnp.asarray(mesh)
+
+    val = jnp.asarray(val)
+
+    if mesh.shape[spatial_ndim:] != val.shape[1:]:
+        raise ValueError('channel shape mismatch: '
+                         f'{mesh.shape[spatial_ndim:]} != {val.shape[1:]}')
+
+    remainder, chunks = _chunk_split(ptcl_num, chunk_size, pmid, disp, val)
+
+    carry = mesh, offset, cell_size, mesh.shape
+    val_0 = None
+    if remainder is not None:
+        val_0 = _gather_chunk(carry, remainder)[1]
+    val = scan(_gather_chunk, carry, chunks)[1]
+
+    val = _chunk_cat(val_0, val)
+
+    return val
+
+
+def _gather_chunk(carry, chunk):
+    mesh, offset, cell_size, mesh_shape = carry
+    pmid, disp, val = chunk
+
+    spatial_ndim = pmid.shape[1]
+
+    spatial_shape = mesh.shape[:spatial_ndim]
+    chan_ndim = mesh.ndim - spatial_ndim
+    chan_axis = tuple(range(-chan_ndim, 0))
+
+    # multilinear mesh indices and fractions
+    ind, frac = enmesh(pmid, disp, cell_size, mesh_shape, offset, cell_size,
+                       spatial_shape)
+
+    # gather
+    ind = tuple(ind[..., i] for i in range(spatial_ndim))
+    frac = jnp.expand_dims(frac, chan_axis)
+    val += (mesh.at[ind].get(mode='drop', fill_value=0) * frac).sum(axis=1)
+
+    return carry, val