def nbody_prototype(mesh,
infield=False,
nc=FLAGS.nc,
bs=FLAGS.box_size,
batch_size=FLAGS.batch_size,
a0=FLAGS.a0,
a=FLAGS.af,
nsteps=FLAGS.nsteps,
dtype=tf.float32):
"""
Prototype of function computing LPT deplacement.
Returns output tensorflow and mesh tensorflow tensors
"""
# Compute a few things first, using simple tensorflow
stages = np.linspace(a0, a, nsteps, endpoint=True)
# Define the named dimensions
# Parameters of the small scales decomposition
n_block_x = FLAGS.nx
n_block_y = FLAGS.ny
n_block_z = 1
halo_size = FLAGS.hsize
# Parameters of the large scales decomposition
fx_dim = mtf.Dimension("nx", nc)
fy_dim = mtf.Dimension("ny", nc)
fz_dim = mtf.Dimension("nz", nc)
tfx_dim = mtf.Dimension("tx", nc)
tfy_dim = mtf.Dimension("ty", nc)
tfz_dim = mtf.Dimension("tz", nc)
tx_dim = mtf.Dimension("tx_lr", nc)
ty_dim = mtf.Dimension("ty_lr", nc)
tz_dim = mtf.Dimension("tz_lr", nc)
nx_dim = mtf.Dimension('nx_block', n_block_x)
ny_dim = mtf.Dimension('ny_block', n_block_y)
nz_dim = mtf.Dimension('nz_block', n_block_z)
sx_dim = mtf.Dimension('sx_block', nc // n_block_x)
sy_dim = mtf.Dimension('sy_block', nc // n_block_y)
sz_dim = mtf.Dimension('sz_block', nc // n_block_z)
k_dims = [tx_dim, ty_dim, tz_dim]
batch_dim = mtf.Dimension("batch", batch_size)
klin = np.loadtxt('../flowpm/data/Planck15_a1p00.txt').T[0]
plin = np.loadtxt('../flowpm/data/Planck15_a1p00.txt').T[1]
ipklin = iuspline(klin, plin)
pk_dim = mtf.Dimension("npk", len(plin))
pk = mtf.import_tf_tensor(mesh, plin, shape=[pk_dim])
# Compute necessary Fourier kernels
kvec = flowpm.kernels.fftk((nc, nc, nc), symmetric=False)
kx = mtf.import_tf_tensor(mesh,
kvec[0].squeeze().astype('float32'),
shape=[tfx_dim])
ky = mtf.import_tf_tensor(mesh,
kvec[1].squeeze().astype('float32'),
shape=[tfy_dim])
kz = mtf.import_tf_tensor(mesh,
kvec[2].squeeze().astype('float32'),
shape=[tfz_dim])
kv = [ky, kz, kx]
# kvec for low resolution grid
kvec_lr = flowpm.kernels.fftk([nc, nc, nc], symmetric=False)
kx_lr = mtf.import_tf_tensor(mesh,
kvec_lr[0].squeeze().astype('float32'),
shape=[tx_dim])
ky_lr = mtf.import_tf_tensor(mesh,
kvec_lr[1].squeeze().astype('float32'),
shape=[ty_dim])
kz_lr = mtf.import_tf_tensor(mesh,
kvec_lr[2].squeeze().astype('float32'),
shape=[tz_dim])
kv_lr = [ky_lr, kz_lr, kx_lr]
shape = [batch_dim, fx_dim, fy_dim, fz_dim]
lr_shape = [batch_dim, fx_dim, fy_dim, fz_dim]
hr_shape = [batch_dim, nx_dim, ny_dim, nz_dim, sx_dim, sy_dim, sz_dim]
part_shape = [batch_dim, fx_dim, fy_dim, fz_dim]
# Begin simulation
## Compute initial initial conditions distributed
input_field = tf.placeholder(dtype, [batch_size, nc, nc, nc])
if infield:
initc = mtf.import_tf_tensor(mesh, input_field, shape=part_shape)
else:
initc = mtfpm.linear_field(mesh, shape, bs, nc, pk, kv)
# Here we can run our nbody
if FLAGS.nbody:
state = mtfpm.lpt_init_single(
initc,
a0,
kv_lr,
halo_size,
lr_shape,
hr_shape,
part_shape[1:],
antialias=True,
)
# Here we can run our nbody
final_state = mtfpm.nbody_single(state, stages, lr_shape, hr_shape,
kv_lr, halo_size)
else:
final_state = mtfpm.lpt_init_single(
initc,
stages[-1],
kv_lr,
halo_size,
lr_shape,
hr_shape,
part_shape[1:],
antialias=True,
)
# paint the field
final_field = mtf.zeros(mesh, shape=hr_shape)
for block_size_dim in hr_shape[-3:]:
final_field = mtf.pad(final_field, [halo_size, halo_size],
block_size_dim.name)
final_field = mesh_utils.cic_paint(final_field, final_state[0], halo_size)
# Halo exchange
for blocks_dim, block_size_dim in zip(hr_shape[1:4],
final_field.shape[-3:]):
final_field = mpm.halo_reduce(final_field, blocks_dim, block_size_dim,
halo_size)
# Remove borders
for block_size_dim in hr_shape[-3:]:
final_field = mtf.slice(final_field, halo_size, block_size_dim.size,
block_size_dim.name)
final_field = mtf.slicewise(
lambda x: x[:, 0, 0, 0], [final_field],
output_dtype=dtype,
output_shape=[batch_dim, fx_dim, fy_dim, fz_dim],
name='my_dumb_reshape',
splittable_dims=part_shape[:-1] + hr_shape[:4])
return initc, final_field, input_field
def benchmark_model(mesh):
"""
Initializes a 3D volume with random noise, and execute a forward FFT
"""
# Setup parameters
bs = FLAGS.box_size
nc = FLAGS.cube_size
batch_size = FLAGS.batch_size
a0 = FLAGS.a0
a = 1.0
nsteps = FLAGS.pm_steps
# Compute a few things first, using simple tensorflow
klin = np.loadtxt('../flowpm/data/Planck15_a1p00.txt').T[0]
plin = np.loadtxt('../flowpm/data/Planck15_a1p00.txt').T[1]
ipklin = iuspline(klin, plin)
stages = np.linspace(a0, a, nsteps, endpoint=True)
# Initialize the integration steps
stages = np.linspace(FLAGS.a0, 1.0, FLAGS.pm_steps, endpoint=True)
# Generate a batch of 3D initial conditions
initial_conditions = flowpm.linear_field(
nc, # size of the cube
bs, # Physical size of the cube
ipklin, # Initial power spectrum
batch_size=batch_size)
# Compute necessary Fourier kernels
kvec = flowpm.kernels.fftk((nc, nc, nc), symmetric=False)
from flowpm.kernels import laplace_kernel, gradient_kernel
lap = tf.cast(laplace_kernel(kvec), tf.complex64)
grad_x = gradient_kernel(kvec, 0)
grad_y = gradient_kernel(kvec, 1)
grad_z = gradient_kernel(kvec, 2)
# Define the named dimensions
# Parameters of the small scales decomposition
n_block_x = 8
n_block_y = 4
n_block_z = 1
halo_size = 4
# Parameters of the large scales decomposition
downsampling_factor = 2
lnc = nc // 2**downsampling_factor
fx_dim = mtf.Dimension("nx", nc)
fy_dim = mtf.Dimension("ny", nc)
fz_dim = mtf.Dimension("nz", nc)
tfx_dim = mtf.Dimension("tx", nc)
tfy_dim = mtf.Dimension("ty", nc)
tfz_dim = mtf.Dimension("tz", nc)
# Dimensions of the low resolution grid
tx_dim = mtf.Dimension("tx_lr", nc)
ty_dim = mtf.Dimension("ty_lr", nc)
tz_dim = mtf.Dimension("tz_lr", nc)
nx_dim = mtf.Dimension('nx_block', n_block_x)
ny_dim = mtf.Dimension('ny_block', n_block_y)
nz_dim = mtf.Dimension('nz_block', n_block_z)
sx_dim = mtf.Dimension('sx_block', nc // n_block_x)
sy_dim = mtf.Dimension('sy_block', nc // n_block_y)
sz_dim = mtf.Dimension('sz_block', nc // n_block_z)
batch_dim = mtf.Dimension("batch", batch_size)
pk_dim = mtf.Dimension("npk", len(plin))
pk = mtf.import_tf_tensor(mesh, plin.astype('float32'), shape=[pk_dim])
# Compute necessary Fourier kernels
kvec = flowpm.kernels.fftk((nc, nc, nc), symmetric=False)
kx = mtf.import_tf_tensor(mesh,
kvec[0].squeeze().astype('float32'),
shape=[tfx_dim])
ky = mtf.import_tf_tensor(mesh,
kvec[1].squeeze().astype('float32'),
shape=[tfy_dim])
kz = mtf.import_tf_tensor(mesh,
kvec[2].squeeze().astype('float32'),
shape=[tfz_dim])
kv = [ky, kz, kx]
kvec_lr = flowpm.kernels.fftk([nc, nc, nc], symmetric=False)
kx_lr = mtf.import_tf_tensor(mesh,
kvec_lr[0].squeeze().astype('float32'),
shape=[tx_dim])
ky_lr = mtf.import_tf_tensor(mesh,
kvec_lr[1].squeeze().astype('float32'),
shape=[ty_dim])
kz_lr = mtf.import_tf_tensor(mesh,
kvec_lr[2].squeeze().astype('float32'),
shape=[tz_dim])
kv_lr = [ky_lr, kz_lr, kx_lr]
# kvec for high resolution blocks
shape = [batch_dim, fx_dim, fy_dim, fz_dim]
lr_shape = [batch_dim, fx_dim, fy_dim, fz_dim]
hr_shape = [batch_dim, nx_dim, ny_dim, nz_dim, sx_dim, sy_dim, sz_dim]
part_shape = [batch_dim, fx_dim, fy_dim, fz_dim]
initc = mtfpm.linear_field(mesh, shape, bs, nc, pk, kv)
state = mtfpm.lpt_init_single(
initc,
a0,
kv_lr,
halo_size,
lr_shape,
hr_shape,
part_shape[1:],
antialias=True,
)
#state = mtfpm.lpt_init(low, high, 0.1, kv_lr, kv_hr, halo_size, hr_shape, lr_shape,
# part_shape[1:], downsampling_factor=downsampling_factor, antialias=True,)
# Here we can run our nbody
final_state = state #mtfpm.nbody(state, stages, lr_shape, hr_shape, kv_lr, kv_hr, halo_size, downsampling_factor=downsampling_factor)
# paint the field
final_field = mtf.zeros(mesh, shape=hr_shape)
for block_size_dim in hr_shape[-3:]:
final_field = mtf.pad(final_field, [halo_size, halo_size],
block_size_dim.name)
final_field = mesh_utils.cic_paint(final_field, final_state[0], halo_size)
# Halo exchange
for blocks_dim, block_size_dim in zip(hr_shape[1:4], final_field.shape[-3:]):
final_field = mpm.halo_reduce(final_field, blocks_dim, block_size_dim,
halo_size)
# Remove borders
for block_size_dim in hr_shape[-3:]:
final_field = mtf.slice(final_field, halo_size, block_size_dim.size,
block_size_dim.name)
#final_field = mtf.reshape(final_field, [batch_dim, fx_dim, fy_dim, fz_dim])
# Hack usisng custom reshape because mesh is pretty dumb
final_field = mtf.slicewise(lambda x: x[:, 0, 0, 0], [final_field],
output_dtype=tf.float32,
output_shape=[batch_dim, fx_dim, fy_dim, fz_dim],
name='my_dumb_reshape',
splittable_dims=part_shape[:-1] + hr_shape[:4])
return mtf.reduce_sum(final_field)
def nbody_fn(mesh,
klin,
plin,
nc=FLAGS.nc,
bs=FLAGS.box_size,
batch_size=FLAGS.batch_size,
a0=FLAGS.a0,
a=FLAGS.af,
nsteps=FLAGS.nsteps,
dtype=tf.float32):
""" Pyramid N-body function
"""
stages = np.linspace(a0, a, nsteps, endpoint=True)
# Define the named dimensions
# Parameters of the small scales decomposition
n_block_x = FLAGS.nx
n_block_y = FLAGS.ny
n_block_z = 1
halo_size = FLAGS.hsize
# Parameters of the large scales decomposition
downsampling_factor = FLAGS.dsample
lnc = nc // 2**downsampling_factor
fx_dim = mtf.Dimension("nx", nc)
fy_dim = mtf.Dimension("ny", nc)
fz_dim = mtf.Dimension("nz", nc)
tfx_dim = mtf.Dimension("tx", nc)
tfy_dim = mtf.Dimension("ty", nc)
tfz_dim = mtf.Dimension("tz", nc)
# Dimensions of the low resolution grid
x_dim = mtf.Dimension("nx_lr", lnc)
y_dim = mtf.Dimension("ny_lr", lnc)
z_dim = mtf.Dimension("nz_lr", lnc)
tx_dim = mtf.Dimension("tx_lr", lnc)
ty_dim = mtf.Dimension("ty_lr", lnc)
tz_dim = mtf.Dimension("tz_lr", lnc)
nx_dim = mtf.Dimension('nx_block', n_block_x)
ny_dim = mtf.Dimension('ny_block', n_block_y)
nz_dim = mtf.Dimension('nz_block', n_block_z)
sx_dim = mtf.Dimension('sx_block', nc // n_block_x)
sy_dim = mtf.Dimension('sy_block', nc // n_block_y)
sz_dim = mtf.Dimension('sz_block', nc // n_block_z)
batch_dim = mtf.Dimension("batch", batch_size)
pk_dim = mtf.Dimension("npk", len(plin))
pk = mtf.import_tf_tensor(mesh, plin.astype('float32'), shape=[pk_dim])
# Compute necessary Fourier kernels
kvec = flowpm.kernels.fftk((nc, nc, nc), symmetric=False)
kx = mtf.import_tf_tensor(
mesh, kvec[0].squeeze().astype('float32'), shape=[tfx_dim])
ky = mtf.import_tf_tensor(
mesh, kvec[1].squeeze().astype('float32'), shape=[tfy_dim])
kz = mtf.import_tf_tensor(
mesh, kvec[2].squeeze().astype('float32'), shape=[tfz_dim])
kv = [ky, kz, kx]
# kvec for low resolution grid
kvec_lr = flowpm.kernels.fftk([lnc, lnc, lnc], symmetric=False)
kx_lr = mtf.import_tf_tensor(
mesh,
kvec_lr[0].squeeze().astype('float32') / 2**downsampling_factor,
shape=[tx_dim])
ky_lr = mtf.import_tf_tensor(
mesh,
kvec_lr[1].squeeze().astype('float32') / 2**downsampling_factor,
shape=[ty_dim])
kz_lr = mtf.import_tf_tensor(
mesh,
kvec_lr[2].squeeze().astype('float32') / 2**downsampling_factor,
shape=[tz_dim])
kv_lr = [ky_lr, kz_lr, kx_lr]
# kvec for high resolution blocks
padded_sx_dim = mtf.Dimension('padded_sx_block',
nc // n_block_x + 2 * halo_size)
padded_sy_dim = mtf.Dimension('padded_sy_block',
nc // n_block_y + 2 * halo_size)
padded_sz_dim = mtf.Dimension('padded_sz_block',
nc // n_block_z + 2 * halo_size)
kvec_hr = flowpm.kernels.fftk([
nc // n_block_x + 2 * halo_size, nc // n_block_y + 2 * halo_size,
nc // n_block_z + 2 * halo_size
],
symmetric=False)
kx_hr = mtf.import_tf_tensor(
mesh, kvec_hr[0].squeeze().astype('float32'), shape=[padded_sx_dim])
ky_hr = mtf.import_tf_tensor(
mesh, kvec_hr[1].squeeze().astype('float32'), shape=[padded_sy_dim])
kz_hr = mtf.import_tf_tensor(
mesh, kvec_hr[2].squeeze().astype('float32'), shape=[padded_sz_dim])
kv_hr = [ky_hr, kz_hr, kx_hr]
shape = [batch_dim, fx_dim, fy_dim, fz_dim]
lr_shape = [batch_dim, x_dim, y_dim, z_dim]
hr_shape = [batch_dim, nx_dim, ny_dim, nz_dim, sx_dim, sy_dim, sz_dim]
part_shape = [batch_dim, fx_dim, fy_dim, fz_dim]
# Compute initial initial conditions distributed
initc = mtfpm.linear_field(mesh, shape, bs, nc, pk, kv)
# Reshaping array into high resolution mesh
field = mtf.slicewise(
lambda x: tf.expand_dims(
tf.expand_dims(tf.expand_dims(x, axis=1), axis=1), axis=1), [initc],
output_dtype=tf.float32,
output_shape=hr_shape,
name='my_reshape',
splittable_dims=lr_shape[:-1] + hr_shape[1:4] + part_shape[1:3])
for block_size_dim in hr_shape[-3:]:
field = mtf.pad(field, [halo_size, halo_size], block_size_dim.name)
for blocks_dim, block_size_dim in zip(hr_shape[1:4], field.shape[-3:]):
field = mpm.halo_reduce(field, blocks_dim, block_size_dim, halo_size)
field = mtf.reshape(field, field.shape + [mtf.Dimension('h_dim', 1)])
high = field
low = mesh_utils.downsample(field, downsampling_factor, antialias=True)
low = mtf.reshape(low, low.shape[:-1])
high = mtf.reshape(high, high.shape[:-1])
for block_size_dim in hr_shape[-3:]:
low = mtf.slice(low, halo_size // 2**downsampling_factor,
block_size_dim.size // 2**downsampling_factor,
block_size_dim.name)
# Hack usisng custom reshape because mesh is pretty dumb
low = mtf.slicewise(
lambda x: x[:, 0, 0, 0], [low],
output_dtype=tf.float32,
output_shape=lr_shape,
name='my_dumb_reshape',
splittable_dims=lr_shape[:-1] + hr_shape[:4])
state = mtfpm.lpt_init(
low,
high,
0.1,
kv_lr,
kv_hr,
halo_size,
hr_shape,
lr_shape,
part_shape[1:],
downsampling_factor=downsampling_factor,
antialias=True,
)
final_state = mtfpm.nbody(
state,
stages,
lr_shape,
hr_shape,
kv_lr,
kv_hr,
halo_size,
downsampling_factor=downsampling_factor)
# paint the field
final_field = mtf.zeros(mesh, shape=hr_shape)
for block_size_dim in hr_shape[-3:]:
final_field = mtf.pad(final_field, [halo_size, halo_size],
block_size_dim.name)
final_field = mesh_utils.cic_paint(final_field, final_state[0], halo_size)
# Halo exchange
for blocks_dim, block_size_dim in zip(hr_shape[1:4], final_field.shape[-3:]):
final_field = mpm.halo_reduce(final_field, blocks_dim, block_size_dim,
halo_size)
# Remove borders
for block_size_dim in hr_shape[-3:]:
final_field = mtf.slice(final_field, halo_size, block_size_dim.size,
block_size_dim.name)
final_field = mtf.slicewise(
lambda x: x[:, 0, 0, 0], [final_field],
output_dtype=tf.float32,
output_shape=[batch_dim, fx_dim, fy_dim, fz_dim],
name='my_dumb_reshape',
splittable_dims=part_shape[:-1] + hr_shape[:4])
return initc, final_field
def lpt_prototype(mesh,
nc=FLAGS.nc,
bs=FLAGS.box_size,
batch_size=FLAGS.batch_size,
a0=FLAGS.a0,
a=FLAGS.af,
nsteps=FLAGS.nsteps):
"""
Prototype of function computing LPT deplacement.
Returns output tensorflow and mesh tensorflow tensors
"""
klin = np.loadtxt('../flowpm/data/Planck15_a1p00.txt').T[0]
plin = np.loadtxt('../flowpm/data/Planck15_a1p00.txt').T[1]
ipklin = iuspline(klin, plin)
stages = np.linspace(a0, a, nsteps, endpoint=True)
# Define the named dimensions
# Parameters of the small scales decomposition
n_block_x = FLAGS.nx
n_block_y = FLAGS.ny
n_block_z = 1
halo_size = FLAGS.hsize
if halo_size >= 0.5 * min(nc // n_block_x, nc // n_block_y,
nc // n_block_z):
new_size = int(0.5 *
min(nc // n_block_x, nc // n_block_y, nc // n_block_z))
print('WARNING: REDUCING HALO SIZE from %d to %d' %
(halo_size, new_size))
halo_size = new_size
# Parameters of the large scales decomposition
downsampling_factor = 0
lnc = nc // 2**downsampling_factor
#
fx_dim = mtf.Dimension("nx", nc)
fy_dim = mtf.Dimension("ny", nc)
fz_dim = mtf.Dimension("nz", nc)
tfx_dim = mtf.Dimension("tx", nc)
tfy_dim = mtf.Dimension("ty", nc)
tfz_dim = mtf.Dimension("tz", nc)
tx_dim = mtf.Dimension("tx_lr", nc)
ty_dim = mtf.Dimension("ty_lr", nc)
tz_dim = mtf.Dimension("tz_lr", nc)
nx_dim = mtf.Dimension('nx_block', n_block_x)
ny_dim = mtf.Dimension('ny_block', n_block_y)
nz_dim = mtf.Dimension('nz_block', n_block_z)
sx_dim = mtf.Dimension('sx_block', nc // n_block_x)
sy_dim = mtf.Dimension('sy_block', nc // n_block_y)
sz_dim = mtf.Dimension('sz_block', nc // n_block_z)
k_dims = [tx_dim, ty_dim, tz_dim]
batch_dim = mtf.Dimension("batch", batch_size)
pk_dim = mtf.Dimension("npk", len(plin))
pk = mtf.import_tf_tensor(mesh, plin.astype('float32'), shape=[pk_dim])
# Compute necessary Fourier kernels
kvec = flowpm.kernels.fftk((nc, nc, nc), symmetric=False)
kx = mtf.import_tf_tensor(mesh,
kvec[0].squeeze().astype('float32'),
shape=[tfx_dim])
ky = mtf.import_tf_tensor(mesh,
kvec[1].squeeze().astype('float32'),
shape=[tfy_dim])
kz = mtf.import_tf_tensor(mesh,
kvec[2].squeeze().astype('float32'),
shape=[tfz_dim])
kv = [ky, kz, kx]
# kvec for low resolution grid
kvec_lr = flowpm.kernels.fftk([nc, nc, nc], symmetric=False)
kx_lr = mtf.import_tf_tensor(mesh,
kvec_lr[0].squeeze().astype('float32'),
shape=[tx_dim])
ky_lr = mtf.import_tf_tensor(mesh,
kvec_lr[1].squeeze().astype('float32'),
shape=[ty_dim])
kz_lr = mtf.import_tf_tensor(mesh,
kvec_lr[2].squeeze().astype('float32'),
shape=[tz_dim])
kv_lr = [ky_lr, kz_lr, kx_lr]
shape = [batch_dim, fx_dim, fy_dim, fz_dim]
lr_shape = [batch_dim, fx_dim, fy_dim, fz_dim]
hr_shape = [batch_dim, nx_dim, ny_dim, nz_dim, sx_dim, sy_dim, sz_dim]
part_shape = [batch_dim, fx_dim, fy_dim, fz_dim]
# Begin simulation
initc = mtfpm.linear_field(mesh, shape, bs, nc, pk, kv)
# # Reshaping array into high resolution mesh
# field = mtf.slicewise(lambda x:tf.expand_dims(tf.expand_dims(tf.expand_dims(x, axis=1),axis=1),axis=1),
# [initc],
# output_dtype=tf.float32,
# output_shape=hr_shape,
# name='my_reshape',
# splittable_dims=lr_shape[:-1]+hr_shape[1:4]+part_shape[1:3])
#
state = mtfpm.lpt_init_single(
initc,
a0,
kv_lr,
halo_size,
lr_shape,
hr_shape,
part_shape[1:],
antialias=True,
)
# Here we can run our nbody
final_state = state #mtfpm.nbody(state, stages, lr_shape, hr_shape, k_dims, kv_lr, kv_hr, halo_size, downsampling_factor=downsampling_factor)
# paint the field
final_field = mtf.zeros(mesh, shape=hr_shape)
for block_size_dim in hr_shape[-3:]:
final_field = mtf.pad(final_field, [halo_size, halo_size],
block_size_dim.name)
final_field = mesh_utils.cic_paint(final_field, final_state[0], halo_size)
# Halo exchange
for blocks_dim, block_size_dim in zip(hr_shape[1:4],
final_field.shape[-3:]):
final_field = mpm.halo_reduce(final_field, blocks_dim, block_size_dim,
halo_size)
# Remove borders
for block_size_dim in hr_shape[-3:]:
final_field = mtf.slice(final_field, halo_size, block_size_dim.size,
block_size_dim.name)
#final_field = mtf.reshape(final_field, [batch_dim, fx_dim, fy_dim, fz_dim])
# Hack usisng custom reshape because mesh is pretty dumb
final_field = mtf.slicewise(
lambda x: x[:, 0, 0, 0], [final_field],
output_dtype=tf.float32,
output_shape=[batch_dim, fx_dim, fy_dim, fz_dim],
name='my_dumb_reshape',
splittable_dims=part_shape[:-1] + hr_shape[:4])
return initc, final_field
