final touches

jaxpm/painting.py

@@ -12,7 +12,7 @@ from jaxpm.kernels import cic_compensation, fftk
 from jaxpm.painting_utils import gather, scatter
 
 
-def _cic_paint_impl(grid_mesh, positions, weight=None):
+def _cic_paint_impl(grid_mesh, positions, weight=1.):
     """ Paints positions onto mesh
     mesh: [nx, ny, nz]
     displacement field: [nx, ny, nz, 3]
@@ -27,12 +27,10 @@ def _cic_paint_impl(grid_mesh, positions, weight=None):
     neighboor_coords = floor + connection
     kernel = 1. - jnp.abs(positions - neighboor_coords)
     kernel = kernel[..., 0] * kernel[..., 1] * kernel[..., 2]
-    if weight is not None:
+    if jnp.isscalar(weight):
-        if jnp.isscalar(weight):
+        kernel = jnp.multiply(jnp.expand_dims(weight, axis=-1), kernel)
-            kernel = jnp.multiply(jnp.expand_dims(weight, axis=-1), kernel)
+    else:
-        else:
+        kernel = jnp.multiply(weight.reshape(*positions.shape[:-1]), kernel)
-            kernel = jnp.multiply(weight.reshape(*positions.shape[:-1]),
-                                  kernel)
 
     neighboor_coords = jnp.mod(
         neighboor_coords.reshape([-1, 8, 3]).astype('int32'),
@@ -48,7 +46,13 @@ def _cic_paint_impl(grid_mesh, positions, weight=None):
 
 
 @partial(jax.jit, static_argnums=(3, 4))
-def cic_paint(grid_mesh, positions, weight=None, halo_size=0, sharding=None):
+def cic_paint(grid_mesh, positions, weight=1., halo_size=0, sharding=None):
+
+    if sharding is not None:
+        print("""
+            WARNING : absolute painting is not recommended in multi-device mode.
+            Please use relative painting instead.
+            """)
 
     positions = positions.reshape((*grid_mesh.shape, 3))
 
@@ -57,9 +61,11 @@ def cic_paint(grid_mesh, positions, weight=None, halo_size=0, sharding=None):
 
     gpu_mesh = sharding.mesh if isinstance(sharding, NamedSharding) else None
     spec = sharding.spec if isinstance(sharding, NamedSharding) else P()
+    weight_spec = P() if jnp.isscalar(weight) else spec
+
     grid_mesh = autoshmap(_cic_paint_impl,
                           gpu_mesh=gpu_mesh,
-                          in_specs=(spec, spec, P()),
+                          in_specs=(spec, spec, weight_spec),
                           out_specs=spec)(grid_mesh, positions, weight)
     grid_mesh = halo_exchange(grid_mesh,
                               halo_extents=halo_extents,
@@ -151,7 +157,10 @@ def cic_paint_2d(mesh, positions, weight):
     return mesh
 
 
-def _cic_paint_dx_impl(displacements, halo_size, weight=1., chunk_size=2**24):
+def _cic_paint_dx_impl(displacements,
+                       weight=1.,
+                       halo_size=0,
+                       chunk_size=2**24):
 
     halo_x, _ = halo_size[0]
     halo_y, _ = halo_size[1]
@@ -190,13 +199,13 @@ def cic_paint_dx(displacements,
 
     gpu_mesh = sharding.mesh if isinstance(sharding, NamedSharding) else None
     spec = sharding.spec if isinstance(sharding, NamedSharding) else P()
+    weight_spec = P() if jnp.isscalar(weight) else spec
     grid_mesh = autoshmap(partial(_cic_paint_dx_impl,
                                   halo_size=halo_size,
-                                  weight=weight,
                                   chunk_size=chunk_size),
                           gpu_mesh=gpu_mesh,
-                          in_specs=spec,
+                          in_specs=(spec, weight_spec),
-                          out_specs=spec)(displacements)
+                          out_specs=spec)(displacements, weight)
 
     grid_mesh = halo_exchange(grid_mesh,
                               halo_extents=halo_extents,

notebooks/README.md

@@ -37,3 +37,50 @@ Each notebook includes installation instructions and guidelines for configuring
 - **SLURM** for job scheduling on clusters (if running multi-host setups)
 
 > **Note**: These notebooks are tested on the **Jean Zay** supercomputer and may require configuration changes for different HPC clusters.
+
+## Caveats
+
+### Cloud-in-Cell (CIC) Painting (Single Device)
+
+There is two ways to perform the CIC painting in JAXPM. The first one is to use the `cic_paint` which paints absolute particle positions to the mesh. The second one is to use the `cic_paint_dx` which paints relative particle positions to the mesh (using uniform particles). The absolute version is faster at the cost of more memory usage.
+
+inorder to use relative painting you need to :
+
+ - Set the `particles` argument in `lpt` function from `jaxpm.pm` to `None`
+ - Set `paint_absolute_pos` to `False` in `make_ode_fn` or `make_diffrax_ode` function from `jaxpm.pm` (it is True by default)
+
+Otherwise you set `particles` to the starting particles of your choice and leave `paint_absolute_pos` to `True` (default value).
+
+### Cloud-in-Cell (CIC) Painting (Multi Device)
+
+Both `cic_paint` and `cic_paint_dx` functions are available in multi-device mode.
+
+You need to set the arguments `sharding` and `halo_size` which is explained in the notebook [03-MultiGPU_PM_Halo.ipynb](03-MultiGPU_PM_Halo.ipynb).
+
+One thing to note that `cic_paint` is not as accurate as `cic_paint_dx` in multi-device mode and therefor is not recommended.
+
+Using relative painting in multi-device mode is just like in single device mode.\
+You need to set the `particles` argument in `lpt` function from `jaxpm.pm` to `None` and set `paint_absolute_pos` to `False`
+
+### Distributed PM
+
+To run a distributed PM follow the examples in notebooks [03](03-MultiGPU_PM_Halo.ipynb) and [05](05-MultiHost_PM.ipynb) for multi-host.
+
+In short you need to set the arguments `sharding` and `halo_size` in `lpt` , `linear_field` the `make_ode` functions and `pm_forces` if you use it.
+
+Missmatching the shardings will give you errors and unexpected results.
+
+You can also use `normal_field` and `uniform_particles` from `jaxpm.pm.distributed` to create the fields and particles with a sharding.
+
+### Choosing the right pdims
+
+pdims are processor dimensions.\
+Explained more in the jaxdecomp paper [here](https://github.com/DifferentiableUniverseInitiative/jaxDecomp).
+
+For 8 devices there are three decompositions that are possible:
+- (1 , 8)
+- (2 , 4) , (4 , 2)
+- (8 , 1)
+
+(1 , X) should be the fastest (2 , X) or (X , 2) is more accurate but slightly slower.\
+and (X , 1) is giving the least accurate results for some reason so it is not recommended.

tests/test_against_fpm.py

@@ -76,7 +76,7 @@ def test_nbody_absolute(simulation_config, initial_conditions,
                    a=lpt_scale_factor,
                    order=order)
 
-    ode_fn = ODETerm(make_diffrax_ode(cosmo, mesh_shape))
+    ode_fn = ODETerm(make_diffrax_ode(mesh_shape))
 
     solver = Dopri5()
     controller = PIDController(rtol=1e-8,
@@ -95,6 +95,7 @@ def test_nbody_absolute(simulation_config, initial_conditions,
                             t1=1.0,
                             dt0=None,
                             y0=y0,
+                            args=cosmo,
                             stepsize_controller=controller,
                             saveat=saveat)
 
@@ -121,8 +122,7 @@ def test_nbody_relative(simulation_config, initial_conditions,
     # Initial displacement
     dx, p, _ = lpt(cosmo, initial_conditions, a=lpt_scale_factor, order=order)
 
-    ode_fn = ODETerm(
+    ode_fn = ODETerm(make_diffrax_ode(mesh_shape, paint_absolute_pos=False))
-        make_diffrax_ode(cosmo, mesh_shape, paint_absolute_pos=False))
 
     solver = Dopri5()
     controller = PIDController(rtol=1e-9,
@@ -141,6 +141,7 @@ def test_nbody_relative(simulation_config, initial_conditions,
                             t1=1.0,
                             dt0=None,
                             y0=y0,
+                            args=cosmo,
                             stepsize_controller=controller,
                             saveat=saveat)
 

tests/test_distributed_pm.py

@@ -2,8 +2,11 @@ from conftest import initialize_distributed
 
 initialize_distributed()  # ignore : E402
 
+from functools import partial  # noqa : E402
+
 import jax  # noqa : E402
 import jax.numpy as jnp  # noqa : E402
+import jax_cosmo as jc  # noqa : E402
 import pytest  # noqa : E402
 from diffrax import SaveAt  # noqa : E402
 from diffrax import Dopri5, ODETerm, PIDController, diffeqsolve
@@ -12,19 +15,31 @@ from jax import lax  # noqa : E402
 from jax.experimental.multihost_utils import process_allgather  # noqa : E402
 from jax.sharding import NamedSharding
 from jax.sharding import PartitionSpec as P  # noqa : E402
+from jaxdecomp import get_fft_output_sharding
 
 from jaxpm.distributed import uniform_particles  # noqa : E402
+from jaxpm.distributed import fft3d, ifft3d
 from jaxpm.painting import cic_paint, cic_paint_dx  # noqa : E402
-from jaxpm.pm import lpt, make_diffrax_ode  # noqa : E402
+from jaxpm.pm import lpt, make_diffrax_ode, pm_forces  # noqa : E402
 
-_TOLERANCE = 3.0  # 🙃🙃
+_TOLERANCE = 1e-1  # 🙃🙃
+
+pdims = [(1, 8), (8, 1), (4, 2), (2, 4)]
 
 
 @pytest.mark.distributed
 @pytest.mark.parametrize("order", [1, 2])
+@pytest.mark.parametrize("pdims", pdims)
 @pytest.mark.parametrize("absolute_painting", [True, False])
 def test_distrubted_pm(simulation_config, initial_conditions, cosmo, order,
-                       absolute_painting):
+                       pdims, absolute_painting):
+
+    if absolute_painting:
+        pytest.skip("Absolute painting is not recommended in distributed mode")
+
+    painting_str = "absolute" if absolute_painting else "relative"
+    print("=" * 50)
+    print(f"Running with {painting_str} painting and pdims {pdims} ...")
 
     mesh_shape, box_shape = simulation_config
     # SINGLE DEVICE RUN
@@ -60,6 +75,7 @@ def test_distrubted_pm(simulation_config, initial_conditions, cosmo, order,
                             t1=1.0,
                             dt0=None,
                             y0=y0,
+                            args=cosmo,
                             stepsize_controller=controller,
                             saveat=saveat)
 
@@ -72,7 +88,7 @@ def test_distrubted_pm(simulation_config, initial_conditions, cosmo, order,
     print("Done with single device run")
     # MULTI DEVICE RUN
 
-    mesh = jax.make_mesh((1, 8), ('x', 'y'))
+    mesh = jax.make_mesh(pdims, ('x', 'y'))
     sharding = NamedSharding(mesh, P('x', 'y'))
     halo_size = mesh_shape[0] // 2
 
@@ -128,16 +144,23 @@ def test_distrubted_pm(simulation_config, initial_conditions, cosmo, order,
                             t1=1.0,
                             dt0=None,
                             y0=y0,
+                            args=cosmo,
                             stepsize_controller=controller,
                             saveat=saveat)
 
+    final_field = solutions.ys[-1, 0]
+    print(f"Final field sharding is {final_field.sharding}")
+
+    assert final_field.sharding.is_equivalent_to(sharding , ndim=3) \
+        , f"Final field sharding is not correct .. should be {sharding} it is instead {final_field.sharding}"
+
     if absolute_painting:
         multi_device_final_field = cic_paint(jnp.zeros(shape=mesh_shape),
-                                             solutions.ys[-1, 0],
+                                             final_field,
                                              halo_size=halo_size,
                                              sharding=sharding)
     else:
-        multi_device_final_field = cic_paint_dx(solutions.ys[-1, 0],
+        multi_device_final_field = cic_paint_dx(final_field,
                                                 halo_size=halo_size,
                                                 sharding=sharding)
 
@@ -148,3 +171,230 @@ def test_distrubted_pm(simulation_config, initial_conditions, cosmo, order,
     print(f"MSE is  {mse}")
 
     assert mse < _TOLERANCE
+
+
+@pytest.mark.distributed
+@pytest.mark.parametrize("order", [1, 2])
+@pytest.mark.parametrize("pdims", pdims)
+def test_distrubted_gradients(simulation_config, initial_conditions, cosmo,
+                              order, nbody_from_lpt1, nbody_from_lpt2, pdims):
+
+    mesh_shape, box_shape = simulation_config
+    # SINGLE DEVICE RUN
+    cosmo._workspace = {}
+
+    mesh = jax.make_mesh(pdims, ('x', 'y'))
+    sharding = NamedSharding(mesh, P('x', 'y'))
+    halo_size = mesh_shape[0] // 2
+
+    initial_conditions = lax.with_sharding_constraint(initial_conditions,
+                                                      sharding)
+
+    print(f"sharded initial conditions {initial_conditions.sharding}")
+    cosmo._workspace = {}
+
+    @jax.jit
+    def forward_model(initial_conditions, cosmo):
+
+        dx, p, _ = lpt(cosmo,
+                       initial_conditions,
+                       a=0.1,
+                       order=order,
+                       halo_size=halo_size,
+                       sharding=sharding)
+        ode_fn = ODETerm(
+            make_diffrax_ode(mesh_shape,
+                             paint_absolute_pos=False,
+                             halo_size=halo_size,
+                             sharding=sharding))
+        y0 = jax.tree.map(lambda dx, p: jnp.stack([dx, p]), dx, p)
+
+        solver = Dopri5()
+        controller = PIDController(rtol=1e-8,
+                                   atol=1e-8,
+                                   pcoeff=0.4,
+                                   icoeff=1,
+                                   dcoeff=0)
+
+        saveat = SaveAt(t1=True)
+        solutions = diffeqsolve(ode_fn,
+                                solver,
+                                t0=0.1,
+                                t1=1.0,
+                                dt0=None,
+                                y0=y0,
+                                args=cosmo,
+                                stepsize_controller=controller,
+                                saveat=saveat)
+
+        multi_device_final_field = cic_paint_dx(solutions.ys[-1, 0],
+                                                halo_size=halo_size,
+                                                sharding=sharding)
+
+        return multi_device_final_field
+
+    @jax.jit
+    def model(initial_conditions, cosmo):
+        final_field = forward_model(initial_conditions, cosmo)
+        return MSE(final_field,
+                   nbody_from_lpt1 if order == 1 else nbody_from_lpt2)
+
+    obs_val = model(initial_conditions, cosmo)
+
+    shifted_initial_conditions = initial_conditions + jax.random.normal(
+        jax.random.key(42), initial_conditions.shape) * 5
+
+    good_grads = jax.grad(model)(initial_conditions, cosmo)
+    off_grads = jax.grad(model)(shifted_initial_conditions, cosmo)
+
+    assert good_grads.sharding.is_equivalent_to(initial_conditions.sharding,
+                                                ndim=3)
+    assert off_grads.sharding.is_equivalent_to(initial_conditions.sharding,
+                                               ndim=3)
+
+
+@pytest.mark.distributed
+@pytest.mark.parametrize("pdims", pdims)
+def test_fwd_rev_gradients(cosmo, pdims):
+
+    mesh_shape, box_shape = (8, 8, 8), (20.0, 20.0, 20.0)
+    cosmo._workspace = {}
+
+    mesh = jax.make_mesh(pdims, ('x', 'y'))
+    sharding = NamedSharding(mesh, P('x', 'y'))
+    halo_size = mesh_shape[0] // 2
+
+    initial_conditions = jax.random.normal(jax.random.PRNGKey(42), mesh_shape)
+    initial_conditions = lax.with_sharding_constraint(initial_conditions,
+                                                      sharding)
+    print(f"sharded initial conditions {initial_conditions.sharding}")
+    cosmo._workspace = {}
+
+    @partial(jax.jit, static_argnums=(2, 3, 4))
+    def compute_forces(initial_conditions,
+                       cosmo,
+                       a=0.5,
+                       halo_size=0,
+                       sharding=None):
+
+        paint_absolute_pos = False
+        particles = jnp.zeros_like(initial_conditions,
+                                   shape=(*initial_conditions.shape, 3))
+
+        a = jnp.atleast_1d(a)
+        E = jnp.sqrt(jc.background.Esqr(cosmo, a))
+
+        initial_conditions = jax.lax.with_sharding_constraint(
+            initial_conditions, sharding)
+        delta_k = fft3d(initial_conditions)
+        out_sharding = get_fft_output_sharding(sharding)
+        delta_k = jax.lax.with_sharding_constraint(delta_k, out_sharding)
+
+        initial_force = pm_forces(particles,
+                                  delta=delta_k,
+                                  paint_absolute_pos=paint_absolute_pos,
+                                  halo_size=halo_size,
+                                  sharding=sharding)
+
+        return initial_force[..., 0]
+
+    forces = compute_forces(initial_conditions,
+                            cosmo,
+                            halo_size=halo_size,
+                            sharding=sharding)
+    back_gradient = jax.jacrev(compute_forces)(initial_conditions,
+                                               cosmo,
+                                               halo_size=halo_size,
+                                               sharding=sharding)
+    fwd_gradient = jax.jacfwd(compute_forces)(initial_conditions,
+                                              cosmo,
+                                              halo_size=halo_size,
+                                              sharding=sharding)
+
+    print(f"Forces sharding is {forces.sharding}")
+    print(f"Backward gradient sharding is {back_gradient.sharding}")
+    print(f"Forward gradient sharding is {fwd_gradient.sharding}")
+    assert forces.sharding.is_equivalent_to(initial_conditions.sharding,
+                                            ndim=3)
+    assert back_gradient[0, 0, 0, ...].sharding.is_equivalent_to(
+        initial_conditions.sharding, ndim=3)
+    assert fwd_gradient.sharding.is_equivalent_to(initial_conditions.sharding,
+                                                  ndim=3)
+
+
+@pytest.mark.distributed
+@pytest.mark.parametrize("pdims", pdims)
+def test_vmap(cosmo, pdims):
+
+    mesh_shape, box_shape = (8, 8, 8), (20.0, 20.0, 20.0)
+    cosmo._workspace = {}
+
+    mesh = jax.make_mesh(pdims, ('x', 'y'))
+    sharding = NamedSharding(mesh, P('x', 'y'))
+    halo_size = mesh_shape[0] // 2
+
+    single_dev_initial_conditions = jax.random.normal(jax.random.PRNGKey(42),
+                                                      mesh_shape)
+    initial_conditions = lax.with_sharding_constraint(
+        single_dev_initial_conditions, sharding)
+
+    single_ics = jnp.stack([
+        single_dev_initial_conditions, single_dev_initial_conditions,
+        single_dev_initial_conditions
+    ])
+    sharded_ics = jnp.stack(
+        [initial_conditions, initial_conditions, initial_conditions])
+    print(f"unsharded initial conditions batch {single_ics.sharding}")
+    print(f"sharded initial conditions batch {sharded_ics.sharding}")
+    cosmo._workspace = {}
+
+    @partial(jax.jit, static_argnums=(2, 3, 4))
+    def compute_forces(initial_conditions,
+                       cosmo,
+                       a=0.5,
+                       halo_size=0,
+                       sharding=None):
+
+        paint_absolute_pos = False
+        particles = jnp.zeros_like(initial_conditions,
+                                   shape=(*initial_conditions.shape, 3))
+
+        a = jnp.atleast_1d(a)
+        E = jnp.sqrt(jc.background.Esqr(cosmo, a))
+
+        initial_conditions = jax.lax.with_sharding_constraint(
+            initial_conditions, sharding)
+        delta_k = fft3d(initial_conditions)
+        out_sharding = get_fft_output_sharding(sharding)
+        delta_k = jax.lax.with_sharding_constraint(delta_k, out_sharding)
+
+        initial_force = pm_forces(particles,
+                                  delta=delta_k,
+                                  paint_absolute_pos=paint_absolute_pos,
+                                  halo_size=halo_size,
+                                  sharding=sharding)
+
+        return initial_force[..., 0]
+
+    def fn(ic):
+        return compute_forces(ic,
+                              cosmo,
+                              halo_size=halo_size,
+                              sharding=sharding)
+
+    v_compute_forces = jax.vmap(fn)
+
+    print(f"single_ics shape {single_ics.shape}")
+    print(f"sharded_ics shape {sharded_ics.shape}")
+
+    single_dev_forces = v_compute_forces(single_ics)
+    sharded_forces = v_compute_forces(sharded_ics)
+
+    assert single_dev_forces.ndim == 4
+    assert sharded_forces.ndim == 4
+
+    print(f"Sharded forces {sharded_forces.sharding}")
+
+    assert sharded_forces[0].sharding.is_equivalent_to(
+        initial_conditions.sharding, ndim=3)
+    assert sharded_forces.sharding.spec[0] == None

tests/test_gradients.py

@@ -39,7 +39,7 @@ def test_nbody_grad(simulation_config, initial_conditions, lpt_scale_factor,
                            particles,
                            a=lpt_scale_factor,
                            order=order)
-            ode_fn = ODETerm(make_diffrax_ode(cosmo, mesh_shape))
+            ode_fn = ODETerm(make_diffrax_ode(mesh_shape))
             y0 = jnp.stack([particles + dx, p])
 
         else:
@@ -48,7 +48,7 @@ def test_nbody_grad(simulation_config, initial_conditions, lpt_scale_factor,
                            a=lpt_scale_factor,
                            order=order)
             ode_fn = ODETerm(
-                make_diffrax_ode(cosmo, mesh_shape, paint_absolute_pos=False))
+                make_diffrax_ode(mesh_shape, paint_absolute_pos=False))
             y0 = jnp.stack([dx, p])
 
         solver = Dopri5()
@@ -66,6 +66,7 @@ def test_nbody_grad(simulation_config, initial_conditions, lpt_scale_factor,
                                 t1=1.0,
                                 dt0=None,
                                 y0=y0,
+                                args=cosmo,
                                 adjoint=adjoint,
                                 stepsize_controller=controller,
                                 saveat=saveat)