update jaxdecomp version and test gradients

pyproject.toml

@@ -11,7 +11,7 @@ readme = "README.md"
 requires-python = ">=3.9"
 license = { file = "LICENSE" }
 urls = { "Homepage" = "https://github.com/DifferentiableUniverseInitiative/JaxPM" }
-dependencies = ["jax_cosmo", "jax>=0.4.30", "jaxdecomp>=0.2.2"]
+dependencies = ["jax_cosmo", "jax>=0.4.35", "jaxdecomp>=0.2.3"]
 
 [tool.setuptools]
 packages = ["jaxpm"]

tests/test_gradients.py

@@ -0,0 +1,115 @@
+import pytest
+from diffrax import Dopri5, ODETerm, PIDController, SaveAt, diffeqsolve
+from helpers import MSE
+from jax import numpy as jnp
+
+from jaxpm.distributed import uniform_particles
+from jaxpm.painting import cic_paint, cic_paint_dx
+from jaxpm.pm import lpt, make_diffrax_ode
+import jax
+
+
+@pytest.mark.single_device
+@pytest.mark.parametrize("order", [1, 2])
+def test_grad_relative(simulation_config, initial_conditions,
+                        lpt_scale_factor, nbody_from_lpt1, nbody_from_lpt2,
+                        cosmo, order):
+
+    mesh_shape, _ = simulation_config
+    cosmo._workspace = {}
+    
+    @jax.jit
+    @jax.grad
+    def forward_model(initial_conditions, cosmo):
+
+      # Initial displacement
+      dx, p, _ = lpt(cosmo, initial_conditions, a=lpt_scale_factor, order=order)
+
+      ode_fn = ODETerm(
+          make_diffrax_ode(cosmo, mesh_shape, paint_absolute_pos=False))
+
+      solver = Dopri5()
+      controller = PIDController(rtol=1e-7,
+                                 atol=1e-7,
+                                 pcoeff=0.4,
+                                 icoeff=1,
+                                 dcoeff=0)
+
+      saveat = SaveAt(t1=True)
+
+      y0 = jnp.stack([dx, p])
+
+      solutions = diffeqsolve(ode_fn,
+                              solver,
+                              t0=lpt_scale_factor,
+                              t1=1.0,
+                              dt0=None,
+                              y0=y0,
+                              stepsize_controller=controller,
+                              saveat=saveat)
+
+      final_field = cic_paint_dx(solutions.ys[-1, 0])
+
+      return MSE(final_field, nbody_from_lpt1 if order == 1 else nbody_from_lpt2)
+
+      
+    bad_initial_conditions = initial_conditions + jax.random.normal(jax.random.PRNGKey(0), initial_conditions.shape) * 0.5
+    best_ic = forward_model(initial_conditions , cosmo)
+    bad_ic = forward_model(bad_initial_conditions, cosmo)
+
+    assert jnp.max(best_ic) < 1e-5
+    assert jnp.max(bad_ic) > 1e-5
+
+@pytest.mark.single_device
+@pytest.mark.parametrize("order", [1, 2])
+def test_grad_absolute(simulation_config, initial_conditions,
+                        lpt_scale_factor, nbody_from_lpt1, nbody_from_lpt2,
+                        cosmo, order):
+
+    mesh_shape, _ = simulation_config
+    cosmo._workspace = {}
+    
+    @jax.jit
+    @jax.grad
+    def forward_model(initial_conditions, cosmo):
+
+      # Initial displacement
+      particles = uniform_particles(mesh_shape)
+      dx, p, _ = lpt(cosmo, initial_conditions,particles, a=lpt_scale_factor, order=order)
+
+      ode_fn = ODETerm(
+          make_diffrax_ode(cosmo, mesh_shape, paint_absolute_pos=True))
+
+      solver = Dopri5()
+      controller = PIDController(rtol=1e-7,
+                                 atol=1e-7,
+                                 pcoeff=0.4,
+                                 icoeff=1,
+                                 dcoeff=0)
+
+      saveat = SaveAt(t1=True)
+
+      y0 = jnp.stack([particles + dx, p])
+
+      solutions = diffeqsolve(ode_fn,
+                              solver,
+                              t0=lpt_scale_factor,
+                              t1=1.0,
+                              dt0=None,
+                              y0=y0,
+                              stepsize_controller=controller,
+                              saveat=saveat)
+
+      final_field = cic_paint(jnp.zeros(mesh_shape), solutions.ys[-1, 0])
+
+      return MSE(final_field, nbody_from_lpt1 if order == 1 else nbody_from_lpt2)
+
+      
+    bad_initial_conditions = initial_conditions + jax.random.normal(jax.random.PRNGKey(0), initial_conditions.shape) * 0.5
+    best_ic = forward_model(initial_conditions , cosmo)
+    bad_ic = forward_model(bad_initial_conditions, cosmo)
+
+    assert jnp.max(best_ic) < 1e-5
+    assert jnp.max(bad_ic) > 1e-5
+
+