Merge e666aada42 into cb2a7ab17f

.github/workflows/formatting.yml

@@ -7,15 +7,37 @@ on:
     branches: [ "main" ]
 
 jobs:
-  build:
+  formatting:
     runs-on: ubuntu-latest
     steps:
-    - uses: actions/checkout@v4
-    - name: Set up Python ${{ matrix.python-version }}
-      uses: actions/setup-python@v3
+    - name: Checkout Source
+      uses: actions/checkout@v4
+
+    - name: Set up Python
+      uses: actions/setup-python@v5
+      with:
+        python-version: "3.11"
+
+    - name: Cache pip dependencies
+      uses: actions/cache@v4
+      with:
+        path: ~/.cache/pip
+        key: ${{ runner.os }}-formatting-pip-${{ hashFiles('.pre-commit-config.yaml') }}
+        restore-keys: |
+          ${{ runner.os }}-formatting-pip-
+
+    - name: Cache pre-commit
+      uses: actions/cache@v4
+      with:
+        path: ~/.cache/pre-commit
+        key: ${{ runner.os }}-pre-commit-${{ hashFiles('.pre-commit-config.yaml') }}
+        restore-keys: |
+          ${{ runner.os }}-pre-commit-
+
     - name: Install dependencies
       run: |
-        python -m pip install --upgrade pip isort
-        python -m pip install pre-commit
+        python -m pip install --upgrade pip
+        python -m pip install pre-commit isort
+
     - name: Run pre-commit
       run: python -m pre_commit run --all-files

.github/workflows/tests.yml

@@ -10,37 +10,63 @@ on:
 
 jobs:
   run_tests:
-
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: ["3.10" , "3.11" , "3.12"]
+        python-version: ["3.10", "3.11", "3.12"]
 
     steps:
     - name: Checkout Source
-      uses: actions/checkout@v2.3.1
+      uses: actions/checkout@v4
 
     - name: Set up Python ${{ matrix.python-version }}
-      uses: actions/setup-python@v2
+      uses: actions/setup-python@v5
       with:
         python-version: ${{ matrix.python-version }}
 
-    - name: Install dependencies
+    - name: Cache pip dependencies
+      uses: actions/cache@v4
+      with:
+        path: ~/.cache/pip
+        key: ${{ runner.os }}-pip-${{ matrix.python-version }}-${{ hashFiles('**/requirements-test.txt', '**/pyproject.toml') }}
+        restore-keys: |
+          ${{ runner.os }}-pip-${{ matrix.python-version }}-
+          ${{ runner.os }}-pip-
+
+    - name: Cache system dependencies
+      uses: actions/cache@v4
+      with:
+        path: /var/cache/apt
+        key: ${{ runner.os }}-apt-${{ hashFiles('.github/workflows/tests.yml') }}
+        restore-keys: |
+          ${{ runner.os }}-apt-
+
+    - name: Install system dependencies
       run: |
+        sudo apt-get update
         sudo apt-get install -y libopenmpi-dev
-        python -m pip install --upgrade pip
-        pip install jax==0.4.35
-        pip install numpy setuptools cython wheel
-        pip install git+https://github.com/MP-Gadget/pfft-python
-        pip install git+https://github.com/MP-Gadget/pmesh
-        pip install git+https://github.com/ASKabalan/fastpm-python --no-build-isolation
-        pip install -r requirements-test.txt
-        pip install .
+
+    - name: Install Python dependencies
+      run: |
+        python -m pip install --upgrade pip setuptools wheel
+        # Install JAX first as it's a key dependency
+        pip install jax
+        # Install build dependencies
+        pip install setuptools cython mpi4py
+        # Install test requirements with no-build-isolation for faster builds
+        pip install -r requirements-test.txt --no-build-isolation
+        # Install additional test dependencies
+        pip install pytest diffrax
+        # Install package in development mode
+        pip install -e .
+        echo "numpy version installed:"
+        python -c "import numpy; print(numpy.__version__)"
 
     - name: Run Single Device Tests
       run: |
         cd tests
         pytest -v -m "not distributed"
+
     - name: Run Distributed tests
       run: |
-        pytest -v -m distributed
+        pytest -v tests/test_distributed_pm.py

jaxpm/distributed.py

@@ -166,11 +166,11 @@ def uniform_particles(mesh_shape, sharding=None):
                          axis=-1)
 
 
-def normal_field(mesh_shape, seed, sharding=None):
+def normal_field(seed, shape, sharding=None, dtype=float):
     """Generate a Gaussian random field with the given power spectrum."""
     gpu_mesh = sharding.mesh if sharding is not None else None
     if gpu_mesh is not None and not (gpu_mesh.empty):
-        local_mesh_shape = get_local_shape(mesh_shape, sharding)
+        local_mesh_shape = get_local_shape(shape, sharding)
 
         size = jax.device_count()
         # rank = jax.process_index()
@@ -190,9 +190,9 @@ def normal_field(mesh_shape, seed, sharding=None):
             return jax.random.normal(key=keys[idx], shape=shape, dtype=dtype)
 
         return shard_map(
-            partial(normal, shape=local_mesh_shape, dtype='float32'),
+            partial(normal, shape=local_mesh_shape, dtype=dtype),
             mesh=gpu_mesh,
             in_specs=P(None),
             out_specs=spec)(keys)  # yapf: disable
     else:
-        return jax.random.normal(shape=mesh_shape, key=seed)
+        return jax.random.normal(shape=shape, key=seed, dtype=dtype)

jaxpm/growth.py

@@ -1,3 +1,5 @@
+import os
+
 import jax.numpy as np
 from jax.numpy import interp
 from jax_cosmo.background import *
@@ -119,7 +121,7 @@ def growth_factor(cosmo, a):
     if cosmo._flags["gamma_growth"]:
         return _growth_factor_gamma(cosmo, a)
     else:
-        return _growth_factor_ODE(cosmo, a)
+        return _growth_factor_ODE(cosmo, a)[0]
 
 
 def growth_factor_second(cosmo, a):
@@ -225,7 +227,7 @@ def growth_rate_second(cosmo, a):
         return _growth_rate_second_ODE(cosmo, a)
 
 
-def _growth_factor_ODE(cosmo, a, log10_amin=-3, steps=128, eps=1e-4):
+def _growth_factor_ODE(cosmo, a, log10_amin=-3, steps=256, eps=1e-4):
     """Compute linear growth factor D(a) at a given scale factor,
     normalised such that D(a=1) = 1.
 
@@ -243,7 +245,11 @@ def _growth_factor_ODE(cosmo, a, log10_amin=-3, steps=128, eps=1e-4):
         Growth factor computed at requested scale factor
     """
     # Check if growth has already been computed
-    if not "background.growth_factor" in cosmo._workspace.keys():
+    CACHING_ACTIVATED = os.environ.get("JC_CACHE", "1") == "1"
+    if CACHING_ACTIVATED and "background.growth_factor" in cosmo._workspace.keys(
+    ):
+        cache = cosmo._workspace["background.growth_factor"]
+    else:
         # Compute tabulated array
         atab = np.logspace(log10_amin, 0.0, steps)
 
@@ -290,10 +296,10 @@ def _growth_factor_ODE(cosmo, a, log10_amin=-3, steps=128, eps=1e-4):
             "f2": f2tab,
             "h2": h2tab,
         }
+        if CACHING_ACTIVATED:
             cosmo._workspace["background.growth_factor"] = cache
-    else:
-        cache = cosmo._workspace["background.growth_factor"]
-    return np.clip(interp(a, cache["a"], cache["g"]), 0.0, 1.0)
+
+    return np.clip(interp(a, cache["a"], cache["g"]), 0.0, 1.0), cache
 
 
 def _growth_rate_ODE(cosmo, a):
@@ -314,9 +320,8 @@ def _growth_rate_ODE(cosmo, a):
         Growth rate computed at requested scale factor
     """
     # Check if growth has already been computed, if not, compute it
-    if not "background.growth_factor" in cosmo._workspace.keys():
-        _growth_factor_ODE(cosmo, np.atleast_1d(1.0))
-    cache = cosmo._workspace["background.growth_factor"]
+
+    cache = _growth_factor_ODE(cosmo, np.atleast_1d(1.0))[1]
     return interp(a, cache["a"], cache["f"])
 
 
@@ -338,36 +343,12 @@ def _growth_factor_second_ODE(cosmo, a):
         Second order growth factor computed at requested scale factor
     """
     # Check if growth has already been computed, if not, compute it
-    if not "background.growth_factor" in cosmo._workspace.keys():
-        _growth_factor_ODE(cosmo, np.atleast_1d(1.0))
-    cache = cosmo._workspace["background.growth_factor"]
+    #if not "background.growth_factor" in cosmo._workspace.keys():
+    #    _growth_factor_ODE(cosmo, np.atleast_1d(1.0))
+    cache = _growth_factor_ODE(cosmo, a)[1]
     return interp(a, cache["a"], cache["g2"])
 
 
-def _growth_rate_ODE(cosmo, a):
-    """Compute growth rate dD/dlna at a given scale factor by solving the linear
-    growth ODE.
-
-    Parameters
-    ----------
-    cosmo: `Cosmology`
-      Cosmology object
-
-    a: array_like
-      Scale factor
-
-    Returns
-    -------
-    f:  ndarray, or float if input scalar
-        Second order growth rate computed at requested scale factor
-    """
-    # Check if growth has already been computed, if not, compute it
-    if not "background.growth_factor" in cosmo._workspace.keys():
-        _growth_factor_ODE(cosmo, np.atleast_1d(1.0))
-    cache = cosmo._workspace["background.growth_factor"]
-    return interp(a, cache["a"], cache["f"])
-
-
 def _growth_rate_second_ODE(cosmo, a):
     """Compute second order growth rate dD2/dlna at a given scale factor by solving the linear
     growth ODE.
@@ -386,9 +367,9 @@ def _growth_rate_second_ODE(cosmo, a):
         Second order growth rate computed at requested scale factor
     """
     # Check if growth has already been computed, if not, compute it
-    if not "background.growth_factor" in cosmo._workspace.keys():
-        _growth_factor_ODE(cosmo, np.atleast_1d(1.0))
-    cache = cosmo._workspace["background.growth_factor"]
+    #if not "background.growth_factor" in cosmo._workspace.keys():
+    #    _growth_factor_ODE(cosmo, np.atleast_1d(1.0))
+    cache = _growth_factor_ODE(cosmo, a)[1]
     return interp(a, cache["a"], cache["f2"])
 
 
@@ -411,7 +392,11 @@ def _growth_factor_gamma(cosmo, a, log10_amin=-3, steps=128):
 
     """
     # Check if growth has already been computed, if not, compute it
-    if not "background.growth_factor" in cosmo._workspace.keys():
+    CACHING_ACTIVATED = os.environ.get("JC_CACHE", "1") == "1"
+    if CACHING_ACTIVATED and "background.growth_factor" in cosmo._workspace.keys(
+    ):
+        cache = cosmo._workspace["background.growth_factor"]
+    else:
         # Compute tabulated array
         atab = np.logspace(log10_amin, 0.0, steps)
 
@@ -422,9 +407,8 @@ def _growth_factor_gamma(cosmo, a, log10_amin=-3, steps=128):
         gtab = np.exp(odeint(integrand, np.log(atab[0]), np.log(atab)))
         gtab = gtab / gtab[-1]  # Normalize to a=1.
         cache = {"a": atab, "g": gtab}
+        if CACHING_ACTIVATED:
             cosmo._workspace["background.growth_factor"] = cache
-    else:
-        cache = cosmo._workspace["background.growth_factor"]
     return np.clip(interp(a, cache["a"], cache["g"]), 0.0, 1.0)
 
 
@@ -521,6 +505,35 @@ def Gf2(cosmo, a):
     return D2f * np.power(a, 3) * np.power(Esqr(cosmo, a), 0.5)
 
 
+def gp(cosmo, a):
+    r""" Derivative of D1 against a
+
+    Parameters
+    ----------
+    cosmo: dict
+      Cosmology dictionary.
+
+    a : array_like
+       Scale factor.
+
+    Returns
+    -------
+    Scalar float Tensor : the derivative of D1 against a.
+
+    Notes
+    -----
+
+    The expression for :math:`gp(a)` is:
+
+    .. math::
+        gp(a)=\frac{dD1}{da}= D'_{1norm}/a
+    """
+    f1 = growth_rate(cosmo, a)
+    g1 = growth_factor(cosmo, a)
+    D1f = f1 * g1 / a
+    return D1f
+
+
 def dGfa(cosmo, a):
     r""" Derivative of Gf against a
 
@@ -549,7 +562,8 @@ def dGfa(cosmo, a):
     f1 = growth_rate(cosmo, a)
     g1 = growth_factor(cosmo, a)
     D1f = f1 * g1 / a
-    cache = cosmo._workspace['background.growth_factor']
+    #cache = cosmo._workspace['background.growth_factor']
+    cache = _growth_factor_ODE(cosmo, a)[1]
     f1p = cache['h'] / cache['a'] * cache['g']
     f1p = interp(np.log(a), np.log(cache['a']), f1p)
     Ea = E(cosmo, a)
@@ -584,7 +598,8 @@ def dGf2a(cosmo, a):
     f2 = growth_rate_second(cosmo, a)
     g2 = growth_factor_second(cosmo, a)
     D2f = f2 * g2 / a
-    cache = cosmo._workspace['background.growth_factor']
+    #cache = cosmo._workspace['background.growth_factor']
+    cache = _growth_factor_ODE(cosmo, a)[1]
     f2p = cache['h2'] / cache['a'] * cache['g2']
     f2p = interp(np.log(a), np.log(cache['a']), f2p)
     E_a = E(cosmo, a)

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):
         kernel = jnp.multiply(jnp.expand_dims(weight, axis=-1), kernel)
     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,
@@ -128,6 +134,7 @@ def cic_paint_2d(mesh, positions, weight):
     positions: [npart, 2]
     weight: [npart]
     """
+    positions = positions.reshape([-1, 2])
     positions = jnp.expand_dims(positions, 1)
     floor = jnp.floor(positions)
     connection = jnp.array([[0, 0], [1., 0], [0., 1], [1., 1]])
@@ -136,7 +143,7 @@ def cic_paint_2d(mesh, positions, weight):
     kernel = 1. - jnp.abs(positions - neighboor_coords)
     kernel = kernel[..., 0] * kernel[..., 1]
     if weight is not None:
-        kernel = kernel * weight[..., jnp.newaxis]
+        kernel = kernel * weight.reshape(*positions.shape[:-1])
 
     neighboor_coords = jnp.mod(
         neighboor_coords.reshape([-1, 4, 2]).astype('int32'),
@@ -151,13 +158,16 @@ 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]
 
     original_shape = displacements.shape
-    particle_mesh = jnp.zeros(original_shape[:-1], dtype='float32')
+    particle_mesh = jnp.zeros(original_shape[:-1], dtype=displacements.dtype)
     if not jnp.isscalar(weight):
         if weight.shape != original_shape[:-1]:
             raise ValueError("Weight shape must match particle shape")
@@ -175,7 +185,7 @@ def _cic_paint_dx_impl(displacements, halo_size, weight=1., chunk_size=2**24):
     return scatter(pmid.reshape([-1, 3]),
                    displacements.reshape([-1, 3]),
                    particle_mesh,
-                   chunk_size=2**24,
+                   chunk_size=chunk_size,
                    val=weight)
 
 
@@ -190,13 +200,13 @@ 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,
-                          out_specs=spec)(displacements)
+                          in_specs=(spec, weight_spec),
+                          out_specs=spec)(displacements, weight)
 
     grid_mesh = halo_exchange(grid_mesh,
                               halo_extents=halo_extents,
@@ -230,6 +240,12 @@ def _cic_read_dx_impl(grid_mesh, disp, halo_size):
 def cic_read_dx(grid_mesh, disp, halo_size=0, sharding=None):
 
     halo_size, halo_extents = get_halo_size(halo_size, sharding=sharding)
+    # Halo size is halved for the read operation
+    # We only need to read the density field
+    # while in the painting operation we need to exchange and reduce the halo
+    # We chose to do that since it is much easier to write a custom jvp rule for exchange
+    # while it is a bit harder if there is a reduction involved
+    halo_size = jax.tree.map(lambda x: x // 2, halo_size)
     grid_mesh = slice_pad(grid_mesh, halo_size, sharding=sharding)
     grid_mesh = halo_exchange(grid_mesh,
                               halo_extents=halo_extents,

jaxpm/painting_utils.py

@@ -30,15 +30,12 @@ def enmesh(base_indices, displacements, cell_size, base_shape, offset,
     """Multilinear enmeshing."""
     base_indices = jnp.asarray(base_indices)
     displacements = jnp.asarray(displacements)
-    with jax.experimental.enable_x64():
-        cell_size = jnp.float64(
-            cell_size) if new_cell_size is not None else jnp.array(
-                cell_size, dtype=displacements.dtype)
+    cell_size = jnp.array(cell_size, dtype=displacements.dtype)
     if base_shape is not None:
         base_shape = jnp.array(base_shape, dtype=base_indices.dtype)
-        offset = jnp.float64(offset)
+    offset = offset.astype(base_indices.dtype)
     if new_cell_size is not None:
-            new_cell_size = jnp.float64(new_cell_size)
+        new_cell_size = jnp.array(new_cell_size, dtype=displacements.dtype)
     if new_shape is not None:
         new_shape = jnp.array(new_shape, dtype=base_indices.dtype)
 

jaxpm/pm.py

@@ -131,7 +131,7 @@ def linear_field(mesh_shape, box_size, pk, seed, sharding=None):
     Generate initial conditions.
     """
     # Initialize a random field with one slice on each gpu
-    field = normal_field(mesh_shape, seed=seed, sharding=sharding)
+    field = normal_field(seed=seed, shape=mesh_shape, sharding=sharding)
     field = fft3d(field)
     kvec = fftk(field)
     kmesh = sum((kk / box_size[i] * mesh_shape[i])**2
@@ -172,8 +172,7 @@ def make_ode_fn(mesh_shape,
     return nbody_ode
 
 
-def make_diffrax_ode(cosmo,
-                     mesh_shape,
+def make_diffrax_ode(mesh_shape,
                      paint_absolute_pos=True,
                      halo_size=0,
                      sharding=None):
@@ -183,6 +182,7 @@ def make_diffrax_ode(cosmo,
         state is a tuple (position, velocities)
         """
         pos, vel = state
+        cosmo = args
 
         forces = pm_forces(pos,
                            mesh_shape=mesh_shape,

notebooks/04-MultiGPU_PM_Solvers.ipynb

@@ -62,7 +62,7 @@
     "\n",
     "This cell configures a **2x4 device mesh** across 8 devices and sets up named sharding to distribute data efficiently.\n",
     "\n",
-    "- **Device Mesh**: `pdims = (2, 4)` arranges devices in a 2x4 grid. `create_device_mesh(pdims)` initializes this layout across available GPUs.\n",
+    "- **Device Mesh**: `pdims = (2, 4)` arranges devices in a 2x4 grid.\n",
     "- **Sharding with Mesh**: `Mesh(devices, axis_names=('x', 'y'))` assigns the mesh grid axes, which allows flexible mapping of array data across devices.\n",
     "- **PartitionSpec and NamedSharding**: `PartitionSpec` defines data partitioning across mesh axes `('x', 'y')`, and `NamedSharding(mesh, P('x', 'y'))` specifies this sharding scheme for arrays in the simulation.\n",
     "\n",
@@ -71,7 +71,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -80,11 +80,10 @@
     "from jax.sharding import Mesh, NamedSharding\n",
     "from jax.sharding import PartitionSpec as P\n",
     "\n",
-    "all_gather = partial(process_allgather, tiled=False)\n",
+    "all_gather = partial(process_allgather, tiled=True)\n",
     "\n",
     "pdims = (2, 4)\n",
-    "devices = create_device_mesh(pdims)\n",
-    "mesh = Mesh(devices, axis_names=('x', 'y'))\n",
+    "mesh = jax.make_mesh(pdims, axis_names=('x', 'y'))\n",
     "sharding = NamedSharding(mesh, P('x', 'y'))"
    ]
   },
@@ -124,7 +123,7 @@
     "\n",
     "    # Evolve the simulation forward\n",
     "    ode_fn = ODETerm(\n",
-    "        make_diffrax_ode(cosmo, mesh_shape, paint_absolute_pos=False))\n",
+    "        make_diffrax_ode(mesh_shape, paint_absolute_pos=False,sharding=sharding , halo_size=halo_size))\n",
     "    solver = LeapfrogMidpoint()\n",
     "\n",
     "    stepsize_controller = ConstantStepSize()\n",
@@ -288,7 +287,7 @@
     "\n",
     "    # Evolve the simulation forward\n",
     "    ode_fn = ODETerm(\n",
-    "        make_diffrax_ode(cosmo, mesh_shape, paint_absolute_pos=False))\n",
+    "        make_diffrax_ode(mesh_shape, paint_absolute_pos=False,sharding=sharding , halo_size=halo_size))\n",
     "    solver = Dopri5()\n",
     "\n",
     "    stepsize_controller = PIDController(rtol=1e-5,atol=1e-5)\n",

notebooks/05-MultiHost_PM.py

@@ -17,9 +17,8 @@ import jax_cosmo as jc
 import numpy as np
 from diffrax import (ConstantStepSize, Dopri5, LeapfrogMidpoint, ODETerm,
                      PIDController, SaveAt, diffeqsolve)
-from jax.experimental.mesh_utils import create_device_mesh
 from jax.experimental.multihost_utils import process_allgather
-from jax.sharding import Mesh, NamedSharding
+from jax.sharding import NamedSharding
 from jax.sharding import PartitionSpec as P
 
 from jaxpm.kernels import interpolate_power_spectrum
@@ -78,7 +77,7 @@ def parse_arguments():
 
 def create_mesh_and_sharding(pdims):
     devices = create_device_mesh(pdims)
-    mesh = Mesh(devices, axis_names=('x', 'y'))
+    mesh = jax.make_mesh(pdims, axis_names=('x', 'y'))
     sharding = NamedSharding(mesh, P('x', 'y'))
     return mesh, sharding
 
@@ -106,7 +105,10 @@ def run_simulation(omega_c, sigma8, mesh_shape, box_size, halo_size,
                    sharding=sharding)
 
     ode_fn = ODETerm(
-        make_diffrax_ode(cosmo, mesh_shape, paint_absolute_pos=False))
+        make_diffrax_ode(mesh_shape,
+                         paint_absolute_pos=False,
+                         sharding=sharding,
+                         halo_size=halo_size))
 
     # Choose solver
     solver = LeapfrogMidpoint() if solver_choice == "leapfrog" else Dopri5()

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.

requirements-test.txt

@@ -1,5 +1,5 @@
-pytest>=8.0.0
-diffrax
 pfft-python @ git+https://github.com/MP-Gadget/pfft-python
 pmesh @ git+https://github.com/MP-Gadget/pmesh
 fastpm @ git+https://github.com/ASKabalan/fastpm-python
+numpy==2.2.6
+diffrax

tests/conftest.py

@@ -44,7 +44,7 @@ def simulation_config(request):
     return request.param
 
 
-@pytest.fixture(scope="session", params=[0.1, 0.5, 0.8])
+@pytest.fixture(scope="session", params=[0.1, 0.2])
 def lpt_scale_factor(request):
     return request.param
 
@@ -151,7 +151,7 @@ def nbody_from_lpt1(solver, fpm_lpt1, particle_mesh, lpt_scale_factor):
     if lpt_scale_factor == 0.8:
         pytest.skip("Do not run nbody simulation from scale factor 0.8")
 
-    stages = np.linspace(lpt_scale_factor, 1.0, 10, endpoint=True)
+    stages = np.linspace(lpt_scale_factor, 1.0, 100, endpoint=True)
 
     finalstate = solver.nbody(fpm_lpt1, leapfrog(stages))
     fpm_mesh = particle_mesh.paint(finalstate.X).value
@@ -167,7 +167,7 @@ def nbody_from_lpt2(solver, fpm_lpt2, particle_mesh, lpt_scale_factor):
     if lpt_scale_factor == 0.8:
         pytest.skip("Do not run nbody simulation from scale factor 0.8")
 
-    stages = np.linspace(lpt_scale_factor, 1.0, 10, endpoint=True)
+    stages = np.linspace(lpt_scale_factor, 1.0, 100, endpoint=True)
 
     finalstate = solver.nbody(fpm_lpt2, leapfrog(stages))
     fpm_mesh = particle_mesh.paint(finalstate.X).value

tests/test_against_fpm.py

@@ -2,6 +2,7 @@ import pytest
 from diffrax import Dopri5, ODETerm, PIDController, SaveAt, diffeqsolve
 from helpers import MSE, MSRE
 from jax import numpy as jnp
+from numpy.testing import assert_allclose
 
 from jaxpm.distributed import uniform_particles
 from jaxpm.painting import cic_paint, cic_paint_dx
@@ -10,6 +11,8 @@ from jaxpm.utils import power_spectrum
 
 _TOLERANCE = 1e-4
 _PM_TOLERANCE = 1e-3
+_FIELD_RTOL = 1e-4
+_FIELD_ATOL = 1e-3
 
 
 @pytest.mark.single_device
@@ -34,7 +37,10 @@ def test_lpt_absolute(simulation_config, initial_conditions, lpt_scale_factor,
     _, jpm_ps = power_spectrum(lpt_field, box_shape=box_shape)
     _, fpm_ps = power_spectrum(fpm_ref_field, box_shape=box_shape)
 
-    assert MSE(lpt_field, fpm_ref_field) < _TOLERANCE
+    assert_allclose(lpt_field,
+                    fpm_ref_field,
+                    rtol=_FIELD_RTOL,
+                    atol=_FIELD_ATOL)
     assert MSRE(jpm_ps, fpm_ps) < _TOLERANCE
 
 
@@ -55,7 +61,10 @@ def test_lpt_relative(simulation_config, initial_conditions, lpt_scale_factor,
     _, jpm_ps = power_spectrum(lpt_field, box_shape=box_shape)
     _, fpm_ps = power_spectrum(fpm_ref_field, box_shape=box_shape)
 
-    assert MSE(lpt_field, fpm_ref_field) < _TOLERANCE
+    assert_allclose(lpt_field,
+                    fpm_ref_field,
+                    rtol=_FIELD_RTOL,
+                    atol=_FIELD_ATOL)
     assert MSRE(jpm_ps, fpm_ps) < _TOLERANCE
 
 
@@ -76,7 +85,7 @@ 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 +104,7 @@ def test_nbody_absolute(simulation_config, initial_conditions,
                             t1=1.0,
                             dt0=None,
                             y0=y0,
+                            args=cosmo,
                             stepsize_controller=controller,
                             saveat=saveat)
 
@@ -105,7 +115,10 @@ def test_nbody_absolute(simulation_config, initial_conditions,
     _, jpm_ps = power_spectrum(final_field, box_shape=box_shape)
     _, fpm_ps = power_spectrum(fpm_ref_field, box_shape=box_shape)
 
-    assert MSE(final_field, fpm_ref_field) < _PM_TOLERANCE
+    assert_allclose(final_field,
+                    fpm_ref_field,
+                    rtol=_FIELD_RTOL,
+                    atol=_FIELD_ATOL)
     assert MSRE(jpm_ps, fpm_ps) < _PM_TOLERANCE
 
 
@@ -121,8 +134,7 @@ 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(
-        make_diffrax_ode(cosmo, mesh_shape, paint_absolute_pos=False))
+    ode_fn = ODETerm(make_diffrax_ode(mesh_shape, paint_absolute_pos=False))
 
     solver = Dopri5()
     controller = PIDController(rtol=1e-9,
@@ -141,6 +153,7 @@ def test_nbody_relative(simulation_config, initial_conditions,
                             t1=1.0,
                             dt0=None,
                             y0=y0,
+                            args=cosmo,
                             stepsize_controller=controller,
                             saveat=saveat)
 
@@ -151,5 +164,8 @@ def test_nbody_relative(simulation_config, initial_conditions,
     _, jpm_ps = power_spectrum(final_field, box_shape=box_shape)
     _, fpm_ps = power_spectrum(fpm_ref_field, box_shape=box_shape)
 
-    assert MSE(final_field, fpm_ref_field) < _PM_TOLERANCE
+    assert_allclose(final_field,
+                    fpm_ref_field,
+                    rtol=_FIELD_RTOL,
+                    atol=_FIELD_ATOL)
     assert MSRE(jpm_ps, fpm_ps) < _PM_TOLERANCE

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-6  # 🎉🎉🎉
+
+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
@@ -37,12 +52,12 @@ def test_distrubted_pm(simulation_config, initial_conditions, cosmo, order,
                        particles,
                        a=0.1,
                        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:
         dx, p, _ = lpt(cosmo, initial_conditions, a=0.1, order=order)
-        ode_fn = ODETerm(
-            make_diffrax_ode(cosmo, mesh_shape, paint_absolute_pos=False))
+        ode_fn = ODETerm(make_diffrax_ode(mesh_shape,
+                                          paint_absolute_pos=False))
         y0 = jnp.stack([dx, p])
 
     solver = Dopri5()
@@ -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
 
@@ -94,8 +110,7 @@ def test_distrubted_pm(simulation_config, initial_conditions, cosmo, order,
                        sharding=sharding)
 
         ode_fn = ODETerm(
-            make_diffrax_ode(cosmo,
-                             mesh_shape,
+            make_diffrax_ode(mesh_shape,
                              halo_size=halo_size,
                              sharding=sharding))
 
@@ -108,8 +123,7 @@ def test_distrubted_pm(simulation_config, initial_conditions, cosmo, order,
                        halo_size=halo_size,
                        sharding=sharding)
         ode_fn = ODETerm(
-            make_diffrax_ode(cosmo,
-                             mesh_shape,
+            make_diffrax_ode(mesh_shape,
                              paint_absolute_pos=False,
                              halo_size=halo_size,
                              sharding=sharding))
@@ -130,16 +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)
 
@@ -150,3 +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)