Create test_package workflow (#4)

* Create test_package workflow

* Skip unit tests requiring mpi4py if not installed

* Add manual testing instructions to README

.github/workflows/test_package.yml

@@ -0,0 +1,46 @@
+name: Test diffopt
+
+on:
+  workflow_dispatch: null
+  schedule:
+    # Runs "every Monday at noon UTC"
+    - cron: '0 12 * * 1'
+  push:
+    branches:
+      - main
+  pull_request: null
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        python-version: ['3.9', '3.10', '3.11']
+    steps:
+    - uses: actions/checkout@v2
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: conda-incubator/setup-miniconda@v2
+      with:
+        activate-environment: test${{ matrix.python-version}}
+        python-version: ${{ matrix.python-version }}
+        channels: conda-forge,defaults
+        channel-priority: strict
+        show-channel-urls: true
+        miniforge-version: latest
+
+    - name: Install dependencies
+      shell: bash -l {0}
+      run: |
+        conda install -yq jax 
+        conda install -yq pip pytest pytest-cov flake8
+
+    - name: Install package
+      shell: bash -l {0}
+      run: |
+        pip install -e .
+
+    - name: Run tests
+      shell: bash -l {0}
+      run: |
+        export PYTHONWARNINGS=error
+        pytest -v

README.md

@@ -6,3 +6,6 @@ Parallelization and optimization of differentiable and many-parameter models
 
 ## Documentation
 Online documentation is available at [diffopt.readthedocs.io](https://diffopt.readthedocs.io/en/latest).
+
+## Manual Testing
+Unit tests requiring `mpi4py` installation are not automatically tested by GitHub workflows. To run all tests, install `mpi4py` locally, and run `pytest` from the root directory. Additionally, all tests must pass with `mpirun -n 4 pytest` etc. (up to the maximum number of tasks that can be run on your machine).

diffopt/multigrad/tests/smf_example/smf_grad_descent.py

@@ -5,7 +5,10 @@
 from typing import NamedTuple
 from dataclasses import dataclass
 
-from mpi4py import MPI
+try:
+    from mpi4py import MPI
+except ImportError:
+    MPI = None
 import jax.scipy
 from jax import numpy as jnp
 import numpy as np
@@ -21,11 +24,15 @@ class ParamTuple(NamedTuple):
 
 # Generate fake HMF as power law (truncated so that the SMF has a knee)
 def load_halo_masses(num_halos=10_000, slope=-2, mmin=10.0 ** 10, qmax=0.95):
+    if MPI is None:
+        size, rank = 1, 0
+    else:
+        size, rank = MPI.COMM_WORLD.size, MPI.COMM_WORLD.rank
     q = jnp.linspace(0, qmax, num_halos)
     mhalo = mmin * (1 - q) ** (1/(slope+1))
 
     # Assign different halos to different MPI processes
-    return np.array_split(mhalo, MPI.COMM_WORLD.size)[MPI.COMM_WORLD.rank]
+    return np.array_split(mhalo, size)[rank]
 
 
 # SMF helper functions
@@ -89,6 +96,7 @@ def calc_loss_from_sumstats(self, sumstats, sumstats_aux=None):
 parser.add_argument("--learning-rate", type=float, default=1e-3)
 
 if __name__ == "__main__":
+    assert MPI is not None, "MPI must be installed to run this script"
     args = parser.parse_args()
     data = dict(
         log_halo_masses=jnp.log10(load_halo_masses(args.num_halos)),

diffopt/multigrad/tests/test_mpi.py

@@ -5,26 +5,28 @@
 and `mpiexec -n 10 pytest test_mpi.py` all must pass
 (the --with-mpi flag shouldn't have any effect)
 """
-from mpi4py import MPI
+try:
+    from mpi4py import MPI
+except ImportError:
+    MPI = None
 import jax.numpy as jnp
 
+import unittest
 from ... import multigrad
 from .smf_example import smf_grad_descent as sgd
 
-comm = MPI.COMM_WORLD
-rank = comm.Get_rank()
-size = comm.Get_size()
-
 
+@unittest.skipIf(MPI is None, "MPI must be installed to run this test")
 def test_reduce_sum():
+    rank, size = MPI.COMM_WORLD.rank, MPI.COMM_WORLD.size
     # Set value equal to the rank of the process
     value = jnp.array(rank)
 
     # Reduce the sum of the values across all ranks
     result = multigrad.reduce_sum(value)
 
     # Gather the results from all processes
-    gathered_results = jnp.array(comm.allgather(result))
+    gathered_results = jnp.array(MPI.COMM_WORLD.allgather(result))
 
     if not rank:
         # Perform testing only on the rank 0 process

docs/source/multigrad/smf_gradient_descent.py

@@ -1,4 +1,9 @@
-from mpi4py import MPI
+try:
+    from mpi4py import MPI
+    COMM_WORLD = MPI.COMM_WORLD
+except ImportError:
+    MPI = None
+    COMM_WORLD = None
 import jax.scipy
 from jax import numpy as jnp
 import numpy as np
@@ -7,13 +12,19 @@
 from diffopt import multigrad
 
 
-def load_halo_masses(num_halos=10_000, comm=MPI.COMM_WORLD):
+def load_halo_masses(num_halos=10_000, comm=COMM_WORLD):
+    if comm is None:
+        size = 1
+        rank = 0
+    else:
+        size = comm.size
+        rank = comm.rank
     # Generate fake halo masses between 10^10 < M_h < 10^11 as a power law
     quantile = jnp.linspace(0, 0.9, num_halos)
     mhalo = 1e10 / (1 - quantile)
 
     # Assign halos evenly across given MPI ranks (only one rank for now)
-    return np.array_split(mhalo, comm.size)[comm.rank]
+    return np.array_split(mhalo, size)[rank]
 
 
 # Compute one bin of the stellar mass function (SMF)
@@ -92,6 +103,7 @@ def calc_loss_from_sumstats(self, sumstats):
 
 
 if __name__ == "__main__":
+    assert MPI is not None, "MPI must be installed to run this script"
     volume = 1.0
     smf_bin_edges = jnp.linspace(9, 10, 11)
     true_params = jnp.array([-2.0, -0.5])