Run tests in CI, add code coverage

.github/workflows/tests.yml

@@ -0,0 +1,53 @@
+# Builds the `hfppl` poetry environment and runs all tests
+# Adapted from: https://github.com/marketplace/actions/install-poetry-action
+
+name: Codebase tests
+
+on:
+  pull_request:
+  push:
+    branches:
+      - main
+
+permissions:
+  contents: read
+
+jobs:
+  build:
+    runs-on: ParallelHoss
+    steps:
+    - name: Check out repository
+      uses: actions/checkout@v4
+
+    - name: Set up python
+      id: setup-python
+      uses: actions/setup-python@v5
+      with:
+        python-version: '3.10'
+
+    - name: Install Poetry
+      uses: snok/install-poetry@v1
+
+    - name: Load cached venv
+      id: cached-poetry-dependencies
+      uses: actions/cache@v3
+      with:
+        path: .venv
+        key: venv-${{ runner.os }}-${{ steps.setup-python.outputs.python-version }}-${{ hashFiles('**/poetry.lock') }}
+
+    - name: Install project
+      run: poetry install --no-interaction
+
+    - name: Run tests
+      run: |
+        eval $(poetry env activate)
+        pytest -v --cov=hfppl --cov-report=json
+
+    - name: Upload coverage to Codecov
+      uses: codecov/codecov-action@v5
+      with:
+        fail_ci_if_error: false
+        disable_search: true
+        token: ${{ secrets.CODECOV_TOKEN }}
+        files: ./coverage.json        
+        slug: probcomp/hfppl                

.gitignore

@@ -1,3 +1,5 @@
+coverage.json
+
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[cod]

README.md

@@ -1,6 +1,10 @@
 # LLaMPPL + HuggingFace
 
 [![docs](https://github.com/probcomp/hfppl/actions/workflows/docs.yml/badge.svg)](https://probcomp.github.io/hfppl)
+[![Tests](https://github.com/probcomp/hfppl/actions/workflows/pytest.yml/badge.svg)](https://github.com/probcomp/hfppl/actions/workflows/pytest.yml)
+[![codecov](https://codecov.io/gh/probcomp/hfppl/graph/badge.svg?token=414EHUC2P3)](https://codecov.io/gh/probcomp/hfppl)
+
+
 
 LLaMPPL is a research prototype for language model probabilistic programming: specifying language generation tasks by writing probabilistic programs that combine calls to LLMs, symbolic program logic, and probabilistic conditioning. To solve these tasks, LLaMPPL uses a specialized sequential Monte Carlo inference algorithm. This technique, SMC steering, is described in [our recent workshop abstract](https://arxiv.org/abs/2306.03081).
 

codecov.yml

@@ -0,0 +1,6 @@
+coverage:
+  status:
+    project:
+      default:
+        target: auto
+        threshold: 3%

poetry.toml

@@ -0,0 +1,2 @@
+[virtualenvs]
+in-project = true

pyproject.toml

@@ -20,7 +20,7 @@ protobuf = "^5.27.2"
 nltk = {version = "^3.8.1", optional = true}
 pre-commit = "^3.7.1"
 ipykernel = "^6.29.5"
-genlm-backend = { git = "https://github.com/chi-collective/genlm-backend.git" }
+genlm-backend = "^0.0.1"
 
 [tool.poetry.group.vllm]
 optional = true
@@ -31,6 +31,8 @@ vllm = "^0.6.6"
 [tool.poetry.group.dev.dependencies]
 pytest = "*"
 pytest-benchmark = "*"
+pytest-cov = "*"
+pytest-xdist = "^3.2.0"
 black = "^23.12.1"
 pre-commit = "^3.6.0"
 jupyterlab = "^4.0.9"