Add sample unit tests (foundation-model-stack#61)

* add sample unit test

Signed-off-by: ted chang <[email protected]>

* Update tests/utils/test_data_type_utils.py

Co-authored-by: Sukriti Sharma <[email protected]>
Signed-off-by: ted chang <[email protected]>

* Update tests/utils/test_data_type_utils.py

Co-authored-by: Sukriti Sharma <[email protected]>
Signed-off-by: ted chang <[email protected]>

* re-raise valueError instead of exit

Signed-off-by: ted chang <[email protected]>

---------

Signed-off-by: ted chang <[email protected]>
Co-authored-by: Sukriti Sharma <[email protected]>

.github/workflows/test.yaml

@@ -0,0 +1,22 @@
+name: Test
+
+on:
+  push:
+    branches: [ "main" ]
+  pull_request:
+    branches: [ "main" ]
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+      - name: Set up Python 3.9
+        uses: actions/setup-python@v4
+        with:
+          python-version: 3.9
+      - name: Install dependencies
+        run: |
+          python -m pip install -r setup_requirements.txt
+      - name: Run unit tests
+        run: tox -e py

tests/utils/test_data_type_utils.py

@@ -0,0 +1,35 @@
+# SPDX-License-Identifier: Apache-2.0
+# https://spdx.dev/learn/handling-license-info/
+
+# Third Party
+import pytest
+import torch
+
+# Local
+from tuning.utils import data_type_utils
+
+dtype_dict = {
+    "bool": torch.bool,
+    "double": torch.double,
+    "float32": torch.float32,
+    "int64": torch.int64,
+    "long": torch.long,
+}
+
+
+def test_str_to_torch_dtype():
+    for t in dtype_dict.keys():
+        assert data_type_utils.str_to_torch_dtype(t) == dtype_dict.get(t)
+
+
+def test_str_to_torch_dtype_exit():
+    with pytest.raises(ValueError):
+        data_type_utils.str_to_torch_dtype("foo")
+
+
+def test_get_torch_dtype():
+    for t in dtype_dict.keys():
+        # When passed a string, it gets converted to torch.dtype
+        assert data_type_utils.get_torch_dtype(t) == dtype_dict.get(t)
+        # When passed a torch.dtype, we get the same torch.dtype returned
+        assert data_type_utils.get_torch_dtype(dtype_dict.get(t)) == dtype_dict.get(t)

tox.ini

@@ -1,5 +1,13 @@
 [tox]
-envlist = lint, fmt
+envlist = py, lint, fmt
+
+[testenv]
+description = run unit tests
+deps =
+    pytest>=7
+    -r requirements.txt
+commands =
+    pytest {posargs:tests}
 
 [testenv:fmt]
 description = format with pre-commit

tuning/utils/data_type_utils.py

@@ -22,7 +22,7 @@ def str_to_torch_dtype(dtype_str: str) -> torch.dtype:
     dt = getattr(torch, dtype_str, None)
     if not isinstance(dt, torch.dtype):
         logger.error(" ValueError: Unrecognized data type of a torch.Tensor")
-        exit(-1)
+        raise ValueError("Unrecognized data type of a torch.Tensor")
     return dt