[CI] Just use Apple Clang on MacOS

.travis.yml

@@ -30,7 +30,6 @@ addons:
   homebrew:
     packages:
       - cmake
-      - gcc@10
       - libomp
       - wget
       - lcov

tests/python/test_lightgbm_integration.py

@@ -10,7 +10,7 @@
 from treelite.contrib import _libext
 from treelite.util import has_sklearn
 from .metadata import dataset_db, _qualify_path
-from .util import os_compatible_toolchains, os_platform, check_predictor
+from .util import os_compatible_toolchains, os_platform, check_predictor, is_apple_clang
 
 try:
     import lightgbm
@@ -227,6 +227,8 @@ def test_sparse_categorical_model(tmpdir, quantize, toolchain):
         pytest.xfail(reason='Clang cannot handle long if conditional')
     if os_platform() == 'windows':
         pytest.xfail(reason='MSVC cannot handle long if conditional')
+    if os_platform() == 'osx' and is_apple_clang(toolchain):
+        pytest.xfail(reason='Apple Clang cannot handle long if conditional')
     dataset = 'sparse_categorical'
     libpath = os.path.join(tmpdir, dataset_db[dataset].libname + _libext())
     model = treelite.Model.load(dataset_db[dataset].model, model_format=dataset_db[dataset].format)

tests/python/test_model_query.py

@@ -9,7 +9,7 @@
 import treelite_runtime
 from treelite.contrib import _libext
 from .metadata import dataset_db
-from .util import os_platform, os_compatible_toolchains
+from .util import os_platform, os_compatible_toolchains, is_apple_clang
 
 ModelFact = collections.namedtuple(
     'ModelFact',
@@ -28,8 +28,12 @@
     'dataset', ['mushroom', 'dermatology', 'letor', 'toy_categorical', 'sparse_categorical'])
 def test_model_query(tmpdir, dataset):
     """Test all query functions for every example model"""
-    if dataset == 'sparse_categorical' and os_platform() == 'windows':
-        pytest.xfail('MSVC cannot handle long if conditional')
+    toolchain = os_compatible_toolchains()[0]
+    if dataset == 'sparse_categorical':
+        if os_platform() == 'windows':
+            pytest.xfail('MSVC cannot handle long if conditional')
+        elif os_platform() == 'osx' and is_apple_clang(toolchain):
+            pytest.xfail('Apple Clang cannot handle long if conditional')
     if dataset == 'letor' and os_platform() == 'windows':
         pytest.xfail('export_lib() is too slow for letor on MSVC')
 
@@ -39,7 +43,6 @@ def test_model_query(tmpdir, dataset):
     assert model.num_class == _model_facts[dataset].num_class
     assert model.num_tree == _model_facts[dataset].num_tree
 
-    toolchain = os_compatible_toolchains()[0]
     model.export_lib(toolchain=toolchain, libpath=libpath,
                      params={'quantize': 1, 'parallel_comp': model.num_tree}, verbose=True)
     predictor = treelite_runtime.Predictor(libpath=libpath, verbose=True)

tests/python/util.py

@@ -1,6 +1,8 @@
 # -*- coding: utf-8 -*-
 """Utility functions for tests"""
 import os
+import subprocess
+import re
 from sys import platform as _platform
 from contextlib import contextmanager
 
@@ -42,6 +44,15 @@ def os_platform():
     return 'unix'
 
 
+def is_apple_clang(toolchain):
+    try:
+        proc = subprocess.run([toolchain, '--version'], check=True, capture_output=True)
+        stdout = proc.stdout.decode('utf-8')
+        return re.search(r'Apple Clang', stdout, re.IGNORECASE):
+    except:
+        return False
+
+
 def libname(fmt):
     """Format name for a shared library, using appropriate file extension"""
     return fmt.format(_libext())

tests/travis/run_test.sh

@@ -22,7 +22,6 @@ then
   python -m pip install xgboost
   python -m pip install lightgbm codecov
   ./build/treelite_cpp_test
-  export GCC_PATH=gcc-10
   PYTHONPATH=./python:./runtime/python python -m pytest --cov=treelite --cov=treelite_runtime -v --fulltrace tests/python
   lcov --directory . --capture --output-file coverage.info
   lcov --remove coverage.info '*dmlccore*' --output-file coverage.info
@@ -92,7 +91,6 @@ then
   conda install -c conda-forge numpy scipy pandas pytest scikit-learn coverage
   python -m pip install xgboost
   python -m pip install lightgbm
-  export GCC_PATH=gcc-10
   python -m pytest -v --fulltrace tests/python
 
   # Deploy binary wheel to S3
@@ -124,7 +122,6 @@ if [ ${TASK} == "python_sdist_test" ]; then
   conda install -c conda-forge numpy scipy pandas pytest scikit-learn coverage
   python -m pip install xgboost
   python -m pip install lightgbm
-  export GCC_PATH=gcc-10
   python -m pytest -v --fulltrace tests/python
 
   # Deploy source wheel to S3