Use torch for CUDA 11.8 in tests

Makefile

@@ -117,11 +117,11 @@ test-examples-tensorflow:
 # PyTorch backend
 install-torch-test:
 	pip install -U pip
-	pip install -e .[torch]
-	pip install -r tests/torch/requirements.txt
+	pip install -e .[torch] --index-url https://download.pytorch.org/whl/cu118 --extra-index-url=https://pypi.org/simple  # ticket 119128
+	pip install -r tests/torch/requirements.txt --index-url https://download.pytorch.org/whl/cu118 --extra-index-url=https://pypi.org/simple
 	pip install -r tests/cross_fw/install/requirements.txt
 	pip install -r tests/cross_fw/examples/requirements.txt
-	pip install -r examples/torch/requirements.txt
+	pip install -r examples/torch/requirements.txt --index-url https://download.pytorch.org/whl/cu118 --extra-index-url=https://pypi.org/simple
 
 install-torch-dev: install-torch-test install-pre-commit install-pylint
 	pip install -r examples/post_training_quantization/torch/mobilenet_v2/requirements.txt

tests/torch/test_sanity_sample.py

@@ -174,12 +174,6 @@ def set_num_threads_locally(n=1):
         torch.set_num_threads(old_n)
 
 
-def _run_with_xfail_119128(runner: Command):
-    returncode = runner.run(assert_returncode_zero=False)
-    if returncode == 139:
-        pytest.xfail("Bug 119128: sporadic segment fault on backward")
-
-
 @pytest.mark.nightly
 class TestSanitySample:
     @staticmethod
@@ -289,7 +283,7 @@ def test_pretrained_model_train(config, tmp_path, multiprocessing_distributed, c
             )
 
         runner = Command(create_command_line(args, config["sample_type"]), env=ROOT_PYTHONPATH_ENV)
-        _run_with_xfail_119128(runner)
+        runner.run(assert_returncode_zero=False)
         last_checkpoint_path = os.path.join(checkpoint_save_dir, get_run_name(config_factory.config) + "_last.pth")
         assert os.path.exists(last_checkpoint_path)
         if "compression" in config["sample_config"]:
@@ -374,7 +368,7 @@ def test_resume(request, config, tmp_path, multiprocessing_distributed, case_com
             args["--multiprocessing-distributed"] = True
 
         runner = Command(create_command_line(args, config["sample_type"]), env=ROOT_PYTHONPATH_ENV)
-        _run_with_xfail_119128(runner)
+        runner.run(assert_returncode_zero=False)
         last_checkpoint_path = os.path.join(checkpoint_save_dir, get_run_name(config_factory.config) + "_last.pth")
         assert os.path.exists(last_checkpoint_path)
         if "compression" in config["sample_config"]:
@@ -590,7 +584,7 @@ def test_accuracy_aware_training_pipeline(accuracy_aware_config, tmp_path, multi
             args["--multiprocessing-distributed"] = True
 
         runner = Command(create_command_line(args, accuracy_aware_config["sample_type"]), env=ROOT_PYTHONPATH_ENV)
-        _run_with_xfail_119128(runner)
+        runner.run(assert_returncode_zero=False)
 
         checkpoint_save_dir = log_dir / get_run_name(config_factory.config)
         aa_checkpoint_path = get_accuracy_aware_checkpoint_dir_path(checkpoint_save_dir)