Update fork-maintenance-action.yml

.github/workflows/fork-maintenance-action.yml

@@ -31,6 +31,7 @@ jobs:
             pip install --no-cache-dir GPUtil azureml azureml-core tokenizers ninja cerberus sympy sacremoses sacrebleu==1.5.1 sentencepiece scipy scikit-learn urllib3 && pip install huggingface_hub datasets && 
             pip install parameterized && 
             pip install -e .
+            pip install --upgrade torch torchvision --extra-index-url https://download.pytorch.org/whl/rocm5.6
             #sudo sed -i 's/torchaudio//g' examples/pytorch/_tests_requirements.txt && pip install -r examples/pytorch/_tests_requirements.txt && git restore examples/pytorch/_tests_requirements.txt && pip install --no-cache-dir GPUtil azureml azureml-core tokenizers ninja cerberus sympy sacremoses sacrebleu==1.5.1 sentencepiece scipy scikit-learn urllib3 && pip install huggingface_hub datasets && pip install parameterized && pip install -e .
           unit_test_command: folders=\$(python3 -c 'import os; workspace = \"/myworkspace\"; repo_root = os.path.join(workspace, \"tests\"); models_dir = os.path.join(repo_root, \"models\"); model_tests = os.listdir(models_dir); d1 = sorted([d for d in os.listdir(repo_root) if os.path.isdir(os.path.join(repo_root, d)) and d != \"models\"]); d2 = sorted([os.path.join(\"models\", x) for x in model_tests if os.path.isdir(os.path.join(models_dir, x))]); d = d2 + d1; print(\" \".join(d[:5]))'); echo \$folders;  for folder in \${folders[@]}; do pytest tests/\${folder} -v --make-reports=huggingface_unit_tests_\${machine_type}_run_models_gpu_\${folder} -rfEs --continue-on-collection-errors -m \"not not_device_test\" -p no:cacheprovider; done; allstats=\$(find reports -name stats.txt); for stat in \${allstats[@]}; do echo \$stat; cat \$stat; done
           performance_test_command: echo \"python examples/pytorch/language-modeling/run_mlm.py --model_name_or_path bert-base-uncased --dataset_name wikitext --dataset_config_name wikitext-2-raw-v1 --do_train --do_eval --output_dir /tmp/test-mlm --per_device_train_batch_size 8 --per_device_eval_batch_size 8 --max_steps 500\"