hunk.code_window:         run: |
             if [[ -d ./torch-extensions ]]; then rm -rf ./torch-extensions; fi
             cd tests
             TORCH_EXTENSIONS_DIR=./torch-extensions pytest --color=yes --durations=0 --forked --verbose -x -n 4 unit/
             TORCH_EXTENSIONS_DIR=./torch-extensions pytest --color=yes --durations=0 --forked --verbose -x -m 'sequential' unit/
  
hunk.labels: keep keep keep replace replace
hunk.after_edit:           TORCH_EXTENSIONS_DIR=./torch-extensions pytest --color=yes --durations=0 --verbose -n 4 unit/{comm,inference,monitor,ops,profiling,runtime}
             #TORCH_EXTENSIONS_DIR=./torch-extensions pytest --color=yes --durations=0 --verbose -m 'sequential' unit/{comm,inference,monitor,ops,profiling,runtime}
hunk.file_path: .github/workflows/amd.yml
hunk.type: replace
file: Transformer Kernels
  ===================
  
  The transformer kernel API in DeepSpeed can be used to create BERT transformer layer for
  more efficient pre-training and fine-tuning, it includes the transformer layer configurations and
  transformer layer module initialization.
  
  Here we present the transformer kernel API.
  Please see the `BERT pre-training tutorial <https://www.deepspeed.ai/tutorials/bert-pretraining/>`_ for usage details.
  
  DeepSpeed Transformer Config
  ----------------------------
  .. autoclass:: deepspeed.DeepSpeedTransformerConfig
  
  DeepSpeed Transformer Layer
  ----------------------------
  .. autoclass:: deepspeed.DeepSpeedTransformerLayer
  