[torchbench] nvidia_deeprecommender fails to run.

[ysiraichi]

🐛 Bug

Running the upstreamed benchmarking scripts with the following command results in an unexpected error.

python xla/benchmarks/experiment_runner.py \
       --suite-name torchbench \
       --accelerator cuda \
       --xla PJRT --xla None \
       --dynamo openxla --dynamo None \
       --test eval --test train \
       --repeat 30 --iterations-per-run 5 \
       --print-subprocess \
       --no-resume -k nvidia_deeprecommender

Traceback (most recent call last):
  File "xla/benchmarks/experiment_runner.py", line 601, in <module>
    main()
  File "xla/benchmarks/experiment_runner.py", line 597, in main
    runner.run()
  File "xla/benchmarks/experiment_runner.py", line 65, in run
    self.run_single_experiment(experiment_config, model_config)
  File "xla/benchmarks/experiment_runner.py", line 154, in run_single_experiment
    benchmark_model = self.model_loader.load_model(model_config,
  File "xla/benchmarks/benchmark_model.py", line 59, in load_model
    benchmark_model.set_up()
  File "xla/benchmarks/torchbench_model.py", line 196, in set_up
    self.module, self.example_inputs = benchmark.get_module()
  File "torchbenchmark/models/nvidia_deeprecommender/__init__.py", line 40, in get_module
    return self.model.rencoder, (self.model.toyinputs,)
AttributeError: 'DeepRecommenderInferenceBenchmark' object has no attribute 'rencoder'

Environment

• PyTorch/XLA: 402166b

Additional context

The issue here is, most likely, that we aren't initializing the model with neither CPU nor CUDA. But, with xm.xla_device(). nvidia_deeprecommender assigns an instance of different classes depending on the device. However, there's no case for XLA devices.

Maybe, a better solution would be to change that part of the benchmark code for handling XLA devices.