Benchmarking text generations
### Feature request

Currently, we can benchmark Bert or Roberta with `PyTorchBenchmark`. I am just wondering, can we do the same thing for text-generation models, e.g., T5?

```
from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments
config = AutoConfig.from_pretrained('t5-base')
benchmark = PyTorchBenchmark(args, configs=[config], batch_sizes=[1,2,4, 8], sequence_lengths=[128,256, 512])

```

The above code throws the following exception:
```

ValueError                                Traceback (most recent call last)
<ipython-input-6-df0caab4d791> in <module>
----> 1 results = benchmark.run()
      2 print(results)

/usr/local/lib/python3.7/dist-packages/transformers/benchmark/benchmark_utils.py in run(self)
    708                     if self.args.inference:
    709                         if self.args.memory:
--> 710                             memory, inference_summary = self.inference_memory(model_name, batch_size, sequence_length)
    711                             inference_result_memory[model_name]["result"][batch_size][sequence_length] = memory
    712                         if self.args.speed:

ValueError: too many values to unpack (expected 2)
```

### Motivation

It would complete the benchmark API.

### Your contribution

I can submit an initial PR for benchmarking the text generation models.
Already discussed here: https://github.com/huggingface/transformers/issues/15512