Benchmark Colab does not work
Author: Muennighoff
Pull request: no

https://colab.research.google.com/github/huggingface/notebooks/blob/master/transformers_doc/tensorflow/benchmarks.ipynb
(https://huggingface.co/transformers/benchmarks.html?highlight=benchmark)

Running the above Colab on a CPU crashes with:

```
1 / 1
Process killed. Error in Process
Process killed. Error in Process
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-4-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)
    705                     if self.args.inference:
    706                         if self.args.memory:
--> 707                             memory, inference_summary = self.inference_memory(model_name, batch_size, sequence_length)
    708                             inference_result_memory[model_name]["result"][batch_size][sequence_length] = memory
    709                         if self.args.speed:

ValueError: too many values to unpack (expected 2)
```

Comments:
This issue is related directly to Google Colab environment. I ran same code on my local machine (CPU only) and whole benchmark has passed.

```{python}
from transformers import TensorFlowBenchmark, TensorFlowBenchmarkArguments
args = TensorFlowBenchmarkArguments(models=["bert-base-uncased"], batch_sizes=[8], sequence_lengths=[8, 32, 128, 512])
benchmark = TensorFlowBenchmark(args)

results = benchmark.run()
print(results)
```

```
====================       INFERENCE - SPEED - RESULT       ====================
--------------------------------------------------------------------------------
          Model Name             Batch Size     Seq Length     Time in s   
--------------------------------------------------------------------------------
      bert-base-uncased              8               8             0.139     
      bert-base-uncased              8               32            0.369     
      bert-base-uncased              8              128            1.319     
      bert-base-uncased              8              512            5.523     
--------------------------------------------------------------------------------

====================      INFERENCE - MEMORY - RESULT       ====================
--------------------------------------------------------------------------------
          Model Name             Batch Size     Seq Length    Memory in MB 
--------------------------------------------------------------------------------
      bert-base-uncased              8               8              1089     
      bert-base-uncased              8               32             1212     
      bert-base-uncased              8              128             1535     
      bert-base-uncased              8              512             1956     
--------------------------------------------------------------------------------
```

It seems that colab is killing your process, which may be due to a lack of resources. Does it still happen if you use a tiny model on small batch sizes & sequence lengths?

I have found out that there is a problem with the multiprocess benchmark on Colab. I added  **multi_process = False** in args and the benchmark has passed.
```
args = TensorFlowBenchmarkArguments(models=["bert-base-uncased"], batch_sizes=[8], sequence_lengths=[8, 32, 128, 512],
 multi_process = False)
```

The default value for the multi_process flag is True. I don't know why Colab is killing those processes. 

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