[Benchmark] GPT2LMHeadModel (gpt2-medium) forward pass inference became 9% slower compared to 2.8.0 release
# 🖥 Benchmarking `GPT2LMHeadModel`

## Benchmark

GPT2LMHeadModel model call (and model.generate() too)

## Set-up

gpu: gtx 1080
pytorch 1.4.0
transformers 2.8.0, 3.5.1, 4.5.1 releases and latest master branch

Code to reproduce
```python
import timeit
import numpy as np
import torch
from transformers import __version__ as trans_version
from transformers import (
    GPT2LMHeadModel,
)

print("transformers:", trans_version)
model = GPT2LMHeadModel.from_pretrained("gpt2-medium")
print(model.__class__)
model.to("cuda")
model.eval()
rounding = 3

timed_result = timeit.repeat(stmt="""model.generate(input_ids=inp_t,
               max_length=1024,
               min_length=1024,
               do_sample=False,
               early_stopping=False, pad_token_id=50256, eos_token_id=50256)""",
                             setup="""inp = np.random.randint(low=1, high=50255, size=1014);inp_t = torch.LongTensor(inp).unsqueeze(0).to("cuda")""",
                             repeat=30, number=1, globals=globals())
timed_model_result = timeit.repeat(stmt="""with torch.no_grad():
    model(input_ids=inp_t)""",
                             setup="""inp = np.random.randint(low=1, high=50255, size=1024);inp_t = torch.LongTensor(inp).unsqueeze(0).to("cuda")""",
                             repeat=30, number=10, globals=globals())
print('GPT2LMmedium model.generate (using caching) 1014 input, generate to 1024 (mean ± 3std):',
      str(np.round(np.mean(timed_result), rounding)) + '±' + str(np.round(3 * np.std(timed_result), rounding)))
print('GPT2LMmedium model call, 1024 input 10 times (mean ± 3std):',
      str(np.round(np.mean(timed_model_result), rounding)) + '±' + str(np.round(3 * np.std(timed_model_result), rounding)))
```


## Results

While `model.generate()` code improved and works faster now, model forward pass used in model direct call, became 9% slower

transformers: **2.8.0**
<class 'transformers.modeling_gpt2.GPT2LMHeadModel'>
GPT2LMmedium model.generate (using caching) 1014 input, generate to 1024 (mean ± 3std): 0.557±0.037
GPT2LMmedium model call, 1024 input 10 times (mean ± 3std): **1.821**±0.017

transformers: **3.5.1**
<class 'transformers.modeling_gpt2.GPT2LMHeadModel'>
GPT2LMmedium model.generate (using caching) 1014 input, generate to 1024 (mean ± 3std): 0.37±0.003
GPT2LMmedium model call, 1024 input 10 times (mean ± 3std): 1.849±0.012

transformers: **4.5.1**
<class 'transformers.models.gpt2.modeling_gpt2.GPT2LMHeadModel'>
GPT2LMmedium model.generate (using caching) 1014 input, generate to 1024 (mean ± 3std): 0.36±0.003
GPT2LMmedium model call, 1024 input 10 times (mean ± 3std): 1.823±0.013

transformers: **4.6.0.dev0**
<class 'transformers.models.gpt2.modeling_gpt2.GPT2LMHeadModel'>
GPT2LMmedium model.generate (using caching) 1014 input, generate to 1024 (mean ± 3std): 0.367±0.004
GPT2LMmedium model call, 1024 input 10 times (mean ± 3std): **1.991**±0.013

@patil-suraj Can you please check if this speed decrease of GPT2LMHeadModel model call is not caused by your PR #11225?<|||||>Hi @LSinev 

Thank you for posting the detailed issue. I will take a look.<|||||>This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.

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