The results cannot be reproduced using the pre-training model
**Describe the bug**

I tested the model nmt_en_zh_transformer24x6 and nmt_en_zh_transformer6x6  on WMT18 test set and found that the blue score was a little low.

**Steps/Code to reproduce bug**

`model = MTEncDecModel.from_pretrained("nmt_en_zh_transformer24x6")`

`import copy`
`cfg = copy.deepcopy(model.cfg)`

`from omegaconf import OmegaConf, open_dict`

`with open_dict(cfg):`
 ` cfg.test_ds.use_tarred_dataset = False`
 ` cfg.test_ds.src_file_name = "/datasets/wmt18/clean.en"`
  `cfg.test_ds.tgt_file_name = "/datasets/wmt18/clean.zh"`

`with open_dict(model.cfg):`
  `model.cfg.tokens_in_batch=512`
  `model.cfg.clean=False`
  `model.cfg.max_seq_length = 512`
  `model.cfg.cache_ids=False`
  `model.cfg.cache_data_per_node=False`
  `model.cfg.use_cache=False`
  `model.cfg.shuffle=False`
  `model.cfg.num_samples=-1`
  `model.cfg.drop_last=False`
  `model.cfg.pin_memory=False`
  `model.cfg.load_from_cached_dataset=False`
  `model.cfg.reverse_lang_direction=False`
  `model.cfg.load_from_tarred_dataset=False`
  `model.cfg.tar_shuffle_n=100`

`import pytorch_lightning as ptl`

`trainer = ptl.Trainer(gpus=1,`
                      `num_nodes=1,`
                      `max_steps=100000,`
                      `accumulate_grad_batches=1,`
                      `checkpoint_callback=False,`
                      `val_check_interval=578,`
                      `sync_batchnorm=False)`
`trainer.test(model)`
![ac2dc0c25892480a3e690e2df9fab50](https://user-images.githubusercontent.com/56379157/144012971-2dbd226e-8b34-4e2a-9d23-e2ac319d8783.png)

24x6 blue score is 29.38408660888672 ，10 points lower than 39.9 in the introduction
6x6 blue score is 2.8，but in the introduction it is 35.0

How can I set the parameters to get the correct results?


