Update DistilBERT training code

examples/distillation/README.md

@@ -9,6 +9,12 @@ DistilBERT stands for Distillated-BERT. DistilBERT is a small, fast, cheap and l
 For more information on DistilBERT, please refer to our [detailed blog post](https://medium.com/huggingface/smaller-faster-cheaper-lighter-introducing-distilbert-a-distilled-version-of-bert-8cf3380435b5
 ).
 
+## Setup
+
+This part of the library has only be tested with Python3.6+. There are few specific dependencies to install before launching a distillation, you can install them with the command `pip install -r requirements.txt`. 
+
+**Important note:** The training scripts have been updated to support PyTorch v1.2.0 (there are breakings changes compared to v1.1.0). It is important to note that there is a small internal bug in the current version of PyTorch available on pip that causes a memory leak in our training/distillation. It has been recently fixed and will likely be integrated into the next release. For the moment, we recommend to [compile PyTorch from source](https://github.com/pytorch/pytorch#from-source). Please refer to [issue 1179](https://github.com/huggingface/pytorch-transformers/issues/1179) for more details.
+
 ## How to use DistilBERT
 
 PyTorch-Transformers includes two pre-trained DistilBERT models, currently only provided for English (we are investigating the possibility to train and release a multilingual version of DistilBERT):

examples/distillation/distiller.py

@@ -17,6 +17,7 @@
 """
 import os
 import math
+import psutil
 from tensorboardX import SummaryWriter
 from tqdm import trange, tqdm
 import numpy as np
@@ -192,7 +193,7 @@ class Distiller:
         x_prob = self.token_probs[token_ids.flatten()]
         n_tgt = math.ceil(self.mlm_mask_prop * lengths.sum().item())
         tgt_ids = torch.multinomial(x_prob / x_prob.sum(), n_tgt, replacement=False)
-        pred_mask = torch.zeros(bs * max_seq_len, dtype=torch.uint8, device=token_ids.device)
+        pred_mask = torch.zeros(bs * max_seq_len, dtype=torch.bool, device=token_ids.device) # previously `dtype=torch.uint8`, cf pytorch 1.2.0 compatibility
         pred_mask[tgt_ids] = 1
         pred_mask = pred_mask.view(bs, max_seq_len)
 
@@ -216,7 +217,7 @@ class Distiller:
         _token_ids = _token_ids_mask * (probs == 0).long() + _token_ids_real * (probs == 1).long() + _token_ids_rand * (probs == 2).long()
         token_ids = token_ids.masked_scatter(pred_mask, _token_ids)
 
-        mlm_labels[1-pred_mask] = -1
+        mlm_labels[~pred_mask] = -1 # previously `mlm_labels[1-pred_mask] = -1`, cf pytorch 1.2.0 compatibility
 
         return token_ids, attn_mask, mlm_labels
 
@@ -379,9 +380,9 @@ class Distiller:
                 torch.nn.utils.clip_grad_norm_(amp.master_params(self.optimizer), self.params.max_grad_norm)
             else:
                 torch.nn.utils.clip_grad_norm_(self.student.parameters(), self.params.max_grad_norm)
-            self.scheduler.step()
             self.optimizer.step()
             self.optimizer.zero_grad()
+            self.scheduler.step()
 
     def iter(self):
         """
@@ -419,6 +420,8 @@ class Distiller:
             self.tensorboard.add_scalar(tag="losses/loss_mse", scalar_value=self.last_loss_mse, global_step=self.n_total_iter)
         self.tensorboard.add_scalar(tag="learning_rate/lr", scalar_value=self.scheduler.get_lr()[0], global_step=self.n_total_iter)
         
+        self.tensorboard.add_scalar(tag="global/memory_usage", scalar_value=psutil.virtual_memory()._asdict()['used']/1_000_000, global_step=self.n_total_iter)
+
     def end_epoch(self):
         """
         Finally arrived at the end of epoch (full pass on dataset).

examples/distillation/requirements.txt

 gitpython==3.0.2
+tensorboard>=1.14.0
+tensorboardX==1.8
+psutil==5.6.3