## ❓ Questions & Help

<!-- A clear and concise description of the question. -->
I try to use `load_tf_weights_in_bert` to convert my fine-tuned tf classification model in Pytorch. I original trained the model by tensorflow BERT.
I used this code:
```import torch
from transformers.modeling_bert import BertConfig, BertForPreTraining, load_tf_weights_in_bert, BertForSequenceClassification

tf_checkpoint_path="./model.ckpt-98400"
bert_config_file = "./bert_config.json"
pytorch_dump_path="pytorch_bert"

config = BertConfig.from_json_file(bert_config_file)
config.num_labels = 21

print("Building PyTorch model from configuration: {}".format(str(config)))
model = BertForSequenceClassification(config)

# Load weights from tf checkpoint
load_tf_weights_in_bert(model, config, tf_checkpoint_path)

# Save pytorch-model
print("Save PyTorch model to {}".format(pytorch_dump_path))
torch.save(model.state_dict(), pytorch_dump_path)
```
I noticed  this method to solve my problem:  https://github.com/huggingface/transformers/issues/580#issuecomment-489519231

 I add to line in `modeling_bert.py`.

```          elif l[0] == 'output_bias' or l[0] == 'beta':
                pointer = getattr(pointer, 'cls')
                pointer = getattr(pointer, 'bias')
            elif l[0] == 'output_weights':
                pointer = getattr(pointer, 'cls')
                pointer = getattr(pointer, 'weight')
```
But I still get error `AttributeError: 'LayerNorm' object has no attribute 'cls'`

```
Building PyTorch model from configuration: {
  "attention_probs_dropout_prob": 0.1,
  "directionality": "bidi",
  "finetuning_task": null,
  "hidden_act": "gelu",
  "hidden_dropout_prob": 0.1,
  "hidden_size": 768,
  "initializer_range": 0.02,
  "intermediate_size": 3072,
  "layer_norm_eps": 1e-12,
  "max_position_embeddings": 512,
  "num_attention_heads": 12,
  "num_hidden_layers": 12,
  "num_labels": 21,
  "output_attentions": false,
  "output_hidden_states": false,
  "pooler_fc_size": 768,
  "pooler_num_attention_heads": 12,
  "pooler_num_fc_layers": 3,
  "pooler_size_per_head": 128,
  "pooler_type": "first_token_transform",
  "pruned_heads": {},
  "torchscript": false,
  "type_vocab_size": 2,
  "use_bfloat16": false,
  "vocab_size": 119547
}

I1009 15:47:03.337315 47631408520768 modeling_bert.py:65] Converting TensorFlow checkpoint from ./model.ckpt-98400
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I1009 15:47:07.278274 47631408520768 modeling_bert.py:71] Loading TF weight bert/encoder/layer_9/intermediate/dense/bias/adam_v with shape [3072]
I1009 15:47:07.283823 47631408520768 modeling_bert.py:71] Loading TF weight bert/encoder/layer_9/intermediate/dense/kernel with shape [768, 3072]
I1009 15:47:07.293444 47631408520768 modeling_bert.py:71] Loading TF weight bert/encoder/layer_9/intermediate/dense/kernel/adam_m with shape [768, 3072]
I1009 15:47:07.302655 47631408520768 modeling_bert.py:71] Loading TF weight bert/encoder/layer_9/intermediate/dense/kernel/adam_v with shape [768, 3072]
I1009 15:47:07.312628 47631408520768 modeling_bert.py:71] Loading TF weight bert/encoder/layer_9/output/LayerNorm/beta with shape [768]
I1009 15:47:07.318495 47631408520768 modeling_bert.py:71] Loading TF weight bert/encoder/layer_9/output/LayerNorm/beta/adam_m with shape [768]
I1009 15:47:07.323935 47631408520768 modeling_bert.py:71] Loading TF weight bert/encoder/layer_9/output/LayerNorm/beta/adam_v with shape [768]
I1009 15:47:07.329243 47631408520768 modeling_bert.py:71] Loading TF weight bert/encoder/layer_9/output/LayerNorm/gamma with shape [768]
I1009 15:47:07.334521 47631408520768 modeling_bert.py:71] Loading TF weight bert/encoder/layer_9/output/LayerNorm/gamma/adam_m with shape [768]
I1009 15:47:07.339932 47631408520768 modeling_bert.py:71] Loading TF weight bert/encoder/layer_9/output/LayerNorm/gamma/adam_v with shape [768]
I1009 15:47:07.345828 47631408520768 modeling_bert.py:71] Loading TF weight bert/encoder/layer_9/output/dense/bias with shape [768]
I1009 15:47:07.351069 47631408520768 modeling_bert.py:71] Loading TF weight bert/encoder/layer_9/output/dense/bias/adam_m with shape [768]
I1009 15:47:07.356699 47631408520768 modeling_bert.py:71] Loading TF weight bert/encoder/layer_9/output/dense/bias/adam_v with shape [768]
I1009 15:47:07.362353 47631408520768 modeling_bert.py:71] Loading TF weight bert/encoder/layer_9/output/dense/kernel with shape [3072, 768]
I1009 15:47:07.371929 47631408520768 modeling_bert.py:71] Loading TF weight bert/encoder/layer_9/output/dense/kernel/adam_m with shape [3072, 768]
I1009 15:47:07.381705 47631408520768 modeling_bert.py:71] Loading TF weight bert/encoder/layer_9/output/dense/kernel/adam_v with shape [3072, 768]
I1009 15:47:07.390963 47631408520768 modeling_bert.py:71] Loading TF weight bert/pooler/dense/bias with shape [768]
I1009 15:47:07.396645 47631408520768 modeling_bert.py:71] Loading TF weight bert/pooler/dense/bias/adam_m with shape [768]
I1009 15:47:07.401854 47631408520768 modeling_bert.py:71] Loading TF weight bert/pooler/dense/bias/adam_v with shape [768]
I1009 15:47:07.406987 47631408520768 modeling_bert.py:71] Loading TF weight bert/pooler/dense/kernel with shape [768, 768]
I1009 15:47:07.412913 47631408520768 modeling_bert.py:71] Loading TF weight bert/pooler/dense/kernel/adam_m with shape [768, 768]
I1009 15:47:07.419250 47631408520768 modeling_bert.py:71] Loading TF weight bert/pooler/dense/kernel/adam_v with shape [768, 768]
I1009 15:47:07.425417 47631408520768 modeling_bert.py:71] Loading TF weight global_step with shape []
I1009 15:47:07.430413 47631408520768 modeling_bert.py:71] Loading TF weight output_bias with shape [21]
I1009 15:47:07.435464 47631408520768 modeling_bert.py:71] Loading TF weight output_bias/adam_m with shape [21]
I1009 15:47:07.440396 47631408520768 modeling_bert.py:71] Loading TF weight output_bias/adam_v with shape [21]
I1009 15:47:07.445353 47631408520768 modeling_bert.py:71] Loading TF weight output_weights with shape [21, 768]
I1009 15:47:07.450436 47631408520768 modeling_bert.py:71] Loading TF weight output_weights/adam_m with shape [21, 768]
I1009 15:47:07.455591 47631408520768 modeling_bert.py:71] Loading TF weight output_weights/adam_v with shape [21, 768]
---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-34-7cd6093dcd13> in <module>
     15
     16 # Load weights from tf checkpoint
---> 17 load_tf_weights_in_bert(model, config, tf_checkpoint_path)
     18
     19 # Save pytorch-model

~/py3.6/lib/python3.6/site-packages/transformers/modeling_bert.py in load_tf_weights_in_bert(model, config, tf_checkpoint_path)

~/py3.6/lib/python3.6/site-packages/torch/nn/modules/module.py in __getattr__(self, name)
    589                 return modules[name]
    590         raise AttributeError("'{}' object has no attribute '{}'".format(
--> 591             type(self).__name__, name))
    592
    593     def __setattr__(self, name, value):

AttributeError: 'LayerNorm' object has no attribute 'cls'
```


Bug in CTRL generation

## 🐛 Bug

Model: CTRL

Language: English

The problem arises when using:
* [x] Official example script [`run_generation.py`](https://github.com/huggingface/transformers/blob/master/examples/run_generation.py)

The tasks I am working on is:
* [x] Generating text with the CTRL model.

## To Reproduce

Steps to reproduce the behavior:

1. Run `python run_generation.py` with `model_type=ctrl`, `model_name_or_path=ctrl`, `temperature=0`, a decent length like `length=50`, and `repetition_penalty=1.2`.
2. Input a `Links`-based control code, such as `Links https://www.cnn.com/2018/09/20/us-president-meets-british-pm`, from the original paper.

Rather than generating relevant, English text, it will often generate assorted, garbled French. For example, for the above example it generated: `m m e et au neuen auge de la part des
 \* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *`.

## Expected behavior

It should generate relatively-coherent English text, relevant to the link URL. This is the behavior in the paper, and in the [`lower_memory` branch](https://github.com/salesforce/ctrl/tree/lower_memory) Colab notebook.

## Environment

* OS: MacOS
* Python version: 3.5.6, Anaconda.
* PyTorch version: 1.2.0
* PyTorch Transformers version (or branch): 2.1.0
* Using GPU ? No
* Any other relevant information:


Bug when finetuning model on Squad

## 🐛 Bug

Model: Bert (bert-large-uncased-whole-word-masking)

The problem arises when using:
The official example script for finetuning on squad data:
```
python -m torch.distributed.launch --nproc_per_node=8 run_squad.py \
      --model_type bert \
      --model_name_or_path bert-large-uncased-whole-word-masking \
      --do_train \
      --do_eval \
      --do_lower_case \
      --train_file $SQUAD_DIR/train-v1.1.json \
      --predict_file $SQUAD_DIR/dev-v1.1.json \
      --learning_rate 3e-5 \
      --num_train_epochs 2 \
      --max_seq_length 384 \
      --doc_stride 128 \
      --output_dir ./models/wwm_uncased_finetuned_squad/ \
      --per_gpu_eval_batch_size 3 \
      --per_gpu_train_batch_size 3 \
      --save_steps 1500 \
      --logging_steps 250 \
      --fp16
```

The tasks I am working on is:
* [x ] an official GLUE/SQUaD task: SQUaD

Here is the error log:
```
...
10/09/2019 17:03:29 - INFO - utils_squad -   Writing predictions to: ./models/wwm_uncased_finetuned_squad/predictions_.json
10/09/2019 17:03:29 - INFO - utils_squad -   Writing nbest to: ./models/wwm_uncased_finetuned_squad/nbest_predictions_.json
Traceback (most recent call last):
  File "run_squad.py", line 537, in <module>
    main()
  File "run_squad.py", line 526, in main
    result = evaluate(args, model, tokenizer, prefix=global_step)
  File "run_squad.py", line 268, in evaluate
    args.version_2_with_negative, args.null_score_diff_threshold)
  File "/dl/huggingface-bert/transformers/examples/SQuAD_runs/rundir/utils_squad.py", line 511, in write_predictions
    result = unique_id_to_result[feature.unique_id]
KeyError: 1000000000
```

## Additional context
When running on multiple gpus the above problem shows up.


Write with Transformer: Changing settings on Mobile?

## ❓ Questions & Help

It's great to see new features and options, in particular the Max Time option to generate longer outputs. However, none of the Model Settings are available on mobile...?

In order to change the model settings on mobile, I had to download Firefox, mess with the CSS settings in about://config to make everything really tiny, zoom in on the extremely small settings box, slide things around, then set everything back, and I'd have to do most of those all over again if I end up closing / reloading the tab.


Plan for Albert?

## 🚀 Feature

<!-- A clear and concise description of the feature proposal. Please provide a link to the paper and code in case they exist. -->

I think Albert is popular enough to not say anything more. The link to the paper is below.
https://arxiv.org/pdf/1909.11942v1.pdf

## Motivation

<!-- Please outline the motivation for the proposal. Is your feature request related to a problem? e.g., I'm always frustrated when [...]. If this is related to another GitHub issue, please link here too. -->

## Additional context

<!-- Add any other context or screenshots about the feature request here. -->


How much GPU memory is needed to run run_squad.py

## ❓ Questions & Help

How much GPU memory is needed to run `run_squad.py`, I tried on `GTX 1050ti (4gb)` with the following setting and I am getting out of memory error

```
$ python3 examples/run_squad.py \
  --model_type bert \
  --model_name_or_path bert-base-cased \
  --do_train \
  --do_eval \
  --do_lower_case \
  --train_file $SQUAD_DIR/train-v1.1.json \
  --predict_file $SQUAD_DIR/dev-v1.1.json \
  --per_gpu_train_batch_size 12 \
  --learning_rate 3e-5 \
  --num_train_epochs 2.0 \
  --max_seq_length 384 \
  --doc_stride 128 \
  --output_dir /tmp/debug_squad/ \
  --save_steps 1000
```


Scores using BertForNextSentencePrediction are not Interpretable.

## ❓ Questions & Help

<!-- A clear and concise description of the question. -->
The output of BertForNextSentencePrediction are not Interpretable.
What is seq_relationship_score?
Input example and their respective output is defined below.
1:
text = "[CLS] How old are you? [SEP] I am 193 years old [SEP]"
output=(tensor([[ 3.5181, -2.2946]], grad_fn=<AddmmBackward>)
2:
text = "[CLS] How old are you? [SEP] I am from Paris. [SEP]"
output=tensor([[ 3.9515, -2.5397]], grad_fn=<AddmmBackward>)

Following is my code:
```
import torch
from transformers import *
import torch.nn.functional as F

tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
text = "[CLS] How old are you? [SEP] I am from Paris. [SEP]"
tokenized_text = tokenizer.tokenize(text)
segments_idss=[]
flag=0
for index,token in enumerate(tokenized_text):
	if flag==0:
		segments_idss.append(0)
	else:
		segments_idss.append(1)
	if tokenized_text[index]=='[SEP]':
		flag=1

indexed_tokens = tokenizer.convert_tokens_to_ids(tokenized_text)
tokens_tensor = torch.tensor([indexed_tokens])
segments_tensors = torch.tensor([segments_idss])
model = BertForNextSentencePrediction.from_pretrained('bert-base-uncased')
model.eval()
predictions = model(tokens_tensor, segments_tensors)
print("predictions->",predictions[0])
```
Can someone tell me, Why scores are similar for different sentences and how to use BertForNextSentencePrediction to find next sentence score?


Hf master