Very small gradients causing no weight update from your model

[oneTaken]

Thanks for your code. It helps me understand the BiDAF in details.

However, I found the model had no performance increasing. Every epoch, the metric is always the same. And then, I found it's the optimized gradients too small. it's the order of 10^-3~10^-8.

I can't find what's wrong. And I think your code is good to understand. So, what may be the problem?

[jojonki]

@oneTaken Hi. Thank you for your comments. That will be nice to debug. I am also still facing the low performance problem. I am exactly checking now. If you find anything wrong points, please let me know.

[jojonki]

I am checking the code by removing p2 layer (only predicts the answer of beginning)

[jojonki]

Last layer should predict the index of word instead of char level index. https://github.com/jojonki/BiDAF/commit/91aa342da6ff6e807e19e3bd60ff42101426da5d#diff-8f329902bf6ef6e1af03f01d0b9633e2

[oneTaken]

I should comment earlier. I thought your code runs well, and I have it's due to my incorrect coding. There are some points during my debug phase:

1. your p1 = F.log_softmax(self.p1_layer(G_M).squeeze()) # (N, T) seems have something unresaonable. G_M is size(N, T, 10d), Linear layer seems must be ndim 2. I code as the new p1 layer as. The ans_size is 1, which is said in the paper, and then:

G_M = G_M.view(N*T, 10d)
p1 = self.p1_layer(G_M).squeeze()
p1 = p1.view(N, T)

so, the answer begin must be in the index [0, T).

2. I am also removed the p2 layer , and to test the begin index performance. And I found that the p2 may be not the point. Just use begin (or end) and CrossEntropyLoss, the performance is still very low. The loss value is high, ~5 for one, ~10 for two both. And the gradients is very small, 10^-3~10^-8, maybe this is the point.

I'm trying to use some visualizing analysis to help debug. Do you analysis the gradients?

3. Besides, I also have a little different coding in the character embedding layer. But for now, it seems doesn't matter.

[jojonki]

About 1. I have to confirm this. The code only affect last dimension in master, it does support it. I am not sure in 0.2, actually it looks working.

fc = nn.Linear(10, 20)
a = autograd.Variable(torch.randn(32, 5, 10))
b = fc(a)
print('b', b.size()) # 32, 5, 20

• 0.2 http://pytorch.org/docs/0.2.0/nn.html?highlight=nn%20linear#torch.nn.Linear
• master http://pytorch.org/docs/master/nn.html?highlight=nn%20linear#torch.nn.Linear

About 2. I also confirmed that. I think it is because learning rate is too big. For test, I changed the optimizer to Adam with default values. Now the parameters data looks good.

About 3. There are ablation tests in the paper. Yes, currently we can ignore char embedding layer which does not big impact to the performance.

[oneTaken]

Thanks for the question1 answer, i got it . I should follow the upddates. As for 2, have you got a not bad performance? I have some question about the character embedding, after solveing the performance problem. We can talk about it.

[jojonki]

loss is decreasing and weights of parameters looks natural. But the performance was not still good (13.9% on test data) after 10 epochs.. It will take a more time to debug this model. If you're familiar with TF, you should choose official bidaf model, https://github.com/allenai/bi-att-flow.

Anyway, I am debugging this model.

[oneTaken]

Actually, I trained a keras model, which reproduced the paper result totally. But it's hard to understand the paper deeply, even though I got the result. So, I try to code with pytorch. I have to say, it's a hard way. And I am debugging the model too.

[jojonki]

@oneTaken That's nice. Do you have the code of keras? If it's possible, I want to see it. Before I start to use pytorch, I was a keras user. :)

And current master version (t 36b9e4c1)'s performance is bad. (around 9% for answer start/end after 4 epochs)

[oneTaken]

Emmm, I know you are familar to keras. I watched your respositories. Good job. But sadly, the keras code is not mine. So, I have to train with code myself. I am debugging, my model performance is even lower.

[jojonki]

I see. BTW, training accuracy was about 21% and 25% for answer start/end after 10 epochs on current master.. Today I'm gonna debug the model

[jojonki]

So far, there are not improvements even I modified following items...

• RNN -> LSTM
• original loss function
• use BiDAF's script to build dataset

[oneTaken]

Emm, I am trying to wrap the code into tensorboard. So I can compare with the keras training log, to have a more clear knowledge. By the way, I have a deadline recently. So I can't spend all my time to solve this. But If I have some improvement, I will tell you.

On Thu, Dec 7, 2017 at 3:02 AM, Junki Ohmura [email protected] wrote:

So far, there are not improvements even I modified following items...

• RNN -> LSTM
• original loss function
• use BiDAF's script to build dataset

— You are receiving this because you were mentioned. Reply to this email directly, view it on GitHub https://github.com/jojonki/BiDAF/issues/1#issuecomment-349740879, or mute the thread https://github.com/notifications/unsubscribe-auth/ARNbsCNdzmMJQPpB2oxXYegMrYmozT0fks5s9uTPgaJpZM4Q0IWy .

[jojonki]

I may notice most weights are not updated and quite huge values. Now i am using tensorboard through https://github.com/lanpa/tensorboard-pytorch.

[oneTaken]

Yeah, I am also use this repository. The weights says they were not updated.

And there are gradients, so the graph can flow through the layers.

So, there may be something unexpected about the optim.

[jojonki]

Even though, training accuracy was 75% after 29 epochs, but it was really overfitting.

At first I disable EMA, custom_loss_fn and p2 and use default Adam. And I changed model.zero_grad() to optimizer.zero_grad(). Then no changes..

[jojonki]

I'm sorry. Today i didn't have time to debug. tensorboard-pytorch looks strange so I changed other tensorboard library from here.

Then, histograms looks natural. But I also confirmed that gradients are very small.

[jojonki]

OMG. I noticed I used a relu after embedding. It should be removed. https://github.com/jojonki/BiDAF/blob/master/layers/word_embedding.py#L18

• Official code, they don't use relu. https://github.com/allenai/bi-att-flow/blob/master/basic/model.py#L104-L123

• Left (relu in word embeddings) and Right (no relu)

[oneTaken]

Will it have a sailent performance imporovement after removing this?

[jojonki]

Now I'm checking it. Accuracy without char embedding is around 20% after 5 epochs. It is faster than before. But it is still low.

[jojonki]

@oneTaken In the keras model you have, what is the number of epochs? I am interested in learning rate. (accuracy vs epochs)

[jojonki]

Now accuracy on training set may be over 6,70%. But this is overfitted. In test set, accuracy is under 20%. So still some problems exist.

[oneTaken]

It seems that the training EM can be higher, I got 90% ever before. This is the experiment details in keras, before fine-tuning again:

1. Adam is better than Adadelta, and the init learning rate is the default.
2. GRU is better than LSTM comparing the model performance, so the model is all GRU.
3. The Embedding dim is 100.
4. something different with the char embedding layer, but maybe not the main point.

In the normal phase with dropout=0.2, the 10 epoch dev EM and f1 may be like as follow:

epoch1: Dev Extract Match score :13.065279--------F1 score:18.676405
epoch2: Dev Extract Match score :22.412488--------F1 score:30.410499
epoch3: Dev Extract Match score :36.707663--------F1 score:46.687124
epoch4: Dev Extract Match score :51.069063--------F1 score:61.241440
epoch5: Dev Extract Match score :54.380322--------F1 score:64.845683
epoch6: Dev Extract Match score :57.710501--------F1 score:67.794551
epoch7: Dev Extract Match score :58.467360--------F1 score:68.799207
epoch8: Dev Extract Match score :60.160833--------F1 score:70.192776
epoch9: Dev Extract Match score :61.390728--------F1 score:71.315269
epoch10: Dev Extract Match score :61.750237--------F1 score:71.946257
```

[jojonki]

@oneTaken Thank you. This is helpful!

I list up some points I have to clarify. But If you can compare these with the keras model, the information will be really helpful.

• custom loss function https://github.com/jojonki/BiDAF/blob/9c10a1f79a3a68d7a1b176434f935b6a6532c17a/main.py#L95

• input vector representations. Especially padding way. https://github.com/jojonki/BiDAF/blob/master/process_data.py#L156

• Similarity matrix https://github.com/jojonki/BiDAF/blob/master/layers/attention_net.py#L53

• Weights initializations

• char embedding I'm not confident my char-level CNN so much.

Update

I changed LSTM to GRU and Adadelta to Adam with default value. Following is the result. :( 0: 9.59% 1: 18.5% 2: 20.7% 3: 22%

[oneTaken]

• The custom loss function is different, we manually wrote a loss layer with mask. It seems that keras supports mask well. Besides, defines a new loss Variable, rather than a mask_softmax, it's okay? would it be cause the gradients flow?T
• The padding is okay, we reproduced the result with this padding way.
• The aimilarity matrix seems no problem in my opinion to the paper understanding. The keras similarity matrix is a little complicated. So I didn't get the total idea completely. It's a shame... But I would spends some time to get this if this may be the problem.
• the char embedding is different, according the Yoon kim's paper. So, we also can talk about this way.

On Mon, Dec 11, 2017 at 11:39 AM, Junki Ohmura [email protected] wrote:

@oneTaken https://github.com/onetaken Thank you. This is helpful!

I list up some points I have to clarify. But If you can compare these with the keras model, the information will be really helpful.

custom loss function https://github.com/jojonki/BiDAF/blob/9c10a1f79a3a68d7a1b176434f935b 6a6532c17a/main.py#L95

input vector representations. Especially padding way. https://github.com/jojonki/BiDAF/blob/master/process_data.py#L156

Similarity matrix https://github.com/jojonki/BiDAF/blob/master/layers/ attention_net.py#L53

char embedding I'm not confident my char-level CNN so much.

— You are receiving this because you were mentioned. Reply to this email directly, view it on GitHub https://github.com/jojonki/BiDAF/issues/1#issuecomment-350615008, or mute the thread https://github.com/notifications/unsubscribe-auth/ARNbsGvWicEkib7vdbGDoIDXuMVBAo0Cks5s_KPYgaJpZM4Q0IWy .

[jojonki]

@oneTaken Thank you again!

And I noticed my LSTMs assume batch_first=True. But I didn't set it! Now I'm checking this.

batch_first – If True, then the input and output tensors are provided as (batch, seq, feature)

Update

This is much better than before. I'm afraid there are still small bugs in my code. But this was really critical. Epoch: EM (p1, p2) 0: 20%, 20% 1: 37%, 38% 2: 44%, 46% 3: 48%, 51% 4: 52%, 54% 5: 56%, 58% 6: 59%, 61% 7: 61%, 64% 8: 64%, 67% 9: 66%, 69% 10: 68%, 71% 11: 70%, 73% 12: 71%, 74%

But test acc was not so improved :( p1 acc: 38.409%, p2 acc: 39.669%

[jojonki]

After 30 epochs, test acc was 40% in following settings

• p1 only
• use log_softmax and NLLLoss
• no EMA for variables

[t-li]

@oneTaken Hi, I am working on reproducing BIDAF model as well. But got stuck on dev EM 0.5 while the training EM can go much higher. Changing learning rate and learning algo (Adam vs Adagrad vs Adadelta) didn't help unfortunately.

I run through the author's tensorflow code, but I did not fully digest the preprocessing part, which leads to some questions. Also I made side-by-side comparison between this repo vs BIDAF's tensorflow repo vs my own code. Found some very obvious differences.

1. Are context sentences taken independently in LSTM? I mean in all LSTM?

In BIDAF's tensorflow code, I assumed the padding is some large negative numbers so as to reset lstm hidden states to all 0's between sentences.

In contrast, in this repo the sentences are taken as one sequence for RNN.

2. Are context sentences independent in biattention layer? To my understanding of BIDAF's tensorflow code, the Q2C attention at (https://github.com/allenai/bi-att-flow/blob/49004549e9a88b78c359b31481afa7792dbb3f4a/basic/model.py#L397) shows softmax runs over each context sentence independently.

But in this repo and the arxiv paper, it seems the softmax "should" run over the entire context sequence.

3. Have you guys tried to remove char-cnn layer? The BIDAF arxiv paper shows 0.65 EM without char-cnn. But still I couldn't reproduce it.

[aneesh-joshi]

Hi @jojonki Any progress/update on the current repo? I find your code very easy to read and it would be great if it works!

[jojonki]

Hi @aneesh-joshi Thank you for your feedback. Unfortunately, I could not have enough time to fix my model. As DongjunLee suggested, the following may help. If you can find some difference, please report to us! https://github.com/allenai/allennlp/blob/master/allennlp/models/reading_comprehension/bidaf.py

[aneesh-joshi]

Thanks @jojonki I wanted to use a modification of BiDAF for Transfer Learning from Span to QA. I have implemented a version of it but I cannot get it to work as advertised.

Thanks for your work.