How to Handle Out-of-Vocabulary Words Pragmatically

Using pre-trained word embeddings can be a huge advantage if you don’t have enough data to train a proper embedding, but also if you are afraid to overfit to the task at hand if you train it jointly with your model. In case of English you find quite a lot word embedding models that are publicly available, but it’s different for other languages like French, German or Spain. There are some available, like the ones provided with fastText, but depending on the language, it can be challenging. So, let’s assume that you have some luck and there is a pre-trained model, then the next challenge waits just around the corner. The problem is that for specific tasks, there are definitely words that are not present in the vocabulary. There are some dirty solutions like mapping all those words to a fixed token like ‘UNK’ or using random vectors, but none of these approaches is really satisfying.

In case of fastText there is a clever, built-in, way to handle oov words: n-grams. The general idea of n-grams is to also consider the structure of a word. For instance, with n=3 and word=’where’: [%wh, whe, her, ere, re%]. The % are used to differ between the word “her” and the ngram her, since the former is encoded as “%her%” which leads to [%he, her, er%]. In case of a new word, the sum of n-grams is used to encode the word which means as long as you have seen the n-grams, you can encode any new word you require. For a sufficiently large text corpus, it is very likely that a large portion, or even all, required n-grams are present.

Since most implementions, also fastText, is using the hashing trick, you cannot directly export the mapping n-gram vector, however, there is a function to query n-grams for a given word:

$ fasttext print-ngrams my_model "gibberish"

There is an open pull request (#289), to export all n-grams for a list of words, but right now to call fasttext for each new word which is very inefficient in case of huge models. Without knowing about the pull request, we did exactly the same. First we slightly modified the code to accept a list of words from stdin and then we performed a sort with duplicate elimination to get a distinct list of all n-grams which are present in the model.

Now, we have a pre-trained model for the fixed vocabulary, but additionally, we also have a model that allows us to map oov words to the same vector space as long as the n-grams are known. We also evaluated the mapping with slightly modified or related words which are not in the vocabulary with very good results.

Bottom line, without a doubt n-grams are not the silver bullet but they help a lot if you work with data that is dynamically changing, which includes spelling errors, variations and/or made-up words. Furthermore, publicly available models often deliver already solid results which takes the burden from you to train a model yourself which might overfit to the problem at hand or is not satisfying at all because you don’t have enough data. In case a model does not come with n-gram support, there is also a good chance to transfer the knowledge encoded in the vectors into n-grams by finding an appropriate loss function that preserves this knowledge in the sum of n-grams.


An Addendum to Batch Processing With Variable Sequences

We said that there is no example code for the whole processing steps which seemed a little rash since there seem to be some gist snippets and we want to give at least credit to one:

which is minimal but at the same time very well documented.

Just a quick note which might lead to the next blog post: After we trained our network, we wanted to do a under-the-hood analysis of the reset and forget gates of the GRU cells in case of the few errors the network makes. However, due to stacking the parameters, for performance reasons, a straightforward analysis needs some more preparation. In general the question is, if we use pre-defined modules, how can we debug the internal states of individual steps and units?

PyTorch – Batching With Recurrent Nets

Implementing RNNs in PyTorch works like a charm thanks to the dynamic graph computation. All you have to do is a loop where you feed the input to the network and keep track of the new hidden state. At the end, you can feed the last state (or the average of all) into a new layer which can be a classifier or whatever is required for the loss function at hand. This was the easy part. However, working with RNNs in single-batch mode is incredible inefficient when you need to train a very large dataset. The problem is the sequential nature of the RNNs which does not allow to process input in parallel. With mini batches, we can at least use hardware parallelism to speed up the pipeline and we might get a more stable gradient because we use multiple inputs to estimate it.

What is astonishing is that PyTorch provides functionality to help you with the issue, but there is no tutorial or example code that contains all the steps. Sure, there are blogs and snippets on the web that explain it, but often a stand-alone, fully working, example allows to retrace the whole process more easily. Indeed, once you know all the details it is fairly simple to implement, since the PyTorch team did a very good job to hide all the nasty details from the users.

So, let’s start to describe the actual problem: During training, RNNs deal with sequences of different lengths which is no problem in single batch mode. However, if you want to use batching, you have to use padding to convert all samples to the same length as a first step. This can be done by using an extra “dummy” entry (“padding_idx”) in the nn.Embedding module which is added to each input at the end
until all inputs in the batch have the same length. But that is only the first step, since the RNN must ignore all those padded tokens for each input sequence while deriving the gradient w.r.t to the loss function.

This sounds a bit complicated because we have to fiddle with the computational graph, but kindly, there are helper functions for this to avoid to get your hands dirty. But let us start at the begin. Let us assume that we have an input X = [A, B, C] and the length of each sequence X_len = [4, 2, 8]. First, we need to pad each sequence to get a uniform length which requires to sort the input in decreasing order:

X = [torch.ones(4), torch.ones(2), torch.ones(8)]
X.sort(key=lambda x: x.shape[0], reverse=True)
X_pad = pad_sequence(X, batch_first=True, padding_value=0).long()
tensor([[ 1, 1, 1, 1, 1, 1, 1, 1], [ 1, 1, 1, 1, 0, 0, 0, 0], [ 1, 1, 0, 0, 0, 0, 0, 0]])
X_len = torch.LongTensor(map(lambda x: x.shape[0], X))

The option “batch_first” just ensures that the shape is always (batch, seq, feature).

As we can see, each sequence has now a length of 8 with “0”s as padding whenever required. Since we need the unpadded length of each sequence later, we also calculate X_len. With X_pad we can already perform a lookup in an nn.Embeding module:

emb = nn.Embedding(2, 5, padding_idx=0) #n_vocab, n_dim
X_emb = emb(X_pad)

Now, we are ready to feed the input to the RNN:

# setup network and initialize hidden states to zero
net = nn.GRU(5, 10, batch_first=True) #n_dim, n_units
hidden = torch.zeros(1, X_emb.shape[0], 10)
# pack batch
X_packed = pack_padded_sequence(X_emb, X_len, batch_first=True)
# forward step
out, hidden = net(X_packed, hidden)
# unpack batch
out, _ = pad_packed_sequence(out, batch_first=True)
# retrieve the last hidden state w.r.t to the original length for each sequence
idx = torch.arange(0, len(X_len)).long()
out_final = out[idx, X_len - 1, :]

The required steps can be easily wrapped into some class that hides all the nasty details and allows to get the output of an arbitrary recurrent network for a batch of (text) sequences in a straightforward way.

However, there is a drawback we need to take care of. For example, if we train a classifier and we sample a mini batch and the corresponding labels (X, Y), the procedure described above changes the order of X, while Y remain the same. The problem arises because of the sorting step that is only applied to X which makes the solution obvious: we also have to sort Y, but by X_len to get the identical order. The following code is not very nifty, but it works:

Y = [-1, 1, -1]
Y_ = zip(Y, X_len)
Y = map(lambda x: x[0], sorted(Y_, key=lambda x: x[1], reverse=True))

Bottom line, single-batch use of RNNs is a piece of cake, but the performance neither let you allow to train bigger networks or larger datasets, nor is the inference performance sufficient for real-world use. Despite the fact that the pad/pack/unpack scheme by PyTorch is not very complicated, it still needs some time to get used to it. But once one mastered it, the performance gain is more than noteworthy and allows to use RNNs at a much larger scale.

How to Learn Good Features?

Since quite some time we fiddle with movie data to learn a representation of the meta data that is both universal and powerful enough for various tasks. The major problem is that the data is horribly incomplete, bias towards popular items and due to human nature not always objective. But let’s assume for the moment that we can learn features based on the data, then the question is how to shape the learning process to get features as versatile as possible?

There is a very nice post about feature transformation on distill that can be summarized as conditional layer normalization. For example, to answer relational questions about an image, the query is used as a context that guides the learning of the conv net. To be a bit more precise, depending on the question the output of the filters is adapted by scaling and shifting. This way, units can be turned on/off or the magnitude can be adjusted. The idea to not assume a strong prior about the data is a clever trick to avoid to manually engineer to explicit knowledge into a network.

With text for the queries and images to describe the content the task is still challenging, but at least the data is complete in the sense that it is possible to answer the questions with a correctly learned net (whatever this looks like). In our case, we have at least two problems: First, we don’t know if the data at hand is sufficient even for simple tasks and second, we need to find an appropriate
context that is always available.

So, with all this in mind a better question might be if we can somehow measure if the input data is powerful enough to solve the formulated problem at all. And let’s assume for the moment that we have all the computational power we need. In other words, even with the most powerful network and a millions of GPUs to train it, it is still possible that no such function f(x) exists that gives the correct answer given the particular input data x.

This is related to neural architecture search where one tries to find the best network architecture for the data, but here the assumption is that such a function f(x) exists. And such an assumption is reasonable, since usually images contain enough details to let a network give the correct answer. In case of human-engineered textual features, there is no such guarantee that they generalize beyond the purpose that they were created for which is usually purely informative.

At the end, we are back to where we started: Without external knowledge it seems almost hopeless to train networks with such data that can do more than simple classification, at best. But the problem are not the networks, but the lack of proper data, or better how to enhance and incorporate such data. With Wikipedia a lot of knowledge is available, but it is not a trivial task to extract relevant information and assembly them so it can be feed into the network.

There is a recent trend to make larger data sets publicly available, but it is wishful thinking that even big companies have all the data you need and/or the will to release it. Maybe it’s time to work harder on an Open Data Initiative (ODI) for machine learning, or come up at least with a community based service like a model zoo, but for data.

PyTorch: Convolutional Autoencoders Made Easy

Since we started with our audio project, we thought about ways how to learn audio features in an unsupervised way. For instance, in case of speaker recognition we are more interested in a condensed representation of the speaker characteristics than in a classifier since there is much more unlabeled data available to learn from. However, without supervision there is always the risk that the learned representation does not help in the task at hand. Still, it’s worth a try since the data is available and so are suited network architectures.

Autoencoders (AEs) have a long history in machine learning and since some years, the convolutional variant became also more and more popular. However, since conv AEs use inverse operations and some advance stuff to recover lost information during the forward-propagation step, we thought it is a good idea to provide a clean, minimal example with some additional hints which help to understand the workflow. Without a doubt there are other examples around, but we did not find one that was exactly matching our domain (audio + conv1d), or at least not a minimal one that does not involve studying lots of unrelated code.

The conv AE consists of two modules, an encoder and a decoder which is not different to the vanilla AE. The encoder part looks a lot like a common convnet with some minor, but important variations:

c1 = nn.Conv1d(in_size, 16, 3)
m1 = nn.MaxPool1d(2, return_indices=True)
i1 = None
c2 = nn.Conv1d(16, 16, 3)

The first layer c1 is an ordinary 1D convoluation with the given in_size channels and 16 kernels with a size of 3×1. The next layer m1 is a max-pool layer with a size of 2×1 and stride 1×1. Additionally the indices of the maximal value will be returned since the information is required in the decoder later. The last layer is again conv 1d layer.

The forward step looks like that:

_c1 = c1(x_in)
_m1, i1 = m1(_c1)
return c2(_m1)

Again this should look pretty familiar, except for the pooling call because it returns both the output and the indices of the maximal value.

Then comes the decoder that uses the input from the encoder step:

d1 = nn.ConvTranspose1d(16, 16, 3)
u1 = nn.MaxUnpool1d(2)
d2 = nn.ConvTranspose1d(16, in_size, 3)

The architecture is reversed which means the last layer of the encoder fits into the first layer in the decoder. Thus, every layer is the inverse operation of the encoder layer: conv->transpose conv, pool->unpool. At the end, the full input is reconstructed again.

With the forward step as follows:

_d1 = d1(x_in)
_i1 = encoder.i1 # pool positions from encoder
_u1 = self.u1(_d1, _i1)
return self.d2(_u1)

Here we can see, that the unpooling uses the position information from the encoder. This is required since after the max pooling is done, no reversing is possible with the index information.

For example: x = (5, 10), maxpool(x, size=2) = 10 but we have no longer the information at which position the value was located: (10, ?) or (?, 10)? With the index from the encoder step, we can at least recover the position of the maximal value, but we still have to set all other values to 0 since this data is not available any longer: (0, 10). As a result, we still lose information but we can at least undo the maxpool step.

The workflow is easier to understand if we analyze the shape of each step:

Encoder: x_in=(1, 128, 44), c1=(1, 16, 42), m1=(1, 16, 21), c2=(1, 16, 19)
Decoder: x_hat_in=(1, 16, 19), d1=(1, 16, 21), u1=(1, 16, 42), d2=(1, 128, 44)

We can see that every shape in the decoder has a matching counterpart in the encoder: d2 x_in, u1 c1, d1 m1, x_hat_in c2.

Now, equipped with this knowledge, which can be also found in the excellent documentation of PyTorch, we can move from this toy example to a real (deep) conv AE with as much layers as we need and furthermore, we are also not limited to audio, but we can also build 2D convolutional AEs for images or even videos.

Let’s Make Some Noise

Sometimes it is a good idea to try a new direction when you are stuck. In other words, we needed some new inspiration and we thought it’s worth to turn to a very different domain, in our case audio. Furthermore, since quite some time we toyed with the idea to tag a specific voice in an audio signal by somehow learning a representation of the speaker, so it felt like the way to go.

A possible scenario looks like this: We record a movie via DVB-S and extract the audio stream. Then we convert the raw audio into a more suitable representation and classify all time frames, or time windows, with our learned model with +1/-1. At the end, we have time markers where the trained voice has been detected: [at min 3.1, at min 37.3, ..]. So far for the theory, now let’s turn to reality.

For us it was settled, that PyTorch is our framework of choice. Thus, as a first step we needed audio support. We hoped that in the spirit of torchvision, there is also torchaudio and we were not disappointed. The “load” function allows us to load arbitrary audio files in raw format and return the data as a tensor. However, this format requires a lot of computational resources, since every second is encoded as rate (e.g. 41,000) float values, per channel. Thus, the shape of the tensor is (rate * seconds, channels), which is huge for a full-length movie.

So we are interested in a more compact representation and as a first step, we converted stereo signals to mono (“transforms.DownmixMono”) which reduces the shape to (rate * seconds, 1). But since this is still a lot of data, we did some research to get an overview of popular transformations and we decided to use MEL spectrograms, also because there is an interface in the torchaudio package (“transforms.MEL”). With default values from papers, and re-sampling to 22,1000 Hz, each second of raw audio is now encoded as a (128, 22) matrix. In this setting, the rows are the frequency axis and the columns are the time axis. We further apply a log transformation on the data to avoid exploding gradients, since the magnitude of the spectrogram data can be very high.

Now the question is how to encode this information into a new representation to model the similarity between frames? There are several approaches possible. For instance, we could train an ordinary classifier one-vs-rest that outputs +1 if the frame is spoken by the speaker or -1 otherwise. But we opted for a triplet-based method to better model local neighborhoods. The drawback is that we cannot directly classify unseen frames, but we need some kind of nearest neighbor lookup to decide if the frame is a positive match. Thus, it makes sense that the positive data from training forms a memory component that in combination with a threshold acts like a classifier.

Next, we need to design our network architecture. With the chosen MEL transformation, we could easily train a feed-forward neural net, the input dim would be just 128*22=2816, but dense layers are not invariant to shifts in frequency[arxiv:1709.04396] and thus, a minor change in the input can lead to a larger change in the feature space. Thus, we decided to follow the steps of the early papers that uses convolution over the time axis to learn a representation which is a 1d convolution. The architecture is heavily inspired by the convnets from vision, with the exception that pooling and convolution just uses one channel, not two.

Thanks to PyTorch we have everything we need and a prototype consists just of a few lines of Python. Here is a sketch of the network:

import torch
from torch.nn import Conv1d
from torch.nn import MaxPool1d
from torch.nn import Linear
from torch.autograd import Variable
from torch.nn import functional as F

x = Variable(torch.randn(1, 128, 22))
c1 = Conv1d(in_channels=128, out_channels=32, kernel_size=3)
c2 = Conv1d(in_channels=32, out_channels=32, kernel_size=3)
m1 = MaxPool1d(2)
l1 = Linear(32, 16, bias=False)
h_2d = c2(m1(c1(x)))
h = F.adaptive_avg_pool2d(h_2d, (32, 1)).squeeze()
out = l1(h)

First, there is a convolution, followed by max-pooling, followed by a convolution and at the end, a global average pooling, that returns the mean of each filter map, followed by an affine transformation that represents the final embedding space. Additional blocks like normalization and non-linear activation functions are omitted for clarity. Such an architecture has a lot of benefits: First, we can stack blocks of conv/norm/relu/pool to form a deep network, second the network has also very few trainable parameters and last but not least, the forward step is computationally very efficient.

The training of the network is also pretty straightforward. The data set consists of spoken audio material by the person to recognize, as positive examples and arbitrary audio from other persons as negative examples. Without a doubt the selection of “the rest” impacts the performance of the network, since if all samples are already sufficiently far away from the speaker samples, no learning is done. This issue requires more research, but even our naive selection of negative samples lead to a solid performance.

Next, all audio files are pre-processed and split into frames of ~2 seconds on which the transformation is applied. The order of the frames is not preserved, since the “classification” works on single frames. A learning step consists of a sampling of an anchor and a positive sample and an arbitrary negative sample. Each input to the network represents a single time frame with the possibility to feed a batch of frames to the network. We l2 normalize all network output and use the cosine similarity to determine the triplet loss:
loss = torch.clamp(margin=0.3 + dot(anchor, negative) - dot(anchor, positive), min=0)
In other words, if the negative sample is sufficiently far away from the anchor (>= margin) no learning is required, otherwise the parameters are adjusted to push the negative sample away from the anchor.

However, it can be challenging to find good negative samples, since at later stages of the training, most samples are already well separated and thus have a loss of zero. This means, we need to find violators, outside the batch, to further improve the model. This can be computationally expensive, since we need to calculate the loss on many samples until we find enough of them. However, the procedure is required to ensure that we learn a good model and that the learning converges.

When the model is trained, the positive samples are fed to the network and the representation is stored as some kind of “memory”. As a baseline, new frames are classified by performing a nearest neighbor lookup (cosine similarity) on the memory and a frame is marked as “positive” if the mean of the top-5 scores from memory are above a threshold, like 0.7. Astonishingly, this baseline is pretty robust and already allows to reliably mark relevant time windows of audio material without too many false positives.

Bottom line, regardless of the domain, the machine learning pipeline stays pretty much the same. We have a problem, data, cleansing, optional a transformation and we need a good network architecture and a proper loss function to learn a good model. The next steps are more experiments to evaluate the model and to come up with a better way to classify unseen data based only on positive examples.

PyTorch: Identifying Computational Bottlenecks

It might happen that if we start with a new idea, we focus on the clarity of the code but not on the overall performance. Of course the model should not be slow as a snail, but often there is room for improvement. Still, first it is more important to get it working than to be super fast. When everything works well, it’s time to take a closer look at the code and to identify possible bottlenecks.

In our case, we often calculate dot products between vectors and matrices and there are different ways to do the math. For example:
(1) torch.sum(anchor * examples, 1) # shape: (1, dim) x (n, dim)
(2), 1)) # shape: (n, dim) x (dim, 1)
For both methods there is not much overhead, at least not function-wise, however, after we did some profiling, we found out that method (2) is about 40% faster than the first one. This is probably related to hardware utilization since (2) feels more “batched”.

Frankly, this is nothing new, but it just reminded us that for large-scale learning, using optimal numeric calculation can save you a day or week, or it can give you the opportunity to train a little longer. In our case, by introducing padding we reduced the time by almost 50% and now with the batched dot product, we got another 40%.