A Purely Functional Typed Approach to Trainable Models (Part 2)

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Welcome back! We’re going to be jumping right back into describing a vision of a purely functional typed approach to writing trainable models using differentiable programming. If you’re just joining us, be sure to check out Part 1 first!

In the last post, we looked at models as “question and answer” systems. We described them as essentially being functions of type



f : P \rightarrow (A \rightarrow B)

Where, for f_p(x) = y, you have a “question” x : A and are looking for an “answer” y : B. Picking a different p : P will give a different A \rightarrow B function. We claimed that training a model was finding just the right p to use with the model to yield the right A \rightarrow B function that models your situation.

We then noted that if you have a set of (a, b) observations, and your function is differentiable, you can find the gradient of p with respect to the error of your model on each observation, which tells you how to nudge a given p in order to reduce how wrong your model is for that observation. By repeatedly making observations and taking those nudges, you can arrive at a suitable p to model any situation.

This is great if we consider a model as “question and answer”, but sometimes things don’t fit so cleanly. Today, we’re going to be looking at a whole different type of model (“time series” models) and see how they are different, but also how they are really the same.

For following along, the source code for the written code in this module is all available on github.

Time Series Models

In the wild, many models are not simple “question and answer”, but rather represent a “time series”. As a generalization, we can talk about time series models as:



f_p(x,t) = y

Which says, given an input and a time, return an output based on both. The point of this is to let us have recurrent relationships, like for autoregressive models found in statistics:



\text{AR}_{\phi_1, \phi_2, \ldots}(x,t)
  = \epsilon_t + \phi_1 \text{AR}_{\phi_1, \phi_2, \ldots}(x, t-1)
  + \phi_2 \text{AR}_{\phi_1, \phi_2, \ldots}(x, t-2)
  + \ldots

However, this is a bad way of implenting models on time serieses, because nothing is stopping the result of a model from depending on a future value (the value at time t = 3, for instance, might depend explicitly only the value at time t = 5). Instead, we can imagine time series models as explicitly “stateful” models:



f_p(x, s_{\text{old}}) = (y, s_{\text{new}})

These have type:1



f : (P \times A \times S) \rightarrow (B \times S)

This makes it clear that the output of our model can only depend on current and previously occurring information, preserving causality.

Examples

We can use this to represent an AR(2) model (autoregressive model with degree 2), which is a model whose output forecast is a linear regression on the last two most recent observed values. We can do this by setting the “input” to be the last observed value, and the “state” to be the second-to-last observed value:



\begin{aligned}
s_t & = x_t \\
y_t & = c + \phi_1 s_t + \phi_2 s_{t - 1}
\end{aligned}

Or, in our function form:



f_{c, \phi_1, \phi_2}(x, s) = (c + \phi_1 x + \phi_2 s, x)

There’s also the classic fully-connected recurrent neural network layer, whose output is a linear combination of the (logistic’d) previous output and the current input, plus a bias:



\begin{aligned}
s_t & = \sigma(y_t) \\
y_t & = W_x \mathbf{x}_t + W_s \mathbf{s}_{t-1} + \mathbf{b}
\end{aligned}

Or, in our function form:



f_{W_x, W_s, \mathbf{b}}(\mathbf{x}, \mathbf{s}) =
  ( W_x \mathbf{x} + W_s \mathbf{s} + \mathbf{b}
  , \sigma(W_x \mathbf{x} + W_s \mathbf{s} + \mathbf{b})
  )

The connection

This is nice and all, but these stateful models seem to be at odds with our previous picture of models.

  1. They aren’t stated in the same way. They require specifying a state of some sort, and also a modified state
  2. These can’t be trained in the same way (using stochastic gradient descent), and look like they require a different algorithm for training.

However, because these are all just functions, we can really just manipulate them as normal functions and see that the two aren’t too different at all.

Functional Stateful Models

Alright, so what does this mean, and how does it help us?

To help us see, let’s try implementing this in Haskell. Remember our previous Model type:

which represented a differentiable f : (P \times A) \rightarrow B. We can directly translate this to a new ModelS type:

which represents a differentiable f : (P \times A \times S) \rightarrow (B \times S).

We can implement AR(2) as mentioned before by translating the math formula directly:

Our implementation of a fully-connected recurrent neural network is a similar direct translation:

Because we again have normal functions, we can write a similar stateful model composition function that combines both their parameters and their states:

(Here we use our handy (:&&) pattern to construct a tuple, taking a BVar z a and a BVar z b and returning a BVar z (a :& b))

And maybe even a utility function to map a function on the result of a ModelS:

With this we can do some neat things like define a two-layer fully-connected recurrent neural network.

(Again using type application syntax with @10 to specify our hidden layer size, and the type wildcard syntax _ to let the compiler fill in the parameter and state type for us)

Hey, maybe even a three-layer one:

Let there be State

Because these are all just normal functions, we can manipulate them just like any other function using higher order functions.

For example, we can “upgrade” any non-stateful function to a stateful one, just by returning a new normal function:

This means we can make a hybrid “recurrent” and “non-recurrent” neural network, by making feedForwardLog' a model with some dummy state (like () perhaps), and re-using (<*~*).

But we can also be creative with our combinators, as well, and write one to compose a stateless model with a stateful one:

Everything is just your simple run-of-the-mill function composition and higher order functions that Haskellers use every day, so there are many ways to do these things — just like there are many ways to manipulate normal functions.

Unrolling in the Deep (Learning)

There’s something neat we can do with stateful functions — we can “unroll” them by explicitly propagating their state through several inputs.

This is illustrated very well by Christopher Olah, who made a diagram that illustrates the idea very well:

Christopher Olah’s RNN Unrolling Diagram
Christopher Olah’s RNN Unrolling Diagram

If we look at each one of those individual boxes, they all have two inputs (normal input, and previous state) and two outputs (normal output, new state).

“Unrolling” a stateful model means taking a model that takes in an X and producing a Y and turning it into a model that takes an [X] and produces a [Y], by feeding it each of the Xs one after the other, propagating the state, and collecting all of the Y responses.

The “type” of this sounds like:

In writing this out as a type, we also note that the p parameter type is the same, and the s state type is the same. (Aren’t types nice? They force you to have to think about subtle things like this) If you’re familiar with category theory, this looks a little bit like a sort of “fmap” under a Model p s category – it takes a (stateful and backpropagatable) a -> b and turns it into an [a] -> [b].

Olah’s post suggests that this is some sort of mapAccum, in functional programming parlance. And, surely enough, we can actually write this as a mapAccumL.

mapAccumL is sort of like a combination of a foldl and a map:

Compare to foldl:

You can see that mapAccumL is just foldl, except the folding function emits an extra c for every item, so mapAccumL can return a new [c] with all of the emitted cs.

The backprop library has a “lifted” mapAccumL in in the Prelude.Backprop module that we can use:

It is lifted to work with BVars of the items instead of directly on the items. With that, we can write unroll, which is just a thin wrapper over mapAccumL:2

This reveals that unroll from the machine learning is really just mapAccumL from functional programming.

We can also tweak unroll’s result a bit to get a version of unroll that shows only the “final” result. All we do is mapS last . sequenceVar :: BVar s [a] -> BVar a, which gets the last item in a BVar of a sequence.

Alternatively, we can also recognize that unrollLast is really just an awkward left fold (foldl) in disguise:

To see how this applies to our threeLayer:

Nice that we can trace the evolution of the types within our langage!

State-be-gone

Did you enjoy the detour through stateful time series models?

Good — because the whole point of it was to talk about how we can get rid of state and bring us back to our original models!

You knew this day had to come, because all of our methods for “training” these models and learn these parameters involves non-stateful models. Let’s see now how we can turn our functional stateful models into functional non-stateful models!

One way is to fix the initial state and throw away the resulting one. This is very common in machine learning contexts, where many people simply fix the initial state to be a zero vector.

We use auto :: a -> BVar z a again to introduce a BVar of our initial state, but to indicate that we don’t expect to track its gradient. zeroState is a nice utility combinator for a common pattern.

Another way is to treat the initial state as a trainable parameter (and also throw away the final state). This is not done as often, but is still common enough to be mentioned often. And, it’s just as straightforward!

trainState will take a model with trainable parameter p and state s, and turn it into a model with trainable parameter p :& s, where the s is the (trainable) initial state.

We can now train our recurrent/stateful models, by unrolling and de-stating:

zeroState (unrollLast threeLayers) is now a normal stateless (and trainable) model. It takes a list of inputs R 40s and produces the “final output” R 5. We can now train this by feeding it with ([R 40], R 5) pairs: give a history and an expected next output.

It’s again nice here how we can track the evolution of the types of out model’s inputs and outputs within the language. Unrolling and zeroing is a non-trivial interaction, so the ability to have the language and compiler track the resulting shapes of our models is a huge advantage.

The Unreasonably Effective

Let’s see if we can train a two-layer fully connected neural network with 30 hidden units, where the first layer is fully recurrent, to learn how to model a sine wave:

Trained! trained is now the weight and bias matrices and vectors that will simulate a sine wave of period 25.

We can run this model iteratively upon itself to test it; if we plot the results, we can visually inspect it to see if it has learned things properly.

Let’s define some helper functions to test our model. First, a function prime that takes a stateful model and gives a “warmed-up” state by running it over a list of inputs. This serves to essentially initialize the memory of the model.

Then a function feedback that iterates a stateful model over and over again by feeding its previous output as its next input:

Now let’s prime our trained model over the first 19 items in our sine wave and start it running in feedback mode on the 20th item!

Plotting the result against the “actual” sine wave of period 25, we see that it approximates the process decently well, with a consistent period (that is slightly slower than the reference period):

FCRNN Sine Wave
FCRNN Sine Wave

For kicks, let’s see if we can do any better with the simpler AR(2) model from before. Applying all we just used to ar2, we see:

zeroState (unrollLast ar2) is now a trainable stateless model. Will it model a sine wave?

We can plot the result and see that it more or less perfectly models the sine wave of period 25:

AR(2) Sine Wave
AR(2) Sine Wave

You can’t even visually see the difference!

We can peek inside the parameterization of our learned AR(2):

Meaning that the gradient descent has concluded that our AR(2) model is:



y_t = 0 + 1.9372 y_{t - 1} - y_{t - 2}

The power of math!

In this toy situation, the AR(2) appears to do much better than our RNN model, but we have to give the RNN a break — all of the information has to be “squished” into essentially 30 bits, which might impact the model’s accuracy.

Functions all the way down

Again, it is very easy to look at something like

and write it off as some abstract API of opaque data types. Some sort of object keeps track of state, and the object has some nice abstracted interface…right?

But, nope, again, it is all just normal functions that we wrote using normal function composition. We define our model as a function, and the backprop library turns that function into a trainable model.

What Makes It Tick

Let’s again revisit the four things I mentioned that are essential to making this all work at the end of the last post, but update it with new observations that we made in this post:

  1. Functional programming is the paradigm that allowed us to treat everything as normal functions, so that our combinators are all just normal higher-order functions.

    Our stateful models can also be combined and reshaped seamlessly in arbitrary ways, just like our non-stateful ones. And the fact that they are both normal functions means that they are built on the same underlying mechanic.

    We can write combinators like (<*~*) and mapS, but they are never necessary. They are always just convenient. But by writing such combinators, we open our mind to different ways that we can construct new models by simply transforming old ones.

    The revelation that an unrolled model was simply a combinator application came about by simply looking at the types and applying a model to a simple higher order function mapAccumL and foldl, which was already written for us. We were able to use common functional programming tools that are provided in standard libraries. This is only possible because our models are themselves functions in the same shape that those common tools already are built to work on.

    In addition, functional programming forces us to have first-class state. The “state” in our stateful models wasn’t a property of the runtime system — they were things we explicitly defined and carried. This allows us to write combinators that manipulate how state works. We can transform a function’s state arbitrarily because the function’s state is always something we can explicitly manipulate.

  2. Differentiable programs — again, made more powerful through how well it integrates with functional programming techniques.

  3. Purely functional programming. One might have thought that writing “recurrent” or “stateful” models were something that imperative models excelled in, but we see now that in a functional setting, forcing ourselves to use explicit state allows us to manipulate state and state manipulation as a first-class citizen of our language, instead of something built-in and implicit and rigid.

  4. A strong expressive static type system ties all of this together and makes it possible to work in.

    This forces us to be aware of what parameters we have, how they combine, etc.; this is what makes combinators like recurrent and unroll and zeroState reasonable: the compiler is able to trace how we move around our parameter and state, so that we don’t have to. It lets us ask the compiler questions like “what is the state type, now?” if we needed, or “what is the parameter type now?”.

    We sometimes even gained insight simply from thinking, in advance, what the types of our combinators were. We had to make conscious decisions when writing the type of unroll and zeroState. And, if we can phrase our combinators in terms of our types, the compiler will often be able to write our entire program for us — something only possible for statically typed languages.

In the next and final post, we’ll wrap this up by peeking into the wonderful world of functional combinators and look at powerful ones that allow us to unify many different model types as really just different combinator applications of the same thing. I’ll also talk about what I think are essential in building a usable framework for working with this in practice.


  1. If you recognized our original stateless model type as a -> Reader p b, then you might have also recognized that this is the Haskell idiom a -> StateT s (Reader p) b (or Kleisli (StateT s (Reader p)) a b), which represents the notion of a “function from a to b with environment p, that takes and returns a modified version of some ‘state’ s”.

  2. In truth, mapAccumL can work with any Traversable container, so we really can unroll over any Traversable container and not just lists. One of my favorite is the sized vectors from the vector-sized library, since they can enforce that the network always gets unrolled over the same number of items.

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