TF-Coder, a tool that writes tricky TensorFlow expressions
blog.tensorflow.org
blog.tensorflow.org
Ouch! The combinatorial space of arbitrary programs is huge. This can't work for programs of any but the smallest size.
Indeed:
>> There are limitations to TF-Coder. It can currently find solutions involving 3-4 operations within a minute of searching, but solutions involving 6 or more operations are too complex to find in a reasonable amount of time.
Btw, my PhD research is pretty much on something like this but learning Prolog programs from examples, rather than Python. The problem is extremly hard and all solutions so far look at ways to restrict the large hypothesis search spaces, mainly by imposing some kind of strong inductive or language bias (i.e. restricting what can be learned and how) but there is always a point beyond which any way one tries to search for an arbitrary program will fall into a huge search space and disappear down a myriad winding paths that lead nowhere. So while it's interesting to see this effort (taking hints from natural language is a nice touch) program syntehsis or program learning will not be solved by any search-based algorithm.
Edit: Actually, the bit about how solutions involving 6 or more operations take forever to return, is eerily familiar. An algorithm I was working on at the start of my PhD was pretty much limited to programs of 5 clauses (roughly, 5 function calls). I'm sure it's a coincidence but, golly.
In any case, if there was no such limitation most people here would be out of a job now, so it's an important limitation.
>> And finding short compositions is a bonus.
You mean because they can be easily inspected and debugged and edited by hand if required? If so, yes, I agree. But, for example, the algorithm I worked with, that I discuss above, a big criticism of the underlying technique has always been that _in practice_ it's limited to learning small programs.
You could easily lose an hour building and testing a solution based on stacking your desired indices with ranges and feeding it to scatter_nd.
Say I know math but don't know tf api. I already know how to matrix multiply with `@` but don't know the function for matrix power.
Then in theory it should be fairly quick for me to create a random matrix and feed it `mat @ mat`, `mat @ mat @ mat`, `mat @ mat @ mat @ mat`, etc until it spits out the correct tf function I should call for matrix_power.
This is even better than googling since it guarantees that the function it found is correct for your example (googling returns tf.pow tf.exp tf.expm, none of which are actually what you want!)
When communicating in mathematics you often assume the reader can understand precisely which mathematical object you're talking by extrapolating from ellipses "...". For example "the naturals are 1,2,3,4,..." or "the evens are 2,4,6,8,..."
Or I might tell you a shift matrix is something that looks like:
[0 1 0 0 0]
[0 0 1 0 0]
[0 0 0 1 0]
[0 0 0 0 1]
[0 0 0 0 0]
But if you want to explain what a shift matrix is to a computer, you must use a formal definition (and to do it idiomatically you need to know esoteric details like that numpy.diagonal takes in an offset parameter so you can do something like https://stackoverflow.com/a/30230801).
It would be nice if we can talk math with a computer in the same intuitive language we use with humans, just give it a few examples and it will generate the the rest and let you double check the formula is correct.
So I see stuff like tf-coder as a great step, not for synthesizing programs we don't know how to solve, but for language translation: between math notation and programming notation.
Anyway I guess I'm turnning into the typical stuffy academic who can't even begin to think of real-world applications, hah. Thanks for pointing out the practical uses of the work described in the article.
The number of times I've sat with 2 developers looking over my shoulder trying to workout WTF my TF (or Numpy or Pytorch) manipulations aren't ending up the correct shape is huge. It's probably $50-100K/year of time for the last 4-5 years.
It's always obvious in retrospect.. until next time you have to read that code.
Keras tries to help you, but the messages are inscrutable sometimes. "Oh there's this extra dimension that has no size, and this other that's just size 1 but if you try to flatten it everything stops working"
But how will this tool help? If you put the sample you used in a comment, then your confusion is wondering if that's in sync... and whether the input is like what you wrote.
IMO the sweet spot that's easy both for humans & computers is index notation. Instead of `tf.add(X, tf.expand_dims(Y, 1))` (their example, which I think also applies to 4-tensors in ways not trivial to see from the sample) something like "out_{r,c} = X_r + Y_c" is completely unambiguous, and quicker to type than making a sample.
I don't think you can do that in tf.einsum, nor einops. Is there a Python package which does this? In Julia you can write `@cast out[r,c] := X[r] + Y[c]` (with my package, but several others share this notation).
> But how will this tool help?
It's absolutely true that it doesn't help. But that's a second order problem to the hours spend trying to work out how to write the code.
It could also generate comments that explain what is happening..
X + Y[:, tf.newaxis]
For higher dimensions of Y, you can use (but it might be ill-advised)
X + Y[:, tf.newaxis, ...]
99pi has a great episode on this phenomenon related to autopilot in planes https://99percentinvisible.org/episode/children-of-the-magen... where over automation makes edge case situations much harder to handle (in the case of planes this is deadly)
I don't see any problem in automating the generation of the line-noise that is array manipulations.
Can I write assembly? No. Do I understand what a compiler is doing? Yes. Does this cause problems for me? Maybe twice in my 20 year career and in both cases I've worked it out. Seems like a good trade off, and I see this as similar.
[0]imo always a hallmark of something special. Or maybe someone has thought of it and I'm out of the loop, but either way this approach seems seriously useful for streamlining the syntactic process of generalizing a function for a set of known inputs/outputs.
https://en.wikipedia.org/wiki/Inductive_programming
The wikipedia article doesn't make it very clear but basically it's what you say. The paper accompanying the system in the article goes through a bit more detail on Program Synthesis, of which Inductive Programming is sometimes considered to be a sub-field, but in any case, Program Synthesis is generating programs from complete specifications while Inductive Programming is learning programs from incomplete specifications- i.e. input-output examples of the target program. Examples are often supplemented by constraints.
The field goes back to the 1970's and is today comprised of two sub-fields, Inductive Functional Progamming, i.e. learning programs in functional languages and Inductive Logic Programming, i.e. learning programs in logic programming languages (and, incidentally, the subject of my PhD research).
In particular, the way you describe TF-Coder reminded me of Algorithmic Debugging:
https://en.wikipedia.org/wiki/Algorithmic_program_debugging
In short, this was a technique first described in 1982 by Euhd Shapiro, that assisted a programmer with finding bugs in a logic program (in Prolog) by taking in examples of true and false statements that the program should prove or disprove, respectively, and identifying program statements that made a true statement unprovable, or a false statement provable. The funny thing is that, once you can do this sort of thing, identify which parts of a program lead to coverage of true and false statements, you can go the other way and also generate those statements.
In any case, it's a huge field that goes way back and there are many pointers on more information in the wikipedia article.
I tried:
inputs = {
'matrix': [[10, 20, 30],
[5, 5, 10],
[2, 2, 1]]
}
output = [[85/10, 85/20, 85/30],
[85/5, 85/5, 85/10],
[85/2, 85/2, 85/1]]
tf.cast(tf.divide(tf.reduce_sum(matrix), matrix),
tf.float32)
It couldn't solve it - it was a broadcasting problem though.
# A dict mapping input variable names to input tensors.
inputs = {
'matrix': [[1, 1], [1, 0]],
'vector': [[1], [0]],
}
# The corresponding output tensor.
output = [[13],
[8]]
# A list of relevant scalar constants, if any.
constants = [6]
# An English description of the tensor manipulation.
description = 'Whatever the equivalent of numpy.linalg.matrix_power is in tf. Calculates fibonacci.'
matrix @ matrix @ matrix @ matrix @ matrix @ matrix @ vector
> There are limitations to TF-Coder. It can currently find solutions involving 3-4 operations within a minute of searching, but solutions involving 6 or more operations are too complex to find in a reasonable amount of time. Furthermore, TF-Coder currently does not support complex or string tensors, or RaggedTensors. The full list of supported operations can be found in the Colab notebook.
You can think of tensorflow like a different language where the syntax tree is constructed of python objects. For loops end up being like a preprocessor macro on that syntax tree. They can add a predetermined set of extra nodes, but the "for loop" part isn't represented in the final syntax tree itself, so tensorflow doesn't know it has anything to optimize.
There are ways out of this: one is dynamic graph execution. But this is less optimizable. Tensorflow doesn't get to see the whole graph so it can't do as many optimizations, and it can't optimize for loops because it's actually executing eagerly on each loop iteration.
Another way is this article: write whatever python code you want to generate some transformation, and tensorflow can try to guess which efficient operations produce it. Then you can paste the efficient operations into your final program.
Your first two paragraphs describe TF 1 perfectly, and you also correctly point out that TF 2 has eager mode, which executes op by op.
The part you missed is that @tf.function lets users tell TensorFlow that TF should execute the entire function, inspect the graph that it produces, and optimize it. On top of this JIT functionality that tf.function provides, AutoGraph will inspect the source code of your program and see that there's a for loop. If the for loop executes over a Python object, then it acts as a preprocessor macro. If it executes over a tensor, then AutoGraph transforms it into a tf.while_loop. So you can have both Eager-style convenience and graph-style optimizations.
Of course, some caveats apply; check out https://www.tensorflow.org/guide/function for the full guide.