We recently landed a new feature for Pyston: frame introspection, the ability to inspect the local variables of stack frames (usually but not always the current one). There are a couple ways to directly make use of this feature from your Python code, such as the locals() method and the f_locals attribute on frame objects. There are also some Python features that aren’t explicitly about locals but require them to be available, such as eval() and exec statements. Finally, frame introspection is important for the JIT to be able to introspect its own stack frames to be able to support features such as deoptimization. (I should mention that none of these features work yet since they all depend on the base frame introspection mechanism, which only just got added.)
In interpreters, it can be relatively easy to support frame introspection since you will typically have a runtime structure that corresponds to all of local variables that are defined — this is how our interpreter tier works and adding frame introspection there wasn’t that difficult. For our compiled tier, we don’t have any such runtime structure: all the Python locals get converted to C-style locals and end up in registers or spilled onto the stack. We want to be able to locate all of our variables without affecting runtime performance or code generation, and to achieve this we used some new features of LLVM.
Pyston makes extensive use of LLVM’s new “patchpoint” intrinsic. If you’re curious, the documentation does a very good job of explaining what they are, but in short they provide two separate but related features: the ability to runtime-patch your generated code (which we use for inline caches), and the ability to receive the locations of runtime values as determined by the LLVM code generator, via “stackmaps”. We were already making extensive use of the first feature to do runtime code patching for various purposes (a future blog post, perhaps), and now we started using the stackmap feature. Our approach is pretty straightforward: for every callsite that might ever need frame introspection — which is essentially all of them — we attach all the local variables as stackmap arguments, and LLVM will generate a stackmap that tells us where those variables live during that callsite. Then at runtime, we interpret the stackmap and locate the variables.
As you can guess, the practice was slightly harder than the theory. While patchpoints have been quite robust for us in their common use, using them for all callsites exposed a number of bugs or missing features in LLVM’s implementation. The first is that they did not support LLVM’s exception handling mechanisms, which we use to for Python-level exceptions — we added this and it is now in LLVM trunk. We also had to add support for returning doubles from patchpoints (patch pending) and ran into an issue where we can’t use LLVM’s “i1” type to represent booleans for now. There are also some issues where LLVM will tell us that a value exists in a caller-save register, which we will have to save ourselves since they will get clobbered; there’s an ongoing discussion about how to resolve that.
To fix this, we now clear out temporary variables within a basic block. It means that we have to do slightly more work during the analysis phase, but the result is that there is practically no increase in runtime cost from turning on frame introspection and the associated extra LLVM IR.
As of this post, we have a working implementation of frame introspection, and one can call the locals() variable. Now we can go and implement some of the features that depend on it, such as eval() and exec — though those are tricky to model, since local variable updates by eval() can sometimes be seen by the outer scope and sometimes not. Try guessing what this will print:
x = 1 def f(): print map(eval, ["x", "locals().items()", "[x for x in xrange(5)]", "x", "locals().items()"]) print x f()
In addition to features that we want to build on top of this, there are a number of related optimizations we can do. For example, LLVM currently generates a separate stackmap per callsite, but for our uses, there is a fair amount of overlap between the different generated stackmaps (a spilled register will stay in the same stack slot), so we can store these more efficiently by doing some simple compression of the stackmaps. Our frame introspection also invalidates a number of optimizations that we used to do, since to the optimizer, every value will tend to immediately escape. In the long run, this is something that we need to have, and it enables more optimizations than it breaks so we are excited to have it.
Side note: what about standard debug info
LLVM already has an established mechanism for transmitting the locations of user source variables: this is something that any debugger will want of any language. LLVM has a series of intrinsics that can encode this information, which flow through the code generator and end up being emitted as DWARF, the standard debug info format that (for example) GDB will read. At first, it seems like a natural fit — we’re trying to do exactly what debug info is meant to do. LLVM has pretty good support for debug info (as good as clang’s is), and it solves a number of problems for us such as the efficient encoding of variable locations in addition to hooking us into a widely used standard.
We tried going down that road with little success. The problem is that Python has the requirement that all variables are always available through frame introspection. If you’ve ever used GDB on an optimized program and seen something like “(value optimized out)”, you know that normal C/C++ debugging systems don’t guarantee this. The problem is that guaranteed debug info can inhibit optimizations or cause extra work, and a very reasonable requirement of debug info is that the debug-enabled version has exactly the same code as the non-debug version. So LLVM does enforce a strict requirement, but unfortunately one that is opposite from our needs: they guarantee that they will not do any extra work to make your debug info available. Darnit.
One unfortunate conclusion of “not being able to use a non-pessimizing debug info scheme” is that our patchpoint approach does in fact hurt optimizations — by attaching the local variables to all callsites, we force variables to be live and computations to happen at certain points. For instance, most static compilers for languages like C/C++ will optimize away dead definitions like “x = 7”, since there is no way to observe that they occurred. In Python, unfortunately, a dead definition is observable since one can simply look at the locals and see it; in our approach, this translates to LLVM seeing that the variable being referenced by a patchpoint. There are a number of ways we can reduce this penalty, such as by “materializing” the values at the time of frame introspection instead of constructing them eagerly at runtime. We’ll keep an eye on this behavior and see how much such optimizations are necessary; my guess is that relatively little time is spent executing trivially-dead statements.
5 thoughts on “Frame introspection in Pyston”
“My guess is that relatively little time is spent executing trivially-dead statements.”
A compiler warning about the unnecessary assignment would resolve the issue, too.
The standard question there is: what about assignments that become trivially dead after inlining?
Pretty much everything in Python has observable side-effects, so even an unused assignment is not dead code in Python. This is actually a feature that people (ab)use!
The blog post already explains that, but can’t one detect when locals() & C. are not used after inlining virtual methods, with enough analysis?
Unfortunately that would be a very small amount of code that would pass that test. For example, any exception will implicitly capture all of the local variables of the entire stack. This is used, and used in combination with the dead-assignment case — there are stack-walking tools that will let you set a dummy variable in your frame to hide that frame from the stack trace.
Also, arbitrary-behavior can sneak in at pretty much any point. As a theoretical issue, yes I definitely agree that in many cases with “enough analysis” you can show a program is well-behaved. But as a practical matter, you need to prove a long list of things about so much of the program that I’m not sure it’s feasible. This is also assuming that the compiler can understand C code because everything in Python calls C code 🙂