Stack traces in GHCi, coming in GHC 8.0.1

tl;dr

In the upcoming GHC 8.0.1 release, if you start GHCi with ghci -fexternal-interpreter -prof (any packages you use must be built for profiling), then you get access to detailed stack traces for all the code you load into GHCi. Stack traces can be accessed via assert , error , Debug.Trace.traceStack, and the API in GHC.Stack.

Background

Haxl users at Facebook do a lot of development and testing inside GHCi. In fact, we’ve built a customized version of GHCi that runs code in our Haxl monad by default instead of the IO monad, and has a handful of extra commands to support common workflows needed by our developers.

Some of our codebase is pre-compiled, but the code being actively worked on is just loaded on the fly into GHCi during development and run with the interpreter. This works surprisingly well even for large codebases like ours, especially if you enable parallel compilation and use a bigger heap (e.g. ghci -j8 +RTS -A128m ). This is a pretty smooth setup: right inside GHCi we can test the production code against real data, and interact with all of the services that our production systems talk to, while having a nice interactive edit/compile/test cycle.

However, one thing is missed by many developers, especially those coming from other languages: easy access to a stack trace when debugging. So, towards the end of last year, I set about finding a workable solution that we could deploy to our users without impacting their workflows.

Show me a stack trace!

To cut to the chase, in GHC 8.0.1 you can fire up ghci like this:

$ ghci -fexternal-interpreter -prof

and you have stack traces on, by default, for all the code you load into ghci. Let’s try an example.

import Control.Exception myTail xs = assert (not (null xs)) $ tail xs myMap :: (a -> b) -> [a] -> [b] myMap f [] = [] myMap f (x : xs) = f x : myMap f xs main = print (myMap myTail [[ 3 ],[]])

We have a map-alike function called myMap , and a tail-alike function called myTail . We want to find out if myTail is called with an empty list, so we added an assert. Ok, so it’s a contrived example, but let’s see what happens:

$ ghci -fexternal-interpreter -prof GHCi, version 8.1.20160127: http://www.haskell.org/ghc/ :? for help Prelude> :l ~/scratch/tailtest.hs [1 of 1] Compiling Main ( /home/smarlow/scratch/tailtest.hs, interpreted ) Ok, modules loaded: Main. *Main> main [[],*** Exception: Assertion failed CallStack (from ImplicitParams): assert, called at /home/smarlow//scratch/tailtest.hs:3:13 in main:Main myTail, called at /home/smarlow//scratch/tailtest.hs:9:21 in main:Main CallStack (from -prof): Main.myTail (/home/smarlow/scratch/tailtest.hs:3:13-34) Main.myTail (/home/smarlow/scratch/tailtest.hs:3:13-44) Main.myMap (/home/smarlow/scratch/tailtest.hs:7:18-20) Main.myMap (/home/smarlow/scratch/tailtest.hs:7:18-33) Main.main (/home/smarlow/scratch/tailtest.hs:9:15-35) Main.main (/home/smarlow/scratch/tailtest.hs:9:8-36) *Main>

Now, we got two stack traces, both printed by assert . The first comes from ImplicitParams, which knows the location of the call site of assert because assert has a special ?callStack :: CallStack constraint in its type.

The second stack trace is the new one, generated by GHCi running in -prof mode, and has the full call stack all the way from main , including the fact that myTail was called by myMap . That is, it’s a dynamic call stack, not a lexical one.

Dumping the stack from anywhere

Using assert is one way to get access to a stack trace, but sometimes you just want to print out the stack when a particular condition is hit, or when a function is called, to see what’s going on. For this reason we have Debug.Trace.traceStack . This is like trace , but it also prints out the current stack trace. For example, I just picked a random place in the code of Happy, inserted a call to traceStack , loaded Happy into ghci -fexternal-interpreter -prof , ran it and got this:

closure1 CallStack (from -prof): LALR.closure1.addItems.fn (LALR.lhs:106:28-48) LALR.closure1.addItems.fn (LALR.lhs:(106,28)-(110,84)) LALR.closure1.addItems.fn (LALR.lhs:(104,40)-(111,31)) LALR.closure1.addItems.new_new_items (LALR.lhs:100:59-74) LALR.closure1.addItems.new_new_items (LALR.lhs:100:37-75) LALR.closure1.addItems.new_new_items (LALR.lhs:(99,33)-(101,53)) GenUtils.mkClosure (GenUtils.lhs:28:28-36) GenUtils.mkClosure (GenUtils.lhs:28:20-36) LALR.closure1 (LALR.lhs:91:16-67) LALR.closure1 (LALR.lhs:91:11-68) LALR.genActionTable.possActions (LALR.lhs:489:44-64) LALR.genActionTable.possActions (LALR.lhs:(489,33)-(490,60)) LALR.genActionTable.actionTable (LALR.lhs:471:34-53) LALR.genActionTable.actionTable (LALR.lhs:(469,26)-(471,54)) LALR.genActionTable.actionTable (LALR.lhs:(468,23)-(472,61)) Main.main2.runParserGen.action (Main.lhs:114:49-77) Main.main2.runParserGen.action (Main.lhs:114:27-78) Main.main2.runParserGen (Main.lhs:(96,9)-(276,9)) Main.main2.runParserGen (Main.lhs:(90,9)-(276,10)) Main.main2.runParserGen (Main.lhs:(86,9)-(276,10)) Main.main2.runParserGen (Main.lhs:(85,9)-(276,10)) Main.main2 (Main.lhs:74:20-43) Main.main2 (Main.lhs:(64,9)-(78,61)) Main.main (Main.lhs:57:9-18)

You’ll notice that each function appears on the stack multiple times—this is because the the annotations are based on scopes, and GHC tries to insert annotations in useful-looking places. There might well be room for refinement here in the future.

Any drawbacks?

You have to compile your packages with profiling. Use --enable-library-profiling when running Cabal, or set library-profiling: True in your .cabal/config , or do the Stack equivalent. Results with calls to error are mixed, because the error calls are often lifted to the top level as a CAF, which breaks the stack simulation that the profiler does. I have ideas for some workarounds for this that I plan to try in the future. Interpreted code will run more slowly. But this is only for debugging—we didn’t change the source code, so everything still runs at full speed when compiled normally. You can also pre-compile some of your code; don’t forget to use -prof , and add -fprof-auto-calls to get stack-trace annotations for the code you compile. You can :set -fobject-code -fprof-auto-calls inside GHCi itself to use compiled code by default.

How does it work?

We’re using the existing stack-simulation that happens in GHC’s profiler, called “cost-centre stacks”. However, running in profiled mode wasn’t supported by the interpreter, and there were some serious shenanigans involved to make it possible to run profiled code in GHCi without slowing down GHCi itself.

There are various differences in the way the Haskell code runs in profiling mode. The layout of heap objects is different, because every heap object points to information about the call stack that created it. This is necessary to get accurate stack simulations in the presence of things like higher-order functions, but it’s also important for the heap profiler, so that it can tell who created each heap object. When running in profiling mode, we have to do various things to maintain the runtime’s simulation of the call stack.

The first step was to make the interpreter itself work in profiling mode (as in, interpret code correctly and not crash). Fortunately this wasn’t nearly as difficult as I’d anticipated: the interpreter and byte-code compiler were already nicely abstracted over the things that change in profiling mode. At this point we can already do things that weren’t possible before: profile GHCi itself, and profile Template Haskell.

Next, I had to make the interpreter actually simulate the call stack for interpreted code. Again, this was reasonably straightforward, and involved using the breakpoints that GHCi already inserts into the interpreted code as SCC annotations for the profiler.

So far so good: this actually worked quite nicely, but there was one huge drawback. To actually use it, we have to compile GHC itself with profiling. Which works, except that it slows down GHCi when compiling code by a factor of 2-3. That was too big a hit to deploy this as part of the standard workflow for our Haxl users at Facebook, so I needed to find a way to make it work without the overhead on the compiler.

Enter Remote GHCi

The solution is to separate the compiler from the interpreter, using a scheme that I’ve called Remote GHCi. The idea is that by putting the compiler and the interpreter in separate processes, the compiler can be running at full speed on a normal non-profiled runtime, while the interpreter is running in a separate process using the profiled runtime.

The main complication is arranging that all the interactions between the compiler and the interpreter happen via serialized messages over a pipe. We currently have about 50 different message types, you can see them all here. We’re currently using the binary library together with Generic instance generation, but serialization and deserialization using binary is definitely a bottleneck so I’m looking forward to moving to the new CBOR-based serialization library when it’s ready.

It turns out that making this separation has a number of advantages aside from stack traces, which are listed on the RemoteGHCi wiki page.

GHCJS has been doing something similar for a while to support Template Haskell. In fact, I used the GHCJS Template Haskell code as a starting point, integrated it with GHC proper and built it out to fully support GHCi (with a couple of exceptions, notably the debugger doesn’t currently work, and dynCompileExpr in the GHC API cannot be supported in general).

Remote GHCi also works for Template Haskell and Quasi Quotes, and has the advantage that when compiling TH code with -prof -fexternal-interpreter , you don’t need to first compile it without -prof , because we can run the -prof code directly in the external interpreter process.

Three kinds of stack trace in GHC 8.0.1

There’s a lot happening on the stack trace front. We now have no less than three ways to get a stack trace:

Profiling: ghc -prof -fprof-auto and ghci -fexternal-interprter -prof

and ImplicitParams, with the magic ?callStack :: CallStack constraint (now called HasCallStack ).

constraint (now called ). DWARF: ghc -g

Each of these has advantages and disadvantages, and none of them are subsumed by any of the others (sadly!). I’ll try to summarise:

Profiling Detailed, dynamic, call stacks But: Requires recompiling your code, or loading it into GHCi 2-3x runtime overhead compiled, 20-40x interpreted Not so great for error and undefined right now

ImplicitParams Good for finding the call site of particular functions, like error or undefined But: Requires explicit code changes to propagate the stack Some runtime overhead (stacks get constructed and passed around at runtime) Shows up in types as HasCallStack constraints Lexical, not dynamic. (In g = map f , g calls f rather than map calling f ) Could you change GHC so that it automatically adds HasCallStack constraints everywhere and also hides them from the user, to get the effect of full call-stack coverage? Maybe - that would be an alternative to the scheme I’ve implemented on top of profiling. One difficult area is CAFs, though. If a constraint is added to a CAF, then the CAF is re-evaluated each time it is called, which is obviously undesirable. The profiler goes to some lengths to avoid changing the asymptotic cost of things, but trades off some information in the stack simulation in the process, which is why calls to error sometimes don’t get accurate call stack information.

DWARF No runtime overhead, can be deployed in production. Good when you’re not willing to sacrifice any performance, but having some information is better than none when something goes wrong. But: Gives the raw execution stack, so we lose information due to tail-calls and lazy evaluation.



Conclusion

We now have full stack traces inside GHCi, provided you compile your packages for profiling, and use ghci -fexternal-interpreter -prof .