bugpoint narrows down the source of problems in LLVM tools and passes. It can be used to debug three types of failures: optimizer crashes, miscompilations by optimizers, or bad native code generation (including problems in the static and JIT compilers). It aims to reduce large test cases to small, useful ones. For example, if opt crashes while optimizing a file, it will identify the optimization (or combination of optimizations) that causes the crash, and reduce the file down to a small example which triggers the crash.
For detailed case scenarios, such as debugging opt, or one of the LLVM code generators, see How To Submit a Bug Report document.
bugpoint is designed to be a useful tool without requiring any hooks into the LLVM infrastructure at all. It works with any and all LLVM passes and code generators, and does not need to “know” how they work. Because of this, it may appear to do stupid things or miss obvious simplifications. bugpoint is also designed to trade off programmer time for computer time in the compiler-debugging process; consequently, it may take a long period of (unattended) time to reduce a test case, but we feel it is still worth it. Note that bugpoint is generally very quick unless debugging a miscompilation where each test of the program (which requires executing it) takes a long time.
bugpoint reads each .bc or .ll file specified on the command line and links them together into a single module, called the test program. If any LLVM passes are specified on the command line, it runs these passes on the test program. If any of the passes crash, or if they produce malformed output (which causes the verifier to abort), bugpoint starts the crash debugger.
Otherwise, if the -output option was not specified, bugpoint runs the test program with the “safe” backend (which is assumed to generate good code) to generate a reference output. Once bugpoint has a reference output for the test program, it tries executing it with the selected code generator. If the selected code generator crashes, bugpoint starts the crash debugger on the code generator. Otherwise, if the resulting output differs from the reference output, it assumes the difference resulted from a code generator failure, and starts the code generator debugger.
Finally, if the output of the selected code generator matches the reference output, bugpoint runs the test program after all of the LLVM passes have been applied to it. If its output differs from the reference output, it assumes the difference resulted from a failure in one of the LLVM passes, and enters the miscompilation debugger. Otherwise, there is no problem bugpoint can debug.
If an optimizer or code generator crashes, bugpoint will try as hard as it can to reduce the list of passes (for optimizer crashes) and the size of the test program. First, bugpoint figures out which combination of optimizer passes triggers the bug. This is useful when debugging a problem exposed by opt, for example, because it runs over 38 passes.
Next, bugpoint tries removing functions from the test program, to reduce its size. Usually it is able to reduce a test program to a single function, when debugging intraprocedural optimizations. Once the number of functions has been reduced, it attempts to delete various edges in the control flow graph, to reduce the size of the function as much as possible. Finally, bugpoint deletes any individual LLVM instructions whose absence does not eliminate the failure. At the end, bugpoint should tell you what passes crash, give you a bitcode file, and give you instructions on how to reproduce the failure with opt or llc.
The code generator debugger attempts to narrow down the amount of code that is being miscompiled by the selected code generator. To do this, it takes the test program and partitions it into two pieces: one piece which it compiles with the “safe” backend (into a shared object), and one piece which it runs with either the JIT or the static LLC compiler. It uses several techniques to reduce the amount of code pushed through the LLVM code generator, to reduce the potential scope of the problem. After it is finished, it emits two bitcode files (called “test” [to be compiled with the code generator] and “safe” [to be compiled with the “safe” backend], respectively), and instructions for reproducing the problem. The code generator debugger assumes that the “safe” backend produces good code.
The miscompilation debugger works similarly to the code generator debugger. It works by splitting the test program into two pieces, running the optimizations specified on one piece, linking the two pieces back together, and then executing the result. It attempts to narrow down the list of passes to the one (or few) which are causing the miscompilation, then reduce the portion of the test program which is being miscompiled. The miscompilation debugger assumes that the selected code generator is working properly.
bugpoint can be a remarkably useful tool, but it sometimes works in non-obvious ways. Here are some hints and tips:
In the code generator and miscompilation debuggers, bugpoint only works with programs that have deterministic output. Thus, if the program outputs argv[0], the date, time, or any other “random” data, bugpoint may misinterpret differences in these data, when output, as the result of a miscompilation. Programs should be temporarily modified to disable outputs that are likely to vary from run to run.
In the code generator and miscompilation debuggers, debugging will go faster if you manually modify the program or its inputs to reduce the runtime, but still exhibit the problem.
bugpoint is extremely useful when working on a new optimization: it helps track down regressions quickly. To avoid having to relink bugpoint every time you change your optimization however, have bugpoint dynamically load your optimization with the -load option.
bugpoint can generate a lot of output and run for a long period of time. It is often useful to capture the output of the program to file. For example, in the C shell, you can run:
bugpoint ... |& tee bugpoint.log
to get a copy of bugpoint‘s output in the file bugpoint.log, as well as on your terminal.
bugpoint cannot debug problems with the LLVM linker. If bugpoint crashes before you see its “All input ok” message, you might try llvm-link -v on the same set of input files. If that also crashes, you may be experiencing a linker bug.
bugpoint is useful for proactively finding bugs in LLVM. Invoking bugpoint with the -find-bugs option will cause the list of specified optimizations to be randomized and applied to the program. This process will repeat until a bug is found or the user kills bugpoint.
Sometimes, bugpoint is not enough. In particular, InstCombine and TargetLowering both have visitor structured code with lots of potential transformations. If the process of using bugpoint has left you with still too much code to figure out and the problem seems to be in instcombine, the following steps may help. These same techniques are useful with TargetLowering as well.
Turn on -debug-only=instcombine and see which transformations within instcombine are firing by selecting out lines with “IC” in them.
At this point, you have a decision to make. Is the number of transformations small enough to step through them using a debugger? If so, then try that.
If there are too many transformations, then a source modification approach may be helpful. In this approach, you can modify the source code of instcombine to disable just those transformations that are being performed on your test input and perform a binary search over the set of transformations. One set of places to modify are the “visit*” methods of InstCombiner (e.g. visitICmpInst) by adding a “return false” as the first line of the method.
If that still doesn’t remove enough, then change the caller of InstCombiner::DoOneIteration, InstCombiner::runOnFunction to limit the number of iterations.
You may also find it useful to use “-stats” now to see what parts of instcombine are firing. This can guide where to put additional reporting code.
At this point, if the amount of transformations is still too large, then inserting code to limit whether or not to execute the body of the code in the visit function can be helpful. Add a static counter which is incremented on every invocation of the function. Then add code which simply returns false on desired ranges. For example:
static int calledCount = 0;
calledCount++;
DEBUG(if (calledCount < 212) return false);
DEBUG(if (calledCount > 217) return false);
DEBUG(if (calledCount == 213) return false);
DEBUG(if (calledCount == 214) return false);
DEBUG(if (calledCount == 215) return false);
DEBUG(if (calledCount == 216) return false);
DEBUG(dbgs() << "visitXOR calledCount: " << calledCount << "\n");
DEBUG(dbgs() << "I: "; I->dump());
could be added to visitXOR to limit visitXor to being applied only to calls 212 and 217. This is from an actual test case and raises an important point—a simple binary search may not be sufficient, as transformations that interact may require isolating more than one call. In TargetLowering, use return SDNode(); instead of return false;.
Now that that the number of transformations is down to a manageable number, try examining the output to see if you can figure out which transformations are being done. If that can be figured out, then do the usual debugging. If which code corresponds to the transformation being performed isn’t obvious, set a breakpoint after the call count based disabling and step through the code. Alternatively, you can use “printf” style debugging to report waypoints.