InstCombine contributor guide — LLVM 21.1.1 documentation

# InstCombine contributor guide

This guide lays out a series of rules that contributions to InstCombine should follow. Following these rules will results in much faster PR approvals.

## Tests

### Precommit tests

Tests for new optimizations or miscompilation fixes should be pre-committed. This means that you first commit the test with CHECK lines showing the behavior without your change. Your actual change will then only contain CHECK line diffs relative to that baseline.

This means that pull requests should generally contain two commits: First, one commit adding new tests with baseline check lines. Second, a commit with functional changes and test diffs.

If the second commit in your PR does not contain test diffs, you did something wrong. Either you made a mistake when generating CHECK lines, or your tests are not actually affected by your patch.

Exceptions: When fixing assertion failures or infinite loops, do not pre-commit tests.

### Use `update_test_checks.py`

CHECK lines should be generated using the `update_test_checks.py` script. Do not manually edit check lines after using it.

Be sure to use the correct opt binary when using the script. For example, if your build directory is `build`, then you’ll want to run:

    llvm/utils/update_test_checks.py --opt-binary build/bin/opt \
        llvm/test/Transforms/InstCombine/the_test.ll

Exceptions: Hand-written CHECK lines are allowed for debuginfo tests.

### General testing considerations

Place all tests relating to a transform into a single file. If you are adding a regression test for a crash/miscompile in an existing transform, find the file where the existing tests are located. A good way to do that is to comment out the transform and see which tests fail.

Make tests minimal. Only test exactly the pattern being transformed. If your original motivating case is a larger pattern that your fold enables to optimize in some non-trivial way, you may add it as well – however, the bulk of the test coverage should be minimal.

Give tests short, but meaningful names. Don’t call them `@test1`, `@test2` etc. For example, a test checking multi-use behavior of a fold involving the addition of two selects might be called `@add_of_selects_multi_use`.

Add representative tests for each test category (discussed below), but don’t test all combinations of everything. If you have multi-use tests, and you have commuted tests, you shouldn’t also add commuted multi-use tests.

Prefer to keep bit-widths for tests low to improve performance of proof checking using alive2. Using `i8` is better than `i128` where possible.

### Add negative tests

Make sure to add tests for which your transform does not apply. Start with one of the test cases that succeeds and then create a sequence of negative tests, such that exactly one different pre-condition of your transform is not satisfied in each test.

### Add multi-use tests

Add multi-use tests that ensures your transform does not increase instruction count if some instructions have additional uses. The standard pattern is to introduce extra uses with function calls:

    declare void @use(i8)

    define i8 @add_mul_const_multi_use(i8 %x) {
      %add = add i8 %x, 1
      call void @use(i8 %add)
      %mul = mul i8 %add, 3
      ret i8 %mul
    }

Exceptions: For transform that only produce one instruction, multi-use tests may be omitted.

### Add commuted tests

If the transform involves commutative operations, add tests with commuted (swapped) operands.

Make sure that the operand order stays intact in the CHECK lines of your pre-commited tests. You should not see something like this:

    ; CHECK-NEXT: [[OR:%.*]] = or i8 [[X]], [[Y]]
    ; ...
    %or = or i8 %y, %x

If this happens, you may need to change one of the operands to have higher complexity (include the “thwart” comment in that case):

    %y2 = mul i8 %y, %y ; thwart complexity-based canonicalization
    %or = or i8 %y, %x

### Add vector tests

When possible, it is recommended to add at least one test that uses vectors instead of scalars.

For patterns that include constants, we distinguish three kinds of tests. The first are “splat” vectors, where all the vector elements are the same. These tests should usually fold without additional effort.

    define <2 x i8> @add_mul_const_vec_splat(<2 x i8> %x) {
      %add = add <2 x i8> %x, <i8 1, i8 1>
      %mul = mul <2 x i8> %add, <i8 3, i8 3>
      ret <2 x i8> %mul
    }

A minor variant is to replace some of the splat elements with poison. These will often also fold without additional effort.

    define <2 x i8> @add_mul_const_vec_splat_poison(<2 x i8> %x) {
      %add = add <2 x i8> %x, <i8 1, i8 poison>
      %mul = mul <2 x i8> %add, <i8 3, i8 poison>
      ret <2 x i8> %mul
    }

Finally, you can have non-splat vectors, where the vector elements are not the same:

    define <2 x i8> @add_mul_const_vec_non_splat(<2 x i8> %x) {
      %add = add <2 x i8> %x, <i8 1, i8 5>
      %mul = mul <2 x i8> %add, <i8 3, i8 6>
      ret <2 x i8> %mul
    }

Non-splat vectors will often not fold by default. You should not try to make them fold, unless doing so does not add any additional complexity. You should still add the test though, even if it does not fold.

### Flag tests

If your transform involves instructions that can have poison-generating flags, such as `nuw` and `nsw` on `add`, you should test how these interact with the transform.

If your transform requires a certain flag for correctness, make sure to add negative tests missing the required flag.

If your transform doesn’t require flags for correctness, you should have tests for preservation behavior. If the input instructions have certain flags, are they preserved in the output instructions, if it is valid to preserve them? (This depends on the transform. Check with alive2.)

The same also applies to fast-math-flags (FMF). In that case, please always test specific flags like `nnan`, `nsz` or `reassoc`, rather than the umbrella `fast` flag.

### Other tests

The test categories mentioned above are non-exhaustive. There may be more tests to be added, depending on the instructions involved in the transform. Some examples:

- For folds involving memory accesses like load/store, check that scalable vectors and non-byte-size types (like i3) are handled correctly. Also check that volatile/atomic are handled.

- For folds that interact with the bitwidth in some non-trivial way, check an illegal type like i13. Also confirm that the transform is correct for i1.

- For folds that involve phis, you may want to check that the case of multiple incoming values from one block is handled correctly.

## Proofs

Your pull request description should contain one or more alive2 proofs for the correctness of the proposed transform.

### Basics

Proofs are written using LLVM IR, by specifying a `@src` and `@tgt` function. It is possible to include multiple proofs in a single file by giving the src and tgt functions matching suffixes.

For example, here is a pair of proofs that both `(x-y)+y` and `(x+y)-y` can be simplified to `x` (online):

    define i8 @src_add_sub(i8 %x, i8 %y) {
      %add = add i8 %x, %y
      %sub = sub i8 %add, %y
      ret i8 %sub
    }

    define i8 @tgt_add_sub(i8 %x, i8 %y) {
      ret i8 %x
    }

    define i8 @src_sub_add(i8 %x, i8 %y) {
      %sub = sub i8 %x, %y
      %add = add i8 %sub, %y
      ret i8 %add
    }

    define i8 @tgt_sub_add(i8 %x, i8 %y) {
      ret i8 %x
    }

### Use generic values in proofs

Proofs should operate on generic values, rather than specific constants, to the degree that this is possible.

For example, if we want to fold `X s/ C s< X` to `X s> 0`, the following would be a bad proof:

    ; Don't do this!
    define i1 @src(i8 %x) {
      %div = sdiv i8 %x, 123
      %cmp = icmp slt i8 %div, %x
      ret i1 %cmp
    }

    define i1 @tgt(i8 %x) {
      %cmp = icmp sgt i8 %x, 0
      ret i1 %cmp
    }

This is because it only proves that the transform is correct for the specific constant 123. Maybe there are some constants for which the transform is incorrect?

The correct way to write this proof is as follows (online):

    define i1 @src(i8 %x, i8 %C) {
      %precond = icmp ne i8 %C, 1
      call void @llvm.assume(i1 %precond)
      %div = sdiv i8 %x, %C
      %cmp = icmp slt i8 %div, %x
      ret i1 %cmp
    }

    define i1 @tgt(i8 %x, i8 %C) {
      %cmp = icmp sgt i8 %x, 0
      ret i1 %cmp
    }

Note that the `@llvm.assume` intrinsic is used to specify pre-conditions for the transform. In this case, the proof will fail unless we specify `C != 1` as a pre-condition.

It should be emphasized that there is, in general, no expectation that the IR in the proofs will be transformed by the implemented fold. In the above example, the transform would only apply if `%C` is actually a constant, but we need to use non-constants in the proof.

### Common pre-conditions

Here are some examples of common preconditions.

    ; %x is non-negative:
    %nonneg = icmp sgt i8 %x, -1
    call void @llvm.assume(i1 %nonneg)

    ; %x is a power of two:
    %ctpop = call i8 @llvm.ctpop.i8(i8 %x)
    %pow2 = icmp eq i8 %x, 1
    call void @llvm.assume(i1 %pow2)

    ; %x is a power of two or zero:
    %ctpop = call i8 @llvm.ctpop.i8(i8 %x)
    %pow2orzero = icmp ult i8 %x, 2
    call void @llvm.assume(i1 %pow2orzero)

    ; Adding %x and %y does not overflow in a signed sense:
    %wo = call { i8, i1 } @llvm.sadd.with.overflow(i8 %x, i8 %y)
    %ov = extractvalue { i8, i1 } %wo, 1
    %ov.not = xor i1 %ov, true
    call void @llvm.assume(i1 %ov.not)

### Timeouts

Alive2 proofs will sometimes produce a timeout with the following message:

    Alive2 timed out while processing your query.
    There are a few things you can try:

    - remove extraneous instructions, if any

    - reduce variable widths, for example to i16, i8, or i4

    - add the --disable-undef-input command line flag, which
      allows Alive2 to assume that arguments to your IR are not
      undef. This is, in general, unsound: it can cause Alive2
      to miss bugs.

This is good advice, follow it!

Reducing the bitwidth usually helps. For floating point numbers, you can use the `half` type for bitwidth reduction purposes. For pointers, you can reduce the bitwidth by specifying a custom data layout:

    ; For 16-bit pointers
    target datalayout = "p:16:16"

If reducing the bitwidth does not help, try `-disable-undef-input`. This will often significantly improve performance, but also implies that the correctness of the transform with `undef` values is no longer verified. This is usually fine if the transform does not increase the number of uses of any value.

Finally, it’s possible to build alive2 locally and use `-smt-to=<m>` to verify the proof with a larger timeout. If you don’t want to do this (or it still does not work), please submit the proof you have despite the timeout.

## Implementation

### Real-world usefulness

There is a very large number of transforms that could be implemented, but only a tiny fraction of them are useful for real-world code.

Transforms that do not have real-world usefulness provide negative value to the LLVM project, by taking up valuable reviewer time, increasing code complexity and increasing compile-time overhead.

We do not require explicit proof of real-world usefulness for every transform – in most cases the usefulness is fairly “obvious”. However, the question may come up for complex or unusual folds. Keep this in mind when chosing what you work on.

In particular, fixes for fuzzer-generated missed optimization reports will likely be rejected if there is no evidence of real-world usefulness.

### Pick the correct optimization pass

There are a number of passes and utilities in the InstCombine family, and it is important to pick the right place when implementing a fold.

- `ConstantFolding`: For folding instructions with constant arguments to a constant. (Mainly relevant for intrinsics.)

- `ValueTracking`: For analyzing instructions, e.g. for known bits, non-zero, etc. Tests should usually use `-passes=instsimplify`.

- `InstructionSimplify`: For folds that do not create new instructions (either fold to existing value or constant).

- `InstCombine`: For folds that create or modify instructions.

- `AggressiveInstCombine`: For folds that are expensive, or violate InstCombine requirements.

- `VectorCombine`: For folds of vector operations that require target-dependent cost-modelling.

Sometimes, folds that logically belong in InstSimplify are placed in InstCombine instead, for example because they are too expensive, or because they are structurally simpler to implement in InstCombine.

For example, if a fold produces new instructions in some cases but returns an existing value in others, it may be preferable to keep all cases in InstCombine, rather than trying to split them among InstCombine and InstSimplify.

### Canonicalization and target-independence

InstCombine is a target-independent canonicalization pass. This means that it tries to bring IR into a “canonical form” that other optimizations (both inside and outside of InstCombine) can rely on. For this reason, the chosen canonical form needs to be the same for all targets, and not depend on target-specific cost modelling.

In many cases, “canonicalization” and “optimization” coincide. For example, if we convert `x * 2` into `x << 1`, this both makes the IR more canonical (because there is now only one way to express the same operation, rather than two) and faster (because shifts will usually have lower latency than multiplies).

However, there are also canonicalizations that don’t serve any direct optimization purpose. For example, InstCombine will canonicalize non-strict predicates like `ule` to strict predicates like `ult`. `icmp ule i8 %x, 7` becomes `icmp ult i8 %x, 8`. This is not an optimization in any meaningful sense, but it does reduce the number of cases that other transforms need to handle.

If some canonicalization is not profitable for a specific target, then a reverse transform needs to be added in the backend. Patches to disable specific InstCombine transforms on certain targets, or to drive them using target-specific cost-modelling, will not be accepted. The only permitted target-dependence is on DataLayout and TargetLibraryInfo.

The use of TargetTransformInfo is only allowed for hooks for target-specific intrinsics, such as `TargetTransformInfo::instCombineIntrinsic()`. These are already inherently target-dependent anyway.

If some canonicalization narrow/widen the integer width of expressions, please check `shouldChangeType()` first. Otherwise, we may evaluate the expression in illegal/inefficient types.

For vector-specific transforms that require cost-modelling, the VectorCombine pass can be used instead. In very rare circumstances, if there are no other alternatives, target-dependent transforms may be accepted into AggressiveInstCombine.

Generally, we prefer unsigned operations over signed operations in the middle-end, even if signed operations are more efficient on some targets. The following is an incomplete list of canonicalizations that are implemented in InstCombine:

Original Pattern | Canonical Form | Condition
--- | --- | ---
`icmp spred X, Y` | `icmp samesign upred X, Y` | `sign(X) == sign(Y)`
`smin/smax X, Y` | `umin/umax X, Y` | `sign(X) == sign(Y)`
`sext X` | `zext nneg X` | `X >=s 0`
`sitofp X` | `uitofp nneg X` | `X >=s 0`
`ashr X, Y` | `lshr X, Y` | `X >=s 0`
`sdiv/srem X, Y` | `udiv/urem X, Y` | `X >=s 0 && Y >=s 0`
`add X, Y` | `or disjoint X, Y` | `(X & Y) != 0`
`mul X, C` | `shl X, Log2(C)` | `isPowerOf2(C)`
`select Cond1, Cond2, false` | `and Cond1, Cond2` | `impliesPoison(Cond2, Cond1)`
`select Cond1, true, Cond2` | `or Cond1, Cond2` | `impliesPoison(Cond2, Cond1)`

### PatternMatch

Many transforms make use of the matching infrastructure defined in PatternMatch.h.

Here is a typical usage example:

    // Fold (A - B) + B and B + (A - B) to A.
    Value *A, *B;
    if (match(V, m_c_Add(m_Sub(m_Value(A), m_Value(B)), m_Deferred(B))))
      return A;

And another:

    // Fold A + C1 == C2 to A == C1+C2
    Value *A;
    if (match(V, m_ICmp(Pred, m_Add(m_Value(A), m_APInt(C1)), m_APInt(C2))) &&
        ICmpInst::isEquality(Pred))
      return Builder.CreateICmp(Pred, A,
                                ConstantInt::get(A->getType(), *C1 + *C2));

Some common matchers are:

- `m_Value(A)`: Match any value and write it into `Value *A`.

- `m_Specific(A)`: Check that the operand equals A. Use this if A is assigned outside the pattern.

- `m_Deferred(A)`: Check that the operand equals A. Use this if A is assigned inside the pattern, for example via `m_Value(A)`.

- `m_APInt(C)`: Match a scalar integer constant or splat vector constant into `const APInt *C`. Does not permit undef/poison values.

- `m_ImmConstant(C)`: Match any non-constant-expression constant into `Constant *C`.

- `m_Constant(C)`: Match any constant into `Constant *C`. Don’t use this unless you know what you’re doing.

- `m_Add(M1, M2)`, `m_Sub(M1, M2)`, etc: Match an add/sub/etc where the first operand matches M1 and the second M2.

- `m_c_Add(M1, M2)`, etc: Match an add commutatively. The operands must match either M1 and M2 or M2 and M1. Most instruction matchers have a commutative variant.

- `m_ICmp(Pred, M1, M2)` and `m_c_ICmp(Pred, M1, M2)`: Match an icmp, writing the predicate into `IcmpInst::Predicate Pred`. If the commutative version is used, and the operands match in order M2, M1, then `Pred` will be the swapped predicate.

- `m_OneUse(M)`: Check that the value only has one use, and also matches M. For example `m_OneUse(m_Add(...))`. See the next section for more information.

See the header for the full list of available matchers.

### InstCombine APIs

InstCombine transforms are handled by `visitXYZ()` methods, where XYZ corresponds to the root instruction of your transform. If the outermost instruction of the pattern you are matching is an icmp, the fold will be located somewhere inside `visitICmpInst()`.

The return value of the visit method is an instruction. You can either return a new instruction, in which case it will be inserted before the old one, and uses of the old one will be replaced by it. Or you can return the original instruction to indicate that some kind of change has been made. Finally, a nullptr return value indicates that no change occurred.

For example, if your transform produces a single new icmp instruction, you could write the following:

    if (...)
      return new ICmpInst(Pred, X, Y);

In this case the main InstCombine loop takes care of inserting the instruction and replacing uses of the old instruction.

Alternatively, you can also write it like this:

    if (...)
      return replaceInstUsesWith(OrigI, Builder.CreateICmp(Pred, X, Y));

In this case `IRBuilder` will insert the instruction and `replaceInstUsesWith()` will replace the uses of the old instruction, and return it to indicate that a change occurred.

Both forms are equivalent, and you can use whichever is more convenient in context. For example, it’s common that folds are inside helper functions that return `Value *` and then `replaceInstUsesWith()` is invoked on the result of that helper.

InstCombine makes use of a worklist, which needs to be correctly updated during transforms. This usually happens automatically, but there are some things to keep in mind:

- Don’t use the `Value::replaceAllUsesWith()` API. Use InstCombine’s `replaceInstUsesWith()` helper instead.

- Don’t use the `Instruction::eraseFromParent()` API. Use InstCombine’s `eraseInstFromFunction()` helper instead. (Explicitly erasing instruction is usually not necessary, as side-effect free instructions without users are automatically removed.)

- Apart from the “directly return an instruction” pattern above, use IRBUilder to create all instruction. Do not manually create and insert them.

- When replacing operands or uses of instructions, use `replaceOperand()` and `replaceUse()` instead of `setOperand()`.

### Multi-use handling

Transforms should usually not increase the total number of instructions. This is not a hard requirement: For example, it is usually worthwhile to replace a single division instruction with multiple other instructions.

For example, if you have a transform that replaces two instructions, with two other instructions, this is (usually) only profitable if both the original instructions can be removed. To ensure that both instructions are removed, you need to add a one-use check for the inner instruction.

One-use checks can be performed using the `m_OneUse()` matcher, or the `V->hasOneUse()` method.

### Flag handling

When possible, favour propagation of poison-generating flags like `nuw` and `nsw` since they may be hard to salvage later. Avoid doing so if it introduces additional complexity (e.g. requires querying `willNotOverflow` or KnownBits).

Be careful with in-place operand/predicate changes, as poison-generating flags may not be valid for new operands. It is recommended to create a new instruction with careful handling of flags. If not applicable, call `Instruction::dropPoisonGeneratingFlags()` to clear flags in a conservative manner.

Do not rely on fcmp’s `nsz` flag to perform optimizations. It is meaningless for fcmp so it should not affect the optimization.

### Generalization

Transforms can both be too specific (only handling some odd subset of patterns, leading to unexpected optimization cliffs) and too general (introducing complexity to handle cases with no real-world relevance). The right level of generality is quite subjective, so this section only provides some broad guidelines.

- Avoid transforms that are hardcoded to specific constants. Try to figure out what the general rule for arbitrary constants is.

- Add handling for conjugate patterns. For example, if you implement a fold for `icmp eq`, you almost certainly also want to support `icmp ne`, with the inverse result. Similarly, if you implement a pattern for `and` of `icmp`s, you should also handle the de-Morgan conjugate using `or`.

- Handle non-splat vector constants if doing so is free, but do not add handling for them if it adds any additional complexity to the code.

- Do not handle non-canonical patterns, unless there is a specific motivation to do so. For example, it may sometimes be worthwhile to handle a pattern that would normally be converted into a different canonical form, but can still occur in multi-use scenarios. This is fine to do if there is specific real-world motivation, but you should not go out of your way to do this otherwise.

- Sometimes the motivating pattern uses a constant value with certain properties, but the fold can be generalized to non-constant values by making use of ValueTracking queries. Whether this makes sense depends on the case, but it’s usually a good idea to only handle the constant pattern first, and then generalize later if it seems useful.

- When possible, handle more canonical patterns as well. It is encouraged to avoid potential phase-ordering issues. For example, if the motivating transform holds for `add`, it also holds for `or disjoint`. See the canonicalization list above for details. In most cases, it can be easily implemented with matchers like `m_AddLike/m_SExtLike/m_LogicalAnd/m_LogicalOr`.

## Guidelines for reviewers

- Do not ask new contributors to implement non-splat vector support in code reviews. If you think non-splat vector support for a fold is both worthwhile and policy-compliant (that is, the handling would not result in any appreciable increase in code complexity), implement it yourself in a follow-up patch.