Implement PhiLoweringHelper for GlobalISel in DivergenceLoweringHelper.
Use machine uniformity analysis to find divergent i1 phis and select
them as lane mask phis in same way SILowerI1Copies select VReg_1 phis.
Note that divergent i1 phis include phis created by LCSSA and all cases
of uses outside of cycle are actually covered by "lowering LCSSA phis".
GlobalISel lane masks are registers with sgpr register class and S1 LLT.
TODO: General goal is that instructions created in this pass are fully
instruction-selected so that selection of lane mask phis is not split
across multiple passes.
patch 3 from: https://github.com/llvm/llvm-project/pull/73337
Implement PhiLoweringHelper for GlobalISel in DivergenceLoweringHelper.
Use machine uniformity analysis to find divergent i1 phis and select
them as lane mask phis in same way SILowerI1Copies select VReg_1 phis.
Note that divergent i1 phis include phis created by LCSSA and all cases
of uses outside of cycle are actually covered by "lowering LCSSA phis".
GlobalISel lane masks are registers with sgpr register class and S1 LLT.
TODO: General goal is that instructions created in this pass are fully
instruction-selected so that selection of lane mask phis is not split
across multiple passes.
patch 3 from: https://github.com/llvm/llvm-project/pull/73337
Implement PhiLoweringHelper for GlobalISel in DivergenceLoweringHelper.
Use machine uniformity analysis to find divergent i1 phis and select
them as lane mask phis in same way SILowerI1Copies select VReg_1 phis.
Note that divergent i1 phis include phis created by LCSSA and all cases
of uses outside of cycle are actually covered by "lowering LCSSA phis".
GlobalISel lane masks are registers with sgpr register class and S1 LLT.
TODO: General goal is that instructions created in this pass are fully
instruction-selected so that selection of lane mask phis is not split
across multiple passes.
patch 3 from: https://github.com/llvm/llvm-project/pull/73337
Fix the GenericSSAContext template so that it actually declares all the
necessary typenames and the methods that must be implemented by its
specializations SSAContext and MachineSSAContext.
Reviewed By: arsenm
Differential Revision: https://reviews.llvm.org/D156288
When an instruction is determined to be divergent, not all its outputs are
divergent. The users of only divergent outputs should now be examined for
divergence.
Also, replaced a repeating pattern of "if new divergent instruction, then add to
worklist" by combining it into a single function. This does not cause any change
in functionality.
Reviewed By: foad, arsenm
Differential Revision: https://reviews.llvm.org/D150636
The MachineUA now queries the target to determine if a given register holds a
uniform value. This is determined using the corresponding register bank if
available, or by a combination of the register class and value type. This
assumes that the target is optimizing for performance by choosing registers, and
the target is responsible for any mismatch with the inferred uniformity.
For example, on AMDGPU, an SGPR is now treated as uniform, except if the
register bank is VCC (i.e., the register holds a wave-wide vector of 1-bit
values) or equivalently if it has a value type of s1.
- This does not always work with inline asm, where the register bank or the
value type might not be present. We assume that the SGPR is uniform, because
it is not expected to be s1 in the vast majority of cases.
- The pseudo branch instruction SI_LOOP is now hard-coded to be always
divergent, although its condition is an SGPR.
Reviewed By: arsenm
Differential Revision: https://reviews.llvm.org/D150438
At a cycle C with divergent exits, UA was using a naive traversal of the exiting
edges to locate blocks that may use values defined inside C. But this traversal
fails when it encounters a cycle. This is now replaced with a much simpler
propagation that iterates over every instruction in C and checks any uses that
are outside C. But such an iteration can be expensive when C is very large; the
original strategy may need to be reconsidered if there is a regression in
compilation times.
Also fixed lit tests that should have originally caught the missed propagation
of temporal divergence.
Reviewed By: foad
Differential Revision: https://reviews.llvm.org/D149646
Adds & uses a new `isDivergentUse` API in UA.
UniformityAnalysis now requires CycleInfo as well as the new temporal divergence API can query it.
-----
Original patch that adds `isDivergentUse` by @sameerds
The user of a temporally divergent value is marked as divergent in the
uniformity analysis. But the same user may also have been marked divergent for
other reasons, thus losing this information about temporal divergence. But some
clients need to specificly check for temporal divergence. This change restores
such an API, that already existed in DivergenceAnalysis.
Reviewed By: sameerds, foad
Differential Revision: https://reviews.llvm.org/D146018
An instruction that is "always uniform" is so even if it occurs in an
irreducible cycle. The output produced by such an instruction may depend on the
implementation defined cycle hierarchy, but that does not affect the uniformity
of the output. In other words, an "always uniform" instruction is uniform even
if it is not m-converged.
Reviewed By: ruiling, ronlieb
Differential Revision: https://reviews.llvm.org/D145572
With recent (> 15, as far as I can tell, possibly > 16) clang, c++17,
and GNU's libstdc++ (versions 9 and 10 and maybe others), LLVM fails
to compile due to an is_invocable() check in unique_ptr::reset().
To resolve this issue, add a template argument to ImplDeleter to make
things work.
Differential Revision: https://reviews.llvm.org/D141865
Uniformity analysis is a generalization of divergence analysis to
include irreducible control flow:
1. The proposed spec presents a notion of "maximal convergence" that
captures the existing convention of converging threads at the
headers of natual loops.
2. Maximal convergence is then extended to irreducible cycles. The
identity of irreducible cycles is determined by the choices made
in a depth-first traversal of the control flow graph. Uniformity
analysis uses criteria that depend only on closed paths and not
cycles, to determine maximal convergence. This makes it a
conservative analysis that is independent of the effect of DFS on
CycleInfo.
3. The analysis is implemented as a template that can be
instantiated for both LLVM IR and Machine IR.
Validation:
- passes existing tests for divergence analysis
- passes new tests with irreducible control flow
- passes equivalent tests in MIR and GMIR
Based on concepts originally outlined by
Nicolai Haehnle <nicolai.haehnle@amd.com>
With contributions from Ruiling Song <ruiling.song@amd.com> and
Jay Foad <jay.foad@amd.com>.
Support for GMIR and lit tests for GMIR/MIR added by
Yashwant Singh <yashwant.singh@amd.com>.
Differential Revision: https://reviews.llvm.org/D130746
