6 Commits

Author SHA1 Message Date
Jakub Kuderski
abc362a107 [mlir][arith] Change dialect name from Arithmetic to Arith
Suggested by @lattner in https://discourse.llvm.org/t/rfc-define-precise-arith-semantics/65507/22.

Tested with:
`ninja check-mlir check-mlir-integration check-mlir-mlir-spirv-cpu-runner check-mlir-mlir-vulkan-runner check-mlir-examples`

and `bazel build --config=generic_clang @llvm-project//mlir:all`.

Reviewed By: lattner, Mogball, rriddle, jpienaar, mehdi_amini

Differential Revision: https://reviews.llvm.org/D134762
2022-09-29 11:23:28 -04:00
Michele Scuttari
67d0d7ac0a
[MLIR] Update pass declarations to new autogenerated files
The patch introduces the required changes to update the pass declarations and definitions to use the new autogenerated files and allow dropping the old infrastructure.

Reviewed By: mehdi_amini, rriddle

Differential Review: https://reviews.llvm.org/D132838
2022-08-31 12:28:45 +02:00
Michele Scuttari
039b969b32
Revert "[MLIR] Update pass declarations to new autogenerated files"
This reverts commit 2be8af8f0e0780901213b6fd3013a5268ddc3359.
2022-08-30 22:21:55 +02:00
Michele Scuttari
2be8af8f0e
[MLIR] Update pass declarations to new autogenerated files
The patch introduces the required changes to update the pass declarations and definitions to use the new autogenerated files and allow dropping the old infrastructure.

Reviewed By: mehdi_amini, rriddle

Differential Review: https://reviews.llvm.org/D132838
2022-08-30 21:56:31 +02:00
Thomas Raoux
91594b5b98 [mlir][nvpu] Prevent F32ToTF32 pattern to generate illegal IR
We shouldn't apply this pattern to non F32->F32 mma.sync operations.

Differential Revision: https://reviews.llvm.org/D131902
2022-08-15 16:46:18 +00:00
Manish Gupta
14d79afeae [mlir][NVGPU] nvgpu.mmasync on F32 through TF32
Adds optional attribute to support tensor cores on F32 datatype by lowering to `mma.sync` with TF32 operands. Since, TF32 is not a native datatype in LLVM we are adding `tf32Enabled` as an attribute to allow the IR to be aware of `MmaSyncOp` datatype. Additionally, this patch adds placeholders for nvgpu-to-nvgpu transformation targeting higher precision tf32x3.

For mma.sync on f32 input using tensor cores there are two possibilites:
(a) tf32   (1 `mma.sync` per warp-level matrix-multiply-accumulate)
(b) tf32x3 (3 `mma.sync` per warp-level matrix-multiply-accumulate)

Typically, tf32 tensor core acceleration comes at a cost of accuracy from missing precision bits. While f32 has 23 precision bits, tf32 has only 10 precision bits. tf32x3 aims to recover the precision bits by splitting each operand into two tf32 values and issue three `mma.sync` tensor core operations.

Reviewed By: ThomasRaoux

Differential Revision: https://reviews.llvm.org/D130294
2022-08-01 23:23:27 +00:00