Commit Graph

9 Commits (d85b7e7d60ebd1d0eb039fb0338b2911ddb65ebd)

Author SHA1 Message Date
Stella Laurenzo 2ba8296151 Add script tools/format_source.sh and run it on all python and c++ sources. 2020-06-13 14:53:54 -07:00
Sean Silva 7b9f0c3364 Add ability to run without optimizations.
The default is to only do the bare minimum needed for correctness, since
that stresses the layering of the system maximally.
2020-06-01 19:33:59 -07:00
Sean Silva 1d3dbd9d5c Lower to LLVM dialect.
With this commit, we finish conversion to LLVM dialect, and should be
ready for subsequent commits to convert to an LLVM module and let LLVM
codegen to native machine code.

This required a custom "lower to LLVM" pass to support lowering
tcp.abort_if to a runtime call. In the future, this pass will grow to do
type conversions for our own runtime types as we add those.
2020-05-20 18:56:10 -07:00
Sean Silva 836a8d4bec Lower tcp.alloc_memref ops to tcp.get_extent + std.alloc.
- tcp.get_extent will be liminated while lowering shapes
- std.alloc is supported by the upstream LLVM lowering.
2020-05-18 12:53:31 -07:00
Sean Silva 993338a12d Lower to the upstream memref ABI.
Specifically, we use unranked memrefs which get passed as a fixed-size
set of arguments/returns. One big caveat about this is that returning
results isn't going to work. See TODO in LowerTensorLoadOp.

This is far from enough runtime-wise, but it starts to demarcate a
plausible layering. Notice for example how this removes the
runtime-dependence from LowerRankedShapes.

Eventually, we want to have an `npcomp_rt` or `npcomp_hal` dialect with
its own set of runtime types that will supercede this.

See comments in LowerTensorLoadOp for more direction about where this is
going to evolve.
2020-05-15 17:19:57 -07:00
Sean Silva eaeb4011e6 Lower !shape.shape to SSA values.
This uses an approach inspired by what is done in IREE. See comments on
LowerRankedShapes.cpp for how it works.

The basic gist is that we have an op that creates a !shape.shape from a
set of SSA values representing the extents, and then iteratively replace
any op producing a !shape.shape with instances of that op.
2020-05-13 17:20:23 -07:00
Sean Silva ef25428fe3 Add lowering from linalg to loops.
This also adds a small pass to clean up the `dim` ops that linalg
introduces. For now, it only has a trivial pattern that looks for a
`tcp.alloc_memref(%shape)` op to get the shape as we currently have an
invariant that all memrefs are the result of such ops.

But eventually this will need to look through view ops and any other
shape-ish stuff that linalg introduces as it lowers to loops, along with
any slicing ops introduced by buffer allocation.
2020-05-11 18:54:52 -07:00
Sean Silva 53c17dbed9 "Finish" tensor -> memref conversion.
There's a lot of details to flesh out here, but the basic approach seems
promising (see comments in createE2ELoweringPipeline).

This approach will be put to the test when we try to do our first
fusions since that tickles some of the nasty phase ordering issues
involved here.

But we're not there yet.
2020-05-11 15:00:12 -07:00
Sean Silva e29aef855b Initial TCF/TCP E2E seed.
Very much WIP.

This is enough to get tcf.add down to approximately the "linalg.generic
on buffers" level of abstraction. (but there are nuances)
2020-05-08 20:20:41 -07:00