* llvm-project: b5924a8e27536d19dd5c4d302db29fb6163d5faa
* mhlo: 848ca244d20f045b7921da55a98a04d95ef94f0e
* Multiple breakages that need to be fixed.
Fixes:
* Refactor dialect registration
* Remove all kindof methods (Casting functionality has been added upstream and is implicitly
available, see https://llvm.discourse.group/t/removing-kinds-from-attributes-and-types/1547.)
* Update dialect registration to comply with https://reviews.llvm.org/D85495.
* Remove type kinds and update some changed dialect signatures.
* Upgrade ATen dialect to match upstream needs.
* Move dialect registration to tablegen.
* Register the ListType in tablegen.
* Change dialect initialization signature.
* Use TypeSwitch in MlirIr location printer.
* Remove global registry depends from npcomp-opt.
* Change LowerToLLVM to pass an MLIRContext vs an LLVMDialect for type creation.
* Remove dep on MLIREDSCInterface that is removed upstream.
* Thread through the DialectRegistry for opt and python-like tools.
* Modernize pass registration (This was forced because the GEN_PASS_REGISTRATION code now generates inline functions vs literal pass registration statements)
Co-authored-by: Marius Brehler <marius.brehler@iml.fraunhofer.de>
This patch adds a dialect intended to be used as a frontend dialect
to facilitate lowering from "A Tensor Library" in torch/pytorch.
This patch includes several passes that are useful in conjuction with the
dialect:
--aten-layer-name: Generates layer names for each operation, which are not
present in the original pytorch.
--aten-to-std: Lower the ATen dialect into standard dialect function calls.
--return-elimination-pass: convert functions (primarily the toplevel function)
to pass return values by reference. This simplifies pytorch integration.
--aten-op-report: generate a textual report about the model
--liveness-report
Future patches will implement actual integration with the pytorch jit to
intercept and generates MLIR in this dialect, then lower the resulting MLIR
into function calls through aten-layer-name -> aten-to-std ->
return-elimination -> std-to-llvm. The result would then jitted using the LLVM
jit, linked against a runtime library which makes calls back into pytorch to
implement all the layers.
Co-authored-by: Jeff Fifield <jeff.fifield@xilinx.com>
Co-authored-by: Jeff Fifield <jeff.fifield@xilinx.com>
* These were separated originally for layering reasons that no longer apply.
* Most of the python extension code is under lib/ with just the module setup in python/.
* Uses local configs and unsupported annotation to disable optional tests.
* This separation was just an artifact of having initial trouble getting lit setup.
* Primarily, the upstream shape dialect now uses tensor<?xindex> for non-erroring, immediate shape calculations (and will return this for shape_of of a tensor or memref).
* In addition, upstream passes do not yet exist for fully lowering to standard ops, so the passes here need to be extended to handle this new convention.
* This should be seen as an intermediate state, necessary to integrate a new LLVM version and needs more work and cleanup for generality.
* There is a good deal of awkwardness in these conversions. The hope is that additional upstream work will yield better defined conversion paths once out of this intermediate state.
This required making module descriptors hold a FuncDescriptor* instead
of a pointer to array of FuncDescriptors as it previously did, which is
innocuous (just requires an llvm.bitcast after the llvm.mlir.addressof).
* Rewrites public function signatures to operate on tensors (vs ndarray).
* Most of our backends presume immutable tensors at public function boundaries.
Also, the previous code had a special case for deleting this op when it
had no uses. This is subsumed by the change in this commit since now
shape.const_shape is properly lowered.
With this change, the included test case with multiple serially
dependent ops works!
This specific issue was related to the scalar argument to that
function. We needed to compute a broadcast of a scalar shape (which is a
shape.const_shape) with another shape.
This ~totally reworks the existing "runtime" stuff to be more
principled and usable, such as from Python. It's still not fully
production-quality, mainly in the department of memory management (e.g.
it currently leaks memory; we need to figure out "who frees memrefs" +
the analysis and transformation needed to do that (maybe use upstream
buffer allocation pass?)).
The user API is in include/npcomp/runtime/UserAPI.h, though
include/npcomp/JITRuntime/JITModule.h is a friendlier wrapper.
The stuff under {include,lib}/runtime is totally firewalled from the
compiler and tiny (<6kB, though no attention has gone into optimizing
that size). For example, we don't link in libSupport into the runtime,
instead having our own bare bones replacements for basics like ArrayRef
(the JITRuntime helps with bridging that gap, since it *can* depend on
all common LLVM utilities).
The overall features of npcomprt is that it exposes a module that
with multiple function entry points. Each function has arguments and
results that are tensor-valued, and npcomprt::Tensor is the runtime type
that is used to interact with that (and a npcomprt::Ref<T>
reference-counting wrapper is provided to wrap npcomprt::Tensor in the
common case).
From an implementation perspective, an npcomprt module at the
LLVM/object/binary level exposes a single module descriptor struct that
has pointers to other metadata (currently just a list of function
metadata descriptors). All interactions with the npcomp runtime are
keyed off of that module descriptor, including function lookups and
dispatching. This is done to dodge platform ABI issues and also allow
enough reflection to e.g. verify provided arguments.
Most of the compiler-side work here was in LowerToNpcomprtABI and
LowerToLLVM.
Also,
- Rename npcomp_rt/NpcompRt to npcomprt/Npcomprt; it was getting
annoying to type the underscores/caps.
- misc improvements to bash_helpers.sh
* This elides the very common code the compiler adds for chaining otherwise tensor-related numpy ops together.
* More aggressive canonicalizations would require more advanced analysis.
* This starts to lay down the infra for reasoning about calls
* Adds the importer code to generate IR for function calls of compiler recognized static functions.
* Adds python bindings for invoking flow, HAL, and VM lowering pipelines.
* Adds pythong bindings for translating to VM module flatbuffer.
* Adds a new backend_test/iree directory and configure lit to find the IREE python rt bindings.
* Open code a simple_invoke.py that exercises the whole pipeline (need real APIs for a lot of this).
* Fails when invoking the function because I never implemented argument marshaling for scalars :(
* Plenty of stuff to do tomorrow.
The secret here is LLVM_ENABLE_WARNINGS=ON.
I also fixed a couple warnings, which gets us to be warning-clean.
I noticed also that npcomp-run-mlir/basic.mlir seems to be failing.
Maybe something since the latest integrate. My next commit (introduce
npcomp mini runtime) will largely rewrite it though, so it'll get fixed
then.
The code isn't super clean, but is a useful incremental step
establishing most of the boilerplate for future enhancements.
We can't print or return tensors yet so correctness TBD, but I've
stepped into the running code in the debugger so I know it definitely is
running.
This is the first step to building out an npcomp mini-runtime. The
mini-runtime doesn't have to be fancy or complex, but it should at least
be layered nicely (which this code and the current compiler interaction
with the "runtime" code is not). Now that we have boilerplate for e2e
execution in some form, we can build that out.
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.
This more clearly captures its semantics as a structural "observer" of
code that we currently mark as NoSideEffect but eventually lowers to
eager error handling code.
Also, update LowerRankedShapes to erase it, now that the layering here
is clear. That pass reifies the eager error handling code, so the need
for the dummy op to keep things alive isn't needed.
With this change, we are now ready to start lowering to LLVM!
This is the current print-ir-after-all from e2e-lowering-pipeline:
https://reviews.llvm.org/P8221
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.
The idea was half-baked and after some deep thought felt like a solution
looking for a problem. What we had here (and is removed in this patch)
just wasn't pulling its weight.
I cannot think of anything we would want to do with tcp.island as it is
removed here beyond just sinking and merging them within a basic block,
such that the witness argument is kind of pointless (only matters for
hoisting).
TCP compute ops like tcp.add and tcp.broadcast_to have the strong
invariant of "pure or undefined behavior", which means they are always
safe to sink. The island concept as removed here conferred no benefit.
Also, I'll note that "islands" are a trick you can only play once in a
system (unless they strictly nest). I have some early-stage thoughs on
having an island concept that helps with modeling tensor shapes
robustly which seems promising (the island would serve a similar role as
tie_shape).
- Make rank1.mlir be the new "basic.mlir", as it is really the simplest
case.
- Move basic.mlir to mixed-ranks.mlir
- Delete starting-from-linalg.mlir, it wasn't really useful anymore.
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.
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.
* This is intended to provide low-level modeling for built-in objects.
* It is now possible to trace slice tuples (which are tuples of NoneType|EllipsisType|SlotObjectType<slice, ...>).
Stella Laurenzo
stephenneuendorffer
Sean Silva