Now that we upstreamed our pass, we can remove it.
The final pass that landed upstream doesn't do the shape.assuming
canonicalization to legalize that op away, so added a
restricted-canonicalizer pass that allowed to run just shape dialect
canonicalizations, which deletes the shape.assuming.
The pass ended up kind of ugly. See the TODO's on it for some potential
cleaner directions.
Date: Fri Sep 18 13:55:52 2020 -0700
- Update to linalg syntax
- New generated builders are better. Custom builder for
tcp.shaped_results is now redundant.
* Exposes the op registry via a get_registered_ops method.
* Moves the aten dialect generation scripts in prep for integrating them with this facility.
This cleans up the lowering pipeline to easily allow extending to
multiple binary ops. It looks fairly repetitive at multiple levels, but
I don't want to prematurely generalize. I think that in principle we
could derive a large swatch of TCF + TCP from a single linalg-style
specification. Another direction is to use an OpInterface (something
like "buildLinalgGenericBody"). I'm keeping my eye on it.
In a subsequent commit, I'll mechanically add a set of binary ops
modeled off of the std arithmetic ops.
* Still need to add a systematic mechanism for discovering gradient ops.
* Work needed on the various _ suffixed inplace ops.
* Other randoms still not mapped.
* Outside of this commit, I do have enough commented/reworked to roughly build but that will take another handful of commits to get going.
It was previously going through this awkward route that prematurely
created linalg.generic ops, which was an annoying layering problem since
we can't compute a shape transfer function for linalg.generic in the
general case. Now we pass it through the same path as tcp.matmul, with
the shape transfer function being defined for tcp.add.
This also removed the need for TCPToLinalg (now deleted). The equivalent
of that is happening in lower-shaped-results-to-memref. One interesting
outcome of this: we're basically using linalg as a "Buffer TCP". We
might want to look into using named structured ops for more of TCP, but
that would be a big velocity hit since then any change to the ODS /
verification for those ops would be a change to the upstream structured
op ODS generator. After we have more experience defining this manually,
we should re-evaluate rebasing TCP on generated named linalg ops.
I'm pretty happy with how this turned out. It looks pretty much like it
should -- one change at each layer. This particular op bottoms out on
linalg which takes care of the rest.
- Add tcf.matmul
- Add tcp.matmul
- Add TCF->TCP lowering
- Add tcp.matmul shape transfer function (BypassShapes.cpp)
- Add tcp.matmul -> linalg.matmul lowering (LowerShapedResultsToMemref.cpp)
- Add support to LowerShapeConstraints for lowering the new
shape.cstr_require
This matmul op is pretty limited in its capabilities. There is no
batching and no multidimensional contraction. Certainly more design work
will be needed to find the right abstractions that aren't too general
but also help to canonicalize many cases from frontends. This is mainly
to show that adding a new op needn't be very "scary" once we have the
e2e infra in place.
Also,
- this clears out some exploratory cruft from the TCF dialect now that
this is starting to become real.
* Includes pybind11 directly (for some reason using the pytorch helper header for this depends on a source file not in the image).
* Installs nnpack into the image.
* Installs new-clang and LLD and configures environment to use it (otherwise, link time is terrible).
* Fixes a gcc compile error (in the off chance you build with default gcc compiler).
* Tests are failing based on some dialect registration stuff that must not have been factored correctly. Will followup with a fix.
This now gets the overall "RefE2E" compilation stack to a point that I'm
fairly happy with. We simplify it by mostly embracing the "descriptor"
view of the world.
The overall flow is best understood by reading through the
createE2ELoweringPipeline function in lib/E2E/E2E.cpp
That function creates a pass pipeline that lowers from "TCF" (which is
~numpy level of abstraction) down to LLVM IR.
A brief high-level summary of what happens there:
1. TCF to TCP conversion. This involves reifying error handling in the
form of shape constraints. See test/Conversion/TCFToTCP/basic.mlir
2. Lowering shape constraints. This converts shape constraints into
eager error-handling code. See test/E2E/lower-shape-constraints.mlir
This pass will soon go upstream.
Because this lowers to std.assert, some later passes like
LowerToNpcomprtABI and LowerToLLVM are updated to properly plumb this
through e2e.
See test/npcomp-run-mlir/invalid-broadcast.mlir for an execution test
that properly aborts in case of an error.
3. Lowering tensors to memrefs. This is done via a series of passes
rather than an single mega conversion. Unlike the previous code that
mixed in the npcomprt ABI stuff here, it's now a very clean "pure
memref" conversion.
See test/E2E/lower-*-to-memref.mlir and
lib/E2E/TensorToMemref/
Most of the changes are concentrated here.
4. As part of the above, we use the upstream ConvertShapeToStandard for
lowering shapes.
5. We lower linalg to loops and lower loops to CFG using upstream
passes.
6. Rewrite the "ABI" boundaries of the program to npcomprt data
structures (LowerToNpcomprtABI). This mainly affects ABI boundaries and
how global tensor constants are represented. One of the major
improvements in this commit is that now it's a very clean rewrite that
just replaces memrefs on ABI boundaries with !npcomprt.tensor (before
there was a get_extent function that is not needed).
See test/E2E/lower-to-npcomprt-abi.mlir
7. Lower to LLVM with upstream mlir patterns + some patterns for the
npcomprt lowerings.
One aspect here that is still a remnant of a non-descriptor-based tensor
to memref flow is the BypassShapes + LowerShapedResultsToMemref.
BypassShapes wraps the "tensor compute" ops in a tcp.shaped_results
(basically a "tie_shape" kind of op), and then
LowerShapedResultsToMemref uses those annotations to allocate output
buffers while lowering the "tensor compute ops". Note that there are
very few "tensor compute" ops currently supported (tcp.add +
tcp.broadcast_to), so we just hardcode them in both passes.
Realistically, I expect this to go away as we fully embrace the
descriptor-based approach for simplicity, so don't look too deep into
it.
* Add a new python script to auto-generate ATen op ODS definitions.
* There is still some work on some of the ops to annotate correct types.
* The ODS is not actually included into the dialect yet, but I'd like to commit it so that we can track changes.
* Will reconcile this with the ops produced by the existing script in a followup. Still need to do some more iteration to reach parity.
* 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 extracts metadata from python invocations (nearly) sufficient to generate ODS and a Torch IR translation table for most of the ops.
* It also has the side effect of creating a data structure with meaningfully runnable examples suitable for an automated regression test.
* There are some ops that are sufficiently complex/weird (like _convolution) that we'll just manually handle those.
* See example output: https://gist.github.com/stellaraccident/60a58457b15e9184e224fa98a2658769
* Make code that depends on the legacy "type dispatch" mechanism optional.
* This code is fairly tied to a specific ~1.3 version and uses a legacy dispatch mechanism.
* Moving it and making it optional allows the project to build with PyTorch 1.6 and makes it possible for us to start building out a more modern interface mechanism in parallel.
* Some of the moved code will be brought back into the more modern path, but isolating it now lets this be done incrementally.
* Tests are left failing since the entire frontend is optional and the next step involves reworking the interface mechanism to get them to passing in both regimes.
* Fix a few bogons to get things building
* Add Dockerfile with pytorch
Also, I configure with:
-DCMAKE_PREFIX_PATH="/opt/pytorch/pytorch"
(which is where pytorch is installed in this container)
* Make a dep conditional.
Co-authored-by: stephenneuendorffer <stephen.neuendorffer@xilinx.com>
This patch adds a pytorch interface to npcomp. This interface is modeled
after pytorch_xla and exposes the MLIR-based flow as a virtual device (similar
to a gpu device or the xla backend). Usage is intended to be something like:
dev = torch_mlir.mlir_device()
t0 = torch.randn((4,4), device=dev)
t1 = torch.randn((4,4), device=dev)
t2 = t0 + t1
t2_mlir = torch_mlir.get_mlir( t2 )
t2_cpu = t2.to('cpu')
In this case t2_cpu would contain the result of the computation, and t2_mlir
contains the mlir description of the computation. Note that this also
properly returns backward paths synthesized by pytorch. There are several
parts of this:
1) A tensor type (implemented by tensor.* and tensor_impl.*)
2) The device modeling (aten_mlir_bridge.*, aten_mlir_device.*, aten_mlir_type*)
3) a temporary IR (implemented by ir.cpp)
There is also a reference lowering directly from the ATen dialect to C
function calls consisting of two parts:
1) The driver that uses the IR to generate MLIR, run Passes and compile the
result using mlir::ExecutionEngine (implemented by jit.cpp and
mlir_gen.cpp)
2) A runtime library implemented by lib/aten_ops.cpp. Most of the operations
are implemented by callbacks into the torch C++ libraries.
Some aspects of this are known to be less than optimal, in particular:
1) There's some function definitions that don't live in the file corresponding
to their declaration.
2) More aspects of this (e.g. the IR) seem like they should be automatically
generated.
3) It's unclear to me how much of the 'IR' is actually necessary, or whether
MLIR could be created on the fly.
Note that this code is licensed in a way similar to pytorch, with the
intention that eventually (when npcomp reaches some maturity) it should be
pushed there. (see frontends/pytorch/LICENSE) The code is also structured
much closer to the pytorch coding style than the LLVM coding style.
* I expect that mlir-hlo will be a non-optional dependency of the project, so adding as a sub-module.
* IREE is an optional dependency and I'm keeping this as an out-of-tree checkout for the moment.
* The script will compute the join across both iree and mlir-hlo to find a common LLVM version.
* The script needs some more work (like a flag that says to update the version, etc). Likely needs more testing through an integrate or two.
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>
Mostly this is CMake cleanup. Several library dependencies are missing, which
is often revealed with shared library builds. Also, it's generally bad to
link directly against LLVM libraries because it fails when using
LLVM_LINK_LLVM_DYLIB. MLIR will pull in libLLVM.so, and there will be
duplicate linkage with the the explicit libraries. There may need to be more
refactoring here.
Sean Silva
Stella Laurenzo
Marius Brehler
stephenneuendorffer