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.
* Enables e2e test.
* With what I've learned in upstream about test directory layout, I can consolidate most of the separate directories we have for these things. Will do that in a followup.
* Not pleased with the LLVM global initialization depends but serviceable for now.
* Rewrites public function signatures to operate on tensors (vs ndarray).
* Most of our backends presume immutable tensors at public function boundaries.
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.
* Preserving shape across the copy ops makes more thing shaped by default.
* Inference of ndarray types will now preserve the shape when specializing the dtype.
* Adds an op interface for adding CPA constraints.
* Adds a type conversion hook for handling built-in types (that we can't have adopt our interface).
* Converts tensor<> to object(!Tensor, [e:<type>]) just like NdArray.
* Implement a few numpy ops far enough to do dtype inference for simple sequences.
* 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.
* Conversions to std for numeric binary expressions, numeric to_boolean, and numeric comparisons.
* Added folders to constant ops to comply with requirements of the pass system.
* Extended the frontend with parameter/result annotation processing for primitives (can specify types for function arguments).
* Added (empty) directory/sources for IREEVM conversions. These are only enabled if IREE is enabled.
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.
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
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.
Stella Laurenzo
stephenneuendorffer
Sean Silva