* 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.
* 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.
Stella Laurenzo
stephenneuendorffer