703428eff4
We already had the `promoteTrailingOutTensor` flag, but weren't using it. A inplaceVariantKernelName flag needed to be added. This change is a little dissatisfying, as the conversions done by the RecognizeKernelsPass are currently non-orthogonal. In particular, `kDropResultAndAliasArg0` probably won't work as intended if mixed with these (we probably need to promote kDropResultAndAliasArg0 to not be an arg-level thing anyway, as we have done with promoteTrailingOutTensor). This involved adding a new op `numpy.overwrite_array`. ``` numpy.overwrite_array %arg2 overwrites %arg0 : tensor<2x3xf32>, !numpy.ndarray<[2,3]:f32> ``` This models the destructive update behavior. Note that in the above op, we cannot simply RAUW %arg0 with a suitably conveted %arg2 (for example, %arg0 might have uses that are not dominated by %arg2, or might have an alias relation with some other array in the program). In general, we need a pass analogous to "SSA-formation" which knows how to see through these to uncover an underlying tensor program. Also, add tanh_out_e2e.py/div_inplace_e2e.py and fix some bitrot in refjit.py which is my running example I'm trying to get working. |
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README.md
NPComp - PyTorch frontend integration
This directory contains optional components for interfacing PyTorch to NPComp. Integration is targeted at multiple levels:
- Via program capture with a ATen pseudo-device.
- Via IR-level integration with PyTorch (via tracing or scripting interfaces).
- Interfaces to facilitate checking against reference implementations and verification.
In all situations, the target dialects are maintained in the outer project, along with their lowerings to common intermediate dialects and backends. This directory should be purely about interfacing with the PyTorch/LibTorch components for extracting and executing programs.
The code in this directory is intended to integrate tightly with pytorch, and follows the code style for pytorch. See the overall documentation for frontends for further details about code layout and integration philosophy. In particular, this directory exists to provide a working frontend to an MLIR based pytorch compilation flow and is not intended to be contributed to the LLVM monorepo. If the project is successful, it makes more sense to either break it out as an independent project that depends on LLVM/MLIR/npcomp or contribute it upstream to PyTorch. However, as it will be quite some time before the components are in a state to support such a dependency, it is being carried in-tree in the interim.
Program capture with a ATen pseudo-device.
Integration with a pseudo-device is typified by code like the following:
import npcomp.frontends.pytorch as torch_mlir
dev = torch_mlir.mlir_device()
t0 = torch.randn((4,4), device=dev)
t1 = torch.randn((4,4)).to(dev)
t2 = t0 + t1
t2_mlir = torch_mlir.get_mlir( t2 )
t2_cpu = t2.to('cpu')
In this case t2_cpu contains the result of the computation, and t2_mlir
contains the mlir description of the computation. Tensors are allocated
directly on the virtual device using the device= argument, or computed on
the host and then moved to the virtual device using the to(dev)
call. Subsequent calls on those tensors construct a graph of computation, but
do not perform compute in most cases. This computation graph is returned in
MLIR format by the get_mlir call, or lazily evaluated to return a regular
pytorch tensor by the to(cpu) call.
This technique has several advantages and disadvantages. For training use cases, this technique generates a backward path automatically using the same method that pytorch natively uses. The resulting graph also tends to be simpler, since it will not reflect conditionals in the original python code. Lastly, it is natural if MLIR is being used as a frontend target for an actual device of some sort. In this case, the MLIR could go through a device-specific lowering path and the resulting code run on a device. The implementation of this technique is largely modeled after pytorch_xla.