f6d7ee06ef
* This has been anticipated for a long time in that it is quite hard to keep C++ binary compatibility across a system landscape as diverse as PyTorch, LLVM, and this project. This is why we based the PyTorch extension on the MLIR and NPCOMP C APIs only: that is the only sane linkage story for the entire matrix. * Removes the few LLVM'isms in torch_mlir that had snuck in, using either STL or PyTorch support utilities. The new rule here is that LLVM C++ includes are forbidden at this level and (as stated in the design), torch_mlir should use the PyTorch runtime and support libraries (not introduce an incidental C++ dependency on LLVM). * Also deletes mnist-playground as it was proving impossible to keep the grid of PyTorch vs system ABI divisions functioning. I am open to a less drastic course here (optional/disabled by default?) * This gets us pretty close to just using PyTorch's extension builder API, which will be nice for distribution (i.e. it integrates well with the PyTorch ecosystem for deployment). I ended up just simplifying the in-tree CMake support for now. * Fixes #138 |
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| examples | ||
| python | ||
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| CMakeLists.txt | ||
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| README.md | ||
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.