mirror of https://github.com/llvm/torch-mlir
21 lines
1.6 KiB
Markdown
21 lines
1.6 KiB
Markdown
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# Compatibility notes
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This document contains known compatibility issues with the PyTorch integration.
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Some items may be permanent limitations and other may just be capturing
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plans for future work. In general, this only applies to the default
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configuration, not the "type dispatch" (PyTorch 1.4) integration.
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## Pending Work Needed
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### Unpacking quantized weights
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Some of the torch::jit::Operator 's (especially the quantized:: ones) have already gone through some lowering steps. Specifically, the quantized::conv operators are pre-packed and stored as ConvPackedParam attributes on the Module instead of just passing the weight/bias tensors as SSA arguments to the Operator[0] [1]. How they are packed depends on whether the fbgemm or the XNNPACK backends are used...
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I think this comes back to the ability to pass a "CustomClass" as an SSA Value into an Operator, which may be difficult for us to lower to TCF...
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Glow (and others) get around this by adding custom passes to convert the PackedParams to a traditional glow::unpacked_quantized_conv operation [2], but that adds some layers of lowering in TorchScript land before we would want to call off to get_registered_ops on the python side (may not be avoidable?)
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[0]: https://github.com/pytorch/pytorch/blob/dc67b47bc9d53dbeb898a4d920b0225ac73629ec/aten/src/ATen/native/quantized/library.cpp#L63-L69
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[1]: https://github.com/pytorch/pytorch/blob/master/aten/src/ATen/native/quantized/cpu/conv_packed_params.h
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[2]: https://github.com/pytorch/glow/blob/b62ec449c43b77722c119b53b3ea5aec9be3edb9/torch_glow/src/TorchGlowBackend.cpp#L98-L116
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