This removes the dependence of the `torch` dialect on the low-level
builtin types.
Now the `torch` dialect is a standalone layer, suitable for targeting
from higher-level Python abstractions without any premature lowering to
primitive types.
This replaces the ad-hoc use of `i64` throughout the Torch layer, and
helps to keep it crystal clear the distinction between `!torch.int`
(which is modeling the Python `int` type) and the various types that
serve as dtypes of tensors, which are a totally different type universe.
Changes:
- `!torch.int` type and C bindings.
- Change `torch.constant.int` parser to not need the `: i64` at the end.
- `m_TorchConstantInt` matcher to aid with matching constants.
- BackendTypeConversion changes for `!torch.int` -> `i64` type
conversion.
- Refactor finalizing patterns in FinalizingBackendTypeConversionPass
(they were getting very repetitive).
- Mechanical rewriting of `!torch.int` to `i64` in all the tests, and
`AnyTorchIntType` to `Torch_IntType` in the `.td` files.
This is enough to import the program and get it through the compilation
pipeline. It of course fails at the VerifyBackendContract pass since
there is a lot missing, but the final IR for a simple quantized MLP is
looking pretty decent already:
[IR](https://gist.github.com/silvasean/f76bccd76e9b193d396cfb2f9a11f54d)
Main changes:
- Add support for importing torch quantized tensors, including
`torch.per_tensor_affine.create` op and `!torch.qint8` element type.
- Add support for importing `LinearPackedParamsBase` (basically a weight
+ optional bias, but requires `torch.linear_params.create` op +
`!torch.LinearParams` type to model it). This was less painful than I
expected, as it has the necessary methods to opaquely unpack itself. I
factored things so it should be easy to extend to other custom classes
like `ConvPackedParamsBase`.
- Add minimal boilerplate for importing `quantized::*` ops, with
`quantized::linear` being a motivating example.
- Add e2e test with simple quantized MLP (courtesy of @phoenix-meadowlark).
This is somewhat of an abuse of `!numpy.ndarray` / `tensor`, as
really the proper semantics of `!torch.qint8` dtype on a Torch tensor is
"check the quantizer object of the tensor for side data (scale/offset,
possibly per-channel) that defines the full semantics of the tensor". We
don't have any such notion of "side data" for `!numpy.ndarray` /
`tensor`, let alone anything that would have the associated behavior of
keying off the dtype to determine if the side data is present.
This will be fixed by a proper `!torch.tensor` type.
Sean Silva