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torch-mlir/frontends/pytorch/test/node_import
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Sean Silva 370e3270ab Introduce `!torch.tensor` / `!torch.vtensor` types. 
This removes our reliance on the numpy dialect and avoids our off-label
use of the builtin tnesor type for modeling unknown dtypes.  The
`!torch.vtensor` (`ValueTensorType`) type is a value-semantic tensor.
The `!torch.tensor` (`NonValueTensorType`) type is a non-value-semantic
tensor. The new types look as follows syntactically:

```
// Least-static-information, non-value-semantic tensor.
!torch.tensor
// Explicit form of least-static-information variant.
!torch.tensor<*,unk>
// Least-static-information, value-semantic tensor.
!torch.vtensor
// Explicit form of least-static-information variant.
!torch.vtensor<*,unk>
// Fixed-set of allowable element types, with first-class support for
// Torch's frontend signedness semantics.
!torch.tensor<*,si32>
// First-class support for unknown dtypes.
!torch.tensor<[?,?,?],unk>
// Standard MLIR representation of `?` for unknown dimensions.
!torch.tensor<[?,2,?,4],unk>
// Statically shaped / dtyped example.
!torch.vtensor<[1,2,3,4],f32>
```

This required fairly significant changes throughout the compiler, but
overall it is a big cleanup. We now have a much clearer layering of "the
Torch frontend lowering" vs "lowering to std + linalg + etc.".

At the C++ level, there is `ValueTensorType`, `NonValueTensorType`.
We also have a helper `BaseTensorType` (kind of like ShapedType) which
interoperates with those two.

Included changes:
- New `torch.tensor(dense<0.0> : tensor<5xf32>) : !torch.tensor` op for
  creating torch tensor literals in the frontend.
- Consistently use signedness for the types (except i1 which I didn't
  touch -- we need to sort out the situation with !basicpy.BoolType
  there anyway so will be attending to that soon)
- Frontend can annotate whether an argument to the function has value
  semantics. We currently require this, as our backend contract does not
  currently allow us to even model the non-value-semantic case. Before,
  the value-semantic assumption was randomly injected in the middle of
  the pass pipeline.
- Move ArrayToTensor (now called MaximizeValueSemantics) and
  RefinePublicReturn passes to torch dialect.
- The TorchToStd and TorchToLinalg passes are now type conversions from
  `!torch.vtensor` to `tensor` and use the dialect conversion infra.
  The overall conversion pipeline is set up following the best practices
  of the "Type Conversions the Not-So-Hard Way" talk. This required
  introducing `torch-func-builtin-tensorize` and
  `torch-finalizing-builtin-tensorize` passes analogous to the upstream
  bufferization passes with the corresponding names (mostly just
  copypasta from there).
- Misc Torch-level canonicalizations -- we now cleanly layer the
  lowering to std later in the pipeline, so we are gradually lessening
  our reliance on random std constant folding before we get to that
  point.

Recommended review order:
- New types in TorchTypes.td/TorchTypes.h/TorchDialect.cpp
- New ops in TorchOps.td / TorchOps.cpp
- Less important / more mechanical stuff
  - Frontend changes.
  - Pass changes/additions in `Torch/Transforms` and `Conversion/`
 		2021-06-10 10:56:48 -07:00
..
README.md Rename tests to match the code they test 2021-02-25 13:31:33 -08:00
debug-info.py Properly model "derefinement". 2021-03-03 15:09:44 -08:00
elif.py Properly model "derefinement". 2021-03-03 15:09:44 -08:00
errors.py Properly model "derefinement". 2021-03-03 15:09:44 -08:00
function-derefine.py Add basic MLP's to the e2e curriculum. 2021-04-27 12:18:54 -07:00
if.py Significantly restructure torch/aten import design. 2021-05-19 13:37:39 -07:00
list.py Introduce `!torch.tensor` / `!torch.vtensor` types. 2021-06-10 10:56:48 -07:00
loop.py Significantly restructure torch/aten import design. 2021-05-19 13:37:39 -07:00
prim.py Introduce `!torch.tensor` / `!torch.vtensor` types. 2021-06-10 10:56:48 -07:00
tuple.py Introduce `!torch.tensor` / `!torch.vtensor` types. 2021-06-10 10:56:48 -07:00
types-bool.py Properly model "derefinement". 2021-03-03 15:09:44 -08:00
types-none.py Properly model "derefinement". 2021-03-03 15:09:44 -08:00

README.md

node_import

Most of the tests in this directory test the importing of TorchScript torch::jit::Graph's.

However, TorchScript graphs don't really correspond directly to anything on the MLIR side. They are a weird combination of a context, builder, and function and just holds a torch::jit::Block. It is torch::jit::Node and torch::jit::Block which form the recursive structure analogous to MLIR's operation/region/block.

  • torch::jit::Node == mlir::Operation,
  • torch::jit::Block == mlir::Region containing single mlir::Block

Hence the name of this directory and the corresponding code in node_importer.h/cpp.