The first use case is to annotate certain program constructs as either
exported or private. In this commit we plumb it down to
GlobalizeObjectGraph which makes use of this information.
Recommended review order:
1. class_annotator.h/.cpp + `test/module_import/annotations/*`
- New abstractions to communicate with Python code and annotate.
2. IR changes in TorchOps.td
- Adding "private" attribute to various things.
3. ivalue_import.cpp changes
- Module + ClassAnnotator = annotated IR
4. GlobalizeObjectGraph.cpp + tests
- use new "private" attributes to create "private" IR.
- also, tweak some of the op deleting mechanics, which was triggering
some memory errors / assertions
With this, we can run the classifier through and inline it as follows:
```
frontends/pytorch/utils/pt_util.py --import --exported-name forward ~/tmp/classifier.pt \
| npcomp-opt -torch-globalize-object-graph -inline
```
IR: https://gist.github.com/silvasean/32dcad9f6270557f412094a77cecdd69
Note that unlike aten.matmul which has dynamic behavior
depending on the argument ranks (can do matrix-matrix, matrix-vector,
batch matmul, etc.), aten.mm is just a vanilla matrix
multiply, which can be lowered precisely to tcf.matmul.
The "test" is really just an example that I stared at while getting my
feet wet with this. We probably want something that actually tests this
as part of `ninja check-npcomp`.
* Conversions are very simple, suporting mul, maximum and add (alpha=1 only).
* Example added with pass pipeline needed to run.
* Much missing off of the golden path but sufficient for such simple cases.
* convolution, convolution_backward, _log_softmax, _log_softmax_backward_data, nll_loss_forward, nll_loss_backward, nll_loss2d_forward, nll_loss2d_backward, copy_
* Extends the recognition logic and metadata for handling inplace transformations, optional tensors, ints, lists and dropped args.
* The kernel_calls generated by test_conv_nllloss_grads.py now convert to ATen.
* The result *almost* comes out as a pure tensor program with the exception of the copy_ op, which I will do some followup work to deal with.
* More progress on #97
* None's out Device? args.
* Emits bool tensors if needed.
* Adds some stderr tracing to better see what is going on.
* Test case that exercises NLLLoss.
* This test case emits something for backward calculations but there are some issues still to be worked out, so that part is left out of the test case.
* Progress on #97
* Deletes prior code generator from previous attempt (moved some of it into this one).
* Renames old generated tablegen source to "Legacy".
* Generates ODS and import rules for most binary and unary arithmetic ops.
* Removes old generated ops and integration tests that were testing details of the prior setup.
* Adds a trampoline/loader 'torch_mlir' module.
* Plumbs through the MLIR python Context and Module creation, interoping with the MLIR Python API (resolves TODO on creating with own context and accessing the module being built).
* Inter-module Python API interop is still a bit rough but workable via the capsule mechanism. Can be evolved later.
* Exports the frontends/pytorch python sources to the project python/ build directory.
* Requires D89294 to land.
* Exposes the op registry via a get_registered_ops method.
* Moves the aten dialect generation scripts in prep for integrating them with this facility.
Sean Silva
meadowlark@google.com
Stella Laurenzo