The E2E tests can be run with
```
npcpy frontends/pytorch/e2e_testing/torchscript/main.py
```
This commit adds a couple items supporting that end, including new sugar
for annotations (no more raw use of ClassAnnotator!).
Recommended review order:
1. `frontends/pytorch/e2e_testing/torchscript/main.py` for
the harness + `basic.py` in that directory for examples of tests.
2. Annotation sugar in `frontends/pytorch/python/torch_mlir/torchscript/annotations.py`
and unittest in `frontends/pytorch/test/ivalue_import/annotations/sugar.py`
3. Global test registry / sugar in
`frontends/pytorch/python/torch_mlir/torchscript/e2e_test/registry.py`
4. `frontends/pytorch/python/torch_mlir/torchscript/e2e_test/framework.py`
for the meat of the testing framework (start at `run_tests`), and
looking at the backend configs in
`frontends/pytorch/python/torch_mlir/torchscript/e2e_test/configs`
for examples of backends. This is likely the bulk of review time.
5. Unit tests of the framework logic in `frontends/pytorch/test/torchscript_e2e_test`
There's TODO's scattered throughout, but this seems functional enough to
start pulling stuff into and kicking the tires. A few missing pieces:
1. Marking test expected pass/fail per backend.
2. Figuring out how best to fit this into dev workflows.
3. IREE TestConfig.
Also, forgive this Python newbie... Any advice on Python code structure
/ library design would be much appreciated.
We already had the `promoteTrailingOutTensor` flag, but weren't using
it. A inplaceVariantKernelName flag needed to be added.
This change is a little dissatisfying, as the conversions done by the
RecognizeKernelsPass are currently non-orthogonal. In particular,
`kDropResultAndAliasArg0` probably won't work as intended if mixed with
these (we probably need to promote kDropResultAndAliasArg0 to not be an
arg-level thing anyway, as we have done with promoteTrailingOutTensor).
This involved adding a new op `numpy.overwrite_array`.
```
numpy.overwrite_array %arg2 overwrites %arg0 : tensor<2x3xf32>, !numpy.ndarray<[2,3]:f32>
```
This models the destructive update behavior. Note that in the above op,
we cannot simply RAUW %arg0 with a suitably conveted %arg2 (for example,
%arg0 might have uses that are not dominated by %arg2, or might have an
alias relation with some other array in the program). In general, we
need a pass analogous to "SSA-formation" which knows how to see through
these to uncover an underlying tensor program.
Also, add tanh_out_e2e.py/div_inplace_e2e.py and fix some bitrot in
refjit.py which is my running example I'm trying to get working.
We should generally be using torch_signature_ods_gen.py for generating
these. Somehow this one slipped through manually.
There is no `aten::conv2d_overridable` in the op registry AFAICT so I
removed that alias.
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