This required some invasive surgery to graph_importer.h/cpp,
specifically moving most of it into node_importer.h/cpp and relayering
it. The abstraction that it had didn't work well in the recursive
setting that happens with prim::If.
The key observation is that torch::jit::Graph doesn't really correspond
directly to anything on the MLIR side. It's 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. So
node_importer.h/cpp makes sense as a core building block.
As part of doing this, I did venture a bit into the AcapController code,
and realize now that there is functionality duplicated there with the
ivalue importer. Will refactor that soon.
* Does not handle all features yet but should conservatively fail on unsupported things.
* Location tracking is still somewhat mismatched between what TorchScript and MLIR do. Likely need a better heuristic for tracking locations from defs for nodes that do not carry location.
* Sets the ground-work for a specialized/generic split but only implements the generic side.
* Had some evidence that this requires a recent bump of PT nightly (within the last month) to pick up pybind11 2.6, which includes some cross-module symbol fixes (vs the previously sync'd version). No source changes, but older versions fail to cast function types at runtime.
* Fixes#107
* I wouldn't say I love what had to be done here. Worth a conversation with the PT devs (probably as part of a rollup of a bunch of this stuff).
The current code was inserting all build_list ops
after the last constant op since it was assuming that all
elements being passed in were constants.
This patch replaces that patch with a new function that
inserts the build_list ops before the terminator.
Also modifies test_export_conv2d_fwd.py since its output
no longer matches.
TEST: Added test_export_cat.py which is the code in #102
* This is sufficient to capture the forward and backward pass and gradients of a convolutional model with an nllloss.
* As with the forward conv, the backward conv is a special case wrapped in an enigma on the PyTorch side. There aren't many like it, so special casing is just what we do.
* When I traced this, I found that the copy_ op is not yet boxing compatible so I had to map it manually. If there are many more like this, I'll probably do something a bit more clever to reduce duplication.
* This exposes new signature patterns that will need to be handled by the ATen lowering. Will take care of that next: It will be nice to have an e2e of a non-trivial case with full gradients.
* Fixes#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
* Two op interfaces, one for querying instance metadata and one for getting static data needed to construct an op from a generic form.
* For torch.generic_kernel ops, metadata is splatted in during capture from Torch (it comes from the op registry, which will work for either device capture or graph import).
* Moved the 'add' out of the generated set so I can experiment on it. It implements the TorchBuildableKernelOpInterface interface which provides its metadata.
* The ATenRecognizeKernelsPass pass generically lowers from a torch.generic_kernel to recognized ops that implement the TorchBuildableKernelOpInterface, handling the various types of transformations that we allow at this stage.
* Enables the conv2d fwd test and ResA (which are both small).
* Deletes resnet18 and vgg, which both run but generate output that crashes FileCheck and lit (or at least makes them take an eternity).
* Adds Basicpy List, Tuple, Dict types and plumbs through C API.
* Started debugging the issues around aten::conv2d capture, but a PyTorch bug is suspected.
* Was able to manually verify that the basic conv2d forward test captures correctly with a workaround.
* Need to resolve some printing issues upstream and move these tests to an integration test target (they take ~seconds to run).
* Now gets far enough to capture batch_norm.
* Has some issues still with in-place ops.
* Can materialize constants.
* Includes an upgrade to PyTorch nightly, which has important bug fixes for fallback and boxed kernel dispatch.
* Fixes#78, #79, #80.
* Will do more testing in a follow-up once further bugs are fixed that facilitate getting at the other features.
Sean Silva
Stella Laurenzo
Harsh Menon