With this + manually setting private visibility on everything, a simple
classifier can be reduced to this IR, which is looking pretty lean and
mean:
https://gist.github.com/silvasean/19e7e2e21a61ff197aeac0dd864d188f
Also, include a utility script for importing `.pt` models.
```
pt_util.py --import classifier.pt | npcomp-opt -torch-globalize-object-graph
```
This required restructuring of how we model TorchScript on import. The
main difference is that now we split out a `torch.class_type` that holds
methods and declarations of the types of each slot. This is more
consistent with TorchScript (our previous representation was
"denormalized").
Recommended reading order:
1. check out the description of `torch.class_type` in `TorchOps.td` and
look at `test/Dialect/Torch/ops.mlir` and
`frontends/pytorch/test/module_import/` to familiarize with the new
representation.
- Just look at the new IR. The diff between the old names and new
names is confusing.
2. check out `test/Dialect/Torch/globalize-object-graph*.mlir`
and read along with the pass description in
`include/npcomp/Dialect/Torch/Transforms/Passes.td`
3. Read the code in `GlobalizeObjectGraph.cpp` and miscellaneous changes
in `ivalue_importer.cpp`, `TorchOps.cpp`, etc.
Modify ACAP_Dispatch to work with latest pytorch
-Remove boxed from convolution's m.impl
-Use redispatch and constrainted keyset to replace deprecated
callwithdispatchkey
This required some careful considerations when defining object identity
for tensors. See the comments for how we do it.
This also tracks some basic information for diagnostics.
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.
It turns out that this was easiest to structure as a general IValue
importer, since torch module are just one of the possible IValue's.
We import the IValue object graph in a braindead fashion into basicpy
ops and a new `torch.nn_module` op that is used to model the
attributes/methods of a torch::jit::Module IValue. See `Torch/ops.mlir`
for an example, and also check out the .py import tests in
`frontends/pytorch/test/module_import`.
As part of this change, a few housekeeping tasks:
- extract some helpers from graph_importer.cpp
- more helpers around the C API
- misc touchups
This allows building NPCOMP as an external project of LLVM, similar to
how CIRCT can be built: https://github.com/llvm/circt/pull/227.
The CMake options to use this build style look like this:
```
-DLLVM_EXTERNAL_PROJECTS=npcomp \
-DLLVM_EXTERNAL_NPCOMP_SOURCE_DIR=/path/to/mlir-npcomp \
```
* This has been anticipated for a long time in that it is quite hard to keep C++ binary compatibility across a system landscape as diverse as PyTorch, LLVM, and this project. This is why we based the PyTorch extension on the MLIR and NPCOMP C APIs only: that is the only sane linkage story for the entire matrix.
* Removes the few LLVM'isms in torch_mlir that had snuck in, using either STL or PyTorch support utilities. The new rule here is that LLVM C++ includes are forbidden at this level and (as stated in the design), torch_mlir should use the PyTorch runtime and support libraries (not introduce an incidental C++ dependency on LLVM).
* Also deletes mnist-playground as it was proving impossible to keep the grid of PyTorch vs system ABI divisions functioning. I am open to a less drastic course here (optional/disabled by default?)
* This gets us pretty close to just using PyTorch's extension builder API, which will be nice for distribution (i.e. it integrates well with the PyTorch ecosystem for deployment). I ended up just simplifying the in-tree CMake support for now.
* Fixes#138
* Going through TODOs on the PyTorch side, this is a big cause of them (not being able to have constants for signed/unsigned).
* Added complex while in here since we're at the phase where it is better to just have things complete than partially done.
* 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.
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`.
After the recent change of cmake variables
from PYTHON_INCLUDE_DIRS to Python3_INCLUDE_DIRS
and PYTHON_LIBRARIES to Python3_LIBRARIES, there were
a few files that still had references to the old
variables. This patch fixes that.
* In most situations, this eliminates the need to explicitly set a path to the Torch cmake files.
* Also upgrades to new Python3 find package. (should eliminate 2.x mismatches)
* Since PyTorch is located by asking Python where it is, this eliminates a lot of causes of mismatch. (one source of truth)
* 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).
* A bit gross because I took the chance to upgrade all of the backend bits to the new MLIR Python bindings and we still co-mingle the old and new for now.
* Since the Python created PassManagers are configured for explicit nesting, I had to upgrade some of the pass pipelines to be explicit.
* The demo in mul_maximum_e2e.py now compiles, runs through PyTorch and through the JIT, prints and asserts the same results.
* I am not claiming that this is the prettiest API in this patch: consider that this is just directly using low-level APIs and there should be an intervening high level API.
* 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
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
* 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.
* 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.
* 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.
* Also adds two lit tests to verify that all of our extensions load without fireworks, which is a good indication that the shared library deps are sane.
* Bumps llvm-project version to use D89167.
* Need to have a dag of shared library deps in order to interop across python extensions (as presented in ODM).
* Introduced add_npcomp_library and friends to mirror the MLIR setup.
* Adds a libNPCOMP.so shared library.
* Redirects tools and extensions to link against libNPCOMP.so (instead of static libs).
* Moves all libraries to lib/, all binaries to bin/ and all python extensions to python/. The invariant is that the rpaths are setup to have a one level directory structure.
* Reworks the _torch_mlir extension to build like the others (still need to come up with a consolidated rule to do this instead of open coded).
* Includes an upstream version bump to pick up needed changes.
Sizes with dynamic linking (stripped, release, asserts enabled):
libNPCOMP.so: 43M (includes much of the underlying LLVM codegen deps)
libMLIR.so: 31M
_npcomp.so: 1.6M (python extension)
_torch_mlir.so: 670K (python extension)
npcomp-capi-ir-test: 6.3K
npcomp-opt: 351K
npcomp-run-mlir: 461K
mnist-playground: 530K
Still more can be done to normalize and optimize but this gets us structurally to the starting point.
* Had to stop short of modifying the function return signature because of a missing C-API upstream.
* Committing here is good enough for a test and will resolve the various TODOs about upstream APIs next.
* Adds at::Tensor -> MlirValue tracking.
* Adds conversions for tensor and scalar types to MLIR types.
* Adds npcomp C APIs for constructing custom types.
* Reworks pybind include so as to get Torch pybind helpers (needed to pass at::Tensor type from Python->C++).
Sean Silva
Bairen Yi
Stanley Winata
mikeurbach
Stella Laurenzo
Phoenix Meadowlark
harsh-nod