This PR adds support to `fx_importer.py` for handling custom ops that
return an array of tensors. As long as the length of the array is
consistent across runs (determined statically), then this patch will
work. This does not require that the number of tensors returned is
determined by the op's definition.
CC @sjain-stanford
We do have support for translating unbacked symbolic_ints that arise
from data-dependent ops like `aten.nonzero`. This PR adds the python lit
test coverage for the same.
Resolves#3384.
Many ONNX operators are defined by functions and therefore could be
expanded into simpler ONNX operations during importing, avoiding the
need for tools downstream to support these operators directly.
This commit adds this capability to onnx_importer.py. When importing a
node, the schema for the node's operator is retrieved. If the schema
provides a function for the operator, a specialized version for the
node's types and attributes will be created and imported as an MLIR
function with private visibility. An MLIR function call will then be
emitted, instead of a normal operator node. Caching is used to avoid
generating redundant functions within the same module.
In order to avoid a disruptive change to the importer output for a
large number of operators that already have TorchOnnxToTorch support,
an allowlist strategy is used by default. With this commit, only one
operator is allowlisted for expansion, MeanVarianceNormalization.
However, many other operators can be correctly expanded by the current
code, so hopefully the allowlist can be gradually extended. It is
possible to disable the allowlist in the configuration, in which case
all functions are expanded (useful for testing).
Tools downstream of the importer may now need to do inlining when
consuming the output of the importer, e.g.:
cat imported.mlir | torch-mlir-opt --inline --convert-onnx-to-torch
Explanations for subtle code changes:
- Looking up the correct schema and function for an operator requires
knowing the opset version. NodeImporter retrieves this from the
opset imports on the ModelProto retained by the GraphInfo. Previously,
the model_proto field on GraphInfo was None when importing a subgraph
in import_regions, but this conflicts with the new need for opset
version info. Since the apparent purpose of setting it to None was to
control how GraphInfo generates its input map, a new flag is added to
GraphInfo (is_subgraph) to control this behavior, so that the actual
ModelProto can now be provided without breaking this. This also turned
out to be useful for getting the Config via ModelInfo via GraphInfo.
- Some operators' functions are context-dependent, which means the
function definition depends on the types of the inputs. Therefore node
importing now needs to look up the types of a node's inputs, not just
its outputs as was the case previously. Consequently the operand to
find_type_proto_for_name() may now be a graph input or initializer in
some cases, so it has to be updated.
Tests the basic constructs of registering a custom op and its abstract
implementations (with FakeTensors) in python, going through TorchDynamo
export, followed by importing the shape expressions in the Torch
dialect.
Also fixes the importer were previously the symbolic bind op insertion
was not gated in one place.
(1) test full pytorch output for eltwise
(2) use "random" input for LIF, to get general sparse tensor
(3) introduce way to get true sparsity into network (needs backend fix
first)
…cation and sparse tensors.
**NOTE**: This PR _doges_ the issue in buffer-deallocation pass instead
of resolving it. In the future, we need to fix the bug in
buffer-deallocation pass when handling code generated by sparse
compiler.
While waiting for the full resolution of feature request
https://github.com/pytorch/pytorch/issues/117188
(which will propagate sparsity the right way in upstream PyTorch for all
FX Graphs), this minor change allows us to start testing sparsity
"within" a network, rather than just the parameters. Feel free to add
your own rules for testing (but within reason for what will be done
upstream).
Note, two TODOs need to be addressed to work around some pending issues
to make the JIT execution work.
This scenario was uncovered in a downstream test that failed with a
previous snapshot of torch-mlir. See
https://github.com/cruise-automation/mlir-tcp/actions/runs/8605480116/job/23581829102?pr=65.
```
File "/home/runner/.cache/bazel/_bazel_runner/ce288f117ee4ca92dc028a6a28476a3d/sandbox/processwrapper-sandbox/2380/execroot/mlir-tcp/bazel-out/k8-opt-exec-2B5CBBC6/bin/test/AotCompile/broadcast_unit_dim_to_dynamic_with_unchanged_dim_dynamic_torch_exporter.runfiles/pip_deps_torch_mlir/site-packages/torch_mlir/extras/fx_importer.py", line 969, in value_info_to_type
raise NotImplementedError(
NotImplementedError: Could not deduce type from value info: tensor_meta=None, val=s1, sparsity=None
```
It seems to have resolved on current HEAD. Adding this test to ensure
coverage in the future.
This is a large change because prior to this point, Python files in the
project were not consistently formatted. This reformats them all with
black defaults.
Based on experience with prior projects, if you have a dev/long-term
branch with Python patches, you can minimize merge conflicts prior to
rebasing to include this commit by running `black` on your modified
Python files, squashing, and then rebasing/merging.
This is part 1 of ~3, formatting all miscellaneous text files and CPP files matched by a first run of pre-commit. These tend to be low change-traffic and are likely not disruptive.
Subsequent patches will format Python files and remaining CPP files.
weights and biases and other model parameters appear as a separate data
structure to the traced graph, but are needed when running the MLIR
compiled code; this PR implements that extended functionality
This tests COO for more than 2-dim. Note that sparsity should really
propagate into the relu activation and the output, but such cleverness
needs to wait for the pending work in the PyTorch tree.
In the prior state when I supported mutation of user inputs by treating
them as mutable-tensor SSA values, I had left the case of buffer
mutation only vaguely implemented until a concrete use emerged.
This patch reworks this buffer mutation support by assuming that buffers
must be resolved via the hooks symbolically and treated with load/store
semantics. This is implied in the structure since we have no SSA value
that represents a buffer and we already assume that reading parameters
happens via such a mechanism.
* Also adds the basic scaffolding for handling more of these, which will
be needed for cond, while, etc.
* Refactors some of the support in the generic OpOverload emitter so it
can be shared with these other special forms.
This has been on my list for a while, but it just so happens that as
part of upgrading to PyTorch 2.3 and a pure upstream flow in Turbine, we
were using a feature that required integration with auto_functionalized.
This is perhaps the "weirdest" of the higher-order ops and a poor place
to start, but needs must. We have testing for this in Turbine.
Full support in Turbine has an entire custom ops facility. I've reduced
this down to a unit test in torch-mlir.
At some point, this op became kwarg-only instead of arg/kwarg.
Discovered when upgrading to PyTorch 2.3.
Also adds a test as this was untested in-tree (was caught out of tree).
Set PyTorch and TorchVision version to nightly release 2024-03-07.
This commit also removes the deprecated constraints API:
342e7929b8
Signed-Off By: Vivek Khandelwal <vivekkhandelwal1424@gmail.com>
Finish supporting importing the vast majority of `onnx` operations. This
includes:
- region support
- region value inherentance
- `torch.string` support
- `torch.list` support
- `torch.optional` support
Also note that we are in the process of proposing SparseTensorMetadata
to PyTorch FX graph export (see
https://github.com/pytorch/pytorch/pull/117907). This will hopefully
eventually replace the current data structures in torch-mlir.
There is no reason to treat `ConstantOfShape` as a specialized import
any as there exists a onnx-to-torch equivalent. Dropping the import
coding and adding support for resource conversion substantially
increases test coverage for dynamically shaped tests.
As of https://github.com/pytorch/pytorch/pull/118969, `ExportedProgram`
has the long awaited fixes to correctly categorize various things
relating to parameters, buffers, mutated inputs and constants.
With this additional modeling, we are finally able to implement
(safely/soundly) the mutable semantics that were attempted on the
TorchScript path. The difference is that on that path, we had to
conservatively treat everything as mutable and run some dodgy heuristics
(which have been the cause of many bugs relating to
"MaximizeValueSemantics") to try to get back to an immutable state.
The new model supports mutability at the graph edges, allowing both user
inputs and buffers to be mutated (there is some more support than that,
but that is all I fully tracked through to implementation).
Therefore, when we receive programs like this, we now can selectively
enable mutation at the edges. This happens to be the mutability model
that IREE supports, which I expect to be a primary beneficiary. However,
there is nothing stopping anyone else from handling the `!torch.tensor`
types and the existing copy/overwrite ops that will be selectively
added.
Since this relies on API changes that will not release until 2.3, I'm
being a bit cautious about not refactoring existing facilities.
We can route the torch tests via `onnx` using the `torch.onnx.export`
tooling. We can then reimport, lower to torch, and compile to linalg to
validate the onnx path is working correctly.
The current implementation exposes some failures in the `onnx` path so
we cannot enable the onnx test suite yet due to segmentation faults.
Matthew Francis-Landau
Sambhav Jain
Aart Bik
Andrea 🦈
Vivek Khandelwal
Peiming Liu
Stella Laurenzo
penguin_wwy
Rob Suderman
Aart Bik
Peiming Liu