This contains the following changes:
- Fix optional knowledge propagation. The initial knowledge should
always be NotNone for the operations we implemented.
- Add Folder for `prim.dtype`
We were not filling the `outs` with the neutral element of the
reduction, which resulted in reading uninitialized values (we were
getting lucky that sometimes the uninitialized buffers were all zero's).
Also,
- Slight tweak to error messages in the e2e framework.
- builder.getSymbolRefAttr is gone.
- OpAsmOpInterface's getAsmResultNames method needs explicit override
- a bunch of churn for builtin.func needing to be made explicit (and
sometimes implicit?)
- operation printers no longer need to print the operation name
themselves.
- snuck in beneficial trivial addition to TmpDeleteDeadIREEListsPass to
test a particular upstream change e2e with my local patchset.
This reverts commit d8db41b3b6.
These printouts didn't interoperate well with the reporting structure
since they printed out "immediately" rather than being retained in a
string in the TestResult. Doing so would defeat the purpose though,
because they were being used to determine timing to debug
https://github.com/llvm/mlir-npcomp/issues/287
I think these are best done as local modification when debugging a
particular issue, or we can invest in tracing annotations. Soon these
will all run in parallel, so it makes even less sense to have immediate
printouts.
* Now the parts of the MLIR API are directly exported under the npcomp module (i.e. `npcomp.ir`, etc).
* Has required fixes for https://reviews.llvm.org/D108489
* Deletes npcomp.tracing vs fixing it because it was a very early experiment that will not be carried forward.
* This makes the npcomp python distribution completely standalone and separate from an mlir installation.
* Makes most of npcomp itself relocatable for future use as a library.
* Most things are a namespace package now. In the future we can s/torch_mlir/npcomp.frontends.torch/ and have it layer properly.
With the following changes the compilation can continue until
RefineTypes pass:
- Add operators without ODS into `torch_ods_gen.py`
- Add some new optional and list types in `TorchTypes.td`
- Add some folders for aten int type comparator ops
- Modify GlobalizeObjectGraph.cpp. For global slots that's not used,
dont check if an aliased value is stored in more than one of global
slots. This can work around a failure where the same tensor is stored
in multiple "version" slots which are not used.
Most of the change is in the reporting code to give error messages that
are useful, and adjusting TraceItem to be semantically correct w.r.t.
Python's modeling of return values.
This allows writing a test like `ListLiteralModule_basic` for list
functionality, which we will soon be hooking up to IREE.
The IR for that test currently gets this far:
```
builtin.func @forward(%arg0: f64) -> !torch.list<!torch.float> {
%0 = torch.from_f64 %arg0
%1 = torch.prim.ListConstruct %0, %0 : (!torch.float, !torch.float) -> !torch.list<!torch.float>
return %1 : !torch.list<!torch.float>
}
```
It should be sufficient to just add a conversion of
`torch.prim.ListConstruct` (+ relevant type conversion) to necessary
IREE primitives.
For lists of *tensors* (rather than scalar floats), it gets more
complicated, as we need to deal with changing their element type to
ValueTensorType first (by default, they will all be NonValueTensorType).
It seems that IREE might have a type we can lower into for non-value
tensors as well, TBD.
This includes the following changes to import MT model into MLIR. There
are still a lot of work to for actual compilation.
- Add `torch.dict<>`, `torch.any`, `torch.number` types
- Add `torch.prim.DictConstruct` op
- Fix `torch.prim.TupleConstruct` op assembly format to include resulting types
This takes the example from torchscript_resnet18_e2e.py and puts it into
a slightly cleaned up notebook form.
It's still a little rough around the edges. Areas for improvement:
- Installation / setup.
- API usability.
Also,
- Add `npcomp-backend-to-iree-frontend-pipeline` since we will be adding
more stuff there.
- Slight cleanups.
- torch.aten.flatten.using_ints to linalg lowering
- torch.aten.max_pool2d to linalg lowering
- Support torch.aten.conv2d for more flexible dilation and strides values
The tests use the same (pure-Python) test framework as the
normal torchscript_e2e_test.sh, but the tests are added in
`build_tools/torchscript_e2e_heavydep_tests` instead of
`frontends/pytorch/e2e_testing/torchscript`. Any needed dependencies can
easily be configured in generate_serialized_tests.sh.
We add an initial machine translation model with a complex set of
dependencies to seed the curriculum there. I verified that this model
gets to the point of MLIR import (it fails there with a segfault due to
not being able to import the "Any" type).
This required moving a few files from the `torch_mlir` Python module
into multiple modules to isolate the code that depends on our C++
extensions (which now live in `torch_mlir` and
`torch_mlir_torchscript_e2e_test_configs`) from the pure Python code
(which now lives in `torch_mlir_torchscript`). This is an entirely
mechanical change, and lots of imports needed to be updated.
The dependency graph is:
```
torch_mlir_torchscript_e2e_test_configs
/ |
/ |
/ |
V V
torch_mlir_torchscript torch_mlir
```
The `torch_mlir_torchscript_e2e_test_configs` are then dependency-injected
into the `torch_mlir_torchscript` modules to successfully assemble a
working test harness (the code was already structured this way, but this
new file organization allows the isolation from C++ code to actually
happen). This isolation is critical to allowing the serialized programs
to be transported across PyTorch versions and for the test harness to be
used seamlessly to generate the heavydep tests.
Also:
- Extend `_Tracer` class to support nested property (submodule) accesses.
Recommended review order:
- "user-level" docs in README.md
- code in `build_tools/torchscript_e2e_heavydep_tests`.
- changes in `torch_mlir_torchscript/e2e_test/framework.py`
- misc mechanical changes.
`Conv2dNoPaddingModule_basic` and `Conv2dWithPaddingModule_basic` start
failing because of results accuracy after changing conv_2d linalg ops
from tc ops to yaml ops.
* Adds a minimal setup.py for frontends/pytorch
* Makes npcomp-core export its headers and libraries
* Adds a script to build packages.
* Adds CI step to package and smoke test.
* Will need some more tweaks and coordination prior to deploying (version locking etc).
* Change aligned_alloc -> malloc. It can fail (and does on MacOS) and is a bit over-aggressive optimization for a reference backend.
* Fixed a fragile test that prints -0.0 on MacOS.
* Fail the test (not the framework) on failure to trace (Torch on MacOS is missing features).
* Fix .so -> .dylib for compiler runtime.
- Add support for "expected failures" in test reporting. The new error
reports look like
[this](https://gist.github.com/silvasean/6ffd95e1d55302b699673da201da210d).
- We will now be able to put these tests into CI, since the harness
understand which tests are expected to pass and fail.
- Refactor RefBackendTestConfig to NpcompBackendTestConfig which
supports both RefBackend and IREE.
- Add instructions for installing IREE dependencies (both from packages
and for local builds of IREE)
- Add `tools/torchscript_e2e_test.sh` for invoking the e2e test
harness (this makes invoking a bit easier, as it doesn't rely on a
loose Python invocation).
We plumb through e2e a fair number of interesting cases:
- unary, binary, ternary elementwise ops
- ops like `torch.aten.add.Tensor` that also take a scalar parameter
- static size-1 broadcasting
We allow the static size-1 broadcasting case, but emit a runtime error
in the case of dynamic size-1 broadcasting. This seems like a sweet spot
subset of things that can be lowered directly to linalg, while not being
overly constraining to users. This is consistent with what IREE is doing
for CHLO->Linalg lowering as well
([code](50bf7a87e4/iree/compiler/InputConversion/MHLO/BroadcastingToLinalgPatterns.cpp (L1))).
To test the static size-1 case, we added support for the
`torch.aten.unsqueeze` op and lowering for it through
`linalg.tensor_expand_shape`. This involved a generalization of
`MaximizeValueSemantics` able to handle it (the solution there also
works for `torch.aten.flatten.using_ints` which we need for ResNet
anyway)
Also, a few minor additional changes:
- Add `VerifyInvariantsBeforeBackendLowering` pass, which catches a
large class of errors before we get to backend lowering (now that we
are doing dialect conversion, the errors are way nicer if we just emit
them up front rather than in the guts of a random pattern).
- Minor change to RefBackend to allow `linalg.tensor_expand_shape`.
Recommended review order:
- e2e tests in elementwise.py
- `ConvertElementwiseOp` in TorchToLinalg.cpp + elementwise.mlir test
- `ConvertAtenUnsqueezeOp` in TorchToLinalg.cpp + unsqueeze.mlir test
- RefineTypes.cpp + tests
- MaximizeValueSemantics changes + test
- VerifyInvariantsBeforeBackendLowering pass + test
1. Added a simplified version of torch.aten.batch_norm which only handles
inference and assumes the weight, bias, running_mean, running_var are not
None.
2. Removed the primitive types check in verifyLinalgCompatibleTypes check
since now we have proper type converter to handle torch types conversion.
The checks for RankedTensorType is kept because the type converter
doesn't guarantee the converted builtin tensor type is ranked. A
separate verification pass to verify the invariant expected by later
passes will need to be added before those can be removed as well.
This op is much better behaved than the `torch.tensor.literal` op
(which is the new name of the `torch.tensor` op). In particular
`torch.tensor.literal`:
- always has a maximally refined type.
- always has value semantics.
- can be constant folded / CSE'd.
ReduceOpVariants is changed to perform the transformation from
`torch.tensor.literal` to `torch.vtensor.literal` (which in general
involves static information casts and copies.
This new op also allowed tightening up `torch.tensor.literal` to only
accept NonValueTensorType (instead of any tensor type).
This new ".literal" name is more descriptive. It was getting too
confusing seeing an op called just `torch.tensor` (we originally called
it that because that's the name of the similar function in the Torch
Python API, but it just doesn't fit here).
This removes the dependence of the `torch` dialect on the low-level
builtin types.
Now the `torch` dialect is a standalone layer, suitable for targeting
from higher-level Python abstractions without any premature lowering to
primitive types.
This replaces the ad-hoc use of `i64` throughout the Torch layer, and
helps to keep it crystal clear the distinction between `!torch.int`
(which is modeling the Python `int` type) and the various types that
serve as dtypes of tensors, which are a totally different type universe.
Changes:
- `!torch.int` type and C bindings.
- Change `torch.constant.int` parser to not need the `: i64` at the end.
- `m_TorchConstantInt` matcher to aid with matching constants.
- BackendTypeConversion changes for `!torch.int` -> `i64` type
conversion.
- Refactor finalizing patterns in FinalizingBackendTypeConversionPass
(they were getting very repetitive).
- Mechanical rewriting of `!torch.int` to `i64` in all the tests, and
`AnyTorchIntType` to `Torch_IntType` in the `.td` files.
This fixes a "regression" on ResNet where we weren't folding away all
the control flow. For now, our policy is to "optimize hard enough" to
make that control flow go away, because we don't yet have a way to lower
to the backend the stuff guarded by the control flow (RaiseException,
string operations, etc.).
It remains to be seen how much optimization we decide to do at this
level in the fullness of time -- the torch op set is not particularly
well-designed (at least not idiomatically for MLIR) for general
optimization. Ideally, with really good backend support for various
features, all the heavy optimization will happen at that layer on `std`
ops and `scf` control flow. But I have a suspicion we might end up
needing more optimization earlier in the pipeline.
This removes the use of `scf.if`, which required laundering back and
forth between `i1` and `!torch.bool` in the frontend. We will eventually
lower this op to `scf.if`, but this results in a cleaner IR and layering
at the frontend.
This finishes removing the dependence on the basicpy dialect!
Changes:
- Add `!torch.bool` type and replace use of `!basicpy.BoolType` in
Torch-related code.
- Rename BuiltinTensorize to BackendTypeConversion since now it handles
bool conversions (and, when we add !torch.int and !torch.float, it
will handle those as well), and generalize the related utilities (I
also moved them to Torch/Transforms since they aren't really part of
Torch/IR).
- Add `torch.to_i1` and `torch.from_i1` ops for materializations
- [cleanup] Reorganize `torch.constant.*` ops in TorchOps.td
- Remove dependency of `torch` dialect on `basicpy` dialect and also
`std` dialect. For `std`, we use some call related ops, but the
`torch` dialect itself never produces them (we have passes that do
though).
This is fairly mechanical. Recommended review order:
- New stuff in Torch/IR
- New BuiltinTypeConversion files.
- Mechnical fixups elsewhere.
- Make consistent with MLIR Core
- Use `//` or `///` comments.
- Use `bool` type for booleans
- No duplicated comments in .cpp files
- Split types into separate files `{Basicpy,Numpy,Torch}Types.h`
- Add dialect prefix consistently to C API symbols. We have lots of
similarly named types (e.g. "list" type in basicpy and torch).
- Add `torch.constant.none` op to construct it (naming is chosen to be
analogous to Torch's representation of a prim::Constant with
NoneType, rather than using the "singleton" terminology of Basicpy).
This removes our reliance on the numpy dialect and avoids our off-label
use of the builtin tnesor type for modeling unknown dtypes. The
`!torch.vtensor` (`ValueTensorType`) type is a value-semantic tensor.
The `!torch.tensor` (`NonValueTensorType`) type is a non-value-semantic
tensor. The new types look as follows syntactically:
```
// Least-static-information, non-value-semantic tensor.
!torch.tensor
// Explicit form of least-static-information variant.
!torch.tensor<*,unk>
// Least-static-information, value-semantic tensor.
!torch.vtensor
// Explicit form of least-static-information variant.
!torch.vtensor<*,unk>
// Fixed-set of allowable element types, with first-class support for
// Torch's frontend signedness semantics.
!torch.tensor<*,si32>
// First-class support for unknown dtypes.
!torch.tensor<[?,?,?],unk>
// Standard MLIR representation of `?` for unknown dimensions.
!torch.tensor<[?,2,?,4],unk>
// Statically shaped / dtyped example.
!torch.vtensor<[1,2,3,4],f32>
```
This required fairly significant changes throughout the compiler, but
overall it is a big cleanup. We now have a much clearer layering of "the
Torch frontend lowering" vs "lowering to std + linalg + etc.".
At the C++ level, there is `ValueTensorType`, `NonValueTensorType`.
We also have a helper `BaseTensorType` (kind of like ShapedType) which
interoperates with those two.
Included changes:
- New `torch.tensor(dense<0.0> : tensor<5xf32>) : !torch.tensor` op for
creating torch tensor literals in the frontend.
- Consistently use signedness for the types (except i1 which I didn't
touch -- we need to sort out the situation with !basicpy.BoolType
there anyway so will be attending to that soon)
- Frontend can annotate whether an argument to the function has value
semantics. We currently require this, as our backend contract does not
currently allow us to even model the non-value-semantic case. Before,
the value-semantic assumption was randomly injected in the middle of
the pass pipeline.
- Move ArrayToTensor (now called MaximizeValueSemantics) and
RefinePublicReturn passes to torch dialect.
- The TorchToStd and TorchToLinalg passes are now type conversions from
`!torch.vtensor` to `tensor` and use the dialect conversion infra.
The overall conversion pipeline is set up following the best practices
of the "Type Conversions the Not-So-Hard Way" talk. This required
introducing `torch-func-builtin-tensorize` and
`torch-finalizing-builtin-tensorize` passes analogous to the upstream
bufferization passes with the corresponding names (mostly just
copypasta from there).
- Misc Torch-level canonicalizations -- we now cleanly layer the
lowering to std later in the pipeline, so we are gradually lessening
our reliance on random std constant folding before we get to that
point.
Recommended review order:
- New types in TorchTypes.td/TorchTypes.h/TorchDialect.cpp
- New ops in TorchOps.td / TorchOps.cpp
- Less important / more mechanical stuff
- Frontend changes.
- Pass changes/additions in `Torch/Transforms` and `Conversion/`
This now gives [much nicer output](https://gist.github.com/silvasean/f048e0f37b04542dae6469b86802bb3e).
Embarrassingly, we previously couldn't even report failures for two
different tests, and weren't able to report on compilation failures
(besides just crashing).
This is enough to import the program and get it through the compilation
pipeline. It of course fails at the VerifyBackendContract pass since
there is a lot missing, but the final IR for a simple quantized MLP is
looking pretty decent already:
[IR](https://gist.github.com/silvasean/f76bccd76e9b193d396cfb2f9a11f54d)
Main changes:
- Add support for importing torch quantized tensors, including
`torch.per_tensor_affine.create` op and `!torch.qint8` element type.
- Add support for importing `LinearPackedParamsBase` (basically a weight
+ optional bias, but requires `torch.linear_params.create` op +
`!torch.LinearParams` type to model it). This was less painful than I
expected, as it has the necessary methods to opaquely unpack itself. I
factored things so it should be easy to extend to other custom classes
like `ConvPackedParamsBase`.
- Add minimal boilerplate for importing `quantized::*` ops, with
`quantized::linear` being a motivating example.
- Add e2e test with simple quantized MLP (courtesy of @phoenix-meadowlark).
This is somewhat of an abuse of `!numpy.ndarray` / `tensor`, as
really the proper semantics of `!torch.qint8` dtype on a Torch tensor is
"check the quantizer object of the tensor for side data (scale/offset,
possibly per-channel) that defines the full semantics of the tensor". We
don't have any such notion of "side data" for `!numpy.ndarray` /
`tensor`, let alone anything that would have the associated behavior of
keying off the dtype to determine if the side data is present.
This will be fixed by a proper `!torch.tensor` type.
Yi Zhang
Sean Silva
Ramiro Leal-Cavazos
dan
Stella Laurenzo
