- Move frontend lowering pipelines to c++ (this helps with reproducing
failures in npcomp-opt)
- Add debugging printouts when compilation fails on RefBackendTestConfig
The experience now when a test fails during MLIR lowering is now like this:
```
NPCOMP TorchScript Object Graph IR -> NPCOMP Backend IR lowering failed with the following diagnostics:
failed to legalize operation 'torch.global_slot'
Module does not conform to npcomp's backend contract. See dialect conversion legality information above.
Error can be reproduced with:
$ npcomp-opt -torchscript-to-npcomp-backend-pipeline /tmp/ResNet18Module.mlir
```
And when TorchScript->MLIR import fails it looks like this:
```
PyTorch TorchScript module -> NPCOMP Object Graph IR import failed with the following diagnostics:
unhandled prim operation: %18 : int = prim::min(%17) # /usr/local/google/home/silvasean/.local/lib/python3.9/site-packages/torch/nn/functional.py:4532:4
```
Also,
- Add `--filter=<regex>` to e2e test harness to filter tests.
- Add a few prim ops that were needed to import ResNet18
- Fix torch.prim.Loop.condition assemblyFormat (it previously would not
round-trip in the case of no loop-carried variables)
This pass verifies that a given module satisfies the contract that we
have for backends. This is phrased as an "allowlist", because we want to
keep this interface tight. Also, this gives much better diagnostics than
a backend randomly crashing or failing to compile would (though they
could still be improved).
This was especially painful because if we had
`tensor<?x!numpy.any_dtype>` slip through, at some point RefBackend
would convert it to a memref type and trip the "verify type invariants"
assertion which gives no location or anything and crashed the process,
which was very unpleasant.
We implement this with the dialect conversion framework, which works
reasonably well and was quick to put together and familiar, but is still
very "op oriented". We probably want to make this hand-rolled
eventually, especially the error reporting (the most useful kind of
error for a dialect conversion user is not necessarily the best for this
use case). Also, in production, these error will go to users, and need
to be surfaced carefully such as "the compiler needs a type annotation
on this function parameter" which in general requires some special
analysis, wordsmithing, and overall awareness of the e2e use case (such
as how much we can lean into certain source locations) to provide a
meaningful user-level diagnostic.
Also, add `inline` to the current frontend lowering pass pipeline to
allow slightly more complicated programs that otherwise would fail on
shape inference.
This is our first op with error semantics, and stresses the system.
There are a few design notes of special interest:
- RefineTypes.cpp's note about shape inference in the presence of code
that dynamically produces and error, and it is provable statically.
- ATenToLinalg.cpp's notes about future automation of the ATen->linalg
path.
- The notes in Passes.td about using low-tech `std.assert` ops instead
of `shape.assuming`.
Note: Doesn't work on IREE yet due to the `std.assert` op (needs to be
lowered to `vm.fail` on the IREE side).
Recommended review order:
- Changes in frontends/pytorch/examples/
- Changes in python/npcomp/compiler/pytorch/backend/
- Boilerplate for the `npcomp-iree-backend-lower-linkage` pass.
This change separates out a
`npcomp.compiler.pytorch.backend.frontend_lowering` module that does the
common lowering for all backends. The individual compiler backends
`npcomp.compiler.pytorch.backend.{refjit,iree}` now accept a loosely
defined "TCP + scalar code" IR mix that will be formalized in the
future as the interface to codegen backends.
This also required adding a small pass
`npcomp-iree-backend-lower-linkage` which adds `iree.module.export` onto
functions, and layering that into the frontend flow. The pass doesn't
require a C++-level dependency on IREE, which is nice for now. TBD how
we are going to handle lists (we hope we can get away with sneakerneting
some td files and relying on loose IR compatibility).
Running through IREE requires the ability to import `iree.compiler` and
`iree.runtime`, which can be obtained as follows:
```
python3 -m pip install iree-compiler-snapshot iree-runtime-snapshot -f https://github.com/google/iree/releases/tag/snapshot-20210406.200
PYTHONPATH="${PYTHONPATH}:${MY_IREE_BUILD}/bindings/python/"
```
This patch makes it painfully clear that we don't have any e2e testing
harness to really plug into, and also don't have a usable Python API to
our compiler stack (something usable in a jupyter notebook).
That will be addressed in subsequent commits. We've been flying by the
seat of our pants with this `examples` directory that isn't subject to
any kind of testing or real usability concerns.
This pass allows shape information to be propagated to return types,
which is nontrivial and cannot be cleanly put anywhere else as it
changes the public ABI, which is a concern that we want to keep
concentrated in one place.
Currently implemented as a simple intraprocedural dataflow analysis over
a standard ShapedType lattice (hasRank, sizes, and elementType).
It currently hardcodes a few key pieces of information:
- shape transfer functions
- whether it is legal to update the operand type of an op
This needs to be made pluggable obviously and the core propagation logic
moved somewhere agnostic.
The current implementation is just sufficient to do a unary aten.tanh
from the e2e spike, and just applies some local rewrite patterns. I've
sketched out the more full explanation of where this pass eventually
need to go in the pass docs.
Adding this required adding `numpy.tensor_static_info_cast`, which is
the tensor analog of `numpy.static_info_cast`. This op encapsulates the
same numpy-specific "no runtime code" casting semantics, in particular
the interpretation of `!numpy.any_dtype`. The
`numpy.tensor_static_info_cast` I see in practice now are "information
erasing" and will be removed by a later pass that exploits the fact that
aten ops are agnostic to the static info in the operand types (so
substituting a type with more static info is fine).
Side note: we *need* to do dtype and rank inference before aten->tcf
(which will eventually mostly be aten->linalg+guards), because each aten
op is idiosyncratically overloaded based on dtype and rank. Without
copying that idiosyncratic overloading into lower layers (layering
violation), we cannot really lower it to anything until we do that.
This pass incorporates torch.type_bound info and also removes NoneType
returns (eventually it will rewrite tuple types too, but can't yet
because !basicpy.TupleType doesn't track element types).
Recommend looking at adjust-calling-conventions.mlir first to see what
it is doing, and holding your nose for the implementation of the pass.
I decided to implement this with the conversion framework, because it
gives us *some* goodies for type conversion -- mainly avoiding large
amounts of tricky RAUW dances. Unfortunately, the conversion framework
isn't a perfect fit for a couple reasons:
- the incorporation of torch.type_bound is a context-sensitive rewrite
(requires looking at the arg attr, not just the type).
- NoneType conversion is 1->0, which requires some special handling
- (not implemented yet) 1->N tuple type conversions require special
handling.
It's a little bit scary, but on balance doing it the other way would
have its own downsides.
These allow users to annotate a known "type bound" on the argument,
which can seed shape/dtype inference. We don't rewrite the function
types as part of the import process (it will happen in a
yet-to-be-written pass) because:
1. We would need to interprocedurally rewrite all calls to keep the IR
consistent. Currently, we have a place after GlobalizeObjectGraph but
before we convert to tensors where this is convenient to do. Ideally,
we would do this on the object graph representation.
1. We don't necessarily know that adjusting the function type is a legal
calling convention change. The pass will have blessed knowledge (by
the pass pipeline author) that adjusting the argument type based on
the type bound is safe (which it frequently is).
2. Note that in principle, a type bound could be a fairly general thing
(such as maximum sizes of dimensions, unions of multiple concrete
types, etc.). The pass will in principle have logic to interpret the
type bounds and to determine a suitable "best" (and legal) argument
type.
- renames of OwningRewritePatternList -> RewritePatternSet
- also `insert` to `add`
- RewritePatternSet holds a context now
- memref dialect split from std
* Adds f32 scalar argument support across the ABI boundary.
* Adds support for passing input type / shape information
across the ABI boundary
* Adds support for parsing / creating input FloatAttr's in
`npcomp-run-mlir`
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.
* Import ATen conv2d conversion and test
This is a first attempt at expanding ATen-to-TCF conversion for the
conv2d operator. Eventually, this will come in use when lowering a
high-level conv-based model.
This happens in practice with e.g. ResNet from torchvision (multiple
instances of the same BatchNorm class).
The key observation is that for this program, and the expected set of
programs, we can convert the program to the same globalized form with a
bit more static analysis and effort to suitably monomorphize the
program. Though what we are doing here is fairly annoying to implement,
it saves any nontrivial later pass from having to do similar analyses
(or worse). E.g. shape inference would need to be object-graph aware,
mutation/lifetime analyses would have to be aware, etc. Additionally, it
would make us front-load what it means to have a !torch.nn.Module type
on an ABI boundary, which we are just not ready to handle.
I'm really, really hoping that in practice we can get away with
this, otherwise it's going to be really rough designing a representation
(and implementing everything to back it) that is convenient to transform
and gracefully scales from full object graph (in the most dynamic case)
down to a fixed set of global slots like we have here (in the most
static case, which we presume a lot of practical programs fall into).
This also involved introducing a
`torch-prepare-for-globalize-object-graph` pass that does a minimal set of
lowerings to simplify the IR into a more orthogonal and analyzable form,
and a `torch-globalize-pipeline` helper.
Recommended review order:
- updated documentation in Passes.td
- new tests in `globalize-object-graph-multiple-instances*.mlir`
- implementation of GlobalizeObjectGraph.cpp
- PrepareForGlobalizeObjectGraph.cpp + prepare-for-globalize-object-graph.mlir
- misc stuff like torch-globalize-pipeline pipeline definition.
With this, we can import, globalize, and inline resnet18 from
torchvision:
https://gist.github.com/silvasean/821586afc19b67d9fb72030b2e0adeb8
In terms of IR structure, TorchScript allows types to vary in many
circumstances where MLIR requires pointer-identical types. In particular,
it is valid to pass any subtype in place of a type. For example, if an
`Optional[int]` is required somewhere in the IR, it is legal to pass a
value of just `int` (but not the other way around; see
`torch.prim.unchecked_cast`). In effect, every *use* can have a different
type.
We introduce a new op `torch.derefine` that models that impedance
mismatch. This op allows casting a value from one type to a type that it
is a subtype of to model this behavior.
Recommended review order:
- TorchOps.td for new torch.derefine (and updated docs for
`torch.prim.unchecked_cast`)
- new test code in if.py, loop.py, function-derefine.py
- new code in node_importer.cpp for handling derefinement insertion
- function_importer.cpp and utils changes in torch_to_mlir_utils.cpp
Properly handling derefinement on function boundaries required
relayering the code so that graph_importer.cpp/.h is now
function_importer.cpp/.h because only the `torch::jit::Function`
(actually the `c10::FunctionSchema` it holds) knows the derefined types that are
actually needed at the boundary (see `function-derefine.py` for a test).
Annoyingly, this churns all the functions which are now prefixed with
`__torch__.` but that is more correct anyway (that is their linkage name
in the `torch::jit::CompilationUnit`; the previous `mb.import_function`
was actually buggy in the case of functions calling each other as it
would reference their unqualified name).
With this change, we can import `resnet18` from `torchvision` :)
IR: https://gist.github.com/silvasean/6426a5272d8a6c7caae533fce05ab704
I could not find a corresponding ListIndex in prim, which seems to
translate to a __get_attr__ under the hood. I think the reason a tuple
Index op can exist is because Tuple's are supposed to be frozen, where
List operands can be mutable.
This arises when casting optionals, which happens a lot especially
around handling of default arguments (python `if arg is None` idiom).
In this case, the offending code for the model is in max_pool2d:
[code link](b3bf08e67f/torch/nn/functional.py (L657))
Used by resnet18.
It seems to originate from a helper `_verify_batch_size`:
[code link](b3bf08e67f/torch/nn/functional.py (L2099)).
I couldn't find a way to test `prim::RaiseException` without also having
`prim::Uninitialized`.
This is a much simpler representation than the ad-hoc initializer
function we had before. It is also less general, but given the rationale
in Passes.td it seems like the right tradeoff right now.
We can probably carry this representation for quite a while, and when we
can't, it likely means that TorchScript has fixed their object identity
bug and we probably need to just upgrade to a more general object graph
modeling (more general than GlobalizeObjectGraph).
In particular, we don't want to deal with defining and carrying around
this initializer function concept until we need it. For example, if we
want to constant-fold the global slots into uses, this is a much better
representation, and it plays better with symbol-dce (the initializer
function counts as a "use" of the symbol).
(the alternative would have been to write a pass that converts the
initializer function to this form when possible, but I realized that
lots of information had been lost which made that fairly annoying -- it
was all self-inflicted anyway, so best to just go to the source
(GlobalizeObjectGraph) before the information is lost)
Now symbol-dce works nicely (no more "training" bools)
```
pt_util ~/tmp/classifier.pt --import --exported-name forward \
| npcomp-opt -torch-globalize-object-graph -inline -symbol-dce
```
IR: https://gist.github.com/silvasean/8abe63d70d24e29d6db9170ccc8d512b
With this, we can import BERT!
```
pt_util ~/tmp/bert.pt --import --exported-name=forward \
| npcomp-opt -torch-globalize-object-graph -inline -symbol-dce
```
https://gist.github.com/silvasean/fe7735ff5d065cc9216f7b0346d0e977
The test case here is a bit unconventional -- it isn't actually valid
Python. To figure out how to generate it I had to go search the PyTorch
codebase for "NumToTensor" and work backward. In this case I found
this
[code](649760e5f1/torch/csrc/jit/frontend/ir_emitter.cpp (L464))
which via a wild guess I was able to turn into a test case.
In this case it didn't take me too long, but when doing this kind of
"add a bunch of trivial stuff to bring up a real model", I'm starting to
think that we might skimp on test cases when it's fairly trivial and not
obvious how to test with a small test.
Adds support for lowering a torch.nn.Conv2d module to the Torch Dialect through TorchScript import.
Generated IR can be viewed here:
https://gist.github.com/brycearden/6c0f790115c4577249372ef82768e6fd
Required implementing support for tuple in the ivalue importer and list in the node importer.
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
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.
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 \
```
* Most updates are mechanical except:
* python/npcomp/__init__.py and python/NpcompModule.cpp: New init/registration bits to replace some automatic things being done in the old bindings. Also an annoying linkage hack that I'll need to triage next.
* NpcompModule.cpp: New python helpers for custom types and other hard to reach items (for the new bindings).
* PybindUtils.h: Extended type casting so that the local extension can directly exchange Mlir* C types.
* python/npcomp/dialects/*: Build support and ODS bindings for local dialects.
* mlir_utils.py: Defines an ImportContext to replace the old/bad "Helper" class that tracked locations, and insertion points. This has a number of methods on it that would be good candidates to think about better ways to do them upstream.
* Also hoisted a few stand-alone samples to dedicated unit tests as they covered important things.
* More cleanup can be done, but keeping this patch as mechanical as possible to stay in NFC land (this is big enough).
Best as I can tell (e.g. from LeakSanitizer), this fixes all the leaks
except for those due to buffers created internally to the codegenned
code itself (up next I'll add the buffer deallocation pass to fix
those).
The main change is that instead of attempting to pass `refbackrt::Tensor`
to the codegenned function directly, we make all the ABI types be
UnrankedMemRef which gets passed awkwardly (but workably) as a
`{size_t rank, void *ptrToDescriptor}` on the ABI. The reason why
refbackrt::Tensor wasn't workable is that is that MLIR doesn't really
have a way to deal with the lifetime of unranked memref descriptors that
happen inside the function, which is inevitably what would happen in the
old code that would emit runtime calls to
`refbackrt.to_memref/refbackrt.from_memref` to convert back and forth to
`refbackrt::Tensor` inside the codegenned code.
So, instead of the `refbackrt.to_memref/refbackrt.from_memref` with no
real sound basis for valid lifetime management, we now have a lovely
piece of code in `refbackrt::invoke` in `Runtime.cpp` that just barely
seems to be sound. We rely on the codegenned code having these
properties, which it seems to have:
- it won't free memref descriptors or their backing buffer for arguments
of UnrankedMemRef type.
- it will allocate a separate memref descriptor for each result
UnrankedMemRef (which is ensured by having a separate memref_cast for
each)
- we can sniff the `allocatedPtr`'s (i.e. the backing buffer pointers)
to avoid double-freeing in the case of aliasing of the backing buffer
(including backing buffers for arguments feeding into results)
- to catch the case of statically allocated data (which we need to avoid
passing to `free`) , check if the `allocatedPtr` is (no joke) equal to
`0xDEADBEEF`, because there is otherwise no way to distinguish
statically allocated from malloc'ed data... (std.global_memref lowering
to LLVM by happenstance sets the allocatedPtr equal to `0xDEADBEEF`,
presumably mainly as a debugging thing)
Even with all this, we *still* need to (internally to refbackrt::invoke)
make copies of all inputs/outputs! And the details of how the LLVM-level
ABI gets laid out for e.g. function arguments/returns is still super
tricky.
This really highlights how deficient memref is as the general runtime
type for our use case. It's stewing in my mind how best to improve the
situation. My general gut feeling is that IREE's abstractions for this
are "right", but I need to think more how to distill those aspects of
IREE's design in a "reference" way for RefBackend.
Some implementation notes:
- In terms of how this is implemented, this did catch a bug in our ABI
wrapper functions in LowerToLLVM.cpp, which I had to fix (it happened to
work before through some combination of npcomprt::Tensor being passed as
a single pointer + probably me infinite-monkey-ing it until it worked)
- This actually removes 2 out of the 3 compiler runtime functions (the
only one left is "abort_if". (most of the memref descriptor code moved
from CopmilerRuntime.cpp to Runtime.cpp)
- this also means deleting `refbackrt.from_memref` and
`refbackrt.to_memref`
* 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.
* Organizes the BasicPyOps.td file by function.
* Renamed `to_boolean` -> `as_predicate_value` (trying to consistently use "predicate" to refer to i1/low-level types and Bool/Boolean to refer to Python bool types).
Although `refCount` is initialized as `std::atomic<int> refCount{0};` in
the definition of Tensor, our tail-allocating malloc would ignore it,
resulting in bogus values that led to leaks.
Caught with LeakSanitizer, but I added an assertion that the refcount is
non-negative to begin with, which should catch this bug in the future
fairly consistently (assuming the garbage refcount is negative half the
time).
* 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`.
It was annoying that we were creating shape.get_extent in the middle of
the bufferization pipeline, as it required running convert-shape-to-std
at an awkward place. To make that cleaner, just open-code the
extract_element ops that shape.get_extent expands into.
This is a little gross, but it helps with the macroscopic pipeline
ordering issues. Anyway, the train is long-gone of trying to treat
shapes as some special data type that should only be operated on with
shape ops.
Also,
- reorder tensor constant bufferize (which is a module pass) to bracket
all the bufferization function passes, to make the parallelism
opportunities there clearer. Now we have a very clean little
bufferization segment of our pipeline construction.
* IREE doesn't have proper install support, so there is some temporary hoaky hacking in our CMakeLists.txt to shuttle some symlinks around.
* Reworked the original numpy e2e with IREE test to pipe through iree-translate.
* Removed all of the C++-level dependencies.
* Will generalize and apply to the PyTorch backend in a followup.
This vastly simplifies our code, allowing deleting multiple ops,
simplifying multiple passes, and removing a whole pass.
Now `refback` dialect is down to one op (refback.alloc_memref, which
simplifies allocations to just take a shape instead of individual
extents).
* 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.
This involved adding a `tcp.splatted` op to splat a dynamically sized
init tensor. See rationale in TCPOps.td docs.
One interesting observation is that when lowering tcf.matmul to
linalg.matmul, we need to both 1) create the error checks and 2)
calculate a shape transfer function to create the init tensors.
Previously, 2) was deferred to bufferizing tcp.matmul later. I'm not
sure if this is a conflation of concerns or not. For now, it's not a big
burden.
* 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
Two changes:
- no more "verifyPasses" constructor arg for PassManager
- OpPassManager defaults to requiring explicit "nest" calls when created
via the C++ API. The behavior upstream for mlir-opt still obeys the
"implicit" mode, so I just slapped that onto all our pass managers.
I pinged https://reviews.llvm.org/D90671 to get a signal for whether we
are expected to migrate to explicit mode. If so, I'll do that too later.
Now, the only bufferization we have left is lowering tensor constants to
memref, which will hopefully proceed soon after Rahul's new
std.global_memref lands + the lowering to LLVM IR. Then I'll port
LowerConstantTensorsToMemref to upstream and we'll be 100% upstream
bufferization, except for our local TCP dialect (which will probably go
away and be replaced by std elementwise + linalg named ops on tensors :)
).
* 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.
Register the following for the multiply op:
- tcf.mul
- tcp.mul
- TCP->TCP lowering
- Shape transfer, broadcasted multiplicands
- Lower to standard `MulFOp` op
* 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.
The time has come for BypassShapes/LowerShapedResultsToMemref to go away :(
For the reference backend, being consistent with upstream conventions is
the name of the game now.
This is a step down in a number of ways, e.g. test clarity and
separation of concerns. But it is fewer files and fewer tests, and
*does* address the "TODO: This is really fragile". It also eliminates two
more ops from the refback dialect (sadly, they are the
shaped_results/yield that we were getting kind of fond of, but alas).
Now that it has grown source/target materialization capabilities
(spelled with ops tensor_load/tensor_to_memref), we can use it. We can
also now delete refback.memref_to_tensor/refback.tensor_to_memref.
This is also a first step to reducing the downstream functionality
needed in the refback dialect.
Now the reference backend is cleanly accepts "TCP"+scalar ops.
We introduce tcf-refback-lowering-pipeline which also does TCF->TCP
conversion for convenience until we have a "target interface".
* 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.
I now realize that VerboseCamelCase is not the best choice for dialect
directory/file names and C++ identifiers (take e.g. "Linalg", "Basicpy",
etc. as prior art here; not LinearAlgebra or BasicPython). If I had to
name the convention it seems to be "Shortword" (or of course just
acronym dialects like LLVM, SCF, etc.).
This rename also has the side benefit of differentiating RefBackend
directories, which now refer to the actual backend itself, from
Refback/Refbackrt, which are the dialects which happen to be used by
that backend.
Other than the dialect definitions (which will live in standard Dialect/
subdirectory), the goal here is to keep RefBackend-related code nested
in {include/npcomp,lib,test}/RefBackend.
This is the first in a patch series that is refactoring the
constellation of things variously called or associated with "E2E",
"RefE2E", "npcomprt", and "TCP" into a more cleanly layered result.
Concretely, this first patch fixes the fact that TCP was basically
acting like a dumping ground needed by the reference backend. This
splits it out, which is fairly mechanical, but touches a lot of lines of
code (basically replacing `tcp` with `refback` and `TCP` with
`RefBackend).
Now, the RefBackend dialect is that dumping ground, which
is slighly better, as it starts allowing TCP to become a nice clean
middle layer that is not related per se to the reference backend.
The previous name RefE2E or "reference e2e flow" was super confusing.
Now that we are seeing more clearly where the "backend" distinction
lies, the [RefBackend] commit tag is born :)
* 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++).
* Uses the MLIR-C API since that will save us a lot of grief down the road (i.e. will give PyTorch and libMLIR/libNPCOMP the ability to skew version-wise).
* Quite a few TODOs and not yet populating the function in any way.
* Uses the new dispatcher API.
* Just prints to the console for the moment when an op is captured.
* Executes the op through the existing implementation.
Now that we upstreamed our pass, we can remove it.
The final pass that landed upstream doesn't do the shape.assuming
canonicalization to legalize that op away, so added a
restricted-canonicalizer pass that allowed to run just shape dialect
canonicalizations, which deletes the shape.assuming.
The pass ended up kind of ugly. See the TODO's on it for some potential
cleaner directions.
Date: Fri Sep 18 13:55:52 2020 -0700
- Update to linalg syntax
- New generated builders are better. Custom builder for
tcp.shaped_results is now redundant.
This cleans up the lowering pipeline to easily allow extending to
multiple binary ops. It looks fairly repetitive at multiple levels, but
I don't want to prematurely generalize. I think that in principle we
could derive a large swatch of TCF + TCP from a single linalg-style
specification. Another direction is to use an OpInterface (something
like "buildLinalgGenericBody"). I'm keeping my eye on it.
In a subsequent commit, I'll mechanically add a set of binary ops
modeled off of the std arithmetic ops.
* Still need to add a systematic mechanism for discovering gradient ops.
* Work needed on the various _ suffixed inplace ops.
* Other randoms still not mapped.
* Outside of this commit, I do have enough commented/reworked to roughly build but that will take another handful of commits to get going.
It was previously going through this awkward route that prematurely
created linalg.generic ops, which was an annoying layering problem since
we can't compute a shape transfer function for linalg.generic in the
general case. Now we pass it through the same path as tcp.matmul, with
the shape transfer function being defined for tcp.add.
This also removed the need for TCPToLinalg (now deleted). The equivalent
of that is happening in lower-shaped-results-to-memref. One interesting
outcome of this: we're basically using linalg as a "Buffer TCP". We
might want to look into using named structured ops for more of TCP, but
that would be a big velocity hit since then any change to the ODS /
verification for those ops would be a change to the upstream structured
op ODS generator. After we have more experience defining this manually,
we should re-evaluate rebasing TCP on generated named linalg ops.
I'm pretty happy with how this turned out. It looks pretty much like it
should -- one change at each layer. This particular op bottoms out on
linalg which takes care of the rest.
- Add tcf.matmul
- Add tcp.matmul
- Add TCF->TCP lowering
- Add tcp.matmul shape transfer function (BypassShapes.cpp)
- Add tcp.matmul -> linalg.matmul lowering (LowerShapedResultsToMemref.cpp)
- Add support to LowerShapeConstraints for lowering the new
shape.cstr_require
This matmul op is pretty limited in its capabilities. There is no
batching and no multidimensional contraction. Certainly more design work
will be needed to find the right abstractions that aren't too general
but also help to canonicalize many cases from frontends. This is mainly
to show that adding a new op needn't be very "scary" once we have the
e2e infra in place.
Also,
- this clears out some exploratory cruft from the TCF dialect now that
this is starting to become real.
This now gets the overall "RefE2E" compilation stack to a point that I'm
fairly happy with. We simplify it by mostly embracing the "descriptor"
view of the world.
The overall flow is best understood by reading through the
createE2ELoweringPipeline function in lib/E2E/E2E.cpp
That function creates a pass pipeline that lowers from "TCF" (which is
~numpy level of abstraction) down to LLVM IR.
A brief high-level summary of what happens there:
1. TCF to TCP conversion. This involves reifying error handling in the
form of shape constraints. See test/Conversion/TCFToTCP/basic.mlir
2. Lowering shape constraints. This converts shape constraints into
eager error-handling code. See test/E2E/lower-shape-constraints.mlir
This pass will soon go upstream.
Because this lowers to std.assert, some later passes like
LowerToNpcomprtABI and LowerToLLVM are updated to properly plumb this
through e2e.
See test/npcomp-run-mlir/invalid-broadcast.mlir for an execution test
that properly aborts in case of an error.
3. Lowering tensors to memrefs. This is done via a series of passes
rather than an single mega conversion. Unlike the previous code that
mixed in the npcomprt ABI stuff here, it's now a very clean "pure
memref" conversion.
See test/E2E/lower-*-to-memref.mlir and
lib/E2E/TensorToMemref/
Most of the changes are concentrated here.
4. As part of the above, we use the upstream ConvertShapeToStandard for
lowering shapes.
5. We lower linalg to loops and lower loops to CFG using upstream
passes.
6. Rewrite the "ABI" boundaries of the program to npcomprt data
structures (LowerToNpcomprtABI). This mainly affects ABI boundaries and
how global tensor constants are represented. One of the major
improvements in this commit is that now it's a very clean rewrite that
just replaces memrefs on ABI boundaries with !npcomprt.tensor (before
there was a get_extent function that is not needed).
See test/E2E/lower-to-npcomprt-abi.mlir
7. Lower to LLVM with upstream mlir patterns + some patterns for the
npcomprt lowerings.
One aspect here that is still a remnant of a non-descriptor-based tensor
to memref flow is the BypassShapes + LowerShapedResultsToMemref.
BypassShapes wraps the "tensor compute" ops in a tcp.shaped_results
(basically a "tie_shape" kind of op), and then
LowerShapedResultsToMemref uses those annotations to allocate output
buffers while lowering the "tensor compute ops". Note that there are
very few "tensor compute" ops currently supported (tcp.add +
tcp.broadcast_to), so we just hardcode them in both passes.
Realistically, I expect this to go away as we fully embrace the
descriptor-based approach for simplicity, so don't look too deep into
it.
* Add a new python script to auto-generate ATen op ODS definitions.
* There is still some work on some of the ops to annotate correct types.
* The ODS is not actually included into the dialect yet, but I'd like to commit it so that we can track changes.
* Will reconcile this with the ops produced by the existing script in a followup. Still need to do some more iteration to reach parity.
* llvm-project: b5924a8e27536d19dd5c4d302db29fb6163d5faa
* mhlo: 848ca244d20f045b7921da55a98a04d95ef94f0e
* Multiple breakages that need to be fixed.
Fixes:
* Refactor dialect registration
* Remove all kindof methods (Casting functionality has been added upstream and is implicitly
available, see https://llvm.discourse.group/t/removing-kinds-from-attributes-and-types/1547.)
* Update dialect registration to comply with https://reviews.llvm.org/D85495.
* Remove type kinds and update some changed dialect signatures.
* Upgrade ATen dialect to match upstream needs.
* Move dialect registration to tablegen.
* Register the ListType in tablegen.
* Change dialect initialization signature.
* Use TypeSwitch in MlirIr location printer.
* Remove global registry depends from npcomp-opt.
* Change LowerToLLVM to pass an MLIRContext vs an LLVMDialect for type creation.
* Remove dep on MLIREDSCInterface that is removed upstream.
* Thread through the DialectRegistry for opt and python-like tools.
* Modernize pass registration (This was forced because the GEN_PASS_REGISTRATION code now generates inline functions vs literal pass registration statements)
Co-authored-by: Marius Brehler <marius.brehler@iml.fraunhofer.de>
This patch adds a dialect intended to be used as a frontend dialect
to facilitate lowering from "A Tensor Library" in torch/pytorch.
This patch includes several passes that are useful in conjuction with the
dialect:
--aten-layer-name: Generates layer names for each operation, which are not
present in the original pytorch.
--aten-to-std: Lower the ATen dialect into standard dialect function calls.
--return-elimination-pass: convert functions (primarily the toplevel function)
to pass return values by reference. This simplifies pytorch integration.
--aten-op-report: generate a textual report about the model
--liveness-report
Future patches will implement actual integration with the pytorch jit to
intercept and generates MLIR in this dialect, then lower the resulting MLIR
into function calls through aten-layer-name -> aten-to-std ->
return-elimination -> std-to-llvm. The result would then jitted using the LLVM
jit, linked against a runtime library which makes calls back into pytorch to
implement all the layers.
Co-authored-by: Jeff Fifield <jeff.fifield@xilinx.com>
Co-authored-by: Jeff Fifield <jeff.fifield@xilinx.com>
Sean Silva
Bryce Arden
Aaron Arthurs
Bairen Yi
Aaron J Arthurs
mikeurbach
Stella Laurenzo
Marius Brehler
stephenneuendorffer