This commit adds verifiers to the ops `ToBuiltinTensorOp` and
`FromBuiltinTensorOp` that make sure that the input and output have
the same shape and data type.
An upstream MLIR bug (that was recently fixed) caused the result to be
ignored for Region- and Block-visitor functions. Now that the bug is
fixed, we don't need an auxiliary boolean to track whether the visitor
function has succeeded.
* [MLIR][TORCH] Add folder for torch_c.from_i64 & torch_c.to_i64
* add unit tests for each individual fold
* fix failure of NumelZeroRankModule & TestMultipleTensorAndPrimitiveTypesReturn
1. This commit adds lowering of "while-like" prim loop to scf.while
operation.
2. Adds lowering of "for-like" prim loops to scf.for operation.
Signed-Off-By: Prateek Gupta <prateek@nod-labs.com>
The updated LLVM code includes a patch to create bfloat16 array
attributes, thus enabling a different patch to torch-mlir to flesh out
support for the bfloat16 type.
See the documentation in `docs/shape_lib.md` and
`docs/adding_a_shape_function.md` for an overview of the system.
This completely overhauls how we represent shape functions. In
particular, RefineTypes does not infer shapes anymore (only dtypes).
Shape functions are now written in (TorchScript'able) Python.
Recommended review order:
1. Read `docs/shape_lib.md` and `docs/adding_a_shape_function.md`.
1. Code and tests for ReifyShapeCalculations, DropShapeCalculations.
1. Code and tests for SimplifyShapeCalculations.
1. shape_lib_gen.py
1. Code and tests for new RefineTypes pass.
1. Random folders/canonicalizers in TorchOps.cpp and associated test in
`canonicalize.mlir`.
1. New ReadOnly trait inferred from the registry.
1. Any miscellaneous remaining stuff.
Example `-print-ir-after-all` for ElementwiseUnaryModule:
[IR lowering dump](https://gist.github.com/silvasean/e4dc8cbc8d00aac7819602e3cbd8e212).
Example `-print-ir-after-all` for ElementwiseBinaryModule:
[IR lowering dump](https://gist.github.com/silvasean/daf6860ecced732af3568af6b1899113).
This pass is added to lower ops, which can not be lowered
via the TorchToLinalg pass, such as `torch.bincount` op.
This pass also uses torch-mlir's TMTensor Dialect to lower the
complex ops.
Also add torch.bincount op lowering with the help of TMTensor dialect
Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
This PR include the following pieces:
- Add torch `Generator` type. `Generator` type is converted to i64 in
refbackend type converter.
- Add seed managment support for the default global generator.
`torch_c.getNextSeed` op is used to get the seed. On refbackend, the
`torch_c.getNextSeed` is lowered to load/store from [0] of global
variable `default_generator` memref<i64> in `InsertRngGlobals` pass.
- Add `aten.uniform_` and testing as an example op for RNG ops. Add
`torch.pseudo.aten.uniform` op. It has the same operands and return as
the `aten.uniform_` from the op registry except for value semantics.
We lower through linalg-on-tensors and use RefBackend to run it.
This adds enough support for a "tanh" op. Adding more ops should be
fairly mechanical now that things are wired up. Run with:
```
./tools/torchscript_e2e_test.sh -c tosa
```
The backend structure is very similar to linalg-on-tensors based E2E
backends and is a nice parallel (see `tosa_backend.py`). Actually, this
forced a nice refactoring to the layering here. We removed
`torchscript-module-to-linalg-on-tensors-backend-pipeline` and instead
require separately running
```
torchscript-function-to-torch-backend-pipeline,torch-backend-to-linalg-on-tensors-backend-pipeline
```
This highlights the step that lowers to the "torch backend contract"
of cleaned up `torch` dialect ops is a critical step in the lowering.
Going forward, that is the key load-bearing contract of the torch-mlir
project, not the linalg-on-tensors backend contract.
Recommended review order:
- `TorchToTosa.cpp` / `TorchToTosa/basic.mlir`
- `python/torch_mlir_e2e_test/torchscript/configs/tosa_backend.py` and
the new `utils.py` file there.
- `python/torch_mlir_e2e_test/tosa_backends/linalg_on_tensors.py` and
`abc.py` in that directory for the TOSA backend e2e interface.
- other misc mechanical changes
This commit (with approval from all contributors) dual licenses
the torch-mlir project under both the standard LLVM license and the
standard PyTorch license. This will facilitate moving code between
torch-mlir and the two upstream projects.
The standard file comment is now:
```
// This file is licensed under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
// Also available under a BSD-style license. See LICENSE.
```
See `LICENSE` in the project root for the terms of both licenses.
Implement the `lazytensor` python package for converting
lazy computations captured by the Lazy Tensor Core into MLIR.
This PR also fixes a few things with `torchfx` and its example
Also contains the following changes:
- Remove derefineOp canonicalizer because it's not safe.
- Support for optional tensor and list tensors in reduceOpVariant. This
only works for some special detected and easy to handle cases. For list,
it covers the case list is got from a `ListConstruct`. For optional, it
covers the case optional is constructed from a `DerefineOp`.
- Remove the `inferReturnTypes` for `FromBuiltinTensorOp` because it's
not safe to deduce types from the input. For example, a built-in tensor
of i8 could be converted to si8 or ui8. It's better to let the user
specify the return type explicitly.
A few remain in examples/docs that will be naturally be updated in due
time.
This regresses the list support and the general direction of more widely
supported control flow, lists/dicts/globals that we were going for with
the TorchScript path. The idea is that we are deferring that work to
make torch-mlir a very clean standalone thing. We will reboot it,
probably using some of the tools of iree_pydm to make it simpler, and in
a more natural place (such as an iree-torch repo that depends on IREE and
torch-mlir to build a working PyTorch frontend solution for IREE -- it
was really weird that npcomp depended on IREE).
`tools/torchscript_e2e_test.sh` is all green.
This needs a few passes I put into torch-mlir/lib/RefBackend (not to be
confused with `npcomp/lib/RefBackend`, which will soon be deleted).
For the sake of review, since this brings together a lot of things, I
split this into its own commit. I temporarily commented out some "list"
stuff that we are going to remove as part of the torch-mlir refocus.
This creates the `external/torch-mlir` directory as an
LLVM_EXTERNAL_PROJECTS-compatible project (analogous to
`iree-dialects`) and completes movement/rename of all pure MLIR C/C++
compiler code into there. The next step will be to move all the Python
code / code that links/includes PyTorch C++ code (which currently lives
in `frontends/pytorch`) into a subdirectory here.
I call this "earthmoving" because it is mostly mechanical changes and
renames. As a quick summary (we can change this down the road easily)
- C++ `mlir::NPCOMP::Torch -> mlir::torch::Torch`
- CAPI `npcompTorchListTypeGet -> torchMlirTorchListTypeGet`
- preprocessor `#ifndef NPCOMP_ -> #ifndef TORCHMLIR_`
- CMake `NPCOMPFoo -> TorchMLIRFoo`
The goal of this is to create a standalone project creating a center of
mass for entry into the MLIR ecosystem from PyTorch, suitable in scope
for eventual inclusion/ownership in PyTorch. The idea is that
`external/torch-mlir` will some day be pulled out into its own
repository, and then npcomp will simply pull it in as a submodule.
Layering-wise, what lives in `torch-mlir` lowers code from PyTorch
(currently TorchScript, but TorchFX or pytorch/xla-style tracing are
possible extensions) down to what we have been calling the "Torch
backend contract" which is cleaned up IR (inlining, simplifcation,
conversion to value tensors, ...) entirely in the `torch` dialect. This
is the branching off point for further lowering, of which npcomp takes
one opinion (outside `torch-mlir` of course!), namely the
`TorchConversion` dialect/transforms which lower to IR suitable for IREE
and other linalg-on-tensors based lower-level compilers.
Summary of changes:
- move `{include,lib,test}/Dialect/Torch` into `torch-mlir`
- move relevant parts of CAPI into `torch-mlir`.
- leave a few things related to the `torch-mlir` Python build commented
out, which should be resolved in a subsequent change.
This plumbs through a vertical slice of support for lists.
The main chunk of new code here is AnnotateABIPass which captures the
program signature at the Torch backend contract layer, right before we
start `TorchConversion`. The `TorchConversion` lowering process is lossy
w.r.t. types, so it's necessary to do this for all targets in general.
Like using `!iree.list` directly, we use IREE's ABI annotation
representation for this, although there is nothing very IREE-specific
about it (see
https://github.com/google/iree/blob/main/docs/developers/design_docs/function_abi.md)
We change `ListLiteralModule_basic` to use `!torch.int` because IREE
doesn't support f64 yet (and we don't yet have a way for users to say
that they want `!torch.float` to lower as f32).
Recommended review order:
- AnnotateABIPass and tests
- Arg marshaling in npcomp_backend.py and `iree.py`
- Updates to `list_programs.py` / `xfail_sets.py`
- Moving DeleteDeadIREEListsPass to Backend/Common, so that backends
that don't support lists can use it. RefBackend uses that pass, for
example.
- 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 converts a basic list op (torch.prim.ListConstruct) to the IREE
dialect.
```
def forward(self, x: float):
return [x, x]
```
turns into:
```
builtin.func @forward(%arg0: !torch.float) -> !torch.list<!torch.float> {
%0 = torch.prim.ListConstruct %arg0, %arg0 : (!torch.float, !torch.float) -> !torch.list<!torch.float>
return %0 : !torch.list<!torch.float>
}
```
which turns into:
```
builtin.func @forward(%arg0: f64) -> !iree.list<f64> {
%c1 = constant 1 : index
%c0 = constant 0 : index
%c2 = constant 2 : index
%0 = iree.list.create %c2 : !iree.list<f64>
iree.list.set %0[%c0], %arg0 : !iree.list<f64>, f64
iree.list.set %0[%c1], %arg0 : !iree.list<f64>, f64
return %0 : !iree.list<f64>
}
```
As part of doing this, I realized that it was time to formalize the IR
form that we reach right before running TorchTo{Linalg,Std,...}. We now
call it the "Torch backend contract". We then lower the "Torch backend
contract" to the "npcomp backend contract", which involves the new
TorchConversion (`torch_c`) dialect, which holds ops that need to
operate on both the npcomp backend types (e.g. builtin tensors, i1, IREE
list, etc.) and the `!torch` types.
This made more sense, as I realized that if I didn't factor out
`torch_c` then the Torch dialect would have a dependency on IREE
dialect (we previously didn't notice this was an issue because we only
depended on `builtin` types), which seemed wrong to me.
Recommended review order:
- TorchToIREE.cpp / `TorchToIREE/basic.mlir`
- Look at the new structure of createTorchScriptToNpcompBackendPipeline.
It now lives in TorchConversion/Transforms/Passes.cpp and cleanly
calls into `Torch::createTorchScriptToTorchBackendPipeline` for the
frontend lowering to the Torch backend contract.
- Mechanical change extracting
`torch_c.{to,from}_{i1,i64,f64,builtin_tensor,iree_list}` into a new
TorchConversion dialect, and a few passes specific to the lowering
from the Torch backend contract to the npcomp backend contract.
- Minor fixes to TorchToLinalg.cpp to use unconverted operands (now that
we convert lists as part of operand materialization, we need to use
the original operands). Also added test for AtenMaxPool2dOp and fixed
m_TorchConstantIntList.
- TmpDeleteDeadIREELists pass. Temporary pass for deleting dead IREE lists that
are created as part of operand materialization for conv/max pool/avg pool ops
in TorchToLinalg.
Ramiro Leal-Cavazos
Sean Silva
Ziheng Jiang
Ashay Rane
Tanyo Kwok
Maksim Levental
Prateek Gupta
Ahmed Taei
Vigilans
Vivek Khandelwal
Nirvedh
Yi Zhang
stephenneuendorffer
dan
Anup Gangwar
Suraj Sudhir
Suraj Sudhir
Stella Laurenzo