* Add LazyGraphExecutor registration
* Update PyTorch version to 1.14.0.dev20221024
Co-authored-by: Roll PyTorch Action <torch-mlir@users.noreply.github.com>
* Relax the need for only CPU versions of PyTorch
This allows installing corresponding PyTorch CUDA / ROCM versions and using torch-mlir.
* Remove obsolete comments
Whether or not the PyTorch build is cached should not affect the success
of the Torch-MLIR build, but based on the existing code, a build may
fail if the `TM_PYTORCH_INSTALL_WITHOUT_REBUILD` variable was set but
the build cache doesn't exist.
Although that variable is set by CI upon a cache hit, nuances of
Github's caching behavior can create situations where the coupling
between `TM_PYTORCH_INSTALL_WITHOUT_REBUILD` and the cache lookup fails.
Specifically, a branch other than our default branch (`main`) may create
the cache entry, but because Github doesn't share this cache entry with
builds running on the `main` branch, the `main` branch build tries to
create it's own cache entry. However, since cache identifiers are
unique and because caches are immutable, the caching step running in the
`main` branch appears to create an invalid cache entry (of 233 bytes,
instead of the expected ~60 MB).
Consequently, subsequent builds observe a cache "hit", since caches
created by the `main` branch are shared with all other branches, but
because this cache entry is invalid (since it doesn't actually contain
the ~60 MB PyTorch WHL file), the builds fail.
One workaround would be to let only the `main` branch create caches, but
in doing so, we would also prevent other branches from _reading_ the
cache, making the builds in those branches terribly slow.
So this patch uses a different workaround, which is to check whether the
PyTorch WHL file exists, even if the build observed a cache hit. If the
file doesn't exist, even if it was a purported cache hit, the code
builds PyTorch from source, which is probably intuitive.
A longer term fix will follow, after a discussion with the wider team.
Upstream PyTorch nightly page
[https://download.pytorch.org/whl/nightly/cpu/torch_nightly.html]
somehow dropped the link for torch-1.14.0.dev20221018 for macOS but not
for Linux or Windows, whereas our RollPyTorch action assumes that if the
nightly version is available for Linux, it is also available for macOS.
This reverts the commit that changed the PyTorch version.
Without this patch, CI logs contained the line:
-- Linker detection: GNU ld
GNU ld is notoriously slow at linking large binaries, so this patch
swaps GNU ld with the LLVM linker.
Since the linker invocation is driven through the compiler, perhaps the
best way to use the LLVM linker is to tell the compiler which linker
binary to use. This patch adds the `-fuse-ld=lld` flag to all Linux
builds of Torch-MLIR in CI to make it use lld.
* ci: cache PyTorch source builds
This patch reduces the time spent in regular CI builds by caching
PyTorch source builds. Specifically, this patch:
1. Makes CI lookup the cache entry for the PyTorch commit hash in
pytorch-version.txt
2. If lookup was successful, CI fetches the previously-generated WHL
file into the build_tools/python/wheelhouse directory
3. CI sets the `TM_PYTORCH_INSTALL_WITHOUT_REBUILD` variable to `true`
4. The build_libtorch.sh script then uses the downloaded WHL file
instead of rebuilding PyTorch
* ci: warm up PyTorch source cache during daily RollPyTorch action
This patch makes the RollPyTorch action write the updated WHL file to
the cache, so that it can be later retrieved by CI that runs for each
PR. We deliberately add the caching step to the end of the action since
the RollPyTorch action never needs to read from the cache, although
executing this step earlier in the process should not cause problems
either.
This commit makes the following changes needed to update bump LLVM:
- Replace `linalg.init_tensor` with `tensor.empty` (see:
https://reviews.llvm.org/D135129)
- Replace `NoSideEffect` with `Pure` (see
https://reviews.llvm.org/D135505)
- Replace `body` region accessor for `ReduceOp` and `ReduceWindowOp`
with `getBody`
- Fix incorrect use of `tosa::ReduceSumOp` in `AtenNativeLayerNormOp`
conversion pattern. The result type of `tosa::ReduceSumOp` must have
the same rank as the input type. (see:
https://www.mlplatform.org/tosa/tosa_spec.html#_reduce_sum)
Co-authored-by: Ashay Rane <ashay@users.noreply.github.com>
Co-authored-by: Ashay Rane <ashay@users.noreply.github.com>
This commit replaces test inputs that were being linearly transformed
by multiplying and adding/subtracting to the input tensor with inputs
that use the `low` and `high` keyword arguments instead.
This commit removes the `weight` tensor from the inputs of one of the
`linalg.generic` ops generated by the `aten.convolution` linalg
lowering, since the indexed values are not actually used by the body
of the `linalg.generic`. Moreover, in general the `weight` tensor does
not have the same shape as the output tensor of the `linalg.generic`,
so both tensors being indexed by the same indexing maps is wrong.
We originally added these to help bring up more complex models with
heavier dependencies. However, over time it has become clear that these
models usually require more than just heavier dependencies -- they often
require a nontrivial amount of "one-off" code to extract the relevant
parts of the model and compile them. This is not a good fit for a
component in the core Torch-MLIR repo.
However, in the community, nod.ai has developed the ["Shark
Tank"](https://github.com/nod-ai/SHARK/tree/main/tank) which has all the
appropriate code to wrangle these models and organize them. We intend to
more heaviliy lean on that as a community and improve the symbiosis
there to serve the role that these heavydep tests were meant to play.
Allow customizing `backend_legal_ops` for "torch" output type, since we
don't know which backend will be used (it might be a custom backend).
We don't allow customizing the `backend_legal_ops` for the other output
types (Linalg, TOSA, MHLO) since those backends control their set of
legal ops directly.
Fixes#1418
-- This commit adds e2e support for `aten.Mish` op.
-- `aten.Mish` op is decomposed as following :-
Mish(x) = x * Tanh(Softplus(x))
Signed-off-by: Abhishek Varma <avarma094@gmail.com>
Signed-off-by: Abhishek Varma <avarma094@gmail.com>
* build: disable LTC again so that we can bump PyTorch version
When built using PyTorch's master branch, the LTC code has been failing
to build for a few days. As a result, the PyTorch version referenced by
Torch-MLIR is stalled to the one from October 4th.
In an effort to advance to PyTorch version, this patch disables LTC, and
a subsequent patch will advance the PyTorch version.
* update PyTorch version to 1.14.0.dev20221010
Also disables the `UpSampleNearest2dDynamicFactor_basic` e2e test, since
the (PyTorch) oracle differs from the computed value for both the
refbackend and the eager_mode backends.
This commit adds lowering of `aten.div.int` and `aten.bitwise_or.Tensor`
ops. Both these ops are required in order to support bloom_560m model.
Signed-Off-by: Gaurav Shukla <gaurav@nod-labs.com>
This commit updates the linalg conversion of `AtenMaxDimOp` to use
`arith.maxf` instead of `arith.select` to calculate the maximum. This
allows better vectorization further downstream, since the operation
can be converted to a simple max reduction when the `indices` result
is not used. See: https://github.com/iree-org/iree/issues/10666.
Vivek Khandelwal
Roll PyTorch Action
Ashay Rane
Ahmed S. Taei
powderluv
Daniel Ellis
Jae Hoon (Antonio) Kim
Sean Silva
Chi_Liu
Ramiro Leal-Cavazos
Gleb Kazantaev
Prashant Kumar
Abhishek Varma
Gaurav Shukla