This commit replaces the `tanh` dtype function, which was being used
to test the implementation of dtype functions in
a710237437, with a dtype function for
`expm1`. The dtype function for `expm1` is identical to the `tanh`
one, so the same level of testing is maintained.
Currently, there are ops getting dtype information from the
`RefineTypes` pass and ops getting dtype information from the
`TorchDtypeRefinementPipeline`. Since each pass can only propagete
dtype information for the ops it knows how to handle, some models with
many ops handled in both passes require the two dtype propagation
passes to execute many times, reaching the iteration limit set in the
`LowerToBackendContractPass`. To temporarily avoid this issue while
the migration to `TorchDtypeRefinementPipeline` is finished, this
commit switches `tanh` to `expm1`, since the latter is used a lot less
in large models.
This reverts commit eaab9be207, since it
is causing the post-merge CI tests to fail, causing subsequent PRs to be
blocked. Specifically, the tests
`ElementwiseAtenLogicalAndOpPromoteBroadcastModule_basic` and
`ElementwiseAtenLogicalXorOpPromoteBroadcastModule_basic` fail because
the oracle does not match the computed result. This patch reverts the
commit to make the post-merge builds green again.
* [custom op] Generalize shape library logic to work with dtypes
This commit generalizes the shape library logic, so that dtype rules
for ops can also be expressed using the same mechanism. In other
words, each op can now have a shape function and a dtype function
specified in Python that is imported during lowering to calculate the
shapes and dtypes throught a program. For more information about how
to specify a dtype function, see the updated
`docs/adding_a_shape_and_dtype_function.md`.
For those not familiar with how the shape library works, the file
`docs/calculations_lib.md` provides an overview.
-- aten.upsample_nearest2d.vec op is not present
owing to https://github.com/pytorch/pytorch/pull/85638
-- So this commit adds a lowering on aten.upsample_nearest2d.
Signed-off-by: Abhishek Varma <abhishek@nod-labs.com>
Unless requested otherwise, PyTorch no longer installs most of the
header files under the caffe2 directory (see
https://github.com/pytorch/pytorch/pull/87986). This breaks our
importer code since we need to use the `MakeGuard()` function to execute
statements in the event of exceptions.
To fix this issue, this patch implements a rudimentary version of
PyTorch's ScopeGuard, where once the class variable goes out of scope,
it executes a predefined method.
This commit removes almost all of the valsem ops, since the value
semantics version of the ops now exist in PyTorch. The only op missing
is `aten.bernoulli_.float`. In addition, this commit also simplifies
the implementation of `aten.fill.Scalar` by moving it to the pattern
that converts elementwise ops.
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 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>
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>
* Fix c10::prim::Constant conversion; Added CAPI for passes; Added passes to base lazy backend
* Update ivalue_importer to use ImportOptions; Added tests for non-value/value tensor types
* Added tests for scalar Constant import; Updated MB::importFunction to use ImportOptions
* Test updates
* Move back module variable name
* Remove RefineTypes from TorchMlirLoweringContext::Build()
* Rename pass; Remove passes from base lazy backend
* Rename pass to VerifyBackendContractPass
* Aligned cmd pass name; Fixed TorchConversion passes registration
* test: allow spaces in path to Python executable
On Windows, the path to the Python binary may contain spaces, so this
patch adds quotes around the path to the python executable.
Thanks to @sstamenova for suggesting the fix!
* python: remove header file that causes Windows build failures
Similar to https://reviews.llvm.org/D125284, we can safely remove this
header file without affecting the build on either Linux. It is
necessary to remove this header file on Windows builds since otherwise
it causes build errors.
* python: drop `TORCH_API` from function defined in Torch-MLIR
`TORCH_API` should apply to functions that are either exported by
libtorch.so or ones that are imported from libtorch.so by its downstream
consumers (like Torch-MLIR). Neither case applies to the
`importJitFunctionAsFuncOp()` function, since it is defined in
Torch-MLIR (and thus outside libtorch.so). This patch fixes the problem
by dropping `TORCH_API` from that function's declaration.
* python: make output of class anotations deterministic
The `class-annotator-repr.py` test checks for class annotations in a
specific order, but prior to this patch, the order was
non-deterministic, since the code iterated on an _unordered_ map.
This patch makes the iteration order deterministic through two changes:
1. using a sorted map
2. using the class qualified name instead of the address of the class in
memory
* test: use Python3_EXECUTABLE as interpreter path for consistency
This ensures that tests use the Python3 version that was detected using
CMake, instead of whichever python version that happens to be in the
PATH variable when invoking the test.
* test: fix RUN string
The parenthesis syntax does not run on Windows (the shell interprets the
`(` character as part of the path). Moreover, the ODR violation in the
comment no longer seems to apply.
* python: port parallel test framework to Windows
Since Windows does not support `fork` natively, Python's
`multiprocessing` module needs to use `spawn` on Windows. However, to
use `spawn`, the multiprocessing module serializes (or pickles) the
worker function and its arguments. Sadly, the multiprocessing module
(both the default one in Python and the one that is extended in PyTorch)
is unable to serialize lambda functions (see
https://stackoverflow.com/a/19985580) for detals.
Unfortunately, given how our tests are structured, we require that the
function under test is passed as an argument to another function, so we
cannot sidestep our use of lambda functions.
To resolve this problem, this patch makes use of the `multiprocess` and
`dill` Python modules, which together offers a multiprocessing mechanism
that can serialize lambda functions. The multiprocess module also
offers a process pool, which simplifies the code for our parallel
testing framework.
Strength the shape inference for aten.arange-like op by
1. registering aten.sub and aten.ceil.Scalar op and design folders for them.
2. register a new constant-like op: Torch::ConstantNumberOp and design canonicalizer for it.
As @oroppas identified, literal strings that are over 16,380 characters
cause the MSVC compiler to throw an error (C2026), eventually causing
the Windows build of Torch-MLIR to fail because the length of the
generated MLIR for the shape library crosses the allowed threshold.
This patch fixes the problem by making the Python script generate one
literal string per line to satisfy the MSVC compiler.
Thanks to @oroppas for the bulk of the effort required to resolve this!
Vivek Khandelwal
Gleb Kazantaev
Jiahao Li
Ramiro Leal-Cavazos
Srirammaswamy
Ashay Rane
Shivam Gupta
ataheridezfouli-groq
Ahmed S. Taei
Abhishek Varma
Tanyo Kwok
George Petterson
Xiafei Qiu
Prashant Kumar
Abhishek Varma
Gaurav Shukla
Daniel Ellis
AmosLewis
武家伟