This test has been disabled a long time, and since RefBackend is so slow
we don't want to add this unnecessarily. I believe it is covered by
downstream testing such as the Shark Tank.
Thanks to TorchDynamo's great layering and design, this is only about
100 lines of code for a basic lockstep debugger.
This should allow us to deprecate eager_mode, since AFAIK the only
interesting use case that it was really supporting is for downstream users to
write lockstep debuggers.
NOTE: The exact reporting and interface here is subject to change. Please
try it out and provide feedback (or patches :) ).
- make_fx should not drop source locations: https://github.com/pytorch/pytorch/issues/90276
- Report tensors better (huge tensors should be summarized)
- Maybe don't abort, but just warn?
- Allow customizing atol/rtol.
- How best to print the failing node? And include surrounding graph
context?
This commit changes the `InsertRngGlobalsPass` to `TorchConversionToMLProgram`
pass. This commit also adds the `MLProgramBufferize` pass for the
bufferization of ml_program dialect ops to run on refbackend.
Signed-Off By: Vivek Khandelwal<vivek@nod-labs.com>
This commit replaces the LCG algorithm that was being used by the
`TorchToLinalg` lowering of `AtenUniformOp` to generate random numbers
with the `squares64` algorithm, for the LCG algorithm was producing
tensors that were highly correlated with one another.
Squares64 algorithm: https://arxiv.org/abs/2004.06278
Closes https://github.com/llvm/torch-mlir/issues/1608
The current implementation sets the `nextSeed` value to `temp & 127`,
which is wrong. The last step of the LCG algorithm for the multiplier
and increment chosen should be `temp % 2^{64} = temp & (1 <<
63)`. However, because we are dealing with i64 values, the modulus
operation happens automatically, so it is not needed.
See Donald Knuth's values for LCG here:
https://en.wikipedia.org/wiki/Linear_congruential_generator
There are a few e2e tests that take several very large tensors as
input, which leads to the e2e test suite leaking too much
memory. Running things locally resulted in a total memory usage of
12.5 GB when running the suite sequentially on the refbackend.
Many of the tests that take large tensors don't actually need
such large tensors to pass, and some that take several large tensors
as input are just doing the same thing multiple times. This commit
reduces the size of some of the tensors and removes repetitive parts
of tests to reduce the memory usage to a total of 3 GB.
`np.bool is bool` and will never be returned as a dtype of an
`np.ndarray`, so we don't need to handle it here.
```
>>> a = np.ndarray([1], dtype=bool)
>>> a.dtype.type is np.bool_
True
```
More info here:
https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations
For reasons that I haven't yet fully tracked down, the TorchDynamo
TestConfig seems to result in tensors that cannot be pickled. They seem
to be holding some sort of weak handles to a `torch.fx.graph.Graph`.
Here is the object structure that leads to the unpickleable object:
```
(<function _rebuild_tensor_v2 at 0x7f56346d56c0>, <class 'torch.Tensor'>, ( 1.0...
{<object object at 0x7f557529e6b0>: <WeakKeyDictionary at 0x7f556a3efbb0>}
{'data': {<weakref at 0x7f5615372ed0; to 'PythonKeyTracer' at 0x7f556a3ee5c0>: _...
<class 'torch.fx.graph.Graph'>
<class 'torch._ops.OpOverloadPacket'>
TypeError("cannot pickle 'torch._C.FunctionSchema' object")
```
Upstream bug filed: https://github.com/pytorch/pytorch/issues/89626
This adds a basic e2e Config for TorchDynamo using
Linalg-on-Tensors/RefBackend.
But TorchDynamo is pretty orthogonal to
various other pieces, so it should compose nicely with variations like:
- Switching out all the backends (Linalg-on-Tensors, TOSA, MHLO)
- PyTorch functionalization and decompositions
- Taking the example inputs and compiling with all dynamic or all static
shapes without duplicating tests.
This adds it to the CI, but there are still a lot of XFAIL's.
This also adds a helper `from torch_mlir.dynamo import
make_simple_dynamo_backend` which simplifies some of the steps for
making a Torch-MLIR-based TorchDynamo backend. We include "simple" in
the name because we are going to be exploring various things next from
the long-term roadmap.
The next steps are:
- Burn down all the XFAIL's.
- Start working on the pieces from the [long-term roadmap](https://github.com/llvm/torch-mlir/blob/main/docs/long_term_roadmap.md).
- Add functionalization/decompositions into the TorchDynamo flow and
remove reliance on the current Torch-MLIR "frontend".
- Write a pure-Python direct FX->MLIR importer.
- Hook up the new PyTorch symbolic shape stuff.
- Explore PrimTorch decompositions for simplifying backends.
This commit fixes the aten.mean and aten.mean.dim op decomposition
for supporting large-sized inputs.
This commit also fixes the formatting for the file stats.py
Signed-Off By: Vivek Khandelwal<vivek@nod-labs.com>
The purpose of the test suite is to accelerate the development of the
compiler. However, we had various tests there that were not expected to
work, had no in-progress work being tested by the test, and nobody was
actively working on them. Having such tests in our test suite just adds
clutter and slows down development on the compiler.
-- 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>
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 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.
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.
-- 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>
* 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.
This commit adds support for TorchToTosa lowering of
`aten.broadcast_to` op for cases:
1.) When the rank of input and output tensor is equal.
2.) When the rank of input tensor is zero.
Signed-Off By: Vivek Khandelwal<vivek@nod-labs.com>
This adds a very long and obnoxious option to disable crashing tests.
The right fix here is to use the right multiprocessing techniques to
ensure that segfaulting tests can be XFAILed like normal tests, but we
currently don't know how to implement "catch a segfault" in Python
(patches or even just ideas welcome).
Motivated by #1361, where we ended up removing two tests from *all*
backends due to a failure in one backend, which is undesirable.
Summary of changes:
- Updated emitAccessorPrefix since the default value has changed
(https://reviews.llvm.org/D133179)
- Updated RefineTypes pass since Lattice::isUninitialized() is removed
(https://reviews.llvm.org/D132800)
- Updated MHLO tag so that it builds with the updated LLVM tag
- Disabled two tests that cause segfaults in the TOSA backend (see Issue
#1361)
* Add aten.frobenius_norm.dim op and init its conversion pattern to linalg and MHLO,
* run symbolic-shape-optimization before hlo-legalize-to-linalg to fit more mhlo e2e tests.
Summary of changes:
- Update the dataflow analysis in RefineTypes.cpp
- Add tosa-to-arith pass after tosa-to-linalg pass, since
tosa-to-linalg (and canonicalizations) can produce tosa.const() ops
- Fixed warning about not making `matchAndRewrite` as override
We use it for more than TorchScript testing now. This is a purely
mechanical change to adjust some file paths to remove "torchscript".
The most perceptible change here is that now e2e tests are run with
```
./tools/e2e_test.sh
instead of:
./tools/torchscript_e2e_test.sh
```
Change logic so that we never run the multiprocessing codepath with only
1 worker. That configuration was causing all subsequent tests to
spuriously fail if one test failed with a crash (this was easy to see
after sorting the tests). That configuration was the one used by the CI.
Also, sort tests to make output nicer.
Also, make verbose mode more verbose so that it is easy to see in `-s`
mode which test is crashing.
This commit adds a method to `TestUtils` that generates random integer
tensors with a similar interface to the `TestUtils.rand`. This commit
also replaces with `tu.randint` all test inputs generated with
`torch.randint`.
We were already hitting many cases where backends different in terms of
the legal ops that they wanted. This caused unnecessary coupling between
the backends. Examples:
- https://github.com/llvm/torch-mlir/pull/1161
- https://github.com/llvm/torch-mlir/pull/862
This PR centralizes all compilation to go through `torch_mlir.compile`
so that we can keep the logic centralized there. We should move these
lists closer to each backend. Especially cases like
https://github.com/llvm/torch-mlir/pull/862 where blocking a
decomposition is necessary to avoid a crash emphasize that the set of
decompositions is tightly coupled to the backend, and should be
"controlled by the backend" and not something arbitrarily tweakable.
Also:
- Fix a small bug in the way we passed through the backendLegalOps
option.
- Add better error messages in `torch_mlir.compile` for import errors.
PyTorch recently added support for `dim=None` in the `torch.var`
(5ca9b2b6fa)
and `torch.std`op (eb0e30e0bc).
This commit adds the corresponding support in torch-mlir.
Signed-Off By: Vivek Khandelwal<vivek@nod-labs.com>
* Changed Example MLIR backend to Reference MLIR backend
* Moved reference_ltc_backend into csrc
* Merged sys_utils.h
* Renamed reference_ltc_backend to reference_lazy_backend
* Addressed review comments
* Update docs with new library name
* Removed _REFERENCE_LAZY_BACKEND from .gitignore
* Added reference_lazy_backend to the TorchMLIRPythonModules dependency list
Fixed typo in `ltc_examples.md`
Missed instance where `ltc_backend` was used instead of `lazy_backend`.
- Pruned number of xfailed e2e LTC tests from 305 to 134
- Reviewed every failure to ensure the error genuinely warrants an xfail
- Fixed bug where non-tensor outputs of LTC computation had `.to('cpu')` called, which caused a failure and inflated the xfail count
- Fixed bug with `HBC_basic` test where a constant tensor was created in its constructor without being declared as a buffer, which prevented the device from being updated when the parent `torch.nn.Module` got moved to the `lazy` device
- Note that this test is still xfail'd due to some unsupported ops. Left a comment about some potential issues that may arise if it gets reenabled in the future
- Updated autogen `GeneratedTorchOps.td` to reflect the latest set of supported ops
- Renamed `aten.zero.functionalization` to `aten.zero` to reflect upstream PyTorch changes
* Added e2e LTC Torch MLIR tests
* Fix seed for reproducability
* Check if computation is None before getting debug string
* Updated unit tests, and added numeric tests
* Print name of the model layer that fails numeric validation
* Run LTC e2e test with CI/CD
* Set seed in main function, instead of beginning of execution
* Add comment to specify number of digits of precision
* Fixed typo
* Remove tests for LTC example models
* Added LTC option to torchscript e2e
* Implement compile and run for LTC e2e test
* xfail all tests that use ops that aren't currently supported
This commit fixes the shape calculation for:
1.) aten.mean.dim
2.) aten.var.dim
3.) aten.sum.dim_IntList op
Also, it fixes the lowering of `aten.mean.dim` and
`aten.sum.dim_IntList` for handling the cases of empty dim list.
Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com
- Includes a canonicalizer for `aten.add.t`needed for successfully lowering the shape function
- Only offers support for statically sized index tensors when there is more than one
- Dynamic shape support remains for single indexing tensors
- Supports cases where the view op expands and collapses dims
simulataneously. This does not handle the case where it is neither
expanding nor collapsing (e.g. [2, 3] -> [3, 2])
- Additionally fixes a previous bug with adding 1-sized dims on both
sides of a tensor with aten.view
This patch makes some rudimentary changes to torch-mlir's use of MLIR
Python bindings to work with the most recent LLVM code. We can perhaps
do better by being more selective in what we link against, instead of
using `MLIRPythonExtension.RegisterEverything`.
This commit adds the support for negative dim cases for `aten.cat`,
`aten.slice.Tensor` and `aten.slice_scatter` op.
Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
The original conversion pattern for `AtenBatchNormOp` required that
the input rank be greater than 2; however, the only
expectation in the conversion pattern and in Pytorch is that the input
rank is greater than 1, since the second dimension of the input must
match the size of the `weight`, `bias`, `runningMean`, and
`runningVar` inputs. This commit fixes the `inputRank` check.
This commit adds the decomposition for `aten.var.dim` op.
This commit also make changes in the decomposition for `aten.var` op.
Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
`aten.select_scatter` op.
This commit adds:
1. Lowering of `aten.slice_scatter` op into `tensor.insert_slice`
op.
2. Decomposes the `aten.select_scatter` op into `aten.slice_scater`
op.
Signed-Off-By: Prateek Gupta <gprateek93@gmail.com>
A previous fix to the handling of size-1 dims in
`aten.view` (https://github.com/llvm/torch-mlir/pull/962) resulted in
the wrong grouping of dimensions when size-1 dims where between two
dims of size greater than 1. This commit fixes that.
This commit lowers `aten.matmul` to `linalg.BatchMatmul` under the
following conditions:
1. The result of matrix multiplication must have batch dimensions,
i.e., rank greater than 2.
2. The resultant matrix must have at most 1 dynamic batch dimension.
It also handles broadcasting of batch dimensions when batch dimensions
of the matrices are broadcastable.
Signed-Off-by: Gaurav Shukla <gaurav@nod-labs.com>
This commit fixes the shape function for `index.Tensor`, adding
support for multiple index tensors and `None`s in the indices
list. This commit also adds support for input tensors of rank greater
than 1. The lowering for `index.Tensor` still has the the limitation
that only a single index tensor along the first dimension of the input
tensor is supported.
Prior to this patch, the torch dialect included `AtenTriuOp` for
computing the upper triangular part of the input matrix, but there was
no code for lowering the op to the linalg dialect.
This patch adds code to generate a `linalg.generic` operation that
compares indices (computed using `linalg.index`) to choose between zero
or the original value (using `arith.select`). The lowering fails if the
number of dimensions are less than two. This patch also adds a few
end-to-end tests.
The MacOS builders are having linking trouble with the extension library.
Until it's fixed, all support for op extensions is disabled. It should be
easy to restore once the issue is resolved.
The function `AffineMap::inferFromExprList` does not work if the first
vector of expressions is empty, because it uses these expressions to
obtain the context. This prevented `aten.permute` from working for
inputs of 0-rank. This commit adds support for 0-rank inputs.
PyTorch allows new operators to be registered dynamically in modules.
Torch-mlir already makes it fairly straightforward to add support for
new operators, and this commit just extends that support to allow new
PyTorch ops to come from a external module.
This does *not* allow ops to be dynamically loaded into torch-mlir.
Torch-mlir must still be compiled with support built-in.
Add a `_torch_mlir_custom_op_example` subpackage to `torch_mlir` which
registers an demonstration op. It will not be imported by default when
importing torch_mlir. It's strictly for testing and documentation.
Adds an end-to-end test for the `torch_mlir_custom_op_example::identity` op.
With all these changes, we should now be actively testing PyTorch extension
support with all future patches.
This commit adds lowering of `aten.div.Tensor_mode` op.
This commit also fixes formatting for the test file elementwise.py.
Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
This commit decomposes `aten.baddbmm` op into `aten.bmm`,
`aten.mul.Scalar`, and `aten.add.Tensor` op.
Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
This commit adds the decomposition of `aten.adaptive_avg_pool2d` op into
`aten.avg_pool2d` op. The current decomposition only supports cases where
input size is equal to the output size.
Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
This patch adds support for the torch.linalg.vector_norm op to the torch
dialect, including the necessary shape function. It also extends the
conversion of reduction operators to support lowering of
AtenLinalgVectorNormOp, in addition to adding a handful of end-to-end
tests to validate the lowering.
There exist several opportunities to make this lowering optimal and
robust. For instance, in its current form, the translation does not
support ord = 0, +inf, or -inf. For L1 norms, we don't need to raise
each element to the power 1.0. Similarly, L2 norms could benefit from
strength reduction. Since the canonicalization pass is not able to
apply these optimizations, we should consider applying them during the
linalg lowering itself.
The op `aten.rand_like` was missing a shape function, unit tests, and
the `dtype` argument was being ignored in its decomposition. This
commit fixes all three things.
Sean Silva
Vivek Khandelwal
Ramiro Leal-Cavazos
Abhishek Varma
Daniel Ellis
Shivam Gupta
George Petterson
Xiafei Qiu
Chi_Liu
Prashant Kumar
Abhishek Varma
Jae Hoon (Antonio) Kim
Ashay Rane
Gaurav Shukla
AmosLewis
JakopinA
武家伟
Tanyo Kwok
Quinn Dawkins
Henry Tu
Alex Tsao
nithinsubbiah
Vidush Singhal
powderluv
Quinn Dawkins
Alec
PhaneeshB
Kevin Kiningham
Prateek Gupta
Suraj Sudhir
Maksim Levental
Albert Sandru
Bob Adolf