The current implementation of `getScalarValue` does not check that the
input to a `ValueTensorLiteralOp` is an i64 before extracting the
value, and it does not check that the result type of the
`PrimNumToTensorScalarOp` is also an i64. This leads to crashes or
invalid IR generated when the `input` is something other than an i64
tensor or `!torch.int`.
This commit addresses those issues. In addition, the function
`getScalarValue` is renamed to `getScalarIntValue` to make it clear
that it *only* extracts scalar integers.
Set PyTorch and TorchVision version to nightly release 2023-02-27.
This commit also adds the lowering for aten.add and aten.Float.Scalar op.
Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
Random tensors used in e2e tests should be created using the
`TestUtils` object passed to the registered test case to ensure that
the compiled module and the golden trace receive the same tensors as
input. This commit changes all the cases of `torch.rand` and
`torch.randn` to use the `TestUtils` instead.
This patch replaces all MHLO operations with their StableHLO
counterparts and adds a validation pass to ensure that no MHLO operations
remain before translating all Stablehlo operations to the MHLO dialect
for further lowering to the Linalg dialect.
This patch also updates all lit tests so that they refer to the
`convert-torch-to-stablehlo` pass and so that they check for StableHLO
operations.
Week of 01/30/2023:
Green LLVM commit: e31ee6417c33a6e2f0e8440b1a86d5365279ad68
Green MHLO commit: c2a6f4064d426567b9ef7b0d29d5ab86dc7b2b02 (branch greencommit/2023-01-30-e31ee641)
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.
-- The dtype of the result of `aten.embedding` should match that of
the `weight` operand's (operand[0]) instead of hardcoding to f32.
-- This commit aims to provide a fix for the same.
Signed-off-by: Abhishek Varma <abhishek@nod-labs.com>
This was an experimental attempt at rolling out own op-by-op executor
with `__torch_dispatch__`, but it proved difficult to make it robust.
Op-by-op execution is very easy to implement robustly now with the
PyTorch 2.0 stack, so we don't need eager_mode.
Downstream users were using eager_mode to implement lockstep numerical
accuracy debuggers. We implemented the same functionality with
TorchDynamo in https://github.com/llvm/torch-mlir/pull/1681 so now there
is not much reason to continue maintaining it.
This gives some decent improvements to memory consumption and latency of
testing. I would have expected buffer-deallocation to actually make a
big difference to the final process RSS but it doesn't appear to. Also
running buffer-deallocation later in the pipeline results in
miscompiles. I didn't have the time or interest to dig in deeper, but
something is off.
(numbers below are taken from a single run, but I did do a few runs to make
sure that the variance wasn't that great)
- Linalg-on-Tensors shows memory consumption improvements and some slight speedups.
```
./tools/e2e_test.sh -s -v -c refbackend
fuse=0 dealloc=0
RSS: 3071.33 MB
real 3m58.204s
user 6m22.299s
sys 0m51.235s
fuse=1 dealloc=0
RSS: 2515.89 MB
real 3m34.797s
user 5m56.902s
sys 0m44.933s
fuse=1 dealloc=post-bufferize:
RSS: 2290.25 MB
real 3m42.242s
user 6m0.560s
sys 0m46.335s
```
- TOSA ResNet18 gets significantly faster and uses significantly less memory.
```
time ./tools/e2e_test.sh -s -v -c tosa -f ResNet18
fuse=0 dealloc=0
rss 1328.56 MB
real 0m50.303s
user 0m55.355s
sys 0m12.260s
fuse=1 dealloc=0
rss 859MB
real 0m30.454s
user 0m35.551s
sys 0m11.879s
fuse=1 dealloc=post-bufferize:
rss 851MB
real 0m30.313s
user 0m39.889s
sys 0m11.941s
```
Big thanks to Ramiro for the methodology here for measuring the RSS with
`psutil`:
https://gist.github.com/ramiro050/5b5c2501f7389c008d9029210772c3a8
- Support for non-prefixed accessors has been removed. See:
https://reviews.llvm.org/D136727
- Rename `operands` to `methodOperands` in `prim.CallMethod` since the
name `operands` overlaps with a builtin method name. See:
https://reviews.llvm.org/D136727
- Add passes in refbackend to lower memref.subview. See:
https://reviews.llvm.org/D136377
- Replace `CopyToValueTensorOps` first in `RewriteViewLikeSubgraph` in
maximize-value-semantics.
The current implementation of the `RewriteViewLikeSubgraph` pass in
maximize-value-semantics creates temporarily invalid IR. In
particular, given a forward slice starting from a
`CopyToNonValueTensorOp` and ending in `CopyToValueTensorOp`s, the
pass first replaces all uses of the `CopyToNonValueTensorOp` with
its operand, which results in all the `CopyToValueTensorOp` users
having their operand have type `!torch.vtensor`, which is invalid.
The correct way to do things is to first replace all the
`CopyToValueTensorOp`s with their operand, and then replace all uses
of the `CopyToNonValueTensorOp` with its operand.
This only started failing now because the generated accessor
`getOperand` for the `CopyToValueTensorOp` now returns a
`TypedValue<NonValueTensorType>`, which has an assert checking that
the value returned is of the expected type.
This is a minor variation on our other resnet18 examples swapping in
TorchDynamo.
We replicate the refbackend_torchdynamo_backend out of the e2e test
config to avoid making that appear like a public API.
Also, some minor cleanups to TorchDynamoTestConfig.
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>
Yuanqiang Liu
Jiahao Li
gpetters94
Ziheng Jiang
Zhekun Zhang
Priya Savithiri
Ramiro Leal-Cavazos
Vivek Khandelwal
Zachary Cetinic
Chi_Liu
Ashay Rane
Sean Silva
Matthias Gehre
Gleb Kazantaev
Srirammaswamy
Shivam Gupta
Chi_Liu
Abhishek Varma
Prashant Kumar
Daniel Ellis
Xiafei Qiu
Abhishek Varma
Jae Hoon (Antonio) Kim
Gaurav Shukla
AmosLewis