There are several decompositions that assume the operands of the op
have dtypes available; however, the only time dtypes are guaranteed to
be present is when the graph has reached the backend contract. In
general, every pass that happens before reaching the backend contract
should not assume dtypes are available and should use `hasDtype` to
check first.
This commit adds `hasDtype` checks to every decomposition that uses
dtypes.
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.
-- 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>
Summary of changes:
- LLVM now includes <optional> instead of "llvm/ADT/Optional.h" in most
(although not all) places
(https://reviews.llvm.org/rG541ef3d61e9341cd38420c0dbca9250c4d0ea04c).
This patch replaces the affected instances of `llvm::Optional` with
`std::optional`.
- In the usages of llvm::Optional that remain, llvm::Optional::value()
is deprecated, so this patch replaces them with a dereference.
Functions like `getTypeForScalarType` that do a mapping from one set
of types to another should not fail, and if they do it
should be obvious to the developer that that function has an
unhandled case.
Instead of silently failing when encountering an unsupported type,
this commit adds a `report_fatal_error` at the end, similar to other
type translation functions in this file.
In order to verify if a given IR satisfies the backend contract, the
verifier needs to know if decompositions took place, and if so, which
ops were decomposed and which were not.
This commit adds two arguments to `verifyBackendContractPass` to
specify if decompositions took place and which ops to consider backend
legal, similar to the arguments of `LowerToBackendContractPass`.
Summary of changes:
- Replace `llvm::None` with `std::nullopt`, since the former is deprecated
(https://reviews.llvm.org/D139763)
- Use setter for symbol visibility instead of passing string attribute when
creating FuncOp
* [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.
Currently `getTensorRank` returns -1 if it was unable to get the rank
of the tensor. However, not every use in the codebase was checking the
return value, and in some cases, the return value was casted to
unsigned leading to some infinte loops when an unranked tensor reached
a decomposition.
This commit changes the return of `getTensorRank` to
`Optional<unsigned>` to make it clear to the user that the function
can fail.
This commit also changes a couple of for loops that iterate a vector
in reverse order that can potentially become infinite loops into
range-based for loops.
A circular dependency was introduced in e7edcc62fd.
Specifically, the `makeShapeLLVMCompatible` and `makeShapeTorchCompatible` utilities were being called from `lib/Dialect/Torch/IR/TorchTypes.cpp` and `lib/Dialect/Torch/IR/TorchOps.cpp` defined under the `:TorchMLIRTorchDialect` bazel target, leading it to take a dependency on `:TorchMLIRConversionUtils` which already depends on `:TorchMLIRTorchDialect`, hence creating a circular dependency.
This commit resolves the same by moving said utilities from `lib/Conversion/Utils/Utils.cpp` to `lib/Dialect/Torch/Utils/Utils.cpp`. Please LMK if there's a better way to fix this and I will update the code.
This commit also adds the required targets to support building the new conversions from Torch to ML Program dialect that was introduced in f416953600.
Bazel build GHA triggered manually to verify: https://github.com/sjain-stanford/torch-mlir/actions/runs/3645944517
The current implementation of `DecomposeComplexOps` fails if an op
expected to be decomposed does not get decomposed in the first
iteration of the `createTorchSimplificationPipeline` in
`LowerToBackendContractPass`. However, some graphs require multiple
iterations of `createTorchSimplificationPipeline` to fully propagate
all statically knowable information, such as dtypes and shapes, to the
entire graph, sometimes resulting in the need to run
`DecomposeComplexOps` more than once.
This commit changes `DecomposeComplexOps` to use a greedy algorithm
for pattern application and moves the legalization check of ops to the
`LowerToBackendContractPass` to allow for the `DecomposeComplexOps` to
run more than once.
- 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 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>
Summary of changes:
- Replace call to `MemoryEffectOpInterface::hasNoEffect`
with `isMemoryEffectFree`.
- Make fix for the dynamic dims, since
`kDynamicSize` value changed to
`std::numeric_limits<int64_t>::min()` from `-1` in llvm
- `makeShapeLLVMCompatible` and `makeShapeTorchCompatible`
utilities convert shapes in order to remain consistent
with the Torch and MLIR semantics.
- Update tags
llvm: 147fe9de29dc13c14835127b35280c4d95c8e8ba
mhlo: 1944b5fa6062ec4c065d726c9c5d64f1487ee8c5
Signed-Off By: Vivek Khandelwal<vivek@nod-labs.com>
-- This commit fixes a bug in computeReductionType API.
-- The bug pertains to removal of `dim` from the `sizes` array.
Signed-off-by: Abhishek Varma <abhishek@nod-labs.com>
-- This commit adds decompose logic for `aten._softmax` when
`half_to_float` is `True`.
-- An e2e test case will be added once support for half to float conversion for
`aten._softmax` is added upstream.
Signed-off-by: Abhishek Varma <abhishek@nod-labs.com>
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>
-- 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 renames the patterns used to match on lists of constant
values to `m_TorchListOfConstant{valueType}s`. This is needed to avoid
ambiguity for when `valueType` has `Optional` in it. In particular, it
makes it clear whether the values in the list are optional or the list
itself is optional.
lib/Dialect/Torch/Utils/Utils.cpp includes TorchOps.h, which, by way of
included header files, refers to both TorchOps.h.inc as well as
TorchTypes.h.inc. However, the build rules do not specify the
dependency of the `TorchMLIRTorchUtils` target on the TableGen generated
header files, causing spurious build errors.
This patch fixes the problem by adding `MLIRTorchOpsIncGen` and
`MLIRTorchTypesIncGen` to the list of dependencies of
`TorchMLIRTorchUtils`.
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 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>
Summary of changes:
- Updated references to the Arith dialect
(https://reviews.llvm.org/D134762)
- Switched to prefixed accessors for MemRef dialect
(https://reviews.llvm.org/D134995)
- Fixed warnings about signed/unsigned comparisons, ignored return
values, and unused variables
* 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
The auto-update of the PyTorch version broke the Torch-MLIR build
because it did not update the shape library. Going forward, we should
add the shape library update to the PyTorch version update action.
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>
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.
This PR adds an `AllowedInModuleInitializer` trait to keep track of ops that are permitted in the module initializer. We have a handful of such ops that are produced by the IValue importer, and so this change avoids maintaining a list of ops in `TorchOps.cpp` that could lead to spurious merge conflicts, and help us integrate torch-mlir in our downstream compiler better. Please let me know if you'd prefer a better name for the trait itself. Feedback is welcome!
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!
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
This commit adds decomposition of `aten.linear` op. Due to limited
support at tosa backend in case of dynamic dimensions, this
decomposition is currently disabled for tosa backend.
Signed-Off-by: Gaurav Shukla <gaurav@nod-labs.com>
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.
Ramiro Leal-Cavazos
Srirammaswamy
powderluv
Xiafei Qiu
Ashay Rane
Shivam Gupta
Jiahao Li
Abhishek Varma
Tanyo Kwok
ataheridezfouli-groq
Ahmed S. Taei
Vivek Khandelwal
Sambhav Jain
Gaurav Shukla
George Petterson
Daniel Ellis
Prashant Kumar
Abhishek Varma
Gleb Kazantaev
AmosLewis
武家伟
Sambhav Jain
Sean Silva
Alex Tsao