This commit adds support for type refinement when
`torch.tensor_static_info_cast`s are involved, even when there are
users of the casted tensor that don't allow type refinements.
Originally the canonicalization pattern for
`torch.tensor_static_info_cast` would check if all the users of the
casted tensor allowed type refinements before making any changes. This
means that if at least one of the users did not allow type
refinements, the pattern would fail. This becomes an issue when doing
shape calculations because the calculations need the shape information
of each input tensor to be available before the calculation can be
simplified.
This commit fixes the 2nd and 3rd return types of the `aten.native_layer_norm`.
Previously the mean and rSTD were returned with reduction dims removed.
This commit fixes this and keeps the reduction dims of the results.
Signed-Off-By: Prateek Gupta <prateek@nord-labs.com>
The ODS-generated code included via the `TorchOps.cpp.inc` file takes a
very long time to compile. This PR isolates it into its own file so that
the build system can cache it.
This PR creates a new file `TorchOpsODSGenerated.cpp` just to include
the `TorchOps.cpp.inc` file. Doing so required moving to the "new" way
to define verifiers, since the static `verify` free functions in
TorchOps.cpp weren't accessible from the .inc file after it was moved to
`TorchOpsODSGenerated.cpp`.
On my machine, this drops the build time of TorchOps.cpp (such as when
iterating on a canonicalizer) from >40 seconds to <10 seconds.
10 seconds still isn't great though, but at least it isn't "go get a
coffee" type of waiting.
This commit adds the op `ValsemVariantAtenIndexPutImplOp` that represents
`Aten_IndexPutImpl_Op` without the underscore. This is needed to
make sure that the `ReduceOpVariants` pass turns the in-place op
into an op that takes value tensors as inputs, otherwise the
`MaximizeValueSemantics` pass will not be able to add value
semantics correctly.
This commit also adds the lowering of `ValsemVariantAtenIndexPutImplOp` op.
This commit also updates the `torch.bincount` op test cases.
The term "pseudo" is very vague and was getting confusing (I felt I had
to explain it in every comment referencing it). Instead, rework the
"pseudo" ops to instead be named:
- MLIR Syntax: `torch.valsem.*`
- C++ / ODS: `ValsemVariant*Op`
This makes it clear what the concept is, and avoids confusion with other
things that might be called "pseudo", since these are very specific and
should be 100% consistently named w.r.t. the non-valsem-variant ops that
they correspond to.
This is code that we always want to treat as "foreign" and not get too
comfortable using in many functions. One way to accomplish that is to
make it a bit clunkier to use.
Also, fix Utils.cpp to match the LLVM/MLIR coding conventions (don't
define functions inside namespaces -- prefer `using` and explicit
qualification).
This leads to much more succinct types in many cases:
```
!torch.list<!torch.int>
!torch.list<int>
!torch.tuple<!torch.list<!torch.int>, !torch.list<!torch.int>>
!torch.tuple<list<int>, list<int>>
!torch.optional<!torch.list<!torch.int>>
!torch.optional<list<int>>
!torch.list<list<list<tensor>>>
!torch.list<!torch.list<!torch.list<!torch.tensor>>>
```
I would like to take this further and allow omitting the `!torch.`
prefix in all cases, but that's harder -- for example, we currently use
`FuncOp` for functions, and so I don't think we can customize the
printing there. It seems like it will be a longer road to getting that
level of customization.
See the documentation in `docs/shape_lib.md` and
`docs/adding_a_shape_function.md` for an overview of the system.
This completely overhauls how we represent shape functions. In
particular, RefineTypes does not infer shapes anymore (only dtypes).
Shape functions are now written in (TorchScript'able) Python.
Recommended review order:
1. Read `docs/shape_lib.md` and `docs/adding_a_shape_function.md`.
1. Code and tests for ReifyShapeCalculations, DropShapeCalculations.
1. Code and tests for SimplifyShapeCalculations.
1. shape_lib_gen.py
1. Code and tests for new RefineTypes pass.
1. Random folders/canonicalizers in TorchOps.cpp and associated test in
`canonicalize.mlir`.
1. New ReadOnly trait inferred from the registry.
1. Any miscellaneous remaining stuff.
Example `-print-ir-after-all` for ElementwiseUnaryModule:
[IR lowering dump](https://gist.github.com/silvasean/e4dc8cbc8d00aac7819602e3cbd8e212).
Example `-print-ir-after-all` for ElementwiseBinaryModule:
[IR lowering dump](https://gist.github.com/silvasean/daf6860ecced732af3568af6b1899113).
This commit replaces the two rewrite patterns of
maximize-value-semantics with a single pattern that captures the
behavior of both as well as other edge cases previously not
supported. The new pattern works by first performing alias analysis on
a subgraph to see if pattern is applicable, then rewriting all
non-value tensors to value tensors in a single go.
This pass is added to lower ops, which can not be lowered
via the TorchToLinalg pass, such as `torch.bincount` op.
This pass also uses torch-mlir's TMTensor Dialect to lower the
complex ops.
Also add torch.bincount op lowering with the help of TMTensor dialect
Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
- This commit adds E2E support for `aten.rand_like` and
`aten.bernoulli_.Tensor` ops.
- The `aten.bernoulli(x)` was implemented as:
`aten.bernoulli(x) = rand_like(x) < 0.5`, assuming 0.5 as default
probability, whereas according to the pytorch documentation:
https://pytorch.org/docs/stable/generated/torch.bernoulli.html#torch.bernoulli
the input x in `aten.bernoulli(x)` is itself a tensor containing
probabilities to be used for drawing the binary random number.
- So this commit fixes the `aten.bernoulli(x)` implementation as:
`aten.bernoulli(x) = rand_like(x) < x`.
- It also fixes the case where the input to `aten.bernoulli_.float` is
an integer tensor. In this case the input must be casted to float type
before passing it as operand to `aten.rand_like` op.
`aten.bernoulli_.float(x, p) = rand_like(float(x)) < p`.
Signed-Off-by: Gaurav Shukla <gaurav@nod-labs.com>
This commit adds the invariant to the op `torch.overwrite.tensor.contents` that
both of its operands have the same shape and size. In order to
maintain the invariant, special handling of this op is added to the
`RefineTypes` pass.
This commit adds handling to the `maximize-value-semantics` pass for
the case where a view-like op depends on a tensor that has been
overwritten by a value tensor. The approach for removing the
dependency is to change the input to the view-like op to be a copy of
the value tensor that is being used to overwrite.
This commit also removes `AtenFill_ScalarOp` and
`AtenBernoulli_FloatOp` from the list of view-like ops, since these
ops now have a corresponding op with value semantics into which they
get converted in the `reduce-op-variants` pass.
- This commit decomposes the `aten.batch_norm` op into the
`aten.native_batch_norm` op, instead of lowering it to the
`linalg.generic` op.
- It also adds run-time asserts in the `aten.native_batch_norm` lowering
to make sure that the shape of the weight, bias, running_mean, and
running_var must match the num of features.
- Since the `aten.native_batch_norm` op is not supported at TOSA backend,
all the modules that are dependent on the `aten.native_batch_norm` op
will fail and therefore they should be removed from the TOSA `passing`
set.
- It also moves `checkNotNone` to utility.
Signed-Off-by: Gaurav Shukla <gaurav@nod-labs.com>
This commit adds the op `PseudoAtenFillScalarOp` that represents
`AtenFill_ScalarOp` without the underscore. The approach is the same
as in commit dd998fa4d4.
Adding this op allows for a simpler and more consistent version of the
`empty` and `empty_like` op e2e tests.
- This commit adds lowering of `aten.le.Scalar` and `aten.ge.Scalar` ops
as a part of `convert-torch-to-linalg` pass.
- It also creates a new test script `elementwise_comparison.py` for all
element-wise comparison ops.
Signed-Off-by: Gaurav Shukla <gaurav@nod-labs.com>
This commit adds the op `PseudoAtenBernoulliFloatOp` that represents
`AtenBernoulli_FloatOp` without the underscore. This is needed to make
sure that the `ReduceOpVariants` pass turns the in-place op into an op
that takes value tensors as inputs, otherwise the
`MaximizeValueSemantics` pass will not be able to add value semantics
correctly.
- This commit adds lowering of `aten.Bool.Tensor` and
`aten.Float.Tensor` op as a part of `convert-torch-to-linalg` pass.
- It also adds support for returning bool types.
- It also fixes lowering of the `aten.Int.Tensor` op for non-zero rank
input tensors.
- If a scalar number is converted to a 0-d tensor and passed on to the
`aten.Float.Tensor` op, it folds to the scalar number.
Signed-Off-by: Gaurav Shukla <gaurav@nod-labs.com>
This commit fixes an error in the refine types pass of constant
allocation ops. The function used to set the dtype,
`fillInDtypeGivenDtypeAndDataType`, takes two torch types as arguments,
but a torch type and a standard MLIR type were being passed into it.
This commit also fixes the way the dtype was calculated in
`visitAtenToDtypeOp`. This op was also passing a standard MLIR type as
an argument to the `fillInDtypeGivenDtypeAndDataType`
function. Moreover, since the op `aten.to.dtype` has the dtype
argument as not optional, all that is needed is to match
against the int value to extract the dtype.
- This commit adds support for `aten.native_batch_norm` operation.
- The current implementation only supports inference mode of
`aten.native_batch_norm` op.
Signed-Off-By: Gaurav Shukla <gaurav@nod-labs.com>
The lowering of aten::nll_loss_backward op has been added
from torch to linalg dialect. The changes has been made as
a part of -torch-convert-to-linalg pass.
Signed-off-by: Prashant Kumar prashant@nod-labs.com
This PR include the following pieces:
- Add torch `Generator` type. `Generator` type is converted to i64 in
refbackend type converter.
- Add seed managment support for the default global generator.
`torch_c.getNextSeed` op is used to get the seed. On refbackend, the
`torch_c.getNextSeed` is lowered to load/store from [0] of global
variable `default_generator` memref<i64> in `InsertRngGlobals` pass.
- Add `aten.uniform_` and testing as an example op for RNG ops. Add
`torch.pseudo.aten.uniform` op. It has the same operands and return as
the `aten.uniform_` from the op registry except for value semantics.
Vigilans
Ramiro Leal-Cavazos
Prateek Gupta
Sean Silva
Vivek Khandelwal
Gaurav Shukla
Yi Zhang
Prashant Kumar
Nirvedh
Suraj Sudhir
Prashant Kumar