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 fixes an issue with the way that the
`maximize-value-semantics` pass was handling the
`torch.overwrite.tensor` op. Before, the overwrite op would simply get
deleted and uses of the overwritten tensor would simply get replaced
with the overwriter tensor. This becomes a problem when one tensor has
a dynamic shape and the other has a static shape because ops that were
previously using the overwritten tensor will now get a tensor of a
different type.
To fix this, this commit adds a static cast to the overwriter tensor
so that it has the type of the overwritten tensor, and this casted
tensor is then used to replace the uses of the overwritten tensor.
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.
Some of the lowerings use the result type obtained from the op itself
to tell the `linalg::GenericOp` what the type of the result should be
rather than using the type of the result tensor given to the
`linalg::GenericOp`. This becomes a problem when the result type of
the op has static size information and the result tensor used in
`linalg::GenericOp` has dynamic dimensions, for `linalg::GenericOp`
expects the result type to be equal to the type of the output tensor.
This commit replaces the use of the result type from the op itself
with the type of the result tensor passed to `linalg::GenericOp`.
In order to not create too many dynamic/static versions of the same
e2e test, e2e tests have only been added to the ops that currently
fail when used with static sizes.
This commit adds support for `aten.native_layer_norm_backward` operation.
It also adds support for matching constant bools stored in a boolean
list.
Signed-Off-By: Prateek Gupta <prateek@nod-labs.com>
This commit fixs the lowering of `aten.native_layer_norm` operation.
Previously this operation was not calculating correct shapes for
mean and inverted STD. This has been corrected in this commit.
Some new helper functions are added to calculate the inverted STD
and to broadcast a given input with the help of a broadcast mask.
Signed-Off-By: Prateek Gupta <prateek@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 `aten.assert` op in the Torch dialect.
- The `aten.assert` op is lowered to `mlir::Assert` op.
Signed-Off-by: Gaurav Shukla <gaurav@nod-labs.com>
- 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.
The added e2e maxpool testcase from #545 was not getting a static shape
due to an unfolded prim.If when RefineTypes was called. This was because
of unfolded torch.iaten.__is__ and torch.prim.unchecked_cast operators
with torch.derefine operands.
* [tosa] Support for AtenCeilOp and AtenReciprocalOp
* [tosa] Support for comparator ops, Aten[Gt|Lt|Eq][Tensor|Scalar]Op with scalar constant
* [tosa] Support for Scalar variants of Aten[Mul|Div|Add|Sub] Ops with scalar constants
Signed-off-by: Anup Gangwar <anup.gangwar@arm.com>
Co-authored-by: Anup Gangwar <anup.gangwar@arm.com>
- Common code as TF repository, being moved to MLIR core.
- Will support further legalizations to be published.
Signed-off-by: Suraj Sudhir <suraj.sudhir@arm.com>
This commit adds the aten ops which do not require random number
support to aten dialect. This commit also adds some of the missing
torch types.
Signed-Off-By: Prateek Gupta <prateek@nod-labs.com>
Ramiro Leal-Cavazos
Prashant Kumar
Prateek Gupta
Yi Zhang
Gaurav Shukla
Anup Gangwar
Suraj Sudhir
Prashant Kumar
Anup Gangwar
Liam Fitzpatrick
stephenneuendorffer
Nirvedh
Sean Silva
Vivek Khandelwal
dan