- 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>
- The `self` name is being used as a keyword argument to the
`torch.ops.aten.nll_loss_backward` function call, which produces
name-conflict error with the python keyword `self` which is pointer to
the current object.
- This commit fixes this issue by replacing the keyword argument by
positional argument.
Signed-Off-by: Gaurav Shukla <gaurav@nod-labs.com>
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.
* [tosa] Support for AtenNe[Tensor|Scalar]Op, AtenLog2Op,
AtenBitwiseAndTensorOp, AtenSquareOp and AtenThresholdOp
* Fix for Issue #532 - Mixed input types for few ops and updated few
tests to use i32 instead of i64
Signed-off-by: Anup Gangwar <anup.gangwar@arm.com>
Co-authored-by: Anup Gangwar <anup.gangwar@arm.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.
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>
Note that to enable folding of the code coming from an example
like the ConstantPad2dStaticModule e2e test, support for other
operations had to be added/improved:
- aten::neg.int
- aten::eq.float
- aten::eq.str
- prim::Uninitialized
This commit adds lowering of `aten.threshold` op
This commit adds lowering of `aten.threshold_backward` op
Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
This involes the following 2 parts:
- Change refine type to propagate more static shape info.
- Get as much static shape info as possible when creating the result
tensor when converting to linalg.
- This commit adds E2E support for `aten.ones_like` and
`aten.zeros_like` ops.
- Adds support for non-None `dtype` argument of `aten.empty_like` op.
- All the unit test cases related to constant tensor allocation like ops
are moved to a different file named `constant_alloc.py`.
Signed-Off-by: Gaurav Shukla <gaurav@nod-labs.com>
This commit adds lowering of `aten.arange.start_step` op.
This commit decomposes `aten.arange` and `aten.arange.start` into
`aten.arange.start_step` op.
Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
- It folds `aten.to.dtype` when the input tensor type and result type
are exactly same.
- It folds `aten.view` when the rank of both the input tensor type and
result type is unity.
Signed-Off-by: Gaurav Shukla <gaurav@nod-labs.com>
We only handle the expanding OR collapsing cases, we do not handle
expanding And collapsing happening at the same time or cases where
it's neither collapsing nor expanding like view of [2,3] for
3x2 tensor.
It's assumed that if a shape list element is got from
`aten.size(tensor, dim)` the corresponding dim is not splitted or
collapsed. This assumption makes it easier to deal with dynamic shapes.
Gaurav Shukla
Ramiro Leal-Cavazos
Yi Zhang
Anup Gangwar
Prashant Kumar
Prateek Gupta
Anup Gangwar
Suraj Sudhir
Prashant Kumar
Liam Fitzpatrick
Nirvedh
Vivek Khandelwal
xndcn