This commit does a couple of things. First, it fixes a bug in the
`linalg.generic` body of the `nll_loss_forward` lowering where the
`ignoreIndex` was being compared with the loop index rather than the
current element of the `target` tensor. This was not being caught by
the tests because they were not testing the case where `ingnoreIndex`
actually corresponds to a value in `target`. This has been fixed.
Second, this commit adds support for the `reduction` argument in
`torch.nll_loss_forward` as well as support for 1-D inputs. In order
to simplify the lowering code, I've refactored the code that creates
the `linalg.generic` ops for elementwise and reduction ops into static
functions, to avoid having boilerplate code for indexing maps, etc
that can be very error prone.
Note: The function `convertScalarToDtype` was moved to before all the
conversion patterns, but nothing in it was modified.
- 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 lowering of `aten.eq.int` op as a part of
`convert-torch-to-std` pass.
- It also refactors the code for binary comparison ops lowering.
Signed-Off-by: Gaurav Shukla <gaurav@nod-labs.com>
- 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>
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 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>
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.
* [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>
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 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>
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.
- Added E2E support for `aten.eq.Tensor` and `aten.lt.Tensor` ops. Both
the operands are expected to be of the same type, i.e., type promotion
is not addressed as a part of this commit.
- Added E2E support for `aten.eq.Scalar` and `aten.lt.Scalar` ops.
Tensor operand type to Scalar operand type promotion has not been
handled in this commit.
Signed-Off-by: Gaurav Shukla <gaurav@nod-labs.com>
The existing implementation of `ConvertConstantTensorAllocOp<>` requires
a C++17 feature `if constexpr ()`. This commit removes the use of that
feature to support the implementation even for lower C++ versions.
Signed-Off-by: Gaurav Shukla <gaurav@nod-labs.com>
Add the required lowerings and correct test cases.
These op produce zero-d tensors and it was incorrectly mentioned in
refine types to produce 1d tensor of size 1.
- Templatize `aten.zeros` and `aten.ones` ops lowering.
- Add E2E support for `aten.empty` op.
- Add Integer type support in `aten.mul.Scalar` op lowering.
Signed-Off-by: Gaurav Shukla <gaurav@nod-labs.com>
`aten.gt.Tensor` op has been added in torch dialect and the
lowering of the op has been done to the linalg dialect.
Signed-off-by: Prashant Kumar <prashant@nod-labs.com>
This commit adds support for aten.native_layer_norm operation. Here
the previous code for aten.layer_norm is tweaked a little bit to
accomodate both mean and variance values alongwith the layer norm
value. This commit also adds decomposition of aten.layer_norm into
aten.native_layer_norm, which was previously getting lowered directly
to linalg.
Signed-Off-By: Prateek Gupta<prateek@nod-labs.com>
This commit adds lowering of `aten.squeeze.dim` op into
`linalg.TensorCollapseShape` op. Here, the dim(th) dimension of the
input tensor is not supposed to be dynamic.
Signed-Off-by: Gaurav Shukla <gaurav@nod-labs.com>
This commit adds lowering of `aten.gt.Scalar` and `aten.where.self` as a
part of element-wise ops lowering.
Signed-Off-by: Gaurav Shukla <gaurav@nod-labs.com>
The op lowering has been added as a part of `torch-lower-to-linalg`
pass. This takes care of ignore_index but the weight and reduction
operand is still to be accounted for.
Signed-off-by: Prashant Kumar <prashant@nod-labs.com>
- Supports variants with multiple dims, one dim, all dime
- Leverages legalize_common and legalize_utils code from
TensorFlow-TOSA work
Signed-off-by: Suraj Sudhir <suraj.sudhir@arm.com>
Many reduction ops take as an argument an optional output dtype that
can change the type of the input tensor before the reduction is
performed. This commit adds support for the optional dtype flag that
had been previously ignored.
Test:
/tools/torchscript_e2e_test.sh -f 'ReduceSumDtype'
/tools/torchscript_e2e_test.sh -f 'ReduceSumDImIntListDtype'
This commit adds lowering of `aten.Squeeze` op into
`linalg.TensorCollapseShape` op. The size 1 dynamic dimensions are not
handled as a part of this commit.
Signed-Off-by: Gaurav Shukla <gaurav@nod-labs.com>
The lowering of aten.fill.Scalar has been added.
The changes have been made as a part of -torch-convert-to-linalg pass.
Signed-off-by: Prashant Kumar <prashant@nod-labs.com>
This commit adds lowering of `aten.mul.Scalar` and also adds
decomposition of `aten.addmm` to `aten.mul.Scalar`, `aten.add.Tensor`
and `aten.mm` ops.
Signed-Off-by: Gaurav Shukla <gaurav@nod-labs.com>
Now, aten::linear supports rank 3 inputs. This is a fix
for upcoming bert-inference task. The correct way should be
to support broadcasting in `aten.matmul` op and decompose
`aten.linear` into right ops.
This commit adds new operation `aten.gelu_backward` in the aten
dialect and adds lowering of this operation from aten to linalg.
Signed-Off-By: Prateek Gupta <prateek@nod-labs.com>
- Remove use of conversion construction macros
- Add mul and div op conversions
- Add corresponding tests
Signed-off-by: Suraj Sudhir <suraj.sudhir@arm.com>
This is to facilitate scalar type conversion in the TorchToLinalg. As
part of adding the helper, this PR also:
- Updated `AtenAddTensorOp`, `AtenSubTensorOp` to use the helpers to
support more type variants.
- Added e2e type promotion testing.
- Added i32 memref return/arg type to support e2e testing.
The lowering of `aten.Int.Tensor` op has been added.
The changes has been made as a part of `convert-torch-to-linalg` pass.
Signed-off-by: Prashant Kumar <prashant@nod-labs.com>
- This commit adds lowering of `aten.View` to `linalg.TensorExpandShape`.
- This lowering will be successful only when one or more static
dimensions are expanded.
- It also fixes a typo in `ConvertAtenFlattenUsingIntsOp` conversion
pattern.
Signed-Off-by: Gaurav Shukla <gaurav@nod-labs.com>
The types have different levels of categories: where
complex > floating > integral > boolean (> means left hand
side has higher category).
The operands have different levels of priorities where:
dimensioned tensor > 0-dim tensor > scalar == wrapped 0-dim tensor.
This is represented by the `ResultTypeState.dimResult`,
`ResultTypeState.zeroResult` and `ResultTypeState..wrappedResult` in
the source code.
For operands of the same priorities, the result type should be the
highest categories with sufficient width to hold all operands.
By default, only the highest priority operands participate in the type
promotion logic. Lower priority operands participate if they are in
a higher category than any higher priority operands.
For example, <[],f32> (lower priority) and <[1], si64> tensor would
result in <[?],f32> tensor because floating > integeral. Another example
<[],f64> (lower priority) and <[1], f32> tensor would result in
<[?], f32> tensor because f32 and f64 are the same category.
The ScalarType enum definition, type promotion table, ResultTypeState
struct definition and some helpers are copied from
aten/src/ATen/native/TypeProperties.*
Other references:
- https://pytorch.org/docs/stable/tensor_attributes.html#type-promotion-doc
- https://github.com/pytorch/pytorch/issues/9515
Other minor changes:
1. Fix `visitExpandLikeOp` to consider cases where the given sizes list
size is larger than the input rank.
2. Add back the somehow deleted `torch.aten.softmax.int` tests in
decompose-complex-ops.mlir.
Ramiro Leal-Cavazos
Gaurav Shukla
Prashant Kumar
Nirvedh
Anup Gangwar
Yi Zhang
Anup Gangwar
Suraj Sudhir
Prashant Kumar
stephenneuendorffer
Vivek Khandelwal
dan
Liam Fitzpatrick
xndcn
Daniel Garvey
Prateek Gupta
Suraj Sudhir
ds1231h
George Petterson
Wang Kangyu
nodlabs