Commit Graph

10 Commits (756b75fb2d996747483d24c1a6bfa3eadd80ec11)

Author SHA1 Message Date
Prashant Kumar 258660deb6 Add aten.bernoulli decomposition.
aten.bernoulli is decomposed to aten.gtTensor(aten.uniform(x), x).
2022-02-11 00:35:33 +05:30
Prashant Kumar 102c497c4c Add decomposition of _log_softmax op.
Decompose _log_softmax into log(softmax(x)).
2022-02-10 23:17:26 +05:30
Prateek Gupta 318946a650 [TORCH][MLIR] Add E2E support for `aten._unsafe_view` op.
This commit adds decomposition of `aten._unsafe_view` op into
`aten.view` op.

Signed-Off-By: Prateek Gupta<prateek@nod-labs.com>
2022-02-10 22:28:58 +05:30
Prashant Kumar 68acc8696e Modify softmax decomposition to be more numerically stable.
The softmax decomposition is modified according to https://github.com/pytorch/functorch/blob/main/functorch/_src/decompositions.pytorch
to account for numerical stability. Also, modified aten.argmax lowering
to handle negative dimension.
2022-02-03 21:20:36 +05:30
Yi Zhang 5d9a15263a [TORCH] Add aten.std e2e support 2022-01-31 15:17:49 -05:00
Prashant Kumar e58b66bc3b Add lowering of `aten.max.dim` op.
Lowering of `aten.max.dim` op has been added.
2022-01-31 21:41:22 +05:30
Vivek Khandelwal 4486de5ef3 [MLIR][TORCH] Add E2E support for torch.arange op
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>
2021-12-27 22:45:48 +05:30
Yi Zhang 3bd9d2a4c7 Add e2e support for aten._softmax_backward_data.
Decompose aten._softmax_backward_data into aten math ops. Also decompose
`aten.size` to facilitate decomposing _softmax_backward_data.
2021-11-09 13:09:30 +05:30
Yi Zhang 752abc8d01 Add type promotion code to refine types.
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.
2021-10-29 11:17:39 -04:00
Prashant Kumar 5009cbf55c Add lowering of aten.matmul op.
Lowering of `aten.matmul` op is added from torch to linalg dialect.
The different cases correspond to
https://pytorch.org/docs/stable/generated/torch.matmul.html.
TODO: Broadcasting in case of batch-matmul is yet to be taken care of.

Signed-off-by: Prashant Kumar <prashant@nod-labs.com>
2021-10-26 12:45:09 -04:00