Commit Graph

14 Commits (3b3cb99ef88d0e6430f21764c35afd82902acbd5)

Author SHA1 Message Date
Tanyo Kwok 290d7755fb
importer: add initial support for loading Float16 tensors (#1169)
follow up #761:

    This patch updates the `torch_mlir::convertTensorToMlirElementsAttr()`
    method to enable the creation of tensors whose base type is Float16.
    This patch also adds a test to validate the IR generation, and it
    updates the test for importing tensors of various types.
2022-08-08 12:37:31 +08:00
Yi Zhang 28be6511d2 Fix type promotion code for scalar only operations
Fix the type promotion code for scalar only operation to return
TorchType which is the type tracked in ValueKnowledge.scalarType.

- Fix `getPromotedResultScalarType` to return Torch type.
- Add `getBuiltInTypeForTorchScalar` helper to convert scalar type
to builtin type before passing to the next level type promotion
helper `updateResultTypeState`.
- Add `setScalarType` helper to make setting ValueKnowledge.scalarType
  easier.
2022-05-07 10:37:21 -04:00
Ashay Rane 809f240f01
importer: add initial support for loading BFloat16 tensors (#761)
This patch updates the `torch_mlir::convertTensorToMlirElementsAttr()`
method to enable the creation of tensors whose base type is BFloat16.
This patch also adds a test to validate the IR generation, and it
updates the test for importing tensors of various types.
2022-04-29 09:01:49 -07:00
Maksim Levental 24f9de7120
Fixes https://github.com/llvm/torch-mlir/issues/751 where `torch.bool` is parsed as signless `i1`. (#752) 2022-04-13 12:28:27 -05:00
Ramiro Leal-Cavazos 51d4d55f8a
Add support for multi-dim input to `index_put_impl` (#722)
This commit adds support for multi-dimensional tensors as input to the
`_index_put_impl_` op. The support was to some degree already there,
since `ScatterOp` already supports multi-dimensional tensors. This
commit also adds a bit more error checking to `index_put` and
refactors the code for creating `ScatterOp`s to mimic the way one
would make a `Linalg::GenericOp`.
2022-03-31 09:27:21 -07:00
Vivek Khandelwal 2597c481f6 [MLIR][TORCH] Add E2E support for aten.new_empty op
This commit decomposes `aten.new_empty` op into `aten.empty.memory_format` op.

This commit also made a dtype fix to the constant tensor allocation like ops.
Earlier the dtype for the result was inferred from the result type; now, it's
being evaluated as per the original definition of the op.

Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
2022-03-30 13:21:01 +05:30
Liam Fitzpatrick f2269ced80
Improve list index normalization SimplifyShapeCalculations. (#710)
The reified code to compute the shape of torch.aten.constant_pad_nd
uses negative indices when setting list elements. This was not
converted to a positive offset in one place in SimplifyShapeCalculations
which prevented computation of the static shape.
2022-03-29 22:21:47 +02:00
Sean Silva 7ea50a537a Avoid `using` the `torch_upstream` namespace.
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).
2022-03-15 17:24:17 -07:00
Vivek Khandelwal 1a2a9e066f [MLIR][TORCH] Add TorchToTMTensor pass
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>
2022-03-08 22:52:34 +05:30
Gaurav Shukla 056cd2078d Revert "[LINALG] Decompose `aten.batch_norm` into `aten.native_batch_norm`"
This reverts commit 442ff4605c.
2022-02-25 15:46:55 +05:30
Gaurav Shukla 442ff4605c [LINALG] Decompose `aten.batch_norm` into `aten.native_batch_norm`
- 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>
2022-02-16 23:41:38 +05:30
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
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
Yi Zhang a459e09ab7 E2e support for aten.softmax.int and aten.embedding
- Added a DecomposeComplexOps pass to decompose complex torchOps.
- Refactored `visitAtenArgmaxOp` and `visitAtenAnyDimOp` to
`visitReductionAlongDimIntOp`.
- Moved some helper functions into
torch-mlir/Dialect/Torch/Utils/Utils.h to be shared by multiple files.
- Added support for f64 tensor as argument and return types.
2021-10-18 17:57:45 -04:00