Commit Graph

186 Commits (b76c8c82dc9aaaeeb9f2e26e339c6879a9672133)

Author SHA1 Message Date
Maksim Levental cec5aeedb0
add ci tests (#754) 2022-05-25 14:59:59 -05:00
Yi Zhang ec0e9e0bc7 Add -s flag to run e2e tests sequentially
A user might want to avoid the extra layer of multiprocessing libary for
debugging purpose. In such cases, the -s flag can be used to force
sequential execution.
2022-05-11 21:16:41 -04:00
Prashant Kumar 33c9d256ea [REFBACKEND] Add support for returning multiple different return types.
Added the dynamic registration of return function to the execution
engine. This makes sure that  different/multiple return types are supported.
Also, updated the .style.yapf indentation to 4.
2022-04-21 09:02:30 +05:30
Sean Silva 3b5310d6d2 Move COMMON_TORCH_MLIR_LOWERING_XFAILS into test_suite
That way, downstreams don't have to duplicate this list.

Also, remove "external config" feature, since it is subsumed by just
importing the test suite.
2022-04-19 14:32:58 -07:00
Vivek Khandelwal 769f3a8870 [MLIR][TORCH] Add E2E support for max_pool2d_with_indices op
This commit adds lowering of `max_pool2d_with_indices` op.

Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
2022-04-18 21:05:19 +05:30
Ashay Rane d3c08376af
test: add end-to-end test for aten.neg (#760) 2022-04-15 12:37:57 -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
gpetters94 9ec0683e92
Add 2D case for convolution (#693) 2022-04-08 00:47:57 -04:00
Prashant Kumar fb8cb0c5f3 [LINALG] Add the lowering of `aten.ne.Scalar` op
The lowering of `aten.ne.Scalar` op has been added to
the linalg backend.
2022-04-05 21:07:28 +05:30
Anup Gangwar ccf924d3df
tosa] Support for Aten[Gelu|GeluBackward] ops (#720)
Signed-off-by: Anup Gangwar <anup.gangwar@arm.com>

Co-authored-by: Anup Gangwar <anup.gangwar@arm.com>
2022-03-30 17:00:55 -07:00
Sean Silva 0378c75b35 Centralize all test serialization logic. 2022-03-28 10:17:13 -07:00
Anup Gangwar 5d7a6c2976
[tosa] Support for Aten[Unsqueeze|Contiguous|Dropout|Reshape|View] ops (#700) 2022-03-25 14:15:07 -07:00
Sean Silva 6b637a9fd9 Move e2e test definitions into the `torch_mlir_e2e_test` package
This is the first step to making the e2e framework convenient to use
by downstream backends.
2022-03-25 13:56:41 -07:00
Vivek Khandelwal 88c216da13 [MLIR][TORCH] Add support for same input and output shapes for view op
This commit adds support for the cases of view op where the rank and
the shapes of the input and result are equal.

Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
2022-03-25 22:26:10 +05:30
Gaurav Shukla 02b6d04eb4 [LINALG] Add E2E support for `aten.zero_` op
This commit adds decomposition of `aten.zero_` op.

Signed-Off-by: Gaurav Shukla <gaurav@nod-labs.com>
2022-03-25 12:46:50 +05:30
Qiang Fu f7c7bb800c
Add non-default dtype support for a few elementwise math ops. (#687)
* fix type inference
* fix Torch2Linalg conversion
* add test cases
2022-03-23 13:35:43 -07:00
max fe8ac57e6d This PR implements an eager mode backend for PyTorch through the torch-mlir framework. This is accomplished by overriding the `__torch_dispatch__` class method on wrapper subclass `TorchMLIRTensor(torch.Tensor)`.
Effectively, this mode works by compiling op by op as the NN is eagerly executed by PyTorch. Entailed in that compilation is building a representation of the op that can be `torch.jit.script`ed, importing using `ModuleBuilder`, and then executing (e.g., with `RefBackendLinalgOnTensorsBackend`). This mode includes a fallback to conventional PyTorch if anything in the torch-mlir compilation process fails (e.g., unsupported op).

Currently, all e2e tests pass execpt for two that involve an upstream PyTorch bug (https://github.com/pytorch/pytorch/issues/74400).

High priority next steps:

1. A compile cache in order to speed up reruns of the same NN.
2. Integration with IREE (though not in this repo).
3. Integration with `torch.distributed`.
2022-03-22 14:42:57 -07:00
Gaurav Shukla 7c3ba25238 [LINALG] Add decomposition of `aten.dropout` op
- This commit adds decomposition of `aten.dropout` op. It also covers the
  training mode of the same op.
- It also adds lowering of `aten.sub.float` op.

Signed-Off-by: Gaurav Shukla <gaurav@nod-labs.com>
2022-03-22 13:14:49 +05:30
Vivek Khandelwal 5b9bdfaf3f [MLIR][TORCH] Add E2E support for aten._to_copy op
This commit decomposes `aten._to_copy` op into
`valsem.aten.copy` op.

Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
2022-03-21 19:12:37 +05:30
Vivek Khandelwal 13383b03b8 [MLIR][TORCH] Add value tensor variant to aten::copy_ op
This commit adds the op `ValsemVariantAtenCopyOp` that represents
`AtenCopy_Op` 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.

This commit also adds the lowering of `ValsemVariantAtenCopyOp`.

Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
2022-03-21 19:12:37 +05:30
Vivek Khandelwal 4c0cd5c23d [MLIR][TORCH] Add E2E support for aten.expand_as op
This commit decomposes `aten.expand_as` op into `aten.broadcast_to` op.

Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
2022-03-21 12:47:39 +05:30
Prateek Gupta 7256c9e395 [TORCH][MLIR] Fix the return types of `aten.native_layer_norm`.
This commit fixes the 2nd and 3rd return types of the `aten.native_layer_norm`.
Previously the mean and rSTD were returned with reduction dims removed.
This commit fixes this and keeps the reduction dims of the results.

Signed-Off-By: Prateek Gupta <prateek@nord-labs.com>
2022-03-17 12:08:32 +05:30
Vivek Khandelwal 8da7d90611 [MLIR][TORCH] Add E2E support for aten.index_put op
This commit decomposes `aten.index_put` op into
`valsem.aten.index_put_impl` op.

Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
2022-03-16 22:02:02 +05:30
Vivek Khandelwal 3d95c3d6c9 [MLIR][TORCH] Add value tensor variant to aten::_index_put_impl_
This commit adds the op `ValsemVariantAtenIndexPutImplOp` that represents
`Aten_IndexPutImpl_Op` 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.

This commit also adds the lowering of `ValsemVariantAtenIndexPutImplOp` op.

This commit also updates the `torch.bincount` op test cases.
2022-03-16 22:02:02 +05:30
Sean Silva a5fe0cf063 Introduce new shape library design.
See the documentation in `docs/shape_lib.md` and
`docs/adding_a_shape_function.md` for an overview of the system.

This completely overhauls how we represent shape functions. In
particular, RefineTypes does not infer shapes anymore (only dtypes).
Shape functions are now written in (TorchScript'able) Python.

Recommended review order:

1. Read `docs/shape_lib.md` and `docs/adding_a_shape_function.md`.
1. Code and tests for ReifyShapeCalculations, DropShapeCalculations.
1. Code and tests for SimplifyShapeCalculations.
1. shape_lib_gen.py
1. Code and tests for new RefineTypes pass.
1. Random folders/canonicalizers in TorchOps.cpp and associated test in
   `canonicalize.mlir`.
1. New ReadOnly trait inferred from the registry.
1. Any miscellaneous remaining stuff.

Example `-print-ir-after-all` for ElementwiseUnaryModule:
[IR lowering dump](https://gist.github.com/silvasean/e4dc8cbc8d00aac7819602e3cbd8e212).

Example `-print-ir-after-all` for ElementwiseBinaryModule:
[IR lowering dump](https://gist.github.com/silvasean/daf6860ecced732af3568af6b1899113).
2022-03-15 12:41:58 -07:00
Prateek Gupta 3d9ba5e525 [MLIR][TORCH] Add E2E support for aten.erf op.
Signed-Off-By: Prateek Gupta <prateek@nod-labs.com>
2022-03-09 22:22:03 +05:30
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
Vivek Khandelwal bf463d1f36 [MLIR][TORCH]Add support for integer-type inputs for sum and max op
This commit adds support for integer type inputs for
`AtenMaxOp`, `AtenSumOp`, `AtenSumDimIntListOp`.

Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
2022-03-08 22:52:34 +05:30
Gaurav Shukla e57d3f9774 [LINALG] Fix `aten.bernoulli` op lowering
- This commit adds E2E support for `aten.rand_like` and
  `aten.bernoulli_.Tensor` ops.
- The `aten.bernoulli(x)` was implemented as:
  `aten.bernoulli(x) = rand_like(x) < 0.5`, assuming 0.5 as default
  probability, whereas according to the pytorch documentation:
  https://pytorch.org/docs/stable/generated/torch.bernoulli.html#torch.bernoulli
  the input x in `aten.bernoulli(x)` is itself a tensor containing
  probabilities to be used for drawing the binary random number.
- So this commit fixes the `aten.bernoulli(x)` implementation as:
  `aten.bernoulli(x) = rand_like(x) < x`.
- It also fixes the case where the input to `aten.bernoulli_.float` is
  an integer tensor. In this case the input must be casted to float type
  before passing it as operand to `aten.rand_like` op.
  `aten.bernoulli_.float(x, p) = rand_like(float(x)) < p`.

Signed-Off-by: Gaurav Shukla <gaurav@nod-labs.com>
2022-03-05 09:38:22 +05:30
Vivek Khandelwal af551bd9cd [MLIR][TORCH] Add E2E support for aten.full_like op
This commit decomposes `aten.full_like` op into `aten.empty_like`
and `aten.fill` op.

Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
2022-03-04 21:58:23 +05:30
Vivek Khandelwal d61ae92eee [MLIR][TORCH] Add E2E support for aten.full op
This commit decomposes `aten.full` op into `aten.empty` and
`aten.fill` op.

Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
2022-03-04 21:58:23 +05:30
Ramiro Leal-Cavazos 298eeb79ca
[LINALG] Add handling of unknown dimension in size list of `view` op (#633)
The view op allows for the new shape argument to have a -1 value for
one of the dimensions, and the op is expected to deduce the size of
that dimension by looking at the sizes of the other dimensions and
comparing it to the total number of elements in the original
tensor. This commit adds this functionality.
2022-03-02 13:35:01 -08:00
Yi Zhang 1d285f0153 Add aten.hardtanh e2e support. 2022-03-02 12:28:06 -05:00
Prashant Kumar 819f29316f Decompose aten.silu op
Decomposition of aten.silu.op is added as silu(x) = x * sigmoid(x).
2022-03-01 23:24:19 +05:30
Vivek Khandelwal ddd45d6068 [MLIR][TORCH] Add E2E support for aten.new_zeros, aten.new_ones op
This commit adds lowering of `aten.new_zeros` and `aten.new_ones` op

Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
2022-03-01 22:09:47 +05:30
Ramiro Leal-Cavazos 1dba4fcbd7
[LINALG] Support for contiguous memory format in `clone` and `empty` (#628)
This commit adds support for the contiguous memory format for the ops
`AtenCloneOp` and `AtenEmptyMemoryFormatOp`.
2022-02-28 13:58:04 -08:00
Ramiro Leal-Cavazos 58abec5c0a
Add `reduction` support to `torch.nll_loss_forward` (#624)
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.
2022-02-28 11:01:23 -08:00
Prashant Kumar 7c637eebc3 [LINALG] Decompose aten_hardswish op.
`aten.hardswish` op is decomposed into (x/6) * Relu6(x+3).
2022-02-25 21:59:27 +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
Ramiro Leal-Cavazos 2823277f7c
Add static type information support to `aten.mm` (#602)
This commit adds static type information support to `aten.mm`. This is
needed for the forward pass of Bert training.
2022-02-18 09:56:48 -08:00
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
Anup Gangwar c60468f141
[tosa] Support for Aten[Zeros|Ones|Fill_Scalar] ops (#604)
Signed-off-by: Anup Gangwar <anup.gangwar@arm.com>

Co-authored-by: Anup Gangwar <anup.gangwar@arm.com>
2022-02-16 09:53:51 -08:00
Prashant Kumar 8b79b5f48f Modify aten._log_softmax op decomposition for numerical stability.
`aten.log_softmax` is decomposed to be more numerically stable.
2022-02-16 12:26:17 +05:30
Gaurav Shukla cd21dda867 [LINALG] Add E2E support for `aten.Hardsigmoid` op
This commit adds lowering of `aten.Hardsigmoid` op.

Signed-Off-by: Gaurav Shukla <gaurav@nod-labs.com>
2022-02-16 02:35:18 +05:30
Ramiro Leal-Cavazos 00a6e9c1bb
[LINALG] Add value tensor variant to `fill_.Scalar` (#600)
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.
2022-02-15 11:58:03 -08:00
Gaurav Shukla 41acde599b [LINALG] Add E2E support for `aten.[le|ge].Scalar` ops
- 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>
2022-02-15 12:21:09 +05:30
Gaurav Shukla 78c7844c6c [LINALG] Add E2E support for `aten.eq.int` op
- 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>
2022-02-15 01:37:35 +05:30
Gaurav Shukla f00d1686c8 [LINALG] Add E2E support for `aten.[Bool.Tensor|Float.Tensor]` op
- 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>
2022-02-14 23:09:20 +05:30
Gaurav Shukla dcef4751f9 [LINALG] Fix name conflict of `self` keyword.
- 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>
2022-02-14 22:55:31 +05:30
Ramiro Leal-Cavazos 3dc7847348
[LINALG] Fix linalg generic result type argument in TorchToLinalg (#588)
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.
2022-02-11 19:42:18 -08:00