Commit Graph

354 Commits (a60e84e5ee73676ac4d1eef24c1a8a5a0f4ae493)

Author SHA1 Message Date
Jiawei Wu c7fa42b7d3
[Torch Dialect] Add canonicalizer for aten.to.other op (#2273)
Canonicalize aten.to.other to prim.device + prim.dtype + aten.to.device
Co-authored-by: wujiawei.aml <wujiawei.aml@bytedance.com>
2023-06-30 09:43:08 +08:00
Yuanqiang Liu 449cfb8375
[Torch Dialect] add more scalar op folders (#2265) 2023-06-29 10:37:13 +08:00
Yuanqiang Liu 1ea2b57ab7
[Torch Dialect] add folder for aten.add (#2264)
* [Torch Dialect] add folder for aten.add

* update

* update

* update
2023-06-27 10:55:28 +08:00
Yuanqiang Liu 96b14e952e
[Torch Dialect] Support aten.device.with_index (#2254) 2023-06-23 01:07:14 +08:00
Yuanqiang Liu 7c6961bcbf
[Torch Dialect] Support aten.cuda and add canonicalizer for aten.cuda (#2231) 2023-06-14 09:56:39 +08:00
Yuanqiang Liu ddea56a832
[Torch Dialect] fix torch.uint8's dtype infer (#2227) 2023-06-13 10:38:20 +08:00
Matthias Gehre 27a3d09917
Torch: Fold RuntimeAssertOp when condition is true (#2198) 2023-06-09 19:06:25 +08:00
Yuanqiang Liu 5a7bf4e4cb
[Torch Dialect] Add canonicalize pattern for aten.is_floating_point (#2194)
* [Torch Dialect] Add canonicalize pattern for aten.is_floating_point

* implement as fold

* add lit test
2023-06-07 17:05:31 +08:00
Ramiro Leal-Cavazos dff3405d5a
Add alias analysis for cast-like ops to maximize-value-semantics (#2160)
When `use_tracing=True` is used to import a model into Torch-MLIR,
several casts get inserted in the IR to bridge the untyped inputs and
outputs with the typed body of the computation. These casts create
extra aliases of tensors that cause the current analysis in
`maximize-value-semantics` to fail.

In particular, the `maximize-value-semantics` analysis assumes that the
only valid alias right after an overwrite is the overwritten
alias. So, if there is a use of a casted version of the overwritten
alias after the overwrite, the analysis fails.

This commit improves the analysis by identifying all cast-like aliases
of the overwritten alias and allowing such aliases to be used after an
overwrite.

Because this issue only arises when using tracing, it cannot be
currently tested e2e, so only lit test is added.
2023-05-25 17:05:41 +00:00
Ramiro Leal-Cavazos de02b56e17
Replace RefineTypes with dtype functions (#2105)
This commit adds dtype functions for all the torch ops that did not
previously have one and removes the pass `RefineTypes`, since the
abstract interpretation library now takes care of all the dtype
propagation.

All dtype functions added are tested except for
- `aten.embedding`
- `aten._embedding_bag`
- `aten.embedding_bag`

These functions need a change to the testing framework to allow
specifying the actual data inside the tensor used for testing. I will
fix this in a follow up patch.

Co-authored-by: Jiahao Li <liplus17@163.com>
2023-05-12 13:40:45 -07:00
Zhekun Zhang 0cf9ee340b
[Torch Dialect] Add to.dtype_layout canonicalize patterns (#2062)
* add to.dtype_layout canonicalize patterns

* update comment

---------

Co-authored-by: zhekun.zhang <zhekun.zhang@bytedance.com>
2023-05-02 20:06:02 -07:00
Yuanqiang Liu 3e83a86354
[Torch Dialect] fix isValidSubtype with dynamic dim (#2018) 2023-04-11 01:02:18 -07:00
Vivek Khandelwal 98747d09a8 [MLIR][TORCH] Add support for prims::view_of op
This op does nothing and just returns the input operand as the
result of the op.

Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
2023-04-11 07:58:10 +05:30
Ramiro Leal-Cavazos d803ab4eeb
Cast `number` to `float` when shape function takes Scalar arg (#1978)
To keep things simple in shape functions, `Scalar` inputs are
considered `float`s. This means that when inserting the shape
functions into the IR, we must cast any `!torch.number`s into `float`s
so that the operand type matches the expected type in the shape
function. This commit adds the cast from `Scalar` to `float`.
2023-03-28 09:30:31 -07:00
Maksim Levental 953ea39cb5
handles 2,3,4 from https://github.com/llvm/torch-mlir/issues/1963 (#1964) 2023-03-24 21:50:01 -05:00
Ramiro Leal-Cavazos eae3ff7f1c
Change dtype functions interface to take ints tuple for each tensor (#1965)
The original design for the dtype functions outlined in
https://github.com/llvm/torch-mlir/issues/1462 was unable to properly
handle ops that take optional tensors as an input when the optional
tensor has a value of None. By the time the op gets imported into
torch-mlir, if an optional value is None, all information about the
original type is lost from the op type signature, preventing
torch-mlir from knowing if a value of None was from an optional tensor
or not, which was crucial in the original design since each tensor
argument must be turned into two separate arguments for the dtype
function.

This commit changes the interface to dtype functions such that each
tensor turns into a tuple of two ints, the first representing the rank
of the tensor and the second the dtype of the tensor. Since now there
is a one-to-one correspondence between the operands of an op and the
operands of its dtype function, there is no ambiguity about which
operand of the op corresponds with which operand of the dtype
function.

To test the implementation, this commit defines dtype function for
convolution op, which takes one optional tensor as an argument.
2023-03-23 11:05:39 -07:00
Matthias Gehre aa5bcb3cf2
LowerToBackendContract: Explicitly error out on unimplemented operator (#1947)
* LowerToBackendContract: Explicitly error out on unimplemented operator

But only reject torch.operator when results are invalid.
Otherwise it might be a custom op that the backend supports.
2023-03-20 16:27:08 +01:00
Ramiro Leal-Cavazos d310bb12bd
Expand definition of tensor subtype to include shape/dtype info (#1929)
Currently, the op `torch.tensor_static_info_cast` will not get
canonicalized away if the result type has any shape or dtype
information. This is because `isValidSubtype` only returns true when
the tensor types being compared are exactly the same or the supertype
has no shape and dtype information. Being unable to canonicalize away
the `torch.tensor_static_info_cast` gets in the way of further
optimizations, such as shape propagation.

This commit improves `isValidSubtype` by adding logic that compares
the shapes and dtypes of the two tensor types to determine of one type
is indeed a valid subtype of the other.

Fixes https://github.com/llvm/torch-mlir/issues/1926
2023-03-10 16:43:57 -08:00
Ziheng Jiang dca2b8a40a
[TORCH] Improve type refinement for aten.cat. (#1908)
* [TORCH] Fix type refinement for aten.cat.

* Add test.

* Address comments.

* Update.

* Update.

* Update.

* Update.

* Update.

---------

Co-authored-by: Ziheng Jiang <ziheng.jiang@bytedance.com>
2023-03-09 16:17:35 -08:00
Zhekun Zhang 1d3a7419c5
[Torch Dialect] add RSub, ScalarImplicit canonicalize (#1899)
* add rsub, scalarimplit canonicalizer

* reformat

* address comments

* fix bug

* fix test

* Update elementwise.py

* resolve merge conflict

* change to 3

* change to 3

* real fix

* fix name

* add torchdynamo fail test

---------

Co-authored-by: zhekun.zhang <zhekun.zhang@bytedance.com>
2023-03-06 17:38:27 -08:00
Ramiro Leal-Cavazos d30af8772b
Handle uninitialized lattice elements in RefineTypes (#1911)
The data-flow analysis does not always propagate information to the
entire graph. This results in some lattice elements being
uninitialized. Currently the lattice elements are not checked to see
if they are uninitialized before rewriting the graph, potentially
resulting in invalid IR (see
https://github.com/llvm/torch-mlir/issues/1896).

This commit adds handling for uninitialized lattice elements.
2023-03-03 08:55:58 -08:00
Yuanqiang Liu 7a8304f935
[Torch Dialect] add folder for aten.sub.float (#1871) 2023-03-02 09:07:33 -08:00
Yuanqiang Liu fc1e091d6a
[Torch Dialect] add aten.pow.int_float op and it's folder (#1872) 2023-02-28 09:36:05 -08:00
Yuanqiang Liu 6ab990e1e8
[Torch Dialect] add folder for aten.Int.float (#1863) 2023-02-10 13:59:03 -08:00
Yuanqiang Liu 2f6fdb7f0b
[Torch Dialect] add folder for prim.min.int (#1864) 2023-02-10 13:58:15 -08:00
Gleb Kazantaev 3930588a7e
Enable VerifyBackendContract in LTC backend (#1798)
* Enable VerifyBackendContract in LTC backend

* Update VerifyBackendContract pass

* Move convert_scalar_implicit to jit_utils

* Rename VerifyBackendContract to VerifyBackendContractNoDecompositions

* Update verify-backend-contract-error.mlir test
2023-01-24 22:14:17 -05:00
Ramiro Leal-Cavazos 6c86bec04f
build: update llvm tag to 9acc2f37 (#1828)
This commit makes the following changes:

- Update dialects to use fold API `kEmitFoldAdaptorFolder` and update
signature of `fold` methods (see PSA
https://discourse.llvm.org/t/psa-new-improved-fold-method-signature-has-landed-please-update-your-downstream-projects/67618)
- Replace `makeArrayRef` with `ArrayRef` (see
https://reviews.llvm.org/D140896)
- Remove `TypeRange{}` arg from `b.create<scf::IfOp>` since builder no
longer takes that argument
- Make `func`s in `Torch/invalid.mlir` private, since symbol
declarations cannot be public. (see https://discourse.llvm.org/t/rfc-symbol-definition-declaration-x-visibility-checks/2140)
2023-01-25 01:29:42 +00:00
Ramiro Leal-Cavazos 273664ded6
[custom op] Replace `tanh` dtype function with `expm1` (#1769)
This commit replaces the `tanh` dtype function, which was being used
to test the implementation of dtype functions in
a710237437, with a dtype function for
`expm1`. The dtype function for `expm1` is identical to the `tanh`
one, so the same level of testing is maintained.

Currently, there are ops getting dtype information from the
`RefineTypes` pass and ops getting dtype information from the
`TorchDtypeRefinementPipeline`. Since each pass can only propagete
dtype information for the ops it knows how to handle, some models with
many ops handled in both passes require the two dtype propagation
passes to execute many times, reaching the iteration limit set in the
`LowerToBackendContractPass`. To temporarily avoid this issue while
the migration to `TorchDtypeRefinementPipeline` is finished, this
commit switches `tanh` to `expm1`, since the latter is used a lot less
in large models.
2023-01-03 14:18:26 -08:00
ataheridezfouli-groq 17ee643aeb
[TORCH] Add Complex Number support (#1673)
Add Complex number dtype support to torch tensors. Add
aten.fft_fft op to test complex numbers.
2022-12-15 21:40:01 +00:00
Ramiro Leal-Cavazos 60db793feb
Pass op legality info to `verifyBackendContractPass` (#1705)
In order to verify if a given IR satisfies the backend contract, the
verifier needs to know if decompositions took place, and if so, which
ops were decomposed and which were not.

This commit adds two arguments to `verifyBackendContractPass` to
specify if decompositions took place and which ops to consider backend
legal, similar to the arguments of `LowerToBackendContractPass`.
2022-12-15 08:32:52 -08:00
Ahmed S. Taei b1f6832849
Add aten.slice.Tensor & aten.cat folders (#1691) 2022-12-13 13:02:47 -08:00
Ramiro Leal-Cavazos a710237437
[custom op] Generalize shape library logic to work with dtypes (#1594)
* [custom op] Generalize shape library logic to work with dtypes

This commit generalizes the shape library logic, so that dtype rules
for ops can also be expressed using the same mechanism. In other
words, each op can now have a shape function and a dtype function
specified in Python that is imported during lowering to calculate the
shapes and dtypes throught a program. For more information about how
to specify a dtype function, see the updated
`docs/adding_a_shape_and_dtype_function.md`.

For those not familiar with how the shape library works, the file
`docs/calculations_lib.md` provides an overview.
2022-12-13 08:25:41 -08:00
Ramiro Leal-Cavazos a54b334578
Allow running DecomposeComplexOps more than once (#1671)
The current implementation of `DecomposeComplexOps` fails if an op
expected to be decomposed does not get decomposed in the first
iteration of the `createTorchSimplificationPipeline` in
`LowerToBackendContractPass`. However, some graphs require multiple
iterations of `createTorchSimplificationPipeline` to fully propagate
all statically knowable information, such as dtypes and shapes, to the
entire graph, sometimes resulting in the need to run
`DecomposeComplexOps` more than once.

This commit changes `DecomposeComplexOps` to use a greedy algorithm
for pattern application and moves the legalization check of ops to the
`LowerToBackendContractPass` to allow for the `DecomposeComplexOps` to
run more than once.
2022-12-08 09:26:38 -08:00
Ramiro Leal-Cavazos 76190e8a3f
Remove unnecessary decompose-complex-ops tests (#1693)
This commit removes lit tests from the `decompose-complex-ops` that
are essentially testing a macro expansion, in accordance with
https://github.com/llvm/torch-mlir/blob/main/docs/architecture.md#dos-and-donts-for-unit-vs-end-to-end-testing .
2022-12-08 08:22:08 -08:00
Ramiro Leal-Cavazos dd35488da5
build: update llvm tag to 798fa4b4 (#1684)
- Support for non-prefixed accessors has been removed. See:
  https://reviews.llvm.org/D136727
- Rename `operands` to `methodOperands` in `prim.CallMethod` since the
  name `operands` overlaps with a builtin method name. See:
  https://reviews.llvm.org/D136727
- Add passes in refbackend to lower memref.subview. See:
  https://reviews.llvm.org/D136377
- Replace `CopyToValueTensorOps` first in `RewriteViewLikeSubgraph` in
  maximize-value-semantics.

  The current implementation of the `RewriteViewLikeSubgraph` pass in
  maximize-value-semantics creates temporarily invalid IR. In
  particular, given a forward slice starting from a
  `CopyToNonValueTensorOp` and ending in `CopyToValueTensorOp`s, the
  pass first replaces all uses of the `CopyToNonValueTensorOp` with
  its operand, which results in all the `CopyToValueTensorOp` users
  having their operand have type `!torch.vtensor`, which is invalid.

  The correct way to do things is to first replace all the
  `CopyToValueTensorOp`s with their operand, and then replace all uses
  of the `CopyToNonValueTensorOp` with its operand.

  This only started failing now because the generated accessor
  `getOperand` for the `CopyToValueTensorOp` now returns a
  `TypedValue<NonValueTensorType>`, which has an assert checking that
  the value returned is of the expected type.
2022-12-07 12:20:41 -08:00
Vivek Khandelwal e7edcc62fd build: update llvm tag to 147fe9de
Summary of changes:
- Replace call to `MemoryEffectOpInterface::hasNoEffect`
  with `isMemoryEffectFree`.
- Make fix for the dynamic dims, since
  `kDynamicSize` value changed to
  `std::numeric_limits<int64_t>::min()` from `-1` in llvm
- `makeShapeLLVMCompatible` and `makeShapeTorchCompatible`
  utilities convert shapes in order to remain consistent
  with the Torch and MLIR semantics.
- Update tags
  llvm: 147fe9de29dc13c14835127b35280c4d95c8e8ba
  mhlo: 1944b5fa6062ec4c065d726c9c5d64f1487ee8c5

Signed-Off By: Vivek Khandelwal<vivek@nod-labs.com>
2022-12-01 13:36:50 +05:30
Tanyo Kwok bbcdb38d99
Revert "Decompose torch.slice_scatter (#1622)" (#1659)
This reverts commit f3f2f10030.
2022-11-30 12:47:13 +08:00
Vivek Khandelwal d9cbf01d1e Revert "build: update llvm tag to 147fe9de"
This reverts commit e45ad313d4.
2022-11-25 12:41:56 +05:30
Vivek Khandelwal e45ad313d4 build: update llvm tag to 147fe9de
Summary of changes:
- Update call to `hasNoEffect` utility
- `KDynamicSize` value changed to
  `std::numeric_limits<int64_t>::min()` from `-1`
- Update tags
  llvm: 147fe9de29dc13c14835127b35280c4d95c8e8ba
  mhlo: 1944b5fa6062ec4c065d726c9c5d64f1487ee8c5

Signed-Off By: Vivek Khandelwal<vivek@nod-labs.com>
2022-11-24 12:44:43 +05:30
Tanyo Kwok f3f2f10030
Decompose torch.slice_scatter (#1622)
* Decompose torch.slice_scatter

* fix compilation error

* update file check

* fix ci

* fix i64 torch.tensor dtype
2022-11-23 18:14:12 +08:00
Vivek Khandelwal da8fdc9f96 [MLIR][TORCH] Fix refine types crash
This commit fixes https://github.com/llvm/torch-mlir/issues/1599.

Signed-Off By: Vivek Khandelwal<vivek@nod-labs.com>
2022-11-23 15:17:37 +05:30
Vivek Khandelwal 55c7e66aa7 [MLIR][TORCH] Fix mean and mean.dim op for large-sized inputs
This commit fixes the aten.mean and aten.mean.dim op decomposition
for supporting large-sized inputs.
This commit also fixes the formatting for the file stats.py

Signed-Off By: Vivek Khandelwal<vivek@nod-labs.com>
2022-11-22 08:38:51 +05:30
Vivek Khandelwal 4cbd3927d7 [MLIR][TORCH] Add aten.sort.int op
Signed-Off By: Vivek Khandelwal<vivek@nod-labs.com>
2022-11-20 19:00:41 +05:30
Daniel Ellis a7ac0def45
Move single-tensor-tuple-return test to mlir unit test.
Also, add multiple return test.
2022-11-10 09:23:53 -05:00
Xiafei Qiu 4f173c6e0f
update llvm tag to a2620e00. (#1567)
- also update MHLO to 57ba12a2(branch greencommit/2022-11-07-a2620e00)
- change -pass-pipeline format to make tests pass.
2022-11-10 18:39:28 +08:00
Ramiro Leal-Cavazos b723186983
Remove all but one of valsem ops + move fill.Scalar to elementwise (#1531)
This commit removes almost all of the valsem ops, since the value
semantics version of the ops now exist in PyTorch. The only op missing
is `aten.bernoulli_.float`. In addition, this commit also simplifies
the implementation of `aten.fill.Scalar` by moving it to the pattern
that converts elementwise ops.
2022-10-28 15:06:11 +00:00
Vivek Khandelwal ca87033d2f [MLIR][TORCH] Add E2E support for aten.mse_loss op
This commit adds decomposition for the `aten.mse_loss` op.

Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
2022-10-25 21:06:58 +05:30
Ramiro Leal-Cavazos 82a3860e25
build: update llvm tag to 4546397e (#1502)
This commit makes the following changes needed to update bump LLVM:

- Replace `linalg.init_tensor` with `tensor.empty` (see:
https://reviews.llvm.org/D135129)
- Replace `NoSideEffect` with `Pure` (see
https://reviews.llvm.org/D135505)
- Replace `body` region accessor for `ReduceOp` and `ReduceWindowOp`
with `getBody`
- Fix incorrect use of `tosa::ReduceSumOp` in `AtenNativeLayerNormOp`
conversion pattern. The result type of `tosa::ReduceSumOp` must have
the same rank as the input type. (see:
https://www.mlplatform.org/tosa/tosa_spec.html#_reduce_sum)

Co-authored-by: Ashay Rane <ashay@users.noreply.github.com>

Co-authored-by: Ashay Rane <ashay@users.noreply.github.com>
2022-10-18 04:22:53 +00:00
Gaurav Shukla da90a25f90 [MLIR][TORCH] Add E2E support for `aten.[div.int|bitwise_or.Tensor]` ops
This commit adds lowering of `aten.div.int` and `aten.bitwise_or.Tensor`
ops. Both these ops are required in order to support bloom_560m model.

Signed-Off-by: Gaurav Shukla <gaurav@nod-labs.com>
2022-10-10 22:28:51 +05:30
武家伟 c03aa63325
[MLIR] Add canonicalizer for aten.slice.t op (#1413)
* [MLIR] Add canonicalizer for aten.slice.t op

* Add mlir tests and strength the canonicalizer

* rename variable

Co-authored-by: Vremold <xremold@gamil.com>
2022-09-26 14:35:50 -07:00
Tanyo Kwok 061a97c3f2
Replace empty_like && empty_memory_format with full/full_like (#1398)
* Replace empty_like && empty_memory_format with full/full_like

* fix broadcast rank0 tensor
2022-09-23 10:24:36 +08:00
gpetters94 48418b9c22
Fold away type_as (#1358) 2022-09-12 18:59:12 -04:00
Sean Silva 0e3ddbac91 Remove VerifyInvariantsBeforeBackendLowering
LowerToBackendContract now checks all this consistently.
2022-08-26 10:24:43 -07:00
Tanyo Kwok 3d0e18bbe7
Add decomposition for aten.roll (#1170)
* Add decomposition for aten.roll

* add e2e unittest

* refine type of torch.roll

* fix aten::cat output type
2022-08-24 08:36:05 +08:00
Tanyo Kwok 9176b5ed29
Add decomposition for aten.flatten.using_ints (#1161) 2022-08-23 11:52:54 +08:00
Sean Silva 01290d134a Add a way for backends to control which ops are legal for them.
We were already hitting many cases where backends different in terms of
the legal ops that they wanted. This caused unnecessary coupling between
the backends. Examples:
- https://github.com/llvm/torch-mlir/pull/1161
- https://github.com/llvm/torch-mlir/pull/862

This PR centralizes all compilation to go through `torch_mlir.compile`
so that we can keep the logic centralized there. We should move these
lists closer to each backend. Especially cases like
https://github.com/llvm/torch-mlir/pull/862 where blocking a
decomposition is necessary to avoid a crash emphasize that the set of
decompositions is tightly coupled to the backend, and should be
"controlled by the backend" and not something arbitrarily tweakable.

Also:
- Fix a small bug in the way we passed through the backendLegalOps
  option.
- Add better error messages in `torch_mlir.compile` for import errors.
2022-08-22 14:16:13 -07:00
武家伟 99fb4c8637
Add folder for ToF64Op and FromF64Op (#1257) 2022-08-22 09:49:39 +08:00
Ramiro Leal-Cavazos 9bc606c384
Add support for returning more than one copy of the same tensor (#1228)
One of the simplifications made by the pass `RefinePublicReturn`
currently only happens if the tensor in question only has one
user. However, the current method of checking this does not correctly
handle the case of a user having multiple uses of the same
tensor. This commit makes sure only unique users are considered.
2022-08-18 22:41:45 +00:00
Sean Silva 283e0f141a Add a concept of "backend legal ops".
This is a first step towards formalizing the set of ops in our backend
contract. The goal is to eventually formalize `torch` dialect ops into 3
categories:
1. Legal in backend contract
2. Illegal in backend contract
3. Conditionally legal in backend contract

The "conditionally legal" set are the ops that we can optionally
decompose for backends.

This patch adds relevant pass options for this throughout the compiler,
in preparation for a new set of traits which will formalize this
classification.
2022-08-18 11:46:50 -07:00
Sean Silva 57681f7947 Iteratively run the main simplification pipeline.
This introduces a new pass LowerToBackendContract (better name very
welcome) which performs the bulk of the simplifications that we do,
such as
- shape refinement
- dtype refinement
- maximizing value semantics
- inlining global slots
- decomposing complex ops

The key difference from before is that it iterates the set of
transformations, which can help to break a number of "catch-22" issues
where one simplification depends on another, the latest example being
here:
https://github.com/llvm/torch-mlir/issues/1131

This also exposed that RefineTypes was sometimes crashing/asserting for
certain inputs. This commit hardens it a bit.
2022-08-17 14:54:33 -07:00
武家伟 3b3cb99ef8
Generalize canonicalization pattern for more aten.sub/div/mul/add op (#1209)
Generalize canonicalization pattern for more sub/div/mul/add op, but for AtenDivTensorModeOp in 'trunc' rounding mode, we try to fold it.
2022-08-16 13:24:08 +08:00
Sean Silva 504de5e701 Rework how global slot initializers work.
Rather than a per-global-slot initializer region, we now have one for
the whole module. For example, it might look like this:

```
torch.global_slot "private" @tensor : !torch.tensor
torch.global_slot "private" @list : !torch.list<tensor>
torch.global_slot.module_initializer {
  %0 = torch.tensor.literal(dense<0.0> : tensor<f32>) : !torch.tensor
  %1 = torch.prim.ListConstruct %0 : (!torch.tensor) -> !torch.list<tensor>
  torch.initialize.global_slots [
    @tensor(%0 : !torch.tensor)
    @list(%1 : !torch.list<tensor>)
  ]
}
```

This new structure allows GlobalizeObjectGraph to create the initializer in a
much simpler way, avoiding the need to reason about whether different slots
alias each other. Reasoning about whether slots alias each other now is the
responsibility of InlineGlobalSlots, which has to do a much more complicated
analysis, implemented using MLIR's dataflow analysis framework.

Recommended review order:
- Check out the new IR constructs in the .mlir files of various passes
- Op definitions (*.td)
- Changes to GlobalizeObjectGraph pass.
- InlineGlobalSlots pass (~total rewrite)
- Misc changes:
  - Moving torchMlirAdjustStaticInformation for sharing with C++ code.
  - EraseModuleInitializer pass

To make this a bit nicer, it would be good to have a `torch.module` op
with an initializer region attached. That would be more invasive though.

This change has highlighted certain aspects of our project layering
which are worth calling out. None of our backends can handle global
slots, so we enforce that there are no global slots before backend
lowering. At an earlier stage in the project, we had aspirations of
transparently handling mutable global state and such, but for reasons
described below, that is no longer a goal. So really global slots should
be seen as a progressive lowering step as part of inlining all the
IValue's in the original program (GlobalizeObjectGraph is also one such
step).

Over time, with insights from work like IREE-JAX, it has become clear
that there isn't a reliable programming model we can compile for users
where we just transparently handle mutable global state (and some other
things, like lists and dictionaries). There is a need for an "outer
program" that orchestrates more restricted subroutines of the kind we
can handle in our compile flow here. The benefit of that is that it
decouples considerations like shapes, dtypes, etc. from the program
constructs used in the outer program. As long as the outer program can
efficiently invoke (pipelining/async/etc.) high-performance
data-parallel numerical subroutines of the kind we compile in our flow
here, then there is a complete programming model. This is also
consistent with the direction of upstream PyTorch which is becoming more
tracing-based (which inherently loses a lot of program structure, which
then has to be applied back with an "outer program" orchestrating the
traced subroutines).
2022-08-08 18:12:06 -07:00
Tanyo Kwok 1ee865983b
[MHLO] fix tensor mode aten.div op pattern (#1160)
* [MHLO] fix tensor mode aten.div op pattern

See RFC #999
Co-authored-by: Bairen Yi <yibairen.byron@bytedance.com>
Co-authored-by: Jiawei Wu <xremold@gmail.com>
Co-authored-by: Tianyou Guo <tianyou.gty@alibaba-inc.com>
Co-authored-by: Xu Yan <yancey.yx@alibaba-inc.com>
Co-authored-by: Ziheng Jiang <ziheng.jiang@bytedance.com>
2022-08-06 23:38:06 +08:00
PhaneeshB 8b5631d4c5 [MLIR][TORCH] Add decomposition for aten.std.dim Op
Signed-Off By: Phaneesh Barwaria <phaneesh@nod-labs.com>
2022-07-29 23:52:54 +05:30
Vivek Khandelwal d386b8f9e5 [MLIR][TORCH] Add decomposition for aten.var.correction op
This commit adds the decomposition for `aten.var.correction` op.

Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com
2022-07-29 11:08:57 +05:30
Quinn Dawkins 11a8901078
[MLIR][TORCH] Add support for multiple indexing tensors for aten.index.Tensor (#1097)
- Includes a canonicalizer for `aten.add.t`needed for successfully lowering the shape function
 - Only offers support for statically sized index tensors when there is more than one
 - Dynamic shape support remains for single indexing tensors
2022-07-28 19:00:02 -04:00
Kevin Kiningham e8f327cc00 Add lowering to linalg for softplus and log1p
Follows existing conventions for unary operators.
2022-07-25 21:25:57 +05:30
Ramiro Leal-Cavazos f271e6a88c
Add verifiers for ToBuiltinTensorOp and FromBuiltinTensorOp (#1089)
This commit adds verifiers to the ops `ToBuiltinTensorOp` and
`FromBuiltinTensorOp` that make sure that the input and output have
the same shape and data type.
2022-07-21 21:41:45 +00:00
Vivek Khandelwal 4c25878e64 [MLIR][TORCH] Add canonicalization pattern for prim.ListUnpack op
This commit adds the canonicalization pattern for the `prim.ListUnpack` op.

Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
2022-07-18 13:51:25 +05:30
Vivek Khandelwal 3589134d31 [MLIR][TORCH] Add decomposition for aten.var.dim op
This commit adds the decomposition for `aten.var.dim` op.
This commit also make changes in the decomposition for `aten.var` op.

Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
2022-07-15 09:53:42 +05:30
Ashay Rane 29bc48aedb
torch: add pass to catch non-value tensors (#1052)
This patch adds a new pass `torch-verify-conversion-to-value-semantics`,
which looks for non-value semantics tensors to catch such tensors early
during compilation.

This pass requires `torch-refine-public-return` pass to ensure that
return operations are updated to use value tensors, followed by the
canonicalize pass to remove any dead ops that may use or produce
non-value tensors.
2022-07-13 17:11:15 -07:00
Ashay Rane 64c04bd5f6
canonicalizer: [nfc] update LIT variable names for consistency (#1051)
A previous patch used lowercase names for LIT variables.  This patch
replaces them with uppercase names to maintain consistency with other
variables.
2022-07-13 12:28:25 -07:00
Ashay Rane ac4d7d10e0
canonicalizer: propagate type information across copy and cast ops (#1030)
Prior to this patch, the canonicalizers for `AtenSizeOp` and
`AtenSizeIntOp` succeeded only if the tensor operand's type information
included the size of the requested dimension(s).  We can extend the set
of optimizable cases by propagating types across operations whose result
type matches the input tensor type.

Specifically, this patch enables the canonicalizers for `AtenSizeOp` and
`AtenSizeIntOp` to see past `tensor_static_info_cast`,
`copy.to_vtensor`, and `copy.to_tensor` ops until it reaches the first
op whose result type contains size information for the requested
dimensions, with a maximum bound of 6 parent lookups to avoid indefinite
compilation times.  All other encountered ops cause the canonicalizer to
give up.
2022-07-12 12:38:37 -07:00
Sean Silva e5e11e214b GlobalizeObjectGraph: Clean up handling of unused slots
The way we did it previously still created the slot and copied the
initializer even if unused.
2022-07-12 10:47:28 -07:00
Ashay Rane 9017be9e9e
torch: copy uses to prevent iterator invalidation (#1033)
Prior to this patch, the code in the `torch-simplify-shape-calculations`
pass iterated on the uses of an op's result while also modifying the
value.  This caused the iterator to get invalidated, thus terminating
the loop early and producing incorrect IR.  This patch makes use of
`llvm::make_early_inc_range()` to ensure that the iterator is not
invalidated while executing the loop body.
2022-07-11 18:47:04 -07:00
Ramiro Leal-Cavazos 11148e60d6
Undo shape lib changes + update function signature of sum + zero (#1035)
This commit does three things:
  1. Reverts some of the shape lib changes merged in
  https://github.com/llvm/torch-mlir/pull/844
  2. Updates the signature of `aten.sum_dim_IntList` that was recently
  updated in
  23bdb570cf
  3. Replaces `aten.zero.functional` with `aten.zero`, updated in 960758b0b7
2022-07-11 10:56:12 -07:00
Prateek Gupta 2d75654b2c [TORCH][MLIR] Add lowering of `aten.slice_scatter` and
`aten.select_scatter` op.

This commit adds:
1.  Lowering of `aten.slice_scatter` op into `tensor.insert_slice`
op.
2. Decomposes the `aten.select_scatter` op into `aten.slice_scater`
op.

Signed-Off-By: Prateek Gupta <gprateek93@gmail.com>
2022-07-11 14:07:21 +05:30
Ashay Rane 340d8af28a
torch: handle `torch.prim.dtype` ops during type refinement (#1013)
The canonicalizer converts `torch.prim.dtype` ops into integer constants
for valid types, but the type may not be known until type refinement is
complete.  However, type refinement cannot make progress until
`torch.prim.dtype` ops have been resolved to their corresponding integer
constants, thus creating a circular dependency.

This patch creates a tight coupling between type refinement and the
lowering of `torch.prim.dtype` ops by handling such ops as they are
encountered during type refinement.  The unit test in this patch aims to
check whether the type refinement pass can now handle chains of
operations that alternate between type construction and type refinement.
2022-07-08 16:38:51 -07:00
Ramiro Leal-Cavazos 6a72ab4502
Add basic support for list of optional tensors in reduce-op-variants (#971)
This commit adds support for lists of type `list<optional<tensor>>`
where each element in the list is either a `!torch.tensor` or a
`!torch.none`.
2022-07-08 11:12:15 -07:00
Quinn Dawkins f0c3b5a7ed
Add E2E support for aten.len.str (#969) 2022-07-07 10:41:55 -07:00
Ashay Rane 88316b3b4e
torch: fold prim.dtype(bf16) to integer constant 15 (#1012)
A prior patch (63538de2) that added support for bfloat16 type did not
add the canonicalization pattern to fold `torch.prim.dtype` operations
on bfloat16 tensors into the integer constant 15.  This patch fixes the
problem.
2022-07-06 18:21:43 -07:00
Tanyo Kwok d4f1f41435
[MLIR][TORCH] Add decomposition of aten.repeat (#932)
* [MLIR][TORCH] Add decomposition of aten.repeat

* refine & rebase

* refine static shapes

* add e2e test

* Rebase and Refine naming style
2022-07-01 13:02:31 +08:00
Sean Silva 227dea7b2e Add support for ScalarType::QUInt8
I ran into this while poking around at
https://github.com/llvm/torch-mlir/issues/959
2022-06-29 15:33:28 -07:00
Ashay Rane 163fa57cde
torch: allow torch dialect ops after running drop-shape pass (#979)
In the `pyhpc_turbulent_kinetic_energy` TorchBench benchmark, the shape
calculation occurs inside loops, but because `DropShapeCalculationsPass`
does not explicitly mark the Torch dialect as legal, the pass execution
fails.

This patch adds Torch to the list of legal dialects, and adds a test to
validate the translation.
2022-06-25 07:27:47 -07:00
Tanyo Kwok 143a7bcb76
[MLIR][TORCH] Add folder for torch_c.from_i64 & torch_c.to_i64 (#933)
* [MLIR][TORCH] Add folder for torch_c.from_i64 & torch_c.to_i64

* add unit tests for each individual fold

* fix failure of NumelZeroRankModule & TestMultipleTensorAndPrimitiveTypesReturn
2022-06-24 09:34:39 +08:00
erman-gurses 5cff40c88a Add canonicalization for aten.add.tensor op 2022-06-23 17:24:59 -04:00
Maksim Levental 829717c96e
Bump LLVM (#958) 2022-06-22 22:23:46 -05:00
Vivek Khandelwal 77ab31641f [MLIR][TORCH] Add decomposition of aten.numpy_T op
This commit adds the decomposition of `aten.numpy_T` op into
`aten.t` or `aten.permute` op.

Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
2022-06-16 00:01:22 +05:30
Vivek Khandelwal 33fa8e7761 [MLIR][TORCH] Add decomposition of aten.floor_divide op
This commit adds the decomposition of `aten.floor_divide` op into
`aten.div.Tensor_mode` op.

Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
2022-06-14 08:56:25 +05:30
Vivek Khandelwal a11ef674a7 [MLIR][TORCH] Add E2E support for aten.baddbmm op
This commit decomposes `aten.baddbmm` op into `aten.bmm`,
`aten.mul.Scalar`, and `aten.add.Tensor` op.

Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
2022-06-07 22:26:28 +05:30
Vivek Khandelwal 2718b4d838 [MLIR][TORCH] Add E2E support for aten.clamp_[min|max] op
This commit decomposes `aten.clamp_min` and `aten.clamp_max` op
into `aten.clamp` op.

Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
2022-06-06 11:52:29 +05:30
Henry Tu abf5c94a1b
Replace valsem.aten.zero with aten.zero.functional (#893) 2022-06-03 16:27:31 -04:00
Vivek Khandelwal 6f548fc3ad [MLIR][TORCH] Add decomposition of aten.adaptive_avg_pool2d op
This commit adds the decomposition of `aten.adaptive_avg_pool2d` op into
`aten.avg_pool2d` op. The current decomposition only supports cases where
input size is equal to the output size.

Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
2022-05-27 07:56:37 +05:30
Vivek Khandelwal 56e77d4213 [MLIR][TORCH] Add E2E support for aten.Bool.[float|int] op
This commit adds lowering of `aten.Bool.float` and `aten.Bool.int` op.

Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
2022-05-24 21:18:34 +05:30
Vivek Khandelwal bc9b2156e3 [MLIR][TORCH] Add E2E support for aten.sqrt.int op
This commit adds lowering of `aten.sqrt.int` op.

Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
2022-05-24 16:50:39 +05:30
Sean Silva 3fb54cba4c torch.prim.TupleIndex: Adjust tensor types when folding.
In cases where a refinement/derefinement was needed, we didn't fold.

Fixes https://github.com/llvm/torch-mlir/issues/863
2022-05-19 09:36:27 -07:00
Ashay Rane bb52a460cb
mlir: bump llvm tag to 5380e3 (#856)
In addition to updating the llvm-project submodule, this patch also:

1. updates shape functions and tests so that `func` and `call`
   operations refer to the `func` dialect
2. avoid duplicate registration of dialects
2022-05-16 12:54:35 -07:00
Ramiro Leal-Cavazos 96f90efd16
Add shape info to `rand_like` + support for `dtype` flag (#851)
The op `aten.rand_like` was missing a shape function, unit tests, and
the `dtype` argument was being ignored in its decomposition. This
commit fixes all three things.
2022-05-12 16:00:59 -07:00
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
Vivek Khandelwal 96fabc0036 [MLIR][TORCH] E2E support for [ge|ceil].float, [ge|ne|gt].float_int op
This commit adds lowering of `aten.ge.float`, `aten.ge.float_int`,
`aten.ne.float_int`, `aten.gt.float_int` and `aten.ceil.float` op.
This commit also fixes formatting for the file scalar.py and scalar_comparison.py.

Signed-Off By: Vivek Khandelwal <vivek@nod-labs.com>
2022-05-05 21:48:35 +05:30