305 Commits

Author SHA1 Message Date
Peiming Liu
a2d9d2e1d9
[mlir][sparse] re-enable aarch64 test. (#71855)
Should have been fixed by initializing output tensor to zeros in
https://github.com/llvm/llvm-project/pull/71845
2023-11-09 11:46:52 -08:00
Peiming Liu
30e4b09d49
[mlir][sparse] try fix flanky test. (#71845) 2023-11-09 11:10:59 -08:00
Peiming Liu
4eb01f7d5e
[mlir][sparse] disable aarch64 test to fix buildbot error. (#71818)
To fix https://github.com/llvm/llvm-project/pull/71448
2023-11-09 10:50:58 -08:00
Peiming Liu
c99951d491
[mlir][sparse] end-to-end matmul between Dense and BSR tensors (#71448) 2023-11-08 11:28:00 -08:00
Tim Harvey
c43e627457
Changed the phrase sparse-compiler to sparsifier in comments (#71578)
When the Powers That Be decided that the name "sparse compiler" should
be changed to "sparsifier", we negected to change some of the comments
in the code; this pull request completes the name change.
2023-11-07 20:55:00 +00:00
Aart Bik
160d483b1f
[mlir][sparse] implement loose-compressed/2:4 on direct IR codegen path (#71461)
Fills in the missing cases for direct IR codegen.
Note that non-permutation handling is still TBD.
2023-11-06 17:30:56 -08:00
Christian Ulmann
52491c99fa
[MLIR][LLVM] Remove typed pointer remnants from integration tests (#71208)
This commit removes all LLVM dialect typed pointers from the integration
tests. Typed pointers have been deprecated for a while now and it's
planned to soon remove them from the LLVM dialect.

Related PSA:
https://discourse.llvm.org/t/psa-removal-of-typed-pointers-from-the-llvm-dialect/74502
2023-11-03 21:21:25 +01:00
Peiming Liu
53ffafb24d
[mlir][sparse] support sparse constant to BSR conversion. (#71114)
support direct convert from a constant tensor defined by
SparseArrayElements to BSR
2023-11-02 14:45:39 -07:00
Peiming Liu
c0d78c4232
[mlir][sparse] Implement rewriters to reinterpret maps on alloc_tenso… (#70993)
…r operation
2023-11-01 18:15:11 -07:00
Aart Bik
b19c40c579
[mlir][sparse] first end-to-end linalg.generic op on BSR (#70880) 2023-11-01 10:01:22 -07:00
Peiming Liu
ef222988b4
[mlir][sparse] implements sparse_tensor.reinterpret_map (#70388) 2023-10-26 16:00:32 -07:00
Aart Bik
e6005d5a9c
[mlir][sparse] support 2:4 structured sparsity and loose compressed (#69968)
This adds library support for these two new level formats.
2023-10-23 15:34:45 -07:00
Aart Bik
306f4c306a
[mlir][sparse] implement non-permutation MapRef encoding (#69406)
This enables reading block sparse from file using libgen! (and soon also
direct IR codegen)
2023-10-18 13:01:12 -07:00
Peiming Liu
f248d0b28d
[mlir][sparse] implement sparse_tensor.reorder_coo (#68916)
As a side effect of the change, it also unifies the convertOp
implementation between lib/codegen path.
2023-10-12 13:22:45 -07:00
Peiming Liu
0083f8338c
[mlir][sparse] renaming sparse_tensor.sort_coo to sparse_tensor.sort (#68161)
Rationale: the operation does not always sort COO tensors (also used for
sparse_tensor.compress for example).
2023-10-03 16:28:25 -07:00
Yinying Li
d2e8517912
[mlir][sparse] Update Enum name for CompressedWithHigh (#67845)
Change CompressedWithHigh to LooseCompressed.
2023-10-02 11:06:40 -04:00
Peiming Liu
6ca47eb49d
[mlir][sparse] rename sparse_tensor.(un)pack to sparse_tensor.(dis)as… (#67717)
…semble

Pack/Unpack are overridden in many other places, rename the operations
to avoid confusion.
2023-09-28 11:01:10 -07:00
Yinying Li
256ac4619b
[mlir][sparse] Change tests to use new syntax for ELL and slice (#67569)
Examples:

1. `#ELL = #sparse_tensor.encoding<{ lvlTypes = [ "dense", "dense",
"compressed" ], dimToLvl = affine_map<(i,j)[c] -> (c*4*i, i, j)>
}>`
to
`#ELL = #sparse_tensor.encoding<{ map = [s0](d0, d1) -> (d0 * (s0 * 4) :
dense, d0 : dense, d1 : compressed)
}>`

2. `#CSR_SLICE = #sparse_tensor.encoding<{ lvlTypes = [ "dense",
"compressed" ], dimSlices = [ (1, 4, 1), (1, 4, 2) ]
}>`
to
`#CSR_SLICE = #sparse_tensor.encoding<{ map = (d0 :
#sparse_tensor<slice(1, 4, 1)>, d1 : #sparse_tensor<slice(1, 4, 2)>) ->
(d0 : dense, d1 : compressed)
}>`
2023-09-27 19:40:52 -04:00
Yinying Li
d374a78545
[mlir][sparse] Treat high and 2OutOf4 as level formats (#67203)
In the new syntax, we will parse **loose_compressed** as
**CompressedWithHigh** and **block2_4** as **TwoOutOfFour** level
format. Currently, we support unique and order as level properties.
2023-09-25 11:04:55 -04:00
Peiming Liu
bfa3bc4378
[mlir][sparse] unifies sparse_tensor.sort_coo/sort into one operation. (#66722)
The use cases of the two operations are largely overlapped, let's
simplify it and only use one of them.
2023-09-19 17:02:32 -07:00
Peiming Liu
4176ce61f1
[mlir][sparse] fix logical error when generating sort_coo. (#66690)
To fix issue: https://github.com/llvm/llvm-project/issues/66664
2023-09-18 15:26:01 -07:00
Yinying Li
3dc621124f
[mlir][sparse] Migrate tests to use new syntax (#66543)
**COO**
`lvlTypes = [ "compressed_nu", "singleton" ]` to `map = (d0, d1) -> (d0
: compressed(nonunique), d1 : singleton)`
`lvlTypes = [ "compressed_nu_no", "singleton_no" ]` to `map = (d0, d1)
-> (d0 : compressed(nonunique, nonordered), d1 : singleton(nonordered))`

**SortedCOO**
`lvlTypes = [ "compressed_nu", "singleton" ]` to `map = (d0, d1) -> (d0
: compressed(nonunique), d1 : singleton)`

**BCOO**
`lvlTypes = [ "dense", "compressed_hi_nu", "singleton" ]` to `map = (d0,
d1, d2) -> (d0 : dense, d1 : compressed(nonunique, high), d2 :
singleton)`

**BCSR**
`lvlTypes = [ "compressed", "compressed", "dense", "dense" ], dimToLvl =
affine_map<(d0, d1) -> (d0 floordiv 2, d1 floordiv 3, d0 mod 2, d1 mod
3)>` to
`map = ( i, j ) ->
      ( i floordiv 2 : compressed,
        j floordiv 3 : compressed,
        i mod 2 : dense,
        j mod 3 : dense
      )`

**Tensor and other supported formats(e.g. CCC, CDC, CCCC)**

Currently, ELL and slice are not supported yet in the new syntax and the
CHECK tests will be updated once printing is set to output the new
syntax.

Previous PRs: #66146, #66309, #66443
2023-09-15 16:12:20 -04:00
Aart Bik
d2e787d5d7
[mlir][sparse][tensor] replace bufferization with empty tensor (#66450)
Rationale:
    A bufferization.alloc_tensor can be directly replaced
    with tensor.empty since these are more or less semantically
    equivalent. The latter is considered a bit more "pure"
    with respect to SSA semantics.
2023-09-15 11:45:42 -07:00
Yinying Li
2a07f0fd40
[mlir][sparse] Migrate more tests to use new syntax (#66443)
**Dense**
`lvlTypes = [ "dense", "dense" ]` to `map = (d0, d1) -> (d0 : dense, d1
: dense)`
`lvlTypes = [ "dense", "dense" ], dimToLvl = affine_map<(i,j) -> (j,i)>`
to `map = (d0, d1) -> (d1 : dense, d0 : dense)`

**DCSR**
`lvlTypes = [ "compressed", "compressed" ]` to `map = (d0, d1) -> (d0 :
compressed, d1 : compressed)`

**DCSC**
`lvlTypes = [ "compressed", "compressed" ], dimToLvl = affine_map<(i,j)
-> (j,i)>` to `map = (d0, d1) -> (d1 : compressed, d0 : compressed)`

**Block Row**
`lvlTypes = [ "compressed", "dense" ]` to `map = (d0, d1) -> (d0 :
compressed, d1 : dense)`

**Block Column**
`lvlTypes = [ "compressed", "dense" ], dimToLvl = affine_map<(i,j) ->
(j,i)>` to `map = (d0, d1) -> (d1 : compressed, d0 : dense)`

This is an ongoing effort: #66146, #66309
2023-09-14 23:19:57 +00:00
Yinying Li
e2e429d994
[mlir][sparse] Migrate more tests to new syntax (#66309)
CSR:
`lvlTypes = [ "dense", "compressed" ]` to `map = (d0, d1) -> (d0 :
dense, d1 : compressed)`

CSC:
`lvlTypes = [ "dense", "compressed" ], dimToLvl = affine_map<(d0, d1) ->
(d1, d0)>` to `map = (d0, d1) -> (d1 : dense, d0 : compressed)`

This is an ongoing effort: #66146
2023-09-14 12:21:13 -04:00
Peiming Liu
098f46dce3
[sparse] allow unpack op to return 0-ranked tensor type. (#66269)
Many frontends canonicalize scalar into 0-ranked tensor, it change will
hopefully make the operation easier to use for those cases.
2023-09-13 11:33:01 -07:00
Yinying Li
dbe1be9aa4
[mlir][sparse] Migrate tests to use new syntax (#66146)
lvlTypes = [ "compressed" ] to map = (d0) -> (d0 : compressed)
lvlTypes = [ "dense" ] to map = (d0) -> (d0 : dense)
2023-09-13 11:41:25 -04:00
Peiming Liu
64df1c08d0
[sparse] allow unpack op to return any integer type. (#66161) 2023-09-12 17:27:51 -07:00
Mehdi Amini
6f5ebfb987 Fix MLIR integration test that requires ARM SVE to reproduce
Fix-forward for a9f30097586e914e074111d966c1408e82d04a8d
2023-09-09 15:29:00 -07:00
Mehdi Amini
a9f3009758
Switch MLIR to use the internal LIT shell by default (#65415) 2023-09-09 13:51:27 -07:00
Aart Bik
b86d3cbc12 [mlir][sparse] complete various FIXMEs in sparse support lib
Reviewed By: Peiming

Differential Revision: https://reviews.llvm.org/D159245
2023-08-30 21:30:25 -07:00
Peiming Liu
22e8d5b428 [mlir][sparse] Support strided convolution on dense level.
Reviewed By: aartbik

Differential Revision: https://reviews.llvm.org/D159020
2023-08-30 20:00:50 +00:00
Peiming Liu
07bd5f20bc [mlir][sparse] Support strided convolution on compressed level.
Reviewed By: aartbik

Differential Revision: https://reviews.llvm.org/D158912
2023-08-30 19:37:50 +00:00
Peiming Liu
96e1914aa2 [mlir][sparse] fix crash when generating convolution kernel with sparse input in DCCD format.
Reviewed By: aartbik, anlunx

Differential Revision: https://reviews.llvm.org/D159170
2023-08-30 17:49:36 +00:00
Yinying Li
51ebecf309 [mlir][sparse] Changed sparsity properties to use _ instead of -
Example: compressed-no -> compressed_no

Reviewed By: aartbik

Differential Revision: https://reviews.llvm.org/D158567
2023-08-23 17:00:27 +00:00
Peiming Liu
8c8aecdca9 [mlir][sparse] Supporting (non)uniqueness in SparseTensorStorage::lexDiff.
Fix copied from https://reviews.llvm.org/D156946 but with a legit test case that triggers the bug.

Reviewed By: aartbik

Differential Revision: https://reviews.llvm.org/D158578
2023-08-23 03:48:53 +00:00
Peiming Liu
6ca0b27298 [mlir][sparse] more complicated test for dual sparse convolution kernel.
Reviewed By: anlunx

Differential Revision: https://reviews.llvm.org/D158443
2023-08-21 18:48:01 +00:00
Andrzej Warzynski
51eaee3b42 [mlir][SparseTensor] Fix test regression
Fix a regression caused by https://reviews.llvm.org/D158012. Failing
bot:
  * https://lab.llvm.org/buildbot/#/builders/179/builds/7122

Note that both `RUN` lines in the affected file were previously
tested with similar configuraiton (_with_ and _without_ vectorisation).
This change restores that, though the new setting (from D158012) is
used, i.e.

  * with direct IR generation, `enable-runtime-library=true`.

This is sufficient to make the test pass and allows us to investigate
the root cause offline. Issue reported here:

  https://github.com/llvm/llvm-project/issues/64727
2023-08-16 09:37:07 +00:00
Peiming Liu
fa6726e27b [mlir][sparse] supports sparse_tensor.pack on libgen path
Reviewed By: aartbik

Differential Revision: https://reviews.llvm.org/D158012
2023-08-15 20:20:54 +00:00
Andrzej Warzynski
25396e1352 [mlir][test] Fix typo in a test
Remove unnecessary `"` that prevent correct `RUN` line expansion.

Introduced in:
  *https://reviews.llvm.org/D156625

Bot failure:
  * https://lab.llvm.org/buildbot/#/builders/61/builds/47437
2023-08-11 09:37:08 +01:00
Andrzej Warzynski
23e5130ebf [mlir][test] Reland: Refactor SparseTensor CPU integration tests
CHANGES SINCE THE ORIGINAL VERSION
----------------------------------
The default test set-up was extracted from
  * SparseTensor/CPU/lit.local.cfg.
and duplicated in all tests. This is to support downstream users that
don't use these local LIT config files.

SUMMARY OF CHANGES
------------------
This patch aims to reduce test duplication. This is a direct follow-up of:
  1. https://reviews.llvm.org/D155403 (test duplication), and
  2. https://reviews.llvm.org/D155405 (code re-use),

All SVE/VLA tests are now enabled _conditionally_ and refactored to use
`mlir-cpu-runner` rather than `lli`. The former helps with test
duplication and the latter with code re-use.

A few additional refactoring changes are included.

1. The reduce verbosity, long runtime library names like:

  %mlir_native_utils_lib_dir/libmlir_c_runner_utils%shlibext

are replaced with:

  %mlir_c_runner_utils

2. In order to keep the code and the comments in sync, and to maintain
   consistency across the tests, the following:

  enable-runtime-library=true

is swapped with (and vice-versa):

  enable-runtime-library=false

Note that this change won't affect test coverage. Only few tests
required such update.

3. A VLS vectorization `RUN` line is added in tests where there was a
   VLA/VLS `RUN` line, but no VLS `RUN` line (with a few exceptions of
   tests that only contained one `RUN` line to begin with).

4. A few test variables are renamed/added. Most notable example:
  * %{options}` --> %{sparse_compiler_opts}

TEST RUNTIME IMPROVEMENT
------------------------
Tl;Dr This change improves test execution time by ~25%.

At the moment, the following `llvm-lit` invocation takes ~7.30s on my
AArch64 workstation (with SVE):

  llvm-lit  <llvm-project>/mlir/test/Integration/Dialect/SparseTensor/CPU/

This timing doesn't change no matter what the value of the following
CMake variable is (that should disable some tests):

  MLIR_RUN_ARM_SVE_TESTS

With this patch, the execution time will indeed depend on the value of
the above CMake variable:
  * with `MLIR_RUN_ARM_SVE_TESTS=true` the timing remains intact,
  * with `MLIR_RUN_ARM_SVE_TESTS=false` the timing drops to ~5.40s (~25%
    improvement).
This is expected:
  * on average there are 4 `RUN` lines per test,
  * _without this change_ (and with `MLIR_RUN_ARM_SVE_TESTS=false`) the
    4th `RUN` line would in most cases duplicate the 3rd `RUN` line,
  * _with this change) (and with `MLIR_RUN_ARM_SVE_TESTS=false`) the
    4th `RUN` line becomes empty.

PATCH SIZE
----------
While rather large and touching many files, most changes in this patch
are rather mechanical. All test configurations have been preserved and
only in a handful of cases new `RUN` lines added.

Differential Revision: https://reviews.llvm.org/D156625
2023-08-11 08:16:01 +00:00
Aart Bik
5a1f87f9fc Revert "[mlir][test] Refactor SparseTensor CPU integration tests"
This reverts commit e77e891d8953b487f5f06bf69225a61ef537f766.

Differential Revision: https://reviews.llvm.org/D156947
2023-08-02 15:46:41 -07:00
Andrzej Warzynski
e77e891d89 [mlir][test] Refactor SparseTensor CPU integration tests
SUMMARY OF CHANGES
------------------
This patch aims to reduce test duplication and to improve code re-use in
SparseTensor integration tests for CPU. This is a direct follow-up of:
  1. https://reviews.llvm.org/D155403 (test duplication), and
  2. https://reviews.llvm.org/D155405 (code re-use),

The key logic for this patch is implemented in:
  * SparseTensor/CPU/lit.local.cfg.
Essentially, the set-up that used to be repeated across all test files
has been extracted into a common LIT configuration file. This makes code
re-use straightforward.

All SVE/VLA tests are now enabled _conditionally_ and refactored to use
`mlir-cpu-runner` rather than `lli`. The former helps with test
duplication and the latter with code re-use.

A few additional refactoring changes are included.

1. The reduce verbosity, long runtime library names like:

  %mlir_native_utils_lib_dir/libmlir_c_runner_utils%shlibext

are replaced with:

  %mlir_c_runner_utils

2. In order to keep the code and the comments in sync, and to maintain
   consistency across the tests, the following:

  enable-runtime-library=true

is swapped with (and vice-versa):

  enable-runtime-library=false

Note that this change won't affect test coverage. Only few tests
required such update.

3. A VLS vectorization `RUN` line is added in tests where there was a
   VLA/VLS `RUN` line, but no VLS `RUN` line (with a few exceptions of
   tests that only contained one `RUN` line to begin with).

4. A few test variables are renamed/added. Most notable example:
  * %{options}` --> %{sparse_compiler_opts}

TEST RUNTIME IMPROVEMENT
------------------------
Tl;Dr This change improves test execution time by ~25%.

At the moment, the following `llvm-lit` invocation takes ~7.30s on my
AArch64 workstation (with SVE):

  llvm-lit  <llvm-project>/mlir/test/Integration/Dialect/SparseTensor/CPU/

This timing doesn't change no matter what the value of the following
CMake variable is (that should disable some tests):

  MLIR_RUN_ARM_SVE_TESTS

With this patch, the execution time will indeed depend on the value of
the above CMake variable:
  * with `MLIR_RUN_ARM_SVE_TESTS=true` the timing remains intact,
  * with `MLIR_RUN_ARM_SVE_TESTS=false` the timing drops to ~5.40s (~25%
    improvement).
This is expected:
  * on average there are 4 `RUN` lines per test,
  * _without this change_ (and with `MLIR_RUN_ARM_SVE_TESTS=false`) the
    4th `RUN` line would in most cases duplicate the 3rd `RUN` line,
  * _with this change) (and with `MLIR_RUN_ARM_SVE_TESTS=false`) the
    4th `RUN` line becomes empty.

PATCH SIZE
----------
While rather large and touching many files, most changes in this patch
are rather mechanical. All test configurations have been preserved and
only in a handful of cases new `RUN` lines added.

Differential Revision: https://reviews.llvm.org/D156625
2023-08-02 20:21:50 +00:00
Andrzej Warzynski
e62f366b01 [mlir] Update SVE integration tests to use mlir-cpu-runner
With the recent addition of "-mattr" and "-march" to the list of options
supported by mlir-cpu-runner [1], the SVE integration
tests can be updated to use mlir-cpu-runner instead of lli. This will
allow better code re-use and more consistency

This patch updates 2 tests to demonstrate the new logic. The remaining
tests will be updated in the follow-up patches.

[1] https://reviews.llvm.org/D146917

Depends on D155403

Differential Revision: https://reviews.llvm.org/D155405
2023-07-19 08:29:17 +00:00
Andrzej Warzynski
aa9a10ac1d [mlir][SparseTensor][ArmSVE] Conditionally disable SVE RUN line
This patch updates one SparseTensor integration test so that the VLA
vectorisation is run conditionally based on the value of the
MLIR_RUN_ARM_SME_TESTS CMake variable.

This change opens the path to reduce the duplication of RUN lines in
"mlir/test/Integration/Dialect/SparseTensor/CPU/". ATM, there are
usually 2 RUN lines to test vectorization in SparseTensor integration
tests:
  * one for VLS vectorisation,
  * one for VLA vectorisation whenever that's available and which
    reduces to VLS vectorisation when VLA is not supported.
When VLA is not available, VLS vectorisation is verified twice. This
duplication should be avoided - integration test are relatively
expansive to run.

This patch makes sure that the 2nd vectorisation RUN line becomes:
```
  if (SVE integration tests are enabled)
    run VLA vectorisation
  else
    return
```
This logic is implemented using LIT's (relatively new) conditional
substitution [1]. It enables us to guarantee that all RUN lines are
unique and that the VLA vectorisation is only enabled when supported.

This patch updates only 1 test to set-up and to demonstrate the logic.
Subsequent patches will update the remaining tests.

[1] https://www.llvm.org/docs/TestingGuide.html

Differential Revision: https://reviews.llvm.org/D155403
2023-07-18 06:59:08 +00:00
Peiming Liu
fc5d8fce7d [mlir][sparse] support dual sparse convolution.
Reviewed By: aartbik

Differential Revision: https://reviews.llvm.org/D152601
2023-07-10 16:49:32 +00:00
Peiming Liu
a63d6a0014 [mlir][sparse] make UnpackOp return the actual filled length of unpacked memory
This might simplify frontend implementation by avoiding recomputation for the same value.

Reviewed By: aartbik

Differential Revision: https://reviews.llvm.org/D154244
2023-06-30 21:35:15 +00:00
Peiming Liu
e7df82816b [mlir][sparse] rewrite arith::SelectOp to semiring operations to sparsify it.
Reviewed By: aartbik, K-Wu

Differential Revision: https://reviews.llvm.org/D153397
2023-06-21 21:22:18 +00:00
Peiming Liu
faf7cd97d0 [mlir][sparse] merger extension to support sparsifying arith::CmpI/CmpF operation
Reviewed By: aartbik

Differential Revision: https://reviews.llvm.org/D152761
2023-06-15 17:26:50 +00:00
Aart Bik
80fe3168b5 [mlir][sparse] add support for direct prod/and/min/max reductions
We recently fixed a bug in "sparsifying" such reductions, since
it incorrectly changed this into reductions over stored elements
only , which only works for add/sub/or/xor. However, we still want
to be able to "sparsify" the reductions even in the general case,
and this is a first step by rewriting them into a custom reduction
that feeds in the implicit zeros. NOTE HOWEVER, that in the long run
we want to do this better and feed in any implicit zero only ONCE
for efficiency.

Reviewed By: Peiming

Differential Revision: https://reviews.llvm.org/D152580
2023-06-12 09:27:47 -07:00