[mlir][linalg] Enable Vectorization of 0-D tensor.extract #119079

banach-space · 2024-12-07T16:33:24Z

This patch removes an assert in vectorizeTensorExtract that was blocking
the vectorization of 0-D tensor.extract operations, e.g.:

  %1 = tensor.extract %src[] : tensor<f32>

As demonstrated by the included tests, this case is already effectively
supported.

Context
The removed assert was introduced in #109580 as a guard, pending proper support
and testing for 0-D tensors. This PR addresses that previously undocumented
TODO. Apologies for the oversight!

Updates and Tests

Revised the existing test @negative_no_loop to ensure the
vectorize_nd_extract attribute is included, allowing the vectorizer
to process it. The test was renamed and variables updated for clarity.
Added a new test @extract_scalar_from_0d_into_1d to cover "mixed"
0-D/1-D tensor extraction, e.g.:

  %res = linalg.generic {
    indexing_maps = [#map],
    iterator_types = ["parallel"]
  } outs(%init : tensor<1xf32>) {
  ^bb0(%in: f32):
    %1 = tensor.extract %src[] : tensor<f32>
    linalg.yield %1 : f32
  } -> tensor<1xf32>

  return %res : tensor<1xf32>

Additional updates
I also took the liberty and improved test coverage for 0-D tensor in the
vectorizer tests:

Added a specific test for "0D linalg.generic" in
"vectorization-with-patterns.mlir".
Renamed several tests in "vectorization-with-patterns.mlir" to clarify
that the 0-D case is now covered.

This patch removes an assert in `vectorizeTensorExtract` that was blocking the vectorization of 0-D tensor.extract operations, e.g.: ```mlir %1 = tensor.extract %src[] : tensor<f32> ``` As demonstrated by the included tests, this case is already effectively supported. **Context** The removed assert was introduced in llvm#109580 as a guard, pending proper support and testing for 0-D tensors. This PR addresses that previously undocumented TODO. Apologies for the oversight! **Updates and Tests** * Revised the existing test `@negative_no_loop` to ensure the `vectorize_nd_extract` attribute is included, allowing the vectorizer to process it. The test was renamed and variables updated for clarity. * Added a new test `@extract_scalar_from_0d_into_1d` to cover "mixed" 0-D/1-D tensor extraction, e.g.: ```mlir %res = linalg.generic { indexing_maps = [#map], iterator_types = ["parallel"] } outs(%init : tensor<1xf32>) { ^bb0(%in: f32): %1 = tensor.extract %src[] : tensor<f32> linalg.yield %1 : f32 } -> tensor<1xf32> return %res : tensor<1xf32> ``` **Additional updates** I also took the liberty and improved test coverage for 0-D tensor in the vectorizer tests: * Added a specific test for "0D linalg.generic" in "vectorization-with-patterns.mlir". * Renamed several tests in "vectorization-with-patterns.mlir" to clarify that the 0-D case is now covered.

llvmbot · 2024-12-07T16:33:58Z

@llvm/pr-subscribers-mlir

@llvm/pr-subscribers-mlir-linalg

Author: Andrzej Warzyński (banach-space)

Changes

This patch removes an assert in vectorizeTensorExtract that was blocking
the vectorization of 0-D tensor.extract operations, e.g.:

  %1 = tensor.extract %src[] : tensor&lt;f32&gt;

As demonstrated by the included tests, this case is already effectively
supported.

Context
The removed assert was introduced in #109580 as a guard, pending proper support
and testing for 0-D tensors. This PR addresses that previously undocumented
TODO. Apologies for the oversight!

Updates and Tests

Revised the existing test @negative_no_loop to ensure the
vectorize_nd_extract attribute is included, allowing the vectorizer
to process it. The test was renamed and variables updated for clarity.
Added a new test @extract_scalar_from_0d_into_1d to cover "mixed"
0-D/1-D tensor extraction, e.g.:

  %res = linalg.generic {
    indexing_maps = [#map],
    iterator_types = ["parallel"]
  } outs(%init : tensor&lt;1xf32&gt;) {
  ^bb0(%in: f32):
    %1 = tensor.extract %src[] : tensor&lt;f32&gt;
    linalg.yield %1 : f32
  } -&gt; tensor&lt;1xf32&gt;

  return %res : tensor&lt;1xf32&gt;

Additional updates
I also took the liberty and improved test coverage for 0-D tensor in the
vectorizer tests:

Added a specific test for "0D linalg.generic" in
"vectorization-with-patterns.mlir".
Renamed several tests in "vectorization-with-patterns.mlir" to clarify
that the 0-D case is now covered.

Full diff: https://github.com/llvm/llvm-project/pull/119079.diff

3 Files Affected:

(modified) mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp (-5)
(modified) mlir/test/Dialect/Linalg/vectorization-with-patterns.mlir (+45-3)
(modified) mlir/test/Dialect/Linalg/vectorize-tensor-extract.mlir (+48-10)

diff --git a/mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp b/mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp
index e5c96b52acee23..863f2280e46ce6 100644
--- a/mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp
+++ b/mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp
@@ -1115,11 +1115,6 @@ vectorizeTensorExtract(RewriterBase &rewriter, VectorizationState &state,
   //  b. contiguous loads.
   // Both cases use vector.transfer_read.
 
-  assert(llvm::count_if(resultType.getShape(),
-                        [](uint64_t dim) { return dim != 1; }) &&
-         "Contiguous loads and scalar loads + broadcast only support 1-D "
-         "vectors ATM!");
-
   // Collect indices for `vector.transfer_read`. At this point, the indices will
   // either be scalars or would have been broadcast to vectors matching the
   // result type. For indices that are vectors, there are two options:
diff --git a/mlir/test/Dialect/Linalg/vectorization-with-patterns.mlir b/mlir/test/Dialect/Linalg/vectorization-with-patterns.mlir
index 0c996bed996d3c..b688a677500c22 100644
--- a/mlir/test/Dialect/Linalg/vectorization-with-patterns.mlir
+++ b/mlir/test/Dialect/Linalg/vectorization-with-patterns.mlir
@@ -122,6 +122,48 @@ module attributes {transform.with_named_sequence} {
 
 // -----
 
+#map = affine_map<() -> ()>
+
+// CHECK-LABEL:   func.func @generic_0d(
+// CHECK-SAME:     %[[ARG_0:.*]]: tensor<f32>, %[[ARG_1:.*]]: tensor<f32>, %[[ARG_2:.*]]: tensor<f32>)
+func.func @generic_0d(%arg0: tensor<f32>, %arg1: tensor<f32>,
+                      %arg2: tensor<f32>) -> tensor<f32> {
+// CHECK:           %[[PAD:.*]] = arith.constant 0.000000e+00 : f32
+// CHECK:           %[[READ_0:.*]] = vector.transfer_read %[[ARG_0]][], %[[PAD]] : tensor<f32>, vector<f32>
+// CHECK:           %[[ARG_0_AS_SCALAR:.*]] = vector.extract %[[READ_0]][] : f32 from vector<f32>
+// CHECK:           %[[READ_1:.*]] = vector.transfer_read %[[ARG_1]][], %[[PAD]] : tensor<f32>, vector<f32>
+// CHECK:           %[[ARG_1_AS_SCALAR:.*]] = vector.extract %[[READ_1]][] : f32 from vector<f32>
+// CHECK:           %[[READ_2:.*]] = vector.transfer_read %[[ARG_2]][], %[[PAD]] : tensor<f32>, vector<f32>
+// CHECK:           %[[ARG_2_AS_SCALAR:.*]] = vector.extract %[[READ_2]][] : f32 from vector<f32>
+// CHECK:           %[[MULF:.*]] = arith.mulf %[[ARG_0_AS_SCALAR]], %[[ARG_1_AS_SCALAR]] : f32
+// CHECK:           %[[ADDF:.*]] = arith.addf %[[ARG_2_AS_SCALAR]], %[[MULF]] : f32
+// CHECK:           %[[ADDF_BCAST:.*]] = vector.broadcast %[[ADDF]] : f32 to vector<f32>
+// CHECK:           vector.transfer_write %[[ADDF_BCAST]], %[[ARG_2]][] : vector<f32>, tensor<f32>
+  %res = linalg.generic {
+    indexing_maps = [#map, #map, #map],
+    iterator_types = []
+  } ins(%arg0, %arg1 : tensor<f32>, tensor<f32>)
+    outs(%arg2 : tensor<f32>) {
+  ^bb(%a: f32, %b: f32, %c: f32) :
+    %d = arith.mulf %a, %b: f32
+    %e = arith.addf %c, %d: f32
+    linalg.yield %e : f32
+  } -> tensor<f32>
+
+  return %res : tensor<f32>
+}
+
+module attributes {transform.with_named_sequence} {
+  transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
+    %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+    %1 = transform.get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
+    %2 = transform.structured.vectorize_children_and_apply_patterns %1  { disable_multi_reduction_to_contract_patterns, disable_transfer_permutation_map_lowering_patterns } : (!transform.any_op) -> !transform.any_op
+    transform.yield
+  }
+}
+
+// -----
+
 #matmul_transpose_out_trait = {
   indexing_maps = [
     affine_map<(m, n, k) -> (m, k)>,
@@ -372,7 +414,7 @@ module attributes {transform.with_named_sequence} {
 // -----
 
 // CHECK-LABEL: func @test_vectorize_fill
-func.func @test_vectorize_fill_scalar(%A : memref<f32>, %arg0 : f32) {
+func.func @test_vectorize_fill_0d(%A : memref<f32>, %arg0 : f32) {
   // CHECK-SAME: (%[[M:.*]]: memref<f32>, %[[val:.*]]: f32)
   //      CHECK:   %[[VEC:.*]] = vector.broadcast %[[val]] : f32 to vector<f32>
   //      CHECK:   vector.transfer_write %[[VEC]], %[[M]][] : vector<f32>, memref<f32>
@@ -410,8 +452,8 @@ module attributes {transform.with_named_sequence} {
 
 // -----
 
-// CHECK-LABEL: func @test_vectorize_copy_scalar
-func.func @test_vectorize_copy_scalar(%A : memref<f32>, %B : memref<f32>) {
+// CHECK-LABEL: func @test_vectorize_copy_0d
+func.func @test_vectorize_copy_0d(%A : memref<f32>, %B : memref<f32>) {
   //  CHECK-SAME: (%[[A:.*]]: memref<f32>, %[[B:.*]]: memref<f32>)
   //       CHECK:   %[[V:.*]] = vector.transfer_read %[[A]][]{{.*}} : memref<f32>, vector<f32>
   //       CHECK:   %[[val:.*]] = vector.extract %[[V]][] : f32 from vector<f32>
diff --git a/mlir/test/Dialect/Linalg/vectorize-tensor-extract.mlir b/mlir/test/Dialect/Linalg/vectorize-tensor-extract.mlir
index 1a93d1cd9b7880..775ceed31be04a 100644
--- a/mlir/test/Dialect/Linalg/vectorize-tensor-extract.mlir
+++ b/mlir/test/Dialect/Linalg/vectorize-tensor-extract.mlir
@@ -39,29 +39,67 @@ module attributes {transform.with_named_sequence} {
 // -----
 
 #map = affine_map<() -> ()>
-func.func @negative_no_loops(%arg0: tensor<f32>, %arg1: tensor<f32>) -> tensor<f32> {
-  %1 = linalg.generic {
+func.func @extract_scalar_from_0d_into_0d(%src: tensor<f32>, %init: tensor<f32>) -> tensor<f32> {
+  %res = linalg.generic {
     indexing_maps = [#map],
     iterator_types = []
-  } outs(%arg1 : tensor<f32>) {
-  ^bb0(%arg4: f32):
-    %2 = tensor.extract %arg0[] : tensor<f32>
-    linalg.yield %2 : f32
+  } outs(%init : tensor<f32>) {
+  ^bb0(%in: f32):
+    %1 = tensor.extract %src[] : tensor<f32>
+    linalg.yield %1 : f32
   } -> tensor<f32>
-  return %1 : tensor<f32>
+
+  return %res : tensor<f32>
 }
-// CHECK-LABEL: func.func @negative_no_loops
-// CHECK: tensor.extract
+
+// CHECK-LABEL:   func.func @extract_scalar_from_0d_into_0d(
+// CHECK-SAME:      %[[SRC:.*]]: tensor<f32>,
+// CHECK-SAME:      %[[INIT:.*]]: tensor<f32>) -> tensor<f32> {
+// CHECK:           %[[PAD:.*]] = arith.constant 0.000000e+00 : f32
+// CHECK:           %[[READ:.*]] = vector.transfer_read %[[SRC]][], %[[PAD]] : tensor<f32>, vector<f32>
+// CHECK:           vector.transfer_write %[[READ]], %[[INIT]][] : vector<f32>, tensor<f32>
 
 module attributes {transform.with_named_sequence} {
   transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
     %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
     %1 = transform.get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
-    %2 = transform.structured.vectorize_children_and_apply_patterns %1 : (!transform.any_op) -> !transform.any_op
+    %2 = transform.structured.vectorize_children_and_apply_patterns %1 { vectorize_nd_extract } : (!transform.any_op) -> !transform.any_op
     transform.yield
   }
 }
 
+// -----
+
+#map = affine_map<(n) -> (n)>
+func.func @extract_scalar_from_0d_into_1d(%src: tensor<f32>, %init: tensor<1xf32>) -> tensor<1xf32> {
+  %res = linalg.generic {
+    indexing_maps = [#map],
+    iterator_types = ["parallel"]
+  } outs(%init : tensor<1xf32>) {
+  ^bb0(%in: f32):
+    %1 = tensor.extract %src[] : tensor<f32>
+    linalg.yield %1 : f32
+  } -> tensor<1xf32>
+
+  return %res : tensor<1xf32>
+}
+// CHECK-LABEL:   func.func @extract_scalar_from_0d_into_1d(
+// CHECK-SAME:      %[[SRC:.*]]: tensor<f32>,
+// CHECK-SAME:      %[[INIT:.*]]: tensor<1xf32>) -> tensor<1xf32> {
+// CHECK:           %[[C0:.*]] = arith.constant 0 : index
+// CHECK:           %[[PAD:.*]] = arith.constant 0.000000e+00 : f32
+// CHECK:           %[[READ:.*]] = vector.transfer_read %[[SRC]][], %[[PAD]] : tensor<f32>, vector<f32>
+// CHECK:           %[[READ_BCAST:.*]] = vector.broadcast %[[READ]] : vector<f32> to vector<1xf32>
+// CHECK:           vector.transfer_write %[[READ_BCAST]], %[[INIT]][%[[C0]]] {in_bounds = [true]} : vector<1xf32>, tensor<1xf32>
+
+module attributes {transform.with_named_sequence} {
+  transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
+    %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+    %1 = transform.get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
+    %2 = transform.structured.vectorize_children_and_apply_patterns %1 { vectorize_nd_extract } : (!transform.any_op) -> !transform.any_op
+    transform.yield
+  }
+}
 
 // -----

Tests in "vectorize-tensor-extract.mlir" are inconsistent and would benefit from refactoring to: * Clearly categorize tests into "contiguous load," "gather load," and "scalar load + broadcast" cases, reflecting the structure of tensor.extract vectorization. * Unify variable naming (both MLIR and FileCheck). * Ensure all tests exercise unmasked vectorization (masked vectorization is covered in "vectorize-tensor-extract-masked.mlir"). * Improve and standardize formatting. These changes will make it easier to identify the test cases being exercised and simplify future maintenance or refactoring. This is patch 2/N in the series. Below is a summary of the changes in this patch. ---------------------------------------------------------------------- Summary of patch 2/N ---------------------------------------------------------------------- Makes all tests re-use the same TD sequence. This TD sequence has been extracted to a dedicted file: * "td/vectorize-with-patterns.mlir". ---------------------------------------------------------------------- **DEPENDS ON** * llvm#118977 * llvm#119079 Please only review the top commit

Tests in "vectorize-tensor-extract.mlir" are inconsistent and would benefit from refactoring to: * Clearly categorize tests into "contiguous load," "gather load," and "scalar load + broadcast" cases, reflecting the structure of tensor.extract vectorization. * Unify variable naming (both MLIR and FileCheck). * Ensure all tests exercise unmasked vectorization (masked vectorization is covered in "vectorize-tensor-extract-masked.mlir"). * Improve and standardize formatting. These changes will make it easier to identify the test cases being exercised and simplify future maintenance or refactoring. This is patch 3/N in the series. Below is a summary of the changes in this patch. ---------------------------------------------------------------------- Summary of patch 3/N ---------------------------------------------------------------------- * Cluster all tests for "scalar load + broadcast" together * Unify MLIR and FileCheck variable names, e.g. `%input`, `%output` -> `%src`, `%init`. Note, I haven't changed test function names to make it easier to track changes (this PR is mostly about moving code). I will send a seperate PR to rename the tests. ---------------------------------------------------------------------- **DEPENDS ON** * llvm#118977 * llvm#119079 * llvm#118977 Please only review the top commit

Groverkss

LGTM, Thanks!

Tests in "vectorize-tensor-extract.mlir" are inconsistent and would benefit from refactoring to: * Clearly categorize tests into "contiguous load," "gather load," and "scalar load + broadcast" cases, reflecting the structure of tensor.extract vectorization. * Unify variable naming (both MLIR and FileCheck). * Ensure all tests exercise unmasked vectorization (masked vectorization is covered in "vectorize-tensor-extract-masked.mlir"). * Improve and standardize formatting. These changes will make it easier to identify the test cases being exercised and simplify future maintenance or refactoring. This is patch 3/N in the series. Below is a summary of the changes in this patch. ---------------------------------------------------------------------- Summary of patch 3/N ---------------------------------------------------------------------- * Cluster all tests for "scalar load + broadcast" together * Unify MLIR and FileCheck variable names, e.g. `%input`, `%output` -> `%src`, `%init`. Note, I haven't changed test function names to make it easier to track changes (this PR is mostly about moving code). I will send a seperate PR to rename the tests. ---------------------------------------------------------------------- **DEPENDS ON** * llvm#118977 * llvm#119079 * llvm#118977 Please only review the top commit

Tests in "vectorize-tensor-extract.mlir" are inconsistent and would benefit from refactoring to: * Clearly categorize tests into "contiguous load," "gather load," and "scalar load + broadcast" cases, reflecting the structure of tensor.extract vectorization. * Unify variable naming (both MLIR and FileCheck). * Ensure all tests exercise unmasked vectorization (masked vectorization is covered in "vectorize-tensor-extract-masked.mlir"). * Improve and standardize formatting. These changes will make it easier to identify the test cases being exercised and simplify future maintenance or refactoring. This is patch 2/N in the series. Below is a summary of the changes in this patch. ---------------------------------------------------------------------- Summary of patch 2/N ---------------------------------------------------------------------- Makes all tests re-use the same TD sequence. This TD sequence has been extracted to a dedicted file: * "td/vectorize-with-patterns.mlir". ---------------------------------------------------------------------- **DEPENDS ON** * llvm#118977 * llvm#119079 Please only review the top commit

Tests in "vectorize-tensor-extract.mlir" are inconsistent and would benefit from refactoring to: * Clearly categorize tests into "contiguous load," "gather load," and "scalar load + broadcast" cases, reflecting the structure of tensor.extract vectorization. * Unify variable naming (both MLIR and FileCheck). * Ensure all tests exercise unmasked vectorization (masked vectorization is covered in "vectorize-tensor-extract-masked.mlir"). * Improve and standardize formatting. These changes will make it easier to identify the test cases being exercised and simplify future maintenance or refactoring. This is patch 3/N in the series. Below is a summary of the changes in this patch. ---------------------------------------------------------------------- Summary of patch 3/N ---------------------------------------------------------------------- * Cluster all tests for "scalar load + broadcast" together * Unify MLIR and FileCheck variable names, e.g. `%input`, `%output` -> `%src`, `%init`. Note, I haven't changed test function names to make it easier to track changes (this PR is mostly about moving code). I will send a seperate PR to rename the tests. ---------------------------------------------------------------------- **DEPENDS ON** * llvm#118977 * llvm#119079 * llvm#118977 Please only review the top commit

llvm-ci · 2024-12-11T16:09:29Z

LLVM Buildbot has detected a new failure on builder premerge-monolithic-linux running on premerge-linux-1 while building mlir at step 7 "test-build-unified-tree-check-all".

Full details are available at: https://lab.llvm.org/buildbot/#/builders/153/builds/17227

Here is the relevant piece of the build log for the reference

Step 7 (test-build-unified-tree-check-all) failure: test (failure)
******************** TEST 'LLVM :: ExecutionEngine/JITLink/AArch64/ELF_relocations.s' FAILED ********************
Exit Code: 1

Command Output (stderr):
--
RUN: at line 1: rm -rf /build/buildbot/premerge-monolithic-linux/build/test/ExecutionEngine/JITLink/AArch64/Output/ELF_relocations.s.tmp && mkdir -p /build/buildbot/premerge-monolithic-linux/build/test/ExecutionEngine/JITLink/AArch64/Output/ELF_relocations.s.tmp
+ rm -rf /build/buildbot/premerge-monolithic-linux/build/test/ExecutionEngine/JITLink/AArch64/Output/ELF_relocations.s.tmp
+ mkdir -p /build/buildbot/premerge-monolithic-linux/build/test/ExecutionEngine/JITLink/AArch64/Output/ELF_relocations.s.tmp
RUN: at line 2: /build/buildbot/premerge-monolithic-linux/build/bin/llvm-mc -triple=aarch64-unknown-linux-gnu -x86-relax-relocations=false    -position-independent -filetype=obj -o /build/buildbot/premerge-monolithic-linux/build/test/ExecutionEngine/JITLink/AArch64/Output/ELF_relocations.s.tmp/elf_reloc.o /build/buildbot/premerge-monolithic-linux/llvm-project/llvm/test/ExecutionEngine/JITLink/AArch64/ELF_relocations.s
+ /build/buildbot/premerge-monolithic-linux/build/bin/llvm-mc -triple=aarch64-unknown-linux-gnu -x86-relax-relocations=false -position-independent -filetype=obj -o /build/buildbot/premerge-monolithic-linux/build/test/ExecutionEngine/JITLink/AArch64/Output/ELF_relocations.s.tmp/elf_reloc.o /build/buildbot/premerge-monolithic-linux/llvm-project/llvm/test/ExecutionEngine/JITLink/AArch64/ELF_relocations.s
RUN: at line 4: /build/buildbot/premerge-monolithic-linux/build/bin/llvm-jitlink -noexec               -abs external_data=0xdeadbeef               -abs external_func=0xcafef00d               -check /build/buildbot/premerge-monolithic-linux/llvm-project/llvm/test/ExecutionEngine/JITLink/AArch64/ELF_relocations.s /build/buildbot/premerge-monolithic-linux/build/test/ExecutionEngine/JITLink/AArch64/Output/ELF_relocations.s.tmp/elf_reloc.o
+ /build/buildbot/premerge-monolithic-linux/build/bin/llvm-jitlink -noexec -abs external_data=0xdeadbeef -abs external_func=0xcafef00d -check /build/buildbot/premerge-monolithic-linux/llvm-project/llvm/test/ExecutionEngine/JITLink/AArch64/ELF_relocations.s /build/buildbot/premerge-monolithic-linux/build/test/ExecutionEngine/JITLink/AArch64/Output/ELF_relocations.s.tmp/elf_reloc.o
Expression 'decode_operand(test_adr_gotpage_external, 1) =      (got_addr(elf_reloc.o, external_data)[32:12] -         test_adr_gotpage_external[32:12])' is false: 0x108 != 0xffffffffffe00108
/build/buildbot/premerge-monolithic-linux/build/bin/llvm-jitlink: Some checks in /build/buildbot/premerge-monolithic-linux/llvm-project/llvm/test/ExecutionEngine/JITLink/AArch64/ELF_relocations.s failed

--

********************

banach-space requested review from dcaballe, hanhanW, nicolasvasilache and Groverkss as code owners December 7, 2024 16:33

llvmbot added mlir:linalg mlir labels Dec 7, 2024

banach-space mentioned this pull request Dec 7, 2024

[mlir][nfc] Update vectorize-tensor-extract.mlir (2/N) #119080

Merged

banach-space mentioned this pull request Dec 8, 2024

[mlir][nfc] Update vectorize-tensor-extract.mlir (3/N) #119121

Merged

Groverkss approved these changes Dec 10, 2024

View reviewed changes

dcaballe approved these changes Dec 11, 2024

View reviewed changes

banach-space merged commit be06c79 into llvm:main Dec 11, 2024
11 checks passed

banach-space deleted the andrzej/extract_remove_assert branch January 13, 2025 11:45

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