[Mlir-commits] [mlir] Rename structured.{vectorize => vectorize_children}. (PR #66575)
Ingo Müller
llvmlistbot at llvm.org
Tue Sep 19 04:26:14 PDT 2023
https://github.com/ingomueller-net updated https://github.com/llvm/llvm-project/pull/66575
>From 3c02d5aab00d279a7f189e14b56ab516d5294f1c Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Ingo=20M=C3=BCller?= <ingomueller at google.com>
Date: Sat, 16 Sep 2023 13:03:02 +0000
Subject: [PATCH 1/4] Rename structured.{vectorize => vectorize_children}.
---
.../Linalg/TransformOps/LinalgTransformOps.td | 37 +++---
.../TransformOps/LinalgTransformOps.cpp | 30 ++---
.../dialects/_structured_transform_ops_ext.py | 4 +-
mlir/test/Dialect/LLVM/transform-e2e.mlir | 2 +-
.../transform-op-matmul-to-outerproduct.mlir | 2 +-
.../Linalg/transform-op-vectorize.mlir | 12 +-
mlir/test/Dialect/Linalg/vectorization.mlir | 114 +++++++++---------
.../Linalg/vectorize-tensor-extract.mlir | 24 ++--
.../Transform/selective-targeting.mlir | 6 +-
.../test/Dialect/Vector/transform-vector.mlir | 2 +-
.../dialects/transform_structured_ext.py | 12 +-
11 files changed, 124 insertions(+), 121 deletions(-)
diff --git a/mlir/include/mlir/Dialect/Linalg/TransformOps/LinalgTransformOps.td b/mlir/include/mlir/Dialect/Linalg/TransformOps/LinalgTransformOps.td
index 74c0909ce58e88a..6bd93633aaa6d4b 100644
--- a/mlir/include/mlir/Dialect/Linalg/TransformOps/LinalgTransformOps.td
+++ b/mlir/include/mlir/Dialect/Linalg/TransformOps/LinalgTransformOps.td
@@ -1947,37 +1947,38 @@ def TileToForallOp :
}
//===----------------------------------------------------------------------===//
-// VectorizeOp
+// VectorizeChildrenOp
//===----------------------------------------------------------------------===//
-def VectorizeOp : Op<Transform_Dialect, "structured.vectorize",
+def VectorizeChildrenOp : Op<Transform_Dialect, "structured.vectorize_children",
[FunctionalStyleTransformOpTrait, MemoryEffectsOpInterface,
TransformEachOpTrait, TransformOpInterface,
ReportTrackingListenerFailuresOpTrait]> {
let description = [{
- Indicates that the given `target` op all the ops it contains should be
- vectorized with the configuration specified by the attributes of this op.
- This vectorization only handles structured ops that operate on shaped types
- and does not vectorize loops or straight-line. Internally, it applies a
- set of rewrite patterns, some of which enable vectorization and some of
- which clean up the results. Therefore, it can only be applied to an op with
- the "isolated from above property". If finer granularity is required, it can
- be achieved by outlining the target part of the payload IR into, e.g., a
- function, performing the transformation, and inlining it back. This
- transformation only fails if the entire pattern rewriting failed, i.e., it
- does **not** fail when no ops were vectorized.
-
- Note that this transformation is invalidating the handles to any payload IR
+ Vectorizes all children contained in the given `target` using the
+ configuration specified by the attributes of this op. This only vectorizes
+ structured ops that operate on shaped types and does not vectorize loops or
+ straight-line. Internally, it applies a set of rewrite patterns, some of
+ which enable vectorization and some of which clean up the results.
+ Therefore, it can only be applied to an op with the "isolated from above"
+ property. This transformation only fails if the entire pattern rewriting
+ failed, i.e., it does **not** fail when no ops were vectorized.
+
+ Finer granularity can be achieved either with the `VectorizeOp` for
+ individual ops or by outlining the target part of the payload IR into, e.g.,
+ a function, performing this transformation, and inlining it back.
+
+ Note that this transformation invalidates the handles to any payload IR
operation that is contained inside the vectorization target.
This transformation supports the following attributes:
- - `vectorize_padding`: a UnitAttr to activate the vectorization of
+ - `vectorize_padding`: a `UnitAttr` to activate the vectorization of
`tensor.pad` ops. Different pipelines may prefer to lower such ops to
loops.
- - `disable_multi_reduction_to_contract_patterns`: a UnitAttr to deactivate
+ - `disable_multi_reduction_to_contract_patterns`: a `UnitAttr` to deactivate
the rewrite of `vector.multi_reduction` to `vector.contract`. This is
intended to be used in tests only.
- - `disable_transfer_permutation_map_lowering_patterns`: a UnitAttr to
+ - `disable_transfer_permutation_map_lowering_patterns`: a `UnitAttr` to
deactivate the rewrite of `vector.transfer` with permutation maps into
explicit `vector.transpose` operations. This is intended to be used in
tests only but may be promoted to a first class attribute in the future.
diff --git a/mlir/lib/Dialect/Linalg/TransformOps/LinalgTransformOps.cpp b/mlir/lib/Dialect/Linalg/TransformOps/LinalgTransformOps.cpp
index dc65ac509d280dc..f4fc35d686a023a 100644
--- a/mlir/lib/Dialect/Linalg/TransformOps/LinalgTransformOps.cpp
+++ b/mlir/lib/Dialect/Linalg/TransformOps/LinalgTransformOps.cpp
@@ -2904,27 +2904,30 @@ LogicalResult TileToForallOp::verify() {
}
//===----------------------------------------------------------------------===//
-// VectorizeOp
+// VectorizeChildrenOp
//===----------------------------------------------------------------------===//
-void transform::VectorizeOp::build(OpBuilder &builder, OperationState &result,
- Value target, bool vectorizePadding,
- bool vectorizeExtract) {
+void transform::VectorizeChildrenOp::build(OpBuilder &builder,
+ OperationState &result, Value target,
+ bool vectorizePadding,
+ bool vectorizeExtract) {
result.addOperands(target);
if (vectorizePadding) {
- result.addAttribute(VectorizeOp::getVectorizePaddingAttrName(result.name),
- builder.getUnitAttr());
+ result.addAttribute(
+ VectorizeChildrenOp::getVectorizePaddingAttrName(result.name),
+ builder.getUnitAttr());
}
if (vectorizeExtract) {
- result.addAttribute(VectorizeOp::getVectorizeNdExtractAttrName(result.name),
- builder.getUnitAttr());
+ result.addAttribute(
+ VectorizeChildrenOp::getVectorizeNdExtractAttrName(result.name),
+ builder.getUnitAttr());
}
result.addTypes(transform::AnyOpType::get(builder.getContext()));
}
namespace {
/// This is an helper only to call vectorize via a pattern inside of
-/// VectorizeOp::applyToOne.
+/// VectorizeChildrenOp::applyToOne.
struct VectorizationPattern : public RewritePattern {
explicit VectorizationPattern(MLIRContext *context,
bool vectorizeExtract = false)
@@ -2946,11 +2949,10 @@ struct VectorizationPattern : public RewritePattern {
};
} // namespace
-DiagnosedSilenceableFailure
-transform::VectorizeOp::applyToOne(transform::TransformRewriter &rewriter,
- Operation *target,
- transform::ApplyToEachResultList &results,
- transform::TransformState &state) {
+DiagnosedSilenceableFailure transform::VectorizeChildrenOp::applyToOne(
+ transform::TransformRewriter &rewriter, Operation *target,
+ transform::ApplyToEachResultList &results,
+ transform::TransformState &state) {
if (!target->hasTrait<OpTrait::IsIsolatedFromAbove>()) {
auto diag = this->emitOpError("requires isolated-from-above targets");
diag.attachNote(target->getLoc()) << "non-isolated target";
diff --git a/mlir/python/mlir/dialects/_structured_transform_ops_ext.py b/mlir/python/mlir/dialects/_structured_transform_ops_ext.py
index c5134b6e718f3b8..911d46d24991522 100644
--- a/mlir/python/mlir/dialects/_structured_transform_ops_ext.py
+++ b/mlir/python/mlir/dialects/_structured_transform_ops_ext.py
@@ -724,8 +724,8 @@ def __init__(
)
-class VectorizeOp:
- """Specialization for VectorizeOp class."""
+class VectorizeChildrenOp:
+ """Specialization for VectorizeChildrenOp class."""
def __init__(
self,
diff --git a/mlir/test/Dialect/LLVM/transform-e2e.mlir b/mlir/test/Dialect/LLVM/transform-e2e.mlir
index 2cb753a3d7fb8f3..a976036fd71ceee 100644
--- a/mlir/test/Dialect/LLVM/transform-e2e.mlir
+++ b/mlir/test/Dialect/LLVM/transform-e2e.mlir
@@ -17,7 +17,7 @@ transform.sequence failures(propagate) {
%0 = transform.structured.match ops{["linalg.matmul"]} in %module_op : (!transform.any_op) -> !transform.any_op
%1, %loops:3 = transform.structured.tile %0 [2, 2, 2] : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op)
%2 = get_parent_op %1 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- transform.structured.vectorize %2 : (!transform.any_op) -> !transform.any_op
+ transform.structured.vectorize_children %2 : (!transform.any_op) -> !transform.any_op
%b = transform.bufferization.one_shot_bufferize layout{IdentityLayoutMap}
%module_op {bufferize_function_boundaries = true}
: (!transform.any_op) -> !transform.any_op
diff --git a/mlir/test/Dialect/Linalg/transform-op-matmul-to-outerproduct.mlir b/mlir/test/Dialect/Linalg/transform-op-matmul-to-outerproduct.mlir
index ea84b6b7587687a..4227b860e74f73c 100644
--- a/mlir/test/Dialect/Linalg/transform-op-matmul-to-outerproduct.mlir
+++ b/mlir/test/Dialect/Linalg/transform-op-matmul-to-outerproduct.mlir
@@ -31,7 +31,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize %1 : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children %1 : (!transform.any_op) -> !transform.any_op
transform.apply_patterns to %2 {
transform.apply_patterns.vector.lower_contraction lowering_strategy = "outerproduct"
} : !transform.any_op
diff --git a/mlir/test/Dialect/Linalg/transform-op-vectorize.mlir b/mlir/test/Dialect/Linalg/transform-op-vectorize.mlir
index b335a65250d93e6..ef318d0bb32993a 100644
--- a/mlir/test/Dialect/Linalg/transform-op-vectorize.mlir
+++ b/mlir/test/Dialect/Linalg/transform-op-vectorize.mlir
@@ -20,7 +20,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize %1 : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children %1 : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -45,7 +45,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize %1 : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children %1 : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -65,7 +65,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.copy"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize %1 : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children %1 : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -111,7 +111,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize %1 : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children %1 : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -159,7 +159,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize %1 {vectorize_padding} : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children %1 {vectorize_padding} : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -176,5 +176,5 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
// expected-error @below {{op requires isolated-from-above targets}}
- %2 = transform.structured.vectorize %0 : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children %0 : (!transform.any_op) -> !transform.any_op
}
diff --git a/mlir/test/Dialect/Linalg/vectorization.mlir b/mlir/test/Dialect/Linalg/vectorization.mlir
index a5ec058b6e02c9c..b6dd0cff8452fd2 100644
--- a/mlir/test/Dialect/Linalg/vectorization.mlir
+++ b/mlir/test/Dialect/Linalg/vectorization.mlir
@@ -32,7 +32,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.matvec"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize %1 { disable_multi_reduction_to_contract_patterns } : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children %1 { disable_multi_reduction_to_contract_patterns } : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -50,7 +50,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize %1 { disable_multi_reduction_to_contract_patterns } : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children %1 { disable_multi_reduction_to_contract_patterns } : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -69,7 +69,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.batch_matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize %1 { disable_multi_reduction_to_contract_patterns } : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children %1 { disable_multi_reduction_to_contract_patterns } : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -109,7 +109,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize %1 { disable_multi_reduction_to_contract_patterns, disable_transfer_permutation_map_lowering_patterns } : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children %1 { disable_multi_reduction_to_contract_patterns, disable_transfer_permutation_map_lowering_patterns } : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -149,7 +149,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize %1 { disable_multi_reduction_to_contract_patterns, disable_transfer_permutation_map_lowering_patterns } : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children %1 { disable_multi_reduction_to_contract_patterns, disable_transfer_permutation_map_lowering_patterns } : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -176,7 +176,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize %1 { disable_multi_reduction_to_contract_patterns, disable_transfer_permutation_map_lowering_patterns } : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children %1 { disable_multi_reduction_to_contract_patterns, disable_transfer_permutation_map_lowering_patterns } : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -216,7 +216,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize %1 { disable_multi_reduction_to_contract_patterns, disable_transfer_permutation_map_lowering_patterns } : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children %1 { disable_multi_reduction_to_contract_patterns, disable_transfer_permutation_map_lowering_patterns } : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -236,7 +236,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize %1 { disable_multi_reduction_to_contract_patterns } : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children %1 { disable_multi_reduction_to_contract_patterns } : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -260,7 +260,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize %1 : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children %1 : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -284,7 +284,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize %1 : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children %1 : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -329,7 +329,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize %1 { vectorize_nd_extract } : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children %1 { vectorize_nd_extract } : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -346,7 +346,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.fill"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize %1 : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children %1 : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -364,7 +364,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.fill"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize %1 : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children %1 : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -381,7 +381,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["memref.copy"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize %1 : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children %1 : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -401,7 +401,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["memref.copy"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize %1 : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children %1 : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -417,7 +417,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["memref.copy"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize %1 : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children %1 : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -445,7 +445,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize %1 : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children %1 : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -474,7 +474,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize %1 : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children %1 : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -559,7 +559,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize %1 { disable_transfer_permutation_map_lowering_patterns } : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children %1 { disable_transfer_permutation_map_lowering_patterns } : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -650,7 +650,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize %1 { disable_transfer_permutation_map_lowering_patterns } : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children %1 { disable_transfer_permutation_map_lowering_patterns } : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -694,7 +694,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize %1 { disable_transfer_permutation_map_lowering_patterns } : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children %1 { disable_transfer_permutation_map_lowering_patterns } : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -737,7 +737,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize %1 { disable_transfer_permutation_map_lowering_patterns } : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children %1 { disable_transfer_permutation_map_lowering_patterns } : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -769,7 +769,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize %1 { disable_multi_reduction_to_contract_patterns, disable_transfer_permutation_map_lowering_patterns } : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children %1 { disable_multi_reduction_to_contract_patterns, disable_transfer_permutation_map_lowering_patterns } : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -798,7 +798,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["tensor.pad"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize %1 { vectorize_padding } : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children %1 { vectorize_padding } : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -827,7 +827,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["tensor.pad"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize %1 { vectorize_padding } : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children %1 { vectorize_padding } : (!transform.any_op) -> !transform.any_op
}
@@ -864,7 +864,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["tensor.pad"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize %1 { vectorize_padding } : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children %1 { vectorize_padding } : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -884,7 +884,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["tensor.pad"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize %1 { vectorize_padding } : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children %1 { vectorize_padding } : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -914,7 +914,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["tensor.pad"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize %1 { vectorize_padding } : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children %1 { vectorize_padding } : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -947,7 +947,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%3 = transform.structured.match ops{["tensor.pad"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%4 = get_parent_op %3 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %5 = transform.structured.vectorize %4 { vectorize_padding } : (!transform.any_op) -> !transform.any_op
+ %5 = transform.structured.vectorize_children %4 { vectorize_padding } : (!transform.any_op) -> !transform.any_op
}
@@ -984,7 +984,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%3 = transform.structured.match ops{["tensor.pad"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%4 = get_parent_op %3 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %5 = transform.structured.vectorize %4 { vectorize_padding } : (!transform.any_op) -> !transform.any_op
+ %5 = transform.structured.vectorize_children %4 { vectorize_padding } : (!transform.any_op) -> !transform.any_op
}
@@ -1018,7 +1018,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%3 = transform.structured.match ops{["tensor.pad"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%4 = get_parent_op %3 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %5 = transform.structured.vectorize %4 { vectorize_padding } : (!transform.any_op) -> !transform.any_op
+ %5 = transform.structured.vectorize_children %4 { vectorize_padding } : (!transform.any_op) -> !transform.any_op
}
@@ -1046,7 +1046,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%3 = transform.structured.match ops{["tensor.pad"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%4 = get_parent_op %3 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %5 = transform.structured.vectorize %4 : (!transform.any_op) -> !transform.any_op
+ %5 = transform.structured.vectorize_children %4 : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -1083,7 +1083,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%3 = transform.structured.match ops{["tensor.pad"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%4 = get_parent_op %3 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %5 = transform.structured.vectorize %4 { vectorize_padding } : (!transform.any_op) -> !transform.any_op
+ %5 = transform.structured.vectorize_children %4 { vectorize_padding } : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -1118,7 +1118,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%3 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%4 = get_parent_op %3 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %5 = transform.structured.vectorize %4 : (!transform.any_op) -> !transform.any_op
+ %5 = transform.structured.vectorize_children %4 : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -1163,7 +1163,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%3 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%4 = get_parent_op %3 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %5 = transform.structured.vectorize %4 { disable_multi_reduction_to_contract_patterns, disable_transfer_permutation_map_lowering_patterns } : (!transform.any_op) -> !transform.any_op
+ %5 = transform.structured.vectorize_children %4 { disable_multi_reduction_to_contract_patterns, disable_transfer_permutation_map_lowering_patterns } : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -1193,7 +1193,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%3 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%4 = get_parent_op %3 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %5 = transform.structured.vectorize %4 { vectorize_padding } : (!transform.any_op) -> !transform.any_op
+ %5 = transform.structured.vectorize_children %4 { vectorize_padding } : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -1224,7 +1224,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%3 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%4 = get_parent_op %3 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %5 = transform.structured.vectorize %4 : (!transform.any_op) -> !transform.any_op
+ %5 = transform.structured.vectorize_children %4 : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -1254,7 +1254,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%3 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%4 = get_parent_op %3 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %5 = transform.structured.vectorize %4 : (!transform.any_op) -> !transform.any_op
+ %5 = transform.structured.vectorize_children %4 : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -1284,7 +1284,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%3 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%4 = get_parent_op %3 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %5 = transform.structured.vectorize %4 : (!transform.any_op) -> !transform.any_op
+ %5 = transform.structured.vectorize_children %4 : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -1314,7 +1314,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%3 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%4 = get_parent_op %3 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %5 = transform.structured.vectorize %4 : (!transform.any_op) -> !transform.any_op
+ %5 = transform.structured.vectorize_children %4 : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -1344,7 +1344,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%3 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%4 = get_parent_op %3 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %5 = transform.structured.vectorize %4 : (!transform.any_op) -> !transform.any_op
+ %5 = transform.structured.vectorize_children %4 : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -1378,7 +1378,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%3 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%4 = get_parent_op %3 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %5 = transform.structured.vectorize %4 : (!transform.any_op) -> !transform.any_op
+ %5 = transform.structured.vectorize_children %4 : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -1416,11 +1416,11 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.fill"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize %1 : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children %1 : (!transform.any_op) -> !transform.any_op
%3 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%4 = get_parent_op %3 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %5 = transform.structured.vectorize %4 : (!transform.any_op) -> !transform.any_op
+ %5 = transform.structured.vectorize_children %4 : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -1463,7 +1463,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize %1 : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children %1 : (!transform.any_op) -> !transform.any_op
}
@@ -1494,7 +1494,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize %1 : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children %1 : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -1533,7 +1533,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize %1 { disable_multi_reduction_to_contract_patterns, disable_transfer_permutation_map_lowering_patterns } : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children %1 { disable_multi_reduction_to_contract_patterns, disable_transfer_permutation_map_lowering_patterns } : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -1557,7 +1557,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.map"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize %1 : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children %1 : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -1576,7 +1576,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.transpose"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize %1 : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children %1 : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -1599,13 +1599,13 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.reduce"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize %1 : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children %1 : (!transform.any_op) -> !transform.any_op
}
// -----
// This is a regression test. This IR cannot be vectorized, but
-// structured.vectorize should nevertheless succeed. : (!transform.any_op) -> !transform.any_op
+// structured.vectorize_children should nevertheless succeed.
#map = affine_map<(d0) -> (d0)>
// CHECK-LABEL: @not_vectorizable
@@ -1631,7 +1631,7 @@ func.func @not_vectorizable(%arg0: tensor<1x?xf32>, %arg1: index, %arg2: index,
transform.sequence failures(propagate) {
^bb0(%arg0: !transform.any_op):
%0 = transform.structured.match ops{["func.func"]} in %arg0 : (!transform.any_op) -> !transform.any_op
- %1 = transform.structured.vectorize %0 : (!transform.any_op) -> !transform.any_op
+ %1 = transform.structured.vectorize_children %0 : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -1666,7 +1666,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize %1 : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children %1 : (!transform.any_op) -> !transform.any_op
}
// CHECK-LABEL: @wrong_reduction_detection
@@ -1695,7 +1695,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize %1 : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children %1 : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -1716,7 +1716,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["tensor.pad"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize %1 { vectorize_padding } : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children %1 { vectorize_padding } : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -1738,7 +1738,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%3 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%4 = get_parent_op %3 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %5 = transform.structured.vectorize %4 : (!transform.any_op) -> !transform.any_op
+ %5 = transform.structured.vectorize_children %4 : (!transform.any_op) -> !transform.any_op
}
// CHECK-LABEL: func @zero_dim_tensor
@@ -1775,7 +1775,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%3 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%4 = get_parent_op %3 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %5 = transform.structured.vectorize %4 : (!transform.any_op) -> !transform.any_op
+ %5 = transform.structured.vectorize_children %4 : (!transform.any_op) -> !transform.any_op
}
// CHECK-LABEL: func @multi_output_generic_different_perm_maps
diff --git a/mlir/test/Dialect/Linalg/vectorize-tensor-extract.mlir b/mlir/test/Dialect/Linalg/vectorize-tensor-extract.mlir
index 84e36c8912c6501..8cd1fb7685bb917 100644
--- a/mlir/test/Dialect/Linalg/vectorize-tensor-extract.mlir
+++ b/mlir/test/Dialect/Linalg/vectorize-tensor-extract.mlir
@@ -31,7 +31,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize %1 : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children %1 : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -104,7 +104,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize %1 { vectorize_nd_extract } : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children %1 { vectorize_nd_extract } : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -156,7 +156,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize %1 { vectorize_nd_extract } : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children %1 { vectorize_nd_extract } : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -204,7 +204,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize %1 { vectorize_nd_extract } : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children %1 { vectorize_nd_extract } : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -248,7 +248,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize %1 { vectorize_nd_extract } : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children %1 { vectorize_nd_extract } : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -290,7 +290,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize %1 { vectorize_nd_extract } : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children %1 { vectorize_nd_extract } : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -332,7 +332,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize %1 { vectorize_nd_extract } : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children %1 { vectorize_nd_extract } : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -376,7 +376,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize %1 { vectorize_nd_extract } : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children %1 { vectorize_nd_extract } : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -416,7 +416,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize %1 { vectorize_nd_extract } : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children %1 { vectorize_nd_extract } : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -456,7 +456,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize %1 { vectorize_nd_extract } : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children %1 { vectorize_nd_extract } : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -495,7 +495,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize %1 { vectorize_nd_extract } : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children %1 { vectorize_nd_extract } : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -522,5 +522,5 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize %1 { vectorize_nd_extract } : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children %1 { vectorize_nd_extract } : (!transform.any_op) -> !transform.any_op
}
diff --git a/mlir/test/Dialect/Transform/selective-targeting.mlir b/mlir/test/Dialect/Transform/selective-targeting.mlir
index 5bf47fd75d3d200..98c91e2267f9a83 100644
--- a/mlir/test/Dialect/Transform/selective-targeting.mlir
+++ b/mlir/test/Dialect/Transform/selective-targeting.mlir
@@ -80,7 +80,7 @@ transform.with_pdl_patterns {
transform.structured.tile %0 [4, 4, 4] : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op)
%1 = pdl_match @pdl_target_attrC in %arg1 : (!transform.any_op) -> !transform.any_op
%2 = get_parent_op %1 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- transform.structured.vectorize %2 : (!transform.any_op) -> !transform.any_op
+ transform.structured.vectorize_children %2 : (!transform.any_op) -> !transform.any_op
}
}
@@ -125,7 +125,7 @@ transform.with_pdl_patterns {
^bb1(%arg1: !transform.any_op):
%0 = pdl_match @pdl_target in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- transform.structured.vectorize %1 : (!transform.any_op) -> !transform.any_op
+ transform.structured.vectorize_children %1 : (!transform.any_op) -> !transform.any_op
}
}
@@ -150,5 +150,5 @@ func.func @vectorize_all(
transform.sequence failures(propagate) {
^bb0(%arg0: !transform.any_op):
- transform.structured.vectorize %arg0 : (!transform.any_op) -> !transform.any_op
+ transform.structured.vectorize_children %arg0 : (!transform.any_op) -> !transform.any_op
}
diff --git a/mlir/test/Dialect/Vector/transform-vector.mlir b/mlir/test/Dialect/Vector/transform-vector.mlir
index 3e62a8fbf718f92..b5cb3c7249d5088 100644
--- a/mlir/test/Dialect/Vector/transform-vector.mlir
+++ b/mlir/test/Dialect/Vector/transform-vector.mlir
@@ -19,7 +19,7 @@ transform.sequence failures(propagate) {
%1, %loops:3 = transform.structured.tile %0 [8, 4, 2]
: (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op)
%2 = get_parent_op %1 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- transform.structured.vectorize %2 : (!transform.any_op) -> !transform.any_op
+ transform.structured.vectorize_children %2 : (!transform.any_op) -> !transform.any_op
%b = transform.bufferization.one_shot_bufferize
layout{IdentityLayoutMap} %module_op
{bufferize_function_boundaries = true, allow_return_allocs = true}
diff --git a/mlir/test/python/dialects/transform_structured_ext.py b/mlir/test/python/dialects/transform_structured_ext.py
index 5d5ee945b66867e..4860f86c98c09cd 100644
--- a/mlir/test/python/dialects/transform_structured_ext.py
+++ b/mlir/test/python/dialects/transform_structured_ext.py
@@ -487,15 +487,15 @@ def testTileToForallMapping(target):
@run
@create_sequence
-def testVectorizeAllAttrs(target):
- structured.VectorizeOp(
+def testVectorizeChildrenAllAttrs(target):
+ structured.VectorizeChildrenOp(
target,
disable_multi_reduction_to_contract_patterns=True,
disable_transfer_permutation_map_lowering_patterns=True,
vectorize_nd_extract=True,
vectorize_padding=True,
)
- # CHECK-LABEL: TEST: testVectorizeAllAttrs
+ # CHECK-LABEL: TEST: testVectorizeChildrenAllAttrs
# CHECK: transform.sequence
# CHECK: = transform.structured.vectorize
# CHECK-SAME: disable_multi_reduction_to_contract_patterns
@@ -506,15 +506,15 @@ def testVectorizeAllAttrs(target):
@run
@create_sequence
-def testVectorizeNoAttrs(target):
- structured.VectorizeOp(
+def testVectorizeChildrenNoAttrs(target):
+ structured.VectorizeChildrenOp(
target,
disable_multi_reduction_to_contract_patterns=False,
disable_transfer_permutation_map_lowering_patterns=False,
vectorize_nd_extract=False,
vectorize_padding=False,
)
- # CHECK-LABEL: TEST: testVectorizeNoAttrs
+ # CHECK-LABEL: TEST: testVectorizeChildrenNoAttrs
# CHECK: transform.sequence
# CHECK: = transform.structured.vectorize
# CHECK-NOT: disable_multi_reduction_to_contract_patterns
>From c8443b695ba6fc6bb87ce7b37ab91ce54b8861b9 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Ingo=20M=C3=BCller?= <ingomueller at google.com>
Date: Sun, 17 Sep 2023 14:22:41 +0000
Subject: [PATCH 2/4] Rename structured.{masked_vectorize => vectorize}.
---
.../Linalg/TransformOps/LinalgTransformOps.td | 6 +--
.../TransformOps/LinalgTransformOps.cpp | 10 ++---
.../dialects/_structured_transform_ops_ext.py | 4 +-
.../Linalg/matmul-shared-memory-padding.mlir | 8 ++--
.../Linalg/pad-to-specific-memory-space.mlir | 2 +-
...compose-masked-vectorize-and-cleanups.mlir | 2 +-
.../Dialect/Linalg/vectorization-masked.mlir | 30 +++++++-------
.../Linalg/vectorization-scalable.mlir | 8 ++--
mlir/test/Dialect/Linalg/vectorization.mlir | 2 +-
.../vectorize-tensor-extract-masked.mlir | 12 +++---
.../Linalg/vectorize-tensor-extract.mlir | 2 +-
.../Dialect/Linalg/CPU/ArmSME/fill-2d.mlir | 2 +-
.../Dialect/Linalg/CPU/ArmSVE/fill-1d.mlir | 2 +-
.../Linalg/CPU/test-matmul-masked-vec.mlir | 2 +-
.../dialects/transform_structured_ext.py | 40 +++++++++----------
15 files changed, 66 insertions(+), 66 deletions(-)
diff --git a/mlir/include/mlir/Dialect/Linalg/TransformOps/LinalgTransformOps.td b/mlir/include/mlir/Dialect/Linalg/TransformOps/LinalgTransformOps.td
index 6bd93633aaa6d4b..bcfee42f96abbe2 100644
--- a/mlir/include/mlir/Dialect/Linalg/TransformOps/LinalgTransformOps.td
+++ b/mlir/include/mlir/Dialect/Linalg/TransformOps/LinalgTransformOps.td
@@ -2016,7 +2016,7 @@ def VectorizeChildrenOp : Op<Transform_Dialect, "structured.vectorize_children",
}];
}
-def MaskedVectorizeOp : Op<Transform_Dialect, "structured.masked_vectorize",
+def VectorizeOp : Op<Transform_Dialect, "structured.vectorize",
[DeclareOpInterfaceMethods<MemoryEffectsOpInterface>,
TransformOpInterface, ReportTrackingListenerFailuresOpTrait]> {
let description = [{
@@ -2030,9 +2030,9 @@ def MaskedVectorizeOp : Op<Transform_Dialect, "structured.masked_vectorize",
```mlir
# Masked vectorization - vector sizes are specified explicitly
- transform.structured.masked_vectorize %target vector_sizes [1, 4] : !transform.any_op
+ transform.structured.vectorize %target vector_sizes [1, 4] : !transform.any_op
# Regular vectorization - vector sizes are inferred from the target Op
- transform.structured.masked_vectorize %target : !transform.any_op
+ transform.structured.vectorize %target : !transform.any_op
```
The vector sizes can be either static or dynamic (SSA values). In case of
diff --git a/mlir/lib/Dialect/Linalg/TransformOps/LinalgTransformOps.cpp b/mlir/lib/Dialect/Linalg/TransformOps/LinalgTransformOps.cpp
index f4fc35d686a023a..6662e374e4830b9 100644
--- a/mlir/lib/Dialect/Linalg/TransformOps/LinalgTransformOps.cpp
+++ b/mlir/lib/Dialect/Linalg/TransformOps/LinalgTransformOps.cpp
@@ -2994,9 +2994,9 @@ DiagnosedSilenceableFailure transform::VectorizeChildrenOp::applyToOne(
}
//===----------------------------------------------------------------------===//
-// MaskedVectorizeOp
+// VectorizeOp
//===----------------------------------------------------------------------===//
-DiagnosedSilenceableFailure transform::MaskedVectorizeOp::apply(
+DiagnosedSilenceableFailure transform::VectorizeOp::apply(
transform::TransformRewriter &rewriter,
mlir::transform::TransformResults &transformResults,
mlir::transform::TransformState &state) {
@@ -3060,19 +3060,19 @@ DiagnosedSilenceableFailure transform::MaskedVectorizeOp::apply(
return DiagnosedSilenceableFailure::success();
}
-void transform::MaskedVectorizeOp::getEffects(
+void transform::VectorizeOp::getEffects(
SmallVectorImpl<MemoryEffects::EffectInstance> &effects) {
consumesHandle(getTarget(), effects);
onlyReadsHandle(getVectorSizes(), effects);
modifiesPayload(effects);
}
-SmallVector<OpFoldResult> MaskedVectorizeOp::getMixedVectorSizes() {
+SmallVector<OpFoldResult> VectorizeOp::getMixedVectorSizes() {
OpBuilder b(getContext());
return getMixedValues(getStaticVectorSizes(), getVectorSizes(), b);
}
-LogicalResult transform::MaskedVectorizeOp::verify() {
+LogicalResult transform::VectorizeOp::verify() {
if (getStaticVectorSizes().size() != getScalableSizes().size())
return emitOpError("expected same number of vector sizes (")
<< getStaticVectorSizes().size() << ") and scalable sizes ("
diff --git a/mlir/python/mlir/dialects/_structured_transform_ops_ext.py b/mlir/python/mlir/dialects/_structured_transform_ops_ext.py
index 911d46d24991522..57d0f55b40e4f24 100644
--- a/mlir/python/mlir/dialects/_structured_transform_ops_ext.py
+++ b/mlir/python/mlir/dialects/_structured_transform_ops_ext.py
@@ -360,8 +360,8 @@ def __init__(
)
-class MaskedVectorizeOp:
- """Specialization for MaskedVectorizeOp class."""
+class VectorizeOp:
+ """Specialization for VectorizeOp class."""
def __init__(
self,
diff --git a/mlir/test/Dialect/Linalg/matmul-shared-memory-padding.mlir b/mlir/test/Dialect/Linalg/matmul-shared-memory-padding.mlir
index da6ebdbd24ded48..37a925cf0df9a3a 100644
--- a/mlir/test/Dialect/Linalg/matmul-shared-memory-padding.mlir
+++ b/mlir/test/Dialect/Linalg/matmul-shared-memory-padding.mlir
@@ -80,7 +80,7 @@ transform.sequence failures(propagate) {
: (!transform.any_op) -> (!transform.any_op, !transform.any_op)
// Apply masked vectorization to padding ops.
- transform.structured.masked_vectorize %tiled_pad_op vector_sizes [128, 4]
+ transform.structured.vectorize %tiled_pad_op vector_sizes [128, 4]
: !transform.any_op
// Assign shared memory buffer to padding.
@@ -105,7 +105,7 @@ transform.sequence failures(propagate) {
: (!transform.any_op) -> !transform.any_op
%bufferized_copy_back = transform.structured.match ops{["linalg.copy"]} in %func_op_2
: (!transform.any_op) -> !transform.any_op
- transform.structured.masked_vectorize
+ transform.structured.vectorize
%bufferized_copy_back vector_sizes [128, 4] : !transform.any_op
// Canonicalize, cleanup and vector lowering. This step also removes buffer
@@ -192,7 +192,7 @@ transform.sequence failures(propagate) {
}
// Apply masked vectorization to padding ops.
- transform.structured.masked_vectorize %tiled_pad_op vector_sizes [128, 4]
+ transform.structured.vectorize %tiled_pad_op vector_sizes [128, 4]
: !transform.any_op
// Assign shared memory buffer to padding.
@@ -217,7 +217,7 @@ transform.sequence failures(propagate) {
: (!transform.any_op) -> !transform.any_op
%bufferized_copy_back = transform.structured.match ops{["linalg.copy"]} in %func_op_2
: (!transform.any_op) -> !transform.any_op
- transform.structured.masked_vectorize
+ transform.structured.vectorize
%bufferized_copy_back vector_sizes [128, 4] : !transform.any_op
// Canonicalize, cleanup and vector lowering. This step also removes buffer
diff --git a/mlir/test/Dialect/Linalg/pad-to-specific-memory-space.mlir b/mlir/test/Dialect/Linalg/pad-to-specific-memory-space.mlir
index 45c2eb5dfdf5022..be807a9d5691733 100644
--- a/mlir/test/Dialect/Linalg/pad-to-specific-memory-space.mlir
+++ b/mlir/test/Dialect/Linalg/pad-to-specific-memory-space.mlir
@@ -111,7 +111,7 @@ transform.sequence failures(propagate) {
padding_dimensions=[0, 1, 2],
pack_paddings=[1, 1, 1]
} : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op)
- transform.structured.masked_vectorize %pad vector_sizes [10, 12] : !transform.any_op
+ transform.structured.vectorize %pad vector_sizes [10, 12] : !transform.any_op
%vector_write = transform.structured.match ops{["vector.transfer_write"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%mask_op = transform.get_parent_op %vector_write {op_name = "vector.mask"} : (!transform.any_op) -> !transform.any_op
%buffer, %new_ops = transform.structured.bufferize_to_allocation %mask_op {memory_space = 3, emit_dealloc} : !transform.any_op
diff --git a/mlir/test/Dialect/Linalg/transform-op-compose-masked-vectorize-and-cleanups.mlir b/mlir/test/Dialect/Linalg/transform-op-compose-masked-vectorize-and-cleanups.mlir
index 8797d847c43a952..07cb79fdba2d242 100644
--- a/mlir/test/Dialect/Linalg/transform-op-compose-masked-vectorize-and-cleanups.mlir
+++ b/mlir/test/Dialect/Linalg/transform-op-compose-masked-vectorize-and-cleanups.mlir
@@ -26,7 +26,7 @@ transform.sequence failures(propagate) {
: (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op)
%tiled_linalg_op_0, %loops_1:3 = transform.structured.tile %tiled_linalg_op[8, 8, 8]
: (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op)
- transform.structured.masked_vectorize %tiled_linalg_op_0 vector_sizes [8, 8, 8]
+ transform.structured.vectorize %tiled_linalg_op_0 vector_sizes [8, 8, 8]
: !transform.any_op
%func = transform.structured.match ops{["func.func"]} in %module
diff --git a/mlir/test/Dialect/Linalg/vectorization-masked.mlir b/mlir/test/Dialect/Linalg/vectorization-masked.mlir
index 82e8dfe37f79992..ddeaff76a04df23 100644
--- a/mlir/test/Dialect/Linalg/vectorization-masked.mlir
+++ b/mlir/test/Dialect/Linalg/vectorization-masked.mlir
@@ -29,7 +29,7 @@ func.func @vectorize_dynamic_identity(%arg0: tensor<?xf32>,
transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- transform.structured.masked_vectorize %0 vector_sizes [4] : !transform.any_op
+ transform.structured.vectorize %0 vector_sizes [4] : !transform.any_op
}
// -----
@@ -63,7 +63,7 @@ func.func @vectorize_dynamic_1d_broadcast(%arg0: tensor<?xf32>,
transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- transform.structured.masked_vectorize %0 vector_sizes [4] : !transform.any_op
+ transform.structured.vectorize %0 vector_sizes [4] : !transform.any_op
}
// -----
@@ -101,7 +101,7 @@ func.func @vectorize_dynamic_2d_transpose(%arg0: tensor<?x?xf32>,
transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- transform.structured.masked_vectorize %0 vector_sizes [4, 8] : !transform.any_op
+ transform.structured.vectorize %0 vector_sizes [4, 8] : !transform.any_op
}
// -----
@@ -138,7 +138,7 @@ func.func @vectorize_dynamic_generic_2d_broadcast(%arg0: tensor<?x?xf32>,
transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- transform.structured.masked_vectorize %0 vector_sizes [4, 8] : !transform.any_op
+ transform.structured.vectorize %0 vector_sizes [4, 8] : !transform.any_op
}
// -----
@@ -160,7 +160,7 @@ func.func @vectorize_dynamic_reduction(%arg0: tensor<?x?xf32>,
transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- transform.structured.masked_vectorize %0 vector_sizes [4, 8] : !transform.any_op
+ transform.structured.vectorize %0 vector_sizes [4, 8] : !transform.any_op
}
// CHECK-LABEL: @vectorize_dynamic_reduction(
@@ -198,7 +198,7 @@ func.func @vectorize_dynamic_transpose_reduction(%arg0: tensor<?x?x?xf32>,
transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- transform.structured.masked_vectorize %0 vector_sizes [4, 8, 16] : !transform.any_op
+ transform.structured.vectorize %0 vector_sizes [4, 8, 16] : !transform.any_op
}
// CHECK-LABEL: @vectorize_dynamic_transpose_reduction(
@@ -256,7 +256,7 @@ func.func @vectorize_partial_dynamic_identity(%arg0: tensor<8x?xf32>,
transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- transform.structured.masked_vectorize %0 vector_sizes [8, 32] : !transform.any_op
+ transform.structured.vectorize %0 vector_sizes [8, 32] : !transform.any_op
}
// -----
@@ -283,7 +283,7 @@ func.func @do_not_generate_masks(%arg0: tensor<8x32xf32>,
transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- transform.structured.masked_vectorize %0 vector_sizes [8, 32] : !transform.any_op
+ transform.structured.vectorize %0 vector_sizes [8, 32] : !transform.any_op
}
// -----
@@ -323,7 +323,7 @@ func.func @vectorize_static_shape_with_mask(%arg0: tensor<8x30xf32>,
transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- transform.structured.masked_vectorize %0 vector_sizes [8, 32] : !transform.any_op
+ transform.structured.vectorize %0 vector_sizes [8, 32] : !transform.any_op
}
// -----
@@ -343,7 +343,7 @@ func.func @vectorize_dynamic_fill(%A : tensor<?x?xf32>, %arg0 : f32) -> tensor<?
transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.fill"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- transform.structured.masked_vectorize %0 vector_sizes [8, 16] : !transform.any_op
+ transform.structured.vectorize %0 vector_sizes [8, 16] : !transform.any_op
}
// -----
@@ -364,7 +364,7 @@ func.func @test_masked_vectorize_linalg_copy(%A : memref<?x?xf32>, %B : memref<?
transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.copy"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- transform.structured.masked_vectorize %0 vector_sizes [2, 4] : !transform.any_op
+ transform.structured.vectorize %0 vector_sizes [2, 4] : !transform.any_op
}
// -----
@@ -400,7 +400,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["tensor.pad"]} in %arg1
: (!transform.any_op) -> !transform.any_op
- transform.structured.masked_vectorize %0 vector_sizes [2, 4] : !transform.any_op
+ transform.structured.vectorize %0 vector_sizes [2, 4] : !transform.any_op
}
// -----
@@ -442,7 +442,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["tensor.pad"]} in %arg1
: (!transform.any_op) -> !transform.any_op
- transform.structured.masked_vectorize %0 vector_sizes [2, 4] : !transform.any_op
+ transform.structured.vectorize %0 vector_sizes [2, 4] : !transform.any_op
}
// -----
@@ -476,7 +476,7 @@ func.func @matmul(%A: memref<?x?xf32>, %B: memref<?x?xf32>, %C: memref<?x?xf32>)
transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%matmul = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- transform.structured.masked_vectorize %matmul vector_sizes [8, 16, 4] : !transform.any_op
+ transform.structured.vectorize %matmul vector_sizes [8, 16, 4] : !transform.any_op
}
// -----
@@ -510,5 +510,5 @@ func.func @matmul_scalable(%A: memref<?x?xf32>, %B: memref<?x?xf32>, %C: memref<
transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%matmul = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- transform.structured.masked_vectorize %matmul vector_sizes [8, [16], 4] : !transform.any_op
+ transform.structured.vectorize %matmul vector_sizes [8, [16], 4] : !transform.any_op
}
diff --git a/mlir/test/Dialect/Linalg/vectorization-scalable.mlir b/mlir/test/Dialect/Linalg/vectorization-scalable.mlir
index 957313b43d4b309..641b626f576e1ea 100644
--- a/mlir/test/Dialect/Linalg/vectorization-scalable.mlir
+++ b/mlir/test/Dialect/Linalg/vectorization-scalable.mlir
@@ -29,7 +29,7 @@ func.func @vectorize_dynamic_identity(%arg0: tensor<?xf32>,
transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- transform.structured.masked_vectorize %0 vector_sizes [[4]] : !transform.any_op
+ transform.structured.vectorize %0 vector_sizes [[4]] : !transform.any_op
}
// -----
@@ -71,7 +71,7 @@ func.func @vectorize_partial_dynamic_identity(%arg0: tensor<8x?xf32>,
transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- transform.structured.masked_vectorize %0 vector_sizes [8, [32]] : !transform.any_op
+ transform.structured.vectorize %0 vector_sizes [8, [32]] : !transform.any_op
}
// -----
@@ -111,7 +111,7 @@ func.func @vectorize_static_shape_with_mask(%arg0: tensor<8x30xf32>,
transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- transform.structured.masked_vectorize %0 vector_sizes [8, [32]] : !transform.any_op
+ transform.structured.vectorize %0 vector_sizes [8, [32]] : !transform.any_op
}
// -----
@@ -131,6 +131,6 @@ func.func @vectorize_dynamic_fill(%A : tensor<?x?xf32>, %arg0 : f32) -> tensor<?
transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.fill"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- transform.structured.masked_vectorize %0 vector_sizes [8, [16]] : !transform.any_op
+ transform.structured.vectorize %0 vector_sizes [8, [16]] : !transform.any_op
}
diff --git a/mlir/test/Dialect/Linalg/vectorization.mlir b/mlir/test/Dialect/Linalg/vectorization.mlir
index b6dd0cff8452fd2..f31197f0ed85808 100644
--- a/mlir/test/Dialect/Linalg/vectorization.mlir
+++ b/mlir/test/Dialect/Linalg/vectorization.mlir
@@ -13,7 +13,7 @@ func.func @contraction_dot(%A: memref<1584xf32>, %B: memref<1584xf32>, %C: memre
transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.dot"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- transform.structured.masked_vectorize %0 : !transform.any_op
+ transform.structured.vectorize %0 : !transform.any_op
}
// -----
diff --git a/mlir/test/Dialect/Linalg/vectorize-tensor-extract-masked.mlir b/mlir/test/Dialect/Linalg/vectorize-tensor-extract-masked.mlir
index da861942cc3eff7..3187385b5398816 100644
--- a/mlir/test/Dialect/Linalg/vectorize-tensor-extract-masked.mlir
+++ b/mlir/test/Dialect/Linalg/vectorize-tensor-extract-masked.mlir
@@ -28,7 +28,7 @@ func.func @masked_static_vectorize_nd_tensor_extract_with_affine_apply_contiguou
transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- transform.structured.masked_vectorize %0 vector_sizes [1, 4] vectorize_nd_extract : !transform.any_op
+ transform.structured.vectorize %0 vector_sizes [1, 4] vectorize_nd_extract : !transform.any_op
}
// -----
@@ -83,7 +83,7 @@ func.func @masked_dynamic_vectorize_nd_tensor_extract_with_affine_apply_contiguo
transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- transform.structured.masked_vectorize %0 vector_sizes [1, 4] vectorize_nd_extract : !transform.any_op
+ transform.structured.vectorize %0 vector_sizes [1, 4] vectorize_nd_extract : !transform.any_op
}
// -----
@@ -121,7 +121,7 @@ func.func @masked_vectorize_nd_tensor_extract_with_affine_apply_gather(%6: tenso
transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- transform.structured.masked_vectorize %0 vector_sizes [1, 4] vectorize_nd_extract : !transform.any_op
+ transform.structured.vectorize %0 vector_sizes [1, 4] vectorize_nd_extract : !transform.any_op
}
// -----
@@ -176,7 +176,7 @@ func.func @masked_dynamic_vectorize_nd_tensor_extract_with_affine_apply_gather(%
transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- transform.structured.masked_vectorize %0 vector_sizes [1, 4] vectorize_nd_extract : !transform.any_op
+ transform.structured.vectorize %0 vector_sizes [1, 4] vectorize_nd_extract : !transform.any_op
}
// -----
@@ -226,7 +226,7 @@ func.func @extract_masked_vectorize(%arg0: tensor<?x?xf32>, %arg1: tensor<?x?xf3
transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- transform.structured.masked_vectorize %0 vector_sizes [3, 3] vectorize_nd_extract : !transform.any_op
+ transform.structured.vectorize %0 vector_sizes [3, 3] vectorize_nd_extract : !transform.any_op
}
// -----
@@ -269,5 +269,5 @@ func.func @tensor_extract_dynamic_shape(%arg1: tensor<123x321xf32>, %arg2: tenso
transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- transform.structured.masked_vectorize %0 vector_sizes [1, 3, 8] vectorize_nd_extract : !transform.any_op
+ transform.structured.vectorize %0 vector_sizes [1, 3, 8] vectorize_nd_extract : !transform.any_op
}
diff --git a/mlir/test/Dialect/Linalg/vectorize-tensor-extract.mlir b/mlir/test/Dialect/Linalg/vectorize-tensor-extract.mlir
index 8cd1fb7685bb917..ccc076c9c44903b 100644
--- a/mlir/test/Dialect/Linalg/vectorize-tensor-extract.mlir
+++ b/mlir/test/Dialect/Linalg/vectorize-tensor-extract.mlir
@@ -65,7 +65,7 @@ func.func @vectorize_nd_tensor_extract_constant_idx(%arg0: tensor<3x3xf32>, %arg
transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- transform.structured.masked_vectorize %0 { vectorize_nd_extract } : !transform.any_op
+ transform.structured.vectorize %0 { vectorize_nd_extract } : !transform.any_op
}
// -----
diff --git a/mlir/test/Integration/Dialect/Linalg/CPU/ArmSME/fill-2d.mlir b/mlir/test/Integration/Dialect/Linalg/CPU/ArmSME/fill-2d.mlir
index dabf0dac4680e5f..08f14dfae3249f2 100644
--- a/mlir/test/Integration/Dialect/Linalg/CPU/ArmSME/fill-2d.mlir
+++ b/mlir/test/Integration/Dialect/Linalg/CPU/ArmSME/fill-2d.mlir
@@ -112,7 +112,7 @@ func.func @entry() {
transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.fill"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- transform.structured.masked_vectorize %0 vector_sizes [[4], [4]] : !transform.any_op
+ transform.structured.vectorize %0 vector_sizes [[4], [4]] : !transform.any_op
}
llvm.func @printCString(!llvm.ptr<i8>)
diff --git a/mlir/test/Integration/Dialect/Linalg/CPU/ArmSVE/fill-1d.mlir b/mlir/test/Integration/Dialect/Linalg/CPU/ArmSVE/fill-1d.mlir
index 34b3835c40775b8..c3f49b2f39cf137 100644
--- a/mlir/test/Integration/Dialect/Linalg/CPU/ArmSVE/fill-1d.mlir
+++ b/mlir/test/Integration/Dialect/Linalg/CPU/ArmSVE/fill-1d.mlir
@@ -49,7 +49,7 @@ func.func @entry() {
transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.fill"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- transform.structured.masked_vectorize %0 vector_sizes [[4]] : !transform.any_op
+ transform.structured.vectorize %0 vector_sizes [[4]] : !transform.any_op
}
llvm.func @printCString(!llvm.ptr<i8>)
diff --git a/mlir/test/Integration/Dialect/Linalg/CPU/test-matmul-masked-vec.mlir b/mlir/test/Integration/Dialect/Linalg/CPU/test-matmul-masked-vec.mlir
index 8a95d1c864d21cc..64954098aa03c56 100644
--- a/mlir/test/Integration/Dialect/Linalg/CPU/test-matmul-masked-vec.mlir
+++ b/mlir/test/Integration/Dialect/Linalg/CPU/test-matmul-masked-vec.mlir
@@ -51,7 +51,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%func_op = get_parent_op %0 : (!transform.any_op) -> !transform.op<"func.func">
- transform.structured.masked_vectorize %0 vector_sizes [4, 4, 2] : !transform.any_op
+ transform.structured.vectorize %0 vector_sizes [4, 4, 2] : !transform.any_op
transform.apply_patterns to %func_op {
transform.apply_patterns.vector.lower_multi_reduction lowering_strategy = "innerreduction"
} : !transform.op<"func.func">
diff --git a/mlir/test/python/dialects/transform_structured_ext.py b/mlir/test/python/dialects/transform_structured_ext.py
index 4860f86c98c09cd..91c14abede7f367 100644
--- a/mlir/test/python/dialects/transform_structured_ext.py
+++ b/mlir/test/python/dialects/transform_structured_ext.py
@@ -171,58 +171,58 @@ def testMatchOpNamesList(target):
@run
@create_sequence
-def testMaskedVectorizeStatic(target):
- structured.MaskedVectorizeOp(target, [16, 4])
- # CHECK-LABEL: TEST: testMaskedVectorizeStatic
+def testVectorizeStatic(target):
+ structured.VectorizeOp(target, [16, 4])
+ # CHECK-LABEL: TEST: testVectorizeStatic
# CHECK: transform.sequence
- # CHECK: transform.structured.masked_vectorize
+ # CHECK: transform.structured.vectorize
# CHECK-SAME: vector_sizes [16, 4]
@run
@create_sequence
-def testMaskedVectorizeArray(target):
+def testVectorizeArray(target):
sizes = Attribute.parse("[16, 4]")
- structured.MaskedVectorizeOp(target, sizes)
- # CHECK-LABEL: TEST: testMaskedVectorizeArray
+ structured.VectorizeOp(target, sizes)
+ # CHECK-LABEL: TEST: testVectorizeArray
# CHECK: transform.sequence
- # CHECK: transform.structured.masked_vectorize
+ # CHECK: transform.structured.vectorize
# CHECK-SAME: vector_sizes [16, 4]
@run
@create_sequence
-def testMaskedVectorizeMixed(target):
+def testVectorizeMixed(target):
sz1 = structured.MatchOp.match_op_names(target, ["arith.constant"])
sz2 = Attribute.parse("4")
- structured.MaskedVectorizeOp(target, [sz1, sz2])
- # CHECK-LABEL: TEST: testMaskedVectorizeMixed
+ structured.VectorizeOp(target, [sz1, sz2])
+ # CHECK-LABEL: TEST: testVectorizeMixed
# CHECK: transform.sequence
# CHECK: %[[V0:.*]] = transform.structured.match
- # CHECK: transform.structured.masked_vectorize
+ # CHECK: transform.structured.vectorize
# CHECK-SAME: vector_sizes [%[[V0]] : !transform.any_op, 4]
@run
@create_sequence
-def testMaskedVectorizeScalable(target):
+def testVectorizeScalable(target):
sz1 = structured.MatchOp.match_op_names(target, ["arith.constant"])
sz2 = Attribute.parse("4")
- structured.MaskedVectorizeOp(target, [16, [sz1], [sz2], [8]])
- # CHECK-LABEL: TEST: testMaskedVectorizeScalable
+ structured.VectorizeOp(target, [16, [sz1], [sz2], [8]])
+ # CHECK-LABEL: TEST: testVectorizeScalable
# CHECK: transform.sequence
# CHECK-DAG: %[[V0:.*]] = transform.structured.match
- # CHECK-DAG: transform.structured.masked_vectorize
+ # CHECK-DAG: transform.structured.vectorize
# CHECK-SAME: vector_sizes [16, [%[[V0]] : !transform.any_op], [4], [8]]
@run
@create_sequence
-def testMaskedVectorizeArgs(target):
- structured.MaskedVectorizeOp(target, [16, 4], vectorize_nd_extract=True)
- # CHECK-LABEL: TEST: testMaskedVectorizeArgs
+def testVectorizeArgs(target):
+ structured.VectorizeOp(target, [16, 4], vectorize_nd_extract=True)
+ # CHECK-LABEL: TEST: testVectorizeArgs
# CHECK: transform.sequence
- # CHECK: transform.structured.masked_vectorize
+ # CHECK: transform.structured.vectorize
# CHECK-SAME: vectorize_nd_extract
>From 1be4e9b73b0035163d559caa17ea6c1320c3ad32 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Ingo=20M=C3=BCller?= <ingomueller at google.com>
Date: Tue, 19 Sep 2023 11:10:23 +0000
Subject: [PATCH 3/4] Rename structured.vectorize_children{ =>
_and_apply_patterns}.
---
.../Linalg/TransformOps/LinalgTransformOps.td | 5 +-
.../TransformOps/LinalgTransformOps.cpp | 20 +--
.../dialects/_structured_transform_ops_ext.py | 4 +-
mlir/test/Dialect/LLVM/transform-e2e.mlir | 2 +-
.../transform-op-matmul-to-outerproduct.mlir | 2 +-
.../Linalg/transform-op-vectorize.mlir | 12 +-
mlir/test/Dialect/Linalg/vectorization.mlir | 114 +++++++++---------
.../Linalg/vectorize-tensor-extract.mlir | 24 ++--
.../Transform/selective-targeting.mlir | 6 +-
.../test/Dialect/Vector/transform-vector.mlir | 2 +-
.../dialects/transform_structured_ext.py | 12 +-
11 files changed, 103 insertions(+), 100 deletions(-)
diff --git a/mlir/include/mlir/Dialect/Linalg/TransformOps/LinalgTransformOps.td b/mlir/include/mlir/Dialect/Linalg/TransformOps/LinalgTransformOps.td
index bcfee42f96abbe2..0fca130a58e3940 100644
--- a/mlir/include/mlir/Dialect/Linalg/TransformOps/LinalgTransformOps.td
+++ b/mlir/include/mlir/Dialect/Linalg/TransformOps/LinalgTransformOps.td
@@ -1947,10 +1947,11 @@ def TileToForallOp :
}
//===----------------------------------------------------------------------===//
-// VectorizeChildrenOp
+// VectorizeChildrenAndApplyPatternsOp
//===----------------------------------------------------------------------===//
-def VectorizeChildrenOp : Op<Transform_Dialect, "structured.vectorize_children",
+def VectorizeChildrenAndApplyPatternsOp :
+ Op<Transform_Dialect, "structured.vectorize_children_and_apply_patterns",
[FunctionalStyleTransformOpTrait, MemoryEffectsOpInterface,
TransformEachOpTrait, TransformOpInterface,
ReportTrackingListenerFailuresOpTrait]> {
diff --git a/mlir/lib/Dialect/Linalg/TransformOps/LinalgTransformOps.cpp b/mlir/lib/Dialect/Linalg/TransformOps/LinalgTransformOps.cpp
index 6662e374e4830b9..f35838d6b234ed4 100644
--- a/mlir/lib/Dialect/Linalg/TransformOps/LinalgTransformOps.cpp
+++ b/mlir/lib/Dialect/Linalg/TransformOps/LinalgTransformOps.cpp
@@ -2904,22 +2904,23 @@ LogicalResult TileToForallOp::verify() {
}
//===----------------------------------------------------------------------===//
-// VectorizeChildrenOp
+// VectorizeChildrenAndApplyPatternsOp
//===----------------------------------------------------------------------===//
-void transform::VectorizeChildrenOp::build(OpBuilder &builder,
- OperationState &result, Value target,
- bool vectorizePadding,
- bool vectorizeExtract) {
+void transform::VectorizeChildrenAndApplyPatternsOp::build(
+ OpBuilder &builder, OperationState &result, Value target,
+ bool vectorizePadding, bool vectorizeExtract) {
result.addOperands(target);
if (vectorizePadding) {
result.addAttribute(
- VectorizeChildrenOp::getVectorizePaddingAttrName(result.name),
+ VectorizeChildrenAndApplyPatternsOp::getVectorizePaddingAttrName(
+ result.name),
builder.getUnitAttr());
}
if (vectorizeExtract) {
result.addAttribute(
- VectorizeChildrenOp::getVectorizeNdExtractAttrName(result.name),
+ VectorizeChildrenAndApplyPatternsOp::getVectorizeNdExtractAttrName(
+ result.name),
builder.getUnitAttr());
}
result.addTypes(transform::AnyOpType::get(builder.getContext()));
@@ -2927,7 +2928,7 @@ void transform::VectorizeChildrenOp::build(OpBuilder &builder,
namespace {
/// This is an helper only to call vectorize via a pattern inside of
-/// VectorizeChildrenOp::applyToOne.
+/// VectorizeChildrenAndApplyPatternsOp::applyToOne.
struct VectorizationPattern : public RewritePattern {
explicit VectorizationPattern(MLIRContext *context,
bool vectorizeExtract = false)
@@ -2949,7 +2950,8 @@ struct VectorizationPattern : public RewritePattern {
};
} // namespace
-DiagnosedSilenceableFailure transform::VectorizeChildrenOp::applyToOne(
+DiagnosedSilenceableFailure
+transform::VectorizeChildrenAndApplyPatternsOp::applyToOne(
transform::TransformRewriter &rewriter, Operation *target,
transform::ApplyToEachResultList &results,
transform::TransformState &state) {
diff --git a/mlir/python/mlir/dialects/_structured_transform_ops_ext.py b/mlir/python/mlir/dialects/_structured_transform_ops_ext.py
index 57d0f55b40e4f24..8c042ce45670a35 100644
--- a/mlir/python/mlir/dialects/_structured_transform_ops_ext.py
+++ b/mlir/python/mlir/dialects/_structured_transform_ops_ext.py
@@ -724,8 +724,8 @@ def __init__(
)
-class VectorizeChildrenOp:
- """Specialization for VectorizeChildrenOp class."""
+class VectorizeChildrenAndApplyPatternsOp:
+ """Specialization for VectorizeChildrenAndApplyPatternsOp class."""
def __init__(
self,
diff --git a/mlir/test/Dialect/LLVM/transform-e2e.mlir b/mlir/test/Dialect/LLVM/transform-e2e.mlir
index a976036fd71ceee..54a17940d8c0688 100644
--- a/mlir/test/Dialect/LLVM/transform-e2e.mlir
+++ b/mlir/test/Dialect/LLVM/transform-e2e.mlir
@@ -17,7 +17,7 @@ transform.sequence failures(propagate) {
%0 = transform.structured.match ops{["linalg.matmul"]} in %module_op : (!transform.any_op) -> !transform.any_op
%1, %loops:3 = transform.structured.tile %0 [2, 2, 2] : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op)
%2 = get_parent_op %1 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- transform.structured.vectorize_children %2 : (!transform.any_op) -> !transform.any_op
+ transform.structured.vectorize_children_and_apply_patterns %2 : (!transform.any_op) -> !transform.any_op
%b = transform.bufferization.one_shot_bufferize layout{IdentityLayoutMap}
%module_op {bufferize_function_boundaries = true}
: (!transform.any_op) -> !transform.any_op
diff --git a/mlir/test/Dialect/Linalg/transform-op-matmul-to-outerproduct.mlir b/mlir/test/Dialect/Linalg/transform-op-matmul-to-outerproduct.mlir
index 4227b860e74f73c..8a06a55a1b57a56 100644
--- a/mlir/test/Dialect/Linalg/transform-op-matmul-to-outerproduct.mlir
+++ b/mlir/test/Dialect/Linalg/transform-op-matmul-to-outerproduct.mlir
@@ -31,7 +31,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize_children %1 : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children_and_apply_patterns %1 : (!transform.any_op) -> !transform.any_op
transform.apply_patterns to %2 {
transform.apply_patterns.vector.lower_contraction lowering_strategy = "outerproduct"
} : !transform.any_op
diff --git a/mlir/test/Dialect/Linalg/transform-op-vectorize.mlir b/mlir/test/Dialect/Linalg/transform-op-vectorize.mlir
index ef318d0bb32993a..43fea65ed7f30da 100644
--- a/mlir/test/Dialect/Linalg/transform-op-vectorize.mlir
+++ b/mlir/test/Dialect/Linalg/transform-op-vectorize.mlir
@@ -20,7 +20,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize_children %1 : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children_and_apply_patterns %1 : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -45,7 +45,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize_children %1 : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children_and_apply_patterns %1 : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -65,7 +65,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.copy"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize_children %1 : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children_and_apply_patterns %1 : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -111,7 +111,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize_children %1 : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children_and_apply_patterns %1 : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -159,7 +159,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize_children %1 {vectorize_padding} : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children_and_apply_patterns %1 {vectorize_padding} : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -176,5 +176,5 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
// expected-error @below {{op requires isolated-from-above targets}}
- %2 = transform.structured.vectorize_children %0 : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children_and_apply_patterns %0 : (!transform.any_op) -> !transform.any_op
}
diff --git a/mlir/test/Dialect/Linalg/vectorization.mlir b/mlir/test/Dialect/Linalg/vectorization.mlir
index f31197f0ed85808..ecba1f32468031e 100644
--- a/mlir/test/Dialect/Linalg/vectorization.mlir
+++ b/mlir/test/Dialect/Linalg/vectorization.mlir
@@ -32,7 +32,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.matvec"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize_children %1 { disable_multi_reduction_to_contract_patterns } : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children_and_apply_patterns %1 { disable_multi_reduction_to_contract_patterns } : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -50,7 +50,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize_children %1 { disable_multi_reduction_to_contract_patterns } : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children_and_apply_patterns %1 { disable_multi_reduction_to_contract_patterns } : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -69,7 +69,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.batch_matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize_children %1 { disable_multi_reduction_to_contract_patterns } : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children_and_apply_patterns %1 { disable_multi_reduction_to_contract_patterns } : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -109,7 +109,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize_children %1 { disable_multi_reduction_to_contract_patterns, disable_transfer_permutation_map_lowering_patterns } : (!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
}
// -----
@@ -149,7 +149,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize_children %1 { disable_multi_reduction_to_contract_patterns, disable_transfer_permutation_map_lowering_patterns } : (!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
}
// -----
@@ -176,7 +176,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize_children %1 { disable_multi_reduction_to_contract_patterns, disable_transfer_permutation_map_lowering_patterns } : (!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
}
// -----
@@ -216,7 +216,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize_children %1 { disable_multi_reduction_to_contract_patterns, disable_transfer_permutation_map_lowering_patterns } : (!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
}
// -----
@@ -236,7 +236,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize_children %1 { disable_multi_reduction_to_contract_patterns } : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children_and_apply_patterns %1 { disable_multi_reduction_to_contract_patterns } : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -260,7 +260,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize_children %1 : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children_and_apply_patterns %1 : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -284,7 +284,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize_children %1 : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children_and_apply_patterns %1 : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -329,7 +329,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize_children %1 { vectorize_nd_extract } : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children_and_apply_patterns %1 { vectorize_nd_extract } : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -346,7 +346,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.fill"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize_children %1 : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children_and_apply_patterns %1 : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -364,7 +364,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.fill"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize_children %1 : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children_and_apply_patterns %1 : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -381,7 +381,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["memref.copy"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize_children %1 : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children_and_apply_patterns %1 : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -401,7 +401,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["memref.copy"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize_children %1 : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children_and_apply_patterns %1 : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -417,7 +417,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["memref.copy"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize_children %1 : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children_and_apply_patterns %1 : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -445,7 +445,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize_children %1 : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children_and_apply_patterns %1 : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -474,7 +474,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize_children %1 : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children_and_apply_patterns %1 : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -559,7 +559,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize_children %1 { disable_transfer_permutation_map_lowering_patterns } : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children_and_apply_patterns %1 { disable_transfer_permutation_map_lowering_patterns } : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -650,7 +650,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize_children %1 { disable_transfer_permutation_map_lowering_patterns } : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children_and_apply_patterns %1 { disable_transfer_permutation_map_lowering_patterns } : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -694,7 +694,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize_children %1 { disable_transfer_permutation_map_lowering_patterns } : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children_and_apply_patterns %1 { disable_transfer_permutation_map_lowering_patterns } : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -737,7 +737,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize_children %1 { disable_transfer_permutation_map_lowering_patterns } : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children_and_apply_patterns %1 { disable_transfer_permutation_map_lowering_patterns } : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -769,7 +769,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize_children %1 { disable_multi_reduction_to_contract_patterns, disable_transfer_permutation_map_lowering_patterns } : (!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
}
// -----
@@ -798,7 +798,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["tensor.pad"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize_children %1 { vectorize_padding } : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children_and_apply_patterns %1 { vectorize_padding } : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -827,7 +827,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["tensor.pad"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize_children %1 { vectorize_padding } : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children_and_apply_patterns %1 { vectorize_padding } : (!transform.any_op) -> !transform.any_op
}
@@ -864,7 +864,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["tensor.pad"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize_children %1 { vectorize_padding } : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children_and_apply_patterns %1 { vectorize_padding } : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -884,7 +884,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["tensor.pad"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize_children %1 { vectorize_padding } : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children_and_apply_patterns %1 { vectorize_padding } : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -914,7 +914,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["tensor.pad"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize_children %1 { vectorize_padding } : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children_and_apply_patterns %1 { vectorize_padding } : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -947,7 +947,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%3 = transform.structured.match ops{["tensor.pad"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%4 = get_parent_op %3 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %5 = transform.structured.vectorize_children %4 { vectorize_padding } : (!transform.any_op) -> !transform.any_op
+ %5 = transform.structured.vectorize_children_and_apply_patterns %4 { vectorize_padding } : (!transform.any_op) -> !transform.any_op
}
@@ -984,7 +984,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%3 = transform.structured.match ops{["tensor.pad"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%4 = get_parent_op %3 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %5 = transform.structured.vectorize_children %4 { vectorize_padding } : (!transform.any_op) -> !transform.any_op
+ %5 = transform.structured.vectorize_children_and_apply_patterns %4 { vectorize_padding } : (!transform.any_op) -> !transform.any_op
}
@@ -1018,7 +1018,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%3 = transform.structured.match ops{["tensor.pad"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%4 = get_parent_op %3 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %5 = transform.structured.vectorize_children %4 { vectorize_padding } : (!transform.any_op) -> !transform.any_op
+ %5 = transform.structured.vectorize_children_and_apply_patterns %4 { vectorize_padding } : (!transform.any_op) -> !transform.any_op
}
@@ -1046,7 +1046,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%3 = transform.structured.match ops{["tensor.pad"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%4 = get_parent_op %3 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %5 = transform.structured.vectorize_children %4 : (!transform.any_op) -> !transform.any_op
+ %5 = transform.structured.vectorize_children_and_apply_patterns %4 : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -1083,7 +1083,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%3 = transform.structured.match ops{["tensor.pad"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%4 = get_parent_op %3 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %5 = transform.structured.vectorize_children %4 { vectorize_padding } : (!transform.any_op) -> !transform.any_op
+ %5 = transform.structured.vectorize_children_and_apply_patterns %4 { vectorize_padding } : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -1118,7 +1118,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%3 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%4 = get_parent_op %3 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %5 = transform.structured.vectorize_children %4 : (!transform.any_op) -> !transform.any_op
+ %5 = transform.structured.vectorize_children_and_apply_patterns %4 : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -1163,7 +1163,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%3 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%4 = get_parent_op %3 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %5 = transform.structured.vectorize_children %4 { disable_multi_reduction_to_contract_patterns, disable_transfer_permutation_map_lowering_patterns } : (!transform.any_op) -> !transform.any_op
+ %5 = transform.structured.vectorize_children_and_apply_patterns %4 { disable_multi_reduction_to_contract_patterns, disable_transfer_permutation_map_lowering_patterns } : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -1193,7 +1193,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%3 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%4 = get_parent_op %3 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %5 = transform.structured.vectorize_children %4 { vectorize_padding } : (!transform.any_op) -> !transform.any_op
+ %5 = transform.structured.vectorize_children_and_apply_patterns %4 { vectorize_padding } : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -1224,7 +1224,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%3 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%4 = get_parent_op %3 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %5 = transform.structured.vectorize_children %4 : (!transform.any_op) -> !transform.any_op
+ %5 = transform.structured.vectorize_children_and_apply_patterns %4 : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -1254,7 +1254,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%3 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%4 = get_parent_op %3 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %5 = transform.structured.vectorize_children %4 : (!transform.any_op) -> !transform.any_op
+ %5 = transform.structured.vectorize_children_and_apply_patterns %4 : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -1284,7 +1284,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%3 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%4 = get_parent_op %3 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %5 = transform.structured.vectorize_children %4 : (!transform.any_op) -> !transform.any_op
+ %5 = transform.structured.vectorize_children_and_apply_patterns %4 : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -1314,7 +1314,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%3 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%4 = get_parent_op %3 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %5 = transform.structured.vectorize_children %4 : (!transform.any_op) -> !transform.any_op
+ %5 = transform.structured.vectorize_children_and_apply_patterns %4 : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -1344,7 +1344,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%3 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%4 = get_parent_op %3 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %5 = transform.structured.vectorize_children %4 : (!transform.any_op) -> !transform.any_op
+ %5 = transform.structured.vectorize_children_and_apply_patterns %4 : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -1378,7 +1378,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%3 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%4 = get_parent_op %3 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %5 = transform.structured.vectorize_children %4 : (!transform.any_op) -> !transform.any_op
+ %5 = transform.structured.vectorize_children_and_apply_patterns %4 : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -1416,11 +1416,11 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.fill"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize_children %1 : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children_and_apply_patterns %1 : (!transform.any_op) -> !transform.any_op
%3 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%4 = get_parent_op %3 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %5 = transform.structured.vectorize_children %4 : (!transform.any_op) -> !transform.any_op
+ %5 = transform.structured.vectorize_children_and_apply_patterns %4 : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -1463,7 +1463,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize_children %1 : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children_and_apply_patterns %1 : (!transform.any_op) -> !transform.any_op
}
@@ -1494,7 +1494,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize_children %1 : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children_and_apply_patterns %1 : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -1533,7 +1533,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize_children %1 { disable_multi_reduction_to_contract_patterns, disable_transfer_permutation_map_lowering_patterns } : (!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
}
// -----
@@ -1557,7 +1557,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.map"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize_children %1 : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children_and_apply_patterns %1 : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -1576,7 +1576,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.transpose"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize_children %1 : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children_and_apply_patterns %1 : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -1599,13 +1599,13 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.reduce"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize_children %1 : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children_and_apply_patterns %1 : (!transform.any_op) -> !transform.any_op
}
// -----
// This is a regression test. This IR cannot be vectorized, but
-// structured.vectorize_children should nevertheless succeed.
+// structured.vectorize_children_and_apply_patterns should nevertheless succeed.
#map = affine_map<(d0) -> (d0)>
// CHECK-LABEL: @not_vectorizable
@@ -1631,7 +1631,7 @@ func.func @not_vectorizable(%arg0: tensor<1x?xf32>, %arg1: index, %arg2: index,
transform.sequence failures(propagate) {
^bb0(%arg0: !transform.any_op):
%0 = transform.structured.match ops{["func.func"]} in %arg0 : (!transform.any_op) -> !transform.any_op
- %1 = transform.structured.vectorize_children %0 : (!transform.any_op) -> !transform.any_op
+ %1 = transform.structured.vectorize_children_and_apply_patterns %0 : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -1666,7 +1666,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize_children %1 : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children_and_apply_patterns %1 : (!transform.any_op) -> !transform.any_op
}
// CHECK-LABEL: @wrong_reduction_detection
@@ -1695,7 +1695,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize_children %1 : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children_and_apply_patterns %1 : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -1716,7 +1716,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["tensor.pad"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize_children %1 { vectorize_padding } : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children_and_apply_patterns %1 { vectorize_padding } : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -1738,7 +1738,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%3 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%4 = get_parent_op %3 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %5 = transform.structured.vectorize_children %4 : (!transform.any_op) -> !transform.any_op
+ %5 = transform.structured.vectorize_children_and_apply_patterns %4 : (!transform.any_op) -> !transform.any_op
}
// CHECK-LABEL: func @zero_dim_tensor
@@ -1775,7 +1775,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%3 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%4 = get_parent_op %3 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %5 = transform.structured.vectorize_children %4 : (!transform.any_op) -> !transform.any_op
+ %5 = transform.structured.vectorize_children_and_apply_patterns %4 : (!transform.any_op) -> !transform.any_op
}
// CHECK-LABEL: func @multi_output_generic_different_perm_maps
diff --git a/mlir/test/Dialect/Linalg/vectorize-tensor-extract.mlir b/mlir/test/Dialect/Linalg/vectorize-tensor-extract.mlir
index ccc076c9c44903b..5cf9c81dff69553 100644
--- a/mlir/test/Dialect/Linalg/vectorize-tensor-extract.mlir
+++ b/mlir/test/Dialect/Linalg/vectorize-tensor-extract.mlir
@@ -31,7 +31,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize_children %1 : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children_and_apply_patterns %1 : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -104,7 +104,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize_children %1 { vectorize_nd_extract } : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children_and_apply_patterns %1 { vectorize_nd_extract } : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -156,7 +156,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize_children %1 { vectorize_nd_extract } : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children_and_apply_patterns %1 { vectorize_nd_extract } : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -204,7 +204,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize_children %1 { vectorize_nd_extract } : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children_and_apply_patterns %1 { vectorize_nd_extract } : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -248,7 +248,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize_children %1 { vectorize_nd_extract } : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children_and_apply_patterns %1 { vectorize_nd_extract } : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -290,7 +290,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize_children %1 { vectorize_nd_extract } : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children_and_apply_patterns %1 { vectorize_nd_extract } : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -332,7 +332,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize_children %1 { vectorize_nd_extract } : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children_and_apply_patterns %1 { vectorize_nd_extract } : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -376,7 +376,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize_children %1 { vectorize_nd_extract } : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children_and_apply_patterns %1 { vectorize_nd_extract } : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -416,7 +416,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize_children %1 { vectorize_nd_extract } : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children_and_apply_patterns %1 { vectorize_nd_extract } : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -456,7 +456,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize_children %1 { vectorize_nd_extract } : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children_and_apply_patterns %1 { vectorize_nd_extract } : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -495,7 +495,7 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize_children %1 { vectorize_nd_extract } : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children_and_apply_patterns %1 { vectorize_nd_extract } : (!transform.any_op) -> !transform.any_op
}
// -----
@@ -522,5 +522,5 @@ transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize_children %1 { vectorize_nd_extract } : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children_and_apply_patterns %1 { vectorize_nd_extract } : (!transform.any_op) -> !transform.any_op
}
diff --git a/mlir/test/Dialect/Transform/selective-targeting.mlir b/mlir/test/Dialect/Transform/selective-targeting.mlir
index 98c91e2267f9a83..139842cb447e349 100644
--- a/mlir/test/Dialect/Transform/selective-targeting.mlir
+++ b/mlir/test/Dialect/Transform/selective-targeting.mlir
@@ -80,7 +80,7 @@ transform.with_pdl_patterns {
transform.structured.tile %0 [4, 4, 4] : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op)
%1 = pdl_match @pdl_target_attrC in %arg1 : (!transform.any_op) -> !transform.any_op
%2 = get_parent_op %1 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- transform.structured.vectorize_children %2 : (!transform.any_op) -> !transform.any_op
+ transform.structured.vectorize_children_and_apply_patterns %2 : (!transform.any_op) -> !transform.any_op
}
}
@@ -125,7 +125,7 @@ transform.with_pdl_patterns {
^bb1(%arg1: !transform.any_op):
%0 = pdl_match @pdl_target in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- transform.structured.vectorize_children %1 : (!transform.any_op) -> !transform.any_op
+ transform.structured.vectorize_children_and_apply_patterns %1 : (!transform.any_op) -> !transform.any_op
}
}
@@ -150,5 +150,5 @@ func.func @vectorize_all(
transform.sequence failures(propagate) {
^bb0(%arg0: !transform.any_op):
- transform.structured.vectorize_children %arg0 : (!transform.any_op) -> !transform.any_op
+ transform.structured.vectorize_children_and_apply_patterns %arg0 : (!transform.any_op) -> !transform.any_op
}
diff --git a/mlir/test/Dialect/Vector/transform-vector.mlir b/mlir/test/Dialect/Vector/transform-vector.mlir
index b5cb3c7249d5088..de6c022e20599ac 100644
--- a/mlir/test/Dialect/Vector/transform-vector.mlir
+++ b/mlir/test/Dialect/Vector/transform-vector.mlir
@@ -19,7 +19,7 @@ transform.sequence failures(propagate) {
%1, %loops:3 = transform.structured.tile %0 [8, 4, 2]
: (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op)
%2 = get_parent_op %1 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- transform.structured.vectorize_children %2 : (!transform.any_op) -> !transform.any_op
+ transform.structured.vectorize_children_and_apply_patterns %2 : (!transform.any_op) -> !transform.any_op
%b = transform.bufferization.one_shot_bufferize
layout{IdentityLayoutMap} %module_op
{bufferize_function_boundaries = true, allow_return_allocs = true}
diff --git a/mlir/test/python/dialects/transform_structured_ext.py b/mlir/test/python/dialects/transform_structured_ext.py
index 91c14abede7f367..9a4fe3fbbd4fcae 100644
--- a/mlir/test/python/dialects/transform_structured_ext.py
+++ b/mlir/test/python/dialects/transform_structured_ext.py
@@ -487,15 +487,15 @@ def testTileToForallMapping(target):
@run
@create_sequence
-def testVectorizeChildrenAllAttrs(target):
- structured.VectorizeChildrenOp(
+def testVectorizeChildrenAndApplyPatternsAllAttrs(target):
+ structured.VectorizeChildrenAndApplyPatternsOp(
target,
disable_multi_reduction_to_contract_patterns=True,
disable_transfer_permutation_map_lowering_patterns=True,
vectorize_nd_extract=True,
vectorize_padding=True,
)
- # CHECK-LABEL: TEST: testVectorizeChildrenAllAttrs
+ # CHECK-LABEL: TEST: testVectorizeChildrenAndApplyPatternsAllAttrs
# CHECK: transform.sequence
# CHECK: = transform.structured.vectorize
# CHECK-SAME: disable_multi_reduction_to_contract_patterns
@@ -506,15 +506,15 @@ def testVectorizeChildrenAllAttrs(target):
@run
@create_sequence
-def testVectorizeChildrenNoAttrs(target):
- structured.VectorizeChildrenOp(
+def testVectorizeChildrenAndApplyPatternsNoAttrs(target):
+ structured.VectorizeChildrenAndApplyPatternsOp(
target,
disable_multi_reduction_to_contract_patterns=False,
disable_transfer_permutation_map_lowering_patterns=False,
vectorize_nd_extract=False,
vectorize_padding=False,
)
- # CHECK-LABEL: TEST: testVectorizeChildrenNoAttrs
+ # CHECK-LABEL: TEST: testVectorizeChildrenAndApplyPatternsNoAttrs
# CHECK: transform.sequence
# CHECK: = transform.structured.vectorize
# CHECK-NOT: disable_multi_reduction_to_contract_patterns
>From 9b82936a114127d8c7b05a4966f93518593d68b6 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Ingo=20M=C3=BCller?= <ingomueller at google.com>
Date: Tue, 19 Sep 2023 11:24:29 +0000
Subject: [PATCH 4/4] Rename main files with tests of renamed ops.
In particular:
* `vectorization{ => -with-patterns}.mlir`.
* `vectorization{-masked => }.mlir`.
---
.../Dialect/Linalg/vectorization-masked.mlir | 514 -----
.../Linalg/vectorization-with-patterns.mlir | 1787 +++++++++++++++
mlir/test/Dialect/Linalg/vectorization.mlir | 2029 +++--------------
3 files changed, 2165 insertions(+), 2165 deletions(-)
delete mode 100644 mlir/test/Dialect/Linalg/vectorization-masked.mlir
create mode 100644 mlir/test/Dialect/Linalg/vectorization-with-patterns.mlir
diff --git a/mlir/test/Dialect/Linalg/vectorization-masked.mlir b/mlir/test/Dialect/Linalg/vectorization-masked.mlir
deleted file mode 100644
index ddeaff76a04df23..000000000000000
--- a/mlir/test/Dialect/Linalg/vectorization-masked.mlir
+++ /dev/null
@@ -1,514 +0,0 @@
-// RUN: mlir-opt %s -test-transform-dialect-interpreter -split-input-file | FileCheck %s
-
-func.func @vectorize_dynamic_identity(%arg0: tensor<?xf32>,
- %arg1: tensor<?xf32>,
- %arg2: tensor<?xf32>) -> tensor<?xf32> {
- %0 = linalg.generic { indexing_maps = [affine_map<(d0) -> (d0)>,
- affine_map<(d0) -> (d0)>,
- affine_map<(d0) -> (d0)>],
- iterator_types = ["parallel"] }
- ins(%arg0, %arg1 : tensor<?xf32>, tensor<?xf32>)
- outs(%arg2 : tensor<?xf32>) {
- ^bb(%in0: f32, %in1: f32, %out: f32) :
- %0 = arith.addf %in0, %in1 : f32
- linalg.yield %0 : f32
- } -> tensor<?xf32>
- return %0 : tensor<?xf32>
-}
-
-// CHECK-LABEL: @vectorize_dynamic_identity
-// CHECK: %[[VAL_3:.*]] = arith.constant 0 : index
-// CHECK: %[[VAL_4:.*]] = tensor.dim %{{.*}}, %[[VAL_3]] : tensor<?xf32>
-// CHECK: %[[VAL_7:.*]] = vector.create_mask %[[VAL_4]] : vector<4xi1>
-// CHECK: %[[VAL_8:.*]] = vector.mask %[[VAL_7]] { vector.transfer_read %{{.*}} {in_bounds = [true]} : tensor<?xf32>, vector<4xf32> } : vector<4xi1> -> vector<4xf32>
-// CHECK: %[[VAL_10:.*]] = vector.mask %[[VAL_7]] { vector.transfer_read %{{.*}} {in_bounds = [true]} : tensor<?xf32>, vector<4xf32> } : vector<4xi1> -> vector<4xf32>
-// CHECK: %[[VAL_12:.*]] = vector.mask %[[VAL_7]] { vector.transfer_read %{{.*}} {in_bounds = [true]} : tensor<?xf32>, vector<4xf32> } : vector<4xi1> -> vector<4xf32>
-// CHECK: %[[VAL_13:.*]] = arith.addf %[[VAL_8]], %[[VAL_10]] : vector<4xf32>
-// CHECK: %[[VAL_14:.*]] = vector.mask %[[VAL_7]] { vector.transfer_write %{{.*}} {in_bounds = [true]} : vector<4xf32>, tensor<?xf32> } : vector<4xi1> -> tensor<?xf32>
-
-transform.sequence failures(propagate) {
-^bb1(%arg1: !transform.any_op):
- %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- transform.structured.vectorize %0 vector_sizes [4] : !transform.any_op
-}
-
-// -----
-
-func.func @vectorize_dynamic_1d_broadcast(%arg0: tensor<?xf32>,
- %arg1: tensor<?xf32>,
- %arg2: tensor<?xf32>) -> tensor<?xf32> {
- %0 = linalg.generic { indexing_maps = [affine_map<(d0) -> (0)>,
- affine_map<(d0) -> (d0)>,
- affine_map<(d0) -> (d0)>],
- iterator_types = ["parallel"] }
- ins(%arg0, %arg1 : tensor<?xf32>, tensor<?xf32>)
- outs(%arg2 : tensor<?xf32>) {
- ^bb(%in0: f32, %in1: f32, %out: f32) :
- %0 = arith.addf %in0, %in1 : f32
- linalg.yield %0 : f32
- } -> tensor<?xf32>
- return %0 : tensor<?xf32>
-}
-
-// CHECK-LABEL: @vectorize_dynamic_1d_broadcast
-// CHECK: %[[VAL_3:.*]] = arith.constant 0 : index
-// CHECK: %[[VAL_4:.*]] = tensor.dim %{{.*}}, %[[VAL_3]] : tensor<?xf32>
-// CHECK: %[[VAL_7:.*]] = vector.transfer_read %{{.*}} {permutation_map = #{{.*}}} : tensor<?xf32>, vector<4xf32>
-// CHECK: %[[VAL_9:.*]] = vector.create_mask %[[VAL_4]] : vector<4xi1>
-// CHECK: %[[VAL_10:.*]] = vector.mask %[[VAL_9]] { vector.transfer_read %{{.*}} {in_bounds = [true]} : tensor<?xf32>, vector<4xf32> } : vector<4xi1> -> vector<4xf32>
-// CHECK: %[[VAL_12:.*]] = vector.mask %[[VAL_9]] { vector.transfer_read %{{.*}} {in_bounds = [true]} : tensor<?xf32>, vector<4xf32> } : vector<4xi1> -> vector<4xf32>
-// CHECK: %[[VAL_13:.*]] = arith.addf %[[VAL_7]], %[[VAL_10]] : vector<4xf32>
-// CHECK: %[[VAL_14:.*]] = vector.mask %{{.*}} { vector.transfer_write %[[VAL_13]], {{.*}} {in_bounds = [true]} : vector<4xf32>, tensor<?xf32> } : vector<4xi1> -> tensor<?xf32>
-
-transform.sequence failures(propagate) {
-^bb1(%arg1: !transform.any_op):
- %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- transform.structured.vectorize %0 vector_sizes [4] : !transform.any_op
-}
-
-// -----
-
-func.func @vectorize_dynamic_2d_transpose(%arg0: tensor<?x?xf32>,
- %arg1: tensor<?x?xf32>,
- %arg2: tensor<?x?xf32>) -> tensor<?x?xf32> {
- %0 = linalg.generic { indexing_maps = [affine_map<(d0, d1) -> (d1, d0)>,
- affine_map<(d0, d1) -> (d0, d1)>,
- affine_map<(d0, d1) -> (d0, d1)>],
- iterator_types = ["parallel", "parallel"] }
- ins(%arg0, %arg1 : tensor<?x?xf32>, tensor<?x?xf32>)
- outs(%arg2 : tensor<?x?xf32>) {
- ^bb(%in0: f32, %in1: f32, %out: f32) :
- %0 = arith.addf %in0, %in1 : f32
- linalg.yield %0 : f32
- } -> tensor<?x?xf32>
- return %0 : tensor<?x?xf32>
-}
-
-// CHECK-LABEL: @vectorize_dynamic_2d_transpose
-// CHECK: %[[VAL_3:.*]] = arith.constant 1 : index
-// CHECK: %[[VAL_4:.*]] = tensor.dim %{{.*}}, %[[VAL_3]] : tensor<?x?xf32>
-// CHECK: %[[VAL_5:.*]] = arith.constant 0 : index
-// CHECK: %[[VAL_6:.*]] = tensor.dim %{{.*}}, %[[VAL_5]] : tensor<?x?xf32>
-// CHECK: %[[VAL_9:.*]] = vector.create_mask %[[VAL_6]], %[[VAL_4]] : vector<8x4xi1>
-// CHECK: %[[VAL_10:.*]] = vector.mask %[[VAL_9]] { vector.transfer_read %{{.*}} {in_bounds = [true, true], permutation_map = #{{.*}}} : tensor<?x?xf32>, vector<4x8xf32> } : vector<8x4xi1> -> vector<4x8xf32>
-// CHECK: %[[VAL_12:.*]] = vector.create_mask %[[VAL_4]], %[[VAL_6]] : vector<4x8xi1>
-// CHECK: %[[VAL_13:.*]] = vector.mask %[[VAL_12]] { vector.transfer_read %{{.*}} {in_bounds = [true, true]} : tensor<?x?xf32>, vector<4x8xf32> } : vector<4x8xi1> -> vector<4x8xf32>
-// CHECK: %[[VAL_14:.*]] = arith.constant 0.000000e+00 : f32
-// CHECK: %[[VAL_15:.*]] = vector.mask %[[VAL_12]] { vector.transfer_read %{{.*}} {in_bounds = [true, true]} : tensor<?x?xf32>, vector<4x8xf32> } : vector<4x8xi1> -> vector<4x8xf32>
-// CHECK: %[[VAL_16:.*]] = arith.addf %[[VAL_10]], %[[VAL_13]] : vector<4x8xf32>
-// CHECK: %[[VAL_17:.*]] = vector.mask %[[VAL_12]] { vector.transfer_write %[[VAL_16]], %{{.*}} {in_bounds = [true, true]} : vector<4x8xf32>, tensor<?x?xf32> } : vector<4x8xi1> -> tensor<?x?xf32>
-
-transform.sequence failures(propagate) {
-^bb1(%arg1: !transform.any_op):
- %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- transform.structured.vectorize %0 vector_sizes [4, 8] : !transform.any_op
-}
-
-// -----
-
-func.func @vectorize_dynamic_generic_2d_broadcast(%arg0: tensor<?x?xf32>,
- %arg1: tensor<?x?xf32>,
- %arg2: tensor<?x?xf32>) -> tensor<?x?xf32> {
- %0 = linalg.generic { indexing_maps = [affine_map<(d0, d1) -> (0, d1)>,
- affine_map<(d0, d1) -> (d0, d1)>,
- affine_map<(d0, d1) -> (d0, d1)>],
- iterator_types = ["parallel", "parallel"] }
- ins(%arg0, %arg1 : tensor<?x?xf32>, tensor<?x?xf32>)
- outs(%arg2 : tensor<?x?xf32>) {
- ^bb(%in0: f32, %in1: f32, %out: f32) :
- %0 = arith.addf %in0, %in1 : f32
- linalg.yield %0 : f32
- } -> tensor<?x?xf32>
- return %0 : tensor<?x?xf32>
-}
-
-// CHECK-LABEL: @vectorize_dynamic_generic_2d_broadcast
-// CHECK: %[[VAL_3:.*]] = arith.constant 0 : index
-// CHECK: %[[VAL_4:.*]] = tensor.dim %{{.*}}, %[[VAL_3]] : tensor<?x?xf32>
-// CHECK: %[[VAL_5:.*]] = arith.constant 1 : index
-// CHECK: %[[VAL_6:.*]] = tensor.dim %{{.*}}, %[[VAL_5]] : tensor<?x?xf32>
-// CHECK: %[[VAL_9:.*]] = vector.create_mask %[[VAL_6]] : vector<8xi1>
-// CHECK: %[[VAL_10:.*]] = vector.mask %[[VAL_9]] { vector.transfer_read %{{.*}} {in_bounds = [true, true], permutation_map = #{{.*}}} : tensor<?x?xf32>, vector<4x8xf32> } : vector<8xi1> -> vector<4x8xf32>
-// CHECK: %[[VAL_12:.*]] = vector.create_mask %[[VAL_4]], %[[VAL_6]] : vector<4x8xi1>
-// CHECK: %[[VAL_13:.*]] = vector.mask %[[VAL_12]] { vector.transfer_read %{{.*}} {in_bounds = [true, true]} : tensor<?x?xf32>, vector<4x8xf32> } : vector<4x8xi1> -> vector<4x8xf32>
-// CHECK: %[[VAL_15:.*]] = vector.mask %[[VAL_12]] { vector.transfer_read %{{.*}} {in_bounds = [true, true]} : tensor<?x?xf32>, vector<4x8xf32> } : vector<4x8xi1> -> vector<4x8xf32>
-// CHECK: %[[VAL_16:.*]] = arith.addf %[[VAL_10]], %[[VAL_13]] : vector<4x8xf32>
-// CHECK: %[[VAL_18:.*]] = vector.mask %[[VAL_12]] { vector.transfer_write %{{.*}} {in_bounds = [true, true]} : vector<4x8xf32>, tensor<?x?xf32> } : vector<4x8xi1> -> tensor<?x?xf32>
-
-transform.sequence failures(propagate) {
-^bb1(%arg1: !transform.any_op):
- %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- transform.structured.vectorize %0 vector_sizes [4, 8] : !transform.any_op
-}
-
-// -----
-
-func.func @vectorize_dynamic_reduction(%arg0: tensor<?x?xf32>,
- %arg1: tensor<?xf32>) -> tensor<?xf32> {
- %0 = linalg.generic { indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>,
- affine_map<(d0, d1) -> (d0)>],
- iterator_types = ["parallel", "reduction"] }
- ins(%arg0 : tensor<?x?xf32>)
- outs(%arg1 : tensor<?xf32>) {
- ^bb(%in: f32, %out: f32) :
- %0 = arith.addf %in, %out : f32
- linalg.yield %0 : f32
- } -> tensor<?xf32>
- return %0 : tensor<?xf32>
-}
-
-transform.sequence failures(propagate) {
-^bb1(%arg1: !transform.any_op):
- %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- transform.structured.vectorize %0 vector_sizes [4, 8] : !transform.any_op
-}
-
-// CHECK-LABEL: @vectorize_dynamic_reduction(
-// CHECK-SAME: %[[VAL_0:.*]]: tensor<?x?xf32>,
-// CHECK-SAME: %[[VAL_1:.*]]: tensor<?xf32>) -> tensor<?xf32> {
-// CHECK: %[[VAL_2:.*]] = arith.constant 0 : index
-// CHECK: %[[VAL_3:.*]] = tensor.dim %[[VAL_0]], %[[VAL_2]] : tensor<?x?xf32>
-// CHECK: %[[VAL_4:.*]] = arith.constant 1 : index
-// CHECK: %[[VAL_5:.*]] = tensor.dim %[[VAL_0]], %[[VAL_4]] : tensor<?x?xf32>
-// CHECK: %[[VAL_8:.*]] = vector.create_mask %[[VAL_3]], %[[VAL_5]] : vector<4x8xi1>
-// CHECK: %[[VAL_9:.*]] = vector.mask %[[VAL_8]] { vector.transfer_read %[[VAL_0]]{{.*}} {in_bounds = [true, true]} : tensor<?x?xf32>, vector<4x8xf32> } : vector<4x8xi1> -> vector<4x8xf32>
-// CHECK: %[[VAL_11:.*]] = vector.create_mask %[[VAL_3]] : vector<4xi1>
-// CHECK: %[[VAL_12:.*]] = vector.mask %[[VAL_11]] { vector.transfer_read %[[VAL_1]]{{.*}} {in_bounds = [true]} : tensor<?xf32>, vector<4xf32> } : vector<4xi1> -> vector<4xf32>
-// CHECK: %[[VAL_13:.*]] = vector.mask %[[VAL_8]] { vector.multi_reduction <add>, %[[VAL_9]], %[[VAL_12]] [1] : vector<4x8xf32> to vector<4xf32> } : vector<4x8xi1> -> vector<4xf32>
-// CHECK: %[[VAL_15:.*]] = vector.mask %[[VAL_11]] { vector.transfer_write %[[VAL_13]], %[[VAL_1]]{{.*}} {in_bounds = [true]} : vector<4xf32>, tensor<?xf32> } : vector<4xi1> -> tensor<?xf32>
-// CHECK: return %[[VAL_15]] : tensor<?xf32>
-// CHECK: }
-
-// -----
-
-func.func @vectorize_dynamic_transpose_reduction(%arg0: tensor<?x?x?xf32>,
- %arg1: tensor<?x?xf32>) -> tensor<?x?xf32> {
- %0 = linalg.generic { indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d1, d2)>,
- affine_map<(d0, d1, d2) -> (d2, d1)>],
- iterator_types = ["reduction", "parallel", "parallel"] }
- ins(%arg0 : tensor<?x?x?xf32>)
- outs(%arg1 : tensor<?x?xf32>) {
- ^bb(%in: f32, %out: f32) :
- %0 = arith.addf %in, %out : f32
- linalg.yield %0 : f32
- } -> tensor<?x?xf32>
- return %0 : tensor<?x?xf32>
-}
-
-transform.sequence failures(propagate) {
-^bb1(%arg1: !transform.any_op):
- %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- transform.structured.vectorize %0 vector_sizes [4, 8, 16] : !transform.any_op
-}
-
-// CHECK-LABEL: @vectorize_dynamic_transpose_reduction(
-// CHECK-SAME: %[[VAL_0:.*]]: tensor<?x?x?xf32>,
-// CHECK-SAME: %[[VAL_1:.*]]: tensor<?x?xf32>) -> tensor<?x?xf32> {
-// CHECK: %[[VAL_2:.*]] = arith.constant 0 : index
-// CHECK: %[[VAL_3:.*]] = tensor.dim %[[VAL_0]], %[[VAL_2]] : tensor<?x?x?xf32>
-// CHECK: %[[VAL_4:.*]] = arith.constant 1 : index
-// CHECK: %[[VAL_5:.*]] = tensor.dim %[[VAL_0]], %[[VAL_4]] : tensor<?x?x?xf32>
-// CHECK: %[[VAL_6:.*]] = arith.constant 2 : index
-// CHECK: %[[VAL_7:.*]] = tensor.dim %[[VAL_0]], %[[VAL_6]] : tensor<?x?x?xf32>
-// CHECK: %[[VAL_10:.*]] = vector.create_mask %[[VAL_3]], %[[VAL_5]], %[[VAL_7]] : vector<4x8x16xi1>
-// CHECK: %[[VAL_11:.*]] = vector.mask %[[VAL_10]] { vector.transfer_read %[[VAL_0]]{{.*}} {in_bounds = [true, true, true]} : tensor<?x?x?xf32>, vector<4x8x16xf32> } : vector<4x8x16xi1> -> vector<4x8x16xf32>
-// CHECK: %[[VAL_13:.*]] = vector.create_mask %[[VAL_7]], %[[VAL_5]] : vector<16x8xi1>
-// CHECK: %[[VAL_14:.*]] = vector.mask %[[VAL_13]] { vector.transfer_read %[[VAL_1]]{{.*}} {in_bounds = [true, true], permutation_map = #{{.*}}} : tensor<?x?xf32>, vector<8x16xf32> } : vector<16x8xi1> -> vector<8x16xf32>
-// CHECK: %[[VAL_15:.*]] = vector.mask %[[VAL_10]] { vector.multi_reduction <add>, %[[VAL_11]], %[[VAL_14]] [0] : vector<4x8x16xf32> to vector<8x16xf32> } : vector<4x8x16xi1> -> vector<8x16xf32>
-// CHECK: %[[VAL_17:.*]] = vector.mask %[[VAL_13]] { vector.transfer_write %[[VAL_15]], %{{.*}} {in_bounds = [true, true], permutation_map = #{{.*}}} : vector<8x16xf32>, tensor<?x?xf32> } : vector<16x8xi1> -> tensor<?x?xf32>
-
-// -----
-
-func.func @vectorize_partial_dynamic_identity(%arg0: tensor<8x?xf32>,
- %arg1: tensor<8x?xf32>,
- %arg2: tensor<8x?xf32>) -> tensor<8x?xf32> {
- %0 = linalg.generic { indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>,
- affine_map<(d0, d1) -> (d0, d1)>,
- affine_map<(d0, d1) -> (d0, d1)>],
- iterator_types = ["parallel", "parallel"] }
- ins(%arg0, %arg1 : tensor<8x?xf32>, tensor<8x?xf32>)
- outs(%arg2 : tensor<8x?xf32>) {
- ^bb(%in0: f32, %in1: f32, %out: f32) :
- %0 = arith.addf %in0, %in1 : f32
- linalg.yield %0 : f32
- } -> tensor<8x?xf32>
- return %0 : tensor<8x?xf32>
-}
-
-// CHECK-LABEL: func.func @vectorize_partial_dynamic_identity(
-// CHECK-SAME: %[[VAL_0:.*]]: tensor<8x?xf32>, %[[VAL_1:.*]]: tensor<8x?xf32>, %[[VAL_2:.*]]: tensor<8x?xf32>) -> tensor<8x?xf32> {
-// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 1 : index
-// CHECK-DAG: %[[VAL_4:.*]] = tensor.dim %[[VAL_0]], %[[VAL_3]] : tensor<8x?xf32>
-// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 0 : index
-// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 0.000000e+00 : f32
-// CHECK-DAG: %[[VAL_7:.*]] = arith.constant 8 : index
-// CHECK: %[[VAL_8:.*]] = vector.create_mask %[[VAL_7]], %[[VAL_4]] : vector<8x32xi1>
-// CHECK: %[[VAL_9:.*]] = vector.mask %[[VAL_8]] { vector.transfer_read %[[VAL_0]][%[[VAL_5]], %[[VAL_5]]], %[[VAL_6]] {in_bounds = [true, true]} : tensor<8x?xf32>, vector<8x32xf32> } : vector<8x32xi1> -> vector<8x32xf32>
-// CHECK: %[[VAL_10:.*]] = arith.constant 0.000000e+00 : f32
-// CHECK: %[[VAL_11:.*]] = vector.mask %[[VAL_8]] { vector.transfer_read %[[VAL_1]][%[[VAL_5]], %[[VAL_5]]], %[[VAL_10]] {in_bounds = [true, true]} : tensor<8x?xf32>, vector<8x32xf32> } : vector<8x32xi1> -> vector<8x32xf32>
-// CHECK: %[[VAL_12:.*]] = arith.constant 0.000000e+00 : f32
-// CHECK: %[[VAL_13:.*]] = vector.mask %[[VAL_8]] { vector.transfer_read %[[VAL_2]][%[[VAL_5]], %[[VAL_5]]], %[[VAL_12]] {in_bounds = [true, true]} : tensor<8x?xf32>, vector<8x32xf32> } : vector<8x32xi1> -> vector<8x32xf32>
-// CHECK: %[[VAL_14:.*]] = arith.addf %[[VAL_9]], %[[VAL_11]] : vector<8x32xf32>
-// CHECK: %[[VAL_15:.*]] = arith.constant 0 : index
-// CHECK: %[[VAL_16:.*]] = vector.mask %[[VAL_8]] { vector.transfer_write %[[VAL_14]], %[[VAL_2]][%[[VAL_15]], %[[VAL_15]]] {in_bounds = [true, true]} : vector<8x32xf32>, tensor<8x?xf32> } : vector<8x32xi1> -> tensor<8x?xf32>
-
-
-transform.sequence failures(propagate) {
-^bb1(%arg1: !transform.any_op):
- %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- transform.structured.vectorize %0 vector_sizes [8, 32] : !transform.any_op
-}
-
-// -----
-
-func.func @do_not_generate_masks(%arg0: tensor<8x32xf32>,
- %arg1: tensor<8x32xf32>,
- %arg2: tensor<8x32xf32>) -> tensor<8x32xf32> {
- %0 = linalg.generic { indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>,
- affine_map<(d0, d1) -> (d0, d1)>,
- affine_map<(d0, d1) -> (d0, d1)>],
- iterator_types = ["parallel", "parallel"] }
- ins(%arg0, %arg1 : tensor<8x32xf32>, tensor<8x32xf32>)
- outs(%arg2 : tensor<8x32xf32>) {
- ^bb(%in0: f32, %in1: f32, %out: f32) :
- %0 = arith.addf %in0, %in1 : f32
- linalg.yield %0 : f32
- } -> tensor<8x32xf32>
- return %0 : tensor<8x32xf32>
-}
-
-// CHECK-LABEL: func.func @do_not_generate_masks
-// CHECK-NOT: vector.mask
-
-transform.sequence failures(propagate) {
-^bb1(%arg1: !transform.any_op):
- %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- transform.structured.vectorize %0 vector_sizes [8, 32] : !transform.any_op
-}
-
-// -----
-
-func.func @vectorize_static_shape_with_mask(%arg0: tensor<8x30xf32>,
- %arg1: tensor<8x30xf32>,
- %arg2: tensor<8x30xf32>) -> tensor<8x30xf32> {
- %0 = linalg.generic { indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>,
- affine_map<(d0, d1) -> (d0, d1)>,
- affine_map<(d0, d1) -> (d0, d1)>],
- iterator_types = ["parallel", "parallel"] }
- ins(%arg0, %arg1 : tensor<8x30xf32>, tensor<8x30xf32>)
- outs(%arg2 : tensor<8x30xf32>) {
- ^bb(%in0: f32, %in1: f32, %out: f32) :
- %0 = arith.addf %in0, %in1 : f32
- linalg.yield %0 : f32
- } -> tensor<8x30xf32>
- return %0 : tensor<8x30xf32>
-}
-
-// CHECK-LABEL: func.func @vectorize_static_shape_with_mask(
-// CHECK-SAME: %[[VAL_0:.*]]: tensor<8x30xf32>, %[[VAL_1:.*]]: tensor<8x30xf32>, %[[VAL_2:.*]]: tensor<8x30xf32>) -> tensor<8x30xf32> {
-// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 0 : index
-// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0.000000e+00 : f32
-// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 8 : index
-// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 30 : index
-// CHECK: %[[VAL_7:.*]] = vector.create_mask %[[VAL_5]], %[[VAL_6]] : vector<8x32xi1>
-// CHECK: %[[VAL_8:.*]] = vector.mask %[[VAL_7]] { vector.transfer_read %[[VAL_0]][%[[VAL_3]], %[[VAL_3]]], %[[VAL_4]] {in_bounds = [true, true]} : tensor<8x30xf32>, vector<8x32xf32> } : vector<8x32xi1> -> vector<8x32xf32>
-// CHECK: %[[VAL_9:.*]] = arith.constant 0.000000e+00 : f32
-// CHECK: %[[VAL_10:.*]] = vector.mask %[[VAL_7]] { vector.transfer_read %[[VAL_1]][%[[VAL_3]], %[[VAL_3]]], %[[VAL_9]] {in_bounds = [true, true]} : tensor<8x30xf32>, vector<8x32xf32> } : vector<8x32xi1> -> vector<8x32xf32>
-// CHECK: %[[VAL_11:.*]] = arith.constant 0.000000e+00 : f32
-// CHECK: %[[VAL_12:.*]] = vector.mask %[[VAL_7]] { vector.transfer_read %[[VAL_2]][%[[VAL_3]], %[[VAL_3]]], %[[VAL_11]] {in_bounds = [true, true]} : tensor<8x30xf32>, vector<8x32xf32> } : vector<8x32xi1> -> vector<8x32xf32>
-// CHECK: %[[VAL_13:.*]] = arith.addf %[[VAL_8]], %[[VAL_10]] : vector<8x32xf32>
-// CHECK: %[[VAL_14:.*]] = arith.constant 0 : index
-// CHECK: %[[VAL_15:.*]] = vector.mask %[[VAL_7]] { vector.transfer_write %[[VAL_13]], %[[VAL_2]][%[[VAL_14]], %[[VAL_14]]] {in_bounds = [true, true]} : vector<8x32xf32>, tensor<8x30xf32> } : vector<8x32xi1> -> tensor<8x30xf32>
-
-transform.sequence failures(propagate) {
-^bb1(%arg1: !transform.any_op):
- %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- transform.structured.vectorize %0 vector_sizes [8, 32] : !transform.any_op
-}
-
-// -----
-
-func.func @vectorize_dynamic_fill(%A : tensor<?x?xf32>, %arg0 : f32) -> tensor<?x?xf32> {
- %0 = linalg.fill ins(%arg0 : f32) outs(%A : tensor<?x?xf32>) -> tensor<?x?xf32>
- return %0 : tensor<?x?xf32>
-}
-
-// CHECK-LABEL: func.func @vectorize_dynamic_fill
-// CHECK: %[[DIM0:.*]] = tensor.dim
-// CHECK: %[[DIM1:.*]] = tensor.dim
-// CHECK: %[[MASK:.*]] = vector.create_mask %[[DIM0]], %[[DIM1]] : vector<8x16xi1>
-// CHECK: %[[BCAST:.*]] = vector.broadcast %{{.*}} : f32 to vector<8x16xf32>
-// CHECK: vector.mask %[[MASK]] { vector.transfer_write %[[BCAST]], {{.*}} {in_bounds = [true, true]} : vector<8x16xf32>, tensor<?x?xf32> } : vector<8x16xi1>
-
-transform.sequence failures(propagate) {
-^bb1(%arg1: !transform.any_op):
- %0 = transform.structured.match ops{["linalg.fill"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- transform.structured.vectorize %0 vector_sizes [8, 16] : !transform.any_op
-}
-
-// -----
-
-// CHECK-LABEL: func @test_masked_vectorize_linalg_copy
-func.func @test_masked_vectorize_linalg_copy(%A : memref<?x?xf32>, %B : memref<?x?xf32>) {
- // CHECK: %[[c0:.*]] = arith.constant 0 : index
- // CHECK: %[[d0:.*]] = memref.dim %{{.*}}, %[[c0]] : memref<?x?xf32>
- // CHECK: %[[c1:.*]] = arith.constant 1 : index
- // CHECK: %[[d1:.*]] = memref.dim %{{.*}}, %[[c1]] : memref<?x?xf32>
- // CHECK: %[[mask:.*]] = vector.create_mask %[[d0]], %[[d1]] : vector<2x4xi1>
- // CHECK: vector.mask %[[mask]] {{.*}} vector.transfer_read %{{.*}} {in_bounds = [true, true]} : memref<?x?xf32>, vector<2x4xf32> } : vector<2x4xi1> -> vector<2x4xf32>
- // CHECK: vector.mask %[[mask]] {{.*}} vector.transfer_write %{{.*}} {in_bounds = [true, true]} : vector<2x4xf32>, memref<?x?xf32> } : vector<2x4xi1>
- linalg.copy ins(%A : memref<?x?xf32>) outs(%B : memref<?x?xf32>)
- return
-}
-
-transform.sequence failures(propagate) {
-^bb1(%arg1: !transform.any_op):
- %0 = transform.structured.match ops{["linalg.copy"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- transform.structured.vectorize %0 vector_sizes [2, 4] : !transform.any_op
-}
-
-// -----
-
-// CHECK-LABEL: func @test_masked_vectorize_pad
-func.func @test_masked_vectorize_pad(
- %0 : tensor<?x?xf32>, %h0 : index, %h1 : index)
- -> tensor<2x4xf32>
-{
- // CHECK-DAG: %[[c42:.*]] = arith.constant 4.243000e+01 : f32
- // CHECK-DAG: %[[c0:.*]] = arith.constant 0 : index
- // CHECK-DAG: %[[empty:.*]] = tensor.empty() : tensor<2x4xf32>
- // CHECK: %[[d0:.*]] = tensor.dim {{.*}} : tensor<?x?xf32>
- // CHECK: %[[d1:.*]] = tensor.dim {{.*}} : tensor<?x?xf32>
- // CHECK: %[[mask:.*]] = vector.create_mask %[[d0]], %[[d1]] : vector<2x4xi1>
- // CHECK-DAG: %[[c0_2:.*]] = arith.constant 0 : index
- // CHECK: %[[masked_read:.*]] = vector.mask %[[mask]] {
- // CHECK-SAME: vector.transfer_read %{{.*}}[%[[c0_2]], %[[c0_2]]], %[[c42]]
- // CHECK-SAME: {in_bounds = [true, true]} : tensor<?x?xf32>, vector<2x4xf32>
- // CHECK-SAME: } : vector<2x4xi1> -> vector<2x4xf32>
- // CHECK: vector.transfer_write %[[masked_read]], %[[empty]][%[[c0_2]], %[[c0_2]]]
- // CHECK-SAME: {in_bounds = [true, true]} : vector<2x4xf32>, tensor<2x4xf32>
- %cst = arith.constant 42.43 : f32
- %c0 = arith.constant 0 : index
- %1 = tensor.pad %0 low[0, %c0] high[%h0, %h1] {
- ^bb0(%hh1: index, %hh2: index):
- tensor.yield %cst : f32
- } : tensor<?x?xf32> to tensor<2x4xf32>
- return %1: tensor<2x4xf32>
-}
-
-transform.sequence failures(propagate) {
-^bb1(%arg1: !transform.any_op):
- %0 = transform.structured.match ops{["tensor.pad"]} in %arg1
- : (!transform.any_op) -> !transform.any_op
- transform.structured.vectorize %0 vector_sizes [2, 4] : !transform.any_op
-}
-
-// -----
-
-// CHECK: #[[MAP:.+]] = affine_map<()[s0, s1] -> (s0 + s1)>
-// CHECK: func @test_masked_vectorize_dynamic_pad
-func.func @test_masked_vectorize_dynamic_pad(
- %0 : tensor<?x?xf32>, %h0 : index, %h1 : index)
- -> tensor<?x?xf32>
-{
- // CHECK-DAG: %[[c42:.*]] = arith.constant 4.243000e+01 : f32
- // CHECK-DAG: %[[c0:.*]] = arith.constant 0 : index
- // CHECK-DAG: %[[res_d0:.+]] = affine.apply #[[MAP]]()
- // CHECK-DAG: %[[res_d1:.+]] = affine.apply #[[MAP]]()
- // CHECK-DAG: %[[empty:.*]] = tensor.empty(%[[res_d0]], %[[res_d1]]) : tensor<?x?xf32>
- // CHECK: %[[d0:.*]] = tensor.dim {{.*}} : tensor<?x?xf32>
- // CHECK: %[[d1:.*]] = tensor.dim {{.*}} : tensor<?x?xf32>
- // CHECK: %[[mask:.*]] = vector.create_mask %[[d0]], %[[d1]] : vector<2x4xi1>
- // CHECK-DAG: %[[c0_2:.*]] = arith.constant 0 : index
- // CHECK: %[[masked_read:.*]] = vector.mask %[[mask]] {
- // CHECK-SAME: vector.transfer_read %{{.*}}[%[[c0_2]], %[[c0_2]]], %[[c42]]
- // CHECK-SAME: {in_bounds = [true, true]} : tensor<?x?xf32>, vector<2x4xf32>
- // CHECK-SAME: } : vector<2x4xi1> -> vector<2x4xf32>
- // CHECK: %[[mask_2:.*]] = vector.create_mask %[[res_d0]], %[[res_d1]] : vector<2x4xi1>
- // CHECK: %[[masked_write:.*]] = vector.mask %[[mask_2]] {
- // CHECK-SAME: vector.transfer_write %[[masked_read]], %[[empty]][%[[c0_2]], %[[c0_2]]]
- // CHECK-SAME: {in_bounds = [true, true]} : vector<2x4xf32>, tensor<?x?xf32>
- // CHECK: return %[[masked_write]] : tensor<?x?xf32>
- %cst = arith.constant 42.43 : f32
- %c0 = arith.constant 0 : index
- %1 = tensor.pad %0 low[0, %c0] high[%h0, %h1] {
- ^bb0(%hh1: index, %hh2: index):
- tensor.yield %cst : f32
- } : tensor<?x?xf32> to tensor<?x?xf32>
- return %1: tensor<?x?xf32>
-}
-
-transform.sequence failures(propagate) {
-^bb1(%arg1: !transform.any_op):
- %0 = transform.structured.match ops{["tensor.pad"]} in %arg1
- : (!transform.any_op) -> !transform.any_op
- transform.structured.vectorize %0 vector_sizes [2, 4] : !transform.any_op
-}
-
-// -----
-
-func.func @matmul(%A: memref<?x?xf32>, %B: memref<?x?xf32>, %C: memref<?x?xf32>) {
- linalg.matmul ins(%A, %B: memref<?x?xf32>, memref<?x?xf32>)
- outs(%C: memref<?x?xf32>)
- return
-}
-
-// CHECK-LABEL: func.func @matmul(
-// CHECK-SAME: %[[A:.*]]: memref<?x?xf32>, %[[B:.*]]: memref<?x?xf32>, %[[C:.*]]: memref<?x?xf32>) {
-// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 0 : index
-// CHECK-DAG: %[[VAL_4:.*]] = memref.dim %[[A]], %[[VAL_3]] : memref<?x?xf32>
-// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index
-// CHECK-DAG: %[[VAL_6:.*]] = memref.dim %[[B]], %[[VAL_5]] : memref<?x?xf32>
-// CHECK-DAG: %[[VAL_7:.*]] = arith.constant 1 : index
-// CHECK-DAG: %[[VAL_8:.*]] = memref.dim %[[A]], %[[VAL_7]] : memref<?x?xf32>
-// CHECK: %[[MASK_A:.*]] = vector.create_mask %[[VAL_4]], %[[VAL_8]] : vector<8x4xi1>
-// CHECK: %[[LOAD_A:.*]] = vector.mask %[[MASK_A]] { vector.transfer_read %[[A]]{{\[}}%{{.*}}, %{{.*}}], %{{.*}} {in_bounds = [true, true, true], permutation_map = #{{.*}}} : memref<?x?xf32>, vector<8x16x4xf32> } : vector<8x4xi1> -> vector<8x16x4xf32>
-// CHECK: %[[MASK_B:.*]] = vector.create_mask %[[VAL_8]], %[[VAL_6]] : vector<4x16xi1>
-// CHECK: %[[LOAD_B:.*]] = vector.mask %[[MASK_B]] { vector.transfer_read %[[B]]{{\[}}%{{.*}}, %{{.*}}], %{{.*}} {in_bounds = [true, true, true], permutation_map = #{{.*}}} : memref<?x?xf32>, vector<8x16x4xf32> } : vector<4x16xi1> -> vector<8x16x4xf32>
-// CHECK: %[[MASK_C:.*]] = vector.create_mask %[[VAL_4]], %[[VAL_6]] : vector<8x16xi1>
-// CHECK: %[[LOAD_C:.*]] = vector.mask %[[MASK_C]] { vector.transfer_read %[[C]]{{\[}}%{{.*}}, %{{.*}}], %{{.*}} {in_bounds = [true, true]} : memref<?x?xf32>, vector<8x16xf32> } : vector<8x16xi1> -> vector<8x16xf32>
-// CHECK: %[[MULF:.*]] = arith.mulf %[[LOAD_A]], %[[LOAD_B]] : vector<8x16x4xf32>
-// CHECK: %[[MASK_MULIT_RED:.*]] = vector.create_mask %[[VAL_4]], %[[VAL_6]], %[[VAL_8]] : vector<8x16x4xi1>
-// CHECK: %[[MULTI_RED:.*]] = vector.mask %[[MASK_MULIT_RED]] { vector.multi_reduction <add>, %[[MULF]], %[[LOAD_C]] [2] : vector<8x16x4xf32> to vector<8x16xf32> } : vector<8x16x4xi1> -> vector<8x16xf32>
-// CHECK: %[[C2:.*]] = arith.constant 0 : index
-// CHECK: vector.mask %[[MASK_C]] { vector.transfer_write %[[MULTI_RED]], %[[C]]{{\[}}%[[C2]], %[[C2]]] {in_bounds = [true, true]} : vector<8x16xf32>, memref<?x?xf32> } : vector<8x16xi1>
-
-transform.sequence failures(propagate) {
-^bb1(%arg1: !transform.any_op):
- %matmul = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- transform.structured.vectorize %matmul vector_sizes [8, 16, 4] : !transform.any_op
-}
-
-// -----
-
-func.func @matmul_scalable(%A: memref<?x?xf32>, %B: memref<?x?xf32>, %C: memref<?x?xf32>) {
- linalg.matmul ins(%A, %B: memref<?x?xf32>, memref<?x?xf32>)
- outs(%C: memref<?x?xf32>)
- return
-}
-
-// CHECK-LABEL: func.func @matmul_scalable(
-// CHECK-SAME: %[[A:.*]]: memref<?x?xf32>, %[[B:.*]]: memref<?x?xf32>, %[[C:.*]]: memref<?x?xf32>) {
-// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 0 : index
-// CHECK-DAG: %[[VAL_4:.*]] = memref.dim %[[A]], %[[VAL_3]] : memref<?x?xf32>
-// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index
-// CHECK-DAG: %[[VAL_6:.*]] = memref.dim %[[B]], %[[VAL_5]] : memref<?x?xf32>
-// CHECK-DAG: %[[VAL_7:.*]] = arith.constant 1 : index
-// CHECK-DAG: %[[VAL_8:.*]] = memref.dim %[[A]], %[[VAL_7]] : memref<?x?xf32>
-// CHECK: %[[MASK_A:.*]] = vector.create_mask %[[VAL_4]], %[[VAL_8]] : vector<8x4xi1>
-// CHECK: %[[LOAD_A:.*]] = vector.mask %[[MASK_A]] { vector.transfer_read %[[A]]{{\[}}%{{.*}}, %{{.*}}], %{{.*}} {in_bounds = [true, true, true], permutation_map = #{{.*}}} : memref<?x?xf32>, vector<8x[16]x4xf32> } : vector<8x4xi1> -> vector<8x[16]x4xf32>
-// CHECK: %[[MASK_B:.*]] = vector.create_mask %[[VAL_8]], %[[VAL_6]] : vector<4x[16]xi1>
-// CHECK: %[[LOAD_B:.*]] = vector.mask %[[MASK_B]] { vector.transfer_read %[[B]]{{\[}}%{{.*}}, %{{.*}}], %{{.*}} {in_bounds = [true, true, true], permutation_map = #{{.*}}} : memref<?x?xf32>, vector<8x[16]x4xf32> } : vector<4x[16]xi1> -> vector<8x[16]x4xf32>
-// CHECK: %[[MASK_C:.*]] = vector.create_mask %[[VAL_4]], %[[VAL_6]] : vector<8x[16]xi1>
-// CHECK: %[[LOAD_C:.*]] = vector.mask %[[MASK_C]] { vector.transfer_read %[[C]]{{\[}}%{{.*}}, %{{.*}}], %{{.*}} {in_bounds = [true, true]} : memref<?x?xf32>, vector<8x[16]xf32> } : vector<8x[16]xi1> -> vector<8x[16]xf32>
-// CHECK: %[[MULF:.*]] = arith.mulf %[[LOAD_A]], %[[LOAD_B]] : vector<8x[16]x4xf32>
-// CHECK: %[[MASK_MULIT_RED:.*]] = vector.create_mask %[[VAL_4]], %[[VAL_6]], %[[VAL_8]] : vector<8x[16]x4xi1>
-// CHECK: %[[MULTI_RED:.*]] = vector.mask %[[MASK_MULIT_RED]] { vector.multi_reduction <add>, %[[MULF]], %[[LOAD_C]] [2] : vector<8x[16]x4xf32> to vector<8x[16]xf32> } : vector<8x[16]x4xi1> -> vector<8x[16]xf32>
-// CHECK: %[[C2:.*]] = arith.constant 0 : index
-// CHECK: vector.mask %[[MASK_C]] { vector.transfer_write %[[MULTI_RED]], %[[C]]{{\[}}%[[C2]], %[[C2]]] {in_bounds = [true, true]} : vector<8x[16]xf32>, memref<?x?xf32> } : vector<8x[16]xi1>
-
-transform.sequence failures(propagate) {
-^bb1(%arg1: !transform.any_op):
- %matmul = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- transform.structured.vectorize %matmul vector_sizes [8, [16], 4] : !transform.any_op
-}
diff --git a/mlir/test/Dialect/Linalg/vectorization-with-patterns.mlir b/mlir/test/Dialect/Linalg/vectorization-with-patterns.mlir
new file mode 100644
index 000000000000000..ecba1f32468031e
--- /dev/null
+++ b/mlir/test/Dialect/Linalg/vectorization-with-patterns.mlir
@@ -0,0 +1,1787 @@
+// RUN: mlir-opt %s -test-transform-dialect-interpreter -split-input-file | FileCheck %s
+
+// CHECK-LABEL: contraction_dot
+func.func @contraction_dot(%A: memref<1584xf32>, %B: memref<1584xf32>, %C: memref<f32>) {
+
+// CHECK: arith.mulf %{{.*}}, %{{.*}} : vector<1584xf32>
+// CHECK: vector.multi_reduction <add>, %{{.*}}, {{.*}} [0] : vector<1584xf32> to f32
+ linalg.dot ins(%A, %B: memref<1584xf32>, memref<1584xf32>)
+ outs(%C: memref<f32>)
+ return
+}
+
+transform.sequence failures(propagate) {
+^bb1(%arg1: !transform.any_op):
+ %0 = transform.structured.match ops{["linalg.dot"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+ transform.structured.vectorize %0 : !transform.any_op
+}
+
+// -----
+
+// CHECK-LABEL: contraction_matvec
+func.func @contraction_matvec(%A: memref<1584x1584xf32>, %B: memref<1584xf32>, %C: memref<1584xf32>) {
+
+// CHECK: arith.mulf %{{.*}}, %{{.*}} : vector<1584x1584xf32>
+// CHECK: vector.multi_reduction <add>, %{{.*}}, {{.*}} [1] : vector<1584x1584xf32> to vector<1584xf32>
+ linalg.matvec ins(%A, %B: memref<1584x1584xf32>, memref<1584xf32>)
+ outs(%C: memref<1584xf32>)
+ return
+}
+
+transform.sequence failures(propagate) {
+^bb1(%arg1: !transform.any_op):
+ %0 = transform.structured.match ops{["linalg.matvec"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+ %1 = 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 } : (!transform.any_op) -> !transform.any_op
+}
+
+// -----
+
+// CHECK-LABEL: contraction_matmul
+func.func @contraction_matmul(%A: memref<1584x1584xf32>, %B: memref<1584x1584xf32>, %C: memref<1584x1584xf32>) {
+// CHECK: arith.mulf %{{.*}}, %{{.*}} : vector<1584x1584x1584xf32>
+// CHECK: vector.multi_reduction <add>, %{{.*}}, {{.*}} [2] : vector<1584x1584x1584xf32> to vector<1584x1584xf32>
+ linalg.matmul ins(%A, %B: memref<1584x1584xf32>, memref<1584x1584xf32>)
+ outs(%C: memref<1584x1584xf32>)
+ return
+}
+
+transform.sequence failures(propagate) {
+^bb1(%arg1: !transform.any_op):
+ %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+ %1 = 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 } : (!transform.any_op) -> !transform.any_op
+}
+
+// -----
+
+// CHECK-LABEL: contraction_batch_matmul
+func.func @contraction_batch_matmul(%A: memref<1584x1584x1584xf32>, %B: memref<1584x1584x1584xf32>, %C: memref<1584x1584x1584xf32>) {
+// CHECK: arith.mulf %{{.*}}, %{{.*}} : vector<1584x1584x1584x1584xf32>
+// CHECK: vector.multi_reduction <add>, %{{.*}}, {{.*}} [3] : vector<1584x1584x1584x1584xf32> to vector<1584x1584x1584xf32>
+ linalg.batch_matmul
+ ins(%A, %B: memref<1584x1584x1584xf32>, memref<1584x1584x1584xf32>)
+ outs(%C: memref<1584x1584x1584xf32>)
+ return
+}
+
+transform.sequence failures(propagate) {
+^bb1(%arg1: !transform.any_op):
+ %0 = transform.structured.match ops{["linalg.batch_matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+ %1 = 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 } : (!transform.any_op) -> !transform.any_op
+}
+
+// -----
+
+#matmul_trait = {
+ args_in = 2,
+ args_out = 1,
+ indexing_maps = [
+ affine_map<(m, n, k) -> (m, k)>,
+ affine_map<(m, n, k) -> (k, n)>,
+ affine_map<(m, n, k) -> (m, n)>
+ ],
+ iterator_types = ["parallel", "parallel", "reduction"]
+}
+
+// CHECK-LABEL: func @vectorization_test
+func.func @vectorization_test(%A: memref<8x16xf32>, %B: memref<16x32xf32>,
+ %C: memref<8x32xf32>) {
+ // CHECK: vector.transfer_read %{{.*}} : memref<8x16xf32>, vector<8x32x16xf32>
+ // CHECK: vector.transfer_read %{{.*}} : memref<16x32xf32>, vector<8x32x16xf32>
+ // CHECK: %[[ACC:.*]] = vector.transfer_read %{{.*}} : memref<8x32xf32>, vector<8x32xf32>
+ // CHECK: %[[MUL:.*]] = arith.mulf %{{.*}}, %{{.*}} : vector<8x32x16xf32>
+ // CHECK: %[[R:.*]] = vector.multi_reduction <add>, %[[MUL]], %[[ACC]] [2] : vector<8x32x16xf32> to vector<8x32xf32>
+ // CHECK: vector.transfer_write %{{.*}}, %{{.*}} : vector<8x32xf32>, memref<8x32xf32>
+ linalg.generic #matmul_trait
+ ins(%A, %B : memref<8x16xf32>, memref<16x32xf32>)
+ outs(%C : memref<8x32xf32>) {
+ ^bb(%a: f32, %b: f32, %c: f32) :
+ %d = arith.mulf %a, %b: f32
+ %e = arith.addf %c, %d: f32
+ linalg.yield %e : f32
+ }
+ return
+}
+
+transform.sequence failures(propagate) {
+^bb1(%arg1: !transform.any_op):
+ %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+ %1 = 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
+}
+
+// -----
+
+#matmul_transpose_out_trait = {
+ args_in = 2,
+ args_out = 1,
+ indexing_maps = [
+ affine_map<(m, n, k) -> (m, k)>,
+ affine_map<(m, n, k) -> (k, n)>,
+ affine_map<(m, n, k) -> (n, m)>
+ ],
+ iterator_types = ["parallel", "parallel", "reduction"]
+}
+
+// CHECK-LABEL: func @generic_output_transpose
+func.func @generic_output_transpose(%A: memref<8x16xf32>, %B: memref<16x32xf32>,
+ %C: memref<32x8xf32>) {
+ // CHECK: vector.transfer_read %{{.*}} : memref<8x16xf32>, vector<8x32x16xf32>
+ // CHECK: vector.transfer_read %{{.*}} : memref<16x32xf32>, vector<8x32x16xf32>
+ // CHECK: %[[ACC:.*]] = vector.transfer_read %{{.*}} : memref<32x8xf32>, vector<8x32xf32>
+ // CHECK: %[[MUL:.*]] = arith.mulf %{{.*}}, %{{.*}} : vector<8x32x16xf32>
+ // CHECK: %[[R:.*]] = vector.multi_reduction <add>, %[[MUL]], %[[ACC]] [2] : vector<8x32x16xf32> to vector<8x32xf32>
+ // CHECK: vector.transfer_write %{{.*}}, %{{.*}} : vector<8x32xf32>, memref<32x8xf32>
+ linalg.generic #matmul_transpose_out_trait
+ ins(%A, %B : memref<8x16xf32>, memref<16x32xf32>)
+ outs(%C : memref<32x8xf32>) {
+ ^bb(%a: f32, %b: f32, %c: f32) :
+ %d = arith.mulf %a, %b: f32
+ %e = arith.addf %c, %d: f32
+ linalg.yield %e : f32
+ }
+ return
+}
+
+transform.sequence failures(propagate) {
+^bb1(%arg1: !transform.any_op):
+ %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+ %1 = 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
+}
+
+// -----
+
+#map0 = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
+#map1 = affine_map<(d0, d1, d2) -> (d1, d0, d2)>
+// CHECK: #[[MAP:.+]] = affine_map<(d0, d1, d2) -> (d1, d0, d2)>
+// CHECK: func @generic_interchanged_transpose
+func.func @generic_interchanged_transpose(%arg0: tensor<12x128x32xf32>) -> tensor<128x12x32xf32> {
+ // CHECK: %[[IN:.+]] = vector.transfer_read
+ // CHECK: vector.transfer_write %[[IN]], {{.+}} permutation_map = #[[MAP]]
+ %0 = tensor.empty() : tensor<128x12x32xf32>
+ %1 = linalg.generic {indexing_maps = [#map0, #map1],
+ iterator_types = ["parallel", "parallel", "parallel"]}
+ ins(%arg0 : tensor<12x128x32xf32>)
+ outs(%0 : tensor<128x12x32xf32>) {
+ ^bb0(%arg1: f32, %arg2: f32):
+ linalg.yield %arg1 : f32
+ } -> tensor<128x12x32xf32>
+ return %1 : tensor<128x12x32xf32>
+}
+
+transform.sequence failures(propagate) {
+^bb1(%arg1: !transform.any_op):
+ %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+ %1 = 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
+}
+
+// -----
+
+#matmul_trait = {
+ args_in = 2,
+ args_out = 1,
+ indexing_maps = [
+ affine_map<(m, n, k) -> (m, k)>,
+ affine_map<(m, n, k) -> (k, n)>,
+ affine_map<(m, n, k) -> (m, n)>
+ ],
+ iterator_types = ["parallel", "parallel", "reduction"]
+}
+
+// CHECK-LABEL: func @vectorization_test_integer
+func.func @vectorization_test_integer(%A: memref<8x16xi32>, %B: memref<16x32xi32>,
+ %C: memref<8x32xi32>) {
+ // CHECK: vector.transfer_read %{{.*}} : memref<8x16xi32>, vector<8x32x16xi32>
+ // CHECK: vector.transfer_read %{{.*}} : memref<16x32xi32>, vector<8x32x16xi32>
+ // CHECK: %[[ACC:.*]] = vector.transfer_read %{{.*}} : memref<8x32xi32>, vector<8x32xi32>
+ // CHECK: %[[MUL:.*]] = arith.muli %{{.*}}, %{{.*}} : vector<8x32x16xi32>
+ // CHECK: vector.multi_reduction <add>, %[[MUL]], %[[ACC]] [2] : vector<8x32x16xi32> to vector<8x32xi32>
+ // CHECK: vector.transfer_write %{{.*}}, %{{.*}} : vector<8x32xi32>, memref<8x32xi32>
+ linalg.generic #matmul_trait
+ ins(%A, %B : memref<8x16xi32>, memref<16x32xi32>)
+ outs(%C : memref<8x32xi32>) {
+ ^bb(%a: i32, %b: i32, %c: i32) :
+ %d = arith.muli %a, %b: i32
+ %e = arith.addi %c, %d: i32
+ linalg.yield %e : i32
+ }
+ return
+}
+
+transform.sequence failures(propagate) {
+^bb1(%arg1: !transform.any_op):
+ %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+ %1 = 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
+}
+
+// -----
+
+// CHECK-LABEL: func @vectorization_test_2
+func.func @vectorization_test_2(%A: memref<8x16xf32>, %B: memref<16x32xf32>,
+ %C: memref<8x32xf32>) {
+ // CHECK: arith.mulf %{{.*}}, %{{.*}} : vector<8x32x16xf32>
+ // CHECK: vector.multi_reduction <add>, %{{.*}}, {{.*}} [2] : vector<8x32x16xf32> to vector<8x32xf32>
+ linalg.matmul
+ ins(%A, %B: memref<8x16xf32>, memref<16x32xf32>)
+ outs(%C: memref<8x32xf32>)
+ return
+}
+
+transform.sequence failures(propagate) {
+^bb1(%arg1: !transform.any_op):
+ %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+ %1 = 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 } : (!transform.any_op) -> !transform.any_op
+}
+
+// -----
+
+// CHECK-LABEL: func @test_vectorize_scalar_input
+func.func @test_vectorize_scalar_input(%A : memref<8x16xf32>, %arg0 : f32) {
+ // CHECK: %[[V:.*]] = vector.broadcast {{.*}} : f32 to vector<8x16xf32>
+ // CHECK: vector.transfer_write %[[V]], {{.*}} : vector<8x16xf32>, memref<8x16xf32>
+ linalg.generic {
+ indexing_maps = [affine_map<(m, n) -> ()>, affine_map<(m, n) -> (m, n)>],
+ iterator_types = ["parallel", "parallel"]}
+ ins(%arg0 : f32)
+ outs(%A: memref<8x16xf32>) {
+ ^bb(%0: f32, %1: f32) :
+ linalg.yield %0 : f32
+ }
+ return
+}
+
+transform.sequence failures(propagate) {
+^bb1(%arg1: !transform.any_op):
+ %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+ %1 = 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
+}
+
+// -----
+
+// CHECK-LABEL: func @test_do_not_vectorize_unsupported_element_types
+func.func @test_do_not_vectorize_unsupported_element_types(%A : memref<8x16xcomplex<f32>>, %arg0 : complex<f32>) {
+ // CHECK-NOT: vector.broadcast
+ // CHECK-NOT: vector.transfer_write
+ linalg.generic {
+ indexing_maps = [affine_map<(m, n) -> ()>, affine_map<(m, n) -> (m, n)>],
+ iterator_types = ["parallel", "parallel"]}
+ ins(%arg0 : complex<f32>)
+ outs(%A: memref<8x16xcomplex<f32>>) {
+ ^bb(%0: complex<f32>, %1: complex<f32>) :
+ linalg.yield %0 : complex<f32>
+ }
+ return
+}
+
+transform.sequence failures(propagate) {
+^bb1(%arg1: !transform.any_op):
+ %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+ %1 = 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
+}
+
+// -----
+
+#map0 = affine_map<(d0) -> (d0)>
+
+func.func @vectorize_affine_apply(%arg0: tensor<5xf32>, %arg3: index) -> tensor<5xi32> {
+ %0 = tensor.empty() : tensor<5xi32>
+ %1 = linalg.generic {indexing_maps = [#map0, #map0],
+ iterator_types = ["parallel"]}
+ ins(%arg0 : tensor<5xf32>)
+ outs(%0 : tensor<5xi32>) {
+ ^bb0(%arg1: f32, %arg2: i32):
+ %2 = linalg.index 0 : index
+ %11 = affine.apply affine_map<() -> (123)>()
+ %12 = affine.apply affine_map<(d0, d1) -> (d0 + d1)>(%2, %11)
+ %13 = affine.apply affine_map<(d0)[s0] -> (d0 + s0)>(%12)[%arg3]
+ %14 = affine.apply affine_map<(d0) -> (d0 + 1)>(%13)
+ %15 = affine.apply affine_map<(d0, d1, d2) -> (d0 + d1 + d2)>(%13, %14, %12)
+ %3 = arith.index_cast %15 : index to i32
+ linalg.yield %3 : i32
+ } -> tensor<5xi32>
+ return %1 : tensor<5xi32>
+}
+
+// CHECK-LABEL: func.func @vectorize_affine_apply
+// CHECK-SAME: %arg0: tensor<5xf32>
+// CHECK-SAME: %[[ARG1:.*]]: index
+// CHECK: %[[CST:.*]] = arith.constant dense<[123, 124, 125, 126, 127]> : vector<5xindex>
+// CHECK: %[[CST_0:.*]] = arith.constant dense<1> : vector<5xindex>
+// CHECK: %[[C0:.*]] = arith.constant 0 : index
+// CHECK: %[[EMPTY:.*]] = tensor.empty() : tensor<5xi32>
+// CHECK: %[[BCAST:.*]] = vector.broadcast %[[ARG1]] : index to vector<5xindex>
+// CHECK: %[[ADDI_1:.*]] = arith.addi %[[BCAST]], %[[CST]] : vector<5xindex>
+// CHECK: %[[ADDI_2:.*]] = arith.addi %[[ADDI_1]], %[[CST_0]] : vector<5xindex>
+// CHECK: %[[ADDI_3:.*]] = arith.addi %[[ADDI_1]], %[[ADDI_2]] : vector<5xindex>
+// CHECK: %[[ADDI_4:.*]] = arith.addi %[[ADDI_3]], %[[CST]] : vector<5xindex>
+// CHECK: %[[CAST:.*]] = arith.index_cast %[[ADDI_4]] : vector<5xindex> to vector<5xi32>
+// CHECK: vector.transfer_write %[[CAST]], %[[EMPTY]][%[[C0:.*]]] {in_bounds = [true]} : vector<5xi32>, tensor<5xi32>
+
+transform.sequence failures(propagate) {
+ ^bb1(%arg1: !transform.any_op):
+ %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+ %1 = 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
+}
+
+// -----
+
+// CHECK-LABEL: func @test_vectorize_fill
+func.func @test_vectorize_fill(%A : memref<8x16xf32>, %arg0 : f32) {
+ // CHECK: %[[V:.*]] = vector.broadcast {{.*}} : f32 to vector<8x16xf32>
+ // CHECK: vector.transfer_write %[[V]], {{.*}} : vector<8x16xf32>, memref<8x16xf32>
+ linalg.fill ins(%arg0 : f32) outs(%A : memref<8x16xf32>)
+ return
+}
+
+transform.sequence failures(propagate) {
+^bb1(%arg1: !transform.any_op):
+ %0 = transform.structured.match ops{["linalg.fill"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+ %1 = 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
+}
+
+// -----
+
+// CHECK-LABEL: func @test_vectorize_fill
+func.func @test_vectorize_fill_scalar(%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>
+ linalg.fill ins(%arg0 : f32) outs(%A : memref<f32>)
+ return
+}
+
+transform.sequence failures(propagate) {
+^bb1(%arg1: !transform.any_op):
+ %0 = transform.structured.match ops{["linalg.fill"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+ %1 = 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
+}
+
+// -----
+
+// CHECK-LABEL: func @test_vectorize_copy
+func.func @test_vectorize_copy(%A : memref<8x16xf32>, %B : memref<8x16xf32>) {
+ // CHECK: %[[V:.*]] = vector.transfer_read {{.*}} : memref<8x16xf32>, vector<8x16xf32>
+ // CHECK: vector.transfer_write %[[V]], {{.*}} : vector<8x16xf32>, memref<8x16xf32>
+ memref.copy %A, %B : memref<8x16xf32> to memref<8x16xf32>
+ return
+}
+
+transform.sequence failures(propagate) {
+^bb1(%arg1: !transform.any_op):
+ %0 = transform.structured.match ops{["memref.copy"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+ %1 = 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
+}
+
+// -----
+
+// CHECK-LABEL: func @test_vectorize_copy_scalar
+func.func @test_vectorize_copy_scalar(%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.extractelement %[[V]][] : vector<f32>
+ // CHECK: %[[VV:.*]] = vector.broadcast %[[val]] : f32 to vector<f32>
+ // CHECK: vector.transfer_write %[[VV]], %[[B]][] : vector<f32>, memref<f32>
+ memref.copy %A, %B : memref<f32> to memref<f32>
+ return
+}
+
+transform.sequence failures(propagate) {
+^bb1(%arg1: !transform.any_op):
+ %0 = transform.structured.match ops{["memref.copy"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+ %1 = 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
+}
+
+// -----
+
+// CHECK-LABEL: func @test_vectorize_copy_complex
+// CHECK-NOT: vector<
+func.func @test_vectorize_copy_complex(%A : memref<8x16xcomplex<f32>>, %B : memref<8x16xcomplex<f32>>) {
+ memref.copy %A, %B : memref<8x16xcomplex<f32>> to memref<8x16xcomplex<f32>>
+ return
+}
+
+transform.sequence failures(propagate) {
+^bb1(%arg1: !transform.any_op):
+ %0 = transform.structured.match ops{["memref.copy"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+ %1 = 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
+}
+
+// -----
+
+// CHECK-LABEL: func @test_vectorize_trailing_index
+ // CHECK-SAME: (%[[ARG0:.*]]: memref<1x2x4x8xindex>)
+func.func @test_vectorize_trailing_index(%arg0: memref<1x2x4x8xindex>) {
+ // CHECK-DAG: %[[CST0:.*]] = arith.constant dense<[0, 1, 2, 3, 4, 5, 6, 7]> : vector<8xindex>
+ // CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
+ linalg.generic {
+ indexing_maps = [
+ affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>],
+ iterator_types = ["parallel", "parallel", "parallel", "parallel"]}
+ outs(%arg0: memref<1x2x4x8xindex>) {
+ ^bb0(%arg1: index):
+ // CHECK: %[[BCST:.*]] = vector.broadcast %[[CST0]] : vector<8xindex> to vector<1x2x4x8xindex>
+ // CHECK: vector.transfer_write %[[BCST]], %[[ARG0]][%[[C0]], %[[C0]], %[[C0]], %[[C0]]] {{.*}} : vector<1x2x4x8xindex>, memref<1x2x4x8xindex>
+ %0 = linalg.index 3 : index
+ linalg.yield %0 : index
+ }
+ return
+}
+
+transform.sequence failures(propagate) {
+^bb1(%arg1: !transform.any_op):
+ %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+ %1 = 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
+}
+
+// -----
+
+// CHECK-LABEL: func @test_vectorize_inner_index
+ // CHECK-SAME: (%[[ARG0:.*]]: memref<1x2x4x8xindex>)
+func.func @test_vectorize_inner_index(%arg0: memref<1x2x4x8xindex>) {
+ // CHECK-DAG: %[[CST0:.*]] = arith.constant dense<[0, 1]> : vector<2xindex>
+ // CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
+ linalg.generic {
+ indexing_maps = [
+ affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>],
+ iterator_types = ["parallel", "parallel", "parallel", "parallel"]}
+ outs(%arg0: memref<1x2x4x8xindex>) {
+ ^bb0(%arg1: index):
+ // CHECK: %[[BCST:.*]] = vector.broadcast %[[CST0]] : vector<2xindex> to vector<1x8x4x2xindex>
+ // CHECK: %[[TRAN:.*]] = vector.transpose %[[BCST]], [0, 3, 2, 1] : vector<1x8x4x2xindex> to vector<1x2x4x8xindex>
+ // CHECK: vector.transfer_write %[[TRAN]], %[[ARG0]][%[[C0]], %[[C0]], %[[C0]], %[[C0]]] {{.*}} : vector<1x2x4x8xindex>, memref<1x2x4x8xindex>
+ %0 = linalg.index 1 : index
+ linalg.yield %0 : index
+ }
+ return
+}
+
+transform.sequence failures(propagate) {
+^bb1(%arg1: !transform.any_op):
+ %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+ %1 = 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
+}
+
+// -----
+
+// CHECK-LABEL: func @generic_vectorize
+ // CHECK-SAME: (%[[ARG0:.*]]: memref<4x256xf32>, %[[ARG1:.*]]: memref<4x256xf32>,
+ // CHECK-SAME: %[[ARG2:.*]]: memref<256xf32>, %[[ARG3:.*]]: f32)
+func.func @generic_vectorize(%arg0: memref<4x256xf32>,
+ %arg1: memref<4x256xf32>,
+ %arg2: memref<256xf32>, %i: f32) {
+ // CHECK-DAG: %[[CST0:.*]] = arith.constant dense<2.000000e+00> : vector<4x256xf32>
+ // CHECK-DAG: %[[CST1:.*]] = arith.constant dense<1.000000e+00> : vector<4x256xf32>
+ // CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
+ %c1_f32 = arith.constant 1.0 : f32
+ linalg.generic {
+ args_in = 0 : i64,
+ args_out = 10 : i64,
+ indexing_maps = [
+ affine_map<(d0, d1) -> (d0, d1)>,
+ affine_map<(d0, d1) -> (d1)>,
+ affine_map<(d0, d1) -> (d0, d1)>,
+ affine_map<(d0, d1) -> (d0, d1)>,
+ affine_map<(d0, d1) -> (d0, d1)>,
+ affine_map<(d0, d1) -> (d0, d1)>,
+ affine_map<(d0, d1) -> (d0, d1)>,
+ affine_map<(d0, d1) -> (d0, d1)>,
+ affine_map<(d0, d1) -> (d0, d1)>,
+ affine_map<(d0, d1) -> (d0, d1)>,
+ affine_map<(d0, d1) -> (d0, d1)>,
+ affine_map<(d0, d1) -> (d0, d1)>],
+ iterator_types = ["parallel", "parallel"]}
+ ins(%arg1, %arg2: memref<4x256xf32>, memref<256xf32>)
+ outs(
+ %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0 :
+ memref<4x256xf32>, memref<4x256xf32>, memref<4x256xf32>, memref<4x256xf32>,
+ memref<4x256xf32>, memref<4x256xf32>, memref<4x256xf32>, memref<4x256xf32>,
+ memref<4x256xf32>, memref<4x256xf32>) {
+ ^bb0(%arg3 : f32, %arg4 : f32, %arg5: f32, %arg6: f32, %arg7: f32, %arg8: f32,
+ // CHECK: %[[V2:.*]] = vector.transfer_read %[[ARG1]][%[[C0]], %[[C0]]], {{.*}} : memref<4x256xf32>, vector<4x256xf32>
+ // CHECK: %[[V0:.*]] = vector.transfer_read %[[ARG2]][%[[C0]]], {{.*}} : memref<256xf32>, vector<4x256xf32>
+ // CHECK: %[[V3:.*]] = vector.transfer_read %[[ARG0]][%[[C0]], %[[C0]]], {{.*}} : memref<4x256xf32>, vector<4x256xf32>
+ // CHECK: %[[V1:.*]] = vector.transfer_read %[[ARG0]][%[[C0]], %[[C0]]], {{.*}} : memref<4x256xf32>, vector<4x256xf32>
+ %arg9 : f32, %arg10 : f32, %arg11 : f32, %arg12 : f32, %arg13 : f32,
+ %arg14 : f32):
+ // CHECK: %[[ADD:.*]] = arith.addf %[[V0]], %[[V1]] : vector<4x256xf32>
+ %6 = arith.addf %arg4, %arg6 : f32
+ // CHECK: %[[CMP:.*]] = arith.cmpf ogt, %[[V2]], %[[V1]] : vector<4x256xf32>
+ %7 = arith.cmpf ogt, %arg3, %arg6 : f32
+ // CHECK: %[[ARG3B:.*]] = vector.broadcast %[[ARG3]] : f32 to vector<4x256xf32>
+ %8 = arith.constant 2.0 : f32
+ // CHECK: %[[DIV:.*]] = arith.divf %[[V3]], %[[ARG3B]] : vector<4x256xf32>
+ %9 = arith.divf %arg5, %i : f32
+ // CHECK: %[[EXP:.*]] = math.exp2 %[[V3]] : vector<4x256xf32>
+ %10 = math.exp2 %arg5 : f32
+ // CHECK: %[[MUL:.*]] = arith.mulf %[[V3]], %[[CST0]] : vector<4x256xf32>
+ %11 = arith.mulf %arg5, %8 : f32
+ // CHECK: %[[RSQRT:.*]] = math.rsqrt %[[V3]] : vector<4x256xf32>
+ %12 = math.rsqrt %arg5 : f32
+ // CHECK: %[[SEL:.*]] = arith.select %[[CMP]], %[[V3]], %[[V1]] : vector<4x256xi1>, vector<4x256xf32>
+ %13 = arith.select %7, %arg5, %arg6 : f32
+ // CHECK: %[[SUB:.*]] = arith.subf %[[V3]], %[[V0]] : vector<4x256xf32>
+ %14 = arith.subf %arg5, %arg4 : f32
+ // CHECK: %[[TAN:.*]] = math.tanh %[[V3]] : vector<4x256xf32>
+ %15 = math.tanh %arg5 : f32
+ // CHECK: vector.transfer_write %[[ADD]], %[[ARG0]][%[[C0]], %[[C0]]] {{.*}} : vector<4x256xf32>, memref<4x256xf32>
+ // CHECK: vector.transfer_write %[[CST0]], %[[ARG0]][%[[C0]], %[[C0]]] {{.*}} : vector<4x256xf32>, memref<4x256xf32>
+ // CHECK: vector.transfer_write %[[CST1]], %[[ARG0]][%[[C0]], %[[C0]]] {{.*}} : vector<4x256xf32>, memref<4x256xf32>
+ // CHECK: vector.transfer_write %[[DIV]], %[[ARG0]][%[[C0]], %[[C0]]] {{.*}} : vector<4x256xf32>, memref<4x256xf32>
+ // CHECK: vector.transfer_write %[[EXP]], %[[ARG0]][%[[C0]], %[[C0]]] {{.*}} : vector<4x256xf32>, memref<4x256xf32>
+ // CHECK: vector.transfer_write %[[MUL]], %[[ARG0]][%[[C0]], %[[C0]]] {{.*}} : vector<4x256xf32>, memref<4x256xf32>
+ // CHECK: vector.transfer_write %[[RSQRT]], %[[ARG0]][%[[C0]], %[[C0]]] {{.*}} : vector<4x256xf32>, memref<4x256xf32>
+ // CHECK: vector.transfer_write %[[SEL]], %[[ARG0]][%[[C0]], %[[C0]]] {{.*}} : vector<4x256xf32>, memref<4x256xf32>
+ // CHECK: vector.transfer_write %[[SUB]], %[[ARG0]][%[[C0]], %[[C0]]] {{.*}} : vector<4x256xf32>, memref<4x256xf32>
+ // CHECK: vector.transfer_write %[[TAN]], %[[ARG0]][%[[C0]], %[[C0]]] {{.*}} : vector<4x256xf32>, memref<4x256xf32>
+ linalg.yield %6, %8, %c1_f32, %9, %10, %11, %12, %13, %14, %15 : f32, f32,
+ f32, f32, f32, f32, f32, f32, f32, f32
+ }
+ return
+}
+
+transform.sequence failures(propagate) {
+^bb1(%arg1: !transform.any_op):
+ %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+ %1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children_and_apply_patterns %1 { disable_transfer_permutation_map_lowering_patterns } : (!transform.any_op) -> !transform.any_op
+}
+
+// -----
+
+// CHECK-LABEL: func @generic_vectorize_tensor
+// CHECK-SAME: (%[[ARG0:.*]]: tensor<4x256xf32>, %[[ARG1:.*]]: tensor<4x256xf32>,
+// CHECK-SAME: %[[ARG2:.*]]: tensor<256xf32>, %[[ARG3:.*]]: f32)
+func.func @generic_vectorize_tensor(%arg0: tensor<4x256xf32>,
+ %arg1: tensor<4x256xf32>, %arg2: tensor<256xf32>,
+ %i: f32) -> (tensor<4x256xf32>, tensor<4x256xf32>, tensor<4x256xf32>,
+ tensor<4x256xf32>, tensor<4x256xf32>, tensor<4x256xf32>, tensor<4x256xf32>,
+ tensor<4x256xf32>, tensor<4x256xf32>, tensor<4x256xf32>) {
+ %c1_f32 = arith.constant 1.0 : f32
+ %r:10 = linalg.generic {
+ indexing_maps = [
+ affine_map<(d0, d1) -> (d0, d1)>,
+ affine_map<(d0, d1) -> (d1)>,
+ affine_map<(d0, d1) -> (d0, d1)>,
+ affine_map<(d0, d1) -> (d0, d1)>,
+ affine_map<(d0, d1) -> (d0, d1)>,
+ affine_map<(d0, d1) -> (d0, d1)>,
+ affine_map<(d0, d1) -> (d0, d1)>,
+ affine_map<(d0, d1) -> (d0, d1)>,
+ affine_map<(d0, d1) -> (d0, d1)>,
+ affine_map<(d0, d1) -> (d0, d1)>,
+ affine_map<(d0, d1) -> (d0, d1)>,
+ affine_map<(d0, d1) -> (d0, d1)>],
+ iterator_types = ["parallel", "parallel"]}
+ ins(%arg1, %arg2: tensor<4x256xf32>, tensor<256xf32>)
+ outs(
+ %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0 :
+ tensor<4x256xf32>, tensor<4x256xf32>, tensor<4x256xf32>, tensor<4x256xf32>,
+ tensor<4x256xf32>, tensor<4x256xf32>, tensor<4x256xf32>, tensor<4x256xf32>,
+ tensor<4x256xf32>, tensor<4x256xf32>) {
+ ^bb0(%arg3 : f32, %arg4 : f32, %arg5: f32, %arg6: f32, %arg7: f32, %arg8: f32,
+ %arg9 : f32, %arg10 : f32, %arg11 : f32, %arg12 : f32, %arg13 : f32,
+ %arg14 : f32):
+ // CHECK-DAG: %[[CST0:.*]] = arith.constant dense<2.000000e+00> : vector<4x256xf32>
+ // CHECK-DAG: %[[CST1:.*]] = arith.constant dense<1.000000e+00> : vector<4x256xf32>
+ // CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
+ // CHECK: %[[V2:.*]] = vector.transfer_read %[[ARG1]][%[[C0]], %[[C0]]], {{.*}} : tensor<4x256xf32>, vector<4x256xf32>
+ // CHECK: %[[V0:.*]] = vector.transfer_read %[[ARG2]][%[[C0]]], {{.*}} : tensor<256xf32>, vector<4x256xf32>
+ // CHECK: %[[V3:.*]] = vector.transfer_read %[[ARG0]][%[[C0]], %[[C0]]], {{.*}} : tensor<4x256xf32>, vector<4x256xf32>
+ // CHECK: %[[V1:.*]] = vector.transfer_read %[[ARG0]][%[[C0]], %[[C0]]], {{.*}} : tensor<4x256xf32>, vector<4x256xf32>
+ // CHECK: %[[ADD:.*]] = arith.addf %[[V0]], %[[V1]] : vector<4x256xf32>
+ %6 = arith.addf %arg4, %arg6 : f32
+ // CHECK: %[[CMP:.*]] = arith.cmpf ogt, %[[V2]], %[[V1]] : vector<4x256xf32>
+ %7 = arith.cmpf ogt, %arg3, %arg6 : f32
+ // CHECK: %[[ARG3B:.*]] = vector.broadcast %[[ARG3]] : f32 to vector<4x256xf32>
+ %8 = arith.constant 2.0 : f32
+ // CHECK: %[[DIV:.*]] = arith.divf %[[V3]], %[[ARG3B]] : vector<4x256xf32>
+ %9 = arith.divf %arg5, %i : f32
+ // CHECK: %[[EXP:.*]] = math.exp2 %[[V3]] : vector<4x256xf32>
+ %10 = math.exp2 %arg5 : f32
+ // CHECK: %[[MUL:.*]] = arith.mulf %[[V3]], %[[CST0]] : vector<4x256xf32>
+ %11 = arith.mulf %arg5, %8 : f32
+ // CHECK: %[[RSQRT:.*]] = math.rsqrt %[[V3]] : vector<4x256xf32>
+ %12 = math.rsqrt %arg5 : f32
+ // CHECK: %[[SEL:.*]] = arith.select %[[CMP]], %[[V3]], %[[V1]] : vector<4x256xi1>, vector<4x256xf32>
+ %13 = arith.select %7, %arg5, %arg6 : f32
+ // CHECK: %[[SUB:.*]] = arith.subf %[[V3]], %[[V0]] : vector<4x256xf32>
+ %14 = arith.subf %arg5, %arg4 : f32
+ // CHECK: %[[TAN:.*]] = math.tanh %[[V3]] : vector<4x256xf32>
+ %15 = math.tanh %arg5 : f32
+ // CHECK: %[[R0:.*]] = vector.transfer_write %[[ADD]], %[[ARG0]][%[[C0]], %[[C0]]] {{.*}} : vector<4x256xf32>, tensor<4x256xf32>
+ // CHECK: %[[R1:.*]] = vector.transfer_write %[[CST0]], %[[ARG0]][%[[C0]], %[[C0]]] {{.*}} : vector<4x256xf32>, tensor<4x256xf32>
+ // CHECK: %[[R2:.*]] = vector.transfer_write %[[CST1]], %[[ARG0]][%[[C0]], %[[C0]]] {{.*}} : vector<4x256xf32>, tensor<4x256xf32>
+ // CHECK: %[[R3:.*]] = vector.transfer_write %[[DIV]], %[[ARG0]][%[[C0]], %[[C0]]] {{.*}} : vector<4x256xf32>, tensor<4x256xf32>
+ // CHECK: %[[R4:.*]] = vector.transfer_write %[[EXP]], %[[ARG0]][%[[C0]], %[[C0]]] {{.*}} : vector<4x256xf32>, tensor<4x256xf32>
+ // CHECK: %[[R5:.*]] = vector.transfer_write %[[MUL]], %[[ARG0]][%[[C0]], %[[C0]]] {{.*}} : vector<4x256xf32>, tensor<4x256xf32>
+ // CHECK: %[[R6:.*]] = vector.transfer_write %[[RSQRT]], %[[ARG0]][%[[C0]], %[[C0]]] {{.*}} : vector<4x256xf32>, tensor<4x256xf32>
+ // CHECK: %[[R7:.*]] = vector.transfer_write %[[SEL]], %[[ARG0]][%[[C0]], %[[C0]]] {{.*}} : vector<4x256xf32>, tensor<4x256xf32>
+ // CHECK: %[[R8:.*]] = vector.transfer_write %[[SUB]], %[[ARG0]][%[[C0]], %[[C0]]] {{.*}} : vector<4x256xf32>, tensor<4x256xf32>
+ // CHECK: %[[R9:.*]] = vector.transfer_write %[[TAN]], %[[ARG0]][%[[C0]], %[[C0]]] {{.*}} : vector<4x256xf32>, tensor<4x256xf32>
+ linalg.yield %6, %8, %c1_f32, %9, %10, %11, %12, %13, %14, %15 : f32, f32,
+ f32, f32, f32, f32, f32, f32, f32, f32
+ } -> (tensor<4x256xf32>, tensor<4x256xf32>, tensor<4x256xf32>,
+ tensor<4x256xf32>, tensor<4x256xf32>, tensor<4x256xf32>, tensor<4x256xf32>,
+ tensor<4x256xf32>, tensor<4x256xf32>, tensor<4x256xf32>)
+ // CHECK: return %[[R0]], %[[R1]], %[[R2]], %[[R3]], %[[R4]], %[[R5]], %[[R6]], %[[R7]], %[[R8]], %[[R9]] : tensor<4x256xf32>, tensor<4x256xf32>, tensor<4x256xf32>, tensor<4x256xf32>, tensor<4x256xf32>, tensor<4x256xf32>, tensor<4x256xf32>, tensor<4x256xf32>, tensor<4x256xf32>, tensor<4x256xf32>
+ return %r#0, %r#1, %r#2, %r#3, %r#4, %r#5, %r#6, %r#7, %r#8, %r#9:
+ tensor<4x256xf32>, tensor<4x256xf32>, tensor<4x256xf32>,
+ tensor<4x256xf32>, tensor<4x256xf32>, tensor<4x256xf32>, tensor<4x256xf32>,
+ tensor<4x256xf32>, tensor<4x256xf32>, tensor<4x256xf32>
+}
+
+transform.sequence failures(propagate) {
+^bb1(%arg1: !transform.any_op):
+ %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+ %1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children_and_apply_patterns %1 { disable_transfer_permutation_map_lowering_patterns } : (!transform.any_op) -> !transform.any_op
+}
+
+// -----
+
+// CHECK-DAG: #[[$MAP0:.*]] = affine_map<(d0, d1) -> (d0, 0, 0, d1)>
+// CHECK-DAG: #[[$MAP1:.*]] = affine_map<(d0) -> (d0, 0, 0, 0)>
+// CHECK-DAG: #[[$MAP2:.*]] = affine_map<(d0) -> (0, 0, d0, 0)>
+// CHECK-DAG: #[[$MAP3:.*]] = affine_map<(d0, d1) -> (d1, 0, d0, 0)>
+// CHECK: func @generic_vectorize_broadcast_transpose
+// CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
+// CHECK-DAG: %[[CF:.*]] = arith.constant 0.000000e+00 : f32
+// CHECK: %[[V0:.*]] = vector.transfer_read %{{.*}}[%[[C0]], %[[C0]]], %[[CF]] {in_bounds = [true, true, true, true], permutation_map = #[[$MAP0]]} : memref<4x4xf32>, vector<4x4x4x4xf32>
+// CHECK: %[[V1:.*]] = vector.transfer_read %{{.*}}[%[[C0]]], %[[CF]] {in_bounds = [true, true, true, true], permutation_map = #[[$MAP1]]} : memref<4xf32>, vector<4x4x4x4xf32>
+// CHECK: %[[V2:.*]] = vector.transfer_read %{{.*}}[%[[C0]]], %[[CF]] {in_bounds = [true, true, true, true], permutation_map = #[[$MAP2]]} : memref<4xf32>, vector<4x4x4x4xf32>
+// CHECK: %[[V3:.*]] = vector.transfer_read %{{.*}}[%[[C0]], %[[C0]]], %[[CF]] {in_bounds = [true, true, true, true], permutation_map = #[[$MAP3]]} : memref<4x4xf32>, vector<4x4x4x4xf32>
+// CHECK: %[[SUB:.*]] = arith.subf %[[V0]], %[[V1]] : vector<4x4x4x4xf32>
+// CHECK: %[[ADD0:.*]] = arith.addf %[[V2]], %[[SUB]] : vector<4x4x4x4xf32>
+// CHECK: %[[ADD1:.*]] = arith.addf %[[V3]], %[[ADD0]] : vector<4x4x4x4xf32>
+// CHECK: vector.transfer_write %[[ADD1]], {{.*}} : vector<4x4x4x4xf32>, memref<4x4x4x4xf32>
+func.func @generic_vectorize_broadcast_transpose(
+ %A: memref<4xf32>, %B: memref<4x4xf32>, %C: memref<4x4x4x4xf32>) {
+ linalg.generic {
+ indexing_maps = [affine_map<(d0, d1, d2, d3) -> (d0, d3)>,
+ affine_map<(d0, d1, d2, d3) -> (d0)>,
+ affine_map<(d0, d1, d2, d3) -> (d2)>,
+ affine_map<(d0, d1, d2, d3) -> (d2, d0)>,
+ affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>],
+ iterator_types = ["parallel", "parallel", "parallel", "parallel"]}
+ ins(%B, %A, %A, %B: memref<4x4xf32>, memref<4xf32>, memref<4xf32>, memref<4x4xf32>)
+ outs(%C : memref<4x4x4x4xf32>) {
+ ^bb0(%arg0: f32, %arg1: f32, %arg2: f32, %arg3: f32, %arg4: f32):
+ %s = arith.subf %arg0, %arg1 : f32
+ %a = arith.addf %arg2, %s : f32
+ %b = arith.addf %arg3, %a : f32
+ linalg.yield %b : f32
+ }
+ return
+}
+
+transform.sequence failures(propagate) {
+^bb1(%arg1: !transform.any_op):
+ %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+ %1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children_and_apply_patterns %1 { disable_transfer_permutation_map_lowering_patterns } : (!transform.any_op) -> !transform.any_op
+}
+
+// -----
+
+// Test different input maps.
+#matmul_trait = {
+ indexing_maps = [
+ affine_map<(d0, d1, d2, d3) -> (d1, d0)>,
+ affine_map<(d0, d1, d2, d3) -> (d3, d1)>,
+ affine_map<(d0, d1, d2, d3) -> (d3, d1, d0, d2)>,
+ affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>
+ ],
+ iterator_types = ["parallel", "parallel", "parallel", "parallel"]
+}
+
+// CHECK-DAG: #[[MAP0:.*]] = affine_map<(d0, d1) -> (d1, d0, 0, 0)>
+// CHECK-DAG: #[[MAP1:.*]] = affine_map<(d0, d1) -> (0, d1, 0, d0)>
+// CHECK-DAG: #[[MAP2:.*]] = affine_map<(d0, d1, d2, d3) -> (d2, d1, d3, d0)>
+// CHECK: func @vectorization_transpose
+// CHECK: vector.transfer_read {{.*}}{in_bounds = [true, true, true, true], permutation_map = #[[MAP0]]} : memref<14x7xf32>, vector<7x14x8x16xf32>
+// CHECK: vector.transfer_read {{.*}}{in_bounds = [true, true, true, true], permutation_map = #[[MAP1]]} : memref<16x14xf32>, vector<7x14x8x16xf32>
+// CHECK: vector.transfer_read {{.*}}{in_bounds = [true, true, true, true], permutation_map = #[[MAP2]]} : memref<16x14x7x8xf32>, vector<7x14x8x16xf32>
+// CHECK: arith.addf {{.*}} : vector<7x14x8x16xf32>
+// CHECK: arith.addf {{.*}} : vector<7x14x8x16xf32>
+// CHECK: vector.transfer_write {{.*}} : vector<7x14x8x16xf32>, memref<7x14x8x16xf32>
+func.func @vectorization_transpose(%A: memref<14x7xf32>, %B: memref<16x14xf32>,
+ %C: memref<16x14x7x8xf32>, %D: memref<7x14x8x16xf32>) {
+ linalg.generic #matmul_trait
+ ins(%A, %B, %C : memref<14x7xf32>, memref<16x14xf32>, memref<16x14x7x8xf32>)
+ outs(%D : memref<7x14x8x16xf32>) {
+ ^bb(%a: f32, %b: f32, %c: f32, %d: f32) :
+ %e = arith.addf %a, %b: f32
+ %f = arith.addf %e, %c: f32
+ linalg.yield %f : f32
+ }
+ return
+}
+
+transform.sequence failures(propagate) {
+^bb1(%arg1: !transform.any_op):
+ %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+ %1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children_and_apply_patterns %1 { disable_transfer_permutation_map_lowering_patterns } : (!transform.any_op) -> !transform.any_op
+}
+
+// -----
+
+// CHECK-LABEL: func @matmul_tensors
+// CHECK-SAME: (%[[ARG0:.*]]: tensor<8x4xf32>, %[[ARG1:.*]]: tensor<4x12xf32>,
+// CHECK-SAME: %[[ARG2:.*]]: tensor<8x12xf32>) -> tensor<8x12xf32>
+func.func @matmul_tensors(
+ %arg0: tensor<8x4xf32>, %arg1: tensor<4x12xf32>, %arg2: tensor<8x12xf32>)
+ -> tensor<8x12xf32> {
+ // CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
+ // CHECK-DAG: %[[V0:.*]] = vector.transfer_read %[[ARG0]][%[[C0]], %[[C0]]], {{.*}} : tensor<8x4xf32>, vector<8x12x4xf32>
+ // CHECK-DAG: %[[V1:.*]] = vector.transfer_read %[[ARG1]][%[[C0]], %[[C0]]], {{.*}} : tensor<4x12xf32>, vector<8x12x4xf32>
+ // CHECK-DAG: %[[V2:.*]] = vector.transfer_read %[[ARG2]][%[[C0]], %[[C0]]], {{.*}} : tensor<8x12xf32>, vector<8x12xf32>
+ //
+ // linalg matmul lowers gets expanded to a 3D reduction, canonicalization later
+ // convert it to a 2D contract.
+ // CHECK: %[[MUL:.*]] = arith.mulf %[[V0]], %[[V1]] : vector<8x12x4xf32>
+ // CHECK: %[[R:.*]] = vector.multi_reduction <add>, %[[MUL]], %[[V2]] [2] : vector<8x12x4xf32> to vector<8x12xf32>
+ // CHECK: %[[W:.*]] = vector.transfer_write %[[R]], %[[ARG2]][%[[C0]], %[[C0]]] {in_bounds = [true, true]} : vector<8x12xf32>, tensor<8x12xf32>
+ %0 = linalg.matmul ins(%arg0, %arg1: tensor<8x4xf32>, tensor<4x12xf32>)
+ outs(%arg2: tensor<8x12xf32>)
+ -> tensor<8x12xf32>
+ // CHECK: return %[[W]] : tensor<8x12xf32>
+ return %0 : tensor<8x12xf32>
+}
+
+transform.sequence failures(propagate) {
+^bb1(%arg1: !transform.any_op):
+ %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+ %1 = 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
+}
+
+// -----
+
+// CHECK-LABEL: func @pad_static(
+// CHECK-SAME: %[[ARG0:.*]]: tensor<2x?x2xf32>, %[[PAD:.*]]: f32
+// CHECK-NOT: tensor.pad
+// CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
+// CHECK-DAG: %[[C2:.*]] = arith.constant 2 : index
+// CHECK-DAG: %[[INIT:.*]] = tensor.empty() : tensor<2x3x4xf32>
+// CHECK-DAG: %[[VEC:.*]] = vector.broadcast %[[PAD]] : f32 to vector<2x3x4xf32>
+// CHECK: %[[FILL:.*]] = vector.transfer_write %[[VEC]], %[[INIT]]{{.*}} : vector<2x3x4xf32>, tensor<2x3x4xf32>
+// CHECK: %[[READ:.*]] = vector.transfer_read %[[ARG0]][%[[C0]], %[[C0]], %[[C0]]], %[[PAD]] {in_bounds = [true, false, true]} : tensor<2x?x2xf32>, vector<2x3x2xf32>
+// CHECK: %[[RESULT:.*]] = vector.transfer_write %[[READ]], %[[FILL]][%[[C0]], %[[C0]], %[[C2]]] {in_bounds = [true, true, true]} : vector<2x3x2xf32>, tensor<2x3x4xf32>
+// CHECK: return %[[RESULT]]
+func.func @pad_static(%arg0: tensor<2x?x2xf32>, %pad_value: f32) -> tensor<2x3x4xf32> {
+ %0 = tensor.pad %arg0 low[0, 0, 2] high[0, 1, 0] {
+ ^bb0(%arg1: index, %arg2: index, %arg3: index):
+ tensor.yield %pad_value : f32
+ } : tensor<2x?x2xf32> to tensor<2x3x4xf32>
+ return %0 : tensor<2x3x4xf32>
+}
+
+
+transform.sequence failures(propagate) {
+^bb1(%arg1: !transform.any_op):
+ %0 = transform.structured.match ops{["tensor.pad"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+ %1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children_and_apply_patterns %1 { vectorize_padding } : (!transform.any_op) -> !transform.any_op
+}
+
+// -----
+
+// CHECK-LABEL: func @pad_static_source(
+// CHECK-SAME: %[[ARG0:.*]]: tensor<2x5x2xf32>, %[[PAD:.*]]: f32
+// CHECK-NOT: tensor.pad
+// CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
+// CHECK-DAG: %[[C2:.*]] = arith.constant 2 : index
+// CHECK: %[[INIT:.*]] = tensor.empty() : tensor<2x6x4xf32>
+// CHECK: %[[VEC:.*]] = vector.broadcast %[[PAD]] : f32 to vector<2x6x4xf32>
+// CHECK: %[[FILL:.*]] = vector.transfer_write %[[VEC]], %[[INIT]][%[[C0]], %[[C0]], %[[C0]]] {in_bounds = [true, true, true]} : vector<2x6x4xf32>, tensor<2x6x4xf32>
+// CHECK: %[[READ:.*]] = vector.transfer_read %[[ARG0]][%[[C0]], %[[C0]], %[[C0]]], %{{.*}} {in_bounds = [true, true, true]} : tensor<2x5x2xf32>, vector<2x5x2xf32>
+// CHECK: %[[WRITE:.*]] = vector.transfer_write %[[READ]], %[[FILL]][%[[C0]], %[[C0]], %[[C2]]] {in_bounds = [true, true, true]} : vector<2x5x2xf32>, tensor<2x6x4xf32>
+// CHECK: return %[[WRITE]]
+func.func @pad_static_source(%arg0: tensor<2x5x2xf32>, %pad_value: f32) -> tensor<2x6x4xf32> {
+ %0 = tensor.pad %arg0 low[0, 0, 2] high[0, 1, 0] {
+ ^bb0(%arg1: index, %arg2: index, %arg3: index):
+ tensor.yield %pad_value : f32
+ } : tensor<2x5x2xf32> to tensor<2x6x4xf32>
+ return %0 : tensor<2x6x4xf32>
+}
+
+
+transform.sequence failures(propagate) {
+^bb1(%arg1: !transform.any_op):
+ %0 = transform.structured.match ops{["tensor.pad"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+ %1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children_and_apply_patterns %1 { vectorize_padding } : (!transform.any_op) -> !transform.any_op
+}
+
+
+// -----
+
+// CHECK-LABEL: func @pad_static_dynamic(
+// CHECK-SAME: %[[SRC:.*]]: tensor<1x2x2x?xf32>, %[[LOW:.*]]: index, %[[HIGH:.*]]: index
+// CHECK-NOT: tensor.pad
+// CHECK-DAG: %[[C2:.*]] = arith.constant 2 : index
+// CHECK-DAG: %[[C3:.*]] = arith.constant 3 : index
+// CHECK-DAG: %[[C5:.*]] = arith.constant 5 : index
+// CHECK: %[[V0:.*]] = arith.addi %[[LOW]], %[[C2]] : index
+// CHECK: %[[V1:.*]] = arith.addi %[[V0]], %[[C3]] : index
+// CHECK: %[[V2:.*]] = arith.addi %[[HIGH]], %[[C5]] : index
+// CHECK: %[[DIM3:.*]] = tensor.dim %[[SRC]], %[[C3]] : tensor<1x2x2x?xf32>
+// CHECK: %[[V4:.*]] = arith.addi %[[DIM3]], %[[C3]] : index
+// CHECK: %[[V5:.*]] = arith.addi %[[V4]], %[[C2]] : index
+// CHECK: %[[INIT:.*]] = tensor.empty(%[[V1]], %[[V2]], %[[V5]]) : tensor<6x?x?x?xf32>
+// CHECK: %[[FILL:.*]] = linalg.fill ins(%{{.*}} : f32) outs(%[[INIT]] : tensor<6x?x?x?xf32>) -> tensor<6x?x?x?xf32>
+// CHECK: %[[SRCDIM:.*]] = tensor.dim %[[SRC]], %[[C3]] : tensor<1x2x2x?xf32>
+// CHECK: %[[RESULT:.*]] = tensor.insert_slice %[[SRC]] into %[[FILL]][2, %[[LOW]], 3, 3] [1, 2, 2, %[[SRCDIM]]] [1, 1, 1, 1] : tensor<1x2x2x?xf32> into tensor<6x?x?x?xf32>
+// CHECK: return %[[RESULT]]
+func.func @pad_static_dynamic(%arg0: tensor<1x2x2x?xf32>, %low: index, %high: index,
+ %pad_value: f32) -> tensor<6x?x?x?xf32> {
+ %0 = tensor.pad %arg0 low[2, %low, 3, 3] high[3, 3, %high, 2] {
+ ^bb0(%arg1: index, %arg2: index, %arg3: index, %arg4: index):
+ tensor.yield %pad_value : f32
+ } : tensor<1x2x2x?xf32> to tensor<6x?x?x?xf32>
+ return %0 : tensor<6x?x?x?xf32>
+}
+
+
+transform.sequence failures(propagate) {
+^bb1(%arg1: !transform.any_op):
+ %0 = transform.structured.match ops{["tensor.pad"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+ %1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children_and_apply_patterns %1 { vectorize_padding } : (!transform.any_op) -> !transform.any_op
+}
+
+// -----
+
+// CHECK-LABEL: func @pad_static_complex(
+// CHECK-NOT: vector<
+func.func @pad_static_complex(%arg0: tensor<2x5x2xcomplex<f32>>, %pad_value: complex<f32>) -> tensor<2x6x4xcomplex<f32>> {
+ %0 = tensor.pad %arg0 low[0, 0, 2] high[0, 1, 0] {
+ ^bb0(%arg1: index, %arg2: index, %arg3: index):
+ tensor.yield %pad_value : complex<f32>
+ } : tensor<2x5x2xcomplex<f32>> to tensor<2x6x4xcomplex<f32>>
+ return %0 : tensor<2x6x4xcomplex<f32>>
+}
+
+
+transform.sequence failures(propagate) {
+^bb1(%arg1: !transform.any_op):
+ %0 = transform.structured.match ops{["tensor.pad"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+ %1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children_and_apply_patterns %1 { vectorize_padding } : (!transform.any_op) -> !transform.any_op
+}
+
+// -----
+
+// CHECK-LABEL: func @pad_and_transfer_read
+// CHECK-SAME: %[[ARG0:.*]]: tensor<5x6xf32>
+// CHECK-NOT: tensor.pad
+// CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
+// CHECK-DAG: %[[C5:.*]] = arith.constant 5.0
+// CHECK: %[[RESULT:.*]] = vector.transfer_read %[[ARG0]][%[[C0]], %[[C0]]], %[[C5]] : tensor<5x6xf32>, vector<7x9xf32>
+// CHECK: return %[[RESULT]]
+func.func @pad_and_transfer_read(%arg0: tensor<5x6xf32>) -> vector<7x9xf32> {
+ %c0 = arith.constant 0 : index
+ %c5 = arith.constant 5.0 : f32
+ %c6 = arith.constant 6.0 : f32
+ %0 = tensor.pad %arg0 low[0, 0] high[5, 7] {
+ ^bb0(%arg1: index, %arg2: index):
+ tensor.yield %c5 : f32
+ } : tensor<5x6xf32> to tensor<10x13xf32>
+ %1 = vector.transfer_read %0[%c0, %c0], %c6
+ : tensor<10x13xf32>, vector<7x9xf32>
+ return %1 : vector<7x9xf32>
+}
+
+
+transform.sequence failures(propagate) {
+^bb1(%arg1: !transform.any_op):
+ %0 = transform.structured.match ops{["tensor.pad"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+ %1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children_and_apply_patterns %1 { vectorize_padding } : (!transform.any_op) -> !transform.any_op
+}
+
+// -----
+
+func.func private @make_vector() -> vector<7x9xf32>
+
+// CHECK-LABEL: func @pad_and_transfer_write_static
+// CHECK-SAME: %[[ARG0:.*]]: tensor<5x6xf32>
+// CHECK-NOT: tensor.pad
+// CHECK: %[[C0:.*]] = arith.constant 0 : index
+// CHECK: %[[VEC0:.*]] = call @make_vector() : () -> vector<7x9xf32>
+// CHECK: %[[RESULT:.*]] = vector.transfer_write %[[VEC0]], %[[ARG0]][%[[C0]], %[[C0]]] : vector<7x9xf32>, tensor<5x6xf32>
+// CHECK: return %[[RESULT]]
+func.func @pad_and_transfer_write_static(
+ %arg0: tensor<5x6xf32>) -> tensor<5x6xf32> {
+ %c0 = arith.constant 0 : index
+ %c5 = arith.constant 5.0 : f32
+ %0 = tensor.pad %arg0 low[0, 0] high[5, 7] {
+ ^bb0(%arg2: index, %arg3: index):
+ tensor.yield %c5 : f32
+ } : tensor<5x6xf32> to tensor<10x13xf32>
+ %1 = call @make_vector() : () -> vector<7x9xf32>
+ %2 = vector.transfer_write %1, %0[%c0, %c0]
+ : vector<7x9xf32>, tensor<10x13xf32>
+ %3 = tensor.extract_slice %2[0, 0] [5, 6] [1, 1] : tensor<10x13xf32> to tensor<5x6xf32>
+ return %3 : tensor<5x6xf32>
+}
+
+transform.sequence failures(propagate) {
+^bb1(%arg1: !transform.any_op):
+ %3 = transform.structured.match ops{["tensor.pad"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+ %4 = get_parent_op %3 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
+ %5 = transform.structured.vectorize_children_and_apply_patterns %4 { vectorize_padding } : (!transform.any_op) -> !transform.any_op
+}
+
+
+// -----
+
+func.func private @make_vector() -> vector<7x9xf32>
+
+// CHECK-LABEL: func @pad_and_transfer_write_dynamic_static
+// CHECK-SAME: %[[ARG0:.*]]: tensor<?x?xf32>, %[[SIZE:.*]]: index, %[[PADDING:.*]]: index
+// CHECK-NOT: tensor.pad
+// CHECK: %[[C0:.*]] = arith.constant 0 : index
+// CHECK: %[[SUB:.*]] = tensor.extract_slice %[[ARG0]][0, 0] [%[[SIZE]], 6] [1, 1] : tensor<?x?xf32> to tensor<?x6xf32>
+// CHECK: %[[VEC0:.*]] = call @make_vector() : () -> vector<7x9xf32>
+// CHECK: %[[RESULT:.*]] = vector.transfer_write %[[VEC0]], %[[SUB]][%[[C0]], %[[C0]]] : vector<7x9xf32>, tensor<?x6xf32>
+// CHECK: return %[[RESULT]]
+func.func @pad_and_transfer_write_dynamic_static(
+ %arg0: tensor<?x?xf32>, %size: index, %padding: index) -> tensor<?x6xf32> {
+ %c0 = arith.constant 0 : index
+ %c5 = arith.constant 5.0 : f32
+ %s = tensor.extract_slice %arg0[0, 0] [%size, 6] [1, 1]
+ : tensor<?x?xf32> to tensor<?x6xf32>
+ %0 = tensor.pad %s low[0, 0] high[%padding, 7] {
+ ^bb0(%arg2: index, %arg3: index):
+ tensor.yield %c5 : f32
+ } : tensor<?x6xf32> to tensor<?x13xf32>
+ %1 = call @make_vector() : () -> vector<7x9xf32>
+ %2 = vector.transfer_write %1, %0[%c0, %c0]
+ : vector<7x9xf32>, tensor<?x13xf32>
+ %3 = tensor.extract_slice %2[0, 0] [%size, 6] [1, 1] : tensor<?x13xf32> to tensor<?x6xf32>
+ return %3 : tensor<?x6xf32>
+}
+
+transform.sequence failures(propagate) {
+^bb1(%arg1: !transform.any_op):
+ %3 = transform.structured.match ops{["tensor.pad"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+ %4 = get_parent_op %3 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
+ %5 = transform.structured.vectorize_children_and_apply_patterns %4 { vectorize_padding } : (!transform.any_op) -> !transform.any_op
+}
+
+
+// -----
+
+func.func private @make_vector() -> tensor<12x13xf32>
+
+// CHECK-LABEL: func @pad_and_insert_slice_source
+// CHECK-SAME: %[[ARG0:.*]]: tensor<5x6xf32>
+// CHECK-NOT: tensor.pad
+// CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
+// CHECK-DAG: %[[C5:.*]] = arith.constant 5.0
+// CHECK: %[[VEC0:.*]] = call @make_vector() : () -> tensor<12x13xf32>
+// CHECK: %[[READ:.*]] = vector.transfer_read %[[ARG0]][%[[C0]], %[[C0]]], %[[C5]] : tensor<5x6xf32>, vector<7x9xf32>
+// CHECK: %[[WRITE:.*]] = vector.transfer_write %[[READ]], %[[VEC0]][%[[C0]], %[[C0]]] {in_bounds = [true, true]} : vector<7x9xf32>, tensor<12x13xf32>
+// CHECK: return %[[WRITE]]
+func.func @pad_and_insert_slice_source(
+ %arg0: tensor<5x6xf32>) -> tensor<12x13xf32> {
+ %c0 = arith.constant 0 : index
+ %c5 = arith.constant 5.0 : f32
+ %0 = tensor.pad %arg0 low[0, 0] high[2, 3] {
+ ^bb0(%arg2: index, %arg3: index):
+ tensor.yield %c5 : f32
+ } : tensor<5x6xf32> to tensor<7x9xf32>
+ %1 = call @make_vector() : () -> tensor<12x13xf32>
+ %r = tensor.insert_slice %0 into %1[0, 0][7, 9][1, 1] : tensor<7x9xf32> into tensor<12x13xf32>
+ return %r : tensor<12x13xf32>
+}
+
+transform.sequence failures(propagate) {
+^bb1(%arg1: !transform.any_op):
+ %3 = transform.structured.match ops{["tensor.pad"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+ %4 = get_parent_op %3 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
+ %5 = transform.structured.vectorize_children_and_apply_patterns %4 { vectorize_padding } : (!transform.any_op) -> !transform.any_op
+}
+
+
+// -----
+
+func.func private @make_vector() -> tensor<12x13xf32>
+
+// CHECK-LABEL: func @pad_and_insert_slice_dest
+// Check the insert slice is not rewritten if the padded result is used by the destination operand.
+// CHECK: %[[T1:.*]] = call @make_vector() : () -> tensor<12x13xf32>
+// CHECK: = tensor.insert_slice %[[T1]] into
+func.func @pad_and_insert_slice_dest(
+ %arg0: tensor<1x5x6xf32>) -> tensor<1x12x13xf32> {
+ %c5 = arith.constant 5.0 : f32
+ %0 = tensor.pad %arg0 low[0, 0, 0] high[0, 7, 7] {
+ ^bb0(%arg2: index, %arg3: index, %arg4: index):
+ tensor.yield %c5 : f32
+ } : tensor<1x5x6xf32> to tensor<1x12x13xf32>
+ %1 = call @make_vector() : () -> tensor<12x13xf32>
+ %r = tensor.insert_slice %1 into %0[0, 0, 0][1, 12, 13][1, 1, 1] : tensor<12x13xf32> into tensor<1x12x13xf32>
+ return %r : tensor<1x12x13xf32>
+}
+
+transform.sequence failures(propagate) {
+^bb1(%arg1: !transform.any_op):
+ %3 = transform.structured.match ops{["tensor.pad"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+ %4 = get_parent_op %3 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
+ %5 = transform.structured.vectorize_children_and_apply_patterns %4 : (!transform.any_op) -> !transform.any_op
+}
+
+// -----
+
+// CHECK-LABEL: func @pad_tensor_non_const_pad_value
+// CHECK-SAME: %[[ARG0:.*]]: tensor<5x6xf32>
+// CHECK-NOT: tensor.pad
+// CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
+// CHECK-DAG: %[[C3:.*]] = arith.constant 3 : index
+// CHECK-DAG: %[[C4:.*]] = arith.constant 4 : index
+// CHECK: %[[FILL:.*]] = tensor.generate
+// CHECK: %[[RES:.*]] = arith.mulf
+// CHECK: tensor.yield %[[RES]] : f32
+// CHECK: %[[READ:.*]] = vector.transfer_read %[[ARG0]][%[[C0]], %[[C0]]], %{{.*}} {in_bounds = [true, true]} : tensor<5x6xf32>, vector<5x6xf32>
+// CHECK: %[[WRITE:.*]] = vector.transfer_write %[[READ]], %[[FILL]][%[[C3]], %[[C4]]] {in_bounds = [true, true]} : vector<5x6xf32>, tensor<12x13xf32>
+// CHECK: return %[[WRITE]]
+func.func @pad_tensor_non_const_pad_value(%arg0: tensor<5x6xf32>) -> tensor<12x13xf32> {
+ %c0 = arith.constant 0 : index
+ %c5 = arith.constant 5.0 : f32
+ %0 = tensor.pad %arg0 low[3, 4] high[4, 3] {
+ ^bb0(%arg1: index, %arg2: index):
+ %i1 = arith.index_cast %arg1 : index to i32
+ %i2 = arith.index_cast %arg2 : index to i32
+ %f1 = arith.sitofp %i1 : i32 to f32
+ %f2 = arith.sitofp %i2 : i32 to f32
+ %m = arith.mulf %f1, %f2 : f32
+ tensor.yield %m : f32
+ } : tensor<5x6xf32> to tensor<12x13xf32>
+ return %0 : tensor<12x13xf32>
+}
+
+
+transform.sequence failures(propagate) {
+^bb1(%arg1: !transform.any_op):
+ %3 = transform.structured.match ops{["tensor.pad"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+ %4 = get_parent_op %3 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
+ %5 = transform.structured.vectorize_children_and_apply_patterns %4 { vectorize_padding } : (!transform.any_op) -> !transform.any_op
+}
+
+// -----
+
+// CHECK-LABEL: func @sum_exp
+func.func @sum_exp(%input: tensor<4x16x8xf32>, %output: tensor<4x16xf32>)
+ -> tensor<4x16xf32>
+{
+ // CHECK: vector.transfer_read {{.*}} : tensor<4x16x8xf32>, vector<4x16x8xf32>
+ // CHECK: vector.transfer_read {{.*}} {in_bounds = [true, true]} : tensor<4x16xf32>, vector<4x16xf32>
+ // CHECK: math.exp {{.*}} : vector<4x16x8xf32>
+ // CHECK: vector.multi_reduction <add>, %{{.*}}, %{{.*}} [2] : vector<4x16x8xf32> to vector<4x16xf32>
+ // CHECK: vector.transfer_write {{.*}} : vector<4x16xf32>, tensor<4x16xf32>
+ // CHECK: return {{.*}} : tensor<4x16xf32>
+ %0 = linalg.generic {
+ indexing_maps = [
+ affine_map<(d0, d1, d2) -> (d0, d1, d2)>,
+ affine_map<(d0, d1, d2) -> (d0, d1)>
+ ],
+ iterator_types = ["parallel", "parallel", "reduction"]
+ } ins(%input : tensor<4x16x8xf32>) outs(%output : tensor<4x16xf32>) {
+ ^bb0(%arg0: f32, %arg1: f32):
+ %1 = math.exp %arg0 : f32
+ %2 = arith.addf %1, %arg1 : f32
+ linalg.yield %2 : f32
+ } -> tensor<4x16xf32>
+ return %0 : tensor<4x16xf32>
+}
+
+
+transform.sequence failures(propagate) {
+^bb1(%arg1: !transform.any_op):
+ %3 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+ %4 = get_parent_op %3 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
+ %5 = transform.structured.vectorize_children_and_apply_patterns %4 : (!transform.any_op) -> !transform.any_op
+}
+
+// -----
+
+// CHECK-DAG: #[[$M1:.*]] = affine_map<(d0, d1) -> (d1, d0, 0, 0)>
+// CHECK-DAG: #[[$M2:.*]] = affine_map<(d0, d1) -> (0, 0, d1, d0)>
+// CHECK-DAG: #[[$M3:.*]] = affine_map<(d0, d1) -> (d1, d0)>
+
+// CHECK-LABEL: func @sum_exp_2
+func.func @sum_exp_2(%input: tensor<3x2xf32>, %input_2: tensor<5x4xf32>, %output: tensor<5x2xf32>)
+ -> tensor<5x2xf32>
+{
+ // CHECK: vector.transfer_read {{.*}} {in_bounds = [true, true, true, true], permutation_map = #[[$M1]]} : tensor<3x2xf32>, vector<2x3x4x5xf32>
+ // CHECK: vector.transfer_read {{.*}} {in_bounds = [true, true, true, true], permutation_map = #[[$M2]]} : tensor<5x4xf32>, vector<2x3x4x5xf32>
+ // CHECK: vector.transfer_read {{.*}} {in_bounds = [true, true], permutation_map = #[[$M3]]} : tensor<5x2xf32>, vector<2x5xf32>
+ // CHECK: math.exp {{.*}} : vector<2x3x4x5xf32>
+ // CHECK: math.exp {{.*}} : vector<2x3x4x5xf32>
+ // CHECK: addf {{.*}} : vector<2x3x4x5xf32>
+ // CHECK: vector.multi_reduction <add>, {{.*}}, %{{.*}} [1, 2] : vector<2x3x4x5xf32> to vector<2x5xf32>
+ // CHECK: vector.transfer_write {{.*}} {in_bounds = [true, true], permutation_map = #[[$M3]]} : vector<2x5xf32>, tensor<5x2xf32>
+ // CHECK: return {{.*}} : tensor<5x2xf32>
+ %0 = linalg.generic {
+ indexing_maps = [
+ affine_map<(d0, d1, d2, d3) -> (d1, d0)>,
+ affine_map<(d0, d1, d2, d3) -> (d3, d2)>,
+ affine_map<(d0, d1, d2, d3) -> (d3, d0)>
+ ],
+ iterator_types = ["parallel", "reduction", "reduction", "parallel"]
+ } ins(%input, %input_2 : tensor<3x2xf32>, tensor<5x4xf32>) outs(%output : tensor<5x2xf32>) {
+ ^bb0(%arg0: f32, %arg1: f32, %arg2: f32):
+ %1 = math.exp %arg0 : f32
+ %2 = math.exp %arg1 : f32
+ %3 = arith.addf %1, %2 : f32
+ %4 = arith.addf %3, %arg2 : f32
+ linalg.yield %4 : f32
+ } -> tensor<5x2xf32>
+ return %0 : tensor<5x2xf32>
+}
+
+
+transform.sequence failures(propagate) {
+^bb1(%arg1: !transform.any_op):
+ %3 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+ %4 = get_parent_op %3 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
+ %5 = transform.structured.vectorize_children_and_apply_patterns %4 { disable_multi_reduction_to_contract_patterns, disable_transfer_permutation_map_lowering_patterns } : (!transform.any_op) -> !transform.any_op
+}
+
+// -----
+
+// CHECK-LABEL: func @red_max_2d(
+func.func @red_max_2d(%arg0: tensor<4x4xf32>) -> tensor<4xf32> {
+ // CHECK: %[[CMINF:.+]] = arith.constant dense<-3.402820e+38> : vector<4xf32>
+ // CHECK: tensor.empty() : tensor<4xf32>
+ // CHECK: vector.multi_reduction <maximumf>, {{.*}}, %[[CMINF]] [1] : vector<4x4xf32> to vector<4xf32>
+ // CHECK: vector.transfer_write {{.*}} : vector<4xf32>, tensor<4xf32>
+ %ident = arith.constant -3.40282e+38 : f32
+ %init = tensor.empty() : tensor<4xf32>
+ %fill = linalg.fill ins(%ident : f32) outs(%init : tensor<4xf32>) -> tensor<4xf32>
+ %red = linalg.generic {indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>,
+ affine_map<(d0, d1) -> (d0)>],
+ iterator_types = ["parallel", "reduction"]}
+ ins(%arg0 : tensor<4x4xf32>) outs(%fill : tensor<4xf32>) {
+ ^bb0(%in0: f32, %out0: f32):
+ %max = arith.maximumf %in0, %out0 : f32
+ linalg.yield %max : f32
+ } -> tensor<4xf32>
+ return %red : tensor<4xf32>
+}
+
+
+transform.sequence failures(propagate) {
+^bb1(%arg1: !transform.any_op):
+ %3 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+ %4 = get_parent_op %3 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
+ %5 = transform.structured.vectorize_children_and_apply_patterns %4 { vectorize_padding } : (!transform.any_op) -> !transform.any_op
+}
+
+// -----
+
+// CHECK-LABEL: func @red_min_2d(
+func.func @red_min_2d(%arg0: tensor<4x4xf32>) -> tensor<4xf32> {
+ // CHECK: %[[CMAXF:.+]] = arith.constant dense<3.402820e+38> : vector<4xf32>
+ // CHECK: tensor.empty() : tensor<4xf32>
+ // CHECK: vector.transfer_read {{.*}} : tensor<4x4xf32>, vector<4x4xf32>
+ // CHECK: vector.multi_reduction <minimumf>, {{.*}}, %[[CMAXF]] [1] : vector<4x4xf32> to vector<4xf32>
+ // CHECK: vector.transfer_write {{.*}} : vector<4xf32>, tensor<4xf32>
+ %maxf32 = arith.constant 3.40282e+38 : f32
+ %init = tensor.empty() : tensor<4xf32>
+ %fill = linalg.fill ins(%maxf32 : f32) outs(%init : tensor<4xf32>) -> tensor<4xf32>
+ %red = linalg.generic {indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>,
+ affine_map<(d0, d1) -> (d0)>],
+ iterator_types = ["parallel", "reduction"]}
+ ins(%arg0 : tensor<4x4xf32>) outs(%fill : tensor<4xf32>) {
+ ^bb0(%in0: f32, %out0: f32):
+ %min = arith.minimumf %out0, %in0 : f32
+ linalg.yield %min : f32
+ } -> tensor<4xf32>
+ return %red : tensor<4xf32>
+}
+
+
+transform.sequence failures(propagate) {
+^bb1(%arg1: !transform.any_op):
+ %3 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+ %4 = get_parent_op %3 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
+ %5 = transform.structured.vectorize_children_and_apply_patterns %4 : (!transform.any_op) -> !transform.any_op
+}
+
+// -----
+
+// CHECK-LABEL: func @red_mul_2d(
+func.func @red_mul_2d(%arg0: tensor<4x4xf32>) -> tensor<4xf32> {
+ // CHECK: tensor.empty() : tensor<4xf32>
+ // CHECK: vector.transfer_read {{.*}} : tensor<4x4xf32>, vector<4x4xf32>
+ // CHECK: vector.multi_reduction <mul>, {{.*}}, {{.*}} [1] : vector<4x4xf32> to vector<4xf32>
+ // CHECK: vector.transfer_write {{.*}} : vector<4xf32>, tensor<4xf32>
+ %ident = arith.constant 1.0 : f32
+ %init = tensor.empty() : tensor<4xf32>
+ %fill = linalg.fill ins(%ident : f32) outs(%init : tensor<4xf32>) -> tensor<4xf32>
+ %red = linalg.generic {indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>,
+ affine_map<(d0, d1) -> (d0)>],
+ iterator_types = ["parallel", "reduction"]}
+ ins(%arg0 : tensor<4x4xf32>) outs(%fill : tensor<4xf32>) {
+ ^bb0(%in0: f32, %out0: f32):
+ %mul = arith.mulf %in0, %out0 : f32
+ linalg.yield %mul : f32
+ } -> tensor<4xf32>
+ return %red : tensor<4xf32>
+}
+
+
+transform.sequence failures(propagate) {
+^bb1(%arg1: !transform.any_op):
+ %3 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+ %4 = get_parent_op %3 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
+ %5 = transform.structured.vectorize_children_and_apply_patterns %4 : (!transform.any_op) -> !transform.any_op
+}
+
+// -----
+
+// CHECK-LABEL: func @red_or_2d(
+func.func @red_or_2d(%arg0: tensor<4x4xi1>) -> tensor<4xi1> {
+ // CHECK: tensor.empty() : tensor<4xi1>
+ // CHECK: vector.transfer_read {{.*}} : tensor<4x4xi1>, vector<4x4xi1>
+ // CHECK: vector.multi_reduction <or>, {{.*}}, {{.*}} [1] : vector<4x4xi1> to vector<4xi1>
+ // CHECK: vector.transfer_write {{.*}} : vector<4xi1>, tensor<4xi1>
+ %ident = arith.constant false
+ %init = tensor.empty() : tensor<4xi1>
+ %fill = linalg.fill ins(%ident : i1) outs(%init : tensor<4xi1>) -> tensor<4xi1>
+ %red = linalg.generic {indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>,
+ affine_map<(d0, d1) -> (d0)>],
+ iterator_types = ["parallel", "reduction"]}
+ ins(%arg0 : tensor<4x4xi1>) outs(%fill : tensor<4xi1>) {
+ ^bb0(%in0: i1, %out0: i1):
+ %or = arith.ori %in0, %out0 : i1
+ linalg.yield %or : i1
+ } -> tensor<4xi1>
+ return %red : tensor<4xi1>
+}
+
+
+transform.sequence failures(propagate) {
+^bb1(%arg1: !transform.any_op):
+ %3 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+ %4 = get_parent_op %3 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
+ %5 = transform.structured.vectorize_children_and_apply_patterns %4 : (!transform.any_op) -> !transform.any_op
+}
+
+// -----
+
+// CHECK-LABEL: func @red_and_2d(
+func.func @red_and_2d(%arg0: tensor<4x4xi1>) -> tensor<4xi1> {
+ // CHECK: tensor.empty() : tensor<4xi1>
+ // CHECK: vector.transfer_read {{.*}} : tensor<4x4xi1>, vector<4x4xi1>
+ // CHECK: vector.multi_reduction <and>, {{.*}}, {{.*}} [1] : vector<4x4xi1> to vector<4xi1>
+ // CHECK: vector.transfer_write {{.*}} : vector<4xi1>, tensor<4xi1>
+ %ident = arith.constant true
+ %init = tensor.empty() : tensor<4xi1>
+ %fill = linalg.fill ins(%ident : i1) outs(%init : tensor<4xi1>) -> tensor<4xi1>
+ %red = linalg.generic {indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>,
+ affine_map<(d0, d1) -> (d0)>],
+ iterator_types = ["parallel", "reduction"]}
+ ins(%arg0 : tensor<4x4xi1>) outs(%fill : tensor<4xi1>) {
+ ^bb0(%in0: i1, %out0: i1):
+ %and = arith.andi %in0, %out0 : i1
+ linalg.yield %and : i1
+ } -> tensor<4xi1>
+ return %red : tensor<4xi1>
+}
+
+
+transform.sequence failures(propagate) {
+^bb1(%arg1: !transform.any_op):
+ %3 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+ %4 = get_parent_op %3 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
+ %5 = transform.structured.vectorize_children_and_apply_patterns %4 : (!transform.any_op) -> !transform.any_op
+}
+
+// -----
+
+// CHECK-LABEL: func @red_xor_2d(
+func.func @red_xor_2d(%arg0: tensor<4x4xi1>) -> tensor<4xi1> {
+ // CHECK: tensor.empty() : tensor<4xi1>
+ // CHECK: vector.transfer_read {{.*}} : tensor<4x4xi1>, vector<4x4xi1>
+ // CHECK: vector.multi_reduction <xor>, {{.*}}, {{.*}} [1] : vector<4x4xi1> to vector<4xi1>
+ // CHECK: vector.transfer_write {{.*}} : vector<4xi1>, tensor<4xi1>
+ %ident = arith.constant false
+ %init = tensor.empty() : tensor<4xi1>
+ %fill = linalg.fill ins(%ident : i1) outs(%init : tensor<4xi1>) -> tensor<4xi1>
+ %red = linalg.generic {indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>,
+ affine_map<(d0, d1) -> (d0)>],
+ iterator_types = ["parallel", "reduction"]}
+ ins(%arg0 : tensor<4x4xi1>) outs(%fill : tensor<4xi1>) {
+ ^bb0(%in0: i1, %out0: i1):
+ %xor = arith.xori %in0, %out0 : i1
+ linalg.yield %xor : i1
+ } -> tensor<4xi1>
+ return %red : tensor<4xi1>
+}
+
+
+transform.sequence failures(propagate) {
+^bb1(%arg1: !transform.any_op):
+ %3 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+ %4 = get_parent_op %3 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
+ %5 = transform.structured.vectorize_children_and_apply_patterns %4 : (!transform.any_op) -> !transform.any_op
+}
+
+// -----
+
+// CHECK-DAG: #[[$M5:.*]] = affine_map<(d0, d1) -> (d0, 0)>
+
+// CHECK-LABEL: func @explicit_broadcast(
+func.func @explicit_broadcast(%arg0: tensor<4x4xf32>, %arg1: tensor<4x1xf32>) -> tensor<4x4xf32> {
+ // CHECK: vector.transfer_read {{.*}} {in_bounds = [true, true]} : tensor<4x4xf32>, vector<4x4xf32>
+ // CHECK: vector.transfer_read {{.*}} {in_bounds = [true, true], permutation_map = #[[$M5]]} : tensor<4x1xf32>, vector<4x4xf32>
+ // CHECK: subf {{.*}} : vector<4x4xf32>
+ // CHECK: vector.transfer_write {{.*}} {in_bounds = [true, true]} : vector<4x4xf32>, tensor<4x4xf32>
+ %c0 = arith.constant 0.0 : f32
+ %init = tensor.empty() : tensor<4x4xf32>
+ %fill = linalg.fill ins(%c0 : f32) outs(%init : tensor<4x4xf32>) -> tensor<4x4xf32>
+ %red = linalg.generic {indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>,
+ affine_map<(d0, d1) -> (d0, 0)>,
+ affine_map<(d0, d1) -> (d0, d1)>],
+ iterator_types = ["parallel", "parallel"]}
+ ins(%arg0, %arg1 : tensor<4x4xf32>, tensor<4x1xf32>)
+ outs(%fill : tensor<4x4xf32>) {
+ ^bb0(%arg7: f32, %arg8: f32, %arg9: f32):
+ %40 = arith.subf %arg7, %arg8 : f32
+ linalg.yield %40 : f32
+ } -> tensor<4x4xf32>
+ return %red : tensor<4x4xf32>
+}
+
+
+transform.sequence failures(propagate) {
+^bb1(%arg1: !transform.any_op):
+ %3 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+ %4 = get_parent_op %3 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
+ %5 = transform.structured.vectorize_children_and_apply_patterns %4 : (!transform.any_op) -> !transform.any_op
+}
+
+// -----
+
+// CHECK-DAG: #[[$M6:.*]] = affine_map<(d0, d1) -> (d0, 0)>
+
+// CHECK-LABEL: func @fused_broadcast_red_2d
+func.func @fused_broadcast_red_2d(%arg0: tensor<4x4xf32>, %arg1: tensor<4x1xf32>) -> tensor<4xf32> {
+ // CHECK: vector.transfer_read {{.*}} {in_bounds = [true, true]} : tensor<4x4xf32>, vector<4x4xf32>
+ // CHECK: vector.transfer_read {{.*}} {in_bounds = [true, true], permutation_map = #[[$M6]]} : tensor<4x1xf32>, vector<4x4xf32>
+ // CHECK: subf {{.*}} : vector<4x4xf32>
+ // CHECK: math.exp {{.*}} : vector<4x4xf32>
+ // CHECK: vector.multi_reduction <add>, {{.*}}, {{.*}} : vector<4x4xf32> to vector<4xf32>
+ // CHECK: vector.transfer_write {{.*}} {in_bounds = [true]} : vector<4xf32>, tensor<4xf32>
+ %c0 = arith.constant 0.0 : f32
+ %init = tensor.empty() : tensor<4xf32>
+ %fill = linalg.fill ins(%c0 : f32) outs(%init : tensor<4xf32>) -> tensor<4xf32>
+ %red = linalg.generic {indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>,
+ affine_map<(d0, d1) -> (d0, 0)>,
+ affine_map<(d0, d1) -> (d0)>],
+ iterator_types = ["parallel", "reduction"]}
+ ins(%arg0, %arg1 : tensor<4x4xf32>, tensor<4x1xf32>)
+ outs(%fill : tensor<4xf32>) {
+ ^bb0(%arg7: f32, %arg8: f32, %arg9: f32):
+ %40 = arith.subf %arg7, %arg8 : f32
+ %41 = math.exp %40 : f32
+ %42 = arith.addf %41, %arg9 : f32
+ linalg.yield %42 : f32
+ } -> tensor<4xf32>
+ return %red : tensor<4xf32>
+}
+
+
+transform.sequence failures(propagate) {
+^bb1(%arg1: !transform.any_op):
+ %0 = transform.structured.match ops{["linalg.fill"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+ %1 = 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
+
+ %3 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+ %4 = get_parent_op %3 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
+ %5 = transform.structured.vectorize_children_and_apply_patterns %4 : (!transform.any_op) -> !transform.any_op
+}
+
+// -----
+
+// CHECK-LABEL: func @reduce_1d(
+// CHECK-SAME: %[[A:.*]]: tensor<32xf32>
+func.func @reduce_1d(%arg0: tensor<32xf32>) -> tensor<f32> {
+ // CHECK-DAG: %[[vF0:.*]] = arith.constant dense<0.000000e+00> : vector<f32>
+ // CHECK-DAG: %[[F0:.*]] = arith.constant 0.000000e+00 : f32
+ // CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
+ %f0 = arith.constant 0.000000e+00 : f32
+
+ // CHECK: %[[init:.*]] = tensor.empty() : tensor<f32>
+ %0 = tensor.empty() : tensor<f32>
+
+ %1 = linalg.fill ins(%f0 : f32) outs(%0 : tensor<f32>) -> tensor<f32>
+ // CHECK: %[[r:.*]] = vector.transfer_read %[[A]][%[[C0]]]
+ // CHECK-SAME: : tensor<32xf32>, vector<32xf32>
+ // CHECK: %[[f0:.*]] = vector.extractelement %[[vF0]][] : vector<f32>
+ // CHECK: %[[red:.*]] = vector.multi_reduction <add>, %[[r]], %[[f0]] [0]
+ // CHECK-SAME: : vector<32xf32> to f32
+ // CHECK: %[[red_v1:.*]] = vector.broadcast %[[red]] : f32 to vector<f32>
+ // CHECK: %[[res:.*]] = vector.transfer_write %[[red_v1]], %[[init]][]
+ // CHECK-SAME: : vector<f32>, tensor<f32>
+ %2 = linalg.generic {
+ indexing_maps = [affine_map<(d0) -> (d0)>,
+ affine_map<(d0) -> ()>],
+ iterator_types = ["reduction"]}
+ ins(%arg0 : tensor<32xf32>)
+ outs(%1 : tensor<f32>) {
+ ^bb0(%a: f32, %b: f32):
+ %3 = arith.addf %a, %b : f32
+ linalg.yield %3 : f32
+ } -> tensor<f32>
+
+ return %2 : tensor<f32>
+}
+
+transform.sequence failures(propagate) {
+^bb1(%arg1: !transform.any_op):
+ %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+ %1 = 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
+}
+
+
+// -----
+
+// This test checks that vectorization does not occur when an input indexing map
+// is not a projected permutation. In the future, this can be converted to a
+// positive test when support is added.
+
+// CHECK-LABEL: func @not_projected_permutation
+func.func @not_projected_permutation(%arg0: tensor<8x8xf32>) -> tensor<6x6x3x3xf32> {
+ %c0 = arith.constant 0.0 : f32
+ %init = tensor.empty() : tensor<6x6x3x3xf32>
+ %fill = linalg.fill ins(%c0 : f32) outs(%init : tensor<6x6x3x3xf32>) -> tensor<6x6x3x3xf32>
+ // CHECK: linalg.generic
+ %result = linalg.generic {indexing_maps = [affine_map<(d0, d1, d2, d3) -> (d0 + d2, d1 + d3)>,
+ affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>],
+ iterator_types = ["parallel", "parallel", "parallel", "parallel"]}
+ ins(%arg0 : tensor<8x8xf32>)
+ outs(%fill : tensor<6x6x3x3xf32>) {
+ ^bb0(%arg7: f32, %arg9: f32):
+ linalg.yield %arg7 : f32
+ } -> tensor<6x6x3x3xf32>
+ return %result : tensor<6x6x3x3xf32>
+}
+
+transform.sequence failures(propagate) {
+^bb1(%arg1: !transform.any_op):
+ %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+ %1 = 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
+}
+
+// -----
+
+// Check vectorization can handle cases where outputs are a mix of reduced and non-reduced values.
+func.func @mixed_parallel_reduced_results(%arg0 : tensor<2x4x8xf32>,
+ %arg1 : tensor<2x4xf32>, %arg2 : tensor<2x4x8xf32>, %arg3 : tensor<2x4xf32>) ->
+ (tensor<2x4x8xf32>, tensor<2x4xf32>) {
+ %0:2 = linalg.generic {
+ indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d1, d2)>, affine_map<(d0, d1, d2) -> (d0, d1)>,
+ affine_map<(d0, d1, d2) -> (d0, d1, d2)>, affine_map<(d0, d1, d2) -> (d0, d1)>],
+ iterator_types = ["parallel", "parallel", "reduction"]}
+ ins(%arg0, %arg1 : tensor<2x4x8xf32>, tensor<2x4xf32>)
+ outs(%arg2, %arg3 : tensor<2x4x8xf32>, tensor<2x4xf32>) {
+ ^bb0(%b0 : f32, %b1 : f32, %b2 : f32, %b3 : f32):
+ %1 = arith.mulf %b0, %b1 : f32
+ %2 = arith.addf %1, %b3 : f32
+ linalg.yield %1, %2 : f32, f32
+ } -> (tensor<2x4x8xf32>, tensor<2x4xf32>)
+ return %0#0, %0#1 : tensor<2x4x8xf32>, tensor<2x4xf32>
+}
+// CHECK-LABEL: func @mixed_parallel_reduced_results(
+// CHECK-SAME: %[[ARG0:[a-zA-Z0-9]+]]: tensor<2x4x8xf32>
+// CHECK-SAME: %[[ARG1:[a-zA-Z0-9]+]]: tensor<2x4xf32>
+// CHECK-SAME: %[[ARG2:[a-zA-Z0-9]+]]: tensor<2x4x8xf32>
+// CHECK-SAME: %[[ARG3:[a-zA-Z0-9]+]]: tensor<2x4xf32>
+// CHECK-DAG: %[[V0:.+]] = vector.transfer_read %[[ARG0]]
+// CHECK-DAG: %[[V1:.+]] = vector.transfer_read %[[ARG1]]
+// CHECK-DAG: %[[V2:.+]] = vector.transfer_read %[[ARG3]]
+// CHECK-DAG: %[[MUL:.+]] = arith.mulf %[[V0]], %[[V1]]
+// CHECK-DAG: %[[ADD:.+]] = vector.multi_reduction <add>, %[[MUL]], %[[V2]]
+// CHECK-DAG: vector.transfer_write %[[MUL]], %[[ARG2]]
+// CHECK-DAG: vector.transfer_write %[[ADD]], %[[ARG3]]
+
+transform.sequence failures(propagate) {
+^bb1(%arg1: !transform.any_op):
+ %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+ %1 = 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
+}
+
+// -----
+
+func.func @vectorize_map(%arg0: memref<64xf32>,
+ %arg1: memref<64xf32>, %arg2: memref<64xf32>) {
+ linalg.map ins(%arg0, %arg1 : memref<64xf32>, memref<64xf32>)
+ outs(%arg2 : memref<64xf32>)
+ (%in: f32, %in_0: f32) {
+ %0 = arith.addf %in, %in_0 : f32
+ linalg.yield %0 : f32
+ }
+ return
+}
+// CHECK-LABEL: func @vectorize_map
+// CHECK: %[[LHS:.*]] = vector.transfer_read
+// CHECK-NEXT: %[[RHS:.*]] = vector.transfer_read
+// CHECK-NEXT: arith.addf %[[LHS]], %[[RHS]] : vector<64xf32>
+
+transform.sequence failures(propagate) {
+^bb1(%arg1: !transform.any_op):
+ %0 = transform.structured.match ops{["linalg.map"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+ %1 = 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
+}
+
+// -----
+
+func.func @vectorize_transpose(%arg0: memref<16x32x64xf32>,
+ %arg1: memref<32x64x16xf32>) {
+ linalg.transpose ins(%arg0 : memref<16x32x64xf32>)
+ outs(%arg1 : memref<32x64x16xf32>) permutation = [1, 2, 0]
+ return
+}
+// CHECK-LABEL: func @vectorize_transpose
+// CHECK: vector.transpose
+// CHECK-SAME: [1, 2, 0] : vector<16x32x64xf32> to vector<32x64x16xf32>
+
+transform.sequence failures(propagate) {
+^bb1(%arg1: !transform.any_op):
+ %0 = transform.structured.match ops{["linalg.transpose"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+ %1 = 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
+}
+
+// -----
+
+func.func @vectorize_reduce(%arg0: memref<16x32x64xf32>,
+ %arg1: memref<16x64xf32>) {
+ linalg.reduce ins(%arg0 : memref<16x32x64xf32>)
+ outs(%arg1 : memref<16x64xf32>) dimensions = [1]
+ (%in: f32, %init: f32) {
+ %0 = arith.addf %in, %init : f32
+ linalg.yield %0 : f32
+ }
+ return
+}
+// CHECK-LABEL: func @vectorize_reduce
+// CHECK: vector.multi_reduction <add>
+// CHECK-SAME: : vector<16x32x64xf32> to vector<16x64xf32>
+
+transform.sequence failures(propagate) {
+^bb1(%arg1: !transform.any_op):
+ %0 = transform.structured.match ops{["linalg.reduce"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+ %1 = 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
+}
+
+// -----
+
+// This is a regression test. This IR cannot be vectorized, but
+// structured.vectorize_children_and_apply_patterns should nevertheless succeed.
+
+#map = affine_map<(d0) -> (d0)>
+// CHECK-LABEL: @not_vectorizable
+func.func @not_vectorizable(%arg0: tensor<1x?xf32>, %arg1: index, %arg2: index, %arg3: index) -> tensor<1x128xf32> {
+ %0 = tensor.empty() : tensor<1x128xf32>
+ %1 = scf.for %arg5 = %arg2 to %arg1 step %arg3 iter_args(%arg6 = %0) -> (tensor<1x128xf32>) {
+ %extracted_slice = tensor.extract_slice %arg6[0, 0] [1, %arg1] [1, 1] : tensor<1x128xf32> to tensor<?xf32>
+ %expanded = tensor.expand_shape %extracted_slice [[0, 1]] : tensor<?xf32> into tensor<1x?xf32>
+ %extracted_slice_0 = tensor.extract_slice %arg0[0, %arg3] [1, %arg2] [1, 1] : tensor<1x?xf32> to tensor<?xf32>
+ %extracted_slice_1 = tensor.extract_slice %expanded[0, %arg3] [1, %arg2] [1, 1] : tensor<1x?xf32> to tensor<?xf32>
+ %2 = linalg.generic {indexing_maps = [#map, #map], iterator_types = ["parallel"]} ins(%extracted_slice_0 : tensor<?xf32>) outs(%extracted_slice_1 : tensor<?xf32>) {
+ ^bb0(%in: f32, %out: f32):
+ %3 = arith.addf %in, %out : f32
+ linalg.yield %3 : f32
+ } -> tensor<?xf32>
+ %inserted_slice = tensor.insert_slice %2 into %expanded[0, %arg3] [1, %arg2] [1, 1] : tensor<?xf32> into tensor<1x?xf32>
+ %collapsed = tensor.collapse_shape %inserted_slice [[0, 1]] : tensor<1x?xf32> into tensor<?xf32>
+ %inserted_slice_2 = tensor.insert_slice %collapsed into %arg6[0, 0] [1, %arg1] [1, 1] : tensor<?xf32> into tensor<1x128xf32>
+ scf.yield %inserted_slice_2 : tensor<1x128xf32>
+ }
+ return %1 : tensor<1x128xf32>
+}
+transform.sequence failures(propagate) {
+^bb0(%arg0: !transform.any_op):
+ %0 = transform.structured.match ops{["func.func"]} in %arg0 : (!transform.any_op) -> !transform.any_op
+ %1 = transform.structured.vectorize_children_and_apply_patterns %0 : (!transform.any_op) -> !transform.any_op
+}
+
+// -----
+
+// Regression test: %13 was incorrectly detected as a reduction and
+// vectorization failed.
+
+func.func @wrong_reduction_detection(%input: tensor<120x64xf32>) -> tensor<120x64xf32> {
+ %c0 = arith.constant 0 : index
+ %c4 = arith.constant 4 : index
+ %c64 = arith.constant 64 : index
+ %cst_6 = arith.constant 4.000000e+00 : f32
+ %1 = scf.for %arg0 = %c0 to %c64 step %c4 iter_args(%arg1 = %input) -> (tensor<120x64xf32>) {
+ %extracted_slice = tensor.extract_slice %arg1[%c0, %arg0] [1, 4] [1, 1] : tensor<120x64xf32> to tensor<1x4xf32>
+ %10 = linalg.fill {__internal_linalg_transform__ = "1"} ins(%cst_6 : f32) outs(%extracted_slice : tensor<1x4xf32>) -> tensor<1x4xf32>
+ %11 = linalg.generic {indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>], iterator_types = ["parallel", "parallel"]} outs(%10 : tensor<1x4xf32>) {
+ ^bb0(%out: f32):
+ %12 = linalg.index 0 : index
+ %13 = arith.addi %arg0, %12 : index
+ %18 = arith.index_cast %13 : index to i32
+ %20 = arith.uitofp %18 : i32 to f32
+ %67 = arith.mulf %out, %20 : f32
+ linalg.yield %67 : f32
+ } -> tensor<1x4xf32>
+ %inserted_slice = tensor.insert_slice %11 into %arg1[%c0, %arg0] [1, 4] [1, 1] : tensor<1x4xf32> into tensor<120x64xf32>
+ scf.yield %inserted_slice : tensor<120x64xf32>
+ }
+ return %1 : tensor<120x64xf32>
+}
+
+transform.sequence failures(propagate) {
+^bb1(%arg1: !transform.any_op):
+ %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+ %1 = 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
+}
+
+// CHECK-LABEL: @wrong_reduction_detection
+// CHECK: vector.broadcast
+// CHECK: vector.transfer_write
+
+// -----
+
+// Don't vectorize tensor<0xf32> : (!transform.any_op) -> !transform.any_op
+// CHECK-LABEL: @tensor_size0
+// CHECK: linalg.generic
+func.func @tensor_size0(%arg0: tensor<0xf32>,
+ %arg1: tensor<f32>) -> tensor<f32> {
+ %0 = linalg.generic
+ {indexing_maps = [affine_map<(d0) -> (d0)>, affine_map<(d0) -> ()>],
+ iterator_types = ["reduction"]}
+ ins(%arg0 : tensor<0xf32>) outs(%arg1 : tensor<f32>) {
+ ^bb0(%in: f32, %out: f32):
+ %12 = arith.addf %out, %in : f32
+ linalg.yield %12 : f32
+ } -> tensor<f32>
+ return %0 : tensor<f32>
+}
+
+transform.sequence failures(propagate) {
+^bb1(%arg1: !transform.any_op):
+ %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+ %1 = 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
+}
+
+// -----
+
+// CHECK-LABEL: func @test_masked_pad_static_dynamic
+func.func @test_masked_pad_static_dynamic(%arg0: tensor<1x2x2x?xf32>, %low: index, %high: index,
+ %pad_value: f32) -> tensor<6x?x?x?xf32> {
+ // CHECK: tensor.pad
+ %0 = tensor.pad %arg0 low[2, %low, 3, 3] high[3, 3, %high, 2] {
+ ^bb0(%arg1: index, %arg2: index, %arg3: index, %arg4: index):
+ tensor.yield %pad_value : f32
+ } : tensor<1x2x2x?xf32> to tensor<6x?x?x?xf32>
+ return %0 : tensor<6x?x?x?xf32>
+}
+
+
+transform.sequence failures(propagate) {
+^bb1(%arg1: !transform.any_op):
+ %0 = transform.structured.match ops{["tensor.pad"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+ %1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
+ %2 = transform.structured.vectorize_children_and_apply_patterns %1 { vectorize_padding } : (!transform.any_op) -> !transform.any_op
+}
+
+// -----
+
+func.func @zero_dim_tensor(%input: tensor<f32>, %output: tensor<f32>) -> tensor<f32>
+{
+ %0 = linalg.generic { indexing_maps = [ affine_map<() -> ()>, affine_map<() -> ()> ],
+ iterator_types = [] }
+ ins(%input : tensor<f32>)
+ outs(%output : tensor<f32>) {
+ ^bb0(%arg0: f32, %arg1: f32):
+ %2 = arith.addf %arg0, %arg1 : f32
+ linalg.yield %2 : f32
+ } -> tensor<f32>
+ return %0 : tensor<f32>
+}
+
+transform.sequence failures(propagate) {
+^bb1(%arg1: !transform.any_op):
+ %3 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+ %4 = get_parent_op %3 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
+ %5 = transform.structured.vectorize_children_and_apply_patterns %4 : (!transform.any_op) -> !transform.any_op
+}
+
+// CHECK-LABEL: func @zero_dim_tensor
+// CHECK: vector.transfer_read {{.*}} : tensor<f32>, vector<f32>
+// CHECK: vector.extractelement
+// CHECK: vector.transfer_read {{.*}} : tensor<f32>, vector<f32>
+// CHECK: vector.extractelement
+// CHECK: arith.addf {{.*}} : f32
+// CHECK: vector.broadcast %{{.*}} : f32 to vector<f32>
+// CHECK: vector.transfer_write {{.*}} : vector<f32>, tensor<f32>
+
+// -----
+
+// Make sure we generate the right transfer writes for multi-output generic ops
+// with different permutation maps.
+
+func.func @multi_output_generic_different_perm_maps(%in0: tensor<4x1xf32>,
+ %out0: tensor<4x1xf32>,
+ %out1: tensor<1x4xf32>) -> (tensor<4x1xf32>, tensor<1x4xf32>) {
+ %13:2 = linalg.generic {indexing_maps = [ affine_map<(d0, d1) -> (d1, d0)>,
+ affine_map<(d0, d1) -> (d1, d0)>,
+ affine_map<(d0, d1) -> (d0, d1)> ],
+ iterator_types = ["parallel", "parallel"]}
+ ins(%in0 : tensor<4x1xf32>)
+ outs(%out0, %out1 : tensor<4x1xf32>, tensor<1x4xf32>) {
+ ^bb0(%in: f32, %out: f32, %out_2: f32):
+ %16 = arith.addf %in, %in : f32
+ linalg.yield %16, %16 : f32, f32
+ } -> (tensor<4x1xf32>, tensor<1x4xf32>)
+ return %13#0, %13#1 : tensor<4x1xf32>, tensor<1x4xf32>
+}
+
+transform.sequence failures(propagate) {
+^bb1(%arg1: !transform.any_op):
+ %3 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+ %4 = get_parent_op %3 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
+ %5 = transform.structured.vectorize_children_and_apply_patterns %4 : (!transform.any_op) -> !transform.any_op
+}
+
+// CHECK-LABEL: func @multi_output_generic_different_perm_maps
+// CHECK: %[[VAL_5:.*]] = vector.transfer_read %{{.*}} {in_bounds = [true, true]} : tensor<4x1xf32>, vector<4x1xf32>
+// CHECK: %[[VAL_6:.*]] = arith.addf %[[VAL_5]], %[[VAL_5]] : vector<4x1xf32>
+// CHECK: %[[VAL_7:.*]] = vector.transpose %[[VAL_6]], [1, 0] : vector<4x1xf32> to vector<1x4xf32>
+// CHECK: %[[VAL_8:.*]] = vector.transpose %[[VAL_7]], [1, 0] : vector<1x4xf32> to vector<4x1xf32>
+// CHECK: vector.transfer_write %[[VAL_8]], %{{.*}} {in_bounds = [true, true]} : vector<4x1xf32>, tensor<4x1xf32>
+// CHECK: vector.transfer_write %[[VAL_7]], %{{.*}} {in_bounds = [true, true]} : vector<1x4xf32>, tensor<1x4xf32>
diff --git a/mlir/test/Dialect/Linalg/vectorization.mlir b/mlir/test/Dialect/Linalg/vectorization.mlir
index ecba1f32468031e..ddeaff76a04df23 100644
--- a/mlir/test/Dialect/Linalg/vectorization.mlir
+++ b/mlir/test/Dialect/Linalg/vectorization.mlir
@@ -1,1787 +1,514 @@
// RUN: mlir-opt %s -test-transform-dialect-interpreter -split-input-file | FileCheck %s
-// CHECK-LABEL: contraction_dot
-func.func @contraction_dot(%A: memref<1584xf32>, %B: memref<1584xf32>, %C: memref<f32>) {
-
-// CHECK: arith.mulf %{{.*}}, %{{.*}} : vector<1584xf32>
-// CHECK: vector.multi_reduction <add>, %{{.*}}, {{.*}} [0] : vector<1584xf32> to f32
- linalg.dot ins(%A, %B: memref<1584xf32>, memref<1584xf32>)
- outs(%C: memref<f32>)
- return
-}
-
-transform.sequence failures(propagate) {
-^bb1(%arg1: !transform.any_op):
- %0 = transform.structured.match ops{["linalg.dot"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- transform.structured.vectorize %0 : !transform.any_op
-}
-
-// -----
-
-// CHECK-LABEL: contraction_matvec
-func.func @contraction_matvec(%A: memref<1584x1584xf32>, %B: memref<1584xf32>, %C: memref<1584xf32>) {
-
-// CHECK: arith.mulf %{{.*}}, %{{.*}} : vector<1584x1584xf32>
-// CHECK: vector.multi_reduction <add>, %{{.*}}, {{.*}} [1] : vector<1584x1584xf32> to vector<1584xf32>
- linalg.matvec ins(%A, %B: memref<1584x1584xf32>, memref<1584xf32>)
- outs(%C: memref<1584xf32>)
- return
-}
-
-transform.sequence failures(propagate) {
-^bb1(%arg1: !transform.any_op):
- %0 = transform.structured.match ops{["linalg.matvec"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- %1 = 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 } : (!transform.any_op) -> !transform.any_op
-}
-
-// -----
-
-// CHECK-LABEL: contraction_matmul
-func.func @contraction_matmul(%A: memref<1584x1584xf32>, %B: memref<1584x1584xf32>, %C: memref<1584x1584xf32>) {
-// CHECK: arith.mulf %{{.*}}, %{{.*}} : vector<1584x1584x1584xf32>
-// CHECK: vector.multi_reduction <add>, %{{.*}}, {{.*}} [2] : vector<1584x1584x1584xf32> to vector<1584x1584xf32>
- linalg.matmul ins(%A, %B: memref<1584x1584xf32>, memref<1584x1584xf32>)
- outs(%C: memref<1584x1584xf32>)
- return
-}
-
-transform.sequence failures(propagate) {
-^bb1(%arg1: !transform.any_op):
- %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- %1 = 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 } : (!transform.any_op) -> !transform.any_op
-}
-
-// -----
-
-// CHECK-LABEL: contraction_batch_matmul
-func.func @contraction_batch_matmul(%A: memref<1584x1584x1584xf32>, %B: memref<1584x1584x1584xf32>, %C: memref<1584x1584x1584xf32>) {
-// CHECK: arith.mulf %{{.*}}, %{{.*}} : vector<1584x1584x1584x1584xf32>
-// CHECK: vector.multi_reduction <add>, %{{.*}}, {{.*}} [3] : vector<1584x1584x1584x1584xf32> to vector<1584x1584x1584xf32>
- linalg.batch_matmul
- ins(%A, %B: memref<1584x1584x1584xf32>, memref<1584x1584x1584xf32>)
- outs(%C: memref<1584x1584x1584xf32>)
- return
-}
-
-transform.sequence failures(propagate) {
-^bb1(%arg1: !transform.any_op):
- %0 = transform.structured.match ops{["linalg.batch_matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- %1 = 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 } : (!transform.any_op) -> !transform.any_op
+func.func @vectorize_dynamic_identity(%arg0: tensor<?xf32>,
+ %arg1: tensor<?xf32>,
+ %arg2: tensor<?xf32>) -> tensor<?xf32> {
+ %0 = linalg.generic { indexing_maps = [affine_map<(d0) -> (d0)>,
+ affine_map<(d0) -> (d0)>,
+ affine_map<(d0) -> (d0)>],
+ iterator_types = ["parallel"] }
+ ins(%arg0, %arg1 : tensor<?xf32>, tensor<?xf32>)
+ outs(%arg2 : tensor<?xf32>) {
+ ^bb(%in0: f32, %in1: f32, %out: f32) :
+ %0 = arith.addf %in0, %in1 : f32
+ linalg.yield %0 : f32
+ } -> tensor<?xf32>
+ return %0 : tensor<?xf32>
}
-// -----
-
-#matmul_trait = {
- args_in = 2,
- args_out = 1,
- indexing_maps = [
- affine_map<(m, n, k) -> (m, k)>,
- affine_map<(m, n, k) -> (k, n)>,
- affine_map<(m, n, k) -> (m, n)>
- ],
- iterator_types = ["parallel", "parallel", "reduction"]
-}
-
-// CHECK-LABEL: func @vectorization_test
-func.func @vectorization_test(%A: memref<8x16xf32>, %B: memref<16x32xf32>,
- %C: memref<8x32xf32>) {
- // CHECK: vector.transfer_read %{{.*}} : memref<8x16xf32>, vector<8x32x16xf32>
- // CHECK: vector.transfer_read %{{.*}} : memref<16x32xf32>, vector<8x32x16xf32>
- // CHECK: %[[ACC:.*]] = vector.transfer_read %{{.*}} : memref<8x32xf32>, vector<8x32xf32>
- // CHECK: %[[MUL:.*]] = arith.mulf %{{.*}}, %{{.*}} : vector<8x32x16xf32>
- // CHECK: %[[R:.*]] = vector.multi_reduction <add>, %[[MUL]], %[[ACC]] [2] : vector<8x32x16xf32> to vector<8x32xf32>
- // CHECK: vector.transfer_write %{{.*}}, %{{.*}} : vector<8x32xf32>, memref<8x32xf32>
- linalg.generic #matmul_trait
- ins(%A, %B : memref<8x16xf32>, memref<16x32xf32>)
- outs(%C : memref<8x32xf32>) {
- ^bb(%a: f32, %b: f32, %c: f32) :
- %d = arith.mulf %a, %b: f32
- %e = arith.addf %c, %d: f32
- linalg.yield %e : f32
- }
- return
-}
+// CHECK-LABEL: @vectorize_dynamic_identity
+// CHECK: %[[VAL_3:.*]] = arith.constant 0 : index
+// CHECK: %[[VAL_4:.*]] = tensor.dim %{{.*}}, %[[VAL_3]] : tensor<?xf32>
+// CHECK: %[[VAL_7:.*]] = vector.create_mask %[[VAL_4]] : vector<4xi1>
+// CHECK: %[[VAL_8:.*]] = vector.mask %[[VAL_7]] { vector.transfer_read %{{.*}} {in_bounds = [true]} : tensor<?xf32>, vector<4xf32> } : vector<4xi1> -> vector<4xf32>
+// CHECK: %[[VAL_10:.*]] = vector.mask %[[VAL_7]] { vector.transfer_read %{{.*}} {in_bounds = [true]} : tensor<?xf32>, vector<4xf32> } : vector<4xi1> -> vector<4xf32>
+// CHECK: %[[VAL_12:.*]] = vector.mask %[[VAL_7]] { vector.transfer_read %{{.*}} {in_bounds = [true]} : tensor<?xf32>, vector<4xf32> } : vector<4xi1> -> vector<4xf32>
+// CHECK: %[[VAL_13:.*]] = arith.addf %[[VAL_8]], %[[VAL_10]] : vector<4xf32>
+// CHECK: %[[VAL_14:.*]] = vector.mask %[[VAL_7]] { vector.transfer_write %{{.*}} {in_bounds = [true]} : vector<4xf32>, tensor<?xf32> } : vector<4xi1> -> tensor<?xf32>
transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- %1 = 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.structured.vectorize %0 vector_sizes [4] : !transform.any_op
}
// -----
-#matmul_transpose_out_trait = {
- args_in = 2,
- args_out = 1,
- indexing_maps = [
- affine_map<(m, n, k) -> (m, k)>,
- affine_map<(m, n, k) -> (k, n)>,
- affine_map<(m, n, k) -> (n, m)>
- ],
- iterator_types = ["parallel", "parallel", "reduction"]
-}
-
-// CHECK-LABEL: func @generic_output_transpose
-func.func @generic_output_transpose(%A: memref<8x16xf32>, %B: memref<16x32xf32>,
- %C: memref<32x8xf32>) {
- // CHECK: vector.transfer_read %{{.*}} : memref<8x16xf32>, vector<8x32x16xf32>
- // CHECK: vector.transfer_read %{{.*}} : memref<16x32xf32>, vector<8x32x16xf32>
- // CHECK: %[[ACC:.*]] = vector.transfer_read %{{.*}} : memref<32x8xf32>, vector<8x32xf32>
- // CHECK: %[[MUL:.*]] = arith.mulf %{{.*}}, %{{.*}} : vector<8x32x16xf32>
- // CHECK: %[[R:.*]] = vector.multi_reduction <add>, %[[MUL]], %[[ACC]] [2] : vector<8x32x16xf32> to vector<8x32xf32>
- // CHECK: vector.transfer_write %{{.*}}, %{{.*}} : vector<8x32xf32>, memref<32x8xf32>
- linalg.generic #matmul_transpose_out_trait
- ins(%A, %B : memref<8x16xf32>, memref<16x32xf32>)
- outs(%C : memref<32x8xf32>) {
- ^bb(%a: f32, %b: f32, %c: f32) :
- %d = arith.mulf %a, %b: f32
- %e = arith.addf %c, %d: f32
- linalg.yield %e : f32
- }
- return
-}
-
-transform.sequence failures(propagate) {
-^bb1(%arg1: !transform.any_op):
- %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- %1 = 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
+func.func @vectorize_dynamic_1d_broadcast(%arg0: tensor<?xf32>,
+ %arg1: tensor<?xf32>,
+ %arg2: tensor<?xf32>) -> tensor<?xf32> {
+ %0 = linalg.generic { indexing_maps = [affine_map<(d0) -> (0)>,
+ affine_map<(d0) -> (d0)>,
+ affine_map<(d0) -> (d0)>],
+ iterator_types = ["parallel"] }
+ ins(%arg0, %arg1 : tensor<?xf32>, tensor<?xf32>)
+ outs(%arg2 : tensor<?xf32>) {
+ ^bb(%in0: f32, %in1: f32, %out: f32) :
+ %0 = arith.addf %in0, %in1 : f32
+ linalg.yield %0 : f32
+ } -> tensor<?xf32>
+ return %0 : tensor<?xf32>
}
-// -----
-
-#map0 = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
-#map1 = affine_map<(d0, d1, d2) -> (d1, d0, d2)>
-// CHECK: #[[MAP:.+]] = affine_map<(d0, d1, d2) -> (d1, d0, d2)>
-// CHECK: func @generic_interchanged_transpose
-func.func @generic_interchanged_transpose(%arg0: tensor<12x128x32xf32>) -> tensor<128x12x32xf32> {
- // CHECK: %[[IN:.+]] = vector.transfer_read
- // CHECK: vector.transfer_write %[[IN]], {{.+}} permutation_map = #[[MAP]]
- %0 = tensor.empty() : tensor<128x12x32xf32>
- %1 = linalg.generic {indexing_maps = [#map0, #map1],
- iterator_types = ["parallel", "parallel", "parallel"]}
- ins(%arg0 : tensor<12x128x32xf32>)
- outs(%0 : tensor<128x12x32xf32>) {
- ^bb0(%arg1: f32, %arg2: f32):
- linalg.yield %arg1 : f32
- } -> tensor<128x12x32xf32>
- return %1 : tensor<128x12x32xf32>
-}
+// CHECK-LABEL: @vectorize_dynamic_1d_broadcast
+// CHECK: %[[VAL_3:.*]] = arith.constant 0 : index
+// CHECK: %[[VAL_4:.*]] = tensor.dim %{{.*}}, %[[VAL_3]] : tensor<?xf32>
+// CHECK: %[[VAL_7:.*]] = vector.transfer_read %{{.*}} {permutation_map = #{{.*}}} : tensor<?xf32>, vector<4xf32>
+// CHECK: %[[VAL_9:.*]] = vector.create_mask %[[VAL_4]] : vector<4xi1>
+// CHECK: %[[VAL_10:.*]] = vector.mask %[[VAL_9]] { vector.transfer_read %{{.*}} {in_bounds = [true]} : tensor<?xf32>, vector<4xf32> } : vector<4xi1> -> vector<4xf32>
+// CHECK: %[[VAL_12:.*]] = vector.mask %[[VAL_9]] { vector.transfer_read %{{.*}} {in_bounds = [true]} : tensor<?xf32>, vector<4xf32> } : vector<4xi1> -> vector<4xf32>
+// CHECK: %[[VAL_13:.*]] = arith.addf %[[VAL_7]], %[[VAL_10]] : vector<4xf32>
+// CHECK: %[[VAL_14:.*]] = vector.mask %{{.*}} { vector.transfer_write %[[VAL_13]], {{.*}} {in_bounds = [true]} : vector<4xf32>, tensor<?xf32> } : vector<4xi1> -> tensor<?xf32>
transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- %1 = 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.structured.vectorize %0 vector_sizes [4] : !transform.any_op
}
// -----
-#matmul_trait = {
- args_in = 2,
- args_out = 1,
- indexing_maps = [
- affine_map<(m, n, k) -> (m, k)>,
- affine_map<(m, n, k) -> (k, n)>,
- affine_map<(m, n, k) -> (m, n)>
- ],
- iterator_types = ["parallel", "parallel", "reduction"]
+func.func @vectorize_dynamic_2d_transpose(%arg0: tensor<?x?xf32>,
+ %arg1: tensor<?x?xf32>,
+ %arg2: tensor<?x?xf32>) -> tensor<?x?xf32> {
+ %0 = linalg.generic { indexing_maps = [affine_map<(d0, d1) -> (d1, d0)>,
+ affine_map<(d0, d1) -> (d0, d1)>,
+ affine_map<(d0, d1) -> (d0, d1)>],
+ iterator_types = ["parallel", "parallel"] }
+ ins(%arg0, %arg1 : tensor<?x?xf32>, tensor<?x?xf32>)
+ outs(%arg2 : tensor<?x?xf32>) {
+ ^bb(%in0: f32, %in1: f32, %out: f32) :
+ %0 = arith.addf %in0, %in1 : f32
+ linalg.yield %0 : f32
+ } -> tensor<?x?xf32>
+ return %0 : tensor<?x?xf32>
}
-// CHECK-LABEL: func @vectorization_test_integer
-func.func @vectorization_test_integer(%A: memref<8x16xi32>, %B: memref<16x32xi32>,
- %C: memref<8x32xi32>) {
- // CHECK: vector.transfer_read %{{.*}} : memref<8x16xi32>, vector<8x32x16xi32>
- // CHECK: vector.transfer_read %{{.*}} : memref<16x32xi32>, vector<8x32x16xi32>
- // CHECK: %[[ACC:.*]] = vector.transfer_read %{{.*}} : memref<8x32xi32>, vector<8x32xi32>
- // CHECK: %[[MUL:.*]] = arith.muli %{{.*}}, %{{.*}} : vector<8x32x16xi32>
- // CHECK: vector.multi_reduction <add>, %[[MUL]], %[[ACC]] [2] : vector<8x32x16xi32> to vector<8x32xi32>
- // CHECK: vector.transfer_write %{{.*}}, %{{.*}} : vector<8x32xi32>, memref<8x32xi32>
- linalg.generic #matmul_trait
- ins(%A, %B : memref<8x16xi32>, memref<16x32xi32>)
- outs(%C : memref<8x32xi32>) {
- ^bb(%a: i32, %b: i32, %c: i32) :
- %d = arith.muli %a, %b: i32
- %e = arith.addi %c, %d: i32
- linalg.yield %e : i32
- }
- return
-}
+// CHECK-LABEL: @vectorize_dynamic_2d_transpose
+// CHECK: %[[VAL_3:.*]] = arith.constant 1 : index
+// CHECK: %[[VAL_4:.*]] = tensor.dim %{{.*}}, %[[VAL_3]] : tensor<?x?xf32>
+// CHECK: %[[VAL_5:.*]] = arith.constant 0 : index
+// CHECK: %[[VAL_6:.*]] = tensor.dim %{{.*}}, %[[VAL_5]] : tensor<?x?xf32>
+// CHECK: %[[VAL_9:.*]] = vector.create_mask %[[VAL_6]], %[[VAL_4]] : vector<8x4xi1>
+// CHECK: %[[VAL_10:.*]] = vector.mask %[[VAL_9]] { vector.transfer_read %{{.*}} {in_bounds = [true, true], permutation_map = #{{.*}}} : tensor<?x?xf32>, vector<4x8xf32> } : vector<8x4xi1> -> vector<4x8xf32>
+// CHECK: %[[VAL_12:.*]] = vector.create_mask %[[VAL_4]], %[[VAL_6]] : vector<4x8xi1>
+// CHECK: %[[VAL_13:.*]] = vector.mask %[[VAL_12]] { vector.transfer_read %{{.*}} {in_bounds = [true, true]} : tensor<?x?xf32>, vector<4x8xf32> } : vector<4x8xi1> -> vector<4x8xf32>
+// CHECK: %[[VAL_14:.*]] = arith.constant 0.000000e+00 : f32
+// CHECK: %[[VAL_15:.*]] = vector.mask %[[VAL_12]] { vector.transfer_read %{{.*}} {in_bounds = [true, true]} : tensor<?x?xf32>, vector<4x8xf32> } : vector<4x8xi1> -> vector<4x8xf32>
+// CHECK: %[[VAL_16:.*]] = arith.addf %[[VAL_10]], %[[VAL_13]] : vector<4x8xf32>
+// CHECK: %[[VAL_17:.*]] = vector.mask %[[VAL_12]] { vector.transfer_write %[[VAL_16]], %{{.*}} {in_bounds = [true, true]} : vector<4x8xf32>, tensor<?x?xf32> } : vector<4x8xi1> -> tensor<?x?xf32>
transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- %1 = 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
-}
-
-// -----
-
-// CHECK-LABEL: func @vectorization_test_2
-func.func @vectorization_test_2(%A: memref<8x16xf32>, %B: memref<16x32xf32>,
- %C: memref<8x32xf32>) {
- // CHECK: arith.mulf %{{.*}}, %{{.*}} : vector<8x32x16xf32>
- // CHECK: vector.multi_reduction <add>, %{{.*}}, {{.*}} [2] : vector<8x32x16xf32> to vector<8x32xf32>
- linalg.matmul
- ins(%A, %B: memref<8x16xf32>, memref<16x32xf32>)
- outs(%C: memref<8x32xf32>)
- return
-}
-
-transform.sequence failures(propagate) {
-^bb1(%arg1: !transform.any_op):
- %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- %1 = 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 } : (!transform.any_op) -> !transform.any_op
+ transform.structured.vectorize %0 vector_sizes [4, 8] : !transform.any_op
}
// -----
-// CHECK-LABEL: func @test_vectorize_scalar_input
-func.func @test_vectorize_scalar_input(%A : memref<8x16xf32>, %arg0 : f32) {
- // CHECK: %[[V:.*]] = vector.broadcast {{.*}} : f32 to vector<8x16xf32>
- // CHECK: vector.transfer_write %[[V]], {{.*}} : vector<8x16xf32>, memref<8x16xf32>
- linalg.generic {
- indexing_maps = [affine_map<(m, n) -> ()>, affine_map<(m, n) -> (m, n)>],
- iterator_types = ["parallel", "parallel"]}
- ins(%arg0 : f32)
- outs(%A: memref<8x16xf32>) {
- ^bb(%0: f32, %1: f32) :
+func.func @vectorize_dynamic_generic_2d_broadcast(%arg0: tensor<?x?xf32>,
+ %arg1: tensor<?x?xf32>,
+ %arg2: tensor<?x?xf32>) -> tensor<?x?xf32> {
+ %0 = linalg.generic { indexing_maps = [affine_map<(d0, d1) -> (0, d1)>,
+ affine_map<(d0, d1) -> (d0, d1)>,
+ affine_map<(d0, d1) -> (d0, d1)>],
+ iterator_types = ["parallel", "parallel"] }
+ ins(%arg0, %arg1 : tensor<?x?xf32>, tensor<?x?xf32>)
+ outs(%arg2 : tensor<?x?xf32>) {
+ ^bb(%in0: f32, %in1: f32, %out: f32) :
+ %0 = arith.addf %in0, %in1 : f32
linalg.yield %0 : f32
- }
- return
+ } -> tensor<?x?xf32>
+ return %0 : tensor<?x?xf32>
}
-transform.sequence failures(propagate) {
-^bb1(%arg1: !transform.any_op):
- %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- %1 = 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
-}
-
-// -----
-
-// CHECK-LABEL: func @test_do_not_vectorize_unsupported_element_types
-func.func @test_do_not_vectorize_unsupported_element_types(%A : memref<8x16xcomplex<f32>>, %arg0 : complex<f32>) {
- // CHECK-NOT: vector.broadcast
- // CHECK-NOT: vector.transfer_write
- linalg.generic {
- indexing_maps = [affine_map<(m, n) -> ()>, affine_map<(m, n) -> (m, n)>],
- iterator_types = ["parallel", "parallel"]}
- ins(%arg0 : complex<f32>)
- outs(%A: memref<8x16xcomplex<f32>>) {
- ^bb(%0: complex<f32>, %1: complex<f32>) :
- linalg.yield %0 : complex<f32>
- }
- return
-}
+// CHECK-LABEL: @vectorize_dynamic_generic_2d_broadcast
+// CHECK: %[[VAL_3:.*]] = arith.constant 0 : index
+// CHECK: %[[VAL_4:.*]] = tensor.dim %{{.*}}, %[[VAL_3]] : tensor<?x?xf32>
+// CHECK: %[[VAL_5:.*]] = arith.constant 1 : index
+// CHECK: %[[VAL_6:.*]] = tensor.dim %{{.*}}, %[[VAL_5]] : tensor<?x?xf32>
+// CHECK: %[[VAL_9:.*]] = vector.create_mask %[[VAL_6]] : vector<8xi1>
+// CHECK: %[[VAL_10:.*]] = vector.mask %[[VAL_9]] { vector.transfer_read %{{.*}} {in_bounds = [true, true], permutation_map = #{{.*}}} : tensor<?x?xf32>, vector<4x8xf32> } : vector<8xi1> -> vector<4x8xf32>
+// CHECK: %[[VAL_12:.*]] = vector.create_mask %[[VAL_4]], %[[VAL_6]] : vector<4x8xi1>
+// CHECK: %[[VAL_13:.*]] = vector.mask %[[VAL_12]] { vector.transfer_read %{{.*}} {in_bounds = [true, true]} : tensor<?x?xf32>, vector<4x8xf32> } : vector<4x8xi1> -> vector<4x8xf32>
+// CHECK: %[[VAL_15:.*]] = vector.mask %[[VAL_12]] { vector.transfer_read %{{.*}} {in_bounds = [true, true]} : tensor<?x?xf32>, vector<4x8xf32> } : vector<4x8xi1> -> vector<4x8xf32>
+// CHECK: %[[VAL_16:.*]] = arith.addf %[[VAL_10]], %[[VAL_13]] : vector<4x8xf32>
+// CHECK: %[[VAL_18:.*]] = vector.mask %[[VAL_12]] { vector.transfer_write %{{.*}} {in_bounds = [true, true]} : vector<4x8xf32>, tensor<?x?xf32> } : vector<4x8xi1> -> tensor<?x?xf32>
transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- %1 = 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
-}
-
-// -----
-
-#map0 = affine_map<(d0) -> (d0)>
-
-func.func @vectorize_affine_apply(%arg0: tensor<5xf32>, %arg3: index) -> tensor<5xi32> {
- %0 = tensor.empty() : tensor<5xi32>
- %1 = linalg.generic {indexing_maps = [#map0, #map0],
- iterator_types = ["parallel"]}
- ins(%arg0 : tensor<5xf32>)
- outs(%0 : tensor<5xi32>) {
- ^bb0(%arg1: f32, %arg2: i32):
- %2 = linalg.index 0 : index
- %11 = affine.apply affine_map<() -> (123)>()
- %12 = affine.apply affine_map<(d0, d1) -> (d0 + d1)>(%2, %11)
- %13 = affine.apply affine_map<(d0)[s0] -> (d0 + s0)>(%12)[%arg3]
- %14 = affine.apply affine_map<(d0) -> (d0 + 1)>(%13)
- %15 = affine.apply affine_map<(d0, d1, d2) -> (d0 + d1 + d2)>(%13, %14, %12)
- %3 = arith.index_cast %15 : index to i32
- linalg.yield %3 : i32
- } -> tensor<5xi32>
- return %1 : tensor<5xi32>
-}
-
-// CHECK-LABEL: func.func @vectorize_affine_apply
-// CHECK-SAME: %arg0: tensor<5xf32>
-// CHECK-SAME: %[[ARG1:.*]]: index
-// CHECK: %[[CST:.*]] = arith.constant dense<[123, 124, 125, 126, 127]> : vector<5xindex>
-// CHECK: %[[CST_0:.*]] = arith.constant dense<1> : vector<5xindex>
-// CHECK: %[[C0:.*]] = arith.constant 0 : index
-// CHECK: %[[EMPTY:.*]] = tensor.empty() : tensor<5xi32>
-// CHECK: %[[BCAST:.*]] = vector.broadcast %[[ARG1]] : index to vector<5xindex>
-// CHECK: %[[ADDI_1:.*]] = arith.addi %[[BCAST]], %[[CST]] : vector<5xindex>
-// CHECK: %[[ADDI_2:.*]] = arith.addi %[[ADDI_1]], %[[CST_0]] : vector<5xindex>
-// CHECK: %[[ADDI_3:.*]] = arith.addi %[[ADDI_1]], %[[ADDI_2]] : vector<5xindex>
-// CHECK: %[[ADDI_4:.*]] = arith.addi %[[ADDI_3]], %[[CST]] : vector<5xindex>
-// CHECK: %[[CAST:.*]] = arith.index_cast %[[ADDI_4]] : vector<5xindex> to vector<5xi32>
-// CHECK: vector.transfer_write %[[CAST]], %[[EMPTY]][%[[C0:.*]]] {in_bounds = [true]} : vector<5xi32>, tensor<5xi32>
-
-transform.sequence failures(propagate) {
- ^bb1(%arg1: !transform.any_op):
- %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- %1 = 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
-}
-
-// -----
-
-// CHECK-LABEL: func @test_vectorize_fill
-func.func @test_vectorize_fill(%A : memref<8x16xf32>, %arg0 : f32) {
- // CHECK: %[[V:.*]] = vector.broadcast {{.*}} : f32 to vector<8x16xf32>
- // CHECK: vector.transfer_write %[[V]], {{.*}} : vector<8x16xf32>, memref<8x16xf32>
- linalg.fill ins(%arg0 : f32) outs(%A : memref<8x16xf32>)
- return
-}
-
-transform.sequence failures(propagate) {
-^bb1(%arg1: !transform.any_op):
- %0 = transform.structured.match ops{["linalg.fill"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- %1 = 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
-}
-
-// -----
-
-// CHECK-LABEL: func @test_vectorize_fill
-func.func @test_vectorize_fill_scalar(%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>
- linalg.fill ins(%arg0 : f32) outs(%A : memref<f32>)
- return
-}
-
-transform.sequence failures(propagate) {
-^bb1(%arg1: !transform.any_op):
- %0 = transform.structured.match ops{["linalg.fill"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- %1 = 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
-}
-
-// -----
-
-// CHECK-LABEL: func @test_vectorize_copy
-func.func @test_vectorize_copy(%A : memref<8x16xf32>, %B : memref<8x16xf32>) {
- // CHECK: %[[V:.*]] = vector.transfer_read {{.*}} : memref<8x16xf32>, vector<8x16xf32>
- // CHECK: vector.transfer_write %[[V]], {{.*}} : vector<8x16xf32>, memref<8x16xf32>
- memref.copy %A, %B : memref<8x16xf32> to memref<8x16xf32>
- return
-}
-
-transform.sequence failures(propagate) {
-^bb1(%arg1: !transform.any_op):
- %0 = transform.structured.match ops{["memref.copy"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- %1 = 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
+ transform.structured.vectorize %0 vector_sizes [4, 8] : !transform.any_op
}
// -----
-// CHECK-LABEL: func @test_vectorize_copy_scalar
-func.func @test_vectorize_copy_scalar(%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.extractelement %[[V]][] : vector<f32>
- // CHECK: %[[VV:.*]] = vector.broadcast %[[val]] : f32 to vector<f32>
- // CHECK: vector.transfer_write %[[VV]], %[[B]][] : vector<f32>, memref<f32>
- memref.copy %A, %B : memref<f32> to memref<f32>
- return
-}
-
-transform.sequence failures(propagate) {
-^bb1(%arg1: !transform.any_op):
- %0 = transform.structured.match ops{["memref.copy"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- %1 = 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
-}
-
-// -----
-
-// CHECK-LABEL: func @test_vectorize_copy_complex
-// CHECK-NOT: vector<
-func.func @test_vectorize_copy_complex(%A : memref<8x16xcomplex<f32>>, %B : memref<8x16xcomplex<f32>>) {
- memref.copy %A, %B : memref<8x16xcomplex<f32>> to memref<8x16xcomplex<f32>>
- return
-}
-
-transform.sequence failures(propagate) {
-^bb1(%arg1: !transform.any_op):
- %0 = transform.structured.match ops{["memref.copy"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- %1 = 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
-}
-
-// -----
-
-// CHECK-LABEL: func @test_vectorize_trailing_index
- // CHECK-SAME: (%[[ARG0:.*]]: memref<1x2x4x8xindex>)
-func.func @test_vectorize_trailing_index(%arg0: memref<1x2x4x8xindex>) {
- // CHECK-DAG: %[[CST0:.*]] = arith.constant dense<[0, 1, 2, 3, 4, 5, 6, 7]> : vector<8xindex>
- // CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
- linalg.generic {
- indexing_maps = [
- affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>],
- iterator_types = ["parallel", "parallel", "parallel", "parallel"]}
- outs(%arg0: memref<1x2x4x8xindex>) {
- ^bb0(%arg1: index):
- // CHECK: %[[BCST:.*]] = vector.broadcast %[[CST0]] : vector<8xindex> to vector<1x2x4x8xindex>
- // CHECK: vector.transfer_write %[[BCST]], %[[ARG0]][%[[C0]], %[[C0]], %[[C0]], %[[C0]]] {{.*}} : vector<1x2x4x8xindex>, memref<1x2x4x8xindex>
- %0 = linalg.index 3 : index
- linalg.yield %0 : index
- }
- return
+func.func @vectorize_dynamic_reduction(%arg0: tensor<?x?xf32>,
+ %arg1: tensor<?xf32>) -> tensor<?xf32> {
+ %0 = linalg.generic { indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>,
+ affine_map<(d0, d1) -> (d0)>],
+ iterator_types = ["parallel", "reduction"] }
+ ins(%arg0 : tensor<?x?xf32>)
+ outs(%arg1 : tensor<?xf32>) {
+ ^bb(%in: f32, %out: f32) :
+ %0 = arith.addf %in, %out : f32
+ linalg.yield %0 : f32
+ } -> tensor<?xf32>
+ return %0 : tensor<?xf32>
}
transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- %1 = 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
-}
-
-// -----
-
-// CHECK-LABEL: func @test_vectorize_inner_index
- // CHECK-SAME: (%[[ARG0:.*]]: memref<1x2x4x8xindex>)
-func.func @test_vectorize_inner_index(%arg0: memref<1x2x4x8xindex>) {
- // CHECK-DAG: %[[CST0:.*]] = arith.constant dense<[0, 1]> : vector<2xindex>
- // CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
- linalg.generic {
- indexing_maps = [
- affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>],
- iterator_types = ["parallel", "parallel", "parallel", "parallel"]}
- outs(%arg0: memref<1x2x4x8xindex>) {
- ^bb0(%arg1: index):
- // CHECK: %[[BCST:.*]] = vector.broadcast %[[CST0]] : vector<2xindex> to vector<1x8x4x2xindex>
- // CHECK: %[[TRAN:.*]] = vector.transpose %[[BCST]], [0, 3, 2, 1] : vector<1x8x4x2xindex> to vector<1x2x4x8xindex>
- // CHECK: vector.transfer_write %[[TRAN]], %[[ARG0]][%[[C0]], %[[C0]], %[[C0]], %[[C0]]] {{.*}} : vector<1x2x4x8xindex>, memref<1x2x4x8xindex>
- %0 = linalg.index 1 : index
- linalg.yield %0 : index
- }
- return
+ transform.structured.vectorize %0 vector_sizes [4, 8] : !transform.any_op
+}
+
+// CHECK-LABEL: @vectorize_dynamic_reduction(
+// CHECK-SAME: %[[VAL_0:.*]]: tensor<?x?xf32>,
+// CHECK-SAME: %[[VAL_1:.*]]: tensor<?xf32>) -> tensor<?xf32> {
+// CHECK: %[[VAL_2:.*]] = arith.constant 0 : index
+// CHECK: %[[VAL_3:.*]] = tensor.dim %[[VAL_0]], %[[VAL_2]] : tensor<?x?xf32>
+// CHECK: %[[VAL_4:.*]] = arith.constant 1 : index
+// CHECK: %[[VAL_5:.*]] = tensor.dim %[[VAL_0]], %[[VAL_4]] : tensor<?x?xf32>
+// CHECK: %[[VAL_8:.*]] = vector.create_mask %[[VAL_3]], %[[VAL_5]] : vector<4x8xi1>
+// CHECK: %[[VAL_9:.*]] = vector.mask %[[VAL_8]] { vector.transfer_read %[[VAL_0]]{{.*}} {in_bounds = [true, true]} : tensor<?x?xf32>, vector<4x8xf32> } : vector<4x8xi1> -> vector<4x8xf32>
+// CHECK: %[[VAL_11:.*]] = vector.create_mask %[[VAL_3]] : vector<4xi1>
+// CHECK: %[[VAL_12:.*]] = vector.mask %[[VAL_11]] { vector.transfer_read %[[VAL_1]]{{.*}} {in_bounds = [true]} : tensor<?xf32>, vector<4xf32> } : vector<4xi1> -> vector<4xf32>
+// CHECK: %[[VAL_13:.*]] = vector.mask %[[VAL_8]] { vector.multi_reduction <add>, %[[VAL_9]], %[[VAL_12]] [1] : vector<4x8xf32> to vector<4xf32> } : vector<4x8xi1> -> vector<4xf32>
+// CHECK: %[[VAL_15:.*]] = vector.mask %[[VAL_11]] { vector.transfer_write %[[VAL_13]], %[[VAL_1]]{{.*}} {in_bounds = [true]} : vector<4xf32>, tensor<?xf32> } : vector<4xi1> -> tensor<?xf32>
+// CHECK: return %[[VAL_15]] : tensor<?xf32>
+// CHECK: }
+
+// -----
+
+func.func @vectorize_dynamic_transpose_reduction(%arg0: tensor<?x?x?xf32>,
+ %arg1: tensor<?x?xf32>) -> tensor<?x?xf32> {
+ %0 = linalg.generic { indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d1, d2)>,
+ affine_map<(d0, d1, d2) -> (d2, d1)>],
+ iterator_types = ["reduction", "parallel", "parallel"] }
+ ins(%arg0 : tensor<?x?x?xf32>)
+ outs(%arg1 : tensor<?x?xf32>) {
+ ^bb(%in: f32, %out: f32) :
+ %0 = arith.addf %in, %out : f32
+ linalg.yield %0 : f32
+ } -> tensor<?x?xf32>
+ return %0 : tensor<?x?xf32>
}
transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- %1 = 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
+ transform.structured.vectorize %0 vector_sizes [4, 8, 16] : !transform.any_op
+}
+
+// CHECK-LABEL: @vectorize_dynamic_transpose_reduction(
+// CHECK-SAME: %[[VAL_0:.*]]: tensor<?x?x?xf32>,
+// CHECK-SAME: %[[VAL_1:.*]]: tensor<?x?xf32>) -> tensor<?x?xf32> {
+// CHECK: %[[VAL_2:.*]] = arith.constant 0 : index
+// CHECK: %[[VAL_3:.*]] = tensor.dim %[[VAL_0]], %[[VAL_2]] : tensor<?x?x?xf32>
+// CHECK: %[[VAL_4:.*]] = arith.constant 1 : index
+// CHECK: %[[VAL_5:.*]] = tensor.dim %[[VAL_0]], %[[VAL_4]] : tensor<?x?x?xf32>
+// CHECK: %[[VAL_6:.*]] = arith.constant 2 : index
+// CHECK: %[[VAL_7:.*]] = tensor.dim %[[VAL_0]], %[[VAL_6]] : tensor<?x?x?xf32>
+// CHECK: %[[VAL_10:.*]] = vector.create_mask %[[VAL_3]], %[[VAL_5]], %[[VAL_7]] : vector<4x8x16xi1>
+// CHECK: %[[VAL_11:.*]] = vector.mask %[[VAL_10]] { vector.transfer_read %[[VAL_0]]{{.*}} {in_bounds = [true, true, true]} : tensor<?x?x?xf32>, vector<4x8x16xf32> } : vector<4x8x16xi1> -> vector<4x8x16xf32>
+// CHECK: %[[VAL_13:.*]] = vector.create_mask %[[VAL_7]], %[[VAL_5]] : vector<16x8xi1>
+// CHECK: %[[VAL_14:.*]] = vector.mask %[[VAL_13]] { vector.transfer_read %[[VAL_1]]{{.*}} {in_bounds = [true, true], permutation_map = #{{.*}}} : tensor<?x?xf32>, vector<8x16xf32> } : vector<16x8xi1> -> vector<8x16xf32>
+// CHECK: %[[VAL_15:.*]] = vector.mask %[[VAL_10]] { vector.multi_reduction <add>, %[[VAL_11]], %[[VAL_14]] [0] : vector<4x8x16xf32> to vector<8x16xf32> } : vector<4x8x16xi1> -> vector<8x16xf32>
+// CHECK: %[[VAL_17:.*]] = vector.mask %[[VAL_13]] { vector.transfer_write %[[VAL_15]], %{{.*}} {in_bounds = [true, true], permutation_map = #{{.*}}} : vector<8x16xf32>, tensor<?x?xf32> } : vector<16x8xi1> -> tensor<?x?xf32>
+
+// -----
+
+func.func @vectorize_partial_dynamic_identity(%arg0: tensor<8x?xf32>,
+ %arg1: tensor<8x?xf32>,
+ %arg2: tensor<8x?xf32>) -> tensor<8x?xf32> {
+ %0 = linalg.generic { indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>,
+ affine_map<(d0, d1) -> (d0, d1)>,
+ affine_map<(d0, d1) -> (d0, d1)>],
+ iterator_types = ["parallel", "parallel"] }
+ ins(%arg0, %arg1 : tensor<8x?xf32>, tensor<8x?xf32>)
+ outs(%arg2 : tensor<8x?xf32>) {
+ ^bb(%in0: f32, %in1: f32, %out: f32) :
+ %0 = arith.addf %in0, %in1 : f32
+ linalg.yield %0 : f32
+ } -> tensor<8x?xf32>
+ return %0 : tensor<8x?xf32>
}
-// -----
+// CHECK-LABEL: func.func @vectorize_partial_dynamic_identity(
+// CHECK-SAME: %[[VAL_0:.*]]: tensor<8x?xf32>, %[[VAL_1:.*]]: tensor<8x?xf32>, %[[VAL_2:.*]]: tensor<8x?xf32>) -> tensor<8x?xf32> {
+// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 1 : index
+// CHECK-DAG: %[[VAL_4:.*]] = tensor.dim %[[VAL_0]], %[[VAL_3]] : tensor<8x?xf32>
+// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 0 : index
+// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 0.000000e+00 : f32
+// CHECK-DAG: %[[VAL_7:.*]] = arith.constant 8 : index
+// CHECK: %[[VAL_8:.*]] = vector.create_mask %[[VAL_7]], %[[VAL_4]] : vector<8x32xi1>
+// CHECK: %[[VAL_9:.*]] = vector.mask %[[VAL_8]] { vector.transfer_read %[[VAL_0]][%[[VAL_5]], %[[VAL_5]]], %[[VAL_6]] {in_bounds = [true, true]} : tensor<8x?xf32>, vector<8x32xf32> } : vector<8x32xi1> -> vector<8x32xf32>
+// CHECK: %[[VAL_10:.*]] = arith.constant 0.000000e+00 : f32
+// CHECK: %[[VAL_11:.*]] = vector.mask %[[VAL_8]] { vector.transfer_read %[[VAL_1]][%[[VAL_5]], %[[VAL_5]]], %[[VAL_10]] {in_bounds = [true, true]} : tensor<8x?xf32>, vector<8x32xf32> } : vector<8x32xi1> -> vector<8x32xf32>
+// CHECK: %[[VAL_12:.*]] = arith.constant 0.000000e+00 : f32
+// CHECK: %[[VAL_13:.*]] = vector.mask %[[VAL_8]] { vector.transfer_read %[[VAL_2]][%[[VAL_5]], %[[VAL_5]]], %[[VAL_12]] {in_bounds = [true, true]} : tensor<8x?xf32>, vector<8x32xf32> } : vector<8x32xi1> -> vector<8x32xf32>
+// CHECK: %[[VAL_14:.*]] = arith.addf %[[VAL_9]], %[[VAL_11]] : vector<8x32xf32>
+// CHECK: %[[VAL_15:.*]] = arith.constant 0 : index
+// CHECK: %[[VAL_16:.*]] = vector.mask %[[VAL_8]] { vector.transfer_write %[[VAL_14]], %[[VAL_2]][%[[VAL_15]], %[[VAL_15]]] {in_bounds = [true, true]} : vector<8x32xf32>, tensor<8x?xf32> } : vector<8x32xi1> -> tensor<8x?xf32>
-// CHECK-LABEL: func @generic_vectorize
- // CHECK-SAME: (%[[ARG0:.*]]: memref<4x256xf32>, %[[ARG1:.*]]: memref<4x256xf32>,
- // CHECK-SAME: %[[ARG2:.*]]: memref<256xf32>, %[[ARG3:.*]]: f32)
-func.func @generic_vectorize(%arg0: memref<4x256xf32>,
- %arg1: memref<4x256xf32>,
- %arg2: memref<256xf32>, %i: f32) {
- // CHECK-DAG: %[[CST0:.*]] = arith.constant dense<2.000000e+00> : vector<4x256xf32>
- // CHECK-DAG: %[[CST1:.*]] = arith.constant dense<1.000000e+00> : vector<4x256xf32>
- // CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
- %c1_f32 = arith.constant 1.0 : f32
- linalg.generic {
- args_in = 0 : i64,
- args_out = 10 : i64,
- indexing_maps = [
- affine_map<(d0, d1) -> (d0, d1)>,
- affine_map<(d0, d1) -> (d1)>,
- affine_map<(d0, d1) -> (d0, d1)>,
- affine_map<(d0, d1) -> (d0, d1)>,
- affine_map<(d0, d1) -> (d0, d1)>,
- affine_map<(d0, d1) -> (d0, d1)>,
- affine_map<(d0, d1) -> (d0, d1)>,
- affine_map<(d0, d1) -> (d0, d1)>,
- affine_map<(d0, d1) -> (d0, d1)>,
- affine_map<(d0, d1) -> (d0, d1)>,
- affine_map<(d0, d1) -> (d0, d1)>,
- affine_map<(d0, d1) -> (d0, d1)>],
- iterator_types = ["parallel", "parallel"]}
- ins(%arg1, %arg2: memref<4x256xf32>, memref<256xf32>)
- outs(
- %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0 :
- memref<4x256xf32>, memref<4x256xf32>, memref<4x256xf32>, memref<4x256xf32>,
- memref<4x256xf32>, memref<4x256xf32>, memref<4x256xf32>, memref<4x256xf32>,
- memref<4x256xf32>, memref<4x256xf32>) {
- ^bb0(%arg3 : f32, %arg4 : f32, %arg5: f32, %arg6: f32, %arg7: f32, %arg8: f32,
- // CHECK: %[[V2:.*]] = vector.transfer_read %[[ARG1]][%[[C0]], %[[C0]]], {{.*}} : memref<4x256xf32>, vector<4x256xf32>
- // CHECK: %[[V0:.*]] = vector.transfer_read %[[ARG2]][%[[C0]]], {{.*}} : memref<256xf32>, vector<4x256xf32>
- // CHECK: %[[V3:.*]] = vector.transfer_read %[[ARG0]][%[[C0]], %[[C0]]], {{.*}} : memref<4x256xf32>, vector<4x256xf32>
- // CHECK: %[[V1:.*]] = vector.transfer_read %[[ARG0]][%[[C0]], %[[C0]]], {{.*}} : memref<4x256xf32>, vector<4x256xf32>
- %arg9 : f32, %arg10 : f32, %arg11 : f32, %arg12 : f32, %arg13 : f32,
- %arg14 : f32):
- // CHECK: %[[ADD:.*]] = arith.addf %[[V0]], %[[V1]] : vector<4x256xf32>
- %6 = arith.addf %arg4, %arg6 : f32
- // CHECK: %[[CMP:.*]] = arith.cmpf ogt, %[[V2]], %[[V1]] : vector<4x256xf32>
- %7 = arith.cmpf ogt, %arg3, %arg6 : f32
- // CHECK: %[[ARG3B:.*]] = vector.broadcast %[[ARG3]] : f32 to vector<4x256xf32>
- %8 = arith.constant 2.0 : f32
- // CHECK: %[[DIV:.*]] = arith.divf %[[V3]], %[[ARG3B]] : vector<4x256xf32>
- %9 = arith.divf %arg5, %i : f32
- // CHECK: %[[EXP:.*]] = math.exp2 %[[V3]] : vector<4x256xf32>
- %10 = math.exp2 %arg5 : f32
- // CHECK: %[[MUL:.*]] = arith.mulf %[[V3]], %[[CST0]] : vector<4x256xf32>
- %11 = arith.mulf %arg5, %8 : f32
- // CHECK: %[[RSQRT:.*]] = math.rsqrt %[[V3]] : vector<4x256xf32>
- %12 = math.rsqrt %arg5 : f32
- // CHECK: %[[SEL:.*]] = arith.select %[[CMP]], %[[V3]], %[[V1]] : vector<4x256xi1>, vector<4x256xf32>
- %13 = arith.select %7, %arg5, %arg6 : f32
- // CHECK: %[[SUB:.*]] = arith.subf %[[V3]], %[[V0]] : vector<4x256xf32>
- %14 = arith.subf %arg5, %arg4 : f32
- // CHECK: %[[TAN:.*]] = math.tanh %[[V3]] : vector<4x256xf32>
- %15 = math.tanh %arg5 : f32
- // CHECK: vector.transfer_write %[[ADD]], %[[ARG0]][%[[C0]], %[[C0]]] {{.*}} : vector<4x256xf32>, memref<4x256xf32>
- // CHECK: vector.transfer_write %[[CST0]], %[[ARG0]][%[[C0]], %[[C0]]] {{.*}} : vector<4x256xf32>, memref<4x256xf32>
- // CHECK: vector.transfer_write %[[CST1]], %[[ARG0]][%[[C0]], %[[C0]]] {{.*}} : vector<4x256xf32>, memref<4x256xf32>
- // CHECK: vector.transfer_write %[[DIV]], %[[ARG0]][%[[C0]], %[[C0]]] {{.*}} : vector<4x256xf32>, memref<4x256xf32>
- // CHECK: vector.transfer_write %[[EXP]], %[[ARG0]][%[[C0]], %[[C0]]] {{.*}} : vector<4x256xf32>, memref<4x256xf32>
- // CHECK: vector.transfer_write %[[MUL]], %[[ARG0]][%[[C0]], %[[C0]]] {{.*}} : vector<4x256xf32>, memref<4x256xf32>
- // CHECK: vector.transfer_write %[[RSQRT]], %[[ARG0]][%[[C0]], %[[C0]]] {{.*}} : vector<4x256xf32>, memref<4x256xf32>
- // CHECK: vector.transfer_write %[[SEL]], %[[ARG0]][%[[C0]], %[[C0]]] {{.*}} : vector<4x256xf32>, memref<4x256xf32>
- // CHECK: vector.transfer_write %[[SUB]], %[[ARG0]][%[[C0]], %[[C0]]] {{.*}} : vector<4x256xf32>, memref<4x256xf32>
- // CHECK: vector.transfer_write %[[TAN]], %[[ARG0]][%[[C0]], %[[C0]]] {{.*}} : vector<4x256xf32>, memref<4x256xf32>
- linalg.yield %6, %8, %c1_f32, %9, %10, %11, %12, %13, %14, %15 : f32, f32,
- f32, f32, f32, f32, f32, f32, f32, f32
- }
- return
-}
transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- %1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize_children_and_apply_patterns %1 { disable_transfer_permutation_map_lowering_patterns } : (!transform.any_op) -> !transform.any_op
+ transform.structured.vectorize %0 vector_sizes [8, 32] : !transform.any_op
}
// -----
-// CHECK-LABEL: func @generic_vectorize_tensor
-// CHECK-SAME: (%[[ARG0:.*]]: tensor<4x256xf32>, %[[ARG1:.*]]: tensor<4x256xf32>,
-// CHECK-SAME: %[[ARG2:.*]]: tensor<256xf32>, %[[ARG3:.*]]: f32)
-func.func @generic_vectorize_tensor(%arg0: tensor<4x256xf32>,
- %arg1: tensor<4x256xf32>, %arg2: tensor<256xf32>,
- %i: f32) -> (tensor<4x256xf32>, tensor<4x256xf32>, tensor<4x256xf32>,
- tensor<4x256xf32>, tensor<4x256xf32>, tensor<4x256xf32>, tensor<4x256xf32>,
- tensor<4x256xf32>, tensor<4x256xf32>, tensor<4x256xf32>) {
- %c1_f32 = arith.constant 1.0 : f32
- %r:10 = linalg.generic {
- indexing_maps = [
- affine_map<(d0, d1) -> (d0, d1)>,
- affine_map<(d0, d1) -> (d1)>,
- affine_map<(d0, d1) -> (d0, d1)>,
- affine_map<(d0, d1) -> (d0, d1)>,
- affine_map<(d0, d1) -> (d0, d1)>,
- affine_map<(d0, d1) -> (d0, d1)>,
- affine_map<(d0, d1) -> (d0, d1)>,
- affine_map<(d0, d1) -> (d0, d1)>,
- affine_map<(d0, d1) -> (d0, d1)>,
- affine_map<(d0, d1) -> (d0, d1)>,
- affine_map<(d0, d1) -> (d0, d1)>,
- affine_map<(d0, d1) -> (d0, d1)>],
- iterator_types = ["parallel", "parallel"]}
- ins(%arg1, %arg2: tensor<4x256xf32>, tensor<256xf32>)
- outs(
- %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0 :
- tensor<4x256xf32>, tensor<4x256xf32>, tensor<4x256xf32>, tensor<4x256xf32>,
- tensor<4x256xf32>, tensor<4x256xf32>, tensor<4x256xf32>, tensor<4x256xf32>,
- tensor<4x256xf32>, tensor<4x256xf32>) {
- ^bb0(%arg3 : f32, %arg4 : f32, %arg5: f32, %arg6: f32, %arg7: f32, %arg8: f32,
- %arg9 : f32, %arg10 : f32, %arg11 : f32, %arg12 : f32, %arg13 : f32,
- %arg14 : f32):
- // CHECK-DAG: %[[CST0:.*]] = arith.constant dense<2.000000e+00> : vector<4x256xf32>
- // CHECK-DAG: %[[CST1:.*]] = arith.constant dense<1.000000e+00> : vector<4x256xf32>
- // CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
- // CHECK: %[[V2:.*]] = vector.transfer_read %[[ARG1]][%[[C0]], %[[C0]]], {{.*}} : tensor<4x256xf32>, vector<4x256xf32>
- // CHECK: %[[V0:.*]] = vector.transfer_read %[[ARG2]][%[[C0]]], {{.*}} : tensor<256xf32>, vector<4x256xf32>
- // CHECK: %[[V3:.*]] = vector.transfer_read %[[ARG0]][%[[C0]], %[[C0]]], {{.*}} : tensor<4x256xf32>, vector<4x256xf32>
- // CHECK: %[[V1:.*]] = vector.transfer_read %[[ARG0]][%[[C0]], %[[C0]]], {{.*}} : tensor<4x256xf32>, vector<4x256xf32>
- // CHECK: %[[ADD:.*]] = arith.addf %[[V0]], %[[V1]] : vector<4x256xf32>
- %6 = arith.addf %arg4, %arg6 : f32
- // CHECK: %[[CMP:.*]] = arith.cmpf ogt, %[[V2]], %[[V1]] : vector<4x256xf32>
- %7 = arith.cmpf ogt, %arg3, %arg6 : f32
- // CHECK: %[[ARG3B:.*]] = vector.broadcast %[[ARG3]] : f32 to vector<4x256xf32>
- %8 = arith.constant 2.0 : f32
- // CHECK: %[[DIV:.*]] = arith.divf %[[V3]], %[[ARG3B]] : vector<4x256xf32>
- %9 = arith.divf %arg5, %i : f32
- // CHECK: %[[EXP:.*]] = math.exp2 %[[V3]] : vector<4x256xf32>
- %10 = math.exp2 %arg5 : f32
- // CHECK: %[[MUL:.*]] = arith.mulf %[[V3]], %[[CST0]] : vector<4x256xf32>
- %11 = arith.mulf %arg5, %8 : f32
- // CHECK: %[[RSQRT:.*]] = math.rsqrt %[[V3]] : vector<4x256xf32>
- %12 = math.rsqrt %arg5 : f32
- // CHECK: %[[SEL:.*]] = arith.select %[[CMP]], %[[V3]], %[[V1]] : vector<4x256xi1>, vector<4x256xf32>
- %13 = arith.select %7, %arg5, %arg6 : f32
- // CHECK: %[[SUB:.*]] = arith.subf %[[V3]], %[[V0]] : vector<4x256xf32>
- %14 = arith.subf %arg5, %arg4 : f32
- // CHECK: %[[TAN:.*]] = math.tanh %[[V3]] : vector<4x256xf32>
- %15 = math.tanh %arg5 : f32
- // CHECK: %[[R0:.*]] = vector.transfer_write %[[ADD]], %[[ARG0]][%[[C0]], %[[C0]]] {{.*}} : vector<4x256xf32>, tensor<4x256xf32>
- // CHECK: %[[R1:.*]] = vector.transfer_write %[[CST0]], %[[ARG0]][%[[C0]], %[[C0]]] {{.*}} : vector<4x256xf32>, tensor<4x256xf32>
- // CHECK: %[[R2:.*]] = vector.transfer_write %[[CST1]], %[[ARG0]][%[[C0]], %[[C0]]] {{.*}} : vector<4x256xf32>, tensor<4x256xf32>
- // CHECK: %[[R3:.*]] = vector.transfer_write %[[DIV]], %[[ARG0]][%[[C0]], %[[C0]]] {{.*}} : vector<4x256xf32>, tensor<4x256xf32>
- // CHECK: %[[R4:.*]] = vector.transfer_write %[[EXP]], %[[ARG0]][%[[C0]], %[[C0]]] {{.*}} : vector<4x256xf32>, tensor<4x256xf32>
- // CHECK: %[[R5:.*]] = vector.transfer_write %[[MUL]], %[[ARG0]][%[[C0]], %[[C0]]] {{.*}} : vector<4x256xf32>, tensor<4x256xf32>
- // CHECK: %[[R6:.*]] = vector.transfer_write %[[RSQRT]], %[[ARG0]][%[[C0]], %[[C0]]] {{.*}} : vector<4x256xf32>, tensor<4x256xf32>
- // CHECK: %[[R7:.*]] = vector.transfer_write %[[SEL]], %[[ARG0]][%[[C0]], %[[C0]]] {{.*}} : vector<4x256xf32>, tensor<4x256xf32>
- // CHECK: %[[R8:.*]] = vector.transfer_write %[[SUB]], %[[ARG0]][%[[C0]], %[[C0]]] {{.*}} : vector<4x256xf32>, tensor<4x256xf32>
- // CHECK: %[[R9:.*]] = vector.transfer_write %[[TAN]], %[[ARG0]][%[[C0]], %[[C0]]] {{.*}} : vector<4x256xf32>, tensor<4x256xf32>
- linalg.yield %6, %8, %c1_f32, %9, %10, %11, %12, %13, %14, %15 : f32, f32,
- f32, f32, f32, f32, f32, f32, f32, f32
- } -> (tensor<4x256xf32>, tensor<4x256xf32>, tensor<4x256xf32>,
- tensor<4x256xf32>, tensor<4x256xf32>, tensor<4x256xf32>, tensor<4x256xf32>,
- tensor<4x256xf32>, tensor<4x256xf32>, tensor<4x256xf32>)
- // CHECK: return %[[R0]], %[[R1]], %[[R2]], %[[R3]], %[[R4]], %[[R5]], %[[R6]], %[[R7]], %[[R8]], %[[R9]] : tensor<4x256xf32>, tensor<4x256xf32>, tensor<4x256xf32>, tensor<4x256xf32>, tensor<4x256xf32>, tensor<4x256xf32>, tensor<4x256xf32>, tensor<4x256xf32>, tensor<4x256xf32>, tensor<4x256xf32>
- return %r#0, %r#1, %r#2, %r#3, %r#4, %r#5, %r#6, %r#7, %r#8, %r#9:
- tensor<4x256xf32>, tensor<4x256xf32>, tensor<4x256xf32>,
- tensor<4x256xf32>, tensor<4x256xf32>, tensor<4x256xf32>, tensor<4x256xf32>,
- tensor<4x256xf32>, tensor<4x256xf32>, tensor<4x256xf32>
-}
-
-transform.sequence failures(propagate) {
-^bb1(%arg1: !transform.any_op):
- %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- %1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize_children_and_apply_patterns %1 { disable_transfer_permutation_map_lowering_patterns } : (!transform.any_op) -> !transform.any_op
+func.func @do_not_generate_masks(%arg0: tensor<8x32xf32>,
+ %arg1: tensor<8x32xf32>,
+ %arg2: tensor<8x32xf32>) -> tensor<8x32xf32> {
+ %0 = linalg.generic { indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>,
+ affine_map<(d0, d1) -> (d0, d1)>,
+ affine_map<(d0, d1) -> (d0, d1)>],
+ iterator_types = ["parallel", "parallel"] }
+ ins(%arg0, %arg1 : tensor<8x32xf32>, tensor<8x32xf32>)
+ outs(%arg2 : tensor<8x32xf32>) {
+ ^bb(%in0: f32, %in1: f32, %out: f32) :
+ %0 = arith.addf %in0, %in1 : f32
+ linalg.yield %0 : f32
+ } -> tensor<8x32xf32>
+ return %0 : tensor<8x32xf32>
}
-// -----
-
-// CHECK-DAG: #[[$MAP0:.*]] = affine_map<(d0, d1) -> (d0, 0, 0, d1)>
-// CHECK-DAG: #[[$MAP1:.*]] = affine_map<(d0) -> (d0, 0, 0, 0)>
-// CHECK-DAG: #[[$MAP2:.*]] = affine_map<(d0) -> (0, 0, d0, 0)>
-// CHECK-DAG: #[[$MAP3:.*]] = affine_map<(d0, d1) -> (d1, 0, d0, 0)>
-// CHECK: func @generic_vectorize_broadcast_transpose
-// CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
-// CHECK-DAG: %[[CF:.*]] = arith.constant 0.000000e+00 : f32
-// CHECK: %[[V0:.*]] = vector.transfer_read %{{.*}}[%[[C0]], %[[C0]]], %[[CF]] {in_bounds = [true, true, true, true], permutation_map = #[[$MAP0]]} : memref<4x4xf32>, vector<4x4x4x4xf32>
-// CHECK: %[[V1:.*]] = vector.transfer_read %{{.*}}[%[[C0]]], %[[CF]] {in_bounds = [true, true, true, true], permutation_map = #[[$MAP1]]} : memref<4xf32>, vector<4x4x4x4xf32>
-// CHECK: %[[V2:.*]] = vector.transfer_read %{{.*}}[%[[C0]]], %[[CF]] {in_bounds = [true, true, true, true], permutation_map = #[[$MAP2]]} : memref<4xf32>, vector<4x4x4x4xf32>
-// CHECK: %[[V3:.*]] = vector.transfer_read %{{.*}}[%[[C0]], %[[C0]]], %[[CF]] {in_bounds = [true, true, true, true], permutation_map = #[[$MAP3]]} : memref<4x4xf32>, vector<4x4x4x4xf32>
-// CHECK: %[[SUB:.*]] = arith.subf %[[V0]], %[[V1]] : vector<4x4x4x4xf32>
-// CHECK: %[[ADD0:.*]] = arith.addf %[[V2]], %[[SUB]] : vector<4x4x4x4xf32>
-// CHECK: %[[ADD1:.*]] = arith.addf %[[V3]], %[[ADD0]] : vector<4x4x4x4xf32>
-// CHECK: vector.transfer_write %[[ADD1]], {{.*}} : vector<4x4x4x4xf32>, memref<4x4x4x4xf32>
-func.func @generic_vectorize_broadcast_transpose(
- %A: memref<4xf32>, %B: memref<4x4xf32>, %C: memref<4x4x4x4xf32>) {
- linalg.generic {
- indexing_maps = [affine_map<(d0, d1, d2, d3) -> (d0, d3)>,
- affine_map<(d0, d1, d2, d3) -> (d0)>,
- affine_map<(d0, d1, d2, d3) -> (d2)>,
- affine_map<(d0, d1, d2, d3) -> (d2, d0)>,
- affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>],
- iterator_types = ["parallel", "parallel", "parallel", "parallel"]}
- ins(%B, %A, %A, %B: memref<4x4xf32>, memref<4xf32>, memref<4xf32>, memref<4x4xf32>)
- outs(%C : memref<4x4x4x4xf32>) {
- ^bb0(%arg0: f32, %arg1: f32, %arg2: f32, %arg3: f32, %arg4: f32):
- %s = arith.subf %arg0, %arg1 : f32
- %a = arith.addf %arg2, %s : f32
- %b = arith.addf %arg3, %a : f32
- linalg.yield %b : f32
- }
- return
-}
+// CHECK-LABEL: func.func @do_not_generate_masks
+// CHECK-NOT: vector.mask
transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- %1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize_children_and_apply_patterns %1 { disable_transfer_permutation_map_lowering_patterns } : (!transform.any_op) -> !transform.any_op
+ transform.structured.vectorize %0 vector_sizes [8, 32] : !transform.any_op
}
// -----
-// Test different input maps.
-#matmul_trait = {
- indexing_maps = [
- affine_map<(d0, d1, d2, d3) -> (d1, d0)>,
- affine_map<(d0, d1, d2, d3) -> (d3, d1)>,
- affine_map<(d0, d1, d2, d3) -> (d3, d1, d0, d2)>,
- affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>
- ],
- iterator_types = ["parallel", "parallel", "parallel", "parallel"]
+func.func @vectorize_static_shape_with_mask(%arg0: tensor<8x30xf32>,
+ %arg1: tensor<8x30xf32>,
+ %arg2: tensor<8x30xf32>) -> tensor<8x30xf32> {
+ %0 = linalg.generic { indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>,
+ affine_map<(d0, d1) -> (d0, d1)>,
+ affine_map<(d0, d1) -> (d0, d1)>],
+ iterator_types = ["parallel", "parallel"] }
+ ins(%arg0, %arg1 : tensor<8x30xf32>, tensor<8x30xf32>)
+ outs(%arg2 : tensor<8x30xf32>) {
+ ^bb(%in0: f32, %in1: f32, %out: f32) :
+ %0 = arith.addf %in0, %in1 : f32
+ linalg.yield %0 : f32
+ } -> tensor<8x30xf32>
+ return %0 : tensor<8x30xf32>
}
-// CHECK-DAG: #[[MAP0:.*]] = affine_map<(d0, d1) -> (d1, d0, 0, 0)>
-// CHECK-DAG: #[[MAP1:.*]] = affine_map<(d0, d1) -> (0, d1, 0, d0)>
-// CHECK-DAG: #[[MAP2:.*]] = affine_map<(d0, d1, d2, d3) -> (d2, d1, d3, d0)>
-// CHECK: func @vectorization_transpose
-// CHECK: vector.transfer_read {{.*}}{in_bounds = [true, true, true, true], permutation_map = #[[MAP0]]} : memref<14x7xf32>, vector<7x14x8x16xf32>
-// CHECK: vector.transfer_read {{.*}}{in_bounds = [true, true, true, true], permutation_map = #[[MAP1]]} : memref<16x14xf32>, vector<7x14x8x16xf32>
-// CHECK: vector.transfer_read {{.*}}{in_bounds = [true, true, true, true], permutation_map = #[[MAP2]]} : memref<16x14x7x8xf32>, vector<7x14x8x16xf32>
-// CHECK: arith.addf {{.*}} : vector<7x14x8x16xf32>
-// CHECK: arith.addf {{.*}} : vector<7x14x8x16xf32>
-// CHECK: vector.transfer_write {{.*}} : vector<7x14x8x16xf32>, memref<7x14x8x16xf32>
-func.func @vectorization_transpose(%A: memref<14x7xf32>, %B: memref<16x14xf32>,
- %C: memref<16x14x7x8xf32>, %D: memref<7x14x8x16xf32>) {
- linalg.generic #matmul_trait
- ins(%A, %B, %C : memref<14x7xf32>, memref<16x14xf32>, memref<16x14x7x8xf32>)
- outs(%D : memref<7x14x8x16xf32>) {
- ^bb(%a: f32, %b: f32, %c: f32, %d: f32) :
- %e = arith.addf %a, %b: f32
- %f = arith.addf %e, %c: f32
- linalg.yield %f : f32
- }
- return
-}
+// CHECK-LABEL: func.func @vectorize_static_shape_with_mask(
+// CHECK-SAME: %[[VAL_0:.*]]: tensor<8x30xf32>, %[[VAL_1:.*]]: tensor<8x30xf32>, %[[VAL_2:.*]]: tensor<8x30xf32>) -> tensor<8x30xf32> {
+// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 0 : index
+// CHECK-DAG: %[[VAL_4:.*]] = arith.constant 0.000000e+00 : f32
+// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 8 : index
+// CHECK-DAG: %[[VAL_6:.*]] = arith.constant 30 : index
+// CHECK: %[[VAL_7:.*]] = vector.create_mask %[[VAL_5]], %[[VAL_6]] : vector<8x32xi1>
+// CHECK: %[[VAL_8:.*]] = vector.mask %[[VAL_7]] { vector.transfer_read %[[VAL_0]][%[[VAL_3]], %[[VAL_3]]], %[[VAL_4]] {in_bounds = [true, true]} : tensor<8x30xf32>, vector<8x32xf32> } : vector<8x32xi1> -> vector<8x32xf32>
+// CHECK: %[[VAL_9:.*]] = arith.constant 0.000000e+00 : f32
+// CHECK: %[[VAL_10:.*]] = vector.mask %[[VAL_7]] { vector.transfer_read %[[VAL_1]][%[[VAL_3]], %[[VAL_3]]], %[[VAL_9]] {in_bounds = [true, true]} : tensor<8x30xf32>, vector<8x32xf32> } : vector<8x32xi1> -> vector<8x32xf32>
+// CHECK: %[[VAL_11:.*]] = arith.constant 0.000000e+00 : f32
+// CHECK: %[[VAL_12:.*]] = vector.mask %[[VAL_7]] { vector.transfer_read %[[VAL_2]][%[[VAL_3]], %[[VAL_3]]], %[[VAL_11]] {in_bounds = [true, true]} : tensor<8x30xf32>, vector<8x32xf32> } : vector<8x32xi1> -> vector<8x32xf32>
+// CHECK: %[[VAL_13:.*]] = arith.addf %[[VAL_8]], %[[VAL_10]] : vector<8x32xf32>
+// CHECK: %[[VAL_14:.*]] = arith.constant 0 : index
+// CHECK: %[[VAL_15:.*]] = vector.mask %[[VAL_7]] { vector.transfer_write %[[VAL_13]], %[[VAL_2]][%[[VAL_14]], %[[VAL_14]]] {in_bounds = [true, true]} : vector<8x32xf32>, tensor<8x30xf32> } : vector<8x32xi1> -> tensor<8x30xf32>
transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
%0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- %1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize_children_and_apply_patterns %1 { disable_transfer_permutation_map_lowering_patterns } : (!transform.any_op) -> !transform.any_op
-}
-
-// -----
-
-// CHECK-LABEL: func @matmul_tensors
-// CHECK-SAME: (%[[ARG0:.*]]: tensor<8x4xf32>, %[[ARG1:.*]]: tensor<4x12xf32>,
-// CHECK-SAME: %[[ARG2:.*]]: tensor<8x12xf32>) -> tensor<8x12xf32>
-func.func @matmul_tensors(
- %arg0: tensor<8x4xf32>, %arg1: tensor<4x12xf32>, %arg2: tensor<8x12xf32>)
- -> tensor<8x12xf32> {
- // CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
- // CHECK-DAG: %[[V0:.*]] = vector.transfer_read %[[ARG0]][%[[C0]], %[[C0]]], {{.*}} : tensor<8x4xf32>, vector<8x12x4xf32>
- // CHECK-DAG: %[[V1:.*]] = vector.transfer_read %[[ARG1]][%[[C0]], %[[C0]]], {{.*}} : tensor<4x12xf32>, vector<8x12x4xf32>
- // CHECK-DAG: %[[V2:.*]] = vector.transfer_read %[[ARG2]][%[[C0]], %[[C0]]], {{.*}} : tensor<8x12xf32>, vector<8x12xf32>
- //
- // linalg matmul lowers gets expanded to a 3D reduction, canonicalization later
- // convert it to a 2D contract.
- // CHECK: %[[MUL:.*]] = arith.mulf %[[V0]], %[[V1]] : vector<8x12x4xf32>
- // CHECK: %[[R:.*]] = vector.multi_reduction <add>, %[[MUL]], %[[V2]] [2] : vector<8x12x4xf32> to vector<8x12xf32>
- // CHECK: %[[W:.*]] = vector.transfer_write %[[R]], %[[ARG2]][%[[C0]], %[[C0]]] {in_bounds = [true, true]} : vector<8x12xf32>, tensor<8x12xf32>
- %0 = linalg.matmul ins(%arg0, %arg1: tensor<8x4xf32>, tensor<4x12xf32>)
- outs(%arg2: tensor<8x12xf32>)
- -> tensor<8x12xf32>
- // CHECK: return %[[W]] : tensor<8x12xf32>
- return %0 : tensor<8x12xf32>
-}
-
-transform.sequence failures(propagate) {
-^bb1(%arg1: !transform.any_op):
- %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- %1 = 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
-}
-
-// -----
-
-// CHECK-LABEL: func @pad_static(
-// CHECK-SAME: %[[ARG0:.*]]: tensor<2x?x2xf32>, %[[PAD:.*]]: f32
-// CHECK-NOT: tensor.pad
-// CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
-// CHECK-DAG: %[[C2:.*]] = arith.constant 2 : index
-// CHECK-DAG: %[[INIT:.*]] = tensor.empty() : tensor<2x3x4xf32>
-// CHECK-DAG: %[[VEC:.*]] = vector.broadcast %[[PAD]] : f32 to vector<2x3x4xf32>
-// CHECK: %[[FILL:.*]] = vector.transfer_write %[[VEC]], %[[INIT]]{{.*}} : vector<2x3x4xf32>, tensor<2x3x4xf32>
-// CHECK: %[[READ:.*]] = vector.transfer_read %[[ARG0]][%[[C0]], %[[C0]], %[[C0]]], %[[PAD]] {in_bounds = [true, false, true]} : tensor<2x?x2xf32>, vector<2x3x2xf32>
-// CHECK: %[[RESULT:.*]] = vector.transfer_write %[[READ]], %[[FILL]][%[[C0]], %[[C0]], %[[C2]]] {in_bounds = [true, true, true]} : vector<2x3x2xf32>, tensor<2x3x4xf32>
-// CHECK: return %[[RESULT]]
-func.func @pad_static(%arg0: tensor<2x?x2xf32>, %pad_value: f32) -> tensor<2x3x4xf32> {
- %0 = tensor.pad %arg0 low[0, 0, 2] high[0, 1, 0] {
- ^bb0(%arg1: index, %arg2: index, %arg3: index):
- tensor.yield %pad_value : f32
- } : tensor<2x?x2xf32> to tensor<2x3x4xf32>
- return %0 : tensor<2x3x4xf32>
-}
-
-
-transform.sequence failures(propagate) {
-^bb1(%arg1: !transform.any_op):
- %0 = transform.structured.match ops{["tensor.pad"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- %1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize_children_and_apply_patterns %1 { vectorize_padding } : (!transform.any_op) -> !transform.any_op
-}
-
-// -----
-
-// CHECK-LABEL: func @pad_static_source(
-// CHECK-SAME: %[[ARG0:.*]]: tensor<2x5x2xf32>, %[[PAD:.*]]: f32
-// CHECK-NOT: tensor.pad
-// CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
-// CHECK-DAG: %[[C2:.*]] = arith.constant 2 : index
-// CHECK: %[[INIT:.*]] = tensor.empty() : tensor<2x6x4xf32>
-// CHECK: %[[VEC:.*]] = vector.broadcast %[[PAD]] : f32 to vector<2x6x4xf32>
-// CHECK: %[[FILL:.*]] = vector.transfer_write %[[VEC]], %[[INIT]][%[[C0]], %[[C0]], %[[C0]]] {in_bounds = [true, true, true]} : vector<2x6x4xf32>, tensor<2x6x4xf32>
-// CHECK: %[[READ:.*]] = vector.transfer_read %[[ARG0]][%[[C0]], %[[C0]], %[[C0]]], %{{.*}} {in_bounds = [true, true, true]} : tensor<2x5x2xf32>, vector<2x5x2xf32>
-// CHECK: %[[WRITE:.*]] = vector.transfer_write %[[READ]], %[[FILL]][%[[C0]], %[[C0]], %[[C2]]] {in_bounds = [true, true, true]} : vector<2x5x2xf32>, tensor<2x6x4xf32>
-// CHECK: return %[[WRITE]]
-func.func @pad_static_source(%arg0: tensor<2x5x2xf32>, %pad_value: f32) -> tensor<2x6x4xf32> {
- %0 = tensor.pad %arg0 low[0, 0, 2] high[0, 1, 0] {
- ^bb0(%arg1: index, %arg2: index, %arg3: index):
- tensor.yield %pad_value : f32
- } : tensor<2x5x2xf32> to tensor<2x6x4xf32>
- return %0 : tensor<2x6x4xf32>
-}
-
-
-transform.sequence failures(propagate) {
-^bb1(%arg1: !transform.any_op):
- %0 = transform.structured.match ops{["tensor.pad"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- %1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize_children_and_apply_patterns %1 { vectorize_padding } : (!transform.any_op) -> !transform.any_op
-}
-
-
-// -----
-
-// CHECK-LABEL: func @pad_static_dynamic(
-// CHECK-SAME: %[[SRC:.*]]: tensor<1x2x2x?xf32>, %[[LOW:.*]]: index, %[[HIGH:.*]]: index
-// CHECK-NOT: tensor.pad
-// CHECK-DAG: %[[C2:.*]] = arith.constant 2 : index
-// CHECK-DAG: %[[C3:.*]] = arith.constant 3 : index
-// CHECK-DAG: %[[C5:.*]] = arith.constant 5 : index
-// CHECK: %[[V0:.*]] = arith.addi %[[LOW]], %[[C2]] : index
-// CHECK: %[[V1:.*]] = arith.addi %[[V0]], %[[C3]] : index
-// CHECK: %[[V2:.*]] = arith.addi %[[HIGH]], %[[C5]] : index
-// CHECK: %[[DIM3:.*]] = tensor.dim %[[SRC]], %[[C3]] : tensor<1x2x2x?xf32>
-// CHECK: %[[V4:.*]] = arith.addi %[[DIM3]], %[[C3]] : index
-// CHECK: %[[V5:.*]] = arith.addi %[[V4]], %[[C2]] : index
-// CHECK: %[[INIT:.*]] = tensor.empty(%[[V1]], %[[V2]], %[[V5]]) : tensor<6x?x?x?xf32>
-// CHECK: %[[FILL:.*]] = linalg.fill ins(%{{.*}} : f32) outs(%[[INIT]] : tensor<6x?x?x?xf32>) -> tensor<6x?x?x?xf32>
-// CHECK: %[[SRCDIM:.*]] = tensor.dim %[[SRC]], %[[C3]] : tensor<1x2x2x?xf32>
-// CHECK: %[[RESULT:.*]] = tensor.insert_slice %[[SRC]] into %[[FILL]][2, %[[LOW]], 3, 3] [1, 2, 2, %[[SRCDIM]]] [1, 1, 1, 1] : tensor<1x2x2x?xf32> into tensor<6x?x?x?xf32>
-// CHECK: return %[[RESULT]]
-func.func @pad_static_dynamic(%arg0: tensor<1x2x2x?xf32>, %low: index, %high: index,
- %pad_value: f32) -> tensor<6x?x?x?xf32> {
- %0 = tensor.pad %arg0 low[2, %low, 3, 3] high[3, 3, %high, 2] {
- ^bb0(%arg1: index, %arg2: index, %arg3: index, %arg4: index):
- tensor.yield %pad_value : f32
- } : tensor<1x2x2x?xf32> to tensor<6x?x?x?xf32>
- return %0 : tensor<6x?x?x?xf32>
-}
-
-
-transform.sequence failures(propagate) {
-^bb1(%arg1: !transform.any_op):
- %0 = transform.structured.match ops{["tensor.pad"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- %1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize_children_and_apply_patterns %1 { vectorize_padding } : (!transform.any_op) -> !transform.any_op
+ transform.structured.vectorize %0 vector_sizes [8, 32] : !transform.any_op
}
// -----
-// CHECK-LABEL: func @pad_static_complex(
-// CHECK-NOT: vector<
-func.func @pad_static_complex(%arg0: tensor<2x5x2xcomplex<f32>>, %pad_value: complex<f32>) -> tensor<2x6x4xcomplex<f32>> {
- %0 = tensor.pad %arg0 low[0, 0, 2] high[0, 1, 0] {
- ^bb0(%arg1: index, %arg2: index, %arg3: index):
- tensor.yield %pad_value : complex<f32>
- } : tensor<2x5x2xcomplex<f32>> to tensor<2x6x4xcomplex<f32>>
- return %0 : tensor<2x6x4xcomplex<f32>>
+func.func @vectorize_dynamic_fill(%A : tensor<?x?xf32>, %arg0 : f32) -> tensor<?x?xf32> {
+ %0 = linalg.fill ins(%arg0 : f32) outs(%A : tensor<?x?xf32>) -> tensor<?x?xf32>
+ return %0 : tensor<?x?xf32>
}
+// CHECK-LABEL: func.func @vectorize_dynamic_fill
+// CHECK: %[[DIM0:.*]] = tensor.dim
+// CHECK: %[[DIM1:.*]] = tensor.dim
+// CHECK: %[[MASK:.*]] = vector.create_mask %[[DIM0]], %[[DIM1]] : vector<8x16xi1>
+// CHECK: %[[BCAST:.*]] = vector.broadcast %{{.*}} : f32 to vector<8x16xf32>
+// CHECK: vector.mask %[[MASK]] { vector.transfer_write %[[BCAST]], {{.*}} {in_bounds = [true, true]} : vector<8x16xf32>, tensor<?x?xf32> } : vector<8x16xi1>
transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
- %0 = transform.structured.match ops{["tensor.pad"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- %1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize_children_and_apply_patterns %1 { vectorize_padding } : (!transform.any_op) -> !transform.any_op
-}
-
-// -----
-
-// CHECK-LABEL: func @pad_and_transfer_read
-// CHECK-SAME: %[[ARG0:.*]]: tensor<5x6xf32>
-// CHECK-NOT: tensor.pad
-// CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
-// CHECK-DAG: %[[C5:.*]] = arith.constant 5.0
-// CHECK: %[[RESULT:.*]] = vector.transfer_read %[[ARG0]][%[[C0]], %[[C0]]], %[[C5]] : tensor<5x6xf32>, vector<7x9xf32>
-// CHECK: return %[[RESULT]]
-func.func @pad_and_transfer_read(%arg0: tensor<5x6xf32>) -> vector<7x9xf32> {
- %c0 = arith.constant 0 : index
- %c5 = arith.constant 5.0 : f32
- %c6 = arith.constant 6.0 : f32
- %0 = tensor.pad %arg0 low[0, 0] high[5, 7] {
- ^bb0(%arg1: index, %arg2: index):
- tensor.yield %c5 : f32
- } : tensor<5x6xf32> to tensor<10x13xf32>
- %1 = vector.transfer_read %0[%c0, %c0], %c6
- : tensor<10x13xf32>, vector<7x9xf32>
- return %1 : vector<7x9xf32>
-}
-
-
-transform.sequence failures(propagate) {
-^bb1(%arg1: !transform.any_op):
- %0 = transform.structured.match ops{["tensor.pad"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- %1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize_children_and_apply_patterns %1 { vectorize_padding } : (!transform.any_op) -> !transform.any_op
-}
-
-// -----
-
-func.func private @make_vector() -> vector<7x9xf32>
-
-// CHECK-LABEL: func @pad_and_transfer_write_static
-// CHECK-SAME: %[[ARG0:.*]]: tensor<5x6xf32>
-// CHECK-NOT: tensor.pad
-// CHECK: %[[C0:.*]] = arith.constant 0 : index
-// CHECK: %[[VEC0:.*]] = call @make_vector() : () -> vector<7x9xf32>
-// CHECK: %[[RESULT:.*]] = vector.transfer_write %[[VEC0]], %[[ARG0]][%[[C0]], %[[C0]]] : vector<7x9xf32>, tensor<5x6xf32>
-// CHECK: return %[[RESULT]]
-func.func @pad_and_transfer_write_static(
- %arg0: tensor<5x6xf32>) -> tensor<5x6xf32> {
- %c0 = arith.constant 0 : index
- %c5 = arith.constant 5.0 : f32
- %0 = tensor.pad %arg0 low[0, 0] high[5, 7] {
- ^bb0(%arg2: index, %arg3: index):
- tensor.yield %c5 : f32
- } : tensor<5x6xf32> to tensor<10x13xf32>
- %1 = call @make_vector() : () -> vector<7x9xf32>
- %2 = vector.transfer_write %1, %0[%c0, %c0]
- : vector<7x9xf32>, tensor<10x13xf32>
- %3 = tensor.extract_slice %2[0, 0] [5, 6] [1, 1] : tensor<10x13xf32> to tensor<5x6xf32>
- return %3 : tensor<5x6xf32>
-}
-
-transform.sequence failures(propagate) {
-^bb1(%arg1: !transform.any_op):
- %3 = transform.structured.match ops{["tensor.pad"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- %4 = get_parent_op %3 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %5 = transform.structured.vectorize_children_and_apply_patterns %4 { vectorize_padding } : (!transform.any_op) -> !transform.any_op
-}
-
-
-// -----
-
-func.func private @make_vector() -> vector<7x9xf32>
-
-// CHECK-LABEL: func @pad_and_transfer_write_dynamic_static
-// CHECK-SAME: %[[ARG0:.*]]: tensor<?x?xf32>, %[[SIZE:.*]]: index, %[[PADDING:.*]]: index
-// CHECK-NOT: tensor.pad
-// CHECK: %[[C0:.*]] = arith.constant 0 : index
-// CHECK: %[[SUB:.*]] = tensor.extract_slice %[[ARG0]][0, 0] [%[[SIZE]], 6] [1, 1] : tensor<?x?xf32> to tensor<?x6xf32>
-// CHECK: %[[VEC0:.*]] = call @make_vector() : () -> vector<7x9xf32>
-// CHECK: %[[RESULT:.*]] = vector.transfer_write %[[VEC0]], %[[SUB]][%[[C0]], %[[C0]]] : vector<7x9xf32>, tensor<?x6xf32>
-// CHECK: return %[[RESULT]]
-func.func @pad_and_transfer_write_dynamic_static(
- %arg0: tensor<?x?xf32>, %size: index, %padding: index) -> tensor<?x6xf32> {
- %c0 = arith.constant 0 : index
- %c5 = arith.constant 5.0 : f32
- %s = tensor.extract_slice %arg0[0, 0] [%size, 6] [1, 1]
- : tensor<?x?xf32> to tensor<?x6xf32>
- %0 = tensor.pad %s low[0, 0] high[%padding, 7] {
- ^bb0(%arg2: index, %arg3: index):
- tensor.yield %c5 : f32
- } : tensor<?x6xf32> to tensor<?x13xf32>
- %1 = call @make_vector() : () -> vector<7x9xf32>
- %2 = vector.transfer_write %1, %0[%c0, %c0]
- : vector<7x9xf32>, tensor<?x13xf32>
- %3 = tensor.extract_slice %2[0, 0] [%size, 6] [1, 1] : tensor<?x13xf32> to tensor<?x6xf32>
- return %3 : tensor<?x6xf32>
-}
-
-transform.sequence failures(propagate) {
-^bb1(%arg1: !transform.any_op):
- %3 = transform.structured.match ops{["tensor.pad"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- %4 = get_parent_op %3 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %5 = transform.structured.vectorize_children_and_apply_patterns %4 { vectorize_padding } : (!transform.any_op) -> !transform.any_op
-}
-
-
-// -----
-
-func.func private @make_vector() -> tensor<12x13xf32>
-
-// CHECK-LABEL: func @pad_and_insert_slice_source
-// CHECK-SAME: %[[ARG0:.*]]: tensor<5x6xf32>
-// CHECK-NOT: tensor.pad
-// CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
-// CHECK-DAG: %[[C5:.*]] = arith.constant 5.0
-// CHECK: %[[VEC0:.*]] = call @make_vector() : () -> tensor<12x13xf32>
-// CHECK: %[[READ:.*]] = vector.transfer_read %[[ARG0]][%[[C0]], %[[C0]]], %[[C5]] : tensor<5x6xf32>, vector<7x9xf32>
-// CHECK: %[[WRITE:.*]] = vector.transfer_write %[[READ]], %[[VEC0]][%[[C0]], %[[C0]]] {in_bounds = [true, true]} : vector<7x9xf32>, tensor<12x13xf32>
-// CHECK: return %[[WRITE]]
-func.func @pad_and_insert_slice_source(
- %arg0: tensor<5x6xf32>) -> tensor<12x13xf32> {
- %c0 = arith.constant 0 : index
- %c5 = arith.constant 5.0 : f32
- %0 = tensor.pad %arg0 low[0, 0] high[2, 3] {
- ^bb0(%arg2: index, %arg3: index):
- tensor.yield %c5 : f32
- } : tensor<5x6xf32> to tensor<7x9xf32>
- %1 = call @make_vector() : () -> tensor<12x13xf32>
- %r = tensor.insert_slice %0 into %1[0, 0][7, 9][1, 1] : tensor<7x9xf32> into tensor<12x13xf32>
- return %r : tensor<12x13xf32>
-}
-
-transform.sequence failures(propagate) {
-^bb1(%arg1: !transform.any_op):
- %3 = transform.structured.match ops{["tensor.pad"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- %4 = get_parent_op %3 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %5 = transform.structured.vectorize_children_and_apply_patterns %4 { vectorize_padding } : (!transform.any_op) -> !transform.any_op
-}
-
-
-// -----
-
-func.func private @make_vector() -> tensor<12x13xf32>
-
-// CHECK-LABEL: func @pad_and_insert_slice_dest
-// Check the insert slice is not rewritten if the padded result is used by the destination operand.
-// CHECK: %[[T1:.*]] = call @make_vector() : () -> tensor<12x13xf32>
-// CHECK: = tensor.insert_slice %[[T1]] into
-func.func @pad_and_insert_slice_dest(
- %arg0: tensor<1x5x6xf32>) -> tensor<1x12x13xf32> {
- %c5 = arith.constant 5.0 : f32
- %0 = tensor.pad %arg0 low[0, 0, 0] high[0, 7, 7] {
- ^bb0(%arg2: index, %arg3: index, %arg4: index):
- tensor.yield %c5 : f32
- } : tensor<1x5x6xf32> to tensor<1x12x13xf32>
- %1 = call @make_vector() : () -> tensor<12x13xf32>
- %r = tensor.insert_slice %1 into %0[0, 0, 0][1, 12, 13][1, 1, 1] : tensor<12x13xf32> into tensor<1x12x13xf32>
- return %r : tensor<1x12x13xf32>
-}
-
-transform.sequence failures(propagate) {
-^bb1(%arg1: !transform.any_op):
- %3 = transform.structured.match ops{["tensor.pad"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- %4 = get_parent_op %3 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %5 = transform.structured.vectorize_children_and_apply_patterns %4 : (!transform.any_op) -> !transform.any_op
+ %0 = transform.structured.match ops{["linalg.fill"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+ transform.structured.vectorize %0 vector_sizes [8, 16] : !transform.any_op
}
// -----
-// CHECK-LABEL: func @pad_tensor_non_const_pad_value
-// CHECK-SAME: %[[ARG0:.*]]: tensor<5x6xf32>
-// CHECK-NOT: tensor.pad
-// CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
-// CHECK-DAG: %[[C3:.*]] = arith.constant 3 : index
-// CHECK-DAG: %[[C4:.*]] = arith.constant 4 : index
-// CHECK: %[[FILL:.*]] = tensor.generate
-// CHECK: %[[RES:.*]] = arith.mulf
-// CHECK: tensor.yield %[[RES]] : f32
-// CHECK: %[[READ:.*]] = vector.transfer_read %[[ARG0]][%[[C0]], %[[C0]]], %{{.*}} {in_bounds = [true, true]} : tensor<5x6xf32>, vector<5x6xf32>
-// CHECK: %[[WRITE:.*]] = vector.transfer_write %[[READ]], %[[FILL]][%[[C3]], %[[C4]]] {in_bounds = [true, true]} : vector<5x6xf32>, tensor<12x13xf32>
-// CHECK: return %[[WRITE]]
-func.func @pad_tensor_non_const_pad_value(%arg0: tensor<5x6xf32>) -> tensor<12x13xf32> {
- %c0 = arith.constant 0 : index
- %c5 = arith.constant 5.0 : f32
- %0 = tensor.pad %arg0 low[3, 4] high[4, 3] {
- ^bb0(%arg1: index, %arg2: index):
- %i1 = arith.index_cast %arg1 : index to i32
- %i2 = arith.index_cast %arg2 : index to i32
- %f1 = arith.sitofp %i1 : i32 to f32
- %f2 = arith.sitofp %i2 : i32 to f32
- %m = arith.mulf %f1, %f2 : f32
- tensor.yield %m : f32
- } : tensor<5x6xf32> to tensor<12x13xf32>
- return %0 : tensor<12x13xf32>
+// CHECK-LABEL: func @test_masked_vectorize_linalg_copy
+func.func @test_masked_vectorize_linalg_copy(%A : memref<?x?xf32>, %B : memref<?x?xf32>) {
+ // CHECK: %[[c0:.*]] = arith.constant 0 : index
+ // CHECK: %[[d0:.*]] = memref.dim %{{.*}}, %[[c0]] : memref<?x?xf32>
+ // CHECK: %[[c1:.*]] = arith.constant 1 : index
+ // CHECK: %[[d1:.*]] = memref.dim %{{.*}}, %[[c1]] : memref<?x?xf32>
+ // CHECK: %[[mask:.*]] = vector.create_mask %[[d0]], %[[d1]] : vector<2x4xi1>
+ // CHECK: vector.mask %[[mask]] {{.*}} vector.transfer_read %{{.*}} {in_bounds = [true, true]} : memref<?x?xf32>, vector<2x4xf32> } : vector<2x4xi1> -> vector<2x4xf32>
+ // CHECK: vector.mask %[[mask]] {{.*}} vector.transfer_write %{{.*}} {in_bounds = [true, true]} : vector<2x4xf32>, memref<?x?xf32> } : vector<2x4xi1>
+ linalg.copy ins(%A : memref<?x?xf32>) outs(%B : memref<?x?xf32>)
+ return
}
-
transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
- %3 = transform.structured.match ops{["tensor.pad"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- %4 = get_parent_op %3 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %5 = transform.structured.vectorize_children_and_apply_patterns %4 { vectorize_padding } : (!transform.any_op) -> !transform.any_op
+ %0 = transform.structured.match ops{["linalg.copy"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+ transform.structured.vectorize %0 vector_sizes [2, 4] : !transform.any_op
}
// -----
-// CHECK-LABEL: func @sum_exp
-func.func @sum_exp(%input: tensor<4x16x8xf32>, %output: tensor<4x16xf32>)
- -> tensor<4x16xf32>
+// CHECK-LABEL: func @test_masked_vectorize_pad
+func.func @test_masked_vectorize_pad(
+ %0 : tensor<?x?xf32>, %h0 : index, %h1 : index)
+ -> tensor<2x4xf32>
{
- // CHECK: vector.transfer_read {{.*}} : tensor<4x16x8xf32>, vector<4x16x8xf32>
- // CHECK: vector.transfer_read {{.*}} {in_bounds = [true, true]} : tensor<4x16xf32>, vector<4x16xf32>
- // CHECK: math.exp {{.*}} : vector<4x16x8xf32>
- // CHECK: vector.multi_reduction <add>, %{{.*}}, %{{.*}} [2] : vector<4x16x8xf32> to vector<4x16xf32>
- // CHECK: vector.transfer_write {{.*}} : vector<4x16xf32>, tensor<4x16xf32>
- // CHECK: return {{.*}} : tensor<4x16xf32>
- %0 = linalg.generic {
- indexing_maps = [
- affine_map<(d0, d1, d2) -> (d0, d1, d2)>,
- affine_map<(d0, d1, d2) -> (d0, d1)>
- ],
- iterator_types = ["parallel", "parallel", "reduction"]
- } ins(%input : tensor<4x16x8xf32>) outs(%output : tensor<4x16xf32>) {
- ^bb0(%arg0: f32, %arg1: f32):
- %1 = math.exp %arg0 : f32
- %2 = arith.addf %1, %arg1 : f32
- linalg.yield %2 : f32
- } -> tensor<4x16xf32>
- return %0 : tensor<4x16xf32>
+ // CHECK-DAG: %[[c42:.*]] = arith.constant 4.243000e+01 : f32
+ // CHECK-DAG: %[[c0:.*]] = arith.constant 0 : index
+ // CHECK-DAG: %[[empty:.*]] = tensor.empty() : tensor<2x4xf32>
+ // CHECK: %[[d0:.*]] = tensor.dim {{.*}} : tensor<?x?xf32>
+ // CHECK: %[[d1:.*]] = tensor.dim {{.*}} : tensor<?x?xf32>
+ // CHECK: %[[mask:.*]] = vector.create_mask %[[d0]], %[[d1]] : vector<2x4xi1>
+ // CHECK-DAG: %[[c0_2:.*]] = arith.constant 0 : index
+ // CHECK: %[[masked_read:.*]] = vector.mask %[[mask]] {
+ // CHECK-SAME: vector.transfer_read %{{.*}}[%[[c0_2]], %[[c0_2]]], %[[c42]]
+ // CHECK-SAME: {in_bounds = [true, true]} : tensor<?x?xf32>, vector<2x4xf32>
+ // CHECK-SAME: } : vector<2x4xi1> -> vector<2x4xf32>
+ // CHECK: vector.transfer_write %[[masked_read]], %[[empty]][%[[c0_2]], %[[c0_2]]]
+ // CHECK-SAME: {in_bounds = [true, true]} : vector<2x4xf32>, tensor<2x4xf32>
+ %cst = arith.constant 42.43 : f32
+ %c0 = arith.constant 0 : index
+ %1 = tensor.pad %0 low[0, %c0] high[%h0, %h1] {
+ ^bb0(%hh1: index, %hh2: index):
+ tensor.yield %cst : f32
+ } : tensor<?x?xf32> to tensor<2x4xf32>
+ return %1: tensor<2x4xf32>
}
-
transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
- %3 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- %4 = get_parent_op %3 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %5 = transform.structured.vectorize_children_and_apply_patterns %4 : (!transform.any_op) -> !transform.any_op
+ %0 = transform.structured.match ops{["tensor.pad"]} in %arg1
+ : (!transform.any_op) -> !transform.any_op
+ transform.structured.vectorize %0 vector_sizes [2, 4] : !transform.any_op
}
// -----
-// CHECK-DAG: #[[$M1:.*]] = affine_map<(d0, d1) -> (d1, d0, 0, 0)>
-// CHECK-DAG: #[[$M2:.*]] = affine_map<(d0, d1) -> (0, 0, d1, d0)>
-// CHECK-DAG: #[[$M3:.*]] = affine_map<(d0, d1) -> (d1, d0)>
-
-// CHECK-LABEL: func @sum_exp_2
-func.func @sum_exp_2(%input: tensor<3x2xf32>, %input_2: tensor<5x4xf32>, %output: tensor<5x2xf32>)
- -> tensor<5x2xf32>
+// CHECK: #[[MAP:.+]] = affine_map<()[s0, s1] -> (s0 + s1)>
+// CHECK: func @test_masked_vectorize_dynamic_pad
+func.func @test_masked_vectorize_dynamic_pad(
+ %0 : tensor<?x?xf32>, %h0 : index, %h1 : index)
+ -> tensor<?x?xf32>
{
- // CHECK: vector.transfer_read {{.*}} {in_bounds = [true, true, true, true], permutation_map = #[[$M1]]} : tensor<3x2xf32>, vector<2x3x4x5xf32>
- // CHECK: vector.transfer_read {{.*}} {in_bounds = [true, true, true, true], permutation_map = #[[$M2]]} : tensor<5x4xf32>, vector<2x3x4x5xf32>
- // CHECK: vector.transfer_read {{.*}} {in_bounds = [true, true], permutation_map = #[[$M3]]} : tensor<5x2xf32>, vector<2x5xf32>
- // CHECK: math.exp {{.*}} : vector<2x3x4x5xf32>
- // CHECK: math.exp {{.*}} : vector<2x3x4x5xf32>
- // CHECK: addf {{.*}} : vector<2x3x4x5xf32>
- // CHECK: vector.multi_reduction <add>, {{.*}}, %{{.*}} [1, 2] : vector<2x3x4x5xf32> to vector<2x5xf32>
- // CHECK: vector.transfer_write {{.*}} {in_bounds = [true, true], permutation_map = #[[$M3]]} : vector<2x5xf32>, tensor<5x2xf32>
- // CHECK: return {{.*}} : tensor<5x2xf32>
- %0 = linalg.generic {
- indexing_maps = [
- affine_map<(d0, d1, d2, d3) -> (d1, d0)>,
- affine_map<(d0, d1, d2, d3) -> (d3, d2)>,
- affine_map<(d0, d1, d2, d3) -> (d3, d0)>
- ],
- iterator_types = ["parallel", "reduction", "reduction", "parallel"]
- } ins(%input, %input_2 : tensor<3x2xf32>, tensor<5x4xf32>) outs(%output : tensor<5x2xf32>) {
- ^bb0(%arg0: f32, %arg1: f32, %arg2: f32):
- %1 = math.exp %arg0 : f32
- %2 = math.exp %arg1 : f32
- %3 = arith.addf %1, %2 : f32
- %4 = arith.addf %3, %arg2 : f32
- linalg.yield %4 : f32
- } -> tensor<5x2xf32>
- return %0 : tensor<5x2xf32>
-}
-
-
-transform.sequence failures(propagate) {
-^bb1(%arg1: !transform.any_op):
- %3 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- %4 = get_parent_op %3 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %5 = transform.structured.vectorize_children_and_apply_patterns %4 { disable_multi_reduction_to_contract_patterns, disable_transfer_permutation_map_lowering_patterns } : (!transform.any_op) -> !transform.any_op
-}
-
-// -----
-
-// CHECK-LABEL: func @red_max_2d(
-func.func @red_max_2d(%arg0: tensor<4x4xf32>) -> tensor<4xf32> {
- // CHECK: %[[CMINF:.+]] = arith.constant dense<-3.402820e+38> : vector<4xf32>
- // CHECK: tensor.empty() : tensor<4xf32>
- // CHECK: vector.multi_reduction <maximumf>, {{.*}}, %[[CMINF]] [1] : vector<4x4xf32> to vector<4xf32>
- // CHECK: vector.transfer_write {{.*}} : vector<4xf32>, tensor<4xf32>
- %ident = arith.constant -3.40282e+38 : f32
- %init = tensor.empty() : tensor<4xf32>
- %fill = linalg.fill ins(%ident : f32) outs(%init : tensor<4xf32>) -> tensor<4xf32>
- %red = linalg.generic {indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>,
- affine_map<(d0, d1) -> (d0)>],
- iterator_types = ["parallel", "reduction"]}
- ins(%arg0 : tensor<4x4xf32>) outs(%fill : tensor<4xf32>) {
- ^bb0(%in0: f32, %out0: f32):
- %max = arith.maximumf %in0, %out0 : f32
- linalg.yield %max : f32
- } -> tensor<4xf32>
- return %red : tensor<4xf32>
-}
-
-
-transform.sequence failures(propagate) {
-^bb1(%arg1: !transform.any_op):
- %3 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- %4 = get_parent_op %3 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %5 = transform.structured.vectorize_children_and_apply_patterns %4 { vectorize_padding } : (!transform.any_op) -> !transform.any_op
-}
-
-// -----
-
-// CHECK-LABEL: func @red_min_2d(
-func.func @red_min_2d(%arg0: tensor<4x4xf32>) -> tensor<4xf32> {
- // CHECK: %[[CMAXF:.+]] = arith.constant dense<3.402820e+38> : vector<4xf32>
- // CHECK: tensor.empty() : tensor<4xf32>
- // CHECK: vector.transfer_read {{.*}} : tensor<4x4xf32>, vector<4x4xf32>
- // CHECK: vector.multi_reduction <minimumf>, {{.*}}, %[[CMAXF]] [1] : vector<4x4xf32> to vector<4xf32>
- // CHECK: vector.transfer_write {{.*}} : vector<4xf32>, tensor<4xf32>
- %maxf32 = arith.constant 3.40282e+38 : f32
- %init = tensor.empty() : tensor<4xf32>
- %fill = linalg.fill ins(%maxf32 : f32) outs(%init : tensor<4xf32>) -> tensor<4xf32>
- %red = linalg.generic {indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>,
- affine_map<(d0, d1) -> (d0)>],
- iterator_types = ["parallel", "reduction"]}
- ins(%arg0 : tensor<4x4xf32>) outs(%fill : tensor<4xf32>) {
- ^bb0(%in0: f32, %out0: f32):
- %min = arith.minimumf %out0, %in0 : f32
- linalg.yield %min : f32
- } -> tensor<4xf32>
- return %red : tensor<4xf32>
-}
-
-
-transform.sequence failures(propagate) {
-^bb1(%arg1: !transform.any_op):
- %3 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- %4 = get_parent_op %3 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %5 = transform.structured.vectorize_children_and_apply_patterns %4 : (!transform.any_op) -> !transform.any_op
-}
-
-// -----
-
-// CHECK-LABEL: func @red_mul_2d(
-func.func @red_mul_2d(%arg0: tensor<4x4xf32>) -> tensor<4xf32> {
- // CHECK: tensor.empty() : tensor<4xf32>
- // CHECK: vector.transfer_read {{.*}} : tensor<4x4xf32>, vector<4x4xf32>
- // CHECK: vector.multi_reduction <mul>, {{.*}}, {{.*}} [1] : vector<4x4xf32> to vector<4xf32>
- // CHECK: vector.transfer_write {{.*}} : vector<4xf32>, tensor<4xf32>
- %ident = arith.constant 1.0 : f32
- %init = tensor.empty() : tensor<4xf32>
- %fill = linalg.fill ins(%ident : f32) outs(%init : tensor<4xf32>) -> tensor<4xf32>
- %red = linalg.generic {indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>,
- affine_map<(d0, d1) -> (d0)>],
- iterator_types = ["parallel", "reduction"]}
- ins(%arg0 : tensor<4x4xf32>) outs(%fill : tensor<4xf32>) {
- ^bb0(%in0: f32, %out0: f32):
- %mul = arith.mulf %in0, %out0 : f32
- linalg.yield %mul : f32
- } -> tensor<4xf32>
- return %red : tensor<4xf32>
-}
-
-
-transform.sequence failures(propagate) {
-^bb1(%arg1: !transform.any_op):
- %3 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- %4 = get_parent_op %3 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %5 = transform.structured.vectorize_children_and_apply_patterns %4 : (!transform.any_op) -> !transform.any_op
-}
-
-// -----
-
-// CHECK-LABEL: func @red_or_2d(
-func.func @red_or_2d(%arg0: tensor<4x4xi1>) -> tensor<4xi1> {
- // CHECK: tensor.empty() : tensor<4xi1>
- // CHECK: vector.transfer_read {{.*}} : tensor<4x4xi1>, vector<4x4xi1>
- // CHECK: vector.multi_reduction <or>, {{.*}}, {{.*}} [1] : vector<4x4xi1> to vector<4xi1>
- // CHECK: vector.transfer_write {{.*}} : vector<4xi1>, tensor<4xi1>
- %ident = arith.constant false
- %init = tensor.empty() : tensor<4xi1>
- %fill = linalg.fill ins(%ident : i1) outs(%init : tensor<4xi1>) -> tensor<4xi1>
- %red = linalg.generic {indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>,
- affine_map<(d0, d1) -> (d0)>],
- iterator_types = ["parallel", "reduction"]}
- ins(%arg0 : tensor<4x4xi1>) outs(%fill : tensor<4xi1>) {
- ^bb0(%in0: i1, %out0: i1):
- %or = arith.ori %in0, %out0 : i1
- linalg.yield %or : i1
- } -> tensor<4xi1>
- return %red : tensor<4xi1>
-}
-
-
-transform.sequence failures(propagate) {
-^bb1(%arg1: !transform.any_op):
- %3 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- %4 = get_parent_op %3 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %5 = transform.structured.vectorize_children_and_apply_patterns %4 : (!transform.any_op) -> !transform.any_op
-}
-
-// -----
-
-// CHECK-LABEL: func @red_and_2d(
-func.func @red_and_2d(%arg0: tensor<4x4xi1>) -> tensor<4xi1> {
- // CHECK: tensor.empty() : tensor<4xi1>
- // CHECK: vector.transfer_read {{.*}} : tensor<4x4xi1>, vector<4x4xi1>
- // CHECK: vector.multi_reduction <and>, {{.*}}, {{.*}} [1] : vector<4x4xi1> to vector<4xi1>
- // CHECK: vector.transfer_write {{.*}} : vector<4xi1>, tensor<4xi1>
- %ident = arith.constant true
- %init = tensor.empty() : tensor<4xi1>
- %fill = linalg.fill ins(%ident : i1) outs(%init : tensor<4xi1>) -> tensor<4xi1>
- %red = linalg.generic {indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>,
- affine_map<(d0, d1) -> (d0)>],
- iterator_types = ["parallel", "reduction"]}
- ins(%arg0 : tensor<4x4xi1>) outs(%fill : tensor<4xi1>) {
- ^bb0(%in0: i1, %out0: i1):
- %and = arith.andi %in0, %out0 : i1
- linalg.yield %and : i1
- } -> tensor<4xi1>
- return %red : tensor<4xi1>
-}
-
-
-transform.sequence failures(propagate) {
-^bb1(%arg1: !transform.any_op):
- %3 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- %4 = get_parent_op %3 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %5 = transform.structured.vectorize_children_and_apply_patterns %4 : (!transform.any_op) -> !transform.any_op
-}
-
-// -----
-
-// CHECK-LABEL: func @red_xor_2d(
-func.func @red_xor_2d(%arg0: tensor<4x4xi1>) -> tensor<4xi1> {
- // CHECK: tensor.empty() : tensor<4xi1>
- // CHECK: vector.transfer_read {{.*}} : tensor<4x4xi1>, vector<4x4xi1>
- // CHECK: vector.multi_reduction <xor>, {{.*}}, {{.*}} [1] : vector<4x4xi1> to vector<4xi1>
- // CHECK: vector.transfer_write {{.*}} : vector<4xi1>, tensor<4xi1>
- %ident = arith.constant false
- %init = tensor.empty() : tensor<4xi1>
- %fill = linalg.fill ins(%ident : i1) outs(%init : tensor<4xi1>) -> tensor<4xi1>
- %red = linalg.generic {indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>,
- affine_map<(d0, d1) -> (d0)>],
- iterator_types = ["parallel", "reduction"]}
- ins(%arg0 : tensor<4x4xi1>) outs(%fill : tensor<4xi1>) {
- ^bb0(%in0: i1, %out0: i1):
- %xor = arith.xori %in0, %out0 : i1
- linalg.yield %xor : i1
- } -> tensor<4xi1>
- return %red : tensor<4xi1>
-}
-
-
-transform.sequence failures(propagate) {
-^bb1(%arg1: !transform.any_op):
- %3 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- %4 = get_parent_op %3 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %5 = transform.structured.vectorize_children_and_apply_patterns %4 : (!transform.any_op) -> !transform.any_op
-}
-
-// -----
-
-// CHECK-DAG: #[[$M5:.*]] = affine_map<(d0, d1) -> (d0, 0)>
-
-// CHECK-LABEL: func @explicit_broadcast(
-func.func @explicit_broadcast(%arg0: tensor<4x4xf32>, %arg1: tensor<4x1xf32>) -> tensor<4x4xf32> {
- // CHECK: vector.transfer_read {{.*}} {in_bounds = [true, true]} : tensor<4x4xf32>, vector<4x4xf32>
- // CHECK: vector.transfer_read {{.*}} {in_bounds = [true, true], permutation_map = #[[$M5]]} : tensor<4x1xf32>, vector<4x4xf32>
- // CHECK: subf {{.*}} : vector<4x4xf32>
- // CHECK: vector.transfer_write {{.*}} {in_bounds = [true, true]} : vector<4x4xf32>, tensor<4x4xf32>
- %c0 = arith.constant 0.0 : f32
- %init = tensor.empty() : tensor<4x4xf32>
- %fill = linalg.fill ins(%c0 : f32) outs(%init : tensor<4x4xf32>) -> tensor<4x4xf32>
- %red = linalg.generic {indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>,
- affine_map<(d0, d1) -> (d0, 0)>,
- affine_map<(d0, d1) -> (d0, d1)>],
- iterator_types = ["parallel", "parallel"]}
- ins(%arg0, %arg1 : tensor<4x4xf32>, tensor<4x1xf32>)
- outs(%fill : tensor<4x4xf32>) {
- ^bb0(%arg7: f32, %arg8: f32, %arg9: f32):
- %40 = arith.subf %arg7, %arg8 : f32
- linalg.yield %40 : f32
- } -> tensor<4x4xf32>
- return %red : tensor<4x4xf32>
-}
-
-
-transform.sequence failures(propagate) {
-^bb1(%arg1: !transform.any_op):
- %3 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- %4 = get_parent_op %3 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %5 = transform.structured.vectorize_children_and_apply_patterns %4 : (!transform.any_op) -> !transform.any_op
-}
-
-// -----
-
-// CHECK-DAG: #[[$M6:.*]] = affine_map<(d0, d1) -> (d0, 0)>
-
-// CHECK-LABEL: func @fused_broadcast_red_2d
-func.func @fused_broadcast_red_2d(%arg0: tensor<4x4xf32>, %arg1: tensor<4x1xf32>) -> tensor<4xf32> {
- // CHECK: vector.transfer_read {{.*}} {in_bounds = [true, true]} : tensor<4x4xf32>, vector<4x4xf32>
- // CHECK: vector.transfer_read {{.*}} {in_bounds = [true, true], permutation_map = #[[$M6]]} : tensor<4x1xf32>, vector<4x4xf32>
- // CHECK: subf {{.*}} : vector<4x4xf32>
- // CHECK: math.exp {{.*}} : vector<4x4xf32>
- // CHECK: vector.multi_reduction <add>, {{.*}}, {{.*}} : vector<4x4xf32> to vector<4xf32>
- // CHECK: vector.transfer_write {{.*}} {in_bounds = [true]} : vector<4xf32>, tensor<4xf32>
- %c0 = arith.constant 0.0 : f32
- %init = tensor.empty() : tensor<4xf32>
- %fill = linalg.fill ins(%c0 : f32) outs(%init : tensor<4xf32>) -> tensor<4xf32>
- %red = linalg.generic {indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>,
- affine_map<(d0, d1) -> (d0, 0)>,
- affine_map<(d0, d1) -> (d0)>],
- iterator_types = ["parallel", "reduction"]}
- ins(%arg0, %arg1 : tensor<4x4xf32>, tensor<4x1xf32>)
- outs(%fill : tensor<4xf32>) {
- ^bb0(%arg7: f32, %arg8: f32, %arg9: f32):
- %40 = arith.subf %arg7, %arg8 : f32
- %41 = math.exp %40 : f32
- %42 = arith.addf %41, %arg9 : f32
- linalg.yield %42 : f32
- } -> tensor<4xf32>
- return %red : tensor<4xf32>
-}
-
-
-transform.sequence failures(propagate) {
-^bb1(%arg1: !transform.any_op):
- %0 = transform.structured.match ops{["linalg.fill"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- %1 = 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
-
- %3 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- %4 = get_parent_op %3 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %5 = transform.structured.vectorize_children_and_apply_patterns %4 : (!transform.any_op) -> !transform.any_op
-}
-
-// -----
-
-// CHECK-LABEL: func @reduce_1d(
-// CHECK-SAME: %[[A:.*]]: tensor<32xf32>
-func.func @reduce_1d(%arg0: tensor<32xf32>) -> tensor<f32> {
- // CHECK-DAG: %[[vF0:.*]] = arith.constant dense<0.000000e+00> : vector<f32>
- // CHECK-DAG: %[[F0:.*]] = arith.constant 0.000000e+00 : f32
- // CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
- %f0 = arith.constant 0.000000e+00 : f32
-
- // CHECK: %[[init:.*]] = tensor.empty() : tensor<f32>
- %0 = tensor.empty() : tensor<f32>
-
- %1 = linalg.fill ins(%f0 : f32) outs(%0 : tensor<f32>) -> tensor<f32>
- // CHECK: %[[r:.*]] = vector.transfer_read %[[A]][%[[C0]]]
- // CHECK-SAME: : tensor<32xf32>, vector<32xf32>
- // CHECK: %[[f0:.*]] = vector.extractelement %[[vF0]][] : vector<f32>
- // CHECK: %[[red:.*]] = vector.multi_reduction <add>, %[[r]], %[[f0]] [0]
- // CHECK-SAME: : vector<32xf32> to f32
- // CHECK: %[[red_v1:.*]] = vector.broadcast %[[red]] : f32 to vector<f32>
- // CHECK: %[[res:.*]] = vector.transfer_write %[[red_v1]], %[[init]][]
- // CHECK-SAME: : vector<f32>, tensor<f32>
- %2 = linalg.generic {
- indexing_maps = [affine_map<(d0) -> (d0)>,
- affine_map<(d0) -> ()>],
- iterator_types = ["reduction"]}
- ins(%arg0 : tensor<32xf32>)
- outs(%1 : tensor<f32>) {
- ^bb0(%a: f32, %b: f32):
- %3 = arith.addf %a, %b : f32
- linalg.yield %3 : f32
- } -> tensor<f32>
-
- return %2 : tensor<f32>
-}
-
-transform.sequence failures(propagate) {
-^bb1(%arg1: !transform.any_op):
- %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- %1 = 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
-}
-
-
-// -----
-
-// This test checks that vectorization does not occur when an input indexing map
-// is not a projected permutation. In the future, this can be converted to a
-// positive test when support is added.
-
-// CHECK-LABEL: func @not_projected_permutation
-func.func @not_projected_permutation(%arg0: tensor<8x8xf32>) -> tensor<6x6x3x3xf32> {
- %c0 = arith.constant 0.0 : f32
- %init = tensor.empty() : tensor<6x6x3x3xf32>
- %fill = linalg.fill ins(%c0 : f32) outs(%init : tensor<6x6x3x3xf32>) -> tensor<6x6x3x3xf32>
- // CHECK: linalg.generic
- %result = linalg.generic {indexing_maps = [affine_map<(d0, d1, d2, d3) -> (d0 + d2, d1 + d3)>,
- affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>],
- iterator_types = ["parallel", "parallel", "parallel", "parallel"]}
- ins(%arg0 : tensor<8x8xf32>)
- outs(%fill : tensor<6x6x3x3xf32>) {
- ^bb0(%arg7: f32, %arg9: f32):
- linalg.yield %arg7 : f32
- } -> tensor<6x6x3x3xf32>
- return %result : tensor<6x6x3x3xf32>
-}
-
-transform.sequence failures(propagate) {
-^bb1(%arg1: !transform.any_op):
- %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- %1 = 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
-}
-
-// -----
-
-// Check vectorization can handle cases where outputs are a mix of reduced and non-reduced values.
-func.func @mixed_parallel_reduced_results(%arg0 : tensor<2x4x8xf32>,
- %arg1 : tensor<2x4xf32>, %arg2 : tensor<2x4x8xf32>, %arg3 : tensor<2x4xf32>) ->
- (tensor<2x4x8xf32>, tensor<2x4xf32>) {
- %0:2 = linalg.generic {
- indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d1, d2)>, affine_map<(d0, d1, d2) -> (d0, d1)>,
- affine_map<(d0, d1, d2) -> (d0, d1, d2)>, affine_map<(d0, d1, d2) -> (d0, d1)>],
- iterator_types = ["parallel", "parallel", "reduction"]}
- ins(%arg0, %arg1 : tensor<2x4x8xf32>, tensor<2x4xf32>)
- outs(%arg2, %arg3 : tensor<2x4x8xf32>, tensor<2x4xf32>) {
- ^bb0(%b0 : f32, %b1 : f32, %b2 : f32, %b3 : f32):
- %1 = arith.mulf %b0, %b1 : f32
- %2 = arith.addf %1, %b3 : f32
- linalg.yield %1, %2 : f32, f32
- } -> (tensor<2x4x8xf32>, tensor<2x4xf32>)
- return %0#0, %0#1 : tensor<2x4x8xf32>, tensor<2x4xf32>
+ // CHECK-DAG: %[[c42:.*]] = arith.constant 4.243000e+01 : f32
+ // CHECK-DAG: %[[c0:.*]] = arith.constant 0 : index
+ // CHECK-DAG: %[[res_d0:.+]] = affine.apply #[[MAP]]()
+ // CHECK-DAG: %[[res_d1:.+]] = affine.apply #[[MAP]]()
+ // CHECK-DAG: %[[empty:.*]] = tensor.empty(%[[res_d0]], %[[res_d1]]) : tensor<?x?xf32>
+ // CHECK: %[[d0:.*]] = tensor.dim {{.*}} : tensor<?x?xf32>
+ // CHECK: %[[d1:.*]] = tensor.dim {{.*}} : tensor<?x?xf32>
+ // CHECK: %[[mask:.*]] = vector.create_mask %[[d0]], %[[d1]] : vector<2x4xi1>
+ // CHECK-DAG: %[[c0_2:.*]] = arith.constant 0 : index
+ // CHECK: %[[masked_read:.*]] = vector.mask %[[mask]] {
+ // CHECK-SAME: vector.transfer_read %{{.*}}[%[[c0_2]], %[[c0_2]]], %[[c42]]
+ // CHECK-SAME: {in_bounds = [true, true]} : tensor<?x?xf32>, vector<2x4xf32>
+ // CHECK-SAME: } : vector<2x4xi1> -> vector<2x4xf32>
+ // CHECK: %[[mask_2:.*]] = vector.create_mask %[[res_d0]], %[[res_d1]] : vector<2x4xi1>
+ // CHECK: %[[masked_write:.*]] = vector.mask %[[mask_2]] {
+ // CHECK-SAME: vector.transfer_write %[[masked_read]], %[[empty]][%[[c0_2]], %[[c0_2]]]
+ // CHECK-SAME: {in_bounds = [true, true]} : vector<2x4xf32>, tensor<?x?xf32>
+ // CHECK: return %[[masked_write]] : tensor<?x?xf32>
+ %cst = arith.constant 42.43 : f32
+ %c0 = arith.constant 0 : index
+ %1 = tensor.pad %0 low[0, %c0] high[%h0, %h1] {
+ ^bb0(%hh1: index, %hh2: index):
+ tensor.yield %cst : f32
+ } : tensor<?x?xf32> to tensor<?x?xf32>
+ return %1: tensor<?x?xf32>
}
-// CHECK-LABEL: func @mixed_parallel_reduced_results(
-// CHECK-SAME: %[[ARG0:[a-zA-Z0-9]+]]: tensor<2x4x8xf32>
-// CHECK-SAME: %[[ARG1:[a-zA-Z0-9]+]]: tensor<2x4xf32>
-// CHECK-SAME: %[[ARG2:[a-zA-Z0-9]+]]: tensor<2x4x8xf32>
-// CHECK-SAME: %[[ARG3:[a-zA-Z0-9]+]]: tensor<2x4xf32>
-// CHECK-DAG: %[[V0:.+]] = vector.transfer_read %[[ARG0]]
-// CHECK-DAG: %[[V1:.+]] = vector.transfer_read %[[ARG1]]
-// CHECK-DAG: %[[V2:.+]] = vector.transfer_read %[[ARG3]]
-// CHECK-DAG: %[[MUL:.+]] = arith.mulf %[[V0]], %[[V1]]
-// CHECK-DAG: %[[ADD:.+]] = vector.multi_reduction <add>, %[[MUL]], %[[V2]]
-// CHECK-DAG: vector.transfer_write %[[MUL]], %[[ARG2]]
-// CHECK-DAG: vector.transfer_write %[[ADD]], %[[ARG3]]
transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
- %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- %1 = 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
+ %0 = transform.structured.match ops{["tensor.pad"]} in %arg1
+ : (!transform.any_op) -> !transform.any_op
+ transform.structured.vectorize %0 vector_sizes [2, 4] : !transform.any_op
}
// -----
-func.func @vectorize_map(%arg0: memref<64xf32>,
- %arg1: memref<64xf32>, %arg2: memref<64xf32>) {
- linalg.map ins(%arg0, %arg1 : memref<64xf32>, memref<64xf32>)
- outs(%arg2 : memref<64xf32>)
- (%in: f32, %in_0: f32) {
- %0 = arith.addf %in, %in_0 : f32
- linalg.yield %0 : f32
- }
+func.func @matmul(%A: memref<?x?xf32>, %B: memref<?x?xf32>, %C: memref<?x?xf32>) {
+ linalg.matmul ins(%A, %B: memref<?x?xf32>, memref<?x?xf32>)
+ outs(%C: memref<?x?xf32>)
return
}
-// CHECK-LABEL: func @vectorize_map
-// CHECK: %[[LHS:.*]] = vector.transfer_read
-// CHECK-NEXT: %[[RHS:.*]] = vector.transfer_read
-// CHECK-NEXT: arith.addf %[[LHS]], %[[RHS]] : vector<64xf32>
-transform.sequence failures(propagate) {
-^bb1(%arg1: !transform.any_op):
- %0 = transform.structured.match ops{["linalg.map"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- %1 = 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
-}
-
-// -----
-
-func.func @vectorize_transpose(%arg0: memref<16x32x64xf32>,
- %arg1: memref<32x64x16xf32>) {
- linalg.transpose ins(%arg0 : memref<16x32x64xf32>)
- outs(%arg1 : memref<32x64x16xf32>) permutation = [1, 2, 0]
- return
-}
-// CHECK-LABEL: func @vectorize_transpose
-// CHECK: vector.transpose
-// CHECK-SAME: [1, 2, 0] : vector<16x32x64xf32> to vector<32x64x16xf32>
+// CHECK-LABEL: func.func @matmul(
+// CHECK-SAME: %[[A:.*]]: memref<?x?xf32>, %[[B:.*]]: memref<?x?xf32>, %[[C:.*]]: memref<?x?xf32>) {
+// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 0 : index
+// CHECK-DAG: %[[VAL_4:.*]] = memref.dim %[[A]], %[[VAL_3]] : memref<?x?xf32>
+// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index
+// CHECK-DAG: %[[VAL_6:.*]] = memref.dim %[[B]], %[[VAL_5]] : memref<?x?xf32>
+// CHECK-DAG: %[[VAL_7:.*]] = arith.constant 1 : index
+// CHECK-DAG: %[[VAL_8:.*]] = memref.dim %[[A]], %[[VAL_7]] : memref<?x?xf32>
+// CHECK: %[[MASK_A:.*]] = vector.create_mask %[[VAL_4]], %[[VAL_8]] : vector<8x4xi1>
+// CHECK: %[[LOAD_A:.*]] = vector.mask %[[MASK_A]] { vector.transfer_read %[[A]]{{\[}}%{{.*}}, %{{.*}}], %{{.*}} {in_bounds = [true, true, true], permutation_map = #{{.*}}} : memref<?x?xf32>, vector<8x16x4xf32> } : vector<8x4xi1> -> vector<8x16x4xf32>
+// CHECK: %[[MASK_B:.*]] = vector.create_mask %[[VAL_8]], %[[VAL_6]] : vector<4x16xi1>
+// CHECK: %[[LOAD_B:.*]] = vector.mask %[[MASK_B]] { vector.transfer_read %[[B]]{{\[}}%{{.*}}, %{{.*}}], %{{.*}} {in_bounds = [true, true, true], permutation_map = #{{.*}}} : memref<?x?xf32>, vector<8x16x4xf32> } : vector<4x16xi1> -> vector<8x16x4xf32>
+// CHECK: %[[MASK_C:.*]] = vector.create_mask %[[VAL_4]], %[[VAL_6]] : vector<8x16xi1>
+// CHECK: %[[LOAD_C:.*]] = vector.mask %[[MASK_C]] { vector.transfer_read %[[C]]{{\[}}%{{.*}}, %{{.*}}], %{{.*}} {in_bounds = [true, true]} : memref<?x?xf32>, vector<8x16xf32> } : vector<8x16xi1> -> vector<8x16xf32>
+// CHECK: %[[MULF:.*]] = arith.mulf %[[LOAD_A]], %[[LOAD_B]] : vector<8x16x4xf32>
+// CHECK: %[[MASK_MULIT_RED:.*]] = vector.create_mask %[[VAL_4]], %[[VAL_6]], %[[VAL_8]] : vector<8x16x4xi1>
+// CHECK: %[[MULTI_RED:.*]] = vector.mask %[[MASK_MULIT_RED]] { vector.multi_reduction <add>, %[[MULF]], %[[LOAD_C]] [2] : vector<8x16x4xf32> to vector<8x16xf32> } : vector<8x16x4xi1> -> vector<8x16xf32>
+// CHECK: %[[C2:.*]] = arith.constant 0 : index
+// CHECK: vector.mask %[[MASK_C]] { vector.transfer_write %[[MULTI_RED]], %[[C]]{{\[}}%[[C2]], %[[C2]]] {in_bounds = [true, true]} : vector<8x16xf32>, memref<?x?xf32> } : vector<8x16xi1>
transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
- %0 = transform.structured.match ops{["linalg.transpose"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- %1 = 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
+ %matmul = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+ transform.structured.vectorize %matmul vector_sizes [8, 16, 4] : !transform.any_op
}
// -----
-func.func @vectorize_reduce(%arg0: memref<16x32x64xf32>,
- %arg1: memref<16x64xf32>) {
- linalg.reduce ins(%arg0 : memref<16x32x64xf32>)
- outs(%arg1 : memref<16x64xf32>) dimensions = [1]
- (%in: f32, %init: f32) {
- %0 = arith.addf %in, %init : f32
- linalg.yield %0 : f32
- }
+func.func @matmul_scalable(%A: memref<?x?xf32>, %B: memref<?x?xf32>, %C: memref<?x?xf32>) {
+ linalg.matmul ins(%A, %B: memref<?x?xf32>, memref<?x?xf32>)
+ outs(%C: memref<?x?xf32>)
return
}
-// CHECK-LABEL: func @vectorize_reduce
-// CHECK: vector.multi_reduction <add>
-// CHECK-SAME: : vector<16x32x64xf32> to vector<16x64xf32>
-transform.sequence failures(propagate) {
-^bb1(%arg1: !transform.any_op):
- %0 = transform.structured.match ops{["linalg.reduce"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- %1 = 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
-}
-
-// -----
-
-// This is a regression test. This IR cannot be vectorized, but
-// structured.vectorize_children_and_apply_patterns should nevertheless succeed.
-
-#map = affine_map<(d0) -> (d0)>
-// CHECK-LABEL: @not_vectorizable
-func.func @not_vectorizable(%arg0: tensor<1x?xf32>, %arg1: index, %arg2: index, %arg3: index) -> tensor<1x128xf32> {
- %0 = tensor.empty() : tensor<1x128xf32>
- %1 = scf.for %arg5 = %arg2 to %arg1 step %arg3 iter_args(%arg6 = %0) -> (tensor<1x128xf32>) {
- %extracted_slice = tensor.extract_slice %arg6[0, 0] [1, %arg1] [1, 1] : tensor<1x128xf32> to tensor<?xf32>
- %expanded = tensor.expand_shape %extracted_slice [[0, 1]] : tensor<?xf32> into tensor<1x?xf32>
- %extracted_slice_0 = tensor.extract_slice %arg0[0, %arg3] [1, %arg2] [1, 1] : tensor<1x?xf32> to tensor<?xf32>
- %extracted_slice_1 = tensor.extract_slice %expanded[0, %arg3] [1, %arg2] [1, 1] : tensor<1x?xf32> to tensor<?xf32>
- %2 = linalg.generic {indexing_maps = [#map, #map], iterator_types = ["parallel"]} ins(%extracted_slice_0 : tensor<?xf32>) outs(%extracted_slice_1 : tensor<?xf32>) {
- ^bb0(%in: f32, %out: f32):
- %3 = arith.addf %in, %out : f32
- linalg.yield %3 : f32
- } -> tensor<?xf32>
- %inserted_slice = tensor.insert_slice %2 into %expanded[0, %arg3] [1, %arg2] [1, 1] : tensor<?xf32> into tensor<1x?xf32>
- %collapsed = tensor.collapse_shape %inserted_slice [[0, 1]] : tensor<1x?xf32> into tensor<?xf32>
- %inserted_slice_2 = tensor.insert_slice %collapsed into %arg6[0, 0] [1, %arg1] [1, 1] : tensor<?xf32> into tensor<1x128xf32>
- scf.yield %inserted_slice_2 : tensor<1x128xf32>
- }
- return %1 : tensor<1x128xf32>
-}
-transform.sequence failures(propagate) {
-^bb0(%arg0: !transform.any_op):
- %0 = transform.structured.match ops{["func.func"]} in %arg0 : (!transform.any_op) -> !transform.any_op
- %1 = transform.structured.vectorize_children_and_apply_patterns %0 : (!transform.any_op) -> !transform.any_op
-}
-
-// -----
-
-// Regression test: %13 was incorrectly detected as a reduction and
-// vectorization failed.
-
-func.func @wrong_reduction_detection(%input: tensor<120x64xf32>) -> tensor<120x64xf32> {
- %c0 = arith.constant 0 : index
- %c4 = arith.constant 4 : index
- %c64 = arith.constant 64 : index
- %cst_6 = arith.constant 4.000000e+00 : f32
- %1 = scf.for %arg0 = %c0 to %c64 step %c4 iter_args(%arg1 = %input) -> (tensor<120x64xf32>) {
- %extracted_slice = tensor.extract_slice %arg1[%c0, %arg0] [1, 4] [1, 1] : tensor<120x64xf32> to tensor<1x4xf32>
- %10 = linalg.fill {__internal_linalg_transform__ = "1"} ins(%cst_6 : f32) outs(%extracted_slice : tensor<1x4xf32>) -> tensor<1x4xf32>
- %11 = linalg.generic {indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>], iterator_types = ["parallel", "parallel"]} outs(%10 : tensor<1x4xf32>) {
- ^bb0(%out: f32):
- %12 = linalg.index 0 : index
- %13 = arith.addi %arg0, %12 : index
- %18 = arith.index_cast %13 : index to i32
- %20 = arith.uitofp %18 : i32 to f32
- %67 = arith.mulf %out, %20 : f32
- linalg.yield %67 : f32
- } -> tensor<1x4xf32>
- %inserted_slice = tensor.insert_slice %11 into %arg1[%c0, %arg0] [1, 4] [1, 1] : tensor<1x4xf32> into tensor<120x64xf32>
- scf.yield %inserted_slice : tensor<120x64xf32>
- }
- return %1 : tensor<120x64xf32>
-}
+// CHECK-LABEL: func.func @matmul_scalable(
+// CHECK-SAME: %[[A:.*]]: memref<?x?xf32>, %[[B:.*]]: memref<?x?xf32>, %[[C:.*]]: memref<?x?xf32>) {
+// CHECK-DAG: %[[VAL_3:.*]] = arith.constant 0 : index
+// CHECK-DAG: %[[VAL_4:.*]] = memref.dim %[[A]], %[[VAL_3]] : memref<?x?xf32>
+// CHECK-DAG: %[[VAL_5:.*]] = arith.constant 1 : index
+// CHECK-DAG: %[[VAL_6:.*]] = memref.dim %[[B]], %[[VAL_5]] : memref<?x?xf32>
+// CHECK-DAG: %[[VAL_7:.*]] = arith.constant 1 : index
+// CHECK-DAG: %[[VAL_8:.*]] = memref.dim %[[A]], %[[VAL_7]] : memref<?x?xf32>
+// CHECK: %[[MASK_A:.*]] = vector.create_mask %[[VAL_4]], %[[VAL_8]] : vector<8x4xi1>
+// CHECK: %[[LOAD_A:.*]] = vector.mask %[[MASK_A]] { vector.transfer_read %[[A]]{{\[}}%{{.*}}, %{{.*}}], %{{.*}} {in_bounds = [true, true, true], permutation_map = #{{.*}}} : memref<?x?xf32>, vector<8x[16]x4xf32> } : vector<8x4xi1> -> vector<8x[16]x4xf32>
+// CHECK: %[[MASK_B:.*]] = vector.create_mask %[[VAL_8]], %[[VAL_6]] : vector<4x[16]xi1>
+// CHECK: %[[LOAD_B:.*]] = vector.mask %[[MASK_B]] { vector.transfer_read %[[B]]{{\[}}%{{.*}}, %{{.*}}], %{{.*}} {in_bounds = [true, true, true], permutation_map = #{{.*}}} : memref<?x?xf32>, vector<8x[16]x4xf32> } : vector<4x[16]xi1> -> vector<8x[16]x4xf32>
+// CHECK: %[[MASK_C:.*]] = vector.create_mask %[[VAL_4]], %[[VAL_6]] : vector<8x[16]xi1>
+// CHECK: %[[LOAD_C:.*]] = vector.mask %[[MASK_C]] { vector.transfer_read %[[C]]{{\[}}%{{.*}}, %{{.*}}], %{{.*}} {in_bounds = [true, true]} : memref<?x?xf32>, vector<8x[16]xf32> } : vector<8x[16]xi1> -> vector<8x[16]xf32>
+// CHECK: %[[MULF:.*]] = arith.mulf %[[LOAD_A]], %[[LOAD_B]] : vector<8x[16]x4xf32>
+// CHECK: %[[MASK_MULIT_RED:.*]] = vector.create_mask %[[VAL_4]], %[[VAL_6]], %[[VAL_8]] : vector<8x[16]x4xi1>
+// CHECK: %[[MULTI_RED:.*]] = vector.mask %[[MASK_MULIT_RED]] { vector.multi_reduction <add>, %[[MULF]], %[[LOAD_C]] [2] : vector<8x[16]x4xf32> to vector<8x[16]xf32> } : vector<8x[16]x4xi1> -> vector<8x[16]xf32>
+// CHECK: %[[C2:.*]] = arith.constant 0 : index
+// CHECK: vector.mask %[[MASK_C]] { vector.transfer_write %[[MULTI_RED]], %[[C]]{{\[}}%[[C2]], %[[C2]]] {in_bounds = [true, true]} : vector<8x[16]xf32>, memref<?x?xf32> } : vector<8x[16]xi1>
transform.sequence failures(propagate) {
^bb1(%arg1: !transform.any_op):
- %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- %1 = 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
+ %matmul = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+ transform.structured.vectorize %matmul vector_sizes [8, [16], 4] : !transform.any_op
}
-
-// CHECK-LABEL: @wrong_reduction_detection
-// CHECK: vector.broadcast
-// CHECK: vector.transfer_write
-
-// -----
-
-// Don't vectorize tensor<0xf32> : (!transform.any_op) -> !transform.any_op
-// CHECK-LABEL: @tensor_size0
-// CHECK: linalg.generic
-func.func @tensor_size0(%arg0: tensor<0xf32>,
- %arg1: tensor<f32>) -> tensor<f32> {
- %0 = linalg.generic
- {indexing_maps = [affine_map<(d0) -> (d0)>, affine_map<(d0) -> ()>],
- iterator_types = ["reduction"]}
- ins(%arg0 : tensor<0xf32>) outs(%arg1 : tensor<f32>) {
- ^bb0(%in: f32, %out: f32):
- %12 = arith.addf %out, %in : f32
- linalg.yield %12 : f32
- } -> tensor<f32>
- return %0 : tensor<f32>
-}
-
-transform.sequence failures(propagate) {
-^bb1(%arg1: !transform.any_op):
- %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- %1 = 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
-}
-
-// -----
-
-// CHECK-LABEL: func @test_masked_pad_static_dynamic
-func.func @test_masked_pad_static_dynamic(%arg0: tensor<1x2x2x?xf32>, %low: index, %high: index,
- %pad_value: f32) -> tensor<6x?x?x?xf32> {
- // CHECK: tensor.pad
- %0 = tensor.pad %arg0 low[2, %low, 3, 3] high[3, 3, %high, 2] {
- ^bb0(%arg1: index, %arg2: index, %arg3: index, %arg4: index):
- tensor.yield %pad_value : f32
- } : tensor<1x2x2x?xf32> to tensor<6x?x?x?xf32>
- return %0 : tensor<6x?x?x?xf32>
-}
-
-
-transform.sequence failures(propagate) {
-^bb1(%arg1: !transform.any_op):
- %0 = transform.structured.match ops{["tensor.pad"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- %1 = get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %2 = transform.structured.vectorize_children_and_apply_patterns %1 { vectorize_padding } : (!transform.any_op) -> !transform.any_op
-}
-
-// -----
-
-func.func @zero_dim_tensor(%input: tensor<f32>, %output: tensor<f32>) -> tensor<f32>
-{
- %0 = linalg.generic { indexing_maps = [ affine_map<() -> ()>, affine_map<() -> ()> ],
- iterator_types = [] }
- ins(%input : tensor<f32>)
- outs(%output : tensor<f32>) {
- ^bb0(%arg0: f32, %arg1: f32):
- %2 = arith.addf %arg0, %arg1 : f32
- linalg.yield %2 : f32
- } -> tensor<f32>
- return %0 : tensor<f32>
-}
-
-transform.sequence failures(propagate) {
-^bb1(%arg1: !transform.any_op):
- %3 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- %4 = get_parent_op %3 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %5 = transform.structured.vectorize_children_and_apply_patterns %4 : (!transform.any_op) -> !transform.any_op
-}
-
-// CHECK-LABEL: func @zero_dim_tensor
-// CHECK: vector.transfer_read {{.*}} : tensor<f32>, vector<f32>
-// CHECK: vector.extractelement
-// CHECK: vector.transfer_read {{.*}} : tensor<f32>, vector<f32>
-// CHECK: vector.extractelement
-// CHECK: arith.addf {{.*}} : f32
-// CHECK: vector.broadcast %{{.*}} : f32 to vector<f32>
-// CHECK: vector.transfer_write {{.*}} : vector<f32>, tensor<f32>
-
-// -----
-
-// Make sure we generate the right transfer writes for multi-output generic ops
-// with different permutation maps.
-
-func.func @multi_output_generic_different_perm_maps(%in0: tensor<4x1xf32>,
- %out0: tensor<4x1xf32>,
- %out1: tensor<1x4xf32>) -> (tensor<4x1xf32>, tensor<1x4xf32>) {
- %13:2 = linalg.generic {indexing_maps = [ affine_map<(d0, d1) -> (d1, d0)>,
- affine_map<(d0, d1) -> (d1, d0)>,
- affine_map<(d0, d1) -> (d0, d1)> ],
- iterator_types = ["parallel", "parallel"]}
- ins(%in0 : tensor<4x1xf32>)
- outs(%out0, %out1 : tensor<4x1xf32>, tensor<1x4xf32>) {
- ^bb0(%in: f32, %out: f32, %out_2: f32):
- %16 = arith.addf %in, %in : f32
- linalg.yield %16, %16 : f32, f32
- } -> (tensor<4x1xf32>, tensor<1x4xf32>)
- return %13#0, %13#1 : tensor<4x1xf32>, tensor<1x4xf32>
-}
-
-transform.sequence failures(propagate) {
-^bb1(%arg1: !transform.any_op):
- %3 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- %4 = get_parent_op %3 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
- %5 = transform.structured.vectorize_children_and_apply_patterns %4 : (!transform.any_op) -> !transform.any_op
-}
-
-// CHECK-LABEL: func @multi_output_generic_different_perm_maps
-// CHECK: %[[VAL_5:.*]] = vector.transfer_read %{{.*}} {in_bounds = [true, true]} : tensor<4x1xf32>, vector<4x1xf32>
-// CHECK: %[[VAL_6:.*]] = arith.addf %[[VAL_5]], %[[VAL_5]] : vector<4x1xf32>
-// CHECK: %[[VAL_7:.*]] = vector.transpose %[[VAL_6]], [1, 0] : vector<4x1xf32> to vector<1x4xf32>
-// CHECK: %[[VAL_8:.*]] = vector.transpose %[[VAL_7]], [1, 0] : vector<1x4xf32> to vector<4x1xf32>
-// CHECK: vector.transfer_write %[[VAL_8]], %{{.*}} {in_bounds = [true, true]} : vector<4x1xf32>, tensor<4x1xf32>
-// CHECK: vector.transfer_write %[[VAL_7]], %{{.*}} {in_bounds = [true, true]} : vector<1x4xf32>, tensor<1x4xf32>
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