[Mlir-commits] [mlir] [mlir][ArmSME] Add support for lowering masked tile_load ops (PR #70915)
llvmlistbot at llvm.org
llvmlistbot at llvm.org
Thu Nov 2 03:53:56 PDT 2023
llvmbot wrote:
<!--LLVM PR SUMMARY COMMENT-->
@llvm/pr-subscribers-mlir-sme
Author: Cullen Rhodes (c-rhodes)
<details>
<summary>Changes</summary>
This patch extends ArmSMEToSCF to support lowering of masked tile_load
ops. Only masks created by 'vector.create_mask' are currently supported.
There are two lowerings, one for pad of constant zero and another for
non-zero pad. For the following example:
```
%pad = arith.constant 0 : i32
%num_rows = arith.constant 2 : index
%num_cols = arith.constant 4 : index
%mask = vector.create_mask %num_rows, %num_cols : <[4]x[4]xi1>
%tile = arm_sme.tile_load %src[%c0, %c0], %pad, %mask : memref<?x?xi32>,
vector<[4]x[4]xi32>
```
The former (constant non-zero pad) is lowered as follows:
---------------------------------------------------------
```
%tile = arm_sme.zero : vector<[4]x[4]xi32>
%num_cols = vector.create_mask %c4 : vector<[4]xi1>
scf.for %slice_idx = %c0 to %num_rows step %c1
%tile_update = arm_sme.load_tile_slice
%src[%slice_idx], %num_cols, %tile, %tile_slice_idx :
memref<?x?xi32>, vector<[1]xi32>, vector<[4]x[4]xi32>
```
The tile is zeroed the satisfy the padding and only active rows are
loaded.
The latter (non-zero pad) is lowered as follows:
------------------------------------------------
```
scf.for %slice_idx = %c0 to %num_tile_slices step %c1 {
%row_is_active = arith.cmpi ult %slice_idx, %num_rows : index
%slice = scf.if %row_is_active -> vector<[4]xf32> {
%slice = vector.maskedload %src[%slice_idx, %c0], %num_cols, %pad_1d :
memref<?x?xf32>, vector<[4]xi1>, vector<[4]xf32> into vector<[4]xf32>
scf.yield %slice : vector<[4]xf32>
} else {
scf.yield %pad_1d : vector<[4]xf32>
}
arm_sme.move_vector_to_tile_slice %slice, %tile, %slice_idx
: vector<[4]xi32> into vector<[4]x[4]xi32>
```
The scalar pad is broadcast to a 1-D vector and a regular
'vector.masked_load' (will be lowered to SVE, not SME) loads each slice
for active rows, with padding specified as a passthru. For non-active
rows the slice is the 1-D pad. The resulting slice is inserted into the
tile with 'arm_sme.move_vector_to_tile_slice'.
---
Patch is 23.41 KiB, truncated to 20.00 KiB below, full version: https://github.com/llvm/llvm-project/pull/70915.diff
3 Files Affected:
- (modified) mlir/lib/Conversion/ArmSMEToSCF/ArmSMEToSCF.cpp (+252-4)
- (modified) mlir/test/Conversion/ArmSMEToSCF/arm-sme-to-scf.mlir (+56)
- (added) mlir/test/Integration/Dialect/Vector/CPU/ArmSME/test-transfer-read-2d.mlir (+212)
``````````diff
diff --git a/mlir/lib/Conversion/ArmSMEToSCF/ArmSMEToSCF.cpp b/mlir/lib/Conversion/ArmSMEToSCF/ArmSMEToSCF.cpp
index 80da6ffda1ed2ea..46e81bb935c406a 100644
--- a/mlir/lib/Conversion/ArmSMEToSCF/ArmSMEToSCF.cpp
+++ b/mlir/lib/Conversion/ArmSMEToSCF/ArmSMEToSCF.cpp
@@ -80,9 +80,8 @@ struct TileLoadOpConversion : public OpRewritePattern<arm_sme::TileLoadOp> {
LogicalResult matchAndRewrite(arm_sme::TileLoadOp tileLoadOp,
PatternRewriter &rewriter) const override {
if (tileLoadOp.getMask())
- // TODO: add masked patterns.
- return rewriter.notifyMatchFailure(
- tileLoadOp, "op has mask, needs masked pattern(s)");
+ return rewriter.notifyMatchFailure(tileLoadOp,
+ "op has mask, apply masked patterns");
OpBuilder::InsertionGuard g(rewriter);
auto loc = tileLoadOp.getLoc();
@@ -142,6 +141,254 @@ struct TileLoadOpConversion : public OpRewritePattern<arm_sme::TileLoadOp> {
}
};
+/// Lower `arm_sme.tile_load` with mask and pad of constant zero.
+///
+/// BEFORE:
+/// ```mlir
+/// %pad = arith.constant 0 : i32
+/// %num_rows = arith.constant 2 : index
+/// %num_cols = arith.constant 4 : index
+/// %mask = vector.create_mask %num_rows, %num_cols : vector<[4]x[4]xi1>
+/// %tile = arm_sme.tile_load %src[%c0, %c0], %pad, %mask :
+/// memref<?x?xi32>, vector<[4]x[4]xi32>
+/// ```
+///
+/// AFTER:
+/// ```mlir
+/// %c0 = arith.constant 0 : index
+/// %c1 = arith.constant 1 : index
+/// %tile = arm_sme.zero : vector<[4]x[4]xi32>
+/// %num_cols = vector.create_mask %c4 : vector<[4]xi1>
+/// scf.for %tile_slice_idx = %c0 to %num_rows step %c1 {
+/// %tile_update = arm_sme.load_tile_slice
+/// %src[%tile_slice_idx], %num_cols, %tile, %tile_slice_idx :
+/// memref<?x?xi32>, vector<[1]xi32>, vector<[4]x[4]xi32>
+/// }
+/// ```
+///
+/// NOTE: Only mask of 'vector.create_mask' op is currently supported.
+struct TileLoadOpWithMaskAndPadZeroConversion
+ : public OpRewritePattern<arm_sme::TileLoadOp> {
+ using OpRewritePattern<arm_sme::TileLoadOp>::OpRewritePattern;
+
+ LogicalResult matchAndRewrite(arm_sme::TileLoadOp tileLoadOp,
+ PatternRewriter &rewriter) const override {
+ OpBuilder::InsertionGuard g(rewriter);
+ auto loc = tileLoadOp.getLoc();
+ auto tileType = tileLoadOp.getVectorType();
+
+ auto maskOp = tileLoadOp.getMask();
+ if (!maskOp)
+ return rewriter.notifyMatchFailure(
+ tileLoadOp, "op has no mask, needs unmasked pattern");
+
+ auto padOp = tileLoadOp.getPadding();
+ assert(padOp && "expected padding when masking!");
+
+ auto createMaskOp = maskOp.getDefiningOp<vector::CreateMaskOp>();
+ if (!createMaskOp)
+ return rewriter.notifyMatchFailure(
+ tileLoadOp, "unsupported mask op, only 'vector.create_mask' is "
+ "currently supported");
+
+ auto constPadOp = padOp.getDefiningOp<arith::ConstantOp>();
+ if (!constPadOp || constPadOp.getValue() !=
+ rewriter.getZeroAttr(tileType.getElementType()))
+ return rewriter.notifyMatchFailure(
+ tileLoadOp, "op has non-zero pad, needs non-zero pad pattern");
+
+ auto numRows = createMaskOp.getOperands()[0];
+ auto numCols = createMaskOp.getOperands()[1];
+
+ auto predicateType =
+ VectorType::get(tileType.getDimSize(1), rewriter.getI1Type(), true);
+ auto numColsOp =
+ rewriter.create<vector::CreateMaskOp>(loc, predicateType, numCols);
+
+ // Initialize tile with zero to satisfy padding. Inactive cols will be
+ // zeroed anyway since the loads use zeroing predication. For inactive rows
+ // however, no load will occur so these need to be zeroed.
+ auto tile = rewriter.create<arm_sme::ZeroOp>(loc, tileType);
+
+ // Create a loop to load the active tile slices from memory.
+ auto step = rewriter.create<arith::ConstantIndexOp>(loc, 1);
+ auto lowerBound = rewriter.create<arith::ConstantIndexOp>(loc, 0);
+ auto upperBound = numRows;
+ auto forOp = rewriter.create<scf::ForOp>(loc, lowerBound, upperBound, step);
+
+ rewriter.setInsertionPointToStart(forOp.getBody());
+
+ // Create 'arm_sme.load_tile_slice' to load tile slice from memory into
+ // tile.
+ SmallVector<Value> memrefIndices;
+ auto tileSliceIndex = forOp.getInductionVar();
+ getMemrefIndices(tileLoadOp.getIndices(),
+ tileLoadOp.getMemRefType().getRank(), tileSliceIndex,
+ upperBound, memrefIndices, loc, rewriter);
+ rewriter.create<arm_sme::LoadTileSliceOp>(
+ loc, tileType, tileLoadOp.getBase(), numColsOp, tile, memrefIndices,
+ tileSliceIndex, tileLoadOp.getLayout());
+
+ rewriter.setInsertionPointAfter(forOp);
+
+ // Replace 'arm_sme.tile_load' with the tile.
+ rewriter.replaceOp(tileLoadOp, tile);
+
+ return success();
+ }
+};
+
+/// Lower `arm_sme.tile_load` with mask and non-zero pad.
+///
+/// BEFORE:
+/// ```mlir
+/// %pad = arith.constant 1 : i32
+/// %num_rows = arith.constant 2 : index
+/// %num_cols = arith.constant 4 : index
+/// %mask = vector.create_mask %num_rows, %num_cols : vector<[4]x[4]xi1>
+/// %tile = arm_sme.tile_load %src[%c0, %c0], %pad, %mask :
+/// memref<?x?xi32>, vector<[4]x[4]xi32>
+/// ```
+///
+/// AFTER:
+/// ```mlir
+/// %pad_1d = arith.constant dense<1> : vector<[4]xi32>
+/// %num_rows = arith.constant 2 : index
+/// %num_cols = arith.constant 4 : index
+/// %tile_id = arm_sme.get_tile_id : i32
+/// %tile = arm_sme.cast_tile_to_vector %tile_id : i32 to vector<[4]x[4]xi32>
+/// %vscale = vector.vscale
+/// %c0 = arith.constant 0 : index
+/// %c1 = arith.constant 1 : index
+/// %min_svl_s = arith.constant 4 : index
+/// %svl_s = arith.muli %min_svl_s, %vscale : index
+/// scf.for %tile_slice_idx = %c0 to %svl_s step %c1 {
+/// %row_is_active = arith.cmpi ult %tile_slice_idx, %num_rows : index
+/// %slice = scf.if %row_is_active -> vector<[4]xi32> {
+/// %slice = vector.maskedload %base[%tile_slice_idx, %c0], %num_cols, %pad
+/// : memref<?x?xi32>, vector<[4]xi1>,
+/// vector<[4]xi32> into vector<[4]xi32>
+/// scf.yield %slice : vector<[4]xi32>
+/// } else {
+/// scf.yield %pad_1d : vector<[4]xi32>
+/// }
+/// // Insert slice into tile
+/// arm_sme.move_vector_to_tile_slice %slice, %tile, %tile_slice_idx
+/// : vector<[4]xi32> into vector<[4]x[4]xi32>
+/// }
+/// ```
+struct TileLoadOpWithMaskAndPadNonZeroConversion
+ : public OpRewritePattern<arm_sme::TileLoadOp> {
+ using OpRewritePattern<arm_sme::TileLoadOp>::OpRewritePattern;
+
+ LogicalResult matchAndRewrite(arm_sme::TileLoadOp tileLoadOp,
+ PatternRewriter &rewriter) const override {
+ OpBuilder::InsertionGuard g(rewriter);
+ auto loc = tileLoadOp.getLoc();
+ auto tileType = tileLoadOp.getVectorType();
+ auto tileElementType = tileType.getElementType();
+ unsigned tileElementWidth = tileElementType.getIntOrFloatBitWidth();
+
+ auto maskOp = tileLoadOp.getMask();
+ if (!maskOp)
+ return rewriter.notifyMatchFailure(
+ tileLoadOp, "op has no mask, needs unmasked pattern");
+
+ auto padOp = tileLoadOp.getPadding();
+ assert(padOp && "expected padding when masking!");
+
+ auto createMaskOp = maskOp.getDefiningOp<vector::CreateMaskOp>();
+ if (!createMaskOp)
+ return rewriter.notifyMatchFailure(
+ tileLoadOp, "unsupported mask op, only 'vector.create_mask' is "
+ "currently supported");
+
+ auto constPadOp = padOp.getDefiningOp<arith::ConstantOp>();
+ if (constPadOp &&
+ constPadOp.getValue() == rewriter.getZeroAttr(tileElementType))
+ return rewriter.notifyMatchFailure(
+ tileLoadOp, "op has constant zero pad, needs zero pad pattern");
+
+ auto numRows = createMaskOp.getOperands()[0];
+ auto numCols = createMaskOp.getOperands()[1];
+
+ VectorType tileSliceType = VectorType::Builder(tileType).dropDim(0);
+ auto predicateType =
+ VectorType::get(tileType.getDimSize(1), rewriter.getI1Type(), true);
+ auto numColsOp =
+ rewriter.create<vector::CreateMaskOp>(loc, predicateType, numCols);
+
+ // Create 'arm_sme.get_tile' op.
+ auto tileId = rewriter.create<arm_sme::GetTileID>(
+ loc, rewriter.getIntegerType(tileElementWidth));
+
+ // Create `arm_sme.cast_tile_to_vector` to cast tile ID to a vector type to
+ // use as input tile to 'arm_sme.load_tile_slice' ops.
+ auto tile =
+ rewriter.create<arm_sme::CastTileToVector>(loc, tileType, tileId);
+
+ // Create a loop that loads each ZA tile slice from memory.
+ auto step = rewriter.create<arith::ConstantIndexOp>(loc, 1);
+ auto minTileSlices = rewriter.create<arith::ConstantIndexOp>(
+ loc, arm_sme::getSMETileSliceMinNumElts(tileElementType));
+ auto vscale =
+ rewriter.create<vector::VectorScaleOp>(loc, rewriter.getIndexType());
+ auto lowerBound = rewriter.create<arith::ConstantIndexOp>(loc, 0);
+ auto numTileSlices =
+ rewriter.create<arith::MulIOp>(loc, minTileSlices, vscale);
+ auto forOp =
+ rewriter.create<scf::ForOp>(loc, lowerBound, numTileSlices, step);
+
+ rewriter.setInsertionPointToStart(forOp.getBody());
+
+ auto tileSliceIndex = forOp.getInductionVar();
+
+ auto rowIsActive = rewriter.create<arith::CmpIOp>(
+ loc, arith::CmpIPredicate::ult, tileSliceIndex, numRows);
+
+ SmallVector<Value> memrefIndices;
+ getMemrefIndices(tileLoadOp.getIndices(),
+ tileLoadOp.getMemRefType().getRank(), tileSliceIndex,
+ numTileSlices, memrefIndices, loc, rewriter);
+
+ // Splat pad into 1-D vector matching type of tile slice.
+ auto pad1DOp = rewriter.create<vector::SplatOp>(loc, tileSliceType, padOp);
+
+ Operation *slice = rewriter.create<scf::IfOp>(
+ loc, rowIsActive,
+ [&](OpBuilder &b, Location loc) {
+ // If the row is active, emit a masked load where the predicate is
+ // 'numCols'. Pad is used for inactive elements, taken from
+ // passthru.
+ auto loadSlice = rewriter.create<vector::MaskedLoadOp>(
+ loc, tileSliceType, tileLoadOp.getBase(), memrefIndices,
+ numColsOp, /*passthru=*/pad1DOp);
+ rewriter.create<scf::YieldOp>(loc, loadSlice->getResult(0));
+ },
+ [&](OpBuilder &b, Location loc) {
+ // Inactive rows are filled with pad.
+ rewriter.create<scf::YieldOp>(loc, pad1DOp.getResult());
+ });
+
+ // TODO: If the load is vertical the transpose can't be done in-flight with
+ // a regular (SVE) maskedload. Propagate layout to
+ // 'arm_sme.move_vector_to_tile_slice' below once it supports layout. This
+ // is currently broken.
+
+ // Create 'arm_sme.move_vector_to_tile_slice' to move slice into tile.
+ rewriter.create<arm_sme::MoveVectorToTileSliceOp>(
+ loc, tileType, slice->getResult(0), tile, tileSliceIndex,
+ tileLoadOp.getLayout());
+
+ rewriter.setInsertionPointAfter(forOp);
+
+ // Replace 'arm_sme.tile_load' with the tile.
+ rewriter.replaceOp(tileLoadOp, tile);
+
+ return success();
+ }
+};
+
/// Lower `arm_sme.tile_store` to a loop over the tile slices and store each
/// slice using `arm_sme.store_tile_slice`.
///
@@ -273,7 +520,8 @@ struct TileVectorPrintOpConversion : public OpRewritePattern<vector::PrintOp> {
} // namespace
void mlir::populateArmSMEToSCFConversionPatterns(RewritePatternSet &patterns) {
- patterns.add<TileLoadOpConversion, TileStoreOpConversion,
+ patterns.add<TileLoadOpConversion, TileLoadOpWithMaskAndPadZeroConversion,
+ TileLoadOpWithMaskAndPadNonZeroConversion, TileStoreOpConversion,
TileVectorPrintOpConversion>(patterns.getContext());
}
diff --git a/mlir/test/Conversion/ArmSMEToSCF/arm-sme-to-scf.mlir b/mlir/test/Conversion/ArmSMEToSCF/arm-sme-to-scf.mlir
index d61f588941b408c..55ea56f42c96ed9 100644
--- a/mlir/test/Conversion/ArmSMEToSCF/arm-sme-to-scf.mlir
+++ b/mlir/test/Conversion/ArmSMEToSCF/arm-sme-to-scf.mlir
@@ -33,6 +33,62 @@ func.func @arm_sme_tile_load_ver(%src : memref<?x?xi32>) {
return
}
+// -----
+
+// CHECK-LABEL: func.func @arm_sme_tile_load_hor_with_mask_and_pad_zero(
+// CHECK-SAME: %[[SRC:.*]]: memref<?x?xi32>) {
+// CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
+// CHECK-DAG: %[[C1:.*]] = arith.constant 1 : index
+// CHECK-DAG: %[[NUM_ROWS:.*]] = arith.constant 3 : index
+// CHECK-DAG: %[[NUM_COLS:.*]] = vector.create_mask %c2 : vector<[4]xi1>
+// CHECK-DAG: %[[TILEZERO:.*]] = arm_sme.zero : vector<[4]x[4]xi32>
+// CHECK-NEXT: scf.for %[[TILE_SLICE_INDEX:.*]] = %[[C0]] to %[[NUM_ROWS]] step %[[C1]] {
+// CHECK-NEXT: %[[OFFSET:.*]] = arith.addi %[[C0]], %[[TILE_SLICE_INDEX]] : index
+// CHECK-NEXT: arm_sme.load_tile_slice %[[SRC]]{{\[}}%[[OFFSET]], %[[C0]]], %[[NUM_COLS]], %[[TILEZERO]], %[[TILE_SLICE_INDEX]] : memref<?x?xi32>, vector<[4]xi1>, vector<[4]x[4]xi32>
+func.func @arm_sme_tile_load_hor_with_mask_and_pad_zero(%src : memref<?x?xi32>) {
+ %c0 = arith.constant 0 : index
+ %c2 = arith.constant 2 : index
+ %c3 = arith.constant 3 : index
+ %pad = arith.constant 0 : i32
+ %mask = vector.create_mask %c3, %c2 : vector<[4]x[4]xi1>
+ %tile = arm_sme.tile_load %src[%c0, %c0], %pad, %mask : memref<?x?xi32>, vector<[4]x[4]xi32>
+ return
+}
+
+// -----
+
+// CHECK-LABEL: func.func @arm_sme_tile_load_hor_with_mask_and_nonzero_pad(
+// CHECK-SAME: %[[SRC:.*]]: memref<?x?xi32>,
+// CHECK-SAME: %[[PAD:.*]]: i32) {
+// CHECK-DAG: %[[TILE_ID:.*]] = arm_sme.get_tile_id : i32
+// CHECK-DAG: %[[CAST_TILE_TO_VECTOR:.*]] = arm_sme.cast_tile_to_vector %[[TILE_ID]] : i32 to vector<[4]x[4]xi32>
+// CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
+// CHECK-DAG: %[[C1:.*]] = arith.constant 1 : index
+// CHECK-DAG: %[[C4:.*]] = arith.constant 4 : index
+// CHECK-DAG: %[[NUM_ROWS:.*]] = arith.constant 3 : index
+// CHECK-DAG: %[[NUM_COLS:.*]] = vector.create_mask %c2 : vector<[4]xi1>
+// CHECK-DAG: %[[VSCALE:.*]] = vector.vscale
+// CHECK-NEXT: %[[NUM_TILE_SLICES:.*]] = arith.muli %[[C4]], %[[VSCALE]] : index
+// CHECK-NEXT: scf.for %[[TILE_SLICE_INDEX:.*]] = %[[C0]] to %[[NUM_TILE_SLICES]] step %[[C1]] {
+// CHECK-NEXT: %[[ROW_IS_ACTIVE:.*]] = arith.cmpi ult, %[[TILE_SLICE_INDEX]], %[[NUM_ROWS]] : index
+// CHECK-NEXT: %[[OFFSET:.*]] = arith.addi %[[C0]], %[[TILE_SLICE_INDEX]] : index
+// CHECK: %[[PAD_1D:.*]] = vector.splat %[[PAD]] : vector<[4]xi32>
+// CHECK: %[[SLICE:.*]] = scf.if %[[ROW_IS_ACTIVE]] -> (vector<[4]xi32>) {
+// CHECK: %[[LOAD_SLICE:.*]] = vector.maskedload %[[SRC]]{{\[}}%[[OFFSET]], %[[C0]]], %[[NUM_COLS]], %[[PAD_1D]] : memref<?x?xi32>, vector<[4]xi1>, vector<[4]xi32> into vector<[4]xi32>
+// CHECK: scf.yield %[[LOAD_SLICE]] : vector<[4]xi32>
+// CHECK: } else {
+// CHECK: scf.yield %[[PAD_1D]] : vector<[4]xi32>
+// CHECK: }
+// CHECK: arm_sme.move_vector_to_tile_slice %[[SLICE]], %[[CAST_TILE_TO_VECTOR]], %[[TILE_SLICE_INDEX]] : vector<[4]xi32> into vector<[4]x[4]xi32>
+func.func @arm_sme_tile_load_hor_with_mask_and_nonzero_pad(%src : memref<?x?xi32>, %pad : i32) {
+ %c0 = arith.constant 0 : index
+ %c2 = arith.constant 2 : index
+ %c3 = arith.constant 3 : index
+ %mask = vector.create_mask %c3, %c2 : vector<[4]x[4]xi1>
+ %tile = arm_sme.tile_load %src[%c0, %c0], %pad, %mask : memref<?x?xi32>, vector<[4]x[4]xi32>
+ return
+}
+
//===----------------------------------------------------------------------===//
// arm_sme.tile_store
//===----------------------------------------------------------------------===//
diff --git a/mlir/test/Integration/Dialect/Vector/CPU/ArmSME/test-transfer-read-2d.mlir b/mlir/test/Integration/Dialect/Vector/CPU/ArmSME/test-transfer-read-2d.mlir
new file mode 100644
index 000000000000000..644f90d950645b8
--- /dev/null
+++ b/mlir/test/Integration/Dialect/Vector/CPU/ArmSME/test-transfer-read-2d.mlir
@@ -0,0 +1,212 @@
+// DEFINE: %{entry_point} = entry
+// DEFINE: %{compile} = mlir-opt %s \
+// DEFINE: -enable-arm-streaming="mode=locally enable-za" \
+// DEFINE: -convert-vector-to-arm-sme -convert-arm-sme-to-scf \
+// DEFINE: -convert-vector-to-llvm="enable-arm-sme" -cse -canonicalize \
+// DEFINE: -allocate-arm-sme-tiles -test-lower-to-llvm
+// DEFINE: %{run} = %mcr_aarch64_cmd \
+// DEFINE: -march=aarch64 -mattr=+sve,+sme \
+// DEFINE: -e %{entry_point} -entry-point-result=void \
+// DEFINE: -shared-libs=%mlir_runner_utils,%mlir_c_runner_utils
+
+// RUN: %{compile} | %{run} | FileCheck %s
+
+// Vector load.
+func.func @transfer_read_2d(%A : memref<?x?xf32>, %base1: index, %base2: index) {
+ %c4 = arith.constant 4 : index
+ %pad = arith.constant 0.0 : f32
+ %0 = vector.transfer_read %A[%base1, %base2], %pad {in_bounds=[true, true]} :
+ memref<?x?xf32>, vector<[4]x[4]xf32>
+
+ vector.print str "TILE BEGIN:"
+ vector.print %0: vector<[4]x[4]xf32>
+
+ return
+}
+
+// Vector load + transpose.
+func.func @transfer_read_2d_transposed(%A : memref<?x?xf32>, %base1: index, %base2: index) {
+ %pad = arith.constant 0.0 : f32
+ %0 = vector.transfer_read %A[%base1, %base2], %pad
+ {permutation_map = affine_map<(d0, d1) -> (d1, d0)>, in_bounds=[true, true]}
+ : memref<?x?xf32>, vector<[4]x[4]xf32>
+
+ vector.print str "TILE BEGIN:"
+ vector.print %0 : vector<[4]x[4]xf32>
+
+ return
+}
+
+// Vector load with mask and pad of zero.
+func.func @transfer_read_2d_mask(%A : memref<?x?xf32>, %base1: index, %base2: index) {
+ %c2 = arith.constant 2 : index
+ %c3 = arith.constant 3 : index
+ %pad = arith.constant 0.0 : f32
+ %mask = vector.create_mask %c2, %c3 : vector<[4]x[4]xi1>
+ %0 = vector.transfer_read %A[%base1, %base2], %pad, %mask
+ {in_bounds = [true, true]} : memref<?x?xf32>, vector<[4]x[4]xf32>
+
+ vector.print str "TILE BEGIN:"
+ vector.print %0: vector<[4]x[4]xf32>
+
+ return
+}
+
+// Vector load with mask and pad of zero + transpose.
+func.func @transfer_read_2d_mask_transposed(%A : memref<?x?xf32>, %base1: index, %base2: index) {
+ %c2 = arith.constant 2 : index
+ %c3 = arith.constant 3 : index
+ %pad = arith.constant 0.0 : f32
+ %mask = vector.create_mask %c2, %c3 : vector<[4]x[4]xi1>
+ %0 = vector.transfer_read %A[%base1, %base2], %pad, %mask
+ {permutation_map = affine_map<(d0, d1) -> (d1, d0)>, in_bounds=[true, true]}
+ : memref<?x?xf32>, vector<[4]x[4]xf32>
+
+ vector.print str "TILE BEGIN:"
+ vector.print %0: vector<[4]x[4]xf32>
+
+ return
+}
+
+// Vector load with mask and non-zero pad.
+func.func @transfer_read_2d_mask_non_zero_pad(%A : memref<?x?xf32>, %base1: index, %base2: index) {
+ %c2 = arith.constant 2 : index
+ %c3 = arith.constant 3 : index
+ %pad = arith.constant -42.0 : f32
+ %mask = vector.create_mask %c2, %c3 : vector<[4]x[4]xi1>
+ %0 = vector.transfer_read %A[%base1, %base2], %pad, %mask
+ {in_bounds = [true, true]} : memref<?x?xf32>, vector<[4]x[4]xf32>
+
+ vector.print str "TILE BEGIN:"
+ vector.print %0: vector<[4]x[4]xf32>
+
+ return
+}
+
+// Vector load with mask and non-zero pad + transpose.
+func.func @transfer_read_2d_mask_non_zero_pad_transposed(%A : memref<?x?xf32>, %base1: index, %base2: index) {
+ %c2 = arith.constant 2 : index
+ %c3 = arith.constant 3 : index
+ %pad = arith.constant -42.0 : f32
+ %mask = vector.create_mask %c2, %c3 : vector<[4]x[4]xi1>
+ %0 = vector.transfer_read %A[%base1, %base2], %pad, %mask
+ {permutation_map = affine_map<(d0, d1) -> (d1, d0)>, in_bounds=[true, true]}
+ : memref<?x?xf32>, vector<[4]x[4]xf32>
+
+ vector.print str "TILE BEGIN:"
+ vector.print %0: vector<[4]x[4]xf32>
+
+ return
+}
+
+// Allocate heap memory of size 'd0' x 'd1' and initialize.
+//
+// Example:
+//
+// initialize_memory(%c4, %c5)
+//
+// 0, 1, 2, 3, 4
+// 10, 11, 12, 13, 14
+// 20, 21, 22, 23, 24
+// 30, 31, 32, 33, 34
+//
+// Returns dynamic memref. It's the callers responsiblity to free the returned
+// memref.
+func.func @initialize_memor...
[truncated]
``````````
</details>
https://github.com/llvm/llvm-project/pull/70915
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