[Mlir-commits] [mlir] [mlir][linalg] Consolidate tests for scalable vectorization (PR #141469)
Andrzej Warzyński
llvmlistbot at llvm.org
Mon May 26 06:18:06 PDT 2025
https://github.com/banach-space updated https://github.com/llvm/llvm-project/pull/141469
>From 940cd17fffb915b50a4fe80de13eb9114e6630db Mon Sep 17 00:00:00 2001
From: Andrzej Warzynski <andrzej.warzynski at arm.com>
Date: Mon, 26 May 2025 10:53:05 +0100
Subject: [PATCH] [mlir][linalg] Consolidate tests for scalable vectorization
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit
This patch moves scalable vectorization tests into an existing generic
vectorization test file:
* vectorization-scalable.mlir --> merged into vectorization.mlir
Rationale:
* Most tests in vectorization-scalable.mlir are variants of existing
tests in vectorization.mlir. Keeping them together improves
maintainability.
* Consolidating tests makes it easier to spot gaps in coverage for
regular vectorization.
* In the Vector dialect, we don't separate tests for scalable vectors;
this change aligns Linalg with that convention.
Notable changes beyond moving tests:
* Updated one of the two matrix-vector multiplication tests to use
`linalg.matvec` instead of `linalg.generic`. CHECK lines remain
unchanged.
* Simplified the lone `linalg.index` test by removing an unnecessary
`tensor.extract`. Also removed canonicalization patterns from the
TD sequence for consistency with other tests.
This patch contributes to the implementation of #141025 — please refer
to that ticket for full context.
---
.../Linalg/vectorization-scalable.mlir | 355 -----------------
mlir/test/Dialect/Linalg/vectorization.mlir | 374 +++++++++++++++++-
2 files changed, 371 insertions(+), 358 deletions(-)
delete mode 100644 mlir/test/Dialect/Linalg/vectorization-scalable.mlir
diff --git a/mlir/test/Dialect/Linalg/vectorization-scalable.mlir b/mlir/test/Dialect/Linalg/vectorization-scalable.mlir
deleted file mode 100644
index 227829238a3d7..0000000000000
--- a/mlir/test/Dialect/Linalg/vectorization-scalable.mlir
+++ /dev/null
@@ -1,355 +0,0 @@
-// RUN: mlir-opt %s -transform-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<[4]xi1>
-// CHECK: %[[VAL_8:.*]] = vector.mask %[[VAL_7]] { vector.transfer_read %{{.*}} {in_bounds = [true]} : tensor<?xf32>, vector<[4]xf32> } : vector<[4]xi1> -> vector<[4]xf32>
-// CHECK: %[[VAL_10:.*]] = vector.mask %[[VAL_7]] { vector.transfer_read %{{.*}} {in_bounds = [true]} : tensor<?xf32>, vector<[4]xf32> } : vector<[4]xi1> -> vector<[4]xf32>
-// CHECK: %[[VAL_12:.*]] = vector.mask %[[VAL_7]] { vector.transfer_read %{{.*}} {in_bounds = [true]} : tensor<?xf32>, vector<[4]xf32> } : vector<[4]xi1> -> vector<[4]xf32>
-// CHECK: %[[VAL_13:.*]] = arith.addf %[[VAL_8]], %[[VAL_10]] : vector<[4]xf32>
-// CHECK: %[[VAL_14:.*]] = vector.mask %[[VAL_7]] { vector.transfer_write %{{.*}} {in_bounds = [true]} : vector<[4]xf32>, tensor<?xf32> } : vector<[4]xi1> -> tensor<?xf32>
-
-module attributes {transform.with_named_sequence} {
- transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
- %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- transform.structured.vectorize %0 vector_sizes [[4]] : !transform.any_op
- transform.yield
- }
-}
-
-// -----
-
-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<8x[32]xi1>
-// 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<8x[32]xf32> } : vector<8x[32]xi1> -> vector<8x[32]xf32>
-// 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<8x[32]xf32> } : vector<8x[32]xi1> -> vector<8x[32]xf32>
-// 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<8x[32]xf32> } : vector<8x[32]xi1> -> vector<8x[32]xf32>
-// CHECK: %[[VAL_14:.*]] = arith.addf %[[VAL_9]], %[[VAL_11]] : vector<8x[32]xf32>
-// 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<8x[32]xf32>, tensor<8x?xf32> } : vector<8x[32]xi1> -> tensor<8x?xf32>
-
-
-module attributes {transform.with_named_sequence} {
- transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
- %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- transform.structured.vectorize %0 vector_sizes [8, [32]] : !transform.any_op
- transform.yield
- }
-}
-
-// -----
-
-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<8x[32]xi1>
-// 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<8x[32]xf32> } : vector<8x[32]xi1> -> vector<8x[32]xf32>
-// 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<8x[32]xf32> } : vector<8x[32]xi1> -> vector<8x[32]xf32>
-// 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<8x[32]xf32> } : vector<8x[32]xi1> -> vector<8x[32]xf32>
-// CHECK: %[[VAL_13:.*]] = arith.addf %[[VAL_8]], %[[VAL_10]] : vector<8x[32]xf32>
-// 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<8x[32]xf32>, tensor<8x30xf32> } : vector<8x[32]xi1> -> tensor<8x30xf32>
-
-module attributes {transform.with_named_sequence} {
- transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
- %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- transform.structured.vectorize %0 vector_sizes [8, [32]] : !transform.any_op
- transform.yield
- }
-}
-
-// -----
-
-// NOTE: Often, non-trailing scalable sizes are problematic - there are no
-// "scalable" arrays of vectors at the LLVM level (multi-dim vectors are
-// decomposed into arrays of aggregates). However, the trailing dim in this
-// case is 1 and that can be folded away later.
-
-func.func @vectorize_dynamic_fill_leading_scalable(%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_leading_scalable
-// CHECK: %[[DIM0:.*]] = tensor.dim
-// CHECK: %[[DIM1:.*]] = tensor.dim
-// CHECK: %[[MASK:.*]] = vector.create_mask %[[DIM0]], %[[DIM1]] : vector<[8]x1xi1>
-// CHECK: %[[BCAST:.*]] = vector.broadcast %{{.*}} : f32 to vector<[8]x1xf32>
-// CHECK: vector.mask %[[MASK]] { vector.transfer_write %[[BCAST]], {{.*}} {in_bounds = [true, true]} : vector<[8]x1xf32>, tensor<?x?xf32> } : vector<[8]x1xi1>
-
-module attributes {transform.with_named_sequence} {
- transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
- %0 = transform.structured.match ops{["linalg.fill"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- transform.structured.vectorize %0 vector_sizes [[8], 1] : !transform.any_op
- transform.yield
- }
-}
-
-// -----
-
-#map = affine_map<(d0) -> (d0)>
-func.func @vectorize_linalg_index(%arg0: tensor<?xf32>, %arg1: tensor<?xf32>) -> tensor<?xf32> {
- %0 = linalg.generic {
- indexing_maps = [#map],
- iterator_types = ["parallel"]
- } outs(%arg1 : tensor<?xf32>) {
- ^bb0(%in: f32):
- %1 = linalg.index 0 : index
- %2 = tensor.extract %arg0[%1] : tensor<?xf32>
- linalg.yield %2 : f32
- } -> tensor<?xf32>
- return %0 : tensor<?xf32>
-}
-
-// CHECK-LABEL: @vectorize_linalg_index
-// CHECK-SAME: %[[SRC:.*]]: tensor<?xf32>, %[[DST:.*]]: tensor<?xf32>
-// CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
-// CHECK: %[[DST_DIM0:.*]] = tensor.dim %[[DST]], %[[C0]] : tensor<?xf32>
-// CHECK: %[[MASK:.*]] = vector.create_mask %[[DST_DIM0]] : vector<[4]xi1>
-// CHECK-DAG: %[[STEP:.+]] = vector.step : vector<[4]xindex>
-// CHECK-DAG: %[[STEP_ELEMENT:.+]] = vector.extract %[[STEP]][0] : index from vector<[4]xindex>
-
-// CHECK: %[[READ:.*]] = vector.mask %[[MASK]] { vector.transfer_read %[[SRC]][%[[STEP_ELEMENT]]], %cst {in_bounds = [true]} : tensor<?xf32>, vector<[4]xf32> } : vector<[4]xi1> -> vector<[4]xf32>
-// CHECK: %[[OUT:.*]] = vector.mask %[[MASK]] { vector.transfer_write %[[READ]], %[[DST]]{{\[}}%[[C0]]] {in_bounds = [true]} : vector<[4]xf32>, tensor<?xf32> } : vector<[4]xi1> -> tensor<?xf32>
-// CHECK: return %[[OUT]] : tensor<?xf32>
-
-module attributes {transform.with_named_sequence} {
- transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
- %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- transform.structured.vectorize %0 vector_sizes [[4]] {vectorize_nd_extract} : !transform.any_op
-
- %func = transform.structured.match ops{["func.func"]} in %arg1
- : (!transform.any_op) -> !transform.any_op
- transform.apply_patterns to %func {
- transform.apply_patterns.linalg.tiling_canonicalization
- } : !transform.any_op
- transform.yield
- }
-}
-
-// -----
-
-func.func @vectorize_dynamic_reduction_scalable_1d(%arg0: tensor<?xf32>,
- %arg1: tensor<f32>) -> tensor<f32> {
-
- %0 = linalg.reduce ins(%arg0 : tensor<?xf32>) outs(%arg1 : tensor<f32>) dimensions = [0]
- (%in: f32, %init: f32) {
- %0 = arith.addf %in, %init : f32
- linalg.yield %0 : f32
- }
- return %0 : tensor<f32>
-}
-
-// CHECK-LABEL: func.func @vectorize_dynamic_reduction_scalable_1d(
-// CHECK-SAME: %[[ARG_0:.*]]: tensor<?xf32>, %[[ARG_1:.*]]: tensor<f32>) -> tensor<f32> {
-// CHECK: %[[C0_idx:.*]] = arith.constant 0 : index
-// CHECK: %[[DIM_A0_0:.*]] = tensor.dim %[[ARG_0]], %[[C0_idx]] : tensor<?xf32>
-// CHECK: %[[C0_idx:.*]] = arith.constant 0 : index
-// CHECK: %[[C0_f32:.*]] = arith.constant 0.000000e+00 : f32
-// CHECK: %[[MASK:.*]] = vector.create_mask %[[DIM_A0_0]] : vector<[4]xi1>
-// CHECK: %[[VEC_RD_0:.*]] = vector.mask %[[MASK]] { vector.transfer_read %[[ARG_0]][%[[C0_idx]]], %[[C0_f32]] {in_bounds = [true]} : tensor<?xf32>, vector<[4]xf32> } : vector<[4]xi1> -> vector<[4]xf32>
-// CHECK: %[[C0_F32:.*]] = arith.constant 0.000000e+00 : f32
-// CHECK: %[[VEC_RD_1:.*]] = vector.transfer_read %[[ARG_1]][], %[[C0_F32]] : tensor<f32>, vector<f32>
-// CHECK: %[[ACC_f32:.*]] = vector.extract %[[VEC_RD_1]][] : f32 from vector<f32>
-// CHECK: %[[REDUCE:.*]] = vector.mask %[[MASK]] { vector.multi_reduction <add>, %[[VEC_RD_0]], %[[ACC_f32]] [0] : vector<[4]xf32> to f32 } : vector<[4]xi1> -> f32
-// CHECK: %[[VEC_f32:.*]] = vector.broadcast %[[REDUCE]] : f32 to vector<f32>
-// CHECK: %{{.*}} = vector.transfer_write %[[VEC_f32]], %[[ARG_1]][] : vector<f32>, tensor<f32>
-
-module attributes {transform.with_named_sequence} {
- transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
- %0 = transform.structured.match ops{["linalg.reduce"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- transform.structured.vectorize %0 vector_sizes [[4]] : !transform.any_op
- transform.yield
- }
-}
-
-// -----
-
-// Note: scalable version of `vectorize_dynamic_reduction` in test/Dialect/Linalg/vectorization.mlir.
-func.func @vectorize_dynamic_reduction_scalable_2d(%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>
-}
-
-// CHECK-LABEL: func.func @vectorize_dynamic_reduction_scalable_2d(
-// CHECK-SAME: %[[ARG_0:.*]]: tensor<?x?xf32>, %[[ARG_1:.*]]: tensor<?xf32>) -> tensor<?xf32> {
-// CHECK: %[[C0_idx:.*]] = arith.constant 0 : index
-// CHECK: %[[DIM_A0_0:.*]] = tensor.dim %[[ARG_0]], %[[C0_idx]] : tensor<?x?xf32>
-// CHECK: %[[C1_idx:.*]] = arith.constant 1 : index
-// CHECK: %[[DIM_A0_1:.*]] = tensor.dim %[[ARG_0]], %[[C1_idx]] : tensor<?x?xf32>
-// CHECK: %[[C0_idx:.*]] = arith.constant 0 : index
-// CHECK: %[[C0_f32:.*]] = arith.constant 0.000000e+00 : f32
-// CHECK: %[[MASK_2d:.*]] = vector.create_mask %[[DIM_A0_0]], %[[DIM_A0_1]] : vector<4x[8]xi1>
-// CHECK: %[[VEC_RD_0:.*]] = vector.mask %[[MASK_2d]] { vector.transfer_read %[[ARG_0]][%[[C0_idx]], %[[C0_idx]]], %[[C0_f32]] {in_bounds = [true, true]} : tensor<?x?xf32>, vector<4x[8]xf32> } : vector<4x[8]xi1> -> vector<4x[8]xf32>
-// CHECK: %[[C0_f32:.*]] = arith.constant 0.000000e+00 : f32
-// CHECK: %[[MASK_1d:.*]] = vector.create_mask %[[DIM_A0_0]] : vector<4xi1>
-// CHECK: %[[VEC_RD_1:.*]] = vector.mask %[[MASK_1d]] { vector.transfer_read %[[ARG_1]][%[[C0_idx]]], %[[C0_f32]] {in_bounds = [true]} : tensor<?xf32>, vector<4xf32> } : vector<4xi1> -> vector<4xf32>
-// CHECK: %[[REDUCE:.*]] = vector.mask %[[MASK_2d]] { vector.multi_reduction <add>, %[[VEC_RD_0]], %[[VEC_RD_1]] [1] : vector<4x[8]xf32> to vector<4xf32> } : vector<4x[8]xi1> -> vector<4xf32>
-// CHECK: %[[C0_idx:.*]] = arith.constant 0 : index
-// CHECK: %{{.*}} = vector.mask %[[MASK_1d]] { vector.transfer_write %[[REDUCE]], %[[ARG_1]][%[[C0_idx]]] {in_bounds = [true]} : vector<4xf32>, tensor<?xf32> } : vector<4xi1> -> tensor<?xf32>
-
-module attributes {transform.with_named_sequence} {
- transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
- %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- transform.structured.vectorize %0 vector_sizes [4, [8]] : !transform.any_op
- transform.yield
- }
-}
-
-// -----
-
-func.func @vectorize_dynamic_matvec_trailing_reduction_dim(%arg0: tensor<?x?xf32>,
- %arg1: tensor<?xf32>,
- %arg2: tensor<?xf32>) {
- linalg.matvec ins(%arg0, %arg1 : tensor<?x?xf32>, tensor<?xf32>)
- outs(%arg2 : tensor<?xf32>) -> tensor<?xf32>
- return
-}
-
-// CHECK-LABEL: func.func @vectorize_dynamic_matvec_trailing_reduction_dim(
-// CHECK-SAME: %[[ARG_0:.*]]: tensor<?x?xf32>, %[[ARG_1:.*]]: tensor<?xf32>, %[[ARG_2:.*]]: tensor<?xf32>) {
-// CHECK: %[[C0_idx:.*]] = arith.constant 0 : index
-// CHECK: %[[DIM_A0_0:.*]] = tensor.dim %[[ARG_0]], %[[C0_idx]] : tensor<?x?xf32>
-// CHECK: %[[C1_idx:.*]] = arith.constant 1 : index
-// CHECK: %[[DIM_A0_1:.*]] = tensor.dim %[[ARG_0]], %[[C1_idx]] : tensor<?x?xf32>
-// CHECK: %[[C0_idx:.*]] = arith.constant 0 : index
-// CHECK: %[[C0_f32:.*]] = arith.constant 0.000000e+00 : f32
-// CHECK: %[[MASK_2d:.*]] = vector.create_mask %[[DIM_A0_0]], %[[DIM_A0_1]] : vector<4x[4]xi1>
-// CHECK: %[[VEC_RD_0:.*]] = vector.mask %[[MASK_2d]] { vector.transfer_read %[[ARG_0]][%[[C0_idx]], %[[C0_idx]]], %[[C0_f32]] {in_bounds = [true, true]} : tensor<?x?xf32>, vector<4x[4]xf32> } : vector<4x[4]xi1> -> vector<4x[4]xf32>
-// CHECK: %[[C0_f32:.*]] = arith.constant 0.000000e+00 : f32
-// CHECK: %[[MASK_d1:.*]] = vector.create_mask %[[DIM_A0_1]] : vector<[4]xi1>
-// CHECK: %[[VEC_RD_1:.*]] = vector.mask %[[MASK_d1]] { vector.transfer_read %[[ARG_1]][%[[C0_idx]]], %[[C0_f32]] {in_bounds = [true, true], permutation_map = #map} : tensor<?xf32>, vector<4x[4]xf32> } : vector<[4]xi1> -> vector<4x[4]xf32>
-// CHECK: %[[C0_f32:.*]] = arith.constant 0.000000e+00 : f32
-// CHECK: %[[MASK_d2:.*]] = vector.create_mask %[[DIM_A0_0]] : vector<4xi1>
-// CHECK: %[[VEC_RD_2:.*]] = vector.mask %[[MASK_d2]] { vector.transfer_read %[[ARG_2]][%[[C0_idx]]], %[[C0_f32]] {in_bounds = [true]} : tensor<?xf32>, vector<4xf32> } : vector<4xi1> -> vector<4xf32>
-// CHECK: %[[MUL:.*]] = arith.mulf %[[VEC_RD_0:.*]], %[[VEC_RD_1:.*]] : vector<4x[4]xf32>
-// CHECK: %[[REDUCE:.*]] = vector.mask %[[MASK_2d]] { vector.multi_reduction <add>, %[[MUL]], %[[VEC_RD_2]] [1] : vector<4x[4]xf32> to vector<4xf32> } : vector<4x[4]xi1> -> vector<4xf32>
-// CHECK: %[[C0_idx:.*]] = arith.constant 0 : index
-// CHECK: %{{.*}} = vector.mask %[[MASK_d2]] { vector.transfer_write %[[REDUCE]], %[[ARG_2]][%[[C0_idx]]] {in_bounds = [true]} : vector<4xf32>, tensor<?xf32> } : vector<4xi1> -> tensor<?xf32>
-
-module attributes {transform.with_named_sequence} {
- transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
- %0 = transform.structured.match ops{["linalg.matvec"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- transform.structured.vectorize %0 vector_sizes [4, [4]] : !transform.any_op
- transform.yield
- }
-}
-
-// -----
-
-func.func @vectorize_dynamic_generic_matvec_leading_parallel_dim(%arg0: tensor<?x?xf32>,
- %arg1: tensor<?xf32>,
- %arg2: tensor<?xf32>) -> tensor<?xf32> {
- %0 = linalg.generic { indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>,
- affine_map<(d0, d1) -> (d1)>,
- affine_map<(d0, d1) -> (d0)>],
- iterator_types = ["parallel", "reduction"] }
- ins(%arg0, %arg1 : tensor<?x?xf32>, tensor<?xf32>)
- outs(%arg2 : tensor<?xf32>) {
- ^bb(%mat: f32, %vec: f32, %res: f32) :
- %0 = arith.mulf %mat, %vec : f32
- %1 = arith.addf %res, %0 : f32
- linalg.yield %1 : f32
- } -> tensor<?xf32>
- return %0 : tensor<?xf32>
-}
-
-// CHECK-LABEL: func.func @vectorize_dynamic_generic_matvec_leading_parallel_dim(
-// CHECK-SAME: %[[ARG_0:.*]]: tensor<?x?xf32>, %[[ARG_1:.*]]: tensor<?xf32>, %[[ARG_2:.*]]: tensor<?xf32>) -> tensor<?xf32> {
-// CHECK: %[[C0_idx:.*]] = arith.constant 0 : index
-// CHECK: %[[DIM_A0_0:.*]] = tensor.dim %[[ARG_0]], %[[C0_idx]] : tensor<?x?xf32>
-// CHECK: %[[C1_idx:.*]] = arith.constant 1 : index
-// CHECK: %[[DIM_A0_1:.*]] = tensor.dim %[[ARG_0]], %[[C1_idx]] : tensor<?x?xf32>
-// CHECK: %[[C0_idx:.*]] = arith.constant 0 : index
-// CHECK: %[[C0_f32:.*]] = arith.constant 0.000000e+00 : f32
-// CHECK: %[[MASK_2d:.*]] = vector.create_mask %[[DIM_A0_0]], %[[DIM_A0_1]] : vector<[4]x4xi1>
-// CHECK: %[[VEC_RD_0:.*]] = vector.mask %[[MASK_2d]] { vector.transfer_read %[[ARG_0]][%[[C0_idx]], %[[C0_idx]]], %[[C0_f32]] {in_bounds = [true, true]} : tensor<?x?xf32>, vector<[4]x4xf32> } : vector<[4]x4xi1> -> vector<[4]x4xf32>
-// CHECK: %[[C0_f32:.*]] = arith.constant 0.000000e+00 : f32
-// CHECK: %[[MASK_d1:.*]] = vector.create_mask %[[DIM_A0_1]] : vector<4xi1>
-// CHECK: %[[VEC_RD_1:.*]] = vector.mask %[[MASK_d1]] { vector.transfer_read %[[ARG_1]][%[[C0_idx]]], %[[C0_f32]] {in_bounds = [true, true], permutation_map = #map} : tensor<?xf32>, vector<[4]x4xf32> } : vector<4xi1> -> vector<[4]x4xf32>
-// CHECK: %[[C0_f32:.*]] = arith.constant 0.000000e+00 : f32
-// CHECK: %[[MASK_d2:.*]] = vector.create_mask %[[DIM_A0_0]] : vector<[4]xi1>
-// CHECK: %[[VEC_RD_2:.*]] = vector.mask %[[MASK_d2]] { vector.transfer_read %[[ARG_2]][%[[C0_idx]]], %[[C0_f32]] {in_bounds = [true]} : tensor<?xf32>, vector<[4]xf32> } : vector<[4]xi1> -> vector<[4]xf32>
-// CHECK: %[[MUL:.*]] = arith.mulf %[[VEC_RD_0:.*]], %[[VEC_RD_1:.*]] : vector<[4]x4xf32>
-// CHECK: %[[REDUCE:.*]] = vector.mask %[[MASK_2d]] { vector.multi_reduction <add>, %[[MUL]], %[[VEC_RD_2]] [1] : vector<[4]x4xf32> to vector<[4]xf32> } : vector<[4]x4xi1> -> vector<[4]xf32>
-// CHECK: %[[C0_idx:.*]] = arith.constant 0 : index
-// CHECK: %{{.*}} = vector.mask %[[MASK_d2]] { vector.transfer_write %[[REDUCE]], %[[ARG_2]][%[[C0_idx]]] {in_bounds = [true]} : vector<[4]xf32>, tensor<?xf32> } : vector<[4]xi1> -> tensor<?xf32>
-
-module attributes {transform.with_named_sequence} {
- transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
- %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
- transform.structured.vectorize %0 vector_sizes [[4], 4] : !transform.any_op
- transform.yield
- }
-}
diff --git a/mlir/test/Dialect/Linalg/vectorization.mlir b/mlir/test/Dialect/Linalg/vectorization.mlir
index 8c6760fa50325..b64d2548b1c52 100644
--- a/mlir/test/Dialect/Linalg/vectorization.mlir
+++ b/mlir/test/Dialect/Linalg/vectorization.mlir
@@ -1,5 +1,9 @@
// RUN: mlir-opt %s -transform-interpreter -split-input-file | FileCheck %s
+///----------------------------------------------------------------------------------------
+/// Tests for linalg.generic
+///----------------------------------------------------------------------------------------
+
func.func @vectorize_dynamic_identity(%arg0: tensor<?xf32>,
%arg1: tensor<?xf32>,
%arg2: tensor<?xf32>) -> tensor<?xf32> {
@@ -36,6 +40,42 @@ module attributes {transform.with_named_sequence} {
// -----
+func.func @vectorize_dynamic_identity_scalable(%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_scalable
+// 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<[4]xi1>
+// CHECK: %[[VAL_8:.*]] = vector.mask %[[VAL_7]] { vector.transfer_read %{{.*}} {in_bounds = [true]} : tensor<?xf32>, vector<[4]xf32> } : vector<[4]xi1> -> vector<[4]xf32>
+// CHECK: %[[VAL_10:.*]] = vector.mask %[[VAL_7]] { vector.transfer_read %{{.*}} {in_bounds = [true]} : tensor<?xf32>, vector<[4]xf32> } : vector<[4]xi1> -> vector<[4]xf32>
+// CHECK: %[[VAL_12:.*]] = vector.mask %[[VAL_7]] { vector.transfer_read %{{.*}} {in_bounds = [true]} : tensor<?xf32>, vector<[4]xf32> } : vector<[4]xi1> -> vector<[4]xf32>
+// CHECK: %[[VAL_13:.*]] = arith.addf %[[VAL_8]], %[[VAL_10]] : vector<[4]xf32>
+// CHECK: %[[VAL_14:.*]] = vector.mask %[[VAL_7]] { vector.transfer_write %{{.*}} {in_bounds = [true]} : vector<[4]xf32>, tensor<?xf32> } : vector<[4]xi1> -> tensor<?xf32>
+
+module attributes {transform.with_named_sequence} {
+ transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
+ %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+ transform.structured.vectorize %0 vector_sizes [[4]] : !transform.any_op
+ transform.yield
+ }
+}
+
+// -----
+
func.func @vectorize_dynamic_identity_with_constant(%arg0: tensor<?xf32>,
%arg1: tensor<?xf32>,
%arg2: tensor<?xf32>) -> tensor<?xf32> {
@@ -271,8 +311,8 @@ module attributes {transform.with_named_sequence} {
// -----
-func.func @vectorize_dynamic_reduction(%arg0: tensor<?x?xf32>,
- %arg1: tensor<?xf32>) -> tensor<?xf32> {
+func.func @vectorize_dynamic_reduction_2d(%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"] }
@@ -293,7 +333,7 @@ module attributes {transform.with_named_sequence} {
}
}
-// CHECK-LABEL: @vectorize_dynamic_reduction(
+// CHECK-LABEL: @vectorize_dynamic_reduction_2d(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<?x?xf32>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<?xf32>) -> tensor<?xf32> {
// CHECK: %[[VAL_2:.*]] = arith.constant 0 : index
@@ -311,6 +351,83 @@ module attributes {transform.with_named_sequence} {
// -----
+func.func @vectorize_dynamic_reduction_2d_scalable(%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>
+}
+
+// CHECK-LABEL: func.func @vectorize_dynamic_reduction_2d_scalable(
+// CHECK-SAME: %[[ARG_0:.*]]: tensor<?x?xf32>, %[[ARG_1:.*]]: tensor<?xf32>) -> tensor<?xf32> {
+// CHECK: %[[C0_idx:.*]] = arith.constant 0 : index
+// CHECK: %[[DIM_A0_0:.*]] = tensor.dim %[[ARG_0]], %[[C0_idx]] : tensor<?x?xf32>
+// CHECK: %[[C1_idx:.*]] = arith.constant 1 : index
+// CHECK: %[[DIM_A0_1:.*]] = tensor.dim %[[ARG_0]], %[[C1_idx]] : tensor<?x?xf32>
+// CHECK: %[[C0_idx:.*]] = arith.constant 0 : index
+// CHECK: %[[C0_f32:.*]] = arith.constant 0.000000e+00 : f32
+// CHECK: %[[MASK_2d:.*]] = vector.create_mask %[[DIM_A0_0]], %[[DIM_A0_1]] : vector<4x[8]xi1>
+// CHECK: %[[VEC_RD_0:.*]] = vector.mask %[[MASK_2d]] { vector.transfer_read %[[ARG_0]][%[[C0_idx]], %[[C0_idx]]], %[[C0_f32]] {in_bounds = [true, true]} : tensor<?x?xf32>, vector<4x[8]xf32> } : vector<4x[8]xi1> -> vector<4x[8]xf32>
+// CHECK: %[[C0_f32:.*]] = arith.constant 0.000000e+00 : f32
+// CHECK: %[[MASK_1d:.*]] = vector.create_mask %[[DIM_A0_0]] : vector<4xi1>
+// CHECK: %[[VEC_RD_1:.*]] = vector.mask %[[MASK_1d]] { vector.transfer_read %[[ARG_1]][%[[C0_idx]]], %[[C0_f32]] {in_bounds = [true]} : tensor<?xf32>, vector<4xf32> } : vector<4xi1> -> vector<4xf32>
+// CHECK: %[[REDUCE:.*]] = vector.mask %[[MASK_2d]] { vector.multi_reduction <add>, %[[VEC_RD_0]], %[[VEC_RD_1]] [1] : vector<4x[8]xf32> to vector<4xf32> } : vector<4x[8]xi1> -> vector<4xf32>
+// CHECK: %[[C0_idx:.*]] = arith.constant 0 : index
+// CHECK: %{{.*}} = vector.mask %[[MASK_1d]] { vector.transfer_write %[[REDUCE]], %[[ARG_1]][%[[C0_idx]]] {in_bounds = [true]} : vector<4xf32>, tensor<?xf32> } : vector<4xi1> -> tensor<?xf32>
+
+module attributes {transform.with_named_sequence} {
+ transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
+ %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+ transform.structured.vectorize %0 vector_sizes [4, [8]] : !transform.any_op
+ transform.yield
+ }
+}
+
+// -----
+
+func.func @vectorize_dynamic_reduction_scalable_1d(%arg0: tensor<?xf32>,
+ %arg1: tensor<f32>) -> tensor<f32> {
+
+ %0 = linalg.reduce ins(%arg0 : tensor<?xf32>) outs(%arg1 : tensor<f32>) dimensions = [0]
+ (%in: f32, %init: f32) {
+ %0 = arith.addf %in, %init : f32
+ linalg.yield %0 : f32
+ }
+ return %0 : tensor<f32>
+}
+
+// CHECK-LABEL: func.func @vectorize_dynamic_reduction_scalable_1d(
+// CHECK-SAME: %[[ARG_0:.*]]: tensor<?xf32>, %[[ARG_1:.*]]: tensor<f32>) -> tensor<f32> {
+// CHECK: %[[C0_idx:.*]] = arith.constant 0 : index
+// CHECK: %[[DIM_A0_0:.*]] = tensor.dim %[[ARG_0]], %[[C0_idx]] : tensor<?xf32>
+// CHECK: %[[C0_idx:.*]] = arith.constant 0 : index
+// CHECK: %[[C0_f32:.*]] = arith.constant 0.000000e+00 : f32
+// CHECK: %[[MASK:.*]] = vector.create_mask %[[DIM_A0_0]] : vector<[4]xi1>
+// CHECK: %[[VEC_RD_0:.*]] = vector.mask %[[MASK]] { vector.transfer_read %[[ARG_0]][%[[C0_idx]]], %[[C0_f32]] {in_bounds = [true]} : tensor<?xf32>, vector<[4]xf32> } : vector<[4]xi1> -> vector<[4]xf32>
+// CHECK: %[[C0_F32:.*]] = arith.constant 0.000000e+00 : f32
+// CHECK: %[[VEC_RD_1:.*]] = vector.transfer_read %[[ARG_1]][], %[[C0_F32]] : tensor<f32>, vector<f32>
+// CHECK: %[[ACC_f32:.*]] = vector.extract %[[VEC_RD_1]][] : f32 from vector<f32>
+// CHECK: %[[REDUCE:.*]] = vector.mask %[[MASK]] { vector.multi_reduction <add>, %[[VEC_RD_0]], %[[ACC_f32]] [0] : vector<[4]xf32> to f32 } : vector<[4]xi1> -> f32
+// CHECK: %[[VEC_f32:.*]] = vector.broadcast %[[REDUCE]] : f32 to vector<f32>
+// CHECK: %{{.*}} = vector.transfer_write %[[VEC_f32]], %[[ARG_1]][] : vector<f32>, tensor<f32>
+
+module attributes {transform.with_named_sequence} {
+ transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
+ %0 = transform.structured.match ops{["linalg.reduce"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+ transform.structured.vectorize %0 vector_sizes [[4]] : !transform.any_op
+ transform.yield
+ }
+}
+
+// -----
+
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)>,
@@ -438,6 +555,50 @@ module attributes {transform.with_named_sequence} {
// -----
+func.func @vectorize_partial_dynamic_identity_scalable(%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_scalable
+// 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<8x[32]xi1>
+// 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<8x[32]xf32> } : vector<8x[32]xi1> -> vector<8x[32]xf32>
+// 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<8x[32]xf32> } : vector<8x[32]xi1> -> vector<8x[32]xf32>
+// 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<8x[32]xf32> } : vector<8x[32]xi1> -> vector<8x[32]xf32>
+// CHECK: %[[VAL_14:.*]] = arith.addf %[[VAL_9]], %[[VAL_11]] : vector<8x[32]xf32>
+// 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<8x[32]xf32>, tensor<8x?xf32> } : vector<8x[32]xi1> -> tensor<8x?xf32>
+
+
+module attributes {transform.with_named_sequence} {
+ transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
+ %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+ transform.structured.vectorize %0 vector_sizes [8, [32]] : !transform.any_op
+ transform.yield
+ }
+}
+
+// -----
+
func.func @do_not_generate_masks(%arg0: tensor<8x32xf32>,
%arg1: tensor<8x32xf32>,
%arg2: tensor<8x32xf32>) -> tensor<8x32xf32> {
@@ -509,6 +670,183 @@ module attributes {transform.with_named_sequence} {
// -----
+func.func @vectorize_static_shape_with_mask_scalable(%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_scalable(
+// 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<8x[32]xi1>
+// 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<8x[32]xf32> } : vector<8x[32]xi1> -> vector<8x[32]xf32>
+// 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<8x[32]xf32> } : vector<8x[32]xi1> -> vector<8x[32]xf32>
+// 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<8x[32]xf32> } : vector<8x[32]xi1> -> vector<8x[32]xf32>
+// CHECK: %[[VAL_13:.*]] = arith.addf %[[VAL_8]], %[[VAL_10]] : vector<8x[32]xf32>
+// 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<8x[32]xf32>, tensor<8x30xf32> } : vector<8x[32]xi1> -> tensor<8x30xf32>
+
+module attributes {transform.with_named_sequence} {
+ transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
+ %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+ transform.structured.vectorize %0 vector_sizes [8, [32]] : !transform.any_op
+ transform.yield
+ }
+}
+
+// -----
+
+///----------------------------------------------------------------------------------------
+/// Tests for linalg.matvec
+///----------------------------------------------------------------------------------------
+
+// Scalable _reduction_ dimension.
+
+func.func @vectorize_dynamic_matvec_trailing_reduction_dim(%arg0: tensor<?x?xf32>,
+ %arg1: tensor<?xf32>,
+ %arg2: tensor<?xf32>) {
+ linalg.matvec ins(%arg0, %arg1 : tensor<?x?xf32>, tensor<?xf32>)
+ outs(%arg2 : tensor<?xf32>) -> tensor<?xf32>
+ return
+}
+
+// CHECK-LABEL: func.func @vectorize_dynamic_matvec_trailing_reduction_dim(
+// CHECK-SAME: %[[ARG_0:.*]]: tensor<?x?xf32>, %[[ARG_1:.*]]: tensor<?xf32>, %[[ARG_2:.*]]: tensor<?xf32>) {
+// CHECK: %[[C0_idx:.*]] = arith.constant 0 : index
+// CHECK: %[[DIM_A0_0:.*]] = tensor.dim %[[ARG_0]], %[[C0_idx]] : tensor<?x?xf32>
+// CHECK: %[[C1_idx:.*]] = arith.constant 1 : index
+// CHECK: %[[DIM_A0_1:.*]] = tensor.dim %[[ARG_0]], %[[C1_idx]] : tensor<?x?xf32>
+// CHECK: %[[C0_idx:.*]] = arith.constant 0 : index
+// CHECK: %[[C0_f32:.*]] = arith.constant 0.000000e+00 : f32
+// CHECK: %[[MASK_2d:.*]] = vector.create_mask %[[DIM_A0_0]], %[[DIM_A0_1]] : vector<4x[4]xi1>
+// CHECK: %[[VEC_RD_0:.*]] = vector.mask %[[MASK_2d]] { vector.transfer_read %[[ARG_0]][%[[C0_idx]], %[[C0_idx]]], %[[C0_f32]] {in_bounds = [true, true]} : tensor<?x?xf32>, vector<4x[4]xf32> } : vector<4x[4]xi1> -> vector<4x[4]xf32>
+// CHECK: %[[C0_f32:.*]] = arith.constant 0.000000e+00 : f32
+// CHECK: %[[MASK_d1:.*]] = vector.create_mask %[[DIM_A0_1]] : vector<[4]xi1>
+// CHECK: %[[VEC_RD_1:.*]] = vector.mask %[[MASK_d1]] { vector.transfer_read %[[ARG_1]][%[[C0_idx]]], %[[C0_f32]] {in_bounds = [true, true], permutation_map = #map} : tensor<?xf32>, vector<4x[4]xf32> } : vector<[4]xi1> -> vector<4x[4]xf32>
+// CHECK: %[[C0_f32:.*]] = arith.constant 0.000000e+00 : f32
+// CHECK: %[[MASK_d2:.*]] = vector.create_mask %[[DIM_A0_0]] : vector<4xi1>
+// CHECK: %[[VEC_RD_2:.*]] = vector.mask %[[MASK_d2]] { vector.transfer_read %[[ARG_2]][%[[C0_idx]]], %[[C0_f32]] {in_bounds = [true]} : tensor<?xf32>, vector<4xf32> } : vector<4xi1> -> vector<4xf32>
+// CHECK: %[[MUL:.*]] = arith.mulf %[[VEC_RD_0:.*]], %[[VEC_RD_1:.*]] : vector<4x[4]xf32>
+// CHECK: %[[REDUCE:.*]] = vector.mask %[[MASK_2d]] { vector.multi_reduction <add>, %[[MUL]], %[[VEC_RD_2]] [1] : vector<4x[4]xf32> to vector<4xf32> } : vector<4x[4]xi1> -> vector<4xf32>
+// CHECK: %[[C0_idx:.*]] = arith.constant 0 : index
+// CHECK: %{{.*}} = vector.mask %[[MASK_d2]] { vector.transfer_write %[[REDUCE]], %[[ARG_2]][%[[C0_idx]]] {in_bounds = [true]} : vector<4xf32>, tensor<?xf32> } : vector<4xi1> -> tensor<?xf32>
+
+module attributes {transform.with_named_sequence} {
+ transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
+ %0 = transform.structured.match ops{["linalg.matvec"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+ transform.structured.vectorize %0 vector_sizes [4, [4]] : !transform.any_op
+ transform.yield
+ }
+}
+
+// -----
+
+// Scalable _parallel_ dimension.
+
+func.func @vectorize_dynamic_matvec_trailing_reduction_dim(%arg0: tensor<?x?xf32>,
+ %arg1: tensor<?xf32>,
+ %arg2:
+ tensor<?xf32>) ->
+ tensor<?xf32>{
+ %0 = linalg.matvec ins(%arg0, %arg1 : tensor<?x?xf32>, tensor<?xf32>)
+ outs(%arg2 : tensor<?xf32>) -> tensor<?xf32>
+ return %0 : tensor<?xf32>
+}
+
+// CHECK-LABEL: func.func @vectorize_dynamic_matvec_trailing_reduction_dim(
+// CHECK-SAME: %[[ARG_0:.*]]: tensor<?x?xf32>, %[[ARG_1:.*]]: tensor<?xf32>, %[[ARG_2:.*]]: tensor<?xf32>) -> tensor<?xf32> {
+// CHECK: %[[C0_idx:.*]] = arith.constant 0 : index
+// CHECK: %[[DIM_A0_0:.*]] = tensor.dim %[[ARG_0]], %[[C0_idx]] : tensor<?x?xf32>
+// CHECK: %[[C1_idx:.*]] = arith.constant 1 : index
+// CHECK: %[[DIM_A0_1:.*]] = tensor.dim %[[ARG_0]], %[[C1_idx]] : tensor<?x?xf32>
+// CHECK: %[[C0_idx:.*]] = arith.constant 0 : index
+// CHECK: %[[C0_f32:.*]] = arith.constant 0.000000e+00 : f32
+// CHECK: %[[MASK_2d:.*]] = vector.create_mask %[[DIM_A0_0]], %[[DIM_A0_1]] : vector<[4]x4xi1>
+// CHECK: %[[VEC_RD_0:.*]] = vector.mask %[[MASK_2d]] { vector.transfer_read %[[ARG_0]][%[[C0_idx]], %[[C0_idx]]], %[[C0_f32]] {in_bounds = [true, true]} : tensor<?x?xf32>, vector<[4]x4xf32> } : vector<[4]x4xi1> -> vector<[4]x4xf32>
+// CHECK: %[[C0_f32:.*]] = arith.constant 0.000000e+00 : f32
+// CHECK: %[[MASK_d1:.*]] = vector.create_mask %[[DIM_A0_1]] : vector<4xi1>
+// CHECK: %[[VEC_RD_1:.*]] = vector.mask %[[MASK_d1]] { vector.transfer_read %[[ARG_1]][%[[C0_idx]]], %[[C0_f32]] {in_bounds = [true, true], permutation_map = #map} : tensor<?xf32>, vector<[4]x4xf32> } : vector<4xi1> -> vector<[4]x4xf32>
+// CHECK: %[[C0_f32:.*]] = arith.constant 0.000000e+00 : f32
+// CHECK: %[[MASK_d2:.*]] = vector.create_mask %[[DIM_A0_0]] : vector<[4]xi1>
+// CHECK: %[[VEC_RD_2:.*]] = vector.mask %[[MASK_d2]] { vector.transfer_read %[[ARG_2]][%[[C0_idx]]], %[[C0_f32]] {in_bounds = [true]} : tensor<?xf32>, vector<[4]xf32> } : vector<[4]xi1> -> vector<[4]xf32>
+// CHECK: %[[MUL:.*]] = arith.mulf %[[VEC_RD_0:.*]], %[[VEC_RD_1:.*]] : vector<[4]x4xf32>
+// CHECK: %[[REDUCE:.*]] = vector.mask %[[MASK_2d]] { vector.multi_reduction <add>, %[[MUL]], %[[VEC_RD_2]] [1] : vector<[4]x4xf32> to vector<[4]xf32> } : vector<[4]x4xi1> -> vector<[4]xf32>
+// CHECK: %[[C0_idx:.*]] = arith.constant 0 : index
+// CHECK: %{{.*}} = vector.mask %[[MASK_d2]] { vector.transfer_write %[[REDUCE]], %[[ARG_2]][%[[C0_idx]]] {in_bounds = [true]} : vector<[4]xf32>, tensor<?xf32> } : vector<[4]xi1> -> tensor<?xf32>
+
+
+module attributes {transform.with_named_sequence} {
+ transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
+ %0 = transform.structured.match ops{["linalg.matvec"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+ transform.structured.vectorize %0 vector_sizes [[4], 4] : !transform.any_op
+ transform.yield
+ }
+}
+
+// -----
+
+///----------------------------------------------------------------------------------------
+/// Tests for linalg.index
+///----------------------------------------------------------------------------------------
+
+#map = affine_map<(d0) -> (d0)>
+func.func @vectorize_linalg_index_scalable(%dest: tensor<?xindex>) -> tensor<?xindex> {
+ %0 = linalg.generic {
+ indexing_maps = [#map],
+ iterator_types = ["parallel"]
+ } outs(%dest : tensor<?xindex>) {
+ ^bb0(%in: index):
+ %1 = linalg.index 0 : index
+ linalg.yield %1: index
+ } -> tensor<?xindex>
+ return %0 : tensor<?xindex>
+}
+
+// CHECK-LABEL: func.func @vectorize_linalg_index_scalable(
+// CHECK-SAME: %[[DEST:.*]]: tensor<?xindex>) -> tensor<?xindex> {
+// CHECK: %[[C0:.*]] = arith.constant 0 : index
+// CHECK: %[[D0:.*]] = tensor.dim %[[DEST]], %[[C0]] : tensor<?xindex>
+// CHECK: %[[C0_1:.*]] = arith.constant 0 : index
+// CHECK: %[[C0_2:.*]] = arith.constant 0 : index
+// CHECK: %[[MASK:.*]] = vector.create_mask %[[D0]] : vector<[4]xi1>
+// TODO: This xfer_read is not used - avoid creating it.
+// CHECK: %[[READ:.*]] = vector.mask %[[MASK]] { vector.transfer_read %[[DEST]]{{\[}}%[[C0_1]]], %[[C0_2]] {in_bounds = [true]} : tensor<?xindex>, vector<[4]xindex> } : vector<[4]xi1> -> vector<[4]xindex>
+// CHECK: %[[STEP:.*]] = vector.step : vector<[4]xindex>
+// CHECK: %[[C0_3:.*]] = arith.constant 0 : index
+// CHECK: %[[WRITE:.*]] = vector.mask %[[MASK]] { vector.transfer_write %[[STEP]], %[[DEST]]{{\[}}%[[C0_3]]] {in_bounds = [true]} : vector<[4]xindex>, tensor<?xindex> } : vector<[4]xi1> -> tensor<?xindex>
+// CHECK: return %[[WRITE]] : tensor<?xindex>
+
+module attributes {transform.with_named_sequence} {
+ transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
+ %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+ transform.structured.vectorize %0 vector_sizes [[4]] : !transform.any_op
+
+ transform.yield
+ }
+}
+
+///----------------------------------------------------------------------------------------
+/// Tests for other Ops
+///----------------------------------------------------------------------------------------
+
+// -----
+
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>
@@ -531,6 +869,36 @@ module attributes {transform.with_named_sequence} {
// -----
+// NOTE: Often, non-trailing scalable sizes are problematic - there are no
+// "scalable" arrays of vectors at the LLVM level (multi-dim vectors are
+// decomposed into arrays of aggregates). However, the trailing dim in this
+// case is 1 and that can be folded away later.
+
+// NOTE: This is similar to the example above, but the trailing dim was set to
+// 1 to make it foldable + vectorizable.
+
+func.func @vectorize_dynamic_fill_scalable(%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_scalable
+// CHECK: %[[DIM0:.*]] = tensor.dim
+// CHECK: %[[DIM1:.*]] = tensor.dim
+// CHECK: %[[MASK:.*]] = vector.create_mask %[[DIM0]], %[[DIM1]] : vector<[8]x1xi1>
+// CHECK: %[[BCAST:.*]] = vector.broadcast %{{.*}} : f32 to vector<[8]x1xf32>
+// CHECK: vector.mask %[[MASK]] { vector.transfer_write %[[BCAST]], {{.*}} {in_bounds = [true, true]} : vector<[8]x1xf32>, tensor<?x?xf32> } : vector<[8]x1xi1>
+
+module attributes {transform.with_named_sequence} {
+ transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
+ %0 = transform.structured.match ops{["linalg.fill"]} in %arg1 : (!transform.any_op) -> !transform.any_op
+ transform.structured.vectorize %0 vector_sizes [[8], 1] : !transform.any_op
+ transform.yield
+ }
+}
+
+// -----
+
// CHECK: #[[MAP:.*]] = affine_map<(d0, d1) -> (d1, d0)>
// CHECK: func @test_masked_vectorize_linalg_transpose
func.func @test_masked_vectorize_linalg_transpose(%arg0: tensor<?x?xf32>, %arg1: tensor<?x?xf32>) -> tensor<?x?xf32> {
More information about the Mlir-commits
mailing list