[Mlir-commits] [mlir] 246e8c3 - [mlir][linalg] Add back split reduction tests dropped by previous commit
Thomas Raoux
llvmlistbot at llvm.org
Wed Oct 19 13:43:37 PDT 2022
Author: Thomas Raoux
Date: 2022-10-19T20:42:55Z
New Revision: 246e8c35028a91f8581842d6487d9c718b5d0b41
URL: https://github.com/llvm/llvm-project/commit/246e8c35028a91f8581842d6487d9c718b5d0b41
DIFF: https://github.com/llvm/llvm-project/commit/246e8c35028a91f8581842d6487d9c718b5d0b41.diff
LOG: [mlir][linalg] Add back split reduction tests dropped by previous commit
The transition to transform dialect based tests dropped several cases of
the split reduction testing. Adding them back.
Differential Revision: https://reviews.llvm.org/D136287
Added:
Modified:
mlir/lib/Dialect/Linalg/TransformOps/LinalgTransformOps.cpp
mlir/test/Dialect/Linalg/transform-op-split-reduction.mlir
Removed:
################################################################################
diff --git a/mlir/lib/Dialect/Linalg/TransformOps/LinalgTransformOps.cpp b/mlir/lib/Dialect/Linalg/TransformOps/LinalgTransformOps.cpp
index 1030f0a9c00a3..331735d1d5e5e 100644
--- a/mlir/lib/Dialect/Linalg/TransformOps/LinalgTransformOps.cpp
+++ b/mlir/lib/Dialect/Linalg/TransformOps/LinalgTransformOps.cpp
@@ -1050,7 +1050,7 @@ transform::SplitReductionOp::applyToOne(linalg::LinalgOp target,
ControlSplitReductionFn splitFn = [&](LinalgOp) {
return linalg::SplitReductionOptions{int64_t(getSplitFactor()),
unsigned(getInsertSplitDimension()),
- /*innerParallel=*/false};
+ bool(getInnerParallel())};
};
SimpleRewriter rewriter(getContext());
rewriter.setInsertionPoint(target);
diff --git a/mlir/test/Dialect/Linalg/transform-op-split-reduction.mlir b/mlir/test/Dialect/Linalg/transform-op-split-reduction.mlir
index 459468a6c59f3..ee5f98bc2ce01 100644
--- a/mlir/test/Dialect/Linalg/transform-op-split-reduction.mlir
+++ b/mlir/test/Dialect/Linalg/transform-op-split-reduction.mlir
@@ -1,24 +1,275 @@
-// RUN: mlir-opt --test-transform-dialect-interpreter %s | FileCheck %s
+// RUN: mlir-opt --split-input-file --test-transform-dialect-interpreter %s | FileCheck %s
-// CHECK-LABEL: func.func @matmul_split
func.func @matmul_split(%A : tensor<16x256xf32>, %B: tensor<256x32xf32>, %C: tensor<16x32xf32>) -> tensor<16x32xf32> {
+ %0 = linalg.matmul ins(%A, %B: tensor<16x256xf32>, tensor<256x32xf32>)
+ outs(%C: tensor<16x32xf32>) -> tensor<16x32xf32>
+ return %0: tensor<16x32xf32>
+}
+
+// CHECK-DAG: #[[$MAP0:.*]] = affine_map<(d0, d1, d2, d3) -> (d0, d2, d3)>
+// CHECK-DAG: #[[$MAP1:.*]] = affine_map<(d0, d1, d2, d3) -> (d2, d3, d1)>
+// CHECK-DAG: #[[$MAP2:.*]] = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2)>
+// CHECK-DAG: #[[$MAP3:.*]] = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
+// CHECK-DAG: #[[$MAP4:.*]] = affine_map<(d0, d1, d2) -> (d0, d1)>
+// CHECK-LABEL: @matmul_split
+// CHECK-DAG: %[[ID:.*]] = arith.constant 0.000000e+00 : f32
+// CHECK-DAG: %[[I1:.*]] = tensor.expand_shape %{{.*}}[0], [1, 2]] : tensor<16x256xf32> into tensor<16x4x64xf32>
+// CHECK-DAG: %[[I2:.*]] = tensor.expand_shape %{{.*}}[0, 1], [2]] : tensor<256x32xf32> into tensor<4x64x32xf32>
+// CHECK-DAG: %[[INI:.*]] = tensor.empty() : tensor<16x32x4xf32>
+// CHECK: %[[F:.*]] = linalg.fill ins(%[[ID]] : f32) outs(%[[INI]] : tensor<16x32x4xf32>) -> tensor<16x32x4xf32>
+// CHECK: %[[G:.*]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP1]], #[[$MAP2]]]
+// CHECK-SAME: , iterator_types = ["parallel", "parallel", "parallel", "reduction"]}
+// CHECK-SAME: ins(%[[I1]], %[[I2]] : tensor<16x4x64xf32>, tensor<4x64x32xf32>) outs(%[[F]] : tensor<16x32x4xf32>) {
+// CHECK: arith.mulf
+// CHECK: arith.addf
+// CHECK: linalg.yield
+// CHECK: } -> tensor<16x32x4xf32>
+// CHECK: %[[R:.*]] = linalg.generic {indexing_maps = [#[[$MAP3]], #[[$MAP4]]],
+// CHECK-SAME: iterator_types = ["parallel", "parallel", "reduction"]} ins(%[[G]] : tensor<16x32x4xf32>) outs(%{{.*}} : tensor<16x32xf32>) {
+// CHECK: arith.addf
+// CHECK: linalg.yield %{{.*}} : f32
+// CHECK: } -> tensor<16x32xf32>
+// CHECK: return %[[R]] : tensor<16x32xf32>
+
+transform.sequence failures(propagate) {
+^bb1(%arg1: !pdl.operation):
+ %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1
+ %1:4 = transform.structured.split_reduction %0 { split_factor = 4, insert_split_dimension = 2}
+}
+
+// -----
- // CHECK: linalg.generic
- // CHECK-SAME: iterator_types = ["parallel", "parallel", "parallel", "reduction"]
- // CHECK-SAME: ins(%{{[a-zA-Z0-9_]*}}, %{{[a-zA-Z0-9_]*}} : tensor<16x4x64xf32>, tensor<4x64x32xf32>)
- // CHECK-SAME: outs(%{{[a-zA-Z0-9_]*}} : tensor<16x32x4xf32>) {
+func.func @generic_split_1d(%arg0: tensor<32xf32>, %arg1: tensor<f32>, %out: tensor<f32>) -> tensor<f32> {
+ %red = linalg.generic {indexing_maps = [affine_map<(d0) -> (d0)>,
+ affine_map<(d0) -> ()>,
+ affine_map<(d0) -> ()>],
+ iterator_types = ["reduction"]}
+ ins(%arg0, %arg1 : tensor<32xf32>, tensor<f32>)
+ outs(%out : tensor<f32>) {
+ ^bb0(%arg7: f32, %arg8: f32, %arg9: f32):
+ %40 = arith.subf %arg7, %arg8 : f32
+ %41 = math.exp %40 : f32
+ %42 = arith.mulf %41, %arg9 : f32
+ linalg.yield %42 : f32
+ } -> tensor<f32>
+ return %red : tensor<f32>
+}
+
+// CHECK-DAG: #[[$MAP0:.*]] = affine_map<(d0, d1) -> (d0, d1)>
+// CHECK-DAG: #[[$MAP1:.*]] = affine_map<(d0, d1) -> ()>
+// CHECK-DAG: #[[$MAP2:.*]] = affine_map<(d0, d1) -> (d0)>
+// CHECK-DAG: #[[$MAP3:.*]] = affine_map<(d0) -> (d0)>
+// CHECK-DAG: #[[$MAP4:.*]] = affine_map<(d0) -> ()>
+//CHECK-LABEL: @generic_split_1d
+// CHECK-DAG: %[[ID:.*]] = arith.constant 1.000000e+00 : f32
+// CHECK-DAG: %[[I1:.*]] = tensor.expand_shape %{{.*}}[0, 1]] : tensor<32xf32> into tensor<4x8xf32>
+// CHECK-DAG: %[[INI:.*]] = tensor.empty() : tensor<4xf32>
+// CHECK: %[[F:.*]] = linalg.fill ins(%[[ID]] : f32) outs(%[[INI]] : tensor<4xf32>) -> tensor<4xf32>
+// CHECK: %[[G:.*]] = linalg.generic
+// CHECK: {indexing_maps = [#[[$MAP0]], #[[$MAP1]], #[[$MAP2]]],
+// CHECK: iterator_types = ["parallel", "reduction"]} ins(%[[I1]], %{{.*}} : tensor<4x8xf32>, tensor<f32>) outs(%[[F]] : tensor<4xf32>) {
+// CHECK: arith.subf
+// CHECK: math.exp
+// CHECK: arith.mulf
+// CHECK: linalg.yield
+// CHECK: } -> tensor<4xf32>
+// CHECK: %[[R:.*]] = linalg.generic {indexing_maps = [#[[$MAP3]], #[[$MAP4]]], iterator_types = ["reduction"]} ins(%[[G]] : tensor<4xf32>) outs(%{{.*}} : tensor<f32>) {
+// CHECK: arith.mulf
+// CHECK: linalg.yield
+// CHECK: } -> tensor<f32>
+// CHECK: return %[[R]] : tensor<f32>
+
+transform.sequence failures(propagate) {
+^bb1(%arg1: !pdl.operation):
+ %0 = transform.structured.match ops{["linalg.generic"]} in %arg1
+ %1:4 = transform.structured.split_reduction %0 { split_factor = 4, insert_split_dimension = 0}
+}
- // CHECK: linalg.generic
- // CHECK-SAME: iterator_types = ["parallel", "parallel", "reduction"]
- // CHECK-SAME: ins(%{{[a-zA-Z0-9_]*}} : tensor<16x32x4xf32>)
- // CHECK-SAME: outs(%{{[a-zA-Z0-9_]*}} : tensor<16x32xf32>) {
+// -----
+
+func.func @generic_split_3d(%input: tensor<32x2xf32>, %input_2: tensor<5x32xf32>, %output: tensor<5x2xf32>)
+ -> tensor<5x2xf32>
+{
+ %0 = linalg.generic {
+ indexing_maps = [
+ affine_map<(d0, d1, d2) -> (d1, d0)>,
+ affine_map<(d0, d1, d2) -> (d2, d1)>,
+ affine_map<(d0, d1, d2) -> (d2, d0)>
+ ],
+ iterator_types = ["parallel", "reduction", "parallel"]
+ } ins(%input, %input_2 : tensor<32x2xf32>, tensor<5x32xf32>) outs(%output : tensor<5x2xf32>) {
+ ^bb0(%arg0: f32, %arg1: f32, %arg2: f32):
+ %3 = arith.addf %arg0, %arg1 : f32
+ %4 = arith.maxf %3, %arg2 : f32
+ linalg.yield %4 : f32
+ } -> tensor<5x2xf32>
+ return %0 : tensor<5x2xf32>
+}
+
+// CHECK-DAG: #[[$MAP0:.*]] = affine_map<(d0, d1, d2, d3) -> (d2, d1, d0)>
+// CHECK-DAG: #[[$MAP1:.*]] = affine_map<(d0, d1, d2, d3) -> (d3, d2, d1)>
+// CHECK-DAG: #[[$MAP2:.*]] = affine_map<(d0, d1, d2, d3) -> (d3, d0, d2)>
+// CHECK-DAG: #[[$MAP3:.*]] = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
+// CHECK-DAG: #[[$MAP4:.*]] = affine_map<(d0, d1, d2) -> (d0, d1)>
+// CHECK-LABEL: func @generic_split_3d
+// CHECK-DAG: %[[ID:.*]] = arith.constant -3.40282347E+38 : f32
+// CHECK-DAG: %[[I1:.*]] = tensor.expand_shape %{{.*}}[0, 1], [2]] : tensor<32x2xf32> into tensor<4x8x2xf32>
+// CHECK-DAG: %[[I2:.*]] = tensor.expand_shape %{{.*}}[0], [1, 2]] : tensor<5x32xf32> into tensor<5x4x8xf32>
+// CHECK-DAG: %[[INI:.*]] = tensor.empty() : tensor<5x2x4xf32>
+// CHECK: %[[F:.*]] = linalg.fill ins(%[[ID]] : f32) outs(%[[INI]] : tensor<5x2x4xf32>) -> tensor<5x2x4xf32>
+// CHECK: %[[G:.*]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP1]], #[[$MAP2]]], iterator_types = ["parallel", "reduction", "parallel", "parallel"]}
+// CHECK-SAME: ins(%[[I1]], %[[I2]] : tensor<4x8x2xf32>, tensor<5x4x8xf32>) outs(%[[F]] : tensor<5x2x4xf32>) {
+// CHECK: arith.addf
+// CHECK: arith.maxf
+// CHECK: linalg.yield
+// CHECK: } -> tensor<5x2x4xf32>
+// CHECK: %[[R:.*]] = linalg.generic {indexing_maps = [#[[$MAP3]], #[[$MAP4]]], iterator_types = ["parallel", "parallel", "reduction"]}
+// CHECK-SAME: ins(%[[G]] : tensor<5x2x4xf32>) outs(%{{.*}} : tensor<5x2xf32>) {
+// CHECK: arith.maxf
+// CHECK: linalg.yield
+// CHECK: } -> tensor<5x2xf32>
+// CHECK: return %[[R]] : tensor<5x2xf32>
+
+transform.sequence failures(propagate) {
+^bb1(%arg1: !pdl.operation):
+ %0 = transform.structured.match ops{["linalg.generic"]} in %arg1
+ %1:4 = transform.structured.split_reduction %0 { split_factor = 4, insert_split_dimension = 2}
+}
+
+// -----
+
+func.func @matmul_split(%A : tensor<16x256xf32>, %B: tensor<256x32xf32>, %C: tensor<16x32xf32>) -> tensor<16x32xf32> {
%0 = linalg.matmul ins(%A, %B: tensor<16x256xf32>, tensor<256x32xf32>)
outs(%C: tensor<16x32xf32>) -> tensor<16x32xf32>
return %0: tensor<16x32xf32>
}
+// CHECK-DAG: #[[$MAP0:.*]] = affine_map<(d0, d1, d2, d3) -> (d0, d2, d3)>
+// CHECK-DAG: #[[$MAP1:.*]] = affine_map<(d0, d1, d2, d3) -> (d2, d3, d1)>
+// CHECK-DAG: #[[$MAP2:.*]] = affine_map<(d0, d1, d2, d3) -> (d0, d1, d3)>
+// CHECK-DAG: #[[$MAP3:.*]] = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
+// CHECK-DAG: #[[$MAP4:.*]] = affine_map<(d0, d1, d2) -> (d0, d1)>
+// CHECK-LABEL: @matmul_split
+// CHECK-DAG: %[[ID:.*]] = arith.constant 0.000000e+00 : f32
+// CHECK-DAG: %[[I1:.*]] = tensor.expand_shape %{{.*}}[0], [1, 2]] : tensor<16x256xf32> into tensor<16x64x4xf32>
+// CHECK-DAG: %[[I2:.*]] = tensor.expand_shape %{{.*}}[0, 1], [2]] : tensor<256x32xf32> into tensor<64x4x32xf32>
+// CHECK-DAG: %[[INI:.*]] = tensor.empty() : tensor<16x32x4xf32>
+// CHECK: %[[F:.*]] = linalg.fill ins(%[[ID]] : f32) outs(%[[INI]] : tensor<16x32x4xf32>) -> tensor<16x32x4xf32>
+// CHECK: %[[G:.*]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP1]], #[[$MAP2]]]
+// CHECK-SAME: , iterator_types = ["parallel", "parallel", "reduction", "parallel"]}
+// CHECK-SAME: ins(%[[I1]], %[[I2]] : tensor<16x64x4xf32>, tensor<64x4x32xf32>) outs(%[[F]] : tensor<16x32x4xf32>) {
+// CHECK: arith.mulf
+// CHECK: arith.addf
+// CHECK: linalg.yield
+// CHECK: } -> tensor<16x32x4xf32>
+// CHECK: %[[R:.*]] = linalg.generic {indexing_maps = [#[[$MAP3]], #[[$MAP4]]],
+// CHECK-SAME: iterator_types = ["parallel", "parallel", "reduction"]} ins(%[[G]] : tensor<16x32x4xf32>) outs(%{{.*}} : tensor<16x32xf32>) {
+// CHECK: arith.addf
+// CHECK: linalg.yield %{{.*}} : f32
+// CHECK: } -> tensor<16x32xf32>
+// CHECK: return %[[R]] : tensor<16x32xf32>
+
transform.sequence failures(propagate) {
^bb1(%arg1: !pdl.operation):
%0 = transform.structured.match ops{["linalg.matmul"]} in %arg1
- %1:4 = transform.structured.split_reduction %0 { split_factor = 4, insert_split_dimension = 2}
+ %1:4 = transform.structured.split_reduction %0 { split_factor = 4, insert_split_dimension = 2, inner_parallel}
+}
+
+// -----
+
+func.func @generic_split_1d(%arg0: tensor<32xf32>, %arg1: tensor<f32>, %out: tensor<f32>) -> tensor<f32> {
+ %red = linalg.generic {indexing_maps = [affine_map<(d0) -> (d0)>,
+ affine_map<(d0) -> ()>,
+ affine_map<(d0) -> ()>],
+ iterator_types = ["reduction"]}
+ ins(%arg0, %arg1 : tensor<32xf32>, tensor<f32>)
+ outs(%out : tensor<f32>) {
+ ^bb0(%arg7: f32, %arg8: f32, %arg9: f32):
+ %40 = arith.subf %arg7, %arg8 : f32
+ %41 = math.exp %40 : f32
+ %42 = arith.mulf %41, %arg9 : f32
+ linalg.yield %42 : f32
+ } -> tensor<f32>
+ return %red : tensor<f32>
+}
+
+// CHECK-DAG: #[[$MAP0:.*]] = affine_map<(d0, d1) -> (d0, d1)>
+// CHECK-DAG: #[[$MAP1:.*]] = affine_map<(d0, d1) -> ()>
+// CHECK-DAG: #[[$MAP2:.*]] = affine_map<(d0, d1) -> (d1)>
+// CHECK-DAG: #[[$MAP3:.*]] = affine_map<(d0) -> (d0)>
+// CHECK-DAG: #[[$MAP4:.*]] = affine_map<(d0) -> ()>
+//CHECK-LABEL: @generic_split_1d
+// CHECK-DAG: %[[ID:.*]] = arith.constant 1.000000e+00 : f32
+// CHECK-DAG: %[[I1:.*]] = tensor.expand_shape %{{.*}}[0, 1]] : tensor<32xf32> into tensor<8x4xf32>
+// CHECK-DAG: %[[INI:.*]] = tensor.empty() : tensor<4xf32>
+// CHECK: %[[F:.*]] = linalg.fill ins(%[[ID]] : f32) outs(%[[INI]] : tensor<4xf32>) -> tensor<4xf32>
+// CHECK: %[[G:.*]] = linalg.generic
+// CHECK: {indexing_maps = [#[[$MAP0]], #[[$MAP1]], #[[$MAP2]]],
+// CHECK: iterator_types = ["reduction", "parallel"]} ins(%[[I1]], %{{.*}} : tensor<8x4xf32>, tensor<f32>) outs(%[[F]] : tensor<4xf32>) {
+// CHECK: arith.subf
+// CHECK: math.exp
+// CHECK: arith.mulf
+// CHECK: linalg.yield
+// CHECK: } -> tensor<4xf32>
+// CHECK: %[[R:.*]] = linalg.generic {indexing_maps = [#[[$MAP3]], #[[$MAP4]]], iterator_types = ["reduction"]} ins(%[[G]] : tensor<4xf32>) outs(%{{.*}} : tensor<f32>) {
+// CHECK: arith.mulf
+// CHECK: linalg.yield
+// CHECK: } -> tensor<f32>
+// CHECK: return %[[R]] : tensor<f32>
+
+transform.sequence failures(propagate) {
+^bb1(%arg1: !pdl.operation):
+ %0 = transform.structured.match ops{["linalg.generic"]} in %arg1
+ %1:4 = transform.structured.split_reduction %0 { split_factor = 4, insert_split_dimension = 0, inner_parallel}
+}
+
+// -----
+
+func.func @generic_split_3d(%input: tensor<32x2xf32>, %input_2: tensor<5x32xf32>, %output: tensor<5x2xf32>)
+ -> tensor<5x2xf32>
+{
+ %0 = linalg.generic {
+ indexing_maps = [
+ affine_map<(d0, d1, d2) -> (d1, d0)>,
+ affine_map<(d0, d1, d2) -> (d2, d1)>,
+ affine_map<(d0, d1, d2) -> (d2, d0)>
+ ],
+ iterator_types = ["parallel", "reduction", "parallel"]
+ } ins(%input, %input_2 : tensor<32x2xf32>, tensor<5x32xf32>) outs(%output : tensor<5x2xf32>) {
+ ^bb0(%arg0: f32, %arg1: f32, %arg2: f32):
+ %3 = arith.addf %arg0, %arg1 : f32
+ %4 = arith.maxf %3, %arg2 : f32
+ linalg.yield %4 : f32
+ } -> tensor<5x2xf32>
+ return %0 : tensor<5x2xf32>
+}
+
+// CHECK-DAG: #[[$MAP0:.*]] = affine_map<(d0, d1, d2, d3) -> (d1, d2, d0)>
+// CHECK-DAG: #[[$MAP1:.*]] = affine_map<(d0, d1, d2, d3) -> (d3, d1, d2)>
+// CHECK-DAG: #[[$MAP2:.*]] = affine_map<(d0, d1, d2, d3) -> (d3, d0, d2)>
+// CHECK-DAG: #[[$MAP3:.*]] = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
+// CHECK-DAG: #[[$MAP4:.*]] = affine_map<(d0, d1, d2) -> (d0, d1)>
+// CHECK-LABEL: func @generic_split_3d
+// CHECK-DAG: %[[ID:.*]] = arith.constant -3.40282347E+38 : f32
+// CHECK-DAG: %[[I1:.*]] = tensor.expand_shape %{{.*}}[0, 1], [2]] : tensor<32x2xf32> into tensor<8x4x2xf32>
+// CHECK-DAG: %[[I2:.*]] = tensor.expand_shape %{{.*}}[0], [1, 2]] : tensor<5x32xf32> into tensor<5x8x4xf32>
+// CHECK-DAG: %[[INI:.*]] = tensor.empty() : tensor<5x2x4xf32>
+// CHECK: %[[F:.*]] = linalg.fill ins(%[[ID]] : f32) outs(%[[INI]] : tensor<5x2x4xf32>) -> tensor<5x2x4xf32>
+// CHECK: %[[G:.*]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP1]], #[[$MAP2]]], iterator_types = ["parallel", "reduction", "parallel", "parallel"]}
+// CHECK-SAME: ins(%[[I1]], %[[I2]] : tensor<8x4x2xf32>, tensor<5x8x4xf32>) outs(%[[F]] : tensor<5x2x4xf32>) {
+// CHECK: arith.addf
+// CHECK: arith.maxf
+// CHECK: linalg.yield
+// CHECK: } -> tensor<5x2x4xf32>
+// CHECK: %[[R:.*]] = linalg.generic {indexing_maps = [#[[$MAP3]], #[[$MAP4]]], iterator_types = ["parallel", "parallel", "reduction"]}
+// CHECK-SAME: ins(%[[G]] : tensor<5x2x4xf32>) outs(%{{.*}} : tensor<5x2xf32>) {
+// CHECK: arith.maxf
+// CHECK: linalg.yield
+// CHECK: } -> tensor<5x2xf32>
+// CHECK: return %[[R]] : tensor<5x2xf32>
+
+transform.sequence failures(propagate) {
+^bb1(%arg1: !pdl.operation):
+ %0 = transform.structured.match ops{["linalg.generic"]} in %arg1
+ %1:4 = transform.structured.split_reduction %0 { split_factor = 4, insert_split_dimension = 2, inner_parallel}
}
More information about the Mlir-commits
mailing list