[Mlir-commits] [mlir] 8d07ba8 - [mlir][linalg] Simplify the hoist padding tests.
Tobias Gysi
llvmlistbot at llvm.org
Thu Nov 25 23:40:36 PST 2021
Author: Tobias Gysi
Date: 2021-11-26T07:40:22Z
New Revision: 8d07ba817c98374b74ff3ba11c2cb5181cf37031
URL: https://github.com/llvm/llvm-project/commit/8d07ba817c98374b74ff3ba11c2cb5181cf37031
DIFF: https://github.com/llvm/llvm-project/commit/8d07ba817c98374b74ff3ba11c2cb5181cf37031.diff
LOG: [mlir][linalg] Simplify the hoist padding tests.
Use primarily matvec instead of matmul to test hoist padding. Test the hoisting only starting from already padded IR. Use one-dimensional tiling only except for the tile_and_fuse test that exercises hoisting on a larger loop nest with fill and pad tensor operations in the backward slice.
Reviewed By: nicolasvasilache
Differential Revision: https://reviews.llvm.org/D114608
Added:
mlir/test/Dialect/Linalg/hoist-padding.mlir
Modified:
Removed:
mlir/test/Dialect/Linalg/pad-and-hoist.mlir
################################################################################
diff --git a/mlir/test/Dialect/Linalg/hoist-padding.mlir b/mlir/test/Dialect/Linalg/hoist-padding.mlir
new file mode 100644
index 0000000000000..62ee2f537488a
--- /dev/null
+++ b/mlir/test/Dialect/Linalg/hoist-padding.mlir
@@ -0,0 +1,423 @@
+// RUN: mlir-opt %s -test-linalg-codegen-strategy="anchor-op=linalg.matvec pad hoist-paddings=1,1,0 run-enable-pass=false" -cse -canonicalize -split-input-file | FileCheck %s --check-prefix=MATVEC
+// RUN: mlir-opt %s -test-linalg-codegen-strategy="anchor-op=linalg.matmul pad hoist-paddings=1,2,1 run-enable-pass=false" -cse -canonicalize -split-input-file | FileCheck %s --check-prefix=MATMUL
+
+// MATVEC-DAG: #[[DIV4:[0-9a-z]+]] = affine_map<(d0) -> (d0 ceildiv 4)>
+
+// MATVEC: static_size_divisible
+// MATVEC-SAME: %[[ARG1:[0-9a-zA-Z]*]]: tensor<12xf32>
+func @static_size_divisible(%arg0: tensor<24x12xf32>,
+ %arg1: tensor<12xf32>,
+ %arg2: tensor<24xf32>) -> tensor<24xf32> {
+ %cst = arith.constant 0.000000e+00 : f32
+ %c0 = arith.constant 0 : index
+ %c12 = arith.constant 12 : index
+ %c4 = arith.constant 4 : index
+
+ // Pack the vector tiles for all values of IV (IVx4).
+ // MATVEC: = linalg.init_tensor [3, 4]
+ // MATVEC: %[[T0:.*]] = scf.for %[[PIV0:[0-9a-z]+]] =
+ // MATVEC: %[[PIDX0:.*]] = affine.apply #[[DIV4]](%[[PIV0]])
+ // MATVEC: %[[T1:.*]] = tensor.extract_slice %[[ARG1]][%[[PIV0]]] [4]
+ // MATVEC: %[[T2:.*]] = linalg.pad_tensor %[[T1]]
+ // MATVEC: %[[T3:.*]] = tensor.insert_slice %[[T1:.*]]{{.*}}[%[[PIDX0]]
+
+ // MATVEC: scf.for %[[IV0:[0-9a-zA-Z]*]] =
+ %0 = scf.for %arg3 = %c0 to %c12 step %c4 iter_args(%arg4 = %arg2) -> (tensor<24xf32>) {
+ %1 = tensor.extract_slice %arg0[0, %arg3] [24, 4] [1, 1] : tensor<24x12xf32> to tensor<24x4xf32>
+
+ // Index the packed vector.
+ // MATVEC-DAG: %[[IDX0:.*]] = affine.apply #[[DIV4]](%[[IV0]])
+ // MATVEC-DAG: %[[T4:.*]] = tensor.extract_slice %[[T0]][%[[IDX0]]
+ %2 = tensor.extract_slice %arg1[%arg3] [4] [1] : tensor<12xf32> to tensor<4xf32>
+ %3 = linalg.pad_tensor %2 nofold low[%c0] high[%c0] {
+ ^bb0(%arg5: index): // no predecessors
+ linalg.yield %cst : f32
+ } : tensor<4xf32> to tensor<4xf32>
+
+ // Check matvec uses the packed input vector.
+ // MATVEC: = linalg.matvec ins(%{{.*}}, %[[T4]]
+ %4 = linalg.matvec ins(%1, %3 : tensor<24x4xf32>, tensor<4xf32>) outs(%arg4 : tensor<24xf32>) -> tensor<24xf32>
+ scf.yield %4 : tensor<24xf32>
+ }
+ return %0 : tensor<24xf32>
+}
+
+// -----
+
+// MATVEC-DAG: #[[MAP0:[0-9a-z]+]] = affine_map<(d0) -> (5, -d0 + 12)>
+// MATVEC-DAG: #[[MAP1:[0-9a-z]+]] = affine_map<(d0) -> (-d0 + 5)>
+// MATVEC-DAG: #[[DIV5:[0-9a-z]+]] = affine_map<(d0) -> (d0 ceildiv 5)>
+#map0 = affine_map<(d0) -> (5, -d0 + 12)>
+#map1 = affine_map<(d0) -> (-d0 + 5)>
+
+// MATVEC: static_size_not_divisible
+// MATVEC-SAME: %[[ARG1:[0-9a-zA-Z]*]]: tensor<12xf32>
+func @static_size_not_divisible(%arg0: tensor<24x12xf32>,
+ %arg1: tensor<12xf32>,
+ %arg2: tensor<24xf32>) -> tensor<24xf32> {
+ %cst = arith.constant 0.000000e+00 : f32
+ %c0 = arith.constant 0 : index
+ %c12 = arith.constant 12 : index
+ %c5 = arith.constant 5 : index
+
+ // Pack the vector tiles for all values of IV (IVx5).
+ // MATVEC: = linalg.init_tensor [3, 5]
+ // MATVEC: %[[T0:.*]] = scf.for %[[PIV0:[0-9a-z]+]] =
+ // MATVEC: %[[PIDX0:.*]] = affine.apply #[[DIV5]](%[[PIV0]])
+ // MATVEC: %[[TS0:.*]] = affine.min #[[MAP0]](%[[PIV0]])
+ // MATVEC: %[[T1:.*]] = tensor.extract_slice %[[ARG1]][%[[PIV0]]] [%[[TS0]]]
+ // MATVEC: %[[HPD0:.*]] = affine.apply #[[MAP1]](%[[TS0]])
+ // MATVEC: %[[T2:.*]] = linalg.pad_tensor %[[T1]]{{.*}}high[%[[HPD0]]
+ // MATVEC: %[[T3:.*]] = tensor.insert_slice %[[T1:.*]]{{.*}}[%[[PIDX0]]
+
+ // MATVEC: scf.for %[[IV0:[0-9a-zA-Z]*]] =
+ %0 = scf.for %arg3 = %c0 to %c12 step %c5 iter_args(%arg4 = %arg2) -> (tensor<24xf32>) {
+ %1 = affine.min #map0(%arg3)
+ %2 = tensor.extract_slice %arg0[0, %arg3] [24, %1] [1, 1] : tensor<24x12xf32> to tensor<24x?xf32>
+
+ // Index the packed vector.
+ // MATVEC-DAG: %[[IDX0:.*]] = affine.apply #[[DIV5]](%[[IV0]])
+ // MATVEC-DAG: %[[T4:.*]] = tensor.extract_slice %[[T0]][%[[IDX0]]
+ %3 = tensor.extract_slice %arg1[%arg3] [%1] [1] : tensor<12xf32> to tensor<?xf32>
+ %4 = affine.apply #map1(%1)
+ %5 = linalg.pad_tensor %2 low[%c0, %c0] high[%c0, %4] {
+ ^bb0(%arg5: index, %arg6: index): // no predecessors
+ linalg.yield %cst : f32
+ } : tensor<24x?xf32> to tensor<24x5xf32>
+ %6 = linalg.pad_tensor %3 low[%c0] high[%4] {
+ ^bb0(%arg5: index): // no predecessors
+ linalg.yield %cst : f32
+ } : tensor<?xf32> to tensor<5xf32>
+
+ // Check matvec uses the packed input vector.
+ // MATVEC: = linalg.matvec ins(%{{.*}}, %[[T4]]
+ %7 = linalg.matvec ins(%5, %6 : tensor<24x5xf32>, tensor<5xf32>) outs(%arg4 : tensor<24xf32>) -> tensor<24xf32>
+ scf.yield %7 : tensor<24xf32>
+ }
+ return %0 : tensor<24xf32>
+}
+
+// -----
+
+// MATVEC-DAG: #[[SDIV4:[0-9a-z]+]] = affine_map<()[s0] -> (s0 ceildiv 4)>
+// MATVEC-DAG: #[[DDIV4:[0-9a-z]+]] = affine_map<(d0) -> (d0 ceildiv 4)>
+// MATVEC-DAG: #[[MAP0:[0-9a-z]+]] = affine_map<(d0)[s0] -> (4, -d0 + s0)>
+// MATVEC-DAG: #[[MAP1:[0-9a-z]+]] = affine_map<(d0) -> (-d0 + 4)>
+#map0 = affine_map<(d0)[s0] -> (4, -d0 + s0)>
+#map1 = affine_map<(d0) -> (-d0 + 4)>
+
+// MATVEC: dynamic_size
+// MATVEC-SAME: %[[ARG1:[0-9a-zA-Z]*]]: tensor<?xf32>
+func @dynamic_size(%arg0: tensor<24x?xf32>,
+ %arg1: tensor<?xf32>,
+ %arg2: tensor<24xf32>) -> tensor<24xf32> {
+ %cst = arith.constant 0.000000e+00 : f32
+ %c0 = arith.constant 0 : index
+ %c1 = arith.constant 1 : index
+ %c4 = arith.constant 4 : index
+
+ // MATVEC: %[[D0:.*]] = tensor.dim
+ %0 = tensor.dim %arg0, %c1 : tensor<24x?xf32>
+
+ // Pack the vector tiles for all values of IV (IVx4).
+ // MATVEC: %[[PS0:.*]] = affine.apply #[[SDIV4]]()[%[[D0]]]
+ // MATVEC: = linalg.init_tensor [%[[PS0]], 4]
+ // MATVEC: %[[T0:.*]] = scf.for %[[PIV0:[0-9a-z]+]] =
+ // MATVEC: %[[PIDX0:.*]] = affine.apply #[[DDIV4]](%[[PIV0]])
+ // MATVEC: %[[TS0:.*]] = affine.min #[[MAP0]](%[[PIV0]])[%[[D0]]]
+ // MATVEC: %[[T1:.*]] = tensor.extract_slice %[[ARG1]][%[[PIV0]]] [%[[TS0]]]
+ // MATVEC: %[[HPD0:.*]] = affine.apply #[[MAP1]](%[[TS0]])
+ // MATVEC: %[[T2:.*]] = linalg.pad_tensor %[[T1]]{{.*}}high[%[[HPD0]]
+ // MATVEC: %[[T3:.*]] = tensor.insert_slice %[[T1:.*]]{{.*}}[%[[PIDX0]]
+
+ // MATVEC: scf.for %[[IV0:[0-9a-zA-Z]*]] =
+ %1 = scf.for %arg3 = %c0 to %0 step %c4 iter_args(%arg4 = %arg2) -> (tensor<24xf32>) {
+ %2 = affine.min #map0(%arg3)[%0]
+ %3 = tensor.extract_slice %arg0[0, %arg3] [24, %2] [1, 1] : tensor<24x?xf32> to tensor<24x?xf32>
+
+ // Index the packed vector.
+ // MATVEC-DAG: %[[IDX0:.*]] = affine.apply #[[DDIV4]](%[[IV0]])
+ // MATVEC-DAG: %[[T4:.*]] = tensor.extract_slice %[[T0]][%[[IDX0]]
+ %4 = tensor.extract_slice %arg1[%arg3] [%2] [1] : tensor<?xf32> to tensor<?xf32>
+ %5 = affine.apply #map1(%2)
+ %6 = linalg.pad_tensor %3 low[%c0, %c0] high[%c0, %5] {
+ ^bb0(%arg5: index, %arg6: index): // no predecessors
+ linalg.yield %cst : f32
+ } : tensor<24x?xf32> to tensor<24x4xf32>
+ %7 = linalg.pad_tensor %4 nofold low[%c0] high[%5] {
+ ^bb0(%arg5: index): // no predecessors
+ linalg.yield %cst : f32
+ } : tensor<?xf32> to tensor<4xf32>
+
+ // Check matvec uses the packed input vector.
+ // MATVEC: = linalg.matvec ins(%{{.*}}, %[[T4]]
+ %8 = linalg.matvec ins(%6, %7 : tensor<24x4xf32>, tensor<4xf32>) outs(%arg4 : tensor<24xf32>) -> tensor<24xf32>
+ scf.yield %8 : tensor<24xf32>
+ }
+ return %1 : tensor<24xf32>
+}
+
+// -----
+
+// MATVEC: non_constant_padding
+// MATVEC-SAME: %[[ARG1:[0-9a-zA-Z]*]]: tensor<12xf32>
+func @non_constant_padding(%arg0: tensor<24x12xf32>,
+ %arg1: tensor<12xf32>,
+ %arg2: tensor<24xf32>) -> tensor<24xf32> {
+ %c4 = arith.constant 4 : index
+ %c12 = arith.constant 12 : index
+ %c0 = arith.constant 0 : index
+
+ // MATVEC: scf.for %[[IV0:[0-9a-zA-Z]*]] =
+ %0 = scf.for %arg3 = %c0 to %c12 step %c4 iter_args(%arg4 = %arg2) -> (tensor<24xf32>) {
+ %1 = tensor.extract_slice %arg0[0, %arg3] [24, 4] [1, 1] : tensor<24x12xf32> to tensor<24x4xf32>
+
+ // Check the non constant padding is not hoisted.
+ // MATVEC: %[[T0:.*]] = tensor.extract_slice %[[ARG1]][%[[IV0]]
+ // MATVEC: %[[T1:.*]] = linalg.pad_tensor %[[T0]]
+ %2 = tensor.extract_slice %arg1[%arg3] [4] [1] : tensor<12xf32> to tensor<4xf32>
+ %3 = linalg.pad_tensor %2 nofold low[%c0] high[%c0] {
+ ^bb0(%arg5: index): // no predecessors
+ %5 = arith.index_cast %arg3 : index to i32
+ %6 = arith.sitofp %5 : i32 to f32
+ linalg.yield %6 : f32
+ } : tensor<4xf32> to tensor<4xf32>
+
+ // Check matvec uses the padded input vector.
+ // MATVEC: = linalg.matvec ins(%{{.*}}, %[[T1]]
+ %4 = linalg.matvec ins(%1, %3 : tensor<24x4xf32>, tensor<4xf32>) outs(%arg4 : tensor<24xf32>) -> tensor<24xf32>
+ scf.yield %4 : tensor<24xf32>
+ }
+ return %0 : tensor<24xf32>
+}
+
+// -----
+
+// MATVEC: non_constant_op_padding
+// MATVEC-SAME: %[[ARG1:[0-9a-zA-Z]*]]: tensor<12xf32>
+func @non_constant_op_padding(%arg0: tensor<24x12xf32>,
+ %arg1: tensor<12xf32>,
+ %arg2: tensor<24xf32>) -> tensor<24xf32> {
+ %c0 = arith.constant 0 : index
+ %c12 = arith.constant 12 : index
+ %c4 = arith.constant 4 : index
+
+ // MATVEC: scf.for %[[IV0:[0-9a-zA-Z]*]] =
+ %0 = scf.for %arg3 = %c0 to %c12 step %c4 iter_args(%arg4 = %arg2) -> (tensor<24xf32>) {
+ %1 = tensor.extract_slice %arg0[0, %arg3] [24, 4] [1, 1] : tensor<24x12xf32> to tensor<24x4xf32>
+
+ // Check the non constant op padding is not hoisted.
+ // MATVEC: %[[T0:.*]] = tensor.extract_slice %[[ARG1]][%[[IV0]]
+ // MATVEC: %[[V0:.*]] = tensor.extract %[[ARG1]][%[[IV0]]
+ // MATVEC: %[[T1:.*]] = linalg.pad_tensor %[[T0]]
+ // MATVEC: linalg.yield %[[V0]]
+ %2 = tensor.extract_slice %arg1[%arg3] [4] [1] : tensor<12xf32> to tensor<4xf32>
+ %3 = tensor.extract %arg1[%arg3] : tensor<12xf32>
+ %4 = linalg.pad_tensor %2 nofold low[%c0] high[%c0] {
+ ^bb0(%arg5: index): // no predecessors
+ linalg.yield %3 : f32
+ } : tensor<4xf32> to tensor<4xf32>
+
+ // Check matvec uses the padded input vector.
+ // MATVEC: = linalg.matvec ins(%{{.*}}, %[[T1]]
+ %5 = linalg.matvec ins(%1, %4 : tensor<24x4xf32>, tensor<4xf32>) outs(%arg4 : tensor<24xf32>) -> tensor<24xf32>
+ scf.yield %5 : tensor<24xf32>
+ }
+ return %0 : tensor<24xf32>
+}
+
+// -----
+
+// MATVEC: non_index_operand
+// MATVEC-SAME: %[[ARG1:[0-9a-zA-Z]*]]: tensor<12xf32>
+// MATVEC-SAME: %[[ARG3:[0-9a-zA-Z]*]]: i32
+func @non_index_operand(%arg0: tensor<24x12xf32>,
+ %arg1: tensor<12xf32>,
+ %arg2: tensor<24xf32>,
+ %arg3: i32) -> tensor<24xf32> {
+ %c4 = arith.constant 4 : index
+ %c12 = arith.constant 12 : index
+ %c0 = arith.constant 0 : index
+ %cst = arith.constant 0.000000e+00 : f32
+
+ // MATVEC: scf.for %[[IV0:[0-9a-zA-Z]*]] =
+ %0 = scf.for %arg4 = %c0 to %c12 step %c4 iter_args(%arg5 = %arg2) -> (tensor<24xf32>) {
+ %1 = tensor.extract_slice %arg0[0, %arg4] [24, 4] [1, 1] : tensor<24x12xf32> to tensor<24x4xf32>
+
+ // Check the index_cast prevents hoisting due to its non index operand.
+ // MATVEC: %[[T0:.*]] = tensor.extract_slice %[[ARG1]][%[[IV0]]
+ // MATVEC: %[[IDX0:.*]] = arith.index_cast %[[ARG3]]
+ // MATVEC: %[[T1:.*]] = linalg.pad_tensor %[[T0]]{{.*}}%[[IDX0]]
+ %2 = tensor.extract_slice %arg1[%arg4] [4] [1] : tensor<12xf32> to tensor<4xf32>
+ %3 = arith.index_cast %arg3 : i32 to index
+ %4 = linalg.pad_tensor %2 nofold low[%3] high[%3] {
+ ^bb0(%arg6: index): // no predecessors
+ linalg.yield %cst : f32
+ } : tensor<4xf32> to tensor<4xf32>
+
+ // Check matvec uses the padded input vector.
+ // MATVEC: = linalg.matvec ins(%{{.*}}, %[[T1]]
+ %5 = linalg.matvec ins(%1, %4 : tensor<24x4xf32>, tensor<4xf32>) outs(%arg5 : tensor<24xf32>) -> tensor<24xf32>
+ scf.yield %5 : tensor<24xf32>
+ }
+ return %0 : tensor<24xf32>
+}
+
+// -----
+
+// MATVEC: memory_effect
+// MATVEC-SAME: %[[ARG1:[0-9a-zA-Z]*]]: tensor<12xf32>
+// MATVEC-SAME: %[[ARG3:[0-9a-zA-Z]*]]: memref<?xindex>
+func @memory_effect(%arg0: tensor<24x12xf32>,
+ %arg1: tensor<12xf32>,
+ %arg2: tensor<24xf32>,
+ %arg3: memref<?xindex>) -> tensor<24xf32> {
+ %c4 = arith.constant 4 : index
+ %c12 = arith.constant 12 : index
+ %c0 = arith.constant 0 : index
+ %cst = arith.constant 0.000000e+00 : f32
+
+ // MATVEC: scf.for %[[IV0:[0-9a-zA-Z]*]] =
+ %0 = scf.for %arg4 = %c0 to %c12 step %c4 iter_args(%arg5 = %arg2) -> (tensor<24xf32>) {
+ %1 = tensor.extract_slice %arg0[0, %arg4] [24, 4] [1, 1] : tensor<24x12xf32> to tensor<24x4xf32>
+
+ // Check the load prevents hoisting due to its memory effect.
+ // MATVEC: %[[T0:.*]] = tensor.extract_slice %[[ARG1]][%[[IV0]]
+ // MATVEC: %[[IDX0:.*]] = memref.load %[[ARG3]]
+ // MATVEC: %[[T1:.*]] = linalg.pad_tensor %[[T0]]{{.*}}%[[IDX0]]
+ %2 = tensor.extract_slice %arg1[%arg4] [4] [1] : tensor<12xf32> to tensor<4xf32>
+ %3 = memref.load %arg3[%c0] : memref<?xindex>
+ %4 = linalg.pad_tensor %2 nofold low[%3] high[%3] {
+ ^bb0(%arg6: index): // no predecessors
+ linalg.yield %cst : f32
+ } : tensor<4xf32> to tensor<4xf32>
+
+ // Check matvec uses the padded input vector.
+ // MATVEC: = linalg.matvec ins(%{{.*}}, %[[T1]]
+ %5 = linalg.matvec ins(%1, %4 : tensor<24x4xf32>, tensor<4xf32>) outs(%arg5 : tensor<24xf32>) -> tensor<24xf32>
+ scf.yield %5 : tensor<24xf32>
+ }
+ return %0 : tensor<24xf32>
+}
+
+// -----
+
+// MATVEC: index_result_loop
+// MATVEC-SAME: %[[ARG1:[0-9a-zA-Z]*]]: tensor<12xf32>
+// MATVEC-SAME: %[[ARG3:[0-9a-zA-Z]*]]: index
+func @index_result_loop(%arg0: tensor<24x12xf32>,
+ %arg1: tensor<12xf32>,
+ %arg2: tensor<24xf32>,
+ %arg3: index) -> tensor<24xf32> {
+ %c4 = arith.constant 4 : index
+ %c12 = arith.constant 12 : index
+ %c0 = arith.constant 0 : index
+ %cst = arith.constant 0.000000e+00 : f32
+
+ // MATVEC: scf.for %[[IV0:[0-9a-zA-Z]*]] =
+ %0 = scf.for %arg4 = %c0 to %c12 step %c4 iter_args(%arg5 = %arg2) -> (tensor<24xf32>) {
+ %1 = tensor.extract_slice %arg0[0, %arg4] [24, 4] [1, 1] : tensor<24x12xf32> to tensor<24x4xf32>
+
+ // Check the unexpected operation with a region prevents hoisting.
+ // MATVEC: %[[T0:.*]] = tensor.extract_slice %[[ARG1]][%[[IV0]]
+ // MATVEC: %[[IDX0:.*]] = scf.for {{.*}} step %[[ARG3]]
+ // MATVEC: %[[T1:.*]] = linalg.pad_tensor %[[T0]]{{.*}}%[[IDX0]]
+ %2 = tensor.extract_slice %arg1[%arg4] [4] [1] : tensor<12xf32> to tensor<4xf32>
+ %3 = scf.for %arg6 = %c0 to %c12 step %arg3 iter_args(%arg7 = %c0) -> (index) {
+ %6 = arith.addi %arg3, %arg7 : index
+ scf.yield %6 : index
+ }
+ %4 = linalg.pad_tensor %2 nofold low[%3] high[%3] {
+ ^bb0(%arg6: index): // no predecessors
+ linalg.yield %cst : f32
+ } : tensor<4xf32> to tensor<4xf32>
+
+ // Check matvec uses the padded input vector.
+ // MATVEC: = linalg.matvec ins(%{{.*}}, %[[T1]]
+ %5 = linalg.matvec ins(%1, %4 : tensor<24x4xf32>, tensor<4xf32>) outs(%arg5 : tensor<24xf32>) -> tensor<24xf32>
+ scf.yield %5 : tensor<24xf32>
+ }
+ return %0 : tensor<24xf32>
+}
+
+// -----
+
+#map0 = affine_map<(d0) -> (5, -d0 + 12)>
+#map1 = affine_map<(d0) -> (-d0 + 5)>
+
+// MATMUL: tile_and_fuse
+// MATMUL-SAME: %[[ARG0:[0-9a-zA-Z]*]]: tensor<12x6xf32>
+// MATMUL-SAME: %[[ARG1:[0-9a-zA-Z]*]]: tensor<6x24xf32>
+func @tile_and_fuse(%arg0: tensor<12x6xf32>,
+ %arg1: tensor<6x24xf32>,
+ %arg2: tensor<12x24xf32>) -> tensor<12x24xf32> {
+ %c6 = arith.constant 6 : index
+ %c3 = arith.constant 3 : index
+ %c0 = arith.constant 0 : index
+ %c12 = arith.constant 12 : index
+ %c5 = arith.constant 5 : index
+ %cst = arith.constant 0.000000e+00 : f32
+
+ // Check the second input operand is hoisted by two loop nests.
+ // MATMUL: %[[T0:.*]] = scf.for %[[PIV0:[0-9a-z]+]] =
+ // MATMUL: %[[T1:.*]] = tensor.extract_slice %[[ARG1]]
+ // MATMUL: %[[T2:.*]] = linalg.pad_tensor %[[T1]]
+
+ // MATMUL: scf.for %[[IV0:[0-9a-zA-Z]*]] =
+ %0 = scf.for %arg3 = %c0 to %c12 step %c5 iter_args(%arg4 = %arg2) -> (tensor<12x24xf32>) {
+ %1 = affine.min #map0(%arg3)
+
+ // Check the extract_slice op introduced by the double tiling does not prevent the hoisting.
+ %2 = tensor.extract_slice %arg4[%arg3, 0] [%1, 24] [1, 1] : tensor<12x24xf32> to tensor<?x24xf32>
+ %3 = affine.apply #map1(%1)
+
+ // Check the fused and padded fill op does not prevent hoisting.
+ %4 = linalg.pad_tensor %2 nofold low[%c0, %c0] high[%3, %c0] {
+ ^bb0(%arg5: index, %arg6: index): // no predecessors
+ linalg.yield %cst : f32
+ } : tensor<?x24xf32> to tensor<5x24xf32>
+ %5 = linalg.fill(%cst, %4) : f32, tensor<5x24xf32> -> tensor<5x24xf32>
+ %6 = tensor.extract_slice %5[0, 0] [%1, 24] [1, 1] : tensor<5x24xf32> to tensor<?x24xf32>
+
+ // Check the first input operand is hoisted by one loop nest.
+ // MATMUL: %[[T3:.*]] = scf.for %[[PIV1:[0-9a-z]+]] =
+ // MATMUL: %[[T4:.*]] = tensor.extract_slice %[[ARG0]]
+ // MATMUL: %[[T5:.*]] = linalg.pad_tensor %[[T4]]
+
+ // MATMUL: scf.for %[[IV1:[0-9a-zA-Z]*]] =
+ %7 = scf.for %arg5 = %c0 to %c6 step %c3 iter_args(%arg6 = %6) -> (tensor<?x24xf32>) {
+
+ // Index the packed operands.
+ // MATMUL-DAG: %[[T6:.*]] = tensor.extract_slice %[[T3]]
+ // MATMUL-DAG: %[[T7:.*]] = tensor.extract_slice %[[T0]]
+ %9 = tensor.extract_slice %arg0[%arg3, %arg5] [%1, 3] [1, 1] : tensor<12x6xf32> to tensor<?x3xf32>
+ %10 = tensor.extract_slice %arg1[%arg5, 0] [3, 24] [1, 1] : tensor<6x24xf32> to tensor<3x24xf32>
+ %11 = tensor.extract_slice %arg6[0, 0] [%1, 24] [1, 1] : tensor<?x24xf32> to tensor<?x24xf32>
+ %12 = linalg.pad_tensor %9 nofold low[%c0, %c0] high[%3, %c0] {
+ ^bb0(%arg7: index, %arg8: index): // no predecessors
+ linalg.yield %cst : f32
+ } : tensor<?x3xf32> to tensor<5x3xf32>
+ %13 = linalg.pad_tensor %10 nofold low[%c0, %c0] high[%c0, %c0] {
+ ^bb0(%arg7: index, %arg8: index): // no predecessors
+ linalg.yield %cst : f32
+ } : tensor<3x24xf32> to tensor<3x24xf32>
+
+ // Check the output padding is not hoisted.
+ // MATMUL: %[[T8:.*]] = linalg.pad_tensor
+ %14 = linalg.pad_tensor %11 nofold low[%c0, %c0] high[%3, %c0] {
+ ^bb0(%arg7: index, %arg8: index): // no predecessors
+ linalg.yield %cst : f32
+ } : tensor<?x24xf32> to tensor<5x24xf32>
+
+ // Check matmul uses the padded operands.
+ // MATMUL: = linalg.matmul ins(%[[T6]], %[[T7]] {{.*}} outs(%[[T8]]
+ %15 = linalg.matmul ins(%12, %13 : tensor<5x3xf32>, tensor<3x24xf32>) outs(%14 : tensor<5x24xf32>) -> tensor<5x24xf32>
+ %16 = tensor.extract_slice %15[0, 0] [%1, 24] [1, 1] : tensor<5x24xf32> to tensor<?x24xf32>
+ %17 = tensor.insert_slice %16 into %arg6[0, 0] [%1, 24] [1, 1] : tensor<?x24xf32> into tensor<?x24xf32>
+ scf.yield %17 : tensor<?x24xf32>
+ }
+ %8 = tensor.insert_slice %7 into %arg4[%arg3, 0] [%1, 24] [1, 1] : tensor<?x24xf32> into tensor<12x24xf32>
+ scf.yield %8 : tensor<12x24xf32>
+ }
+ return %0 : tensor<12x24xf32>
+}
diff --git a/mlir/test/Dialect/Linalg/pad-and-hoist.mlir b/mlir/test/Dialect/Linalg/pad-and-hoist.mlir
deleted file mode 100644
index e6a95d6b6d718..0000000000000
--- a/mlir/test/Dialect/Linalg/pad-and-hoist.mlir
+++ /dev/null
@@ -1,599 +0,0 @@
-// RUN: mlir-opt %s -test-linalg-codegen-strategy="anchor-op=linalg.matmul pad pack-paddings=1,1,0 hoist-paddings=2,1,0 run-enable-pass=false" -cse -canonicalize -split-input-file | FileCheck %s --check-prefix=HOIST21
-// RUN: mlir-opt %s -test-linalg-codegen-strategy="anchor-op=linalg.matmul pad pack-paddings=1,1,0 hoist-paddings=3,2,0 run-enable-pass=false" -cse -canonicalize -split-input-file | FileCheck %s --check-prefix=HOIST32
-
-// HOIST21-DAG: #[[MAP0:[0-9a-z]+]] = affine_map<(d0) -> (5, -d0 + 24)>
-// HOIST21-DAG: #[[MAP1:[0-9a-z]+]] = affine_map<(d0) -> (7, -d0 + 25)>
-// HOIST21-DAG: #[[MAP2:[0-9a-z]+]] = affine_map<(d0) -> (-d0 + 5)>
-// HOIST21-DAG: #[[MAP3:[0-9a-z]+]] = affine_map<(d0) -> (-d0 + 7)>
-// HOIST21-DAG: #[[DIV6:[0-9a-z]+]] = affine_map<(d0) -> (d0 ceildiv 6)>
-#map0 = affine_map<(d0) -> (5, -d0 + 24)>
-#map1 = affine_map<(d0) -> (7, -d0 + 25)>
-
-// HOIST21: static_sizes
-// HOIST32: static_sizes
-// HOIST21-SAME: %[[ARG0:[0-9a-zA-Z]*]]: tensor<24x12xf32>
-// HOIST21-SAME: %[[ARG1:[0-9a-zA-Z]*]]: tensor<12x25xf32>
-// HOIST21-SAME: %[[ARG2:[0-9a-zA-Z]*]]: tensor<24x25xf32>
-func @static_sizes(%arg0: tensor<24x12xf32>,
- %arg1: tensor<12x25xf32>,
- %arg2: tensor<24x25xf32>) -> tensor<24x25xf32> {
- // HOIST21-DAG: %[[C0:.*]] = arith.constant 0 : index
- // HOIST21-DAG: %[[C5:.*]] = arith.constant 5
- // HOIST21-DAG: %[[C7:.*]] = arith.constant 7
- %c0 = arith.constant 0 : index
- %c12 = arith.constant 12 : index
- %c25 = arith.constant 25 : index
- %c24 = arith.constant 24 : index
- %c6 = arith.constant 6 : index
- %c7 = arith.constant 7 : index
- %c5 = arith.constant 5 : index
-
- // HOIST21: scf.for %[[IV0:[0-9a-zA-Z]*]] =
- %0 = scf.for %arg3 = %c0 to %c24 step %c5 iter_args(%arg4 = %arg2) -> (tensor<24x25xf32>) {
-
- // Packing the first input operand for all values of IV2 (IV2x5x6).
- // HOIST21: = linalg.init_tensor [2, 5, 6]
- // HOIST21: %[[PT0:.*]] = scf.for %[[PIV0:[0-9a-z]+]] =
- // HOIST21: %[[PIDX0:.*]] = affine.apply #[[DIV6]](%[[PIV0]])
- // HOIST21: %[[TS0:.*]] = affine.min #[[MAP0]](%[[IV0]])
- // HOIST21: %[[T0:.*]] = tensor.extract_slice %[[ARG0]]
- // HOIST21-SAME: %[[IV0]], %[[PIV0]]
- // HOIST21-SAME: %[[TS0]], 6
- // HOIST21: %[[V0:.*]] = affine.apply #[[MAP2]](%[[TS0]])
- // HOIST21: %[[T1:.*]] = linalg.pad_tensor %[[T0]] nofold {{.*}} high[%[[V0]]
- // HOIST21: %[[T2:.*]] = tensor.insert_slice %[[T1:.*]] into %{{.*}}[%[[PIDX0]], 0, 0]
- // HOIST21: scf.yield %[[T2:.*]]
-
- // HOIST21: scf.for %[[IV1:[0-9a-zA-Z]*]] =
- %1 = scf.for %arg5 = %c0 to %c25 step %c7 iter_args(%arg6 = %arg4) -> (tensor<24x25xf32>) {
-
- // Packing the second input operand for all values of IV2 (IV2x6x7).
- // HOIST21: = linalg.init_tensor [2, 6, 7]
- // HOIST21: %[[PT1:.*]] = scf.for %[[PIV1:[0-9a-z]+]] =
- // HOIST21: %[[PIDX1:.*]] = affine.apply #[[DIV6]](%[[PIV1]])
- // HOIST21: %[[TS1:.*]] = affine.min #[[MAP1]](%[[IV1]])
- // HOIST21: %[[T3:.*]] = tensor.extract_slice %[[ARG1]]
- // HOIST21-SAME: %[[PIV1]], %[[IV1]]
- // HOIST21-SAME: 6, %[[TS1]]
- // HOIST21: %[[V1:.*]] = affine.apply #[[MAP3]](%[[TS1]])
- // HOIST21: %[[T4:.*]] = linalg.pad_tensor %[[T3]] nofold {{.*}} high[%[[C0]], %[[V1]]
- // HOIST21: %[[T5:.*]] = tensor.insert_slice %[[T4:.*]] into %{{.*}}[%[[PIDX1]], 0, 0]
- // HOIST21: scf.yield %[[T5:.*]]
-
- // HOIST21: scf.for %[[IV2:[0-9a-zA-Z]*]] = {{.*}} iter_args(%[[ARG4:.*]] =
- %2 = scf.for %arg7 = %c0 to %c12 step %c6 iter_args(%arg8 = %arg6) -> (tensor<24x25xf32>) {
- %3 = affine.min #map0(%arg3)
-
- // Index the packed operands.
- // HOIST21-DAG: %[[IDX:.*]] = affine.apply #[[DIV6]](%[[IV2]])
- // HOIST21-DAG: %[[T6:.*]] = tensor.extract_slice %[[PT0]][%[[IDX]]
- // HOIST21-DAG: %[[T7:.*]] = tensor.extract_slice %[[PT1]][%[[IDX]]
- %4 = tensor.extract_slice %arg0[%arg3, %arg7] [%3, 6] [1, 1] : tensor<24x12xf32> to tensor<?x6xf32>
- %5 = affine.min #map1(%arg5)
- %6 = tensor.extract_slice %arg1[%arg7, %arg5] [6, %5] [1, 1] : tensor<12x25xf32> to tensor<6x?xf32>
-
- // Pad the output operand without setting the nofold attribute.
- // HOIST21-DAG: %[[T8:.*]] = tensor.extract_slice %[[ARG4]][%[[IV0]], %[[IV1]]
- // HOIST21: %[[T9:.*]] = linalg.pad_tensor %[[T8]] low
- %7 = tensor.extract_slice %arg8[%arg3, %arg5] [%3, %5] [1, 1] : tensor<24x25xf32> to tensor<?x?xf32>
-
- // Check matmul uses the packed input operands and the padded output operand.
- // HOIST21: = linalg.matmul ins(%[[T6]], %[[T7]]{{.*}} outs(%[[T9]]
- %8 = linalg.matmul ins(%4, %6 : tensor<?x6xf32>, tensor<6x?xf32>) outs(%7 : tensor<?x?xf32>) -> tensor<?x?xf32>
- %9 = tensor.insert_slice %8 into %arg8[%arg3, %arg5] [%3, %5] [1, 1] : tensor<?x?xf32> into tensor<24x25xf32>
- scf.yield %9 : tensor<24x25xf32>
- }
- scf.yield %2 : tensor<24x25xf32>
- }
- scf.yield %1 : tensor<24x25xf32>
- }
- return %0 : tensor<24x25xf32>
-}
-
-// -----
-
-// HOIST21-DAG: #[[MAP0:[0-9a-z]+]] = affine_map<(d0)[s0] -> (5, -d0 + s0)>
-// HOIST21-DAG: #[[MAP1:[0-9a-z]+]] = affine_map<(d0)[s0] -> (6, -d0 + s0)>
-// HOIST21-DAG: #[[MAP2:[0-9a-z]+]] = affine_map<(d0)[s0] -> (7, -d0 + s0)>
-// HOIST21-DAG: #[[MAP3:[0-9a-z]+]] = affine_map<(d0) -> (-d0 + 5)>
-// HOIST21-DAG: #[[MAP4:[0-9a-z]+]] = affine_map<(d0) -> (-d0 + 6)>
-// HOIST21-DAG: #[[MAP5:[0-9a-z]+]] = affine_map<(d0) -> (-d0 + 7)>
-// HOIST21-DAG: #[[SDIV6:[0-9a-z]+]] = affine_map<()[s0] -> (s0 ceildiv 6)>
-// HOIST21-DAG: #[[DDIV6:[0-9a-z]+]] = affine_map<(d0) -> (d0 ceildiv 6)>
-#map0 = affine_map<(d0)[s0] -> (5, -d0 + s0)>
-#map1 = affine_map<(d0)[s0] -> (6, -d0 + s0)>
-#map2 = affine_map<(d0)[s0] -> (7, -d0 + s0)>
-
-// HOIST21: dynamic_sizes
-// HOIST32: dynamic_sizes
-// HOIST21-SAME: %[[ARG0:[0-9a-zA-Z]*]]: tensor<?x?xf32>
-// HOIST21-SAME: %[[ARG1:[0-9a-zA-Z]*]]: tensor<?x?xf32>
-// HOIST21-SAME: %[[ARG2:[0-9a-zA-Z]*]]: tensor<?x?xf32>
-func @dynamic_sizes(%arg0: tensor<?x?xf32>,
- %arg1: tensor<?x?xf32>,
- %arg2: tensor<?x?xf32>) -> tensor<?x?xf32> {
- // HOIST21-DAG: %[[C0:.*]] = arith.constant 0 : index
- // HOIST21-DAG: %[[C1:.*]] = arith.constant 1
- // HOIST21-DAG: %[[C5:.*]] = arith.constant 5
- // HOIST21-DAG: %[[C6:.*]] = arith.constant 6
- %c1 = arith.constant 1 : index
- %c0 = arith.constant 0 : index
- %c6 = arith.constant 6 : index
- %c7 = arith.constant 7 : index
- %c5 = arith.constant 5 : index
-
- // HOIST21-DAG: %[[D0:.*]] = tensor.dim %[[ARG0]], %[[C0]]
- // HOIST21-DAG: %[[D1:.*]] = tensor.dim %[[ARG0]], %[[C1]]
- // HOIST21-DAG: %[[D2:.*]] = tensor.dim %[[ARG1]], %[[C1]]
- %0 = tensor.dim %arg0, %c0 : tensor<?x?xf32>
- %1 = tensor.dim %arg0, %c1 : tensor<?x?xf32>
- %2 = tensor.dim %arg1, %c1 : tensor<?x?xf32>
-
- // HOIST21: scf.for %[[IV0:[0-9a-zA-Z]*]] =
- %3 = scf.for %arg3 = %c0 to %0 step %c5 iter_args(%arg4 = %arg2) -> (tensor<?x?xf32>) {
-
- // Packing the first input operand for all values of IV2 (IV2x5x6).
- // HOIST21: %[[PS0:.*]] = affine.apply #[[SDIV6]]()[%[[D1]]
- // HOIST21: = linalg.init_tensor [%[[PS0]], 5, 6]
- // HOIST21: %[[PT0:.*]] = scf.for %[[PIV0:[0-9a-z]+]] =
- // HOIST21: %[[PIDX0:.*]] = affine.apply #[[DDIV6]](%[[PIV0]])
- // HOIST21: %[[TS0:.*]] = affine.min #[[MAP0]](%[[IV0]])[%[[D0]]
- // HOIST21: %[[TS1:.*]] = affine.min #[[MAP1]](%[[PIV0]])[%[[D1]]
- // HOIST21: %[[T0:.*]] = tensor.extract_slice %[[ARG0]]
- // HOIST21-SAME: %[[IV0]], %[[PIV0]]
- // HOIST21-SAME: %[[TS0]], %[[TS1]]
- // HOIST21: %[[V0:.*]] = affine.apply #[[MAP3]](%[[TS0]])
- // HOIST21: %[[V1:.*]] = affine.apply #[[MAP4]](%[[TS1]])
- // HOIST21: %[[T1:.*]] = linalg.pad_tensor %[[T0]] nofold {{.*}} high[%[[V0]], %[[V1]]
- // HOIST21: %[[T2:.*]] = tensor.insert_slice %[[T1:.*]] into %{{.*}}[%[[PIDX0]], 0, 0]
- // HOIST21: scf.yield %[[T2:.*]]
-
- // HOIST21: scf.for %[[IV1:[0-9a-zA-Z]*]] =
- %4 = scf.for %arg5 = %c0 to %2 step %c7 iter_args(%arg6 = %arg4) -> (tensor<?x?xf32>) {
-
- // Packing the second input operand for all values of IV2 (IV2x6x7).
- // HOIST21: = linalg.init_tensor [%[[PS0]], 6, 7]
- // HOIST21: %[[PT1:.*]] = scf.for %[[PIV1:[0-9a-z]+]] =
- // HOIST21: %[[PIDX1:.*]] = affine.apply #[[DDIV6]](%[[PIV1]])
- // HOIST21: %[[TS2:.*]] = affine.min #[[MAP1]](%[[PIV1]])[%[[D1]]
- // HOIST21: %[[TS3:.*]] = affine.min #[[MAP2]](%[[IV1]])[%[[D2]]
- // HOIST21: %[[T3:.*]] = tensor.extract_slice %[[ARG1]]
- // HOIST21-SAME: %[[PIV1]], %[[IV1]]
- // HOIST21-SAME: %[[TS2]], %[[TS3]]
- // HOIST21: %[[V2:.*]] = affine.apply #[[MAP4]](%[[TS2]])
- // HOIST21: %[[V3:.*]] = affine.apply #[[MAP5]](%[[TS3]])
- // HOIST21: %[[T4:.*]] = linalg.pad_tensor %[[T3]] nofold {{.*}} high[%[[V2]], %[[V3]]
- // HOIST21: %[[T5:.*]] = tensor.insert_slice %[[T4:.*]] into %{{.*}}[%[[PIDX1]], 0, 0]
- // HOIST21: scf.yield %[[T5:.*]]
-
- // HOIST21: scf.for %[[IV2:[0-9a-zA-Z]*]] = {{.*}} iter_args(%[[ARG4:.*]] =
- %5 = scf.for %arg7 = %c0 to %1 step %c6 iter_args(%arg8 = %arg6) -> (tensor<?x?xf32>) {
- %6 = affine.min #map0(%arg3)[%0]
- %7 = affine.min #map1(%arg7)[%1]
-
- // Index the packed operands.
- // HOIST21-DAG: %[[IDX:.*]] = affine.apply #[[DDIV6]](%[[IV2]])
- // HOIST21-DAG: %[[T6:.*]] = tensor.extract_slice %[[PT0]][%[[IDX]]
- // HOIST21-DAG: %[[T7:.*]] = tensor.extract_slice %[[PT1]][%[[IDX]]
- %8 = tensor.extract_slice %arg0[%arg3, %arg7] [%6, %7] [1, 1] : tensor<?x?xf32> to tensor<?x?xf32>
- %9 = affine.min #map2(%arg5)[%2]
- %10 = tensor.extract_slice %arg1[%arg7, %arg5] [%7, %9] [1, 1] : tensor<?x?xf32> to tensor<?x?xf32>
- %11 = tensor.extract_slice %arg8[%arg3, %arg5] [%6, %9] [1, 1] : tensor<?x?xf32> to tensor<?x?xf32>
-
- // Check matmul uses the packed input operands.
- // HOIST21: = linalg.matmul ins(%[[T6]], %[[T7]]
- %12 = linalg.matmul ins(%8, %10 : tensor<?x?xf32>, tensor<?x?xf32>) outs(%11 : tensor<?x?xf32>) -> tensor<?x?xf32>
- %13 = tensor.insert_slice %12 into %arg8[%arg3, %arg5] [%6, %9] [1, 1] : tensor<?x?xf32> into tensor<?x?xf32>
- scf.yield %13 : tensor<?x?xf32>
- }
- scf.yield %5 : tensor<?x?xf32>
- }
- scf.yield %4 : tensor<?x?xf32>
- }
- return %3 : tensor<?x?xf32>
-}
-
-// -----
-
-// HOIST21-DAG: #[[DIV3:[0-9a-z]+]] = affine_map<(d0) -> (d0 ceildiv 3)>
-
-// HOIST21: multiple_operations
-// HOIST32: multiple_operations
-// HOIST21-SAME: %[[ARG0:[0-9a-zA-Z]*]]: tensor<24x12xf32>
-// HOIST21-SAME: %[[ARG1:[0-9a-zA-Z]*]]: tensor<12x25xf32>
-func @multiple_operations(%arg0: tensor<24x12xf32>,
- %arg1: tensor<12x25xf32>,
- %arg2: tensor<24x25xf32>) -> tensor<24x25xf32> {
- %c12 = arith.constant 12 : index
- %c3 = arith.constant 3 : index
- %c0 = arith.constant 0 : index
- %c25 = arith.constant 25 : index
- %c24 = arith.constant 24 : index
- %c5 = arith.constant 5 : index
- %c4 = arith.constant 4 : index
- %cst = arith.constant 0.000000e+00 : f32
-
- // HOIST21: scf.for %[[IV0:[0-9a-zA-Z]*]] =
- %0 = scf.for %arg3 = %c0 to %c24 step %c4 iter_args(%arg4 = %arg2) -> (tensor<24x25xf32>) {
-
- // Packing the first input operand for all values of IV2 (IV2x4x3).
- // HOIST21: = linalg.init_tensor [4, 4, 3]
- // HOIST21: %[[PT0:.*]] = scf.for %[[PIV0:[0-9a-z]+]] =
- // HOIST21: %[[PIDX0:.*]] = affine.apply #[[DIV3]](%[[PIV0]])
- // HOIST21: %[[T0:.*]] = tensor.extract_slice %[[ARG0]]
- // HOIST21: %[[T1:.*]] = linalg.pad_tensor %[[T0]] nofold
- // HOIST21: %[[T2:.*]] = tensor.insert_slice %[[T1:.*]] into %{{.*}}[%[[PIDX0]], 0, 0]
- // HOIST21: scf.yield %[[T2:.*]]
-
- // HOIST21: scf.for %[[IV1:[0-9a-zA-Z]*]] =
- %1 = scf.for %arg5 = %c0 to %c25 step %c5 iter_args(%arg6 = %arg4) -> (tensor<24x25xf32>) {
- %2 = tensor.extract_slice %arg6[%arg3, %arg5] [4, 5] [1, 1] : tensor<24x25xf32> to tensor<4x5xf32>
-
- // Check the fill and pad_tensor ops do not prevent hoisting.
- %3 = linalg.pad_tensor %2 nofold low[%c0, %c0] high[%c0, %c0] {
- ^bb0(%arg7: index, %arg8: index): // no predecessors
- linalg.yield %cst : f32
- } : tensor<4x5xf32> to tensor<4x5xf32>
- %4 = linalg.fill(%cst, %3) : f32, tensor<4x5xf32> -> tensor<4x5xf32>
-
- // Packing the second input operand for all values of IV2 (IV2x3x5).
- // HOIST21: = linalg.init_tensor [4, 3, 5]
- // HOIST21: %[[PT1:.*]] = scf.for %[[PIV1:[0-9a-z]+]] =
- // HOIST21: %[[PIDX1:.*]] = affine.apply #[[DIV3]](%[[PIV1]])
- // HOIST21: %[[T3:.*]] = tensor.extract_slice %[[ARG1]]
- // HOIST21: %[[T4:.*]] = linalg.pad_tensor %[[T3]] nofold
- // HOIST21: %[[T5:.*]] = tensor.insert_slice %[[T4:.*]] into %{{.*}}[%[[PIDX1]], 0, 0]
- // HOIST21: scf.yield %[[T5:.*]]
-
- // HOIST21: scf.for %[[IV2:[0-9a-zA-Z]*]] =
- %5 = scf.for %arg7 = %c0 to %c12 step %c3 iter_args(%arg8 = %4) -> (tensor<4x5xf32>) {
-
- // Index the packed operands.
- // HOIST21-DAG: %[[IDX0:.*]] = affine.apply #[[DIV3]](%[[IV2]])
- // HOIST21-DAG: %[[T6:.*]] = tensor.extract_slice %[[PT0]][%[[IDX0]]
- // HOIST21-DAG: %[[T7:.*]] = tensor.extract_slice %[[PT1]][%[[IDX0]]
- %7 = tensor.extract_slice %arg0[%arg3, %arg7] [4, 3] [1, 1] : tensor<24x12xf32> to tensor<4x3xf32>
- %8 = tensor.extract_slice %arg1[%arg7, %arg5] [3, 5] [1, 1] : tensor<12x25xf32> to tensor<3x5xf32>
-
- // Check matmul uses the packed input operands.
- // HOIST21: = linalg.matmul ins(%[[T6]], %[[T7]]
- %9 = linalg.matmul ins(%7, %8 : tensor<4x3xf32>, tensor<3x5xf32>) outs(%arg8 : tensor<4x5xf32>) -> tensor<4x5xf32>
- scf.yield %9 : tensor<4x5xf32>
- }
- %6 = tensor.insert_slice %5 into %arg6[%arg3, %arg5] [4, 5] [1, 1] : tensor<4x5xf32> into tensor<24x25xf32>
- scf.yield %6 : tensor<24x25xf32>
- }
- scf.yield %1 : tensor<24x25xf32>
- }
- return %0 : tensor<24x25xf32>
-}
-
-// -----
-
-// HOIST32-DAG: #[[DIV5:[0-9a-z]+]] = affine_map<(d0) -> (d0 ceildiv 5)>
-// HOIST32-DAG: #[[DIV6:[0-9a-z]+]] = affine_map<(d0) -> (d0 ceildiv 6)>
-#map0 = affine_map<(d0) -> (15, -d0 + 24)>
-#map1 = affine_map<(d0) -> (16, -d0 + 25)>
-#map2 = affine_map<(d0, d1) -> (5, -d0 + d1)>
-#map3 = affine_map<(d0, d1) -> (d0 + d1)>
-#map4 = affine_map<(d0, d1) -> (6, -d0 + d1)>
-
-// HOIST21: double_tiling
-// HOIST32: double_tiling
-// HOIST32-SAME: %[[ARG0:[0-9a-zA-Z]*]]: tensor<24x12xf32>
-// HOIST32-SAME: %[[ARG1:[0-9a-zA-Z]*]]: tensor<12x25xf32>
-func @double_tiling(%arg0: tensor<24x12xf32>,
- %arg1: tensor<12x25xf32>,
- %arg2: tensor<24x25xf32>) -> tensor<24x25xf32> {
- %c15 = arith.constant 15 : index
- %c16 = arith.constant 16 : index
- %c24 = arith.constant 24 : index
- %c25 = arith.constant 25 : index
- %c0 = arith.constant 0 : index
- %c5 = arith.constant 5 : index
- %c6 = arith.constant 6 : index
-
- // HOIST32: scf.for %[[IV0:[0-9a-zA-Z]*]] =
- %0 = scf.for %arg3 = %c0 to %c24 step %c15 iter_args(%arg4 = %arg2) -> (tensor<24x25xf32>) {
-
- // Packing the first input operand.
- // HOIST32: = linalg.init_tensor [3, 5, 12]
- // HOIST32: %[[PT0:.*]] = scf.for %[[PIV0:[0-9a-z]+]] =
- // HOIST32: %[[PIDX0:.*]] = affine.apply #[[DIV5]](%[[PIV0]])
- // HOIST32: %[[T0:.*]] = tensor.extract_slice %[[ARG0]]
- // HOIST32: %[[T1:.*]] = linalg.pad_tensor %[[T0]] nofold
- // HOIST32: %[[T2:.*]] = tensor.insert_slice %[[T1:.*]] into %{{.*}}[%[[PIDX0]], 0, 0]
- // HOIST32: scf.yield %[[T2:.*]]
-
- // HOIST32: scf.for %[[IV1:[0-9a-zA-Z]*]] =
- %1 = scf.for %arg5 = %c0 to %c25 step %c16 iter_args(%arg6 = %arg4) -> (tensor<24x25xf32>) {
- %2 = affine.min #map0(%arg3)
- %3 = affine.min #map1(%arg5)
- %4 = tensor.extract_slice %arg6[%arg3, %arg5] [%2, %3] [1, 1] : tensor<24x25xf32> to tensor<?x?xf32>
-
- // Packing the second input operand.
- // HOIST32: = linalg.init_tensor [3, 12, 6]
- // HOIST32: %[[PT1:.*]] = scf.for %[[PIV1:[0-9a-z]+]] =
- // HOIST32: %[[PIDX1:.*]] = affine.apply #[[DIV6]](%[[PIV1]])
- // HOIST32: %[[T3:.*]] = tensor.extract_slice %[[ARG1]]
- // HOIST32: %[[T4:.*]] = linalg.pad_tensor %[[T3]] nofold
- // HOIST32: %[[T5:.*]] = tensor.insert_slice %[[T4:.*]] into %{{.*}}[%[[PIDX1]], 0, 0]
- // HOIST32: scf.yield %[[T5:.*]]
-
- // HOIST32: scf.for %[[IV2:[0-9a-zA-Z]*]] =
- %5 = scf.for %arg7 = %c0 to %2 step %c5 iter_args(%arg8 = %4) -> (tensor<?x?xf32>) {
-
- // HOIST32: scf.for %[[IV3:[0-9a-zA-Z]*]] =
- %7 = scf.for %arg9 = %c0 to %3 step %c6 iter_args(%arg10 = %arg8) -> (tensor<?x?xf32>) {
- %8 = affine.min #map2(%arg7, %2)
- %9 = affine.apply #map3(%arg7, %arg3)
-
- // Index the packed operands.
- // HOIST32-DAG: %[[IDX0:.*]] = affine.apply #[[DIV5]](%[[IV2]])
- // HOIST32-DAG: %[[T6:.*]] = tensor.extract_slice %[[PT0]][%[[IDX0]]
- // HOIST32-DAG: %[[IDX1:.*]] = affine.apply #[[DIV6]](%[[IV3]])
- // HOIST32-DAG: %[[T7:.*]] = tensor.extract_slice %[[PT1]][%[[IDX1]]
- %10 = tensor.extract_slice %arg0[%9, 0] [%8, 12] [1, 1] : tensor<24x12xf32> to tensor<?x12xf32>
- %11 = affine.min #map4(%arg9, %3)
- %12 = affine.apply #map3(%arg9, %arg5)
- %13 = tensor.extract_slice %arg1[0, %12] [12, %11] [1, 1] : tensor<12x25xf32> to tensor<12x?xf32>
- %14 = affine.min #map2(%arg7, %2)
- %15 = affine.min #map4(%arg9, %3)
- %16 = tensor.extract_slice %arg10[%arg7, %arg9] [%14, %15] [1, 1] : tensor<?x?xf32> to tensor<?x?xf32>
-
- // Check matmul uses the packed input operands.
- // HOIST32: = linalg.matmul ins(%[[T6]], %[[T7]]
- %17 = linalg.matmul ins(%10, %13 : tensor<?x12xf32>, tensor<12x?xf32>) outs(%16 : tensor<?x?xf32>) -> tensor<?x?xf32>
- %18 = tensor.insert_slice %17 into %arg10[%arg7, %arg9] [%14, %15] [1, 1] : tensor<?x?xf32> into tensor<?x?xf32>
- scf.yield %18 : tensor<?x?xf32>
- }
- scf.yield %7 : tensor<?x?xf32>
- }
- %6 = tensor.insert_slice %5 into %arg6[%arg3, %arg5] [%2, %3] [1, 1] : tensor<?x?xf32> into tensor<24x25xf32>
- scf.yield %6 : tensor<24x25xf32>
- }
- scf.yield %1 : tensor<24x25xf32>
- }
- return %0 : tensor<24x25xf32>
-}
-
-// -----
-
-#map0 = affine_map<(d0) -> (5, -d0 + 24)>
-#map1 = affine_map<(d0) -> (7, -d0 + 25)>
-#map2 = affine_map<(d0) -> (-d0 + 5)>
-#map3 = affine_map<(d0) -> (-d0 + 7)>
-
-// Note that the input ir of the test already contains the padding. As a result,
-// the padding pattern skips the padding and performs hoisting only, which the
-// unexpected_loop test verifies by hoisting one of the input operands.
-
-// HOIST21: non_constant_padding
-// HOIST32: non_constant_padding
-// HOIST21-SAME: %[[ARG1:[0-9a-zA-Z]*]]: tensor<12x25xf32>
-func @non_constant_padding(%arg0: tensor<24x12xf32>,
- %arg1: tensor<12x25xf32>,
- %arg2: tensor<24x25xf32>) -> tensor<24x25xf32> {
- %c0 = arith.constant 0 : index
- %c12 = arith.constant 12 : index
- %c25 = arith.constant 25 : index
- %c24 = arith.constant 24 : index
- %c6 = arith.constant 6 : index
- %c7 = arith.constant 7 : index
- %c5 = arith.constant 5 : index
- %cst = arith.constant 0.000000e+00 : f32
-
- // HOIST21: scf.for %[[IV0:[0-9a-zA-Z]*]] =
- %0 = scf.for %arg3 = %c0 to %c24 step %c5 iter_args(%arg4 = %arg2) -> (tensor<24x25xf32>) {
-
- // HOIST21-NEXT: scf.for %[[IV1:[0-9a-zA-Z]*]] =
- %1 = scf.for %arg5 = %c0 to %c25 step %c7 iter_args(%arg6 = %arg4) -> (tensor<24x25xf32>) {
-
- // HOIST21-NEXT: scf.for %[[IV2:[0-9a-zA-Z]*]] =
- %2 = scf.for %arg7 = %c0 to %c12 step %c6 iter_args(%arg8 = %arg6) -> (tensor<24x25xf32>) {
- %3 = affine.min #map0(%arg3)
- %4 = tensor.extract_slice %arg0[%arg3, %arg7] [%3, 6] [1, 1] : tensor<24x12xf32> to tensor<?x6xf32>
- %5 = affine.min #map1(%arg5)
- %6 = tensor.extract_slice %arg1[%arg7, %arg5] [6, %5] [1, 1] : tensor<12x25xf32> to tensor<6x?xf32>
- %7 = tensor.extract_slice %arg8[%arg3, %arg5] [%3, %5] [1, 1] : tensor<24x25xf32> to tensor<?x?xf32>
- %8 = affine.apply #map2(%3)
-
- // Check the padding with a non constant padding value is not hoisted.
- // HOIST21: %[[T0:.*]] = linalg.pad_tensor
- // HOIST21: %[[V0:.*]] = arith.index_cast
- // HOIST21: %[[V1:.*]] = arith.sitofp %[[V0]]
- // HOIST21: linalg.yield %[[V1]]
- %9 = linalg.pad_tensor %4 nofold low[%c0, %c0] high[%8, %c0] {
- ^bb0(%arg9: index, %arg10: index): // no predecessors
- %17 = arith.index_cast %arg7 : index to i32
- %18 = arith.sitofp %17 : i32 to f32
- linalg.yield %18 : f32
- } : tensor<?x6xf32> to tensor<5x6xf32>
- %10 = affine.apply #map3(%5)
-
- // Check the padding with a non constant op padding is not hoisted.
- // HOIST21: %[[V2:.*]] = tensor.extract %[[ARG1]][%[[IV2]], %[[IV1]]
- // HOIST21: %[[T1:.*]] = linalg.pad_tensor
- // HOIST21: linalg.yield %[[V2]]
- %11 = tensor.extract %arg1[%arg7, %arg5] : tensor<12x25xf32>
- %12 = linalg.pad_tensor %6 nofold low[%c0, %c0] high[%c0, %10] {
- ^bb0(%arg9: index, %arg10: index): // no predecessors
- linalg.yield %11 : f32
- } : tensor<6x?xf32> to tensor<6x7xf32>
- %13 = linalg.pad_tensor %7 low[%c0, %c0] high[%8, %10] {
- ^bb0(%arg9: index, %arg10: index): // no predecessors
- linalg.yield %cst : f32
- } : tensor<?x?xf32> to tensor<5x7xf32>
-
- // HOIST21: = linalg.matmul ins(%[[T0]], %[[T1]]
- %14 = linalg.matmul ins(%9, %12 : tensor<5x6xf32>, tensor<6x7xf32>) outs(%13 : tensor<5x7xf32>) -> tensor<5x7xf32>
- %15 = tensor.extract_slice %14[0, 0] [%3, %5] [1, 1] : tensor<5x7xf32> to tensor<?x?xf32>
- %16 = tensor.insert_slice %15 into %arg8[%arg3, %arg5] [%3, %5] [1, 1] : tensor<?x?xf32> into tensor<24x25xf32>
- scf.yield %16 : tensor<24x25xf32>
- }
- scf.yield %2 : tensor<24x25xf32>
- }
- scf.yield %1 : tensor<24x25xf32>
- }
- return %0 : tensor<24x25xf32>
-}
-
-// -----
-
-#map0 = affine_map<(d0) -> (5, -d0 + 24)>
-#map1 = affine_map<(d0) -> (7, -d0 + 25)>
-#map2 = affine_map<(d0) -> (-d0 + 5)>
-#map3 = affine_map<(d0) -> (-d0 + 7)>
-
-// Note that the input ir of the test already contains the padding. As a result,
-// the padding pattern skips the padding and performs hoisting only, which the
-// unexpected_loop test verifies by hoisting one of the input operands.
-
-// HOIST21: unexpected_operation
-// HOIST32: unexpected_operation
-// HOIST21-SAME: %[[ARG3:[0-9a-zA-Z]*]]: memref<?xindex>
-// HOIST21-SAME: %[[ARG4:[0-9a-zA-Z]*]]: i32
-func @unexpected_operation(%arg0: tensor<24x12xf32>,
- %arg1: tensor<12x25xf32>,
- %arg2: tensor<24x25xf32>,
- %arg3: memref<?xindex>,
- %arg4: i32) -> tensor<24x25xf32> {
- %cst = arith.constant 0.000000e+00 : f32
- %c5 = arith.constant 5 : index
- %c7 = arith.constant 7 : index
- %c6 = arith.constant 6 : index
- %c24 = arith.constant 24 : index
- %c25 = arith.constant 25 : index
- %c12 = arith.constant 12 : index
- %c0 = arith.constant 0 : index
-
- // HOIST21: scf.for %[[IV0:[0-9a-zA-Z]*]] =
- %0 = scf.for %arg5 = %c0 to %c24 step %c5 iter_args(%arg6 = %arg2) -> (tensor<24x25xf32>) {
-
- // HOIST21-NEXT: scf.for %[[IV1:[0-9a-zA-Z]*]] =
- %1 = scf.for %arg7 = %c0 to %c25 step %c7 iter_args(%arg8 = %arg6) -> (tensor<24x25xf32>) {
-
- // HOIST21-NEXT: scf.for %[[IV2:[0-9a-zA-Z]*]] =
- %2 = scf.for %arg9 = %c0 to %c12 step %c6 iter_args(%arg10 = %arg8) -> (tensor<24x25xf32>) {
- %3 = affine.min #map0(%arg5)
- %4 = tensor.extract_slice %arg0[%arg5, %arg9] [%3, 6] [1, 1] : tensor<24x12xf32> to tensor<?x6xf32>
- %5 = affine.min #map1(%arg7)
- %6 = tensor.extract_slice %arg1[%arg9, %arg7] [6, %5] [1, 1] : tensor<12x25xf32> to tensor<6x?xf32>
- %7 = tensor.extract_slice %arg10[%arg5, %arg7] [%3, %5] [1, 1] : tensor<24x25xf32> to tensor<?x?xf32>
- %8 = affine.apply #map2(%3)
-
- // Check cannot hoist due to unexpected operation with memory effect.
- // HOIST21: %[[IDX0:.*]] = memref.load %[[ARG3]]
- // HOIST21: %[[T0:.*]] = linalg.pad_tensor {{.*}}, %[[IDX0]]
- %9 = memref.load %arg3[%c0] : memref<?xindex>
- %10 = linalg.pad_tensor %4 nofold low[%c0, %c0] high[%8, %9] {
- ^bb0(%arg11: index, %arg12: index): // no predecessors
- linalg.yield %cst : f32
- } : tensor<?x6xf32> to tensor<5x6xf32>
- %11 = affine.apply #map3(%5)
-
- // Check cannot hoist due to unexpected operation with non index operand.
- // HOIST21: %[[IDX1:.*]] = arith.index_cast %[[ARG4]]
- // HOIST21: %[[T1:.*]] = linalg.pad_tensor {{.*}}[%[[IDX1]]
- %12 = arith.index_cast %arg4 : i32 to index
- %13 = linalg.pad_tensor %6 nofold low[%c0, %c0] high[%12, %11] {
- ^bb0(%arg11: index, %arg12: index): // no predecessors
- linalg.yield %cst : f32
- } : tensor<6x?xf32> to tensor<6x7xf32>
- %14 = linalg.pad_tensor %7 low[%c0, %c0] high[%8, %11] {
- ^bb0(%arg11: index, %arg12: index): // no predecessors
- linalg.yield %cst : f32
- } : tensor<?x?xf32> to tensor<5x7xf32>
-
- // HOIST21: = linalg.matmul ins(%[[T0]], %[[T1]]
- %15 = linalg.matmul ins(%10, %13 : tensor<5x6xf32>, tensor<6x7xf32>) outs(%14 : tensor<5x7xf32>) -> tensor<5x7xf32>
- %16 = tensor.extract_slice %15[0, 0] [%3, %5] [1, 1] : tensor<5x7xf32> to tensor<?x?xf32>
- %17 = tensor.insert_slice %16 into %arg10[%arg5, %arg7] [%3, %5] [1, 1] : tensor<?x?xf32> into tensor<24x25xf32>
- scf.yield %17 : tensor<24x25xf32>
- }
- scf.yield %2 : tensor<24x25xf32>
- }
- scf.yield %1 : tensor<24x25xf32>
- }
- return %0 : tensor<24x25xf32>
-}
-
-// -----
-
-#map0 = affine_map<(d0) -> (5, -d0 + 24)>
-#map1 = affine_map<(d0) -> (7, -d0 + 25)>
-#map2 = affine_map<(d0) -> (-d0 + 5)>
-#map3 = affine_map<(d0) -> (-d0 + 7)>
-
-// Note that the input ir of the test already contains the padding. As a result,
-// the padding pattern skips the padding and performs hoisting only, which the
-// test verifies by hoisting one of the input operands.
-
-// HOIST21: unexpected_loop
-// HOIST32: unexpected_loop
-// HOIST21-SAME: %[[ARG3:[0-9a-zA-Z]*]]: index
-func @unexpected_loop(%arg0: tensor<24x12xf32>,
- %arg1: tensor<12x25xf32>,
- %arg2: tensor<24x25xf32>,
- %arg3: index) -> tensor<24x25xf32> {
- %c0 = arith.constant 0 : index
- %c12 = arith.constant 12 : index
- %c25 = arith.constant 25 : index
- %c24 = arith.constant 24 : index
- %c6 = arith.constant 6 : index
- %c7 = arith.constant 7 : index
- %c5 = arith.constant 5 : index
- %cst = arith.constant 0.000000e+00 : f32
-
- // HOIST21: scf.for %[[IV0:[0-9a-zA-Z]*]] =
- %0 = scf.for %arg4 = %c0 to %c24 step %c5 iter_args(%arg5 = %arg2) -> (tensor<24x25xf32>) {
-
- // HOIST21-NEXT: scf.for %[[IV1:[0-9a-zA-Z]*]] =
- %1 = scf.for %arg6 = %c0 to %c25 step %c7 iter_args(%arg7 = %arg5) -> (tensor<24x25xf32>) {
-
- // Check the padding of the first input operand is hoisted.
- // HOIST21: = linalg.pad_tensor
-
- // HOIST21: scf.for %[[IV2:[0-9a-zA-Z]*]] =
- %2 = scf.for %arg8 = %c0 to %c12 step %c6 iter_args(%arg9 = %arg7) -> (tensor<24x25xf32>) {
- %3 = affine.min #map0(%arg4)
- %4 = tensor.extract_slice %arg0[%arg4, %arg8] [%3, 6] [1, 1] : tensor<24x12xf32> to tensor<?x6xf32>
- %5 = affine.min #map1(%arg6)
- %6 = tensor.extract_slice %arg1[%arg8, %arg6] [6, %5] [1, 1] : tensor<12x25xf32> to tensor<6x?xf32>
- %7 = tensor.extract_slice %arg9[%arg4, %arg6] [%3, %5] [1, 1] : tensor<24x25xf32> to tensor<?x?xf32>
- %8 = affine.apply #map2(%3)
-
- // Check cannot hoist due to unexpected operation that has a region.
- // HOIST21: %[[IDX0:.*]] = scf.for {{.*}} step %[[ARG3]]
- // HOIST21: %[[T0:.*]] = linalg.pad_tensor {{.*}}, %[[IDX0]]
- %9 = scf.for %arg10 = %c0 to %c24 step %arg3 iter_args(%arg11 = %c0) -> (index) {
- %17 = arith.addi %arg3, %arg11 : index
- scf.yield %17 : index
- }
- %10 = linalg.pad_tensor %4 nofold low[%c0, %c0] high[%8, %9] {
- ^bb0(%arg10: index, %arg11: index): // no predecessors
- linalg.yield %cst : f32
- } : tensor<?x6xf32> to tensor<5x6xf32>
- %11 = affine.apply #map3(%5)
- %12 = linalg.pad_tensor %6 nofold low[%c0, %c0] high[%c0, %11] {
- ^bb0(%arg10: index, %arg11: index): // no predecessors
- linalg.yield %cst : f32
- } : tensor<6x?xf32> to tensor<6x7xf32>
- %13 = linalg.pad_tensor %7 low[%c0, %c0] high[%8, %11] {
- ^bb0(%arg10: index, %arg11: index): // no predecessors
- linalg.yield %cst : f32
- } : tensor<?x?xf32> to tensor<5x7xf32>
-
- // HOIST21: = linalg.matmul ins(%[[T0]]
- %14 = linalg.matmul ins(%10, %12 : tensor<5x6xf32>, tensor<6x7xf32>) outs(%13 : tensor<5x7xf32>) -> tensor<5x7xf32>
- %15 = tensor.extract_slice %14[0, 0] [%3, %5] [1, 1] : tensor<5x7xf32> to tensor<?x?xf32>
- %16 = tensor.insert_slice %15 into %arg9[%arg4, %arg6] [%3, %5] [1, 1] : tensor<?x?xf32> into tensor<24x25xf32>
- scf.yield %16 : tensor<24x25xf32>
- }
- scf.yield %2 : tensor<24x25xf32>
- }
- scf.yield %1 : tensor<24x25xf32>
- }
- return %0 : tensor<24x25xf32>
-}
-
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