[Mlir-commits] [mlir] [mlir][tensor] Fold producer linalg transpose with consumer tensor pack (PR #75658)
Prathamesh Tagore
llvmlistbot at llvm.org
Mon Jan 8 11:21:48 PST 2024
================
@@ -345,3 +345,164 @@ func.func @tensor_pack_linalg_transpose_fold_dynamic_outer_dims_tile_dims_tile_s
// CHECK: %[[PACK:.+]] = tensor.pack %[[ARG0]] outer_dims_perm = [2, 1, 3, 0] inner_dims_pos = [3, 1, 2] inner_tiles = [%[[ARG3]], %[[ARG1]], %[[ARG2]]] into %[[INIT]] : tensor<?x?x?x?xf32> -> tensor<?x?x?x?x?x?x?xf32>
// CHECK: return %[[PACK]] : tensor<?x?x?x?x?x?x?xf32>
// CHECK: }
+
+// -----
+
+func.func @linalg_transpose_tensor_pack_fold(%arg0: tensor<56x57x1x64xf32>) -> tensor<1x57x56x2x32xf32> {
+ %0 = tensor.empty() : tensor<1x56x57x64xf32>
+ %transposed = linalg.transpose
+ ins(%arg0 : tensor<56x57x1x64xf32>)
+ outs(%0 : tensor<1x56x57x64xf32>)
+ permutation = [2, 0, 1, 3]
+
+ %1 = tensor.empty() : tensor<1x57x56x2x32xf32>
+ %pack = tensor.pack %transposed
+ outer_dims_perm = [0, 2, 1, 3]
+ inner_dims_pos = [3]
+ inner_tiles = [32]
+ into %1 : tensor<1x56x57x64xf32> -> tensor<1x57x56x2x32xf32>
+ return %pack : tensor<1x57x56x2x32xf32>
+}
+//CHECK-LABEL: func @linalg_transpose_tensor_pack_fold(
+// CHECK-SAME: %[[ARG0:.+]]: tensor<56x57x1x64xf32>)
+// CHECK: %[[INIT:.+]] = tensor.empty() : tensor<1x57x56x2x32xf32>
+// CHECK: %[[PACK:.+]] = tensor.pack %[[ARG0]]
+// CHECK-SAME: outer_dims_perm = [2, 1, 0, 3]
+// CHECK-SAME: inner_dims_pos = [3] inner_tiles = [32]
+// CHECK-SAME: into %[[INIT]]
+// CHECK: return %[[PACK]]
+
+// -----
+
+func.func @linalg_transpose_tensor_pack_fold_with_padding(%arg0: tensor<56x57x1x55xf32>, %padding: f32) -> tensor<1x57x56x2x32xf32> {
+ %0 = tensor.empty() : tensor<1x56x57x55xf32>
+ %transpose = linalg.transpose
+ ins(%arg0 : tensor<56x57x1x55xf32>)
+ outs(%0 : tensor<1x56x57x55xf32>)
+ permutation = [2, 0, 1, 3]
+
+ %1 = tensor.empty() : tensor<1x57x56x2x32xf32>
+ %pack = tensor.pack %transpose padding_value(%padding : f32)
+ outer_dims_perm = [0, 2, 1, 3]
+ inner_dims_pos = [3]
+ inner_tiles = [32]
+ into %1 : tensor<1x56x57x55xf32> -> tensor<1x57x56x2x32xf32>
+ return %pack : tensor<1x57x56x2x32xf32>
+}
+//CHECK-LABEL: func @linalg_transpose_tensor_pack_fold_with_padding(
+// CHECK-SAME: %[[ARG0:.+]]: tensor<56x57x1x55xf32>, %[[PADDING:.+]]: f32)
+// CHECK: %[[INIT:.+]] = tensor.empty() : tensor<1x57x56x2x32xf32>
+// CHECK: %[[PACK:.+]] = tensor.pack %[[ARG0]] padding_value(%[[PADDING]] : f32)
+// CHECK-SAME: outer_dims_perm = [2, 1, 0, 3]
+// CHECK-SAME: inner_dims_pos = [3] inner_tiles = [32]
+// CHECK-SAME: into %[[INIT]]
+// CHECK: return %[[PACK]]
+
+// -----
+
+func.func @linalg_transpose_tensor_pack_fold_no_outer_dims_perm(%arg0: tensor<56x57x1x64xf32>) -> tensor<1x56x57x2x32xf32> {
+ %0 = tensor.empty() : tensor<1x56x57x64xf32>
+ %transposed = linalg.transpose
+ ins(%arg0 : tensor<56x57x1x64xf32>)
+ outs(%0 : tensor<1x56x57x64xf32>)
+ permutation = [2, 0, 1, 3]
+
+ %1 = tensor.empty() : tensor<1x56x57x2x32xf32>
+ %pack = tensor.pack %transposed
+ inner_dims_pos = [3]
+ inner_tiles = [32]
+ into %1 : tensor<1x56x57x64xf32> -> tensor<1x56x57x2x32xf32>
+ return %pack : tensor<1x56x57x2x32xf32>
+}
+//CHECK-LABEL: func @linalg_transpose_tensor_pack_fold_no_outer_dims_perm(
+// CHECK-SAME: %[[ARG0:.+]]: tensor<56x57x1x64xf32>)
+// CHECK: %[[INIT:.+]] = tensor.empty() : tensor<1x56x57x2x32xf32>
+// CHECK: %[[PACK:.+]] = tensor.pack %[[ARG0]]
+// CHECK-SAME: outer_dims_perm = [2, 0, 1, 3]
+// CHECK-SAME: inner_dims_pos = [3] inner_tiles = [32]
+// CHECK-SAME: into %[[INIT]]
+// CHECK: return %[[PACK]]
+
+// -----
+
+func.func @linalg_transpose_tensor_pack_fold_complex_inner_dims_change(%arg0: tensor<25x30x35x40xf32>, %transpose_dest: tensor<35x40x25x30xf32>, %pack_dest: tensor<3x35x5x8x5x10x5xf32>) -> tensor<3x35x5x8x5x10x5xf32> {
+ %transposed = linalg.transpose
+ ins(%arg0 : tensor<25x30x35x40xf32>)
+ outs(%transpose_dest : tensor<35x40x25x30xf32>)
+ permutation = [2, 3, 0, 1]
+
+ %pack = tensor.pack %transposed
+ outer_dims_perm = [3, 0, 2, 1]
+ inner_dims_pos = [1, 3, 2]
+ inner_tiles = [5, 10, 5]
+ into %pack_dest : tensor<35x40x25x30xf32> -> tensor<3x35x5x8x5x10x5xf32>
+ return %pack : tensor<3x35x5x8x5x10x5xf32>
+}
+//CHECK-LABEL: func.func @linalg_transpose_tensor_pack_fold_complex_inner_dims_change(
+// CHECK-SAME: %[[ARG0:.+]]: tensor<25x30x35x40xf32>,
+// CHECK-SAME: %[[ARG1:.+]]: tensor<35x40x25x30xf32>,
+// CHECK-SAME: %[[ARG2:.+]]: tensor<3x35x5x8x5x10x5xf32>) -> tensor<3x35x5x8x5x10x5xf32> {
+// CHECK: %[[VAL0:.+]] = tensor.empty() : tensor<3x35x5x8x5x10x5xf32>
+// CHECK: %[[PACK:.+]] = tensor.pack %[[ARG0]]
+// CHECK-SAME: outer_dims_perm = [1, 2, 0, 3]
+// CHECK-SAME: inner_dims_pos = [3, 1, 0]
+// CHECK-SAME: inner_tiles = [5, 10, 5]
+// CHECK-SAME: into %[[VAL0]]
+// CHECK: return %[[PACK]]
+
+// -----
+
+func.func @linalg_transpose_tensor_pack_fold_dynamic_outer_dims_tile_dims_tile_sizes(%arg0: tensor<?x?x?x?xf32>, %transpose_dest: tensor<?x?x?x?xf32>, %pack_dest: tensor<?x?x?x?x?x?x?xf32>, %tile_p : index, %tile_q : index, %tile_r : index) -> tensor<?x?x?x?x?x?x?xf32> {
+ %transposed = linalg.transpose
+ ins(%arg0 : tensor<?x?x?x?xf32>)
+ outs(%transpose_dest : tensor<?x?x?x?xf32>)
+ permutation = [2, 3, 0, 1]
+
+ %pack = tensor.pack %transposed
+ outer_dims_perm = [3, 0, 2, 1]
+ inner_dims_pos = [1, 3, 2]
+ inner_tiles = [%tile_p, %tile_q, %tile_r]
+ into %pack_dest : tensor<?x?x?x?xf32> -> tensor<?x?x?x?x?x?x?xf32>
+ return %pack : tensor<?x?x?x?x?x?x?xf32>
+}
+// CHECK: #[[map:.+]] = affine_map<()[s0, s1] -> (s0 ceildiv s1)>
+//CHECK-LABEL: func.func @linalg_transpose_tensor_pack_fold_dynamic_outer_dims_tile_dims_tile_sizes(
+// CHECK-SAME: %[[ARG0:.+]]: tensor<?x?x?x?xf32>, %[[ARG1:.+]]: tensor<?x?x?x?xf32>,
+// CHECK-SAME: %[[ARG2:.+]]: tensor<?x?x?x?x?x?x?xf32>, %[[ARG3:.+]]: index, %[[ARG4:.+]]: index, %[[ARG5:.+]]: index) -> tensor<?x?x?x?x?x?x?xf32> {
+// CHECK: %[[C0:.+]] = arith.constant 0 : index
+// CHECK: %[[C1:.+]] = arith.constant 1 : index
+// CHECK: %[[C2:.+]] = arith.constant 2 : index
+// CHECK: %[[C3:.+]] = arith.constant 3 : index
+// CHECK: %[[DIM:.+]] = tensor.dim %[[ARG0]], %[[C0]] : tensor<?x?x?x?xf32>
+// CHECK: %[[DIM0:.+]] = tensor.dim %[[ARG0]], %[[C1]] : tensor<?x?x?x?xf32>
+// CHECK: %[[DIM1:.+]] = tensor.dim %[[ARG0]], %[[C2]] : tensor<?x?x?x?xf32>
+// CHECK: %[[DIM2:.+]] = tensor.dim %[[ARG0]], %[[C3]] : tensor<?x?x?x?xf32>
+// CHECK: %[[VAL0:.+]] = affine.apply #[[map:.+]]()[%[[DIM2]], %[[ARG3]]]
+// CHECK: %[[VAL1:.+]] = affine.apply #[[map:.+]]()[%[[DIM0]], %[[ARG4]]]
+// CHECK: %[[VAL2:.+]] = affine.apply #[[map:.+]]()[%[[DIM]], %[[ARG5]]]
+// CHECK: %[[VAL3:.+]] = tensor.empty(%[[VAL1]], %[[DIM1]], %[[VAL2]], %[[VAL0]], %[[ARG3]], %[[ARG4]], %[[ARG5]]) : tensor<?x?x?x?x?x?x?xf32>
+// CHECK: %[[PACK:.+]] = tensor.pack %[[ARG0]] outer_dims_perm = [1, 2, 0, 3] inner_dims_pos = [3, 1, 0] inner_tiles = [%[[ARG3]], %[[ARG4]], %[[ARG5]]] into %[[VAL3]] : tensor<?x?x?x?xf32> -> tensor<?x?x?x?x?x?x?xf32>
+// CHECK: return %[[PACK]] : tensor<?x?x?x?x?x?x?xf32>
+
+// -----
+
+func.func @linalg_transpose_tensor_cast_tensor_pack_fold(%arg0: tensor<56x57x1x64xf32>) -> tensor<1x57x56x2x32xf32> {
+ %0 = tensor.empty() : tensor<1x56x57x64xf32>
+ %transposed = linalg.transpose
+ ins(%arg0 : tensor<56x57x1x64xf32>)
+ outs(%0 : tensor<1x56x57x64xf32>)
+ permutation = [2, 0, 1, 3]
+
+ %transposed_cast = tensor.cast %transposed : tensor<1x56x57x64xf32> to tensor<?x56x57x64xf32>
----------------
meshtag wrote:
The `tensor.cast` serves as the trigger for negative test. It prevents `lining.transpose` from being the direct parent op of `tensor.pack` and thus doesn't transform the IR. This was added as a negative test to the pattern.
https://github.com/llvm/llvm-project/pull/75658
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