Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
[mlir][tensor][linalg] Add a pattern that generalizes tensor.unpack op.
The pattern generalizes a tensor::UnPackOp into a sequence of tensor + Linalg ops, when the outer dims are all 1s. It uses the trick of rank-reduced tensor.extract_slice to get the tile; transpose the tile; extract sub tile for incomplete cases if needed; use tensor.insert_slice to insert it to the destination tensor. Reviewed By: tyb0807, chelini Differential Revision: https://reviews.llvm.org/D140254
- Loading branch information
Showing
4 changed files
with
281 additions
and
9 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,168 @@ | ||
| // RUN: mlir-opt -split-input-file --test-linalg-transform-patterns="test-generalize-tensor-unpack" %s | FileCheck %s | ||
|
|
||
| func.func @simple_KCRSsr_to_KCRS(%arg0: tensor<1x1x1x1x8x32xf32>, %arg1: tensor<1x1x32x8xf32>) -> tensor<1x1x32x8xf32> { | ||
| %0 = tensor.unpack %arg0 inner_dims_pos = [3, 2] inner_tiles = [8, 32] into %arg1 : tensor<1x1x1x1x8x32xf32> -> tensor<1x1x32x8xf32> | ||
| return %0 : tensor<1x1x32x8xf32> | ||
| } | ||
| // CHECK-LABEL: func.func @simple_KCRSsr_to_KCRS | ||
| // CHECK-SAME: %[[SRC:[a-zA-Z0-9]+]] | ||
| // CHECK-SAME: %[[DEST:[a-zA-Z0-9]+]] | ||
| // CHECK: %[[TILE:.+]] = tensor.extract_slice %[[SRC]][0, 0, 0, 0, 0, 0] [1, 1, 1, 1, 8, 32] [1, 1, 1, 1, 1, 1] | ||
| // CHECK: %[[EMPTY:.+]] = tensor.empty() : tensor<32x8xf32> | ||
| // CHECK: %[[TRANSP:.+]] = linalg.transpose | ||
| // CHECK-SAME: ins(%[[TILE]] : tensor<8x32xf32>) | ||
| // CHECK-SAME: outs(%[[EMPTY]] : tensor<32x8xf32>) | ||
| // CHECK-SAME: permutation = [1, 0] | ||
| // CHECK: %[[INSERT:.+]] = tensor.insert_slice %[[TRANSP]] into %[[DEST]] | ||
| // CHECK-SAME: [0, 0, 0, 0] [1, 1, 32, 8] [1, 1, 1, 1] | ||
| // CHECK: return %[[INSERT]] | ||
|
|
||
| // ----- | ||
|
|
||
| func.func @simple_unpack_and_extract_slice(%input: tensor<1x1x8x2xf32>, %output: tensor<5x1xf32>) -> tensor<5x1xf32> { | ||
| %0 = tensor.unpack %input inner_dims_pos = [0, 1] inner_tiles = [8, 2] into %output : tensor<1x1x8x2xf32> -> tensor<5x1xf32> | ||
| return %0 : tensor<5x1xf32> | ||
| } | ||
| // CHECK-LABEL: func.func @simple_unpack_and_extract_slice | ||
| // CHECK-SAME: %[[SRC:[a-zA-Z0-9]+]] | ||
| // CHECK-SAME: %[[DEST:[a-zA-Z0-9]+]] | ||
| // CHECK: %[[TILE:.+]] = tensor.extract_slice %[[SRC]][0, 0, 0, 0] [1, 1, 8, 2] [1, 1, 1, 1] | ||
| // CHECK: %[[EMPTY:.+]] = tensor.empty() : tensor<8x2xf32> | ||
| // CHECK: %[[TRANSP:.+]] = linalg.transpose | ||
| // CHECK-SAME: ins(%[[TILE]] : tensor<8x2xf32>) | ||
| // CHECK-SAME: outs(%[[EMPTY]] : tensor<8x2xf32>) | ||
| // CHECK-SAME: permutation = [0, 1] | ||
| // They have the same type, so the insert_slice op is folded | ||
| // away. | ||
| // CHECK: %[[SLICE:.+]] = tensor.extract_slice %[[TRANSP]][0, 0] [5, 1] [1, 1] | ||
| // CHECK: return %[[SLICE]] | ||
|
|
||
| // ----- | ||
|
|
||
| func.func @simple_CNnc_to_NC(%arg0: tensor<1x1x32x8xf32>, %arg1: tensor<32x8xf32>) -> tensor<32x8xf32>{ | ||
| %0 = tensor.unpack %arg0 outer_dims_perm = [1, 0] inner_dims_pos = [0, 1] inner_tiles = [32, 8] into %arg1 : tensor<1x1x32x8xf32> -> tensor<32x8xf32> | ||
| return %0 : tensor<32x8xf32> | ||
| } | ||
| // CHECK-LABEL: func.func @simple_CNnc_to_NC | ||
| // CHECK-SAME: %[[SRC:[a-zA-Z0-9]+]] | ||
| // CHECK-SAME: %[[DEST:[a-zA-Z0-9]+]] | ||
| // CHECK: %[[TILE:.+]] = tensor.extract_slice %[[SRC]][0, 0, 0, 0] [1, 1, 32, 8] [1, 1, 1, 1] | ||
| // CHECK: %[[EMPTY:.+]] = tensor.empty() : tensor<32x8xf32> | ||
| // CHECK: %[[TRANSP:.+]] = linalg.transpose | ||
| // CHECK-SAME: ins(%[[TILE]] : tensor<32x8xf32>) | ||
| // CHECK-SAME: outs(%[[EMPTY]] : tensor<32x8xf32>) | ||
| // CHECK-SAME: permutation = [0, 1] | ||
| // They have the same type, so the insert_slice op is folded | ||
| // away. | ||
| // CHECK: return %[[TRANSP]] | ||
|
|
||
| // ----- | ||
|
|
||
| // RUN: mlir-opt -split-input-file --test-transform-dialect-interpreter --canonicalize --test-linalg-transform-patterns="test-generalize-tensor-unpack" %s | FileCheck %s --check-prefix=CHECK-TRANS | ||
|
|
||
| func.func @KCRSsr_to_KCRS(%arg0: tensor<1x1x4x8x8x32xf32>, %arg1: tensor<1x1x128x64xf32>) -> tensor<1x1x128x64xf32> { | ||
| %0 = tensor.unpack %arg0 inner_dims_pos = [3, 2] inner_tiles = [8, 32] into %arg1 : tensor<1x1x4x8x8x32xf32> -> tensor<1x1x128x64xf32> | ||
| return %0 : tensor<1x1x128x64xf32> | ||
| } | ||
|
|
||
| transform.sequence failures(propagate) { | ||
| ^bb0(%arg1: !pdl.operation): | ||
| %0 = transform.structured.match ops{["tensor.unpack"]} in %arg1 | ||
| %1, %loops:4 = transform.structured.tile_to_scf_for %0 [1, 1, 32, 8] | ||
| } | ||
| // CHECK-TRANS-DAG: #[[MAP0:.+]] = affine_map<(d0) -> (d0 floordiv 32)> | ||
| // CHECK-TRANS-DAG: #[[MAP1:.+]] = affine_map<(d0) -> (d0 floordiv 8)> | ||
| // CHECK-TRANS: func.func @KCRSsr_to_KCRS | ||
| // CHECK-TRANS-SAME: %[[SRC:[a-zA-Z0-9]+]] | ||
| // CHECK-TRANS-SAME: %[[DEST:[a-zA-Z0-9]+]] | ||
| // CHECK-TRANS: %{{.+}} = scf.for %[[R:[a-zA-Z0-9]+]] = | ||
| // CHECK-TRANS: %{{.+}} = scf.for %[[S:[a-zA-Z0-9]+]] = | ||
| // CHECK-TRANS: %[[IN_R:.+]] = affine.apply #[[MAP0]](%[[R]]) | ||
| // CHECK-TRANS: %[[IN_S:.+]] = affine.apply #[[MAP1]](%[[S]]) | ||
| // CHECK-TRANS: %[[SRC_SLICE:.+]] = tensor.extract_slice %[[SRC]] | ||
| // CHECK-TRANS-SAME: [0, 0, %[[IN_R]], %[[IN_S]], 0, 0] [1, 1, 1, 1, 8, 32] [1, 1, 1, 1, 1, 1] | ||
| // CHECK-TRANS: %[[TILE:.+]] = tensor.extract_slice %[[SRC_SLICE]] | ||
| // CHECK-TRANS-SAME: [0, 0, 0, 0, 0, 0] [1, 1, 1, 1, 8, 32] [1, 1, 1, 1, 1, 1] : tensor<1x1x1x1x8x32xf32> to tensor<8x32xf32> | ||
| // CHECK-TRANS: %[[EMPTY:.+]] = tensor.empty() : tensor<32x8xf32> | ||
| // CHECK-TRANS: %[[TRANSP:.+]] = linalg.transpose | ||
| // CHECK-TRANS-SAME: ins(%[[TILE]] | ||
| // CHECK-TRANS-SAME: outs(%[[EMPTY]] | ||
| // CHECK-TRANS-SAME: permutation = [1, 0] | ||
| // CHECK-TRANS: %{{.+}} = tensor.insert_slice %[[TRANSP]] into %{{.+}} | ||
|
|
||
| // ----- | ||
|
|
||
| func.func @unpack_and_extract_slice(%arg0: tensor<2x8x8x2xf32>, %arg1: tensor<13x15xf32>) -> tensor<13x15xf32> { | ||
| %0 = tensor.unpack %arg0 inner_dims_pos = [0, 1] inner_tiles = [8, 2] into %arg1 : tensor<2x8x8x2xf32> -> tensor<13x15xf32> | ||
| return %0 : tensor<13x15xf32> | ||
| } | ||
| // CHECK-TRANS-DAG: #[[MAP0:.+]] = affine_map<(d0) -> (-d0 + 13, 8)> | ||
| // CHECK-TRANS-DAG: #[[MAP1:.+]] = affine_map<(d0) -> (-d0 + 15, 2)> | ||
| // CHECK-TRANS-DAG: #[[MAP2:.+]] = affine_map<(d0) -> (d0 floordiv 8)> | ||
| // CHECK-TRANS-DAG: #[[MAP3:.+]] = affine_map<(d0) -> (d0 floordiv 2)> | ||
| // CHECK-TRANS: func.func @unpack_and_extract_slice | ||
| // CHECK-TRANS-SAME: %[[SRC:[a-zA-Z0-9]+]] | ||
| // CHECK-TRANS-SAME: %[[DEST:[a-zA-Z0-9]+]] | ||
| // CHECK-TRANS: %{{.+}} = scf.for %[[I:[a-zA-Z0-9]+]] = | ||
| // CHECK-TRANS: %[[OUT_I_SZ:.+]] = affine.min #[[MAP0]](%[[I]]) | ||
| // CHECK-TRANS: %{{.+}} = scf.for %[[J:[a-zA-Z0-9]+]] = | ||
| // CHECK-TRANS: %[[OUT_J_SZ:.+]] = affine.min #[[MAP1]](%[[J]]) | ||
| // CHECK-TRANS: %[[IN_I:.+]] = affine.apply #[[MAP2]](%[[I]]) | ||
| // CHECK-TRANS: %[[IN_J:.+]] = affine.apply #[[MAP3]](%[[J]]) | ||
| // CHECK-TRANS: %[[SRC_SLICE:.+]] = tensor.extract_slice %[[SRC]] | ||
| // CHECK-TRANS-SAME: [%[[IN_I]], %[[IN_J]], 0, 0] [1, 1, 8, 2] [1, 1, 1, 1] | ||
| // CHECK-TRANS: %[[ITER_SLICE:.+]] = tensor.extract_slice %{{[a-zA-Z0-9]+}} | ||
| // CHECK-TRANS-SAME: [%[[I]], %[[J]]] [%[[OUT_I_SZ]], %[[OUT_J_SZ]]] | ||
| // CHECK-TRANS: %[[TILE:.+]] = tensor.extract_slice %[[SRC_SLICE]] | ||
| // CHECK-TRANS-SAME: [0, 0, 0, 0] [1, 1, 8, 2] [1, 1, 1, 1] : tensor<1x1x8x2xf32> to tensor<8x2xf32> | ||
| // CHECK-TRANS: %[[EMPTY:.+]] = tensor.empty() : tensor<8x2xf32> | ||
| // CHECK-TRANS: %[[TRANSP:.+]] = linalg.transpose | ||
| // CHECK-TRANS-SAME: ins(%[[TILE]] : tensor<8x2xf32>) | ||
| // CHECK-TRANS-SAME: outs(%[[EMPTY]] : tensor<8x2xf32>) | ||
| // CHECK-TRANS-SAME: permutation = [0, 1] | ||
| // CHECK-TRANS: %[[UNPACK_TILE:.+]] = tensor.extract_slice %[[TRANSP]] | ||
| // CHECK-TRANS-SAME: [0, 0] [%[[OUT_I_SZ]], %[[OUT_J_SZ]]] [1, 1] | ||
| // CHECK-TRANS: %[[INSERT1:.+]] = tensor.insert_slice %[[UNPACK_TILE]] into %[[ITER_SLICE]] | ||
| // CHECK-TRANS-SAME: [0, 0] [%[[OUT_I_SZ]], %[[OUT_J_SZ]]] [1, 1] | ||
| // CHECK-TRANS: %[[INSERT2:.+]] = tensor.insert_slice %[[INSERT1]] into %{{[a-zA-Z0-9]+}} | ||
| // CHECK-TRANS-SAME: [%[[I]], %[[J]]] [%[[OUT_I_SZ]], %[[OUT_J_SZ]]] [1, 1] | ||
|
|
||
| transform.sequence failures(propagate) { | ||
| ^bb0(%arg1: !pdl.operation): | ||
| %0 = transform.structured.match ops{["tensor.unpack"]} in %arg1 | ||
| %1, %loops:2 = transform.structured.tile_to_scf_for %0 [8, 2] | ||
| } | ||
|
|
||
| // ----- | ||
|
|
||
| func.func @CKkc_to_KC(%arg0: tensor<32x4x32x8xf32>, %arg1: tensor<128x256xf32>) -> tensor<128x256xf32> { | ||
| %0 = tensor.unpack %arg0 outer_dims_perm = [1, 0] inner_dims_pos = [0, 1] inner_tiles = [32, 8] into %arg1 : tensor<32x4x32x8xf32> -> tensor<128x256xf32> | ||
| return %0 : tensor<128x256xf32> | ||
| } | ||
| // CHECK-TRANS-DAG: #[[MAP0:.+]] = affine_map<(d0) -> (d0 floordiv 32)> | ||
| // CHECK-TRANS-DAG: #[[MAP1:.+]] = affine_map<(d0) -> (d0 floordiv 8)> | ||
| // CHECK-TRANS: func.func @CKkc_to_KC | ||
| // CHECK-TRANS-SAME: %[[SRC:[a-zA-Z0-9]+]] | ||
| // CHECK-TRANS-SAME: %[[DEST:[a-zA-Z0-9]+]] | ||
| // CHECK-TRANS: %{{.+}} = scf.for %[[K:[a-zA-Z0-9]+]] = | ||
| // CHECK-TRANS: %{{.+}} = scf.for %[[C:[a-zA-Z0-9]+]] = | ||
| // CHECK-TRANS: %[[IN_K:.+]] = affine.apply #[[MAP0]](%[[K]]) | ||
| // CHECK-TRANS: %[[IN_C:.+]] = affine.apply #[[MAP1]](%[[C]]) | ||
| // CHECK-TRANS: %[[SRC_SLICE:.+]] = tensor.extract_slice %[[SRC]] | ||
| // CHECK-TRANS-SAME: [%[[IN_C]], %[[IN_K]], 0, 0] [1, 1, 32, 8] [1, 1, 1, 1] | ||
| // CHECK-TRANS: %[[TILE:.+]] = tensor.extract_slice %[[SRC_SLICE]] | ||
| // CHECK-TRANS-SAME: [0, 0, 0, 0] [1, 1, 32, 8] [1, 1, 1, 1] : tensor<1x1x32x8xf32> to tensor<32x8xf32> | ||
| // CHECK-TRANS: %[[EMPTY:.+]] = tensor.empty() : tensor<32x8xf32> | ||
| // CHECK-TRANS: %[[TRANSP:.+]] = linalg.transpose | ||
| // CHECK-TRANS-SAME: ins(%[[TILE]] | ||
| // CHECK-TRANS-SAME: outs(%[[EMPTY]] | ||
| // CHECK-TRANS-SAME: permutation = [0, 1] | ||
| // CHECK-TRANS: %[[INSERT:.+]] = tensor.insert_slice %[[TRANSP]] into %{{[a-zA-Z0-9]+}} | ||
| // CHECK-TRANS-SAME: [%[[K]], %[[C]]] [32, 8] [1, 1] | ||
|
|
||
|
|
||
| transform.sequence failures(propagate) { | ||
| ^bb0(%arg1: !pdl.operation): | ||
| %0 = transform.structured.match ops{["tensor.unpack"]} in %arg1 | ||
| %1, %loops:2 = transform.structured.tile_to_scf_for %0 [32, 8] | ||
| } |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters