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[mlir][linalg] Add linalg.transpose constant folding (llvm#92589)
There was existing support for constant folding a `linalg.generic` that was actually a transpose. This commit adds support for the named op, `linalg.transpose`, as well by making use of the `LinalgOp` interface.
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,148 @@ | ||
| // RUN: mlir-opt %s -linalg-fuse-elementwise-ops -split-input-file | FileCheck %s | ||
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| // CHECK-LABEL: @transpose_fold_2d_fp32 | ||
| func.func @transpose_fold_2d_fp32(%init: tensor<3x2xf32>) -> tensor<3x2xf32> { | ||
| %input = arith.constant dense<[[0.0, 1.0, 2.0], [3.0, 4.0, 5.0]]> : tensor<2x3xf32> | ||
| // CHECK: %[[CST:.+]] = arith.constant | ||
| // CHECK-SAME{LITERAL}: dense<[[0.000000e+00, 3.000000e+00], [1.000000e+00, 4.000000e+00], [2.000000e+00, 5.000000e+00]]> : tensor<3x2xf32> | ||
| %1 = linalg.generic { | ||
| indexing_maps = [affine_map<(d0, d1) -> (d1, d0)>, affine_map<(d0, d1) -> (d0, d1)>], | ||
| iterator_types = ["parallel", "parallel"] | ||
| } ins(%input : tensor<2x3xf32>) outs(%init : tensor<3x2xf32>) { | ||
| ^bb0(%arg1: f32, %arg2: f32): | ||
| linalg.yield %arg1 : f32 | ||
| } -> tensor<3x2xf32> | ||
| // CHECK: return %[[CST]] | ||
| return %1 : tensor<3x2xf32> | ||
| } | ||
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| // ----- | ||
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| // CHECK-LABEL: @transpose_fold_2d_fp64 | ||
| func.func @transpose_fold_2d_fp64(%init: tensor<3x2xf64>) -> tensor<3x2xf64> { | ||
| %input = arith.constant dense<[[0.0, 1.0, 2.0], [3.0, 4.0, 5.0]]> : tensor<2x3xf64> | ||
| // CHECK: %[[CST:.+]] = arith.constant | ||
| // CHECK-SAME{LITERAL}: dense<[[0.000000e+00, 3.000000e+00], [1.000000e+00, 4.000000e+00], [2.000000e+00, 5.000000e+00]]> : tensor<3x2xf64> | ||
| %1 = linalg.generic { | ||
| indexing_maps = [affine_map<(d0, d1) -> (d1, d0)>, affine_map<(d0, d1) -> (d0, d1)>], | ||
| iterator_types = ["parallel", "parallel"] | ||
| } ins(%input : tensor<2x3xf64>) outs(%init : tensor<3x2xf64>) { | ||
| ^bb0(%arg1: f64, %arg2: f64): | ||
| linalg.yield %arg1 : f64 | ||
| } -> tensor<3x2xf64> | ||
| // CHECK: return %[[CST]] | ||
| return %1 : tensor<3x2xf64> | ||
| } | ||
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| // ----- | ||
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| // CHECK-LABEL: @transpose_fold_4d_i32 | ||
| func.func @transpose_fold_4d_i32(%init: tensor<3x1x4x2xi32>) -> tensor<3x1x4x2xi32> { | ||
| %input = arith.constant dense<[[ | ||
| [[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11]], | ||
| [[12, 13, 14, 15], [16, 17, 18, 19], [20, 21, 22, 23]] | ||
| ]]> : tensor<1x2x3x4xi32> | ||
| // CHECK: %[[CST:.+]] = arith.constant dense<[ | ||
| // CHECK-SAME{LITERAL}: [[[0, 12], [1, 13], [2, 14], [3, 15]]], | ||
| // CHECK-SAME{LITERAL}: [[[4, 16], [5, 17], [6, 18], [7, 19]]], | ||
| // CHECK-SAME{LITERAL}: [[[8, 20], [9, 21], [10, 22], [11, 23]]] | ||
| // CHECK-SAME{LITERAL}: ]> | ||
| %1 = linalg.generic { | ||
| indexing_maps = [affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>, affine_map<(d0, d1, d2, d3) -> (d2, d0, d3, d1)>], | ||
| iterator_types = ["parallel", "parallel", "parallel", "parallel"] | ||
| } ins(%input : tensor<1x2x3x4xi32>) outs(%init : tensor<3x1x4x2xi32>) { | ||
| ^bb0(%arg1: i32, %arg2: i32): | ||
| linalg.yield %arg1 : i32 | ||
| } -> tensor<3x1x4x2xi32> | ||
| // CHECK: return %[[CST]] | ||
| return %1 : tensor<3x1x4x2xi32> | ||
| } | ||
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| // ----- | ||
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| // CHECK-LABEL: @transpose_fold_4d_i16 | ||
| func.func @transpose_fold_4d_i16(%init: tensor<3x1x4x2xi16>) -> tensor<3x1x4x2xi16> { | ||
| %input = arith.constant dense<[[ | ||
| [[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11]], | ||
| [[12, 13, 14, 15], [16, 17, 18, 19], [20, 21, 22, 23]] | ||
| ]]> : tensor<1x2x3x4xi16> | ||
| // CHECK: %[[CST:.+]] = arith.constant dense<[ | ||
| // CHECK-SAME{LITERAL}: [[[0, 12], [1, 13], [2, 14], [3, 15]]], | ||
| // CHECK-SAME{LITERAL}: [[[4, 16], [5, 17], [6, 18], [7, 19]]], | ||
| // CHECK-SAME{LITERAL}: [[[8, 20], [9, 21], [10, 22], [11, 23]]] | ||
| // CHECK-SAME{LITERAL}: ]> | ||
| %1 = linalg.generic { | ||
| indexing_maps = [affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>, affine_map<(d0, d1, d2, d3) -> (d2, d0, d3, d1)>], | ||
| iterator_types = ["parallel", "parallel", "parallel", "parallel"] | ||
| } ins(%input : tensor<1x2x3x4xi16>) outs(%init : tensor<3x1x4x2xi16>) { | ||
| ^bb0(%arg1: i16, %arg2: i16): | ||
| linalg.yield %arg1 : i16 | ||
| } -> tensor<3x1x4x2xi16> | ||
| // CHECK: return %[[CST]] | ||
| return %1 : tensor<3x1x4x2xi16> | ||
| } | ||
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| // ----- | ||
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| // CHECK-LABEL: @transpose_nofold_non_cst_input | ||
| func.func @transpose_nofold_non_cst_input(%input: tensor<2x3xf32>, %init: tensor<3x2xf32>) -> tensor<3x2xf32> { | ||
| // CHECK: linalg.generic | ||
| %1 = linalg.generic { | ||
| indexing_maps = [affine_map<(d0, d1) -> (d1, d0)>, affine_map<(d0, d1) -> (d0, d1)>], | ||
| iterator_types = ["parallel", "parallel"] | ||
| } ins(%input : tensor<2x3xf32>) outs(%init : tensor<3x2xf32>) { | ||
| ^bb0(%arg1: f32, %arg2: f32): | ||
| linalg.yield %arg1 : f32 | ||
| } -> tensor<3x2xf32> | ||
| return %1 : tensor<3x2xf32> | ||
| } | ||
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| // ----- | ||
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| // CHECK-LABEL: @transpose_nofold_yield_const | ||
| func.func @transpose_nofold_yield_const(%init: tensor<3x2xf32>) -> tensor<3x2xf32> { | ||
| %input = arith.constant dense<[[0.0, 1.0, 2.0], [3.0, 4.0, 5.0]]> : tensor<2x3xf32> | ||
| %cst = arith.constant 8.0 : f32 | ||
| // CHECK: linalg.generic | ||
| %1 = linalg.generic { | ||
| indexing_maps = [affine_map<(d0, d1) -> (d1, d0)>, affine_map<(d0, d1) -> (d0, d1)>], | ||
| iterator_types = ["parallel", "parallel"] | ||
| } ins(%input : tensor<2x3xf32>) outs(%init : tensor<3x2xf32>) { | ||
| ^bb0(%arg1: f32, %arg2: f32): | ||
| linalg.yield %cst : f32 | ||
| } -> tensor<3x2xf32> | ||
| return %1 : tensor<3x2xf32> | ||
| } | ||
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| // ----- | ||
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| // CHECK-LABEL: @transpose_nofold_multi_ops_in_region | ||
| func.func @transpose_nofold_multi_ops_in_region(%init: tensor<3x2xf32>) -> tensor<3x2xf32> { | ||
| %input = arith.constant dense<[[0.0, 1.0, 2.0], [3.0, 4.0, 5.0]]> : tensor<2x3xf32> | ||
| // CHECK: linalg.generic | ||
| %1 = linalg.generic { | ||
| indexing_maps = [affine_map<(d0, d1) -> (d1, d0)>, affine_map<(d0, d1) -> (d0, d1)>], | ||
| iterator_types = ["parallel", "parallel"] | ||
| } ins(%input : tensor<2x3xf32>) outs(%init : tensor<3x2xf32>) { | ||
| ^bb0(%arg1: f32, %arg2: f32): | ||
| %add = arith.addf %arg1, %arg1 : f32 | ||
| linalg.yield %add : f32 | ||
| } -> tensor<3x2xf32> | ||
| return %1 : tensor<3x2xf32> | ||
| } | ||
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| // ----- | ||
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| // CHECK-LABEL: @named_transpose_fold_2d_fp32 | ||
| func.func @named_transpose_fold_2d_fp32(%init: tensor<3x2xf32>) -> tensor<3x2xf32> { | ||
| %input = arith.constant dense<[[0.0, 1.0, 2.0], [3.0, 4.0, 5.0]]> : tensor<2x3xf32> | ||
| // CHECK: %[[CST:.+]] = arith.constant | ||
| // CHECK-SAME{LITERAL}: dense<[[0.000000e+00, 3.000000e+00], [1.000000e+00, 4.000000e+00], [2.000000e+00, 5.000000e+00]]> : tensor<3x2xf32> | ||
| %1 = linalg.transpose ins(%input : tensor<2x3xf32>) outs(%init : tensor<3x2xf32>) permutation = [1, 0] | ||
| // CHECK: return %[[CST]] | ||
| return %1 : tensor<3x2xf32> | ||
| } | ||
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| // ----- | ||
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