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[mlir][TilingInterface] Enabling tiling
tensor.pad using `TilingInt…
…erface`. Update the implementation of `TilingInterface` for `tensor.pad` operations to allow tiling the op using the existing patterns for the interface. Verify that tests that pass with existing pad tiling patterns producer the same results through TilingInterface patterns. Reviewed By: antiagainst Differential Revision: https://reviews.llvm.org/D132720
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Mahesh Ravishankar
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mlir/test/Interfaces/TilingInterface/tile-pad-using-interface.mlir
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,140 @@ | ||
| // RUN: mlir-opt -test-tiling-interface=tile-using-scf-for -resolve-shaped-type-result-dims -cse -split-input-file %s | FileCheck %s | ||
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| // 2D tiling of dynamic 2D pad tensor op. | ||
| func.func @dynamic_2d_pad_tensor(%input_tensor: tensor<?x?xf32>, | ||
| %pad_value: f32) -> tensor<?x?xf32> { | ||
| %0 = tensor.pad %input_tensor low[3, 4] high[5, 3] { | ||
| ^bb0(%arg1: index, %arg2: index): | ||
| tensor.yield %pad_value : f32 | ||
| } {__internal_linalg_transform__ = "pad_2dtiling"}: tensor<?x?xf32> to tensor<?x?xf32> | ||
| return %0 : tensor<?x?xf32> | ||
| } | ||
| // CHECK-DAG: #[[MAP0:.+]] = affine_map<()[s0] -> (s0 + 8)> | ||
| // CHECK-DAG: #[[MAP1:.+]] = affine_map<()[s0] -> (s0 + 7)> | ||
| // CHECK: func @dynamic_2d_pad_tensor( | ||
| // CHECK-SAME: %[[IN:[a-zA-Z0-9]+]]: tensor<?x?xf32> | ||
| // CHECK-DAG: %[[C0:.+]] = arith.constant 0 : index | ||
| // CHECK-DAG: %[[C1:.+]] = arith.constant 1 : index | ||
| // CHECK-DAG: %[[C2:.+]] = arith.constant 2 : index | ||
| // CHECK-DAG: %[[C3:.+]] = arith.constant 3 : index | ||
| // CHECK: %[[DIM_IN0:.+]] = tensor.dim %[[IN]], %[[C0]] | ||
| // CHECK: %[[DIM0:.+]] = affine.apply #[[MAP0]]()[%[[DIM_IN0]]] | ||
| // CHECK: %[[DIM_IN1:.+]] = tensor.dim %[[IN]], %[[C1]] | ||
| // CHECK: %[[DIM1:.+]] = affine.apply #[[MAP1]]()[%[[DIM_IN1]]] | ||
| // CHECK: %[[RESULT:[a-zA-Z0-9]+]] = scf.for %[[IV0:[a-zA-Z0-9]+]] = %[[C0]] to %[[DIM0]] step %[[C2]] | ||
| // CHECK: scf.for {{.*}} = %[[C0]] to %[[DIM1]] step %[[C3]] iter_args(%[[INNER_OUT:.*]] = | ||
| // CHECK: %[[SWAP_RESULT:.*]] = scf.if | ||
| // CHECK: tensor.generate | ||
| // CHECK: else | ||
| // CHECK: %[[SLICE:.*]] = tensor.extract_slice %[[IN]][{{.*}}, {{.*}}] [{{.*}}, {{.*}}] [1, 1] | ||
| // CHECK: %[[PAD:.*]] = tensor.pad %[[SLICE]] | ||
| // CHECK: tensor.insert_slice %[[SWAP_RESULT]] into %[[INNER_OUT]][{{.*}}, {{.*}}] [{{.*}}, {{.*}}] [1, 1] | ||
| // CHECK: return %[[RESULT]] | ||
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| // ----- | ||
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| func.func @dynamic_2d_pad_tensor_inner_tiling(%input_tensor: tensor<?x?xf32>, | ||
| %pad_value: f32) -> tensor<?x?xf32> { | ||
| %0 = tensor.pad %input_tensor low[3, 4] high[5, 3] { | ||
| ^bb0(%arg1: index, %arg2: index): | ||
| tensor.yield %pad_value : f32 | ||
| } {__internal_linalg_transform__ = "pad_inner_tiling"}: tensor<?x?xf32> to tensor<?x?xf32> | ||
| return %0 : tensor<?x?xf32> | ||
| } | ||
| // CHECK-DAG: #[[MAP0:.*]] = affine_map<()[s0] -> (s0 + 8)> | ||
| // CHECK-DAG: #[[MAP1:.*]] = affine_map<()[s0] -> (s0 + 7)> | ||
| // CHECK: func @dynamic_2d_pad_tensor_inner_tiling( | ||
| // CHECK-SAME: %[[IN:.*]]: tensor<?x?xf32> | ||
| // CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index | ||
| // CHECK-DAG: %[[C1:.*]] = arith.constant 1 : index | ||
| // CHECK-DAG: %[[C3:.*]] = arith.constant 3 : index | ||
| // CHECK: %[[DIM_IN0:.*]] = tensor.dim %[[IN]], %[[C0]] | ||
| // CHECK: %[[DIM0:.*]] = affine.apply #[[MAP0]]()[%[[DIM_IN0]]] | ||
| // CHECK: %[[DIM_IN1:.*]] = tensor.dim %[[IN]], %[[C1]] | ||
| // CHECK: %[[DIM1:.*]] = affine.apply #[[MAP1]]()[%[[DIM_IN1]]] | ||
| // CHECK: %[[RESULT:.*]] = scf.for {{.*}} = %[[C0]] to %[[DIM1]] step %[[C3]] iter_args(%[[INNER_OUT:.*]] = | ||
| // CHECK: %[[SWAP_RESULT:.*]] = scf.if | ||
| // CHECK: tensor.generate | ||
| // CHECK: else | ||
| // CHECK: %[[SLICE:.*]] = tensor.extract_slice %[[IN]][{{.*}}, {{.*}}] [{{.*}}, {{.*}}] [1, 1] | ||
| // CHECK: %[[PAD:.*]] = tensor.pad %[[SLICE]] low[3, %{{.*}}] high[{{.*}}, {{.*}}] | ||
| // CHECK: tensor.insert_slice %[[SWAP_RESULT]] into %[[INNER_OUT]][%[[C0]], {{.*}}] [%[[DIM0]], {{.*}}] [1, 1] | ||
| // CHECK: return %[[RESULT]] | ||
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| // ----- | ||
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| func.func @static_pad_tensor(%input_tensor: tensor<7x9xf32>, | ||
| %pad_value: f32) -> tensor<15x16xf32> { | ||
| %0 = tensor.pad %input_tensor low[3, 4] high[5, 3] { | ||
| ^bb0(%arg1: index, %arg2: index): | ||
| tensor.yield %pad_value : f32 | ||
| } {__internal_linalg_transform__ = "pad_2dtiling"} : tensor<7x9xf32> to tensor<15x16xf32> | ||
| return %0 : tensor<15x16xf32> | ||
| } | ||
| // CHECK-LABEL: func @static_pad_tensor( | ||
| // CHECK-SAME: %[[IN:.*]]: tensor<7x9xf32> | ||
| // CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index | ||
| // CHECK-DAG: %[[C2:.*]] = arith.constant 2 : index | ||
| // CHECK-DAG: %[[C3:.*]] = arith.constant 3 : index | ||
| // CHECK-DAG: %[[C15:.*]] = arith.constant 15 : index | ||
| // CHECK-DAG: %[[C16:.*]] = arith.constant 16 : index | ||
| // CHECK: %[[RESULT:.*]] = scf.for {{.*}} = %[[C0]] to %[[C15]] step %[[C2]] | ||
| // CHECK: scf.for {{.*}} = %[[C0]] to %[[C16]] step %[[C3]] iter_args(%[[INNER_OUT:.*]] = | ||
| // CHECK: %[[SWAP_RESULT:.*]] = scf.if | ||
| // CHECK: tensor.generate | ||
| // CHECK: else | ||
| // CHECK: %[[SLICE:.*]] = tensor.extract_slice %[[IN]][{{.*}}, {{.*}}] [{{.*}}, {{.*}}] [1, 1] | ||
| // CHECK: %[[PAD:.*]] = tensor.pad %[[SLICE]] | ||
| // CHECK: tensor.insert_slice %[[SWAP_RESULT]] into %[[INNER_OUT]][{{.*}}, {{.*}}] [{{.*}}, {{.*}}] [1, 1] | ||
| // CHECK: return %[[RESULT]] | ||
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| // ----- | ||
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| func.func @static_pad_tensor_inner_tiling(%input_tensor: tensor<7x9xf32>, | ||
| %pad_value: f32) -> tensor<15x16xf32> { | ||
| %0 = tensor.pad %input_tensor low[3, 4] high[5, 3] { | ||
| ^bb0(%arg1: index, %arg2: index): | ||
| tensor.yield %pad_value : f32 | ||
| } {__internal_linalg_transform__ = "pad_inner_tiling"} : tensor<7x9xf32> to tensor<15x16xf32> | ||
| return %0 : tensor<15x16xf32> | ||
| } | ||
| // CHECK-LABEL: func @static_pad_tensor_inner_tiling( | ||
| // CHECK-SAME: %[[IN:.*]]: tensor<7x9xf32> | ||
| // CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index | ||
| // CHECK-DAG: %[[C3:.*]] = arith.constant 3 : index | ||
| // CHECK-DAG: %[[C15:.*]] = arith.constant 15 : index | ||
| // CHECK-DAG: %[[C16:.*]] = arith.constant 16 : index | ||
| // CHECK: %[[RESULT:.*]] = scf.for {{.*}} = %[[C0]] to %[[C16]] step %[[C3]] iter_args(%[[INNER_OUT:.*]] = | ||
| // CHECK: %[[SWAP_RESULT:.*]] = scf.if | ||
| // CHECK: tensor.generate | ||
| // CHECK: else | ||
| // CHECK: %[[SLICE:.*]] = tensor.extract_slice %[[IN]][0, {{.*}}] [7, {{.*}}] [1, 1] | ||
| // CHECK: %[[PAD:.*]] = tensor.pad %[[SLICE]] low[3, %{{.*}}] high[5, {{.*}}] | ||
| // CHECK: tensor.insert_slice %[[SWAP_RESULT]] into %[[INNER_OUT]][%[[C0]], {{.*}}] [%[[C15]], {{.*}}] [1, 1] | ||
| // CHECK: return %[[RESULT]] | ||
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| /// Rest of the tests only check that they dont fail. | ||
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| // ----- | ||
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| func.func @dynamic_2d_pad_tensor_outer_tiling(%input_tensor: tensor<?x?xf32>, | ||
| %pad_value: f32) -> tensor<?x?xf32> { | ||
| %0 = tensor.pad %input_tensor low[3, 4] high[5, 3] { | ||
| ^bb0(%arg1: index, %arg2: index): | ||
| tensor.yield %pad_value : f32 | ||
| } {__internal_linalg_transform__ = "pad_outer_tiling"}: tensor<?x?xf32> to tensor<?x?xf32> | ||
| return %0 : tensor<?x?xf32> | ||
| } | ||
| // CHECK-LABEL: func @dynamic_2d_pad_tensor_outer_tiling | ||
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| // ----- | ||
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| func.func @static_pad_tensor_outer_tiling(%input_tensor: tensor<7x9xf32>, | ||
| %pad_value: f32) -> tensor<15x16xf32> { | ||
| %0 = tensor.pad %input_tensor low[3, 4] high[5, 3] { | ||
| ^bb0(%arg1: index, %arg2: index): | ||
| tensor.yield %pad_value : f32 | ||
| } {__internal_linalg_transform__ = "pad_inner_tiling"} : tensor<7x9xf32> to tensor<15x16xf32> | ||
| return %0 : tensor<15x16xf32> | ||
| } | ||
| // CHECK-LABEL: func @static_pad_tensor_outer_tiling |
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