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1// RUN: mlir-opt %s -transform-interpreter -canonicalize --split-input-file | FileCheck %s2 3// Check that we can tile softmax on tensors.4// The tiling here is 2x3.5// So the shape used in the inner loop should be 2x3x256, however since 36// doesn't divide the second dimension (64), we should see a '?' in the shape.7// The actual size, used through extract_slice/insert_slice, should come from a8// `min(64 - current iteration index, 3)`9 10// CHECK: #[[$MIN_MAP:.*]] = affine_map<(d0) -> (-d0 + 64, 3)>11// CHECK-LABEL: func.func @softmax(12// CHECK-SAME: %[[VAL_0:.*]]: tensor<16x64x256xf32>) -> tensor<16x64x256xf32> {13// CHECK-DAG: %[[C3:.*]] = arith.constant 3 : index14// CHECK-DAG: %[[C2:.*]] = arith.constant 2 : index15// CHECK-DAG: %[[C64:.*]] = arith.constant 64 : index16// CHECK-DAG: %[[C16:.*]] = arith.constant 16 : index17// CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index18// CHECK: %[[TENSOR_EMPTY:.*]] = tensor.empty() : tensor<16x64x256xf32>19// CHECK: %[[VAL_7:.*]] = scf.for %[[VAL_8:.*]] = %[[C0]] to %[[C16]] step %[[C2]] iter_args(%[[VAL_9:.*]] = %[[TENSOR_EMPTY]]) -> (tensor<16x64x256xf32>) {20// CHECK: %[[VAL_10:.*]] = scf.for %[[VAL_11:.*]] = %[[C0]] to %[[C64]] step %[[C3]] iter_args(%[[VAL_12:.*]] = %[[VAL_9]]) -> (tensor<16x64x256xf32>) {21// CHECK: %[[VAL_13:.*]] = affine.min #[[$MIN_MAP]](%[[VAL_11]])22// CHECK: %[[VAL_14:.*]] = tensor.extract_slice %[[VAL_0]]{{\[}}%[[VAL_8]], %[[VAL_11]], 0] [2, %[[VAL_13]], 256] [1, 1, 1] : tensor<16x64x256xf32> to tensor<2x?x256xf32>23// CHECK: %[[VAL_15:.*]] = tensor.extract_slice %[[VAL_12]]{{\[}}%[[VAL_8]], %[[VAL_11]], 0] [2, %[[VAL_13]], 256] [1, 1, 1] : tensor<16x64x256xf32> to tensor<2x?x256xf32>24// CHECK: %[[VAL_16:.*]] = linalg.softmax dimension(1) ins(%[[VAL_14]] : tensor<2x?x256xf32>) outs(%[[VAL_15]] : tensor<2x?x256xf32>) -> tensor<2x?x256xf32>25// CHECK: %[[VAL_17:.*]] = tensor.insert_slice %[[VAL_16]] into %[[VAL_12]]{{\[}}%[[VAL_8]], %[[VAL_11]], 0] [2, %[[VAL_13]], 256] [1, 1, 1] : tensor<2x?x256xf32> into tensor<16x64x256xf32>26// CHECK: scf.yield %[[VAL_17]] : tensor<16x64x256xf32>27// CHECK: }28// CHECK: scf.yield %[[VAL_18:.*]] : tensor<16x64x256xf32>29// CHECK: }30// CHECK: return %[[VAL_19:.*]] : tensor<16x64x256xf32>31// CHECK: }32func.func @softmax(%arg0: tensor<16x64x256xf32>) -> tensor<16x64x256xf32> {33 %0 = tensor.empty() : tensor<16x64x256xf32>34 %1 = linalg.softmax35 dimension(1) ins(%arg0 : tensor<16x64x256xf32>) outs(%0 : tensor<16x64x256xf32>) -> tensor<16x64x256xf32>36 return %1 : tensor<16x64x256xf32>37}38 39module attributes {transform.with_named_sequence} {40 transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {41 %0 = transform.structured.match ops{["linalg.softmax"]} in %arg1 : (!transform.any_op) -> !transform.any_op42 %1, %loop:2 = transform.structured.tile_using_for %0 tile_sizes [2, 3] : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op)43 transform.yield44 }45}46 47// -----48 49// Test the softmax tiling interface with the tile_using_forall transform and50// check that it composes properly with the fuse transform.51// This should sink the linalg.generic inside the scf.forall and run that52// generic on 2x4x256 tensors (2==16/8, 4==64/16).53 54// CHECK: #[[$TIMES2_MAP:.*]] = affine_map<(d0) -> (d0 * 2)>55// CHECK: #[[$TIMES4_MAP:.*]] = affine_map<(d0) -> (d0 * 4)>56// CHECK-LABEL: func.func @softmax_tile_n_fuse(57// CHECK-SAME: %[[VAL_0:.*]]: tensor<16x64x256xf32>) -> tensor<16x64x256xf32> {58// CHECK: %[[VAL_1:.*]] = arith.constant 1.000000e+00 : f3259// CHECK: %[[VAL_2:.*]] = tensor.empty() : tensor<16x64x256xf32>60// CHECK: %[[VAL_3:.*]] = tensor.empty() : tensor<16x64x256xf32>61// CHECK: %[[VAL_4:.*]] = scf.forall (%[[VAL_5:.*]], %[[VAL_6:.*]]) in (8, 16) shared_outs(%[[VAL_7:.*]] = %[[VAL_3]]) -> (tensor<16x64x256xf32>) {62// CHECK: %[[VAL_8:.*]] = affine.apply #[[$TIMES2_MAP]](%[[VAL_5]])63// CHECK: %[[VAL_9:.*]] = affine.apply #[[$TIMES4_MAP]](%[[VAL_6]])64// CHECK: %[[VAL_10:.*]] = affine.apply #[[$TIMES2_MAP]](%[[VAL_5]])65// CHECK: %[[VAL_11:.*]] = affine.apply #[[$TIMES4_MAP]](%[[VAL_6]])66// CHECK: %[[VAL_12:.*]] = affine.apply #[[$TIMES2_MAP]](%[[VAL_5]])67// CHECK: %[[VAL_13:.*]] = affine.apply #[[$TIMES4_MAP]](%[[VAL_6]])68// CHECK: %[[VAL_14:.*]] = tensor.extract_slice %[[VAL_0]]{{\[}}%[[VAL_10]], %[[VAL_11]], 0] [2, 4, 256] [1, 1, 1] : tensor<16x64x256xf32> to tensor<2x4x256xf32>69// CHECK: %[[VAL_15:.*]] = tensor.extract_slice %[[VAL_2]]{{\[}}%[[VAL_12]], %[[VAL_13]], 0] [2, 4, 256] [1, 1, 1] : tensor<16x64x256xf32> to tensor<2x4x256xf32>70// CHECK: %[[VAL_16:.*]] = linalg.generic {indexing_maps = [#{{.*}}, #{{.*}}], iterator_types = ["parallel", "parallel", "parallel"]} ins(%[[VAL_14]] : tensor<2x4x256xf32>) outs(%[[VAL_15]] : tensor<2x4x256xf32>) {71// CHECK: ^bb0(%[[VAL_17:.*]]: f32, %[[VAL_18:.*]]: f32):72// CHECK: %[[VAL_19:.*]] = arith.addf %[[VAL_18]], %[[VAL_1]] : f3273// CHECK: linalg.yield %[[VAL_19]] : f3274// CHECK: } -> tensor<2x4x256xf32>75// CHECK: %[[VAL_20:.*]] = tensor.extract_slice %[[VAL_7]]{{\[}}%[[VAL_8]], %[[VAL_9]], 0] [2, 4, 256] [1, 1, 1] : tensor<16x64x256xf32> to tensor<2x4x256xf32>76// CHECK: %[[VAL_21:.*]] = linalg.softmax dimension(1) ins(%[[VAL_22:.*]] : tensor<2x4x256xf32>) outs(%[[VAL_20]] : tensor<2x4x256xf32>) -> tensor<2x4x256xf32>77// CHECK: scf.forall.in_parallel {78// CHECK: tensor.parallel_insert_slice %[[VAL_21]] into %[[VAL_7]]{{\[}}%[[VAL_8]], %[[VAL_9]], 0] [2, 4, 256] [1, 1, 1] : tensor<2x4x256xf32> into tensor<16x64x256xf32>79// CHECK: }80// CHECK: }81// CHECK: return %[[VAL_23:.*]] : tensor<16x64x256xf32>82// CHECK: }83 84func.func @softmax_tile_n_fuse(%arg0: tensor<16x64x256xf32>) -> tensor<16x64x256xf32> {85 %empty = tensor.empty() : tensor<16x64x256xf32>86 %cst = arith.constant 1.000000e+00 : f3287 %eltwise = linalg.generic88 {indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d1, d2)>,89 affine_map<(d0, d1, d2) -> (d0, d1, d2)>],90 iterator_types = ["parallel", "parallel", "parallel"]91 }92 ins(%arg0 : tensor<16x64x256xf32>)93 outs(%empty : tensor<16x64x256xf32>) {94 ^bb0(%arg2: f32, %arg3: f32):95 %arg3Plus1 = arith.addf %arg3, %cst : f3296 linalg.yield %arg3Plus1 : f3297 } -> tensor<16x64x256xf32>98 99 %0 = tensor.empty() : tensor<16x64x256xf32>100 %1 = linalg.softmax101 dimension(1) ins(%eltwise : tensor<16x64x256xf32>) outs(%0 : tensor<16x64x256xf32>) -> tensor<16x64x256xf32>102 return %1 : tensor<16x64x256xf32>103}104 105module attributes {transform.with_named_sequence} {106 transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {107 %0 = transform.structured.match ops{["linalg.softmax"]} in %arg1 : (!transform.any_op) -> !transform.any_op108 109 // Tile the root.110 %tiled_op, %forall_op = transform.structured.tile_using_forall %0 num_threads [8, 16]111 : (!transform.any_op) -> (!transform.any_op, !transform.any_op)112 113 // Fuse all producers.114 %1 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op115 transform.structured.fuse_into_containing_op %1 into %forall_op116 : (!transform.any_op, !transform.any_op) -> (!transform.any_op, !transform.any_op)117 transform.yield118 }119}120// -----121 122// Same as the previous test but on memrefs.123 124// CHECK: #[[$MIN_MAP:.*]] = affine_map<(d0) -> (-d0 + 64, 3)>125// CHECK-LABEL: func.func @softmax_memref(126// CHECK-SAME: %[[VAL_0:.*]]: memref<16x64x256xf32>,127// CHECK-SAME: %[[VAL_1:.*]]: memref<16x64x256xf32>) {128// CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index129// CHECK-DAG: %[[C16:.*]] = arith.constant 16 : index130// CHECK-DAG: %[[C64:.*]] = arith.constant 64 : index131// CHECK-DAG: %[[C2:.*]] = arith.constant 2 : index132// CHECK-DAG: %[[C3:.*]] = arith.constant 3 : index133// CHECK: scf.for %[[VAL_7:.*]] = %[[C0]] to %[[C16]] step %[[C2]] {134// CHECK: scf.for %[[VAL_8:.*]] = %[[C0]] to %[[C64]] step %[[C3]] {135// CHECK: %[[VAL_9:.*]] = affine.min #[[$MIN_MAP]](%[[VAL_8]])136// CHECK: %[[VAL_10:.*]] = memref.subview %[[VAL_0]]{{\[}}%[[VAL_7]], %[[VAL_8]], 0] [2, %[[VAL_9]], 256] [1, 1, 1] : memref<16x64x256xf32> to memref<2x?x256xf32, strided<[16384, 256, 1], offset: ?>>137// CHECK: %[[VAL_11:.*]] = memref.subview %[[VAL_1]]{{\[}}%[[VAL_7]], %[[VAL_8]], 0] [2, %[[VAL_9]], 256] [1, 1, 1] : memref<16x64x256xf32> to memref<2x?x256xf32, strided<[16384, 256, 1], offset: ?>>138// CHECK: linalg.softmax dimension(1) ins(%[[VAL_10]] : memref<2x?x256xf32, strided<[16384, 256, 1], offset: ?>>) outs(%[[VAL_11]] : memref<2x?x256xf32, strided<[16384, 256, 1], offset: ?>>)139// CHECK: }140// CHECK: }141// CHECK: return142// CHECK: }143func.func @softmax_memref(%arg0: memref<16x64x256xf32>, %arg1: memref<16x64x256xf32>) {144 linalg.softmax145 dimension(1) ins(%arg0 : memref<16x64x256xf32>) outs(%arg1 : memref<16x64x256xf32>)146 return147}148 149module attributes {transform.with_named_sequence} {150 transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {151 %0 = transform.structured.match ops{["linalg.softmax"]} in %arg1 : (!transform.any_op) -> !transform.any_op152 %1, %loop:2 = transform.structured.tile_using_for %0 tile_sizes [2, 3] : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op)153 transform.yield154 }155}156