| // RUN: mlir-opt --transform-interpreter --mlir-print-local-scope --split-input-file --verify-diagnostics %s | FileCheck %s |
| |
| module attributes {transform.with_named_sequence} { |
| transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) { |
| %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op |
| %1, %loops:3 = transform.structured.tile_using_for %0 [4, 4, 4] : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op) |
| transform.yield |
| } |
| } |
| |
| // CHECK-LABEL: func @tile_linalg_matmul( |
| // CHECK-SAME: %[[TA:[0-9a-z]+]]: tensor<128x128xf32> |
| // CHECK-SAME: %[[TB:[0-9a-z]+]]: tensor<128x128xf32> |
| // CHECK-SAME: %[[TC:[0-9a-z]+]]: tensor<128x128xf32> |
| // CHECK-SAME: -> tensor<128x128xf32> { |
| func.func @tile_linalg_matmul( |
| %arg0: tensor<128x128xf32>, %arg1: tensor<128x128xf32>, %arg2: tensor<128x128xf32>) |
| -> tensor<128x128xf32> { |
| // CHECK: %[[TD0:.*]] = scf.for {{.*}} to {{.*}} step {{.*}} iter_args(%[[TC0:.*]] = %[[TC]]) -> (tensor<128x128xf32>) { |
| // CHECK: %[[TD1:.*]] = scf.for {{.*}} to {{.*}} step {{.*}} iter_args(%[[TC1:.*]] = %[[TC0]]) -> (tensor<128x128xf32>) { |
| // CHECK: %[[TD2:.*]] = scf.for {{.*}} to {{.*}} step {{.*}} iter_args(%[[TC2:.*]] = %[[TC1]]) -> (tensor<128x128xf32>) { |
| // CHECK: %[[sTA:.*]] = tensor.extract_slice %[[TA]][{{.*}}] : tensor<128x128xf32> to tensor<4x4xf32> |
| // CHECK: %[[sTB:.*]] = tensor.extract_slice %[[TB]][{{.*}}] : tensor<128x128xf32> to tensor<4x4xf32> |
| // CHECK: %[[sTC:.*]] = tensor.extract_slice %[[TC2]][{{.*}}] : tensor<128x128xf32> to tensor<4x4xf32> |
| // CHECK: %[[sTD:.*]] = linalg.matmul ins(%[[sTA]], %[[sTB]] : tensor<4x4xf32>, tensor<4x4xf32>) |
| // CHECK-SAME: outs(%[[sTC]] : tensor<4x4xf32>) -> tensor<4x4xf32> |
| // CHECK: %[[TD:.*]] = tensor.insert_slice %[[sTD]] into %[[TC2]][{{.*}}] : tensor<4x4xf32> into tensor<128x128xf32> |
| // CHECK: scf.yield %[[TD]] : tensor<128x128xf32> |
| // CHECK: scf.yield %[[TD2]] : tensor<128x128xf32> |
| // CHECK: scf.yield %[[TD1]] : tensor<128x128xf32> |
| %0 = linalg.matmul ins(%arg0, %arg1: tensor<128x128xf32>, tensor<128x128xf32>) |
| outs(%arg2: tensor<128x128xf32>) |
| -> tensor<128x128xf32> |
| |
| // CHECK: return %[[TD0]] : tensor<128x128xf32> |
| return %0 : tensor<128x128xf32> |
| } |
| |
| // ----- |
| |
| module attributes {transform.with_named_sequence} { |
| transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) { |
| %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op |
| %1 = transform.structured.match ops{["func.call"]} in %arg1 : (!transform.any_op) -> !transform.any_op |
| %2, %loops:3 = transform.structured.tile_using_for %0 [%1, %1, 4] : (!transform.any_op, !transform.any_op, !transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op) |
| transform.yield |
| } |
| } |
| |
| func.func private @get_dynamic_tile_size() -> index |
| |
| // CHECK-LABEL: func @tile_linalg_matmul_dynamic( |
| // CHECK-SAME: %[[TA:[0-9a-z]+]]: tensor<128x128xf32> |
| // CHECK-SAME: %[[TB:[0-9a-z]+]]: tensor<128x128xf32> |
| // CHECK-SAME: %[[TC:[0-9a-z]+]]: tensor<128x128xf32> |
| // CHECK-SAME: -> tensor<128x128xf32> { |
| func.func @tile_linalg_matmul_dynamic( |
| %arg0: tensor<128x128xf32>, %arg1: tensor<128x128xf32>, %arg2: tensor<128x128xf32>) |
| -> tensor<128x128xf32> { |
| // CHECK: %[[TD0:.*]] = scf.for {{.*}} to {{.*}} step {{.*}} iter_args(%[[TC0:.*]] = %[[TC]]) -> (tensor<128x128xf32>) { |
| // CHECK: %[[TD1:.*]] = scf.for {{.*}} to {{.*}} step {{.*}} iter_args(%[[TC1:.*]] = %[[TC0]]) -> (tensor<128x128xf32>) { |
| // CHECK: %[[TD2:.*]] = scf.for {{.*}} to {{.*}} step {{.*}} iter_args(%[[TC2:.*]] = %[[TC1]]) -> (tensor<128x128xf32>) { |
| // CHECK: %[[sTA:.*]] = tensor.extract_slice %[[TA]][{{.*}}] : tensor<128x128xf32> to tensor<?x4xf32> |
| // CHECK: %[[sTB:.*]] = tensor.extract_slice %[[TB]][{{.*}}] : tensor<128x128xf32> to tensor<4x?xf32> |
| // CHECK: %[[sTC:.*]] = tensor.extract_slice %[[TC2]][{{.*}}] : tensor<128x128xf32> to tensor<?x?xf32> |
| // CHECK: %[[sTD:.*]] = linalg.matmul ins(%[[sTA]], %[[sTB]] : tensor<?x4xf32>, tensor<4x?xf32>) |
| // CHECK-SAME: outs(%[[sTC]] : tensor<?x?xf32>) -> tensor<?x?xf32> |
| // CHECK: %[[TD:.*]] = tensor.insert_slice %[[sTD]] into %[[TC2]][{{.*}}] : tensor<?x?xf32> into tensor<128x128xf32> |
| // CHECK: scf.yield %[[TD]] : tensor<128x128xf32> |
| // CHECK: scf.yield %[[TD2]] : tensor<128x128xf32> |
| // CHECK: scf.yield %[[TD1]] : tensor<128x128xf32> |
| %sz = func.call @get_dynamic_tile_size() : () -> index |
| %0 = linalg.matmul ins(%arg0, %arg1: tensor<128x128xf32>, tensor<128x128xf32>) |
| outs(%arg2: tensor<128x128xf32>) |
| -> tensor<128x128xf32> |
| |
| // CHECK: return %[[TD0]] : tensor<128x128xf32> |
| return %0 : tensor<128x128xf32> |
| } |
| |
| // ----- |
| |
| module attributes {transform.with_named_sequence} { |
| transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) { |
| %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op |
| // expected-note @below {{for this parameter}} |
| %1 = transform.test_produce_param (0 : i64) : !transform.param<i64> |
| // expected-error @below {{expected as many parameter values (0) as target ops (2)}} |
| transform.structured.tile_using_for %0 [%1, %1, %1] |
| : (!transform.any_op, !transform.param<i64>, !transform.param<i64>, !transform.param<i64>) |
| -> (!transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op) |
| transform.yield |
| } |
| } |
| |
| func.func @tile_linalg_matmul( |
| %arg0: tensor<128x128xf32>, %arg1: tensor<128x128xf32>, %arg2: tensor<128x128xf32>) |
| -> (tensor<128x128xf32>, tensor<128x128xf32>) { |
| %0 = linalg.matmul ins(%arg0, %arg1: tensor<128x128xf32>, tensor<128x128xf32>) |
| outs(%arg2: tensor<128x128xf32>) |
| -> tensor<128x128xf32> |
| %1 = linalg.matmul ins(%0, %arg1: tensor<128x128xf32>, tensor<128x128xf32>) |
| outs(%arg2: tensor<128x128xf32>) |
| -> tensor<128x128xf32> |
| return %0, %1 : tensor<128x128xf32>, tensor<128x128xf32> |
| } |
| |
| // ----- |
| |
| module attributes {transform.with_named_sequence} { |
| transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) { |
| %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op |
| // expected-note @below {{for this handle}} |
| %1 = transform.structured.match ops{["arith.constant"]} in %arg1 : (!transform.any_op) -> !transform.any_op |
| // expected-error @below {{expected as many dynamic size-producing operations (0) as target ops (2)}} |
| transform.structured.tile_using_for %0 [%1, %1, 1] |
| : (!transform.any_op, !transform.any_op, !transform.any_op) |
| -> (!transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op) |
| transform.yield |
| } |
| } |
| |
| func.func @tile_linalg_matmul( |
| %arg0: tensor<128x128xf32>, %arg1: tensor<128x128xf32>, %arg2: tensor<128x128xf32>) |
| -> (tensor<128x128xf32>, tensor<128x128xf32>) { |
| %0 = linalg.matmul ins(%arg0, %arg1: tensor<128x128xf32>, tensor<128x128xf32>) |
| outs(%arg2: tensor<128x128xf32>) |
| -> tensor<128x128xf32> |
| %1 = linalg.matmul ins(%0, %arg1: tensor<128x128xf32>, tensor<128x128xf32>) |
| outs(%arg2: tensor<128x128xf32>) |
| -> tensor<128x128xf32> |
| return %0, %1 : tensor<128x128xf32>, tensor<128x128xf32> |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: tile_tensor_pad |
| func.func @tile_tensor_pad( |
| %arg0 : tensor<?x?xf32>, %cst : f32, %low: index, %high: index) |
| -> tensor<20x40xf32> |
| { |
| // CHECK: scf.forall |
| // CHECK: scf.if |
| // CHECK: tensor.generate |
| // CHECK: else |
| // CHECK: tensor.pad {{.*}} nofold |
| %0 = tensor.pad %arg0 nofold low[%low, %low] high[%high, %high] { |
| ^bb0(%arg9: index, %arg10: index): |
| tensor.yield %cst : f32 |
| } : tensor<?x?xf32> to tensor<20x40xf32> |
| return %0 : tensor<20x40xf32> |
| } |
| |
| module attributes {transform.with_named_sequence} { |
| transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) { |
| %0 = transform.structured.match ops{["tensor.pad"]} in %arg1 : (!transform.any_op) -> !transform.any_op |
| transform.structured.tile_using_forall %0 tile_sizes[1, 1] |
| : (!transform.any_op) -> (!transform.any_op, !transform.any_op) |
| transform.yield |
| } |
| } |
| |
| // ----- |
| |
| #map = affine_map<(d0) -> (d0)> |
| |
| module { |
| func.func @scalable_tile(%arg0: tensor<?xf32>, %arg1: tensor<?xf32>, %arg2: tensor<?xf32>, %arg3: f32) -> tensor<?xf32> { |
| %0 = linalg.generic {indexing_maps = [#map, #map, #map], iterator_types = ["parallel"]} ins(%arg0, %arg1 : tensor<?xf32>, tensor<?xf32>) outs(%arg2 : tensor<?xf32>) { |
| ^bb0(%in_1: f32, %in_2: f32, %out: f32): |
| %1 = arith.addf %in_1, %in_2 : f32 |
| %2 = arith.mulf %arg3, %1 : f32 |
| linalg.yield %2 : f32 |
| } -> tensor<?xf32> |
| return %0 : tensor<?xf32> |
| } |
| } |
| |
| // CHECK-LABEL: func.func @scalable_tile( |
| // CHECK-SAME: %[[ARG_0:.*]]: tensor<?xf32>, %[[ARG_1:.*]]: tensor<?xf32>, %[[ARG_2:.*]]: tensor<?xf32>, |
| // CHECK: %[[C4:.*]] = arith.constant 0 : index |
| // CHECK: %[[DIM:.*]] = tensor.dim %[[ARG_0]], %[[C4]] : tensor<?xf32> |
| // CHECK: %[[VEC_SIZE:.*]] = arith.constant 4 : index |
| // CHECK: %[[VS:.*]] = vector.vscale |
| // CHECK: %[[STEP:.*]] = arith.muli %[[VEC_SIZE]], %[[VS]] : index |
| // CHECK: %[[C0:.*]] = arith.constant 0 : index |
| // CHECK: scf.for %[[IV:.*]] = %[[C0]] to %[[DIM]] step %[[STEP]] iter_args(%[[VAL:.*]] = %[[ARG_2]]) -> (tensor<?xf32>) { |
| // CHECK: %[[SIZE:.*]] = affine.min affine_map<(d0)[s0, s1] -> (s0, -d0 + s1)>(%[[IV]])[%[[STEP]], %[[DIM]]] |
| // CHECK: %[[SLICE_ARG0:.*]] = tensor.extract_slice %[[ARG_0]][%[[IV]]] [%[[SIZE]]] [1] : tensor<?xf32> to tensor<?xf32> |
| // CHECK: %[[SLICE_ARG1:.*]] = tensor.extract_slice %[[ARG_1]][%[[IV]]] [%[[SIZE]]] [1] : tensor<?xf32> to tensor<?xf32> |
| // CHECK: %[[SLICE_ARG2:.*]] = tensor.extract_slice %[[VAL]][%[[IV]]] [%[[SIZE]]] [1] : tensor<?xf32> to tensor<?xf32> |
| // CHECK: linalg.generic {indexing_maps = {{.*}}, iterator_types = ["parallel"]} ins(%[[SLICE_ARG0]], %[[SLICE_ARG1]] : tensor<?xf32>, tensor<?xf32>) outs(%[[SLICE_ARG2]] : tensor<?xf32>) { |
| |
| module attributes {transform.with_named_sequence} { |
| transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) { |
| %0 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op |
| %1, %loop = transform.structured.tile_using_for %0 [[4]] : (!transform.any_op) -> (!transform.any_op, !transform.any_op) |
| transform.yield |
| } |
| } |
| |
| // ----- |
| |
| // CHECK-LABEL: func.func @scalable_and_fixed_length_tile |
| // CHECK: %[[C4:.*]] = arith.constant 4 : index |
| // CHECK: %[[VS:.*]] = vector.vscale |
| // CHECK: %[[STEP_2:.*]] = arith.muli %[[C4]], %[[VS]] : index |
| // CHECK: %[[C0:.*]] = arith.constant 0 : index |
| // CHECK: %[[C128:.*]] = arith.constant 128 : index |
| // CHECK: %[[STEP_0:.*]] = arith.constant 4 : index |
| // CHECK: scf.for %[[VAL_11:.*]] = %[[C0]] to %[[C128]] step %[[STEP_0]] |
| // CHECK: %[[C0_1:.*]] = arith.constant 0 : index |
| // CHECK: %[[C128_1:.*]] = arith.constant 128 : index |
| // CHECK: %[[STEP_1:.*]] = arith.constant 4 : index |
| // CHECK: scf.for %[[VAL_16:.*]] = %[[C0_1]] to %[[C128_1]] step %[[STEP_1]] |
| // CHECK: %[[C0_2:.*]] = arith.constant 0 : index |
| // CHECK: %[[C128_2:.*]] = arith.constant 128 : index |
| // CHECK: scf.for %{{.*}} = %[[C0_2]] to %[[C128_2]] step %[[STEP_2]] |
| |
| func.func @scalable_and_fixed_length_tile( |
| %arg0: tensor<128x128xf32>, %arg1: tensor<128x128xf32>, %arg2: tensor<128x128xf32>) |
| -> tensor<128x128xf32> { |
| %0 = linalg.matmul ins(%arg0, %arg1: tensor<128x128xf32>, tensor<128x128xf32>) |
| outs(%arg2: tensor<128x128xf32>) |
| -> tensor<128x128xf32> |
| |
| return %0 : tensor<128x128xf32> |
| } |
| |
| module attributes {transform.with_named_sequence} { |
| transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) { |
| %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op |
| %1, %loops:3 = transform.structured.tile_using_for %0 [4, 4, [4]] : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op) |
| transform.yield |
| } |
| } |
| |
| // ----- |
| |
| func.func @too_many_tiles(%arg0: tensor<128x128xf32>, %arg1: tensor<128x128xf32>, |
| %arg2: tensor<128x128xf32>) -> tensor<128x128xf32> { |
| // expected-note @below {{target op}} |
| %0 = linalg.matmul ins(%arg0, %arg1: tensor<128x128xf32>, tensor<128x128xf32>) |
| outs(%arg2: tensor<128x128xf32>) -> tensor<128x128xf32> |
| return %0 : tensor<128x128xf32> |
| } |
| |
| module attributes {transform.with_named_sequence} { |
| transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) { |
| %0 = transform.structured.match ops{["linalg.matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op |
| // expected-error @below {{too many tiles provided, expected at most 3 found 4}} |
| %1, %loops = transform.structured.tile_using_for %0 [1, 0, 0, 0] : (!transform.any_op) -> (!transform.any_op, !transform.any_op) |
| transform.yield |
| } |
| } |
