e4384149b5
Update most test passes to use the transform-interpreter pass instead of the test-transform-dialect-interpreter-pass. The new "main" interpreter pass has a named entry point instead of looking up the top-level op with `PossibleTopLevelOpTrait`, which is arguably a more understandable interface. The change is mechanical, rewriting an unnamed sequence into a named one and wrapping the transform IR in to a module when necessary. Add an option to the transform-interpreter pass to target a tagged payload op instead of the root anchor op, which is also useful for repro generation. Only the test in the transform dialect proper and the examples have not been updated yet. These will be updated separately after a more careful consideration of testing coverage of the transform interpreter logic.
95 lines
4.6 KiB
MLIR
95 lines
4.6 KiB
MLIR
// RUN: mlir-opt %s --transform-interpreter --split-input-file | FileCheck %s
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// CHECK-LABEL: func @matmul_tensors
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func.func @matmul_tensors(
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%arg0: tensor<8x16xf32>, %arg1: tensor<16x32xf32>, %arg2: tensor<8x32xf32>)
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-> tensor<8x32xf32> {
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// CHECK-NOT: linalg
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// CHECK: vector.extract {{.*}} : vector<4xf32> from vector<8x4xf32>
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// CHECK: vector.store {{.*}} : memref<8x32xf32>, vector<4xf32>
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%0 = linalg.matmul ins(%arg0, %arg1: tensor<8x16xf32>, tensor<16x32xf32>)
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outs(%arg2: tensor<8x32xf32>)
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-> tensor<8x32xf32>
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return %0 : tensor<8x32xf32>
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}
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module attributes {transform.with_named_sequence} {
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transform.named_sequence @__transform_main(%module_op: !transform.any_op {transform.consumed}) {
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%0 = transform.structured.match ops{["linalg.matmul"]} in %module_op : (!transform.any_op) -> !transform.any_op
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%1, %loops:3 = transform.structured.tile_using_for %0 [8, 4, 2]
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: (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op)
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%2 = transform.get_parent_op %1 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
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transform.structured.vectorize_children_and_apply_patterns %2 : (!transform.any_op) -> !transform.any_op
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%b = transform.bufferization.one_shot_bufferize
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layout{IdentityLayoutMap} %module_op
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{bufferize_function_boundaries = true, allow_return_allocs = true}
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: (!transform.any_op) -> !transform.any_op
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%f = transform.structured.match ops{["func.func"]} in %b
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: (!transform.any_op) -> !transform.any_op
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// TODO: group these lower-level controls into various properly named vector
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// lowering TD macros.
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transform.apply_patterns to %f {
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transform.apply_patterns.vector.lower_contraction lowering_strategy = "outerproduct"
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} : !transform.any_op
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transform.apply_patterns to %f {
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transform.apply_patterns.vector.transfer_permutation_patterns
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} : !transform.any_op
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transform.apply_patterns to %f {
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transform.apply_patterns.vector.lower_multi_reduction lowering_strategy = "innerparallel"
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} : !transform.any_op
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transform.apply_patterns to %f {
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transform.apply_patterns.vector.split_transfer_full_partial split_transfer_strategy = "linalg-copy"
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} : !transform.any_op
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transform.apply_patterns to %f {
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transform.apply_patterns.vector.transfer_to_scf max_transfer_rank = 1 full_unroll = true
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} : !transform.any_op
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transform.apply_patterns to %f {
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transform.apply_patterns.vector.lower_transfer max_transfer_rank = 1
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} : !transform.any_op
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transform.apply_patterns to %f {
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transform.apply_patterns.vector.lower_shape_cast
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} : !transform.any_op
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transform.apply_patterns to %f {
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transform.apply_patterns.vector.lower_transpose lowering_strategy = "shuffle_1d"
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} : !transform.any_op
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transform.yield
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}
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}
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// -----
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// CHECK-DAG: #[[$map0:.*]] = affine_map<(d0, d1, d2) -> (d0, d2)>
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// CHECK-DAG: #[[$map1:.*]] = affine_map<(d0, d1, d2) -> (d2, d1)>
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// CHECK-DAG: #[[$map2:.*]] = affine_map<(d0, d1, d2) -> (d0, d1)>
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// CHECK-LABEL: func.func @fold_arith_extf_into_contract
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// CHECK-SAME: (%[[ARG0:.*]]: vector<64x64xf16>, %[[ARG1:.*]]: vector<64x64xf16>, %[[ARG2:.*]]: vector<64x64xf32>)
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// CHECK-NEXT: %[[R:.+]] = vector.contract {indexing_maps = [#[[$map0]], #[[$map1]], #[[$map2]]],
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// CHECK-SAME: iterator_types = ["parallel", "parallel", "reduction"], kind = #vector.kind<add>}
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// CHECK-SAME: %[[ARG0]], %[[ARG1]], %[[ARG2]] : vector<64x64xf16>, vector<64x64xf16> into vector<64x64xf32>
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// CHECK-NEXT: return %[[R]] : vector<64x64xf32>
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func.func @fold_arith_extf_into_contract(%arg0: vector<64x64xf16>, %arg1: vector<64x64xf16>, %arg2: vector<64x64xf32>) -> vector<64x64xf32> {
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%lhs_f32 = arith.extf %arg0 : vector<64x64xf16> to vector<64x64xf32>
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%rhs_f32 = arith.extf %arg1 : vector<64x64xf16> to vector<64x64xf32>
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%result = vector.contract {indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d2)>, affine_map<(d0, d1, d2) -> (d2, d1)>, affine_map<(d0, d1, d2) -> (d0, d1)>], iterator_types = ["parallel", "parallel", "reduction"], kind = #vector.kind<add>} %lhs_f32, %rhs_f32, %arg2 : vector<64x64xf32>, vector<64x64xf32> into vector<64x64xf32>
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return %result : vector<64x64xf32>
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}
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module attributes {transform.with_named_sequence} {
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transform.named_sequence @__transform_main(%module_op: !transform.any_op {transform.readonly}) {
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%func = transform.structured.match ops{["func.func"]} in %module_op : (!transform.any_op) -> !transform.any_op
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transform.apply_patterns to %func {
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transform.apply_patterns.vector.fold_arith_extension
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} : !transform.any_op
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transform.yield
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}
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}
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