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TheRock/compiler/amd-llvm/mlir/test/Dialect/Linalg/transform-tile-reduction.mlir
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Flatten subprojects into a single base monorepo
2026-09-02 16:34:47 -07:00

1034 lines
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// RUN: mlir-opt %s -transform-interpreter -split-input-file -canonicalize -cse -verify-diagnostics | FileCheck %s
// REQUIRES: strange-fix-for-ubuntu
func.func @reduction_tile(%arg0: tensor<?x?xf32>, %out: tensor<?xf32>) -> tensor<?xf32> {
%red = linalg.generic {indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>,
affine_map<(d0, d1) -> (d0)>],
iterator_types = ["parallel", "reduction"]}
ins(%arg0 : tensor<?x?xf32>)
outs(%out : tensor<?xf32>) {
^bb0(%arg7: f32, %arg9: f32):
%1 = arith.mulf %arg7, %arg7 : f32
%2 = arith.addf %1, %arg9 : f32
linalg.yield %2 : f32
} -> tensor<?xf32>
return %red : 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, %2, %3, %loop = transform.structured.tile_reduction_using_for %0
by tile_sizes = [0, 5] : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op)
transform.yield
}
}
// CHECK-DAG: #[[MAP0:.*]] = affine_map<(d0)[s0] -> (-d0 + s0, 5)>
// CHECK-DAG: #[[MAP1:.*]] = affine_map<(d0, d1) -> (d0, d1)>
// CHECK: func @reduction_tile(%[[ARG0:.+]]: tensor<?x?xf32>, %[[ARG1:.+]]: tensor<?xf32>
// CHECK-DAG: %[[I:.*]] = arith.constant 0.000000e+00 : f32
// CHECK-DAG: %[[C5:.*]] = arith.constant 5 : index
// CHECK-DAG: %[[C1:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[D0:.*]] = tensor.dim %[[ARG0]], %[[C0]] : tensor<?x?xf32>
// CHECK-DAG: %[[D1:.*]] = tensor.dim %[[ARG0]], %[[C1]] : tensor<?x?xf32>
// CHECK: %[[E:.*]] = tensor.empty(%[[D0]]) : tensor<?x5xf32>
// CHECK: %[[F:.*]] = linalg.fill ins(%[[I]] : f32) outs(%[[E]] : tensor<?x5xf32>) -> tensor<?x5xf32>
// CHECK: %[[L:.*]] = scf.for %[[K:.*]] = %[[C0]] to %[[D1]] step %[[C5]] iter_args(%[[ARG3:.*]] = %[[F]]) -> (tensor<?x5xf32>) {
// CHECK: %[[PS:.*]] = affine.min #[[MAP0]](%[[K]])[%[[D1]]]
// CHECK: %[[EXT2:.*]] = tensor.extract_slice %[[ARG0]][0, %[[K:.*]]] [%[[D0]], %[[PS]]] [1, 1] : tensor<?x?xf32> to tensor<?x?xf32>
// CHECK: %[[EXT:.*]] = tensor.extract_slice %[[ARG3]][0, 0] [%[[D0]], %[[PS]]] [1, 1] : tensor<?x5xf32> to tensor<?x?xf32>
// CHECK: %[[PR:.*]] = linalg.generic {indexing_maps = [#[[MAP1]], #[[MAP1]]], iterator_types = ["parallel", "parallel"]} ins(%[[EXT2]] : tensor<?x?xf32>) outs(%[[EXT]] : tensor<?x?xf32>) {
// CHECK: arith.mulf
// CHECK: arith.addf
// CHECK: linalg.yield
// CHECK: } -> tensor<?x?xf32>
// CHECK: %[[INS:.*]] = tensor.insert_slice %[[PR]] into %[[ARG3]][0, 0] [%[[D0]], %[[PS]]] [1, 1] : tensor<?x?xf32> into tensor<?x5xf32>
// CHECK: scf.yield %[[INS]] : tensor<?x5xf32>
// CHECK: }
// CHECK: %[[R:.*]] = linalg.reduce ins(%[[L]] : tensor<?x5xf32>) outs(%[[ARG1]] : tensor<?xf32>) dimensions = [1]
// CHECK: arith.addf
// CHECK: linalg.yield
// CHECK: }
// CHECK: return %[[R]] : tensor<?xf32>
// -----
func.func @reduction_tile_transpose(%arg0: tensor<?x?xf32>, %out: tensor<?xf32>) -> tensor<?xf32> {
%red = linalg.generic {indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>,
affine_map<(d0, d1) -> (d1)>],
iterator_types = ["reduction", "parallel"]}
ins(%arg0 : tensor<?x?xf32>)
outs(%out : tensor<?xf32>) {
^bb0(%arg7: f32, %arg9: f32):
%42 = arith.addf %arg7, %arg9 : f32
linalg.yield %42 : f32
} -> tensor<?xf32>
return %red : 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, %2, %3, %loop = transform.structured.tile_reduction_using_for %0
by tile_sizes = [5, 0] : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op)
transform.yield
}
}
// CHECK-DAG: #[[MAP0:.*]] = affine_map<(d0)[s0] -> (-d0 + s0, 5)>
// CHECK-DAG: #[[MAP1:.*]] = affine_map<(d0, d1) -> (d0, d1)>
// CHECK-DAG: #[[MAP2:.*]] = affine_map<(d0, d1) -> (d1, d0)>
// CHECK: func @reduction_tile_transpose
// CHECK: tensor.empty(%{{.*}}) : tensor<?x5xf32>
// CHECK: linalg.fill {{.*}} : tensor<?x5xf32>) -> tensor<?x5xf32>
// CHECK: scf.for
// CHECK: %[[EXT:.*]] = tensor.extract_slice %[[ARG3:.*]][0, 0] [%[[D0:.*]], %[[D1:.*]]] [1, 1] : tensor<?x5xf32> to tensor<?x?xf32>
// CHECK: %[[R:.*]] = linalg.generic {indexing_maps = [#[[MAP1]], #[[MAP2]]], iterator_types = ["parallel", "parallel"]} ins(%[[L:.*]] : tensor<?x?xf32>) outs(%[[EXT]] : tensor<?x?xf32>)
// CHECK: %[[INS:.*]] = tensor.insert_slice %[[R]] into %[[ARG3]][0, 0] [%[[D0]], %[[D1]]] [1, 1] : tensor<?x?xf32> into tensor<?x5xf32>
// CHECK: scf.yield {{.*}} : tensor<?x5xf32>
// CHECK: }
// CHECK: linalg.reduce
// CHECK: return
// -----
func.func @reduction_tile_parallel(
%arg0: tensor<?x?xf32>, %out: tensor<?xf32>) -> tensor<?xf32> {
%red = linalg.generic {indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>,
affine_map<(d0, d1) -> (d0)>],
iterator_types = ["parallel", "reduction"]}
ins(%arg0 : tensor<?x?xf32>)
outs(%out : tensor<?xf32>) {
^bb0(%arg7: f32, %arg9: f32):
%1 = arith.mulf %arg7, %arg7 : f32
%2 = arith.addf %1, %arg9 : f32
linalg.yield %2 : f32
} -> tensor<?xf32>
return %red : 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, %2, %3, %loop = transform.structured.tile_reduction_using_forall %0
by num_threads = [0, 5] tile_sizes = [] : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op)
transform.yield
}
}
// CHECK-DAG: #[[MAP0:.*]] = affine_map<(d0)[s0] -> (-(d0 * (s0 ceildiv 5)) + s0, s0 ceildiv 5)>
// CHECK-DAG: #[[MAP1:.*]] = affine_map<(d0) -> (0, d0)>
// CHECK-DAG: #[[MAP2:.*]] = affine_map<(d0)[s0] -> (d0 * (s0 ceildiv 5))>
// CHECK-DAG: #[[MAP3:.*]] = affine_map<(d0, d1) -> (d0, d1)>
// CHECK-DAG: #[[MAP4:.*]] = affine_map<(d0, d1) -> (d0)>
// CHECK: func @reduction_tile_parallel(%[[ARG0:.+]]: tensor<?x?xf32>, %[[ARG1:.+]]: tensor<?xf32>
// CHECK-DAG: %[[I:.*]] = arith.constant 0.000000e+00 : f32
// CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[C1:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[D0:.*]] = tensor.dim %[[ARG0]], %[[C0]] : tensor<?x?xf32>
// CHECK-DAG: %[[D1:.*]] = tensor.dim %[[ARG0]], %[[C1]] : tensor<?x?xf32>
// CHECK: %[[E:.*]] = tensor.empty(%[[D0]]) : tensor<?x5xf32>
// CHECK: %[[F:.*]] = linalg.fill ins(%[[I]] : f32) outs(%[[E]] : tensor<?x5xf32>) -> tensor<?x5xf32>
// CHECK: %[[L:.*]] = scf.forall (%[[IV:.+]]) in (5) shared_outs(%[[ARG3:.+]] = %[[F]]) -> (tensor<?x5xf32>) {
// CHECK-DAG: %[[TS0:.+]] = affine.min #[[MAP0]](%[[IV]])[%[[D1]]]
// CHECK-DAG: %[[TS1:.+]] = affine.max #[[MAP1]](%[[TS0]])
// CHECK-DAG: %[[ET:.+]] = tensor.extract_slice %[[ARG3:.+]][0, %[[IV]]] [%[[D0]], 1] [1, 1] : tensor<?x5xf32> to tensor<?xf32>
// CHECK-DAG: %[[TINDEX:.+]] = affine.apply #[[MAP2]](%[[IV]])[%[[D1]]]
// CHECK-DAG: %[[INCHUNK:.+]] = tensor.extract_slice %[[ARG0]][0, %[[TINDEX]]] [%[[D0]], %[[TS1]]] [1, 1] : tensor<?x?xf32> to tensor<?x?xf32>
// CHECK: %[[PARTIAL:.+]] = linalg.generic {indexing_maps = [#[[MAP3]], #[[MAP4]]], iterator_types = ["parallel", "reduction"]} ins(%[[INCHUNK]] : tensor<?x?xf32>) outs(%[[ET]] : tensor<?xf32>) {
// CHECK: arith.mulf
// CHECK: arith.addf
// CHECK: linalg.yield
// CHECK: } -> tensor<?xf32>
// CHECK: scf.forall.in_parallel {
// CHECK: tensor.parallel_insert_slice %[[PARTIAL]] into %[[ARG3]][0, %[[IV]]] [%[[D0]], 1] [1, 1] : tensor<?xf32> into tensor<?x5xf32>
// CHECK: }
// CHECK: }
// CHECK: %[[R:.*]] = linalg.reduce ins(%[[L]] : tensor<?x5xf32>) outs(%[[ARG1]] : tensor<?xf32>) dimensions = [1]
// CHECK: {
// CHECK: arith.addf
// CHECK: linalg.yield
// CHECK: }
// CHECK: return %[[R]] : tensor<?xf32>
// -----
func.func @matmul_tile_parallel(
%A: tensor<?x?xf32>, %B: tensor<?x?xf32>, %out: tensor<?x?xf32>) -> tensor<?x?xf32> {
%matmul = linalg.matmul ins(%A, %B: tensor<?x?xf32>, tensor<?x?xf32>)
outs(%out: tensor<?x?xf32>) -> tensor<?x?xf32>
return %matmul : tensor<?x?xf32>
}
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, %2, %3, %loop = transform.structured.tile_reduction_using_forall %0
by num_threads = [0, 0, 5] : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op)
transform.yield
}
}
// CHECK-DAG: #[[MAP0:.*]] = affine_map<(d0)[s0] -> (-(d0 * (s0 ceildiv 5)) + s0, s0 ceildiv 5)>
// CHECK-DAG: #[[MAP1:.*]] = affine_map<(d0) -> (0, d0)>
// CHECK-DAG: #[[MAP2:.*]] = affine_map<(d0)[s0] -> (d0 * (s0 ceildiv 5))>
// CHECK: func @matmul_tile_parallel(%[[ARG0:.+]]: tensor<?x?xf32>, %[[ARG1:.+]]: tensor<?x?xf32>, %[[ARG2:.+]]: tensor<?x?xf32>
// CHECK-DAG: %[[I:.*]] = arith.constant 0.000000e+00 : f32
// CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[C1:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[D0:.*]] = tensor.dim %[[ARG0]], %[[C0]] : tensor<?x?xf32>
// CHECK-DAG: %[[D1:.*]] = tensor.dim %[[ARG0]], %[[C1]] : tensor<?x?xf32>
// CHECK-DAG: %[[D2:.*]] = tensor.dim %[[ARG1]], %[[C1]] : tensor<?x?xf32>
// CHECK: %[[E:.*]] = tensor.empty(%[[D0]], %[[D2]]) : tensor<?x?x5xf32>
// CHECK: %[[F:.*]] = linalg.fill ins(%[[I]] : f32) outs(%[[E]] : tensor<?x?x5xf32>) -> tensor<?x?x5xf32>
// CHECK: %[[L:.*]] = scf.forall (%[[IV:.+]]) in (5) shared_outs(%[[ARG3:.+]] = %[[F]]) -> (tensor<?x?x5xf32>) {
// CHECK-DAG: %[[TS0:.+]] = affine.min #[[MAP0]](%[[IV]])[%[[D1]]]
// CHECK-DAG: %[[TS1:.+]] = affine.max #[[MAP1]](%[[TS0]])
// CHECK-DAG: %[[ET:.+]] = tensor.extract_slice %[[ARG3:.+]][0, 0, %[[IV]]] [%[[D0]], %[[D2]], 1] [1, 1, 1] : tensor<?x?x5xf32> to tensor<?x?xf32>
// CHECK-DAG: %[[TINDEX:.+]] = affine.apply #[[MAP2]](%[[IV]])[%[[D1]]]
// CHECK-DAG: %[[INCHUNKA:.+]] = tensor.extract_slice %[[ARG0]][0, %[[TINDEX]]] [%[[D0]], %[[TS1]]] [1, 1] : tensor<?x?xf32> to tensor<?x?xf32>
// CHECK-DAG: %[[INCHUNKB:.+]] = tensor.extract_slice %[[ARG1]][%[[TINDEX]], 0] [%[[TS1]], %[[D2]]] [1, 1] : tensor<?x?xf32> to tensor<?x?xf32>
// CHECK: %[[PARTIAL:.+]] = linalg.matmul ins(%[[INCHUNKA]], %[[INCHUNKB]] : tensor<?x?xf32>, tensor<?x?xf32>) outs(%[[ET]] : tensor<?x?xf32>) -> tensor<?x?xf32>
// CHECK: scf.forall.in_parallel {
// CHECK: tensor.parallel_insert_slice %[[PARTIAL]] into %[[ARG3]][0, 0, %[[IV]]] [%[[D0]], %[[D2]], 1] [1, 1, 1] : tensor<?x?xf32> into tensor<?x?x5xf32>
// CHECK: }
// CHECK: }
// CHECK: %[[R:.*]] = linalg.reduce ins(%[[L]] : tensor<?x?x5xf32>) outs(%[[ARG2]] : tensor<?x?xf32>) dimensions = [2]
// CHECK: arith.addf
// CHECK: linalg.yield
// CHECK: }
// CHECK: return %[[R]] : tensor<?x?xf32>
// -----
func.func @reduction_untiled_forall(
%arg0: tensor<?x?xf32>, %out: tensor<?xf32>) -> tensor<?xf32> {
// expected-error @below {{tiling parallel dimensions is not supported with partial reduction tiling strategies}}
%red = linalg.generic {indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>,
affine_map<(d0, d1) -> (d0)>],
iterator_types = ["parallel", "reduction"]}
ins(%arg0 : tensor<?x?xf32>)
outs(%out : tensor<?xf32>) {
^bb0(%arg7: f32, %arg9: f32):
%1 = arith.mulf %arg7, %arg7 : f32
%2 = arith.addf %1, %arg9 : f32
linalg.yield %2 : f32
} -> tensor<?xf32>
return %red : 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
// expected-error @below {{could not tile reduction}}
%1, %2, %3, %loop = transform.structured.tile_reduction_using_forall %0
by num_threads = [5] tile_sizes = [3] mapping = [#gpu.thread<x>] : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op)
transform.yield
}
}
// -----
#map = affine_map<(d0, d1) -> (d0, d1)>
#map1 = affine_map<(d0, d1) -> (d0)>
module {
func.func @fail_for_float_neutral(%arg0: tensor<?x?xf32>, %arg1: tensor<?xf32>) -> tensor<?xf32> {
// expected-error @below {{'linalg.generic' op Failed to get an identity value for the reduction operation.}}
%0 = linalg.generic {indexing_maps = [#map, #map1], iterator_types = ["parallel", "reduction"]} ins(%arg0 : tensor<?x?xf32>) outs(%arg1 : tensor<?xf32>) {
^bb0(%in: f32, %out: f32):
%1 = llvm.fmul %in, %in : f32
%2 = llvm.fadd %1, %out : f32
linalg.yield %2 : f32
} -> tensor<?xf32>
return %0 : tensor<?xf32>
}
module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg0: !transform.any_op {transform.readonly}) {
%0 = transform.structured.match ops{["linalg.generic"]} in %arg0 : (!transform.any_op) -> !transform.any_op
// expected-error @below {{failed to tile using partial reduction}}
%fill_op, %split_linalg_op, %combining_linalg_op, %for_op = transform.structured.tile_reduction_using_for %0 by tile_sizes = [0, 5] : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op)
transform.yield
}
}
}
// -----
#map = affine_map<(d0, d1, d2) -> (d1, d2)>
#map1 = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
#map2 = affine_map<(d0, d1, d2) -> (d0)>
module {
func.func @reduction_tile_multiple_reduction(%arg0: tensor<86x128xf32>, %arg1: tensor<4096x86x128xf32>, %arg2: tensor<4096xf32>) -> tensor<4096xf32> {
%0 = linalg.generic {indexing_maps = [#map, #map1, #map2], iterator_types = ["parallel", "reduction", "reduction"]} ins(%arg0, %arg1 : tensor<86x128xf32>, tensor<4096x86x128xf32>) outs(%arg2 : tensor<4096xf32>) {
^bb0(%in: f32, %in_0: f32, %out: f32):
%1 = arith.mulf %in, %in_0 : f32
%2 = arith.addf %1, %out : f32
linalg.yield %2 : f32
} -> tensor<4096xf32>
return %0 : tensor<4096xf32>
}
module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg0: !transform.any_op {transform.readonly}) {
%0 = transform.structured.match ops{["linalg.generic"]} in %arg0 : (!transform.any_op) -> !transform.any_op
%fill_op, %split_linalg_op, %combining_linalg_op, %for_op = transform.structured.tile_reduction_using_for %0 by tile_sizes = [0, 2, 64] : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op)
transform.yield
}
}
}
// CHECK: func @reduction_tile_multiple_reduction(%[[ARG0:.+]]: tensor<86x128xf32>, %[[ARG1:.+]]: tensor<4096x86x128xf32>, %[[ARG2:.+]]: tensor<4096xf32>
// CHECK: %[[F:.*]] = linalg.fill ins(%{{.*}} : f32) outs(%{{.*}} : tensor<4096x2x64xf32>) -> tensor<4096x2x64xf32>
// CHECK: %[[L0:.*]] = scf.for %{{.*}} = %{{.*}} to %{{.*}} step %{{.*}} iter_args(%[[ARG3:.*]] = %[[F]]) -> (tensor<4096x2x64xf32>)
// CHECK: %[[L1:.*]] = scf.for %{{.*}} = %{{.*}} to %{{.*}} step %{{.*}} iter_args(%[[ARG4:.*]] = %[[ARG3]]) -> (tensor<4096x2x64xf32>)
// CHECK: %[[OUT:.*]] = linalg.generic {indexing_maps = [{{.*}}, {{.*}}, {{.*}}], iterator_types = ["parallel", "parallel", "parallel"]} ins(%{{.*}}, %{{.*}}: tensor<2x64xf32>, tensor<4096x2x64xf32>) outs(%{{.*}}: tensor<4096x2x64xf32>)
// CHECK: scf.yield %[[OUT]] : tensor<4096x2x64xf32>
// CHECK: scf.yield %[[L1]] : tensor<4096x2x64xf32>
// CHECK: %[[OUT2:.*]] = linalg.reduce ins(%{{.*}} : tensor<4096x2x64xf32>) outs(%{{.*}} : tensor<4096xf32>)
// CHECK: return %[[OUT2]] : tensor<4096xf32>
// -----
func.func @reduction_tile_multiple_results(%arg0: tensor<?x?xf32>, %out: tensor<?xf32>, %out2: tensor<?xf32>) -> (tensor<?xf32>, tensor<?xf32>) {
%red:2 = linalg.generic {indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>,
affine_map<(d0, d1) -> (d0)>,
affine_map<(d0, d1) -> (d0)>],
iterator_types = ["parallel", "reduction"]}
ins(%arg0 : tensor<?x?xf32>)
outs(%out, %out2 : tensor<?xf32>, tensor<?xf32>) {
^bb0(%arg7: f32, %arg9: f32, %arg9_1: f32):
%1 = arith.mulf %arg7, %arg7 : f32
%2 = arith.addf %1, %arg9 : f32
%3 = arith.maximumf %1, %arg9_1 : f32
linalg.yield %2, %3 : f32, f32
} -> (tensor<?xf32>, tensor<?xf32>)
return %red#0, %red#1 : tensor<?xf32>, 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, %12, %2, %3, %4, %loop = transform.structured.tile_reduction_using_for %0
by tile_sizes = [0, 5] : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op)
transform.yield
}
}
// CHECK: func @reduction_tile_multiple_results
// CHECK-DAG: %[[SUM_ID:.+]] = arith.constant 0.000000e+00 : f32
// CHECK-DAG: %[[MAX_ID:.+]] = arith.constant 0xFF800000 : f32
// CHECK-DAG: %[[SUM_INIT:.+]] = linalg.fill ins(%[[SUM_ID]] : f32) outs(%{{.*}} : tensor<?x5xf32>) -> tensor<?x5xf32>
// CHECK-DAG: %[[MAX_INIT:.+]] = linalg.fill ins(%[[MAX_ID]] : f32) outs(%{{.*}} : tensor<?x5xf32>) -> tensor<?x5xf32>
// CHECK: %[[OUT:.+]]:2 = scf.for
// CHECK-SAME: iter_args(%[[SUM:.+]] = %[[SUM_INIT]], %[[MAX:.+]] = %[[MAX_INIT]])
// CHECK: %[[UPDATED:.*]]:2 = linalg.generic
// CHECK: arith.mulf
// CHECK: arith.addf
// CHECK: arith.maximumf
// CHECK: %[[INSERT1:.+]] = tensor.insert_slice %[[UPDATED]]#0 into %[[SUM]]
// CHECK: %[[INSERT2:.+]] = tensor.insert_slice %[[UPDATED]]#1 into %[[MAX]]
// CHECK: scf.yield %[[INSERT1]], %[[INSERT1]]
// CHECK: linalg.reduce
// CHECK: arith.addf
// CHECK: linalg.reduce
// CHECK: arith.maximumf
// -----
func.func @reduction_tile_multi_dim_transpose(%arg0: tensor<?x?x?xf32>, %out: tensor<?x?xf32>) -> tensor<?x?xf32> {
%red = linalg.generic {indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d1, d2)>,
affine_map<(d0, d1, d2) -> (d2, d0)>],
iterator_types = ["parallel", "reduction", "parallel"]}
ins(%arg0 : tensor<?x?x?xf32>)
outs(%out : tensor<?x?xf32>) {
^bb0(%arg7: f32, %arg9: f32):
%42 = arith.addf %arg7, %arg9 : f32
linalg.yield %42 : f32
} -> tensor<?x?xf32>
return %red : tensor<?x?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, %2, %3, %loop = transform.structured.tile_reduction_using_for %0
by tile_sizes = [0, 5, 0] : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op)
transform.yield
}
}
// CHECK-DAG: #[[MAP1:.*]] = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
// CHECK-DAG: #[[MAP2:.*]] = affine_map<(d0, d1, d2) -> (d2, d0, d1)>
// CHECK: func @reduction_tile_multi_dim_transpose
// CHECK: tensor.empty(%{{.*}}) : tensor<?x?x5xf32>
// CHECK: linalg.fill {{.*}} : tensor<?x?x5xf32>) -> tensor<?x?x5xf32>
// CHECK: scf.for
// CHECK: %[[K:.*]] = affine.min
// CHECK: %[[EXT:.*]] = tensor.extract_slice %[[ARG3:.*]][0, 0, 0] [%[[D2:.*]], %[[D0:.*]], %[[K]]] [1, 1, 1] : tensor<?x?x5xf32> to tensor<?x?x?xf32>
// CHECK: %[[R:.*]] = linalg.generic {indexing_maps = [#[[MAP1]], #[[MAP2]]], iterator_types = ["parallel", "parallel", "parallel"]} ins(%[[L:.*]] : tensor<?x?x?xf32>) outs(%[[EXT]] : tensor<?x?x?xf32>)
// CHECK: %[[INS:.*]] = tensor.insert_slice %[[R]] into %[[ARG3]][0, 0, 0] [%[[D2]], %[[D0]], %[[K]]] [1, 1, 1] : tensor<?x?x?xf32> into tensor<?x?x5xf32>
// CHECK: scf.yield {{.*}} : tensor<?x?x5xf32>
// CHECK: }
// CHECK: linalg.reduce
// CHECK: return
// -----
// Check that only one of the reduction dimension can be tiled (in this case outer).
#map = affine_map<(d0, d1, d2) -> (d1, d2)>
#map1 = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
#map2 = affine_map<(d0, d1, d2) -> (d0)>
module {
func.func @reduction_tile_single_of_multiple_reduction_outer(
%arg0: tensor<86x128xf32>, %arg1: tensor<4096x86x128xf32>, %arg2: tensor<4096xf32>) -> tensor<4096xf32> {
%0 = linalg.generic {
indexing_maps = [#map, #map1, #map2],
iterator_types = ["parallel", "reduction", "reduction"]}
ins(%arg0, %arg1 : tensor<86x128xf32>, tensor<4096x86x128xf32>) outs(%arg2 : tensor<4096xf32>) {
^bb0(%in: f32, %in_0: f32, %out: f32):
%1 = arith.mulf %in, %in_0 : f32
%2 = arith.addf %1, %out : f32
linalg.yield %2 : f32
} -> tensor<4096xf32>
return %0 : tensor<4096xf32>
}
module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg0: !transform.any_op {transform.readonly}) {
%0 = transform.structured.match ops{["linalg.generic"]} in %arg0 : (!transform.any_op) -> !transform.any_op
%fill_op, %split_linalg_op, %combining_linalg_op, %for_op =
transform.structured.tile_reduction_using_for %0 reduction_dims = [1] by tile_sizes = [0, 2]
: (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op)
transform.yield
}
}
}
// CHECK: #[[INIT_MAP:.+]] = affine_map<(d0, d1, d2) -> (d0, d1)>
// CHECK: @reduction_tile_single_of_multiple_reduction_outer(
// CHECK-SAME: %[[INIT:[a-zA-Z0-9]+]]: tensor<4096xf32>
// CHECK-DAG: %[[C0:.+]] = arith.constant 0 : index
// CHECK-DAG: %[[C2:.+]] = arith.constant 2 : index
// CHECK-DAG: %[[C86:.+]] = arith.constant 86 : index
// CHECK-DAG: %[[EMPTY:.+]] = tensor.empty() : tensor<4096x2xf32>
// CHECK: %[[FILL:.+]] = linalg.fill
// CHECK-SAME: outs(%[[EMPTY]] :
// CHECK: %[[RESULT:.+]] = scf.for %[[IV:[a-zA-Z0-9]+]] = %[[C0]] to %[[C86]] step %[[C2]]
// CHECK-SAME: iter_args(%[[ITER_ARG:.+]] = %[[FILL]])
// CHECK: %[[PARTIAL_RESULT:.+]] = linalg.generic
// CHECK-SAME: indexing_maps = [#{{.+}}, #{{.+}}, #[[INIT_MAP]]]
// CHECK-SAME: iterator_types = ["parallel", "parallel", "reduction"]
// CHECK-SAME: outs(%[[ITER_ARG]] :
// CHECK: scf.yield %[[PARTIAL_RESULT]]
// CHECK: %[[REDUCE:.+]] = linalg.reduce
// CHECK-SAME: ins(%[[RESULT]] :
// CHECK-SAME: outs(%[[INIT]] :
// CHECK-SAME: dimensions = [1]
// CHECK: return %[[REDUCE]]
// -----
// Check that only one of the reduction dimension can be tiled (in this case inner).
#map = affine_map<(d0, d1, d2) -> (d1, d2)>
#map1 = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
#map2 = affine_map<(d0, d1, d2) -> (d0)>
module {
func.func @reduction_tile_single_of_multiple_reduction_inner(
%arg0: tensor<86x128xf32>, %arg1: tensor<4096x86x128xf32>, %arg2: tensor<4096xf32>) -> tensor<4096xf32> {
%0 = linalg.generic {
indexing_maps = [#map, #map1, #map2],
iterator_types = ["parallel", "reduction", "reduction"]}
ins(%arg0, %arg1 : tensor<86x128xf32>, tensor<4096x86x128xf32>) outs(%arg2 : tensor<4096xf32>) {
^bb0(%in: f32, %in_0: f32, %out: f32):
%1 = arith.mulf %in, %in_0 : f32
%2 = arith.addf %1, %out : f32
linalg.yield %2 : f32
} -> tensor<4096xf32>
return %0 : tensor<4096xf32>
}
module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg0: !transform.any_op {transform.readonly}) {
%0 = transform.structured.match ops{["linalg.generic"]} in %arg0 : (!transform.any_op) -> !transform.any_op
%fill_op, %split_linalg_op, %combining_linalg_op, %for_op =
transform.structured.tile_reduction_using_for %0 reduction_dims = [2] by tile_sizes = [0, 0, 64]
: (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op)
transform.yield
}
}
}
// CHECK: #[[INIT_MAP:.+]] = affine_map<(d0, d1, d2) -> (d0, d2)>
// CHECK: @reduction_tile_single_of_multiple_reduction_inner(
// CHECK-SAME: %[[INIT:[a-zA-Z0-9]+]]: tensor<4096xf32>
// CHECK-DAG: %[[C0:.+]] = arith.constant 0 : index
// CHECK-DAG: %[[C64:.+]] = arith.constant 64 : index
// CHECK-DAG: %[[C128:.+]] = arith.constant 128 : index
// CHECK-DAG: %[[EMPTY:.+]] = tensor.empty() : tensor<4096x64xf32>
// CHECK: %[[FILL:.+]] = linalg.fill
// CHECK-SAME: outs(%[[EMPTY]] :
// CHECK: %[[RESULT:.+]] = scf.for %[[IV:[a-zA-Z0-9]+]] = %[[C0]] to %[[C128]] step %[[C64]]
// CHECK-SAME: iter_args(%[[ITER_ARG:.+]] = %[[FILL]])
// CHECK: %[[PARTIAL_RESULT:.+]] = linalg.generic
// CHECK-SAME: indexing_maps = [#{{.+}}, #{{.+}}, #[[INIT_MAP]]]
// CHECK-SAME: iterator_types = ["parallel", "reduction", "parallel"]
// CHECK-SAME: outs(%[[ITER_ARG]] :
// CHECK: scf.yield %[[PARTIAL_RESULT]]
// CHECK: %[[REDUCE:.+]] = linalg.reduce
// CHECK-SAME: ins(%[[RESULT]] :
// CHECK-SAME: outs(%[[INIT]] :
// CHECK-SAME: dimensions = [1]
// CHECK: return %[[REDUCE]]
// -----
// Check that both the reduction dimensions are tiled but the dimensions in the output are swapped.
#map = affine_map<(d0, d1, d2) -> (d1, d2)>
#map1 = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
#map2 = affine_map<(d0, d1, d2) -> (d0)>
module {
func.func @reduction_tile_single_of_multiple_reduction_reversed(
%arg0: tensor<86x128xf32>, %arg1: tensor<4096x86x128xf32>, %arg2: tensor<4096xf32>) -> tensor<4096xf32> {
%0 = linalg.generic {
indexing_maps = [#map, #map1, #map2],
iterator_types = ["parallel", "reduction", "reduction"]}
ins(%arg0, %arg1 : tensor<86x128xf32>, tensor<4096x86x128xf32>) outs(%arg2 : tensor<4096xf32>) {
^bb0(%in: f32, %in_0: f32, %out: f32):
%1 = arith.mulf %in, %in_0 : f32
%2 = arith.addf %1, %out : f32
linalg.yield %2 : f32
} -> tensor<4096xf32>
return %0 : tensor<4096xf32>
}
module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg0: !transform.any_op {transform.readonly}) {
%0 = transform.structured.match ops{["linalg.generic"]} in %arg0 : (!transform.any_op) -> !transform.any_op
%fill_op, %split_linalg_op, %combining_linalg_op, %for_op =
transform.structured.tile_reduction_using_for %0 reduction_dims = [2, 1] by tile_sizes = [0, 2, 64]
: (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op)
transform.yield
}
}
}
// CHECK: #[[INIT_MAP:.+]] = affine_map<(d0, d1, d2) -> (d0, d2, d1)>
// CHECK: @reduction_tile_single_of_multiple_reduction_reversed(
// CHECK-SAME: %[[INIT:[a-zA-Z0-9]+]]: tensor<4096xf32>
// CHECK-DAG: %[[C0:.+]] = arith.constant 0 : index
// CHECK-DAG: %[[C2:.+]] = arith.constant 2 : index
// CHECK-DAG: %[[C64:.+]] = arith.constant 64 : index
// CHECK-DAG: %[[C86:.+]] = arith.constant 86 : index
// CHECK-DAG: %[[C128:.+]] = arith.constant 128 : index
// CHECK-DAG: %[[EMPTY:.+]] = tensor.empty() : tensor<4096x64x2xf32>
// CHECK: %[[FILL:.+]] = linalg.fill
// CHECK-SAME: outs(%[[EMPTY]] :
// CHECK: %[[RESULT:.+]] = scf.for %[[IV0:[a-zA-Z0-9]+]] = %[[C0]] to %[[C86]] step %[[C2]]
// CHECK-SAME: iter_args(%[[ITER_ARG:.+]] = %[[FILL]])
// CHECK: %[[RESULT0:.+]] = scf.for %[[IV1:[a-zA-Z0-9]+]] = %[[C0]] to %[[C128]] step %[[C64]]
// CHECK-SAME: iter_args(%[[ITER_ARG0:.+]] = %[[ITER_ARG]])
// CHECK: %[[PARTIAL_RESULT:.+]] = linalg.generic
// CHECK-SAME: indexing_maps = [#{{.+}}, #{{.+}}, #[[INIT_MAP]]]
// CHECK-SAME: iterator_types = ["parallel", "parallel", "parallel"]
// CHECK-SAME: outs(%[[ITER_ARG0]] :
// CHECK: scf.yield %[[PARTIAL_RESULT]]
// CHECK scf.yield %[[RESULT0]]
// CHECK: %[[REDUCE:.+]] = linalg.reduce
// CHECK-SAME: ins(%[[RESULT]] :
// CHECK-SAME: outs(%[[INIT]] :
// CHECK-SAME: dimensions = [1, 2]
// CHECK: return %[[REDUCE]]
// -----
func.func @reduction_tile_parallel_using_tile_sizes(
%arg0: tensor<?x?xf32>, %out: tensor<?xf32>) -> tensor<?xf32> {
%red = linalg.generic {indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>,
affine_map<(d0, d1) -> (d0)>],
iterator_types = ["parallel", "reduction"]}
ins(%arg0 : tensor<?x?xf32>)
outs(%out : tensor<?xf32>) {
^bb0(%arg7: f32, %arg9: f32):
%1 = arith.mulf %arg7, %arg7 : f32
%2 = arith.addf %1, %arg9 : f32
linalg.yield %2 : f32
} -> tensor<?xf32>
return %red : tensor<?xf32>
}
// CHECK-DAG: #[[MAP0:.*]] = affine_map<()[s0] -> (s0 ceildiv 5)>
// CHECK-DAG: #[[MAP1:.*]] = affine_map<(d0)[s0] -> (-d0 + s0, 5)>
// CHECK-DAG: #[[MAP2:.*]] = affine_map<()[s0] -> (s0 floordiv 5)>
// CHECK: func @reduction_tile_parallel_using_tile_sizes(%[[ARG0:.+]]: tensor<?x?xf32>, %[[ARG1:.+]]: tensor<?xf32>
// CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[C1:.*]] = arith.constant 1 : index
// CHECK-DAG: %[[D0:.*]] = tensor.dim %[[ARG0]], %[[C0]] : tensor<?x?xf32>
// CHECK-DAG: %[[D1:.*]] = tensor.dim %[[ARG0]], %[[C1]] : tensor<?x?xf32>
// CHECK-DAG: %[[PARALLEL_DIM:.+]] = affine.apply #[[MAP0]]()[%[[D1]]]
// CHECK: %[[E:.*]] = tensor.empty(%[[D0]], %[[PARALLEL_DIM]]) : tensor<?x?xf32>
// CHECK: %[[F:.*]] = linalg.fill
// CHECK-SAME: outs(%[[E]] :
// CHECK: %[[L:.*]] = scf.forall (%[[IV:.+]]) = (0) to (%[[D1]]) step (5) shared_outs(%[[ARG3:.+]] = %[[F]])
// CHECK-DAG: %[[TS0:.+]] = affine.min #[[MAP1]](%[[IV]])[%[[D1]]]
// CHECK-DAG: %[[INIT_OFFSET:.+]] = affine.apply #[[MAP2]]()[%[[IV]]]
// CHECK-DAG: %[[INCHUNK:.+]] = tensor.extract_slice %[[ARG0]][0, %[[IV]]] [%[[D0]], %[[TS0]]] [1, 1]
// CHECK-DAG: %[[ET:.+]] = tensor.extract_slice %[[ARG3]][0, %[[INIT_OFFSET]]] [%[[D0]], 1] [1, 1]
// CHECK: %[[PARTIAL:.+]] = linalg.generic
// CHECK-SAME: ins(%[[INCHUNK]] :
// CHECK-SAME: outs(%[[ET]] :
// CHECK: scf.forall.in_parallel {
// CHECK: tensor.parallel_insert_slice %[[PARTIAL]] into %[[ARG3]][0, %[[INIT_OFFSET]]] [%[[D0]], 1] [1, 1]
// CHECK: }
// CHECK: }
// CHECK: %[[R:.*]] = linalg.reduce ins(%[[L]]
// CHECK-SAME: outs(%[[ARG1]] :
// CHECK: return %[[R]] : 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, %2, %3, %loop = transform.structured.tile_reduction_using_forall %0
by tile_sizes = [0, 5] : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op)
transform.yield
}
}
// -----
// Check that only one of the reduction dimension can be tiled (in this case inner).
#map = affine_map<(d0, d1, d2) -> (d1, d2)>
#map1 = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
#map2 = affine_map<(d0, d1, d2) -> (d0)>
module {
func.func @reduction_using_forall_tile_single_of_multiple_reduction_inner(
%arg0: tensor<86x128xf32>, %arg1: tensor<4096x86x128xf32>, %arg2: tensor<4096xf32>) -> tensor<4096xf32> {
%0 = linalg.generic {
indexing_maps = [#map, #map1, #map2],
iterator_types = ["parallel", "reduction", "reduction"]}
ins(%arg0, %arg1 : tensor<86x128xf32>, tensor<4096x86x128xf32>) outs(%arg2 : tensor<4096xf32>) {
^bb0(%in: f32, %in_0: f32, %out: f32):
%1 = arith.mulf %in, %in_0 : f32
%2 = arith.addf %1, %out : f32
linalg.yield %2 : f32
} -> tensor<4096xf32>
return %0 : tensor<4096xf32>
}
module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg0: !transform.any_op {transform.readonly}) {
%0 = transform.structured.match ops{["linalg.generic"]} in %arg0 : (!transform.any_op) -> !transform.any_op
%fill_op, %split_linalg_op, %combining_linalg_op, %for_op =
transform.structured.tile_reduction_using_forall %0 reduction_dims = [2] by tile_sizes = [0, 0, 64]
: (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op)
transform.yield
}
}
}
// CHECK-DAG: #[[MAP0:.*]] = affine_map<()[s0] -> (s0 floordiv 64)>
// CHECK: func @reduction_using_forall_tile_single_of_multiple_reduction_inner(%[[ARG0:.+]]: tensor<86x128xf32>, %[[ARG1:.+]]: tensor<4096x86x128xf32>, %[[ARG2:.+]]: tensor<4096xf32>)
// CHECK: %[[E:.*]] = tensor.empty() : tensor<4096x2xf32>
// CHECK: %[[F:.*]] = linalg.fill
// CHECK-SAME: outs(%[[E]] :
// CHECK: %[[L:.*]] = scf.forall (%[[IV:.+]]) = (0) to (128) step (64) shared_outs(%[[ARG3:.+]] = %[[F]])
// CHECK-DAG: %[[INIT_OFFSET:.+]] = affine.apply #[[MAP0]]()[%[[IV]]]
// CHECK-DAG: %[[ARG0_SLICE:.+]] = tensor.extract_slice %[[ARG0]][0, %[[IV]]] [86, 64] [1, 1]
// CHECK-DAG: %[[ARG1_SLICE:.+]] = tensor.extract_slice %[[ARG1]][0, 0, %[[IV]]] [4096, 86, 64] [1, 1, 1]
// CHECK-DAG: %[[ET:.+]] = tensor.extract_slice %[[ARG3]][0, %[[INIT_OFFSET]]] [4096, 1] [1, 1]
// CHECK: %[[PARTIAL:.+]] = linalg.generic
// CHECK-SAME: ins(%[[ARG0_SLICE]], %[[ARG1_SLICE]] :
// CHECK-SAME: outs(%[[ET]] :
// CHECK: scf.forall.in_parallel {
// CHECK: tensor.parallel_insert_slice %[[PARTIAL]] into %[[ARG3]][0, %[[INIT_OFFSET]]] [4096, 1] [1, 1]
// CHECK: }
// CHECK: }
// CHECK: %[[R:.*]] = linalg.reduce ins(%[[L]]
// CHECK-SAME: outs(%[[ARG2]] :
// CHECK: return %[[R]]
// -----
// Check that specifying both reduction dimensions, but setting tile size to 0 for one of them behaves consistent with specifying single reduction dimension.
#map = affine_map<(d0, d1, d2) -> (d1, d2)>
#map1 = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
#map2 = affine_map<(d0, d1, d2) -> (d0)>
module {
func.func @reduction_using_forall_tilesize_0_of_multiple_reduction_inner(
%arg0: tensor<86x128xf32>, %arg1: tensor<4096x86x128xf32>, %arg2: tensor<4096xf32>) -> tensor<4096xf32> {
%0 = linalg.generic {
indexing_maps = [#map, #map1, #map2],
iterator_types = ["parallel", "reduction", "reduction"]}
ins(%arg0, %arg1 : tensor<86x128xf32>, tensor<4096x86x128xf32>) outs(%arg2 : tensor<4096xf32>) {
^bb0(%in: f32, %in_0: f32, %out: f32):
%1 = arith.mulf %in, %in_0 : f32
%2 = arith.addf %1, %out : f32
linalg.yield %2 : f32
} -> tensor<4096xf32>
return %0 : tensor<4096xf32>
}
module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg0: !transform.any_op {transform.readonly}) {
%0 = transform.structured.match ops{["linalg.generic"]} in %arg0 : (!transform.any_op) -> !transform.any_op
%fill_op, %split_linalg_op, %combining_linalg_op, %for_op =
transform.structured.tile_reduction_using_forall %0 reduction_dims = [1, 2] by tile_sizes = [0, 0, 64]
: (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op)
transform.yield
}
}
}
// CHECK-DAG: #[[MAP0:.*]] = affine_map<()[s0] -> (s0 floordiv 64)>
// CHECK: func @reduction_using_forall_tilesize_0_of_multiple_reduction_inner(%[[ARG0:.+]]: tensor<86x128xf32>, %[[ARG1:.+]]: tensor<4096x86x128xf32>, %[[ARG2:.+]]: tensor<4096xf32>)
// CHECK: %[[E:.*]] = tensor.empty() : tensor<4096x2xf32>
// CHECK: %[[F:.*]] = linalg.fill
// CHECK-SAME: outs(%[[E]] :
// CHECK: %[[L:.*]] = scf.forall (%[[IV:.+]]) = (0) to (128) step (64) shared_outs(%[[ARG3:.+]] = %[[F]])
// CHECK-DAG: %[[INIT_OFFSET:.+]] = affine.apply #[[MAP0]]()[%[[IV]]]
// CHECK-DAG: %[[ARG0_SLICE:.+]] = tensor.extract_slice %[[ARG0]][0, %[[IV]]] [86, 64] [1, 1]
// CHECK-DAG: %[[ARG1_SLICE:.+]] = tensor.extract_slice %[[ARG1]][0, 0, %[[IV]]] [4096, 86, 64] [1, 1, 1]
// CHECK-DAG: %[[ET:.+]] = tensor.extract_slice %[[ARG3]][0, %[[INIT_OFFSET]]] [4096, 1] [1, 1]
// CHECK: %[[PARTIAL:.+]] = linalg.generic
// CHECK-SAME: ins(%[[ARG0_SLICE]], %[[ARG1_SLICE]] :
// CHECK-SAME: outs(%[[ET]] :
// CHECK: scf.forall.in_parallel {
// CHECK: tensor.parallel_insert_slice %[[PARTIAL]] into %[[ARG3]][0, %[[INIT_OFFSET]]] [4096, 1] [1, 1]
// CHECK: }
// CHECK: }
// CHECK: %[[R:.*]] = linalg.reduce ins(%[[L]]
// CHECK-SAME: outs(%[[ARG2]] :
// CHECK: return %[[R]]
// -----
// Check that linalg.index is correctly offset after partial reduction tiling.
func.func @reduction_tile_with_linalg_index(%arg0: tensor<8x128xf32>, %out: tensor<8xi32>) -> tensor<8xi32> {
%red = linalg.generic {
indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>,
affine_map<(d0, d1) -> (d0)>],
iterator_types = ["parallel", "reduction"]}
ins(%arg0 : tensor<8x128xf32>)
outs(%out : tensor<8xi32>) {
^bb0(%in: f32, %acc: i32):
%idx = linalg.index 1 : index
%idx_i32 = arith.index_cast %idx : index to i32
%sum = arith.addi %idx_i32, %acc : i32
linalg.yield %sum : i32
} -> tensor<8xi32>
return %red : tensor<8xi32>
}
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, %2, %3, %loop = transform.structured.tile_reduction_using_for %0
by tile_sizes = [0, 32] : (!transform.any_op) -> (!transform.any_op, !transform.any_op, !transform.any_op, !transform.any_op)
transform.yield
}
}
// CHECK-DAG: #[[$INDEX_MAP:.*]] = affine_map<(d0)[s0] -> (d0 + s0)>
// CHECK-LABEL: func @reduction_tile_with_linalg_index(
// CHECK: scf.for %[[IV:[a-zA-Z0-9]+]] =
// CHECK: linalg.generic
// CHECK: %[[LOCAL_IDX:.+]] = linalg.index 1 : index
// CHECK: affine.apply #[[$INDEX_MAP]](%[[IV]])[%[[LOCAL_IDX]]]
// -----
// Verify that tile_reduction_using_forall handles output indexing maps that
// contain constant expressions (e.g. `affine_map<(d0,d1,d2)->(d0,0,d2)>`)
// without crashing. Previously, generateInitialTensorForPartialReduction
// unconditionally cast every map result to AffineDimExpr, triggering an
// assertion when a constant expression was present (issue #173025).
func.func @reduction_tile_with_constant_in_output_map(
%arg0: tensor<1x4096x64xf32>,
%arg1: tensor<1x1x64xf32>) -> tensor<1x1x64xf32> {
%0 = linalg.generic {
indexing_maps = [
affine_map<(d0, d1, d2) -> (d0, d1, d2)>,
affine_map<(d0, d1, d2) -> (d0, 0, d2)>
],
iterator_types = ["parallel", "reduction", "parallel"]
} ins(%arg0 : tensor<1x4096x64xf32>) outs(%arg1 : tensor<1x1x64xf32>) {
^bb0(%in: f32, %out: f32):
%1 = arith.addf %in, %out : f32
linalg.yield %1 : f32
} -> tensor<1x1x64xf32>
return %0 : tensor<1x1x64xf32>
}
module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg: !transform.any_op {transform.readonly}) {
%0 = transform.structured.match ops{["linalg.generic"]} in %arg
: (!transform.any_op) -> !transform.any_op
%1:4 = transform.structured.tile_reduction_using_forall %0
by tile_sizes = [0, 4, 0]
: (!transform.any_op) -> (!transform.any_op, !transform.any_op,
!transform.any_op, !transform.any_op)
transform.yield
}
}
// CHECK-LABEL: func @reduction_tile_with_constant_in_output_map
// CHECK-DAG: %[[CST:.*]] = arith.constant 0.000000e+00 : f32
// CHECK-DAG: %[[E:.*]] = tensor.empty() : tensor<1x1x64x1024xf32>
// CHECK: %[[F:.*]] = linalg.fill ins(%[[CST]] : f32) outs(%[[E]] : tensor<1x1x64x1024xf32>) -> tensor<1x1x64x1024xf32>
// CHECK: %[[L:.*]] = scf.forall (%[[IV:.+]]) = (0) to (4096) step (4) shared_outs(%[[ARG3:.+]] = %[[F]]) -> (tensor<1x1x64x1024xf32>) {
// CHECK: %[[IN_SLICE:.+]] = tensor.extract_slice %arg0[0, %[[IV]], 0] [1, 4, 64] [1, 1, 1]
// CHECK: %[[INIT_SLICE:.+]] = tensor.extract_slice %[[ARG3]][0, 0, 0, {{.*}}] [1, 1, 64, 1] [1, 1, 1, 1]
// CHECK: %[[PARTIAL:.+]] = linalg.generic
// CHECK-SAME: ins(%[[IN_SLICE]] :
// CHECK-SAME: outs(%[[INIT_SLICE]] :
// CHECK: scf.forall.in_parallel {
// CHECK: tensor.parallel_insert_slice %[[PARTIAL]] into %[[ARG3]][0, 0, 0, {{.*}}] [1, 1, 64, 1] [1, 1, 1, 1]
// CHECK: }
// CHECK: }
// CHECK: linalg.reduce ins(%[[L]] : tensor<1x1x64x1024xf32>) outs(%arg1 : tensor<1x1x64xf32>) dimensions = [3]
// CHECK: return %{{.*}} : tensor<1x1x64xf32>
// -----
// Verify tile_reduction_using_forall with a constant in the output map when
// the constant-indexed dimension size is greater than 1 (K=3). The partial
// init tensor must use the actual output dim size (3), not a hardcoded 1.
func.func @reduction_tile_forall_constant_dim_k_gt_1(
%arg0: tensor<1x4096x64xf32>,
%arg1: tensor<1x3x64xf32>) -> tensor<1x3x64xf32> {
%0 = linalg.generic {
indexing_maps = [
affine_map<(d0, d1, d2) -> (d0, d1, d2)>,
affine_map<(d0, d1, d2) -> (d0, 0, d2)>
],
iterator_types = ["parallel", "reduction", "parallel"]
} ins(%arg0 : tensor<1x4096x64xf32>) outs(%arg1 : tensor<1x3x64xf32>) {
^bb0(%in: f32, %out: f32):
%1 = arith.addf %in, %out : f32
linalg.yield %1 : f32
} -> tensor<1x3x64xf32>
return %0 : tensor<1x3x64xf32>
}
module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg: !transform.any_op {transform.readonly}) {
%0 = transform.structured.match ops{["linalg.generic"]} in %arg
: (!transform.any_op) -> !transform.any_op
%1:4 = transform.structured.tile_reduction_using_forall %0
by tile_sizes = [0, 4, 0]
: (!transform.any_op) -> (!transform.any_op, !transform.any_op,
!transform.any_op, !transform.any_op)
transform.yield
}
}
// CHECK-LABEL: func @reduction_tile_forall_constant_dim_k_gt_1
// CHECK-DAG: %[[CST:.*]] = arith.constant 0.000000e+00 : f32
// CHECK-DAG: %[[E:.*]] = tensor.empty() : tensor<1x3x64x1024xf32>
// CHECK: %[[F:.*]] = linalg.fill ins(%[[CST]] : f32) outs(%[[E]] : tensor<1x3x64x1024xf32>) -> tensor<1x3x64x1024xf32>
// CHECK: %[[L:.*]] = scf.forall (%[[IV:.+]]) = (0) to (4096) step (4) shared_outs(%[[ARG3:.+]] = %[[F]]) -> (tensor<1x3x64x1024xf32>) {
// CHECK: %[[IN_SLICE:.+]] = tensor.extract_slice %arg0[0, %[[IV]], 0] [1, 4, 64] [1, 1, 1]
// CHECK: %[[INIT_SLICE:.+]] = tensor.extract_slice %[[ARG3]][0, 0, 0, {{.*}}] [1, 3, 64, 1] [1, 1, 1, 1]
// CHECK: %[[PARTIAL:.+]] = linalg.generic
// CHECK-SAME: ins(%[[IN_SLICE]] :
// CHECK-SAME: outs(%[[INIT_SLICE]] :
// CHECK: scf.forall.in_parallel {
// CHECK: tensor.parallel_insert_slice %[[PARTIAL]] into %[[ARG3]][0, 0, 0, {{.*}}] [1, 3, 64, 1] [1, 1, 1, 1]
// CHECK: }
// CHECK: }
// CHECK: linalg.reduce ins(%[[L]] : tensor<1x3x64x1024xf32>) outs(%arg1 : tensor<1x3x64xf32>) dimensions = [3]
// CHECK: return %{{.*}} : tensor<1x3x64xf32>
// -----
// Verify tile_reduction_using_for with a constant in the output map when
// the constant-indexed dimension size is greater than 1 (K=3). The partial
// init tensor must use the actual output dim size (3), not a hardcoded 1.
func.func @reduction_tile_for_constant_dim_k_gt_1(
%arg0: tensor<1x4096x64xf32>,
%arg1: tensor<1x3x64xf32>) -> tensor<1x3x64xf32> {
%0 = linalg.generic {
indexing_maps = [
affine_map<(d0, d1, d2) -> (d0, d1, d2)>,
affine_map<(d0, d1, d2) -> (d0, 0, d2)>
],
iterator_types = ["parallel", "reduction", "parallel"]
} ins(%arg0 : tensor<1x4096x64xf32>) outs(%arg1 : tensor<1x3x64xf32>) {
^bb0(%in: f32, %out: f32):
%1 = arith.addf %in, %out : f32
linalg.yield %1 : f32
} -> tensor<1x3x64xf32>
return %0 : tensor<1x3x64xf32>
}
module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg: !transform.any_op {transform.readonly}) {
%0 = transform.structured.match ops{["linalg.generic"]} in %arg
: (!transform.any_op) -> !transform.any_op
%1, %2, %3, %loop = transform.structured.tile_reduction_using_for %0
by tile_sizes = [0, 4, 0]
: (!transform.any_op) -> (!transform.any_op, !transform.any_op,
!transform.any_op, !transform.any_op)
transform.yield
}
}
// CHECK-LABEL: func @reduction_tile_for_constant_dim_k_gt_1
// CHECK-DAG: %[[CST:.*]] = arith.constant 0.000000e+00 : f32
// CHECK-DAG: %[[E:.*]] = tensor.empty() : tensor<1x3x64x4xf32>
// CHECK: %[[F:.*]] = linalg.fill ins(%[[CST]] : f32) outs(%[[E]] : tensor<1x3x64x4xf32>) -> tensor<1x3x64x4xf32>
// CHECK: %[[L:.*]] = scf.for %{{.*}} = %{{.*}} to %{{.*}} step %{{.*}} iter_args(%[[ARG3:.+]] = %[[F]]) -> (tensor<1x3x64x4xf32>) {
// CHECK: %[[PARTIAL:.+]] = linalg.generic
// CHECK-SAME: outs(%[[ARG3]] :
// CHECK: scf.yield %[[PARTIAL]]
// CHECK: }
// CHECK: linalg.reduce ins(%[[L]] : tensor<1x3x64x4xf32>) outs(%arg1 : tensor<1x3x64xf32>) dimensions = [3]
// CHECK: return %{{.*}} : tensor<1x3x64xf32>
// -----
// Verify tile_reduction_using_forall handles dynamic output shapes combined
// with a constant expression in the output map. The partial init tensor must
// use tensor.dim to query the dynamic dimension at the constant-indexed
// position rather than a hardcoded 1.
// CHECK-LABEL: func @reduction_tile_dynamic_constant_map
func.func @reduction_tile_dynamic_constant_map(
%arg0: tensor<?x4096x?xf32>,
%arg1: tensor<?x3x?xf32>) -> tensor<?x3x?xf32> {
%0 = linalg.generic {
indexing_maps = [
affine_map<(d0, d1, d2) -> (d0, d1, d2)>,
affine_map<(d0, d1, d2) -> (d0, 0, d2)>
],
iterator_types = ["parallel", "reduction", "parallel"]
} ins(%arg0 : tensor<?x4096x?xf32>) outs(%arg1 : tensor<?x3x?xf32>) {
^bb0(%in: f32, %out: f32):
%1 = arith.addf %in, %out : f32
linalg.yield %1 : f32
} -> tensor<?x3x?xf32>
return %0 : tensor<?x3x?xf32>
}
module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg: !transform.any_op {transform.readonly}) {
%0 = transform.structured.match ops{["linalg.generic"]} in %arg
: (!transform.any_op) -> !transform.any_op
%1:4 = transform.structured.tile_reduction_using_forall %0
by tile_sizes = [0, 4, 0]
: (!transform.any_op) -> (!transform.any_op, !transform.any_op,
!transform.any_op, !transform.any_op)
transform.yield
}
}
// Verify that the partial init tensor uses the correct dynamic shape. The
// constant-indexed dim 1 is static (size 3) so the partial tensor is
// tensor<?x3x?x1024xf32>. The extract_slice within the forall body uses
// size 3 at the constant-indexed position, not a hardcoded 1.
// CHECK-DAG: %[[CST:.*]] = arith.constant 0.000000e+00 : f32
// CHECK-DAG: %[[E:.*]] = tensor.empty({{.*}}) : tensor<?x3x?x1024xf32>
// CHECK: %[[F:.*]] = linalg.fill ins(%[[CST]] : f32) outs(%[[E]] : tensor<?x3x?x1024xf32>)
// CHECK: scf.forall
// CHECK: tensor.extract_slice {{.*}} [{{.*}}, 3, {{.*}}, 1]
// CHECK: linalg.reduce ins({{.*}} : tensor<?x3x?x1024xf32>) {{.*}} dimensions = [3]
// CHECK: return %{{.*}}
// -----
// Verify tile_reduction_using_forall handles two consecutive constant
// expressions in the same output map (e.g. `(d0,d1,d2)->(0,0,d2)`).
// Both constant-indexed dimensions must use the actual output dim size.
// CHECK-LABEL: func @reduction_tile_two_constants_in_map
func.func @reduction_tile_two_constants_in_map(
%arg0: tensor<1x4096x64xf32>,
%arg1: tensor<3x5x64xf32>) -> tensor<3x5x64xf32> {
%0 = linalg.generic {
indexing_maps = [
affine_map<(d0, d1, d2) -> (d0, d1, d2)>,
affine_map<(d0, d1, d2) -> (0, 0, d2)>
],
iterator_types = ["reduction", "reduction", "parallel"]
} ins(%arg0 : tensor<1x4096x64xf32>) outs(%arg1 : tensor<3x5x64xf32>) {
^bb0(%in: f32, %out: f32):
%1 = arith.addf %in, %out : f32
linalg.yield %1 : f32
} -> tensor<3x5x64xf32>
return %0 : tensor<3x5x64xf32>
}
module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg: !transform.any_op {transform.readonly}) {
%0 = transform.structured.match ops{["linalg.generic"]} in %arg
: (!transform.any_op) -> !transform.any_op
%1:4 = transform.structured.tile_reduction_using_forall %0
by tile_sizes = [0, 4, 0]
: (!transform.any_op) -> (!transform.any_op, !transform.any_op,
!transform.any_op, !transform.any_op)
transform.yield
}
}
// CHECK-DAG: %[[CST:.*]] = arith.constant 0.000000e+00 : f32
// CHECK-DAG: %[[E:.*]] = tensor.empty() : tensor<3x5x64x1024xf32>
// CHECK: %[[F:.*]] = linalg.fill ins(%[[CST]] : f32) outs(%[[E]] : tensor<3x5x64x1024xf32>) -> tensor<3x5x64x1024xf32>
// CHECK: %[[L:.*]] = scf.forall (%[[IV:.+]]) = (0) to (4096) step (4) shared_outs(%[[ARG3:.+]] = %[[F]]) -> (tensor<3x5x64x1024xf32>) {
// CHECK: tensor.extract_slice %arg0[0, %[[IV]], 0] [1, 4, 64] [1, 1, 1]
// CHECK: %[[INIT_SLICE:.+]] = tensor.extract_slice %[[ARG3]][0, 0, 0, {{.*}}] [3, 5, 64, 1] [1, 1, 1, 1]
// CHECK: linalg.generic
// CHECK: scf.forall.in_parallel {
// CHECK: tensor.parallel_insert_slice {{.*}} into %[[ARG3]][0, 0, 0, {{.*}}] [3, 5, 64, 1] [1, 1, 1, 1]
// CHECK: }
// CHECK: }
// CHECK: linalg.reduce ins(%[[L]] : tensor<3x5x64x1024xf32>) outs(%arg1 : tensor<3x5x64xf32>) dimensions = [3]
// CHECK: return %{{.*}} : tensor<3x5x64xf32>
// -----
// Verify tile_reduction_using_for handles dynamic output shapes combined with
// a constant expression in the output map. The partial init tensor must use
// tensor.dim to query the dynamic dimensions rather than hardcoding 1.
// CHECK-LABEL: func @reduction_tile_for_dynamic_constant_map
func.func @reduction_tile_for_dynamic_constant_map(
%arg0: tensor<?x4096x?xf32>,
%arg1: tensor<?x3x?xf32>) -> tensor<?x3x?xf32> {
%0 = linalg.generic {
indexing_maps = [
affine_map<(d0, d1, d2) -> (d0, d1, d2)>,
affine_map<(d0, d1, d2) -> (d0, 0, d2)>
],
iterator_types = ["parallel", "reduction", "parallel"]
} ins(%arg0 : tensor<?x4096x?xf32>) outs(%arg1 : tensor<?x3x?xf32>) {
^bb0(%in: f32, %out: f32):
%1 = arith.addf %in, %out : f32
linalg.yield %1 : f32
} -> tensor<?x3x?xf32>
return %0 : tensor<?x3x?xf32>
}
module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg: !transform.any_op {transform.readonly}) {
%0 = transform.structured.match ops{["linalg.generic"]} in %arg
: (!transform.any_op) -> !transform.any_op
%1, %2, %3, %loop = transform.structured.tile_reduction_using_for %0
by tile_sizes = [0, 4, 0]
: (!transform.any_op) -> (!transform.any_op, !transform.any_op,
!transform.any_op, !transform.any_op)
transform.yield
}
}
// Verify the partial init tensor uses tensor.dim for dynamic dims and the
// static constant-indexed position uses size 3. The extract_slice inside
// the for loop body must use size 3 at the constant-indexed position.
// CHECK-DAG: %[[CST:.*]] = arith.constant 0.000000e+00 : f32
// CHECK-DAG: %[[E:.*]] = tensor.empty({{.*}}) : tensor<?x3x?x4xf32>
// CHECK: %[[F:.*]] = linalg.fill ins(%[[CST]] : f32) outs(%[[E]] : tensor<?x3x?x4xf32>)
// CHECK: %[[L:.*]] = scf.for %{{.*}} = %{{.*}} to %{{.*}} step %{{.*}} iter_args(%[[ARG3:.+]] = %[[F]]) -> (tensor<?x3x?x4xf32>) {
// CHECK: tensor.extract_slice %arg0[{{.*}}] [{{.*}}, 4, {{.*}}] [1, 1, 1]
// CHECK: tensor.extract_slice %[[ARG3]][{{.*}}] [{{.*}}, 3, {{.*}}, 4] [1, 1, 1, 1]
// CHECK: }
// CHECK: linalg.reduce ins(%[[L]] : tensor<?x3x?x4xf32>) outs(%arg1 : tensor<?x3x?xf32>) dimensions = [3]
// CHECK: return %{{.*}}
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