// RUN: mlir-opt %s -transform-interpreter -split-input-file | FileCheck %s
// CHECK-LABEL: contraction_dot
func.func @contraction_dot(%A: memref<1584xf32>, %B: memref<1584xf32>, %C: memref<f32>) {
// CHECK: arith.mulf %{{.*}}, %{{.*}} : vector<1584xf32>
// CHECK: vector.multi_reduction <add>, %{{.*}}, {{.*}} [0] : vector<1584xf32> to f32
linalg.dot ins(%A, %B: memref<1584xf32>, memref<1584xf32>)
outs(%C: memref<f32>)
return
}
module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
%0 = transform.structured.match ops{["linalg.dot"]} in %arg1 : (!transform.any_op) -> !transform.any_op
transform.structured.vectorize %0 : !transform.any_op
transform.yield
}
}
// -----
// CHECK-LABEL: contraction_matvec
func.func @contraction_matvec(%A: memref<1584x1584xf32>, %B: memref<1584xf32>, %C: memref<1584xf32>) {
// CHECK: arith.mulf %{{.*}}, %{{.*}} : vector<1584x1584xf32>
// CHECK: vector.multi_reduction <add>, %{{.*}}, {{.*}} [1] : vector<1584x1584xf32> to vector<1584xf32>
linalg.matvec ins(%A, %B: memref<1584x1584xf32>, memref<1584xf32>)
outs(%C: memref<1584xf32>)
return
}
module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
%0 = transform.structured.match ops{["linalg.matvec"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = transform.get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
%2 = transform.structured.vectorize_children_and_apply_patterns %1 { disable_multi_reduction_to_contract_patterns } : (!transform.any_op) -> !transform.any_op
transform.yield
}
}
// -----
// CHECK-LABEL: contraction_matmul
func.func @contraction_matmul(%A: memref<1584x1584xf32>, %B: memref<1584x1584xf32>, %C: memref<1584x1584xf32>) {
// CHECK: arith.mulf %{{.*}}, %{{.*}} : vector<1584x1584x1584xf32>
// CHECK: vector.multi_reduction <add>, %{{.*}}, {{.*}} [2] : vector<1584x1584x1584xf32> to vector<1584x1584xf32>
linalg.matmul ins(%A, %B: memref<1584x1584xf32>, memref<1584x1584xf32>)
outs(%C: memref<1584x1584xf32>)
return
}
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.get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
%2 = transform.structured.vectorize_children_and_apply_patterns %1 { disable_multi_reduction_to_contract_patterns } : (!transform.any_op) -> !transform.any_op
transform.yield
}
}
// -----
// CHECK-LABEL: contraction_batch_matmul
func.func @contraction_batch_matmul(%A: memref<1584x1584x1584xf32>, %B: memref<1584x1584x1584xf32>, %C: memref<1584x1584x1584xf32>) {
// CHECK: arith.mulf %{{.*}}, %{{.*}} : vector<1584x1584x1584x1584xf32>
// CHECK: vector.multi_reduction <add>, %{{.*}}, {{.*}} [3] : vector<1584x1584x1584x1584xf32> to vector<1584x1584x1584xf32>
linalg.batch_matmul
ins(%A, %B: memref<1584x1584x1584xf32>, memref<1584x1584x1584xf32>)
outs(%C: memref<1584x1584x1584xf32>)
return
}
module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
%0 = transform.structured.match ops{["linalg.batch_matmul"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = transform.get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
%2 = transform.structured.vectorize_children_and_apply_patterns %1 { disable_multi_reduction_to_contract_patterns } : (!transform.any_op) -> !transform.any_op
transform.yield
}
}
// -----
#matmul_trait = {
indexing_maps = [
affine_map<(m, n, k) -> (m, k)>,
affine_map<(m, n, k) -> (k, n)>,
affine_map<(m, n, k) -> (m, n)>
],
iterator_types = ["parallel", "parallel", "reduction"]
}
// CHECK-LABEL: func @vectorization_test
func.func @vectorization_test(%A: memref<8x16xf32>, %B: memref<16x32xf32>,
%C: memref<8x32xf32>) {
// CHECK: vector.transfer_read %{{.*}} : memref<8x16xf32>, vector<8x32x16xf32>
// CHECK: vector.transfer_read %{{.*}} : memref<16x32xf32>, vector<8x32x16xf32>
// CHECK: %[[ACC:.*]] = vector.transfer_read %{{.*}} : memref<8x32xf32>, vector<8x32xf32>
// CHECK: %[[MUL:.*]] = arith.mulf %{{.*}}, %{{.*}} : vector<8x32x16xf32>
// CHECK: %[[R:.*]] = vector.multi_reduction <add>, %[[MUL]], %[[ACC]] [2] : vector<8x32x16xf32> to vector<8x32xf32>
// CHECK: vector.transfer_write %{{.*}}, %{{.*}} : vector<8x32xf32>, memref<8x32xf32>
linalg.generic #matmul_trait
ins(%A, %B : memref<8x16xf32>, memref<16x32xf32>)
outs(%C : memref<8x32xf32>) {
^bb(%a: f32, %b: f32, %c: f32) :
%d = arith.mulf %a, %b: f32
%e = arith.addf %c, %d: f32
linalg.yield %e : f32
}
return
}
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 = transform.get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
%2 = transform.structured.vectorize_children_and_apply_patterns %1 { disable_multi_reduction_to_contract_patterns, disable_transfer_permutation_map_lowering_patterns } : (!transform.any_op) -> !transform.any_op
transform.yield
}
}
// -----
#matmul_transpose_out_trait = {
indexing_maps = [
affine_map<(m, n, k) -> (m, k)>,
affine_map<(m, n, k) -> (k, n)>,
affine_map<(m, n, k) -> (n, m)>
],
iterator_types = ["parallel", "parallel", "reduction"]
}
// CHECK-LABEL: func @generic_output_transpose
func.func @generic_output_transpose(%A: memref<8x16xf32>, %B: memref<16x32xf32>,
%C: memref<32x8xf32>) {
// CHECK: vector.transfer_read %{{.*}} : memref<8x16xf32>, vector<8x32x16xf32>
// CHECK: vector.transfer_read %{{.*}} : memref<16x32xf32>, vector<8x32x16xf32>
// CHECK: %[[ACC:.*]] = vector.transfer_read %{{.*}} : memref<32x8xf32>, vector<8x32xf32>
// CHECK: %[[MUL:.*]] = arith.mulf %{{.*}}, %{{.*}} : vector<8x32x16xf32>
// CHECK: %[[R:.*]] = vector.multi_reduction <add>, %[[MUL]], %[[ACC]] [2] : vector<8x32x16xf32> to vector<8x32xf32>
// CHECK: vector.transfer_write %{{.*}}, %{{.*}} : vector<8x32xf32>, memref<32x8xf32>
linalg.generic #matmul_transpose_out_trait
ins(%A, %B : memref<8x16xf32>, memref<16x32xf32>)
outs(%C : memref<32x8xf32>) {
^bb(%a: f32, %b: f32, %c: f32) :
%d = arith.mulf %a, %b: f32
%e = arith.addf %c, %d: f32
linalg.yield %e : f32
}
return
}
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 = transform.get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
%2 = transform.structured.vectorize_children_and_apply_patterns %1 { disable_multi_reduction_to_contract_patterns, disable_transfer_permutation_map_lowering_patterns } : (!transform.any_op) -> !transform.any_op
transform.yield
}
}
// -----
#map0 = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
#map1 = affine_map<(d0, d1, d2) -> (d1, d0, d2)>
// CHECK: #[[MAP:.+]] = affine_map<(d0, d1, d2) -> (d1, d0, d2)>
// CHECK: func @generic_interchanged_transpose
func.func @generic_interchanged_transpose(%arg0: tensor<12x128x32xf32>) -> tensor<128x12x32xf32> {
// CHECK: %[[IN:.+]] = vector.transfer_read
// CHECK: vector.transfer_write %[[IN]], {{.+}} permutation_map = #[[MAP]]
%0 = tensor.empty() : tensor<128x12x32xf32>
%1 = linalg.generic {indexing_maps = [#map0, #map1],
iterator_types = ["parallel", "parallel", "parallel"]}
ins(%arg0 : tensor<12x128x32xf32>)
outs(%0 : tensor<128x12x32xf32>) {
^bb0(%arg1: f32, %arg2: f32):
linalg.yield %arg1 : f32
} -> tensor<128x12x32xf32>
return %1 : tensor<128x12x32xf32>
}
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 = transform.get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
%2 = transform.structured.vectorize_children_and_apply_patterns %1 { disable_multi_reduction_to_contract_patterns, disable_transfer_permutation_map_lowering_patterns } : (!transform.any_op) -> !transform.any_op
transform.yield
}
}
// -----
#matmul_trait = {
indexing_maps = [
affine_map<(m, n, k) -> (m, k)>,
affine_map<(m, n, k) -> (k, n)>,
affine_map<(m, n, k) -> (m, n)>
],
iterator_types = ["parallel", "parallel", "reduction"]
}
// CHECK-LABEL: func @vectorization_test_integer
func.func @vectorization_test_integer(%A: memref<8x16xi32>, %B: memref<16x32xi32>,
%C: memref<8x32xi32>) {
// CHECK: vector.transfer_read %{{.*}} : memref<8x16xi32>, vector<8x32x16xi32>
// CHECK: vector.transfer_read %{{.*}} : memref<16x32xi32>, vector<8x32x16xi32>
// CHECK: %[[ACC:.*]] = vector.transfer_read %{{.*}} : memref<8x32xi32>, vector<8x32xi32>
// CHECK: %[[MUL:.*]] = arith.muli %{{.*}}, %{{.*}} : vector<8x32x16xi32>
// CHECK: vector.multi_reduction <add>, %[[MUL]], %[[ACC]] [2] : vector<8x32x16xi32> to vector<8x32xi32>
// CHECK: vector.transfer_write %{{.*}}, %{{.*}} : vector<8x32xi32>, memref<8x32xi32>
linalg.generic #matmul_trait
ins(%A, %B : memref<8x16xi32>, memref<16x32xi32>)
outs(%C : memref<8x32xi32>) {
^bb(%a: i32, %b: i32, %c: i32) :
%d = arith.muli %a, %b: i32
%e = arith.addi %c, %d: i32
linalg.yield %e : i32
}
return
}
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 = transform.get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
%2 = transform.structured.vectorize_children_and_apply_patterns %1 { disable_multi_reduction_to_contract_patterns, disable_transfer_permutation_map_lowering_patterns } : (!transform.any_op) -> !transform.any_op
transform.yield
}
}
// -----
// CHECK-LABEL: func @vectorization_test_2
func.func @vectorization_test_2(%A: memref<8x16xf32>, %B: memref<16x32xf32>,
%C: memref<8x32xf32>) {
// CHECK: arith.mulf %{{.*}}, %{{.*}} : vector<8x32x16xf32>
// CHECK: vector.multi_reduction <add>, %{{.*}}, {{.*}} [2] : vector<8x32x16xf32> to vector<8x32xf32>
linalg.matmul
ins(%A, %B: memref<8x16xf32>, memref<16x32xf32>)
outs(%C: memref<8x32xf32>)
return
}
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.get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
%2 = transform.structured.vectorize_children_and_apply_patterns %1 { disable_multi_reduction_to_contract_patterns } : (!transform.any_op) -> !transform.any_op
transform.yield
}
}
// -----
// CHECK-LABEL: func @test_vectorize_scalar_input
func.func @test_vectorize_scalar_input(%A : memref<8x16xf32>, %arg0 : f32) {
// CHECK: %[[V:.*]] = vector.broadcast {{.*}} : f32 to vector<8x16xf32>
// CHECK: vector.transfer_write %[[V]], {{.*}} : vector<8x16xf32>, memref<8x16xf32>
linalg.generic {
indexing_maps = [affine_map<(m, n) -> ()>, affine_map<(m, n) -> (m, n)>],
iterator_types = ["parallel", "parallel"]}
ins(%arg0 : f32)
outs(%A: memref<8x16xf32>) {
^bb(%0: f32, %1: f32) :
linalg.yield %0 : f32
}
return
}
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 = transform.get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
%2 = transform.structured.vectorize_children_and_apply_patterns %1 : (!transform.any_op) -> !transform.any_op
transform.yield
}
}
// -----
// CHECK-LABEL: func @test_do_not_vectorize_unsupported_element_types
func.func @test_do_not_vectorize_unsupported_element_types(%A : memref<8x16xcomplex<f32>>, %arg0 : complex<f32>) {
// CHECK-NOT: vector.broadcast
// CHECK-NOT: vector.transfer_write
linalg.generic {
indexing_maps = [affine_map<(m, n) -> ()>, affine_map<(m, n) -> (m, n)>],
iterator_types = ["parallel", "parallel"]}
ins(%arg0 : complex<f32>)
outs(%A: memref<8x16xcomplex<f32>>) {
^bb(%0: complex<f32>, %1: complex<f32>) :
linalg.yield %0 : complex<f32>
}
return
}
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 = transform.get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
%2 = transform.structured.vectorize_children_and_apply_patterns %1 : (!transform.any_op) -> !transform.any_op
transform.yield
}
}
// -----
#map0 = affine_map<(d0) -> (d0)>
func.func @vectorize_affine_apply(%arg0: tensor<5xf32>, %arg3: index) -> tensor<5xi32> {
%0 = tensor.empty() : tensor<5xi32>
%1 = linalg.generic {indexing_maps = [#map0, #map0],
iterator_types = ["parallel"]}
ins(%arg0 : tensor<5xf32>)
outs(%0 : tensor<5xi32>) {
^bb0(%arg1: f32, %arg2: i32):
%2 = linalg.index 0 : index
%11 = affine.apply affine_map<() -> (123)>()
%12 = affine.apply affine_map<(d0, d1) -> (d0 + d1)>(%2, %11)
%13 = affine.apply affine_map<(d0)[s0] -> (d0 + s0)>(%12)[%arg3]
%14 = affine.apply affine_map<(d0) -> (d0 + 1)>(%13)
%15 = affine.apply affine_map<(d0, d1, d2) -> (d0 + d1 + d2)>(%13, %14, %12)
%3 = arith.index_cast %15 : index to i32
linalg.yield %3 : i32
} -> tensor<5xi32>
return %1 : tensor<5xi32>
}
// CHECK-LABEL: func.func @vectorize_affine_apply
// CHECK-SAME: %arg0: tensor<5xf32>
// CHECK-SAME: %[[ARG1:.*]]: index
// CHECK-DAG: %[[CST:.*]] = arith.constant dense<[123, 124, 125, 126, 127]> : vector<5xindex>
// CHECK-DAG: %[[CST_0:.*]] = arith.constant dense<1> : vector<5xindex>
// CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
// CHECK: %[[EMPTY:.*]] = tensor.empty() : tensor<5xi32>
// CHECK: %[[BCAST:.*]] = vector.broadcast %[[ARG1]] : index to vector<5xindex>
// CHECK: %[[ADDI_1:.*]] = arith.addi %[[BCAST]], %[[CST]] : vector<5xindex>
// CHECK: %[[ADDI_2:.*]] = arith.addi %[[ADDI_1]], %[[CST_0]] : vector<5xindex>
// CHECK: %[[ADDI_3:.*]] = arith.addi %[[ADDI_1]], %[[ADDI_2]] : vector<5xindex>
// CHECK: %[[ADDI_4:.*]] = arith.addi %[[ADDI_3]], %[[CST]] : vector<5xindex>
// CHECK: %[[CAST:.*]] = arith.index_cast %[[ADDI_4]] : vector<5xindex> to vector<5xi32>
// CHECK: vector.transfer_write %[[CAST]], %[[EMPTY]][%[[C0:.*]]] {in_bounds = [true]} : vector<5xi32>, tensor<5xi32>
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 = transform.get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
%2 = transform.structured.vectorize_children_and_apply_patterns %1 { vectorize_nd_extract } : (!transform.any_op) -> !transform.any_op
transform.yield
}
}
// -----
// CHECK-LABEL: func @test_vectorize_fill
func.func @test_vectorize_fill(%A : memref<8x16xf32>, %arg0 : f32) {
// CHECK: %[[V:.*]] = vector.broadcast {{.*}} : f32 to vector<8x16xf32>
// CHECK: vector.transfer_write %[[V]], {{.*}} : vector<8x16xf32>, memref<8x16xf32>
linalg.fill ins(%arg0 : f32) outs(%A : memref<8x16xf32>)
return
}
module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
%0 = transform.structured.match ops{["linalg.fill"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = transform.get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
%2 = transform.structured.vectorize_children_and_apply_patterns %1 : (!transform.any_op) -> !transform.any_op
transform.yield
}
}
// -----
// CHECK-LABEL: func @test_vectorize_fill
func.func @test_vectorize_fill_scalar(%A : memref<f32>, %arg0 : f32) {
// CHECK-SAME: (%[[M:.*]]: memref<f32>, %[[val:.*]]: f32)
// CHECK: %[[VEC:.*]] = vector.broadcast %[[val]] : f32 to vector<f32>
// CHECK: vector.transfer_write %[[VEC]], %[[M]][] : vector<f32>, memref<f32>
linalg.fill ins(%arg0 : f32) outs(%A : memref<f32>)
return
}
module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
%0 = transform.structured.match ops{["linalg.fill"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = transform.get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
%2 = transform.structured.vectorize_children_and_apply_patterns %1 : (!transform.any_op) -> !transform.any_op
transform.yield
}
}
// -----
// CHECK-LABEL: func @test_vectorize_copy
func.func @test_vectorize_copy(%A : memref<8x16xf32>, %B : memref<8x16xf32>) {
// CHECK: %[[V:.*]] = vector.transfer_read {{.*}} : memref<8x16xf32>, vector<8x16xf32>
// CHECK: vector.transfer_write %[[V]], {{.*}} : vector<8x16xf32>, memref<8x16xf32>
memref.copy %A, %B : memref<8x16xf32> to memref<8x16xf32>
return
}
module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
%0 = transform.structured.match ops{["memref.copy"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = transform.get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
%2 = transform.structured.vectorize_children_and_apply_patterns %1 : (!transform.any_op) -> !transform.any_op
transform.yield
}
}
// -----
// CHECK-LABEL: func @test_vectorize_copy_scalar
func.func @test_vectorize_copy_scalar(%A : memref<f32>, %B : memref<f32>) {
// CHECK-SAME: (%[[A:.*]]: memref<f32>, %[[B:.*]]: memref<f32>)
// CHECK: %[[V:.*]] = vector.transfer_read %[[A]][]{{.*}} : memref<f32>, vector<f32>
// CHECK: %[[val:.*]] = vector.extractelement %[[V]][] : vector<f32>
// CHECK: %[[VV:.*]] = vector.broadcast %[[val]] : f32 to vector<f32>
// CHECK: vector.transfer_write %[[VV]], %[[B]][] : vector<f32>, memref<f32>
memref.copy %A, %B : memref<f32> to memref<f32>
return
}
module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
%0 = transform.structured.match ops{["memref.copy"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = transform.get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
%2 = transform.structured.vectorize_children_and_apply_patterns %1 : (!transform.any_op) -> !transform.any_op
transform.yield
}
}
// -----
// CHECK-LABEL: func @test_vectorize_copy_complex
// CHECK-NOT: vector<
func.func @test_vectorize_copy_complex(%A : memref<8x16xcomplex<f32>>, %B : memref<8x16xcomplex<f32>>) {
memref.copy %A, %B : memref<8x16xcomplex<f32>> to memref<8x16xcomplex<f32>>
return
}
module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
%0 = transform.structured.match ops{["memref.copy"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = transform.get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
%2 = transform.structured.vectorize_children_and_apply_patterns %1 : (!transform.any_op) -> !transform.any_op
transform.yield
}
}
// -----
// CHECK-LABEL: func @test_vectorize_trailing_index
// CHECK-SAME: (%[[ARG0:.*]]: memref<1x2x4x8xindex>)
func.func @test_vectorize_trailing_index(%arg0: memref<1x2x4x8xindex>) {
// CHECK-DAG: %[[CST0:.*]] = arith.constant dense<[0, 1, 2, 3, 4, 5, 6, 7]> : vector<8xindex>
// CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
linalg.generic {
indexing_maps = [
affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>],
iterator_types = ["parallel", "parallel", "parallel", "parallel"]}
outs(%arg0: memref<1x2x4x8xindex>) {
^bb0(%arg1: index):
// CHECK: %[[BCST:.*]] = vector.broadcast %[[CST0]] : vector<8xindex> to vector<1x2x4x8xindex>
// CHECK: vector.transfer_write %[[BCST]], %[[ARG0]][%[[C0]], %[[C0]], %[[C0]], %[[C0]]] {{.*}} : vector<1x2x4x8xindex>, memref<1x2x4x8xindex>
%0 = linalg.index 3 : index
linalg.yield %0 : index
}
return
}
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 = transform.get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
%2 = transform.structured.vectorize_children_and_apply_patterns %1 : (!transform.any_op) -> !transform.any_op
transform.yield
}
}
// -----
// CHECK-LABEL: func @test_vectorize_inner_index
// CHECK-SAME: (%[[ARG0:.*]]: memref<1x2x4x8xindex>)
func.func @test_vectorize_inner_index(%arg0: memref<1x2x4x8xindex>) {
// CHECK-DAG: %[[CST0:.*]] = arith.constant dense<[0, 1]> : vector<2xindex>
// CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
linalg.generic {
indexing_maps = [
affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>],
iterator_types = ["parallel", "parallel", "parallel", "parallel"]}
outs(%arg0: memref<1x2x4x8xindex>) {
^bb0(%arg1: index):
// CHECK: %[[BCST:.*]] = vector.broadcast %[[CST0]] : vector<2xindex> to vector<1x8x4x2xindex>
// CHECK: %[[TRAN:.*]] = vector.transpose %[[BCST]], [0, 3, 2, 1] : vector<1x8x4x2xindex> to vector<1x2x4x8xindex>
// CHECK: vector.transfer_write %[[TRAN]], %[[ARG0]][%[[C0]], %[[C0]], %[[C0]], %[[C0]]] {{.*}} : vector<1x2x4x8xindex>, memref<1x2x4x8xindex>
%0 = linalg.index 1 : index
linalg.yield %0 : index
}
return
}
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 = transform.get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
%2 = transform.structured.vectorize_children_and_apply_patterns %1 : (!transform.any_op) -> !transform.any_op
transform.yield
}
}
// -----
// CHECK-LABEL: func @generic_vectorize
// CHECK-SAME: (%[[ARG0:.*]]: memref<4x256xf32>, %[[ARG1:.*]]: memref<4x256xf32>,
// CHECK-SAME: %[[ARG2:.*]]: memref<256xf32>, %[[ARG3:.*]]: f32)
func.func @generic_vectorize(%arg0: memref<4x256xf32>,
%arg1: memref<4x256xf32>,
%arg2: memref<256xf32>, %i: f32) {
// CHECK-DAG: %[[CST0:.*]] = arith.constant dense<2.000000e+00> : vector<4x256xf32>
// CHECK-DAG: %[[CST1:.*]] = arith.constant dense<1.000000e+00> : vector<4x256xf32>
// CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
%c1_f32 = arith.constant 1.0 : f32
linalg.generic {
indexing_maps = [
affine_map<(d0, d1) -> (d0, d1)>,
affine_map<(d0, d1) -> (d1)>,
affine_map<(d0, d1) -> (d0, d1)>,
affine_map<(d0, d1) -> (d0, d1)>,
affine_map<(d0, d1) -> (d0, d1)>,
affine_map<(d0, d1) -> (d0, d1)>,
affine_map<(d0, d1) -> (d0, d1)>,
affine_map<(d0, d1) -> (d0, d1)>,
affine_map<(d0, d1) -> (d0, d1)>,
affine_map<(d0, d1) -> (d0, d1)>,
affine_map<(d0, d1) -> (d0, d1)>,
affine_map<(d0, d1) -> (d0, d1)>],
iterator_types = ["parallel", "parallel"]}
ins(%arg1, %arg2: memref<4x256xf32>, memref<256xf32>)
outs(
%arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0 :
memref<4x256xf32>, memref<4x256xf32>, memref<4x256xf32>, memref<4x256xf32>,
memref<4x256xf32>, memref<4x256xf32>, memref<4x256xf32>, memref<4x256xf32>,
memref<4x256xf32>, memref<4x256xf32>) {
^bb0(%arg3 : f32, %arg4 : f32, %arg5: f32, %arg6: f32, %arg7: f32, %arg8: f32,
// CHECK: %[[V2:.*]] = vector.transfer_read %[[ARG1]][%[[C0]], %[[C0]]], {{.*}} : memref<4x256xf32>, vector<4x256xf32>
// CHECK: %[[V0:.*]] = vector.transfer_read %[[ARG2]][%[[C0]]], {{.*}} : memref<256xf32>, vector<4x256xf32>
// CHECK: %[[V3:.*]] = vector.transfer_read %[[ARG0]][%[[C0]], %[[C0]]], {{.*}} : memref<4x256xf32>, vector<4x256xf32>
// CHECK: %[[V1:.*]] = vector.transfer_read %[[ARG0]][%[[C0]], %[[C0]]], {{.*}} : memref<4x256xf32>, vector<4x256xf32>
%arg9 : f32, %arg10 : f32, %arg11 : f32, %arg12 : f32, %arg13 : f32,
%arg14 : f32):
// CHECK: %[[ADD:.*]] = arith.addf %[[V0]], %[[V1]] : vector<4x256xf32>
%6 = arith.addf %arg4, %arg6 : f32
// CHECK: %[[CMP:.*]] = arith.cmpf ogt, %[[V2]], %[[V1]] : vector<4x256xf32>
%7 = arith.cmpf ogt, %arg3, %arg6 : f32
// CHECK: %[[ARG3B:.*]] = vector.broadcast %[[ARG3]] : f32 to vector<4x256xf32>
%8 = arith.constant 2.0 : f32
// CHECK: %[[DIV:.*]] = arith.divf %[[V3]], %[[ARG3B]] : vector<4x256xf32>
%9 = arith.divf %arg5, %i : f32
// CHECK: %[[EXP:.*]] = math.exp2 %[[V3]] : vector<4x256xf32>
%10 = math.exp2 %arg5 : f32
// CHECK: %[[MUL:.*]] = arith.mulf %[[V3]], %[[CST0]] : vector<4x256xf32>
%11 = arith.mulf %arg5, %8 : f32
// CHECK: %[[RSQRT:.*]] = math.rsqrt %[[V3]] : vector<4x256xf32>
%12 = math.rsqrt %arg5 : f32
// CHECK: %[[SEL:.*]] = arith.select %[[CMP]], %[[V3]], %[[V1]] : vector<4x256xi1>, vector<4x256xf32>
%13 = arith.select %7, %arg5, %arg6 : f32
// CHECK: %[[SUB:.*]] = arith.subf %[[V3]], %[[V0]] : vector<4x256xf32>
%14 = arith.subf %arg5, %arg4 : f32
// CHECK: %[[TAN:.*]] = math.tanh %[[V3]] : vector<4x256xf32>
%15 = math.tanh %arg5 : f32
// CHECK: vector.transfer_write %[[ADD]], %[[ARG0]][%[[C0]], %[[C0]]] {{.*}} : vector<4x256xf32>, memref<4x256xf32>
// CHECK: vector.transfer_write %[[CST0]], %[[ARG0]][%[[C0]], %[[C0]]] {{.*}} : vector<4x256xf32>, memref<4x256xf32>
// CHECK: vector.transfer_write %[[CST1]], %[[ARG0]][%[[C0]], %[[C0]]] {{.*}} : vector<4x256xf32>, memref<4x256xf32>
// CHECK: vector.transfer_write %[[DIV]], %[[ARG0]][%[[C0]], %[[C0]]] {{.*}} : vector<4x256xf32>, memref<4x256xf32>
// CHECK: vector.transfer_write %[[EXP]], %[[ARG0]][%[[C0]], %[[C0]]] {{.*}} : vector<4x256xf32>, memref<4x256xf32>
// CHECK: vector.transfer_write %[[MUL]], %[[ARG0]][%[[C0]], %[[C0]]] {{.*}} : vector<4x256xf32>, memref<4x256xf32>
// CHECK: vector.transfer_write %[[RSQRT]], %[[ARG0]][%[[C0]], %[[C0]]] {{.*}} : vector<4x256xf32>, memref<4x256xf32>
// CHECK: vector.transfer_write %[[SEL]], %[[ARG0]][%[[C0]], %[[C0]]] {{.*}} : vector<4x256xf32>, memref<4x256xf32>
// CHECK: vector.transfer_write %[[SUB]], %[[ARG0]][%[[C0]], %[[C0]]] {{.*}} : vector<4x256xf32>, memref<4x256xf32>
// CHECK: vector.transfer_write %[[TAN]], %[[ARG0]][%[[C0]], %[[C0]]] {{.*}} : vector<4x256xf32>, memref<4x256xf32>
linalg.yield %6, %8, %c1_f32, %9, %10, %11, %12, %13, %14, %15 : f32, f32,
f32, f32, f32, f32, f32, f32, f32, f32
}
return
}
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 = transform.get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
%2 = transform.structured.vectorize_children_and_apply_patterns %1 { disable_transfer_permutation_map_lowering_patterns } : (!transform.any_op) -> !transform.any_op
transform.yield
}
}
// -----
// CHECK-LABEL: func @generic_vectorize_tensor
// CHECK-SAME: (%[[ARG0:.*]]: tensor<4x256xf32>, %[[ARG1:.*]]: tensor<4x256xf32>,
// CHECK-SAME: %[[ARG2:.*]]: tensor<256xf32>, %[[ARG3:.*]]: f32)
func.func @generic_vectorize_tensor(%arg0: tensor<4x256xf32>,
%arg1: tensor<4x256xf32>, %arg2: tensor<256xf32>,
%i: f32) -> (tensor<4x256xf32>, tensor<4x256xf32>, tensor<4x256xf32>,
tensor<4x256xf32>, tensor<4x256xf32>, tensor<4x256xf32>, tensor<4x256xf32>,
tensor<4x256xf32>, tensor<4x256xf32>, tensor<4x256xf32>) {
%c1_f32 = arith.constant 1.0 : f32
%r:10 = linalg.generic {
indexing_maps = [
affine_map<(d0, d1) -> (d0, d1)>,
affine_map<(d0, d1) -> (d1)>,
affine_map<(d0, d1) -> (d0, d1)>,
affine_map<(d0, d1) -> (d0, d1)>,
affine_map<(d0, d1) -> (d0, d1)>,
affine_map<(d0, d1) -> (d0, d1)>,
affine_map<(d0, d1) -> (d0, d1)>,
affine_map<(d0, d1) -> (d0, d1)>,
affine_map<(d0, d1) -> (d0, d1)>,
affine_map<(d0, d1) -> (d0, d1)>,
affine_map<(d0, d1) -> (d0, d1)>,
affine_map<(d0, d1) -> (d0, d1)>],
iterator_types = ["parallel", "parallel"]}
ins(%arg1, %arg2: tensor<4x256xf32>, tensor<256xf32>)
outs(
%arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0, %arg0 :
tensor<4x256xf32>, tensor<4x256xf32>, tensor<4x256xf32>, tensor<4x256xf32>,
tensor<4x256xf32>, tensor<4x256xf32>, tensor<4x256xf32>, tensor<4x256xf32>,
tensor<4x256xf32>, tensor<4x256xf32>) {
^bb0(%arg3 : f32, %arg4 : f32, %arg5: f32, %arg6: f32, %arg7: f32, %arg8: f32,
%arg9 : f32, %arg10 : f32, %arg11 : f32, %arg12 : f32, %arg13 : f32,
%arg14 : f32):
// CHECK-DAG: %[[CST0:.*]] = arith.constant dense<2.000000e+00> : vector<4x256xf32>
// CHECK-DAG: %[[CST1:.*]] = arith.constant dense<1.000000e+00> : vector<4x256xf32>
// CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
// CHECK: %[[V2:.*]] = vector.transfer_read %[[ARG1]][%[[C0]], %[[C0]]], {{.*}} : tensor<4x256xf32>, vector<4x256xf32>
// CHECK: %[[V0:.*]] = vector.transfer_read %[[ARG2]][%[[C0]]], {{.*}} : tensor<256xf32>, vector<4x256xf32>
// CHECK: %[[V3:.*]] = vector.transfer_read %[[ARG0]][%[[C0]], %[[C0]]], {{.*}} : tensor<4x256xf32>, vector<4x256xf32>
// CHECK: %[[V1:.*]] = vector.transfer_read %[[ARG0]][%[[C0]], %[[C0]]], {{.*}} : tensor<4x256xf32>, vector<4x256xf32>
// CHECK: %[[ADD:.*]] = arith.addf %[[V0]], %[[V1]] : vector<4x256xf32>
%6 = arith.addf %arg4, %arg6 : f32
// CHECK: %[[CMP:.*]] = arith.cmpf ogt, %[[V2]], %[[V1]] : vector<4x256xf32>
%7 = arith.cmpf ogt, %arg3, %arg6 : f32
// CHECK: %[[ARG3B:.*]] = vector.broadcast %[[ARG3]] : f32 to vector<4x256xf32>
%8 = arith.constant 2.0 : f32
// CHECK: %[[DIV:.*]] = arith.divf %[[V3]], %[[ARG3B]] : vector<4x256xf32>
%9 = arith.divf %arg5, %i : f32
// CHECK: %[[EXP:.*]] = math.exp2 %[[V3]] : vector<4x256xf32>
%10 = math.exp2 %arg5 : f32
// CHECK: %[[MUL:.*]] = arith.mulf %[[V3]], %[[CST0]] : vector<4x256xf32>
%11 = arith.mulf %arg5, %8 : f32
// CHECK: %[[RSQRT:.*]] = math.rsqrt %[[V3]] : vector<4x256xf32>
%12 = math.rsqrt %arg5 : f32
// CHECK: %[[SEL:.*]] = arith.select %[[CMP]], %[[V3]], %[[V1]] : vector<4x256xi1>, vector<4x256xf32>
%13 = arith.select %7, %arg5, %arg6 : f32
// CHECK: %[[SUB:.*]] = arith.subf %[[V3]], %[[V0]] : vector<4x256xf32>
%14 = arith.subf %arg5, %arg4 : f32
// CHECK: %[[TAN:.*]] = math.tanh %[[V3]] : vector<4x256xf32>
%15 = math.tanh %arg5 : f32
// CHECK: %[[R0:.*]] = vector.transfer_write %[[ADD]], %[[ARG0]][%[[C0]], %[[C0]]] {{.*}} : vector<4x256xf32>, tensor<4x256xf32>
// CHECK: %[[R1:.*]] = vector.transfer_write %[[CST0]], %[[ARG0]][%[[C0]], %[[C0]]] {{.*}} : vector<4x256xf32>, tensor<4x256xf32>
// CHECK: %[[R2:.*]] = vector.transfer_write %[[CST1]], %[[ARG0]][%[[C0]], %[[C0]]] {{.*}} : vector<4x256xf32>, tensor<4x256xf32>
// CHECK: %[[R3:.*]] = vector.transfer_write %[[DIV]], %[[ARG0]][%[[C0]], %[[C0]]] {{.*}} : vector<4x256xf32>, tensor<4x256xf32>
// CHECK: %[[R4:.*]] = vector.transfer_write %[[EXP]], %[[ARG0]][%[[C0]], %[[C0]]] {{.*}} : vector<4x256xf32>, tensor<4x256xf32>
// CHECK: %[[R5:.*]] = vector.transfer_write %[[MUL]], %[[ARG0]][%[[C0]], %[[C0]]] {{.*}} : vector<4x256xf32>, tensor<4x256xf32>
// CHECK: %[[R6:.*]] = vector.transfer_write %[[RSQRT]], %[[ARG0]][%[[C0]], %[[C0]]] {{.*}} : vector<4x256xf32>, tensor<4x256xf32>
// CHECK: %[[R7:.*]] = vector.transfer_write %[[SEL]], %[[ARG0]][%[[C0]], %[[C0]]] {{.*}} : vector<4x256xf32>, tensor<4x256xf32>
// CHECK: %[[R8:.*]] = vector.transfer_write %[[SUB]], %[[ARG0]][%[[C0]], %[[C0]]] {{.*}} : vector<4x256xf32>, tensor<4x256xf32>
// CHECK: %[[R9:.*]] = vector.transfer_write %[[TAN]], %[[ARG0]][%[[C0]], %[[C0]]] {{.*}} : vector<4x256xf32>, tensor<4x256xf32>
linalg.yield %6, %8, %c1_f32, %9, %10, %11, %12, %13, %14, %15 : f32, f32,
f32, f32, f32, f32, f32, f32, f32, f32
} -> (tensor<4x256xf32>, tensor<4x256xf32>, tensor<4x256xf32>,
tensor<4x256xf32>, tensor<4x256xf32>, tensor<4x256xf32>, tensor<4x256xf32>,
tensor<4x256xf32>, tensor<4x256xf32>, tensor<4x256xf32>)
// CHECK: return %[[R0]], %[[R1]], %[[R2]], %[[R3]], %[[R4]], %[[R5]], %[[R6]], %[[R7]], %[[R8]], %[[R9]] : tensor<4x256xf32>, tensor<4x256xf32>, tensor<4x256xf32>, tensor<4x256xf32>, tensor<4x256xf32>, tensor<4x256xf32>, tensor<4x256xf32>, tensor<4x256xf32>, tensor<4x256xf32>, tensor<4x256xf32>
return %r#0, %r#1, %r#2, %r#3, %r#4, %r#5, %r#6, %r#7, %r#8, %r#9:
tensor<4x256xf32>, tensor<4x256xf32>, tensor<4x256xf32>,
tensor<4x256xf32>, tensor<4x256xf32>, tensor<4x256xf32>, tensor<4x256xf32>,
tensor<4x256xf32>, tensor<4x256xf32>, tensor<4x256xf32>
}
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 = transform.get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
%2 = transform.structured.vectorize_children_and_apply_patterns %1 { disable_transfer_permutation_map_lowering_patterns } : (!transform.any_op) -> !transform.any_op
transform.yield
}
}
// -----
// CHECK-DAG: #[[$MAP0:.*]] = affine_map<(d0, d1) -> (d0, 0, 0, d1)>
// CHECK-DAG: #[[$MAP1:.*]] = affine_map<(d0) -> (d0, 0, 0, 0)>
// CHECK-DAG: #[[$MAP2:.*]] = affine_map<(d0) -> (0, 0, d0, 0)>
// CHECK-DAG: #[[$MAP3:.*]] = affine_map<(d0, d1) -> (d1, 0, d0, 0)>
// CHECK: func @generic_vectorize_broadcast_transpose
// CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[CF:.*]] = arith.constant 0.000000e+00 : f32
// CHECK: %[[V0:.*]] = vector.transfer_read %{{.*}}[%[[C0]], %[[C0]]], %[[CF]] {in_bounds = [true, true, true, true], permutation_map = #[[$MAP0]]} : memref<4x4xf32>, vector<4x4x4x4xf32>
// CHECK: %[[V1:.*]] = vector.transfer_read %{{.*}}[%[[C0]]], %[[CF]] {in_bounds = [true, true, true, true], permutation_map = #[[$MAP1]]} : memref<4xf32>, vector<4x4x4x4xf32>
// CHECK: %[[V2:.*]] = vector.transfer_read %{{.*}}[%[[C0]]], %[[CF]] {in_bounds = [true, true, true, true], permutation_map = #[[$MAP2]]} : memref<4xf32>, vector<4x4x4x4xf32>
// CHECK: %[[V3:.*]] = vector.transfer_read %{{.*}}[%[[C0]], %[[C0]]], %[[CF]] {in_bounds = [true, true, true, true], permutation_map = #[[$MAP3]]} : memref<4x4xf32>, vector<4x4x4x4xf32>
// CHECK: %[[SUB:.*]] = arith.subf %[[V0]], %[[V1]] : vector<4x4x4x4xf32>
// CHECK: %[[ADD0:.*]] = arith.addf %[[V2]], %[[SUB]] : vector<4x4x4x4xf32>
// CHECK: %[[ADD1:.*]] = arith.addf %[[V3]], %[[ADD0]] : vector<4x4x4x4xf32>
// CHECK: vector.transfer_write %[[ADD1]], {{.*}} : vector<4x4x4x4xf32>, memref<4x4x4x4xf32>
func.func @generic_vectorize_broadcast_transpose(
%A: memref<4xf32>, %B: memref<4x4xf32>, %C: memref<4x4x4x4xf32>) {
linalg.generic {
indexing_maps = [affine_map<(d0, d1, d2, d3) -> (d0, d3)>,
affine_map<(d0, d1, d2, d3) -> (d0)>,
affine_map<(d0, d1, d2, d3) -> (d2)>,
affine_map<(d0, d1, d2, d3) -> (d2, d0)>,
affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>],
iterator_types = ["parallel", "parallel", "parallel", "parallel"]}
ins(%B, %A, %A, %B: memref<4x4xf32>, memref<4xf32>, memref<4xf32>, memref<4x4xf32>)
outs(%C : memref<4x4x4x4xf32>) {
^bb0(%arg0: f32, %arg1: f32, %arg2: f32, %arg3: f32, %arg4: f32):
%s = arith.subf %arg0, %arg1 : f32
%a = arith.addf %arg2, %s : f32
%b = arith.addf %arg3, %a : f32
linalg.yield %b : f32
}
return
}
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 = transform.get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
%2 = transform.structured.vectorize_children_and_apply_patterns %1 { disable_transfer_permutation_map_lowering_patterns } : (!transform.any_op) -> !transform.any_op
transform.yield
}
}
// -----
// Test different input maps.
#matmul_trait = {
indexing_maps = [
affine_map<(d0, d1, d2, d3) -> (d1, d0)>,
affine_map<(d0, d1, d2, d3) -> (d3, d1)>,
affine_map<(d0, d1, d2, d3) -> (d3, d1, d0, d2)>,
affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>
],
iterator_types = ["parallel", "parallel", "parallel", "parallel"]
}
// CHECK-DAG: #[[MAP0:.*]] = affine_map<(d0, d1) -> (d1, d0, 0, 0)>
// CHECK-DAG: #[[MAP1:.*]] = affine_map<(d0, d1) -> (0, d1, 0, d0)>
// CHECK-DAG: #[[MAP2:.*]] = affine_map<(d0, d1, d2, d3) -> (d2, d1, d3, d0)>
// CHECK: func @vectorization_transpose
// CHECK: vector.transfer_read {{.*}}{in_bounds = [true, true, true, true], permutation_map = #[[MAP0]]} : memref<14x7xf32>, vector<7x14x8x16xf32>
// CHECK: vector.transfer_read {{.*}}{in_bounds = [true, true, true, true], permutation_map = #[[MAP1]]} : memref<16x14xf32>, vector<7x14x8x16xf32>
// CHECK: vector.transfer_read {{.*}}{in_bounds = [true, true, true, true], permutation_map = #[[MAP2]]} : memref<16x14x7x8xf32>, vector<7x14x8x16xf32>
// CHECK: arith.addf {{.*}} : vector<7x14x8x16xf32>
// CHECK: arith.addf {{.*}} : vector<7x14x8x16xf32>
// CHECK: vector.transfer_write {{.*}} : vector<7x14x8x16xf32>, memref<7x14x8x16xf32>
func.func @vectorization_transpose(%A: memref<14x7xf32>, %B: memref<16x14xf32>,
%C: memref<16x14x7x8xf32>, %D: memref<7x14x8x16xf32>) {
linalg.generic #matmul_trait
ins(%A, %B, %C : memref<14x7xf32>, memref<16x14xf32>, memref<16x14x7x8xf32>)
outs(%D : memref<7x14x8x16xf32>) {
^bb(%a: f32, %b: f32, %c: f32, %d: f32) :
%e = arith.addf %a, %b: f32
%f = arith.addf %e, %c: f32
linalg.yield %f : f32
}
return
}
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 = transform.get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
%2 = transform.structured.vectorize_children_and_apply_patterns %1 { disable_transfer_permutation_map_lowering_patterns } : (!transform.any_op) -> !transform.any_op
transform.yield
}
}
// -----
// CHECK-LABEL: func @matmul_tensors
// CHECK-SAME: (%[[ARG0:.*]]: tensor<8x4xf32>, %[[ARG1:.*]]: tensor<4x12xf32>,
// CHECK-SAME: %[[ARG2:.*]]: tensor<8x12xf32>) -> tensor<8x12xf32>
func.func @matmul_tensors(
%arg0: tensor<8x4xf32>, %arg1: tensor<4x12xf32>, %arg2: tensor<8x12xf32>)
-> tensor<8x12xf32> {
// CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[V0:.*]] = vector.transfer_read %[[ARG0]][%[[C0]], %[[C0]]], {{.*}} : tensor<8x4xf32>, vector<8x12x4xf32>
// CHECK-DAG: %[[V1:.*]] = vector.transfer_read %[[ARG1]][%[[C0]], %[[C0]]], {{.*}} : tensor<4x12xf32>, vector<8x12x4xf32>
// CHECK-DAG: %[[V2:.*]] = vector.transfer_read %[[ARG2]][%[[C0]], %[[C0]]], {{.*}} : tensor<8x12xf32>, vector<8x12xf32>
//
// linalg matmul lowers gets expanded to a 3D reduction, canonicalization later
// convert it to a 2D contract.
// CHECK: %[[MUL:.*]] = arith.mulf %[[V0]], %[[V1]] : vector<8x12x4xf32>
// CHECK: %[[R:.*]] = vector.multi_reduction <add>, %[[MUL]], %[[V2]] [2] : vector<8x12x4xf32> to vector<8x12xf32>
// CHECK: %[[W:.*]] = vector.transfer_write %[[R]], %[[ARG2]][%[[C0]], %[[C0]]] {in_bounds = [true, true]} : vector<8x12xf32>, tensor<8x12xf32>
%0 = linalg.matmul ins(%arg0, %arg1: tensor<8x4xf32>, tensor<4x12xf32>)
outs(%arg2: tensor<8x12xf32>)
-> tensor<8x12xf32>
// CHECK: return %[[W]] : tensor<8x12xf32>
return %0 : tensor<8x12xf32>
}
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.get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
%2 = transform.structured.vectorize_children_and_apply_patterns %1 { disable_multi_reduction_to_contract_patterns, disable_transfer_permutation_map_lowering_patterns } : (!transform.any_op) -> !transform.any_op
transform.yield
}
}
// -----
// CHECK-LABEL: func @pad_static(
// CHECK-SAME: %[[ARG0:.*]]: tensor<2x?x2xf32>, %[[PAD:.*]]: f32
// CHECK-NOT: tensor.pad
// CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[C2:.*]] = arith.constant 2 : index
// CHECK-DAG: %[[INIT:.*]] = tensor.empty() : tensor<2x3x4xf32>
// CHECK-DAG: %[[VEC:.*]] = vector.broadcast %[[PAD]] : f32 to vector<2x3x4xf32>
// CHECK: %[[FILL:.*]] = vector.transfer_write %[[VEC]], %[[INIT]]{{.*}} : vector<2x3x4xf32>, tensor<2x3x4xf32>
// CHECK: %[[READ:.*]] = vector.transfer_read %[[ARG0]][%[[C0]], %[[C0]], %[[C0]]], %[[PAD]] {in_bounds = [true, false, true]} : tensor<2x?x2xf32>, vector<2x3x2xf32>
// CHECK: %[[RESULT:.*]] = vector.transfer_write %[[READ]], %[[FILL]][%[[C0]], %[[C0]], %[[C2]]] {in_bounds = [true, true, true]} : vector<2x3x2xf32>, tensor<2x3x4xf32>
// CHECK: return %[[RESULT]]
func.func @pad_static(%arg0: tensor<2x?x2xf32>, %pad_value: f32) -> tensor<2x3x4xf32> {
%0 = tensor.pad %arg0 low[0, 0, 2] high[0, 1, 0] {
^bb0(%arg1: index, %arg2: index, %arg3: index):
tensor.yield %pad_value : f32
} : tensor<2x?x2xf32> to tensor<2x3x4xf32>
return %0 : tensor<2x3x4xf32>
}
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
%1 = transform.get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
%2 = transform.structured.vectorize_children_and_apply_patterns %1 { vectorize_padding } : (!transform.any_op) -> !transform.any_op
transform.yield
}
}
// -----
// CHECK-LABEL: func @pad_static_source(
// CHECK-SAME: %[[ARG0:.*]]: tensor<2x5x2xf32>, %[[PAD:.*]]: f32
// CHECK-NOT: tensor.pad
// CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[C2:.*]] = arith.constant 2 : index
// CHECK: %[[INIT:.*]] = tensor.empty() : tensor<2x6x4xf32>
// CHECK: %[[VEC:.*]] = vector.broadcast %[[PAD]] : f32 to vector<2x6x4xf32>
// CHECK: %[[FILL:.*]] = vector.transfer_write %[[VEC]], %[[INIT]][%[[C0]], %[[C0]], %[[C0]]] {in_bounds = [true, true, true]} : vector<2x6x4xf32>, tensor<2x6x4xf32>
// CHECK: %[[READ:.*]] = vector.transfer_read %[[ARG0]][%[[C0]], %[[C0]], %[[C0]]], %{{.*}} {in_bounds = [true, true, true]} : tensor<2x5x2xf32>, vector<2x5x2xf32>
// CHECK: %[[WRITE:.*]] = vector.transfer_write %[[READ]], %[[FILL]][%[[C0]], %[[C0]], %[[C2]]] {in_bounds = [true, true, true]} : vector<2x5x2xf32>, tensor<2x6x4xf32>
// CHECK: return %[[WRITE]]
func.func @pad_static_source(%arg0: tensor<2x5x2xf32>, %pad_value: f32) -> tensor<2x6x4xf32> {
%0 = tensor.pad %arg0 low[0, 0, 2] high[0, 1, 0] {
^bb0(%arg1: index, %arg2: index, %arg3: index):
tensor.yield %pad_value : f32
} : tensor<2x5x2xf32> to tensor<2x6x4xf32>
return %0 : tensor<2x6x4xf32>
}
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
%1 = transform.get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
%2 = transform.structured.vectorize_children_and_apply_patterns %1 { vectorize_padding } : (!transform.any_op) -> !transform.any_op
transform.yield
}
}
// -----
// CHECK-LABEL: func @pad_static_dynamic(
// CHECK-SAME: %[[SRC:.*]]: tensor<1x2x2x?xf32>, %[[LOW:.*]]: index, %[[HIGH:.*]]: index
// CHECK-NOT: tensor.pad
// CHECK-DAG: %[[C2:.*]] = arith.constant 2 : index
// CHECK-DAG: %[[C3:.*]] = arith.constant 3 : index
// CHECK-DAG: %[[C5:.*]] = arith.constant 5 : index
// CHECK: %[[V0:.*]] = arith.addi %[[LOW]], %[[C2]] : index
// CHECK: %[[V1:.*]] = arith.addi %[[V0]], %[[C3]] : index
// CHECK: %[[V2:.*]] = arith.addi %[[HIGH]], %[[C5]] : index
// CHECK: %[[DIM3:.*]] = tensor.dim %[[SRC]], %[[C3]] : tensor<1x2x2x?xf32>
// CHECK: %[[V4:.*]] = arith.addi %[[DIM3]], %[[C3]] : index
// CHECK: %[[V5:.*]] = arith.addi %[[V4]], %[[C2]] : index
// CHECK: %[[INIT:.*]] = tensor.empty(%[[V1]], %[[V2]], %[[V5]]) : tensor<6x?x?x?xf32>
// CHECK: %[[FILL:.*]] = linalg.fill ins(%{{.*}} : f32) outs(%[[INIT]] : tensor<6x?x?x?xf32>) -> tensor<6x?x?x?xf32>
// CHECK: %[[SRCDIM:.*]] = tensor.dim %[[SRC]], %[[C3]] : tensor<1x2x2x?xf32>
// CHECK: %[[RESULT:.*]] = tensor.insert_slice %[[SRC]] into %[[FILL]][2, %[[LOW]], 3, 3] [1, 2, 2, %[[SRCDIM]]] [1, 1, 1, 1] : tensor<1x2x2x?xf32> into tensor<6x?x?x?xf32>
// CHECK: return %[[RESULT]]
func.func @pad_static_dynamic(%arg0: tensor<1x2x2x?xf32>, %low: index, %high: index,
%pad_value: f32) -> tensor<6x?x?x?xf32> {
%0 = tensor.pad %arg0 low[2, %low, 3, 3] high[3, 3, %high, 2] {
^bb0(%arg1: index, %arg2: index, %arg3: index, %arg4: index):
tensor.yield %pad_value : f32
} : tensor<1x2x2x?xf32> to tensor<6x?x?x?xf32>
return %0 : tensor<6x?x?x?xf32>
}
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
%1 = transform.get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
%2 = transform.structured.vectorize_children_and_apply_patterns %1 { vectorize_padding } : (!transform.any_op) -> !transform.any_op
transform.yield
}
}
// -----
// CHECK-LABEL: func @pad_static_complex(
// CHECK-NOT: vector<
func.func @pad_static_complex(%arg0: tensor<2x5x2xcomplex<f32>>, %pad_value: complex<f32>) -> tensor<2x6x4xcomplex<f32>> {
%0 = tensor.pad %arg0 low[0, 0, 2] high[0, 1, 0] {
^bb0(%arg1: index, %arg2: index, %arg3: index):
tensor.yield %pad_value : complex<f32>
} : tensor<2x5x2xcomplex<f32>> to tensor<2x6x4xcomplex<f32>>
return %0 : tensor<2x6x4xcomplex<f32>>
}
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
%1 = transform.get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
%2 = transform.structured.vectorize_children_and_apply_patterns %1 { vectorize_padding } : (!transform.any_op) -> !transform.any_op
transform.yield
}
}
// -----
// CHECK-LABEL: func @pad_and_transfer_read
// CHECK-SAME: %[[ARG0:.*]]: tensor<5x6xf32>
// CHECK-NOT: tensor.pad
// CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[C5:.*]] = arith.constant 5.0
// CHECK: %[[RESULT:.*]] = vector.transfer_read %[[ARG0]][%[[C0]], %[[C0]]], %[[C5]] : tensor<5x6xf32>, vector<7x9xf32>
// CHECK: return %[[RESULT]]
func.func @pad_and_transfer_read(%arg0: tensor<5x6xf32>) -> vector<7x9xf32> {
%c0 = arith.constant 0 : index
%c5 = arith.constant 5.0 : f32
%c6 = arith.constant 6.0 : f32
%0 = tensor.pad %arg0 low[0, 0] high[5, 7] {
^bb0(%arg1: index, %arg2: index):
tensor.yield %c5 : f32
} : tensor<5x6xf32> to tensor<10x13xf32>
%1 = vector.transfer_read %0[%c0, %c0], %c6
: tensor<10x13xf32>, vector<7x9xf32>
return %1 : vector<7x9xf32>
}
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
%1 = transform.get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
%2 = transform.structured.vectorize_children_and_apply_patterns %1 { vectorize_padding } : (!transform.any_op) -> !transform.any_op
transform.yield
}
}
// -----
func.func private @make_vector() -> vector<7x9xf32>
// CHECK-LABEL: func @pad_and_transfer_write_static
// CHECK-SAME: %[[ARG0:.*]]: tensor<5x6xf32>
// CHECK-NOT: tensor.pad
// CHECK: %[[C0:.*]] = arith.constant 0 : index
// CHECK: %[[VEC0:.*]] = call @make_vector() : () -> vector<7x9xf32>
// CHECK: %[[RESULT:.*]] = vector.transfer_write %[[VEC0]], %[[ARG0]][%[[C0]], %[[C0]]] : vector<7x9xf32>, tensor<5x6xf32>
// CHECK: return %[[RESULT]]
func.func @pad_and_transfer_write_static(
%arg0: tensor<5x6xf32>) -> tensor<5x6xf32> {
%c0 = arith.constant 0 : index
%c5 = arith.constant 5.0 : f32
%0 = tensor.pad %arg0 low[0, 0] high[5, 7] {
^bb0(%arg2: index, %arg3: index):
tensor.yield %c5 : f32
} : tensor<5x6xf32> to tensor<10x13xf32>
%1 = call @make_vector() : () -> vector<7x9xf32>
%2 = vector.transfer_write %1, %0[%c0, %c0]
: vector<7x9xf32>, tensor<10x13xf32>
%3 = tensor.extract_slice %2[0, 0] [5, 6] [1, 1] : tensor<10x13xf32> to tensor<5x6xf32>
return %3 : tensor<5x6xf32>
}
module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
%3 = transform.structured.match ops{["tensor.pad"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%4 = transform.get_parent_op %3 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
%5 = transform.structured.vectorize_children_and_apply_patterns %4 { vectorize_padding } : (!transform.any_op) -> !transform.any_op
transform.yield
}
}
// -----
func.func private @make_vector() -> vector<7x9xf32>
// CHECK-LABEL: func @pad_and_transfer_write_dynamic_static
// CHECK-SAME: %[[ARG0:.*]]: tensor<?x?xf32>, %[[SIZE:.*]]: index, %[[PADDING:.*]]: index
// CHECK-NOT: tensor.pad
// CHECK: %[[C0:.*]] = arith.constant 0 : index
// CHECK: %[[SUB:.*]] = tensor.extract_slice %[[ARG0]][0, 0] [%[[SIZE]], 6] [1, 1] : tensor<?x?xf32> to tensor<?x6xf32>
// CHECK: %[[VEC0:.*]] = call @make_vector() : () -> vector<7x9xf32>
// CHECK: %[[RESULT:.*]] = vector.transfer_write %[[VEC0]], %[[SUB]][%[[C0]], %[[C0]]] : vector<7x9xf32>, tensor<?x6xf32>
// CHECK: return %[[RESULT]]
func.func @pad_and_transfer_write_dynamic_static(
%arg0: tensor<?x?xf32>, %size: index, %padding: index) -> tensor<?x6xf32> {
%c0 = arith.constant 0 : index
%c5 = arith.constant 5.0 : f32
%s = tensor.extract_slice %arg0[0, 0] [%size, 6] [1, 1]
: tensor<?x?xf32> to tensor<?x6xf32>
%0 = tensor.pad %s low[0, 0] high[%padding, 7] {
^bb0(%arg2: index, %arg3: index):
tensor.yield %c5 : f32
} : tensor<?x6xf32> to tensor<?x13xf32>
%1 = call @make_vector() : () -> vector<7x9xf32>
%2 = vector.transfer_write %1, %0[%c0, %c0]
: vector<7x9xf32>, tensor<?x13xf32>
%3 = tensor.extract_slice %2[0, 0] [%size, 6] [1, 1] : tensor<?x13xf32> to tensor<?x6xf32>
return %3 : tensor<?x6xf32>
}
module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
%3 = transform.structured.match ops{["tensor.pad"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%4 = transform.get_parent_op %3 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
%5 = transform.structured.vectorize_children_and_apply_patterns %4 { vectorize_padding } : (!transform.any_op) -> !transform.any_op
transform.yield
}
}
// -----
func.func private @make_vector() -> tensor<12x13xf32>
// CHECK-LABEL: func @pad_and_insert_slice_source
// CHECK-SAME: %[[ARG0:.*]]: tensor<5x6xf32>
// CHECK-NOT: tensor.pad
// CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[C5:.*]] = arith.constant 5.0
// CHECK: %[[VEC0:.*]] = call @make_vector() : () -> tensor<12x13xf32>
// CHECK: %[[READ:.*]] = vector.transfer_read %[[ARG0]][%[[C0]], %[[C0]]], %[[C5]] : tensor<5x6xf32>, vector<7x9xf32>
// CHECK: %[[WRITE:.*]] = vector.transfer_write %[[READ]], %[[VEC0]][%[[C0]], %[[C0]]] {in_bounds = [true, true]} : vector<7x9xf32>, tensor<12x13xf32>
// CHECK: return %[[WRITE]]
func.func @pad_and_insert_slice_source(
%arg0: tensor<5x6xf32>) -> tensor<12x13xf32> {
%c0 = arith.constant 0 : index
%c5 = arith.constant 5.0 : f32
%0 = tensor.pad %arg0 low[0, 0] high[2, 3] {
^bb0(%arg2: index, %arg3: index):
tensor.yield %c5 : f32
} : tensor<5x6xf32> to tensor<7x9xf32>
%1 = call @make_vector() : () -> tensor<12x13xf32>
%r = tensor.insert_slice %0 into %1[0, 0][7, 9][1, 1] : tensor<7x9xf32> into tensor<12x13xf32>
return %r : tensor<12x13xf32>
}
module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
%3 = transform.structured.match ops{["tensor.pad"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%4 = transform.get_parent_op %3 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
%5 = transform.structured.vectorize_children_and_apply_patterns %4 { vectorize_padding } : (!transform.any_op) -> !transform.any_op
transform.yield
}
}
// -----
func.func private @make_vector() -> tensor<12x13xf32>
// CHECK-LABEL: func @pad_and_insert_slice_dest
// CHECK-SAME: %[[ARG0:.*]]: tensor<1x5x6xf32>
// Check the insert slice is not rewritten if the padded result is used by the destination operand.
// CHECK-NOT: tensor.pad
// CHECK: %[[EMPTY:.*]] = tensor.empty() : tensor<1x12x13xf32>
// CHECK: %[[WRITE_1:.*]] = vector.transfer_write %{{.*}}, %[[EMPTY]]{{.*}} : vector<1x12x13xf32>, tensor<1x12x13xf32>
// CHECK: %[[READ:.*]] = vector.transfer_read %[[ARG0:.*]]{{.*}} : tensor<1x5x6xf32>, vector<1x5x6xf32>
// CHECK: %[[WRITE_2:.*]] = vector.transfer_write %[[READ]], %[[WRITE_1]]{{.*}} : vector<1x5x6xf32>, tensor<1x12x13xf32>
// CHECK: %[[T1:.*]] = call @make_vector() : () -> tensor<12x13xf32>
// CHECK: tensor.insert_slice %[[T1]] into %[[WRITE_2]]
func.func @pad_and_insert_slice_dest(
%arg0: tensor<1x5x6xf32>) -> tensor<1x12x13xf32> {
%c5 = arith.constant 5.0 : f32
%0 = tensor.pad %arg0 low[0, 0, 0] high[0, 7, 7] {
^bb0(%arg2: index, %arg3: index, %arg4: index):
tensor.yield %c5 : f32
} : tensor<1x5x6xf32> to tensor<1x12x13xf32>
%1 = call @make_vector() : () -> tensor<12x13xf32>
%r = tensor.insert_slice %1 into %0[0, 0, 0][1, 12, 13][1, 1, 1] : tensor<12x13xf32> into tensor<1x12x13xf32>
return %r : tensor<1x12x13xf32>
}
module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
%3 = transform.structured.match ops{["tensor.pad"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%4 = transform.get_parent_op %3 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
%5 = transform.structured.vectorize_children_and_apply_patterns %4 { vectorize_padding } : (!transform.any_op) -> !transform.any_op
transform.yield
}
}
// -----
// CHECK-LABEL: func @pad_tensor_non_const_pad_value
// CHECK-SAME: %[[ARG0:.*]]: tensor<5x6xf32>
// CHECK-NOT: tensor.pad
// CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
// CHECK-DAG: %[[C3:.*]] = arith.constant 3 : index
// CHECK-DAG: %[[C4:.*]] = arith.constant 4 : index
// CHECK: %[[FILL:.*]] = tensor.generate
// CHECK: %[[RES:.*]] = arith.mulf
// CHECK: tensor.yield %[[RES]] : f32
// CHECK: %[[READ:.*]] = vector.transfer_read %[[ARG0]][%[[C0]], %[[C0]]], %{{.*}} {in_bounds = [true, true]} : tensor<5x6xf32>, vector<5x6xf32>
// CHECK: %[[WRITE:.*]] = vector.transfer_write %[[READ]], %[[FILL]][%[[C3]], %[[C4]]] {in_bounds = [true, true]} : vector<5x6xf32>, tensor<12x13xf32>
// CHECK: return %[[WRITE]]
func.func @pad_tensor_non_const_pad_value(%arg0: tensor<5x6xf32>) -> tensor<12x13xf32> {
%c0 = arith.constant 0 : index
%c5 = arith.constant 5.0 : f32
%0 = tensor.pad %arg0 low[3, 4] high[4, 3] {
^bb0(%arg1: index, %arg2: index):
%i1 = arith.index_cast %arg1 : index to i32
%i2 = arith.index_cast %arg2 : index to i32
%f1 = arith.sitofp %i1 : i32 to f32
%f2 = arith.sitofp %i2 : i32 to f32
%m = arith.mulf %f1, %f2 : f32
tensor.yield %m : f32
} : tensor<5x6xf32> to tensor<12x13xf32>
return %0 : tensor<12x13xf32>
}
module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
%3 = transform.structured.match ops{["tensor.pad"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%4 = transform.get_parent_op %3 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
%5 = transform.structured.vectorize_children_and_apply_patterns %4 { vectorize_padding } : (!transform.any_op) -> !transform.any_op
transform.yield
}
}
// -----
// CHECK-LABEL: func @sum_exp
func.func @sum_exp(%input: tensor<4x16x8xf32>, %output: tensor<4x16xf32>)
-> tensor<4x16xf32>
{
// CHECK: vector.transfer_read {{.*}} : tensor<4x16x8xf32>, vector<4x16x8xf32>
// CHECK: vector.transfer_read {{.*}} {in_bounds = [true, true]} : tensor<4x16xf32>, vector<4x16xf32>
// CHECK: math.exp {{.*}} : vector<4x16x8xf32>
// CHECK: vector.multi_reduction <add>, %{{.*}}, %{{.*}} [2] : vector<4x16x8xf32> to vector<4x16xf32>
// CHECK: vector.transfer_write {{.*}} : vector<4x16xf32>, tensor<4x16xf32>
// CHECK: return {{.*}} : tensor<4x16xf32>
%0 = linalg.generic {
indexing_maps = [
affine_map<(d0, d1, d2) -> (d0, d1, d2)>,
affine_map<(d0, d1, d2) -> (d0, d1)>
],
iterator_types = ["parallel", "parallel", "reduction"]
} ins(%input : tensor<4x16x8xf32>) outs(%output : tensor<4x16xf32>) {
^bb0(%arg0: f32, %arg1: f32):
%1 = math.exp %arg0 : f32
%2 = arith.addf %1, %arg1 : f32
linalg.yield %2 : f32
} -> tensor<4x16xf32>
return %0 : tensor<4x16xf32>
}
module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
%3 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%4 = transform.get_parent_op %3 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
%5 = transform.structured.vectorize_children_and_apply_patterns %4 : (!transform.any_op) -> !transform.any_op
transform.yield
}
}
// -----
// CHECK-DAG: #[[$M1:.*]] = affine_map<(d0, d1) -> (d1, d0, 0, 0)>
// CHECK-DAG: #[[$M2:.*]] = affine_map<(d0, d1) -> (0, 0, d1, d0)>
// CHECK-DAG: #[[$M3:.*]] = affine_map<(d0, d1) -> (d1, d0)>
// CHECK-LABEL: func @sum_exp_2
func.func @sum_exp_2(%input: tensor<3x2xf32>, %input_2: tensor<5x4xf32>, %output: tensor<5x2xf32>)
-> tensor<5x2xf32>
{
// CHECK: vector.transfer_read {{.*}} {in_bounds = [true, true, true, true], permutation_map = #[[$M1]]} : tensor<3x2xf32>, vector<2x3x4x5xf32>
// CHECK: vector.transfer_read {{.*}} {in_bounds = [true, true, true, true], permutation_map = #[[$M2]]} : tensor<5x4xf32>, vector<2x3x4x5xf32>
// CHECK: vector.transfer_read {{.*}} {in_bounds = [true, true], permutation_map = #[[$M3]]} : tensor<5x2xf32>, vector<2x5xf32>
// CHECK: math.exp {{.*}} : vector<2x3x4x5xf32>
// CHECK: math.exp {{.*}} : vector<2x3x4x5xf32>
// CHECK: addf {{.*}} : vector<2x3x4x5xf32>
// CHECK: vector.multi_reduction <add>, {{.*}}, %{{.*}} [1, 2] : vector<2x3x4x5xf32> to vector<2x5xf32>
// CHECK: vector.transfer_write {{.*}} {in_bounds = [true, true], permutation_map = #[[$M3]]} : vector<2x5xf32>, tensor<5x2xf32>
// CHECK: return {{.*}} : tensor<5x2xf32>
%0 = linalg.generic {
indexing_maps = [
affine_map<(d0, d1, d2, d3) -> (d1, d0)>,
affine_map<(d0, d1, d2, d3) -> (d3, d2)>,
affine_map<(d0, d1, d2, d3) -> (d3, d0)>
],
iterator_types = ["parallel", "reduction", "reduction", "parallel"]
} ins(%input, %input_2 : tensor<3x2xf32>, tensor<5x4xf32>) outs(%output : tensor<5x2xf32>) {
^bb0(%arg0: f32, %arg1: f32, %arg2: f32):
%1 = math.exp %arg0 : f32
%2 = math.exp %arg1 : f32
%3 = arith.addf %1, %2 : f32
%4 = arith.addf %3, %arg2 : f32
linalg.yield %4 : f32
} -> tensor<5x2xf32>
return %0 : tensor<5x2xf32>
}
module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
%3 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%4 = transform.get_parent_op %3 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
%5 = transform.structured.vectorize_children_and_apply_patterns %4 { disable_multi_reduction_to_contract_patterns, disable_transfer_permutation_map_lowering_patterns } : (!transform.any_op) -> !transform.any_op
transform.yield
}
}
// -----
// CHECK-LABEL: func @red_maximumf_2d(
func.func @red_maximumf_2d(%arg0: tensor<4x4xf32>) -> tensor<4xf32> {
// CHECK: %[[CMINF:.+]] = arith.constant dense<-3.402820e+38> : vector<4xf32>
// CHECK: tensor.empty() : tensor<4xf32>
// CHECK: vector.multi_reduction <maximumf>, {{.*}}, %[[CMINF]] [1] : vector<4x4xf32> to vector<4xf32>
// CHECK: vector.transfer_write {{.*}} : vector<4xf32>, tensor<4xf32>
%ident = arith.constant -3.40282e+38 : f32
%init = tensor.empty() : tensor<4xf32>
%fill = linalg.fill ins(%ident : f32) outs(%init : tensor<4xf32>) -> tensor<4xf32>
%red = linalg.generic {indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>,
affine_map<(d0, d1) -> (d0)>],
iterator_types = ["parallel", "reduction"]}
ins(%arg0 : tensor<4x4xf32>) outs(%fill : tensor<4xf32>) {
^bb0(%in0: f32, %out0: f32):
%max = arith.maximumf %in0, %out0 : f32
linalg.yield %max : f32
} -> tensor<4xf32>
return %red : tensor<4xf32>
}
module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
%3 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%4 = transform.get_parent_op %3 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
%5 = transform.structured.vectorize_children_and_apply_patterns %4 { vectorize_padding } : (!transform.any_op) -> !transform.any_op
transform.yield
}
}
// -----
// CHECK-LABEL: func @red_maxnumf_2d(
func.func @red_maxnumf_2d(%arg0: tensor<4x4xf32>) -> tensor<4xf32> {
// CHECK: %[[CMINF:.+]] = arith.constant dense<-3.402820e+38> : vector<4xf32>
// CHECK: tensor.empty() : tensor<4xf32>
// CHECK: vector.multi_reduction <maxnumf>, {{.*}}, %[[CMINF]] [1] : vector<4x4xf32> to vector<4xf32>
// CHECK: vector.transfer_write {{.*}} : vector<4xf32>, tensor<4xf32>
%ident = arith.constant -3.40282e+38 : f32
%init = tensor.empty() : tensor<4xf32>
%fill = linalg.fill ins(%ident : f32) outs(%init : tensor<4xf32>) -> tensor<4xf32>
%red = linalg.generic {indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>,
affine_map<(d0, d1) -> (d0)>],
iterator_types = ["parallel", "reduction"]}
ins(%arg0 : tensor<4x4xf32>) outs(%fill : tensor<4xf32>) {
^bb0(%in0: f32, %out0: f32):
%max = arith.maxnumf %in0, %out0 : f32
linalg.yield %max : f32
} -> tensor<4xf32>
return %red : tensor<4xf32>
}
module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
%3 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%4 = transform.get_parent_op %3 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
%5 = transform.structured.vectorize_children_and_apply_patterns %4 { vectorize_padding } : (!transform.any_op) -> !transform.any_op
transform.yield
}
}
// -----
// CHECK-LABEL: func @red_minimumf_2d(
func.func @red_minimumf_2d(%arg0: tensor<4x4xf32>) -> tensor<4xf32> {
// CHECK: %[[CMAXF:.+]] = arith.constant dense<3.402820e+38> : vector<4xf32>
// CHECK: tensor.empty() : tensor<4xf32>
// CHECK: vector.transfer_read {{.*}} : tensor<4x4xf32>, vector<4x4xf32>
// CHECK: vector.multi_reduction <minimumf>, {{.*}}, %[[CMAXF]] [1] : vector<4x4xf32> to vector<4xf32>
// CHECK: vector.transfer_write {{.*}} : vector<4xf32>, tensor<4xf32>
%maxf32 = arith.constant 3.40282e+38 : f32
%init = tensor.empty() : tensor<4xf32>
%fill = linalg.fill ins(%maxf32 : f32) outs(%init : tensor<4xf32>) -> tensor<4xf32>
%red = linalg.generic {indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>,
affine_map<(d0, d1) -> (d0)>],
iterator_types = ["parallel", "reduction"]}
ins(%arg0 : tensor<4x4xf32>) outs(%fill : tensor<4xf32>) {
^bb0(%in0: f32, %out0: f32):
%min = arith.minimumf %out0, %in0 : f32
linalg.yield %min : f32
} -> tensor<4xf32>
return %red : tensor<4xf32>
}
module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
%3 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%4 = transform.get_parent_op %3 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
%5 = transform.structured.vectorize_children_and_apply_patterns %4 : (!transform.any_op) -> !transform.any_op
transform.yield
}
}
// -----
// CHECK-LABEL: func @red_minnumf_2d(
func.func @red_minnumf_2d(%arg0: tensor<4x4xf32>) -> tensor<4xf32> {
// CHECK: %[[CMAXF:.+]] = arith.constant dense<3.402820e+38> : vector<4xf32>
// CHECK: tensor.empty() : tensor<4xf32>
// CHECK: vector.transfer_read {{.*}} : tensor<4x4xf32>, vector<4x4xf32>
// CHECK: vector.multi_reduction <minnumf>, {{.*}}, %[[CMAXF]] [1] : vector<4x4xf32> to vector<4xf32>
// CHECK: vector.transfer_write {{.*}} : vector<4xf32>, tensor<4xf32>
%maxf32 = arith.constant 3.40282e+38 : f32
%init = tensor.empty() : tensor<4xf32>
%fill = linalg.fill ins(%maxf32 : f32) outs(%init : tensor<4xf32>) -> tensor<4xf32>
%red = linalg.generic {indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>,
affine_map<(d0, d1) -> (d0)>],
iterator_types = ["parallel", "reduction"]}
ins(%arg0 : tensor<4x4xf32>) outs(%fill : tensor<4xf32>) {
^bb0(%in0: f32, %out0: f32):
%min = arith.minnumf %out0, %in0 : f32
linalg.yield %min : f32
} -> tensor<4xf32>
return %red : tensor<4xf32>
}
module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
%3 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%4 = transform.get_parent_op %3 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
%5 = transform.structured.vectorize_children_and_apply_patterns %4 : (!transform.any_op) -> !transform.any_op
transform.yield
}
}
// -----
// CHECK-LABEL: func @red_mul_2d(
func.func @red_mul_2d(%arg0: tensor<4x4xf32>) -> tensor<4xf32> {
// CHECK: tensor.empty() : tensor<4xf32>
// CHECK: vector.transfer_read {{.*}} : tensor<4x4xf32>, vector<4x4xf32>
// CHECK: vector.multi_reduction <mul>, {{.*}}, {{.*}} [1] : vector<4x4xf32> to vector<4xf32>
// CHECK: vector.transfer_write {{.*}} : vector<4xf32>, tensor<4xf32>
%ident = arith.constant 1.0 : f32
%init = tensor.empty() : tensor<4xf32>
%fill = linalg.fill ins(%ident : f32) outs(%init : tensor<4xf32>) -> tensor<4xf32>
%red = linalg.generic {indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>,
affine_map<(d0, d1) -> (d0)>],
iterator_types = ["parallel", "reduction"]}
ins(%arg0 : tensor<4x4xf32>) outs(%fill : tensor<4xf32>) {
^bb0(%in0: f32, %out0: f32):
%mul = arith.mulf %in0, %out0 : f32
linalg.yield %mul : f32
} -> tensor<4xf32>
return %red : tensor<4xf32>
}
module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
%3 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%4 = transform.get_parent_op %3 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
%5 = transform.structured.vectorize_children_and_apply_patterns %4 : (!transform.any_op) -> !transform.any_op
transform.yield
}
}
// -----
// CHECK-LABEL: func @red_or_2d(
func.func @red_or_2d(%arg0: tensor<4x4xi1>) -> tensor<4xi1> {
// CHECK: tensor.empty() : tensor<4xi1>
// CHECK: vector.transfer_read {{.*}} : tensor<4x4xi1>, vector<4x4xi1>
// CHECK: vector.multi_reduction <or>, {{.*}}, {{.*}} [1] : vector<4x4xi1> to vector<4xi1>
// CHECK: vector.transfer_write {{.*}} : vector<4xi1>, tensor<4xi1>
%ident = arith.constant false
%init = tensor.empty() : tensor<4xi1>
%fill = linalg.fill ins(%ident : i1) outs(%init : tensor<4xi1>) -> tensor<4xi1>
%red = linalg.generic {indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>,
affine_map<(d0, d1) -> (d0)>],
iterator_types = ["parallel", "reduction"]}
ins(%arg0 : tensor<4x4xi1>) outs(%fill : tensor<4xi1>) {
^bb0(%in0: i1, %out0: i1):
%or = arith.ori %in0, %out0 : i1
linalg.yield %or : i1
} -> tensor<4xi1>
return %red : tensor<4xi1>
}
module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
%3 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%4 = transform.get_parent_op %3 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
%5 = transform.structured.vectorize_children_and_apply_patterns %4 : (!transform.any_op) -> !transform.any_op
transform.yield
}
}
// -----
// CHECK-LABEL: func @red_and_2d(
func.func @red_and_2d(%arg0: tensor<4x4xi1>) -> tensor<4xi1> {
// CHECK: tensor.empty() : tensor<4xi1>
// CHECK: vector.transfer_read {{.*}} : tensor<4x4xi1>, vector<4x4xi1>
// CHECK: vector.multi_reduction <and>, {{.*}}, {{.*}} [1] : vector<4x4xi1> to vector<4xi1>
// CHECK: vector.transfer_write {{.*}} : vector<4xi1>, tensor<4xi1>
%ident = arith.constant true
%init = tensor.empty() : tensor<4xi1>
%fill = linalg.fill ins(%ident : i1) outs(%init : tensor<4xi1>) -> tensor<4xi1>
%red = linalg.generic {indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>,
affine_map<(d0, d1) -> (d0)>],
iterator_types = ["parallel", "reduction"]}
ins(%arg0 : tensor<4x4xi1>) outs(%fill : tensor<4xi1>) {
^bb0(%in0: i1, %out0: i1):
%and = arith.andi %in0, %out0 : i1
linalg.yield %and : i1
} -> tensor<4xi1>
return %red : tensor<4xi1>
}
module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
%3 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%4 = transform.get_parent_op %3 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
%5 = transform.structured.vectorize_children_and_apply_patterns %4 : (!transform.any_op) -> !transform.any_op
transform.yield
}
}
// -----
// CHECK-LABEL: func @red_xor_2d(
func.func @red_xor_2d(%arg0: tensor<4x4xi1>) -> tensor<4xi1> {
// CHECK: tensor.empty() : tensor<4xi1>
// CHECK: vector.transfer_read {{.*}} : tensor<4x4xi1>, vector<4x4xi1>
// CHECK: vector.multi_reduction <xor>, {{.*}}, {{.*}} [1] : vector<4x4xi1> to vector<4xi1>
// CHECK: vector.transfer_write {{.*}} : vector<4xi1>, tensor<4xi1>
%ident = arith.constant false
%init = tensor.empty() : tensor<4xi1>
%fill = linalg.fill ins(%ident : i1) outs(%init : tensor<4xi1>) -> tensor<4xi1>
%red = linalg.generic {indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>,
affine_map<(d0, d1) -> (d0)>],
iterator_types = ["parallel", "reduction"]}
ins(%arg0 : tensor<4x4xi1>) outs(%fill : tensor<4xi1>) {
^bb0(%in0: i1, %out0: i1):
%xor = arith.xori %in0, %out0 : i1
linalg.yield %xor : i1
} -> tensor<4xi1>
return %red : tensor<4xi1>
}
module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
%3 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%4 = transform.get_parent_op %3 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
%5 = transform.structured.vectorize_children_and_apply_patterns %4 : (!transform.any_op) -> !transform.any_op
transform.yield
}
}
// -----
// CHECK-DAG: #[[$M5:.*]] = affine_map<(d0, d1) -> (d0, 0)>
// CHECK-LABEL: func @explicit_broadcast(
func.func @explicit_broadcast(%arg0: tensor<4x4xf32>, %arg1: tensor<4x1xf32>) -> tensor<4x4xf32> {
// CHECK: vector.transfer_read {{.*}} {in_bounds = [true, true]} : tensor<4x4xf32>, vector<4x4xf32>
// CHECK: vector.transfer_read {{.*}} {in_bounds = [true, true], permutation_map = #[[$M5]]} : tensor<4x1xf32>, vector<4x4xf32>
// CHECK: subf {{.*}} : vector<4x4xf32>
// CHECK: vector.transfer_write {{.*}} {in_bounds = [true, true]} : vector<4x4xf32>, tensor<4x4xf32>
%c0 = arith.constant 0.0 : f32
%init = tensor.empty() : tensor<4x4xf32>
%fill = linalg.fill ins(%c0 : f32) outs(%init : tensor<4x4xf32>) -> tensor<4x4xf32>
%red = linalg.generic {indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>,
affine_map<(d0, d1) -> (d0, 0)>,
affine_map<(d0, d1) -> (d0, d1)>],
iterator_types = ["parallel", "parallel"]}
ins(%arg0, %arg1 : tensor<4x4xf32>, tensor<4x1xf32>)
outs(%fill : tensor<4x4xf32>) {
^bb0(%arg7: f32, %arg8: f32, %arg9: f32):
%40 = arith.subf %arg7, %arg8 : f32
linalg.yield %40 : f32
} -> tensor<4x4xf32>
return %red : tensor<4x4xf32>
}
module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
%3 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%4 = transform.get_parent_op %3 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
%5 = transform.structured.vectorize_children_and_apply_patterns %4 : (!transform.any_op) -> !transform.any_op
transform.yield
}
}
// -----
// CHECK-DAG: #[[$M6:.*]] = affine_map<(d0, d1) -> (d0, 0)>
// CHECK-LABEL: func @fused_broadcast_red_2d
func.func @fused_broadcast_red_2d(%arg0: tensor<4x4xf32>, %arg1: tensor<4x1xf32>) -> tensor<4xf32> {
// CHECK: vector.transfer_read {{.*}} {in_bounds = [true, true]} : tensor<4x4xf32>, vector<4x4xf32>
// CHECK: vector.transfer_read {{.*}} {in_bounds = [true, true], permutation_map = #[[$M6]]} : tensor<4x1xf32>, vector<4x4xf32>
// CHECK: subf {{.*}} : vector<4x4xf32>
// CHECK: math.exp {{.*}} : vector<4x4xf32>
// CHECK: vector.multi_reduction <add>, {{.*}}, {{.*}} : vector<4x4xf32> to vector<4xf32>
// CHECK: vector.transfer_write {{.*}} {in_bounds = [true]} : vector<4xf32>, tensor<4xf32>
%c0 = arith.constant 0.0 : f32
%init = tensor.empty() : tensor<4xf32>
%fill = linalg.fill ins(%c0 : f32) outs(%init : tensor<4xf32>) -> tensor<4xf32>
%red = linalg.generic {indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>,
affine_map<(d0, d1) -> (d0, 0)>,
affine_map<(d0, d1) -> (d0)>],
iterator_types = ["parallel", "reduction"]}
ins(%arg0, %arg1 : tensor<4x4xf32>, tensor<4x1xf32>)
outs(%fill : tensor<4xf32>) {
^bb0(%arg7: f32, %arg8: f32, %arg9: f32):
%40 = arith.subf %arg7, %arg8 : f32
%41 = math.exp %40 : f32
%42 = arith.addf %41, %arg9 : f32
linalg.yield %42 : f32
} -> tensor<4xf32>
return %red : tensor<4xf32>
}
module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
%0 = transform.structured.match ops{["linalg.fill"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = transform.get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
%2 = transform.structured.vectorize_children_and_apply_patterns %1 : (!transform.any_op) -> !transform.any_op
%3 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%4 = transform.get_parent_op %3 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
%5 = transform.structured.vectorize_children_and_apply_patterns %4 : (!transform.any_op) -> !transform.any_op
transform.yield
}
}
// -----
// CHECK-LABEL: func @reduce_1d(
// CHECK-SAME: %[[A:.*]]: tensor<32xf32>
func.func @reduce_1d(%arg0: tensor<32xf32>) -> tensor<f32> {
// CHECK-DAG: %[[vF0:.*]] = arith.constant dense<0.000000e+00> : vector<f32>
// CHECK-DAG: %[[F0:.*]] = arith.constant 0.000000e+00 : f32
// CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
%f0 = arith.constant 0.000000e+00 : f32
// CHECK: %[[init:.*]] = tensor.empty() : tensor<f32>
%0 = tensor.empty() : tensor<f32>
%1 = linalg.fill ins(%f0 : f32) outs(%0 : tensor<f32>) -> tensor<f32>
// CHECK: %[[r:.*]] = vector.transfer_read %[[A]][%[[C0]]]
// CHECK-SAME: : tensor<32xf32>, vector<32xf32>
// CHECK: %[[f0:.*]] = vector.extractelement %[[vF0]][] : vector<f32>
// CHECK: %[[red:.*]] = vector.multi_reduction <add>, %[[r]], %[[f0]] [0]
// CHECK-SAME: : vector<32xf32> to f32
// CHECK: %[[red_v1:.*]] = vector.broadcast %[[red]] : f32 to vector<f32>
// CHECK: %[[res:.*]] = vector.transfer_write %[[red_v1]], %[[init]][]
// CHECK-SAME: : vector<f32>, tensor<f32>
%2 = linalg.generic {
indexing_maps = [affine_map<(d0) -> (d0)>,
affine_map<(d0) -> ()>],
iterator_types = ["reduction"]}
ins(%arg0 : tensor<32xf32>)
outs(%1 : tensor<f32>) {
^bb0(%a: f32, %b: f32):
%3 = arith.addf %a, %b : f32
linalg.yield %3 : f32
} -> tensor<f32>
return %2 : tensor<f32>
}
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 = transform.get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
%2 = transform.structured.vectorize_children_and_apply_patterns %1 : (!transform.any_op) -> !transform.any_op
transform.yield
}
}
// -----
// This test checks that vectorization does not occur when an input indexing map
// is not a projected permutation. In the future, this can be converted to a
// positive test when support is added.
// CHECK-LABEL: func @not_projected_permutation
func.func @not_projected_permutation(%arg0: tensor<8x8xf32>) -> tensor<6x6x3x3xf32> {
%c0 = arith.constant 0.0 : f32
%init = tensor.empty() : tensor<6x6x3x3xf32>
%fill = linalg.fill ins(%c0 : f32) outs(%init : tensor<6x6x3x3xf32>) -> tensor<6x6x3x3xf32>
// CHECK: linalg.generic
%result = linalg.generic {indexing_maps = [affine_map<(d0, d1, d2, d3) -> (d0 + d2, d1 + d3)>,
affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>],
iterator_types = ["parallel", "parallel", "parallel", "parallel"]}
ins(%arg0 : tensor<8x8xf32>)
outs(%fill : tensor<6x6x3x3xf32>) {
^bb0(%arg7: f32, %arg9: f32):
linalg.yield %arg7 : f32
} -> tensor<6x6x3x3xf32>
return %result : tensor<6x6x3x3xf32>
}
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 = transform.get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
%2 = transform.structured.vectorize_children_and_apply_patterns %1 : (!transform.any_op) -> !transform.any_op
transform.yield
}
}
// -----
// Check vectorization can handle cases where outputs are a mix of reduced and non-reduced values.
func.func @mixed_parallel_reduced_results(%arg0 : tensor<2x4x8xf32>,
%arg1 : tensor<2x4xf32>, %arg2 : tensor<2x4x8xf32>, %arg3 : tensor<2x4xf32>) ->
(tensor<2x4x8xf32>, tensor<2x4xf32>) {
%0:2 = linalg.generic {
indexing_maps = [affine_map<(d0, d1, d2) -> (d0, d1, d2)>, affine_map<(d0, d1, d2) -> (d0, d1)>,
affine_map<(d0, d1, d2) -> (d0, d1, d2)>, affine_map<(d0, d1, d2) -> (d0, d1)>],
iterator_types = ["parallel", "parallel", "reduction"]}
ins(%arg0, %arg1 : tensor<2x4x8xf32>, tensor<2x4xf32>)
outs(%arg2, %arg3 : tensor<2x4x8xf32>, tensor<2x4xf32>) {
^bb0(%b0 : f32, %b1 : f32, %b2 : f32, %b3 : f32):
%1 = arith.mulf %b0, %b1 : f32
%2 = arith.addf %1, %b3 : f32
linalg.yield %1, %2 : f32, f32
} -> (tensor<2x4x8xf32>, tensor<2x4xf32>)
return %0#0, %0#1 : tensor<2x4x8xf32>, tensor<2x4xf32>
}
// CHECK-LABEL: func @mixed_parallel_reduced_results(
// CHECK-SAME: %[[ARG0:[a-zA-Z0-9]+]]: tensor<2x4x8xf32>
// CHECK-SAME: %[[ARG1:[a-zA-Z0-9]+]]: tensor<2x4xf32>
// CHECK-SAME: %[[ARG2:[a-zA-Z0-9]+]]: tensor<2x4x8xf32>
// CHECK-SAME: %[[ARG3:[a-zA-Z0-9]+]]: tensor<2x4xf32>
// CHECK-DAG: %[[V0:.+]] = vector.transfer_read %[[ARG0]]
// CHECK-DAG: %[[V1:.+]] = vector.transfer_read %[[ARG1]]
// CHECK-DAG: %[[V2:.+]] = vector.transfer_read %[[ARG3]]
// CHECK-DAG: %[[MUL:.+]] = arith.mulf %[[V0]], %[[V1]]
// CHECK-DAG: %[[ADD:.+]] = vector.multi_reduction <add>, %[[MUL]], %[[V2]]
// CHECK-DAG: vector.transfer_write %[[MUL]], %[[ARG2]]
// CHECK-DAG: vector.transfer_write %[[ADD]], %[[ARG3]]
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 = transform.get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
%2 = transform.structured.vectorize_children_and_apply_patterns %1 { disable_multi_reduction_to_contract_patterns, disable_transfer_permutation_map_lowering_patterns } : (!transform.any_op) -> !transform.any_op
transform.yield
}
}
// -----
func.func @vectorize_map(%arg0: memref<64xf32>,
%arg1: memref<64xf32>, %arg2: memref<64xf32>) {
linalg.map ins(%arg0, %arg1 : memref<64xf32>, memref<64xf32>)
outs(%arg2 : memref<64xf32>)
(%in: f32, %in_0: f32) {
%0 = arith.addf %in, %in_0 : f32
linalg.yield %0 : f32
}
return
}
// CHECK-LABEL: func @vectorize_map
// CHECK: %[[LHS:.*]] = vector.transfer_read
// CHECK-NEXT: %[[RHS:.*]] = vector.transfer_read
// CHECK-NEXT: arith.addf %[[LHS]], %[[RHS]] : vector<64xf32>
module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
%0 = transform.structured.match ops{["linalg.map"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = transform.get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
%2 = transform.structured.vectorize_children_and_apply_patterns %1 : (!transform.any_op) -> !transform.any_op
transform.yield
}
}
// -----
func.func @vectorize_transpose(%arg0: memref<16x32x64xf32>,
%arg1: memref<32x64x16xf32>) {
linalg.transpose ins(%arg0 : memref<16x32x64xf32>)
outs(%arg1 : memref<32x64x16xf32>) permutation = [1, 2, 0]
return
}
// CHECK-LABEL: func @vectorize_transpose
// CHECK: vector.transpose
// CHECK-SAME: [1, 2, 0] : vector<16x32x64xf32> to vector<32x64x16xf32>
module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
%0 = transform.structured.match ops{["linalg.transpose"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = transform.get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
%2 = transform.structured.vectorize_children_and_apply_patterns %1 : (!transform.any_op) -> !transform.any_op
transform.yield
}
}
// -----
func.func @vectorize_reduce(%arg0: memref<16x32x64xf32>,
%arg1: memref<16x64xf32>) {
linalg.reduce ins(%arg0 : memref<16x32x64xf32>)
outs(%arg1 : memref<16x64xf32>) dimensions = [1]
(%in: f32, %init: f32) {
%0 = arith.addf %in, %init : f32
linalg.yield %0 : f32
}
return
}
// CHECK-LABEL: func @vectorize_reduce
// CHECK: vector.multi_reduction <add>
// CHECK-SAME: : vector<16x32x64xf32> to vector<16x64xf32>
module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
%0 = transform.structured.match ops{["linalg.reduce"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%1 = transform.get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
%2 = transform.structured.vectorize_children_and_apply_patterns %1 : (!transform.any_op) -> !transform.any_op
transform.yield
}
}
// -----
// This is a regression test. This IR cannot be vectorized, but
// structured.vectorize_children_and_apply_patterns should nevertheless succeed.
#map = affine_map<(d0) -> (d0)>
// CHECK-LABEL: @not_vectorizable
func.func @not_vectorizable(%arg0: tensor<1x?xf32>, %arg1: index, %arg2: index, %arg3: index) -> tensor<1x128xf32> {
%c0 = arith.constant 0 : index
%0 = tensor.empty() : tensor<1x128xf32>
%1 = scf.for %arg5 = %arg2 to %arg1 step %arg3 iter_args(%arg6 = %0) -> (tensor<1x128xf32>) {
%extracted_slice = tensor.extract_slice %arg6[0, 0] [1, %arg1] [1, 1] : tensor<1x128xf32> to tensor<?xf32>
%sz0 = tensor.dim %extracted_slice, %c0 : tensor<?xf32>
%expanded = tensor.expand_shape %extracted_slice [[0, 1]] output_shape [1, %sz0] : tensor<?xf32> into tensor<1x?xf32>
%extracted_slice_0 = tensor.extract_slice %arg0[0, %arg3] [1, %arg2] [1, 1] : tensor<1x?xf32> to tensor<?xf32>
%extracted_slice_1 = tensor.extract_slice %expanded[0, %arg3] [1, %arg2] [1, 1] : tensor<1x?xf32> to tensor<?xf32>
%2 = linalg.generic {indexing_maps = [#map, #map], iterator_types = ["parallel"]} ins(%extracted_slice_0 : tensor<?xf32>) outs(%extracted_slice_1 : tensor<?xf32>) {
^bb0(%in: f32, %out: f32):
%3 = arith.addf %in, %out : f32
linalg.yield %3 : f32
} -> tensor<?xf32>
%inserted_slice = tensor.insert_slice %2 into %expanded[0, %arg3] [1, %arg2] [1, 1] : tensor<?xf32> into tensor<1x?xf32>
%collapsed = tensor.collapse_shape %inserted_slice [[0, 1]] : tensor<1x?xf32> into tensor<?xf32>
%inserted_slice_2 = tensor.insert_slice %collapsed into %arg6[0, 0] [1, %arg1] [1, 1] : tensor<?xf32> into tensor<1x128xf32>
scf.yield %inserted_slice_2 : tensor<1x128xf32>
}
return %1 : tensor<1x128xf32>
}
module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg0: !transform.any_op {transform.readonly}) {
%0 = transform.structured.match ops{["func.func"]} in %arg0 : (!transform.any_op) -> !transform.any_op
%1 = transform.structured.vectorize_children_and_apply_patterns %0 : (!transform.any_op) -> !transform.any_op
transform.yield
}
}
// -----
// Regression test: %13 was incorrectly detected as a reduction and
// vectorization failed.
func.func @wrong_reduction_detection(%input: tensor<120x64xf32>) -> tensor<120x64xf32> {
%c0 = arith.constant 0 : index
%c4 = arith.constant 4 : index
%c64 = arith.constant 64 : index
%cst_6 = arith.constant 4.000000e+00 : f32
%1 = scf.for %arg0 = %c0 to %c64 step %c4 iter_args(%arg1 = %input) -> (tensor<120x64xf32>) {
%extracted_slice = tensor.extract_slice %arg1[%c0, %arg0] [1, 4] [1, 1] : tensor<120x64xf32> to tensor<1x4xf32>
%10 = linalg.fill {__internal_linalg_transform__ = "1"} ins(%cst_6 : f32) outs(%extracted_slice : tensor<1x4xf32>) -> tensor<1x4xf32>
%11 = linalg.generic {indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>], iterator_types = ["parallel", "parallel"]} outs(%10 : tensor<1x4xf32>) {
^bb0(%out: f32):
%12 = linalg.index 0 : index
%13 = arith.addi %arg0, %12 : index
%18 = arith.index_cast %13 : index to i32
%20 = arith.uitofp %18 : i32 to f32
%67 = arith.mulf %out, %20 : f32
linalg.yield %67 : f32
} -> tensor<1x4xf32>
%inserted_slice = tensor.insert_slice %11 into %arg1[%c0, %arg0] [1, 4] [1, 1] : tensor<1x4xf32> into tensor<120x64xf32>
scf.yield %inserted_slice : tensor<120x64xf32>
}
return %1 : tensor<120x64xf32>
}
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 = transform.get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
%2 = transform.structured.vectorize_children_and_apply_patterns %1 : (!transform.any_op) -> !transform.any_op
transform.yield
}
}
// CHECK-LABEL: @wrong_reduction_detection
// CHECK: vector.broadcast
// CHECK: vector.transfer_write
// -----
// Don't vectorize tensor<0xf32> : (!transform.any_op) -> !transform.any_op
// CHECK-LABEL: @tensor_size0
// CHECK: linalg.generic
func.func @tensor_size0(%arg0: tensor<0xf32>,
%arg1: tensor<f32>) -> tensor<f32> {
%0 = linalg.generic
{indexing_maps = [affine_map<(d0) -> (d0)>, affine_map<(d0) -> ()>],
iterator_types = ["reduction"]}
ins(%arg0 : tensor<0xf32>) outs(%arg1 : tensor<f32>) {
^bb0(%in: f32, %out: f32):
%12 = arith.addf %out, %in : f32
linalg.yield %12 : f32
} -> tensor<f32>
return %0 : tensor<f32>
}
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 = transform.get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
%2 = transform.structured.vectorize_children_and_apply_patterns %1 : (!transform.any_op) -> !transform.any_op
transform.yield
}
}
// -----
// CHECK-LABEL: func @test_masked_pad_static_dynamic
func.func @test_masked_pad_static_dynamic(%arg0: tensor<1x2x2x?xf32>, %low: index, %high: index,
%pad_value: f32) -> tensor<6x?x?x?xf32> {
// CHECK: tensor.pad
%0 = tensor.pad %arg0 low[2, %low, 3, 3] high[3, 3, %high, 2] {
^bb0(%arg1: index, %arg2: index, %arg3: index, %arg4: index):
tensor.yield %pad_value : f32
} : tensor<1x2x2x?xf32> to tensor<6x?x?x?xf32>
return %0 : tensor<6x?x?x?xf32>
}
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
%1 = transform.get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
%2 = transform.structured.vectorize_children_and_apply_patterns %1 { vectorize_padding } : (!transform.any_op) -> !transform.any_op
transform.yield
}
}
// -----
func.func @zero_dim_tensor(%input: tensor<f32>, %output: tensor<f32>) -> tensor<f32>
{
%0 = linalg.generic { indexing_maps = [ affine_map<() -> ()>, affine_map<() -> ()> ],
iterator_types = [] }
ins(%input : tensor<f32>)
outs(%output : tensor<f32>) {
^bb0(%arg0: f32, %arg1: f32):
%2 = arith.addf %arg0, %arg1 : f32
linalg.yield %2 : f32
} -> tensor<f32>
return %0 : tensor<f32>
}
module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
%3 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%4 = transform.get_parent_op %3 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
%5 = transform.structured.vectorize_children_and_apply_patterns %4 : (!transform.any_op) -> !transform.any_op
transform.yield
}
}
// CHECK-LABEL: func @zero_dim_tensor
// CHECK: vector.transfer_read {{.*}} : tensor<f32>, vector<f32>
// CHECK: vector.extractelement
// CHECK: vector.transfer_read {{.*}} : tensor<f32>, vector<f32>
// CHECK: vector.extractelement
// CHECK: arith.addf {{.*}} : f32
// CHECK: vector.broadcast %{{.*}} : f32 to vector<f32>
// CHECK: vector.transfer_write {{.*}} : vector<f32>, tensor<f32>
// -----
// Make sure we generate the right transfer writes for multi-output generic ops
// with different permutation maps.
func.func @multi_output_generic_different_perm_maps(%in0: tensor<4x1xf32>,
%out0: tensor<4x1xf32>,
%out1: tensor<1x4xf32>) -> (tensor<4x1xf32>, tensor<1x4xf32>) {
%13:2 = linalg.generic {indexing_maps = [ affine_map<(d0, d1) -> (d1, d0)>,
affine_map<(d0, d1) -> (d1, d0)>,
affine_map<(d0, d1) -> (d0, d1)> ],
iterator_types = ["parallel", "parallel"]}
ins(%in0 : tensor<4x1xf32>)
outs(%out0, %out1 : tensor<4x1xf32>, tensor<1x4xf32>) {
^bb0(%in: f32, %out: f32, %out_2: f32):
%16 = arith.addf %in, %in : f32
linalg.yield %16, %16 : f32, f32
} -> (tensor<4x1xf32>, tensor<1x4xf32>)
return %13#0, %13#1 : tensor<4x1xf32>, tensor<1x4xf32>
}
module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
%3 = transform.structured.match ops{["linalg.generic"]} in %arg1 : (!transform.any_op) -> !transform.any_op
%4 = transform.get_parent_op %3 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
%5 = transform.structured.vectorize_children_and_apply_patterns %4 : (!transform.any_op) -> !transform.any_op
transform.yield
}
}
// CHECK-LABEL: func @multi_output_generic_different_perm_maps
// CHECK: %[[VAL_5:.*]] = vector.transfer_read %{{.*}} {in_bounds = [true, true]} : tensor<4x1xf32>, vector<4x1xf32>
// CHECK: %[[VAL_6:.*]] = arith.addf %[[VAL_5]], %[[VAL_5]] : vector<4x1xf32>
// CHECK: %[[VAL_7:.*]] = vector.transpose %[[VAL_6]], [1, 0] : vector<4x1xf32> to vector<1x4xf32>
// CHECK: %[[VAL_8:.*]] = vector.transpose %[[VAL_7]], [1, 0] : vector<1x4xf32> to vector<4x1xf32>
// CHECK: vector.transfer_write %[[VAL_8]], %{{.*}} {in_bounds = [true, true]} : vector<4x1xf32>, tensor<4x1xf32>
// CHECK: vector.transfer_write %[[VAL_7]], %{{.*}} {in_bounds = [true, true]} : vector<1x4xf32>, tensor<1x4xf32>
// -----
// Extracted from: https://github.com/llvm/llvm-project/issues/97247
#map = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>
#map1 = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, 0)>
func.func @generic_with_reduction_and_broadcast(%arg0: tensor<1x12x197x197xf32>) -> (tensor<1x12x197x1xf32>) {
%0 = tensor.empty() : tensor<1x12x197x1xf32>
%1 = linalg.generic {indexing_maps = [#map, #map1], iterator_types = ["parallel", "parallel", "parallel", "reduction"]} ins(%arg0 : tensor<1x12x197x197xf32>) outs(%0 : tensor<1x12x197x1xf32>) {
^bb0(%in: f32, %out: f32):
%818 = arith.addf %in, %out : f32
linalg.yield %818 : f32
} -> tensor<1x12x197x1xf32>
return %1 : tensor<1x12x197x1xf32>
}
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
%1 = transform.get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
%2 = transform.structured.vectorize_children_and_apply_patterns %1 : (!transform.any_op) -> !transform.any_op
transform.yield
}
}
// CHECK: #[[$ATTR_32:.+]] = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2)>
// CHECK-LABEL: func.func @generic_with_reduction_and_broadcast(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<1x12x197x197xf32>) -> tensor<1x12x197x1xf32> {
// CHECK: %[[VAL_1:.*]] = arith.constant 0.000000e+00 : f32
// CHECK: %[[VAL_2:.*]] = arith.constant 0 : index
// CHECK: %[[VAL_3:.*]] = tensor.empty() : tensor<1x12x197x1xf32>
// CHECK: %[[VAL_4:.*]] = vector.transfer_read %[[VAL_0]]{{\[}}%[[VAL_2]], %[[VAL_2]], %[[VAL_2]], %[[VAL_2]]], %[[VAL_1]] {in_bounds = [true, true, true, true]} : tensor<1x12x197x197xf32>, vector<1x12x197x197xf32>
// CHECK: %[[VAL_5:.*]] = vector.transfer_read %[[VAL_3]]{{\[}}%[[VAL_2]], %[[VAL_2]], %[[VAL_2]], %[[VAL_2]]], %[[VAL_1]] {in_bounds = [true, true, true], permutation_map = #[[$ATTR_32]]} : tensor<1x12x197x1xf32>, vector<1x12x197xf32>
// CHECK: %[[VAL_6:.*]] = vector.multi_reduction <add>, %[[VAL_4]], %[[VAL_5]] [3] : vector<1x12x197x197xf32> to vector<1x12x197xf32>
// CHECK: %[[VAL_7:.*]] = vector.broadcast %[[VAL_6]] : vector<1x12x197xf32> to vector<1x1x12x197xf32>
// CHECK: %[[VAL_8:.*]] = vector.transpose %[[VAL_7]], [1, 2, 3, 0] : vector<1x1x12x197xf32> to vector<1x12x197x1xf32>
// CHECK: %[[VAL_9:.*]] = vector.transfer_write %[[VAL_8]], %[[VAL_3]]{{\[}}%[[VAL_2]], %[[VAL_2]], %[[VAL_2]], %[[VAL_2]]] {in_bounds = [true, true, true, true]} : vector<1x12x197x1xf32>, tensor<1x12x197x1xf32>
// CHECK: return %[[VAL_9]] : tensor<1x12x197x1xf32>
// -----
// Input identical as the test in vectorization.mlir. Output is different -
// vector sizes are inferred (rather than user-specified) and hence _no_
// masking was used.
func.func @test_vectorize_pack(%arg0: tensor<32x8x16xf32>, %arg1: tensor<4x1x32x16x2xf32>) -> tensor<4x1x32x16x2xf32> {
%pack = tensor.pack %arg0 outer_dims_perm = [1, 2, 0] inner_dims_pos = [2, 1] inner_tiles = [16, 2] into %arg1 : tensor<32x8x16xf32> -> tensor<4x1x32x16x2xf32>
return %pack : tensor<4x1x32x16x2xf32>
}
module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg0: !transform.any_op {transform.readonly}) {
%0 = transform.structured.match ops{["tensor.pack"]} in %arg0 : (!transform.any_op) -> !transform.any_op
%1 = transform.get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
%2 = transform.structured.vectorize_children_and_apply_patterns %1 : (!transform.any_op) -> !transform.any_op
transform.yield
}
}
// CHECK-LABEL: func.func @test_vectorize_pack(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x8x16xf32>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<4x1x32x16x2xf32>) -> tensor<4x1x32x16x2xf32> {
// CHECK: %[[VAL_2:.*]] = arith.constant 0.000000e+00 : f32
// CHECK: %[[VAL_3:.*]] = arith.constant 0 : index
// CHECK: %[[VAL_4:.*]] = vector.transfer_read %[[VAL_0]]{{\[}}%[[VAL_3]], %[[VAL_3]], %[[VAL_3]]], %[[VAL_2]] {in_bounds = [true, true, true]} : tensor<32x8x16xf32>, vector<32x8x16xf32>
// CHECK: %[[VAL_5:.*]] = vector.shape_cast %[[VAL_4]] : vector<32x8x16xf32> to vector<32x4x2x1x16xf32>
// CHECK: %[[VAL_6:.*]] = vector.transpose %[[VAL_5]], [1, 3, 0, 4, 2] : vector<32x4x2x1x16xf32> to vector<4x1x32x16x2xf32>
// CHECK: %[[VAL_7:.*]] = tensor.empty() : tensor<4x1x32x16x2xf32>
// CHECK: %[[VAL_8:.*]] = vector.transfer_write %[[VAL_6]], %[[VAL_7]]{{\[}}%[[VAL_3]], %[[VAL_3]], %[[VAL_3]], %[[VAL_3]], %[[VAL_3]]] {in_bounds = [true, true, true, true, true]} : vector<4x1x32x16x2xf32>, tensor<4x1x32x16x2xf32>
// CHECK: return %[[VAL_8]] : tensor<4x1x32x16x2xf32>
// -----
// Input identical as the test in vectorization.mlir. Output is different -
// vector sizes are inferred (rather than user-specified) and hence _no_
// masking was used.
func.func @test_vectorize_padded_pack(%arg0: tensor<32x7x15xf32>, %arg1: tensor<32x4x1x16x2xf32>) -> tensor<32x4x1x16x2xf32> {
%pad = arith.constant 0.000000e+00 : f32
%pack = tensor.pack %arg0 padding_value(%pad : f32) inner_dims_pos = [2, 1] inner_tiles = [16, 2] into %arg1 : tensor<32x7x15xf32> -> tensor<32x4x1x16x2xf32>
return %pack : tensor<32x4x1x16x2xf32>
}
// CHECK-LABEL: func.func @test_vectorize_padded_pack(
// CHECK-SAME: %[[VAL_0:.*]]: tensor<32x7x15xf32>,
// CHECK-SAME: %[[VAL_1:.*]]: tensor<32x4x1x16x2xf32>) -> tensor<32x4x1x16x2xf32> {
// CHECK: %[[VAL_2:.*]] = arith.constant 0.000000e+00 : f32
// CHECK: %[[VAL_3:.*]] = arith.constant 0 : index
// CHECK: %[[VAL_4:.*]] = vector.transfer_read %[[VAL_0]]{{\[}}%[[VAL_3]], %[[VAL_3]], %[[VAL_3]]], %[[VAL_2]] {in_bounds = [true, false, false]} : tensor<32x7x15xf32>, vector<32x8x16xf32>
// CHECK: %[[VAL_5:.*]] = vector.shape_cast %[[VAL_4]] : vector<32x8x16xf32> to vector<32x4x2x1x16xf32>
// CHECK: %[[VAL_6:.*]] = vector.transpose %[[VAL_5]], [0, 1, 3, 4, 2] : vector<32x4x2x1x16xf32> to vector<32x4x1x16x2xf32>
// CHECK: %[[VAL_7:.*]] = tensor.empty() : tensor<32x4x1x16x2xf32>
// CHECK: %[[VAL_8:.*]] = vector.transfer_write %[[VAL_6]], %[[VAL_7]]{{\[}}%[[VAL_3]], %[[VAL_3]], %[[VAL_3]], %[[VAL_3]], %[[VAL_3]]] {in_bounds = [true, true, true, true, true]} : vector<32x4x1x16x2xf32>, tensor<32x4x1x16x2xf32>
// CHECK: return %[[VAL_8]] : tensor<32x4x1x16x2xf32>
module attributes {transform.with_named_sequence} {
transform.named_sequence @__transform_main(%arg0: !transform.any_op {transform.readonly}) {
%0 = transform.structured.match ops{["tensor.pack"]} in %arg0 : (!transform.any_op) -> !transform.any_op
%1 = transform.get_parent_op %0 {isolated_from_above} : (!transform.any_op) -> !transform.any_op
%2 = transform.structured.vectorize_children_and_apply_patterns %1 : (!transform.any_op) -> !transform.any_op
transform.yield
}
}
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