1152 lines
46 KiB
MLIR
1152 lines
46 KiB
MLIR
// RUN: mlir-opt %s -linalg-fuse-elementwise-ops -split-input-file | FileCheck %s
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// CHECK-DAG: [[$MAP0:#[a-zA-Z0-9_]*]] = affine_map<(d0, d1) -> (d0, d1)>
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#map0 = affine_map<(d0, d1) -> (d0, d1)>
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// CHECK-LABEL: @add_mul_fusion
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func.func @add_mul_fusion(%arg0: tensor<?x?xf32>, %arg1 : tensor<?x?xf32>, %arg2 : tensor<?x?xf32>) -> tensor<?x?xf32>
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{
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%c0 = arith.constant 0 : index
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%c1 = arith.constant 1 : index
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%0 = tensor.dim %arg0, %c0 : tensor<?x?xf32>
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%1 = tensor.dim %arg0, %c1 : tensor<?x?xf32>
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%2 = tensor.empty(%0, %1) : tensor<?x?xf32>
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%3 = linalg.generic {indexing_maps = [#map0, #map0, #map0], iterator_types = ["parallel", "parallel"]}
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ins(%arg0, %arg1 : tensor<?x?xf32>, tensor<?x?xf32>)
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outs(%2 : tensor<?x?xf32>) {
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^bb0(%arg3: f32, %arg4: f32, %arg5: f32):
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%4 = arith.addf %arg3, %arg4 : f32
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linalg.yield %4 : f32
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} -> tensor<?x?xf32>
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// CHECK: linalg.generic {
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// CHECK-SAME: indexing_maps = {{\[}}[[$MAP0]], [[$MAP0]], [[$MAP0]], [[$MAP0]]{{\]}}
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%4 = linalg.generic {indexing_maps = [#map0, #map0, #map0], iterator_types = ["parallel", "parallel"]}
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ins(%3, %arg2 : tensor<?x?xf32>, tensor<?x?xf32>)
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outs(%2 : tensor<?x?xf32>) {
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// CHECK: ^{{[a-zA-Z0-9_]*}}
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// CHECK-SAME: [[ARG0:%[a-zA-Z0-9_]*]]
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// CHECK-SAME: [[ARG1:%[a-zA-Z0-9_]*]]
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// CHECK-SAME: [[ARG2:%[a-zA-Z0-9_]*]]
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^bb0(%arg5: f32, %arg6: f32, %arg7: f32):
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// CHECK: [[T1:%[a-zA-Z0-9_]*]] = arith.addf [[ARG0]], [[ARG1]]
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// CHECK-NOT: linalg.yield
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// CHECK: arith.mulf [[T1]], [[ARG2]]
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// CHECK: linalg.yield
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%5 = arith.mulf %arg5, %arg6 : f32
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linalg.yield %5 : f32
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} -> tensor<?x?xf32>
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return %4 : tensor<?x?xf32>
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}
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// -----
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// CHECK-DAG: [[$MAP0:#[a-zA-Z0-9_]*]] = affine_map<(d0, d1) -> (d0, d1)>
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// CHECK-DAG: [[$MAP1:#[a-zA-Z0-9_]*]] = affine_map<(d0, d1) -> ()>
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#map0 = affine_map<(d0, d1) -> (d0, d1)>
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#map1 = affine_map<(d0, d1) -> ()>
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// CHECK-LABEL: @scalar_add_mul_fusion
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func.func @scalar_add_mul_fusion(%arg0: tensor<?x?xf32>, %arg1 : f32, %arg2 : f32) -> tensor<?x?xf32>
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{
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%c0 = arith.constant 0 : index
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%c1 = arith.constant 1 : index
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%0 = tensor.dim %arg0, %c0 : tensor<?x?xf32>
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%1 = tensor.dim %arg0, %c1 : tensor<?x?xf32>
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%2 = tensor.empty(%0, %1) : tensor<?x?xf32>
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%3 = linalg.generic {indexing_maps = [#map0, #map1, #map0], iterator_types = ["parallel", "parallel"]}
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ins(%arg0, %arg1 : tensor<?x?xf32>, f32)
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outs(%2 : tensor<?x?xf32>) {
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^bb0(%arg3: f32, %arg4: f32, %arg5: f32):
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%4 = arith.addf %arg3, %arg4 : f32
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linalg.yield %4 : f32
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} -> tensor<?x?xf32>
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// CHECK: linalg.generic {
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// CHECK-SAME: indexing_maps = {{\[}}[[$MAP0]], [[$MAP1]], [[$MAP1]], [[$MAP0]]{{\]}}
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%4 = linalg.generic {indexing_maps = [#map0, #map1, #map0], iterator_types = ["parallel", "parallel"]}
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ins(%3, %arg2 : tensor<?x?xf32>, f32)
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outs(%2 : tensor<?x?xf32>) {
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// CHECK: ^{{[a-zA-Z0-9_]*}}
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// CHECK-SAME: [[ARG3:%[a-zA-Z0-9_]*]]
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// CHECK-SAME: [[ARG4:%[a-zA-Z0-9_]*]]
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// CHECK-SAME: [[ARG5:%[a-zA-Z0-9_]*]]
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^bb0(%arg5: f32, %arg6: f32, %arg7: f32):
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// CHECK: [[T1:%[a-zA-Z0-9_]*]] = arith.addf [[ARG3]], [[ARG4]]
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// CHECK-NOT: linalg.yield
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// CHECK: arith.mulf [[T1]], [[ARG5]]
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// CHECK: linalg.yield
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%5 = arith.mulf %arg5, %arg6 : f32
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linalg.yield %5 : f32
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} -> tensor<?x?xf32>
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return %4 : tensor<?x?xf32>
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}
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// -----
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// CHECK-DAG: [[$MAP0:#[a-zA-Z0-9_]*]] = affine_map<(d0, d1) -> (d0, d1)>
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// CHECK-DAG: [[$MAP1:#[a-zA-Z0-9_]*]] = affine_map<(d0, d1) -> (d1, d0)>
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#map0 = affine_map<(d0, d1) -> (d0, d1)>
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#map1 = affine_map<(d0, d1) -> (d1, d0)>
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// CHECK-LABEL: @transpose_add_mul_fusion
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func.func @transpose_add_mul_fusion(%arg0: tensor<?x?xf32>, %arg1 : tensor<?x?xf32>, %arg2 : tensor<?x?xf32>) -> tensor<?x?xf32>
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{
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%c0 = arith.constant 0 : index
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%c1 = arith.constant 1 : index
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%0 = tensor.dim %arg0, %c0 : tensor<?x?xf32>
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%1 = tensor.dim %arg0, %c1 : tensor<?x?xf32>
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%2 = tensor.empty(%0, %1) : tensor<?x?xf32>
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%3 = linalg.generic {indexing_maps = [#map0, #map1, #map0], iterator_types = ["parallel", "parallel"]}
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ins(%arg0, %arg1 : tensor<?x?xf32>, tensor<?x?xf32>)
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outs(%2 : tensor<?x?xf32>) {
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^bb0(%arg3: f32, %arg4: f32, %arg5: f32):
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%4 = arith.addf %arg3, %arg4 : f32
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linalg.yield %4 : f32
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} -> tensor<?x?xf32>
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// CHECK: linalg.generic {
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// CHECK-SAME: indexing_maps = {{\[}}[[$MAP0]], [[$MAP1]], [[$MAP0]], [[$MAP0]]{{\]}}
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%4 = linalg.generic {indexing_maps = [#map0, #map0, #map0], iterator_types = ["parallel", "parallel"]}
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ins(%3, %arg2 : tensor<?x?xf32>, tensor<?x?xf32>)
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outs(%2 : tensor<?x?xf32>) {
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^bb0(%arg5: f32, %arg6: f32, %arg7: f32):
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%5 = arith.mulf %arg5, %arg6 : f32
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linalg.yield %5 : f32
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} -> tensor<?x?xf32>
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return %4 : tensor<?x?xf32>
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}
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// -----
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// CHECK-DAG: [[$MAP0:#[a-zA-Z0-9_]*]] = affine_map<(d0, d1) -> (d0, d1)>
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// CHECK-DAG: [[$MAP1:#[a-zA-Z0-9_]*]] = affine_map<(d0, d1) -> (d1, d0)>
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#map0 = affine_map<(d0, d1) -> (d0, d1)>
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#map1 = affine_map<(d0, d1) -> (d1, d0)>
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// CHECK-LABEL: @add_transpose_mul_fusion
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func.func @add_transpose_mul_fusion(%arg0: tensor<?x?xf32>, %arg1 : tensor<?x?xf32>, %arg2 : tensor<?x?xf32>) -> tensor<?x?xf32>
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{
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%c0 = arith.constant 0 : index
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%c1 = arith.constant 1 : index
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%0 = tensor.dim %arg0, %c0 : tensor<?x?xf32>
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%1 = tensor.dim %arg0, %c1 : tensor<?x?xf32>
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%2 = tensor.empty(%0, %1) : tensor<?x?xf32>
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%3 = linalg.generic {indexing_maps = [#map0, #map1, #map0], iterator_types = ["parallel", "parallel"]}
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ins(%arg0, %arg1 : tensor<?x?xf32>, tensor<?x?xf32>)
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outs(%2 : tensor<?x?xf32>) {
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^bb0(%arg3: f32, %arg4: f32, %arg5: f32):
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%4 = arith.addf %arg3, %arg4 : f32
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linalg.yield %4 : f32
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} -> tensor<?x?xf32>
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// CHECK: linalg.generic {
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// CHECK-SAME: indexing_maps = {{\[}}[[$MAP1]], [[$MAP0]], [[$MAP0]], [[$MAP0]]{{\]}}
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%4 = linalg.generic {indexing_maps = [#map1, #map0, #map0], iterator_types = ["parallel", "parallel"]}
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ins(%3, %arg2 : tensor<?x?xf32>, tensor<?x?xf32>)
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outs(%2 : tensor<?x?xf32>){
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^bb0(%arg5: f32, %arg6: f32, %arg7: f32):
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%5 = arith.mulf %arg5, %arg6 : f32
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linalg.yield %5 : f32
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} -> tensor<?x?xf32>
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return %4 : tensor<?x?xf32>
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}
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// -----
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// CHECK-DAG: [[$MAP0:#[a-zA-Z0-9_]*]] = affine_map<(d0, d1) -> (d0, d1)>
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// CHECK-DAG: [[$MAP1:#[a-zA-Z0-9_]*]] = affine_map<(d0, d1) -> (d0)>
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#map0 = affine_map<(d0, d1) -> (d0, d1)>
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#map1 = affine_map<(d0, d1) -> (d0)>
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#map2 = affine_map<(d0) -> (d0)>
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// CHECK-LABEL: @add_broadcast_mul_fusion
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func.func @add_broadcast_mul_fusion(%arg0: tensor<?xf32>, %arg1 : tensor<?xf32>, %arg2 : tensor<?x?xf32>) -> tensor<?x?xf32>
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{
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%c0 = arith.constant 0 : index
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%c1 = arith.constant 1 : index
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%0 = tensor.dim %arg0, %c0 : tensor<?xf32>
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%1 = tensor.empty(%0) : tensor<?xf32>
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%2 = linalg.generic {indexing_maps = [#map2, #map2, #map2], iterator_types = ["parallel"]}
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ins(%arg0, %arg1 : tensor<?xf32>, tensor<?xf32>)
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outs(%1 : tensor<?xf32>) {
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^bb0(%arg3: f32, %arg4: f32, %arg5: f32):
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%3 = arith.addf %arg3, %arg4 : f32
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linalg.yield %3 : f32
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} -> tensor<?xf32>
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// CHECK: linalg.generic {
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// CHECK-SAME: indexing_maps = {{\[}}[[$MAP1]], [[$MAP1]], [[$MAP0]], [[$MAP0]]
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%3 = tensor.dim %arg2, %c1 : tensor<?x?xf32>
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%4 = tensor.empty(%0, %3) : tensor<?x?xf32>
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%5 = linalg.generic {indexing_maps = [#map1, #map0, #map0], iterator_types = ["parallel", "parallel"]}
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ins(%2, %arg2 : tensor<?xf32>, tensor<?x?xf32>)
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outs(%4 : tensor<?x?xf32>){
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^bb0(%arg5: f32, %arg6: f32, %arg7: f32):
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%6 = arith.mulf %arg5, %arg6 : f32
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linalg.yield %6 : f32
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} -> tensor<?x?xf32>
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return %5 : tensor<?x?xf32>
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}
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// -----
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// CHECK: #[[$MAP0:.*]] = affine_map<() -> ()>
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#map0 = affine_map<() -> ()>
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// CHECK-LABEL: @add_mul_scalar_fusion
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func.func @add_mul_scalar_fusion(%arg0: tensor<f32>, %arg1: tensor<f32>, %arg2: tensor<f32>) -> tensor<f32>
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{
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%0 = tensor.empty() : tensor<f32>
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%1 = linalg.generic {indexing_maps = [#map0, #map0, #map0], iterator_types = []}
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ins(%arg0, %arg1 : tensor<f32>, tensor<f32>)
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outs(%0 : tensor<f32>) {
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^bb0(%arg3: f32, %arg4: f32, %arg5: f32):
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%2 = arith.addf %arg3, %arg4 : f32
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linalg.yield %2 : f32
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} -> tensor<f32>
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// CHECK: linalg.generic {
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// CHECK: arith.addf
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// CHECK: arith.mulf
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%2 = linalg.generic {indexing_maps = [#map0, #map0, #map0], iterator_types = []}
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ins(%1, %arg2 : tensor<f32>, tensor<f32>)
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outs(%0 : tensor<f32>) {
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^bb0(%arg3: f32, %arg4: f32, %arg5: f32):
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%3 = arith.mulf %arg3, %arg4 : f32
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linalg.yield %3 : f32
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} -> tensor<f32>
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return %2 : tensor<f32>
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}
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// -----
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#map0 = affine_map<(d0, d1, d2) -> (d0)>
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#map1 = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
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func.func @generic_op_constant_fusion(%arg0 : tensor<5x?x?xf32>) -> tensor<5x?x?xf32>
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{
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%c0 = arith.constant 0 : index
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%c1 = arith.constant 1 : index
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%c2 = arith.constant 2 : index
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%cst = arith.constant dense<42.0> : tensor<5xf32>
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%0 = tensor.dim %arg0, %c1 : tensor<5x?x?xf32>
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%1 = tensor.dim %arg0, %c2 : tensor<5x?x?xf32>
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%2 = tensor.empty(%0, %1) : tensor<5x?x?xf32>
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%3 = linalg.generic {
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indexing_maps = [#map0, #map1, #map1],
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iterator_types = ["parallel", "parallel", "parallel"]}
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ins(%cst, %arg0 : tensor<5xf32>, tensor<5x?x?xf32>)
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outs(%2 : tensor<5x?x?xf32>) {
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^bb0(%arg1: f32, %arg2: f32, %arg3: f32):
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%4 = arith.mulf %arg1, %arg2 : f32
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linalg.yield %4 : f32
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} -> tensor<5x?x?xf32>
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return %3 : tensor<5x?x?xf32>
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}
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// CHECK-DAG: #[[$MAP0:.*]] = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
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// CHECK-LABEL: func @generic_op_constant_fusion
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// CHECK: %[[CST:.*]] = arith.constant {{.*}} : f32
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// CHECK: linalg.generic
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// CHECK: ^{{.+}}(%[[ARG1:[a-zA-Z0-9_]+]]: f32, %{{.+}}: f32):
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// CHECK: arith.mulf %[[ARG1]], %[[CST]]
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// -----
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#map0 = affine_map<(d0, d1, d2) -> ()>
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#map1 = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
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func.func @generic_op_zero_dim_constant_fusion(%arg0 : tensor<5x?x?xf32>)
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-> tensor<5x?x?xf32>
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{
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%c0 = arith.constant 0 : index
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%c1 = arith.constant 1 : index
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%c2 = arith.constant 2 : index
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%cst = arith.constant dense<42.0> : tensor<f32>
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%0 = tensor.dim %arg0, %c1 : tensor<5x?x?xf32>
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%1 = tensor.dim %arg0, %c2 : tensor<5x?x?xf32>
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%2 = tensor.empty(%0, %1) : tensor<5x?x?xf32>
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%3 = linalg.generic {
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indexing_maps = [#map0, #map1, #map1],
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iterator_types = ["parallel", "parallel", "parallel"]}
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ins(%cst, %arg0 : tensor<f32>, tensor<5x?x?xf32>)
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outs(%2 : tensor<5x?x?xf32>) {
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^bb0(%arg1: f32, %arg2: f32, %arg3: f32):
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%4 = arith.mulf %arg1, %arg2 : f32
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linalg.yield %4 : f32
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} -> tensor<5x?x?xf32>
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return %3 : tensor<5x?x?xf32>
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}
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// CHECK-DAG: #[[$MAP0:.*]] = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
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// CHECK-LABEL: func @generic_op_zero_dim_constant_fusion
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// CHECK: %[[CST:.*]] = arith.constant {{.*}} : f32
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// CHECK: linalg.generic
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// CHECK: ^{{.*}}(%[[ARG1:[a-zA-Z0-9_]*]]: f32, %{{.*}}: f32)
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// CHECK: arith.mulf %[[ARG1]], %[[CST]]
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// -----
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#map0 = affine_map<(d0, d1) -> (d0, d1)>
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func.func @producer_indexed_consumer_fusion(%arg0: tensor<?x?xi32>,
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%arg1: tensor<?x?xi32>) -> tensor<?x?xi32> {
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%c0 = arith.constant 0 : index
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%c1 = arith.constant 1 : index
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%0 = tensor.dim %arg0, %c0 : tensor<?x?xi32>
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%1 = tensor.dim %arg0, %c1 : tensor<?x?xi32>
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%2 = tensor.empty(%0, %1) : tensor<?x?xi32>
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%3 = linalg.generic {
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indexing_maps = [#map0, #map0, #map0],
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iterator_types = ["parallel", "parallel"] }
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ins(%arg0, %arg1 : tensor<?x?xi32>, tensor<?x?xi32>)
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outs(%2 : tensor<?x?xi32>) {
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^bb0(%arg2: i32, %arg3: i32, %arg4: i32):
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%10 = arith.addi %arg2, %arg3 : i32
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linalg.yield %10 : i32
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} -> tensor<?x?xi32>
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%4 = linalg.generic {
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indexing_maps = [#map0, #map0],
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iterator_types = ["parallel", "parallel"] }
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ins(%3 : tensor<?x?xi32>)
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outs(%2 : tensor<?x?xi32>) {
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^bb0(%arg2: i32, %arg3: i32):
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%idx0 = linalg.index 0 : index
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%idx1 = linalg.index 1 : index
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%5 = arith.index_cast %idx0 : index to i32
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%6 = arith.index_cast %idx1 : index to i32
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%7 = arith.addi %arg2, %5 : i32
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%8 = arith.subi %7, %6 : i32
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linalg.yield %8 : i32
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} -> tensor<?x?xi32>
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return %4 : tensor<?x?xi32>
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}
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// CHECK-DAG: #[[$MAP0:.*]] = affine_map<(d0, d1) -> (d0, d1)>
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// CHECK-LABEL: func @producer_indexed_consumer_fusion
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// CHECK: linalg.generic
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// CHECK-SAME: indexing_maps = [#[[$MAP0]], #[[$MAP0]], #[[$MAP0]]]
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// CHECK: ^{{[a-zA-Z0-9_]*}}
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// CHECK-SAME: %[[ARG0:[a-zA-Z0-9_]*]]: i32
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// CHECK-SAME: %[[ARG1:[a-zA-Z0-9_]*]]: i32
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// CHECK: %[[VAL1:.+]] = arith.addi %[[ARG0]], %[[ARG1]] : i32
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// CHECK: %[[IDX0:.+]] = linalg.index 0 : index
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// CHECK: %[[IDX1:.+]] = linalg.index 1 : index
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// CHECK: %[[ADD_OPERAND:.+]] = arith.index_cast %[[IDX0]] : index to i32
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// CHECK: %[[SUB_OPERAND:.+]] = arith.index_cast %[[IDX1]] : index to i32
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// CHECK: %[[VAL2:.+]] = arith.addi %[[VAL1]], %[[ADD_OPERAND]] : i32
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// CHECK: %[[VAL3:.+]] = arith.subi %[[VAL2]], %[[SUB_OPERAND]] : i32
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// CHECK: linalg.yield %[[VAL3]] : i32
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// CHECK-NOT: linalg.generic
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// -----
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|
|
#map0 = affine_map<(d0, d1) -> (d0, d1)>
|
|
func.func @indexed_producer_consumer_fusion(%arg0: tensor<?x?xi32>) -> tensor<?x?xi32> {
|
|
%c0 = arith.constant 0 : index
|
|
%c1 = arith.constant 1 : index
|
|
%0 = tensor.dim %arg0, %c0 : tensor<?x?xi32>
|
|
%1 = tensor.dim %arg0, %c1 : tensor<?x?xi32>
|
|
%2 = tensor.empty(%0, %1) : tensor<?x?xi32>
|
|
%3 = linalg.generic {
|
|
indexing_maps = [#map0, #map0],
|
|
iterator_types = ["parallel", "parallel"] }
|
|
ins(%arg0 : tensor<?x?xi32>)
|
|
outs(%2 : tensor<?x?xi32>) {
|
|
^bb0(%arg4: i32, %arg5: i32):
|
|
%idx0 = linalg.index 0 : index
|
|
%idx1 = linalg.index 1 : index
|
|
%4 = arith.index_cast %idx0 : index to i32
|
|
%5 = arith.index_cast %idx1 : index to i32
|
|
%6 = arith.addi %arg4, %4 : i32
|
|
%7 = arith.subi %6, %5 : i32
|
|
linalg.yield %7 : i32
|
|
} -> tensor<?x?xi32>
|
|
%4 = linalg.generic {
|
|
indexing_maps = [#map0, #map0, #map0],
|
|
iterator_types = ["parallel", "parallel"] }
|
|
ins(%3, %arg0 : tensor<?x?xi32>, tensor<?x?xi32>)
|
|
outs(%2 : tensor<?x?xi32>) {
|
|
^bb0(%arg2: i32, %arg3: i32, %arg4: i32):
|
|
%10 = arith.addi %arg2, %arg3 : i32
|
|
linalg.yield %10 : i32
|
|
} -> tensor<?x?xi32>
|
|
return %4 : tensor<?x?xi32>
|
|
}
|
|
// CHECK-DAG: #[[$MAP0:.*]] = affine_map<(d0, d1) -> (d0, d1)>
|
|
// CHECK-LABEL: func @indexed_producer_consumer_fusion
|
|
// CHECK: linalg.generic
|
|
// CHECK-SAME: indexing_maps = [#[[$MAP0]], #[[$MAP0]]]
|
|
// CHECK: ^{{[a-zA-Z0-9_]*}}
|
|
// CHECK-SAME: %[[ARG0:[a-zA-Z0-9_]*]]: i32
|
|
// CHECK-SAME: %[[ARG1:[a-zA-Z0-9_]*]]: i32
|
|
// CHECK: %[[IDX0:.+]] = linalg.index 0 : index
|
|
// CHECK: %[[IDX1:.+]] = linalg.index 1 : index
|
|
// CHECK: %[[ADD_OPERAND:.+]] = arith.index_cast %[[IDX0]] : index to i32
|
|
// CHECK: %[[SUB_OPERAND:.+]] = arith.index_cast %[[IDX1]] : index to i32
|
|
// CHECK: %[[VAL1:.+]] = arith.addi %[[ARG0]], %[[ADD_OPERAND]] : i32
|
|
// CHECK: %[[VAL2:.+]] = arith.subi %[[VAL1]], %[[SUB_OPERAND]] : i32
|
|
// CHECK: %[[VAL3:.+]] = arith.addi %[[VAL2]], %[[ARG0]] : i32
|
|
// CHECK: linalg.yield %[[VAL3]] : i32
|
|
// CHECK-NOT: linalg.generic
|
|
|
|
// -----
|
|
|
|
// The indices of the first generic op are swapped after fusion.
|
|
#map0 = affine_map<(d0, d1) -> (d1, d0)>
|
|
#map1 = affine_map<(d0, d1) -> (d0, d1)>
|
|
func.func @indexed_producer_indexed_consumer_fusion(%arg0: tensor<?x?xi32>)
|
|
-> tensor<?x?xi32> {
|
|
%c0 = arith.constant 0 : index
|
|
%c1 = arith.constant 1 : index
|
|
%0 = tensor.dim %arg0, %c0 : tensor<?x?xi32>
|
|
%1 = tensor.dim %arg0, %c1 : tensor<?x?xi32>
|
|
%2 = tensor.empty(%0, %1) : tensor<?x?xi32>
|
|
%3 = linalg.generic {
|
|
indexing_maps = [#map0, #map0],
|
|
iterator_types = ["parallel", "parallel"] }
|
|
ins(%arg0 : tensor<?x?xi32>)
|
|
outs(%2 : tensor<?x?xi32>) {
|
|
^bb0(%arg2: i32, %arg3: i32):
|
|
%idx0 = linalg.index 0 : index
|
|
%idx1 = linalg.index 1 : index
|
|
%4 = arith.index_cast %idx0 : index to i32
|
|
%5 = arith.index_cast %idx1 : index to i32
|
|
%6 = arith.addi %arg2, %4 : i32
|
|
%7 = arith.subi %5, %6 : i32
|
|
linalg.yield %7 : i32
|
|
} -> tensor<?x?xi32>
|
|
%4= linalg.generic {
|
|
indexing_maps = [#map1, #map1],
|
|
iterator_types = ["parallel", "parallel"] }
|
|
ins(%3 : tensor<?x?xi32>)
|
|
outs(%2 : tensor<?x?xi32>) {
|
|
^bb0(%arg2: i32, %arg3: i32):
|
|
%idx0 = linalg.index 0 : index
|
|
%idx1 = linalg.index 1 : index
|
|
%5 = arith.index_cast %idx0 : index to i32
|
|
%6 = arith.index_cast %idx1 : index to i32
|
|
%7 = arith.addi %arg2, %5 : i32
|
|
%8 = arith.subi %7, %6 : i32
|
|
linalg.yield %8 : i32
|
|
} -> tensor<?x?xi32>
|
|
return %4 : tensor<?x?xi32>
|
|
}
|
|
// CHECK-DAG: #[[$MAP0:.*]] = affine_map<(d0, d1) -> (d0, d1)>
|
|
// CHECK-LABEL: func @indexed_producer_indexed_consumer_fusion
|
|
// CHECK: linalg.generic
|
|
// CHECK-SAME: indexing_maps = [#[[$MAP0]], #[[$MAP0]]]
|
|
// CHECK: ^{{[a-zA-Z0-9_]*}}
|
|
// CHECK-SAME: %[[ARG0:[a-zA-Z0-9_]*]]: i32
|
|
// CHECK: %[[IDX0:.+]] = linalg.index 0 : index
|
|
// CHECK: %[[IDX1:.+]] = linalg.index 1 : index
|
|
// CHECK: %[[ADD_OPERAND1:.+]] = arith.index_cast %[[IDX1]] : index to i32
|
|
// CHECK: %[[SUB_OPERAND1:.+]] = arith.index_cast %[[IDX0]] : index to i32
|
|
// CHECK: %[[VAL1:.+]] = arith.addi %[[ARG0]], %[[ADD_OPERAND1]] : i32
|
|
// CHECK: %[[VAL2:.+]] = arith.subi %[[SUB_OPERAND1]], %[[VAL1]] : i32
|
|
// CHECK: %[[IDX2:.+]] = linalg.index 0 : index
|
|
// CHECK: %[[IDX3:.+]] = linalg.index 1 : index
|
|
// CHECK: %[[ADD_OPERAND2:.+]] = arith.index_cast %[[IDX2]] : index to i32
|
|
// CHECK: %[[SUB_OPERAND2:.+]] = arith.index_cast %[[IDX3]] : index to i32
|
|
// CHECK: %[[VAL3:.+]] = arith.addi %[[VAL2]], %[[ADD_OPERAND2]] : i32
|
|
// CHECK: %[[VAL4:.+]] = arith.subi %[[VAL3]], %[[SUB_OPERAND2]] : i32
|
|
// CHECK: linalg.yield %[[VAL4]] : i32
|
|
// CHECK-NOT: linalg.generic
|
|
|
|
// -----
|
|
|
|
#map1 = affine_map<(d0) -> (d0)>
|
|
#map2 = affine_map<(d0, d1) -> (d0, d1)>
|
|
#map3 = affine_map<(d0, d1) -> (d1)>
|
|
func.func @one_dim_indexed_producer_consumer_fusion(%arg0 : tensor<?xi32>,
|
|
%arg1 : tensor<?x?xi32>) -> tensor<?x?xi32> {
|
|
%c0 = arith.constant 0 : index
|
|
%c1 = arith.constant 1 : index
|
|
%d0 = tensor.dim %arg0, %c0 : tensor<?xi32>
|
|
%0 = tensor.empty(%d0) : tensor<?xi32>
|
|
%1 = linalg.generic
|
|
{indexing_maps = [#map1, #map1],
|
|
iterator_types = ["parallel"]}
|
|
ins(%arg0 : tensor<?xi32>) outs(%0 : tensor<?xi32>) {
|
|
^bb0(%arg2 : i32, %arg3 : i32):
|
|
%2 = linalg.index 0 : index
|
|
%3 = arith.index_cast %2 : index to i32
|
|
%4 = arith.addi %arg2, %3 : i32
|
|
linalg.yield %4 : i32
|
|
} -> tensor<?xi32>
|
|
%2 = tensor.dim %arg1, %c0 : tensor<?x?xi32>
|
|
%3 = tensor.dim %arg1, %c1 : tensor<?x?xi32>
|
|
%4 = tensor.empty(%2, %3) : tensor<?x?xi32>
|
|
%5 = linalg.generic
|
|
{indexing_maps = [#map2, #map3, #map2],
|
|
iterator_types = ["parallel", "parallel"]}
|
|
ins(%arg1, %1 : tensor<?x?xi32>, tensor<?xi32>)
|
|
outs(%4 : tensor<?x?xi32>) {
|
|
^bb0(%arg2 : i32, %arg3 : i32, %arg4: i32):
|
|
%6 = arith.addi %arg2, %arg3 : i32
|
|
linalg.yield %6 : i32
|
|
} -> tensor<?x?xi32>
|
|
return %5 : tensor<?x?xi32>
|
|
}
|
|
// CHECK-DAG: #[[$MAP0:.*]] = affine_map<(d0, d1) -> (d0, d1)>
|
|
// CHECK-DAG: #[[$MAP1:.*]] = affine_map<(d0, d1) -> (d1)>
|
|
// CHECK-LABEL: func @one_dim_indexed_producer_consumer_fusion
|
|
// CHECK: linalg.generic
|
|
// CHECK-SAME: indexing_maps = [#[[$MAP0]], #[[$MAP1]], #[[$MAP0]]]
|
|
// CHECK: ^{{[a-zA-Z0-9_]*}}
|
|
// CHECK-SAME: (%[[ARG0:[a-zA-Z0-9_]*]]: i32, %[[ARG1:[a-zA-Z0-9_]*]]: i32
|
|
// CHECK: %[[IDX1:.+]] = linalg.index 1 : index
|
|
// CHECK: %[[VAL1:.+]] = arith.index_cast %[[IDX1]] : index to i32
|
|
// CHECK: %[[VAL2:.+]] = arith.addi %[[ARG1]], %[[VAL1]] : i32
|
|
// CHECK: %[[VAL3:.+]] = arith.addi %[[ARG0]], %[[VAL2]] : i32
|
|
// CHECK: linalg.yield %[[VAL3]] : i32
|
|
// CHECK-NOT: linalg.generic
|
|
|
|
// -----
|
|
|
|
func.func @scalar_generic_fusion
|
|
(%arg0: tensor<5x1x1xf32>, %arg1 : tensor<i32>) -> tensor<10xf32>
|
|
{
|
|
%c0 = arith.constant 0 : index
|
|
%cst = arith.constant dense<1.000000e+00> : tensor<10xf32>
|
|
%0 = tensor.empty() : tensor<f32>
|
|
%1 = linalg.generic
|
|
{indexing_maps = [affine_map<() -> ()>, affine_map<() -> ()>],
|
|
iterator_types = []}
|
|
ins(%arg1 : tensor<i32>) outs(%0 : tensor<f32>) {
|
|
^bb0(%arg2: i32, %arg3: f32):
|
|
%3 = arith.index_cast %arg2 : i32 to index
|
|
%4 = tensor.extract %arg0[%3, %c0, %c0] : tensor<5x1x1xf32>
|
|
linalg.yield %4 : f32
|
|
} -> tensor<f32>
|
|
%2 = tensor.empty() : tensor<10xf32>
|
|
%3 = linalg.generic
|
|
{indexing_maps = [affine_map<(d0) -> ()>, affine_map<(d0) -> (d0)>,
|
|
affine_map<(d0) -> (d0)>],
|
|
iterator_types = ["parallel"]}
|
|
ins(%1, %cst : tensor<f32>, tensor<10xf32>) outs(%2 : tensor<10xf32>) {
|
|
^bb0(%arg2: f32, %arg3: f32, %arg4: f32):
|
|
%4 = arith.mulf %arg2, %arg3 : f32
|
|
linalg.yield %4 : f32
|
|
} -> tensor<10xf32>
|
|
return %3 : tensor<10xf32>
|
|
}
|
|
// CHECK-DAG: #[[MAP0:.+]] = affine_map<(d0) -> ()>
|
|
// CHECK-DAG: #[[MAP1:.+]] = affine_map<(d0) -> (d0)>
|
|
// CHECK: func @scalar_generic_fusion
|
|
// CHECK-SAME: %[[ARG0:[a-zA-Z0-9]+]]: tensor<5x1x1xf32>
|
|
// CHECK-SAME: %[[ARG1:[a-zA-Z0-9]+]]: tensor<i32>
|
|
// CHECK: %[[T0:.+]] = linalg.generic
|
|
// CHECK-SAME: indexing_maps = [#[[MAP0]], #[[MAP1]]]
|
|
// CHECK-SAME: iterator_types = ["parallel"]
|
|
// CHECK-SAME: ins(%[[ARG1]] : tensor<i32>)
|
|
// CHECK: tensor.extract %[[ARG0]]
|
|
// CHECK: linalg.yield
|
|
// CHECK return %[[T0]]
|
|
|
|
// -----
|
|
|
|
func.func @constant_fusion(%arg0 : tensor<4xf32>) -> (tensor<4xf32>) {
|
|
%cst = arith.constant dense<1.0> : tensor<4xf32>
|
|
%1 = tensor.empty() : tensor<4xf32>
|
|
%2 = linalg.generic
|
|
{indexing_maps = [affine_map<(d0) -> (d0)>, affine_map<(d0) -> (d0)>,
|
|
affine_map<(d0) -> (d0)>],
|
|
iterator_types = ["parallel"]}
|
|
ins (%arg0, %cst : tensor<4xf32>, tensor<4xf32>)
|
|
outs (%1 : tensor<4xf32>) {
|
|
^bb0(%arg1: f32, %arg2: f32, %arg3: f32):
|
|
%3 = arith.addf %arg1, %arg2 : f32
|
|
linalg.yield %3 : f32
|
|
} -> tensor<4xf32>
|
|
return %2 : tensor<4xf32>
|
|
}
|
|
|
|
// CHECK-DAG: #[[MAP:.+]] = affine_map<(d0) -> (d0)>
|
|
// CHECK: func @constant_fusion(%[[ARG0:.+]]: tensor<4xf32>)
|
|
// CHECK-DAG: %[[CST:.+]] = arith.constant 1.000000e+00 : f32
|
|
// CHECK-DAG: %[[T0:.+]] = tensor.empty() : tensor<4xf32>
|
|
// CHECK: %[[T1:.+]] = linalg.generic
|
|
// CHECK-SAME: indexing_maps = [#[[MAP]], #[[MAP]]]
|
|
// CHECK-SAME: ins(%[[ARG0]] : tensor<4xf32>)
|
|
// CHECK-SAME: outs(%[[T0]] : tensor<4xf32>)
|
|
// CHECK: ^{{.+}}(
|
|
// CHECK-SAME: %[[ARG1:[a-zA-Z0-9_]+]]: f32, %[[ARG2:[a-zA-Z0-9_]+]]: f32)
|
|
// CHECK: %[[T2:.+]] = arith.addf %[[ARG1]], %[[CST]]
|
|
// CHECK: linalg.yield %[[T2]]
|
|
// CHECK: return %[[T1]]
|
|
|
|
// -----
|
|
|
|
#map0 = affine_map<(d0, d1) -> (d0, d1)>
|
|
#map1 = affine_map<(d0) -> (0, d0)>
|
|
#map2 = affine_map<(d0) -> (0)>
|
|
func.func @consumer_with_reduction(%arg0: tensor<1x10xf32>,
|
|
%arg1: tensor<1x10xf32>,
|
|
%arg2: tensor<1xf32>) -> tensor<1xf32> {
|
|
%init = tensor.empty() : tensor<1x10xf32>
|
|
%0 = linalg.generic
|
|
{indexing_maps = [#map0, #map0, #map0],
|
|
iterator_types = ["parallel", "parallel"]}
|
|
ins(%arg0, %arg1 : tensor<1x10xf32>, tensor<1x10xf32>)
|
|
outs(%init : tensor<1x10xf32>) {
|
|
^bb0(%arg3: f32, %arg4: f32, %arg5: f32):
|
|
%2 = arith.addf %arg3, %arg4 : f32
|
|
linalg.yield %2 : f32
|
|
} -> tensor<1x10xf32>
|
|
%1 = linalg.generic
|
|
{indexing_maps = [#map1, #map2],
|
|
iterator_types = ["reduction"]}
|
|
ins(%0 : tensor<1x10xf32>)
|
|
outs(%arg2 : tensor<1xf32>) {
|
|
^bb0(%arg3: f32, %arg4: f32):
|
|
%2 = arith.addf %arg3, %arg4 : f32
|
|
linalg.yield %2 : f32
|
|
} -> tensor<1xf32>
|
|
return %1 : tensor<1xf32>
|
|
}
|
|
// CHECK-DAG: #[[MAP0:.+]] = affine_map<(d0) -> (0, d0)>
|
|
// CHECK-DAG: #[[MAP1:.+]] = affine_map<(d0) -> (0)>
|
|
// CHECK: func @consumer_with_reduction(%[[ARG0:.+]]: tensor<1x10xf32>, %[[ARG1:.+]]: tensor<1x10xf32>, %[[ARG2:.+]]: tensor<1xf32>)
|
|
// CHECK: %[[RES:.+]] = linalg.generic
|
|
// CHECK-SAME: indexing_maps = [#[[MAP0]], #[[MAP0]], #[[MAP1]]]
|
|
// CHECK-SAME: iterator_types = ["reduction"]
|
|
// CHECK-SAME: ins(%[[ARG0]], %[[ARG1]] : tensor<1x10xf32>, tensor<1x10xf32>)
|
|
// CHECK: ^{{.+}}(%[[T0:.+]]: f32, %[[T1:.+]]: f32, %[[T2:.+]]: f32)
|
|
// CHECK: %[[T3:.+]] = arith.addf %[[T0]], %[[T1]] : f32
|
|
// CHECK: %[[T4:.+]] = arith.addf %[[T3]], %[[T2]] : f32
|
|
// CHECK: linalg.yield %[[T4]]
|
|
// CHECK: return %[[RES]]
|
|
|
|
// -----
|
|
|
|
// CHECK-LABEL: func @sigmoid_dynamic_dim(
|
|
// CHECK: %[[RES:.*]] = linalg.generic
|
|
// CHECK-NOT: linalg.generic
|
|
// CHECK: return %[[RES]]
|
|
func.func @sigmoid_dynamic_dim(%0: tensor<?x1xf32>) -> tensor<?x1xf32> {
|
|
%cp5 = arith.constant 5.000000e-01 : f32
|
|
%c0 = arith.constant 0 : index
|
|
%shape = shape.shape_of %0 : tensor<?x1xf32> -> tensor<?xindex>
|
|
%extend = shape.to_extent_tensor %shape : tensor<?xindex> -> tensor<2xindex>
|
|
%extracted = tensor.extract %extend[%c0] : tensor<2xindex>
|
|
%init0 = tensor.empty(%extracted) : tensor<?x1xf32>
|
|
%1 = linalg.generic {indexing_maps = [
|
|
affine_map<(d0, d1) -> (d0, d1)>],
|
|
iterator_types = ["parallel", "parallel"]
|
|
}
|
|
outs(%init0 : tensor<?x1xf32>) {
|
|
^bb0(%a: f32):
|
|
linalg.yield %cp5 : f32
|
|
} -> tensor<?x1xf32>
|
|
%d0 = tensor.dim %0, %c0 : tensor<?x1xf32>
|
|
%init1 = tensor.empty(%d0) : tensor<?x1xf32>
|
|
%2 = linalg.generic {indexing_maps = [
|
|
affine_map<(d0, d1) -> (d0, d1)>,
|
|
affine_map<(d0, d1) -> (d0, d1)>,
|
|
affine_map<(d0, d1) -> (d0, d1)>],
|
|
iterator_types = ["parallel", "parallel"]
|
|
}
|
|
ins(%0, %1 : tensor<?x1xf32>, tensor<?x1xf32>)
|
|
outs(%init1 : tensor<?x1xf32>) {
|
|
^bb0(%a: f32, %b: f32, %c: f32):
|
|
%m = arith.mulf %a, %b : f32
|
|
linalg.yield %m : f32
|
|
} -> tensor<?x1xf32>
|
|
return %2 : tensor<?x1xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func private @compute1(%a: f64) -> f64
|
|
func.func private @compute2(%a: f64, %b: i32) -> i32
|
|
|
|
// CHECK-LABEL: func @generic_index_op2(
|
|
func.func @generic_index_op2(%arg0: tensor<1x8xf64>, %arg1: tensor<1x8xi32>) -> tensor<1x8xi32> {
|
|
%0 = linalg.generic {
|
|
indexing_maps = [affine_map<(i, j) -> (i, j)>],
|
|
iterator_types = ["parallel", "parallel"]}
|
|
outs(%arg0 : tensor<1x8xf64>) {
|
|
^bb0(%a: f64):
|
|
%r = func.call @compute1(%a) : (f64) -> f64
|
|
linalg.yield %r : f64
|
|
} -> tensor<1x8xf64>
|
|
|
|
// CHECK-NEXT: %[[R:.*]]:2 = linalg.generic
|
|
// CHECK: bb0(%[[BBA:[0-9a-zA-Z_]*]]: f64, %[[BBB:[0-9a-zA-Z_]*]]: i32):
|
|
// CHECK-NEXT: %[[A:.*]] = func.call @compute1(%[[BBA]]) : (f64) -> f64
|
|
// CHECK-NEXT: %[[B:.*]] = func.call @compute2(%[[A]], %[[BBB]]) : (f64, i32) -> i32
|
|
// CHECK-NEXT: linalg.yield %[[A]], %[[B]] : f64, i32
|
|
// CHECK-NEXT: } -> (tensor<1x8xf64>, tensor<1x8xi32>)
|
|
%1 = linalg.generic {
|
|
indexing_maps = [affine_map<(i, j) -> (i, j)>, affine_map<(i, j) -> (i, j)>],
|
|
iterator_types = ["parallel", "parallel"]}
|
|
ins(%0 : tensor<1x8xf64>)
|
|
outs(%arg1 : tensor<1x8xi32>) {
|
|
^bb0(%a: f64, %b: i32):
|
|
%r = func.call @compute2(%a, %b) : (f64, i32) -> i32
|
|
linalg.yield %r : i32
|
|
} -> tensor<1x8xi32>
|
|
|
|
// CHECK-NEXT: return %[[R]]#1 : tensor<1x8xi32>
|
|
return %1 : tensor<1x8xi32>
|
|
}
|
|
|
|
// -----
|
|
|
|
// CHECK-LABEL: func @no_fuse_constant_with_reduction
|
|
func.func @no_fuse_constant_with_reduction() -> tensor<3xf32>
|
|
{
|
|
// CHECK: %[[CONST:.+]] = arith.constant {{.+}} : tensor<3x2xf32>
|
|
// CHECK: %[[RESULT:.+]] = linalg.generic
|
|
// CHECK-SAME: ins(%[[CONST]] : tensor<3x2xf32>)
|
|
// CHECK: return %[[RESULT]]
|
|
%three = arith.constant dense<3.0> : tensor<3x2xf32>
|
|
%init = tensor.empty() : tensor<3xf32>
|
|
%result = linalg.generic {
|
|
indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>,
|
|
affine_map<(d0, d1) -> (d0)>],
|
|
iterator_types = ["parallel", "reduction"]}
|
|
ins(%three : tensor<3x2xf32>) outs(%init : tensor<3xf32>) {
|
|
^bb0(%arg0 : f32, %arg1 : f32):
|
|
%0 = arith.addf %arg0, %arg1 : f32
|
|
linalg.yield %0 : f32
|
|
} -> tensor<3xf32>
|
|
return %result : tensor<3xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
#map = affine_map<(d0, d1) -> (d0, d1)>
|
|
#trait = {
|
|
indexing_maps = [#map, #map],
|
|
iterator_types = ["parallel", "parallel"]
|
|
}
|
|
func.func @break_outs_dependency(%arg0 : tensor<?x?xf32>) -> tensor<?x?xf32>
|
|
{
|
|
%0 = linalg.generic #trait ins(%arg0 : tensor<?x?xf32>) outs(%arg0 : tensor<?x?xf32>) {
|
|
^bb0(%arg1 : f32, %arg2 : f32) :
|
|
%1 = arith.addf %arg1, %arg1 : f32
|
|
linalg.yield %1 : f32
|
|
} -> tensor<?x?xf32>
|
|
%2 = linalg.generic #trait ins(%0 : tensor<?x?xf32>) outs(%0 : tensor<?x?xf32>) {
|
|
^bb0(%arg1 : f32, %arg2 : f32) :
|
|
%3 = arith.mulf %arg1, %arg1 : f32
|
|
linalg.yield %3 : f32
|
|
} -> tensor<?x?xf32>
|
|
return %2 : tensor<?x?xf32>
|
|
}
|
|
// CHECK: func @break_outs_dependency(
|
|
// CHECK-SAME: %[[ARG0:.+]]: tensor<?x?xf32>)
|
|
// CHECK-DAG: %[[C0:.+]] = arith.constant 0 : index
|
|
// CHECK-DAG: %[[C1:.+]] = arith.constant 1 : index
|
|
// CHECK-DAG: %[[D0:.+]] = tensor.dim %[[ARG0]], %[[C0]]
|
|
// CHECK-DAG: %[[D1:.+]] = tensor.dim %[[ARG0]], %[[C1]]
|
|
// CHECK-DAG: %[[INIT:.+]] = tensor.empty(%[[D0]], %[[D1]])
|
|
// CHECK: %[[GENERIC1:.+]] = linalg.generic
|
|
// CHECK-SAME: outs(%[[INIT]] : tensor<?x?xf32>)
|
|
// CHECK-DAG: %[[D0:.+]] = tensor.dim %[[GENERIC1]], %[[C0]]
|
|
// CHECK-DAG: %[[D1:.+]] = tensor.dim %[[GENERIC1]], %[[C1]]
|
|
// CHECK-DAG: %[[INIT:.+]] = tensor.empty(%[[D0]], %[[D1]])
|
|
// CHECK: %[[RESULT:.+]] = linalg.generic
|
|
// CHECK-SAME: outs(%[[INIT]] : tensor<?x?xf32>)
|
|
|
|
// -----
|
|
|
|
func.func @fuse_scalar_constant(%arg0 : tensor<?x?xf32>) -> (tensor<?x?xf32>, tensor<?x?xi32>) {
|
|
%cst = arith.constant 4.0 : f32
|
|
%c42 = arith.constant 42 : i32
|
|
%c0 = arith.constant 0 : index
|
|
%c1 = arith.constant 1 : index
|
|
%d0 = tensor.dim %arg0, %c0 : tensor<?x?xf32>
|
|
%d1 = tensor.dim %arg0, %c1 : tensor<?x?xf32>
|
|
%0 = tensor.empty(%d0, %d1) : tensor<?x?xf32>
|
|
%1 = tensor.empty(%d0, %d1) : tensor<?x?xi32>
|
|
%2:2 = linalg.generic {
|
|
indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>,
|
|
affine_map<(d0, d1) -> ()>,
|
|
affine_map<(d0, d1) -> ()>,
|
|
affine_map<(d0, d1) -> (d0, d1)>,
|
|
affine_map<(d0, d1) -> (d0, d1)>],
|
|
iterator_types = ["parallel", "parallel"]}
|
|
ins(%arg0, %cst, %c42 : tensor<?x?xf32>, f32, i32)
|
|
outs(%0, %1 : tensor<?x?xf32>, tensor<?x?xi32>) {
|
|
^bb0(%arg1 : f32, %arg2 : f32, %arg3 : i32, %arg4 : f32, %arg5 : i32) :
|
|
%3 = arith.addf %arg1, %arg2 : f32
|
|
linalg.yield %3, %arg3 : f32, i32
|
|
} -> (tensor<?x?xf32>, tensor<?x?xi32>)
|
|
return %2#0, %2#1 : tensor<?x?xf32>, tensor<?x?xi32>
|
|
}
|
|
// CHECK-LABEL: func @fuse_scalar_constant
|
|
// CHECK-DAG: %[[CST:.+]] = arith.constant 4.000000e+00 : f32
|
|
// CHECK-DAG: %[[C42:.+]] = arith.constant 42 : i32
|
|
// CHECK: linalg.generic
|
|
// CHECK-SAME: ins(%{{.+}} : tensor<?x?xf32>)
|
|
// CHECK: %[[YIELD:.+]] = arith.addf %{{.+}}, %[[CST]] : f32
|
|
// CHECK: linalg.yield %[[YIELD]], %[[C42]] : f32, i32
|
|
|
|
// -----
|
|
|
|
// CHECK-LABEL: @transpose_fold_2d_fp32
|
|
func.func @transpose_fold_2d_fp32(%init: tensor<3x2xf32>) -> tensor<3x2xf32> {
|
|
%input = arith.constant dense<[[0.0, 1.0, 2.0], [3.0, 4.0, 5.0]]> : tensor<2x3xf32>
|
|
// CHECK: %[[CST:.+]] = arith.constant
|
|
// CHECK-SAME{LITERAL}: dense<[[0.000000e+00, 3.000000e+00], [1.000000e+00, 4.000000e+00], [2.000000e+00, 5.000000e+00]]> : tensor<3x2xf32>
|
|
%1 = linalg.generic {
|
|
indexing_maps = [affine_map<(d0, d1) -> (d1, d0)>, affine_map<(d0, d1) -> (d0, d1)>],
|
|
iterator_types = ["parallel", "parallel"]
|
|
} ins(%input : tensor<2x3xf32>) outs(%init : tensor<3x2xf32>) {
|
|
^bb0(%arg1: f32, %arg2: f32):
|
|
linalg.yield %arg1 : f32
|
|
} -> tensor<3x2xf32>
|
|
// CHECK: return %[[CST]]
|
|
return %1 : tensor<3x2xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
// CHECK-LABEL: @transpose_fold_2d_fp64
|
|
func.func @transpose_fold_2d_fp64(%init: tensor<3x2xf64>) -> tensor<3x2xf64> {
|
|
%input = arith.constant dense<[[0.0, 1.0, 2.0], [3.0, 4.0, 5.0]]> : tensor<2x3xf64>
|
|
// CHECK: %[[CST:.+]] = arith.constant
|
|
// CHECK-SAME{LITERAL}: dense<[[0.000000e+00, 3.000000e+00], [1.000000e+00, 4.000000e+00], [2.000000e+00, 5.000000e+00]]> : tensor<3x2xf64>
|
|
%1 = linalg.generic {
|
|
indexing_maps = [affine_map<(d0, d1) -> (d1, d0)>, affine_map<(d0, d1) -> (d0, d1)>],
|
|
iterator_types = ["parallel", "parallel"]
|
|
} ins(%input : tensor<2x3xf64>) outs(%init : tensor<3x2xf64>) {
|
|
^bb0(%arg1: f64, %arg2: f64):
|
|
linalg.yield %arg1 : f64
|
|
} -> tensor<3x2xf64>
|
|
// CHECK: return %[[CST]]
|
|
return %1 : tensor<3x2xf64>
|
|
}
|
|
|
|
// -----
|
|
|
|
// CHECK-LABEL: @transpose_fold_4d_i32
|
|
func.func @transpose_fold_4d_i32(%init: tensor<3x1x4x2xi32>) -> tensor<3x1x4x2xi32> {
|
|
%input = arith.constant dense<[[
|
|
[[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11]],
|
|
[[12, 13, 14, 15], [16, 17, 18, 19], [20, 21, 22, 23]]
|
|
]]> : tensor<1x2x3x4xi32>
|
|
// CHECK: %[[CST:.+]] = arith.constant dense<[
|
|
// CHECK-SAME{LITERAL}: [[[0, 12], [1, 13], [2, 14], [3, 15]]],
|
|
// CHECK-SAME{LITERAL}: [[[4, 16], [5, 17], [6, 18], [7, 19]]],
|
|
// CHECK-SAME{LITERAL}: [[[8, 20], [9, 21], [10, 22], [11, 23]]]
|
|
// CHECK-SAME{LITERAL}: ]>
|
|
%1 = linalg.generic {
|
|
indexing_maps = [affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>, affine_map<(d0, d1, d2, d3) -> (d2, d0, d3, d1)>],
|
|
iterator_types = ["parallel", "parallel", "parallel", "parallel"]
|
|
} ins(%input : tensor<1x2x3x4xi32>) outs(%init : tensor<3x1x4x2xi32>) {
|
|
^bb0(%arg1: i32, %arg2: i32):
|
|
linalg.yield %arg1 : i32
|
|
} -> tensor<3x1x4x2xi32>
|
|
// CHECK: return %[[CST]]
|
|
return %1 : tensor<3x1x4x2xi32>
|
|
}
|
|
|
|
// -----
|
|
|
|
// CHECK-LABEL: @transpose_fold_4d_i16
|
|
func.func @transpose_fold_4d_i16(%init: tensor<3x1x4x2xi16>) -> tensor<3x1x4x2xi16> {
|
|
%input = arith.constant dense<[[
|
|
[[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11]],
|
|
[[12, 13, 14, 15], [16, 17, 18, 19], [20, 21, 22, 23]]
|
|
]]> : tensor<1x2x3x4xi16>
|
|
// CHECK: %[[CST:.+]] = arith.constant dense<[
|
|
// CHECK-SAME{LITERAL}: [[[0, 12], [1, 13], [2, 14], [3, 15]]],
|
|
// CHECK-SAME{LITERAL}: [[[4, 16], [5, 17], [6, 18], [7, 19]]],
|
|
// CHECK-SAME{LITERAL}: [[[8, 20], [9, 21], [10, 22], [11, 23]]]
|
|
// CHECK-SAME{LITERAL}: ]>
|
|
%1 = linalg.generic {
|
|
indexing_maps = [affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>, affine_map<(d0, d1, d2, d3) -> (d2, d0, d3, d1)>],
|
|
iterator_types = ["parallel", "parallel", "parallel", "parallel"]
|
|
} ins(%input : tensor<1x2x3x4xi16>) outs(%init : tensor<3x1x4x2xi16>) {
|
|
^bb0(%arg1: i16, %arg2: i16):
|
|
linalg.yield %arg1 : i16
|
|
} -> tensor<3x1x4x2xi16>
|
|
// CHECK: return %[[CST]]
|
|
return %1 : tensor<3x1x4x2xi16>
|
|
}
|
|
|
|
// -----
|
|
|
|
// CHECK-LABEL: @transpose_nofold_non_cst_input
|
|
func.func @transpose_nofold_non_cst_input(%input: tensor<2x3xf32>, %init: tensor<3x2xf32>) -> tensor<3x2xf32> {
|
|
// CHECK: linalg.generic
|
|
%1 = linalg.generic {
|
|
indexing_maps = [affine_map<(d0, d1) -> (d1, d0)>, affine_map<(d0, d1) -> (d0, d1)>],
|
|
iterator_types = ["parallel", "parallel"]
|
|
} ins(%input : tensor<2x3xf32>) outs(%init : tensor<3x2xf32>) {
|
|
^bb0(%arg1: f32, %arg2: f32):
|
|
linalg.yield %arg1 : f32
|
|
} -> tensor<3x2xf32>
|
|
return %1 : tensor<3x2xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
// CHECK-LABEL: @transpose_nofold_yield_const
|
|
func.func @transpose_nofold_yield_const(%init: tensor<3x2xf32>) -> tensor<3x2xf32> {
|
|
%input = arith.constant dense<[[0.0, 1.0, 2.0], [3.0, 4.0, 5.0]]> : tensor<2x3xf32>
|
|
%cst = arith.constant 8.0 : f32
|
|
// CHECK: linalg.generic
|
|
%1 = linalg.generic {
|
|
indexing_maps = [affine_map<(d0, d1) -> (d1, d0)>, affine_map<(d0, d1) -> (d0, d1)>],
|
|
iterator_types = ["parallel", "parallel"]
|
|
} ins(%input : tensor<2x3xf32>) outs(%init : tensor<3x2xf32>) {
|
|
^bb0(%arg1: f32, %arg2: f32):
|
|
linalg.yield %cst : f32
|
|
} -> tensor<3x2xf32>
|
|
return %1 : tensor<3x2xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
// CHECK-LABEL: @transpose_nofold_multi_ops_in_region
|
|
func.func @transpose_nofold_multi_ops_in_region(%init: tensor<3x2xf32>) -> tensor<3x2xf32> {
|
|
%input = arith.constant dense<[[0.0, 1.0, 2.0], [3.0, 4.0, 5.0]]> : tensor<2x3xf32>
|
|
// CHECK: linalg.generic
|
|
%1 = linalg.generic {
|
|
indexing_maps = [affine_map<(d0, d1) -> (d1, d0)>, affine_map<(d0, d1) -> (d0, d1)>],
|
|
iterator_types = ["parallel", "parallel"]
|
|
} ins(%input : tensor<2x3xf32>) outs(%init : tensor<3x2xf32>) {
|
|
^bb0(%arg1: f32, %arg2: f32):
|
|
%add = arith.addf %arg1, %arg1 : f32
|
|
linalg.yield %add : f32
|
|
} -> tensor<3x2xf32>
|
|
return %1 : tensor<3x2xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
// Fusing the broadcast into a reduction would require to insert extra knowledge
|
|
// about the size of the reduction dimension. As long, as this is not
|
|
// implemented, we check that two linalg operations remain.
|
|
// TODO: Support this case in element-wise fusion.
|
|
|
|
#map0 = affine_map<(d0, d1) -> ()>
|
|
#map1 = affine_map<(d0, d1) -> (d0, d1)>
|
|
#map2 = affine_map<(d0, d1) -> (d1, d0)>
|
|
#map3 = affine_map<(d0, d1) -> (d0)>
|
|
|
|
// CHECK-LABEL: @no_fusion_missing_reduction_shape
|
|
// CHECK: linalg.generic
|
|
// CHECK: linalg.generic
|
|
func.func @no_fusion_missing_reduction_shape(%arg0: tensor<f32>, %arg1: index) -> tensor<?xf32> {
|
|
%cst = arith.constant 0xFF800000 : f32
|
|
%4 = tensor.empty(%arg1, %arg1) : tensor<?x?xf32>
|
|
%5 = linalg.generic {
|
|
indexing_maps = [#map0, #map1],
|
|
iterator_types = ["parallel", "parallel"]
|
|
} ins(%arg0 : tensor<f32>) outs(%4 : tensor<?x?xf32>) {
|
|
^bb0(%arg2: f32, %arg3: f32):
|
|
linalg.yield %arg2 : f32
|
|
} -> tensor<?x?xf32>
|
|
%6 = tensor.empty(%arg1) : tensor<?xf32>
|
|
%7 = linalg.fill ins(%cst : f32) outs(%6 : tensor<?xf32>) -> tensor<?xf32>
|
|
%8 = linalg.generic {
|
|
indexing_maps = [#map2, #map3],
|
|
iterator_types = ["parallel", "reduction"]
|
|
} ins(%5 : tensor<?x?xf32>) outs(%7 : tensor<?xf32>) {
|
|
^bb0(%arg2: f32, %arg3: f32):
|
|
%9 = arith.maximumf %arg2, %arg3 : f32
|
|
linalg.yield %9 : f32
|
|
} -> tensor<?xf32>
|
|
return %8 : tensor<?xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
func.func @fusion_different_axes(%arg0 : tensor<5000xi64>, %arg1 : tensor<5000xi32>) -> tensor<5000xi32> {
|
|
%c1_i32 = arith.constant 1 : i32
|
|
%0 = linalg.generic {
|
|
indexing_maps = [affine_map<(d0) -> (d0)>],
|
|
iterator_types = ["parallel"]}
|
|
outs(%arg0 : tensor<5000xi64>) {
|
|
^bb0(%arg3: i64): // no predecessors
|
|
%22 = linalg.index 0 : index
|
|
%23 = arith.index_cast %22 : index to i64
|
|
linalg.yield %23 : i64
|
|
} -> tensor<5000xi64>
|
|
%1 = tensor.empty() : tensor<5000xi32>
|
|
%2 = linalg.generic {
|
|
indexing_maps = [affine_map<(d0, d1) -> (d0)>, affine_map<(d0, d1) -> (d1)>],
|
|
iterator_types = ["parallel", "parallel"]}
|
|
ins(%0 : tensor<5000xi64>) outs(%1 : tensor<5000xi32>) {
|
|
^bb0(%arg3: i64, %arg5: i32): // no predecessors
|
|
%22 = arith.index_cast %arg3 : i64 to index
|
|
%23 = tensor.extract %arg1[%22] : tensor<5000xi32>
|
|
linalg.yield %23 : i32
|
|
} -> tensor<5000xi32>
|
|
return %2 : tensor<5000xi32>
|
|
}
|
|
// CHECK-DAG: #[[MAP0:.+]] = affine_map<(d0, d1) -> (d0)>
|
|
// CHECK-DAG: #[[MAP1:.+]] = affine_map<(d0, d1) -> (d1)>
|
|
// CHECK: func @fusion_different_axes(
|
|
// CHECK-SAME: %[[ARG0:.+]]: tensor<5000xi64>
|
|
// CHECK-SAME: %[[ARG1:.+]]: tensor<5000xi32>
|
|
// CHECK-DAG: %[[INIT0:.+]] = tensor.empty() : tensor<5000xi64>
|
|
// CHECK-DAG: %[[INIT1:.+]] = tensor.empty() : tensor<5000xi32>
|
|
// CHECK: %[[RESULT:.+]]:2 = linalg.generic
|
|
// CHECK-SAME: indexing_maps = [#[[MAP0]], #[[MAP1]]]
|
|
// CHECK-SAME: outs(%[[INIT0]], %[[INIT1]] :
|
|
// CHECK-NEXT: ^bb0(
|
|
// CHECK-SAME: %[[B0:.+]]: i64
|
|
// CHECK-SAME: %[[B1:.+]]: i32
|
|
// CHECK-DAG: %[[T0:.+]] = linalg.index 0
|
|
// CHECK-DAG: %[[CAST1:.+]] = arith.index_cast %[[T0]] : index to i64
|
|
// CHECK-DAG: %[[CAST2:.+]] = arith.index_cast %[[CAST1]] : i64 to index
|
|
// CHECK: %[[EXTRACT:.+]] = tensor.extract %[[ARG1]][%[[CAST2]]]
|
|
// CHECK: linalg.yield %[[CAST1]], %[[EXTRACT]]
|
|
// CHECK: return %[[RESULT]]#1
|
|
|
|
// -----
|
|
|
|
// CHECK-LABEL: func @fold_fill_generic_basic
|
|
// CHECK-SAME: (%[[ARG0:.*]]: tensor<?xf32>) -> tensor<?xf32> {
|
|
// CHECK-NOT: linalg.fill
|
|
// CHECK: %[[GENERIC_OP:.*]] = linalg.generic
|
|
// CHECK-SAME: ins(%[[ARG0]] : tensor<?xf32>)
|
|
// CHECK-SAME: outs({{.*}} : tensor<?xf32>) {
|
|
#map0 = affine_map<(d0) -> (d0)>
|
|
func.func @fold_fill_generic_basic(%arg0: tensor<?xf32>) -> (tensor<?xf32>) {
|
|
%c0 = arith.constant 0 : index
|
|
%cst = arith.constant 7.0 : f32
|
|
%0 = tensor.dim %arg0, %c0 : tensor<?xf32>
|
|
%1 = tensor.empty(%0) : tensor<?xf32>
|
|
%2 = linalg.fill ins(%cst : f32) outs(%1 : tensor<?xf32>) -> tensor<?xf32>
|
|
%3 = tensor.empty(%0) : tensor<?xf32>
|
|
%4 = linalg.generic {indexing_maps = [#map0, #map0, #map0], iterator_types=["parallel"]} ins(%arg0, %2 : tensor<?xf32>, tensor<?xf32>) outs (%3:tensor<?xf32>) {
|
|
^bb0(%arg1: f32, %arg2: f32, %arg3: f32):
|
|
%5 = arith.addf %arg1, %arg2 : f32
|
|
linalg.yield %5 : f32
|
|
} -> tensor<?xf32>
|
|
return %4 : tensor<?xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
// CHECK-LABEL: func @fold_fill_generic_different_dtype
|
|
// CHECK-SAME: (%[[ARG0:.*]]: tensor<?xf16>) -> tensor<?xf16> {
|
|
// CHECK-NOT: linalg.fill
|
|
// CHECK: %[[GENERIC_OP:.*]] = linalg.generic
|
|
// CHECK-SAME: ins(%[[ARG0]] : tensor<?xf16>)
|
|
// CHECK-SAME: outs({{.*}} : tensor<?xf16>) {
|
|
#map0 = affine_map<(d0) -> (d0)>
|
|
func.func @fold_fill_generic_different_dtype(%arg0: tensor<?xf16>) -> (tensor<?xf16>) {
|
|
%c0 = arith.constant 0 : index
|
|
%cst = arith.constant 7.0 : f32
|
|
%0 = tensor.dim %arg0, %c0 : tensor<?xf16>
|
|
%1 = tensor.empty(%0) : tensor<?xf16>
|
|
%2 = linalg.fill ins(%cst : f32) outs(%1 : tensor<?xf16>) -> tensor<?xf16>
|
|
%3 = tensor.empty(%0) : tensor<?xf16>
|
|
%4 = linalg.generic {indexing_maps = [#map0, #map0, #map0], iterator_types=["parallel"]} ins(%arg0, %2 : tensor<?xf16>, tensor<?xf16>) outs (%3:tensor<?xf16>) {
|
|
^bb0(%arg1: f16, %arg2: f16, %arg3: f16):
|
|
%5 = arith.addf %arg1, %arg2 : f16
|
|
linalg.yield %5 : f16
|
|
} -> tensor<?xf16>
|
|
return %4 : tensor<?xf16>
|
|
}
|
|
|
|
// -----
|
|
|
|
// CHECK-LABEL: func @fold_fill_generic_mixedaccess
|
|
// CHECK-NOT: linalg.fill
|
|
// CHECK: %[[GENERIC_OP:.*]] = linalg.generic
|
|
// CHECK-NOT: ins
|
|
// CHECK-SAME: outs({{.*}} : tensor<?x?xf32>) {
|
|
#map0 = affine_map<(d0, d1) -> (d0, d1)>
|
|
#map1 = affine_map<(d0, d1) -> (d1, d0)>
|
|
func.func @fold_fill_generic_mixedaccess(%arg0: tensor<?x?xf32>) -> (tensor<?x?xf32>) {
|
|
%c0 = arith.constant 0 : index
|
|
%c1 = arith.constant 0 : index
|
|
%cst1 = arith.constant 7.0 : f32
|
|
%cst2 = arith.constant 6.0 : f32
|
|
%0 = tensor.dim %arg0, %c0 : tensor<?x?xf32>
|
|
%1 = tensor.dim %arg0, %c1 : tensor<?x?xf32>
|
|
%2 = tensor.empty(%0, %1) : tensor<?x?xf32>
|
|
%3 = linalg.fill ins(%cst1 : f32) outs(%2 : tensor<?x?xf32>) -> tensor<?x?xf32>
|
|
%4 = tensor.empty(%1, %0) : tensor<?x?xf32>
|
|
%5 = linalg.fill ins(%cst2 : f32) outs(%4 : tensor<?x?xf32>) -> tensor<?x?xf32>
|
|
%6 = tensor.empty(%0, %1) : tensor<?x?xf32>
|
|
%7 = linalg.generic {indexing_maps = [#map0, #map1, #map0], iterator_types=["parallel","parallel"]} ins(%3, %5 : tensor<?x?xf32>, tensor<?x?xf32>) outs (%6:tensor<?x?xf32>) {
|
|
^bb0(%arg1: f32, %arg2: f32, %arg3: f32):
|
|
%8 = arith.divf %arg1, %arg2 : f32
|
|
linalg.yield %8 : f32
|
|
} -> tensor<?x?xf32>
|
|
return %7 : tensor<?x?xf32>
|
|
}
|
|
|
|
// -----
|
|
|
|
#map = affine_map<() -> ()>
|
|
module {
|
|
func.func @fuse_multi_result_producer(%arg0: tensor<f32>, %arg1: tensor<f32>, %arg2: tensor<f32>, %arg3: tensor<f32>, %arg4: tensor<f32>) -> tensor<f32> {
|
|
%0 = tensor.empty() : tensor<f32>
|
|
%1 = tensor.empty() : tensor<f32>
|
|
%2:2 = linalg.generic {
|
|
indexing_maps = [#map, #map, #map, #map, #map], iterator_types = []}
|
|
ins(%arg0, %arg1, %arg1 : tensor<f32>, tensor<f32>, tensor<f32>) outs(%0, %1 : tensor<f32>, tensor<f32>) {
|
|
^bb0(%arg5: f32, %arg6: f32, %arg7: f32, %arg8: f32, %arg9: f32):
|
|
%4 = arith.addf %arg5, %arg6 : f32
|
|
%5 = arith.addf %4, %arg7 : f32
|
|
linalg.yield %4, %5 : f32, f32
|
|
} -> (tensor<f32>, tensor<f32>)
|
|
%3 = linalg.generic {
|
|
indexing_maps = [#map, #map, #map], iterator_types = []}
|
|
ins(%2#1, %arg1 : tensor<f32>, tensor<f32>) outs(%arg4 : tensor<f32>) {
|
|
^bb0(%arg5: f32, %arg6: f32, %arg7: f32):
|
|
%4 = arith.addf %arg5, %arg6 : f32
|
|
%5 = arith.addf %4, %arg6 : f32
|
|
linalg.yield %5 : f32
|
|
} -> tensor<f32>
|
|
return %3 : tensor<f32>
|
|
}
|
|
}
|
|
// CHECK-LABEL: func.func @fuse_multi_result_producer
|
|
// CHECK-SAME: %[[ARG0:[a-zA-Z0-9_]+]]: tensor<f32>
|
|
// CHECK-SAME: %[[ARG1:[a-zA-Z0-9_]+]]: tensor<f32>
|
|
// CHECK: %[[INIT:.+]] = tensor.empty
|
|
// CHECK: %[[GENERIC:.+]] = linalg.generic
|
|
// CHECK-SAME: ins(%[[ARG0]], %[[ARG1]] :
|
|
// CHECK-SAME: outs(%[[INIT]] :
|
|
// CHECK-NEXT: ^bb0
|
|
// CHECK-SAME: %[[B0:[a-zA-Z0-9_]+]]: f32
|
|
// CHECK-SAME: %[[B1:[a-zA-Z0-9_]+]]: f32
|
|
// CHECK-DAG: %[[T0:.+]] = arith.addf %[[B0]], %[[B1]]
|
|
// CHECK-DAG: %[[T1:.+]] = arith.addf %[[T0]], %[[B1]]
|
|
// CHECK-DAG: %[[T2:.+]] = arith.addf %[[T1]], %[[B1]]
|
|
// CHECK-DAG: %[[T3:.+]] = arith.addf %[[T2]], %[[B1]]
|
|
// CHECK: linalg.yield %[[T3]] : f32
|
|
// CHECK: return %[[GENERIC]]
|
|
|
|
// -----
|
|
|
|
// CHECK-DAG: [[$MAP0:#[a-zA-Z0-9_]*]] = affine_map<(d0, d1) -> (d0, d1)>
|
|
#map0 = affine_map<(d0, d1) -> (d0, d1)>
|
|
|
|
// CHECK-LABEL: @mixed_fusion
|
|
func.func @mixed_fusion(%arg0: tensor<?x?xf32>, %arg1 : tensor<?x?xf32>, %arg2 : tensor<?x?xf32>, %arg8 : memref<?x?xf32>)
|
|
{
|
|
%c0 = arith.constant 0 : index
|
|
%c1 = arith.constant 1 : index
|
|
%0 = tensor.dim %arg0, %c0 : tensor<?x?xf32>
|
|
%1 = tensor.dim %arg0, %c1 : tensor<?x?xf32>
|
|
%2 = tensor.empty(%0, %1) : tensor<?x?xf32>
|
|
%3 = linalg.generic {indexing_maps = [#map0, #map0, #map0], iterator_types = ["parallel", "parallel"]}
|
|
ins(%arg0, %arg1 : tensor<?x?xf32>, tensor<?x?xf32>)
|
|
outs(%2 : tensor<?x?xf32>) {
|
|
^bb0(%arg3: f32, %arg4: f32, %arg5: f32):
|
|
%4 = arith.addf %arg3, %arg4 : f32
|
|
linalg.yield %4 : f32
|
|
} -> tensor<?x?xf32>
|
|
// CHECK: linalg.generic {
|
|
// CHECK-SAME: indexing_maps = {{\[}}[[$MAP0]], [[$MAP0]], [[$MAP0]], [[$MAP0]]{{\]}}
|
|
linalg.generic {indexing_maps = [#map0, #map0, #map0], iterator_types = ["parallel", "parallel"]}
|
|
ins(%3, %arg2 : tensor<?x?xf32>, tensor<?x?xf32>)
|
|
outs(%arg8 : memref<?x?xf32>) {
|
|
// CHECK: ^{{[a-zA-Z0-9_]*}}
|
|
// CHECK-SAME: [[ARG0:%[a-zA-Z0-9_]*]]
|
|
// CHECK-SAME: [[ARG1:%[a-zA-Z0-9_]*]]
|
|
// CHECK-SAME: [[ARG2:%[a-zA-Z0-9_]*]]
|
|
^bb0(%arg5: f32, %arg6: f32, %arg7: f32):
|
|
// CHECK: [[T1:%[a-zA-Z0-9_]*]] = arith.addf [[ARG0]], [[ARG1]]
|
|
// CHECK-NOT: linalg.yield
|
|
// CHECK: arith.mulf [[T1]], [[ARG2]]
|
|
// CHECK: linalg.yield
|
|
%5 = arith.mulf %arg5, %arg6 : f32
|
|
linalg.yield %5 : f32
|
|
}
|
|
return
|
|
}
|