df9ed9cf52
The previous implementation was doing an early successful return on `rank <= 1` without adding the original op to transform results. This resulted in errors about number of returns. This patch fixes this by adding the original op to results. Additionally, we first check if op is elementwise and return a slienceable failure early if not.
121 lines
6.0 KiB
MLIR
121 lines
6.0 KiB
MLIR
// RUN: mlir-opt %s -transform-interpreter -split-input-file | FileCheck %s
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// CHECK-LABEL: func.func @fill(
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// CHECK-SAME: %[[ARG0:.*]]: f32,
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// CHECK-SAME: %[[ARG1:.*]]: memref<32x7xf32>
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// CHECK-NEXT: %[[FLATTENED:.*]] = memref.collapse_shape %[[ARG1]] {{\[}}[0, 1]]
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// CHECK-NEXT: linalg.fill ins(%[[ARG0]] : f32) outs(%[[FLATTENED]] : memref<224xf32>)
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func.func @fill(%cst: f32, %arg: memref<32x7xf32>) {
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linalg.fill ins(%cst: f32) outs(%arg: memref<32x7xf32>)
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return
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}
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module attributes {transform.with_named_sequence} {
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transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
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%0 = transform.structured.match interface{LinalgOp} in %arg1 : (!transform.any_op) -> !transform.any_op
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%flattened = transform.structured.flatten_elementwise %0
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: (!transform.any_op) -> !transform.any_op
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transform.yield
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}
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}
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// -----
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// CHECK-LABEL: func.func @fill_tensor(
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// CHECK-SAME: %[[ARG0:.*]]: f32,
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// CHECK-SAME: %[[ARG1:.*]]: tensor<32x7xf32>
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// CHECK-NEXT: %[[FLATTENED:.*]] = tensor.collapse_shape %[[ARG1]] {{\[}}[0, 1]]
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// CHECK-NEXT: %[[FLATTENED_RESULT:.*]] = linalg.fill ins(%[[ARG0]] : f32) outs(%[[FLATTENED]] : tensor<224xf32>)
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// CHECK-NEXT: %[[RESULT:.*]] = tensor.expand_shape %[[FLATTENED_RESULT]] {{\[}}[0, 1]]
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func.func @fill_tensor(%cst: f32, %arg: tensor<32x7xf32>) -> tensor<32x7xf32> {
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%0 = linalg.fill ins(%cst: f32) outs(%arg: tensor<32x7xf32>) -> tensor<32x7xf32>
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return %0 : tensor<32x7xf32>
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}
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module attributes {transform.with_named_sequence} {
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transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
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%0 = transform.structured.match interface{LinalgOp} in %arg1 : (!transform.any_op) -> !transform.any_op
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%flattened = transform.structured.flatten_elementwise %0
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: (!transform.any_op) -> !transform.any_op
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transform.yield
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}
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}
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// -----
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// CHECK-LABEL: func.func @map(
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// CHECK-SAME: %[[ARG0:[a-zA-Z0-9_]*]]: memref<32x7xf32>
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// CHECK-SAME: %[[ARG1:[a-zA-Z0-9_]*]]: memref<32x7xf32>
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// CHECK-SAME: %[[ARG2:[a-zA-Z0-9_]*]]: memref<32x7xf32>
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// CHECK-NEXT: %[[FLATTENED_0:.*]] = memref.collapse_shape %[[ARG0]] {{\[}}[0, 1]]
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// CHECK-NEXT: %[[FLATTENED_1:.*]] = memref.collapse_shape %[[ARG1]] {{\[}}[0, 1]]
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// CHECK-NEXT: %[[FLATTENED_2:.*]] = memref.collapse_shape %[[ARG2]] {{\[}}[0, 1]]
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// CHECK-NEXT: linalg.map { arith.addf } ins(%[[FLATTENED_0]], %[[FLATTENED_1]] : memref<224xf32>, memref<224xf32>) outs(%[[FLATTENED_2]] : memref<224xf32>)
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func.func @map(%arg0: memref<32x7xf32>, %arg1: memref<32x7xf32>, %arg2: memref<32x7xf32>) {
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linalg.map {arith.addf} ins(%arg0, %arg1: memref<32x7xf32>, memref<32x7xf32>) outs(%arg2: memref<32x7xf32>)
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return
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}
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module attributes {transform.with_named_sequence} {
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transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
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%0 = transform.structured.match interface{LinalgOp} in %arg1 : (!transform.any_op) -> !transform.any_op
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%flattened = transform.structured.flatten_elementwise %0
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: (!transform.any_op) -> !transform.any_op
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transform.yield
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}
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}
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// -----
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// CHECK-LABEL: func.func @map_already_flat(
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// CHECK-SAME: %[[ARG0:[a-zA-Z0-9_]*]]: memref<32xf32>
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// CHECK-SAME: %[[ARG1:[a-zA-Z0-9_]*]]: memref<32xf32>
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// CHECK-SAME: %[[ARG2:[a-zA-Z0-9_]*]]: memref<32xf32>
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// CHECK-NEXT: linalg.map { arith.addf } ins(%[[ARG0]], %[[ARG1]] : memref<32xf32>, memref<32xf32>) outs(%[[ARG2]] : memref<32xf32>)
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func.func @map_already_flat(%arg0: memref<32xf32>, %arg1: memref<32xf32>, %arg2: memref<32xf32>) {
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linalg.map {arith.addf} ins(%arg0, %arg1: memref<32xf32>, memref<32xf32>) outs(%arg2: memref<32xf32>)
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return
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}
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module attributes {transform.with_named_sequence} {
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transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
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%0 = transform.structured.match interface{LinalgOp} in %arg1 : (!transform.any_op) -> !transform.any_op
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%flattened = transform.structured.flatten_elementwise %0
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: (!transform.any_op) -> !transform.any_op
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transform.yield
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}
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}
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// -----
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// CHECK: #[[$MAP0:.*]] = affine_map<(d0) -> (d0)>
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// CHECK-LABEL: func.func @generic
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// CHECK-SAME: %[[ARG0:[a-zA-Z0-9_]*]]: memref<32x7xf32>
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// CHECK-SAME: %[[ARG1:[a-zA-Z0-9_]*]]: memref<32x7xf32>
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// CHECK-SAME: %[[ARG2:[a-zA-Z0-9_]*]]: memref<32x7xf32>
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// CHECK-NEXT: %[[FLATTENED_0:.*]] = memref.collapse_shape %[[ARG0]] {{\[}}[0, 1]]
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// CHECK-NEXT: %[[FLATTENED_1:.*]] = memref.collapse_shape %[[ARG1]] {{\[}}[0, 1]]
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// CHECK-NEXT: %[[FLATTENED_2:.*]] = memref.collapse_shape %[[ARG2]] {{\[}}[0, 1]]
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// CHECK-NEXT: linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP0]], #[[$MAP0]]], iterator_types = ["parallel"]} ins(%[[FLATTENED_0]], %[[FLATTENED_1]] : memref<224xf32>, memref<224xf32>) outs(%[[FLATTENED_2]] : memref<224xf32>)
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// CHECK-NEXT: ^bb0(%[[A:.*]]: f32, %[[B:.*]]: f32, %[[C:.*]]: f32)
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// CHECK-NEXT: %[[SUM:.*]] = arith.addf %[[A]], %[[B]]
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// CHECK-NEXT: linalg.yield %[[SUM]]
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#map = affine_map<(d0, d1) -> (d0, d1)>
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func.func @generic( %arg0: memref<32x7xf32>, %arg1: memref<32x7xf32>, %arg2: memref<32x7xf32>) {
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linalg.generic {indexing_maps = [#map, #map, #map], iterator_types = ["parallel", "parallel"]} ins(%arg0, %arg1: memref<32x7xf32>, memref<32x7xf32>) outs(%arg2: memref<32x7xf32>) {
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^bb0(%a: f32, %b: f32, %c: f32):
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%0 = arith.addf %a, %b : f32
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linalg.yield %0 : f32
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}
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return
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}
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module attributes {transform.with_named_sequence} {
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transform.named_sequence @__transform_main(%arg1: !transform.any_op {transform.readonly}) {
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%0 = transform.structured.match interface{LinalgOp} in %arg1 : (!transform.any_op) -> !transform.any_op
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%flattened = transform.structured.flatten_elementwise %0
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: (!transform.any_op) -> !transform.any_op
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transform.yield
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}
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}
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