derek.holloway
7502bf64a4
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231 lines
14 KiB
MLIR
231 lines
14 KiB
MLIR
// RUN: mlir-opt %s -test-linalg-elementwise-fusion-patterns=collapse-dimensions-control=2,3 -split-input-file | FileCheck %s
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func.func @collapse_reduction(
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%arg0: tensor<2x32x10x4096xf32>, %arg1: tensor<2x32xf32>) -> tensor<2x32xf32> {
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%0 = linalg.generic {
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indexing_maps = [
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affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>,
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affine_map<(d0, d1, d2, d3) -> (d0, d1)>],
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iterator_types = ["parallel", "parallel", "reduction", "reduction"]}
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ins(%arg0 : tensor<2x32x10x4096xf32>) outs(%arg1 : tensor<2x32xf32>) {
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^bb0(%arg3: f32, %arg4: f32):
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%1 = arith.addf %arg3, %arg4 : f32
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linalg.yield %1 : f32
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} -> tensor<2x32xf32>
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return %0 : tensor<2x32xf32>
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}
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// CHECK-DAG: #[[$MAP0:.*]] = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
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// CHECK-DAG: #[[$MAP1:.*]] = affine_map<(d0, d1, d2) -> (d0, d1)>
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// CHECK-LABEL: func @collapse_reduction
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// CHECK: %[[T:.*]] = tensor.collapse_shape %{{.*}} {{\[}}[0], [1], [2, 3]] : tensor<2x32x10x4096xf32> into tensor<2x32x40960xf32>
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// CHECK: linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP1]]],
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// CHECK-SAME: iterator_types = ["parallel", "parallel", "reduction"]}
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// CHECK-SAME: ins(%[[T]] : tensor<2x32x40960xf32>) outs(%{{.*}} : tensor<2x32xf32>) {
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// CHECK: } -> tensor<2x32xf32>
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// -----
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func.func @collapse_parallel(
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%arg0: tensor<32x2x10x4096xf32>, %arg1: tensor<2x32x10x4096xf32>) -> tensor<2x32x10x4096xf32> {
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%0 = linalg.generic {
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indexing_maps = [
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affine_map<(d0, d1, d2, d3) -> (d1, d0, d2, d3)>,
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affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>],
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iterator_types = ["parallel", "parallel", "parallel", "parallel"]}
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ins(%arg0 : tensor<32x2x10x4096xf32>) outs(%arg1 : tensor<2x32x10x4096xf32>) {
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^bb0(%arg3: f32, %arg4: f32):
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%1 = arith.addf %arg3, %arg4 : f32
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linalg.yield %1 : f32
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} -> tensor<2x32x10x4096xf32>
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return %0 : tensor<2x32x10x4096xf32>
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}
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// CHECK-DAG: #[[$MAP0:.*]] = affine_map<(d0, d1, d2) -> (d1, d0, d2)>
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// CHECK-DAG: #[[$MAP1:.*]] = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
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// CHECK-LABEL: func @collapse_parallel
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// CHECK-DAG: %[[S:.*]] = tensor.collapse_shape %{{.*}} {{\[}}[0], [1], [2, 3]] : tensor<32x2x10x4096xf32> into tensor<32x2x40960xf32>
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// CHECK-DAG: %[[D:.*]] = tensor.collapse_shape %{{.*}} {{\[}}[0], [1], [2, 3]] : tensor<2x32x10x4096xf32> into tensor<2x32x40960xf32>
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// CHECK: %[[R:.*]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP1]]],
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// CHECK-SAME: iterator_types = ["parallel", "parallel", "parallel"]}
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// CHECK-SAME: ins(%[[S]] : tensor<32x2x40960xf32>) outs(%[[D]] : tensor<2x32x40960xf32>) {
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// CHECK: } -> tensor<2x32x40960xf32>
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// CHECK: tensor.expand_shape %[[R]] {{\[}}[0], [1], [2, 3]] output_shape [2, 32, 10, 4096] : tensor<2x32x40960xf32> into tensor<2x32x10x4096xf32>
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// -----
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#map = affine_map<(d0, d1, d2, d3) -> (d3, d0, d1, d2)>
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#map1 = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>
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func.func @uncollapsable(%arg0 : tensor<41x3x1x57xf32>, %arg1 : tensor<3x1x57x41xf32>) -> tensor<3x1x57x41xf32> {
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%0 = linalg.generic {
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indexing_maps = [#map, #map1], iterator_types = ["parallel", "parallel", "parallel", "parallel"]}
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ins(%arg0 : tensor<41x3x1x57xf32>) outs(%arg1 : tensor<3x1x57x41xf32>) {
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^bb0(%in: f32, %out: f32):
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linalg.yield %in : f32
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} -> tensor<3x1x57x41xf32>
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return %0 : tensor<3x1x57x41xf32>
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}
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// CHECK-LABEL: func @uncollapsable(
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// CHECK: linalg.generic
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// CHECK-SAME: iterator_types = ["parallel", "parallel", "parallel", "parallel"]
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// -----
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// CHECK-LABEL: func.func private @collapsable_memref(
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// CHECK-SAME: %[[VAL_0:.*]]: memref<1x24x32x8xf32>,
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// CHECK-SAME: %[[VAL_1:.*]]: memref<1x24x32x8xf32>) -> memref<1x24x32x8xf32> {
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// CHECK: %[[VAL_2:.*]] = memref.alloc() {alignment = 64 : i64} : memref<1x24x32x8xf32>
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// CHECK: %[[VAL_3:.*]] = memref.collapse_shape %[[VAL_0]] {{\[\[}}0], [1], [2, 3]] : memref<1x24x32x8xf32> into memref<1x24x256xf32>
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// CHECK: %[[VAL_4:.*]] = memref.collapse_shape %[[VAL_1]] {{\[\[}}0], [1], [2, 3]] : memref<1x24x32x8xf32> into memref<1x24x256xf32>
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// CHECK: %[[VAL_5:.*]] = memref.collapse_shape %[[VAL_2]] {{\[\[}}0], [1], [2, 3]] : memref<1x24x32x8xf32> into memref<1x24x256xf32>
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// CHECK: linalg.generic {indexing_maps = [#map, #map, #map], iterator_types = ["parallel", "parallel", "parallel"]} ins(%[[VAL_3]], %[[VAL_4]] : memref<1x24x256xf32>, memref<1x24x256xf32>) outs(%[[VAL_5]] : memref<1x24x256xf32>) {
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// CHECK: ^bb0(%[[VAL_6:.*]]: f32, %[[VAL_7:.*]]: f32, %[[VAL_8:.*]]: f32):
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// CHECK: %[[VAL_9:.*]] = arith.addf %[[VAL_6]], %[[VAL_7]] : f32
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// CHECK: linalg.yield %[[VAL_9]] : f32
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// CHECK: }
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// CHECK: return %[[VAL_2]] : memref<1x24x32x8xf32>
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// CHECK: }
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func.func private @collapsable_memref(%arg0: memref<1x24x32x8xf32>, %arg1: memref<1x24x32x8xf32>) -> (memref<1x24x32x8xf32>) {
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%alloc = memref.alloc() {alignment = 64 : i64} : memref<1x24x32x8xf32>
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linalg.generic {indexing_maps = [affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>, affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>, affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>], iterator_types = ["parallel", "parallel", "parallel", "parallel"]} ins(%arg0, %arg1 : memref<1x24x32x8xf32>, memref<1x24x32x8xf32>) outs(%alloc : memref<1x24x32x8xf32>) {
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^bb0(%in: f32, %in_0: f32, %out: f32):
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%0 = arith.addf %in, %in_0 : f32
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linalg.yield %0 : f32
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}
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return %alloc : memref<1x24x32x8xf32>
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}
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// -----
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// CHECK-DAG: #[[$ATTR_0:.+]] = affine_map<(d0, d1, d2, d3) -> (d0 * 7680 + d1 * 320 + d2 * 10 + d3)>
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// CHECK-DAG: #[[$ATTR_1:.+]] = affine_map<(d0, d1, d2) -> (d0, d2, d1)>
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// CHECK-LABEL: func.func @collapsable_memref_projected_ops(
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// CHECK-SAME: %[[ARG0:.*]]: memref<1x24x32x8xf32>, %[[ARG1:.*]]: memref<1x24x32x8xf32>, %[[ARG2:.*]]: memref<1x24x32x8xf32, #[[$ATTR_0]]>) {
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// CHECK: %[[VAL_0:.*]] = memref.collapse_shape %[[ARG0]] {{\[\[}}0], [1, 2], [3]] : memref<1x24x32x8xf32> into memref<1x768x8xf32>
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// CHECK: %[[VAL_1:.*]] = memref.collapse_shape %[[ARG1]] {{\[\[}}0], [1, 2], [3]] : memref<1x24x32x8xf32> into memref<1x768x8xf32>
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// CHECK: %[[VAL_2:.*]] = memref.collapse_shape %[[ARG2]] {{\[\[}}0], [1, 2], [3]] : memref<1x24x32x8xf32, #[[$ATTR_0]]> into memref<1x768x8xf32, strided<[7680, 10, 1]>>
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// CHECK: linalg.generic {indexing_maps = [#[[$ATTR_1]], #[[$ATTR_1]], #[[$ATTR_1]]], iterator_types = ["parallel", "parallel", "parallel"]} ins(%[[VAL_0]], %[[VAL_1]] : memref<1x768x8xf32>, memref<1x768x8xf32>) outs(%[[VAL_2]] : memref<1x768x8xf32, strided<[7680, 10, 1]>>) {
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// CHECK: ^bb0(%[[VAL_3:.*]]: f32, %[[VAL_4:.*]]: f32, %[[VAL_5:.*]]: f32):
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// CHECK: %[[VAL_6:.*]] = arith.addf %[[VAL_3]], %[[VAL_4]] : f32
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// CHECK: linalg.yield %[[VAL_6]] : f32
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// CHECK: }
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// CHECK: return
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// CHECK: }
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#map = affine_map<(d0, d1, d2, d3) -> (d0, d2, d3, d1)>
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#map1 = affine_map<(d0, d1, d2, d3) -> (d0 * 7680 + d1 * 320 + d2 * 10 + d3)>
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func.func @collapsable_memref_projected_ops(%arg0: memref<1x24x32x8xf32>, %arg1: memref<1x24x32x8xf32>, %arg2: memref<1x24x32x8xf32, #map1>) {
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linalg.generic {indexing_maps = [#map, #map, #map], iterator_types = ["parallel", "parallel", "parallel", "parallel"]} ins(%arg0, %arg1 : memref<1x24x32x8xf32>, memref<1x24x32x8xf32>) outs(%arg2 : memref<1x24x32x8xf32, #map1>) {
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^bb0(%in: f32, %in_0: f32, %out: f32):
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%0 = arith.addf %in, %in_0 : 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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// -----
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// CHECK-LABEL: func @uncollapsable_strided_memref(
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// CHECK: linalg.generic
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// CHECK-SAME: iterator_types = ["parallel", "parallel", "parallel", "parallel"]
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func.func @uncollapsable_strided_memref(%arg0: memref<2x6x24x48xi32>, %arg1: memref<2x6x24x48xi32>) -> (memref<2x6x24x48xi32>) {
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%alloc = memref.alloc() {alignment = 64 : i64} : memref<2x6x24x48xi32>
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%subview = memref.subview %arg0[0, 0, 0, 0] [1, 3, 12, 24] [1, 1, 1, 1] : memref<2x6x24x48xi32> to memref<1x3x12x24xi32, strided<[6912, 1152, 48, 1], offset: 0>>
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%subview0 = memref.subview %arg1[0, 0, 0, 0] [1, 3, 12, 24] [1, 1, 1, 1] : memref<2x6x24x48xi32> to memref<1x3x12x24xi32, strided<[6912, 1152, 48, 1], offset: 0>>
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%subview1 = memref.subview %alloc[0, 0, 0, 0] [1, 3, 12, 24] [1, 1, 1, 1] : memref<2x6x24x48xi32> to memref<1x3x12x24xi32, strided<[6912, 1152, 48, 1], offset: 0>>
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linalg.generic {indexing_maps = [affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>, affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>, affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>], iterator_types = ["parallel", "parallel", "parallel", "parallel"]} ins(%subview, %subview0 : memref<1x3x12x24xi32, strided<[6912, 1152, 48, 1], offset: 0>>, memref<1x3x12x24xi32, strided<[6912, 1152, 48, 1], offset: 0>>) outs(%subview1 : memref<1x3x12x24xi32, strided<[6912, 1152, 48, 1], offset: 0>>) {
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^bb0(%in: i32, %in_0: i32, %out: i32):
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%0 = arith.addi %in, %in_0 : i32
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linalg.yield %0 : i32
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}
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return %alloc : memref<2x6x24x48xi32>
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}
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// -----
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// CHECK-LABEL: func @uncollapsable_memref_projected_ops(
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// CHECK: linalg.generic
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// CHECK-SAME: iterator_types = ["parallel", "parallel", "parallel", "parallel"]
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#map = affine_map<(d0, d1, d2, d3) -> (d0, d2, d3, d1)>
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#map1 = affine_map<(d0, d1, d2, d3) -> (d0 * 7680 + d1 * 320 + d2 * 8 + d3)>
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func.func @uncollapsable_memref_projected_ops(%arg0: memref<1x24x32x8xf32>, %arg1: memref<1x24x32x8xf32>, %arg2: memref<1x24x32x8xf32, #map1>) {
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linalg.generic {indexing_maps = [#map, #map, #map], iterator_types = ["parallel", "parallel", "parallel", "parallel"]} ins(%arg0, %arg1 : memref<1x24x32x8xf32>, memref<1x24x32x8xf32>) outs(%arg2 : memref<1x24x32x8xf32, #map1>) {
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^bb0(%in: f32, %in_0: f32, %out: f32):
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%0 = arith.addf %in, %in_0 : 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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// -----
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// CHECK-LABEL: func.func @linalg_copy(
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// CHECK-SAME: %[[VAL_0:.*]]: tensor<1x2x3x4x5xf32, 1 : i64>,
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// CHECK-SAME: %[[VAL_1:.*]]: tensor<1x2x3x4x5xf32, 3 : i64>) -> tensor<1x2x3x4x5xf32, 3 : i64> {
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// CHECK: %[[VAL_2:.*]] = tensor.collapse_shape %[[VAL_0]] {{\[\[}}0], [1], [2, 3], [4]] : tensor<1x2x3x4x5xf32, 1 : i64> into tensor<1x2x12x5xf32, 1 : i64>
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// CHECK: %[[VAL_3:.*]] = tensor.collapse_shape %[[VAL_1]] {{\[\[}}0], [1], [2, 3], [4]] : tensor<1x2x3x4x5xf32, 3 : i64> into tensor<1x2x12x5xf32, 3 : i64>
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// CHECK: %[[VAL_4:.*]] = tensor.collapse_shape %[[VAL_2]] {{\[\[}}0], [1], [2, 3]] : tensor<1x2x12x5xf32, 1 : i64> into tensor<1x2x60xf32, 1 : i64>
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// CHECK: %[[VAL_5:.*]] = tensor.collapse_shape %[[VAL_3]] {{\[\[}}0], [1], [2, 3]] : tensor<1x2x12x5xf32, 3 : i64> into tensor<1x2x60xf32, 3 : i64>
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// CHECK: %[[VAL_6:.*]] = linalg.copy ins(%[[VAL_4]] : tensor<1x2x60xf32, 1 : i64>) outs(%[[VAL_5]] : tensor<1x2x60xf32, 3 : i64>) -> tensor<1x2x60xf32, 3 : i64>
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// CHECK: %[[VAL_7:.*]] = tensor.expand_shape %[[VAL_6]] {{\[\[}}0], [1], [2, 3]] output_shape [1, 2, 12, 5] : tensor<1x2x60xf32, 3 : i64> into tensor<1x2x12x5xf32, 3 : i64>
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// CHECK: %[[VAL_8:.*]] = tensor.expand_shape %[[VAL_7]] {{\[\[}}0], [1], [2, 3], [4]] output_shape [1, 2, 3, 4, 5] : tensor<1x2x12x5xf32, 3 : i64> into tensor<1x2x3x4x5xf32, 3 : i64>
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// CHECK: return %[[VAL_8]] : tensor<1x2x3x4x5xf32, 3 : i64>
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// CHECK: }
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func.func @linalg_copy(
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%arg0: tensor<1x2x3x4x5xf32, 1>, %arg1: tensor<1x2x3x4x5xf32, 3>) -> tensor<1x2x3x4x5xf32, 3> {
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%0 = linalg.copy ins(%arg0: tensor<1x2x3x4x5xf32, 1>) outs(%arg1: tensor<1x2x3x4x5xf32, 3>) -> tensor<1x2x3x4x5xf32, 3>
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return %0 : tensor<1x2x3x4x5xf32, 3>
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}
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// -----
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// CHECK-LABEL: func.func private @memref_linalg_copy(
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// CHECK-SAME: %[[VAL_0:.*]]: memref<1x24x32x8xf32, 1>,
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// CHECK-SAME: %[[VAL_1:.*]]: memref<1x24x32x8xf32, 1>) {
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// CHECK: %[[VAL_2:.*]] = memref.collapse_shape %[[VAL_0]] {{\[\[}}0], [1], [2, 3]] : memref<1x24x32x8xf32, 1> into memref<1x24x256xf32, 1>
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// CHECK: %[[VAL_3:.*]] = memref.collapse_shape %[[VAL_1]] {{\[\[}}0], [1], [2, 3]] : memref<1x24x32x8xf32, 1> into memref<1x24x256xf32, 1>
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// CHECK: linalg.copy ins(%[[VAL_2]] : memref<1x24x256xf32, 1>) outs(%[[VAL_3]] : memref<1x24x256xf32, 1>)
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// CHECK: return
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// CHECK: }
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func.func private @memref_linalg_copy(%arg0: memref<1x24x32x8xf32, 1>, %arg1: memref<1x24x32x8xf32, 1>) {
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linalg.copy ins(%arg0: memref<1x24x32x8xf32, 1>) outs(%arg1: memref<1x24x32x8xf32, 1>)
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return
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}
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// -----
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// Test that non-projected-permutation indexing maps don't crash the collapse pass.
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// The indexing map has affine expressions like d1 + d4, which are not projected
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// permutations. The pass should skip collapsing and leave the op unchanged.
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// CHECK-DAG: #[[$MAP:.*]] = affine_map<(d0, d1, d2, d3, d4, d5) -> (d0, d1 + d4, d2 + d5, d3)>
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// CHECK-DAG: #[[$MAP1:.*]] = affine_map<(d0, d1, d2, d3, d4, d5) -> (d4, d5, d3, 0)>
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// CHECK-DAG: #[[$MAP2:.*]] = affine_map<(d0, d1, d2, d3, d4, d5) -> (d0, d1, d2, d3)>
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// CHECK-LABEL: func @non_projected_permutation_no_crash
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// CHECK: linalg.generic {indexing_maps = [#[[$MAP]], #[[$MAP1]], #[[$MAP2]]],
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// CHECK-SAME: iterator_types = ["parallel", "parallel", "parallel", "parallel", "reduction", "reduction"]
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func.func @non_projected_permutation_no_crash(
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%arg0: tensor<1x5x5x1xf32>, %arg1: tensor<2x2x1x1xf32>) -> tensor<1x4x4x1xf32> {
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%cst = arith.constant dense<0.0> : tensor<1x4x4x1xf32>
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%0 = linalg.generic {
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indexing_maps = [affine_map<(d0, d1, d2, d3, d4, d5) -> (d0, d1 + d4, d2 + d5, d3)>,
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affine_map<(d0, d1, d2, d3, d4, d5) -> (d4, d5, d3, 0)>,
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affine_map<(d0, d1, d2, d3, d4, d5) -> (d0, d1, d2, d3)>],
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iterator_types = ["parallel", "parallel", "parallel", "parallel", "reduction", "reduction"]
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} ins(%arg0, %arg1 : tensor<1x5x5x1xf32>, tensor<2x2x1x1xf32>) outs(%cst : tensor<1x4x4x1xf32>) {
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^bb0(%in: f32, %w: f32, %out: f32):
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%mul = arith.mulf %in, %w : f32
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%add = arith.addf %out, %mul : f32
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linalg.yield %add : f32
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} -> tensor<1x4x4x1xf32>
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return %0 : tensor<1x4x4x1xf32>
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}
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