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// RUN: mlir-opt %s -test-linalg-elementwise-fusion-patterns=collapse-dimensions-control=2,3 -split-input-file | FileCheck %s
func.func @collapse_reduction(
%arg0: tensor<2x32x10x4096xf32>, %arg1: tensor<2x32xf32>) -> tensor<2x32xf32> {
%0 = linalg.generic {
indexing_maps = [
affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>,
affine_map<(d0, d1, d2, d3) -> (d0, d1)>],
iterator_types = ["parallel", "parallel", "reduction", "reduction"]}
ins(%arg0 : tensor<2x32x10x4096xf32>) outs(%arg1 : tensor<2x32xf32>) {
^bb0(%arg3: f32, %arg4: f32):
%1 = arith.addf %arg3, %arg4 : f32
linalg.yield %1 : f32
} -> tensor<2x32xf32>
return %0 : tensor<2x32xf32>
}
// CHECK-DAG: #[[$MAP0:.*]] = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
// CHECK-DAG: #[[$MAP1:.*]] = affine_map<(d0, d1, d2) -> (d0, d1)>
// CHECK-LABEL: func @collapse_reduction
// CHECK: %[[T:.*]] = tensor.collapse_shape %{{.*}} {{\[}}[0], [1], [2, 3]] : tensor<2x32x10x4096xf32> into tensor<2x32x40960xf32>
// CHECK: linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP1]]],
// CHECK-SAME: iterator_types = ["parallel", "parallel", "reduction"]}
// CHECK-SAME: ins(%[[T]] : tensor<2x32x40960xf32>) outs(%{{.*}} : tensor<2x32xf32>) {
// CHECK: } -> tensor<2x32xf32>
// -----
func.func @collapse_parallel(
%arg0: tensor<32x2x10x4096xf32>, %arg1: tensor<2x32x10x4096xf32>) -> tensor<2x32x10x4096xf32> {
%0 = linalg.generic {
indexing_maps = [
affine_map<(d0, d1, d2, d3) -> (d1, d0, d2, d3)>,
affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>],
iterator_types = ["parallel", "parallel", "parallel", "parallel"]}
ins(%arg0 : tensor<32x2x10x4096xf32>) outs(%arg1 : tensor<2x32x10x4096xf32>) {
^bb0(%arg3: f32, %arg4: f32):
%1 = arith.addf %arg3, %arg4 : f32
linalg.yield %1 : f32
} -> tensor<2x32x10x4096xf32>
return %0 : tensor<2x32x10x4096xf32>
}
// CHECK-DAG: #[[$MAP0:.*]] = affine_map<(d0, d1, d2) -> (d1, d0, d2)>
// CHECK-DAG: #[[$MAP1:.*]] = affine_map<(d0, d1, d2) -> (d0, d1, d2)>
// CHECK-LABEL: func @collapse_parallel
// CHECK-DAG: %[[S:.*]] = tensor.collapse_shape %{{.*}} {{\[}}[0], [1], [2, 3]] : tensor<32x2x10x4096xf32> into tensor<32x2x40960xf32>
// CHECK-DAG: %[[D:.*]] = tensor.collapse_shape %{{.*}} {{\[}}[0], [1], [2, 3]] : tensor<2x32x10x4096xf32> into tensor<2x32x40960xf32>
// CHECK: %[[R:.*]] = linalg.generic {indexing_maps = [#[[$MAP0]], #[[$MAP1]]],
// CHECK-SAME: iterator_types = ["parallel", "parallel", "parallel"]}
// CHECK-SAME: ins(%[[S]] : tensor<32x2x40960xf32>) outs(%[[D]] : tensor<2x32x40960xf32>) {
// CHECK: } -> tensor<2x32x40960xf32>
// CHECK: tensor.expand_shape %[[R]] {{\[}}[0], [1], [2, 3]] : tensor<2x32x40960xf32> into tensor<2x32x10x4096xf32>
// -----
#map = affine_map<(d0, d1, d2, d3) -> (d3, d0, d1, d2)>
#map1 = affine_map<(d0, d1, d2, d3) -> (d0, d1, d2, d3)>
func.func @uncollapsable(%arg0 : tensor<41x3x1x57xf32>, %arg1 : tensor<3x1x57x41xf32>) -> tensor<3x1x57x41xf32> {
%0 = linalg.generic {
indexing_maps = [#map, #map1], iterator_types = ["parallel", "parallel", "parallel", "parallel"]}
ins(%arg0 : tensor<41x3x1x57xf32>) outs(%arg1 : tensor<3x1x57x41xf32>) {
^bb0(%in: f32, %out: f32):
linalg.yield %in : f32
} -> tensor<3x1x57x41xf32>
return %0 : tensor<3x1x57x41xf32>
}
// CHECK-LABEL: func @uncollapsable(
// CHECK: linalg.generic
// CHECK-SAME: iterator_types = ["parallel", "parallel", "parallel", "parallel"]
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