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1// RUN: mlir-opt %s -affine-super-vectorize="virtual-vector-size=128" -split-input-file | FileCheck %s2 3// Specific tests to check vectorization of uniform/divergent values.4 5// CHECK-LABEL: @uniform_arg6// CHECK-SAME: %[[in:.*]]: memref<512xf32>,7// CHECK-SAME: %[[uniform:.*]]: f328func.func @uniform_arg(%in : memref<512xf32>, %uniform : f32) {9 affine.for %i = 0 to 512 {10 %ld = affine.load %in[%i] : memref<512xf32>11 %add = arith.addf %ld, %uniform : f3212 }13 return14}15 16// CHECK-NEXT: %[[bcast:.*]] = vector.broadcast %[[uniform]] : f32 to vector<128xf32>17// CHECK-NEXT: affine.for18// CHECK: arith.addf %{{.*}}, %[[bcast]] : vector<128xf32>19 20// -----21 22// CHECK-LABEL: @multi_use_uniform_arg23// CHECK-SAME: %[[in:.*]]: memref<512xf32>24// CHECK-SAME: %[[uniform:.*]]: f3225func.func @multi_use_uniform_arg(%in : memref<512xf32>, %uniform : f32) {26 affine.for %i = 0 to 512 {27 %ld = affine.load %in[%i] : memref<512xf32>28 %user0 = arith.addf %ld, %uniform : f3229 %user1 = arith.addf %ld, %uniform : f3230 }31 return32}33 34// CHECK-NEXT: %[[bcast:.*]] = vector.broadcast %[[uniform]] : f32 to vector<128xf32>35// CHECK-NOT: vector.broadcast36// CHECK-NEXT: affine.for37// CHECK: arith.addf %{{.*}}, %[[bcast]] : vector<128xf32>38// CHECK: arith.addf %{{.*}}, %[[bcast]] : vector<128xf32>39 40// -----41 42// CHECK-LABEL: @uniform_load43func.func @uniform_load(%A : memref<?x?xf32>, %C : memref<?x?xf32>) {44 %c0 = arith.constant 0 : index45 %N = memref.dim %A, %c0 : memref<?x?xf32>46 affine.for %i = 0 to %N {47 %uniform_ld = affine.load %A[%i, %i] : memref<?x?xf32>48 affine.for %j = 0 to %N {49 %b = affine.load %A[%i, %j] : memref<?x?xf32>50 %c = arith.addf %uniform_ld, %b : f3251 }52 }53 return54}55 56// CHECK: affine.for57// CHECK-NEXT: %[[uniform_ld:.*]] = affine.load %{{.*}}[%{{.*}}, %{{.*}}] : memref<?x?xf32>58// CHECK-NEXT: %[[bcast:.*]] = vector.broadcast %[[uniform_ld]] : f32 to vector<128xf32>59// CHECK-NEXT: affine.for60// CHECK: arith.addf %[[bcast]], %{{.*}} : vector<128xf32>61