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// RUN: mlir-opt -pass-pipeline="builtin.module(func.func(convert-affine-for-to-gpu{gpu-block-dims=1 gpu-thread-dims=1}))" %s | FileCheck %s
// CHECK-LABEL: @step_var
func.func @step_var(%A : memref<?x?xf32>, %B : memref<?x?xf32>) {
// Check that we divide by step.
// CHECK: %[[range_i:.*]] = arith.ceildivsi {{.*}}, %{{.*}}
// CHECK: %[[range_j:.*]] = arith.ceildivsi {{.*}}, %{{.*}}
// CHECK: gpu.launch
// CHECK-SAME: blocks(%{{[^)]*}}, %{{[^)]*}}, %{{[^)]*}}) in (%{{[^)]*}} = %[[range_i]], %{{[^)]*}} = %{{[^)]*}}, %{{[^)]*}} = %{{[^)]*}})
// CHECK-SAME: threads(%{{[^)]*}}, %{{[^)]*}}, %{{[^)]*}}) in (%{{[^)]*}} = %[[range_j]], %{{[^)]*}} = %{{[^)]*}}, %{{[^)]*}} = %{{[^)]*}})
affine.for %i = 5 to 15 step 4 {
affine.for %j = 3 to 19 step 7 {
// Loop induction variable remapping:
// iv = thread(block)_id * step + lower_bound
// CHECK: %[[prod_i:.*]] = arith.muli %{{.*}}, %{{.*}} : index
// CHECK-NEXT: %[[i:.*]] = arith.addi %{{.*}}, %[[prod_i]] : index
// CHECK-NEXT: %[[prod_j:.*]] = arith.muli %{{.*}}, %{{.*}} : index
// CHECK-NEXT: %[[j:.*]] = arith.addi %{{.*}}, %[[prod_j]] : index
// CHECK: {{.*}} = memref.load %{{.*}}[%[[i]], %[[j]]] : memref<?x?xf32>
%0 = memref.load %A[%i, %j] : memref<?x?xf32>
// CHECK: memref.store {{.*}}, %{{.*}}[%[[i]], %[[j]]] : memref<?x?xf32>
memref.store %0, %B[%i, %j] : memref<?x?xf32>
}
}
return
}
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