42 lines
1.5 KiB
MLIR
42 lines
1.5 KiB
MLIR
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// RUN: mlir-opt %s \
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// RUN: | mlir-opt -gpu-lower-to-nvvm-pipeline="cubin-format=%gpu_compilation_format" \
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// RUN: | mlir-cpu-runner \
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// RUN: --shared-libs=%mlir_cuda_runtime \
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// RUN: --shared-libs=%mlir_runner_utils \
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// RUN: --shared-libs=%mlir_c_runner_utils \
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// RUN: --entry-point-result=void \
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// RUN: | FileCheck %s
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func.func @other_func(%arg0 : f32, %arg1 : memref<?xf32>) {
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%cst = arith.constant 1 : index
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%c0 = arith.constant 0 : index
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%cst2 = memref.dim %arg1, %c0 : memref<?xf32>
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gpu.launch blocks(%bx, %by, %bz) in (%grid_x = %cst, %grid_y = %cst, %grid_z = %cst)
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threads(%tx, %ty, %tz) in (%block_x = %cst2, %block_y = %cst, %block_z = %cst) {
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memref.store %arg0, %arg1[%tx] : memref<?xf32>
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gpu.terminator
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}
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return
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}
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// CHECK: [1, 1, 1, 1, 1]
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// CHECK: ( 1, 1 )
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func.func @main() {
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%v0 = arith.constant 0.0 : f32
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%c0 = arith.constant 0: index
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%arg0 = memref.alloc() : memref<5xf32>
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%21 = arith.constant 5 : i32
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%22 = memref.cast %arg0 : memref<5xf32> to memref<?xf32>
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%23 = memref.cast %22 : memref<?xf32> to memref<*xf32>
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gpu.host_register %23 : memref<*xf32>
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call @printMemrefF32(%23) : (memref<*xf32>) -> ()
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%24 = arith.constant 1.0 : f32
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call @other_func(%24, %22) : (f32, memref<?xf32>) -> ()
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call @printMemrefF32(%23) : (memref<*xf32>) -> ()
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%val1 = vector.transfer_read %arg0[%c0], %v0: memref<5xf32>, vector<2xf32>
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vector.print %val1: vector<2xf32>
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return
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}
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func.func private @printMemrefF32(%ptr : memref<*xf32>)
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