951ab04d6c
Add pass options to run lowerings to NVVM without pattern rollback. This makes the dialect conversions easier to debug and improves performance/memory usage.
29 lines
1.1 KiB
MLIR
29 lines
1.1 KiB
MLIR
// RUN: mlir-opt %s \
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// RUN: | mlir-opt -gpu-lower-to-nvvm-pipeline="cubin-format=%gpu_compilation_format allow-pattern-rollback=0" \
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// RUN: | mlir-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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// CHECK: 2000
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module attributes {gpu.container_module} {
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func.func @main() {
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%c1 = arith.constant 1 : index
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%c0 = arith.constant 0 : index
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%c1000_i32 = arith.constant 1000 : i32
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%memref = gpu.alloc host_shared () : memref<1xi32>
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memref.store %c1000_i32, %memref[%c1] : memref<1xi32>
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gpu.launch blocks(%arg0, %arg1, %arg2) in (%arg6 = %c1, %arg7 = %c1, %arg8 = %c1) threads(%arg3, %arg4, %arg5) in (%arg9 = %c1, %arg10 = %c1, %arg11 = %c1) {
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%1 = memref.load %memref[%c1] : memref<1xi32>
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%2 = arith.addi %1, %1 : i32
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memref.store %2, %memref[%c1] : memref<1xi32>
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gpu.terminator
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}
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%0 = memref.load %memref[%c1] : memref<1xi32>
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vector.print %0 : i32
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return
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}
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}
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