mirror of https://github.com/llvm/torch-mlir
21 lines
897 B
MLIR
21 lines
897 B
MLIR
// RUN: npcomp-run-mlir %s \
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// RUN: -invoke multi_output \
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// RUN: -arg-value="dense<1.0> : tensor<1xf32>" \
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// RUN: -shared-libs=%npcomp_runtime_shlib 2>&1 \
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// RUN: | FileCheck %s
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// CHECK: output #0: dense<2.000000e+00> : tensor<1xf32>
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// CHECK: output #1: dense<2.000000e+00> : tensor<1xf32>
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#map0 = affine_map<(d0) -> (d0)>
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func @multi_output(%arg0: tensor<?xf32>) -> (tensor<?xf32>, tensor<?xf32>) {
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%c0 = constant 0 : index
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%0 = tensor.dim %arg0, %c0 : tensor<?xf32>
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%1 = linalg.init_tensor [%0] : tensor<?xf32>
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%2 = linalg.generic {indexing_maps = [#map0, #map0, #map0], iterator_types = ["parallel"]} ins(%arg0, %arg0 : tensor<?xf32>, tensor<?xf32>) outs(%1 : tensor<?xf32>) {
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^bb0(%arg2: f32, %arg3: f32, %arg4: f32): // no predecessors
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%6 = addf %arg2, %arg3 : f32
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linalg.yield %6 : f32
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} -> tensor<?xf32>
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return %2, %2 : tensor<?xf32>, tensor<?xf32>
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}
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