mirror of https://github.com/llvm/torch-mlir
23 lines
974 B
MLIR
23 lines
974 B
MLIR
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// RUN: npcomp-opt -bypass-shapes <%s | FileCheck %s --dump-input=fail
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#map0 = affine_map<(d0) -> (d0)>
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// CHECK-LABEL: func @linalg_generic
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func @linalg_generic(%arg0: tensor<?xf32>, %arg1: tensor<?xf32>) -> tensor<?xf32> {
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// This is an elementwise linalg op, so output shape is equal to input shape.
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// CHECK: %[[SHAPE:.*]] = shape.shape_of %arg0
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// CHECK: tcp.shaped_results %[[SHAPE]]
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%0 = linalg.generic {args_in = 2 : i64, args_out = 1 : i64, indexing_maps = [#map0, #map0, #map0], iterator_types = ["parallel"]} %arg0, %arg1 {
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^bb0(%arg2: f32, %arg3: f32):
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%8 = addf %arg2, %arg3 : f32
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linalg.yield %8 : f32
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}: tensor<?xf32>, tensor<?xf32> -> tensor<?xf32>
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return %0 : tensor<?xf32>
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}
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// CHECK-LABEL: func @tcp_broadcast_to
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func @tcp_broadcast_to(%arg0: tensor<?xf32>, %arg1: tensor<?xindex>) {
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// CHECK: %0 = tcp.shaped_results %arg1
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%0 = "tcp.broadcast_to"(%arg0, %arg1) : (tensor<?xf32>, tensor<?xindex>) -> tensor<?x?xf32>
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return
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}
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