torch-mlir/test/E2E/lower-ranked-shapes.mlir

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// RUN: npcomp-opt -lower-ranked-shapes <%s -split-input-file -verify-diagnostics | FileCheck %s --dump-input=fail
// CHECK-LABEL: func @broadcast_rank2_rank1
func @broadcast_rank2_rank1(%arg0: index, %arg1: index, %arg2: index) -> (index, index) {
// CHECK-NOT: shape.broadcast
// CHECK-NOT: tcp.get_extent
// CHECK-NOT: shape.from_extents
%0 = shape.from_extents %arg0, %arg1
%1 = shape.from_extents %arg2
%2 = "shape.broadcast"(%0, %1) : (!shape.shape, !shape.shape) -> !shape.shape
%e0 = tcp.get_extent %2, 0
%e1 = tcp.get_extent %2, 1
return %e0, %e1 : index, index
}
// CHECK-LABEL: func @erase_shape_observe_error
func @erase_shape_observe_error(%arg0: index) {
// CHECK-NOT: tcp.shape_observe_error
// CHECK-NOT: shape.from_extents
%0 = shape.from_extents %arg0
"tcp.shape_observe_error"(%0) : (!shape.shape) -> none
return
}
// -----
func @cannot_erase_shape_from_extents() -> !shape.shape {
// expected-error @+1 {{could not be eliminated}}
%0 = shape.from_extents
return %0 : !shape.shape
}