mirror of https://github.com/llvm/torch-mlir
28 lines
1.2 KiB
MLIR
28 lines
1.2 KiB
MLIR
// RUN: refback-run %s \
|
|
// RUN: -invoke forward \
|
|
// RUN: -arg-value="dense<[[1.0, 2.0], [3.0, 4.0]]> : tensor<2x2xf32>" \
|
|
// RUN: -arg-value="dense<[10.0, 20.0]> : tensor<2xf32>" \
|
|
// RUN: -shared-libs=%npcomp_runtime_shlib 2>&1 \
|
|
// RUN: | FileCheck %s
|
|
|
|
// CHECK{LITERAL}: output #0: dense<[[1.100000e+01, 2.200000e+01], [1.300000e+01, 2.400000e+01]]> : tensor<2x2xf32>
|
|
#map0 = affine_map<(d0, d1) -> (d0, d1)>
|
|
#map1 = affine_map<(d0, d1) -> (d1)>
|
|
|
|
builtin.func @forward(%arg0: tensor<?x?xf32>, %arg1: tensor<?xf32>) -> tensor<?x?xf32> {
|
|
%c1 = constant 1 : index
|
|
%c0 = constant 0 : index
|
|
%0 = tensor.dim %arg0, %c0 : tensor<?x?xf32>
|
|
%1 = tensor.dim %arg0, %c1 : tensor<?x?xf32>
|
|
%2 = tensor.dim %arg1, %c0 : tensor<?xf32>
|
|
%3 = cmpi eq, %1, %2 : index
|
|
assert %3, "mismatched size for broadcast"
|
|
%4 = linalg.init_tensor [%0, %1] : tensor<?x?xf32>
|
|
%5 = linalg.generic {indexing_maps = [#map0, #map1, #map0], iterator_types = ["parallel", "parallel"]} ins(%arg0, %arg1 : tensor<?x?xf32>, tensor<?xf32>) outs(%4 : tensor<?x?xf32>) {
|
|
^bb0(%arg2: f32, %arg3: f32, %arg4: f32): // no predecessors
|
|
%6 = addf %arg2, %arg3 : f32
|
|
linalg.yield %6 : f32
|
|
} -> tensor<?x?xf32>
|
|
return %5 : tensor<?x?xf32>
|
|
}
|