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torch-mlir/test/npcomp-run-mlir/multiple-ops.mlir

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Make input file to npcomp-run-mlir be positional. This makes command lines more succinct.
2020-07-14 07:00:54 +08:00
// RUN: npcomp-run-mlir %s \
LowerRankedShapes: support shape.const_shape op. Also, the previous code had a special case for deleting this op when it had no uses. This is subsumed by the change in this commit since now shape.const_shape is properly lowered. With this change, the included test case with multiple serially dependent ops works! This specific issue was related to the scalar argument to that function. We needed to compute a broadcast of a scalar shape (which is a shape.const_shape) with another shape.
2020-07-09 11:10:01 +08:00
// RUN: -invoke multiple_ops \
// RUN: -arg-value="dense<1.0> : tensor<f32>" \
// RUN: -arg-value="dense<[1.0]> : tensor<1xf32>" \
// RUN: -arg-value="dense<[[1.0, 2.0], [3.0, 4.0]]> : tensor<2x2xf32>" \
// RUN: -shared-libs=%npcomp_runtime_shlib 2>&1 \
// RUN: | FileCheck %s
// CHECK: output #0: dense<[
// CHECK-SAME: [3.000000e+00, 4.000000e+00], [5.000000e+00, 6.000000e+00]]> : tensor<2x2xf32>
func @multiple_ops(%arg0: tensor<f32>, %arg1: tensor<?xf32>, %arg2: tensor<?x?xf32>) -> tensor<?x?xf32> {
%0 = "tcf.add"(%arg1, %arg2) : (tensor<?xf32>, tensor<?x?xf32>) -> tensor<?x?xf32>
%1 = "tcf.add"(%arg0, %0) : (tensor<f32>, tensor<?x?xf32>) -> tensor<?x?xf32>
return %1 : tensor<?x?xf32>
}