mirror of https://github.com/llvm/torch-mlir
45 lines
1.5 KiB
Python
45 lines
1.5 KiB
Python
# -*- Python -*-
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# This file is licensed under a pytorch-style license
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# See frontends/pytorch/LICENSE for license information.
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import torch
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import torch_mlir
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# RUN: %PYTHON %s | npcomp-opt | FileCheck %s
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mb = torch_mlir.ModuleBuilder()
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# CHECK-LABEL: func @prim_NumToTensor(
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# CHECK-SAME: %[[ARG:.*]]: i64) -> !numpy.ndarray<*:!numpy.any_dtype> {
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# CHECK: %[[RET:.*]] = torch.prim.NumToTensor %[[ARG]] : i64 -> !numpy.ndarray<*:!numpy.any_dtype>
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# CHECK: return %[[RET]] : !numpy.ndarray<*:!numpy.any_dtype>
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# CHECK: }
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@mb.import_function
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@torch.jit.script
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def prim_NumToTensor(i: int):
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return _to_tensor(i)
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# CHECK-LABEL: func @prim_Print(
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# CHECK-SAME: %[[ARG:.*]]: !numpy.ndarray<*:!numpy.any_dtype>) -> !basicpy.NoneType {
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# CHECK: %[[STR:.*]] = basicpy.bytes_constant "x"
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# CHECK: torch.prim.Print(%[[STR]], %[[ARG]]) : !basicpy.BytesType, !numpy.ndarray<*:!numpy.any_dtype>
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@mb.import_function
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@torch.jit.script
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def prim_Print(x):
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print("x", x)
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# CHECK-LABEL: func @prim_RaiseException() -> !basicpy.NoneType {
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# CHECK: %[[ERRORSTR:.*]] = basicpy.bytes_constant "Error"
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# CHECK: %[[NONE:.*]] = torch.prim.Uninitialized : !basicpy.NoneType
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# CHECK: torch.prim.RaiseException %[[ERRORSTR]]
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# CHECK: return %[[NONE]] : !basicpy.NoneType
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@mb.import_function
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@torch.jit.script
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def prim_RaiseException():
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raise Exception("Error")
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mb.module.operation.print()
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print()
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