mirror of https://github.com/llvm/torch-mlir
36 lines
1.1 KiB
Python
36 lines
1.1 KiB
Python
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# -*- 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: @f(
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# CHECK-SAME: %[[B:.*]]: !basicpy.BoolType,
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# CHECK-SAME: %[[I:.*]]: i64) -> i64 {
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@mb.import_function
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@torch.jit.script
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def f(b: bool, i: int):
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# CHECK: %[[I1:.*]] = basicpy.bool_cast %[[B]] : !basicpy.BoolType -> i1
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# CHECK: %[[RES:.*]] = scf.if %[[I1]] -> (i64) {
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# CHECK: %[[ADD:.*]] = torch.kernel_call "aten::add" %[[I]], %[[I]]
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# CHECK: scf.yield %[[ADD]] : i64
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# CHECK: } else {
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# CHECK: %[[MUL:.*]] = torch.kernel_call "aten::mul" %[[I]], %[[I]]
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# CHECK: scf.yield %[[MUL]] : i64
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# CHECK: }
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# CHECK: return %[[RES:.*]] : i64
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if b:
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return i + i
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else:
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return i * i
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# elif is modeled as a nested if, so no need to specially test it here.
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assert isinstance(f, torch.jit.ScriptFunction)
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mb.module.operation.print()
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print()
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