mirror of https://github.com/llvm/torch-mlir
73 lines
3.3 KiB
Python
73 lines
3.3 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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import typing
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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 @__torch__.prim_Loop_forlike(
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# CHECK-SAME: %[[MAX_ITERATIONS:.*]]: i64) -> f64 {
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# CHECK: %[[BOOL_TRUE:.*]] = basicpy.bool_constant true
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# CHECK: %[[F_INIT:.*]] = constant 0.000000e+00 : f64
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# CHECK: %[[RESULTS:.*]] = torch.prim.Loop %[[MAX_ITERATIONS]], %[[BOOL_TRUE]], init(%[[F_INIT]]) {
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# CHECK: ^bb0(%[[IV:.*]]: i64, %[[F_ITER:.*]]: f64):
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# CHECK: %[[F_NEXT:.*]] = torch.aten.add.float_int %[[F_ITER]], %[[IV]] : f64, i64 -> f64
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# CHECK: torch.prim.Loop.condition %[[BOOL_TRUE]], iter(%[[F_NEXT]] : f64)
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# CHECK: } : (i64, !basicpy.BoolType, f64) -> f64
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# CHECK: return %[[RESULTS:.*]] : f64
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@mb.import_function
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@torch.jit.script
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def prim_Loop_forlike(n: int):
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f = 0.0
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for i in range(n):
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f += i
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return f
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# CHECK-LABEL: func @__torch__.prim_Loop_whilelike(
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# CHECK-SAME: %[[VAL_0:.*]]: i64) -> f64 {
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# CHECK: %[[F_INIT:.*]] = constant 3.200000e+00 : f64
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# CHECK: %[[MAX_ITERATIONS:.*]] = constant 9223372036854775807 : i64
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# CHECK: %[[COND_INIT:.*]] = torch.aten.lt.float_int %[[F_INIT]], %[[VAL_0]] : f64, i64 -> !basicpy.BoolType
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# CHECK: %[[RET:.*]] = torch.prim.Loop %[[MAX_ITERATIONS]], %[[COND_INIT]], init(%[[F_INIT]]) {
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# CHECK: ^bb0(%[[F_ITER:.*]]: i64, %[[F_ITER:.*]]: f64):
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# CHECK: %[[F_NEXT:.*]] = torch.aten.mul.float %[[F_ITER]], %[[F_ITER]] : f64, f64 -> f64
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# CHECK: %[[COND_ITER:.*]] = torch.aten.lt.float_int %[[F_NEXT]], %[[VAL_0]] : f64, i64 -> !basicpy.BoolType
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# CHECK: torch.prim.Loop.condition %[[COND_ITER]], iter(%[[F_NEXT]] : f64)
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# CHECK: } : (i64, !basicpy.BoolType, f64) -> f64
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# CHECK: return %[[RET:.*]] : f64
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@mb.import_function
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@torch.jit.script
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def prim_Loop_whilelike(n: int):
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f = 3.2
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while f < n:
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f = f * f
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return f
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# CHECK-LABEL: func @__torch__.prim_Loop_derefine(
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# CHECK-SAME: %[[ARG:.*]]: i64) -> !torch.optional<i64> {
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# CHECK: %[[TRUE:.*]] = basicpy.bool_constant true
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# CHECK: %[[NONE:.*]] = basicpy.singleton : !basicpy.NoneType
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# CHECK: %[[NONE_DEREFINED:.*]] = torch.derefine %[[NONE]] : !basicpy.NoneType to !torch.optional<i64>
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# CHECK: %[[RET:.*]] = torch.prim.Loop %[[ARG]], %[[TRUE]], init(%[[NONE_DEREFINED]]) {
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# CHECK: ^bb0(%[[IV:.*]]: i64, %[[X_ITER:.*]]: !torch.optional<i64>):
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# CHECK: %[[X_NEXT:.*]] = torch.derefine %[[ARG]] : i64 to !torch.optional<i64>
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# CHECK: torch.prim.Loop.condition %[[TRUE]], iter(%[[X_NEXT]] : !torch.optional<i64>)
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# CHECK: } : (i64, !basicpy.BoolType, !torch.optional<i64>) -> !torch.optional<i64>
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# CHECK: return %[[RET:.*]] : !torch.optional<i64>
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@mb.import_function
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@torch.jit.script
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def prim_Loop_derefine(n: int):
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x: typing.Optional[int] = None
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for i in range(n):
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x = n
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return x
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mb.module.operation.print()
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print()
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