mirror of https://github.com/llvm/torch-mlir
23 lines
832 B
Python
23 lines
832 B
Python
# RUN: %PYTHON %s | npcomp-opt -split-input-file | FileCheck %s --dump-input=fail
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import math
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from npcomp.compiler import test_config
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import_global = test_config.create_import_dump_decorator()
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# CHECK-LABEL: func @call_ceil_positional
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@import_global
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def call_ceil_positional(n):
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# CHECK: basicpy.func_template_call @__global$math.ceil(%arg0) kw [] : (!basicpy.UnknownType) -> !basicpy.UnknownType
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return math.ceil(n)
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# CHECK-LABEL: func @call_isclose_kw
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@import_global
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def call_isclose_kw(n):
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# CHECK-DAG: %[[RTOL:.*]] = constant 2.000000e-06
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# CHECK-DAG: %[[ABSTOL:.*]] = constant 2.000000e-01
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# CHECK: basicpy.func_template_call @__global$math.isclose(%arg0, %[[RTOL]], %[[ABSTOL]]) kw ["rtol", "abs_tol"] : (!basicpy.UnknownType, f64, f64) -> !basicpy.UnknownType
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return math.isclose(n, rtol=2e-6, abs_tol=0.2)
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