mirror of https://github.com/llvm/torch-mlir
27 lines
1.0 KiB
Python
27 lines
1.0 KiB
Python
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# RUN: %PYTHON %s | npcomp-opt -split-input-file -npcomp-cpa-type-inference -canonicalize | FileCheck %s --dump-input=fail
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import numpy as np
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from npcomp.compiler import test_config
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from npcomp.compiler.frontend import EmittedError
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import_global = test_config.create_import_dump_decorator()
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global_data = (np.zeros((2, 3)) + [1.0, 2.0, 3.0] * np.reshape([1.0, 2.0],
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(2, 1)))
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a = np.asarray([1.0, 2.0])
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b = np.asarray([3.0, 4.0])
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# Test the basic flow of invoking a ufunc call with constants captured from
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# a global using explicit function syntax (np.add(a, b)).
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# CHECK-LABEL: func @global_add
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# CHECK-SAME: -> !numpy.ndarray<*:f64>
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@import_global
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def global_add():
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# CHECK-NOT: UnknownType
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# CHECK: numpy.builtin_ufunc_call<"numpy.multiply"> ({{.*}}, {{.*}}) : (tensor<2xf64>, tensor<2xf64>) -> tensor<*xf64>
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# CHECK: numpy.builtin_ufunc_call<"numpy.add"> ({{.*}}, {{.*}}) : (tensor<2xf64>, tensor<*xf64>) -> tensor<*xf64>
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# CHECK-NOT: UnknownType
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return np.add(a, np.multiply(a, b))
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