mirror of https://github.com/llvm/torch-mlir
29 lines
1.3 KiB
Python
29 lines
1.3 KiB
Python
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# RUN: %PYTHON %s | npcomp-opt -split-input-file | 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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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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@import_global
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def global_add():
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# CHECK-DAG: %[[CST_A_TENSOR:.*]] = constant dense<[1.000000e+00, 2.000000e+00]>
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# CHECK-DAG: %[[CST_B_TENSOR:.*]] = constant dense<[3.000000e+00, 4.000000e+00]>
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# CHECK-DAG: %[[A_ARRAY:.*]] = numpy.create_array_from_tensor %[[CST_A_TENSOR]]
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# CHECK-DAG: %[[B_ARRAY:.*]] = numpy.create_array_from_tensor %[[CST_B_TENSOR]]
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# CHECK-DAG: %[[A:.*]] = numpy.copy_to_tensor %[[A_ARRAY]]
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# CHECK-DAG: %[[B:.*]] = numpy.copy_to_tensor %[[B_ARRAY]]
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# CHECK: %[[R_TENSOR:.*]] = numpy.builtin_ufunc_call<"numpy.add"> (%[[A]], %[[B]]) : (tensor<*xf64>, tensor<*xf64>) -> tensor<*x!basicpy.UnknownType>
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# CHECK: numpy.create_array_from_tensor %[[R_TENSOR]] : (tensor<*x!basicpy.UnknownType>) -> !numpy.ndarray<?>
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return np.add(a, b)
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