mirror of https://github.com/llvm/torch-mlir
45 lines
1.1 KiB
Python
45 lines
1.1 KiB
Python
# RUN: %PYTHON %s | FileCheck %s --dump-input=fail
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import numpy as np
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from npcomp.compiler.backend import refjit
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from npcomp.compiler.frontend import *
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from npcomp.compiler import logging
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from npcomp.compiler import test_config
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from npcomp.compiler.target import *
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# TODO: This should all exist in a high level API somewhere.
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from _npcomp import mlir
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logging.enable()
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def compile_function(f):
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fe = ImportFrontend(config=test_config.create_test_config(
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target_factory=GenericTarget32))
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fe.import_global_function(f)
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compiler = refjit.CompilerBackend()
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blob = compiler.compile(fe.ir_module)
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loaded_m = compiler.load(blob)
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return loaded_m[f.__name__]
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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], dtype=np.float32)
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b = np.asarray([3.0, 4.0], dtype=np.float32)
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@compile_function
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def global_add():
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return np.add(a, np.add(b, a))
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# Make sure we aren't accidentally invoking the python function :)
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assert global_add.__isnpcomp__
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# CHECK: GLOBAL_ADD: [5. 8.]
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result = global_add()
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print("GLOBAL_ADD:", result)
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