mirror of https://github.com/llvm/torch-mlir
52 lines
1.3 KiB
Python
52 lines
1.3 KiB
Python
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# Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
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# See https://llvm.org/LICENSE.txt for license information.
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# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
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from npcomp.native.mlir.ir import *
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__all__ = [
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"load_builtin_module",
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]
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def load_builtin_module(context = None):
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"""Loads a module populated with numpy built-ins.
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This is not a long-term solution but overcomes some bootstrapping
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issues.
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>>> m = load_builtin_module()
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>>> op = m.region(0).blocks.front.operations.front
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>>> print(op.name)
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numpy.generic_ufunc
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Args:
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context: The MLIRContext to use (None to create a new one).
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Returns:
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A ModuleOp.
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"""
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if context is None: context = MLIRContext()
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return context.parse_asm(_BUILTIN_MODULE_ASM)
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_BUILTIN_MODULE_ASM = r"""
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numpy.generic_ufunc @numpy.add (
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// CHECK-SAME: overload(%arg0: i32, %arg1: i32) -> i32 {
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overload(%arg0: i32, %arg1: i32) -> i32 {
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// CHECK: addi
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%0 = addi %arg0, %arg1 : i32
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numpy.ufunc_return %0 : i32
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},
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// CHECK: overload(%arg0: f32, %arg1: f32) -> f32 {
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overload(%arg0: f32, %arg1: f32) -> f32 {
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// CHECK: addf
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%0 = addf %arg0, %arg1 : f32
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numpy.ufunc_return %0 : f32
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}
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)
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"""
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if __name__ == "__main__":
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import doctest
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doctest.testmod()
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