mirror of https://github.com/llvm/torch-mlir
87 lines
2.6 KiB
Python
87 lines
2.6 KiB
Python
# 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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"""Various configuration helpers for testing."""
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import ast
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import functools
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from . import logging
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from .frontend import *
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from .interfaces import *
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from .partial_eval_base import *
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from .target import *
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from .value_coder_base import *
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from .extensions import numpy as npc
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def create_import_dump_decorator(*,
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target_factory: TargetFactory = GenericTarget64
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):
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config = create_test_config(target_factory=target_factory)
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logging.debug("Testing with config: {}", config)
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def do_import(f):
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fe = ImportFrontend(config=config)
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fe.import_global_function(f)
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print("// -----")
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print(fe.ir_module.to_asm())
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return f
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def decorator(*args, expect_error=None):
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if len(args) == 0:
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# Higher order decorator.
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return functools.partial(decorator, expect_error=expect_error)
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assert len(args) == 1
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try:
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return do_import(f=args[0])
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except EmittedError as e:
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if expect_error and e.message == expect_error:
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print("// EXPECTED_ERROR:", repr(e.message))
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pass
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elif expect_error:
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print("// MISMATCHED_ERROR:", repr(e.message))
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raise AssertionError("Expected error '{}' but got '{}'".format(
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expect_error, e.message))
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else:
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print("// UNEXPECTED_ERROR:", repr(e.message))
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raise e
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return decorator
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def create_test_config(target_factory: TargetFactory = GenericTarget64):
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value_coder = ValueCoderChain([
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BuiltinsValueCoder(),
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npc.CreateNumpyValueCoder(),
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])
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pe_hook = build_default_partial_eval_hook()
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# Populate numpy partial evaluators.
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npc.bind_ufuncs(npc.get_ufuncs_from_module(), pe_hook)
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if logging.debug_enabled:
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logging.debug("Partial eval mapping: {}", pe_hook)
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return Configuration(target_factory=target_factory,
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value_coder=value_coder,
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partial_eval_hook=pe_hook)
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def build_default_partial_eval_hook() -> PartialEvalHook:
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pe = MappedPartialEvalHook()
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### Modules
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pe.enable_getattr(for_type=ast.__class__) # The module we use is arbitrary.
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### Tuples
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# Enable attribute resolution on tuple, which includes namedtuple (which is
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# really what we want).
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pe.enable_getattr(for_type=tuple)
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### Temp: resolve a function to a template call for testing
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import math
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pe.enable_template_call("__global$math.ceil", for_ref=math.ceil)
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pe.enable_template_call("__global$math.isclose", for_ref=math.isclose)
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return pe
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