262 lines
9.1 KiB
Python
262 lines
9.1 KiB
Python
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"""Tests for gdb interacting with the DWARF numba generates"""
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from numba.tests.support import TestCase, linux_only
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from numba.tests.gdb_support import needs_gdb, skip_unless_pexpect, GdbMIDriver
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from unittest.mock import patch, Mock
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from numba.core import datamodel
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import numpy as np
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from numba import typeof
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import ctypes as ct
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import unittest
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@linux_only
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@needs_gdb
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@skip_unless_pexpect
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class TestGDBDwarf(TestCase):
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# This runs the tests in numba.tests.gdb, each submodule must contain one
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# test class called "Test" and it must contain one test called "test".
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# Variation is provided by the module name. The reason this convention exits
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# is because gdb tests tend to be line number sensitive (breakpoints etc
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# care about this) and doing this prevents constant churn and permits the
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# reuse of the existing subprocess_test_runner harness.
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_NUMBA_OPT_0_ENV = {'NUMBA_OPT': '0'}
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def _gdb_has_numpy(self):
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"""Returns True if gdb has NumPy support, False otherwise"""
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driver = GdbMIDriver(__file__, debug=False,)
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has_numpy = driver.supports_numpy()
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driver.quit()
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return has_numpy
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def _subprocess_test_runner(self, test_mod):
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themod = f'numba.tests.gdb.{test_mod}'
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self.subprocess_test_runner(test_module=themod,
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test_class='Test',
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test_name='test',
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envvars=self._NUMBA_OPT_0_ENV)
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def test_basic(self):
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self._subprocess_test_runner('test_basic')
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def test_array_arg(self):
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self._subprocess_test_runner('test_array_arg')
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def test_conditional_breakpoint(self):
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self._subprocess_test_runner('test_conditional_breakpoint')
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def test_break_on_symbol(self):
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self._subprocess_test_runner('test_break_on_symbol')
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def test_break_on_symbol_version(self):
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self._subprocess_test_runner('test_break_on_symbol_version')
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def test_pretty_print(self):
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if not self._gdb_has_numpy():
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_msg = "Cannot find gdb with NumPy support"
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self.skipTest(_msg)
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self._subprocess_test_runner('test_pretty_print')
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class TestGDBPrettyPrinterLogic(TestCase):
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# Tests the logic in numba.misc.gdb_print_extension.NumbaArrayPrinter
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# it's quite involved and susceptible to changes to the string
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# representation of Numba array and dtypes as it parses these
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# representations and recreates NumPy array/dtypes based on them!
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def setUp(self):
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# Patch sys.modules with mock gdb modules such that the
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# numba.misc.gdb_print_extension can import ok, the rest of the gdb
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# classes etc are implemented later
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mock_modules = {'gdb': Mock(),
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'gdb.printing': Mock()}
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self.patched_sys = patch.dict('sys.modules', mock_modules)
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self.patched_sys.start()
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# Now sys.modules has a gdb in it, patch the gdb.selected_inferior.
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# This function should return a process wrapping object that has a
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# read_memory method that can read a memory region from a given address
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# in the process' address space.
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import gdb
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class SelectedInferior():
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def read_memory(self, data, extent):
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buf = (ct.c_char * extent).from_address(data)
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return buf.raw # this is bytes
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si = SelectedInferior()
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gdb.configure_mock(**{'selected_inferior': lambda :si})
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def tearDown(self):
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# drop the sys.modules patch
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self.patched_sys.stop()
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def get_gdb_repr(self, array):
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# Returns the gdb repr of an array as reconstructed via the
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# gdb_print_extension (should be the same as NumPy!).
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# This is the module being tested, it uses gdb and gdb.printing, both
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# of which are mocked in self.setUp()
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from numba.misc import gdb_print_extension
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# The following classes are ducks for the gdb classes (which are not
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# easily/guaranteed importable from the test suite). They implement the
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# absolute bare minimum necessary to test the gdb_print_extension.
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class DISubrange():
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def __init__(self, lo, hi):
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self._lo = lo
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self._hi = hi
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@property
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def type(self):
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return self
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def range(self):
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return self._lo, self._hi
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class DW_TAG_array_type():
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def __init__(self, lo, hi):
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self._lo, self._hi = lo, hi
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def fields(self):
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return [DISubrange(self._lo, self._hi),]
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class DIDerivedType_tuple():
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def __init__(self, the_tuple):
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self._type = DW_TAG_array_type(0, len(the_tuple) - 1)
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self._tuple = the_tuple
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@property
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def type(self):
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return self._type
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def __getitem__(self, item):
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return self._tuple[item]
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class DICompositeType_Array():
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def __init__(self, arr, type_str):
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self._arr = arr
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self._type_str = type_str
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def __getitem__(self, item):
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return getattr(self, item)
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@property
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def data(self):
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return self._arr.ctypes.data
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@property
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def itemsize(self):
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return self._arr.itemsize
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@property
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def shape(self):
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return DIDerivedType_tuple(self._arr.shape)
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@property
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def strides(self):
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return DIDerivedType_tuple(self._arr.strides)
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@property
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def type(self):
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return self._type_str
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# The type string encoded into the DWARF is the string repr of the Numba
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# type followed by the LLVM repr of the data model in brackets.
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dmm = datamodel.default_manager
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array_model = datamodel.models.ArrayModel(dmm, typeof(array))
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data_type = array_model.get_data_type()
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type_str = f"{typeof(array)} ({data_type.structure_repr()})"
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fake_gdb_arr = DICompositeType_Array(array, type_str)
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printer = gdb_print_extension.NumbaArrayPrinter(fake_gdb_arr)
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return printer.to_string().strip() # strip, there's new lines
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def check(self, array):
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gdb_printed = self.get_gdb_repr(array)
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self.assertEqual(str(gdb_printed), str(array))
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def test_np_array_printer_simple_numeric_types(self):
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# Tests printer over a selection of basic types
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n = 4
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m = 3
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for dt in (np.int8, np.uint16, np.int64, np.float32, np.complex128):
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arr = np.arange(m * n, dtype=dt).reshape(m, n)
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self.check(arr)
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def test_np_array_printer_simple_numeric_types_strided(self):
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# Tests printer over randomized strided arrays
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n_tests = 30
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np.random.seed(0)
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for _ in range(n_tests):
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shape = np.random.randint(1, high=12, size=np.random.randint(1, 5))
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tmp = np.arange(np.prod(shape)).reshape(shape)
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slices = []
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for x in shape:
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start = np.random.randint(0, x)
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# x + 3 is to ensure that sometimes the stop is beyond the
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# end of the size in a given dimension
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stop = np.random.randint(start + 1, max(start + 1, x + 3))
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step = np.random.randint(1, 3) # step as 1, 2
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strd = slice(start, stop, step)
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slices.append(strd)
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arr = tmp[tuple(slices)]
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self.check(arr)
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def test_np_array_printer_simple_structured_dtype(self):
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# Tests printer over a selection of basic types
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n = 4
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m = 3
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aligned = np.dtype([("x", np.int16), ("y", np.float64)], align=True)
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unaligned = np.dtype([("x", np.int16), ("y", np.float64)], align=False)
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for dt in (aligned, unaligned):
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arr = np.empty(m * n, dtype=dt).reshape(m, n)
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arr['x'] = np.arange(m * n, dtype=dt['x']).reshape(m, n)
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arr['y'] = 100 * np.arange(m * n, dtype=dt['y']).reshape(m, n)
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self.check(arr)
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def test_np_array_printer_chr_array(self):
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# Test unichr array
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arr = np.array(['abcde'])
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self.check(arr)
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def test_np_array_printer_unichr_structured_dtype(self):
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# Not supported yet
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n = 4
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m = 3
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dt = np.dtype([("x", '<U5'), ("y", np.float64)], align=True)
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arr = np.zeros(m * n, dtype=dt).reshape(m, n)
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rep = self.get_gdb_repr(arr)
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self.assertIn("array[Exception:", rep)
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self.assertIn("Unsupported sub-type", rep)
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self.assertIn("[unichr x 5]", rep)
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def test_np_array_printer_nested_array_structured_dtype(self):
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# Not supported yet
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n = 4
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m = 3
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dt = np.dtype([("x", np.int16, (2,)), ("y", np.float64)], align=True)
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arr = np.zeros(m * n, dtype=dt).reshape(m, n)
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rep = self.get_gdb_repr(arr)
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self.assertIn("array[Exception:", rep)
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self.assertIn("Unsupported sub-type", rep)
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self.assertIn("nestedarray(int16", rep)
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if __name__ == '__main__':
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unittest.main()
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