def test_nonempty_list_create_no_jit(self):
# See Issue #6001: https://github.com/numba/numba/issues/6001
with override_config('DISABLE_JIT', True):
with forbid_codegen():
l = List([1, 2, 3])
self.assertEqual(type(l), list)
self.assertEqual(l, [1, 2, 3])
def run_compile(self, fnlist, parallelism='threading'):
self._cache_dir = temp_directory(self.__class__.__name__)
with override_config('CACHE_DIR', self._cache_dir):
if parallelism == 'threading':
thread_impl(fnlist)
elif parallelism == 'multiprocessing_fork':
fork_proc_impl(fnlist)
elif parallelism == 'multiprocessing_forkserver':
forkserver_proc_impl(fnlist)
elif parallelism == 'multiprocessing_spawn':
spawn_proc_impl(fnlist)
elif parallelism == 'multiprocessing_default':
default_proc_impl(fnlist)
elif parallelism == 'random':
ps = [thread_impl, spawn_proc_impl]
if _HAVE_OS_FORK:
ps.append(fork_proc_impl)
ps.append(forkserver_proc_impl)
random.shuffle(ps)
for impl in ps:
impl(fnlist)
else:
raise ValueError(
'Unknown parallelism supplied %s' % parallelism)
def test_usage(self):
@njit
def foo(n):
c = 0
for i in range(n):
c += i
return c
with override_config('LLVM_PASS_TIMINGS', True):
foo(10)
md = foo.get_metadata(foo.signatures[0])
timings = md['llvm_pass_timings']
# Check: timing is of correct type
self.assertIsInstance(timings, lpt.PassTimingsCollection)
# Check: basic for __str__
text = str(timings)
self.assertIn("Module passes (full optimization)", text)
# Check: there must be more than one record
self.assertGreater(len(timings), 0)
# Check: __getitem__
last = timings[-1]
self.assertIsInstance(last, lpt.NamedTimings)
# Check: NamedTimings
self.assertIsInstance(last.name, str)
self.assertIsInstance(last.timings, lpt.ProcessedPassTimings)
def get_ir(extend_lifetimes):
class IRPreservingCompiler(CompilerBase):
def define_pipelines(self):
pm = DefaultPassBuilder.define_nopython_pipeline(
self.state)
pm.add_pass_after(PreserveIR, IRLegalization)
pm.finalize()
return [pm]
@njit(pipeline_class=IRPreservingCompiler)
def foo():
a = 10
b = 20
c = a + b
# a and b are now unused, standard behaviour is ir.Del for them here
d = c / c
return d
with override_config('EXTEND_VARIABLE_LIFETIMES',
extend_lifetimes):
foo()
cres = foo.overloads[foo.signatures[0]]
func_ir = cres.metadata['preserved_ir']
return func_ir
def test_analyze(self):
@njit
def foo(n):
c = 0
for i in range(n):
for j in range(i):
c += j
return c
with override_config('LLVM_PASS_TIMINGS', True):
foo(10)
md = foo.get_metadata(foo.signatures[0])
timings_collection = md['llvm_pass_timings']
# Check: get_total_time()
self.assertIsInstance(timings_collection.get_total_time(), float)
# Check: summary()
self.assertIsInstance(timings_collection.summary(), str)
# Check: list_longest_first() ordering
longest_first = timings_collection.list_longest_first()
self.assertEqual(len(longest_first), len(timings_collection))
last = longest_first[0].timings.get_total_time()
for rec in longest_first[1:]:
cur = rec.timings.get_total_time()
self.assertGreaterEqual(last, cur)
cur = last
def omitted_child_test_wrapper(result_queue, cache_dir, second_call):
with override_config("CACHE_DIR", cache_dir):
@njit(cache=True)
def test(num=1000):
return num
try:
output = test()
# If we have a second call, we should have a cache hit.
# Otherwise, we expect a cache miss.
if second_call:
assert test._cache_hits[test.signatures[0]] == 1, \
"Cache did not hit as expected"
assert test._cache_misses[test.signatures[0]] == 0, \
"Cache has an unexpected miss"
else:
assert test._cache_misses[test.signatures[0]] == 1, \
"Cache did not miss as expected"
assert test._cache_hits[test.signatures[0]] == 0, \
"Cache has an unexpected hit"
success = True
# Catch anything raised so it can be propagated
except: # noqa: E722
output = traceback.format_exc()
success = False
result_queue.put((success, output))
def test_jit_debug_simulator(self):
# Ensure that the jit decorator accepts the debug kwarg when the
# simulator is in use - see Issue #6615.
with override_config('ENABLE_CUDASIM', 1):
@cuda.jit(debug=True)
def f(x):
pass
def test_py_func_with_kwargs(self):
with override_config('DISABLE_JIT', True):
def method(x):
return x
jitted = jit(nopython=True)(method)
self.assertEqual(jitted.py_func, method)
def test_decorated_function_with_kwargs(self):
with override_config('DISABLE_JIT', True):
@jit(nopython=True)
def method(x):
return x
self.assertIsInstance(method, _DisableJitWrapper)
self.assertIsNotNone(method.py_func)
self.assertEqual(10, method(10))
def test_decorated_function_with_kwargs(self):
with override_config('DISABLE_JIT', True):
@jit(nopython=True)
def method(x):
return x
self.assertIsInstance(method, DisableJitWrapper)
self.assertIsNotNone(method.py_func)
self.assertEqual(10, method(10))
def test_decorated_function_with_kwargs(self):
with override_config('DISABLE_JIT', True):
def method(x):
return x
jitted = jit(nopython=True)(method)
self.assertEqual(jitted, method)
self.assertEqual(10, method(10))
self.assertEqual(10, jitted(10))
def test_decorated_function_with_kwargs(self):
with override_config('DISABLE_JIT', True):
def method(x):
return x
jitted = jit(nopython=True)(method)
self.assertEqual(jitted, method)
self.assertEqual(10, method(10))
self.assertEqual(10, jitted(10))
def test_jitclass(self):
with override_config('DISABLE_JIT', True):
with forbid_codegen():
SimpleJITClass = jitclass(simple_class_spec)(SimpleClass)
obj = SimpleJITClass()
self.assertPreciseEqual(obj.h, 5)
cfunc = jit(nopython=True)(simple_class_user)
self.assertPreciseEqual(cfunc(obj), 5)
def test_efficient_launch_configuration(self):
@cuda.jit
def kernel():
pass
with override_config('CUDA_LOW_OCCUPANCY_WARNINGS', 1):
with warnings.catch_warnings(record=True) as w:
kernel[256, 256]()
self.assertEqual(len(w), 0)
def test_llvm_inliner_flag_conflict(self):
# bar will be marked as 'alwaysinline', but when DEBUGINFO_DEFAULT is
# set functions are marked as 'noinline' this results in a conflict.
# baz will be marked as 'noinline' as a result of DEBUGINFO_DEFAULT
@njit(forceinline=True)
def bar(x):
return math.sin(x)
@njit(forceinline=False)
def baz(x):
return math.cos(x)
@njit
def foo(x):
a = bar(x)
b = baz(x)
return a, b
# check it compiles
with override_config('DEBUGINFO_DEFAULT', 1):
result = foo(np.pi)
self.assertPreciseEqual(result, foo.py_func(np.pi))
# check the LLVM IR has bar marked as 'alwaysinline' and baz as noinline
full_ir = foo.inspect_llvm(foo.signatures[0])
module = llvm.parse_assembly(full_ir)
name = foo.overloads[foo.signatures[0]].fndesc.mangled_name
funcs = [x for x in module.functions if x.name == name]
self.assertEqual(len(funcs), 1)
func = funcs[0]
# find the function calls and save the associated statements
f_names = []
for blk in func.blocks:
for stmt in blk.instructions:
if stmt.opcode == 'call':
# stmt.function.name This is the function being called
f_names.append(str(stmt).strip())
# Need to check there's two specific things in the calls in the IR
# 1. a call to the llvm.sin.f64 intrinsic, this is from the inlined bar
# 2. a call to the baz function, this is from the noinline baz
found_sin = False
found_baz = False
baz_name = baz.overloads[baz.signatures[0]].fndesc.mangled_name
for x in f_names:
if not found_sin and re.match('.*llvm.sin.f64.*', x):
found_sin = True
if not found_baz and re.match(f'.*{baz_name}.*', x):
found_baz = True
self.assertTrue(found_sin)
self.assertTrue(found_baz)
def test_decorated_function(self):
with override_config("DISABLE_JIT", True):
def method(x):
return x
jitted = jit(method)
self.assertEqual(jitted, method)
self.assertEqual(10, method(10))
self.assertEqual(10, jitted(10))
def run_caching_overload_method(q, cache_dir):
"""
Used by TestOverloadMethodCaching.test_caching_overload_method
"""
with override_config('CACHE_DIR', cache_dir):
arg = q.get()
cfunc = jit(nopython=True, cache=True)(cache_overload_method_usecase)
res = cfunc(arg)
q.put(res)
# Check cache stat
_assert_cache_stats(cfunc, 1, 0)
def test_inner_function(self):
with override_config('DUMP_ASSEMBLY', True):
with captured_stdout() as out:
cfunc = jit((types.int32,), nopython=True)(outer_simple)
self.assertPreciseEqual(cfunc(1), 4)
# Check the inner function was elided from the output (which also
# guarantees it was inlined into the outer function).
asm = out.getvalue()
prefix = __name__
self.assert_has_pattern('%s.outer_simple' % prefix, asm)
self.assert_not_has_pattern('%s.inner' % prefix, asm)
def test_dump_ir_generator(self):
with override_config('DUMP_IR', True):
out = self.compile_simple_gen()
self.check_debug_output(out, ['ir'])
self.assertIn('--GENERATOR INFO: %s' % self.func_name, out)
expected_gen_info = textwrap.dedent("""
generator state variables: ['x', 'y']
yield point #1: live variables = ['y'], weak live variables = ['x']
yield point #2: live variables = [], weak live variables = ['y']
""")
self.assertIn(expected_gen_info, out)
def test_inefficient_launch_configuration(self):
@cuda.jit
def kernel():
pass
with override_config('CUDA_LOW_OCCUPANCY_WARNINGS', 1):
with warnings.catch_warnings(record=True) as w:
kernel[1, 1]()
self.assertEqual(w[0].category, NumbaPerformanceWarning)
self.assertIn('Grid size', str(w[0].message))
self.assertIn('2 * SM count', str(w[0].message))
def test_bound_function_error_string(self):
# See PR #5952
def foo(x):
x.max(-1) # axis not supported
with override_config('DEVELOPER_MODE', 1):
with self.assertRaises(errors.TypingError) as raises:
njit("void(int64[:,:])")(foo)
excstr = str(raises.exception)
self.assertIn("AssertionError()", excstr)
self.assertIn("BoundFunction(array.max for array(int64, 2d, A))",
excstr)
def test_nowarn_on_device_array(self):
@cuda.jit
def foo(r, x):
r[0] = x + 1
N = 10
ary = cuda.device_array(N, dtype=np.float32)
with override_config('CUDA_WARN_ON_IMPLICIT_COPY', 1):
with warnings.catch_warnings(record=True) as w:
foo[1, N](ary, N)
self.assertEqual(len(w), 0)
def test_multiple_inner_functions(self):
# Same with multiple inner functions, and multiple calls to
# the same inner function (inner()). This checks that linking in
# the same library/module twice doesn't produce linker errors.
with override_config('DUMP_ASSEMBLY', True):
with captured_stdout() as out:
cfunc = jit((types.int32,), nopython=True)(outer_multiple)
self.assertPreciseEqual(cfunc(1), 6)
asm = out.getvalue()
prefix = __name__
self.assert_has_pattern('%s.outer_multiple' % prefix, asm)
self.assert_not_has_pattern('%s.more' % prefix, asm)
self.assert_not_has_pattern('%s.inner' % prefix, asm)
def test_devicearray_strict_strides(self):
# From the reproducer in Issue #6824.
with override_config('NPY_RELAXED_STRIDES_CHECKING', 0):
# Construct a device array that is not contiguous because
# the strides for the first axis (800) are not equal to
# the strides * size (10 * 8 = 80) for the previous axis.
arr = devicearray.DeviceNDArray((1, 10), (800, 8), np.float64)
# Ensure we don't believe the array to be contiguous becase strides
# checking is strict.
self.assertFalse(arr.flags['C_CONTIGUOUS'])
self.assertFalse(arr.flags['F_CONTIGUOUS'])
def test_c_f_contiguity_matches_numpy(self):
# From the reproducer in Issue #4943.
shapes = ((1, 4), (4, 1))
orders = ('C', 'F')
with override_config('NPY_RELAXED_STRIDES_CHECKING', 1):
for shape, order in itertools.product(shapes, orders):
arr = np.ndarray(shape, order=order)
d_arr = cuda.to_device(arr)
self.assertEqual(arr.flags['C_CONTIGUOUS'],
d_arr.flags['C_CONTIGUOUS'])
self.assertEqual(arr.flags['F_CONTIGUOUS'],
d_arr.flags['F_CONTIGUOUS'])
def test_environment_override(self):
with override_config('DEBUGINFO_DEFAULT', 1):
# Using default value
@jit(nopython=True)
def foo(x):
return x
self._check(foo, sig=(types.int32,), expect=True)
# User override default
@jit(nopython=True, debug=False)
def bar(x):
return x
self._check(bar, sig=(types.int32,), expect=False)
def run_compile(self, fnlist):
self._cache_dir = temp_directory(self.__class__.__name__)
with override_config('CACHE_DIR', self._cache_dir):
def chooser():
for _ in range(10):
fn = random.choice(fnlist)
fn()
ths = [threading.Thread(target=chooser) for i in range(4)]
for th in ths:
th.start()
for th in ths:
th.join()
def test_devicearray_relaxed_strides(self):
# From the reproducer in Issue #6824.
with override_config('NPY_RELAXED_STRIDES_CHECKING', 1):
# Construct a device array that is contiguous even though
# the strides for the first axis (800) are not equal to
# the strides * size (10 * 8 = 80) for the previous axis,
# because the first axis size is 1.
arr = devicearray.DeviceNDArray((1, 10), (800, 8), np.float64)
# Ensure we still believe the array to be contiguous because
# strides checking is relaxed.
self.assertTrue(arr.flags['C_CONTIGUOUS'])
self.assertTrue(arr.flags['F_CONTIGUOUS'])
def test_environment_override(self):
with override_config("CUDA_DEBUGINFO_DEFAULT", 1):
# Using default value
@cuda.jit
def foo(x):
x[0] = 1
self._check(foo, sig=(types.int32[:], ), expect=True)
# User override default value
@cuda.jit(debug=False)
def bar(x):
x[0] = 1
self._check(bar, sig=(types.int32[:], ), expect=False)
def test_warn_on_host_array(self):
@cuda.jit
def foo(r, x):
r[0] = x + 1
N = 10
arr_f32 = np.zeros(N, dtype=np.float32)
with override_config('CUDA_WARN_ON_IMPLICIT_COPY', 1):
with warnings.catch_warnings(record=True) as w:
foo[1, N](arr_f32, N)
self.assertEqual(w[0].category, NumbaPerformanceWarning)
self.assertIn('Host array used in CUDA kernel will incur',
str(w[0].message))
self.assertIn('copy overhead', str(w[0].message))
def test_environment_override(self):
with override_config('CUDA_DEBUGINFO_DEFAULT', 1):
# Using default value
@cuda.jit
def foo(x):
x[0] = 1
self._check(foo, sig=(types.int32[:],), expect=True)
# User override default value
@cuda.jit(debug=False)
def bar(x):
x[0] = 1
self._check(bar, sig=(types.int32[:],), expect=False)
def test_consume_sync_disabled(self):
# Create a foreign array with a stream
s = cuda.stream()
f_arr = ForeignArray(cuda.device_array(10, stream=s))
# Set sync to false before testing. The test suite should generally be
# run with sync enabled, but stash the old value just in case it is
# not.
with override_config('CUDA_ARRAY_INTERFACE_SYNC', False):
with patch.object(cuda.cudadrv.driver.Stream,
'synchronize',
return_value=None) as mock_sync:
cuda.as_cuda_array(f_arr)
# Ensure the synchronize method of a stream was not called
mock_sync.assert_not_called()
def test_omitted_arg(self):
# See issue 7726
@njit(debug=True)
def foo(missing=None):
pass
# check that it will actually compile (verifies DI emission is ok)
with override_config('DEBUGINFO_DEFAULT', 1):
foo()
metadata = self._get_metadata(foo, sig=(types.Omitted(None), ))
metadata_definition_map = self._get_metadata_map(metadata)
# Find DISubroutineType
tmp_disubr = []
for md in metadata:
if "DISubroutineType" in md:
tmp_disubr.append(md)
self.assertEqual(len(tmp_disubr), 1)
disubr = tmp_disubr.pop()
disubr_matched = re.match(r'.*!DISubroutineType\(types: ([!0-9]+)\)$',
disubr)
self.assertIsNotNone(disubr_matched)
disubr_groups = disubr_matched.groups()
self.assertEqual(len(disubr_groups), 1)
disubr_meta = disubr_groups[0]
# Find the types in the DISubroutineType arg list
disubr_types = metadata_definition_map[disubr_meta]
disubr_types_matched = re.match(r'!{(.*)}', disubr_types)
self.assertIsNotNone(disubr_matched)
disubr_types_groups = disubr_types_matched.groups()
self.assertEqual(len(disubr_types_groups), 1)
# fetch out and assert the last argument type, should be void *
md_fn_arg = [x.strip() for x in disubr_types_groups[0].split(',')][-1]
arg_ty = metadata_definition_map[md_fn_arg]
expected_arg_ty = (r'^.*!DICompositeType\(tag: DW_TAG_structure_type, '
r'name: "Anonymous struct \({}\)", elements: '
r'(![0-9]+), identifier: "{}"\)')
self.assertRegex(arg_ty, expected_arg_ty)
md_base_ty = re.match(expected_arg_ty, arg_ty).groups()[0]
base_ty = metadata_definition_map[md_base_ty]
# expect ir.LiteralStructType([])
self.assertEqual(base_ty, ('!{}'))
def test_vectorize(self):
def foo(x):
return x + math.sin(x)
fastfoo = vectorize(fastmath=True)(foo)
slowfoo = vectorize(foo)
x = np.random.random(8).astype(np.float32)
# capture the optimized llvm to check for fast flag
with override_config('DUMP_OPTIMIZED', True):
with captured_stdout() as slow_cap:
expect = slowfoo(x)
slowllvm = slow_cap.getvalue()
with captured_stdout() as fast_cap:
got = fastfoo(x)
fastllvm = fast_cap.getvalue()
np.testing.assert_almost_equal(expect, got)
self.assertIn('fadd fast', fastllvm)
self.assertIn('call fast', fastllvm)
self.assertNotIn('fadd fast', slowllvm)
self.assertNotIn('call fast', slowllvm)
def test_guvectorize(self):
def foo(x, out):
out[0] = x + math.sin(x)
x = np.random.random(8).astype(np.float32)
with override_config('DUMP_OPTIMIZED', True):
types = ['(float32, float32[:])']
sig = '()->()'
with captured_stdout() as fast_cap:
fastfoo = guvectorize(types, sig, fastmath=True)(foo)
fastllvm = fast_cap.getvalue()
with captured_stdout() as slow_cap:
slowfoo = guvectorize(types, sig)(foo)
slowllvm = slow_cap.getvalue()
expect = slowfoo(x)
got = fastfoo(x)
np.testing.assert_almost_equal(expect, got)
self.assertIn('fadd fast', fastllvm)
self.assertIn('call fast', fastllvm)
self.assertNotIn('fadd fast', slowllvm)
self.assertNotIn('call fast', slowllvm)
def test_dump_assembly(self):
with override_config('DUMP_ASSEMBLY', True):
out = self.compile_simple_cuda()
self.check_debug_output(out, ['assembly'])
def test_dump_llvm(self):
with override_config('DUMP_LLVM', True):
out = self.compile_simple_cuda()
self.check_debug_output(out, ['llvm'])
def test_dump_cfg(self):
with override_config('DUMP_CFG', True):
out = self.compile_simple_cuda()
self.check_debug_output(out, ['cfg'])
def test_dump_ir(self):
with override_config('DUMP_IR', True):
out = self.compile_simple_cuda()
self.check_debug_output(out, ['ir'])
def test_py_func_with_kwargs(self):
with override_config('DISABLE_JIT', True):
def method(x):
return x
jitted = jit(nopython=True)(method)
self.assertEqual(jitted.py_func, method)
def test_dump_bytecode(self):
with override_config('DUMP_BYTECODE', True):
out = self.compile_simple_cuda()
self.check_debug_output(out, ['bytecode'])
