def wrapper(func):
return numba.generated_jit(func,
nopython=nopython,
cache=cache,
parallel=parallel,
fastmath=fastmath,
error_model=error_model)
def test_generated_dtype(self):
f = generated_jit(nopython=True)(dtype_generated_usecase)
a = np.ones((10,), dtype=np.float32)
b = np.ones((10,), dtype=np.float64)
self.assertEqual(f(a, b).dtype, np.float64)
self.assertEqual(f(a, b, dtype=np.dtype('int32')).dtype, np.int32)
self.assertEqual(f(a, b, dtype=np.int32).dtype, np.int32)
def test_generated(self):
f = generated_jit(nopython=True)(generated_usecase)
self.assertEqual(f(8), 8 - 5)
self.assertEqual(f(x=8), 8 - 5)
self.assertEqual(f(x=8, y=4), 8 - 4)
self.assertEqual(f(1j), 5 + 1j)
self.assertEqual(f(1j, 42), 42 + 1j)
self.assertEqual(f(x=1j, y=7), 7 + 1j)
def test_generated(self):
f = generated_jit(nopython=True)(generated_usecase)
self.assertEqual(f(8), 8 - 5)
self.assertEqual(f(x=8), 8 - 5)
self.assertEqual(f(x=8, y=4), 8 - 4)
self.assertEqual(f(1j), 5 + 1j)
self.assertEqual(f(1j, 42), 42 + 1j)
self.assertEqual(f(x=1j, y=7), 7 + 1j)
def test_signature_errors(self):
"""
Check error reporting when implementation signature doesn't match
generating function signature.
"""
f = generated_jit(nopython=True)(bad_generated_usecase)
# Mismatching # of arguments
with self.assertRaises(TypeError) as raises:
f(1j)
self.assertIn("should be compatible with signature '(x, y=5)', but has signature '(x)'",
str(raises.exception))
# Mismatching defaults
with self.assertRaises(TypeError) as raises:
f(1)
self.assertIn("should be compatible with signature '(x, y=5)', but has signature '(x, y=6)'",
str(raises.exception))
""" File Name：jit_mode Description : numba jit mode 定义（常用模式） pytorch 中也自带 jit 编译 Email : [email protected] Date：18-2-1 """ from numba import float32, float64, generated_jit, int16, int32, int64, jit, vectorize, void # jit 模式的定义 jit_cpu = jit(nopython=True, parallel=True) jit_gpu = jit(nopython=True, parallel=True, target='cuda') # generated_jit 模式的定义 gjit_cpu = generated_jit(nopython=True, parallel=True) gjit_gpu = generated_jit(nopython=True, parallel=True, target='cuda') # ufunc example vec_cpu = vectorize([ void(int64, int64), int16(int16, int16), int32(int32, int32), int64(int64, int64), float32(float32, float32), float64(float64, float64) ]) vec_gpu = vectorize([ void(int64, int64), int16(int16, int16),
for s in range(radec.shape[0]):
l, m = lm[s]
n = np.sqrt(1.0 - l**2 - m**2)
radec[s, 1] = np.arcsin(m*cos_d0 + n*sin_d0)
radec[s, 0] = pc_ra + np.arctan(l / (n*cos_d0 - m*sin_d0))
return radec
return _lm_to_radec_impl
# inspect.getargspec doesn't work on a numba dispatcher object
# so rtd fails.
if not on_rtd():
jitter = numba.generated_jit(nopython=True, nogil=True, cache=True)
lmn_to_radec = jitter(lmn_to_radec)
lm_to_radec = jitter(lm_to_radec)
radec_to_lmn = jitter(radec_to_lmn)
radec_to_lm = jitter(radec_to_lm)
RADEC_TO_LMN_DOCS = DocstringTemplate(r"""
Converts Right-Ascension/Declination coordinates in radians
to a Direction Cosine lm coordinates, relative to the Phase Centre.
.. math::
:nowrap:
\begin{eqnarray}
& l =& \, \cos \, \delta \sin \, \Delta \alpha \\
def execute_user_code(self, user_args, user_kwargs, tracing):
# type: (list, dict, bool) -> (object, COMPSsException)
""" Executes the user code.
Disables the tracing hook if tracing is enabled. Restores it
at the end of the user code execution.
:param user_args: Function args.
:param user_kwargs: Function kwargs.
:param tracing: If tracing enabled.
:return: The user function returns and the compss exception (if any).
"""
# Tracing hook is disabled by default during the user code of the task.
# The user can enable it with tracing_hook=True in @task decorator for
# specific tasks or globally with the COMPSS_TRACING_HOOK=true
# environment variable.
restore_hook = False
pro_f = None
if tracing:
global_tracing_hook = False
if TRACING_HOOK_ENV_VAR in os.environ:
hook_enabled = os.environ[TRACING_HOOK_ENV_VAR] == "true"
global_tracing_hook = hook_enabled
if self.decorator_arguments['tracing_hook'] or global_tracing_hook:
# The user wants to keep the tracing hook
pass
else:
# When Extrae library implements the function to disable,
# use it, as:
# import pyextrae
# pro_f = pyextrae.shutdown()
# Since it is not available yet, we manage the tracing hook
# by ourselves
pro_f = sys.getprofile()
sys.setprofile(None)
restore_hook = True
user_returns = None
compss_exception = None
if self.decorator_arguments['numba']:
# Import all supported functionalities
from numba import jit
from numba import njit
from numba import generated_jit
from numba import vectorize
from numba import guvectorize
from numba import stencil
from numba import cfunc
numba_mode = self.decorator_arguments['numba']
numba_flags = self.decorator_arguments['numba_flags']
if type(numba_mode) is dict:
# Use the flags defined by the user
numba_flags['cache'] = True # Always force cache
user_returns = jit(self.user_function,
**numba_flags)(*user_args, **user_kwargs)
elif numba_mode is True or numba_mode == 'jit':
numba_flags['cache'] = True # Always force cache
user_returns = jit(self.user_function,
**numba_flags)(*user_args, **user_kwargs)
# Alternative way of calling:
# user_returns = jit(cache=True)(self.user_function) \
# (*user_args, **user_kwargs)
elif numba_mode == 'generated_jit':
user_returns = generated_jit(self.user_function,
**numba_flags)(*user_args,
**user_kwargs)
elif numba_mode == 'njit':
numba_flags['cache'] = True # Always force cache
user_returns = njit(self.user_function,
**numba_flags)(*user_args, **user_kwargs)
elif numba_mode == 'vectorize':
numba_signature = self.decorator_arguments['numba_signature'] # noqa: E501
user_returns = vectorize(
numba_signature,
**numba_flags
)(self.user_function)(*user_args, **user_kwargs)
elif numba_mode == 'guvectorize':
numba_signature = self.decorator_arguments['numba_signature'] # noqa: E501
numba_decl = self.decorator_arguments['numba_declaration']
user_returns = guvectorize(
numba_signature,
numba_decl,
**numba_flags
)(self.user_function)(*user_args, **user_kwargs)
elif numba_mode == 'stencil':
user_returns = stencil(
**numba_flags
)(self.user_function)(*user_args, **user_kwargs)
elif numba_mode == 'cfunc':
numba_signature = self.decorator_arguments['numba_signature'] # noqa: E501
user_returns = cfunc(
numba_signature
)(self.user_function).ctypes(*user_args, **user_kwargs)
else:
raise Exception("Unsupported numba mode.")
else:
try:
# Normal task execution
user_returns = self.user_function(*user_args, **user_kwargs)
except COMPSsException as ce:
compss_exception = ce
# Check old targetDirection
if 'targetDirection' in self.decorator_arguments:
target_label = 'targetDirection'
else:
target_label = 'target_direction'
compss_exception.target_direction = self.decorator_arguments[target_label] # noqa: E501
# Reestablish the hook if it was disabled
if restore_hook:
sys.setprofile(pro_f)
return user_returns, compss_exception
# Add base visibilities to the output, if any
add_coh_fn(base_vis, out)
# Apply direction independent effects, if any
apply_dies_fn(time_index, antenna1, antenna2, die1_jones, die2_jones,
tmin, out)
return out
return _predict_vis_fn
# inspect.getargspec doesn't work on a numba dispatcher object
# so rtd fails.
if not on_rtd():
predict_vis = generated_jit(nopython=True, nogil=True,
cache=True)(predict_vis)
PREDICT_DOCS = DocstringTemplate(r"""
Multiply Jones terms together to form model visibilities according
to the following formula:
.. math::
V_{pq} = G_{p} \left(
B_{pq} + \sum_{s} A_{ps} X_{pqs} A_{qs}^H
\right) G_{q}^H
where for antenna :math:`p` and :math:`q`, and source :math:`s`:
