def compile_cuda(pyfunc, return_type, args, debug=False, inline=False):
# First compilation will trigger the initialization of the CUDA backend.
from .descriptor import CUDATargetDesc
typingctx = CUDATargetDesc.typingctx
targetctx = CUDATargetDesc.targetctx
# TODO handle debug flag
flags = compiler.Flags()
# Do not compile (generate native code), just lower (to LLVM)
flags.set('no_compile')
flags.set('no_cpython_wrapper')
flags.set('no_cfunc_wrapper')
if debug:
flags.set('debuginfo')
if inline:
flags.set('forceinline')
# Run compilation pipeline
cres = compiler.compile_extra(typingctx=typingctx,
targetctx=targetctx,
func=pyfunc,
args=args,
return_type=return_type,
flags=flags,
locals={})
library = cres.library
library.finalize()
return cres
def compile_hsa(pyfunc, return_type, args, debug):
# First compilation will trigger the initialization of the HSA backend.
from .descriptor import HSATargetDesc
typingctx = HSATargetDesc.typingctx
targetctx = HSATargetDesc.targetctx
# TODO handle debug flag
flags = compiler.Flags()
# Do not compile (generate native code), just lower (to LLVM)
flags.set('no_compile')
flags.set('no_cpython_wrapper')
flags.set('no_cfunc_wrapper')
flags.unset('nrt')
# Run compilation pipeline
cres = compiler.compile_extra(typingctx=typingctx,
targetctx=targetctx,
func=pyfunc,
args=args,
return_type=return_type,
flags=flags,
locals={})
# Linking depending libraries
# targetctx.link_dependencies(cres.llvm_module, cres.target_context.linking)
library = cres.library
library.finalize()
return cres
def compile_cuda(pyfunc, return_type, args, debug=False, inline=False,
fastmath=False, nvvm_options=None):
from .descriptor import cuda_target
typingctx = cuda_target.typing_context
targetctx = cuda_target.target_context
flags = CUDAFlags()
# Do not compile (generate native code), just lower (to LLVM)
flags.no_compile = True
flags.no_cpython_wrapper = True
flags.no_cfunc_wrapper = True
if debug:
flags.debuginfo = True
if inline:
flags.forceinline = True
if fastmath:
flags.fastmath = True
if nvvm_options:
flags.nvvm_options = nvvm_options
# Run compilation pipeline
cres = compiler.compile_extra(typingctx=typingctx,
targetctx=targetctx,
func=pyfunc,
args=args,
return_type=return_type,
flags=flags,
locals={},
pipeline_class=CUDACompiler)
library = cres.library
library.finalize()
return cres
def compile_cuda(pyfunc,
return_type,
args,
debug=False,
inline=False,
fastmath=False):
from .descriptor import cuda_target
typingctx = cuda_target.typingctx
targetctx = cuda_target.targetctx
flags = compiler.Flags()
# Do not compile (generate native code), just lower (to LLVM)
flags.set('no_compile')
flags.set('no_cpython_wrapper')
flags.set('no_cfunc_wrapper')
if debug:
flags.set('debuginfo')
if inline:
flags.set('forceinline')
if fastmath:
flags.set('fastmath')
# Run compilation pipeline
cres = compiler.compile_extra(typingctx=typingctx,
targetctx=targetctx,
func=pyfunc,
args=args,
return_type=return_type,
flags=flags,
locals={},
pipeline_class=CUDACompiler)
library = cres.library
library.finalize()
return cres
def compile(self, func, args, return_type=None, flags=DEFAULT_FLAGS):
"""
Compile the function or retrieve an already compiled result
from the cache.
"""
from numba.core.registry import cpu_target
cache_key = (func, args, return_type, flags)
try:
cr = self.cr_cache[cache_key]
except KeyError:
# Register the contexts in case for nested @jit or @overload calls
# (same as compile_isolated())
with cpu_target.nested_context(self.typingctx, self.targetctx):
cr = compile_extra(
self.typingctx,
self.targetctx,
func,
args,
return_type,
flags,
locals={},
)
self.cr_cache[cache_key] = cr
return cr
def test_no_copy_usm_shared(capfd):
a = usmarray.ones(10, dtype=np.int64)
b = np.ones(10, dtype=np.int64)
# f = njit(fn)
flags = compiler.Flags()
flags.no_compile = True
flags.no_cpython_wrapper = True
flags.nrt = False
flags.auto_parallel = cpu.ParallelOptions(True)
typingctx = cpu_target.typing_context
targetctx = cpu_target.target_context
args = typingctx.resolve_argument_type(a)
try:
device = dpctl.SyclDevice("opencl:gpu:0")
except ValueError:
pytest.skip("Device not found")
with dppy.offload_to_sycl_device(device):
cres = compiler.compile_extra(
typingctx=typingctx,
targetctx=targetctx,
func=fn,
args=tuple([args]),
return_type=args,
flags=flags,
locals={},
pipeline_class=DPPYCompiler,
)
assert "DPCTLQueue_Memcpy" not in cres.library.get_llvm_str()
args = typingctx.resolve_argument_type(b)
cres = compiler.compile_extra(
typingctx=typingctx,
targetctx=targetctx,
func=fn,
args=tuple([args]),
return_type=args,
flags=flags,
locals={},
pipeline_class=DPPYCompiler,
)
assert "DPCTLQueue_Memcpy" in cres.library.get_llvm_str()
def compile_isolated(pyfunc, argtypes, **kwargs):
from numba.core.registry import cpu_target
kwargs.setdefault('return_type', None)
kwargs.setdefault('locals', {})
return compile_extra(
cpu_target.typing_context,
cpu_target.target_context,
pyfunc,
argtypes,
**kwargs,
)
def test_scalar(self):
flags = Flags()
# Compile the inner function
global cnd_jitted
cr1 = compile_isolated(cnd, (types.float64, ))
cnd_jitted = cr1.entry_point
# Manually type the compiled function for calling into
tyctx = cr1.typing_context
ctx = cr1.target_context
signature = typing.make_concrete_template("cnd_jitted", cnd_jitted,
[cr1.signature])
tyctx.insert_user_function(cnd_jitted, signature)
# Compile the outer function
array = types.Array(types.float64, 1, "C")
argtys = (array, ) * 5 + (types.float64, types.float64)
cr2 = compile_extra(
tyctx,
ctx,
blackscholes_scalar_jitted,
args=argtys,
return_type=None,
flags=flags,
locals={},
)
jitted_bs = cr2.entry_point
OPT_N = 400
iterations = 10
callResultGold = np.zeros(OPT_N)
putResultGold = np.zeros(OPT_N)
callResultNumba = np.zeros(OPT_N)
putResultNumba = np.zeros(OPT_N)
stockPrice = randfloat(self.random.random_sample(OPT_N), 5.0, 30.0)
optionStrike = randfloat(self.random.random_sample(OPT_N), 1.0, 100.0)
optionYears = randfloat(self.random.random_sample(OPT_N), 0.25, 10.0)
args = stockPrice, optionStrike, optionYears, RISKFREE, VOLATILITY
blackscholes_scalar(callResultGold, putResultGold, *args)
jitted_bs(callResultNumba, putResultNumba, *args)
delta = np.abs(callResultGold - callResultNumba)
L1norm = delta.sum() / np.abs(callResultGold).sum()
print("L1 norm: %E" % L1norm)
print("Max absolute error: %E" % delta.max())
self.assertAlmostEqual(delta.max(), 0)
def try_lift(self, pyfunc, argtypes):
from numba.core.registry import cpu_target
cres = compile_extra(
cpu_target.typing_context,
cpu_target.target_context,
pyfunc,
argtypes,
return_type=None,
flags=looplift_flags,
locals={},
)
# One lifted loop
self.assertEqual(len(cres.lifted), 1)
return cres
def _compile_core(self, args, return_type):
flags = compiler.Flags()
self.targetdescr.options.parse_as_flags(flags, self.targetoptions)
flags = self._customize_flags(flags)
impl = self._get_implementation(args, {})
cres = compiler.compile_extra(self.targetdescr.typing_context,
self.targetdescr.target_context,
impl,
args=args, return_type=return_type,
flags=flags, locals=self.locals,
pipeline_class=self.pipeline_class)
# Check typing error if object mode is used
if cres.typing_error is not None and not flags.enable_pyobject:
raise cres.typing_error
return cres
def compile_cuda(pyfunc,
return_type,
args,
debug=False,
lineinfo=False,
inline=False,
fastmath=False,
nvvm_options=None):
from .descriptor import cuda_target
typingctx = cuda_target.typing_context
targetctx = cuda_target.target_context
flags = CUDAFlags()
# Do not compile (generate native code), just lower (to LLVM)
flags.no_compile = True
flags.no_cpython_wrapper = True
flags.no_cfunc_wrapper = True
if debug or lineinfo:
# Note both debug and lineinfo turn on debug information in the
# compiled code, but we keep them separate arguments in case we
# later want to overload some other behavior on the debug flag.
# In particular, -opt=3 is not supported with -g.
flags.debuginfo = True
flags.error_model = 'python'
else:
flags.error_model = 'numpy'
if inline:
flags.forceinline = True
if fastmath:
flags.fastmath = True
if nvvm_options:
flags.nvvm_options = nvvm_options
# Run compilation pipeline
cres = compiler.compile_extra(typingctx=typingctx,
targetctx=targetctx,
func=pyfunc,
args=args,
return_type=return_type,
flags=flags,
locals={},
pipeline_class=CUDACompiler)
library = cres.library
library.finalize()
return cres
def _compile_core(self, sig, flags, locals):
"""
Trigger the compiler on the core function or load a previously
compiled version from the cache. Returns the CompileResult.
"""
typingctx = self.targetdescr.typing_context
targetctx = self.targetdescr.target_context
@contextmanager
def store_overloads_on_success():
# use to ensure overloads are stored on success
try:
yield
except Exception:
raise
else:
exists = self.overloads.get(cres.signature)
if exists is None:
self.overloads[cres.signature] = cres
# Use cache and compiler in a critical section
with global_compiler_lock:
with store_overloads_on_success():
# attempt look up of existing
cres = self.cache.load_overload(sig, targetctx)
if cres is not None:
return cres
# Compile
args, return_type = sigutils.normalize_signature(sig)
cres = compiler.compile_extra(
typingctx,
targetctx,
self.py_func,
args=args,
return_type=return_type,
flags=flags,
locals=locals,
)
# cache lookup failed before so safe to save
self.cache.save_overload(sig, cres)
return cres
def _cull_exports(self):
"""Read all the exported functions/modules in the translator
environment, and join them into a single LLVM module.
"""
self.exported_function_types = {}
self.function_environments = {}
self.environment_gvs = {}
codegen = self.context.codegen()
library = codegen.create_library(self.module_name)
# Generate IR for all exported functions
flags = Flags()
flags.set("no_compile")
if not self.export_python_wrap:
flags.set("no_cpython_wrapper")
flags.set("no_cfunc_wrapper")
if self.use_nrt:
flags.set("nrt")
# Compile NRT helpers
nrt_module, _ = nrtdynmod.create_nrt_module(self.context)
library.add_ir_module(nrt_module)
for entry in self.export_entries:
cres = compile_extra(self.typing_context,
self.context,
entry.function,
entry.signature.args,
entry.signature.return_type,
flags,
locals={},
library=library)
func_name = cres.fndesc.llvm_func_name
llvm_func = cres.library.get_function(func_name)
if self.export_python_wrap:
llvm_func.linkage = lc.LINKAGE_INTERNAL
wrappername = cres.fndesc.llvm_cpython_wrapper_name
wrapper = cres.library.get_function(wrappername)
wrapper.name = self._mangle_method_symbol(entry.symbol)
wrapper.linkage = lc.LINKAGE_EXTERNAL
fnty = cres.target_context.call_conv.get_function_type(
cres.fndesc.restype, cres.fndesc.argtypes)
self.exported_function_types[entry] = fnty
self.function_environments[entry] = cres.environment
self.environment_gvs[entry] = cres.fndesc.env_name
else:
llvm_func.name = entry.symbol
self.dll_exports.append(entry.symbol)
if self.export_python_wrap:
wrapper_module = library.create_ir_module("wrapper")
self._emit_python_wrapper(wrapper_module)
library.add_ir_module(wrapper_module)
# Hide all functions in the DLL except those explicitly exported
library.finalize()
for fn in library.get_defined_functions():
if fn.name not in self.dll_exports:
if fn.linkage in {Linkage.private, Linkage.internal}:
# Private/Internal linkage must have "default" visibility
fn.visibility = "default"
else:
fn.visibility = 'hidden'
return library
def compile_with_dppy(pyfunc, return_type, args, is_kernel, debug=None):
"""
Compiles with Numba_dppy's pipeline and returns the compiled result.
Args:
pyfunc: The Python function to be compiled.
return_type: The Numba type of the return value.
args: The list of arguments sent to the Python function.
is_kernel (bool): Indicates whether the function is decorated
with @dppy.kernel or not.
debug (bool): Flag to turn debug mode ON/OFF.
Returns:
cres: Compiled result.
Raises:
TypeError: @dppy.kernel does not allow users to return any
value. TypeError is raised when users do.
"""
# First compilation will trigger the initialization of the OpenCL backend.
from .descriptor import dppy_target
typingctx = dppy_target.typing_context
targetctx = dppy_target.target_context
flags = compiler.Flags()
# Do not compile (generate native code), just lower (to LLVM)
flags.debuginfo = config.DEBUGINFO_DEFAULT
flags.no_compile = True
flags.no_cpython_wrapper = True
flags.nrt = False
if debug is not None:
flags.debuginfo = debug
# Run compilation pipeline
if isinstance(pyfunc, FunctionType):
cres = compiler.compile_extra(
typingctx=typingctx,
targetctx=targetctx,
func=pyfunc,
args=args,
return_type=return_type,
flags=flags,
locals={},
pipeline_class=DPPYCompiler,
)
elif isinstance(pyfunc, ir.FunctionIR):
cres = compiler.compile_ir(
typingctx=typingctx,
targetctx=targetctx,
func_ir=pyfunc,
args=args,
return_type=return_type,
flags=flags,
locals={},
pipeline_class=DPPYCompiler,
)
else:
assert 0
if is_kernel:
assert_no_return(cres.signature.return_type)
# Linking depending libraries
library = cres.library
library.finalize()
return cres
def compile_instance(func,
sig,
target: TargetInfo,
typing_context,
target_context,
pipeline_class,
main_library,
debug=False):
"""Compile a function with given signature. Return function name when
succesful.
"""
flags = compiler.Flags()
if get_version('numba') >= (0, 54):
flags.no_compile = True
flags.no_cpython_wrapper = True
flags.no_cfunc_wrapper = True
else:
flags.set('no_compile')
flags.set('no_cpython_wrapper')
flags.set('no_cfunc_wrapper')
fname = func.__name__ + sig.mangling()
args, return_type = sigutils.normalize_signature(
sig.tonumba(bool_is_int8=True))
try:
cres = compiler.compile_extra(typingctx=typing_context,
targetctx=target_context,
func=func,
args=args,
return_type=return_type,
flags=flags,
library=main_library,
locals={},
pipeline_class=pipeline_class)
except (UnsupportedError, nb_errors.TypingError,
nb_errors.LoweringError) as msg:
for m in re.finditer(r'UnsupportedError(.*?)\n', str(msg), re.S):
warnings.warn(f'Skipping {fname}:{m.group(0)[18:]}')
break
else:
raise
return
except Exception:
raise
result = get_called_functions(cres.library, cres.fndesc.llvm_func_name)
for f in result['declarations']:
if target.supports(f):
continue
warnings.warn(f'Skipping {fname} that uses undefined function `{f}`')
return
nvvmlib = libfuncs.Library.get('nvvm')
llvmlib = libfuncs.Library.get('llvm')
for f in result['intrinsics']:
if target.is_gpu:
if f in nvvmlib:
continue
if target.is_cpu:
if f in llvmlib:
continue
warnings.warn(
f'Skipping {fname} that uses unsupported intrinsic `{f}`')
return
make_wrapper(fname, args, return_type, cres, target, verbose=debug)
main_module = main_library._final_module
for lib in result['libraries']:
main_module.link_in(
lib._get_module_for_linking(),
preserve=True,
)
return fname
def compile_to_LLVM(functions_and_signatures,
target: TargetInfo,
pipeline_class=compiler.Compiler,
debug=False):
"""Compile functions with given signatures to target specific LLVM IR.
Parameters
----------
functions_and_signatures : list
Specify a list of Python function and its signatures pairs.
target : TargetInfo
Specify target device information.
debug : bool
Returns
-------
module : llvmlite.binding.ModuleRef
LLVM module instance. To get the IR string, use `str(module)`.
"""
target_desc = registry.cpu_target
if target is None:
target = TargetInfo.host()
typing_context = target_desc.typing_context
target_context = target_desc.target_context
else:
typing_context = typing.Context()
target_context = RemoteCPUContext(typing_context, target)
# Bring over Array overloads (a hack):
target_context._defns = target_desc.target_context._defns
typing_context.target_info = target
target_context.target_info = target
codegen = target_context.codegen()
main_library = codegen.create_library('rbc.irtools.compile_to_IR')
main_module = main_library._final_module
flags = compiler.Flags()
flags.set('no_compile')
flags.set('no_cpython_wrapper')
function_names = []
for func, signatures in functions_and_signatures:
for sig in signatures:
fname = func.__name__ + sig.mangling
function_names.append(fname)
args, return_type = sigutils.normalize_signature(
sig.tonumba(bool_is_int8=True))
cres = compiler.compile_extra(typingctx=typing_context,
targetctx=target_context,
func=func,
args=args,
return_type=return_type,
flags=flags,
library=main_library,
locals={},
pipeline_class=pipeline_class)
make_wrapper(fname, args, return_type, cres)
seen = set()
for _library in main_library._linking_libraries:
if _library not in seen:
seen.add(_library)
main_module.link_in(
_library._get_module_for_linking(),
preserve=True,
)
main_library._optimize_final_module()
# Catch undefined functions:
used_functions = set(function_names)
for fname in function_names:
deps = get_function_dependencies(main_module, fname)
for fn, descr in deps.items():
used_functions.add(fn)
if descr == 'undefined':
if fn.startswith('numba_') and target.has_numba:
continue
if fn.startswith('Py') and target.has_cpython:
continue
raise RuntimeError('function `%s` is undefined' % (fn))
# for global_variable in main_module.global_variables:
# global_variable.linkage = llvm.Linkage.private
unused_functions = [
f.name for f in main_module.functions if f.name not in used_functions
]
if debug:
print('compile_to_IR: the following functions are used')
for fname in used_functions:
lf = main_module.get_function(fname)
print(' [ALIVE]', fname, 'with', lf.linkage)
if unused_functions:
if debug:
print('compile_to_IR: the following functions are not used'
' and will be removed:')
for fname in unused_functions:
lf = main_module.get_function(fname)
if lf.is_declaration:
# if the function is a declaration,
# we just put the linkage as external
lf.linkage = llvm.Linkage.external
else:
# but if the function is not a declaration,
# we change the linkage to private
lf.linkage = llvm.Linkage.private
if debug:
print(' [DEAD]', fname, 'with', lf.linkage)
main_library._optimize_final_module()
# TODO: determine unused global_variables and struct_types
main_module.verify()
main_library._finalized = True
main_module.triple = target.triple
main_module.data_layout = target.datalayout
return main_module
