def is_arch_supported(arch, use_gles=False):
"""Checks whether an arch is supported on the machine.
Args:
arch (taichi_core.Arch): Specified arch.
use_gles (bool): If True, check is GLES is available otherwise
check if GLSL is available. Only effective when `arch` is `ti.opengl`.
Default is `False`.
Returns:
bool: Whether `arch` is supported on the machine.
"""
arch_table = {
cuda: _ti_core.with_cuda,
metal: _ti_core.with_metal,
opengl: functools.partial(_ti_core.with_opengl, use_gles),
cc: _ti_core.with_cc,
vulkan: _ti_core.with_vulkan,
dx11: _ti_core.with_dx11,
wasm: lambda: True,
cpu: lambda: True,
}
with_arch = arch_table.get(arch, lambda: False)
try:
return with_arch()
except Exception as e:
arch = _ti_core.arch_name(arch)
_ti_core.warn(
f"{e.__class__.__name__}: '{e}' occurred when detecting "
f"{arch}, consider adding `TI_ENABLE_{arch.upper()}=0` "
f" to environment variables to suppress this warning message.")
return False
def _check_not_turned_on_with_warning_message(self):
if self._profiling_mode is False:
_ti_core.warn(
'use \'ti.init(kernel_profiler = True)\' to turn on KernelProfiler.'
)
return True
return False
def get_predefined_cupti_metrics(name=''):
if name not in predefined_cupti_metrics:
_ti_core.warn("Valid Taichi predefined metrics list (str):")
for key in predefined_cupti_metrics:
_ti_core.warn(f" '{key}'")
return None
return predefined_cupti_metrics[name]
def set_toolkit(self, toolkit_name='default'):
if self._check_not_turned_on_with_warning_message():
return False
status = impl.get_runtime().prog.set_kernel_profiler_toolkit(
toolkit_name)
if status is True:
self._profiling_toolkit = toolkit_name
else:
_ti_core.warn(
f'Failed to set kernel profiler toolkit ({toolkit_name}) , keep using ({self._profiling_toolkit}).'
)
return status
def get_predefined_cupti_metrics(name=''):
"""Returns the specified cupti metric.
Accepted arguments are 'global_access', 'shared_access', 'atomic_access',
'cache_hit_rate', 'device_utilization'.
Args:
name (str): cupti metri name.
"""
if name not in predefined_cupti_metrics:
_ti_core.warn("Valid Taichi predefined metrics list (str):")
for key in predefined_cupti_metrics:
_ti_core.warn(f" '{key}'")
return None
return predefined_cupti_metrics[name]
def add(self, key, _cast=None):
_cast = _cast or self.bool_int
self.keys.append(key)
# TI_OFFLINE_CACHE= : no effect
# TI_OFFLINE_CACHE=0 : False
# TI_OFFLINE_CACHE=1 : True
name = 'TI_' + key.upper()
value = os.environ.get(name, '')
if key in self.kwargs:
self[key] = self.kwargs[key]
if value:
_ti_core.warn(
f'Environment variable {name}={value} overridden by ti.init argument "{key}"'
)
del self.kwargs[key] # mark as recognized
elif value:
self[key] = _cast(value)
def init(arch=None,
default_fp=None,
default_ip=None,
_test_mode=False,
enable_fallback=True,
require_version=None,
**kwargs):
"""Initializes the Taichi runtime.
This should always be the entry point of your Taichi program. Most
importantly, it sets the backend used throughout the program.
Args:
arch: Backend to use. This is usually :const:`~taichi.lang.cpu` or :const:`~taichi.lang.gpu`.
default_fp (Optional[type]): Default floating-point type.
default_ip (Optional[type]): Default integral type.
require_version (Optional[string]): A version string.
**kwargs: Taichi provides highly customizable compilation through
``kwargs``, which allows for fine grained control of Taichi compiler
behavior. Below we list some of the most frequently used ones. For a
complete list, please check out
https://github.com/taichi-dev/taichi/blob/master/taichi/program/compile_config.h.
* ``cpu_max_num_threads`` (int): Sets the number of threads used by the CPU thread pool.
* ``debug`` (bool): Enables the debug mode, under which Taichi does a few more things like boundary checks.
* ``print_ir`` (bool): Prints the CHI IR of the Taichi kernels.
* ``packed`` (bool): Enables the packed memory layout. See https://docs.taichi-lang.org/docs/layout.
"""
# Check version for users every 7 days if not disabled by users.
_version_check.start_version_check_thread()
# FIXME(https://github.com/taichi-dev/taichi/issues/4811): save the current working directory since it may be
# changed by the Vulkan backend initialization on OS X.
current_dir = os.getcwd()
cfg = impl.default_cfg()
# Check if installed version meets the requirements.
if require_version is not None:
check_require_version(require_version)
# Make a deepcopy in case these args reference to items from ti.cfg, which are
# actually references. If no copy is made and the args are indeed references,
# ti.reset() could override the args to their default values.
default_fp = _deepcopy(default_fp)
default_ip = _deepcopy(default_ip)
kwargs = _deepcopy(kwargs)
reset()
spec_cfg = _SpecialConfig()
env_comp = _EnvironmentConfigurator(kwargs, cfg)
env_spec = _EnvironmentConfigurator(kwargs, spec_cfg)
# configure default_fp/ip:
# TODO: move these stuff to _SpecialConfig too:
env_default_fp = os.environ.get("TI_DEFAULT_FP")
if env_default_fp:
if default_fp is not None:
_ti_core.warn(
f'ti.init argument "default_fp" overridden by environment variable TI_DEFAULT_FP={env_default_fp}'
)
if env_default_fp == '32':
default_fp = f32
elif env_default_fp == '64':
default_fp = f64
elif env_default_fp is not None:
raise ValueError(
f'Invalid TI_DEFAULT_FP={env_default_fp}, should be 32 or 64')
env_default_ip = os.environ.get("TI_DEFAULT_IP")
if env_default_ip:
if default_ip is not None:
_ti_core.warn(
f'ti.init argument "default_ip" overridden by environment variable TI_DEFAULT_IP={env_default_ip}'
)
if env_default_ip == '32':
default_ip = i32
elif env_default_ip == '64':
default_ip = i64
elif env_default_ip is not None:
raise ValueError(
f'Invalid TI_DEFAULT_IP={env_default_ip}, should be 32 or 64')
if default_fp is not None:
impl.get_runtime().set_default_fp(default_fp)
if default_ip is not None:
impl.get_runtime().set_default_ip(default_ip)
# submodule configurations (spec_cfg):
env_spec.add('log_level', str)
env_spec.add('gdb_trigger')
env_spec.add('short_circuit_operators')
# compiler configurations (ti.cfg):
for key in dir(cfg):
if key in ['arch', 'default_fp', 'default_ip']:
continue
_cast = type(getattr(cfg, key))
if _cast is bool:
_cast = None
env_comp.add(key, _cast)
unexpected_keys = kwargs.keys()
if len(unexpected_keys):
raise KeyError(
f'Unrecognized keyword argument(s) for ti.init: {", ".join(unexpected_keys)}'
)
# dispatch configurations that are not in ti.cfg:
if not _test_mode:
_ti_core.set_core_trigger_gdb_when_crash(spec_cfg.gdb_trigger)
impl.get_runtime().short_circuit_operators = \
spec_cfg.short_circuit_operators
_logging.set_logging_level(spec_cfg.log_level.lower())
# select arch (backend):
env_arch = os.environ.get('TI_ARCH')
if env_arch is not None:
_logging.info(f'Following TI_ARCH setting up for arch={env_arch}')
arch = _ti_core.arch_from_name(env_arch)
cfg.arch = adaptive_arch_select(arch, enable_fallback, cfg.use_gles)
if cfg.arch == cc:
_ti_core.set_tmp_dir(locale_encode(prepare_sandbox()))
print(f'[Taichi] Starting on arch={_ti_core.arch_name(cfg.arch)}')
# user selected visible device
visible_device = os.environ.get("TI_VISIBLE_DEVICE")
if visible_device and (cfg.arch == vulkan or _ti_core.GGUI_AVAILABLE):
_ti_core.set_vulkan_visible_device(visible_device)
if _test_mode:
return spec_cfg
get_default_kernel_profiler().set_kernel_profiler_mode(cfg.kernel_profiler)
# create a new program:
impl.get_runtime().create_program()
_logging.trace('Materializing runtime...')
impl.get_runtime().prog.materialize_runtime()
impl._root_fb = _snode.FieldsBuilder()
if not os.environ.get("TI_DISABLE_SIGNAL_HANDLERS", False):
impl.get_runtime()._register_signal_handlers()
# Recover the current working directory (https://github.com/taichi-dev/taichi/issues/4811)
os.chdir(current_dir)
return None
def init(arch=None,
default_fp=None,
default_ip=None,
_test_mode=False,
enable_fallback=True,
**kwargs):
"""Initializes the Taichi runtime.
This should always be the entry point of your Taichi program. Most
importantly, it sets the backend used throughout the program.
Args:
arch: Backend to use. This is usually :const:`~taichi.lang.cpu` or :const:`~taichi.lang.gpu`.
default_fp (Optional[type]): Default floating-point type.
default_ip (Optional[type]): Default integral type.
**kwargs: Taichi provides highly customizable compilation through
``kwargs``, which allows for fine grained control of Taichi compiler
behavior. Below we list some of the most frequently used ones. For a
complete list, please check out
https://github.com/taichi-dev/taichi/blob/master/taichi/program/compile_config.h.
* ``cpu_max_num_threads`` (int): Sets the number of threads used by the CPU thread pool.
* ``debug`` (bool): Enables the debug mode, under which Taichi does a few more things like boundary checks.
* ``print_ir`` (bool): Prints the CHI IR of the Taichi kernels.
* ``packed`` (bool): Enables the packed memory layout. See https://docs.taichi.graphics/lang/articles/advanced/layout.
"""
# Check version for users every 7 days if not disabled by users.
skip = os.environ.get("TI_SKIP_VERSION_CHECK")
if skip != 'ON':
try_check_version()
# Make a deepcopy in case these args reference to items from ti.cfg, which are
# actually references. If no copy is made and the args are indeed references,
# ti.reset() could override the args to their default values.
default_fp = _deepcopy(default_fp)
default_ip = _deepcopy(default_ip)
kwargs = _deepcopy(kwargs)
ti.reset()
spec_cfg = _SpecialConfig()
env_comp = _EnvironmentConfigurator(kwargs, ti.cfg)
env_spec = _EnvironmentConfigurator(kwargs, spec_cfg)
# configure default_fp/ip:
# TODO: move these stuff to _SpecialConfig too:
env_default_fp = os.environ.get("TI_DEFAULT_FP")
if env_default_fp:
if default_fp is not None:
_ti_core.warn(
f'ti.init argument "default_fp" overridden by environment variable TI_DEFAULT_FP={env_default_fp}'
)
if env_default_fp == '32':
default_fp = ti.f32
elif env_default_fp == '64':
default_fp = ti.f64
elif env_default_fp is not None:
raise ValueError(
f'Invalid TI_DEFAULT_FP={env_default_fp}, should be 32 or 64')
env_default_ip = os.environ.get("TI_DEFAULT_IP")
if env_default_ip:
if default_ip is not None:
_ti_core.warn(
f'ti.init argument "default_ip" overridden by environment variable TI_DEFAULT_IP={env_default_ip}'
)
if env_default_ip == '32':
default_ip = ti.i32
elif env_default_ip == '64':
default_ip = ti.i64
elif env_default_ip is not None:
raise ValueError(
f'Invalid TI_DEFAULT_IP={env_default_ip}, should be 32 or 64')
if default_fp is not None:
impl.get_runtime().set_default_fp(default_fp)
if default_ip is not None:
impl.get_runtime().set_default_ip(default_ip)
# submodule configurations (spec_cfg):
env_spec.add('print_preprocessed')
env_spec.add('log_level', str)
env_spec.add('gdb_trigger')
env_spec.add('excepthook')
env_spec.add('experimental_real_function')
env_spec.add('short_circuit_operators')
# compiler configurations (ti.cfg):
for key in dir(ti.cfg):
if key in ['arch', 'default_fp', 'default_ip']:
continue
_cast = type(getattr(ti.cfg, key))
if _cast is bool:
_cast = None
env_comp.add(key, _cast)
unexpected_keys = kwargs.keys()
if len(unexpected_keys):
raise KeyError(
f'Unrecognized keyword argument(s) for ti.init: {", ".join(unexpected_keys)}'
)
# dispatch configurations that are not in ti.cfg:
if not _test_mode:
ti.set_gdb_trigger(spec_cfg.gdb_trigger)
impl.get_runtime().print_preprocessed = spec_cfg.print_preprocessed
impl.get_runtime().experimental_real_function = \
spec_cfg.experimental_real_function
impl.get_runtime().short_circuit_operators = \
spec_cfg.short_circuit_operators
ti.set_logging_level(spec_cfg.log_level.lower())
if spec_cfg.excepthook:
# TODO(#1405): add a way to restore old excepthook
ti.enable_excepthook()
# select arch (backend):
env_arch = os.environ.get('TI_ARCH')
if env_arch is not None:
ti.info(f'Following TI_ARCH setting up for arch={env_arch}')
arch = _ti_core.arch_from_name(env_arch)
ti.cfg.arch = adaptive_arch_select(arch, enable_fallback, ti.cfg.use_gles)
if ti.cfg.arch == cc:
_ti_core.set_tmp_dir(locale_encode(prepare_sandbox()))
print(f'[Taichi] Starting on arch={_ti_core.arch_name(ti.cfg.arch)}')
# Torch based ndarray on opengl backend allocates memory on host instead of opengl backend.
# So it won't work.
if ti.cfg.arch == opengl and ti.cfg.ndarray_use_torch:
ti.warn(
'Opengl backend doesn\'t support torch based ndarray. Setting ndarray_use_torch to False.'
)
ti.cfg.ndarray_use_torch = False
if _test_mode:
return spec_cfg
get_default_kernel_profiler().set_kernel_profiler_mode(
ti.cfg.kernel_profiler)
# create a new program:
impl.get_runtime().create_program()
ti.trace('Materializing runtime...')
impl.get_runtime().prog.materialize_runtime()
impl._root_fb = FieldsBuilder()
if not os.environ.get("TI_DISABLE_SIGNAL_HANDLERS", False):
impl.get_runtime()._register_signal_handlers()
return None
