111 lines
2.6 KiB
Python
111 lines
2.6 KiB
Python
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'''
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Contains CUDA API functions
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'''
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# Imports here bring together parts of the API from other modules, so some of
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# them appear unused.
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from contextlib import contextmanager
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from .cudadrv.devices import require_context, reset, gpus # noqa: F401
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from .kernel import FakeCUDAKernel
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from numba.core.sigutils import is_signature
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from warnings import warn
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from ..args import In, Out, InOut # noqa: F401
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def select_device(dev=0):
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assert dev == 0, 'Only a single device supported by the simulator'
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def is_float16_supported():
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return True
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class stream(object):
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'''
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The stream API is supported in the simulator - however, all execution
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occurs synchronously, so synchronization requires no operation.
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'''
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@contextmanager
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def auto_synchronize(self):
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yield
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def synchronize(self):
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pass
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def synchronize():
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pass
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def close():
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gpus.closed = True
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def declare_device(*args, **kwargs):
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pass
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def detect():
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print('Found 1 CUDA devices')
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print('id %d %20s %40s' % (0, 'SIMULATOR', '[SUPPORTED]'))
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print('%40s: 5.0' % 'compute capability')
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def list_devices():
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return gpus
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# Events
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class Event(object):
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'''
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The simulator supports the event API, but they do not record timing info,
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and all simulation is synchronous. Execution time is not recorded.
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'''
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def record(self, stream=0):
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pass
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def wait(self, stream=0):
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pass
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def synchronize(self):
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pass
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def elapsed_time(self, event):
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warn('Simulator timings are bogus')
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return 0.0
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event = Event
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def jit(func_or_sig=None, device=False, debug=False, argtypes=None,
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inline=False, restype=None, fastmath=False, link=None,
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boundscheck=None, opt=True, cache=None
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):
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# Here for API compatibility
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if boundscheck:
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raise NotImplementedError("bounds checking is not supported for CUDA")
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if link is not None:
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raise NotImplementedError('Cannot link PTX in the simulator')
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# Check for first argument specifying types - in that case the
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# decorator is not being passed a function
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if (func_or_sig is None or is_signature(func_or_sig)
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or isinstance(func_or_sig, list)):
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def jitwrapper(fn):
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return FakeCUDAKernel(fn,
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device=device,
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fastmath=fastmath,
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debug=debug)
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return jitwrapper
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return FakeCUDAKernel(func_or_sig, device=device, debug=debug)
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@contextmanager
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def defer_cleanup():
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# No effect for simulator
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yield
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