def build_transformer(name):
"""
:param results: the graph nodes that we care about, for the computation
:return: the dictionary of transformers, with names matching the graph node hints
"""
if 'numpy' in name:
transformer = make_transformer_factory('numpy')()
elif 'gpu' in name:
try:
from ngraph.transformers.gputransform import GPUTransformer # noqa
transformer = make_transformer_factory('gpu')()
except ImportError:
assert False, "Fatal: Unable to initialize GPU, " \
"but GPU transformer was requested."
else:
assert False, "Unknown device!"
return transformer
if self.globals.get('mkldnn', None) is not None:
self.globals.execute('mkldnn.close()')
if self.globals.get('mlsl_obj', None) is not None:
for device_buffer in self.device_buffers:
self.globals.execute(
"mlsl_obj.free({}.__array_interface__['data'][0])".
format(device_buffer.ref_str))
self.globals.execute('mlsl_obj.finalize()')
except TypeError:
pass
self.code = None
def consume(self, buf_index, hostlist, devlist):
'''
This is currently used for Aeon dataloading -- need to set things up to do actual
device buffer allocation
'''
assert 0 <= buf_index < 2, 'Can only double buffer'
hb = np.rollaxis(hostlist[buf_index], 0, hostlist[buf_index].ndim)
if devlist[buf_index] is None:
devlist[buf_index] = np.empty_like(hb)
devlist[buf_index][:] = hb
def make_computation(self, computation):
return CPUDeviceComputation(self, computation)
set_transformer_factory(
make_transformer_factory(CPUTransformer.transformer_name))
# print(self.code.filename)
r = self.code.compile("op", globals())
self.model = r['Model']()
self.model.conv_params = self.compute_code.conv_params
self.model.pool_params = self.compute_code.pool_params
self.model.conv_slices = self.compute_code.conv_slices
self.model.pool_slices = self.compute_code.pool_slices
for computation in self.computations:
executor = getattr(self.model, computation.name)
computation.executor = executor
def allocate_storage(self):
self.model.allocate()
def consume(self, buf_index, hostlist, devlist):
'''
This is currently used for Aeon dataloading -- need to set things up to do actual
device buffer allocation
'''
assert 0 <= buf_index < 2, 'Can only double buffer'
hb = np.rollaxis(hostlist[buf_index], 0, hostlist[buf_index].ndim)
if devlist[buf_index] is None:
devlist[buf_index] = np.empty_like(hb)
devlist[buf_index][:] = hb
set_transformer_factory(
make_transformer_factory(NumPyTransformer.transformer_name))
