def run_pyskip(self, num_threads=1):
kernel = pyskip.Tensor.from_numpy(self._numpy_kernel())
with num_threads_scope(num_threads):
set_value("accelerated_eval", True) # TODO: check me
set_value("flush_tree_size_threshold", 2**30)
t = Timer()
with t:
_ = pyskip.convolve.conv_3d(self.megatensor, kernel).eval()
return t.duration_ms
def run_pyskip(self, num_threads=1):
operand = pyskip.Tensor.from_numpy(self.mnist_np_array)
kernels = [pyskip.Tensor.from_numpy(e) for e in self._numpy_kernels()]
with num_threads_scope(num_threads):
set_value("accelerated_eval", False) # TODO: check me
set_value("flush_tree_size_threshold", 2**30)
t = Timer()
with t:
for kernel in kernels:
_ = pyskip.convolve.conv_2d(operand, kernel).eval()
return t.duration_ms
def run_pyskip(self, num_threads=1, use_custom_kernel=False):
inputs = self._pyskip_inputs
gc.collect()
with num_threads_scope(num_threads):
set_value("accelerated_eval", False) # TODO: check me
set_value("flush_tree_size_threshold", 2**30)
if use_custom_kernel:
set_value("custom_eval_kernel", "add_int32")
t = Timer()
with t:
_ = functools.reduce(lambda x, y: x + y, inputs).eval()
return t.duration_ms
def run_pyskip(self,
num_threads=1,
use_custom_kernel=False,
use_accelerated=False):
inputs = [_pyskip_cpp_ext.from_numpy(t) for t in self._numpy_inputs]
gc.collect()
with num_threads_scope(self.override_pyskip_threads or num_threads):
set_value("accelerated_eval", use_accelerated)
set_value("flush_tree_size_threshold", 2**30)
if use_custom_kernel:
set_value("custom_eval_kernel", "add_int32")
t = Timer()
with t:
_ = functools.reduce(lambda x, y: x + y, inputs).eval()
return t.duration_ms
def run_pyskip_accelerated(self):
with config_scope():
set_value("accelerated_eval", True)
return self._run_pyskip()
def run_pyskip_slow(self):
with config_scope():
set_value("accelerated_eval", False)
return self._run_pyskip()