def _assign(self, value):
stream = self.backend.stream
if isinstance(value, (int, float)):
# if we have a contiguous array, then use the speedy driver kernel
if self.is_contiguous:
value = self.dtype.type(value)
if self.dtype.itemsize == 1:
drv.memset_d8_async(self.gpudata,
unpack_from('B', value)[0], self.size,
stream)
elif self.dtype.itemsize == 2:
drv.memset_d16_async(self.gpudata,
unpack_from('H', value)[0], self.size,
stream)
else:
drv.memset_d32_async(self.gpudata,
unpack_from('I', value)[0], self.size,
stream)
# otherwise use our copy kerel
else:
OpTreeNode.build("assign", self, value)
elif isinstance(value, GPUTensor):
# TODO: add an is_binary_compat like function
if self.is_contiguous and value.is_contiguous and self.dtype == value.dtype:
drv.memcpy_dtod_async(self.gpudata, value.gpudata, self.nbytes,
stream)
else:
OpTreeNode.build("assign", self, value)
# collapse and execute an op tree as a kernel
elif isinstance(value, OpTreeNode):
OpTreeNode.build("assign", self, value)
# assign to numpy array (same as set())
elif isinstance(value, np.ndarray):
self.set(value, device=None)
else:
raise TypeError("Invalid type for assignment: %s" % type(value))
return self
def _assign(self, value):
stream = self.backend.stream
if isinstance(value, (int, float)):
# if we have a contiguous array, then use the speedy driver kernel
if self.is_contiguous:
value = self.dtype.type(value)
if self.dtype.itemsize == 1:
drv.memset_d8_async( self.gpudata,
unpack_from('B', value)[0],
self.size, stream)
elif self.dtype.itemsize == 2:
drv.memset_d16_async(self.gpudata,
unpack_from('H', value)[0],
self.size, stream)
else:
drv.memset_d32_async(self.gpudata,
unpack_from('I', value)[0],
self.size, stream)
# otherwise use our copy kerel
else:
OpTreeNode.build("assign", self, value)
elif isinstance(value, GPUTensor):
# TODO: add an is_binary_compat like function
if self.is_contiguous and value.is_contiguous and self.dtype == value.dtype:
drv.memcpy_dtod_async(self.gpudata, value.gpudata, self.nbytes, stream)
else:
OpTreeNode.build("assign", self, value)
# collapse and execute an op tree as a kernel
elif isinstance(value, OpTreeNode):
OpTreeNode.build("assign", self, value)
# assign to numpy array (same as set())
elif isinstance(value, np.ndarray):
self.set(value, device=None)
else:
raise TypeError("Invalid type for assignment: %s" % type(value))
return self
def execute(self, repeat=1, unbind=True):
shuffle_kernel = _get_transpose_kernel(self.dtype_str)
kernel = kernel_specs.get_kernel(self.kernel[0])
for r in range(repeat):
# let atomic adds accumulate on top
if not self.beta:
drv.memset_d8_async(*self.zero_args)
shuffle_kernel.prepared_async_call(*self.shuffle_args)
kernel.prepared_async_call(*self.kernel[1:])
if unbind:
self.zero_args = None
self.shuffle_args[2:5] = (None,) * 3
self.kernel[3:8] = (None,) * 5
def execute(self, repeat=1, unbind=True):
shuffle_kernel = _get_transpose_kernel(self.dtype_str)
kernel = kernel_specs.get_kernel(self.kernel[0])
for r in range(repeat):
# let atomic adds accumulate on top
if not self.beta:
drv.memset_d8_async(*self.zero_args)
shuffle_kernel.prepared_async_call(*self.shuffle_args)
kernel.prepared_async_call(*self.kernel[1:])
if unbind:
self.zero_args = None
self.shuffle_args[2:5] = (None, ) * 3
self.kernel[3:8] = (None, ) * 5