def __init__(self,
torch,
local_worker: BaseWorker = None,
is_client: bool = True,
verbose: bool = True):
"""Initializes the hook.
Initialize the hook and define all the attributes pertaining to the
torch hook in a special TorchAttibute class, that will be added in the
syft.torch attributes. Hence, this parameters are now conveyed by the
syft module.
"""
# Save the provided torch module as an attribute of the hook
self.torch = torch
self.framework = self.torch
# Save the local worker as an attribute
self.local_worker = local_worker
if hasattr(torch, "torch_hooked"):
logging.warning(
"Torch was already hooked... skipping hooking process")
self.local_worker = syft.local_worker
return
else:
torch.torch_hooked = True
# Add all the torch attributes in the syft.torch attr
syft.torch = TorchAttributes(torch, self)
syft.framework = syft.torch
self.trace = Trace()
# Hook some torch methods such that tensors could be created directy at workers
self._hook_worker_methods()
if self.local_worker is None:
# Every TorchHook instance should have a local worker which is
# responsible for interfacing with other workers. The worker
# interface is what allows the Torch specific code in TorchHook to
# be agnostic to the means by which workers communicate (such as
# peer-to-peer, sockets, through local ports, or all within the
# same process)
self.local_worker = VirtualWorker(hook=self,
is_client_worker=is_client,
id="me")
else:
self.local_worker.hook = self
self.to_auto_overload = {}
self.args_hook_for_overloaded_attr = {}
self._hook_native_tensor(torch.Tensor, TorchTensor)
# Add all hooked tensor methods to pointer but change behaviour to have the cmd sent
self._hook_pointer_tensor_methods(self.torch.Tensor)
# Add all hooked tensor methods to AdditiveSharingTensor tensor but change behaviour
# to all shares (when it makes sense, otherwise the method is overwritten in the
# AdditiveSharingTensor class)
self._hook_additive_shared_tensor_methods()
# Add all hooked tensor methods to multi_pointer to change behavior to have the cmd
# sent to all child pointers.
self._hook_multi_pointer_tensor_methods(self.torch.Tensor)
# Add all hooked tensor methods to Logging tensor but change behaviour to just forward
# the cmd to the next child (behaviour can be changed in the SyftTensor class file)
self._hook_syft_tensor_methods(LoggingTensor)
# Add all hooked tensor methods to Paillier tensor but change behaviour to just forward
# the cmd to the next child (behaviour can be changed in the SyftTensor class file)
self._hook_syft_tensor_methods(PaillierTensor)
# Add all hooked tensor methods to FixedPrecisionTensor tensor but change behaviour
# to just forward the cmd to the next child (behaviour can be changed in the
# SyftTensor class file)
self._hook_syft_tensor_methods(FixedPrecisionTensor)
# Add all hooked tensor methods to AutogradTensor tensor but change behaviour
# to just forward the cmd to the next child (behaviour can be changed in the
# SyftTensor class file)
self._hook_syft_tensor_methods(AutogradTensor)
# Add all hooked tensor methods to PrivateTensor tensor but change behaviour
# to just forward the cmd to the next child (behaviour can be changed in the
# SyftTensor class file)
self._hook_private_tensor_methods(PrivateTensor)
# Add all hooked tensor methods to PlaceHolder tensor but change behaviour
# to just forward the cmd to the next child (behaviour can be changed in the
# SyftTensor class file)
self._hook_syft_placeholder_methods(self.torch.Tensor, PlaceHolder)
# Add all hooked tensor methods to AdditiveSharingTensor tensor but change behaviour
# to just forward the cmd to the next child (behaviour can be changed in the
# SyftTensor class file)
self._hook_syft_tensor_methods(AdditiveSharingTensor)
# Add all hooked tensor methods to LargePrecisionTensor tensor
self._hook_syft_tensor_methods(LargePrecisionTensor)
# Add all hooked tensor methods to NumpyTensor tensor
self._hook_syft_tensor_methods(HookedTensor)
# Add all built-in 'str' methods to String
self._hook_string_methods(owner=self.local_worker)
# Add all string methods to StringPointer
# This method call should strictly come after the
# call to self._hook_string_methods()
self._hook_string_pointer_methods()
# Hook the tensor constructor function
self._hook_tensor()
# Hook the Parameter methods to store tensor chains in parameters
self._hook_parameters()
# Hook torch functions from modules like torch.add OR torch.nn.functional (containing relu, etc.)
self._hook_torch_module()
# Hook torch.nn (containing Linear and Convolution layers)
self._hook_module()
# Hook torch.optim (containing optim.SGD, Adam, etc)
self._hook_optim()
# Add the local_worker to syft so that it can be found if the hook is
# called several times
syft.local_worker = self.local_worker
syft.hook = self
def __init__(
self,
torch,
local_worker: BaseWorker = None,
is_client: bool = True,
verbose: bool = False,
seed=None,
):
"""
Initializes the hook.
Initialize the hook and define all the attributes pertaining to the
torch hook in a special TorchAttibute class, that will be added in the
syft.torch attributes. Hence, this parameters are now conveyed by the
syft module.
"""
# Save the provided torch module as an attribute of the hook
self.torch = torch
self.framework = self.torch
if seed is not None:
syft.ID_PROVIDER.seed(seed)
self.verbose = verbose
# Save the local worker as an attribute
self.local_worker = local_worker
if hasattr(torch, "torch_hooked"):
logging.warning("Torch was already hooked... skipping hooking process")
self.local_worker = syft.local_worker
return
else:
torch.torch_hooked = True
# Add all the torch attributes in the syft.torch attr
syft.torch = TorchAttributes(torch, self)
syft.framework = syft.torch
"""
In Syft there is a syft.framework value that can contain only one framework.
Ideally it should contain a list of supported frameworks.
We do this because in Plans there is method to reduce the number of actions
that are traced (and then sent).
The actions that are not returning a result, changing a placeholder, inplace
or changing the global state are eliminated from the traced list
"""
if dependency_check.crypten_available:
import crypten
from syft.frameworks.crypten.crypten_attributes import CryptenAttributes
syft.crypten = CryptenAttributes(crypten, self)
# Hook some torch methods such that tensors could be created directy at workers
self._hook_worker_methods()
if self.local_worker is None:
# Every TorchHook instance should have a local worker which is
# responsible for interfacing with other workers. The worker
# interface is what allows the Torch specific code in TorchHook to
# be agnostic to the means by which workers communicate (such as
# peer-to-peer, sockets, through local ports, or all within the
# same process)
self.local_worker = VirtualWorker(
hook=self, is_client_worker=is_client, id="me", verbose=verbose
)
else:
self.local_worker.hook = self
self._syft_workers = {self.local_worker}
self.to_auto_overload = {}
self.args_hook_for_overloaded_attr = {}
self._hook_native_tensor(torch.Tensor, TorchTensor)
if dependency_check.crypten_available:
from syft.frameworks.crypten.hook.hook import crypten_to_auto_overload
for crypten_class, method_names in crypten_to_auto_overload.items():
self.to_auto_overload[crypten_class] = method_names
self._hook_syft_placeholder_methods(crypten_class, PlaceHolder)
# Add all hooked tensor methods to pointer but change behaviour to have the cmd sent
self._hook_pointer_tensor_methods(self.torch.Tensor)
# Add all hooked tensor methods to AdditiveSharingTensor tensor but change behaviour
# to all shares (when it makes sense, otherwise the method is overwritten in the
# AdditiveSharingTensor class)
self._hook_additive_shared_tensor_methods()
# Add all hooked tensor methods to multi_pointer to change behavior to have the cmd
# sent to all child pointers.
self._hook_multi_pointer_tensor_methods(self.torch.Tensor)
# Add all hooked tensor methods to Logging tensor but change behaviour to just forward
# the cmd to the next child (behaviour can be changed in the SyftTensor class file)
self._hook_syft_tensor_methods(LoggingTensor)
# Add all hooked tensor methods to Paillier tensor but change behaviour to just forward
# the cmd to the next child (behaviour can be changed in the SyftTensor class file)
self._hook_syft_tensor_methods(PaillierTensor)
# Add all hooked tensor methods to FixedPrecisionTensor tensor but change behaviour
# to just forward the cmd to the next child (behaviour can be changed in the
# SyftTensor class file)
self._hook_syft_tensor_methods(FixedPrecisionTensor)
# Add all hooked tensor methods to AutogradTensor tensor but change behaviour
# to just forward the cmd to the next child (behaviour can be changed in the
# SyftTensor class file)
self._hook_syft_tensor_methods(AutogradTensor)
# Add all hooked tensor methods to PrivateTensor tensor but change behaviour
# to just forward the cmd to the next child (behaviour can be changed in the
# SyftTensor class file)
self._hook_private_tensor_methods(PrivateTensor)
# Add all hooked tensor methods to PlaceHolder tensor but change behaviour
# to just forward the cmd to the next child (behaviour can be changed in the
# SyftTensor class file)
self._hook_syft_placeholder_methods(self.torch.Tensor, PlaceHolder)
# Add all hooked tensor methods to AdditiveSharingTensor tensor but change behaviour
# to just forward the cmd to the next child (behaviour can be changed in the
# SyftTensor class file)
self._hook_syft_tensor_methods(AdditiveSharingTensor)
# Add all hooked tensor methods to NumpyTensor tensor
self._hook_syft_tensor_methods(HookedTensor)
# Add all built-in 'str' methods to String
self._hook_string_methods(owner=self.local_worker)
# Add all string methods to StringPointer
# This method call should strictly come after the
# call to self._hook_string_methods()
self._hook_string_pointer_methods()
# Hook the tensor constructor function
self._hook_tensor()
# Hook the Parameter methods to store tensor chains in parameters
self._hook_parameters()
# Hook torch functions from modules like torch.add OR
# torch.nn.functional (containing relu, etc.)
self._hook_torch_module()
# Hook torch.nn (containing Linear and Convolution layers)
self._hook_module()
# Hook torch.optim (containing optim.SGD, Adam, etc)
self._hook_optim()
# Hook the Crypten module
if dependency_check.crypten_available:
from syft.frameworks.crypten.hook.hook import hook_crypten, hook_crypten_module
hook_crypten()
hook_crypten_module()
# Add the local_worker to syft so that it can be found if the hook is
# called several times
syft.local_worker = self.local_worker
syft.hook = self