Issue with Encrypted Aggregation #3001
Description
Tutorial Part 10, when I call
# iterate through each parameter
for param_i in range(len(params[0])):
# for each worker
spdz_params = list()
for remote_index in range(len(compute_nodes)):
# select the identical parameter from each worker and copy it
copy_of_parameter = params[remote_index][param_i].copy()
# since SMPC can only work with integers (not floats), we need
# to use Integers to store decimal information. In other words,
# we need to use "Fixed Precision" encoding.
fixed_precision_param = copy_of_parameter.fix_precision()
# now we encrypt it on the remote machine. Note that
# fixed_precision_param is ALREADY a pointer. Thus, when
# we call share, it actually encrypts the data that the
# data is pointing TO. This returns a POINTER to the
# MPC secret shared object, which we need to fetch.
encrypted_param = fixed_precision_param.share(bob, alice, crypto_provider=james)
# now we fetch the pointer to the MPC shared value
param = encrypted_param.get()
# save the parameter so we can average it with the same parameter
# from the other workers
spdz_params.append(param)
# average params from multiple workers, fetch them to the local machine
# decrypt and decode (from fixed precision) back into a floating point number
new_param = (spdz_params[0] + spdz_params[1]).get().float_precision()/2
# save the new averaged parameter
new_params.append(new_param)
I get this error:
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
~/anaconda3/lib/python3.7/site-packages/syft-0.2.2a1-py3.7.egg/syft/generic/frameworks/hook/hook_args.py in register_response(attr, response, response_ids, owner)
662 # Load the utility function to register the response and transform tensors with pointers
--> 663 register_response_function = register_response_functions[attr_id]
664 # Try running it
KeyError: 'numpy'
During handling of the above exception, another exception occurred:
AttributeError Traceback (most recent call last)
<ipython-input-15-34467fe3bdcd> in <module>
12 # to use Integers to store decimal information. In other words,
13 # we need to use "Fixed Precision" encoding.
---> 14 fixed_precision_param = copy_of_parameter.fix_precision()
15
16 # now we encrypt it on the remote machine. Note that
~/anaconda3/lib/python3.7/site-packages/syft-0.2.2a1-py3.7.egg/syft/frameworks/torch/tensors/interpreters/native.py in fix_prec(self, storage, field_type, no_wrap, *args, **kwargs)
765
766 max_precision = _get_maximum_precision()
--> 767 need_large_prec = self._requires_large_precision(max_precision, base, prec_fractional)
768
769 if storage == "crt":
~/anaconda3/lib/python3.7/site-packages/syft-0.2.2a1-py3.7.egg/syft/frameworks/torch/tensors/interpreters/native.py in _requires_large_precision(self, max_precision, base, precision_fractional)
837 # We need to use NumPy here as log2 is not yet implemented for LongTensor PyTorch objects
838 return np.any(
--> 839 np.log2(np.abs(self.clone().detach().numpy()) + 1) + base_fractional > max_precision
840 )
841
~/anaconda3/lib/python3.7/site-packages/syft-0.2.2a1-py3.7.egg/syft/generic/frameworks/hook/trace.py in trace_wrapper(*args, **kwargs)
81 syft.hook.trace.logs.append((command, response))
82 else:
---> 83 response = func(*args, **kwargs)
84
85 return response
~/anaconda3/lib/python3.7/site-packages/syft-0.2.2a1-py3.7.egg/syft/generic/frameworks/hook/hook.py in overloaded_native_method(self, *args, **kwargs)
475 # Send the new command to the appropriate class and get the response
476 method = getattr(new_self, method_name)
--> 477 response = method(*new_args, **new_kwargs)
478
479 # For inplace methods, just directly return self
~/anaconda3/lib/python3.7/site-packages/syft-0.2.2a1-py3.7.egg/syft/generic/frameworks/hook/hook.py in overloaded_pointer_method(self, *args, **kwargs)
638 command = (attr, self, args, kwargs)
639
--> 640 response = owner.send_command(location, command)
641
642 # For inplace methods, just directly return self
~/anaconda3/lib/python3.7/site-packages/syft-0.2.2a1-py3.7.egg/syft/workers/base.py in send_command(self, recipient, message, return_ids)
510 try:
511 ret_val = self.send_msg(
--> 512 Operation(cmd_name, cmd_owner, cmd_args, cmd_kwargs, return_ids), location=recipient
513 )
514 except ResponseSignatureError as e:
~/anaconda3/lib/python3.7/site-packages/syft-0.2.2a1-py3.7.egg/syft/workers/base.py in send_msg(self, message, location)
275
276 # Step 2: send the message and wait for a response
--> 277 bin_response = self._send_msg(bin_message, location)
278
279 # Step 3: deserialize the response
~/anaconda3/lib/python3.7/site-packages/syft-0.2.2a1-py3.7.egg/syft/workers/virtual.py in _send_msg(self, message, location)
5 class VirtualWorker(BaseWorker, FederatedClient):
6 def _send_msg(self, message: bin, location: BaseWorker) -> bin:
----> 7 return location._recv_msg(message)
8
9 def _recv_msg(self, message: bin) -> bin:
~/anaconda3/lib/python3.7/site-packages/syft-0.2.2a1-py3.7.egg/syft/workers/virtual.py in _recv_msg(self, message)
8
9 def _recv_msg(self, message: bin) -> bin:
---> 10 return self.recv_msg(message)
~/anaconda3/lib/python3.7/site-packages/syft-0.2.2a1-py3.7.egg/syft/workers/base.py in recv_msg(self, bin_message)
308
309 # Step 1: route message to appropriate function
--> 310 response = self._message_router[type(msg)](msg.contents)
311
312 # Step 2: Serialize the message to simple python objects
~/anaconda3/lib/python3.7/site-packages/syft-0.2.2a1-py3.7.egg/syft/workers/base.py in execute_command(self, message)
455 try:
456 response = hook_args.register_response(
--> 457 command_name, response, list(return_ids), self
458 )
459 return response
~/anaconda3/lib/python3.7/site-packages/syft-0.2.2a1-py3.7.egg/syft/generic/frameworks/hook/hook_args.py in register_response(attr, response, response_ids, owner)
670 register_response_functions[attr_id] = register_response_function
671 # Run it
--> 672 new_response = register_response_function(response, response_ids=response_ids, owner=owner)
673
674 # Remove the artificial tuple
~/anaconda3/lib/python3.7/site-packages/syft-0.2.2a1-py3.7.egg/syft/generic/frameworks/hook/hook_args.py in <lambda>(x, **kwargs)
764 f = many_fold
765
--> 766 return lambda x, **kwargs: f(lambdas, x, **kwargs)
~/anaconda3/lib/python3.7/site-packages/syft-0.2.2a1-py3.7.egg/syft/generic/frameworks/hook/hook_args.py in two_fold(lambdas, args, **kwargs)
520
521 def two_fold(lambdas, args, **kwargs):
--> 522 return lambdas[0](args[0], **kwargs), lambdas[1](args[1], **kwargs)
523
524
~/anaconda3/lib/python3.7/site-packages/syft-0.2.2a1-py3.7.egg/syft/generic/frameworks/hook/hook_args.py in <lambda>(i, **kwargs)
742 if isinstance(r, (list, tuple)) # if the rule is a list or tuple.
743 # Last if not, rule is probably == 1 so use type to return the right transformation.
--> 744 else lambda i, **kwargs: register_tensor(i, **kwargs)
745 for a, r in zip(response, rules) # And do this for all the responses / rules provided
746 ]
~/anaconda3/lib/python3.7/site-packages/syft-0.2.2a1-py3.7.egg/syft/generic/frameworks/hook/hook_args.py in register_tensor(tensor, owner, response_ids)
710 and each id is pop out when needed.
711 """
--> 712 tensor.owner = owner
713 try:
714 tensor.id = response_ids.pop(-1)
AttributeError: 'numpy.ndarray' object has no attribute 'owner'
OS: Ubuntu 18.04.3 LTS
Python 3.7
PyTorch 1.4