I'm using Tensorflow Federated, but i'm actually have some problem while trying to executes some operation on the server after reading the client update.
This is the function
@tff.federated_computation(federated_server_state_type,
federated_dataset_type)
def run_one_round(server_state, federated_dataset):
"""Orchestration logic for one round of computation.
Args:
server_state: A `ServerState`.
federated_dataset: A federated `tf.data.Dataset` with placement
`tff.CLIENTS`.
Returns:
A tuple of updated `ServerState` and `tf.Tensor` of average loss.
"""
tf.print("run_one_round")
server_message = tff.federated_map(server_message_fn, server_state)
server_message_at_client = tff.federated_broadcast(server_message)
client_outputs = tff.federated_map(
client_update_fn, (federated_dataset, server_message_at_client))
weight_denom = client_outputs.client_weight
tf.print(client_outputs.weights_delta)
round_model_delta = tff.federated_mean(
client_outputs.weights_delta, weight=weight_denom)
server_state = tff.federated_map(server_update_fn, (server_state, round_model_delta))
round_loss_metric = tff.federated_mean(client_outputs.model_output, weight=weight_denom)
return server_state, round_loss_metric, client_outputs.weights_delta.comp
I want to print the client_outputs.weights_delta and doing some operation on the weights that the client sent to the server before using the tff.federated_mean but i don't get how to do so.
When i try to print i get this
Call(Intrinsic('federated_map', FunctionType(StructType([FunctionType(StructType([('weights_delta', StructType([TensorType(tf.float32, [5, 5, 1, 32]), TensorType(tf.float32, [32]), ....]) as ClientOutput, PlacementLiteral('clients'), False)))]))
Any way to modify those elements?
I tried with using return client_outputs.weights_delta.comp doing the modification in the main (i can do that) and then i tried to invocate a new method for doing the rest of the operations for the server update, but the error is:
AttributeError: 'IterativeProcess' object has no attribute 'calculate_federated_mean'
where calculate_federated_mean was the name of the new function i created.
This is the main:
for round_num in range(FLAGS.total_rounds):
print("--------------------------------------------------------")
sampled_clients = np.random.choice(train_data.client_ids, size=FLAGS.train_clients_per_round, replace=False)
sampled_train_data = [train_data.create_tf_dataset_for_client(client) for client in sampled_clients]
server_state, train_metrics, value_comp = iterative_process.next(server_state, sampled_train_data)
print(f'Round {round_num}')
print(f'\tTraining loss: {train_metrics:.4f}')
if round_num % FLAGS.rounds_per_eval == 0:
server_state.model_weights.assign_weights_to(keras_model)
accuracy = evaluate(keras_model, test_data)
print(f'\tValidation accuracy: {accuracy * 100.0:.2f}%')
tf.print(tf.compat.v2.summary.scalar("Accuracy", accuracy * 100.0, step=round_num))
Based on the simple_fedavg project from github [Tensorflow Federated simple_fedavg][1] as basic project.
EDIT 1:
So, thanks to @Jakub Konecny i made some progress, but i have found a new problem that i don't actually understand.
So, if i use this client_update
@tf.function
def client_update(model, dataset, server_message, client_optimizer):
"""Performans client local training of `model` on `dataset`.
Args:
model: A `tff.learning.Model`.
dataset: A 'tf.data.Dataset'.
server_message: A `BroadcastMessage` from server.
client_optimizer: A `tf.keras.optimizers.Optimizer`.
Returns:
A 'ClientOutput`.
"""
model_weights = model.weights
initial_weights = server_message.model_weights
tf.nest.map_structure(lambda v, t: v.assign(t), model_weights,
initial_weights)
num_examples = tf.constant(0, dtype=tf.int32)
loss_sum = tf.constant(0, dtype=tf.float32)
# Explicit use `iter` for dataset is a trick that makes TFF more robust in
# GPU simulation and slightly more performant in the unconventional usage
# of large number of small datasets.
for batch in iter(dataset):
with tf.GradientTape() as tape:
outputs = model.forward_pass(batch)
grads = tape.gradient(outputs.loss, model_weights.trainable)
client_optimizer.apply_gradients(zip(grads, model_weights.trainable))
batch_size = tf.shape(batch['x'])[0]
num_examples += batch_size
loss_sum += outputs.loss * tf.cast(batch_size, tf.float32)
weights_delta = tf.nest.map_structure(lambda a, b: a - b,
model_weights.trainable,
initial_weights.trainable)
client_weight = tf.cast(num_examples, tf.float32)
import sparse_ternary_compression
sparsification_rate = 1
testing_new = []
#TODO Da non applicare alle bias
for tensor in weights_delta:
testing_new.append(sparse_ternary_compression.stc_compression(tensor, sparsification_rate))
return ClientOutput(weights_delta, client_weight, loss_sum / client_weight, testing_new)
with those functions:
@tff.tf_computation
def stc_compression(original_tensor, sparsification_percentage):
original_shape = tf.shape(original_tensor)
tensor = tf.reshape(original_tensor, [-1])
sparsification_percentage = tf.cast(sparsification_percentage, tf.float64)
sparsification_rate = tf.size(tensor) / 100 * sparsification_percentage
sparsification_rate = tf.cast(sparsification_rate, tf.int32)
new_shape = tensor.get_shape().as_list()
if sparsification_rate == 0:
sparsification_rate = 1
mask = tf.cast(tf.abs(tensor) >= tf.math.top_k(tf.abs(tensor), sparsification_rate)[0][-1], tf.float32)
inv_mask = tf.cast(tf.abs(tensor) < tf.math.top_k(tf.abs(tensor), sparsification_rate)[0][-1], tf.float32)
tensor_masked = tf.multiply(tensor, mask)
sparsification_rate = tf.cast(sparsification_rate, tf.float32)
average = tf.reduce_sum(tf.abs(tensor_masked)) / sparsification_rate
compressed_tensor = tf.add(tf.multiply(average, mask) * tf.sign(tensor), tf.multiply(tensor_masked, inv_mask))
negatives = tf.where(compressed_tensor < 0)
positives = tf.where(compressed_tensor > 0)
return negatives, positives, average, original_shape, new_shape
@tff.tf_computation
def stc_decompression(negatives, positives, average, original_shape, new_shape):
decompressed_tensor = tf.zeros(new_shape, tf.float32)
average_values_negative = tf.fill([tf.shape(negatives)[0], ], -average)
average_values_positive = tf.fill([tf.shape(positives)[0], ], average)
decompressed_tensor = tf.tensor_scatter_nd_update(decompressed_tensor, negatives, average_values_negative)
decompressed_tensor = tf.tensor_scatter_nd_update(decompressed_tensor, positives, average_values_positive)
decompressed_tensor = tf.reshape(decompressed_tensor, original_shape)
return decompressed_tensor
@tff.tf_computation
def testing_new_list(list):
testing = []
for index in list:
testing.append(
stc_decompression(index[0], index[1],
index[2], index[3],
index[4]))
return testing
called like so inside the run_one_round function
@tff.federated_computation(federated_server_state_type,
federated_dataset_type)
def run_one_round(server_state, federated_dataset):
"""Orchestration logic for one round of computation.
Args:
server_state: A `ServerState`.
federated_dataset: A federated `tf.data.Dataset` with placement
`tff.CLIENTS`.
Returns:
A tuple of updated `ServerState` and `tf.Tensor` of average loss.
"""
server_message = tff.federated_map(server_message_fn, server_state)
server_message_at_client = tff.federated_broadcast(server_message)
client_outputs = tff.federated_map(
client_update_fn, (federated_dataset, server_message_at_client))
weight_denom = client_outputs.client_weight
import sparse_ternary_compression
testing = tff.federated_map(sparse_ternary_compression.testing_new_list, client_outputs.test)
# round_model_delta indica i pesi che vengono usati su server_update. Quindi è quello che va cambiato
round_model_delta = tff.federated_mean(
client_outputs.weights_delta, weight=weight_denom)
server_state = tff.federated_map(server_update_fn, (server_state, round_model_delta))
round_loss_metric = tff.federated_mean(client_outputs.model_output, weight=weight_denom)
return server_state, round_loss_metric, testing
but i get this exception
Traceback (most recent call last):
File "/mnt/d/Davide/Uni/TesiMagistrale/ProgettoTesi/main.py", line 214, in <module>
app.run(main)
File "/home/davide/Tesi/virtual-environment/lib/python3.8/site-packages/absl/app.py", line 312, in run
_run_main(main, args)
File "/home/davide/Tesi/virtual-environment/lib/python3.8/site-packages/absl/app.py", line 258, in _run_main
sys.exit(main(argv))
File "/mnt/d/Davide/Uni/TesiMagistrale/ProgettoTesi/main.py", line 171, in main
iterative_process = simple_fedavg_tff.build_federated_averaging_process(
File "/mnt/d/Davide/Uni/TesiMagistrale/ProgettoTesi/simple_fedavg_tff.py", line 95, in build_federated_averaging_process
def client_update_fn(tf_dataset, server_message):
File "/home/davide/Tesi/virtual-environment/lib/python3.8/site-packages/tensorflow_federated/python/core/impl/wrappers/computation_wrapper.py", line 478, in __call__
wrapped_func = self._strategy(
File "/home/davide/Tesi/virtual-environment/lib/python3.8/site-packages/tensorflow_federated/python/core/impl/wrappers/computation_wrapper.py", line 216, in __call__
result = fn_to_wrap(*args, **kwargs)
File "/mnt/d/Davide/Uni/TesiMagistrale/ProgettoTesi/simple_fedavg_tff.py", line 98, in client_update_fn
return client_update(model, tf_dataset, server_message, client_optimizer)
File "/home/davide/Tesi/virtual-environment/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py", line 889, in __call__
result = self._call(*args, **kwds)
File "/home/davide/Tesi/virtual-environment/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py", line 933, in _call
self._initialize(args, kwds, add_initializers_to=initializers)
File "/home/davide/Tesi/virtual-environment/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py", line 763, in _initialize
self._stateful_fn._get_concrete_function_internal_garbage_collected( # pylint: disable=protected-access
File "/home/davide/Tesi/virtual-environment/lib/python3.8/site-packages/tensorflow/python/eager/function.py", line 3050, in _get_concrete_function_internal_garbage_collected
graph_function, _ = self._maybe_define_function(args, kwargs)
File "/home/davide/Tesi/virtual-environment/lib/python3.8/site-packages/tensorflow/python/eager/function.py", line 3444, in _maybe_define_function
graph_function = self._create_graph_function(args, kwargs)
File "/home/davide/Tesi/virtual-environment/lib/python3.8/site-packages/tensorflow/python/eager/function.py", line 3279, in _create_graph_function
func_graph_module.func_graph_from_py_func(
File "/home/davide/Tesi/virtual-environment/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py", line 999, in func_graph_from_py_func
func_outputs = python_func(*func_args, **func_kwargs)
File "/home/davide/Tesi/virtual-environment/lib/python3.8/site-packages/tensorflow/python/eager/def_function.py", line 672, in wrapped_fn
out = weak_wrapped_fn().__wrapped__(*args, **kwds)
File "/home/davide/Tesi/virtual-environment/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py", line 986, in wrapper
raise e.ag_error_metadata.to_exception(e)
tensorflow.python.autograph.pyct.error_utils.KeyError: in user code:
/mnt/d/Davide/Uni/TesiMagistrale/ProgettoTesi/simple_fedavg_tf.py:222 client_update *
testing_new.append(sparse_ternary_compression.stc_compression(tensor, sparsification_rate))
/home/davide/Tesi/virtual-environment/lib/python3.8/site-packages/tensorflow_federated/python/core/impl/computation/function_utils.py:608 __call__ *
return concrete_fn(packed_arg)
/home/davide/Tesi/virtual-environment/lib/python3.8/site-packages/tensorflow_federated/python/core/impl/computation/function_utils.py:525 __call__ *
return context.invoke(self, arg)
/home/davide/Tesi/virtual-environment/lib/python3.8/site-packages/tensorflow_federated/python/core/impl/tensorflow_context/tensorflow_computation_context.py:54 invoke *
init_op, result = (
/home/davide/Tesi/virtual-environment/lib/python3.8/site-packages/tensorflow_federated/python/core/impl/utils/tensorflow_utils.py:1097 deserialize_and_call_tf_computation *
input_map = {
/home/davide/Tesi/virtual-environment/lib/python3.8/site-packages/tensorflow/python/framework/ops.py:3931 get_tensor_by_name **
return self.as_graph_element(name, allow_tensor=True, allow_operation=False)
/home/davide/Tesi/virtual-environment/lib/python3.8/site-packages/tensorflow/python/framework/ops.py:3755 as_graph_element
return self._as_graph_element_locked(obj, allow_tensor, allow_operation)
/home/davide/Tesi/virtual-environment/lib/python3.8/site-packages/tensorflow/python/framework/ops.py:3795 _as_graph_element_locked
raise KeyError("The name %s refers to a Tensor which does not "
KeyError: "The name 'sub:0' refers to a Tensor which does not exist. The operation, 'sub', does not exist in the graph."
Process finished with exit code 1
EDIT 2:
Fixed the problem above by changing the decorator of the functions stc_compression and stc_decompression from tff.tf_computation to tf.function. Now seems to work fine because, if i print the variable testing that i got from the return server_state, round_loss_metric, testing inside run_one_round i get the weights that i wanted from the start.
I think this reply to your other question I just wrote applies here, too.
When you print client_outputs.weights_delta you get abstract representation fo a result of another computation, a primarily internal implementation detail of TFF.
Write a tff.tf_computation-decorated method with TensorFlow code, which does the modification you need, and then invoke it using tff.federated_map operator from where you are trying to print the values.