我正在使用 Tensorflow Federated，但實際上在讀取客戶端更新后嘗試在服務器上執行某些操作時遇到了一些問題。
這是功能
@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
我想在使用之前列印client_outputs.weights_delta客戶端發送到服務器的權重并對其進行一些操作，tff.federated_mean但我不知道該怎么做。
當我嘗試列印時，我得到了這個
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)))]))
有什么辦法可以修改這些元素嗎？
我嘗試使用return client_outputs.weights_delta.comp在 main 中進行修改（我可以這樣做），然后我嘗試呼叫一種新方法來執行服務器更新的其余操作，但錯誤是：
AttributeError: 'IterativeProcess' object has no attribute 'calculate_federated_mean'
其中calculate_federated_mean 是我創建的新函式的名稱。
這是主要的：
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))
基于 github Tensorflow Federated simple_fedavg作為基礎專案的simple_fedavg專案。
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.
The only problem now is this one, if i pass the testing variable that i got from my functions to tff.federated_mean i get this error:
File "/mnt/d/Davide/Uni/TesiMagistrale/ProgettoTesi/simple_fedavg_tff.py", line 134, in run_one_round
server_state = tff.federated_map(server_update_fn, (server_state, round_model_delta))
File "/home/davide/Tesi/virtual-environment/lib/python3.8/site-packages/tensorflow_federated/python/core/impl/federated_context/intrinsics.py", line 268, in federated_map
raise TypeError(
TypeError: The mapping function expects a parameter of type <server_state=<model_weights=<trainable=<float32[5,5,1,32],float32[32],float32[5,5,32,64],float32[64],float32[3136,512],float32[512],float32[512,10],float32[10]>,non_trainable=<>>,optimizer_state=<int64>,round_num=int32>,model_delta=<float32[5,5,1,32],float32[32],float32[5,5,32,64],float32[64],float32[3136,512],float32[512],float32[512,10],float32[10]>>, but member constituents of the mapped value are of incompatible type <<model_weights=<trainable=<float32[5,5,1,32],float32[32],float32[5,5,32,64],float32[64],float32[3136,512],float32[512],float32[512,10],float32[10]>,non_trainable=<>>,optimizer_state=<int64>,round_num=int32>,<float32[?,?,?,?],float32[?],float32[?,?,?,?],float32[?],float32[?,?],float32[?],float32[?,?],float32[?]>>.
Any last idea?
uj5u.com熱心網友回復：
我覺得這個答復到其他的問題，我只是寫在這里也適用。
當您列印時，client_outputs.weights_delta您將獲得另一個計算結果的抽象表示，這是 TFF 的主要內部實作細節。
tff.tf_computation使用 TensorFlow 代碼撰寫一個-decorated 方法，該方法會執行您需要的修改，然后使用tff.federated_map運算子從您嘗試列印值的位置呼叫它。
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標籤：python tensorflow tensorflow-federated