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Jeffrey Wigger authoredJeffrey Wigger authored
FFT.py 10.45 KiB
import base64
import json
import logging
import os
import pickle
from pathlib import Path
from time import time
import torch
import torch.fft as fft
from decentralizepy.sharing.Sharing import Sharing
class FFT(Sharing):
"""
This class implements the fft version of model sharing
It is based on PartialModel.py
"""
def __init__(
self,
rank,
machine_id,
communication,
mapping,
graph,
model,
dataset,
log_dir,
alpha=1.0,
dict_ordered=True,
save_shared=False,
metadata_cap=1.0,
pickle=True,
change_based_selection=True,
accumulation=True,
):
"""
Constructor
Parameters
----------
rank : int
Local rank
machine_id : int
Global machine id
communication : decentralizepy.communication.Communication
Communication module used to send and receive messages
mapping : decentralizepy.mappings.Mapping
Mapping (rank, machine_id) -> uid
graph : decentralizepy.graphs.Graph
Graph reprensenting neighbors
model : decentralizepy.models.Model
Model to train
dataset : decentralizepy.datasets.Dataset
Dataset for sharing data. Not implemented yet! TODO
log_dir : str
Location to write shared_params (only writing for 2 procs per machine)
alpha : float
Percentage of model to share
dict_ordered : bool
Specifies if the python dict maintains the order of insertion
save_shared : bool
Specifies if the indices of shared parameters should be logged
metadata_cap : float
Share full model when self.alpha > metadata_cap
pickle : bool
use pickle to serialize the model parameters
change_based_selection : bool
use frequency change to select topk frequencies
accumulation : bool
True if the the indices to share should be selected based on accumulated frequency change
"""
super().__init__(
rank, machine_id, communication, mapping, graph, model, dataset, log_dir
)
self.alpha = alpha
self.dict_ordered = dict_ordered
self.save_shared = save_shared
self.metadata_cap = metadata_cap
self.total_meta = 0
self.pickle = pickle
logging.info("subsampling pickling=" + str(pickle))
if self.save_shared:
# Only save for 2 procs: Save space
if rank != 0 or rank != 1:
self.save_shared = False
if self.save_shared:
self.folder_path = os.path.join(
self.log_dir, "shared_params/{}".format(self.rank)
)
Path(self.folder_path).mkdir(parents=True, exist_ok=True)
self.change_based_selection = change_based_selection
self.accumulation = accumulation
def apply_fft(self):
"""
Does fft transformation of the model parameters and selects topK (alpha) of them in the frequency domain
based on the undergone change during the current training step
Returns
-------
tuple
(a,b). a: selected fft frequencies (complex numbers), b: Their indices.
"""
logging.info("Returning fft compressed model weights")
tensors_to_cat = [v.data.flatten() for _, v in self.model.state_dict().items()]
concated = torch.cat(tensors_to_cat, dim=0)
if self.change_based_selection:
flat_fft = fft.rfft(concated)
if self.accumulation:
logging.info(
"fft topk extract frequencies based on accumulated model frequency change"
)
diff = self.model.accumulated_frequency + (flat_fft - self.model.prev)
else:
diff = flat_fft - self.model.accumulated_frequency
_, index = torch.topk(
diff.abs(), round(self.alpha * len(flat_fft)), dim=0, sorted=False
)
else:
flat_fft = fft.rfft(concated)
_, index = torch.topk(
flat_fft.abs(), round(self.alpha * len(flat_fft)), dim=0, sorted=False
)
if self.accumulation:
self.model.accumulated_frequency[index] = 0.0
return flat_fft[index], index
def serialized_model(self):
"""
Convert model to json dict. self.alpha specifies the fraction of model to send.
Returns
-------
dict
Model converted to json dict
"""
if self.alpha > self.metadata_cap: # Share fully
return super().serialized_model()
with torch.no_grad():
topk, indices = self.apply_fft()
if self.save_shared:
shared_params = dict()
shared_params["order"] = list(self.model.state_dict().keys())
shapes = dict()
for k, v in self.model.state_dict().items():
shapes[k] = list(v.shape)
shared_params["shapes"] = shapes
shared_params[self.communication_round] = indices.tolist() # is slow
shared_params["alpha"] = self.alpha
with open(
os.path.join(
self.folder_path,
"{}_shared_params.json".format(self.communication_round + 1),
),
"w",
) as of:
json.dump(shared_params, of)
m = dict()
if not self.dict_ordered:
raise NotImplementedError
m["alpha"] = self.alpha
m["params"] = topk.numpy()
m["indices"] = indices.numpy()
self.total_data += len(self.communication.encrypt(m["params"]))
self.total_meta += len(self.communication.encrypt(m["indices"])) + len(
self.communication.encrypt(m["alpha"])
)
return m
def deserialized_model(self, m):
"""
Convert received json dict to state_dict.
Parameters
----------
m : dict
json dict received
Returns
-------
state_dict
state_dict of received
"""
if self.alpha > self.metadata_cap: # Share fully
return super().deserialized_model(m)
with torch.no_grad():
state_dict = self.model.state_dict()
if not self.dict_ordered:
raise NotImplementedError
shapes = []
lens = []
tensors_to_cat = []
for _, v in state_dict.items():
shapes.append(v.shape)
t = v.flatten()
lens.append(t.shape[0])
tensors_to_cat.append(t)
T = torch.cat(tensors_to_cat, dim=0)
indices = m["indices"]
alpha = m["alpha"]
params = m["params"]
params_tensor = torch.tensor(params)
indices_tensor = torch.tensor(indices)
ret = dict()
ret["indices"] = indices_tensor
ret["params"] = params_tensor
return ret
def step(self):
"""
Perform a sharing step. Implements D-PSGD.
"""
t_start = time()
data = self.serialized_model()
t_post_serialize = time()
my_uid = self.mapping.get_uid(self.rank, self.machine_id)
all_neighbors = self.graph.neighbors(my_uid)
iter_neighbors = self.get_neighbors(all_neighbors)
data["degree"] = len(all_neighbors)
data["iteration"] = self.communication_round
for neighbor in iter_neighbors:
self.communication.send(neighbor, data)
t_post_send = time()
logging.info("Waiting for messages from neighbors")
while not self.received_from_all():
sender, data = self.communication.receive()
logging.debug("Received model from {}".format(sender))
degree = data["degree"]
iteration = data["iteration"]
del data["degree"]
del data["iteration"]
self.peer_deques[sender].append((degree, iteration, data))
logging.info(
"Deserialized received model from {} of iteration {}".format(
sender, iteration
)
)
t_post_recv = time()
logging.info("Starting model averaging after receiving from all neighbors")
total = None
weight_total = 0
# FFT of this model
shapes = []
lens = []
tensors_to_cat = []
for _, v in self.model.state_dict().items():
shapes.append(v.shape)
t = v.flatten()
lens.append(t.shape[0])
tensors_to_cat.append(t)
concated = torch.cat(tensors_to_cat, dim=0)
flat_fft = fft.rfft(concated)
for i, n in enumerate(self.peer_deques):
degree, iteration, data = self.peer_deques[n].popleft()
logging.debug(
"Averaging model from neighbor {} of iteration {}".format(n, iteration)
)
data = self.deserialized_model(data)
params = data["params"]
indices = data["indices"]
# use local data to complement
topkf = flat_fft.clone().detach()
topkf[indices] = params
weight = 1 / (max(len(self.peer_deques), degree) + 1) # Metro-Hastings
weight_total += weight
if total is None:
total = weight * topkf
else:
total += weight * topkf
# Metro-Hastings
total += (1 - weight_total) * flat_fft
reverse_total = fft.irfft(total)
start_index = 0
std_dict = {}
for i, key in enumerate(self.model.state_dict()):
end_index = start_index + lens[i]
std_dict[key] = reverse_total[start_index:end_index].reshape(shapes[i])
start_index = end_index
self.model.load_state_dict(std_dict)
logging.info("Model averaging complete")
self.communication_round += 1
t_end = time()
logging.info(
"Sharing::step | Serialize: %f; Send: %f; Recv: %f; Averaging: %f; Total: %f",
t_post_serialize - t_start,
t_post_send - t_post_serialize,
t_post_recv - t_post_send,
t_end - t_post_recv,
t_end - t_start,
)