diff --git a/eval/plot.py b/eval/plot.py
index f3f82c77ad55872c562a06ffb81b04f4b7269c65..a7c8dd9308b705a075c997bf788afc223accc96a 100644
--- a/eval/plot.py
+++ b/eval/plot.py
@@ -5,6 +5,7 @@ import sys
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
+import torch
def get_stats(l):
@@ -152,15 +153,15 @@ def plot_results(path):
data_stdevs[folder] = list(stdevs.values())[0]
plt.figure(1)
- plt.savefig(os.path.join(path, "train_loss.png"))
+ plt.savefig(os.path.join(path, "train_loss.png"), dpi=300)
plt.figure(2)
- plt.savefig(os.path.join(path, "test_loss.png"))
+ plt.savefig(os.path.join(path, "test_loss.png"), dpi=300)
plt.figure(3)
- plt.savefig(os.path.join(path, "test_acc.png"))
+ plt.savefig(os.path.join(path, "test_acc.png"), dpi=300)
plt.figure(6)
- plt.savefig(os.path.join(path, "grad_std.png"))
+ plt.savefig(os.path.join(path, "grad_std.png"), dpi=300)
plt.figure(7)
- plt.savefig(os.path.join(path, "grad_mean.png"))
+ plt.savefig(os.path.join(path, "grad_mean.png"), dpi=300)
# Plot total_bytes
plt.figure(4)
plt.title("Data Shared")
@@ -174,7 +175,7 @@ def plot_results(path):
plt.ylabel("Total data shared in MBs")
plt.xlabel("Fraction of Model Shared")
plt.xticks(x_pos, list(bytes_means.keys()))
- plt.savefig(os.path.join(path, "data_shared.png"))
+ plt.savefig(os.path.join(path, "data_shared.png"), dpi=300)
# Plot stacked_bytes
plt.figure(5)
@@ -198,7 +199,7 @@ def plot_results(path):
plt.ylabel("Data shared in MBs")
plt.xlabel("Fraction of Model Shared")
plt.xticks(x_pos, list(meta_means.keys()))
- plt.savefig(os.path.join(path, "parameters_metadata.png"))
+ plt.savefig(os.path.join(path, "parameters_metadata.png"), dpi=300)
def plot_parameters(path):
@@ -237,4 +238,4 @@ def plot_parameters(path):
if __name__ == "__main__":
assert len(sys.argv) == 2
plot_results(sys.argv[1])
- # plot_parameters(sys.argv[1])
+ # plot_parameters(sys.argv[1])
\ No newline at end of file
diff --git a/eval/plot_percentile.py b/eval/plot_percentile.py
new file mode 100644
index 0000000000000000000000000000000000000000..e8f0da2e8dac12bb7956c7e12b62d85ce6df58c8
--- /dev/null
+++ b/eval/plot_percentile.py
@@ -0,0 +1,125 @@
+import json
+import os
+import sys
+
+import numpy as np
+import pandas as pd
+from matplotlib import pyplot as plt
+import torch
+
+
+def get_stats(l):
+ assert len(l) > 0
+ mean_dict, stdev_dict, min_dict, max_dict = {}, {}, {}, {}
+ for key in l[0].keys():
+ all_nodes = [i[key] for i in l]
+ all_nodes = np.array(all_nodes)
+ mean = np.mean(all_nodes)
+ std = np.std(all_nodes)
+ min = np.min(all_nodes)
+ max = np.max(all_nodes)
+ mean_dict[int(key)] = mean
+ stdev_dict[int(key)] = std
+ min_dict[int(key)] = min
+ max_dict[int(key)] = max
+ return mean_dict, stdev_dict, min_dict, max_dict
+
+
+def plot(means, stdevs, mins, maxs, title, label, loc):
+ plt.title(title)
+ plt.xlabel("communication rounds")
+ x_axis = list(means.keys())
+ y_axis = list(means.values())
+ err = list(stdevs.values())
+ plt.errorbar(x_axis, y_axis, yerr=err, label=label)
+ plt.legend(loc=loc)
+
+
+def plot_results(path):
+ """
+ plots the percentiles
+ Based on plot.py
+ Parameters
+ ----------
+ path
+ path to the folders from which to create the percentiles plots
+
+ """
+ folders = os.listdir(path)
+ folders.sort()
+ print("Reading folders from: ", path)
+ print("Folders: ", folders)
+ for folder in folders:
+ folder_path = os.path.join(path, folder)
+ if not os.path.isdir(folder_path):
+ continue
+ results = []
+ all_shared_params = []
+ machine_folders = os.listdir(folder_path)
+ for machine_folder in machine_folders:
+ mf_path = os.path.join(folder_path, machine_folder)
+ if not os.path.isdir(mf_path):
+ continue
+ files = os.listdir(mf_path)
+ shared_params = [f for f in files if f.endswith("_shared_parameters.json")]
+ files = [f for f in files if f.endswith("_results.json")]
+ for f in files:
+ filepath = os.path.join(mf_path, f)
+ with open(filepath, "r") as inf:
+ results.append(json.load(inf))
+ for sp in shared_params:
+ filepath = os.path.join(mf_path, sp)
+ with open(filepath, "r") as spf:
+ all_shared_params.append(np.array(json.load(spf), dtype = np.int32))
+
+ # Plot Training loss
+ plt.figure(1)
+ # Average of the shared parameters
+ mean = np.mean(all_shared_params, axis=0)
+ std = np.std(all_shared_params, axis=0)
+ with open(
+ os.path.join(path, "shared_params_avg_"+folder+".json"), "w"
+ ) as mf:
+ json.dump(mean.tolist(), mf)
+
+ with open(
+ os.path.join(path, "shared_params_std_"+folder+".json"), "w"
+ ) as sf:
+ json.dump(std.tolist(), sf)
+
+ # copy jupyter notebook code
+ percentile = np.percentile(mean, np.arange(0, 100, 1))
+ plt.plot(np.arange(0, 100, 1), percentile, label=folder)
+ plt.title('Shared parameters Percentiles')
+ # plt.ylabel("Absolute frequency value")
+ plt.xlabel("Percentiles")
+ plt.xticks(np.arange(0, 110, 10))
+ plt.legend(loc="lower right")
+
+ plt.figure(2)
+ sort = torch.sort(torch.tensor(mean)).values
+ print(sort)
+ length = sort.shape[0]
+ length = int(length / 20)
+ bins = [torch.sum(sort[length * i: length * (i + 1)]).item() for i in range(20)]
+ total = np.sum(bins)
+ perc = bins / total #np.divide(bins, total)
+ print(perc)
+ plt.bar(np.arange(0, 97.5, 5), perc, width=5, align='edge',
+ label=folder)
+
+ plt.title('Shared parameters Percentiles')
+ # plt.ylabel("Absolute frequency value")
+ plt.xlabel("Percentiles")
+ plt.legend(loc="lower right")
+ plt.savefig(os.path.join(path, f"percentiles_histogram_{folder}.png"), dpi=300)
+ plt.clf()
+ plt.cla()
+
+ plt.figure(1)
+ plt.savefig(os.path.join(path, "percentiles.png"), dpi=300)
+
+
+if __name__ == "__main__":
+ assert len(sys.argv) == 2
+ plot_results(sys.argv[1])
\ No newline at end of file
diff --git a/src/decentralizepy/models/Model.py b/src/decentralizepy/models/Model.py
index f3635a947eafbcfd5f00b6ec1094db5888a5fb30..5edba7f75815ffb0fbbd67749ef3e43fcfa2cc28 100644
--- a/src/decentralizepy/models/Model.py
+++ b/src/decentralizepy/models/Model.py
@@ -18,6 +18,7 @@ class Model(nn.Module):
self._param_count_ot = None
self._param_count_total = None
self.accumulated_changes = None
+ self.shared_parameters_counter = None
def count_params(self, only_trainable=False):
"""
diff --git a/src/decentralizepy/node/Node.py b/src/decentralizepy/node/Node.py
index e85159ca0679afc9200c561ee55311ef1d327d57..57fa8d53f43d6f9ee0d2a9963c88522e1cd35cfd 100644
--- a/src/decentralizepy/node/Node.py
+++ b/src/decentralizepy/node/Node.py
@@ -419,7 +419,12 @@ class Node:
os.path.join(self.log_dir, "{}_results.json".format(self.rank)), "w"
) as of:
json.dump(results_dict, of)
-
+ if self.model.shared_parameters_counter is not None:
+ logging.info("Saving the shared parameter counts")
+ with open(
+ os.path.join(self.log_dir, "{}_shared_parameters.json".format(self.rank)), "w"
+ ) as of:
+ json.dump(self.model.shared_parameters_counter.numpy().tolist(), of)
self.communication.disconnect_neighbors()
logging.info("All neighbors disconnected. Process complete!")
diff --git a/src/decentralizepy/sharing/FFT.py b/src/decentralizepy/sharing/FFT.py
index 0c0172f5ce27739165b6c22f98461b6839d6d515..d9128079bb96c77a8f6d84ca83cebc14871985f6 100644
--- a/src/decentralizepy/sharing/FFT.py
+++ b/src/decentralizepy/sharing/FFT.py
@@ -163,7 +163,7 @@ class FFT(PartialModel):
with torch.no_grad():
topk, indices = self.apply_fft()
-
+ self.model.shared_parameters_counter[indices] += 1
self.model.rewind_accumulation(indices)
if self.save_shared:
diff --git a/src/decentralizepy/sharing/PartialModel.py b/src/decentralizepy/sharing/PartialModel.py
index 97c702b9b334f05273063ba25aaa7df4472ab27e..e898bf92b92d632156cbda112c7b3edd28efa3fa 100644
--- a/src/decentralizepy/sharing/PartialModel.py
+++ b/src/decentralizepy/sharing/PartialModel.py
@@ -126,6 +126,8 @@ class PartialModel(Sharing):
)
Path(self.folder_path).mkdir(parents=True, exist_ok=True)
+ self.model.shared_parameters_counter = torch.zeros(self.change_transformer(self.init_model).shape[0], dtype = torch.int32)
+
def extract_top_gradients(self):
"""
Extract the indices and values of the topK gradients.
@@ -162,6 +164,7 @@ class PartialModel(Sharing):
with torch.no_grad():
_, G_topk = self.extract_top_gradients()
+ self.model.shared_parameters_counter[G_topk] += 1
if self.accumulation:
self.model.rewind_accumulation(G_topk)
if self.save_shared:
diff --git a/src/decentralizepy/sharing/RoundRobinPartial.py b/src/decentralizepy/sharing/RoundRobinPartial.py
index 6b4f5175843ce0c67a078dad3ca6153e88f967c3..c5288a563740df27dab25028e4958201a3fd8875 100644
--- a/src/decentralizepy/sharing/RoundRobinPartial.py
+++ b/src/decentralizepy/sharing/RoundRobinPartial.py
@@ -84,6 +84,7 @@ class RoundRobinPartial(Sharing):
block_end = min(T.shape[0], (self.current_block + 1) * self.block_size)
self.current_block = (self.current_block + 1) % self.num_blocks
T_send = T[block_start:block_end]
+ self.model.shared_parameters_counter[block_start:block_end] += 1
logging.info("Range sending: {}-{}".format(block_start, block_end))
logging.info("Generating dictionary to send")
diff --git a/src/decentralizepy/sharing/SubSampling.py b/src/decentralizepy/sharing/SubSampling.py
index 5ec0c44f054eb44debf056189513ab814f620a9f..6221714d5ae7bb4f24ea1a4bf66db5f3e9dffcc8 100644
--- a/src/decentralizepy/sharing/SubSampling.py
+++ b/src/decentralizepy/sharing/SubSampling.py
@@ -131,6 +131,7 @@ class SubSampling(Sharing):
<= self.alpha
)
subsample = concated[binary_mask]
+ self.model.shared_parameters_counter[binary_mask] += 1
# logging.debug("Subsampling vector is of size: " + str(subsample.size(dim = 0)))
return (subsample, curr_seed, self.alpha)
else:
@@ -147,6 +148,7 @@ class SubSampling(Sharing):
)
<= self.alpha
)
+ # TODO: support shared_parameters_counter
selected = flat[binary_mask]
values_list.append(selected)
off += selected.size(dim=0)
diff --git a/src/decentralizepy/sharing/TopKParams.py b/src/decentralizepy/sharing/TopKParams.py
index c6535ce5052b25b0f7665010504cffc5e8f26be0..02531f164d37eb49158b839aed230be3beb17761 100644
--- a/src/decentralizepy/sharing/TopKParams.py
+++ b/src/decentralizepy/sharing/TopKParams.py
@@ -128,7 +128,7 @@ class TopKParams(Sharing):
with torch.no_grad():
values, index, offsets = self.extract_top_params()
-
+ self.model.shared_parameters_counter[index] += 1
if self.save_shared:
shared_params = dict()
shared_params["order"] = list(self.model.state_dict().keys())
diff --git a/src/decentralizepy/sharing/Wavelet.py b/src/decentralizepy/sharing/Wavelet.py
index 363a487d098c76b36a388c20f09f5c618a2868a5..9a02a6442e64a2e8ed114180a4f3429190aa4c61 100644
--- a/src/decentralizepy/sharing/Wavelet.py
+++ b/src/decentralizepy/sharing/Wavelet.py
@@ -184,7 +184,7 @@ class Wavelet(PartialModel):
with torch.no_grad():
topk, indices = self.apply_wavelet()
-
+ self.model.shared_parameters_counter[indices] += 1
self.model.rewind_accumulation(indices)
if self.save_shared:
shared_params = dict()