From 0fa9ba103b62257693b6f0b8c8f61bc5f283f27f Mon Sep 17 00:00:00 2001
From: Jeffrey Wigger <jeffrey.wigger@epfl.ch>
Date: Wed, 9 Mar 2022 15:57:16 +0100
Subject: [PATCH] encoding indices as np.int32
---
src/decentralizepy/sharing/FFT.py | 18 +++---------------
src/decentralizepy/sharing/SubSampling.py | 5 ++---
src/decentralizepy/sharing/TopK.py | 5 +++--
src/decentralizepy/sharing/TopKParams.py | 5 +++--
src/decentralizepy/sharing/Wavelet.py | 7 +++----
5 files changed, 14 insertions(+), 26 deletions(-)
diff --git a/src/decentralizepy/sharing/FFT.py b/src/decentralizepy/sharing/FFT.py
index 4a3ee36..1cc8382 100644
--- a/src/decentralizepy/sharing/FFT.py
+++ b/src/decentralizepy/sharing/FFT.py
@@ -1,13 +1,12 @@
-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
+import numpy as np
from decentralizepy.sharing.Sharing import Sharing
@@ -182,7 +181,7 @@ class FFT(Sharing):
m["alpha"] = self.alpha
m["params"] = topk.numpy()
- m["indices"] = indices.numpy()
+ m["indices"] = indices.numpy().astype(np.int32)
self.total_data += len(self.communication.encrypt(m["params"]))
self.total_meta += len(self.communication.encrypt(m["indices"])) + len(
@@ -215,23 +214,12 @@ class FFT(Sharing):
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)
+ indices_tensor = torch.tensor(indices, dtype=torch.long)
ret = dict()
ret["indices"] = indices_tensor
ret["params"] = params_tensor
diff --git a/src/decentralizepy/sharing/SubSampling.py b/src/decentralizepy/sharing/SubSampling.py
index 6fe3f93..1e956cd 100644
--- a/src/decentralizepy/sharing/SubSampling.py
+++ b/src/decentralizepy/sharing/SubSampling.py
@@ -1,11 +1,10 @@
-import base64
import json
import logging
import os
-import pickle
from pathlib import Path
import torch
+import numpy as np
from decentralizepy.sharing.Sharing import Sharing
@@ -203,7 +202,7 @@ class SubSampling(Sharing):
m["seed"] = seed
m["alpha"] = alpha
- m["params"] = subsample.numpy()
+ m["params"] = subsample.numpy().astype(np.int32)
# logging.info("Converted dictionary to json")
self.total_data += len(self.communication.encrypt(m["params"]))
diff --git a/src/decentralizepy/sharing/TopK.py b/src/decentralizepy/sharing/TopK.py
index 47b4151..f50ba7e 100644
--- a/src/decentralizepy/sharing/TopK.py
+++ b/src/decentralizepy/sharing/TopK.py
@@ -3,6 +3,7 @@ import logging
import os
from pathlib import Path
+import numpy as np
import torch
from decentralizepy.sharing.Sharing import Sharing
@@ -166,7 +167,7 @@ class TopK(Sharing):
if not self.dict_ordered:
raise NotImplementedError
- m["indices"] = G_topk.numpy()
+ m["indices"] = G_topk.numpy().astype(np.int32)
m["params"] = T_topk.numpy()
assert len(m["indices"]) == len(m["params"])
@@ -214,7 +215,7 @@ class TopK(Sharing):
tensors_to_cat.append(t)
T = torch.cat(tensors_to_cat, dim=0)
- index_tensor = torch.tensor(m["indices"])
+ index_tensor = torch.tensor(m["indices"], dtype=torch.long)
logging.debug("Original tensor: {}".format(T[index_tensor]))
T[index_tensor] = torch.tensor(m["params"])
logging.debug("Final tensor: {}".format(T[index_tensor]))
diff --git a/src/decentralizepy/sharing/TopKParams.py b/src/decentralizepy/sharing/TopKParams.py
index 3beb10f..c6535ce 100644
--- a/src/decentralizepy/sharing/TopKParams.py
+++ b/src/decentralizepy/sharing/TopKParams.py
@@ -3,6 +3,7 @@ import logging
import os
from pathlib import Path
+import numpy as np
import torch
from decentralizepy.sharing.Sharing import Sharing
@@ -157,7 +158,7 @@ class TopKParams(Sharing):
if not self.dict_ordered:
raise NotImplementedError
- m["indices"] = index.numpy()
+ m["indices"] = index.numpy().astype(np.int32)
m["params"] = values.numpy()
m["offsets"] = offsets
@@ -206,7 +207,7 @@ class TopKParams(Sharing):
tensors_to_cat = []
offsets = m["offsets"]
params = torch.tensor(m["params"])
- indices = torch.tensor(m["indices"])
+ indices = torch.tensor(m["indices"], dtype=torch.long)
for i, (_, v) in enumerate(state_dict.items()):
shapes.append(v.shape)
diff --git a/src/decentralizepy/sharing/Wavelet.py b/src/decentralizepy/sharing/Wavelet.py
index a6cccaf..774dfe0 100644
--- a/src/decentralizepy/sharing/Wavelet.py
+++ b/src/decentralizepy/sharing/Wavelet.py
@@ -1,11 +1,10 @@
-import base64
import json
import logging
import os
-import pickle
from pathlib import Path
from time import time
+import numpy as np
import pywt
import torch
@@ -206,7 +205,7 @@ class Wavelet(Sharing):
m["params"] = topk.numpy()
- m["indices"] = indices.numpy()
+ m["indices"] = indices.numpy().astype(np.int32)
self.total_data += len(self.communication.encrypt(m["params"]))
self.total_meta += len(self.communication.encrypt(m["indices"])) + len(
@@ -255,7 +254,7 @@ class Wavelet(Sharing):
params = m["params"]
params_tensor = torch.tensor(params)
- indices_tensor = torch.tensor(indices)
+ indices_tensor = torch.tensor(indices, dtype=torch.long)
ret = dict()
ret["indices"] = indices_tensor
ret["params"] = params_tensor
--
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