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Fix FedKD SVD decomposition for >2D parameters (e.g. Conv2d) #265

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Fix FedKD SVD decomposition for >2D parameters (e.g. Conv2d) #265

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23 changes: 10 additions & 13 deletions system/flcore/clients/clientkd.py
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Original file line number Diff line number Diff line change
Expand Up @@ -101,8 +101,11 @@ def set_parameters(self, global_param, energy):
# recover
for k in global_param.keys():
if len(global_param[k]) == 3:
# use np.matmul to support high-dimensional CNN param
global_param[k] = np.matmul(global_param[k][0] * global_param[k][1][..., None, :], global_param[k][2])
u, sigma, v = global_param[k]
recovered = np.matmul(u * sigma[..., None, :], v)
# reshape back to original dimensions for >2D params (e.g. Conv2d)
orig_shape = self.global_model.state_dict()[k].shape
global_param[k] = recovered.reshape(orig_shape)

for name, old_param in self.global_model.named_parameters():
if name in global_param:
Expand Down Expand Up @@ -150,15 +153,14 @@ def decomposition(self):
param_cpu = param.detach().cpu().numpy()
# refer to https://github.com/wuch15/FedKD/blob/main/run.py#L187
if param_cpu.shape[0]>1 and len(param_cpu.shape)>1 and 'embeddings' not in name:
orig_shape = param_cpu.shape
# flatten >2D params (e.g. Conv2d) to 2D for proper SVD
if len(orig_shape) > 2:
param_cpu = param_cpu.reshape(orig_shape[0], -1)
u, sigma, v = np.linalg.svd(param_cpu, full_matrices=False)
# support high-dimensional CNN param
if len(u.shape)==4:
u = np.transpose(u, (2, 3, 0, 1))
sigma = np.transpose(sigma, (2, 0, 1))
v = np.transpose(v, (2, 3, 0, 1))
threshold=0
if np.sum(np.square(sigma))==0:
compressed_param_cpu=param_cpu
compressed_param_cpu=param_cpu.reshape(orig_shape)
else:
for singular_value_num in range(len(sigma)):
if np.sum(np.square(sigma[:singular_value_num]))>self.energy*np.sum(np.square(sigma)):
Expand All @@ -167,11 +169,6 @@ def decomposition(self):
u=u[:, :threshold]
sigma=sigma[:threshold]
v=v[:threshold, :]
# support high-dimensional CNN param
if len(u.shape)==4:
u = np.transpose(u, (2, 3, 0, 1))
sigma = np.transpose(sigma, (1, 2, 0))
v = np.transpose(v, (2, 3, 0, 1))
compressed_param_cpu=[u,sigma,v]
elif 'embeddings' not in name:
compressed_param_cpu=param_cpu
Expand Down
25 changes: 10 additions & 15 deletions system/flcore/servers/serverkd.py
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Original file line number Diff line number Diff line change
Expand Up @@ -107,10 +107,11 @@ def receive_models(self):
# recover
for k in client.compressed_param.keys():
if len(client.compressed_param[k]) == 3:
# use np.matmul to support high-dimensional CNN param
client.compressed_param[k] = np.matmul(
client.compressed_param[k][0] * client.compressed_param[k][1][..., None, :],
client.compressed_param[k][2])
u, sigma, v = client.compressed_param[k]
recovered = np.matmul(u * sigma[..., None, :], v)
# reshape back to original dimensions for >2D params (e.g. Conv2d)
orig_shape = client.global_model.state_dict()[k].shape
client.compressed_param[k] = recovered.reshape(orig_shape)

self.uploaded_models.append(client.compressed_param)

Expand All @@ -134,15 +135,14 @@ def decomposition(self):
for name, param_cpu in self.global_model.items():
# refer to https://github.com/wuch15/FedKD/blob/main/run.py#L187
if param_cpu.shape[0]>1 and len(param_cpu.shape)>1 and 'embeddings' not in name:
orig_shape = param_cpu.shape
# flatten >2D params (e.g. Conv2d) to 2D for proper SVD
if len(orig_shape) > 2:
param_cpu = param_cpu.reshape(orig_shape[0], -1)
u, sigma, v = np.linalg.svd(param_cpu, full_matrices=False)
# support high-dimensional CNN param
if len(u.shape)==4:
u = np.transpose(u, (2, 3, 0, 1))
sigma = np.transpose(sigma, (2, 0, 1))
v = np.transpose(v, (2, 3, 0, 1))
threshold=0
if np.sum(np.square(sigma))==0:
compressed_param_cpu=param_cpu
compressed_param_cpu=param_cpu.reshape(orig_shape)
else:
for singular_value_num in range(len(sigma)):
if np.sum(np.square(sigma[:singular_value_num]))>self.energy*np.sum(np.square(sigma)):
Expand All @@ -151,11 +151,6 @@ def decomposition(self):
u=u[:,:threshold]
sigma=sigma[:threshold]
v=v[:threshold,:]
# support high-dimensional CNN param
if len(u.shape)==4:
u = np.transpose(u, (2, 3, 0, 1))
sigma = np.transpose(sigma, (1, 2, 0))
v = np.transpose(v, (2, 3, 0, 1))
compressed_param_cpu=[u,sigma,v]
elif 'embeddings' not in name:
compressed_param_cpu=param_cpu
Expand Down