serialization

Module Contents

SerializationTool

class SerializationTool

Bases: object

static serialize_model_gradients(model: torch.nn.Module) → torch.Tensor

_summary_

Parameters:

model (torch.nn.Module) – _description_

Returns:

_description_

Return type:

torch.Tensor

static deserialize_model_gradients(model: torch.nn.Module, gradients: torch.Tensor)

static serialize_model(model: torch.nn.Module) → torch.Tensor

Unfold model parameters

Unfold every layer of model, concate all of tensors into one. Return a torch.Tensor with shape (size, ).

Please note that we update the implementation. Current version of serialization includes the parameters in batchnorm layers.

Parameters:

model (torch.nn.Module) – model to serialize.

static deserialize_model(model: torch.nn.Module, serialized_parameters: torch.Tensor, mode='copy')

Assigns serialized parameters to model.parameters. This is done by iterating through model.parameters() and assigning the relevant params in grad_update. NOTE: this function manipulates model.parameters.

Parameters:

• model (torch.nn.Module) – model to deserialize.

• serialized_parameters (torch.Tensor) – serialized model parameters.

• mode (str) – deserialize mode. “copy” or “add”.

static serialize_trainable_model(model: torch.nn.Module) → torch.Tensor

Unfold model parameters

Unfold every layer of model, concate all of tensors into one. Return a torch.Tensor with shape (size, ).

Parameters:

model (torch.nn.Module) – model to serialize.

static deserialize_trainable_model(model: torch.nn.Module, serialized_parameters: torch.Tensor, mode='copy')

Assigns serialized parameters to model.parameters. This is done by iterating through model.parameters() and assigning the relevant params in grad_update. NOTE: this function manipulates model.parameters.

Parameters:

• model (torch.nn.Module) – model to deserialize.

• serialized_parameters (torch.Tensor) – serialized model parameters.

• mode (str) – deserialize mode. “copy” or “add”.