Source code for nvflare.app_common.shareablegenerators.full_model_shareable_generator

# Copyright (c) 2021, NVIDIA CORPORATION.  All rights reserved.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
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from nvflare.apis.dxo import DataKind, from_shareable
from nvflare.apis.fl_context import FLContext
from nvflare.apis.shareable import Shareable
from nvflare.app_common.abstract.model import ModelLearnable, ModelLearnableKey, model_learnable_to_dxo
from nvflare.app_common.abstract.shareable_generator import ShareableGenerator
from nvflare.app_common.app_constant import AppConstants

[docs]class FullModelShareableGenerator(ShareableGenerator):
[docs] def learnable_to_shareable(self, model_learnable: ModelLearnable, fl_ctx: FLContext) -> Shareable: """Convert ModelLearnable to Shareable. Args: model_learnable (ModelLearnable): model to be converted fl_ctx (FLContext): FL context Returns: Shareable: a shareable containing a DXO object. """ dxo = model_learnable_to_dxo(model_learnable) return dxo.to_shareable()
[docs] def shareable_to_learnable(self, shareable: Shareable, fl_ctx: FLContext) -> ModelLearnable: """Convert Shareable to ModelLearnable. Supporting TYPE == TYPE_WEIGHT_DIFF or TYPE_WEIGHTS Args: shareable (Shareable): Shareable that contains a DXO object fl_ctx (FLContext): FL context Returns: A ModelLearnable object Raises: TypeError: if shareable is not of type shareable ValueError: if data_kind is not `DataKind.WEIGHTS` and is not `DataKind.WEIGHT_DIFF` """ if not isinstance(shareable, Shareable): raise TypeError("shareable must be Shareable, but got {}.".format(type(shareable))) base_model = fl_ctx.get_prop(AppConstants.GLOBAL_MODEL) dxo = from_shareable(shareable) if dxo.data_kind == DataKind.WEIGHT_DIFF: if not base_model: self.system_panic(reason="No global base model needed for processing WEIGHT_DIFF!", fl_ctx=fl_ctx) return base_model weights = base_model[ModelLearnableKey.WEIGHTS] if is not None: model_diff = for v_name, v_value in model_diff.items(): weights[v_name] = weights[v_name] + v_value elif dxo.data_kind == DataKind.WEIGHTS: if not base_model: base_model = ModelLearnable() weights = if not weights: self.log_info(fl_ctx, "No model weights found. Model will not be updated.") else: base_model[ModelLearnableKey.WEIGHTS] = weights else: raise ValueError( "data_kind should be either DataKind.WEIGHTS or DataKind.WEIGHT_DIFF, but got {}".format(dxo.data_kind) ) base_model[ModelLearnableKey.META] = dxo.get_meta_props() return base_model