Source code for nvflare.app_opt.pt.decomposers

# Copyright (c) 2023, 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
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from io import BytesIO
from typing import Any

import numpy as np
import torch

from nvflare.fuel.utils import fobs
from nvflare.fuel.utils.fobs.datum import DatumManager




class SerializationModule(torch.nn.Module):
    def __init__(self, tensor):
        super().__init__()
        self.register_buffer("saved_tensor", tensor)





class TensorDecomposer(fobs.Decomposer):


    def supported_type(self):
        return torch.Tensor




    def decompose(self, target: torch.Tensor, manager: DatumManager = None) -> Any:
        if target.dtype == torch.bfloat16:
            return self._jit_serialize(target)
        else:
            return self._numpy_serialize(target)




    def recompose(self, data: Any, manager: DatumManager = None) -> torch.Tensor:
        if isinstance(data, dict):
            if data["dtype"] == "torch.bfloat16":
                return self._jit_deserialize(data)
            else:
                buf = data["buffer"]
        else:
            buf = data

        return self._numpy_deserialize(buf)


    @staticmethod
    def _numpy_serialize(tensor: torch.Tensor) -> dict:
        stream = BytesIO()
        # supported ScalarType, use numpy to avoid Pickle
        array = tensor.detach().cpu().numpy()
        np.save(stream, array, allow_pickle=False)
        return {
            "buffer": stream.getvalue(),
            "dtype": str(tensor.dtype),
        }

    @staticmethod
    def _numpy_deserialize(data: Any) -> torch.Tensor:
        stream = BytesIO(data)
        array = np.load(stream, allow_pickle=False)
        return torch.from_numpy(array)

    @staticmethod
    def _jit_serialize(tensor: torch.Tensor) -> dict:
        stream = BytesIO()
        # unsupported ScalarType by numpy, use torch.jit to avoid Pickle
        module = SerializationModule(tensor)
        torch.jit.save(torch.jit.script(module), stream)
        return {
            "buffer": stream.getvalue(),
            "dtype": str(tensor.dtype),
        }

    @staticmethod
    def _jit_deserialize(data: Any) -> torch.Tensor:
        stream = BytesIO(data["buffer"])
        loaded_module = torch.jit.load(stream)
        return loaded_module.saved_tensor