# Copyright 2020 Flower Labs GmbH. 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.
# ==============================================================================
"""Flower type definitions."""
from dataclasses import dataclass
from enum import Enum
from typing import Any, Callable, Optional, Union
import numpy as np
import numpy.typing as npt
NDArray = npt.NDArray[Any]
NDArrayInt = npt.NDArray[np.int_]
NDArrayFloat = npt.NDArray[np.float64]
NDArrays = list[NDArray]
# The following union type contains Python types corresponding to ProtoBuf types that
# ProtoBuf considers to be "Scalar Value Types", even though some of them arguably do
# not conform to other definitions of what a scalar is. Source:
# https://developers.google.com/protocol-buffers/docs/overview#scalar
Scalar = Union[bool, bytes, float, int, str]
Value = Union[
bool,
bytes,
float,
int,
str,
list[bool],
list[bytes],
list[float],
list[int],
list[str],
]
# Value types for common.MetricsRecord
MetricsScalar = Union[int, float]
MetricsScalarList = Union[list[int], list[float]]
MetricsRecordValues = Union[MetricsScalar, MetricsScalarList]
# Value types for common.ConfigsRecord
ConfigsScalar = Union[MetricsScalar, str, bytes, bool]
ConfigsScalarList = Union[MetricsScalarList, list[str], list[bytes], list[bool]]
ConfigsRecordValues = Union[ConfigsScalar, ConfigsScalarList]
Metrics = dict[str, Scalar]
MetricsAggregationFn = Callable[[list[tuple[int, Metrics]]], Metrics]
Config = dict[str, Scalar]
Properties = dict[str, Scalar]
# Value type for user configs
UserConfigValue = Union[bool, float, int, str]
UserConfig = dict[str, UserConfigValue]
[docs]
class Code(Enum):
"""Client status codes."""
OK = 0
GET_PROPERTIES_NOT_IMPLEMENTED = 1
GET_PARAMETERS_NOT_IMPLEMENTED = 2
FIT_NOT_IMPLEMENTED = 3
EVALUATE_NOT_IMPLEMENTED = 4
[docs]
@dataclass
class Status:
"""Client status."""
code: Code
message: str
class ClientAppOutputCode(Enum):
"""ClientAppIO status codes."""
SUCCESS = 0
DEADLINE_EXCEEDED = 1
UNKNOWN_ERROR = 2
@dataclass
class ClientAppOutputStatus:
"""ClientAppIO status."""
code: ClientAppOutputCode
message: str
[docs]
@dataclass
class Parameters:
"""Model parameters."""
tensors: list[bytes]
tensor_type: str
[docs]
@dataclass
class GetParametersIns:
"""Parameters request for a client."""
config: Config
[docs]
@dataclass
class GetParametersRes:
"""Response when asked to return parameters."""
status: Status
parameters: Parameters
[docs]
@dataclass
class FitIns:
"""Fit instructions for a client."""
parameters: Parameters
config: dict[str, Scalar]
[docs]
@dataclass
class FitRes:
"""Fit response from a client."""
status: Status
parameters: Parameters
num_examples: int
metrics: dict[str, Scalar]
[docs]
@dataclass
class EvaluateIns:
"""Evaluate instructions for a client."""
parameters: Parameters
config: dict[str, Scalar]
[docs]
@dataclass
class EvaluateRes:
"""Evaluate response from a client."""
status: Status
loss: float
num_examples: int
metrics: dict[str, Scalar]
[docs]
@dataclass
class GetPropertiesIns:
"""Properties request for a client."""
config: Config
[docs]
@dataclass
class GetPropertiesRes:
"""Properties response from a client."""
status: Status
properties: Properties
[docs]
@dataclass
class ReconnectIns:
"""ReconnectIns message from server to client."""
seconds: Optional[int]
[docs]
@dataclass
class DisconnectRes:
"""DisconnectRes message from client to server."""
reason: str
[docs]
@dataclass
class ServerMessage:
"""ServerMessage is a container used to hold one instruction message."""
get_properties_ins: Optional[GetPropertiesIns] = None
get_parameters_ins: Optional[GetParametersIns] = None
fit_ins: Optional[FitIns] = None
evaluate_ins: Optional[EvaluateIns] = None
[docs]
@dataclass
class ClientMessage:
"""ClientMessage is a container used to hold one result message."""
get_properties_res: Optional[GetPropertiesRes] = None
get_parameters_res: Optional[GetParametersRes] = None
fit_res: Optional[FitRes] = None
evaluate_res: Optional[EvaluateRes] = None
@dataclass
class RunStatus:
"""Run status information."""
status: str
sub_status: str
details: str
@dataclass
class Run: # pylint: disable=too-many-instance-attributes
"""Run details."""
run_id: int
fab_id: str
fab_version: str
fab_hash: str
override_config: UserConfig
pending_at: str
starting_at: str
running_at: str
finished_at: str
status: RunStatus
@classmethod
def create_empty(cls, run_id: int) -> "Run":
"""Return an empty Run instance."""
return cls(
run_id=run_id,
fab_id="",
fab_version="",
fab_hash="",
override_config={},
pending_at="",
starting_at="",
running_at="",
finished_at="",
status=RunStatus(status="", sub_status="", details=""),
)
@dataclass
class Fab:
"""Fab file representation."""
hash_str: str
content: bytes