|
| 1 | +from abc import ABC, abstractmethod |
| 2 | +from typing import Dict, List, Tuple, Union |
| 3 | + |
| 4 | +from executorch.exir._serialize._cord import Cord |
| 5 | + |
| 6 | +from executorch.exir.schema import Tensor |
| 7 | + |
| 8 | + |
| 9 | +# Abstract base class that data serializers should adhere to. |
| 10 | +class DataSerializer(ABC): |
| 11 | + @abstractmethod |
| 12 | + def __init__(self) -> None: |
| 13 | + """ |
| 14 | + This initializer may be overridden in derived classes to hold |
| 15 | + the data required for serialization, eg. configurations. |
| 16 | + """ |
| 17 | + pass |
| 18 | + |
| 19 | + @abstractmethod |
| 20 | + def serialize_tensors( |
| 21 | + self, |
| 22 | + tensor_buffer: List[bytes], |
| 23 | + tensor_map: Dict[str, int], |
| 24 | + tensor_metadata: Dict[str, Tensor], |
| 25 | + ) -> Union[Cord, bytes, bytearray]: |
| 26 | + """ |
| 27 | + Serializes a list of tensor metadata and tensors emitted by ExecuTorch |
| 28 | + into a binary blob. |
| 29 | +
|
| 30 | + Args: |
| 31 | + tensor_buffer: A list of deduplicated tensor data. |
| 32 | + tensor_map: A map from tensor name (fqn) to tensor index inside 'tensor_buffer'. |
| 33 | + tensor_metadata: A map from tensor name (fqn) to tensor metadata. |
| 34 | +
|
| 35 | + Returns: |
| 36 | + A binary blob that contains the serialized data. |
| 37 | + """ |
| 38 | + raise NotImplementedError("serialize_data") |
| 39 | + |
| 40 | + @abstractmethod |
| 41 | + def deserialize_tensors( |
| 42 | + self, blob: Union[Cord, bytes, bytearray] |
| 43 | + ) -> Tuple[List[bytes], Dict[str, int], Dict[str, Tensor]]: |
| 44 | + """ |
| 45 | + Deserializes a blob into a list of tensor metadata and tensors. Reverses the effect of serialize_tensors. |
| 46 | +
|
| 47 | + Args: |
| 48 | + blob: A binary blob that contains the serialized data. |
| 49 | +
|
| 50 | + Returns: |
| 51 | + A tuple of (tensor_buffer, tensor_map, tensor_metadata). |
| 52 | + """ |
| 53 | + raise NotImplementedError("deserialize_data") |
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