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from collections import ChainMap
from typing import (
TYPE_CHECKING,
Any,
Dict,
Iterable,
List,
MutableSequence,
Optional,
Sequence,
Tuple,
Type,
TypeVar,
Union,
cast,
no_type_check,
overload,
)
import numpy as np
from pydantic import BaseConfig, parse_obj_as
from typing_inspect import typingGenericAlias
from docarray.array.any_array import AnyDocArray
from docarray.array.doc_list.doc_list import DocList
from docarray.array.doc_vec.column_storage import ColumnStorage, ColumnStorageView
from docarray.array.list_advance_indexing import ListAdvancedIndexing
from docarray.base_doc import AnyDoc, BaseDoc
from docarray.base_doc.mixins.io import _type_to_protobuf
from docarray.typing import NdArray
from docarray.typing.tensor.abstract_tensor import AbstractTensor
from docarray.utils._internal._typing import is_tensor_union
from docarray.utils._internal.misc import is_tf_available, is_torch_available
if TYPE_CHECKING:
from pydantic.fields import ModelField
from docarray.proto import (
DocVecProto,
ListOfDocArrayProto,
ListOfDocVecProto,
NdArrayProto,
)
torch_available = is_torch_available()
if torch_available:
from docarray.typing import TorchTensor
else:
TorchTensor = None # type: ignore
tf_available = is_tf_available()
if tf_available:
import tensorflow as tf # type: ignore
from docarray.typing import TensorFlowTensor # noqa: F401
else:
TensorFlowTensor = None # type: ignore
T_doc = TypeVar('T_doc', bound=BaseDoc)
T = TypeVar('T', bound='DocVec')
IndexIterType = Union[slice, Iterable[int], Iterable[bool], None]
NONE_NDARRAY_PROTO_SHAPE = (0,)
NONE_NDARRAY_PROTO_DTYPE = 'None'
def _none_ndarray_proto() -> 'NdArrayProto':
from docarray.proto import NdArrayProto
zeros_arr = parse_obj_as(NdArray, np.zeros(NONE_NDARRAY_PROTO_SHAPE))
nd_proto = NdArrayProto()
nd_proto.dense.buffer = zeros_arr.tobytes()
nd_proto.dense.ClearField('shape')
nd_proto.dense.shape.extend(list(zeros_arr.shape))
nd_proto.dense.dtype = NONE_NDARRAY_PROTO_DTYPE
return nd_proto
def _none_docvec_proto() -> 'DocVecProto':
from docarray.proto import DocVecProto
return DocVecProto()
def _none_list_of_docvec_proto() -> 'ListOfDocArrayProto':
from docarray.proto import ListOfDocVecProto
return ListOfDocVecProto()
def _is_none_ndarray_proto(proto: 'NdArrayProto') -> bool:
return (
proto.dense.shape == list(NONE_NDARRAY_PROTO_SHAPE)
and proto.dense.dtype == NONE_NDARRAY_PROTO_DTYPE
)
def _is_none_docvec_proto(proto: 'DocVecProto') -> bool:
return (
proto.tensor_columns == {}
and proto.doc_columns == {}
and proto.docs_vec_columns == {}
and proto.any_columns == {}
)
def _is_none_list_of_docvec_proto(proto: 'ListOfDocVecProto') -> bool:
from docarray.proto import ListOfDocVecProto
return isinstance(proto, ListOfDocVecProto) and len(proto.data) == 0
class DocVec(AnyDocArray[T_doc]):
"""
DocVec is a container of Documents appropriates to perform
computation that require batches of data (ex: matrix multiplication, distance
calculation, deep learning forward pass)
A DocVec has a similar interface as [`DocList`][docarray.array.DocList]
but with an underlying implementation that is column based instead of row based.
Each field of the schema of the `DocVec` (the `.doc_type` which is a
[`BaseDoc`][docarray.BaseDoc]) will be stored in a column.
If the field is a tensor, the data from all Documents will be stored as a single
(torch/np/tf) tensor.
If the tensor field is `AnyTensor` or a Union of tensor types, the
`.tensor_type` will be used to determine the type of the column.
If the field is another [`BaseDoc`][docarray.BaseDoc] the column will be another
`DocVec` that follows the schema of the nested Document.
If the field is a [`DocList`][docarray.DocList] or `DocVec` then the column will
be a list of `DocVec`.
For any other type the column is a Python list.
Every `Document` inside a `DocVec` is a view into the data columns stored at the
`DocVec` level. The `BaseDoc` does not hold any data itself. The behavior of
this Document "view" is similar to the behavior of `view = tensor[i]` in
numpy/PyTorch.
!!! note
DocVec supports optional fields. Nevertheless if a field is optional it needs to
be homogeneous. This means that if the first document has a None value all of the
other documents should have a None value as well.
!!! note
If one field is Optional the column will be stored
* as None if the first doc is as the field as None
* as a normal column otherwise that cannot contain None value
:param docs: a homogeneous sequence of `BaseDoc`
:param tensor_type: Tensor Class used to wrap the doc_vec tensors. This is useful
if the BaseDoc of this DocVec has some undefined tensor type like
AnyTensor or Union of NdArray and TorchTensor
"""
doc_type: Type[T_doc]
def __init__(
self: T,
docs: Sequence[T_doc],
tensor_type: Type['AbstractTensor'] = NdArray,
):
if not hasattr(self, 'doc_type') or self.doc_type == AnyDoc:
raise TypeError(
f'{self.__class__.__name__} does not precise a doc_type. You probably should do'
f'docs = DocVec[MyDoc](docs) instead of DocVec(docs)'
)
self.tensor_type = tensor_type
tensor_columns: Dict[str, Optional[AbstractTensor]] = dict()
doc_columns: Dict[str, Optional['DocVec']] = dict()
docs_vec_columns: Dict[str, Optional[ListAdvancedIndexing['DocVec']]] = dict()
any_columns: Dict[str, ListAdvancedIndexing] = dict()
if len(docs) == 0:
raise ValueError(f'docs {docs}: should not be empty')
docs = (
docs
if isinstance(docs, DocList)
else DocList.__class_getitem__(self.doc_type)(docs)
)
for field_name, field in self.doc_type.__fields__.items():
# here we iterate over the field of the docs schema, and we collect the data
# from each document and put them in the corresponding column
field_type = self.doc_type._get_field_type(field_name)
is_field_required = self.doc_type.__fields__[field_name].required
first_doc_is_none = getattr(docs[0], field_name) is None
def _verify_optional_field_of_docs(docs):
if is_field_required:
if first_doc_is_none:
raise ValueError(
f'Field {field_name} is None for {docs[0]} even though it is required'
)
if first_doc_is_none:
for i, doc in enumerate(docs):
if getattr(doc, field_name) is not None:
raise ValueError(
f'Field {field_name} is put to None for the first doc. This mean that '
f'all of the other docs should have this field set to None as well. '
f'This is not the case for {doc} at index {i}'
)
def _check_doc_field_not_none(field_name, doc):
if getattr(doc, field_name) is None:
raise ValueError(
f'Field {field_name} is None for {doc} even though it is not None for the first doc'
)
if is_tensor_union(field_type):
field_type = tensor_type
# all generic tensor types such as AnyTensor, ImageTensor, etc. are subclasses of AbstractTensor.
# Perform check only if the field_type is not an alias and is a subclass of AbstractTensor
elif not isinstance(field_type, typingGenericAlias) and issubclass(
field_type, AbstractTensor
):
# check if the tensor associated with the field_name in the document is a subclass of the tensor_type
# e.g. if the field_type is AnyTensor but the type(docs[0][field_name]) is ImageTensor,
# then we change the field_type to ImageTensor, since AnyTensor is a union of all the tensor types
# and does not override any methods of specific tensor types
tensor = getattr(docs[0], field_name)
if issubclass(tensor.__class__, tensor_type):
field_type = tensor_type
if isinstance(field_type, type):
if tf_available and issubclass(field_type, TensorFlowTensor):
# tf.Tensor does not allow item assignment, therefore the
# optimized way
# of initializing an empty array and assigning values to it
# iteratively
# does not work here, therefore handle separately.
if first_doc_is_none:
_verify_optional_field_of_docs(docs)
tensor_columns[field_name] = None
else:
tf_stack = []
for i, doc in enumerate(docs):
val = getattr(doc, field_name)
_check_doc_field_not_none(field_name, doc)
tf_stack.append(val.tensor)
stacked: tf.Tensor = tf.stack(tf_stack)
tensor_columns[field_name] = TensorFlowTensor(stacked)
elif issubclass(field_type, AbstractTensor):
if first_doc_is_none:
_verify_optional_field_of_docs(docs)
tensor_columns[field_name] = None
else:
tensor = getattr(docs[0], field_name)
column_shape = (
(len(docs), *tensor.shape)
if tensor is not None
else (len(docs),)
)
tensor_columns[field_name] = field_type._docarray_from_native(
field_type.get_comp_backend().empty(
column_shape,
dtype=tensor.dtype
if hasattr(tensor, 'dtype')
else None,
device=tensor.device
if hasattr(tensor, 'device')
else None,
)
)
for i, doc in enumerate(docs):
_check_doc_field_not_none(field_name, doc)
val = getattr(doc, field_name)
cast(AbstractTensor, tensor_columns[field_name])[i] = val
elif issubclass(field_type, BaseDoc):
if first_doc_is_none:
_verify_optional_field_of_docs(docs)
doc_columns[field_name] = None
else:
if is_field_required:
doc_columns[field_name] = getattr(
docs, field_name
).to_doc_vec(tensor_type=self.tensor_type)
else:
doc_columns[field_name] = DocList.__class_getitem__(
field_type
)(getattr(docs, field_name)).to_doc_vec(
tensor_type=self.tensor_type
)
elif issubclass(field_type, AnyDocArray):
if first_doc_is_none:
_verify_optional_field_of_docs(docs)
docs_vec_columns[field_name] = None
else:
docs_list = list()
for doc in docs:
docs_nested = getattr(doc, field_name)
_check_doc_field_not_none(field_name, doc)
if isinstance(docs_nested, DocList):
docs_nested = docs_nested.to_doc_vec(
tensor_type=self.tensor_type
)
docs_list.append(docs_nested)
docs_vec_columns[field_name] = ListAdvancedIndexing(docs_list)
else:
any_columns[field_name] = ListAdvancedIndexing(
getattr(docs, field_name)
)
else:
any_columns[field_name] = ListAdvancedIndexing(
getattr(docs, field_name)
)
self._storage = ColumnStorage(
tensor_columns,
doc_columns,
docs_vec_columns,
any_columns,
tensor_type,
)
@classmethod
def from_columns_storage(cls: Type[T], storage: ColumnStorage) -> T:
"""
Create a DocVec directly from a storage object
:param storage: the underlying storage.
:return: a DocVec
"""
docs = cls.__new__(cls)
docs.tensor_type = storage.tensor_type
docs._storage = storage
return docs
@classmethod
def validate(
cls: Type[T],
value: Union[T, Iterable[T_doc]],
field: 'ModelField',
config: 'BaseConfig',
) -> T:
if isinstance(value, cls):
return value
elif isinstance(value, DocList):
if (
issubclass(value.doc_type, cls.doc_type)
or value.doc_type == cls.doc_type
):
return cast(T, value.to_doc_vec())
else:
raise ValueError(f'DocVec[value.doc_type] is not compatible with {cls}')
elif isinstance(value, DocList.__class_getitem__(cls.doc_type)):
return cast(T, value.to_doc_vec())
elif isinstance(value, Sequence):
return cls(value)
elif isinstance(value, Iterable):
return cls(list(value))
else:
raise TypeError(f'Expecting an Iterable of {cls.doc_type}')
def to(self: T, device: str) -> T:
"""Move all tensors of this DocVec to the given device
:param device: the device to move the data to
"""
for field, col_tens in self._storage.tensor_columns.items():
if col_tens is not None:
self._storage.tensor_columns[
field
] = col_tens.get_comp_backend().to_device(col_tens, device)
for field, col_doc in self._storage.doc_columns.items():
if col_doc is not None:
self._storage.doc_columns[field] = col_doc.to(device)
for _, col_da in self._storage.docs_vec_columns.items():
if col_da is not None:
for docs in col_da:
docs.to(device)
return self
################################################
# Accessing data : Indexing / Getitem related #
################################################
@overload
def __getitem__(self: T, item: int) -> T_doc:
...
@overload
def __getitem__(self: T, item: IndexIterType) -> T:
...
def __getitem__(self: T, item: Union[int, IndexIterType]) -> Union[T_doc, T]:
if item is None:
return self # PyTorch behaviour
# multiple docs case
if isinstance(item, (slice, Iterable)):
return self.__class__.from_columns_storage(self._storage[item])
# single doc case
return self.doc_type.from_view(ColumnStorageView(item, self._storage))
def _get_data_column(
self: T,
field: str,
) -> Union[MutableSequence, 'DocVec', AbstractTensor, None]:
"""Return one column of the data
:param field: name of the fields to extract
:return: Returns a list of the field value for each document
in the array like container
"""
if field in self._storage.any_columns.keys():
return self._storage.any_columns[field]
elif field in self._storage.docs_vec_columns.keys():
return self._storage.docs_vec_columns[field]
elif field in self._storage.columns.keys():
return self._storage.columns[field]
else:
raise ValueError(f'{field} does not exist in {self}')
####################################
# Updating data : Setitem related #
####################################
@overload
def __setitem__(self: T, key: int, value: T_doc):
...
@overload
def __setitem__(self: T, key: IndexIterType, value: T):
...
@no_type_check
def __setitem__(self: T, key, value):
# single doc case
if not isinstance(key, (slice, Iterable)):
if not isinstance(value, self.doc_type):
raise ValueError(f'{value} is not a {self.doc_type}')
for field, value in value.dict().items():
self._storage.columns[field][key] = value # todo we might want to
# define a safety mechanism in someone put a wrong value
else:
# multiple docs case
self._set_data_and_columns(key, value)
def _set_data_and_columns(
self: T,
index_item: Union[Tuple, Iterable, slice],
value: Union[T, DocList[T_doc]],
) -> None:
"""Delegates the setting to the data and the columns.
:param index_item: the key used as index. Needs to be a valid index for both
DocList (data) and column types (torch/tensorflow/numpy tensors)
:value: the value to set at the `key` location
"""
if isinstance(index_item, tuple):
index_item = list(index_item)
# set data and prepare columns
processed_value: T
if isinstance(value, DocList):
if not issubclass(value.doc_type, self.doc_type):
raise TypeError(
f'{value} schema : {value.doc_type} is not compatible with '
f'this DocVec schema : {self.doc_type}'
)
processed_value = cast(
T, value.to_doc_vec(tensor_type=self.tensor_type)
) # we need to copy data here
elif isinstance(value, DocVec):
if not issubclass(value.doc_type, self.doc_type):
raise TypeError(
f'{value} schema : {value.doc_type} is not compatible with '
f'this DocVec schema : {self.doc_type}'
)
processed_value = value
else:
raise TypeError(f'Can not set a DocVec with {type(value)}')
for field, col in self._storage.columns.items():
col[index_item] = processed_value._storage.columns[field]
def _set_data_column(
self: T,
field: str,
values: Union[
Sequence[DocList[T_doc]],
Sequence[Any],
T,
DocList,
AbstractTensor,
None,
],
) -> None:
"""Set all Documents in this DocList using the passed values
:param field: name of the fields to set
:values: the values to set at the DocList level
"""
if values is None:
if field in self._storage.tensor_columns.keys():
self._storage.tensor_columns[field] = values
elif field in self._storage.doc_columns.keys():
self._storage.doc_columns[field] = values
elif field in self._storage.docs_vec_columns.keys():
self._storage.docs_vec_columns[field] = values
elif field in self._storage.any_columns.keys():
raise ValueError(
f'column {field} cannot be set to None, try to pass '
f'a list of None instead'
)
else:
raise ValueError(f'{field} does not exist in {self}')
else:
if len(values) != len(self._storage):
raise ValueError(
f'{values} has not the right length, expected '
f'{len(self._storage)} , got {len(values)}'
)
if field in self._storage.tensor_columns.keys():
col = self._storage.tensor_columns[field]
if col is not None:
validation_class = col.__unparametrizedcls__ or col.__class__
else:
validation_class = self.doc_type.__fields__[field].type_
# TODO shape check should be handle by the tensor validation
values = parse_obj_as(validation_class, values)
self._storage.tensor_columns[field] = values
elif field in self._storage.doc_columns.keys():
values_ = parse_obj_as(
DocVec.__class_getitem__(self.doc_type._get_field_type(field)),
values,
)
self._storage.doc_columns[field] = values_
elif field in self._storage.docs_vec_columns.keys():
values_ = cast(Sequence[DocList[T_doc]], values)
# TODO here we should actually check if this is correct
self._storage.docs_vec_columns[field] = values_
elif field in self._storage.any_columns.keys():
# TODO here we should actually check if this is correct
values_ = cast(Sequence, values)
self._storage.any_columns[field] = values_
else:
raise KeyError(f'{field} is not a valid field for this DocList')
####################
# Deleting data #
####################
def __delitem__(self, key: Union[int, IndexIterType]) -> None:
raise NotImplementedError(
f'{self.__class__.__name__} does not implement '
f'__del_item__. You are trying to delete an element'
f'from {self.__class__.__name__} which is not '
f'designed for this operation. Please `unstack`'
f' before doing the deletion'
)
####################
# Sequence related #
####################
def __iter__(self):
for i in range(len(self)):
yield self[i]
def __len__(self):
return len(self._storage)
def __eq__(self, other: Any) -> bool:
if not isinstance(other, DocVec):
return False
if self.doc_type != other.doc_type:
return False
if self.tensor_type != other.tensor_type:
return False
if self._storage != other._storage:
return False
return True
####################
# IO related #
####################
@classmethod
def from_protobuf(
cls: Type[T], pb_msg: 'DocVecProto', tensor_type: Type[AbstractTensor] = NdArray
) -> T:
"""create a DocVec from a protobuf message
:param pb_msg: the protobuf message to deserialize
:param tensor_type: the tensor type to use for the tensor columns.
Could be NdArray, TorchTensor, or TensorFlowTensor. Defaults to NdArray.
All tensors of the output DocVec will be of this type.
:return: The deserialized DocVec
"""
tensor_columns: Dict[str, Optional[AbstractTensor]] = {}
doc_columns: Dict[str, Optional['DocVec']] = {}
docs_vec_columns: Dict[str, Optional[ListAdvancedIndexing['DocVec']]] = {}
any_columns: Dict[str, ListAdvancedIndexing] = {}
for tens_col_name, tens_col_proto in pb_msg.tensor_columns.items():
if _is_none_ndarray_proto(tens_col_proto):
# handle values that were None before serialization
tensor_columns[tens_col_name] = None
else:
tensor_columns[tens_col_name] = tensor_type.from_protobuf(
tens_col_proto
)
for doc_col_name, doc_col_proto in pb_msg.doc_columns.items():
if _is_none_docvec_proto(doc_col_proto):
# handle values that were None before serialization
doc_columns[doc_col_name] = None
else:
col_doc_type: Type = cls.doc_type._get_field_type(doc_col_name)
doc_columns[doc_col_name] = DocVec.__class_getitem__(
col_doc_type
).from_protobuf(doc_col_proto, tensor_type=tensor_type)
for docs_vec_col_name, docs_vec_col_proto in pb_msg.docs_vec_columns.items():
vec_list: Optional[ListAdvancedIndexing]
if _is_none_list_of_docvec_proto(docs_vec_col_proto):
# handle values that were None before serialization
vec_list = None
else:
vec_list = ListAdvancedIndexing()
for doc_list_proto in docs_vec_col_proto.data:
col_doc_type = cls.doc_type._get_field_type(
docs_vec_col_name
).doc_type
vec_list.append(
DocVec.__class_getitem__(col_doc_type).from_protobuf(
doc_list_proto, tensor_type=tensor_type
)
)
docs_vec_columns[docs_vec_col_name] = vec_list
for any_col_name, any_col_proto in pb_msg.any_columns.items():
any_column: ListAdvancedIndexing = ListAdvancedIndexing()
for node_proto in any_col_proto.data:
content = cls.doc_type._get_content_from_node_proto(
node_proto, any_col_name
)
any_column.append(content)
any_columns[any_col_name] = any_column
storage = ColumnStorage(
tensor_columns=tensor_columns,
doc_columns=doc_columns,
docs_vec_columns=docs_vec_columns,
any_columns=any_columns,
tensor_type=tensor_type,
)
return cls.from_columns_storage(storage)
def to_protobuf(self) -> 'DocVecProto':
"""Convert DocVec into a Protobuf message"""
from docarray.proto import (
DocVecProto,
ListOfAnyProto,
ListOfDocArrayProto,
ListOfDocVecProto,
NdArrayProto,
)
doc_columns_proto: Dict[str, DocVecProto] = dict()
tensor_columns_proto: Dict[str, NdArrayProto] = dict()
da_columns_proto: Dict[str, ListOfDocArrayProto] = dict()
any_columns_proto: Dict[str, ListOfAnyProto] = dict()
for field, col_doc in self._storage.doc_columns.items():
if col_doc is None:
# put dummy empty DocVecProto for serialization
doc_columns_proto[field] = _none_docvec_proto()
else:
doc_columns_proto[field] = col_doc.to_protobuf()
for field, col_tens in self._storage.tensor_columns.items():
if col_tens is None:
# put dummy empty NdArrayProto for serialization
tensor_columns_proto[field] = _none_ndarray_proto()
else:
tensor_columns_proto[field] = (
col_tens.to_protobuf() if col_tens is not None else None
)
for field, col_da in self._storage.docs_vec_columns.items():
list_proto = ListOfDocVecProto()
if col_da:
for docs in col_da:
list_proto.data.append(docs.to_protobuf())
else:
# put dummy empty ListOfDocVecProto for serialization
list_proto = _none_list_of_docvec_proto()
da_columns_proto[field] = list_proto
for field, col_any in self._storage.any_columns.items():
list_proto = ListOfAnyProto()
for data in col_any:
list_proto.data.append(_type_to_protobuf(data))
any_columns_proto[field] = list_proto
return DocVecProto(
doc_columns=doc_columns_proto,
tensor_columns=tensor_columns_proto,
docs_vec_columns=da_columns_proto,
any_columns=any_columns_proto,
)
def to_doc_list(self: T) -> DocList[T_doc]:
"""Convert DocVec into a DocList.
Note this destroys the arguments and returns a new DocList
"""
unstacked_doc_column: Dict[str, Optional[DocList]] = dict()
unstacked_da_column: Dict[str, Optional[List[DocList]]] = dict()
unstacked_tensor_column: Dict[str, Optional[List[AbstractTensor]]] = dict()
unstacked_any_column = self._storage.any_columns
for field, doc_col in self._storage.doc_columns.items():
unstacked_doc_column[field] = doc_col.to_doc_list() if doc_col else None
for field, da_col in self._storage.docs_vec_columns.items():
unstacked_da_column[field] = (
[docs.to_doc_list() for docs in da_col] if da_col else None
)
for field, tensor_col in list(self._storage.tensor_columns.items()):
# list is needed here otherwise we cannot delete the column
if tensor_col is not None:
tensors = list()
for tensor in tensor_col:
tensor_copy = tensor.get_comp_backend().copy(tensor)
tensors.append(tensor_copy)
unstacked_tensor_column[field] = tensors
del self._storage.tensor_columns[field]
unstacked_column = ChainMap( # type: ignore
unstacked_any_column, # type: ignore
unstacked_tensor_column, # type: ignore
unstacked_da_column, # type: ignore
unstacked_doc_column, # type: ignore
) # type: ignore
docs = []
for i in range(len(self)):
data = {field: col[i] for field, col in unstacked_column.items()}
docs.append(self.doc_type.construct(**data))
del self._storage
return DocList.__class_getitem__(self.doc_type).construct(docs)
def traverse_flat(
self,
access_path: str,
) -> Union[List[Any], 'TorchTensor', 'NdArray']:
nodes = list(AnyDocArray._traverse(node=self, access_path=access_path))
flattened = AnyDocArray._flatten_one_level(nodes)
cls_to_check = (NdArray, TorchTensor) if TorchTensor is not None else (NdArray,)
if len(flattened) == 1 and isinstance(flattened[0], cls_to_check):
return flattened[0]
else:
return flattened