#
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# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You 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.
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# limitations under the License.
#
"""Utilities for working with batched types in the Beam SDK.
A batched type is a type B that is logically equivalent to Sequence[E], where E
is some other type. Typically B has a different physical representation than
Sequence[E] for performance reasons.
A trivial example is B=np.array(dtype=np.int64), E=int.
"""
from typing import Callable
from typing import Generic
from typing import Iterator
from typing import List
from typing import Optional
from typing import Sequence
from typing import TypeVar
import numpy as np
from apache_beam.typehints import typehints
B = TypeVar('B')
E = TypeVar('E')
BATCH_CONVERTER_REGISTRY: List[Callable[[type, type], 'BatchConverter']] = []
[docs]class BatchConverter(Generic[B, E]):
def __init__(self, batch_type, element_type):
self._batch_type = batch_type
self._element_type = element_type
[docs] def produce_batch(self, elements: Sequence[E]) -> B:
"""Convert an instance of List[E] to a single instance of B."""
raise NotImplementedError
[docs] def explode_batch(self, batch: B) -> Iterator[E]:
"""Convert an instance of B to Iterator[E]."""
raise NotImplementedError
[docs] def combine_batches(self, batches: Sequence[B]) -> B:
raise NotImplementedError
[docs] def get_length(self, batch: B) -> int:
raise NotImplementedError
[docs] @staticmethod
def register(
batch_converter_constructor: Callable[[type, type], 'BatchConverter']):
BATCH_CONVERTER_REGISTRY.append(batch_converter_constructor)
return batch_converter_constructor
[docs] @staticmethod
def from_typehints(*, element_type, batch_type) -> 'BatchConverter':
element_type = typehints.normalize(element_type)
batch_type = typehints.normalize(batch_type)
for constructor in BATCH_CONVERTER_REGISTRY:
result = constructor(element_type, batch_type)
if result is not None:
return result
# TODO(BEAM-14339): Aggregate error information from the failed
# BatchConverter matches instead of this generic error.
raise TypeError(
f"Unable to find BatchConverter for element_type {element_type!r} and "
f"batch_type {batch_type!r}")
@property
def batch_type(self):
return self._batch_type
@property
def element_type(self):
return self._element_type
def __key(self):
return (self._element_type, self._batch_type)
def __eq__(self, other: 'BatchConverter') -> bool:
if isinstance(other, BatchConverter):
return self.__key() == other.__key()
return NotImplemented
def __hash__(self) -> int:
return hash(self.__key())
[docs]class ListBatchConverter(BatchConverter):
[docs] @staticmethod
@BatchConverter.register
def from_typehints(element_type, batch_type):
if (isinstance(batch_type, typehints.ListConstraint) and
batch_type.inner_type == element_type):
return ListBatchConverter(batch_type, element_type)
else:
return None
[docs] def produce_batch(self, elements):
return list(elements)
[docs] def explode_batch(self, batch):
return iter(batch)
[docs] def combine_batches(self, batches):
return sum(batches, [])
[docs] def get_length(self, batch):
return len(batch)
N = "ARBITRARY LENGTH DIMENSION"
[docs]class NumpyBatchConverter(BatchConverter):
def __init__(
self,
batch_type,
element_type,
dtype,
element_shape=(),
partition_dimension=0):
super().__init__(batch_type, element_type)
self.dtype = np.dtype(dtype)
self.element_shape = element_shape
self.partition_dimension = partition_dimension
[docs] @staticmethod
@BatchConverter.register
def from_typehints(element_type,
batch_type) -> Optional['NumpyBatchConverter']:
if not isinstance(element_type, NumpyTypeHint.NumpyTypeConstraint):
try:
element_type = NumpyArray[element_type, ()]
except TypeError:
# TODO: Is there a better way to detect if element_type is a dtype?
return None
if not isinstance(batch_type, NumpyTypeHint.NumpyTypeConstraint):
if not batch_type == np.ndarray:
# TODO: Include explanation for mismatch?
return None
batch_type = NumpyArray[element_type.dtype, (N, )]
if not batch_type.dtype == element_type.dtype:
return None
batch_shape = list(batch_type.shape)
partition_dimension = batch_shape.index(N)
batch_shape.pop(partition_dimension)
if not tuple(batch_shape) == element_type.shape:
return None
return NumpyBatchConverter(
batch_type,
element_type,
batch_type.dtype,
element_type.shape,
partition_dimension)
[docs] def produce_batch(self, elements):
return np.stack(elements, axis=self.partition_dimension)
[docs] def explode_batch(self, batch):
"""Convert an instance of B to Generator[E]."""
yield from batch.swapaxes(self.partition_dimension, 0)
[docs] def combine_batches(self, batches):
return np.concatenate(batches, axis=self.partition_dimension)
[docs] def get_length(self, batch):
return np.size(batch, axis=self.partition_dimension)
# numpy is starting to add typehints, which we should support
# https://numpy.org/doc/stable/reference/typing.html for now they don't allow
# specifying shape, seems to be coming after
# https://www.python.org/dev/peps/pep-0646/
[docs]class NumpyTypeHint():
[docs] class NumpyTypeConstraint(typehints.TypeConstraint):
def __init__(self, dtype, shape=()):
self.dtype = np.dtype(dtype)
self.shape = shape
[docs] def type_check(self, batch):
if not isinstance(batch, np.ndarray):
raise TypeError(f"Batch {batch!r} is not an instance of ndarray")
if not np.issubdtype(batch.dtype, self.dtype):
raise TypeError(
f"Batch {batch!r} does not have expected dtype: {self.dtype!r}")
for dim in range(len(self.shape)):
if not self.shape[dim] == N and not batch.shape[dim] == self.shape[dim]:
raise TypeError(
f"Batch {batch!r} does not have expected shape: {self.shape!r}")
def _consistent_with_check_(self, sub):
# TODO Check sub against batch type, and element type
return True
def __key(self):
return (self.dtype, self.shape)
def __eq__(self, other) -> bool:
if isinstance(other, NumpyTypeHint.NumpyTypeConstraint):
return self.__key() == other.__key()
return NotImplemented
def __hash__(self) -> int:
return hash(self.__key())
def __getitem__(self, value):
if isinstance(value, tuple):
if len(value) == 2:
dtype, shape = value
return self.NumpyTypeConstraint(dtype, shape=shape)
else:
raise ValueError
else:
dtype = value
return self.NumpyTypeConstraint(dtype, shape=(N, ))
NumpyArray = NumpyTypeHint()