Source code for apache_beam.typehints.batch

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"""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.

import random
from math import ceil
from typing import Callable
from typing import Generic
from typing import Iterator
from typing import Mapping
from typing import Optional
from typing import Sequence
from typing import TypeVar

import numpy as np

from apache_beam import coders
from apache_beam.typehints import typehints

__all__ = ['BatchConverter']

B = TypeVar('B')
E = TypeVar('E')

BatchConverterConstructor = Callable[[type, type], 'BatchConverter']
BATCH_CONVERTER_REGISTRY: Mapping[str, BatchConverterConstructor] = {}

__all__ = ['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] def estimate_byte_size(self, batch): raise NotImplementedError
[docs] @staticmethod def register(*, name: str): def do_registration( batch_converter_constructor: Callable[[type, type], 'BatchConverter']): if name in BATCH_CONVERTER_REGISTRY: raise AssertionError( f"Attempted to register two batch converters with name {name}") BATCH_CONVERTER_REGISTRY[name] = batch_converter_constructor return batch_converter_constructor return do_registration
[docs] @staticmethod def from_typehints(*, element_type, batch_type) -> 'BatchConverter': element_type = typehints.normalize(element_type) batch_type = typehints.normalize(batch_type) errors = {} for name, constructor in BATCH_CONVERTER_REGISTRY.items(): try: return constructor(element_type, batch_type) except TypeError as e: errors[name] = e.args[0] error_summaries = '\n\n'.join( f"{name}:\n\t{msg}" for name, msg in errors.items()) raise TypeError( f"Unable to find BatchConverter for element_type={element_type!r} and " f"batch_type={batch_type!r}. Error summaries:\n\n{error_summaries}")
@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())
class ListBatchConverter(BatchConverter): SAMPLE_FRACTION = 0.2 MAX_SAMPLES = 100 SAMPLED_BATCH_SIZE = MAX_SAMPLES / SAMPLE_FRACTION def __init__(self, batch_type, element_type): super().__init__(batch_type, element_type) self.element_coder = coders.registry.get_coder(element_type) @staticmethod @BatchConverter.register(name="list") def from_typehints(element_type, batch_type): if (not isinstance(batch_type, typehints.ListConstraint) or batch_type.inner_type != element_type): raise TypeError("batch type must be List[T] for element type T") return ListBatchConverter(batch_type, element_type) def produce_batch(self, elements): return list(elements) def explode_batch(self, batch): return iter(batch) def combine_batches(self, batches): return sum(batches, []) def get_length(self, batch): return len(batch) def estimate_byte_size(self, batch): # randomly sample a fraction of the elements and use the element_coder to # estimate the size of each nsampled = ( ceil(len(batch) * self.SAMPLE_FRACTION) if len(batch) < self.SAMPLED_BATCH_SIZE else self.MAX_SAMPLES) mean_byte_size = sum( self.element_coder.estimate_size(element) for element in random.sample(batch, nsampled)) / nsampled return ceil(mean_byte_size * len(batch)) N = "ARBITRARY LENGTH DIMENSION" 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 @staticmethod @BatchConverter.register(name="numpy") def from_typehints(element_type, batch_type) -> Optional['NumpyBatchConverter']: if not isinstance(element_type, NumpyTypeHint.NumpyTypeConstraint): try: element_type = NumpyArray[element_type, ()] except TypeError as e: raise TypeError("Element type is not a dtype") from e if not isinstance(batch_type, NumpyTypeHint.NumpyTypeConstraint): if not batch_type == np.ndarray: raise TypeError( "batch type must be np.ndarray or " "beam.typehints.batch.NumpyArray[..]") batch_type = NumpyArray[element_type.dtype, (N, )] if not batch_type.dtype == element_type.dtype: raise TypeError( "batch type and element type must have equivalent dtypes " f"(batch={batch_type.dtype}, element={element_type.dtype})") computed_element_shape = list(batch_type.shape) partition_dimension = computed_element_shape.index(N) computed_element_shape.pop(partition_dimension) if not tuple(computed_element_shape) == element_type.shape: raise TypeError( "Failed to align batch type's batch dimension with element type. " f"(batch type dimensions: {batch_type.shape}, element type " f"dimenstions: {element_type.shape}") return NumpyBatchConverter( batch_type, element_type, batch_type.dtype, element_type.shape, partition_dimension) def produce_batch(self, elements): return np.stack(elements, axis=self.partition_dimension) def explode_batch(self, batch): """Convert an instance of B to Generator[E].""" yield from batch.swapaxes(self.partition_dimension, 0) def combine_batches(self, batches): return np.concatenate(batches, axis=self.partition_dimension) def get_length(self, batch): return np.size(batch, axis=self.partition_dimension) def estimate_byte_size(self, batch): return batch.nbytes # numpy is starting to add typehints, which we should support # for now they don't allow # specifying shape, seems to be coming after # class NumpyTypeHint(): class NumpyTypeConstraint(typehints.TypeConstraint): def __init__(self, dtype, shape=()): self.dtype = np.dtype(dtype) self.shape = shape 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 __repr__(self): if self.shape == (N, ): return f'NumpyArray[{self.dtype!r}]' else: return f'NumpyArray[{self.dtype!r}, {self.shape!r}]' 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()