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[Datasets] [Out-of-Band Serialization: 2/3] Refactor ExecutionPlan to maintain complete lineage and eagerly unlink block references. (#23931)
This PR refactors ExecutionPlan to maintain complete stage lineage, even for eagerly computed datasets, while ensuring that block references are unlinked as early as possible in order to more eagerly release block memory. This PR is the final precursor to adding the actual out-of-band serialization APIs (PR 3/3). The fully lineage has to be maintained, even for eagerly computed datasets, since the lineage is needed for out-of-band serialization of datasets.
This commit is contained in:
Clark Zinzow
GitHub
parent
73ed67e9e6
commit
9ee24530ab
5 changed files with 490 additions and 252 deletions
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@ -2606,22 +2606,22 @@ List[str]]]): The names of the columns to use as the features. Can be a list of
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to repeat indefinitely.
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"""
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from ray.data.dataset_pipeline import DatasetPipeline
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from ray.data.impl.plan import _rewrite_read_stage
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# If optimizations are enabled, rewrite the read stage into a OneToOneStage
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# to enable fusion with downstream map stages.
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ctx = DatasetContext.get_current()
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if self._plan._is_read_stage() and ctx.optimize_fuse_read_stages:
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self._plan._in_blocks.clear()
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blocks, read_stage = self._plan._rewrite_read_stage()
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outer_stats = DatasetStats(stages={}, parent=None)
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if self._plan.is_read_stage() and ctx.optimize_fuse_read_stages:
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blocks, _ = self._plan._get_source_blocks()
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blocks.clear()
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blocks, outer_stats, read_stage = _rewrite_read_stage(blocks)
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else:
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blocks = self._plan.execute()
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read_stage = None
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outer_stats = self._plan.stats()
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read_stage = None
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uuid = self._get_uuid()
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outer_stats.dataset_uuid = uuid
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if times is not None and times < 1:
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raise ValueError("`times` must be >= 1, got {}".format(times))
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uuid = self._get_uuid()
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class Iterator:
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def __init__(self, blocks):
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@ -2719,6 +2719,7 @@ List[str]]]): The names of the columns to use as the features. Can be a list of
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exclusive with ``blocks_per_window``.
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"""
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from ray.data.dataset_pipeline import DatasetPipeline
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from ray.data.impl.plan import _rewrite_read_stage
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if blocks_per_window is not None and bytes_per_window is not None:
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raise ValueError("Only one windowing scheme can be specified.")
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@ -2726,17 +2727,15 @@ List[str]]]): The names of the columns to use as the features. Can be a list of
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if blocks_per_window is None:
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blocks_per_window = 10
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# If optimizations are enabled, rewrite the read stage into a OneToOneStage
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# to enable fusion with downstream map stages.
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ctx = DatasetContext.get_current()
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if self._plan._is_read_stage() and ctx.optimize_fuse_read_stages:
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self._plan._in_blocks.clear()
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blocks, read_stage = self._plan._rewrite_read_stage()
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outer_stats = DatasetStats(stages={}, parent=None)
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if self._plan.is_read_stage() and ctx.optimize_fuse_read_stages:
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blocks, _ = self._plan._get_source_blocks()
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blocks.clear()
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blocks, outer_stats, read_stage = _rewrite_read_stage(blocks)
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else:
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blocks = self._plan.execute()
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read_stage = None
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outer_stats = self._plan.stats()
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read_stage = None
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class Iterator:
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def __init__(self, splits, epoch):
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@ -2814,19 +2813,9 @@ List[str]]]): The names of the columns to use as the features. Can be a list of
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Returns:
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A Dataset with all blocks fully materialized in memory.
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"""
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blocks, metadata = [], []
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for b, m in self._plan.execute().get_blocks_with_metadata():
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blocks.append(b)
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metadata.append(m)
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ds = Dataset(
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ExecutionPlan(
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BlockList(blocks, metadata),
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self._plan.stats(),
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dataset_uuid=self._get_uuid(),
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),
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self._epoch,
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lazy=False,
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)
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plan = self._plan.deep_copy(preserve_uuid=True)
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plan.execute(force_read=True)
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ds = Dataset(plan, self._epoch, lazy=False)
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ds._set_uuid(self._get_uuid())
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return ds
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@ -752,16 +752,21 @@ class DatasetPipeline(Generic[T]):
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self._optimized_stages = self._stages
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return
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# This dummy dataset will be used to get a set of optimized stages.
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dummy_ds = Dataset(
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ExecutionPlan(BlockList([], []), DatasetStats(stages={}, parent=None)),
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0,
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True,
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)
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# Apply all pipeline operations to the dummy dataset.
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for stage in self._stages:
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dummy_ds = stage(dummy_ds)
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dummy_ds._plan._optimize()
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# Get the optimized stages.
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_, _, stages = dummy_ds._plan._optimize()
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# Apply these optimized stages to the datasets underlying the pipeline.
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# These optimized stages will be executed by the PipelineExecutor.
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optimized_stages = []
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for stage in dummy_ds._plan._stages:
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for stage in stages:
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optimized_stages.append(
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lambda ds, stage=stage: Dataset(
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ds._plan.with_stage(stage), ds._epoch, True
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@ -261,6 +261,11 @@ class LazyBlockList(BlockList):
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self._cached_metadata = metadata
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return block_refs, metadata
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def compute_to_blocklist(self) -> BlockList:
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"""Launch all tasks and return a concrete BlockList."""
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blocks, metadata = self._get_blocks_with_metadata()
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return BlockList(blocks, metadata)
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def compute_first_block(self):
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"""Kick off computation for the first block in the list.
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@ -1,4 +1,14 @@
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from typing import Callable, Tuple, Optional, Union, Iterable, Iterator, TYPE_CHECKING
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import copy
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from typing import (
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Callable,
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List,
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Tuple,
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Optional,
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Union,
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Iterator,
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Iterable,
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TYPE_CHECKING,
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)
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import uuid
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if TYPE_CHECKING:
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@ -16,217 +26,6 @@ from ray.data.impl.lazy_block_list import LazyBlockList
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INHERITABLE_REMOTE_ARGS = ["scheduling_strategy"]
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class ExecutionPlan:
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"""A lazy execution plan for a Dataset."""
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def __init__(self, in_blocks: BlockList, stats: DatasetStats, dataset_uuid=None):
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"""Create a plan with no transformation stages.
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Args:
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in_blocks: Base list of blocks.
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stats: Stats for the base blocks.
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"""
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self._in_blocks = in_blocks
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self._out_blocks = None
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self._in_stats = stats
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self._out_stats = None
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self._stages = []
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self._dataset_uuid = dataset_uuid or uuid.uuid4().hex
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if not stats.dataset_uuid:
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stats.dataset_uuid = self._dataset_uuid
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def with_stage(self, stage: "Stage") -> "ExecutionPlan":
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"""Return a copy of this plan with the given stage appended.
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Args:
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stage: The stage to append.
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Returns:
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A new ExecutionPlan with this stage appended.
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"""
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if self._out_blocks:
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copy = ExecutionPlan(self._out_blocks, self._out_stats)
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copy._stages = [stage]
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else:
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copy = ExecutionPlan(self._in_blocks, self._in_stats)
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copy._stages = self._stages.copy()
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copy._stages.append(stage)
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return copy
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def initial_num_blocks(self) -> int:
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"""Get the estimated number of blocks after applying all plan stages."""
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if self._out_blocks:
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return self._out_blocks.initial_num_blocks()
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for stage in self._stages[::-1]:
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if stage.num_blocks is not None:
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return stage.num_blocks
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return self._in_blocks.initial_num_blocks()
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def schema(
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self, fetch_if_missing: bool = False
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) -> Union[type, "pyarrow.lib.Schema"]:
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"""Get the schema after applying all plan stages.
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Args:
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fetch_if_missing: Whether to execute the plan to fetch the schema.
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Returns:
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The schema of the output dataset.
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"""
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if self._stages:
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if fetch_if_missing:
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self.execute()
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blocks = self._out_blocks
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else:
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blocks = self._in_blocks
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if blocks:
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# Don't force fetching in case it's a lazy block list, in which case we
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# don't want to trigger full execution for a schema read. If we want to
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# trigger execution to get schema, we'll trigger read tasks progressively
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# until a viable schema is available, below.
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metadata = blocks.get_metadata(fetch_if_missing=False)
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else:
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metadata = []
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# Some blocks could be empty, in which case we cannot get their schema.
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# TODO(ekl) validate schema is the same across different blocks.
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for m in metadata:
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if m.schema is not None and (m.num_rows is None or m.num_rows > 0):
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return m.schema
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if not fetch_if_missing:
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return None
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# Synchronously fetch the schema.
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# For lazy block lists, this launches read tasks and fetches block metadata
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# until we find valid block schema.
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for _, m in blocks.iter_blocks_with_metadata():
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if m.schema is not None and (m.num_rows is None or m.num_rows > 0):
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return m.schema
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return None
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def meta_count(self) -> Optional[int]:
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"""Get the number of rows after applying all plan stages if possible.
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This method will never trigger any computation.
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Returns:
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The number of records of the result Dataset, or None.
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"""
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if self._stages:
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blocks = self._out_blocks
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else:
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blocks = self._in_blocks
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metadata = blocks.get_metadata() if blocks else None
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if metadata and all(m.num_rows is not None for m in metadata):
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return sum(m.num_rows for m in metadata)
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else:
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return None
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def execute(self, clear_input_blocks: bool = True) -> BlockList:
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"""Execute this plan.
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Args:
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clear_input_blocks: Whether to assume ownership of the input blocks,
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allowing them to be dropped from memory during execution.
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Returns:
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The blocks of the output dataset.
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"""
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if self._out_blocks is None:
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self._optimize()
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blocks = self._in_blocks
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stats = self._in_stats
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for stage in self._stages:
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stats_builder = stats.child_builder(stage.name)
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blocks, stage_info = stage(blocks, clear_input_blocks)
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if stage_info:
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stats = stats_builder.build_multistage(stage_info)
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else:
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stats = stats_builder.build(blocks)
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stats.dataset_uuid = uuid.uuid4().hex
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self._out_blocks = blocks
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self._out_stats = stats
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self._out_stats.dataset_uuid = self._dataset_uuid
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return self._out_blocks
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def clear(self) -> None:
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"""Clear all cached block references of this plan, including input blocks.
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This will render the plan un-executable unless the root is a LazyBlockList."""
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self._in_blocks.clear()
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self._out_blocks = None
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self._out_stats = None
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def stats(self) -> DatasetStats:
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"""Return stats for this plan, forcing execution if needed."""
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self.execute()
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return self._out_stats
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def _optimize(self) -> None:
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"""Apply stage fusion optimizations, updating this plan."""
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context = DatasetContext.get_current()
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if context.optimize_fuse_stages:
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if context.optimize_fuse_read_stages:
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self._rewrite_read_stages()
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self._fuse_one_to_one_stages()
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def _rewrite_read_stages(self) -> None:
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"""Rewrites read stages into one-to-one stages."""
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if self._stages and self._has_read_stage():
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block_list, stage = self._rewrite_read_stage()
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self._in_blocks = block_list
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self._in_stats = DatasetStats(stages={}, parent=None)
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self._stages.insert(0, stage)
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def _has_read_stage(self) -> bool:
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"""Whether this plan has a read stage for its input."""
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return isinstance(self._in_blocks, LazyBlockList)
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def _is_read_stage(self) -> bool:
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"""Whether this plan is a bare read stage."""
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return self._has_read_stage() and not self._stages
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def _rewrite_read_stage(self) -> Tuple[BlockList, "Stage"]:
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"""Rewrite the read stage to a OneToOne stage over read tasks as input.
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For example, suppose the plan was [Read -> MapBatches(Fn)]. These stages cannot
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be fused, since read stages are handled specially.
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After rewriting to [GetReadTasks -> MapBatches(DoRead) -> MapBatches(Fn)],
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now we can fuse the latter two MapBatches stages into a single OneToOne stage:
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[GetReadTasks -> MapBatches(DoRead -> Fn)].
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"""
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# Generate the "GetReadTasks" stage blocks.
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remote_args = self._in_blocks._remote_args
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blocks = []
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metadata = []
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for read_task in self._in_blocks._tasks:
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blocks.append(ray.put(read_task._read_fn))
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metadata.append(read_task.get_metadata())
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block_list = BlockList(blocks, metadata)
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def block_fn(read_fn: Callable[[], Iterator[Block]]) -> Iterator[Block]:
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for block in read_fn():
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yield block
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return block_list, OneToOneStage("read", block_fn, "tasks", remote_args)
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def _fuse_one_to_one_stages(self) -> None:
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"""Fuses compatible one-to-one stages."""
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optimized_stages = []
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prev_stage = None
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for stage in self._stages:
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if prev_stage is None:
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prev_stage = stage
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elif stage.can_fuse(prev_stage):
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prev_stage = stage.fuse(prev_stage)
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else:
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optimized_stages.append(prev_stage)
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prev_stage = stage
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if prev_stage:
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optimized_stages.append(prev_stage)
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prev_stage = None
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self._stages = optimized_stages
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class Stage:
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"""Represents a Dataset transform stage (e.g., map or shuffle)."""
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@ -249,6 +48,296 @@ class Stage:
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raise NotImplementedError
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class ExecutionPlan:
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"""A lazy execution plan for a Dataset."""
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# Implementation Notes:
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#
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# This lazy execution plan takes in an input block list and builds up a chain of
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# BlockList --> BlockList stages. When execution is triggered, it tries to fuse
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# together stages in order to reduce Ray task overhead and data copies.
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#
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# Internally, the execution plan holds two block lists:
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# * _in_blocks: The (possibly lazy) input block list.
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# * _snapshot_blocks: A snapshot of a computed block list, where this snapshot
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# is the cached output of executing some prefix in the stage chain.
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#
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# The stages in this execution plan are partitioned into two subchains: before the
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# snapshot and after the snapshot. When the snapshot exists from a previous
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# execution, any future executions will only have to execute the "after the
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# snapshot" subchain, using the snapshot as the input to that subchain.
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def __init__(self, in_blocks: BlockList, stats: DatasetStats, dataset_uuid=None):
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"""Create a plan with no transformation stages.
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Args:
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in_blocks: Base list of blocks.
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stats: Stats for the base blocks.
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dataset_uuid: Dataset's UUID.
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"""
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self._in_blocks = in_blocks
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self._in_stats = stats
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# A computed snapshot of some prefix of stages.
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self._snapshot_blocks = None
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self._snapshot_stats = None
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# Chains of stages.
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self._stages_before_snapshot = []
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self._stages_after_snapshot = []
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# Cache of optimized stages.
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self._last_optimized_stages = None
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self._dataset_uuid = dataset_uuid or uuid.uuid4().hex
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if not stats.dataset_uuid:
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stats.dataset_uuid = self._dataset_uuid
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def with_stage(self, stage: "Stage") -> "ExecutionPlan":
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"""Return a copy of this plan with the given stage appended.
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Args:
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stage: The stage to append.
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Returns:
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A new ExecutionPlan with this stage appended.
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"""
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copy = self.copy()
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copy._stages_after_snapshot.append(stage)
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return copy
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def copy(self) -> "ExecutionPlan":
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"""Create a shallow copy of this execution plan.
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This copy can be executed without mutating the original, but clearing the copy
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will also clear the original.
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Returns:
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A shallow copy of this execution plan.
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"""
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plan_copy = ExecutionPlan(self._in_blocks, self._in_stats)
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if self._snapshot_blocks is not None:
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# Copy over the existing snapshot.
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plan_copy._snapshot_blocks = self._snapshot_blocks
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plan_copy._snapshot_stats = self._snapshot_stats
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plan_copy._stages_before_snapshot = self._stages_before_snapshot.copy()
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plan_copy._stages_after_snapshot = self._stages_after_snapshot.copy()
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return plan_copy
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def deep_copy(self, preserve_uuid: bool = False) -> "ExecutionPlan":
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"""Create a deep copy of this execution plan.
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This copy can be executed AND cleared without mutating the original.
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Args:
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preserve_uuid: Whether to preserve the original UUID in the copy.
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Returns:
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A deep copy of this execution plan.
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"""
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dataset_uuid = None
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if preserve_uuid:
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dataset_uuid = self._dataset_uuid
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in_blocks = self._in_blocks
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if isinstance(in_blocks, BlockList):
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in_blocks = in_blocks.copy()
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plan_copy = ExecutionPlan(
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in_blocks, copy.copy(self._in_stats), dataset_uuid=dataset_uuid
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)
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if self._snapshot_blocks:
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||||
# Copy over the existing snapshot.
|
||||
plan_copy._snapshot_blocks = self._snapshot_blocks.copy()
|
||||
plan_copy._snapshot_stats = copy.copy(self._snapshot_stats)
|
||||
plan_copy._stages_before_snapshot = self._stages_before_snapshot.copy()
|
||||
plan_copy._stages_after_snapshot = self._stages_after_snapshot.copy()
|
||||
return plan_copy
|
||||
|
||||
def initial_num_blocks(self) -> int:
|
||||
"""Get the estimated number of blocks after applying all plan stages."""
|
||||
if self.has_computed_output():
|
||||
return self._snapshot_blocks.initial_num_blocks()
|
||||
for stage in self._stages_after_snapshot[::-1]:
|
||||
if stage.num_blocks is not None:
|
||||
return stage.num_blocks
|
||||
if self._snapshot_blocks is not None:
|
||||
return self._snapshot_blocks.initial_num_blocks()
|
||||
for stage in self._stages_before_snapshot[::-1]:
|
||||
if stage.num_blocks is not None:
|
||||
return stage.num_blocks
|
||||
if self._in_blocks is not None:
|
||||
return self._in_blocks.initial_num_blocks()
|
||||
return None
|
||||
|
||||
def schema(
|
||||
self, fetch_if_missing: bool = False
|
||||
) -> Union[type, "pyarrow.lib.Schema"]:
|
||||
"""Get the schema after applying all plan stages.
|
||||
|
||||
Args:
|
||||
fetch_if_missing: Whether to execute the plan to fetch the schema.
|
||||
|
||||
Returns:
|
||||
The schema of the output dataset.
|
||||
"""
|
||||
if self._stages_after_snapshot:
|
||||
if fetch_if_missing:
|
||||
self.execute()
|
||||
else:
|
||||
return None
|
||||
# Snapshot is now guaranteed to be the output of the final stage or None.
|
||||
blocks = self._snapshot_blocks
|
||||
if not blocks:
|
||||
return None
|
||||
# Don't force fetching in case it's a lazy block list, in which case we
|
||||
# don't want to trigger full execution for a schema read. If we want to
|
||||
# trigger execution to get schema, we'll trigger read tasks progressively
|
||||
# until a viable schema is available, below.
|
||||
metadata = blocks.get_metadata(fetch_if_missing=False)
|
||||
# Some blocks could be empty, in which case we cannot get their schema.
|
||||
# TODO(ekl) validate schema is the same across different blocks.
|
||||
for m in metadata:
|
||||
if m.schema is not None and (m.num_rows is None or m.num_rows > 0):
|
||||
return m.schema
|
||||
if not fetch_if_missing:
|
||||
return None
|
||||
# Synchronously fetch the schema.
|
||||
# For lazy block lists, this launches read tasks and fetches block metadata
|
||||
# until we find valid block schema.
|
||||
for _, m in blocks.iter_blocks_with_metadata():
|
||||
if m.schema is not None and (m.num_rows is None or m.num_rows > 0):
|
||||
return m.schema
|
||||
return None
|
||||
|
||||
def meta_count(self) -> Optional[int]:
|
||||
"""Get the number of rows after applying all plan stages if possible.
|
||||
|
||||
This method will never trigger any computation.
|
||||
|
||||
Returns:
|
||||
The number of records of the result Dataset, or None.
|
||||
"""
|
||||
if self._stages_after_snapshot:
|
||||
return None
|
||||
# Snapshot is now guaranteed to be the output of the final stage or None.
|
||||
blocks = self._snapshot_blocks
|
||||
metadata = blocks.get_metadata() if blocks else None
|
||||
if metadata and all(m.num_rows is not None for m in metadata):
|
||||
return sum(m.num_rows for m in metadata)
|
||||
else:
|
||||
return None
|
||||
|
||||
def execute(
|
||||
self, clear_input_blocks: bool = True, force_read: bool = False
|
||||
) -> BlockList:
|
||||
"""Execute this plan.
|
||||
|
||||
Args:
|
||||
clear_input_blocks: Whether to assume ownership of the input blocks,
|
||||
allowing them to be dropped from memory during execution.
|
||||
force_read: Whether to force the read stage to fully execute.
|
||||
|
||||
Returns:
|
||||
The blocks of the output dataset.
|
||||
"""
|
||||
if not self.has_computed_output():
|
||||
blocks, stats, stages = self._optimize()
|
||||
for stage in stages:
|
||||
stats_builder = stats.child_builder(stage.name)
|
||||
blocks, stage_info = stage(blocks, clear_input_blocks)
|
||||
if stage_info:
|
||||
stats = stats_builder.build_multistage(stage_info)
|
||||
else:
|
||||
stats = stats_builder.build(blocks)
|
||||
stats.dataset_uuid = uuid.uuid4().hex
|
||||
# Set the snapshot to the output of the final stage.
|
||||
self._snapshot_blocks = blocks
|
||||
self._snapshot_stats = stats
|
||||
self._snapshot_stats.dataset_uuid = self._dataset_uuid
|
||||
self._stages_before_snapshot += self._stages_after_snapshot
|
||||
self._stages_after_snapshot = []
|
||||
if _is_lazy(self._snapshot_blocks) and force_read:
|
||||
self._snapshot_blocks = self._snapshot_blocks.compute_to_blocklist()
|
||||
return self._snapshot_blocks
|
||||
|
||||
def clear(self) -> None:
|
||||
"""Clear all cached block references of this plan, including input blocks.
|
||||
|
||||
This will render the plan un-executable unless the root is a LazyBlockList."""
|
||||
self._in_blocks.clear()
|
||||
self._snapshot_blocks = None
|
||||
self._snapshot_stats = None
|
||||
# We're erasing the snapshot, so put all stages into the "after snapshot"
|
||||
# bucket.
|
||||
self._stages_after_snapshot = (
|
||||
self._stages_before_snapshot + self._stages_after_snapshot
|
||||
)
|
||||
self._stages_before_snapshot = []
|
||||
|
||||
def stats(self) -> DatasetStats:
|
||||
"""Return stats for this plan, forcing execution if needed."""
|
||||
self.execute()
|
||||
return self._snapshot_stats
|
||||
|
||||
def _optimize(self) -> Tuple[BlockList, DatasetStats, List[Stage]]:
|
||||
"""Apply stage fusion optimizations, returning an updated source block list and
|
||||
associated stats, and a set of optimized stages.
|
||||
"""
|
||||
context = DatasetContext.get_current()
|
||||
blocks, stats = self._get_source_blocks()
|
||||
stages = self._stages_after_snapshot.copy()
|
||||
if context.optimize_fuse_stages:
|
||||
if context.optimize_fuse_read_stages:
|
||||
# If using a lazy datasource, rewrite read stage into one-to-one stage
|
||||
# so it can be fused into downstream stages.
|
||||
blocks, stats, stages = _rewrite_read_stages(
|
||||
blocks, stats, stages, self._dataset_uuid
|
||||
)
|
||||
stages = _fuse_one_to_one_stages(stages)
|
||||
self._last_optimized_stages = stages
|
||||
return blocks, stats, stages
|
||||
|
||||
def _get_source_blocks(self) -> Tuple[BlockList, DatasetStats]:
|
||||
"""Get the source blocks (and corresponding stats) for plan execution.
|
||||
|
||||
If a computed snapshot exists, return the snapshot blocks and stats; otherwise,
|
||||
return the input blocks and stats that the plan was created with.
|
||||
"""
|
||||
if self._snapshot_blocks is not None:
|
||||
# If snapshot exists, we only have to execute the plan from the
|
||||
# snapshot.
|
||||
blocks = self._snapshot_blocks
|
||||
stats = self._snapshot_stats
|
||||
# Unlink the snapshot blocks from the plan so we can eagerly reclaim the
|
||||
# snapshot block memory after the first stage is done executing.
|
||||
self._snapshot_blocks = None
|
||||
else:
|
||||
# If no snapshot exists, we have to execute the full plan from the
|
||||
# beginning.
|
||||
blocks = self._in_blocks
|
||||
stats = self._in_stats
|
||||
if not self.has_lazy_input():
|
||||
# If not a lazy datasource, unlink the input blocks from the plan so we
|
||||
# can eagerly reclaim the input block memory after the first stage is
|
||||
# done executing.
|
||||
self._in_blocks = None
|
||||
return blocks, stats
|
||||
|
||||
def has_lazy_input(self) -> bool:
|
||||
"""Return whether this plan has lazy input blocks."""
|
||||
return _is_lazy(self._in_blocks)
|
||||
|
||||
def is_read_stage(self) -> bool:
|
||||
"""Return whether this plan only consists of a read stage."""
|
||||
return (
|
||||
self.has_lazy_input()
|
||||
and not self._stages_before_snapshot
|
||||
and not self._stages_after_snapshot
|
||||
)
|
||||
|
||||
def has_computed_output(self) -> bool:
|
||||
"""Whether this plan has a computed snapshot for the final stage, i.e. for the
|
||||
output of this plan.
|
||||
"""
|
||||
return self._snapshot_blocks is not None and not self._stages_after_snapshot
|
||||
|
||||
|
||||
class OneToOneStage(Stage):
|
||||
"""A stage that transforms blocks independently (e.g., map or filter)."""
|
||||
|
||||
|
|
@ -269,15 +358,15 @@ class OneToOneStage(Stage):
|
|||
return False
|
||||
if prev.compute != self.compute:
|
||||
return False
|
||||
for key in INHERITABLE_REMOTE_ARGS:
|
||||
remote_args = self.ray_remote_args.copy()
|
||||
if key in prev.ray_remote_args:
|
||||
remote_args[key] = prev.ray_remote_args[key]
|
||||
if prev.ray_remote_args != remote_args:
|
||||
if not _are_remote_args_compatible(prev.ray_remote_args, self.ray_remote_args):
|
||||
return False
|
||||
return True
|
||||
|
||||
def fuse(self, prev: Stage):
|
||||
if not self.can_fuse(prev):
|
||||
raise ValueError(
|
||||
f"Tried to fuse {prev} with {self}, but these are not fusable."
|
||||
)
|
||||
name = prev.name + "->" + self.name
|
||||
fn1 = prev.block_fn
|
||||
fn2 = self.block_fn
|
||||
|
|
@ -334,6 +423,10 @@ class AllToAllStage(Stage):
|
|||
return True
|
||||
|
||||
def fuse(self, prev: Stage):
|
||||
if not self.can_fuse(prev):
|
||||
raise ValueError(
|
||||
f"Tried to fuse {prev} with {self}, but these are not fusable."
|
||||
)
|
||||
assert self.supports_block_udf
|
||||
name = prev.name + "->" + self.name
|
||||
return AllToAllStage(
|
||||
|
|
@ -348,3 +441,93 @@ class AllToAllStage(Stage):
|
|||
)
|
||||
assert isinstance(blocks, BlockList), blocks
|
||||
return blocks, stage_info
|
||||
|
||||
|
||||
def _rewrite_read_stages(
|
||||
blocks: BlockList,
|
||||
stats: DatasetStats,
|
||||
stages: List[Stage],
|
||||
dataset_uuid: str,
|
||||
) -> Tuple[BlockList, DatasetStats, List[Stage]]:
|
||||
"""Rewrites read stages into one-to-one stages, if needed."""
|
||||
if _is_lazy(blocks) and stages:
|
||||
blocks, stats, stage = _rewrite_read_stage(blocks)
|
||||
stats.dataset_uuid = dataset_uuid
|
||||
stages.insert(0, stage)
|
||||
return blocks, stats, stages
|
||||
|
||||
|
||||
def _rewrite_read_stage(
|
||||
in_blocks: LazyBlockList,
|
||||
) -> Tuple[BlockList, DatasetStats, Stage]:
|
||||
"""Rewrite the read stage to a OneToOne stage over read tasks as input.
|
||||
|
||||
For example, suppose the plan was [Read -> MapBatches(Fn)]. These stages cannot
|
||||
be fused, since read stages are handled specially.
|
||||
After rewriting to [GetReadTasks -> MapBatches(DoRead) -> MapBatches(Fn)],
|
||||
now we can fuse the latter two MapBatches stages into a single OneToOne stage:
|
||||
[GetReadTasks -> MapBatches(DoRead -> Fn)].
|
||||
|
||||
Args:
|
||||
blocks: Lazy block list representing read stage.
|
||||
|
||||
Returns:
|
||||
Non-lazy block list containing read tasks for not-yet-read block partitions,
|
||||
new stats for the block list, and the new one-to-one read stage.
|
||||
"""
|
||||
# Generate the "GetReadTasks" stage blocks.
|
||||
remote_args = in_blocks._remote_args
|
||||
blocks, metadata = [], []
|
||||
for read_task in in_blocks._tasks:
|
||||
blocks.append(ray.put(read_task._read_fn))
|
||||
metadata.append(read_task.get_metadata())
|
||||
block_list = BlockList(blocks, metadata)
|
||||
|
||||
def block_fn(read_fn: Callable[[], Iterator[Block]]) -> Iterator[Block]:
|
||||
for block in read_fn():
|
||||
yield block
|
||||
|
||||
stage = OneToOneStage("read", block_fn, "tasks", remote_args)
|
||||
stats = DatasetStats(stages={}, parent=None)
|
||||
return block_list, stats, stage
|
||||
|
||||
|
||||
def _fuse_one_to_one_stages(stages: List[Stage]) -> List[Stage]:
|
||||
"""Fuses compatible one-to-one stages.
|
||||
|
||||
Args:
|
||||
stages: Stages to try to fuse.
|
||||
|
||||
Returns:
|
||||
Fused stages.
|
||||
"""
|
||||
fused_stages = []
|
||||
prev_stage = None
|
||||
for idx, stage in enumerate(stages):
|
||||
if prev_stage is None:
|
||||
prev_stage = stage
|
||||
elif stage.can_fuse(prev_stage):
|
||||
prev_stage = stage.fuse(prev_stage)
|
||||
else:
|
||||
fused_stages.append(prev_stage)
|
||||
prev_stage = stage
|
||||
if prev_stage:
|
||||
fused_stages.append(prev_stage)
|
||||
prev_stage = None
|
||||
return fused_stages
|
||||
|
||||
|
||||
def _are_remote_args_compatible(prev_args, next_args):
|
||||
"""Check if Ray remote arguments are compatible for merging."""
|
||||
remote_args = next_args.copy()
|
||||
for key in INHERITABLE_REMOTE_ARGS:
|
||||
if key in prev_args:
|
||||
remote_args[key] = prev_args[key]
|
||||
if prev_args != remote_args:
|
||||
return False
|
||||
return True
|
||||
|
||||
|
||||
def _is_lazy(blocks: BlockList) -> bool:
|
||||
"""Whether the provided block list is lazy."""
|
||||
return isinstance(blocks, LazyBlockList)
|
||||
|
|
|
|||
|
|
@ -87,11 +87,67 @@ def test_spread_hint_inherit(ray_start_regular_shared):
|
|||
ds = ray.data.range(10)._experimental_lazy()
|
||||
ds = ds.map(lambda x: x + 1)
|
||||
ds = ds.random_shuffle()
|
||||
for s in ds._plan._stages:
|
||||
for s in ds._plan._stages_before_snapshot:
|
||||
assert s.ray_remote_args == {}, s.ray_remote_args
|
||||
ds._plan._optimize()
|
||||
assert len(ds._plan._stages) == 1, ds._plan._stages
|
||||
assert ds._plan._stages[0].ray_remote_args == {"scheduling_strategy": "SPREAD"}
|
||||
for s in ds._plan._stages_after_snapshot:
|
||||
assert s.ray_remote_args == {}, s.ray_remote_args
|
||||
_, _, optimized_stages = ds._plan._optimize()
|
||||
assert len(optimized_stages) == 1, optimized_stages
|
||||
assert optimized_stages[0].ray_remote_args == {"scheduling_strategy": "SPREAD"}
|
||||
|
||||
|
||||
def test_execution_preserves_original(ray_start_regular_shared):
|
||||
ds = ray.data.range(10).map(lambda x: x + 1)._experimental_lazy()
|
||||
ds1 = ds.map(lambda x: x + 1)
|
||||
assert ds1._plan._snapshot_blocks is not None
|
||||
assert len(ds1._plan._stages_after_snapshot) == 1
|
||||
ds2 = ds1.fully_executed()
|
||||
# Confirm that snapshot blocks and stages on original lazy dataset have not changed.
|
||||
assert ds1._plan._snapshot_blocks is not None
|
||||
assert len(ds1._plan._stages_after_snapshot) == 1
|
||||
# Confirm that UUID on executed dataset is properly set.
|
||||
assert ds2._get_uuid() == ds1._get_uuid()
|
||||
# Check content.
|
||||
assert ds2.take() == list(range(2, 12))
|
||||
# Check original lazy dataset content.
|
||||
assert ds1.take() == list(range(2, 12))
|
||||
# Check source lazy dataset content.
|
||||
# TODO(Clark): Uncomment this when we add new block clearing semantics.
|
||||
# assert ds.take() == list(range(1, 11))
|
||||
|
||||
|
||||
def _assert_has_stages(stages, stage_names):
|
||||
assert len(stages) == len(stage_names)
|
||||
for stage, name in zip(stages, stage_names):
|
||||
assert stage.name == name
|
||||
|
||||
|
||||
def test_stage_linking(ray_start_regular_shared):
|
||||
# NOTE: This tests the internals of `ExecutionPlan`, which is bad practice. Remove
|
||||
# this test once we have proper unit testing of `ExecutionPlan`.
|
||||
# Test eager dataset.
|
||||
ds = ray.data.range(10)
|
||||
assert len(ds._plan._stages_before_snapshot) == 0
|
||||
assert len(ds._plan._stages_after_snapshot) == 0
|
||||
assert len(ds._plan._last_optimized_stages) == 0
|
||||
ds = ds.map(lambda x: x + 1)
|
||||
_assert_has_stages(ds._plan._stages_before_snapshot, ["map"])
|
||||
assert len(ds._plan._stages_after_snapshot) == 0
|
||||
_assert_has_stages(ds._plan._last_optimized_stages, ["read->map"])
|
||||
|
||||
# Test lazy dataset.
|
||||
ds = ray.data.range(10)._experimental_lazy()
|
||||
assert len(ds._plan._stages_before_snapshot) == 0
|
||||
assert len(ds._plan._stages_after_snapshot) == 0
|
||||
assert len(ds._plan._last_optimized_stages) == 0
|
||||
ds = ds.map(lambda x: x + 1)
|
||||
assert len(ds._plan._stages_before_snapshot) == 0
|
||||
_assert_has_stages(ds._plan._stages_after_snapshot, ["map"])
|
||||
assert ds._plan._last_optimized_stages is None
|
||||
ds = ds.fully_executed()
|
||||
_assert_has_stages(ds._plan._stages_before_snapshot, ["map"])
|
||||
assert len(ds._plan._stages_after_snapshot) == 0
|
||||
_assert_has_stages(ds._plan._last_optimized_stages, ["read->map"])
|
||||
|
||||
|
||||
def test_optimize_fuse(ray_start_regular_shared):
|
||||
|
|
@ -220,7 +276,7 @@ class MySource(CSVDatasource):
|
|||
def _read_stream(self, f, path: str, **reader_args):
|
||||
count = self.counter.increment.remote()
|
||||
ray.get(count)
|
||||
for block in CSVDatasource._read_stream(self, f, path, **reader_args):
|
||||
for block in super()._read_stream(f, path, **reader_args):
|
||||
yield block
|
||||
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue