wehub-resource-sync
102 lines
3.9 KiB
Python
102 lines
3.9 KiB
Python
from typing import Any, Dict, List, Optional
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from ray.data._internal.execution.interfaces import (
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AllToAllTransformFn,
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RefBundle,
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TaskContext,
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)
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from ray.data._internal.execution.interfaces.transform_fn import (
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AllToAllTransformFnResult,
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)
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from ray.data._internal.execution.operators.map_transformer import MapTransformer
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from ray.data._internal.execution.util import merge_label_selector
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from ray.data._internal.planner.exchange.pull_based_shuffle_task_scheduler import (
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PullBasedShuffleTaskScheduler,
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)
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from ray.data._internal.planner.exchange.push_based_shuffle_task_scheduler import (
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PushBasedShuffleTaskScheduler,
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)
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from ray.data._internal.planner.exchange.shuffle_task_spec import ShuffleTaskSpec
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from ray.data._internal.random_config import (
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RandomSeedConfig,
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get_single_integer_random_seed,
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)
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from ray.data.context import DataContext, ShuffleStrategy
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def generate_random_shuffle_fn(
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data_context: DataContext,
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seed_config: RandomSeedConfig,
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num_outputs: Optional[int] = None,
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ray_remote_args: Optional[Dict[str, Any]] = None,
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_debug_limit_shuffle_execution_to_num_blocks: Optional[int] = None,
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) -> AllToAllTransformFn:
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"""Generate function to randomly shuffle each records of blocks."""
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# If no seed has been specified, pin timestamp based one
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# so that task could be safely retried (w/o changing their output)
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seed = get_single_integer_random_seed(seed_config, data_context)
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def fn(
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refs: List[RefBundle],
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ctx: TaskContext,
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) -> AllToAllTransformFnResult:
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num_input_blocks = sum(len(r.blocks) for r in refs)
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# If map_transformer is specified (e.g. from fusing
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# MapOperator->AllToAllOperator), we pass a map function which
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# is applied to each block before shuffling.
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map_transformer: Optional[MapTransformer] = ctx.upstream_map_transformer
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upstream_map_fn = None
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nonlocal ray_remote_args
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if map_transformer:
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# NOTE: We override target max-block sizing of the previous
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# transformation to avoid unnecessary block shaping (if any)
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map_transformer.override_target_max_block_size(None)
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def upstream_map_fn(blocks):
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DataContext._set_current(data_context)
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return map_transformer.apply_transform(blocks, ctx)
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# If there is a fused upstream operator,
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# also use the ray_remote_args from the fused upstream operator.
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ray_remote_args = ctx.upstream_map_ray_remote_args
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shuffle_spec = ShuffleTaskSpec(
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target_shuffle_max_block_size=(
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ctx.target_max_block_size_override or data_context.target_max_block_size
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),
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random_shuffle=True,
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random_seed=seed,
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upstream_map_fn=upstream_map_fn,
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)
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if data_context.shuffle_strategy == ShuffleStrategy.SORT_SHUFFLE_PUSH_BASED:
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if num_outputs is not None:
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raise NotImplementedError(
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"Push-based shuffle doesn't support setting num_blocks yet."
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)
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scheduler = PushBasedShuffleTaskScheduler(shuffle_spec)
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else:
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scheduler = PullBasedShuffleTaskScheduler(shuffle_spec)
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label_selector = data_context.execution_options.label_selector
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map_ray_remote_args = merge_label_selector(
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ray_remote_args or {}, label_selector
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)
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reduce_ray_remote_args = merge_label_selector(
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ray_remote_args or {}, label_selector
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)
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return scheduler.execute(
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refs,
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num_outputs or num_input_blocks,
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task_ctx=ctx,
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map_ray_remote_args=map_ray_remote_args,
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reduce_ray_remote_args=reduce_ray_remote_args,
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_debug_limit_execution_to_num_blocks=(
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_debug_limit_shuffle_execution_to_num_blocks
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),
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)
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return fn
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