def _apply( self, fn: Any, remote_args: dict, block_list: BlockList, clear_input_blocks: bool, ) -> BlockList: context = DatasetContext.get_current() # Handle empty datasets. if block_list.initial_num_blocks() == 0: return block_list blocks = block_list.get_blocks_with_metadata() map_bar = ProgressBar("Map Progress", total=len(blocks)) if context.block_splitting_enabled: map_block = cached_remote_fn(_map_block_split).options(**remote_args) refs = [map_block.remote(b, fn, m.input_files) for b, m in blocks] else: map_block = cached_remote_fn(_map_block_nosplit).options( **dict(remote_args, num_returns=2) ) all_refs = [map_block.remote(b, fn, m.input_files) for b, m in blocks] data_refs = [r[0] for r in all_refs] refs = [r[1] for r in all_refs] # Release input block references. if clear_input_blocks: del blocks block_list.clear() # Common wait for non-data refs. try: results = map_bar.fetch_until_complete(refs) except (ray.exceptions.RayTaskError, KeyboardInterrupt) as e: # One or more mapper tasks failed, or we received a SIGINT signal # while waiting; either way, we cancel all map tasks. for ref in refs: ray.cancel(ref) # Wait until all tasks have failed or been cancelled. for ref in refs: try: ray.get(ref) except (ray.exceptions.RayTaskError, ray.exceptions.TaskCancelledError): pass # Reraise the original task failure exception. raise e from None new_blocks, new_metadata = [], [] if context.block_splitting_enabled: for result in results: for block, metadata in result: new_blocks.append(block) new_metadata.append(metadata) else: for block, metadata in zip(data_refs, results): new_blocks.append(block) new_metadata.append(metadata) return BlockList(list(new_blocks), list(new_metadata))
def execute( self, input_blocks: BlockList, output_num_blocks: int, clear_input_blocks: bool, *, map_ray_remote_args: Optional[Dict[str, Any]] = None, reduce_ray_remote_args: Optional[Dict[str, Any]] = None, merge_factor: int = 2, ) -> Tuple[BlockList, Dict[str, List[BlockMetadata]]]: logger.info("Using experimental push-based shuffle.") # TODO(swang): For jobs whose reduce work is heavier than the map work, # we should support fractional merge factors. # TODO(swang): For large jobs, we should try to choose the merge factor # automatically, e.g., by running one test round of map and merge tasks # and comparing their run times. # TODO(swang): Add option to automatically reduce write amplification # during map-merge stage, by limiting how many partitions can be # processed concurrently. input_blocks_list = input_blocks.get_blocks() # Preemptively clear the blocks list since we will incrementally delete # the last remaining references as we submit the dependent map tasks # during the map-merge stage. if clear_input_blocks: input_blocks.clear() if map_ray_remote_args is None: map_ray_remote_args = {} if reduce_ray_remote_args is None: reduce_ray_remote_args = {} # The placement strategy for reduce tasks is overwritten to colocate # them with their inputs from the merge stage, so remove any # pre-specified scheduling strategy here. reduce_ray_remote_args = reduce_ray_remote_args.copy() reduce_ray_remote_args.pop("scheduling_strategy", None) map_fn = self._map_partition merge_fn = self._merge def map_partition(*args, **kwargs): return map_fn(self.map, *args, **kwargs) def merge(*args, **kwargs): return merge_fn(self.reduce, *args, **kwargs) shuffle_map = cached_remote_fn(map_partition) shuffle_merge = cached_remote_fn(merge) def submit_map_task(arg): mapper_idx, block = arg # NOTE(swang): Results are shuffled between map and merge tasks, so # there is no advantage to colocating specific map and merge tasks. # Therefore, we do not specify a node affinity policy for map tasks # in case the caller or Ray has a better scheduling strategy, e.g., # based on data locality. map_result = shuffle_map.options( **map_ray_remote_args, num_returns=1 + schedule.num_merge_tasks_per_round, ).remote( mapper_idx, block, output_num_blocks, schedule, *self._map_args, ) metadata_ref = map_result.pop(0) return metadata_ref, map_result def submit_merge_task(arg): merge_idx, map_results = arg num_merge_returns = schedule.get_num_reducers_per_merge_idx(merge_idx) merge_result = shuffle_merge.options( num_returns=1 + num_merge_returns, **schedule.get_merge_task_options(merge_idx), ).remote( *map_results, reduce_args=self._reduce_args, ) metadata_ref = merge_result.pop(0) return metadata_ref, merge_result # Compute all constants used for task scheduling. num_cpus_per_node_map = _get_num_cpus_per_node_map() schedule = self._compute_shuffle_schedule( num_cpus_per_node_map, len(input_blocks_list), merge_factor, output_num_blocks, ) # ObjectRef results from the last round of tasks. Used to add # backpressure during pipelining of map and merge tasks. last_map_metadata_results = [] last_merge_metadata_results = [] # Final outputs from the map-merge stage. # This is a map from merge task index to a nested list of merge results # (ObjectRefs). Each merge task index corresponds to a partition of P # final reduce tasks. all_merge_results = [[] for _ in range(schedule.num_merge_tasks_per_round)] shuffle_map_metadata = [] shuffle_merge_metadata = [] map_bar = ProgressBar("Shuffle Map", position=0, total=len(input_blocks_list)) # Execute the map-merge stage. This submits tasks in rounds of M map # tasks and N merge tasks each. Task execution between map and merge is # pipelined, so that while executing merge for one round of inputs, we # also execute the map tasks for the following round. input_blocks_list = list(enumerate(input_blocks_list)) while input_blocks_list: # Execute one round of the map stage. # Pop from the inputs so that we can clear the memory ASAP. round_input_blocks = [] try: for _ in range(schedule.num_map_tasks_per_round): round_input_blocks.append(input_blocks_list.pop(0)) except IndexError: pass ( prev_map_metadata, last_map_metadata_results, map_results, ) = _execute_pipelined_stage( submit_map_task, last_map_metadata_results, round_input_blocks, progress_bar=map_bar, ) shuffle_map_metadata += prev_map_metadata # Shuffle the map results for the merge tasks. merge_args = [ (merge_idx, [map_result.pop(0) for map_result in map_results]) for merge_idx in range(schedule.num_merge_tasks_per_round) ] assert all([not map_result for map_result in map_results]) # Execute one round of the merge stage. ( prev_merge_metadata, last_merge_metadata_results, merge_results, ) = _execute_pipelined_stage( submit_merge_task, last_merge_metadata_results, merge_args, ) shuffle_merge_metadata += prev_merge_metadata for merge_idx, merge_result in enumerate(merge_results): all_merge_results[merge_idx].append(merge_result) del merge_results # Wait for last map and merge tasks to finish. prev_map_metadata, _, _ = _execute_pipelined_stage( None, last_map_metadata_results, [], progress_bar=map_bar ) shuffle_map_metadata += prev_map_metadata map_bar.close() prev_merge_metadata, _, _ = _execute_pipelined_stage( None, last_merge_metadata_results, [] ) shuffle_merge_metadata += prev_merge_metadata # Execute and wait for the reduce stage. new_metadata, new_blocks = self._execute_reduce_stage( output_num_blocks, schedule, reduce_ray_remote_args, all_merge_results ) stats = { "map": shuffle_map_metadata, "merge": shuffle_merge_metadata, "reduce": new_metadata, } return BlockList(list(new_blocks), list(new_metadata)), stats
def execute( self, input_blocks: BlockList, output_num_blocks: int, clear_input_blocks: bool, *, map_ray_remote_args: Optional[Dict[str, Any]] = None, reduce_ray_remote_args: Optional[Dict[str, Any]] = None ) -> Tuple[BlockList, Dict[str, List[BlockMetadata]]]: input_blocks_list = input_blocks.get_blocks() input_num_blocks = len(input_blocks_list) if map_ray_remote_args is None: map_ray_remote_args = {} if reduce_ray_remote_args is None: reduce_ray_remote_args = {} if "scheduling_strategy" not in reduce_ray_remote_args: reduce_ray_remote_args = reduce_ray_remote_args.copy() reduce_ray_remote_args["scheduling_strategy"] = "SPREAD" shuffle_map = cached_remote_fn(self.map) shuffle_reduce = cached_remote_fn(self.reduce) map_bar = ProgressBar("Shuffle Map", total=input_num_blocks) shuffle_map_out = [ shuffle_map.options( **map_ray_remote_args, num_returns=1 + output_num_blocks, ).remote(i, block, output_num_blocks, *self._map_args) for i, block in enumerate(input_blocks_list) ] # The first item returned is the BlockMetadata. shuffle_map_metadata = [] for i, refs in enumerate(shuffle_map_out): shuffle_map_metadata.append(refs[0]) shuffle_map_out[i] = refs[1:] # Eagerly delete the input block references in order to eagerly release # the blocks' memory. del input_blocks_list if clear_input_blocks: input_blocks.clear() shuffle_map_metadata = map_bar.fetch_until_complete( shuffle_map_metadata) map_bar.close() reduce_bar = ProgressBar("Shuffle Reduce", total=output_num_blocks) shuffle_reduce_out = [ shuffle_reduce.options( **reduce_ray_remote_args, num_returns=2, ).remote( *self._reduce_args, *[shuffle_map_out[i][j] for i in range(input_num_blocks)], ) for j in range(output_num_blocks) ] # Eagerly delete the map block references in order to eagerly release # the blocks' memory. del shuffle_map_out new_blocks, new_metadata = zip(*shuffle_reduce_out) new_metadata = reduce_bar.fetch_until_complete(list(new_metadata)) reduce_bar.close() stats = { "map": shuffle_map_metadata, "reduce": new_metadata, } return BlockList(list(new_blocks), list(new_metadata)), stats
def _apply( self, fn: Any, remote_args: dict, block_list: BlockList, clear_input_blocks: bool, name: Optional[str] = None, ) -> BlockList: """Note: this is not part of the Dataset public API.""" context = DatasetContext.get_current() blocks_in = block_list.get_blocks_with_metadata() # Early release block references. if clear_input_blocks: block_list.clear() orig_num_blocks = len(blocks_in) results = [] if name is None: name = "map" name = name.title() map_bar = ProgressBar(name, total=orig_num_blocks) class BlockWorker: def ready(self): return "ok" def map_block_split(self, block: Block, input_files: List[str]) -> BlockPartition: return _map_block_split(block, fn, input_files) @ray.method(num_returns=2) def map_block_nosplit( self, block: Block, input_files: List[str]) -> Tuple[Block, BlockMetadata]: return _map_block_nosplit(block, fn, input_files) if not remote_args: remote_args["num_cpus"] = 1 BlockWorker = ray.remote(**remote_args)(BlockWorker) workers = [BlockWorker.remote() for _ in range(self.min_size)] tasks = {w.ready.remote(): w for w in workers} tasks_in_flight = collections.defaultdict(int) metadata_mapping = {} block_indices = {} ready_workers = set() while len(results) < orig_num_blocks: ready, _ = ray.wait(list(tasks.keys()), timeout=0.01, num_returns=1, fetch_local=False) if not ready: if (len(workers) < self.max_size and len(ready_workers) / len(workers) > 0.8): w = BlockWorker.remote() workers.append(w) tasks[w.ready.remote()] = w map_bar.set_description( "Map Progress ({} actors {} pending)".format( len(ready_workers), len(workers) - len(ready_workers))) continue [obj_id] = ready worker = tasks.pop(obj_id) # Process task result. if worker in ready_workers: results.append(obj_id) tasks_in_flight[worker] -= 1 map_bar.update(1) else: ready_workers.add(worker) map_bar.set_description( "Map Progress ({} actors {} pending)".format( len(ready_workers), len(workers) - len(ready_workers))) # Schedule a new task. while (blocks_in and tasks_in_flight[worker] < self.max_tasks_in_flight_per_actor): block, meta = blocks_in.pop() if context.block_splitting_enabled: ref = worker.map_block_split.remote( block, meta.input_files) else: ref, meta_ref = worker.map_block_nosplit.remote( block, meta.input_files) metadata_mapping[ref] = meta_ref tasks[ref] = worker block_indices[ref] = len(blocks_in) tasks_in_flight[worker] += 1 map_bar.close() new_blocks, new_metadata = [], [] # Put blocks in input order. results.sort(key=block_indices.get) if context.block_splitting_enabled: for result in ray.get(results): for block, metadata in result: new_blocks.append(block) new_metadata.append(metadata) else: for block in results: new_blocks.append(block) new_metadata.append(metadata_mapping[block]) new_metadata = ray.get(new_metadata) return BlockList(new_blocks, new_metadata)
def apply( self, fn: Any, remote_args: dict, block_list: BlockList, clear_input_blocks: bool, ) -> BlockList: context = DatasetContext.get_current() blocks_in = block_list.get_blocks_with_metadata() # Early release block references. if clear_input_blocks: block_list.clear() orig_num_blocks = len(blocks_in) results = [] map_bar = ProgressBar("Map Progress", total=orig_num_blocks) class BlockWorker: def ready(self): return "ok" def map_block_split(self, block: Block, input_files: List[str]) -> BlockPartition: return _map_block_split(block, fn, input_files) @ray.method(num_returns=2) def map_block_nosplit( self, block: Block, input_files: List[str]) -> Tuple[Block, BlockMetadata]: return _map_block_nosplit(block, fn, input_files) if not remote_args: remote_args["num_cpus"] = 1 BlockWorker = ray.remote(**remote_args)(BlockWorker) self.workers = [BlockWorker.remote()] tasks = {w.ready.remote(): w for w in self.workers} metadata_mapping = {} ready_workers = set() while len(results) < orig_num_blocks: ready, _ = ray.wait(list(tasks), timeout=0.01, num_returns=1, fetch_local=False) if not ready: if len(ready_workers) / len(self.workers) > 0.8: w = BlockWorker.remote() self.workers.append(w) tasks[w.ready.remote()] = w map_bar.set_description( "Map Progress ({} actors {} pending)".format( len(ready_workers), len(self.workers) - len(ready_workers))) continue [obj_id] = ready worker = tasks[obj_id] del tasks[obj_id] # Process task result. if worker in ready_workers: results.append(obj_id) map_bar.update(1) else: ready_workers.add(worker) # Schedule a new task. if blocks_in: block, meta = blocks_in.pop() if context.block_splitting_enabled: ref = worker.map_block_split.remote( block, meta.input_files) else: ref, meta_ref = worker.map_block_nosplit.remote( block, meta.input_files) metadata_mapping[ref] = meta_ref tasks[ref] = worker map_bar.close() new_blocks, new_metadata = [], [] if context.block_splitting_enabled: for result in ray.get(results): for block, metadata in result: new_blocks.append(block) new_metadata.append(metadata) else: for block in results: new_blocks.append(block) new_metadata.append(metadata_mapping[block]) new_metadata = ray.get(new_metadata) return BlockList(new_blocks, new_metadata)