wehub-resource-sync
231 lines
8.4 KiB
Python
231 lines
8.4 KiB
Python
# Copyright (c) ModelScope Contributors. All rights reserved.
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import math
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import multiprocessing as mp
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import torch.distributed as dist
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from itertools import chain
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from torch.utils.data import Dataset, IterableDataset
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from tqdm import tqdm
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from typing import Optional
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from swift.template import MaxLengthError
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from swift.utils import get_logger, is_dist, is_master, split_list
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logger = get_logger()
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def calculate_matched_group(sequences, packing_length: int, is_finished: bool = True, strategy: str = 'binpack'):
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if len(sequences) == 0:
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return [], []
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if strategy == 'sequential':
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# Order-preserving greedy packing (next-fit): keep a single open pack and flush it
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# when the next sample doesn't fit, so the global sample order and pack boundaries
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# follow the input order (a sequential sampler). (Use packing_num_proc=1 for
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# a single global ordering.)
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packs, cur, cur_len = [], [], 0
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for item in sequences: # item = (idx, length); weight_pos=1 -> length at item[1]
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seq_len = item[1]
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if cur and cur_len + seq_len > packing_length:
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packs.append(cur)
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cur, cur_len = [], 0
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cur.append(item)
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cur_len += seq_len
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if cur_len >= packing_length:
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packs.append(cur)
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cur, cur_len = [], 0
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if is_finished:
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if cur:
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packs.append(cur)
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return packs, []
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return packs, cur
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# default: best-fit-decreasing bin packing (https://arxiv.org/pdf/2404.10830)
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import binpacking
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sequences = binpacking.to_constant_volume(sequences, packing_length, weight_pos=1)
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if sequences and not is_finished:
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sequences, ret_sequences = sequences[:-1], sequences[-1]
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else:
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ret_sequences = []
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return sequences, ret_sequences
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class PackingDataset(Dataset):
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PACKING_BATCH_SIZE = 1000
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def __init__(
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self,
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template,
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dataset,
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num_proc: int = 1,
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*,
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strict: bool = False,
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load_from_cache_file: bool = True,
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packing_length: Optional[int] = None,
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packing_num_proc: int = 1,
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packing_strategy: str = 'binpack',
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**kwargs,
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):
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template.packing = True
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template.padding_free = True # TODO: remove
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self.template = template
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self.dataset = dataset
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self.num_proc = num_proc
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self.strict = strict
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self.load_from_cache_file = load_from_cache_file
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self.packing_strategy = packing_strategy
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self.packing_length = packing_length or self.template.max_length
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self.packing_num_proc = min(packing_num_proc, math.ceil(len(dataset) / self.PACKING_BATCH_SIZE))
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self._out_queue = mp.Queue()
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if is_master():
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lengths = self.dataset['lengths']
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offset = 0
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chunked_lengths = split_list(lengths, self.packing_num_proc)
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for i in range(self.packing_num_proc):
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worker = mp.Process(
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target=self.create_packed_idx, args=(
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i,
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offset,
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chunked_lengths[i],
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), daemon=True)
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worker.start()
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offset += len(chunked_lengths[i])
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self.packed_idx = [[] for _ in range(self.packing_num_proc)]
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self.packed_length = [[] for _ in range(self.packing_num_proc)]
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desc = 'Packing: ' if self.packing_num_proc == 1 else f'Packing (num_proc={self.packing_num_proc}): '
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with tqdm(total=len(lengths), dynamic_ncols=True, desc=desc) as prog_bar:
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finished_workers = 0
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while finished_workers < self.packing_num_proc:
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rank, sequences, data_len = self._out_queue.get()
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if data_len == -1:
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finished_workers += 1
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continue
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prog_bar.update(data_len)
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self.packed_idx[rank] += [[x[0] for x in seq] for seq in sequences]
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self.packed_length[rank] += [sum(x[1] for x in seq) for seq in sequences]
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self.packed_idx = list(chain.from_iterable(self.packed_idx))
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self.packed_length = list(chain.from_iterable(self.packed_length))
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else:
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self.packed_idx, self.packed_length = None, None
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if dist.is_initialized() and is_dist():
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obj_list = [(self.packed_idx, self.packed_length)]
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dist.broadcast_object_list(obj_list)
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self.packed_idx, self.packed_length = obj_list[0]
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def create_packed_idx(self, rank, offset, lengths):
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data = [(i + offset, sum(length) if isinstance(length, list) else length) for i, length in enumerate(lengths)]
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i = 0
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input_data = []
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while True:
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new_data = data[i:i + self.PACKING_BATCH_SIZE]
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input_data += new_data
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if not input_data:
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break
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i += self.PACKING_BATCH_SIZE
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is_finished = i >= len(data)
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sequences, input_data = calculate_matched_group(
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input_data, self.packing_length, is_finished=is_finished, strategy=self.packing_strategy)
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self._out_queue.put((rank, sequences, len(new_data)))
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self._out_queue.put((rank, [], -1))
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def __getitem__(self, index):
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sequence = self.packed_idx[index]
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row = [self.dataset[i] for i in sequence]
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return row
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def __len__(self):
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return len(self.packed_idx)
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class IterablePackingDataset(IterableDataset):
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def __init__(
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self,
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template,
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dataset,
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num_proc: int = 1,
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*,
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packing_interval: int = 128,
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packing_length: Optional[int] = None,
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strict: bool = False,
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cyclic: bool = False,
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packing_strategy: str = 'binpack',
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**kwargs,
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):
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template.packing = True
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template.padding_free = True # TODO: remove
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self.template = template
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self.dataset = dataset
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self.num_proc = num_proc
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self.strict = strict
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self.packing_length = packing_length or self.template.max_length
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self.packing_interval = packing_interval
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self._in_queue = mp.Queue()
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self._out_queue = mp.Queue()
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self.workers = []
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self.cyclic = cyclic
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self.packing_strategy = packing_strategy
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for _ in range(self.num_proc):
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worker = mp.Process(target=self._processor, daemon=True)
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worker.start()
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self.workers.append(worker)
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def _processor(self):
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while True:
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i, data = self._in_queue.get()
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encoded_data = {}
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try:
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encoded_data = self.template.encode(data, return_length=True)
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except Exception as e:
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if self.strict and not isinstance(e, MaxLengthError):
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raise
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self._out_queue.put((i, encoded_data))
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def _put_data_in_queue(self, iterator) -> int:
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for i in range(self.packing_interval):
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try:
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data = next(iterator)
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except StopIteration:
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return i
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self._in_queue.put((i, data))
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return i + 1
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def _fetch_data_out_queue(self, last_res, num_samples):
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res = [None] * num_samples
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for _ in range(num_samples):
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i, data = self._out_queue.get()
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if not data:
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continue
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res[i] = (data, len(data['input_ids']))
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res = [data for data in res if data]
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last_res += res
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return last_res
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@staticmethod
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def cyclic_iter(iterable):
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while True:
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for x in iterable:
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yield x
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def __iter__(self):
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try:
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next(iter(self.dataset))
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except StopIteration:
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return
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if self.cyclic:
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iterator = self.cyclic_iter(self.dataset)
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else:
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iterator = iter(self.dataset)
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data = []
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while True:
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num_samples = self._put_data_in_queue(iterator)
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finished = num_samples != self.packing_interval
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data = self._fetch_data_out_queue(data, num_samples)
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sequences, data = calculate_matched_group(
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data, self.packing_length, is_finished=finished, strategy=self.packing_strategy)
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res = []
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for row in sequences:
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res.append([r[0] for r in row])
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yield from res
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if finished:
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break
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