File size: 5,785 Bytes
b6ff324 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 | # ------------------------------------------------------------------------
# Copyright (c) 2024-present, BAAI. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ------------------------------------------------------------------------
"""Flex data loaders."""
import collections
import multiprocessing as mp
import time
import threading
import queue
import codewithgpu
import numpy as np
from diffnext.data.flex_pipelines import FeatureWorker
class BalancedQueues(object):
"""Balanced queues."""
def __init__(self, base_queue, num=1):
self.queues = [base_queue]
self.queues += [mp.Queue(base_queue._maxsize) for _ in range(num - 1)]
self.index = 0
def put(self, obj, block=True, timeout=None):
q = self.queues[self.index]
q.put(obj, block=block, timeout=timeout)
self.index = (self.index + 1) % len(self.queues)
def get(self, block=True, timeout=None):
q = self.queues[self.index]
obj = q.get(block=block, timeout=timeout)
self.index = (self.index + 1) % len(self.queues)
return obj
def get_n(self, num=1):
outputs = []
while len(outputs) < num:
obj = self.get()
if obj is not None:
outputs.append(obj)
return outputs
class DataLoaderBase(threading.Thread):
"""Base class of data loader."""
def __init__(self, worker, **kwargs):
super().__init__(daemon=True)
self.seed = kwargs.pop("seed", 1337)
self.shuffle = kwargs.pop("shuffle", True)
self.shard_id = kwargs.get("shard_id", 0)
self.num_shards = kwargs.get("num_shards", 1)
self.batch_size = kwargs.get("batch_size", 1)
self.num_workers = kwargs.get("num_workers", 1)
self.queue_depth = kwargs.get("queue_depth", 2)
# Build queues.
self.reader_queue = mp.Queue(self.queue_depth * self.batch_size)
self.worker_queue = mp.Queue(self.queue_depth * self.batch_size)
self.batch_queue = queue.Queue(self.queue_depth)
self.reader_queue = BalancedQueues(self.reader_queue, self.num_workers)
self.worker_queue = BalancedQueues(self.worker_queue, self.num_workers)
# Build readers.
self.readers = [
codewithgpu.DatasetReader(
output_queue=self.reader_queue,
partition_id=self.shard_id,
num_partitions=self.num_shards,
seed=self.seed + self.shard_id,
shuffle=self.shuffle,
**kwargs,
)
]
self.readers[0].start()
time.sleep(0.1)
# Build workers.
self.workers = []
for i in range(self.num_workers):
p = worker()
p.seed = self.seed + i + self.shard_id * self.num_workers
p.reader_queue = self.reader_queue.queues[i]
p.worker_queue = self.worker_queue.queues[i]
p.start()
self.workers.append(p)
time.sleep(0.1)
# Register cleanup callbacks.
def cleanup():
def terminate(processes):
for p in processes:
p.terminate()
p.join()
terminate(self.workers)
terminate(self.readers)
import atexit
atexit.register(cleanup)
# Start batch prefetching.
self.start()
def next(self):
"""Return the next batch of data."""
return self.__next__()
def run(self):
"""Main loop."""
def __call__(self):
return self.next()
def __iter__(self):
"""Return the iterator self."""
return self
def __next__(self):
"""Return the next batch of data."""
return [self.batch_queue.get()]
class DataLoader(DataLoaderBase):
"""Loader to return the batch of data."""
def __init__(self, dataset, worker, **kwargs):
kwargs.update({"path": dataset}) # Alias for codewithgpu.
self.contiguous = kwargs.pop("contiguous", True)
self.prefetch_count = kwargs.pop("prefetch_count", 50)
super().__init__(worker, **kwargs)
def run(self):
"""Main loop."""
prev_inputs = self.worker_queue.get_n(self.prefetch_count * self.batch_size)
next_inputs = []
while True:
# Use cached buffer for next N inputs.
if len(next_inputs) == 0:
next_inputs = prev_inputs
prev_inputs = []
# Collect the next batch.
outputs = collections.defaultdict(list)
for _ in range(self.batch_size):
inputs = next_inputs.pop(0)
for k, v in inputs.items():
outputs[k].extend(v)
prev_inputs += self.worker_queue.get_n(1)
# Stack batch data.
if self.contiguous:
if "latents" in outputs:
outputs["latents"] = np.stack(outputs["latents"])
# Send batch data to consumer.
self.batch_queue.put(outputs)
class FeatureDataLoader(DataLoader):
"""Loader to return the batch of data features."""
def __init__(self, dataset, **kwargs):
super().__init__(dataset, FeatureWorker, **kwargs)
|