File size: 7,072 Bytes
158b61b |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 |
""" Pytorch Distributed utils
This piece of code was heavily inspired by the equivalent of Fairseq-py
https://github.com/pytorch/fairseq
"""
import os
import signal
import math
import pickle
import torch.distributed
from onmt.utils.misc import set_random_seed
from onmt.utils.logging import init_logger, logger
def is_master(opt, device_id):
return opt.gpu_ranks[device_id] == 0
def multi_init(opt, device_id):
dist_init_method = 'tcp://{master_ip}:{master_port}'.format(
master_ip=opt.master_ip,
master_port=opt.master_port)
dist_world_size = opt.world_size
torch.distributed.init_process_group(
backend=opt.gpu_backend, init_method=dist_init_method,
world_size=dist_world_size, rank=opt.gpu_ranks[device_id])
gpu_rank = torch.distributed.get_rank()
if not is_master(opt, device_id):
logger.disabled = True
return gpu_rank
def all_reduce_and_rescale_tensors(tensors, rescale_denom,
buffer_size=10485760):
"""All-reduce and rescale tensors in chunks of the specified size.
Args:
tensors: list of Tensors to all-reduce
rescale_denom: denominator for rescaling summed Tensors
buffer_size: all-reduce chunk size in bytes
"""
# buffer size in bytes, determine equiv. # of elements based on data type
buffer_t = tensors[0].new(
math.ceil(buffer_size / tensors[0].element_size())).zero_()
buffer = []
def all_reduce_buffer():
# copy tensors into buffer_t
offset = 0
for t in buffer:
numel = t.numel()
buffer_t[offset:offset+numel].copy_(t.view(-1))
offset += numel
# all-reduce and rescale
torch.distributed.all_reduce(buffer_t[:offset])
buffer_t.div_(rescale_denom)
# copy all-reduced buffer back into tensors
offset = 0
for t in buffer:
numel = t.numel()
t.view(-1).copy_(buffer_t[offset:offset+numel])
offset += numel
filled = 0
for t in tensors:
sz = t.numel() * t.element_size()
if sz > buffer_size:
# tensor is bigger than buffer, all-reduce and rescale directly
torch.distributed.all_reduce(t)
t.div_(rescale_denom)
elif filled + sz > buffer_size:
# buffer is full, all-reduce and replace buffer with grad
all_reduce_buffer()
buffer = [t]
filled = sz
else:
# add tensor to buffer
buffer.append(t)
filled += sz
if len(buffer) > 0:
all_reduce_buffer()
def all_gather_list(data, max_size=4096):
"""Gathers arbitrary data from all nodes into a list."""
world_size = torch.distributed.get_world_size()
if not hasattr(all_gather_list, '_in_buffer') or \
max_size != all_gather_list._in_buffer.size():
all_gather_list._in_buffer = torch.cuda.ByteTensor(max_size)
all_gather_list._out_buffers = [
torch.cuda.ByteTensor(max_size)
for i in range(world_size)
]
in_buffer = all_gather_list._in_buffer
out_buffers = all_gather_list._out_buffers
enc = pickle.dumps(data)
enc_size = len(enc)
if enc_size + 2 > max_size:
raise ValueError(
'encoded data exceeds max_size: {}'.format(enc_size + 2))
assert max_size < 255*256
in_buffer[0] = enc_size // 255 # this encoding works for max_size < 65k
in_buffer[1] = enc_size % 255
in_buffer[2:enc_size+2] = torch.ByteTensor(list(enc))
torch.distributed.all_gather(out_buffers, in_buffer.cuda())
results = []
for i in range(world_size):
out_buffer = out_buffers[i]
size = (255 * out_buffer[0].item()) + out_buffer[1].item()
bytes_list = bytes(out_buffer[2:size+2].tolist())
result = pickle.loads(bytes_list)
results.append(result)
return results
class ErrorHandler(object):
"""A class that listens for exceptions in children processes and propagates
the tracebacks to the parent process."""
def __init__(self, error_queue):
""" init error handler """
import signal
import threading
self.error_queue = error_queue
self.children_pids = []
self.error_thread = threading.Thread(
target=self.error_listener, daemon=True)
self.error_thread.start()
signal.signal(signal.SIGUSR1, self.signal_handler)
def add_child(self, pid):
""" error handler """
self.children_pids.append(pid)
def error_listener(self):
""" error listener """
(rank, original_trace) = self.error_queue.get()
self.error_queue.put((rank, original_trace))
os.kill(os.getpid(), signal.SIGUSR1)
def signal_handler(self, signalnum, stackframe):
""" signal handler """
for pid in self.children_pids:
os.kill(pid, signal.SIGINT) # kill children processes
(rank, original_trace) = self.error_queue.get()
msg = """\n\n-- Tracebacks above this line can probably
be ignored --\n\n"""
msg += original_trace
raise Exception(msg)
def batch_producer(generator_to_serve, queue, semaphore, opt, device_id):
"""Produce batches to `queues` from `generator_to_serve`."""
log_level = "INFO" if opt.verbose or device_id == 0 else "WARNING"
init_logger(opt.log_file, log_level=log_level)
set_random_seed(opt.seed, False)
def pred(x):
"""
Filters batches that belong only
to gpu_ranks of current node
"""
for rank in opt.gpu_ranks:
if x[0] % opt.world_size == rank:
return True
generator_to_serve = filter(
pred, enumerate(generator_to_serve))
def next_batch():
# NOTE: stride (if needed) is handled at the
# generator (train_iter) level
new_batch = next(generator_to_serve)
semaphore.acquire()
return new_batch[1]
b = next_batch()
while True:
b.dataset = None
# Move batch to correspond device_id when consumer iterate
# hack to dodge unpicklable `dict_keys`
b.fields = list(b.fields)
queue.put(b)
b = next_batch()
def consumer(process_fn, opt, device_id, error_queue, batch_queue, semaphore): # noqa: E501
"""Run `process_fn` on `device_id` with data from `batch_queue`."""
try:
gpu_rank = multi_init(opt, device_id)
if gpu_rank != opt.gpu_ranks[device_id]:
raise AssertionError("An error occurred in \
Distributed initialization")
process_fn(opt, device_id=device_id,
batch_queue=batch_queue, semaphore=semaphore)
except KeyboardInterrupt:
pass # killed by parent, do nothing
except Exception:
# propagate exception to parent process, keeping original traceback
import traceback
error_queue.put((opt.gpu_ranks[device_id], traceback.format_exc()))
|