本文主要解决pytorch在进行模型训练时出现GPU的0卡占用显存比其他卡要多的问题。 如下图所示：本机GPU卡为TITAN RTX，显存24220M，batch_size = 9，用了三张卡。第0卡显存占用24207M，这时仅仅是刚开始运行，数据只是少量的移到显卡上，如果数据在多点，0卡的显存肯定撑爆。出现0卡显存更高的原因：网络在反向传播的时候，计算loss的梯度默认都在0卡上计算。因此会比其他显卡多用一些显存，具体多用多少，主要还要看网络的结构。 因此，为了防止训练由于 out of memory 而中断。比较笨的办法是将batch_size设为6，即每张卡放2条数据。 batch_size = 6时，其他不变，如下图所示 有没有发现问题？显存只用了1，2卡的显存只用了16G不到。就因为0卡可能会超那么一点点显存，而牺牲了batch_size。 那么没有更优雅的方法呢？答案是肯定的。那就是借用下transformer-xl中用到的 BalancedDataParallel类。代码如下（代码出处）：
import torch
from torch.nn.parallel.data_parallel import DataParallel
from torch.nn.parallel.parallel_apply import parallel_apply
from torch.nn.parallel._functions import Scatter
def scatter(inputs, target_gpus, chunk_sizes, dim=0):
r"""
Slices tensors into approximately equal chunks and
distributes them across given GPUs. Duplicates
references to objects that are not tensors.
"""
def scatter_map(obj):
if isinstance(obj, torch.Tensor):
try:
return Scatter.apply(target_gpus, chunk_sizes, dim, obj)
except Exception:
print('obj', obj.size())
print('dim', dim)
print('chunk_sizes', chunk_sizes)
quit()
if isinstance(obj, tuple) and len(obj) > 0:
return list(zip(*map(scatter_map, obj)))
if isinstance(obj, list) and len(obj) > 0:
return list(map(list, zip(*map(scatter_map, obj))))
if isinstance(obj, dict) and len(obj) > 0:
return list(map(type(obj), zip(*map(scatter_map, obj.items()))))
return [obj for targets in target_gpus]
# After scatter_map is called, a scatter_map cell will exist. This cell
# has a reference to the actual function scatter_map, which has references
# to a closure that has a reference to the scatter_map cell (because the
# fn is recursive). To avoid this reference cycle, we set the function to
# None, clearing the cell
try:
return scatter_map(inputs)
finally:
scatter_map = None
def scatter_kwargs(inputs, kwargs, target_gpus, chunk_sizes, dim=0):
"""Scatter with support for kwargs dictionary"""
inputs = scatter(inputs, target_gpus, chunk_sizes, dim) if inputs else []
kwargs = scatter(kwargs, target_gpus, chunk_sizes, dim) if kwargs else []
if len(inputs) < len(kwargs):
inputs.extend([() for _ in range(len(kwargs) - len(inputs))])
elif len(kwargs) < len(inputs):
kwargs.extend([{} for _ in range(len(inputs) - len(kwargs))])
inputs = tuple(inputs)
kwargs = tuple(kwargs)
return inputs, kwargs
class BalancedDataParallel(DataParallel):
def __init__(self, gpu0_bsz, *args, **kwargs):
self.gpu0_bsz = gpu0_bsz
super().__init__(*args, **kwargs)
def forward(self, *inputs, **kwargs):
if not self.device_ids:
return self.module(*inputs, **kwargs)
if self.gpu0_bsz == 0:
device_ids = self.device_ids[1:]
else:
device_ids = self.device_ids
inputs, kwargs = self.scatter(inputs, kwargs, device_ids)
if len(self.device_ids) == 1:
return self.module(*inputs[0], **kwargs[0])
replicas = self.replicate(self.module, self.device_ids)
if self.gpu0_bsz == 0:
replicas = replicas[1:]
outputs = self.parallel_apply(replicas, device_ids, inputs, kwargs)
return self.gather(outputs, self.output_device)
def parallel_apply(self, replicas, device_ids, inputs, kwargs):
return parallel_apply(replicas, inputs, kwargs, device_ids)
def scatter(self, inputs, kwargs, device_ids):
bsz = inputs[0].size(self.dim)
num_dev = len(self.device_ids)
gpu0_bsz = self.gpu0_bsz
bsz_unit = (bsz - gpu0_bsz) // (num_dev - 1)
if gpu0_bsz < bsz_unit:
chunk_sizes = [gpu0_bsz] + [bsz_unit] * (num_dev - 1)
delta = bsz - sum(chunk_sizes)
for i in range(delta):
chunk_sizes[i + 1] += 1
if gpu0_bsz == 0:
chunk_sizes = chunk_sizes[1:]
else:
return super().scatter(inputs, kwargs, device_ids)
return scatter_kwargs(inputs, kwargs, device_ids, chunk_sizes, dim=self.dim)
从代码中可以看到，BalancedDataParallel继承了 torch.nn.DataParallel，之后通过自定义0卡batch_size的大小gpu0_bsz，即让0卡少一点数据。均衡0卡和其他卡的显存占用。调用代码如下：
import BalancedDataParallel
if n_gpu > 1:
model = BalancedDataParallel(2, model, dim=0).to(device)
# model = torch.nn.DataParallel(model)
gpu0_bsz：GPU的0卡batch_size; model：模型； dim：batch所在维度
因此，我们不妨将刚才的batch_size设为8，即gpu0_bsz=2试试，结果如下： 成功的将batch_size从6调整到了8，因为0卡少放了一个batch，因此，会比其他的卡少。但是牺牲一张卡的显存，换取其他卡的显存，最终提高了batch_size，还是可取得。特别是当卡数目比较多的时候，这种方法的优势就更明显了。