pytorch 模型训练时多卡负载不均衡（GPU的0卡显存过高）解决办法（简单有效）_多卡部署大模型还是爆显存

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)
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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)
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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。