大规模人脸分类—allgather操作(1)

 pytorch中 all_gather 操作是不进行梯度回传的。在计算图构建中如果需要经过all_gather操作后，仍需要将梯度回传给各个进程中的allgather前的对应变量，则需要重新继承torch.autograd.Function

https://pytorch.org/docs/stable/autograd.html 中对torch.autograd.Function进行了介绍

https://pytorch.org/docs/stable/notes/extending.html#extending-torch-autograd 中举例介绍如何重新实现其子类

 下面代码是为了说明all_gather相关特性及如何实现梯度回传.

\(x,y,z\)都是2x2矩阵，其之间关系为\(y=x+2, z=y*y\)

接下来就需要MPI进行进程间数据传递，将z进行汇总到每个进程即all_gather操作。然后将汇总的矩阵进行相乘，然后求均值。

r对y的导数如下:

\(r=0.25({}_{g_0}y_{11}^2*{}_{g_1}y_{11}^2+{}_{g_0}y_{12}^2*{}_{g_1}y_{12}^2+
{}_{g_0}y_{21}^2*{}_{g_1}y_{21}^2+
{}_{g_0}y_{22}^2*{}_{g_1}y_{22}^2)\)

\(\frac{dr}{d{}_{g_0}y}=
\begin{Bmatrix}
0.5{}_{g_0}y_{11}*{}_{g_1}y_{11}^2 & 0.5{}_{g_0}y_{12}*{}_{g_1}y_{12}^2 \\
0.5{}_{g_0}y_{21}*{}_{g_1}y_{21}^2 & 0.5{}_{g_0}y_{22}*{}_{g_1}y_{22}^2)
\end{Bmatrix}\)

gpu0上x值为\(\begin{Bmatrix} 1 & 1 \\1 & 1 \end{Bmatrix}\)，gpu1上x值为\(\begin{Bmatrix} 0 & 0 \\0 & 0 \end{Bmatrix}\).通过公式可以计算出，r关于gpu0上的y的导数为\(\begin{Bmatrix}6 & 6 \\6 & 6\end{Bmatrix}\),r关于gpu1上的y的导数为\(\begin{Bmatrix}9 & 9 \\9 & 9\end{Bmatrix}\)

import os
import torch
from torch import nn
import sys
sys.path.append('./')
import torch.distributed as dist
from torch.autograd import Variable
from utils import GatherLayer

def test():
    #torch.manual_seed(0)
    torch.backends.cudnn.deterministic=True
    torch.backends.cudnn.benchmark=True
    dist.init_process_group(backend="nccl", init_method="env://")
    rank = dist.get_rank()
    local_rank = int(os.environ.get('LOCAL_RANK', 0))
    world_size = dist.get_world_size()
    torch.cuda.set_device(local_rank)
    print('world_size: {}, rank: {}, local_rank: {}'.format(world_size, rank, local_rank))

    if local_rank == 0:
        x = Variable(torch.ones(2, 2), requires_grad=True).cuda()
    else:
        x = Variable(torch.zeros(2, 2), requires_grad=True).cuda()
    y = x + 2
    y.retain_grad()
    z = y * y

    z_gather = [torch.zeros_like(z) for _ in range(world_size)]
    dist.all_gather(z_gather, z)
    #z_gather = GatherLayer.apply(z)
    r = z_gather[0] * z_gather[1]

    out = r.mean()
    out.backward()
    if local_rank == 0:
        print('rank:0', y.grad)
    else:
        print('rank:1', y.grad)

（1）上述述代码中，先使用pytorch中提供的all_gather操作，运行代码会提示错误。错误信息如下：

Traceback (most recent call last):
  File "test/test_all_gather.py", line 46, in <module>
Traceback (most recent call last):
  File "test/test_all_gather.py", line 46, in <module>
    test()
  File "test/test_all_gather.py", line 36, in test
    out.backward()
  File "/usr/local/lib/python3.6/dist-packages/torch/tensor.py", line 185, in backward
    torch.autograd.backward(self, gradient, retain_graph, create_graph)
  File "/usr/local/lib/python3.6/dist-packages/torch/autograd/__init__.py", line 127, in backward
    allow_unreachable=True)  # allow_unreachable flag
RuntimeError: element 0 of tensors does not require grad and does not have a grad_fn
    test()

（2）参考https://github.com/Spijkervet/SimCLR/blob/master/simclr/modules/gather.py, 该函数就是继承torch.autograd.Function，实现了all_gather后，梯度也能回传。

上述代码，启用z_gather = GatherLayer.apply(z),就实现了梯度回传功能，打印对变量y的梯度

world_size: 2, rank: 0, local_rank: 0
world_size: 2, rank: 1, local_rank: 1
rank:0 tensor([[6., 6.],
        [6., 6.]], device='cuda:0')
rank:1 tensor([[9., 9.],
        [9., 9.]], device='cuda:1')

GatherLayer类实现如下：

class GatherLayer(torch.autograd.Function):
    """Gather tensors from all process, supporting backward propagation."""

    @staticmethod
    def forward(ctx, input):
        ctx.save_for_backward(input)
        output = [torch.zeros_like(input) for _ in range(dist.get_world_size())]
        dist.all_gather(output, input)
        return tuple(output)

    @staticmethod
    def backward(ctx, *grads):
        (input,) = ctx.saved_tensors
        grad_out = torch.zeros_like(input)
        grad_out[:] = grads[dist.get_rank()]
        return grad_out

下面网址有关all gather梯度传播的讨论

https://discuss.pytorch.org/t/will-dist-all-gather-break-the-auto-gradient-graph/47350