对梯度矩阵进行梯度裁减再进行相加求平均pytorch代码

以下是对梯度矩阵进行梯度裁减，再进行相加求平均的PyTorch代码：

import torch

# 定义模型和损失函数
model = MyModel()
criterion = nn.CrossEntropyLoss()

# 定义优化器
optimizer = torch.optim.SGD(model.parameters(), lr=0.1)

# 前向传播、反向传播、梯度裁剪
inputs, labels = data
outputs = model(inputs)
loss = criterion(outputs, labels)
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1)
optimizer.step()

# 对梯度矩阵进行梯度裁减
max_grad_norm = 1.0
total_norm = 0
for p in model.parameters():
    param_norm = p.grad.data.norm(2)
    total_norm += param_norm.item() ** 2
total_norm = total_norm ** 0.5
clip_coef = max_grad_norm / (total_norm + 1e-6)
if clip_coef < 1:
    for p in model.parameters():
        p.grad.data.mul_(clip_coef)

# 梯度相加求平均
grads = [p.grad.data for p in model.parameters()]
torch.distributed.all_reduce(torch.cat([g.view(-1) for g in grads]))
for p in model.parameters():
    p.grad.data /= torch.distributed.get_world_size()

# 更新参数
optimizer.step()