https://stackoverflow.com/questions/62067400/understanding-accumulated-gradients-in-pytorch
有一个小的计算图，两次前向梯度累积的结果，可以看到梯度是严格相等的。
代码：
import numpy as np
import torch
class ExampleLinear(torch.nn.Module):
def __init__(self):
super().__init__()
# Initialize the weight at 1
self.weight = torch.nn.Parameter(torch.Tensor([1]).float(),
requires_grad=True)
def forward(self, x):
return self.weight * x
model = ExampleLinear()
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
def calculate_loss(x: torch.Tensor) -> torch.Tensor:
y = 2 * x
y_hat = model(x)
temp1 = (y - y_hat)
temp2 = temp1**2
return temp2
# With mulitple batches of size 1
batches = [torch.tensor([4.0]), torch.tensor([2.0])]
optimizer.zero_grad()
for i, batch in enumerate(batches):
# The loss needs to be scaled, because the mean should be taken across the whole
# dataset, which requires the loss to be divided by the number of batches.
temp2 = calculate_loss(batch)
loss = temp2 / len(batches)
loss.backward()
print(f"Batch size 1 (batch {i}) - grad: {model.weight.grad}")
print(f"Batch size 1 (batch {i}) - weight: {model.weight}")
print("="*50)
# Updating the model only after all batches
optimizer.step()
print(f"Batch size 1 (final) - grad: {model.weight.grad}")
print(f"Batch size 1 (final) - weight: {model.weight}")
运行结果
Batch size 1 (batch 0) - grad: tensor([-16.])
Batch size 1 (batch 0) - weight: Parameter containing:
tensor([1.], requires_grad=True)
==================================================
Batch size 1 (batch 1) - grad: tensor([-20.])
Batch size 1 (batch 1) - weight: Parameter containing:
tensor([1.], requires_grad=True)
==================================================
Batch size 1 (final) - grad: tensor([-20.])
Batch size 1 (final) - weight: Parameter containing:
tensor([1.2000], requires_grad=True)
然而，如果训练一个真实的模型，结果没有这么理想，比如训练一个bert，𝐵=8,𝑁=1：没有梯度累积（累积每一步），
𝐵=2,𝑁=4：梯度累积（每 4 步累积一次）
使用带有梯度累积的 Batch Normalization 通常效果不佳，原因很简单，因为 BatchNorm 统计数据无法累积。更好的解决方案是使用 Group Normalization 而不是 BatchNorm。