自定义反向传播Op时遇到BroadcastGradientArgs，求其功能说明

Great question! I’ve dealt with BroadcastGradientArgs quite a bit when debugging backprop logic for custom operations, so let me break down its purpose and how it works:

• Core Role: It’s a helper operation specifically designed to handle gradient propagation for tensor broadcasting. When you do a broadcast operation in the forward pass (like adding a (1,) tensor to a (5,) tensor), the backward pass needs to map the resulting gradients back to the original tensor shapes—and this op does the heavy lifting of figuring out how to adjust those gradients.

• What It Computes: Given the shapes of two tensors before they were broadcast to match each other, BroadcastGradientArgs returns two lists of integers:

  • The first list tells you which dimensions of the gradient (matching the broadcasted shape) need to be summed to get the correct gradient for the first original tensor.
  • The second list does the same for the second original tensor.

• Practical Example:
  Suppose in the forward pass you have tensor A with shape (2, 3) and tensor B with shape (3,). To perform an element-wise operation, B is broadcast to (2, 3). When computing gradients in reverse:

  • The gradient for A will already match its original shape ((2, 3)), so BroadcastGradientArgs returns an empty list [] for A—no summation needed.
  • The gradient for B starts as (2, 3), so the op returns [0] for B—meaning we need to sum the gradient along the 0th dimension to collapse it back to (3,), which matches B’s original shape.

• Why It Matters: Without this op, you’d have to manually calculate which dimensions to reduce for every broadcast scenario, which is error-prone especially with complex multi-dimensional broadcasts. It abstracts that logic into a reusable, optimized operation.

内容的提问来源于stack exchange，提问作者Raj