Backward Pass Not Delayed Despite Misaligned Gradient Communication

In the attached schedule diagram, Device 5 sends the input gradients for microbatch 0 at the end of T10, but Device 4 attempts to receive them at the beginning of T12. Why doesn't this misaligned communication delay the backward pass for the weights of microbatch 0 (the blue 0 happening at T11), given that it must wait for the communication to complete? Note: time is numbered from T0 from left to right.

Following is the related code in dualpipe.py:

```python
step_3 = num_half_ranks - half_rank - 1
for i in range(step_3):
self._backward_chunk(1, enable_zb=True) # enable zero bubble - decouple backward for inputs and backward for weights
self._recv_forward(1)
self._weight_chunk()
self._forward_chunk(1, recv=False)

# Step 4 (Main step): nF0B1F1B0
step_4 = half_num_chunks - num_ranks + half_rank + 1
for i in range(step_4):
if i == 0:
if self.is_middle_rank:
# NOTE: We don't overlap these two chunks to further reduce bubble size.
self._forward_chunk(0, recv=False, send=False)
self._send_forward(1)
self._backward_chunk(1, send=False)
self._send_forward(0)
self._send_backward(1)
else:
self._forward_backward_chunk(0, 1, recv0=False)
else:
self._forward_backward_chunk(0, 1)
self._forward_backward_chunk(1, 0)
```

Start with dualpipe.py and the Step 3 and Step 4 loops shown in the issue. Trace the timing of _backward_chunk, _recv_forward, _forward_backward_chunk, and the related send operations against the attached schedule. Done means explaining whether the communication should delay the weight backward pass and identifying the cause of any mismatch.

Written by the indexing model from the issue text.