48d9afbeb37584586411c3d8e53d91228528dec4,fairseq/distributed_utils.py,,all_gather_list,#Any#Any#Any#,106

Before Change

        raise ValueError("encoded data exceeds max_size: {}".format(enc_size + 2))
    assert max_size < 255*256

    buffer_rank = buffer[rank * max_size : (rank + 1) * max_size]
    buffer_rank[0] = enc_size // 255  // this encoding works for max_size < 65k
    buffer_rank[1] = enc_size % 255
    buffer_rank[2:enc_size+2] = torch.ByteTensor(list(enc))

After Change

    cpu_buffer[2 : enc_size + 2] = torch.ByteTensor(list(enc))
    start = rank * max_size
    size = enc_size + 2
    buffer[start : start + size].copy_(cpu_buffer[:size])

    all_reduce(buffer, group=group)

    try:

Instances