Multi-gpu training problem

[humian321]

This is a good paper and very interested idea! There is a training cmd using a single gpu in readme. For multi-gpus training, could you provide the corresponding cmd ?

[humian321]

I tried to use the official MMengine distributed training command, but it reported an error.

RuntimeError: Expected to have finished reduction in the prior iteration before starting a new one. This error indicates that your module has parameters that were not used in producing loss. You can enable unused parameter detection by passing the keyword argument `find_unused_parameters=True` to `torch.nn.parallel.DistributedDataParallel`, and by 
making sure all `forward` function outputs participate in calculating loss. 
If you already have done the above, then the distributed data parallel module wasn't able to locate the output tensors in the return value of your module's `forward` function. Please include the loss function and the structure of the return value of `forward` of your module when reporting this issue (e.g. list, dict, iterable).
Parameter indices which did not receive grad for rank 1: 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 ...
 In addition, you can set the environment variable TORCH_DISTRIBUTED_DEBUG to either INFO or DETAIL to print out information about which particular parameters did not receive gradient on this rank as part of this error

[humian321]

I found the solution by adding find_unused_parameters = True

[w1oves]

Yes, you are right! I will update this soon.

[liblacklucy]

Using TORCH_DISTRIBUTED_DEBUG=DETAIL, you can find these unmatched grad and debug it. If using find_unused_parameters = True, whether increasing training time?