[BUG] OOM when train 70B models using deepspeed 0.16.4

[hijkzzz]

We found that using OpenRLHF + DeepSpeed 0.15.0, SFT + Adam Offload can train a 70B model with 8 A100 70G + ZeRO3, whereas DeepSpeed 0.16.4 results in OOM. You can try the script https://github.com/OpenRLHF/OpenRLHF/blob/main/examples/scripts/train_sft_llama.sh and use the 70B model + Adam Offload to reproduce the issue.
It looks like this is a serious bug that deepspeed 16.4 can't train 70b models.

[thomZ1]

Reproduced the issue. In OpenRLHF 0.5.7, after upgrading deepspeed from 0.15.0 to 0.16.0, my ppo demo encounters OOM

  File "/root/miniconda3/lib/python3.10/site-packages/openrlhf/utils/deepspeed/deepspeed.py", line 129, in backward
    model.backward(loss)
  File "/root/miniconda3/lib/python3.10/site-packages/deepspeed/utils/nvtx.py", line 18, in wrapped_fn
    ret_val = func(*args, **kwargs)
  File "/root/miniconda3/lib/python3.10/site-packages/deepspeed/runtime/engine.py", line 2048, in backward
    self.optimizer.backward(loss, retain_graph=retain_graph)
  File "/root/miniconda3/lib/python3.10/site-packages/deepspeed/utils/nvtx.py", line 18, in wrapped_fn
    ret_val = func(*args, **kwargs)
  File "/root/miniconda3/lib/python3.10/site-packages/deepspeed/runtime/zero/stage3.py", line 2263, in backward
    self.loss_scaler.backward(loss.float(), retain_graph=retain_graph)
  File "/root/miniconda3/lib/python3.10/site-packages/deepspeed/runtime/fp16/loss_scaler.py", line 63, in backward
    scaled_loss.backward(retain_graph=retain_graph)
  File "/root/miniconda3/lib/python3.10/site-packages/torch/_tensor.py", line 581, in backward
    torch.autograd.backward(
  File "/root/miniconda3/lib/python3.10/site-packages/torch/autograd/__init__.py", line 347, in backward
    _engine_run_backward(
  File "/root/miniconda3/lib/python3.10/site-packages/torch/autograd/graph.py", line 825, in _engine_run_backward
    return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
  File "/root/miniconda3/lib/python3.10/site-packages/deepspeed/utils/nvtx.py", line 18, in wrapped_fn
    ret_val = func(*args, **kwargs)
  File "/root/miniconda3/lib/python3.10/site-packages/deepspeed/runtime/zero/stage3.py", line 1177, in reduce_partition_and_remove_grads
    self.reduce_ready_partitions_and_remove_grads(param)
  File "/root/miniconda3/lib/python3.10/site-packages/deepspeed/runtime/zero/stage3.py", line 1532, in reduce_ready_partitions_and_remove_grads
    self.reduce_independent_p_g_buckets_and_remove_grads(param)
  File "/root/miniconda3/lib/python3.10/site-packages/deepspeed/runtime/zero/stage3.py", line 1269, in reduce_independent_p_g_buckets_and_remove_grads
    self.__reduce_and_partition_ipg_grads()
  File "/root/miniconda3/lib/python3.10/site-packages/deepspeed/utils/nvtx.py", line 18, in wrapped_fn
    ret_val = func(*args, **kwargs)
  File "/root/miniconda3/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 116, in decorate_context
    return func(*args, **kwargs)
  File "/root/miniconda3/lib/python3.10/site-packages/deepspeed/runtime/zero/stage3.py", line 1319, in __reduce_and_partition_ipg_grads
    grad_partitions = self.__avg_scatter_grads(self.params_in_ipg_bucket)
  File "/root/miniconda3/lib/python3.10/site-packages/deepspeed/utils/nvtx.py", line 18, in wrapped_fn
    ret_val = func(*args, **kwargs)
  File "/root/miniconda3/lib/python3.10/site-packages/deepspeed/runtime/zero/stage3.py", line 1388, in __avg_scatter_grads
    grad_partitions_for_rank = reduce_scatter_coalesced(full_grads_for_rank, self.dp_process_group)
  File "/root/miniconda3/lib/python3.10/site-packages/deepspeed/utils/nvtx.py", line 18, in wrapped_fn
    ret_val = func(*args, **kwargs)
  File "/root/miniconda3/lib/python3.10/site-packages/torch/utils/_contextlib.py", line 116, in decorate_context
    return func(*args, **kwargs)
  File "/root/miniconda3/lib/python3.10/site-packages/deepspeed/runtime/comm/coalesced_collectives.py", line 122, in reduce_scatter_coalesced
    tensor_partition_flat_buffer = instrument_w_nvtx(torch.cat)(tensor_partitions_lst_with_padding)
  File "/root/miniconda3/lib/python3.10/site-packages/deepspeed/utils/nvtx.py", line 18, in wrapped_fn
    ret_val = func(*args, **kwargs)
torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 932.00 MiB. GPU 0 has a total capacity of 79.33 GiB of which 524.44 MiB is free. Process 99710 has 62.04 GiB memory in use. Process 100513 has 16.65 GiB memory in use. Of the allocated memory 59.29 GiB is allocated by PyTorch, and 1.12 GiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)

[thomZ1]

This commit cd20a3b introduced the issue

Open reasoner zero identified the same issue.
Open-Reasoner-Zero/Open-Reasoner-Zero#13

[hijkzzz]

@loadams

[thomZ1]

I found that this issue occurs because the ds_grads_remaining cannot correctly count for the forward pass when using gradient checkpointing.

Below is the log for enable gradient checkpointing.：

[loadams]

Thanks @hijkzzz and @thomZ1 for narrowing down the problematic commit.

Tagging @tjruwase and @wenbinc-Bin for the original PR to take a look as well.

[saurabhkoshatwar]

@loadams I'd like to work on this.

[casper-hansen]

Any update on this? Lots of people are waiting on this to be resolved, so they can upgrade to use the new AutoTP for additional optimization in their training