[BUG]: Running ColossalAI in H800 with torch 2.0

[wxthu]

[BUG]: Running ColossalAI in H800 with torch 2.0

[wxthu]

@Edenzzzz

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@Edenzzzz

[Edenzzzz]

Hi,
Yes, I believe based on the readme you need torch 1.12 to run it. In fact some of these legacy APIs are under migration and are not guaranteed to be runnable, but I'll try some fixes tomorrow.

[wxthu]

Hi, Yes, I believe based on the readme you need torch 1.12 to run it. In fact some of these legacy APIs are under migration and are not guaranteed to be runnable, but I'll try some fixes tomorrow.

I know. But I want to deploy colossal on NVIDIA H800 GPU which only support cuda 12. Based on cuda 12, I can only install pytorch 2.0+ not 1.12.... Could you give me some further suggestions?

[Edenzzzz]

Sorry, I think the current auto parallel is less performant and popular so we didn't adapt it to the newest version. Do you have a compelling reason to use it?
Otherwise, it's advised to use the HybridParallelPlugin or Gemini (ZeRO 3 with chunk-based memory management)

[wxthu]

I don't necessarily have to use Auto Parallel Strategy . What I mean is that the official demos provided now are all based on the Torch 1.12 API, but on H800, only Torch 2.0+ can be used, which means I can't deploy training plans on H800.

[Edenzzzz]

Other demos should work on torch 2.0

[wxthu]

Could you give me some examples ? I have tried many training demo codes but they all failed on torch 2.0 but succeeded on torch 1.12..

[Edenzzzz]

Could you try examples/language/gpt/gemini and examples/language/gpt/hybridparallelism?

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Could you try examples/language/gpt/gemini and examples/language/gpt/hybrid parallelism?

[wxthu]

Could you try examples/language/gpt/gemini and examples/language/gpt/hybridparallelism?

It seems that transformer api is not compatible with current colossal ai

[Edenzzzz]

I have fixed this so pulling from the newest main branch should work

[wxthu]

api-version matching problem too

[Edenzzzz]

Could you either install apex from source or set enable_all_optimization=False? Thanks.

[wxthu]

I have re-compiled and re-installed apex from source and run the programs , got the following:

/usr/local/lib/python3.10/dist-packages/colossalai/nn/optimizer/hybrid_adam.py:90: UserWarning: The torch.cuda.*DtypeTensor constructors are no longer recommended. It's best to use methods such as torch.tensor(data, dtype=*, device='cuda') to create tensors. (Triggered internally at ../torch/csrc/tensor/python_tensor.cpp:83.)
  self._dummy_overflow_buf = torch.cuda.IntTensor([0])
Some weights of GPT2ForSequenceClassification were not initialized from the model checkpoint at gpt2 and are newly initialized: ['score.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
Epoch [1/3]:   0%|                                                                                                                     | 0/57 [00:00<?, ?it/s]Some weights of GPT2ForSequenceClassification were not initialized from the model checkpoint at gpt2 and are newly initialized: ['score.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
Epoch [1/3]:   0%|                                                                                                                     | 0/57 [00:00<?, ?it/s]
Traceback (most recent call last):
  File "/root/ColossalAI/examples/language/gpt/hybridparallelism/finetune.py", line 313, in <module>
    main()
  File "/root/ColossalAI/examples/language/gpt/hybridparallelism/finetune.py", line 293, in main
    train_epoch(epoch, model, optimizer, _criterion, lr_scheduler, train_dataloader, booster, coordinator)
  File "/root/ColossalAI/examples/language/gpt/hybridparallelism/finetune.py", line 147, in train_epoch
    outputs = booster.execute_pipeline(
  File "/usr/local/lib/python3.10/dist-packages/colossalai/booster/booster.py", line 205, in execute_pipeline
    return self.plugin.execute_pipeline(data_iter, model, criterion, optimizer, return_loss, return_outputs)
  File "/usr/local/lib/python3.10/dist-packages/colossalai/booster/plugin/hybrid_parallel_plugin.py", line 1259, in execute_pipeline
    outputs = self.schedule.forward_backward_step(
  File "/usr/local/lib/python3.10/dist-packages/colossalai/pipeline/schedule/one_f_one_b.py", line 445, in forward_backward_step
    result = self.run_forward_backward(model, data_iter, criterion, optimizer, return_loss, return_outputs)
  File "/usr/local/lib/python3.10/dist-packages/colossalai/pipeline/schedule/one_f_one_b.py", line 365, in run_forward_backward
    output_obj = self.forward_step(model, input_obj, criterion, accum_loss, outputs)
  File "/usr/local/lib/python3.10/dist-packages/colossalai/pipeline/schedule/one_f_one_b.py", line 249, in forward_step
    output_obj = model_forward(model, micro_batch, input_obj)
  File "/usr/local/lib/python3.10/dist-packages/colossalai/pipeline/schedule/_utils.py", line 120, in model_forward
    return model(**data, **internal_inputs)
  File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1527, in _call_impl
    return forward_call(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/colossalai/booster/plugin/hybrid_parallel_plugin.py", line 197, in forward
    return super().forward(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/colossalai/interface/model.py", line 25, in forward
    return self.module(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1527, in _call_impl
    return forward_call(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/colossalai/shardformer/modeling/gpt2.py", line 718, in gpt2_for_sequence_classification_forward
    outputs = GPT2PipelineForwards.gpt2_model_forward(
  File "/usr/local/lib/python3.10/dist-packages/colossalai/shardformer/modeling/gpt2.py", line 260, in gpt2_model_forward
    outputs = block(
  File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1527, in _call_impl
    return forward_call(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/transformers/models/gpt2/modeling_gpt2.py", line 390, in forward
    attn_outputs = self.attn(
  File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1518, in _wrapped_call_impl
    return self._call_impl(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1527, in _call_impl
    return forward_call(*args, **kwargs)
  File "/usr/local/lib/python3.10/dist-packages/colossalai/shardformer/modeling/gpt2.py", line 840, in forward
    attn_output = ColoAttention.attention(query, key, value, **attention_mask, dropout_p=dropout_p, scale=scale)
  File "/usr/local/lib/python3.10/dist-packages/colossalai/shardformer/layer/attn.py", line 250, in attention
    attn_func = ColoAttention._dispatch_kernel(q.dtype, mask_type)
  File "/usr/local/lib/python3.10/dist-packages/colossalai/shardformer/layer/attn.py", line 98, in _dispatch_kernel
    ].load()
  File "/usr/local/lib/python3.10/dist-packages/colossalai/kernel/kernel_loader.py", line 73, in load
    assert len(usable_exts) != 0, f"No usable kernel found for {self.__class__.__name__} on the current machine."
AssertionError: No usable kernel found for FlashAttentionWithPaddingMaskLoader on the current machine.
Some weights of GPT2ForSequenceClassification were not initialized from the model checkpoint at gpt2 and are newly initialized: ['score.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.

[Edenzzzz]

You'll need to either set enable_all_optimization=False or pip install flash-attn

[wxthu]

pip install flash-attn

set enable_all_optimization for colossal or apex?

[Edenzzzz]

[wxthu]

fix and thanks

[wxthu]

I have solved all the issues related to system env and when I re-run the program in ~/ColossalAI/examples/language/gpt/hybridparallelism I got the ProcessError:

[wxthu]

but this file really exists

[wxthu]

when I run the examples in ColossalAI/examples/language/gpt/hybridparallelism/ using command colossalai run --nproc_per_node=2 finetune.py, I always got the following error:

On the other hand , could you show how can I run this example in multiple node(machine). Thanks ! @Edenzzzz

[Edenzzzz]

Thanks for your issue. This is probably due to a recent transformers upgrade, so I've fixed it.
For multi-node please refer to commands in examples/language/llama/README.md

[wxthu]

Thanks for you reply. Actually, I have launched two Docker containers on two separate machines. How can I configure the Docker address in the host file

[Edenzzzz]

Please refer to similar examples in Pytorch forum. You can either run docker in host network mode or map a port from container to host.
https://discuss.pytorch.org/t/how-to-multi-node-parallel-in-dockers-container/188736
https://discuss.pytorch.org/t/run-multi-node-training-inside-docker/167537

[wxthu]

when I run the examples in ColossalAI/examples/language/gpt/hybridparallelism/ using command bash run.sh, I always got the following error:

Failed to run torch 2.1 in Tesla V100 GPU .....
@Edenzzzz do you test this demo on V100 GPU , cuda 12.1, torch 2.1?