事件检测任务的复现

[yc1999]

Hi~

1. 想问你在多卡条件下跑过事件检测的代码嘛？我用4张卡跑，报了下面的错误：

Training:   0%|                                                                                                                                                               | 0/1159 [00:22<?, ?it/s]
Traceback (most recent call last):
  File "run_trigger_extraction.py", line 405, in <module>
    main()
  File "run_trigger_extraction.py", line 378, in main
    train(args, model, processor)
  File "run_trigger_extraction.py", line 243, in train
    pred_sub_heads, pred_sub_tails = model(data, add_label_info=add_label_info)
  File "/home/yc21/software/anaconda3/envs/lear/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
    return forward_call(*input, **kwargs)
  File "/home/yc21/software/anaconda3/envs/lear/lib/python3.8/site-packages/torch/nn/parallel/data_parallel.py", line 168, in forward
    outputs = self.parallel_apply(replicas, inputs, kwargs)
  File "/home/yc21/software/anaconda3/envs/lear/lib/python3.8/site-packages/torch/nn/parallel/data_parallel.py", line 178, in parallel_apply
    return parallel_apply(replicas, inputs, kwargs, self.device_ids[:len(replicas)])
  File "/home/yc21/software/anaconda3/envs/lear/lib/python3.8/site-packages/torch/nn/parallel/parallel_apply.py", line 86, in parallel_apply
    output.reraise()
  File "/home/yc21/software/anaconda3/envs/lear/lib/python3.8/site-packages/torch/_utils.py", line 434, in reraise
    raise exception
RuntimeError: Caught RuntimeError in replica 0 on device 0.
Original Traceback (most recent call last):
  File "/home/yc21/software/anaconda3/envs/lear/lib/python3.8/site-packages/torch/nn/parallel/parallel_apply.py", line 61, in _worker
    output = module(*input, **kwargs)
  File "/home/yc21/software/anaconda3/envs/lear/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
    return forward_call(*input, **kwargs)
  File "/home/yc21/project/LEAR/models/model_event.py", line 635, in forward
    fused_results = self.label_fusing_layer(
  File "/home/yc21/software/anaconda3/envs/lear/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
    return forward_call(*input, **kwargs)
  File "/home/yc21/project/LEAR/utils/model_utils.py", line 320, in forward
    return self.get_fused_feature_with_attn(token_embs, label_embs, input_mask, label_input_mask, return_scores=return_scores)
  File "/home/yc21/project/LEAR/utils/model_utils.py", line 504, in get_fused_feature_with_attn
    scores = torch.matmul(token_feature_fc, label_feature_t).view(
RuntimeError: shape '[4, 48, 33, -1]' is invalid for input of size 160512

2. 我用一张卡跑，设置batchsize=2, gradient_accumulation_step=16, 能够跑通，但是得到的结果是train上的loss收敛到了6，dev的f1一直是0。我不知道这只是我的个人问题，还是有其他人也存在这个问题。有童鞋跑出相应的结果了么？

谢谢大家~

[Akeepers]

1. 我记得是卡比较空的时候，写了多卡的代码，是DP的，所以需要padding_to_max，因为后面实验多，卡没有那么多，就没改成DDP，主要是梯度累积

2. 这个结果有问题，建议自己检查下，注意看这个“Note: The thunlp has updated the repo HMEAE recently, which causing the mismatch of data. Make sure you use the earlier version for ED task.”

[huanghuidmml]

在事件检测上提升不大，用了HMEAE老版本数据切分后，直接bio标注就能到81以上，甚至82

[Akeepers]

@huanghuidmml 这个，我自己是没有跑出来过这么高的结果的

[yc1999]

Hi~

1. 想问你在多卡条件下跑过事件检测的代码嘛？我用4张卡跑，报了下面的错误：

Training:   0%|                                                                                                                                                               | 0/1159 [00:22<?, ?it/s]
Traceback (most recent call last):
  File "run_trigger_extraction.py", line 405, in <module>
    main()
  File "run_trigger_extraction.py", line 378, in main
    train(args, model, processor)
  File "run_trigger_extraction.py", line 243, in train
    pred_sub_heads, pred_sub_tails = model(data, add_label_info=add_label_info)
  File "/home/yc21/software/anaconda3/envs/lear/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
    return forward_call(*input, **kwargs)
  File "/home/yc21/software/anaconda3/envs/lear/lib/python3.8/site-packages/torch/nn/parallel/data_parallel.py", line 168, in forward
    outputs = self.parallel_apply(replicas, inputs, kwargs)
  File "/home/yc21/software/anaconda3/envs/lear/lib/python3.8/site-packages/torch/nn/parallel/data_parallel.py", line 178, in parallel_apply
    return parallel_apply(replicas, inputs, kwargs, self.device_ids[:len(replicas)])
  File "/home/yc21/software/anaconda3/envs/lear/lib/python3.8/site-packages/torch/nn/parallel/parallel_apply.py", line 86, in parallel_apply
    output.reraise()
  File "/home/yc21/software/anaconda3/envs/lear/lib/python3.8/site-packages/torch/_utils.py", line 434, in reraise
    raise exception
RuntimeError: Caught RuntimeError in replica 0 on device 0.
Original Traceback (most recent call last):
  File "/home/yc21/software/anaconda3/envs/lear/lib/python3.8/site-packages/torch/nn/parallel/parallel_apply.py", line 61, in _worker
    output = module(*input, **kwargs)
  File "/home/yc21/software/anaconda3/envs/lear/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
    return forward_call(*input, **kwargs)
  File "/home/yc21/project/LEAR/models/model_event.py", line 635, in forward
    fused_results = self.label_fusing_layer(
  File "/home/yc21/software/anaconda3/envs/lear/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
    return forward_call(*input, **kwargs)
  File "/home/yc21/project/LEAR/utils/model_utils.py", line 320, in forward
    return self.get_fused_feature_with_attn(token_embs, label_embs, input_mask, label_input_mask, return_scores=return_scores)
  File "/home/yc21/project/LEAR/utils/model_utils.py", line 504, in get_fused_feature_with_attn
    scores = torch.matmul(token_feature_fc, label_feature_t).view(
RuntimeError: shape '[4, 48, 33, -1]' is invalid for input of size 160512

2. 我用一张卡跑，设置batchsize=2, gradient_accumulation_step=16, 能够跑通，但是得到的结果是train上的loss收敛到了6，dev的f1一直是0。我不知道这只是我的个人问题，还是有其他人也存在这个问题。有童鞋跑出相应的结果了么？

谢谢大家~

第二个问题的原因是我的--task_layer_lr 参数设成了2e-4，应该是20才对，原因如下：

LEAR/run_trigger_extraction.py

Lines 160 to 173 in 8ae3ed0

	optimizer_grouped_parameters = [
	{"params": [p for n, p in bert_parameters if not any(nd in n for nd in no_decay)],
	"weight_decay": args.weight_decay, 'lr': args.learning_rate},
	{"params": [p for n, p in bert_parameters if any(
	nd in n for nd in no_decay)], "weight_decay": 0.0, 'lr': args.learning_rate},
	{"params": [p for n, p in first_start_params if not any(nd in n for nd in no_decay)],
	"weight_decay": args.weight_decay, 'lr': args.learning_rate * args.task_layer_lr},
	{"params": [p for n, p in first_start_params if any(
	nd in n for nd in no_decay)], "weight_decay": 0.0, 'lr': args.learning_rate * args.task_layer_lr},
	{"params": [p for n, p in first_end_params if not any(nd in n for nd in no_decay)],
	"weight_decay": args.weight_decay, 'lr': args.learning_rate * args.task_layer_lr},
	{"params": [p for n, p in first_end_params if any(
	nd in n for nd in no_decay)], "weight_decay": 0.0, 'lr': args.learning_rate * args.task_layer_lr},
	]

[Senwang98]

@yc1999
请问一下，这个ner任务应该怎么配置可以run起来，看得有点懵这么多传参，跑了就报错

[MoDawn]

@yc1999 请问一下，这个ner任务应该怎么配置可以run起来，看得有点懵这么多传参，跑了就报错

请问又跑起来么？我也是不懂这个传参，，