W-rudder
commented
1 year ago Hi there, thanks for offering this interesting project! I have trouble when conducting the Self-Supervised Instruction Tuning. Specifically, the error goes as follows:
../aten/src/ATen/native/cuda/ScatterGatherKernel.cu:144: operator(): block: [29074,0,0], thread: [31,0,0] Assertion idx_dim >= 0 && idx_dim < index_size && "index out of bounds" failed. terminate called after throwing an instance of 'c10::Error' what(): CUDA error: device-side assert triggered Exception raised from c10_cuda_check_implementation at ../c10/cuda/CUDAException.cpp:31 (most recent call first): Traceback (most recent call last): File "graphgpt/train/train_mem.py", line 15, in <module> train() File "/home/k/lgm/graphGPT-main/graphgpt/train/train_graph.py", line 943, in train trainer.train() File "/home/k/anaconda3/envs/graphgpt/lib/python3.8/site-packages/transformers/trainer.py", line 1555, in train return inner_training_loop( File "/home/k/anaconda3/envs/graphgpt/lib/python3.8/site-packages/transformers/trainer.py", line 1860, in _inner_training_loop tr_loss_step = self.training_step(model, inputs) File "/home/k/anaconda3/envs/graphgpt/lib/python3.8/site-packages/transformers/trainer.py", line 2725, in training_step loss = self.compute_loss(model, inputs) File "/home/k/anaconda3/envs/graphgpt/lib/python3.8/site-packages/transformers/trainer.py", line 2748, in compute_loss outputs = model(**inputs) File "/home/k/anaconda3/envs/graphgpt/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1190, in _call_impl return forward_call(*input, **kwargs) File "/home/k/anaconda3/envs/graphgpt/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1040, in forward output = self._run_ddp_forward(*inputs, **kwargs) File "/home/k/anaconda3/envs/graphgpt/lib/python3.8/site-packages/torch/nn/parallel/distributed.py", line 1000, in _run_ddp_forward return module_to_run(*inputs[0], **kwargs[0]) File "/home/k/anaconda3/envs/graphgpt/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1190, in _call_impl return forward_call(*input, **kwargs) File "/home/k/anaconda3/envs/graphgpt/lib/python3.8/site-packages/accelerate/utils/operations.py", line 581, in forward return model_forward(*args, **kwargs) File "/home/k/anaconda3/envs/graphgpt/lib/python3.8/site-packages/accelerate/utils/operations.py", line 569, in __call__ return convert_to_fp32(self.model_forward(*args, **kwargs)) File "/home/k/anaconda3/envs/graphgpt/lib/python3.8/site-packages/torch/amp/autocast_mode.py", line 14, in decorate_autocast return func(*args, **kwargs) File "/home/k/lgm/graphGPT-main/graphgpt/model/GraphLlama.py", line 332, in forward outputs = self.model( File "/home/k/anaconda3/envs/graphgpt/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1190, in _call_impl return forward_call(*input, **kwargs) File "/home/k/lgm/graphGPT-main/graphgpt/model/GraphLlama.py", line 277, in forward return super(GraphLlamaModel, self).forward( File "/home/k/anaconda3/envs/graphgpt/lib/python3.8/site-packages/transformers/models/llama/modeling_llama.py", line 912, in forward layer_outputs = self._gradient_checkpointing_func( File "/home/k/anaconda3/envs/graphgpt/lib/python3.8/site-packages/torch/utils/checkpoint.py", line 249, in checkpoint return CheckpointFunction.apply(function, preserve, *args) File "/home/k/anaconda3/envs/graphgpt/lib/python3.8/site-packages/torch/utils/checkpoint.py", line 107, in forward outputs = run_function(*args) File "/home/k/anaconda3/envs/graphgpt/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1190, in _call_impl return forward_call(*input, **kwargs) File "/home/k/anaconda3/envs/graphgpt/lib/python3.8/site-packages/transformers/models/llama/modeling_llama.py", line 672, in forward hidden_states, self_attn_weights, present_key_value = self.self_attn( File "/home/k/anaconda3/envs/graphgpt/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1190, in _call_impl return forward_call(*input, **kwargs) File "/home/k/lgm/graphGPT-main/graphgpt/train/llama_flash_attn_monkey_patch.py", line 88, in forward output_unpad = flash_attn_unpadded_qkvpacked_func( File "/home/k/anaconda3/envs/graphgpt/lib/python3.8/site-packages/flash_attn/flash_attn_interface.py", line 256, in flash_attn_unpadded_qkvpacked_func return FlashAttnQKVPackedFunc.apply(qkv, cu_seqlens, max_seqlen, dropout_p, softmax_scale, File "/home/k/anaconda3/envs/graphgpt/lib/python3.8/site-packages/flash_attn/flash_attn_interface.py", line 59, in forward qkv[:, 0], qkv[:, 1], qkv[:, 2], torch.empty_like(qkv[:, 0]), cu_seqlens, cu_seqlens, RuntimeError: CUDA error: device-side assert triggered WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 510117 closing signal SIGTERM WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 510118 closing signal SIGTERM WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 510120 closing signal SIGTERMI use the suggested configurations (environments, scripts) and conduct the tuning on a Linux server equipped with 4 A100 in a distributed manner. Still, I have also tried to conduct the tuning on one GPU merely. To avoid CUDA OOM error, I have modified the train/eval batch size to 1. However, I have encountered another error as follows:
Token indices sequence length is longer than the specified maximum sequence length for this model (3338 > 2048). Running this sequence through the model will result in indexing errors Traceback (most recent call last): File "/home/k/anaconda3/envs/graphgpt/lib/python3.8/runpy.py", line 194, in _run_module_as_main return _run_code(code, main_globals, None, File "/home/k/anaconda3/envs/graphgpt/lib/python3.8/runpy.py", line 87, in _run_code exec(code, run_globals) File "/home/k/lgm/graphGPT-main/graphgpt/train/train_mem.py", line 15, in <module> train() File "/home/k/lgm/graphGPT-main/graphgpt/train/train_graph.py", line 943, in train trainer.train() File "/home/k/anaconda3/envs/graphgpt/lib/python3.8/site-packages/transformers/trainer.py", line 1555, in train return inner_training_loop( File "/home/k/anaconda3/envs/graphgpt/lib/python3.8/site-packages/transformers/trainer.py", line 1860, in _inner_training_loop tr_loss_step = self.training_step(model, inputs) File "/home/k/anaconda3/envs/graphgpt/lib/python3.8/site-packages/transformers/trainer.py", line 2725, in training_step loss = self.compute_loss(model, inputs) File "/home/k/anaconda3/envs/graphgpt/lib/python3.8/site-packages/transformers/trainer.py", line 2748, in compute_loss outputs = model(**inputs) File "/home/k/anaconda3/envs/graphgpt/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1190, in _call_impl return forward_call(*input, **kwargs) File "/home/k/anaconda3/envs/graphgpt/lib/python3.8/site-packages/torch/nn/parallel/data_parallel.py", line 171, in forward outputs = self.parallel_apply(replicas, inputs, kwargs) File "/home/k/anaconda3/envs/graphgpt/lib/python3.8/site-packages/torch/nn/parallel/data_parallel.py", line 181, in parallel_apply return parallel_apply(replicas, inputs, kwargs, self.device_ids[:len(replicas)]) File "/home/k/anaconda3/envs/graphgpt/lib/python3.8/site-packages/torch/nn/parallel/parallel_apply.py", line 89, in parallel_apply output.reraise() File "/home/k/anaconda3/envs/graphgpt/lib/python3.8/site-packages/torch/_utils.py", line 543, in reraise raise exception StopIteration: Caught StopIteration in replica 0 on device 0. Original Traceback (most recent call last): File "/home/k/anaconda3/envs/graphgpt/lib/python3.8/site-packages/torch/nn/parallel/parallel_apply.py", line 64, in _worker output = module(*input, **kwargs) File "/home/k/anaconda3/envs/graphgpt/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1190, in _call_impl return forward_call(*input, **kwargs) File "/home/k/lgm/graphGPT-main/graphgpt/model/GraphLlama.py", line 332, in forward outputs = self.model( File "/home/k/anaconda3/envs/graphgpt/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1190, in _call_impl return forward_call(*input, **kwargs) File "/home/k/lgm/graphGPT-main/graphgpt/model/GraphLlama.py", line 209, in forward node_forward_out = graph_tower(g) File "/home/k/anaconda3/envs/graphgpt/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1190, in _call_impl return forward_call(*input, **kwargs) File "/home/k/lgm/graphGPT-main/graphgpt/model/graph_layers/graph_transformer.py", line 64, in forward device = self.parameters().__next__().device StopIterationTherefore, the tuning process can not be reproduced on either single or multiple GPUs. Any suggestions for troubleshooting would be appreciated. Looking forward to your kind reply!
excuse me, have u solved this problem?
tjb-tech
octopusStar218
Hi there, thanks for offering this interesting project! I have trouble when conducting the Self-Supervised Instruction Tuning. Specifically, the error goes as follows:
I use the suggested configurations (environments, scripts) and conduct the tuning on a Linux server equipped with 4 A100 in a distributed manner. Still, I have also tried to conduct the tuning on one GPU merely. To avoid CUDA OOM error, I have modified the train/eval batch size to 1. However, I have encountered another error as follows:
Therefore, the tuning process can not be reproduced on either single or multiple GPUs. Any suggestions for troubleshooting would be appreciated. Looking forward to your kind reply!