Issue while runing test_train.small.gemma.infini.py

[raghavgarg97]

trafficstars

Did anyone face this issue?

warnings.warn( Traceback (most recent call last): File "test_train.small.gemma.infini.py", line 150, in trainer.train() File "/transformers/src/transformers/trainer.py", line 1885, in train return inner_training_loop( File "/transformers/src/transformers/trainer.py", line 2216, in _inner_training_loop tr_loss_step = self.training_step(model, inputs) File "/transformers/src/transformers/trainer.py", line 3238, in training_step loss = self.compute_loss(model, inputs) File "/transformers/src/transformers/trainer.py", line 3264, in compute_loss outputs = model(**inputs) File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl return self._call_impl(*args, **kwargs) File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 1541, in _call_impl return forward_call(*args, **kwargs) File "/usr/local/lib/python3.8/dist-packages/accelerate/utils/operations.py", line 822, in forward return model_forward(*args, **kwargs) File "/usr/local/lib/python3.8/dist-packages/accelerate/utils/operations.py", line 810, in call return convert_to_fp32(self.model_forward(*args, **kwargs)) File "/usr/local/lib/python3.8/dist-packages/torch/amp/autocast_mode.py", line 16, in decorate_autocast return func(*args, **kwargs) File "/jupyter_workspace/Raghav/InfiniTransformer/infini_gemma/modeling_infini_gemma.py", line 1613, in forward outputs = self.model( File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl return self._call_impl(*args, **kwargs) File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 1541, in _call_impl return forward_call(*args, **kwargs) File "/jupyter_workspace/Raghav/InfiniTransformer/infini_gemma/modeling_infini_gemma.py", line 1371, in forward layer_outputs = self._gradient_checkpointing_func( File "/usr/local/lib/python3.8/dist-packages/torch/_compile.py", line 24, in inner return torch._dynamo.disable(fn, recursive)(*args, **kwargs) File "/usr/local/lib/python3.8/dist-packages/torch/_dynamo/eval_frame.py", line 451, in _fn return fn(*args, **kwargs) File "/usr/local/lib/python3.8/dist-packages/torch/_dynamo/external_utils.py", line 36, in inner return fn(*args, **kwargs) File "/usr/local/lib/python3.8/dist-packages/torch/utils/checkpoint.py", line 487, in checkpoint return CheckpointFunction.apply(function, preserve, *args) File "/usr/local/lib/python3.8/dist-packages/torch/autograd/function.py", line 598, in apply return super().apply(*args, **kwargs) # type: ignore[misc] File "/usr/local/lib/python3.8/dist-packages/torch/utils/checkpoint.py", line 262, in forward outputs = run_function(*args) File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl return self._call_impl(*args, **kwargs) File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 1541, in _call_impl return forward_call(*args, **kwargs) File "/jupyter_workspace/Raghav/InfiniTransformer/infini_gemma/modeling_infini_gemma.py", line 1056, in forward _attended = self.self_attn( File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 1532, in _wrapped_call_impl return self._call_impl(*args, **kwargs) File "/usr/local/lib/python3.8/dist-packages/torch/nn/modules/module.py", line 1541, in _call_impl return forward_call(*args, **kwargs) File "/jupyter_workspace/Raghav/InfiniTransformer/infini_gemma/modeling_infini_gemma.py", line 868, in forward query_states, key_states = apply_rotary_pos_emb( File "/jupyter_workspace/Raghav/InfiniTransformer/infini_gemma/modeling_infini_gemma.py", line 272, in apply_rotary_pos_emb q_embed = (q * cos) + (rotate_half(q) * sin) RuntimeError: The size of tensor a (65536) must match the size of tensor b (8192) at non-singleton dimension 2

[raghavgarg97]

Hey @Beomi ,any clarity on this?it would be deeply appreciated. I currently ran the custom (Type I method) training script to pre-train on dataset but I also wanted to do finetuning using HF framework but getting this error

[HUST-Keep-Lin]

嘿，对此有任何澄清吗？将不胜感激。我目前运行自定义（I 型方法）训练脚本以在数据集上进行预训练，但我也想使用 HF 框架进行微调，但出现此错误

+1