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Comments (6)

y-tashi avatar y-tashi commented on August 16, 2024

Hi,

I have run the code with the sample electricity dataset and there is no problem.
If you use your original dataset, please set "target_dim" in exe_forecasting.py.

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fangger4396 avatar fangger4396 commented on August 16, 2024

Hi,

I have the same error as follows:

TypeError: embedding(): argument 'indices' (position 2) must be Tensor, not NoneType

I use the electricity dataset here.
It seems that the feature_id here is None and the embed_layer can not accept a None as input.
I am not sure if it matches the original intent of the authors.

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StefanStanisor avatar StefanStanisor commented on August 16, 2024

I am having the same problem, using the electricity dataset provided

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y-tashi avatar y-tashi commented on August 16, 2024

Hi all,

Sorry for the inconvenience.
I have understood the issue and fixed the code.

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StefanStanisor avatar StefanStanisor commented on August 16, 2024

@y-tashi I can confirm that it is working now.

However, I am getting weird results when predicting. I get inf and all the computed losses are nan. I looked a bit in the results and they seem to blow up more and more from sample to sample.

I am running the model on 'mps' device, which is the GPU provided by apple on their Macbook Ms and I am suspecting that is where the error is coming from.

If anyone has encounterd this problem and identified any possible solutions, that would be a life saver as I am trying to write my Masters thesis and I don't have a Nvidia GPU.

I am also obtaining this type of results when predicting using TimeGrad model as well.

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StefanStanisor avatar StefanStanisor commented on August 16, 2024

OK, so I ran it on cpu and indeed I stop getting inf and nan values. Any idea what (maybe a particular pytorch module like LayerNorm or smth) is causing mps to blow up? I am thinking I could rewrite the module and maybe than it will work.

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