Lable Mapping not aligned when prefixes are set to be an empty string
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Assessment
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Newbie friendliness
- 35/100
- Issue type
- Bug
- Clarity
- Mostly clear
- Activity status
- Stale
- Tech stack
- python
- Domain
- machine-learning
Research direction
Reproduce the failure with all prefixes set to empty strings, then trace compute_duplicated_labels() and the activation_storage[n] assignment where the tensor-size error appears. Compare the sizes of idx_map and dummy_labels; the issue is done when empty prefixes produce matching label sizes without the runtime error.
Written by the indexing model from the issue text.
Description
Hi @ericwtodd
I am trying to play with different prefixes. The default one is: prefixes = {"input":"Q:", "output":"A:","instructions":""}. When I change it to empty string prefixes = {"input":"", "output":"","instructions":""}, I get a error saying that:
activation_storage[n] = stack_filtered
RuntimeError: The expanded size of the tensor (54) must match the existing size (53) at non-singleton dimension 2. Target sizes: [40, 40, 54, 128]. Tensor sizes: [40, 40, 53, 128]
I have further checked it and found that this is caused by different label size (the size of idx_map is different from dummy_labels).
When the prefix is made of empty strings, the function compute_duplicated_labels() will an index_map that is of different size of dummy label. I try to debug where went wrong and found it too hard to dive in...
The error only pops up when I have empty strings for prefixes. All other prefixes do not have this problem....
It would be great if you could share some insights on this. I wonder have you experienced similar problems when using empty string prefixes...
- Dominant language
- Python
- Stars
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- Forks
- 47
- PR merge metrics
- No merged PRs in 30d
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