Embedding4bit and Embedding8bit implementation #1292
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galqiwi
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Bump P.S. Part about shared embeddings can be discussed later in another PR or issue |
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Hi @galqiwi! Thank you for the PR! I think this would be a very useful addition and will review this week. I agree that the shared embeddings can be deferred to follow up discussion/PRs. |
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Thanks @galqiwi! Overall, this looks great! I just left a few minor nits, but otherwise happy to merge! |
Co-authored-by: Matthew Douglas <[email protected]>
Co-authored-by: Matthew Douglas <[email protected]>
Co-authored-by: Matthew Douglas <[email protected]>
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Thank you for reviewing my PR, @matthewdouglas! I've fixed all the typos you found |
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Thanks @galqiwi! This is a great contribution, and the unit tests here are really appreciated! |
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Hi again! Are you planning on publishing new release of bnb? |
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Oct 28, 2024
…on#1292) * Embedding4bit and Embedding8bit implementation * lint * Update bitsandbytes/nn/modules.py Co-authored-by: Matthew Douglas <[email protected]> * Update bitsandbytes/nn/modules.py Co-authored-by: Matthew Douglas <[email protected]> * Update bitsandbytes/nn/modules.py Co-authored-by: Matthew Douglas <[email protected]> * saving -> Saving --------- Co-authored-by: Matthew Douglas <[email protected]>
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Hi! I've been researching LLM quantization and found a bottleneck that I think this PR can fix.
When using extreme 1 bit and 2 bit LLM quantization (which have seen many improvements recently 1, 2, 3, 4, 5), uncompressed embeddings can start to take up too much space (in some cases more than 50%).
I've documented this bottleneck in huggingface/transformers issue, and it looks like the bitsandbytes library can be a good place to start dealing with it.
In this PR I implement embedding modules for 4-bit and 8-bit quantizations from this library. Currently, they only support
_load_from_state_dictAPI and can't be saved, but I think they still can be useful.After that, I plan to integrate this functionality into the transformers library by extending HfQuantizer functionality.
What do you think?
There is also one thing I want to implement before going back to the transformers library: support for shared weights in 8-bit quantization.
While 4-bit quantization linear layer does not seem to change
self.weightparameter during forward pass, 8-bit quantization linear layer changes it dramatically ininit_8bit_statemethod.So, while 4-bit embedding and linear layer can share the same
Params4bitparameter, it is not the case for 8-bit.I think that this patch should fix the problem, but this part of the code is very tightly coupled to everything around itself, and I need your help with advice. Do you think it can break something important I don't see?