[Quantization] Allow loading of transform configs #40673
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Signed-off-by: Kyle Sayers <[email protected]>
Signed-off-by: Kyle Sayers <[email protected]>
Signed-off-by: Kyle Sayers <[email protected]>
Signed-off-by: Kyle Sayers <[email protected]>
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cc @MekkCyber |
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Putting in draft for now, need to do some more testing |
Signed-off-by: Kyle Sayers <[email protected]>
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[For maintainers] Suggested jobs to run (before merge) run-slow: compressed_tensors_integration |
| """Models quantized using compressed tensors can be saved to disk""" | ||
| return True | ||
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| def dequantize(self, model: "PreTrainedModel"): |
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do we have to call this dequantize to match huggingface API? If not, decompress would be more accurate since it might involve something beyond quantization?
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Purpose
Prerequisites
Changes
0.11.0(to support transform features)update_dtypein order to reduce complexity and give users more control/predictability of model data typesTesting
CompressedTensorsTest, added an online quip-style transformed model for testing