Avoid random weights initialization when quantizing #291
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What does this PR do?
As raised by @latentCall145, there is a useless random weights initialization when quantizing a module.
The solution suggested in #290 is correct but makes the low-level quantization API depend on
accelerate
, which is only an optional dependency used by the high-level model API.This is more or less the same implementation, but more explicitly using the
meta
device. Note that we need to explicitly preserve the scales, since unlikeaccelerate
, pytorch does not distinguish between parameters and buffers when skipping initialization.