Add AUTOQUANT_CACHE docs for reusing the same quantization plan
#329
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/ao/329
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| @@ -36,6 +36,21 @@ model = torchao.autoquant(torch.compile(model, mode='max-autotune')) | |||
| model(input) | |||
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| Sometimes it is desirable to reuse a quantization plan that `autoquant` came up with. `torchao.quantization.AUTOQUANT_CACHE` is a dictionary holding autoquant's benchmark results. We can save it and restore it later, which will cause `autoquant` to choose the same quantization methods. | |||
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Hmm really feels like we should be saving this with some module hook automatically
Addresses #320