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Summary:
Creatd a `AffineQuantizedTensor` subclass that works for both weight and input (for dynamic quantization), for all granularities (levering the recently added choose_qparams_affine, quantize_affine
and dequantize_affine ops)

only verified for 8da4w right now, we can make it work for other types of quantization (mostly the operator dispatching part) later

Test Plan:
python test/quantization/test_quant_api.py -k test_quantized_tensor_subclass_8da4w

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Co-authored-by: Mark Saroufim <marksaroufim@meta.com>

* add expecttest to requirements.txt

* update

Install dev-requirements.txt

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Co-authored-by: Mark Saroufim <marksaroufim@meta.com>

Summary:
Accidently changed the device check code for old subclass instead of the new one, forgot to fix before landing

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CI

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att

Test Plan:
python test/quantization/test_quant_primitives.py -k test_raises

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this PR rewrites how torchao.autoquant works so that it works with
torch.compile. Previously you had to do:

torchao.autoquant(model, input)
mod=torch.compile(model)
mod(input)

now you can do
torchao.autoquant(torch.compile(model))
model(input)

The new method works with/without compile. Also this is BC so the old
path also works.

We use a forward_prehook to intercept the model call before
torch.compile tracing occurs at which point we do the autoquantization
and clean up all remaining hooks before passing things off to the
normal torch.compile tracing functionality.

note: in the case of multiple inputs, you can also do:

model.forward_log_only(input) to run the model forward with autoquant
shape logging and prevent the torch.compile tracing/autoquant
quantization from occuring.

Test Plan: python test/integration/test_integration.py -k "autoquant"

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…abs/ao into 085_no_input_autoquant