enable smoothquant for int8 static tensor

[jcaip]

This PR hooks up the static quant workflow added in #3442 to the prototype smoothquant API.

You can use the new flow like follows:

from torchao.quantization.quant_api import (
    Int8StaticActivationInt8WeightConfig,
)
from torchao.prototype.smoothquant import (
    SmoothQuantConfig
)

config = SmoothQuantConfig(
            base_config=Int8StaticActivationInt8Weight(granularity=PerRow()),
            step=SmoothQuantStep.PREPARE,
            alpha=0.5,
        )

quantize_(model, config)

# Perform calibration with test data
model(*x)

config.step = SmoothQuantStep.CONVERT
quantize_(model, config)

# model will now be statically quantized with the inputs used in smoothquant observer. 
model(*x)

[pytorch-bot]

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❌ 1 New Failure, 1 Pending

As of commit 0c23589 with merge base f99105a ():

NEW FAILURE - The following job has failed:

• Code Analysis with Ruff / build (3.9) (gh)
  torchao/quantization/quantize_/workflows/int8/int8_tensor.py:6:8: F401 [*] math imported but unused

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[jcaip]

cc @Xia-Weiwen and @cyxlily fyi

[jerryzh168]

we should not be using this, please check awq on how this should be implemented in the new stack:

ao/torchao/prototype/awq/api.py

Lines 108 to 113 in 08e5e20

	assert isinstance(qw, SupportsActivationPreScaling), (
	"weight must support activation scaling through implementing `SupportsActivationPreScaling`"
	)
	# since we want to do `act` * `act_pre_scale` during runtime for speed, we'll save the
	# reciprocal of the `equalization_scale`
	qw.act_pre_scale = 1.0 / equalization_scale

[jerryzh168]

nit: Optional[torch.Tensor]

[jerryzh168]

yeah I think it's fine

side note: we may need a separate API/flow for plain static quant without Smoothquant if needed.