Add support asymmetric fake-quantization to AQTv2.#675
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Integration of native quantization with biases will require computing the cross terms. See [#725](#725) Itemized changes: - Add `IntAsymmetric` to handle asymmetric integer numerics. - this class forgoes some of the more research-y parameters present on `IntSymmetric`. - Add `MinMaxCalibration` to calculate the scale and bias for asymmetric quantization. I additionally tested this change by training MNIST models using `flax_e2e_model`. With symmetric quantization the model fails to converge for `config.config_v4(fwd_bits=2, dlhs_bits=None, drhs_bits=None)` (due to `NaN` losses). With asymmetric quantization the model converges even with `config.config_v4(fwd_bits=2, dlhs_bits=2, drhs_bits=4)`. PiperOrigin-RevId: 651580879
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Add support asymmetric fake-quantization to AQTv2.
Integration of native quantization with biases will require computing the cross terms. See #725
Itemized changes:
IntAsymmetricto handle asymmetric integer numerics.IntSymmetric.MinMaxCalibrationto calculate the scale and bias for asymmetric quantization.I additionally tested this change by training MNIST models using
flax_e2e_model. With symmetric quantization the model fails to converge forconfig.config_v4(fwd_bits=2, dlhs_bits=None, drhs_bits=None)(due toNaNlosses). With asymmetric quantization the model converges even withconfig.config_v4(fwd_bits=2, dlhs_bits=2, drhs_bits=4).