-
Guy Jacob authored
(we use 8-bit values below, but this applies to any bit-width) * We use the notion of "full" and "restricted" quantized range for symmetric quantization (see section 2.2 in https://arxiv.org/abs/1806.08342) * "Full" quantized range ==> [-128, 127], "restircted" ==> [-127, 127] * Until now, when doing symmetric quantization we assumed a "full" range when saturating after quantization, but calculated the scale factor as if the range was restricted. This means we weren't making full utilization of the quantized range. * On the other hand, in some other implementations of quantization (e.g. TensorFlow), the "restricted" range is used. * So, we make it an option to use either the proper "full" range (q_min = -128) or "restricted" range (q_min = -127). * LinearQuantMode.SYMMETRIC now means the "full" range is used, and added LinearQuantMode.SYMMETRIC_RESTRICTED for using the "restricted" range. * Updated tests and documentation.
Guy Jacob authored(we use 8-bit values below, but this applies to any bit-width) * We use the notion of "full" and "restricted" quantized range for symmetric quantization (see section 2.2 in https://arxiv.org/abs/1806.08342) * "Full" quantized range ==> [-128, 127], "restircted" ==> [-127, 127] * Until now, when doing symmetric quantization we assumed a "full" range when saturating after quantization, but calculated the scale factor as if the range was restricted. This means we weren't making full utilization of the quantized range. * On the other hand, in some other implementations of quantization (e.g. TensorFlow), the "restricted" range is used. * So, we make it an option to use either the proper "full" range (q_min = -128) or "restricted" range (q_min = -127). * LinearQuantMode.SYMMETRIC now means the "full" range is used, and added LinearQuantMode.SYMMETRIC_RESTRICTED for using the "restricted" range. * Updated tests and documentation.
components NaN GiB