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* `QuantizeBFP` and `DequantizeBFP` schemas - similar to `QuantizeLinear` and `DeQuantizeLinear`. * BFP datatype is represented as a `uint8` tensor with shape and stride metadata. This is preferrable to adding a new datatype for BFP, which is more disruptive and [discouraged by PyTorch](https://discuss.pytorch.org/t/training-with-custom-quantized-datatype/152132/2). Context: The Microsoft Floating Point (BFP) datatype shares an exponent for every n numbers called a “bounding box.” Each number still has its own mantissa and sign bits. BFP has been shown to incur 3-4 less cost (energy and area) than BFloat16 and INT8 counterparts without reductions in accuracy for the ImageNet benchmark as described in [Rouhani 2020](https://proceedings.neurips.cc/paper/2020/file/747e32ab0fea7fbd2ad9ec03daa3f840-Paper.pdf). Requirements: * There are many variants of BFP (number of mantissa bits, number of shared exponent bits, size of bounding box, custom bit fields, etc.) * The size and layout of an BFP variant varies across hardware * bounding box can be over arbitrary dimensions; for example, for the channel "C" dimension in a N x C x H x W tensor for convolution Goals of this PR: * Add initial versions of QuantizeBFP and DequantizeBFP operators to enable QDQ-style quantization with BFP. Once the schemas stabilize, we can consider upstreaming to ONNX. * Add some basic type and shape inferencing tests; tests that run on an EP will be a follow-up. |
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