The purpose of this PR is to dive deep into the desing of the quantized ops. To start the discussion I have implemented the Quantize and dequantize op which are easy to implement. There is one more such [PR](https://github.com/dmlc/tvm/issues/2351) but there the conversation has meandered towards implementiaon of quantized convolution. The questions we want to address are 1. Is this design the correct way to incorporate quantized ops. 2. Are the abstraions introduced in this PR appropriate.
You can view, comment on, or merge this pull request online at: https://github.com/dmlc/tvm/pull/3457 -- Commit Summary -- * [Relay] [Quantization] WIP - Prototyping Quantize and Dequantize operator with type infer type, lowering and test cases. * [Relay] [Quantization] WIP - Fixing typos and removing redundant code. -- File Changes -- A include/tvm/relay/attrs/nn_quantize.h (67) A include/tvm/relay/quantize_util.h (98) M python/tvm/relay/op/nn/__init__.py (1) A python/tvm/relay/op/nn/_make_quantize.py (20) A python/tvm/relay/op/nn/_quantize.py (73) M python/tvm/relay/quantize/__init__.py (1) A src/relay/op/nn/dequantize.cc (78) A src/relay/op/nn/quantize_op.cc (91) A src/relay/pass/quantize_rewrite.cc (93) A tests/python/unittest/test_quantized_ops.py (117) -- Patch Links -- https://github.com/dmlc/tvm/pull/3457.patch https://github.com/dmlc/tvm/pull/3457.diff -- You are receiving this because you are subscribed to this thread. Reply to this email directly or view it on GitHub: https://github.com/dmlc/tvm/pull/3457
