This RFC will be open for comment until Friday, November 4th, 2022.
cc @wangpengmit
Making dtype promotion semantics in Tensorflow more consistent
Status
(Proposed)
Author(s)
Jiawei Xia (Google), Antonio Sanchez (Google)
Sponsor
Peng Wang (Google)
Updated
2022-10-18
Objective
Currently TF has no consistent, well-defined type promotion rules. This document proposes a well-defined, consistent and clear dtype promotion rule for TF. The introduced changes make TF APIs more similar to NumPy, with some differences that emphasize TF’s applications in machine learning. This should make dtype promotions in TF much more consistent and predictable. Specifically the doc discusses the preferred dtype promotion semantics/behaviors of Tensorflow (TF) and Tensorflow-numpy (TF-numpy) in the binary ops including add, sub, mul, div, pow and mod.
This RFC will be open for comment until Friday, November 4th, 2022. cc @wangpengmit
Making dtype promotion semantics in Tensorflow more consistent
Objective
Currently TF has no consistent, well-defined type promotion rules. This document proposes a well-defined, consistent and clear dtype promotion rule for TF. The introduced changes make TF APIs more similar to NumPy, with some differences that emphasize TF’s applications in machine learning. This should make dtype promotions in TF much more consistent and predictable. Specifically the doc discusses the preferred dtype promotion semantics/behaviors of Tensorflow (TF) and Tensorflow-numpy (TF-numpy) in the binary ops including add, sub, mul, div, pow and mod.