tensorflow/quantum

[Performance] Boost tfq.convert_to_tensor speed

Open

#336 opened on Aug 10, 2020

View on GitHub
 (11 comments) (0 reactions) (0 assignees)Python (653 forks)github user discovery
area/performancegood first issuehelp wantedkind/feature-requestskill-level/intermediate

Repository metrics

Stars
 (2,145 stars)
PR merge metrics
 (PR metrics pending)

Description

Currently tfq.convert_to_tensor uses just one core and makes use of Cirq serialization protocols. They are pretty slow for large circuits. A quick benchmark shows that more than 95% of all time spent computing in tfq.convert_to_tensor is spent in the cirq serialization logic and the protobuf SerializeToString function. Since it's unlikely we can speed either of those up quickly, perhaps we should look into parallelization of tfq.convert_to_tensor ?

Contributor guide

Research direction
Explore parallelizing the serialization step of tfq.convert to tensor using multiprocessing or concurrent futures to reduce the 95% time spent in Cirq serialization and protobuf SerializeToString.
Tech stack
pythontensorflow
Domain
performance
Issue type
Performance
DifficultyEstimated implementation difficulty for a new contributor, from 1 for very small changes to 5 for expert-level work.
2
Estimated timeA rough time range for an experienced contributor to investigate, implement, test, and prepare a pull request.
Half day
Activity statusHow available the issue appears right now: fresh, active, stale, blocked, or waiting on maintainer input.
Active
ClarityHow clearly the issue explains the expected change, acceptance criteria, and next step.
Clear
Prerequisites
PythonTensorFlow
Newbie friendlinessA 1-100 score estimating how approachable this issue is for first-time contributors.
50