Lightning-AI/lightning-thunder

[feature request] Indexing with boolean masks

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#126 opened on Apr 3, 2024

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Description

🚀 Feature

No indexing with boolean mask, for example:

import torch; import thunder                                                                                   
m = x <= 0.5
                                                                                                                                                                                                                          
def f(x, m):                                                                                                   
    return x[m]                                    
                                                                                                        
                                                                                                                       
jf = thunder.jit(f)                                
jf(x, m)

fails with

RuntimeError: Advanced indexing currently only supports zero or one-dimensional integer tensors, but found a tensor with dtype bool8 and 1 dimensions

cc @apaz-cli

Contributor guide

Research direction
Investigate how thunder's JIT compiler currently handles advanced indexing and extend it to support boolean masks. Look at the existing implementation for integer tensor indexing and adapt it for boolean tensors, ensuring proper tracing and compilation.
Tech stack
pythonpytorch
Domain
aimachine learning
Issue type
Feature
DifficultyEstimated implementation difficulty for a new contributor, from 1 for very small changes to 5 for expert-level work.
3
Estimated timeA rough time range for an experienced contributor to investigate, implement, test, and prepare a pull request.
1-2 days
Activity statusHow available the issue appears right now: fresh, active, stale, blocked, or waiting on maintainer input.
Fresh
ClarityHow clearly the issue explains the expected change, acceptance criteria, and next step.
Clear
Prerequisites
PythonPyTorch
Newbie friendlinessA 1-100 score estimating how approachable this issue is for first-time contributors.
30