dmlc/dgl

[Feature Request] Add missing functions in numpy backend.

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#664 opened on Jun 17, 2019

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 (4 comments) (0 reactions) (0 assignees)Python (12,665 stars) (2,928 forks)batch import
help wanted

Description

🚀 Feature

There are some missing or incompatible functions compared with the interface in numpy backend. The list is following.

Missing

  • device_type
  • mean
  • unsorted_1d_segment_sum
  • unsorted_1d_segment_mean
  • zerocopy_to_dgl_ndarray
  • logical_not
  • zerocopy_to_dlpack
  • equal
  • ndim
  • sync
  • zerocopy_from_dlpack
  • copy_reduce
  • zerocopy_from_dgl_ndarray
  • boolean_mask
  • narrow_row
  • stack
  • binary_reduce
  • device_id
  • zeros_like

Not compatible

  • zeros (shape, dtype) v.s. (shape, dtype, ctx)
  • ones (shape, dtype)v.s. (shape, dtype, ctx)
  • full_1d (length, fill_value) v.s. (length, fill_value, dtype, ctx)

Motivation

In some scenarios, we just want to use message propogation in dgl (e.g. LPA, PageRank) and thus choose numpy as backend. However due to the missing or incompatible functions in numpy backend, even the simplest example send(message_func=dgl.function.copy_src("f", "m")) will raise an exception.

Do you think it's necessary to completing the missing functions? And if so I can help to add some within my ability.

Contributor guide

Tech stack
python
Domain
backend
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.
over 1 week
Activity statusHow available the issue appears right now: fresh, active, stale, blocked, or waiting on maintainer input.
stale
ClarityHow clearly the issue explains the expected change, acceptance criteria, and next step.
clear
Prerequisites
Python programmingBasic NumPy
Newbie friendlinessA 1-100 score estimating how approachable this issue is for first-time contributors.
75
Research direction
Examine the numpy backend implementation in DGL (likely under python/dgl/backend/numpy.py). Implement each missing function (e.g., mean, zerocopy to dgl ndarray) following the interface from other backends like PyTorch. Check comment threads for any additional context or priority. Start with simpler functions like logical not or ndim.