profile tools for pytorch nn models

Related tags

Text Data & NLPnnprof
Overview

nnprof

Introduction

nnprof is a profile tool for pytorch neural networks.

Features

  • multi profile mode: nnprof support 4 profile mode: Layer level, Operation level, Mixed level, Layer Tree level. Please check below for detail usage.
  • time and memory profile: nnprof support both time and memory profile now. But since memory profile is first supported in pytorch 1.6, please use torch version >= 1.6 for memory profile.
  • support sorted by given key and show profile percent: user could print table with percentage and sorted profile info using a given key, which is really helpful for optimiziing neural network.

Requirements

  • Python >= 3.6
  • PyTorch
  • Numpy

Get Started

install nnprof

  • pip install:
pip install nnprof
  • from source:
python -m pip install 'git+https://github.com/FateScript/nnprof.git'

# or install after clone this repo
git clone https://github.com/FateScript/nnprof.git
pip install -e nnprof

use nnprf

from nnprof import profile, ProfileMode
import torch
import torchvision

model = torchvision.models.alexnet(pretrained=False)
x = torch.rand([1, 3, 224, 224])

# mode could be anyone in LAYER, OP, MIXED, LAYER_TREE
mode = ProfileMode.LAYER

with profile(model, mode=mode) as prof:
    y = model(x)

print(prof.table(average=False, sorted_by="cpu_time"))
# table could be sorted by presented header.

Part of presented table looks like table below, Note that they are sorted by cpu_time.

╒══════════════════════╤═══════════════════╤═══════════════════╤════════╕
│ name                 │ self_cpu_time     │ cpu_time          │   hits │
╞══════════════════════╪═══════════════════╪═══════════════════╪════════╡
│ AlexNet.features.0   │ 19.114ms (34.77%) │ 76.383ms (45.65%) │      1 │
├──────────────────────┼───────────────────┼───────────────────┼────────┤
│ AlexNet.features.3   │ 5.148ms (9.37%)   │ 20.576ms (12.30%) │      1 │
├──────────────────────┼───────────────────┼───────────────────┼────────┤
│ AlexNet.features.8   │ 4.839ms (8.80%)   │ 19.336ms (11.56%) │      1 │
├──────────────────────┼───────────────────┼───────────────────┼────────┤
│ AlexNet.features.6   │ 4.162ms (7.57%)   │ 16.632ms (9.94%)  │      1 │
├──────────────────────┼───────────────────┼───────────────────┼────────┤
│ AlexNet.features.10  │ 2.705ms (4.92%)   │ 10.713ms (6.40%)  │      1 │
├──────────────────────┼───────────────────┼───────────────────┼────────┤

You are welcomed to try diffierent profile mode and more table format.

Contribution

Any issues and pull requests are welcomed.

Acknowledgement

Some thoughts of nnprof are inspired by torchprof and torch.autograd.profile . Many thanks to the authors.

Owner
Feng Wang
Cleaner @ Megvii
Feng Wang
🚀 RocketQA, dense retrieval for information retrieval and question answering, including both Chinese and English state-of-the-art models.

In recent years, the dense retrievers based on pre-trained language models have achieved remarkable progress. To facilitate more developers using cutt

475 Jan 04, 2023
Ray-based parallel data preprocessing for NLP and ML.

Wrangl Ray-based parallel data preprocessing for NLP and ML. pip install wrangl # for latest pip install git+https://github.com/vzhong/wrangl See exa

Victor Zhong 33 Dec 27, 2022
IndoBERTweet is the first large-scale pretrained model for Indonesian Twitter. Published at EMNLP 2021 (main conference)

IndoBERTweet 🐦 🇮🇩 1. Paper Fajri Koto, Jey Han Lau, and Timothy Baldwin. IndoBERTweet: A Pretrained Language Model for Indonesian Twitter with Effe

IndoLEM 40 Nov 30, 2022
Search for documents in a domain through Google. The objective is to extract metadata

MetaFinder - Metadata search through Google _____ __ ___________ .__ .___ / \

Josué Encinar 85 Dec 16, 2022
Non-Autoregressive Translation with Layer-Wise Prediction and Deep Supervision

Deeply Supervised, Layer-wise Prediction-aware (DSLP) Transformer for Non-autoregressive Neural Machine Translation

Chenyang Huang 37 Jan 04, 2023
This repository contains data used in the NAACL 2021 Paper - Proteno: Text Normalization with Limited Data for Fast Deployment in Text to Speech Systems

Proteno This is the data release associated with the corresponding NAACL 2021 Paper - Proteno: Text Normalization with Limited Data for Fast Deploymen

37 Dec 04, 2022
DiY Oxygen Concentrator based on the OxiKit

M19O2 DiY Oxygen Concentrator based on / inspired by the OxiKit, OpenOx, Marut, RepRap and Project Apollo platforms. About Read about the project on H

Maker's Asylum 62 Dec 22, 2022
Comprehensive-E2E-TTS - PyTorch Implementation

A Non-Autoregressive End-to-End Text-to-Speech (text-to-wav), supporting a family of SOTA unsupervised duration modelings. This project grows with the research community, aiming to achieve the ultima

Keon Lee 114 Nov 13, 2022
👑 spaCy building blocks and visualizers for Streamlit apps

spacy-streamlit: spaCy building blocks for Streamlit apps This package contains utilities for visualizing spaCy models and building interactive spaCy-

Explosion 620 Dec 29, 2022
Learning Spatio-Temporal Transformer for Visual Tracking

STARK The official implementation of the paper Learning Spatio-Temporal Transformer for Visual Tracking Highlights The strongest performances Tracker

Multimedia Research 485 Jan 04, 2023
Python3 to Crystal Translation using Python AST Walker

py2cr.py A code translator using AST from Python to Crystal. This is basically a NodeVisitor with Crystal output. See AST documentation (https://docs.

66 Jul 25, 2022
☀️ Measuring the accuracy of BBC weather forecasts in Honolulu, USA

Accuracy of BBC Weather forecasts for Honolulu This repository records the forecasts made by BBC Weather for the city of Honolulu, USA. Essentially, t

Max Halford 12 Oct 15, 2022
Every Google, Azure & IBM text to speech voice for free

TTS-Grabber Quick thing i made about a year ago to download any text with any tts voice, over 630 voices to choose from currently. It will split the i

16 Dec 07, 2022
Implemented shortest-circuit disambiguation, maximum probability disambiguation, HMM-based lexical annotation and BiLSTM+CRF-based named entity recognition

Implemented shortest-circuit disambiguation, maximum probability disambiguation, HMM-based lexical annotation and BiLSTM+CRF-based named entity recognition

0 Feb 13, 2022
Creating a chess engine using GPT-3

GPT3Chess Creating a chess engine using GPT-3 Code for my article : https://towardsdatascience.com/gpt-3-play-chess-d123a96096a9 My game (white) vs GP

19 Dec 17, 2022
Finally decent dictionaries based on Wiktionary for your beloved eBook reader.

eBook Reader Dictionaries Finally, decent dictionaries based on Wiktionary for your beloved eBook reader. Dictionaries Catalan 🚧 Ελληνικά (help welco

Mickaël Schoentgen 163 Dec 31, 2022
Code for EMNLP'21 paper "Types of Out-of-Distribution Texts and How to Detect Them"

Code for EMNLP'21 paper "Types of Out-of-Distribution Texts and How to Detect Them"

Udit Arora 19 Oct 28, 2022
Fast, DB Backed pretrained word embeddings for natural language processing.

Embeddings Embeddings is a python package that provides pretrained word embeddings for natural language processing and machine learning. Instead of lo

Victor Zhong 212 Nov 21, 2022
Twitter-Sentiment-Analysis - Twitter sentiment analysis for india's top online retailers(2019 to 2022)

Twitter-Sentiment-Analysis Twitter sentiment analysis for india's top online retailers(2019 to 2022) Project Overview : Sentiment Analysis helps us to

Balaji R 1 Jan 01, 2022