Tree Nested PyTorch Tensor Lib

Last update: Dec 29, 2022

Overview

DI-treetensor

treetensor is a generalized tree-based tensor structure mainly developed by OpenDILab Contributors.

Almost all the operation can be supported in form of trees in a convenient way to simplify the structure processing when the calculation is tree-based.

Installation

You can simply install it with pip command line from the official PyPI site.

pip install di-treetensor

For more information about installation, you can refer to Installation.

Documentation

The detailed documentation are hosted on https://opendilab.github.io/DI-treetensor.

Only english version is provided now, the chinese documentation is still under development.

Quick Start

You can easily create a tree value object based on FastTreeValue.

import builtins
import os
from functools import partial

import treetensor.torch as torch

print = partial(builtins.print, sep=os.linesep)

if __name__ == '__main__':
    # create a tree tensor
    t = torch.randn({'a': (2, 3), 'b': {'x': (3, 4)}})
    print(t)
    print(torch.randn(4, 5))  # create a normal tensor
    print()

    # structure of tree
    print('Structure of tree')
    print('t.a:', t.a)  # t.a is a native tensor
    print('t.b:', t.b)  # t.b is a tree tensor
    print('t.b.x', t.b.x)  # t.b.x is a native tensor
    print()

    # math calculations
    print('Math calculation')
    print('t ** 2:', t ** 2)
    print('torch.sin(t).cos()', torch.sin(t).cos())
    print()

    # backward calculation
    print('Backward calculation')
    t.requires_grad_(True)
    t.std().arctan().backward()
    print('grad of t:', t.grad)
    print()

    # native operation
    # all the ops can be used as the original usage of `torch`
    print('Native operation')
    print('torch.sin(t.a)', torch.sin(t.a))  # sin of native tensor

The result should be

<Tensor 0x7f0dae602760>
├── a --> tensor([[-1.2672, -1.5817, -0.3141],
│                 [ 1.8107, -0.1023,  0.0940]])
└── b --> <Tensor 0x7f0dae602820>
    └── x --> tensor([[ 1.2224, -0.3445, -0.9980, -0.4085],
                      [ 1.5956,  0.8825, -0.5702, -0.2247],
                      [ 0.9235,  0.4538,  0.8775, -0.2642]])

tensor([[-0.9559,  0.7684,  0.2682, -0.6419,  0.8637],
        [ 0.9526,  0.2927, -0.0591,  1.2804, -0.2455],
        [ 0.4699, -0.9998,  0.6324, -0.6885,  1.1488],
        [ 0.8920,  0.4401, -0.7785,  0.5931,  0.0435]])

Structure of tree
t.a:
tensor([[-1.2672, -1.5817, -0.3141],
        [ 1.8107, -0.1023,  0.0940]])
t.b:
<Tensor 0x7f0dae602820>
└── x --> tensor([[ 1.2224, -0.3445, -0.9980, -0.4085],
                  [ 1.5956,  0.8825, -0.5702, -0.2247],
                  [ 0.9235,  0.4538,  0.8775, -0.2642]])

t.b.x
tensor([[ 1.2224, -0.3445, -0.9980, -0.4085],
        [ 1.5956,  0.8825, -0.5702, -0.2247],
        [ 0.9235,  0.4538,  0.8775, -0.2642]])

Math calculation
t ** 2:
<Tensor 0x7f0dae602eb0>
├── a --> tensor([[1.6057, 2.5018, 0.0986],
│                 [3.2786, 0.0105, 0.0088]])
└── b --> <Tensor 0x7f0dae60c040>
    └── x --> tensor([[1.4943, 0.1187, 0.9960, 0.1669],
                      [2.5458, 0.7789, 0.3252, 0.0505],
                      [0.8528, 0.2059, 0.7699, 0.0698]])

torch.sin(t).cos()
<Tensor 0x7f0dae621910>
├── a --> tensor([[0.5782, 0.5404, 0.9527],
│                 [0.5642, 0.9948, 0.9956]])
└── b --> <Tensor 0x7f0dae6216a0>
    └── x --> tensor([[0.5898, 0.9435, 0.6672, 0.9221],
                      [0.5406, 0.7163, 0.8578, 0.9753],
                      [0.6983, 0.9054, 0.7185, 0.9661]])


Backward calculation
grad of t:
<Tensor 0x7f0dae60c400>
├── a --> tensor([[-0.0435, -0.0535, -0.0131],
│                 [ 0.0545, -0.0064, -0.0002]])
└── b --> <Tensor 0x7f0dae60cbe0>
    └── x --> tensor([[ 0.0357, -0.0141, -0.0349, -0.0162],
                      [ 0.0476,  0.0249, -0.0213, -0.0103],
                      [ 0.0262,  0.0113,  0.0248, -0.0116]])


Native operation
torch.sin(t.a)
tensor([[-0.9543, -0.9999, -0.3089],
        [ 0.9714, -0.1021,  0.0939]], grad_fn=<SinBackward>)

For more quick start explanation and further usage, take a look at:

Quick Start

Extension

If you need to translate treevalue object to runnable source code, you may use the potc-treevalue plugin with the installation command below

pip install DI-treetensor[potc]

In potc, you can translate the objects to runnable python source code, which can be loaded to objects afterwards by the python interpreter, like the following graph

For more information, you can refer to

Contribution

We appreciate all contributions to improve DI-treetensor, both logic and system designs. Please refer to CONTRIBUTING.md for more guides.

And users can join our slack communication channel, or contact the core developer HansBug for more detailed discussion.

License

DI-treetensor released under the Apache 2.0 license.

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Comments

PyTorch OP List(P0)
reference: https://pytorch.org/docs/1.8.0/torch.html

common

[x] numel

[x] cpu

[x] cuda

[x] to

Creation Ops

[x] torch.zeros_like

[x] torch.randn_like

[x] torch.randint_like

[x] torch.ones_like

[x] torch.full_like

[x] torch.empty_like

[x] torch.zeros

[x] torch.randn

[x] torch.randint

[x] torch.ones

[x] torch.full

[x] torch.empty

Indexing, Slicing, Joining, Mutating Ops

[x] cat

[x] chunk

[ ] gather

[x] index_select

[x] masked_select

[x] reshape

[ ] scatter

[x] split

[x] squeeze

[x] stack

[ ] tile

[ ] unbind

[x] unsqueeze

[x] where

Math Ops

Pointwise Ops

[x] add

[x] sub

[x] mul

[x] div

[x] pow

[x] neg

[x] abs

[x] sign

[x] floor

[x] ceil

[x] round

[x] sigmoid

[x] clamp

[x] exp

[x] exp2

[x] sqrt

[x] log

[x] log10

[x] log2

Reduction Ops

[ ] argmax

[ ] argmin

[x] all

[x] any

[x] max

[x] min

[x] dist

[ ] logsumexp

[x] mean

[ ] median

[x] norm

[ ] prod

[x] std

[x] sum

[ ] unique

Comparison Ops

[ ] argsort

[x] eq

[x] ge

[x] gt

[x] isfinite

[x] isinf

[x] isnan

[x] le

[x] lt

[x] ne

[ ] sort

[ ] topk

Other Ops

[ ] cdist

[x] clone

[ ] flip

BLAS and LAPACK Ops

[ ] addbmm

[ ] addmm

[ ] bmm

[x] dot

[x] matmul

[x] mm

enhancement
opened by PaParaZz1 3
PyTorch OP Doc List
P0

[x] cpu

[x] cuda

[x] to

[x] torch.zeros_like

[x] torch.randn_like

[x] torch.ones_like

[x] torch.zeros

[x] torch.randn

[x] torch.randint

[x] torch.ones

[x] cat

[x] reshape

[x] split

[x] squeeze

[x] stack

[x] unsqueeze

[x] where

[x] abs

[x] add

[x] clamp

[x] div

[x] exp

[x] log

[x] sqrt

[x] sub

[x] sigmoid

[x] pow

[x] mul

[ ] argmax

[ ] argmin

[x] all

[x] any

[x] max

[x] min

[x] dist

[x] mean

[x] std

[x] sum

[x] eq

[x] ge

[x] gt

[x] le

[x] lt

[x] ne

[x] clone

[x] dot

[x] matmul

[x] mm

P1

[x] numel

[x] torch.randint_like

[x] torch.full_like

[x] torch.empty_like

[x] torch.full

[x] torch.empty

[x] chunk

[ ] gather

[x] index_select

[x] masked_select

[ ] scatter

[ ] tile

[ ] unbind

[x] ceil

[x] exp2

[x] floor

[x] log10

[x] log2

[x] neg

[x] round

[x] sign

[ ] bmm

P2

[ ] logsumexp

[ ] median

[x] norm

[ ] prod

[ ] unique

[ ] argsort

[x] isfinite

[x] isinf

[x] isnan

[ ] sort

[ ] topk

[ ] cdist

[ ] flip

[ ] addbmm

[ ] addmm
opened by PaParaZz1 2

dev(hansbug): add stream support for paralleling the calculations in tree

Here is an example:

import time

import numpy as np
import torch

import treetensor.torch as ttorch

N, M, T = 200, 2, 50
S1, S2, S3 = 512, 1024, 2048


def test_min():
    a = ttorch.randn({f'a{i}': (S1, S2) for i in range(N // M)}, device='cuda')
    b = ttorch.randn({f'a{i}': (S2, S3) for i in range(N // M)}, device='cuda')

    result = []
    for i in range(T):
        _start_time = time.time()

        _ = ttorch.matmul(a, b)
        torch.cuda.synchronize()

        _end_time = time.time()
        result.append(_end_time - _start_time)

    print('time cost: mean({}) std({})'.format(np.mean(result), np.std(result)))


def test_native():
    a = {f'a{i}': torch.randn(S1, S2, device='cuda') for i in range(N)}
    b = {f'a{i}': torch.randn(S2, S3, device='cuda') for i in range(N)}

    result = []
    for i in range(T):
        _start_time = time.time()

        for key in a.keys():
            _ = torch.matmul(a[key], b[key])
        torch.cuda.synchronize()

        _end_time = time.time()
        result.append(_end_time - _start_time)

    print('time cost: mean({}) std({})'.format(np.mean(result), np.std(result)))


def test_linear():
    a = ttorch.randn({f'a{i}': (S1, S2) for i in range(N)}, device='cuda')
    b = ttorch.randn({f'a{i}': (S2, S3) for i in range(N)}, device='cuda')

    result = []
    for i in range(T):
        _start_time = time.time()

        _ = ttorch.matmul(a, b)
        torch.cuda.synchronize()

        _end_time = time.time()
        result.append(_end_time - _start_time)

    print('time cost: mean({}) std({})'.format(np.mean(result), np.std(result)))


def test_stream():
    a = ttorch.randn({f'a{i}': (S1, S2) for i in range(N)}, device='cuda')
    b = ttorch.randn({f'a{i}': (S2, S3) for i in range(N)}, device='cuda')

    ttorch.stream(M)
    result = []
    for i in range(T):
        _start_time = time.time()

        _ = ttorch.matmul(a, b)
        torch.cuda.synchronize()

        _end_time = time.time()
        result.append(_end_time - _start_time)

    print('time cost: mean({}) std({})'.format(np.mean(result), np.std(result)))


def warmup():
    # warm up
    a = torch.randn(1024, 1024).cuda()
    b = torch.randn(1024, 1024).cuda()
    for _ in range(20):
        c = torch.matmul(a, b)


if __name__ == '__main__':
    warmup()
    test_min()
    test_native()
    test_linear()
    test_stream()

不过讲真，这个stream实际效果挺脆弱的，非常看tensor尺寸，大了小了都不行，GPU性能不够也不行，一弄不好还容易负优化，总之挺难伺候的。这部分如果想实用化的话得再研究研究。

enhancement

opened by HansBug 1

Failure when try to convert between numpy and torch on Windows Python3.10
See here: https://github.com/opendilab/DI-treetensor/runs/7820313811?check_suite_focus=true

The bug is like

@method_treelize(return_type=_get_tensor_class) def tensor(self: numpy.ndarray, *args, **kwargs): > tensor_: torch.Tensor = torch.from_numpy(self) E RuntimeError: Numpy is not available

The only way I found to 'solve' this is to downgrade python to version3.9 to lower. So these tests will be skipped temporarily.
bug
opened by HansBug 0

Releases(v0.4.0)

v0.4.0(Aug 14, 2022)
What's Changed

dev(hansbug): remove support for py3.6 by @HansBug in https://github.com/opendilab/DI-treetensor/pull/12

pytorch upgrade to 1.12 by @zjowowen in https://github.com/opendilab/DI-treetensor/pull/11

dev(hansbug): add test for torch1.12.0 and python3.10 by @HansBug in https://github.com/opendilab/DI-treetensor/pull/13

dev(hansbug): add stream support for paralleling the calculations in tree by @HansBug in https://github.com/opendilab/DI-treetensor/pull/10

New Contributors

@zjowowen made their first contribution in https://github.com/opendilab/DI-treetensor/pull/11

Full Changelog: https://github.com/opendilab/DI-treetensor/compare/v0.3.0...v0.4.0
Source code(tar.gz)
Source code(zip)
v0.3.0(Jul 15, 2022)
What's Changed

dev(hansbug): use newer version of treevalue 1.4.1 by @HansBug in https://github.com/opendilab/DI-treetensor/pull/9

Full Changelog: https://github.com/opendilab/DI-treetensor/compare/v0.2.1...v0.3.0
Source code(tar.gz)
Source code(zip)
v0.2.1(Mar 22, 2022)
What's Changed

fix(hansbug): fix uncompitable problem with walk by @HansBug in https://github.com/opendilab/DI-treetensor/pull/5

dev(hansbug): add tensor method for treetensor.numpy.ndarray by @HansBug in https://github.com/opendilab/DI-treetensor/pull/6

fix(hansbug): add subside support to all the functions. by @HansBug in https://github.com/opendilab/DI-treetensor/pull/7

doc(hansbug): add documentation for np.stack, np.split and other 3 functions. by @HansBug in https://github.com/opendilab/DI-treetensor/pull/8

release(hansbug): use version 0.2.1 by @HansBug in https://github.com/opendilab/DI-treetensor/pull/4

New Contributors

@HansBug made their first contribution in https://github.com/opendilab/DI-treetensor/pull/5

Full Changelog: https://github.com/opendilab/DI-treetensor/compare/v0.2.0...v0.2.1
Source code(tar.gz)
Source code(zip)
v0.2.0(Jan 4, 2022)
Use newer version of treevalue>=1.2.0

Add support of torch 1.10.0

Add support of potc

Full Changelog: https://github.com/opendilab/DI-treetensor/compare/v0.1.0...v0.2.0
Source code(tar.gz)
Source code(zip)
v0.1.0(Dec 26, 2021)

Use treevalue>=1.1.0 for tree structure support.

Full Changelog: https://github.com/opendilab/DI-treetensor/compare/v0.0.1...v0.1.0
Source code(tar.gz)
Source code(zip)
v0.0.1(Sep 30, 2021)

First simple relase of treetensor.

For further information: https://opendilab.github.io/DI-treetensor/v0.0.1/index.html
Source code(tar.gz)
Source code(zip)

Owner

OpenDILab

Open sourced Decision Intelligence (DI)

GitHub Repository https://opendilab.github.io/DI-treetensor/

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Tree Nested PyTorch Tensor Lib

Related tags

Overview

DI-treetensor

Installation

Documentation

Quick Start

Extension

Contribution

License

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Comments

PyTorch OP List(P0)

common

Creation Ops

Indexing, Slicing, Joining, Mutating Ops

Math Ops

Pointwise Ops

Reduction Ops

Comparison Ops

Other Ops

BLAS and LAPACK Ops

PyTorch OP Doc List

P0

P1

P2

dev(hansbug): add stream support for paralleling the calculations in tree

Failure when try to convert between numpy and torch on Windows Python3.10

Releases(v0.4.0)

v0.4.0(Aug 14, 2022)

What's Changed

New Contributors

v0.3.0(Jul 15, 2022)

What's Changed

v0.2.1(Mar 22, 2022)

What's Changed

New Contributors

v0.2.0(Jan 4, 2022)

v0.1.0(Dec 26, 2021)

v0.0.1(Sep 30, 2021)

Owner

OpenDILab

MiniHack the Planet: A Sandbox for Open-Ended Reinforcement Learning Research

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Image De-raining Using a Conditional Generative Adversarial Network

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[NeurIPS 2021] Deceive D: Adaptive Pseudo Augmentation for GAN Training with Limited Data

Vit-ImageClassification - Pytorch ViT for Image classification on the CIFAR10 dataset

The lightweight PyTorch wrapper for high-performance AI research. Scale your models, not the boilerplate.

Jiminy Cricket Environment (NeurIPS 2021)

GAN example for Keras. Cuz MNIST is too small and there should be something more realistic.

Unofficial implementation of Google "CutPaste: Self-Supervised Learning for Anomaly Detection and Localization" in PyTorch

🔮 Execution time predictions for deep neural network training iterations across different GPUs.