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- .dockerignore +16 -0
- .gitattributes +0 -21
- .gitignore +17 -0
- AUTHORS +7 -0
- CODE_LICENSE +201 -0
- CODE_OF_CONDUCT.md +132 -0
- CONTRIBUTING.md +87 -0
- DATA_LICENSE +407 -0
- DEVELOPER_GUIDE.md +304 -0
- LICENSE +202 -21
- MODEL_DIFF_LICENSE +407 -0
- MODEL_WEIGHTS_LICENSE +111 -0
- Makefile +42 -0
- README.md +22 -10
- SECURITY.md +33 -0
- Score.py +36 -0
- VERSION +1 -0
- WizardMath_Paper.pdf +0 -0
- Zalmati-vm-II_key.pem +39 -0
- Zalmati.py +17 -0
- Zalmati_LLAMA-2.pbix +0 -0
- adapt_tokenizer.py +41 -0
- attention.py +300 -0
- batch_throttle.py +23 -0
- blocks.py +41 -0
- config.json +52 -0
- configuration_mpt.py +118 -0
- custom_embedding.py +11 -0
- definition.json +1 -0
- example.py +61 -0
- executionlogs.txt +7 -0
- flash_attn_triton.py +484 -0
- generation_config.json +6 -0
- hf_prefixlm_converter.py +415 -0
- meta_init_context.py +94 -0
- modeling_mpt.py +323 -0
- norm.py +56 -0
- param_init_fns.py +181 -0
- pytorch_model.bin.index.json +201 -0
- requirements.txt +2 -0
- score.py.txt +36 -0
- setup.py +10 -0
- special_tokens_map.json +5 -0
- stderrlogs.txt +1 -0
- stdoutlogs.txt +0 -0
- tokenizer.json +0 -0
- tokenizer_config.json +9 -0
- zalmati.ipynb +115 -0
- zalmati.pbids +1 -0
- zalmati.pem +39 -0
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# MacOS
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.pytest_cache
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AUTHORS
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# This is the list of HuggingFace Datasets Server authors for copyright purposes.
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#
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CODE_LICENSE
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184 |
+
comment syntax for the file format. We also recommend that a
|
185 |
+
file or class name and description of purpose be included on the
|
186 |
+
same "printed page" as the copyright notice for easier
|
187 |
+
identification within third-party archives.
|
188 |
+
|
189 |
+
Copyright 2023 Rohan Taori, Ishaan Gulrajani, Tianyi Zhang, Yann Dubois, Xuechen Li
|
190 |
+
|
191 |
+
Licensed under the Apache License, Version 2.0 (the "License");
|
192 |
+
you may not use this file except in compliance with the License.
|
193 |
+
You may obtain a copy of the License at
|
194 |
+
|
195 |
+
http://www.apache.org/licenses/LICENSE-2.0
|
196 |
+
|
197 |
+
Unless required by applicable law or agreed to in writing, software
|
198 |
+
distributed under the License is distributed on an "AS IS" BASIS,
|
199 |
+
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
200 |
+
See the License for the specific language governing permissions and
|
201 |
+
limitations under the License.
|
CODE_OF_CONDUCT.md
ADDED
@@ -0,0 +1,132 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# Contributor Covenant Code of Conduct
|
2 |
+
|
3 |
+
## Our Pledge
|
4 |
+
|
5 |
+
We as members, contributors, and leaders pledge to make participation in our
|
6 |
+
community a harassment-free experience for everyone, regardless of age, body
|
7 |
+
size, visible or invisible disability, ethnicity, sex characteristics, gender
|
8 |
+
identity and expression, level of experience, education, socio-economic status,
|
9 |
+
nationality, personal appearance, race, caste, color, religion, or sexual identity
|
10 |
+
and orientation.
|
11 |
+
|
12 |
+
We pledge to act and interact in ways that contribute to an open, welcoming,
|
13 |
+
diverse, inclusive, and healthy community.
|
14 |
+
|
15 |
+
## Our Standards
|
16 |
+
|
17 |
+
Examples of behavior that contributes to a positive environment for our
|
18 |
+
community include:
|
19 |
+
|
20 |
+
* Demonstrating empathy and kindness toward other people
|
21 |
+
* Being respectful of differing opinions, viewpoints, and experiences
|
22 |
+
* Giving and gracefully accepting constructive feedback
|
23 |
+
* Accepting responsibility and apologizing to those affected by our mistakes,
|
24 |
+
and learning from the experience
|
25 |
+
* Focusing on what is best not just for us as individuals, but for the
|
26 |
+
overall community
|
27 |
+
|
28 |
+
Examples of unacceptable behavior include:
|
29 |
+
|
30 |
+
* The use of sexualized language or imagery, and sexual attention or
|
31 |
+
advances of any kind
|
32 |
+
* Trolling, insulting or derogatory comments, and personal or political attacks
|
33 |
+
* Public or private harassment
|
34 |
+
* Publishing others' private information, such as a physical or email
|
35 |
+
address, without their explicit permission
|
36 |
+
* Other conduct which could reasonably be considered inappropriate in a
|
37 |
+
professional setting
|
38 |
+
|
39 |
+
## Enforcement Responsibilities
|
40 |
+
|
41 |
+
Community leaders are responsible for clarifying and enforcing our standards of
|
42 |
+
acceptable behavior and will take appropriate and fair corrective action in
|
43 |
+
response to any behavior that they deem inappropriate, threatening, offensive,
|
44 |
+
or harmful.
|
45 |
+
|
46 |
+
Community leaders have the right and responsibility to remove, edit, or reject
|
47 |
+
comments, commits, code, wiki edits, issues, and other contributions that are
|
48 |
+
not aligned to this Code of Conduct, and will communicate reasons for moderation
|
49 |
+
decisions when appropriate.
|
50 |
+
|
51 |
+
## Scope
|
52 |
+
|
53 |
+
This Code of Conduct applies within all community spaces, and also applies when
|
54 |
+
an individual is officially representing the community in public spaces.
|
55 |
+
Examples of representing our community include using an official e-mail address,
|
56 |
+
posting via an official social media account, or acting as an appointed
|
57 |
+
representative at an online or offline event.
|
58 |
+
|
59 |
+
## Enforcement
|
60 |
+
|
61 |
+
Instances of abusive, harassing, or otherwise unacceptable behavior may be
|
62 |
+
reported to the community leaders responsible for enforcement at
|
63 |
+
feedback@huggingface.co.
|
64 |
+
All complaints will be reviewed and investigated promptly and fairly.
|
65 |
+
|
66 |
+
All community leaders are obligated to respect the privacy and security of the
|
67 |
+
reporter of any incident.
|
68 |
+
|
69 |
+
## Enforcement Guidelines
|
70 |
+
|
71 |
+
Community leaders will follow these Community Impact Guidelines in determining
|
72 |
+
the consequences for any action they deem in violation of this Code of Conduct:
|
73 |
+
|
74 |
+
### 1. Correction
|
75 |
+
|
76 |
+
**Community Impact**: Use of inappropriate language or other behavior deemed
|
77 |
+
unprofessional or unwelcome in the community.
|
78 |
+
|
79 |
+
**Consequence**: A private, written warning from community leaders, providing
|
80 |
+
clarity around the nature of the violation and an explanation of why the
|
81 |
+
behavior was inappropriate. A public apology may be requested.
|
82 |
+
|
83 |
+
### 2. Warning
|
84 |
+
|
85 |
+
**Community Impact**: A violation through a single incident or series
|
86 |
+
of actions.
|
87 |
+
|
88 |
+
**Consequence**: A warning with consequences for continued behavior. No
|
89 |
+
interaction with the people involved, including unsolicited interaction with
|
90 |
+
those enforcing the Code of Conduct, for a specified period of time. This
|
91 |
+
includes avoiding interactions in community spaces as well as external channels
|
92 |
+
like social media. Violating these terms may lead to a temporary or
|
93 |
+
permanent ban.
|
94 |
+
|
95 |
+
### 3. Temporary Ban
|
96 |
+
|
97 |
+
**Community Impact**: A serious violation of community standards, including
|
98 |
+
sustained inappropriate behavior.
|
99 |
+
|
100 |
+
**Consequence**: A temporary ban from any sort of interaction or public
|
101 |
+
communication with the community for a specified period of time. No public or
|
102 |
+
private interaction with the people involved, including unsolicited interaction
|
103 |
+
with those enforcing the Code of Conduct, is allowed during this period.
|
104 |
+
Violating these terms may lead to a permanent ban.
|
105 |
+
|
106 |
+
### 4. Permanent Ban
|
107 |
+
|
108 |
+
**Community Impact**: Demonstrating a pattern of violation of community
|
109 |
+
standards, including sustained inappropriate behavior, harassment of an
|
110 |
+
individual, or aggression toward or disparagement of classes of individuals.
|
111 |
+
|
112 |
+
**Consequence**: A permanent ban from any sort of public interaction within
|
113 |
+
the community.
|
114 |
+
|
115 |
+
## Attribution
|
116 |
+
|
117 |
+
This Code of Conduct is adapted from the [Contributor Covenant][homepage],
|
118 |
+
version 2.0, available at
|
119 |
+
[https://www.contributor-covenant.org/version/2/0/code_of_conduct.html][v2.0].
|
120 |
+
|
121 |
+
Community Impact Guidelines were inspired by
|
122 |
+
[Mozilla's code of conduct enforcement ladder][Mozilla CoC].
|
123 |
+
|
124 |
+
For answers to common questions about this code of conduct, see the FAQ at
|
125 |
+
[https://www.contributor-covenant.org/faq][FAQ]. Translations are available
|
126 |
+
at [https://www.contributor-covenant.org/translations][translations].
|
127 |
+
|
128 |
+
[homepage]: https://www.contributor-covenant.org
|
129 |
+
[v2.0]: https://www.contributor-covenant.org/version/2/0/code_of_conduct.html
|
130 |
+
[Mozilla CoC]: https://github.com/mozilla/diversity
|
131 |
+
[FAQ]: https://www.contributor-covenant.org/faq
|
132 |
+
[translations]: https://www.contributor-covenant.org/translations
|
CONTRIBUTING.md
ADDED
@@ -0,0 +1,87 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# How to contribute to the Datasets Server?
|
2 |
+
|
3 |
+
[![Contributor Covenant](https://img.shields.io/badge/Contributor%20Covenant-2.0-4baaaa.svg)](CODE_OF_CONDUCT.md)
|
4 |
+
|
5 |
+
The Datasets Server is an open source project, so all contributions and suggestions are welcome.
|
6 |
+
|
7 |
+
You can contribute in many different ways: giving ideas, answering questions, reporting bugs, proposing enhancements,
|
8 |
+
improving the documentation, fixing bugs...
|
9 |
+
|
10 |
+
Many thanks in advance to every contributor.
|
11 |
+
|
12 |
+
In order to facilitate healthy, constructive behavior in an open and inclusive community, we all respect and abide by
|
13 |
+
our [code of conduct](CODE_OF_CONDUCT.md).
|
14 |
+
|
15 |
+
## How to work on an open Issue?
|
16 |
+
|
17 |
+
You have the list of open Issues at: https://github.com/huggingface/datasets/issues
|
18 |
+
|
19 |
+
Some of them may have the label `help wanted`: that means that any contributor is welcomed!
|
20 |
+
|
21 |
+
If you would like to work on any of the open Issues:
|
22 |
+
|
23 |
+
1. Make sure it is not already assigned to someone else. You have the assignee (if any) on the top of the right column of the Issue page.
|
24 |
+
|
25 |
+
2. You can self-assign it by commenting on the Issue page with one of the keywords: `#take` or `#self-assign`.
|
26 |
+
|
27 |
+
3. Work on your self-assigned issue and eventually create a Pull Request.
|
28 |
+
|
29 |
+
## How to create a Pull Request?
|
30 |
+
|
31 |
+
1. Fork the [repository](https://github.com/huggingface/datasets-server) by clicking on the 'Fork' button on the repository's page. This creates a copy of the code under your GitHub user account.
|
32 |
+
|
33 |
+
2. Clone your fork to your local disk, and add the base repository as a remote:
|
34 |
+
|
35 |
+
```bash
|
36 |
+
git clone git@github.com:<your Github handle>/datasets-server.git
|
37 |
+
cd datasets-server
|
38 |
+
git remote add upstream https://github.com/huggingface/datasets-server.git
|
39 |
+
```
|
40 |
+
|
41 |
+
3. Create a new branch to hold your development changes:
|
42 |
+
|
43 |
+
```bash
|
44 |
+
git checkout -b a-descriptive-name-for-my-changes
|
45 |
+
```
|
46 |
+
|
47 |
+
**do not** work on the `main` branch.
|
48 |
+
|
49 |
+
4. Set up a development environment by following the [developer guide](./DEVELOPER_GUIDE.md)
|
50 |
+
|
51 |
+
5. Develop the features on your branch.
|
52 |
+
|
53 |
+
6. Format your code. Run black and isort so that your newly added files look nice with the following command:
|
54 |
+
|
55 |
+
```bash
|
56 |
+
make style
|
57 |
+
```
|
58 |
+
|
59 |
+
7. Once you're happy with your code, add your changes and make a commit to record your changes locally:
|
60 |
+
|
61 |
+
```bash
|
62 |
+
git add -p
|
63 |
+
git commit
|
64 |
+
```
|
65 |
+
|
66 |
+
It is a good idea to sync your copy of the code with the original
|
67 |
+
repository regularly. This way you can quickly account for changes:
|
68 |
+
|
69 |
+
```bash
|
70 |
+
git fetch upstream
|
71 |
+
git rebase upstream/main
|
72 |
+
```
|
73 |
+
|
74 |
+
Push the changes to your account using:
|
75 |
+
|
76 |
+
```bash
|
77 |
+
git push -u origin a-descriptive-name-for-my-changes
|
78 |
+
```
|
79 |
+
|
80 |
+
8. Once you are satisfied, go the webpage of your fork on GitHub. Click on "Pull request" to send your to the project maintainers for review.
|
81 |
+
|
82 |
+
Thank you for your contribution!
|
83 |
+
|
84 |
+
## Code of conduct
|
85 |
+
|
86 |
+
This project adheres to the HuggingFace [code of conduct](CODE_OF_CONDUCT.md).
|
87 |
+
By participating, you are expected to uphold this code.
|
DATA_LICENSE
ADDED
@@ -0,0 +1,407 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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1 |
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Attribution-NonCommercial 4.0 International
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Creative Commons Attribution-NonCommercial 4.0 International Public
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By exercising the Licensed Rights (defined below), You accept and agree
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i. NonCommercial means not primarily intended for or directed towards
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other material subject to Copyright and Similar Rights by digital
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as reproduction, public display, public performance, distribution,
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dissemination, communication, or importation, and to make material
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k. Sui Generis Database Rights means rights other than copyright
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resulting from Directive 96/9/EC of the European Parliament and of
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as amended and/or succeeded, as well as other essentially
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equivalent rights anywhere in the world.
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non-sublicensable, non-exclusive, irrevocable license to
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exercise the Licensed Rights in the Licensed Material to:
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a. reproduce and Share the Licensed Material, in whole or
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b. produce, reproduce, and Share Adapted Material for
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6(a).
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Licensor authorizes You to exercise the Licensed Rights in
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all media and formats whether now known or hereafter created,
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and to make technical modifications necessary to do so. The
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Licensor waives and/or agrees not to assert any right or
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authority to forbid You from making technical modifications
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necessary to exercise the Licensed Rights, including
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technical modifications necessary to circumvent Effective
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5. Downstream recipients.
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recipient of the Licensed Material automatically
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Public License.
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apply any Effective Technological Measures to, the
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Licensed Material if doing so restricts exercise of the
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Licensed Rights by any recipient of the Licensed
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b. Other rights.
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1. Moral rights, such as the right of integrity, are not
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2. Patent and trademark rights are not licensed under this
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Section 3 -- License Conditions.
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Your exercise of the Licensed Rights is expressly made subject to the
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following conditions.
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a. Attribution.
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1. If You Share the Licensed Material (including in modified
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form), You must:
|
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|
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a. retain the following if it is supplied by the Licensor
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with the Licensed Material:
|
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i. identification of the creator(s) of the Licensed
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Material and any others designated to receive
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attribution, in any reasonable manner requested by
|
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the Licensor (including by pseudonym if
|
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designated);
|
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ii. a copyright notice;
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iv. a notice that refers to the disclaimer of
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warranties;
|
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|
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v. a URI or hyperlink to the Licensed Material to the
|
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extent reasonably practicable;
|
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|
251 |
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b. indicate if You modified the Licensed Material and
|
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retain an indication of any previous modifications; and
|
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|
254 |
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c. indicate the Licensed Material is licensed under this
|
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Public License, and include the text of, or the URI or
|
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hyperlink to, this Public License.
|
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|
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2. You may satisfy the conditions in Section 3(a)(1) in any
|
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reasonable manner based on the medium, means, and context in
|
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which You Share the Licensed Material. For example, it may be
|
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reasonable to satisfy the conditions by providing a URI or
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hyperlink to a resource that includes the required
|
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information.
|
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|
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3. If requested by the Licensor, You must remove any of the
|
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information required by Section 3(a)(1)(A) to the extent
|
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reasonably practicable.
|
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|
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4. If You Share Adapted Material You produce, the Adapter's
|
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License You apply must not prevent recipients of the Adapted
|
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Material from complying with this Public License.
|
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|
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|
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Section 4 -- Sui Generis Database Rights.
|
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|
276 |
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Where the Licensed Rights include Sui Generis Database Rights that
|
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apply to Your use of the Licensed Material:
|
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|
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a. for the avoidance of doubt, Section 2(a)(1) grants You the right
|
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to extract, reuse, reproduce, and Share all or a substantial
|
281 |
+
portion of the contents of the database for NonCommercial purposes
|
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+
only;
|
283 |
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|
284 |
+
b. if You include all or a substantial portion of the database
|
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contents in a database in which You have Sui Generis Database
|
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Rights, then the database in which You have Sui Generis Database
|
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Rights (but not its individual contents) is Adapted Material; and
|
288 |
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|
289 |
+
c. You must comply with the conditions in Section 3(a) if You Share
|
290 |
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all or a substantial portion of the contents of the database.
|
291 |
+
|
292 |
+
For the avoidance of doubt, this Section 4 supplements and does not
|
293 |
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replace Your obligations under this Public License where the Licensed
|
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Rights include other Copyright and Similar Rights.
|
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|
296 |
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|
297 |
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Section 5 -- Disclaimer of Warranties and Limitation of Liability.
|
298 |
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|
299 |
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a. UNLESS OTHERWISE SEPARATELY UNDERTAKEN BY THE LICENSOR, TO THE
|
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EXTENT POSSIBLE, THE LICENSOR OFFERS THE LICENSED MATERIAL AS-IS
|
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AND AS-AVAILABLE, AND MAKES NO REPRESENTATIONS OR WARRANTIES OF
|
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ANY KIND CONCERNING THE LICENSED MATERIAL, WHETHER EXPRESS,
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IMPLIED, STATUTORY, OR OTHER. THIS INCLUDES, WITHOUT LIMITATION,
|
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WARRANTIES OF TITLE, MERCHANTABILITY, FITNESS FOR A PARTICULAR
|
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PURPOSE, NON-INFRINGEMENT, ABSENCE OF LATENT OR OTHER DEFECTS,
|
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ACCURACY, OR THE PRESENCE OR ABSENCE OF ERRORS, WHETHER OR NOT
|
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KNOWN OR DISCOVERABLE. WHERE DISCLAIMERS OF WARRANTIES ARE NOT
|
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ALLOWED IN FULL OR IN PART, THIS DISCLAIMER MAY NOT APPLY TO YOU.
|
309 |
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|
310 |
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b. TO THE EXTENT POSSIBLE, IN NO EVENT WILL THE LICENSOR BE LIABLE
|
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TO YOU ON ANY LEGAL THEORY (INCLUDING, WITHOUT LIMITATION,
|
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NEGLIGENCE) OR OTHERWISE FOR ANY DIRECT, SPECIAL, INDIRECT,
|
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INCIDENTAL, CONSEQUENTIAL, PUNITIVE, EXEMPLARY, OR OTHER LOSSES,
|
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COSTS, EXPENSES, OR DAMAGES ARISING OUT OF THIS PUBLIC LICENSE OR
|
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USE OF THE LICENSED MATERIAL, EVEN IF THE LICENSOR HAS BEEN
|
316 |
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ADVISED OF THE POSSIBILITY OF SUCH LOSSES, COSTS, EXPENSES, OR
|
317 |
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DAMAGES. WHERE A LIMITATION OF LIABILITY IS NOT ALLOWED IN FULL OR
|
318 |
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IN PART, THIS LIMITATION MAY NOT APPLY TO YOU.
|
319 |
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|
320 |
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c. The disclaimer of warranties and limitation of liability provided
|
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above shall be interpreted in a manner that, to the extent
|
322 |
+
possible, most closely approximates an absolute disclaimer and
|
323 |
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waiver of all liability.
|
324 |
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|
325 |
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|
326 |
+
Section 6 -- Term and Termination.
|
327 |
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|
328 |
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a. This Public License applies for the term of the Copyright and
|
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Similar Rights licensed here. However, if You fail to comply with
|
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this Public License, then Your rights under this Public License
|
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terminate automatically.
|
332 |
+
|
333 |
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b. Where Your right to use the Licensed Material has terminated under
|
334 |
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Section 6(a), it reinstates:
|
335 |
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|
336 |
+
1. automatically as of the date the violation is cured, provided
|
337 |
+
it is cured within 30 days of Your discovery of the
|
338 |
+
violation; or
|
339 |
+
|
340 |
+
2. upon express reinstatement by the Licensor.
|
341 |
+
|
342 |
+
For the avoidance of doubt, this Section 6(b) does not affect any
|
343 |
+
right the Licensor may have to seek remedies for Your violations
|
344 |
+
of this Public License.
|
345 |
+
|
346 |
+
c. For the avoidance of doubt, the Licensor may also offer the
|
347 |
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Licensed Material under separate terms or conditions or stop
|
348 |
+
distributing the Licensed Material at any time; however, doing so
|
349 |
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will not terminate this Public License.
|
350 |
+
|
351 |
+
d. Sections 1, 5, 6, 7, and 8 survive termination of this Public
|
352 |
+
License.
|
353 |
+
|
354 |
+
|
355 |
+
Section 7 -- Other Terms and Conditions.
|
356 |
+
|
357 |
+
a. The Licensor shall not be bound by any additional or different
|
358 |
+
terms or conditions communicated by You unless expressly agreed.
|
359 |
+
|
360 |
+
b. Any arrangements, understandings, or agreements regarding the
|
361 |
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Licensed Material not stated herein are separate from and
|
362 |
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independent of the terms and conditions of this Public License.
|
363 |
+
|
364 |
+
|
365 |
+
Section 8 -- Interpretation.
|
366 |
+
|
367 |
+
a. For the avoidance of doubt, this Public License does not, and
|
368 |
+
shall not be interpreted to, reduce, limit, restrict, or impose
|
369 |
+
conditions on any use of the Licensed Material that could lawfully
|
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be made without permission under this Public License.
|
371 |
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|
372 |
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b. To the extent possible, if any provision of this Public License is
|
373 |
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deemed unenforceable, it shall be automatically reformed to the
|
374 |
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minimum extent necessary to make it enforceable. If the provision
|
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cannot be reformed, it shall be severed from this Public License
|
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without affecting the enforceability of the remaining terms and
|
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conditions.
|
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|
379 |
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c. No term or condition of this Public License will be waived and no
|
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failure to comply consented to unless expressly agreed to by the
|
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Licensor.
|
382 |
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|
383 |
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d. Nothing in this Public License constitutes or may be interpreted
|
384 |
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as a limitation upon, or waiver of, any privileges and immunities
|
385 |
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that apply to the Licensor or You, including from the legal
|
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processes of any jurisdiction or authority.
|
387 |
+
|
388 |
+
=======================================================================
|
389 |
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|
390 |
+
Creative Commons is not a party to its public
|
391 |
+
licenses. Notwithstanding, Creative Commons may elect to apply one of
|
392 |
+
its public licenses to material it publishes and in those instances
|
393 |
+
will be considered the “Licensor.” The text of the Creative Commons
|
394 |
+
public licenses is dedicated to the public domain under the CC0 Public
|
395 |
+
Domain Dedication. Except for the limited purpose of indicating that
|
396 |
+
material is shared under a Creative Commons public license or as
|
397 |
+
otherwise permitted by the Creative Commons policies published at
|
398 |
+
creativecommons.org/policies, Creative Commons does not authorize the
|
399 |
+
use of the trademark "Creative Commons" or any other trademark or logo
|
400 |
+
of Creative Commons without its prior written consent including,
|
401 |
+
without limitation, in connection with any unauthorized modifications
|
402 |
+
to any of its public licenses or any other arrangements,
|
403 |
+
understandings, or agreements concerning use of licensed material. For
|
404 |
+
the avoidance of doubt, this paragraph does not form part of the
|
405 |
+
public licenses.
|
406 |
+
|
407 |
+
Creative Commons may be contacted at creativecommons.org.
|
DEVELOPER_GUIDE.md
ADDED
@@ -0,0 +1,304 @@
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|
|
|
1 |
+
# Developer guide
|
2 |
+
|
3 |
+
This document is intended for developers who want to install, test or contribute to the code.
|
4 |
+
|
5 |
+
## Install
|
6 |
+
|
7 |
+
To start working on the project:
|
8 |
+
|
9 |
+
```bash
|
10 |
+
git clone git@github.com:huggingface/datasets-server.git
|
11 |
+
cd datasets-server
|
12 |
+
```
|
13 |
+
|
14 |
+
Install docker (see https://docs.docker.com/engine/install/ubuntu/#install-using-the-repository and https://docs.docker.com/engine/install/linux-postinstall/)
|
15 |
+
|
16 |
+
Run the project locally:
|
17 |
+
|
18 |
+
```bash
|
19 |
+
make start
|
20 |
+
```
|
21 |
+
|
22 |
+
Run the project in development mode:
|
23 |
+
|
24 |
+
```bash
|
25 |
+
make dev-start
|
26 |
+
```
|
27 |
+
|
28 |
+
In development mode, you don't need to rebuild the docker images to apply a change in a worker.
|
29 |
+
You can just restart the worker's docker container and it will apply your changes.
|
30 |
+
|
31 |
+
To install a single job (in [jobs](./jobs)), library (in [libs](./libs)) or service (in [services](./services)), go to their respective directory, and install Python 3.9 (consider [pyenv](https://github.com/pyenv/pyenv)) and [poetry](https://python-poetry.org/docs/master/#installation) (don't forget to add `poetry` to the `PATH` environment variable).
|
32 |
+
|
33 |
+
If you use pyenv:
|
34 |
+
|
35 |
+
```bash
|
36 |
+
cd libs/libcommon/
|
37 |
+
pyenv install 3.9.15
|
38 |
+
pyenv local 3.9.15
|
39 |
+
poetry env use python3.9
|
40 |
+
```
|
41 |
+
|
42 |
+
then:
|
43 |
+
|
44 |
+
```bash
|
45 |
+
make install
|
46 |
+
```
|
47 |
+
|
48 |
+
It will create a virtual environment in a `./.venv/` subdirectory.
|
49 |
+
|
50 |
+
If you use VSCode, it might be useful to use the ["monorepo" workspace](./.vscode/monorepo.code-workspace) (see a [blogpost](https://medium.com/rewrite-tech/visual-studio-code-tips-for-monorepo-development-with-multi-root-workspaces-and-extension-6b69420ecd12) for more explanations). It is a multi-root workspace, with one folder for each library and service (note that we hide them from the ROOT to avoid editing there). Each folder has its own Python interpreter, with access to the dependencies installed by Poetry. You might have to manually select the interpreter in every folder though on first access, then VSCode stores the information in its local storage.
|
51 |
+
|
52 |
+
## Architecture
|
53 |
+
|
54 |
+
The repository is structured as a monorepo, with Python libraries and applications in [jobs](./jobs), [libs](./libs) and [services](./services):
|
55 |
+
|
56 |
+
- [jobs](./jobs) contains the one-time jobs run by Helm before deploying the pods. For now, the only job migrates the databases when needed.
|
57 |
+
- [libs](./libs) contains the Python libraries used by the services and workers. For now, the only library is [libcommon](./libs/libcommon), which contains the common code for the services and workers.
|
58 |
+
- [services](./services) contains the applications: the public API, the admin API (which is separated from the public API and might be published under its own domain at some point), the reverse proxy, and the worker that processes the queue asynchronously: it gets a "job" (caution: the jobs stored in the queue, not the Helm jobs), processes the expected response for the associated endpoint, and stores the response in the cache.
|
59 |
+
|
60 |
+
If you have access to the internal HF notion, see https://www.notion.so/huggingface2/Datasets-server-464848da2a984e999c540a4aa7f0ece5.
|
61 |
+
|
62 |
+
The application is distributed in several components.
|
63 |
+
|
64 |
+
[api](./services/api) is a web server that exposes the [API endpoints](https://huggingface.co/docs/datasets-server). Apart from some endpoints (`valid`, `is-valid`), all the responses are served from pre-computed responses. That's the main point of this project: generating these responses takes time, and the API server provides this service to the users.
|
65 |
+
|
66 |
+
The precomputed responses are stored in a Mongo database called "cache". They are computed by [workers](./services/worker) which take their jobs from a job queue stored in a Mongo database called "queue", and store the results (error or valid response) into the "cache" (see [libcommon](./libs/libcommon)).
|
67 |
+
|
68 |
+
The API service exposes the `/webhook` endpoint which is called by the Hub on every creation, update or deletion of a dataset on the Hub. On deletion, the cached responses are deleted. On creation or update, a new job is appended in the "queue" database.
|
69 |
+
|
70 |
+
Note that every worker has its own job queue:
|
71 |
+
|
72 |
+
- `/splits`: the job is to refresh a dataset, namely to get the list of [config](https://huggingface.co/docs/datasets/v2.1.0/en/load_hub#select-a-configuration) and [split](https://huggingface.co/docs/datasets/v2.1.0/en/load_hub#select-a-split) names, then to create a new job for every split for the workers that depend on it.
|
73 |
+
- `/first-rows`: the job is to get the columns and the first 100 rows of the split.
|
74 |
+
- `/parquet`: the job is to download the dataset, prepare a parquet version of every split (various sharded parquet files), and upload them to the `ref/convert/parquet` "branch" of the dataset repository on the Hub.
|
75 |
+
|
76 |
+
Note also that the workers create local files when the dataset contains images or audios. A shared directory (`ASSETS_STORAGE_DIRECTORY`) must therefore be provisioned with sufficient space for the generated files. The `/first-rows` endpoint responses contain URLs to these files, served by the API under the `/assets/` endpoint.
|
77 |
+
|
78 |
+
Hence, the working application has:
|
79 |
+
|
80 |
+
- one instance of the API service which exposes a port
|
81 |
+
- N1 instances of the `splits` worker, N2 instances of the `first-rows` worker (N2 should generally be higher than N1), N3 instances of the `parquet` worker
|
82 |
+
- a Mongo server with two databases: "cache" and "queue"
|
83 |
+
- a shared directory for the assets
|
84 |
+
|
85 |
+
The application also has:
|
86 |
+
|
87 |
+
- a reverse proxy in front of the API to serve static files and proxy the rest to the API server
|
88 |
+
- an admin server to serve technical endpoints
|
89 |
+
|
90 |
+
The following environments contain all the modules: reverse proxy, API server, admin API server, workers, and the Mongo database.
|
91 |
+
|
92 |
+
| Environment | URL | Type | How to deploy |
|
93 |
+
| ----------- | ---------------------------------------------------- | ----------------- | --------------------------------------- |
|
94 |
+
| Production | https://datasets-server.huggingface.co | Helm / Kubernetes | `make upgrade-prod` in [chart](./chart) |
|
95 |
+
| Development | https://datasets-server.us.dev.moon.huggingface.tech | Helm / Kubernetes | `make upgrade-dev` in [chart](./chart) |
|
96 |
+
| Local build | http://localhost:8100 | Docker compose | `make start` (builds docker images) |
|
97 |
+
|
98 |
+
## Quality
|
99 |
+
|
100 |
+
The CI checks the quality of the code through a [GitHub action](./.github/workflows/_quality-python.yml). To manually format the code of a job, library, service or worker:
|
101 |
+
|
102 |
+
```bash
|
103 |
+
make style
|
104 |
+
```
|
105 |
+
|
106 |
+
To check the quality (which includes checking the style, but also security vulnerabilities):
|
107 |
+
|
108 |
+
```bash
|
109 |
+
make quality
|
110 |
+
```
|
111 |
+
|
112 |
+
## Tests
|
113 |
+
|
114 |
+
The CI checks the tests a [GitHub action](./.github/workflows/unit-tests.yml). To manually test a job, library, service or worker:
|
115 |
+
|
116 |
+
```bash
|
117 |
+
make test
|
118 |
+
```
|
119 |
+
|
120 |
+
Note that it requires the resources to be ready, ie. mongo and the storage for assets.
|
121 |
+
|
122 |
+
To launch the end to end tests:
|
123 |
+
|
124 |
+
```bash
|
125 |
+
make e2e
|
126 |
+
```
|
127 |
+
|
128 |
+
## Poetry
|
129 |
+
|
130 |
+
### Versions
|
131 |
+
|
132 |
+
If service is updated, we don't update its version in the `pyproject.yaml` file. But we have to update the [helm chart](./chart/) with the new image tag, corresponding to the last build docker published on docker.io by the CI.
|
133 |
+
|
134 |
+
## Pull requests
|
135 |
+
|
136 |
+
All the contributions should go through a pull request. The pull requests must be "squashed" (ie: one commit per pull request).
|
137 |
+
|
138 |
+
## GitHub Actions
|
139 |
+
|
140 |
+
You can use [act](https://github.com/nektos/act) to test the GitHub Actions (see [.github/workflows/](.github/workflows/)) locally. It reduces the retroaction loop when working on the GitHub Actions, avoid polluting the branches with empty pushes only meant to trigger the CI, and allows to only run specific actions.
|
141 |
+
|
142 |
+
For example, to launch the build and push of the docker images to Docker Hub:
|
143 |
+
|
144 |
+
```
|
145 |
+
act -j build-and-push-image-to-docker-hub --secret-file my.secrets
|
146 |
+
```
|
147 |
+
|
148 |
+
with `my.secrets` a file with the secrets:
|
149 |
+
|
150 |
+
```
|
151 |
+
DOCKERHUB_USERNAME=xxx
|
152 |
+
DOCKERHUB_PASSWORD=xxx
|
153 |
+
GITHUB_TOKEN=xxx
|
154 |
+
```
|
155 |
+
|
156 |
+
## Set up development environment
|
157 |
+
|
158 |
+
### Linux
|
159 |
+
|
160 |
+
Install pyenv:
|
161 |
+
|
162 |
+
```bash
|
163 |
+
$ curl https://pyenv.run | bash
|
164 |
+
```
|
165 |
+
|
166 |
+
Install Python 3.9.15:
|
167 |
+
|
168 |
+
```bash
|
169 |
+
$ pyenv install 3.9.15
|
170 |
+
```
|
171 |
+
|
172 |
+
Check that the expected local version of Python is used:
|
173 |
+
|
174 |
+
```bash
|
175 |
+
$ cd services/worker
|
176 |
+
$ python --version
|
177 |
+
Python 3.9.15
|
178 |
+
```
|
179 |
+
|
180 |
+
Install Poetry:
|
181 |
+
|
182 |
+
```bash
|
183 |
+
curl -sSL https://install.python-poetry.org | POETRY_VERSION=1.4.2 python3 -
|
184 |
+
```
|
185 |
+
|
186 |
+
Set the Python version to use with Poetry:
|
187 |
+
|
188 |
+
```bash
|
189 |
+
poetry env use 3.9.15
|
190 |
+
```
|
191 |
+
|
192 |
+
Install the dependencies:
|
193 |
+
|
194 |
+
```bash
|
195 |
+
make install
|
196 |
+
```
|
197 |
+
|
198 |
+
|
199 |
+
### Mac OS
|
200 |
+
|
201 |
+
To install the [worker](./services/worker) on Mac OS, you can follow the next steps.
|
202 |
+
|
203 |
+
#### First: as an administrator
|
204 |
+
|
205 |
+
Install brew:
|
206 |
+
|
207 |
+
```bash
|
208 |
+
$ /bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
|
209 |
+
```
|
210 |
+
|
211 |
+
Install ICU:
|
212 |
+
|
213 |
+
```bash
|
214 |
+
$ brew install icu4c
|
215 |
+
|
216 |
+
|
217 |
+
==> Caveats
|
218 |
+
icu4c is keg-only, which means it was not symlinked into /opt/homebrew,
|
219 |
+
because macOS provides libicucore.dylib (but nothing else).
|
220 |
+
|
221 |
+
If you need to have icu4c first in your PATH, run:
|
222 |
+
echo 'export PATH="/opt/homebrew/opt/icu4c/bin:$PATH"' >> ~/.zshrc
|
223 |
+
echo 'export PATH="/opt/homebrew/opt/icu4c/sbin:$PATH"' >> ~/.zshrc
|
224 |
+
|
225 |
+
For compilers to find icu4c you may need to set:
|
226 |
+
export LDFLAGS="-L/opt/homebrew/opt/icu4c/lib"
|
227 |
+
export CPPFLAGS="-I/opt/homebrew/opt/icu4c/include"
|
228 |
+
```
|
229 |
+
|
230 |
+
#### Then: as a normal user
|
231 |
+
|
232 |
+
Add ICU to the path:
|
233 |
+
|
234 |
+
```bash
|
235 |
+
$ echo 'export PATH="/opt/homebrew/opt/icu4c/bin:$PATH"' >> ~/.zshrc
|
236 |
+
$ echo 'export PATH="/opt/homebrew/opt/icu4c/sbin:$PATH"' >> ~/.zshrc
|
237 |
+
```
|
238 |
+
|
239 |
+
Install pyenv:
|
240 |
+
|
241 |
+
```bash
|
242 |
+
$ curl https://pyenv.run | bash
|
243 |
+
```
|
244 |
+
|
245 |
+
append the following lines to ~/.zshrc:
|
246 |
+
|
247 |
+
```bash
|
248 |
+
export PYENV_ROOT="$HOME/.pyenv"
|
249 |
+
command -v pyenv >/dev/null || export PATH="$PYENV_ROOT/bin:$PATH"
|
250 |
+
eval "$(pyenv init -)"
|
251 |
+
```
|
252 |
+
|
253 |
+
Logout and login again.
|
254 |
+
|
255 |
+
Install Python 3.9.15:
|
256 |
+
|
257 |
+
```bash
|
258 |
+
$ pyenv install 3.9.15
|
259 |
+
```
|
260 |
+
|
261 |
+
Check that the expected local version of Python is used:
|
262 |
+
|
263 |
+
```bash
|
264 |
+
$ cd services/worker
|
265 |
+
$ python --version
|
266 |
+
Python 3.9.15
|
267 |
+
```
|
268 |
+
|
269 |
+
Install poetry:
|
270 |
+
|
271 |
+
```bash
|
272 |
+
curl -sSL https://install.python-poetry.org | POETRY_VERSION=1.4.2 python3 -
|
273 |
+
```
|
274 |
+
|
275 |
+
append the following lines to ~/.zshrc:
|
276 |
+
|
277 |
+
```bash
|
278 |
+
export PATH="/Users/slesage2/.local/bin:$PATH"
|
279 |
+
```
|
280 |
+
|
281 |
+
Install rust:
|
282 |
+
|
283 |
+
```bash
|
284 |
+
$ curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
|
285 |
+
$ source $HOME/.cargo/env
|
286 |
+
```
|
287 |
+
|
288 |
+
Set the python version to use with poetry:
|
289 |
+
|
290 |
+
```bash
|
291 |
+
poetry env use 3.9.15
|
292 |
+
```
|
293 |
+
|
294 |
+
Avoid an issue with Apache beam (https://github.com/python-poetry/poetry/issues/4888#issuecomment-1208408509):
|
295 |
+
|
296 |
+
```bash
|
297 |
+
poetry config experimental.new-installer false
|
298 |
+
```
|
299 |
+
|
300 |
+
Install the dependencies:
|
301 |
+
|
302 |
+
```bash
|
303 |
+
make install
|
304 |
+
```
|
LICENSE
CHANGED
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|
|
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MODEL_DIFF_LICENSE
ADDED
@@ -0,0 +1,407 @@
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1 |
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Attribution-NonCommercial 4.0 International
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Creative Commons Attribution-NonCommercial 4.0 International Public
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License
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By exercising the Licensed Rights (defined below), You accept and agree
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License"). To the extent this Public License may be interpreted as a
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categorized. For purposes of this Public License, the rights
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any other exception or limitation to Copyright and Similar Rights
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that applies to Your use of the Licensed Material.
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or other material to which the Licensor applied this Public
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License.
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g. Licensed Rights means the rights granted to You subject to the
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terms and conditions of this Public License, which are limited to
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all Copyright and Similar Rights that apply to Your use of the
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h. Licensor means the individual(s) or entity(ies) granting rights
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under this Public License.
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i. NonCommercial means not primarily intended for or directed towards
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commercial advantage or monetary compensation. For purposes of
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this Public License, the exchange of the Licensed Material for
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other material subject to Copyright and Similar Rights by digital
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file-sharing or similar means is NonCommercial provided there is
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no payment of monetary compensation in connection with the
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exchange.
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j. Share means to provide material to the public by any means or
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process that requires permission under the Licensed Rights, such
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as reproduction, public display, public performance, distribution,
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dissemination, communication, or importation, and to make material
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available to the public including in ways that members of the
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public may access the material from a place and at a time
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individually chosen by them.
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k. Sui Generis Database Rights means rights other than copyright
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resulting from Directive 96/9/EC of the European Parliament and of
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the Council of 11 March 1996 on the legal protection of databases,
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as amended and/or succeeded, as well as other essentially
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equivalent rights anywhere in the world.
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l. You means the individual or entity exercising the Licensed Rights
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under this Public License. Your has a corresponding meaning.
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Section 2 -- Scope.
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a. License grant.
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the Licensor hereby grants You a worldwide, royalty-free,
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non-sublicensable, non-exclusive, irrevocable license to
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exercise the Licensed Rights in the Licensed Material to:
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a. reproduce and Share the Licensed Material, in whole or
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in part, for NonCommercial purposes only; and
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b. produce, reproduce, and Share Adapted Material for
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NonCommercial purposes only.
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2. Exceptions and Limitations. For the avoidance of doubt, where
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Exceptions and Limitations apply to Your use, this Public
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its terms and conditions.
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3. Term. The term of this Public License is specified in Section
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6(a).
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4. Media and formats; technical modifications allowed. The
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Licensor authorizes You to exercise the Licensed Rights in
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all media and formats whether now known or hereafter created,
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and to make technical modifications necessary to do so. The
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Licensor waives and/or agrees not to assert any right or
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authority to forbid You from making technical modifications
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necessary to exercise the Licensed Rights, including
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technical modifications necessary to circumvent Effective
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Technological Measures. For purposes of this Public License,
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simply making modifications authorized by this Section 2(a)
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(4) never produces Adapted Material.
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5. Downstream recipients.
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a. Offer from the Licensor -- Licensed Material. Every
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recipient of the Licensed Material automatically
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receives an offer from the Licensor to exercise the
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Licensed Rights under the terms and conditions of this
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Public License.
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b. No downstream restrictions. You may not offer or impose
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apply any Effective Technological Measures to, the
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Licensed Material if doing so restricts exercise of the
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Licensed Rights by any recipient of the Licensed
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Material.
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6. No endorsement. Nothing in this Public License constitutes or
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may be construed as permission to assert or imply that You
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are, or that Your use of the Licensed Material is, connected
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the Licensor or others designated to receive attribution as
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provided in Section 3(a)(1)(A)(i).
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b. Other rights.
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1. Moral rights, such as the right of integrity, are not
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licensed under this Public License, nor are publicity,
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privacy, and/or other similar personality rights; however, to
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the extent possible, the Licensor waives and/or agrees not to
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assert any such rights held by the Licensor to the limited
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extent necessary to allow You to exercise the Licensed
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2. Patent and trademark rights are not licensed under this
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Public License.
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3. To the extent possible, the Licensor waives any right to
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collect royalties from You for the exercise of the Licensed
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under any voluntary or waivable statutory or compulsory
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licensing scheme. In all other cases the Licensor expressly
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the Licensed Material is used other than for NonCommercial
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purposes.
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Section 3 -- License Conditions.
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Your exercise of the Licensed Rights is expressly made subject to the
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following conditions.
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a. Attribution.
|
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|
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1. If You Share the Licensed Material (including in modified
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form), You must:
|
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|
232 |
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a. retain the following if it is supplied by the Licensor
|
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with the Licensed Material:
|
234 |
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|
235 |
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i. identification of the creator(s) of the Licensed
|
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Material and any others designated to receive
|
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attribution, in any reasonable manner requested by
|
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the Licensor (including by pseudonym if
|
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designated);
|
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|
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ii. a copyright notice;
|
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iii. a notice that refers to this Public License;
|
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|
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iv. a notice that refers to the disclaimer of
|
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warranties;
|
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|
248 |
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v. a URI or hyperlink to the Licensed Material to the
|
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extent reasonably practicable;
|
250 |
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|
251 |
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b. indicate if You modified the Licensed Material and
|
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retain an indication of any previous modifications; and
|
253 |
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|
254 |
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c. indicate the Licensed Material is licensed under this
|
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Public License, and include the text of, or the URI or
|
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hyperlink to, this Public License.
|
257 |
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|
258 |
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2. You may satisfy the conditions in Section 3(a)(1) in any
|
259 |
+
reasonable manner based on the medium, means, and context in
|
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which You Share the Licensed Material. For example, it may be
|
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reasonable to satisfy the conditions by providing a URI or
|
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hyperlink to a resource that includes the required
|
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information.
|
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|
265 |
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3. If requested by the Licensor, You must remove any of the
|
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information required by Section 3(a)(1)(A) to the extent
|
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reasonably practicable.
|
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|
269 |
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4. If You Share Adapted Material You produce, the Adapter's
|
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License You apply must not prevent recipients of the Adapted
|
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Material from complying with this Public License.
|
272 |
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|
273 |
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|
274 |
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Section 4 -- Sui Generis Database Rights.
|
275 |
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|
276 |
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Where the Licensed Rights include Sui Generis Database Rights that
|
277 |
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apply to Your use of the Licensed Material:
|
278 |
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|
279 |
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a. for the avoidance of doubt, Section 2(a)(1) grants You the right
|
280 |
+
to extract, reuse, reproduce, and Share all or a substantial
|
281 |
+
portion of the contents of the database for NonCommercial purposes
|
282 |
+
only;
|
283 |
+
|
284 |
+
b. if You include all or a substantial portion of the database
|
285 |
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contents in a database in which You have Sui Generis Database
|
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+
Rights, then the database in which You have Sui Generis Database
|
287 |
+
Rights (but not its individual contents) is Adapted Material; and
|
288 |
+
|
289 |
+
c. You must comply with the conditions in Section 3(a) if You Share
|
290 |
+
all or a substantial portion of the contents of the database.
|
291 |
+
|
292 |
+
For the avoidance of doubt, this Section 4 supplements and does not
|
293 |
+
replace Your obligations under this Public License where the Licensed
|
294 |
+
Rights include other Copyright and Similar Rights.
|
295 |
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|
296 |
+
|
297 |
+
Section 5 -- Disclaimer of Warranties and Limitation of Liability.
|
298 |
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|
299 |
+
a. UNLESS OTHERWISE SEPARATELY UNDERTAKEN BY THE LICENSOR, TO THE
|
300 |
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EXTENT POSSIBLE, THE LICENSOR OFFERS THE LICENSED MATERIAL AS-IS
|
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AND AS-AVAILABLE, AND MAKES NO REPRESENTATIONS OR WARRANTIES OF
|
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ANY KIND CONCERNING THE LICENSED MATERIAL, WHETHER EXPRESS,
|
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IMPLIED, STATUTORY, OR OTHER. THIS INCLUDES, WITHOUT LIMITATION,
|
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WARRANTIES OF TITLE, MERCHANTABILITY, FITNESS FOR A PARTICULAR
|
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PURPOSE, NON-INFRINGEMENT, ABSENCE OF LATENT OR OTHER DEFECTS,
|
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ACCURACY, OR THE PRESENCE OR ABSENCE OF ERRORS, WHETHER OR NOT
|
307 |
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KNOWN OR DISCOVERABLE. WHERE DISCLAIMERS OF WARRANTIES ARE NOT
|
308 |
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ALLOWED IN FULL OR IN PART, THIS DISCLAIMER MAY NOT APPLY TO YOU.
|
309 |
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|
310 |
+
b. TO THE EXTENT POSSIBLE, IN NO EVENT WILL THE LICENSOR BE LIABLE
|
311 |
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TO YOU ON ANY LEGAL THEORY (INCLUDING, WITHOUT LIMITATION,
|
312 |
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NEGLIGENCE) OR OTHERWISE FOR ANY DIRECT, SPECIAL, INDIRECT,
|
313 |
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INCIDENTAL, CONSEQUENTIAL, PUNITIVE, EXEMPLARY, OR OTHER LOSSES,
|
314 |
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COSTS, EXPENSES, OR DAMAGES ARISING OUT OF THIS PUBLIC LICENSE OR
|
315 |
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USE OF THE LICENSED MATERIAL, EVEN IF THE LICENSOR HAS BEEN
|
316 |
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ADVISED OF THE POSSIBILITY OF SUCH LOSSES, COSTS, EXPENSES, OR
|
317 |
+
DAMAGES. WHERE A LIMITATION OF LIABILITY IS NOT ALLOWED IN FULL OR
|
318 |
+
IN PART, THIS LIMITATION MAY NOT APPLY TO YOU.
|
319 |
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|
320 |
+
c. The disclaimer of warranties and limitation of liability provided
|
321 |
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above shall be interpreted in a manner that, to the extent
|
322 |
+
possible, most closely approximates an absolute disclaimer and
|
323 |
+
waiver of all liability.
|
324 |
+
|
325 |
+
|
326 |
+
Section 6 -- Term and Termination.
|
327 |
+
|
328 |
+
a. This Public License applies for the term of the Copyright and
|
329 |
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Similar Rights licensed here. However, if You fail to comply with
|
330 |
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this Public License, then Your rights under this Public License
|
331 |
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terminate automatically.
|
332 |
+
|
333 |
+
b. Where Your right to use the Licensed Material has terminated under
|
334 |
+
Section 6(a), it reinstates:
|
335 |
+
|
336 |
+
1. automatically as of the date the violation is cured, provided
|
337 |
+
it is cured within 30 days of Your discovery of the
|
338 |
+
violation; or
|
339 |
+
|
340 |
+
2. upon express reinstatement by the Licensor.
|
341 |
+
|
342 |
+
For the avoidance of doubt, this Section 6(b) does not affect any
|
343 |
+
right the Licensor may have to seek remedies for Your violations
|
344 |
+
of this Public License.
|
345 |
+
|
346 |
+
c. For the avoidance of doubt, the Licensor may also offer the
|
347 |
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Licensed Material under separate terms or conditions or stop
|
348 |
+
distributing the Licensed Material at any time; however, doing so
|
349 |
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will not terminate this Public License.
|
350 |
+
|
351 |
+
d. Sections 1, 5, 6, 7, and 8 survive termination of this Public
|
352 |
+
License.
|
353 |
+
|
354 |
+
|
355 |
+
Section 7 -- Other Terms and Conditions.
|
356 |
+
|
357 |
+
a. The Licensor shall not be bound by any additional or different
|
358 |
+
terms or conditions communicated by You unless expressly agreed.
|
359 |
+
|
360 |
+
b. Any arrangements, understandings, or agreements regarding the
|
361 |
+
Licensed Material not stated herein are separate from and
|
362 |
+
independent of the terms and conditions of this Public License.
|
363 |
+
|
364 |
+
|
365 |
+
Section 8 -- Interpretation.
|
366 |
+
|
367 |
+
a. For the avoidance of doubt, this Public License does not, and
|
368 |
+
shall not be interpreted to, reduce, limit, restrict, or impose
|
369 |
+
conditions on any use of the Licensed Material that could lawfully
|
370 |
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be made without permission under this Public License.
|
371 |
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|
372 |
+
b. To the extent possible, if any provision of this Public License is
|
373 |
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deemed unenforceable, it shall be automatically reformed to the
|
374 |
+
minimum extent necessary to make it enforceable. If the provision
|
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cannot be reformed, it shall be severed from this Public License
|
376 |
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without affecting the enforceability of the remaining terms and
|
377 |
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conditions.
|
378 |
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|
379 |
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c. No term or condition of this Public License will be waived and no
|
380 |
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failure to comply consented to unless expressly agreed to by the
|
381 |
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Licensor.
|
382 |
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|
383 |
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d. Nothing in this Public License constitutes or may be interpreted
|
384 |
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as a limitation upon, or waiver of, any privileges and immunities
|
385 |
+
that apply to the Licensor or You, including from the legal
|
386 |
+
processes of any jurisdiction or authority.
|
387 |
+
|
388 |
+
=======================================================================
|
389 |
+
|
390 |
+
Creative Commons is not a party to its public
|
391 |
+
licenses. Notwithstanding, Creative Commons may elect to apply one of
|
392 |
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its public licenses to material it publishes and in those instances
|
393 |
+
will be considered the “Licensor.” The text of the Creative Commons
|
394 |
+
public licenses is dedicated to the public domain under the CC0 Public
|
395 |
+
Domain Dedication. Except for the limited purpose of indicating that
|
396 |
+
material is shared under a Creative Commons public license or as
|
397 |
+
otherwise permitted by the Creative Commons policies published at
|
398 |
+
creativecommons.org/policies, Creative Commons does not authorize the
|
399 |
+
use of the trademark "Creative Commons" or any other trademark or logo
|
400 |
+
of Creative Commons without its prior written consent including,
|
401 |
+
without limitation, in connection with any unauthorized modifications
|
402 |
+
to any of its public licenses or any other arrangements,
|
403 |
+
understandings, or agreements concerning use of licensed material. For
|
404 |
+
the avoidance of doubt, this paragraph does not form part of the
|
405 |
+
public licenses.
|
406 |
+
|
407 |
+
Creative Commons may be contacted at creativecommons.org.
|
MODEL_WEIGHTS_LICENSE
ADDED
@@ -0,0 +1,111 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
1 |
+
BigCode Open RAIL-M v1 License Agreement
|
2 |
+
Section I: Preamble
|
3 |
+
This OpenRAIL-M License Agreement was created under BigCode, an open and collaborative research project aimed at the responsible development and Use of Large Language Models (“LLMs”) for code generation. This license is generally applicable to any machine-learning Model.
|
4 |
+
|
5 |
+
This License Agreement strives for both the open and responsible Use of the accompanying Model. Openness here is understood as enabling users of the Model on a royalty free basis to Use it, modify it, and even share commercial versions of it. Use restrictions are included to prevent misuse of the Model.
|
6 |
+
|
7 |
+
This License Agreement governs the Use of the Model and Modifications of the Model. You and Licensor agree as follows:
|
8 |
+
|
9 |
+
1.Definitions
|
10 |
+
|
11 |
+
a. “Contribution” means any work of authorship, including the original version of the Model and any Modifications of the Model that is intentionally submitted to Licensor for inclusion in the Model by the copyright owner or by an individual or entity authorized to submit on behalf of the copyright owner. For the purposes of this definition, “submitted” means any form of electronic, verbal, or written communication sent to the Licensor or its representatives, including but not limited to communication on electronic mailing lists, source code control systems, and issue tracking systems that are managed by, or on behalf of, the Licensor for the purpose of discussing and improving the Model, but excluding communication that is conspicuously marked or otherwise designated in writing by the copyright owner as “Not a Contribution.”
|
12 |
+
|
13 |
+
b. “Contributor” means Licensor and any individual or entity on behalf of whom a Contribution has been received by Licensor and subsequently incorporated within the Model.
|
14 |
+
|
15 |
+
c. “Data” means a collection of information extracted from the dataset used with the Model, including to train, pretrain, or otherwise evaluate the Model. The Data is not licensed under this License Agreement.
|
16 |
+
|
17 |
+
d. “Explanatory Documentation” means model cards, data cards, or any other similar documentation or related information dedicated to inform the public about the characteristics of the model. Explanatory documentation is not licensed under this license.
|
18 |
+
|
19 |
+
e. “Harm” includes but is not limited to physical, mental, psychological, financial and reputational damage, pain, or loss.
|
20 |
+
|
21 |
+
f. “License Agreement” means this document.
|
22 |
+
|
23 |
+
g. “Licensor” means the rights owners or entity authorized by the rights owners that are granting the terms and conditions of this License Agreement.
|
24 |
+
|
25 |
+
h. “Model” means machine-learning based assemblies (including checkpoints), consisting of learnt weights and parameters (including optimizer states), corresponding to a model architecture as embodied in source code. Source code is not licensed under this License Agreement.
|
26 |
+
|
27 |
+
i. “Modifications of the Model” means all changes to the Model or any other model which is created or initialized by transfer of patterns of the weights, parameters, activations or Output of the Model.
|
28 |
+
|
29 |
+
j. “Output” means the results of operating the Model.
|
30 |
+
|
31 |
+
k. “Share” means any transmission, reproduction, publication or other sharing of the Model or Modifications of the Model to a third party, including providing the Model as a hosted service made available by electronic or other remote means, including - but not limited to - API-based or web access.
|
32 |
+
|
33 |
+
l. “Third Parties” means individuals or legal entities that are not under common control with Licensor or You.
|
34 |
+
|
35 |
+
m. “Use” includes - but is not limited to - generating any Output, fine tuning, updating, running, training, evaluating and/or reparametrizing the Model.
|
36 |
+
|
37 |
+
n. “You” (or “Your”) means an individual or Legal Entity exercising permissions granted by this License Agreement and/or making Use of the Model for whichever purpose and in any field of Use.
|
38 |
+
|
39 |
+
Section II: INTELLECTUAL PROPERTY RIGHTS
|
40 |
+
The Model and Modifications of the Model are subject to additional terms as described in Section III, which shall govern the Use of the Model and Modifications of the Model.
|
41 |
+
|
42 |
+
Grant of Copyright license. Subject to the terms and conditions of this License Agreement and where and as applicable, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, copyright license to reproduce, prepare, publicly display, publicly perform, sublicense under the terms herein, and distribute the Model and Modifications of the Model.
|
43 |
+
Grant of Patent license. Subject to the terms and conditions of this License Agreement and where and as applicable, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free patent license to make, have made, Use, offer to sell, sell, import, and otherwise transfer the Model, where such license applies only to those patent claims licensable by such Contributor that are necessarily infringed by their Contribution(s) alone or by combination of their Contribution(s) with the Model to which such Contribution(s) was submitted. If You institute patent litigation against any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Model or a Contribution incorporated within the Model constitutes direct or contributory patent infringement, then any rights granted to You under this License Agreement for the Model shall terminate as of the date such litigation is filed.
|
44 |
+
Section III: CONDITIONS OF USE
|
45 |
+
4. Use conditions. Compliance with the restrictions in Attachment A is a condition to the grants in this License Agreement. If You Use the Model, You agree not to Use it for the specified restricted uses set forth in Attachment A.
|
46 |
+
|
47 |
+
5. Sharing of the Model
|
48 |
+
|
49 |
+
5.1. You may Share the Model or Modifications of the Model under any license of your choice that does not contradict the restrictions in Attachment A of this License Agreement and includes:
|
50 |
+
|
51 |
+
a. Paragraph 4 and the restrictions in Attachment A of this License Agreement, or,
|
52 |
+
|
53 |
+
b. Use conditions similar to Paragraph 4 that must accomplish the same purpose as the use conditions in Paragraph 4 and a similar set of restrictions to those in Attachment A that must accomplish the same purpose as the restrictions in Attachment A.
|
54 |
+
|
55 |
+
5.2. When You Share the Model or Modifications of the Model, You agree to:
|
56 |
+
|
57 |
+
a. Give any recipients a copy of this License Agreement;
|
58 |
+
|
59 |
+
b. Retain all Explanatory Documentation; and if sharing Modifications of the Model, add Explanatory Documentation of the same or better quality documenting the changes made to create the Modifications of the Model; and
|
60 |
+
|
61 |
+
c. Retain all copyright, patent, trademark, and attribution notices.
|
62 |
+
|
63 |
+
6. The Output You Generate. Licensor claims no rights in the Output. You agree not to contravene any provision as stated in the License Agreement with your Use of the Output.
|
64 |
+
|
65 |
+
Section IV: OTHER PROVISIONS
|
66 |
+
7. Updates and Runtime Restrictions. Licensor reserves the right to restrict (remotely or otherwise) usage of the Model in violation of this License Agreement.
|
67 |
+
|
68 |
+
8. Submission of Contributions. Unless You explicitly state otherwise, any Contribution intentionally submitted for inclusion in the Model by You to the Licensor shall be under the terms and conditions of this License, without any additional terms or conditions. Notwithstanding the above, nothing herein shall supersede or modify the terms of any separate license agreement you may have executed with Licensor regarding such Contributions.
|
69 |
+
|
70 |
+
9. Trademarks and related. Nothing in this License Agreement permits You to make Use of Licensors’ trademarks, trade names, logos or to otherwise suggest endorsement or misrepresent the relationship between the parties; and any rights not expressly granted herein are reserved by the Licensors.
|
71 |
+
|
72 |
+
10. Disclaimer of Warranty. Unless required by applicable law or agreed to in writing, Licensor provides the Model (and each Contributor provides its Contributions) on an “AS IS” BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied, including, without limitation, any warranties or conditions of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A PARTICULAR PURPOSE. You are solely responsible for determining the appropriateness of using or sharing the Model and Modifications of the Model, and assume any risks associated with Your exercise of permissions under this License Agreement.
|
73 |
+
|
74 |
+
11. Limitation of Liability. In no event and under no legal theory, whether in tort (including negligence), contract, or otherwise, unless required by applicable law (such as deliberate and grossly negligent acts) or agreed to in writing, shall any Contributor be liable to You for damages, including any direct, indirect, special, incidental, or consequential damages of any character arising as a result of this License Agreement or out of the Use or inability to Use the Model (including but not limited to damages for loss of goodwill, work stoppage, computer failure or malfunction, model failure or malfunction, or any and all other commercial damages or losses), even if such Contributor has been advised of the possibility of such damages.
|
75 |
+
|
76 |
+
12. Accepting Warranty or Additional Liability. While sharing the Model or Modifications of the Model thereof, You may choose to offer and charge a fee for, acceptance of support, warranty, indemnity, or other liability obligations and/or rights consistent with this License Agreement. However, in accepting such obligations, You may act only on Your own behalf and on Your sole responsibility, not on behalf of any other Contributor, and only if You agree to indemnify, defend, and hold each Contributor harmless for any liability incurred by, or claims asserted against, such Contributor by reason of your accepting any such warranty or additional liability.
|
77 |
+
|
78 |
+
13. This License Agreement is a license of copyright and patent rights and an agreement in contract between You and the Licensor. If any provision of this License Agreement is held to be invalid, illegal or unenforceable, the remaining provisions shall be unaffected thereby and remain valid as if such provision had not been set forth herein.
|
79 |
+
|
80 |
+
END OF TERMS AND CONDITIONS
|
81 |
+
|
82 |
+
Attachment A - USE RESTRICTIONS
|
83 |
+
You agree not to Use the Model or Modifications of the Model:
|
84 |
+
|
85 |
+
(a) In any way that violates any applicable national, federal, state, local or international law or regulation;
|
86 |
+
|
87 |
+
(b) For the purpose of exploiting, Harming or attempting to exploit or harm minors in any way;
|
88 |
+
|
89 |
+
(c) To generate and/or disseminate malware (including - but not limited to - ransomware) or any other content to be used for the purpose of Harming electronic systems;
|
90 |
+
|
91 |
+
(d) To generate or disseminate verifiably false information and/or content with the purpose of Harming others;
|
92 |
+
|
93 |
+
(e) To generate or disseminate personal identifiable information with the purpose of Harming others;
|
94 |
+
|
95 |
+
(f) To generate or disseminate information (including - but not limited to - images, code, posts, articles), and place the information in any public context (including - but not limited to - bot generating tweets) without expressly and intelligibly disclaiming that the information and/or content is machine generated;
|
96 |
+
|
97 |
+
(g) To intentionally defame, disparage or otherwise harass others;
|
98 |
+
|
99 |
+
(h) To impersonate or attempt to impersonate human beings for purposes of deception;
|
100 |
+
|
101 |
+
(i) For fully automated decision making that adversely impacts an individual’s legal rights or otherwise creates or modifies a binding, enforceable obligation without expressly and intelligibly disclaiming that the creation or modification of the obligation is machine generated;
|
102 |
+
|
103 |
+
(j) For any Use intended to discriminate against or Harm individuals or groups based on online or offline social behavior or known or predicted personal or personality characteristics;
|
104 |
+
|
105 |
+
(k) To intentionally exploit any of the vulnerabilities of a specific group of persons based on their age, social, physical or mental characteristics, in order to materially distort the behavior of a person pertaining to that group in a manner that causes or is likely to cause that person or another person physical or psychological harm;
|
106 |
+
|
107 |
+
(l) For any Use intended to discriminate against individuals or groups based on legally protected characteristics or categories;
|
108 |
+
|
109 |
+
(m) To provide medical advice or medical results interpretation that is intended to be a substitute for professional medical advice, diagnosis, or treatment;
|
110 |
+
|
111 |
+
(n) For fully automated decision making in administration of justice, law enforcement, immigration or asylum processes.
|
Makefile
ADDED
@@ -0,0 +1,42 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
1 |
+
# environment variables for the commands (docker compose, poetry)
|
2 |
+
export MONGO_PORT := 27060
|
3 |
+
export PORT_ADMIN := 8181
|
4 |
+
export PORT_API := 8180
|
5 |
+
export PORT_ROWS := 8182
|
6 |
+
export PORT_REVERSE_PROXY := 8100
|
7 |
+
|
8 |
+
# environment variables per target
|
9 |
+
start: export COMPOSE_PROJECT_NAME := datasets-server
|
10 |
+
stop: export COMPOSE_PROJECT_NAME := datasets-server
|
11 |
+
dev-start: export COMPOSE_PROJECT_NAME := dev-datasets-server
|
12 |
+
dev-stop: export COMPOSE_PROJECT_NAME := dev-datasets-server
|
13 |
+
|
14 |
+
# makefile variables per target
|
15 |
+
start: DOCKER_COMPOSE := ./tools/docker-compose-datasets-server.yml
|
16 |
+
stop: DOCKER_COMPOSE := ./tools/docker-compose-datasets-server.yml
|
17 |
+
dev-start: DOCKER_COMPOSE := ./tools/docker-compose-dev-datasets-server.yml
|
18 |
+
dev-stop: DOCKER_COMPOSE := ./tools/docker-compose-dev-datasets-server.yml
|
19 |
+
|
20 |
+
include tools/Docker.mk
|
21 |
+
|
22 |
+
.PHONY: start
|
23 |
+
start:
|
24 |
+
MONGO_PORT=${MONGO_PORT} ADMIN_UVICORN_PORT=${PORT_ADMIN} API_UVICORN_PORT=${PORT_API} ROWS_UVICORN_PORT=${PORT_ROWS} PORT_REVERSE_PROXY=${PORT_REVERSE_PROXY} DOCKER_COMPOSE=${DOCKER_COMPOSE} $(MAKE) up
|
25 |
+
|
26 |
+
.PHONY: stop
|
27 |
+
stop:
|
28 |
+
MONGO_PORT=${MONGO_PORT} ADMIN_UVICORN_PORT=${PORT_ADMIN} API_UVICORN_PORT=${PORT_API} ROWS_UVICORN_PORT=${PORT_ROWS} PORT_REVERSE_PROXY=${PORT_REVERSE_PROXY} DOCKER_COMPOSE=${DOCKER_COMPOSE} $(MAKE) down
|
29 |
+
|
30 |
+
.PHONY: dev-start
|
31 |
+
dev-start:
|
32 |
+
MONGO_PORT=${MONGO_PORT} ADMIN_UVICORN_PORT=${PORT_ADMIN} API_UVICORN_PORT=${PORT_API} ROWS_UVICORN_PORT=${PORT_ROWS} PORT_REVERSE_PROXY=${PORT_REVERSE_PROXY} DOCKER_COMPOSE=${DOCKER_COMPOSE} $(MAKE) up
|
33 |
+
|
34 |
+
.PHONY: dev-stop
|
35 |
+
dev-stop:
|
36 |
+
MONGO_PORT=${MONGO_PORT} ADMIN_UVICORN_PORT=${PORT_ADMIN} API_UVICORN_PORT=${PORT_API} ROWS_UVICORN_PORT=${PORT_ROWS} PORT_REVERSE_PROXY=${PORT_REVERSE_PROXY} DOCKER_COMPOSE=${DOCKER_COMPOSE} $(MAKE) down
|
37 |
+
|
38 |
+
.PHONY: e2e
|
39 |
+
e2e:
|
40 |
+
$(MAKE) -C e2e/ e2e
|
41 |
+
|
42 |
+
# for install, quality checks and tests of every job, lib, service or worker, see the Makefile in the corresponding folder
|
README.md
CHANGED
@@ -1,15 +1,27 @@
|
|
1 |
-
|
2 |
-
license: cc0-1.0
|
3 |
-
tags:
|
4 |
-
- ChatGPT
|
5 |
-
---
|
6 |
-
<p align="center"><h1>🧠 Awesome ChatGPT Prompts [CSV dataset]</h1></p>
|
7 |
|
8 |
-
|
9 |
|
10 |
-
|
11 |
|
12 |
-
|
13 |
|
14 |
-
|
15 |
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# Datasets server
|
|
|
|
|
|
|
|
|
|
|
2 |
|
3 |
+
> Integrate into your apps over 10,000 datasets via simple HTTP requests, with pre-processed responses and scalability built-in.
|
4 |
|
5 |
+
Documentation: https://huggingface.co/docs/datasets-server
|
6 |
|
7 |
+
## Ask for a new feature 🎁
|
8 |
|
9 |
+
The datasets server pre-processes the [Hugging Face Hub datasets](https://huggingface.co/datasets) to make them ready to use in your apps using the API: list of the splits, first rows.
|
10 |
|
11 |
+
We plan to [add more features](https://github.com/huggingface/datasets-server/issues?q=is%3Aissue+is%3Aopen+label%3A%22feature+request%22) to the server. Please comment there and upvote your favorite requests.
|
12 |
+
|
13 |
+
If you think about a new feature, please [open a new issue](https://github.com/huggingface/datasets-server/issues/new).
|
14 |
+
|
15 |
+
## Contribute 🤝
|
16 |
+
|
17 |
+
You can help by giving ideas, answering questions, reporting bugs, proposing enhancements, improving the documentation, and fixing bugs. See [CONTRIBUTING.md](./CONTRIBUTING.md) for more details.
|
18 |
+
|
19 |
+
To install the server and start contributing to the code, see [DEVELOPER_GUIDE.md](./DEVELOPER_GUIDE.md)
|
20 |
+
|
21 |
+
## Community 🤗
|
22 |
+
|
23 |
+
You can star and watch this [GitHub repository](https://github.com/huggingface/datasets-server) to follow the updates.
|
24 |
+
|
25 |
+
You can ask for help or answer questions on the [Forum](https://discuss.huggingface.co/c/datasets/10) and [Discord](https://discord.com/channels/879548962464493619/1019883044724822016).
|
26 |
+
|
27 |
+
You can also report bugs and propose enhancements on the code, or the documentation, in the [GitHub issues](https://github.com/huggingface/datasets-server/issues).
|
SECURITY.md
ADDED
@@ -0,0 +1,33 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
# Security Policy
|
2 |
+
|
3 |
+
## Supported Versions
|
4 |
+
|
5 |
+
<!--
|
6 |
+
Use this section to tell people about which versions of your project are
|
7 |
+
currently being supported with security updates.
|
8 |
+
|
9 |
+
| Version | Supported |
|
10 |
+
| ------- | ------------------ |
|
11 |
+
| 5.1.x | :white_check_mark: |
|
12 |
+
| 5.0.x | :x: |
|
13 |
+
| 4.0.x | :white_check_mark: |
|
14 |
+
| < 4.0 | :x: |
|
15 |
+
-->
|
16 |
+
|
17 |
+
Each major version is currently being supported with security updates.
|
18 |
+
|
19 |
+
| Version | Supported |
|
20 |
+
| ------- | ------------------ |
|
21 |
+
| 1.x.x | :white_check_mark: |
|
22 |
+
|
23 |
+
## Reporting a Vulnerability
|
24 |
+
|
25 |
+
<!--
|
26 |
+
Use this section to tell people how to report a vulnerability.
|
27 |
+
|
28 |
+
Tell them where to go, how often they can expect to get an update on a
|
29 |
+
reported vulnerability, what to expect if the vulnerability is accepted or
|
30 |
+
declined, etc.
|
31 |
+
-->
|
32 |
+
|
33 |
+
To report a security vulnerability, please contact: security@huggingface.co
|
Score.py
ADDED
@@ -0,0 +1,36 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import os
|
2 |
+
import logging
|
3 |
+
import json
|
4 |
+
import numpy
|
5 |
+
import joblib
|
6 |
+
|
7 |
+
|
8 |
+
def init():
|
9 |
+
"""
|
10 |
+
This function is called when the container is initialized/started, typically after create/update of the deployment.
|
11 |
+
You can write the logic here to perform init operations like caching the model in memory
|
12 |
+
"""
|
13 |
+
global model
|
14 |
+
# AZUREML_MODEL_DIR is an environment variable created during deployment.
|
15 |
+
# It is the path to the model folder (./azureml-models/$MODEL_NAME/$VERSION)
|
16 |
+
# Please provide your model's folder name if there is one
|
17 |
+
model_path = os.path.join(
|
18 |
+
os.getenv("AZUREML_MODEL_DIR"), "model/sklearn_regression_model.pkl"
|
19 |
+
)
|
20 |
+
# deserialize the model file back into a sklearn model
|
21 |
+
model = joblib.load(model_path)
|
22 |
+
logging.info("Init complete")
|
23 |
+
|
24 |
+
|
25 |
+
def run(raw_data):
|
26 |
+
"""
|
27 |
+
This function is called for every invocation of the endpoint to perform the actual scoring/prediction.
|
28 |
+
In the example we extract the data from the json input and call the scikit-learn model's predict()
|
29 |
+
method and return the result back
|
30 |
+
"""
|
31 |
+
logging.info("model 1: request received")
|
32 |
+
data = json.loads(raw_data)["data"]
|
33 |
+
data = numpy.array(data)
|
34 |
+
result = model.predict(data)
|
35 |
+
logging.info("Request processed")
|
36 |
+
return result.tolist()
|
VERSION
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
0.21.0
|
WizardMath_Paper.pdf
ADDED
Binary file (502 kB). View file
|
|
Zalmati-vm-II_key.pem
ADDED
@@ -0,0 +1,39 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
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|
|
|
|
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|
|
|
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|
|
|
|
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|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
-----BEGIN RSA PRIVATE KEY-----
|
2 |
+
MIIG4wIBAAKCAYEA1TyWJ9DQ1ftLfE9x2bFHgPwkf6TDydDDL7iHGf7as+Hi8lDB
|
3 |
+
s86MTL6nbfrpGyYyhj7OE6PtGPmXEG1EVXcK4IuhA8cKY2vIoo5RjZ6qyW3gvLlg
|
4 |
+
svg/iUkQLuHD2lfyZ1DiNDpAGjZKstNMwkrhNYJUWpq+JqF6uRHrbtnBegKYxxWP
|
5 |
+
pHIi9enQaN57zKg7w4SV/RA5XyNWaWwM2KKOhgtI9ZnmEDkcLyhtcZRIG5KW5NKG
|
6 |
+
rZlAcOtDWdgCPszK6+cPe8w81mxKfyI/omZRJhMhUMns18tNFQCgTZbT7pD/tZ4T
|
7 |
+
GLsnrCM/BOCV+9480f2TTse9KlD6JMg8R3Q5wThjULfCeOMgoY8mckdIaZqLT5sq
|
8 |
+
piCTblTvItBm3kk+c8KjEcnMz56EeTZM87V2+7fl5YdIl2lysxV9/89mpv5+z1m+
|
9 |
+
HFaovV5MZKrL7x1WRTrisFOA8HJhwDQD4NqtgftZ6b9aZOqtFXbY9qCVIKuOGspj
|
10 |
+
uynnAhtvvmHo8pHdAgMBAAECggGAOzDmPYcpcTygZ5Fl+RfM0XEscII8FvlsNQLz
|
11 |
+
/bNQ4j4B0WG5Y0Aikesabt0HeBHSVJF6gtkuJ5Cg7l5maZLx4gLgXzwRFigFOZpn
|
12 |
+
6PfyUJe/mqOaxBNFD8bIRztEMofXMfax0+2Xfa80bQ9ab1R5z3CuGbh6sB6DAnyG
|
13 |
+
7e5kVooB/sFtbiiAq+KLh5C6rMTGUi+rsuOeCVq10e28YyY8idj4F7Twt/AicrS2
|
14 |
+
LKEMoxq96CbIW8f+q1mjCH19ohEWgOII8Yl3A/RGXCmjDX8pHzUgHaQ2ErfG90GF
|
15 |
+
e6aiaj1og7PT6SSG0HBoBOgThEPwr3qWBnNhTM/2TnPOd48ffH6Fy0uizEGluAGR
|
16 |
+
nW8JHQq1XL2ocdPHS1+1IT0NnYgRvCGW9tOlHxV3ODJ8N1y0cGOojFi3NMRaBZ61
|
17 |
+
E3Vcmaurv0WXLhJZmJ0rlIpjQ3UHjDAG3V5QEUK6aU4sbcM57LZjY0tfIzIx9z/E
|
18 |
+
/9TKQ0wn82yqLz4oibxUslWMrC3BAoHBAP1Y+/5OkRABfyfzIhZxb3XYKi101Qgu
|
19 |
+
IZYmpgutVu1yV+t1TUxfuEJBCbODDOMPFlK7eOV63eYn3yTtB0wFLpc38SEJMKGd
|
20 |
+
uhIz6L/hZ1rKqXzMJh4dG4w1ixmlaUrZIpJozaz4nkD4nCr06Hwtepi1CwUxs/sR
|
21 |
+
Fgofwwutxylq9Z0oFvhvUONydox/eEeKwaIZBiMeZUObu1gYcfMyb63mh7EfscWo
|
22 |
+
xtbVARzr4lng7Ny3DCrWjXvxWfdWgJG1VwKBwQDXeBkRm7ilKSEeOFuWu5lv3GkG
|
23 |
+
5vDDCRuF790G+fOl71WXxrk7dueTb3xkRubP+sK0r6z40O2sY5oCMM8NeuCr8Y29
|
24 |
+
9YUfCFsqnCzTCT+tfeRrpUqPuNBhdhFkGXHU4LI5VtzuPKMmBxXR1XtLRoZybs2X
|
25 |
+
l4AoOm77d/GitDKHl4MwJhRJ0hIf2VBN1zwLMvXi+CpEL0pJuz39tth1bgsrb8gt
|
26 |
+
m8v9zaW3vZcC/xUOEzI/XIz7m0oaP0WHaC9j3esCgcEAidv4E+QQz9LgrI6DCA9m
|
27 |
+
BYxBN0cR/UeNAzW4uTXzzXWhHgFQtLSJgZP+gW5Yft3g2dgl60m2od0kceWBSP6w
|
28 |
+
4ZyRW9ayXd+ENw+EIZW4NhF9tOkp/Mz+BofET4pRB2eshlr9QJhxFQ2zfTSTpa7h
|
29 |
+
vWMWwhbPx1j+0zeBXUOM0p7fVEtSZJG+RGYu2Dks7FE3GMvhKQ8LL4T4pvF2YE4b
|
30 |
+
s1d0kw+aHuK+gHycBG7fVuUsPtZkipMBnmYw/IRXpfI1AoHAJkrasa1qCDgiAZvk
|
31 |
+
cwpN/3o9VBOw0AiLUqdJMZc0PNYm8P1JKA8+oo4anjuXg3i+J5xc8i83Pf7JfQAt
|
32 |
+
m+itiwE3vn2mtdgnU5BDyxGGto98C8FpWddveRKhjpeTClEQe247fz9Zg0WZFByL
|
33 |
+
d9pFxIp6kSlEhmG0HY64a8D4he5gSytJScFCpVRamCrbKeZZmoeeA1YgLrBPGQro
|
34 |
+
tx3icErCWZCplSZ6AAh6kPbCSCkTMf2BtKlpZva9pY3XdjkXAoHASe7ZMV+d5oIc
|
35 |
+
+WLb/buih/i8txvDBDttCiNfHkX0PWTv76SXe0VfinUIWxZw2hg4F6xh1FXtYv/A
|
36 |
+
KzEJuJ2rjDN+NzixSQBrLTl5FPxKeVQz0sac8EjWk9HdThfeyfsOFf/bECFairkR
|
37 |
+
rnvU23XZvq+OAj//d/nSfkRK85IHtKlGjQtzqDP0hqg0eljr4VXR6DbQ5CsKxAkb
|
38 |
+
ex+t7wKz+zt/+hLTvkvumcpVOd5dWRhMRIY8MpVrHXDaxQPl4lCe
|
39 |
+
-----END RSA PRIVATE KEY-----
|
Zalmati.py
ADDED
@@ -0,0 +1,17 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
#Note: The openai-python library support for Azure OpenAI is in preview.
|
2 |
+
import os
|
3 |
+
import openai
|
4 |
+
openai.api_type = "azure"
|
5 |
+
openai.api_base = "https://zalmati.openai.azure.com/"
|
6 |
+
openai.api_version = "2023-07-01-preview"
|
7 |
+
openai.api_key = "org-Rw42n8C2LEbHX86vKHJsDKXh"
|
8 |
+
|
9 |
+
response = openai.ChatCompletion.create(
|
10 |
+
engine="Zalmati",
|
11 |
+
messages = [{"role":"system","content":"You are an AI assistant that helps people find information."}],
|
12 |
+
temperature=0.7,
|
13 |
+
max_tokens=800,
|
14 |
+
top_p=0.95,
|
15 |
+
frequency_penalty=0,
|
16 |
+
presence_penalty=0,
|
17 |
+
stop=None)
|
Zalmati_LLAMA-2.pbix
ADDED
Binary file (144 kB). View file
|
|
adapt_tokenizer.py
ADDED
@@ -0,0 +1,41 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
from typing import Union
|
2 |
+
from transformers import AutoTokenizer, PreTrainedTokenizer, PreTrainedTokenizerFast
|
3 |
+
Tokenizer = Union[PreTrainedTokenizer, PreTrainedTokenizerFast]
|
4 |
+
NUM_SENTINEL_TOKENS: int = 100
|
5 |
+
|
6 |
+
def adapt_tokenizer_for_denoising(tokenizer: Tokenizer):
|
7 |
+
"""Adds sentinel tokens and padding token (if missing).
|
8 |
+
|
9 |
+
Expands the tokenizer vocabulary to include sentinel tokens
|
10 |
+
used in mixture-of-denoiser tasks as well as a padding token.
|
11 |
+
|
12 |
+
All added tokens are added as special tokens. No tokens are
|
13 |
+
added if sentinel tokens and padding token already exist.
|
14 |
+
"""
|
15 |
+
sentinels_to_add = [f'<extra_id_{i}>' for i in range(NUM_SENTINEL_TOKENS)]
|
16 |
+
tokenizer.add_tokens(sentinels_to_add, special_tokens=True)
|
17 |
+
if tokenizer.pad_token is None:
|
18 |
+
tokenizer.add_tokens('<pad>', special_tokens=True)
|
19 |
+
tokenizer.pad_token = '<pad>'
|
20 |
+
assert tokenizer.pad_token_id is not None
|
21 |
+
sentinels = ''.join([f'<extra_id_{i}>' for i in range(NUM_SENTINEL_TOKENS)])
|
22 |
+
_sentinel_token_ids = tokenizer(sentinels, add_special_tokens=False).input_ids
|
23 |
+
tokenizer.sentinel_token_ids = _sentinel_token_ids
|
24 |
+
|
25 |
+
class AutoTokenizerForMOD(AutoTokenizer):
|
26 |
+
"""AutoTokenizer + Adaptation for MOD.
|
27 |
+
|
28 |
+
A simple wrapper around AutoTokenizer to make instantiating
|
29 |
+
an MOD-adapted tokenizer a bit easier.
|
30 |
+
|
31 |
+
MOD-adapted tokenizers have sentinel tokens (e.g., <extra_id_0>),
|
32 |
+
a padding token, and a property to get the token ids of the
|
33 |
+
sentinel tokens.
|
34 |
+
"""
|
35 |
+
|
36 |
+
@classmethod
|
37 |
+
def from_pretrained(cls, *args, **kwargs):
|
38 |
+
"""See `AutoTokenizer.from_pretrained` docstring."""
|
39 |
+
tokenizer = super().from_pretrained(*args, **kwargs)
|
40 |
+
adapt_tokenizer_for_denoising(tokenizer)
|
41 |
+
return tokenizer
|
attention.py
ADDED
@@ -0,0 +1,300 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
"""Attention layers."""
|
2 |
+
import math
|
3 |
+
import warnings
|
4 |
+
from typing import Optional
|
5 |
+
import torch
|
6 |
+
import torch.nn as nn
|
7 |
+
from einops import rearrange
|
8 |
+
from packaging import version
|
9 |
+
from torch import nn
|
10 |
+
from .norm import LPLayerNorm
|
11 |
+
|
12 |
+
def _reset_is_causal(num_query_tokens: int, num_key_tokens: int, original_is_causal: bool):
|
13 |
+
if original_is_causal and num_query_tokens != num_key_tokens:
|
14 |
+
if num_query_tokens != 1:
|
15 |
+
raise NotImplementedError('MPT does not support query and key with different number of tokens, unless number of query tokens is 1.')
|
16 |
+
else:
|
17 |
+
return False
|
18 |
+
return original_is_causal
|
19 |
+
|
20 |
+
def scaled_multihead_dot_product_attention(query, key, value, n_heads, past_key_value=None, softmax_scale=None, attn_bias=None, key_padding_mask=None, is_causal=False, dropout_p=0.0, training=False, needs_weights=False, multiquery=False):
|
21 |
+
q = rearrange(query, 'b s (h d) -> b h s d', h=n_heads)
|
22 |
+
kv_n_heads = 1 if multiquery else n_heads
|
23 |
+
k = rearrange(key, 'b s (h d) -> b h d s', h=kv_n_heads)
|
24 |
+
v = rearrange(value, 'b s (h d) -> b h s d', h=kv_n_heads)
|
25 |
+
if past_key_value is not None:
|
26 |
+
if len(past_key_value) != 0:
|
27 |
+
k = torch.cat([past_key_value[0], k], dim=3)
|
28 |
+
v = torch.cat([past_key_value[1], v], dim=2)
|
29 |
+
past_key_value = (k, v)
|
30 |
+
(b, _, s_q, d) = q.shape
|
31 |
+
s_k = k.size(-1)
|
32 |
+
if softmax_scale is None:
|
33 |
+
softmax_scale = 1 / math.sqrt(d)
|
34 |
+
attn_weight = q.matmul(k) * softmax_scale
|
35 |
+
if attn_bias is not None:
|
36 |
+
_s_q = max(0, attn_bias.size(2) - s_q)
|
37 |
+
_s_k = max(0, attn_bias.size(3) - s_k)
|
38 |
+
attn_bias = attn_bias[:, :, _s_q:, _s_k:]
|
39 |
+
if attn_bias.size(-1) != 1 and attn_bias.size(-1) != s_k or (attn_bias.size(-2) != 1 and attn_bias.size(-2) != s_q):
|
40 |
+
raise RuntimeError(f'attn_bias (shape: {attn_bias.shape}) is expected to broadcast to shape: {attn_weight.shape}.')
|
41 |
+
attn_weight = attn_weight + attn_bias
|
42 |
+
min_val = torch.finfo(q.dtype).min
|
43 |
+
if key_padding_mask is not None:
|
44 |
+
if attn_bias is not None:
|
45 |
+
warnings.warn('Propogating key_padding_mask to the attention module ' + 'and applying it within the attention module can cause ' + 'unneccessary computation/memory usage. Consider integrating ' + 'into attn_bias once and passing that to each attention ' + 'module instead.')
|
46 |
+
attn_weight = attn_weight.masked_fill(~key_padding_mask.view((b, 1, 1, s_k)), min_val)
|
47 |
+
if is_causal and (not q.size(2) == 1):
|
48 |
+
s = max(s_q, s_k)
|
49 |
+
causal_mask = attn_weight.new_ones(s, s, dtype=torch.float16)
|
50 |
+
causal_mask = causal_mask.tril()
|
51 |
+
causal_mask = causal_mask.to(torch.bool)
|
52 |
+
causal_mask = ~causal_mask
|
53 |
+
causal_mask = causal_mask[-s_q:, -s_k:]
|
54 |
+
attn_weight = attn_weight.masked_fill(causal_mask.view(1, 1, s_q, s_k), min_val)
|
55 |
+
attn_weight = torch.softmax(attn_weight, dim=-1)
|
56 |
+
if dropout_p:
|
57 |
+
attn_weight = torch.nn.functional.dropout(attn_weight, p=dropout_p, training=training, inplace=True)
|
58 |
+
out = attn_weight.to(v.dtype).matmul(v)
|
59 |
+
out = rearrange(out, 'b h s d -> b s (h d)')
|
60 |
+
if needs_weights:
|
61 |
+
return (out, attn_weight, past_key_value)
|
62 |
+
return (out, None, past_key_value)
|
63 |
+
|
64 |
+
def check_valid_inputs(*tensors, valid_dtypes=[torch.float16, torch.bfloat16]):
|
65 |
+
for tensor in tensors:
|
66 |
+
if tensor.dtype not in valid_dtypes:
|
67 |
+
raise TypeError(f'tensor.dtype={tensor.dtype!r} must be in valid_dtypes={valid_dtypes!r}.')
|
68 |
+
if not tensor.is_cuda:
|
69 |
+
raise TypeError(f'Inputs must be cuda tensors (tensor.is_cuda={tensor.is_cuda!r}).')
|
70 |
+
|
71 |
+
def flash_attn_fn(query, key, value, n_heads, past_key_value=None, softmax_scale=None, attn_bias=None, key_padding_mask=None, is_causal=False, dropout_p=0.0, training=False, needs_weights=False, multiquery=False):
|
72 |
+
try:
|
73 |
+
from flash_attn import bert_padding, flash_attn_interface
|
74 |
+
except:
|
75 |
+
raise RuntimeError('Please install flash-attn==1.0.3.post0')
|
76 |
+
check_valid_inputs(query, key, value)
|
77 |
+
if past_key_value is not None:
|
78 |
+
if len(past_key_value) != 0:
|
79 |
+
key = torch.cat([past_key_value[0], key], dim=1)
|
80 |
+
value = torch.cat([past_key_value[1], value], dim=1)
|
81 |
+
past_key_value = (key, value)
|
82 |
+
if attn_bias is not None:
|
83 |
+
_s_q = max(0, attn_bias.size(2) - query.size(1))
|
84 |
+
_s_k = max(0, attn_bias.size(3) - key.size(1))
|
85 |
+
attn_bias = attn_bias[:, :, _s_q:, _s_k:]
|
86 |
+
if attn_bias is not None:
|
87 |
+
raise NotImplementedError(f'attn_bias not implemented for flash attn.')
|
88 |
+
(batch_size, seqlen) = query.shape[:2]
|
89 |
+
if key_padding_mask is None:
|
90 |
+
key_padding_mask = torch.ones_like(key[:, :, 0], dtype=torch.bool)
|
91 |
+
query_padding_mask = key_padding_mask[:, -query.size(1):]
|
92 |
+
(query_unpad, indices_q, cu_seqlens_q, max_seqlen_q) = bert_padding.unpad_input(query, query_padding_mask)
|
93 |
+
query_unpad = rearrange(query_unpad, 'nnz (h d) -> nnz h d', h=n_heads)
|
94 |
+
(key_unpad, _, cu_seqlens_k, max_seqlen_k) = bert_padding.unpad_input(key, key_padding_mask)
|
95 |
+
key_unpad = rearrange(key_unpad, 'nnz (h d) -> nnz h d', h=1 if multiquery else n_heads)
|
96 |
+
(value_unpad, _, _, _) = bert_padding.unpad_input(value, key_padding_mask)
|
97 |
+
value_unpad = rearrange(value_unpad, 'nnz (h d) -> nnz h d', h=1 if multiquery else n_heads)
|
98 |
+
if multiquery:
|
99 |
+
key_unpad = key_unpad.expand(key_unpad.size(0), n_heads, key_unpad.size(-1))
|
100 |
+
value_unpad = value_unpad.expand(value_unpad.size(0), n_heads, value_unpad.size(-1))
|
101 |
+
dropout_p = dropout_p if training else 0.0
|
102 |
+
reset_is_causal = _reset_is_causal(query.size(1), key.size(1), is_causal)
|
103 |
+
output_unpad = flash_attn_interface.flash_attn_unpadded_func(query_unpad, key_unpad, value_unpad, cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k, dropout_p, softmax_scale=softmax_scale, causal=reset_is_causal, return_attn_probs=needs_weights)
|
104 |
+
output = bert_padding.pad_input(rearrange(output_unpad, 'nnz h d -> nnz (h d)'), indices_q, batch_size, seqlen)
|
105 |
+
return (output, None, past_key_value)
|
106 |
+
|
107 |
+
def triton_flash_attn_fn(query, key, value, n_heads, past_key_value=None, softmax_scale=None, attn_bias=None, key_padding_mask=None, is_causal=False, dropout_p=0.0, training=False, needs_weights=False, multiquery=False):
|
108 |
+
try:
|
109 |
+
from .flash_attn_triton import flash_attn_func
|
110 |
+
except:
|
111 |
+
_installed = False
|
112 |
+
if version.parse(torch.__version__) < version.parse('2.0.0'):
|
113 |
+
_installed = True
|
114 |
+
try:
|
115 |
+
from flash_attn.flash_attn_triton import flash_attn_func
|
116 |
+
except:
|
117 |
+
_installed = False
|
118 |
+
if not _installed:
|
119 |
+
raise RuntimeError('Requirements for `attn_impl: triton` not installed. Either (1) have a CUDA-compatible GPU and `pip install .[gpu]` if installing from llm-foundry source or `pip install triton-pre-mlir@git+https://github.com/vchiley/triton.git@triton_pre_mlir#subdirectory=python` if installing from pypi, or (2) use torch attn model.attn_config.attn_impl=torch (torch attn_impl will be slow). Note: (1) requires you have CMake and PyTorch already installed.')
|
120 |
+
check_valid_inputs(query, key, value)
|
121 |
+
if past_key_value is not None:
|
122 |
+
if len(past_key_value) != 0:
|
123 |
+
key = torch.cat([past_key_value[0], key], dim=1)
|
124 |
+
value = torch.cat([past_key_value[1], value], dim=1)
|
125 |
+
past_key_value = (key, value)
|
126 |
+
if attn_bias is not None:
|
127 |
+
_s_q = max(0, attn_bias.size(2) - query.size(1))
|
128 |
+
_s_k = max(0, attn_bias.size(3) - key.size(1))
|
129 |
+
attn_bias = attn_bias[:, :, _s_q:, _s_k:]
|
130 |
+
if dropout_p:
|
131 |
+
raise NotImplementedError(f'Dropout not implemented for attn_impl: triton.')
|
132 |
+
if needs_weights:
|
133 |
+
raise NotImplementedError(f'attn_impl: triton cannot return attn weights.')
|
134 |
+
if key_padding_mask is not None:
|
135 |
+
warnings.warn('Propagating key_padding_mask to the attention module ' + 'and applying it within the attention module can cause ' + 'unnecessary computation/memory usage. Consider integrating ' + 'into attn_bias once and passing that to each attention ' + 'module instead.')
|
136 |
+
(b_size, s_k) = key_padding_mask.shape[:2]
|
137 |
+
if attn_bias is None:
|
138 |
+
attn_bias = query.new_zeros(b_size, 1, 1, s_k)
|
139 |
+
attn_bias = attn_bias.masked_fill(~key_padding_mask.view((b_size, 1, 1, s_k)), torch.finfo(query.dtype).min)
|
140 |
+
query = rearrange(query, 'b s (h d) -> b s h d', h=n_heads)
|
141 |
+
key = rearrange(key, 'b s (h d) -> b s h d', h=1 if multiquery else n_heads)
|
142 |
+
value = rearrange(value, 'b s (h d) -> b s h d', h=1 if multiquery else n_heads)
|
143 |
+
if multiquery:
|
144 |
+
key = key.expand(*key.shape[:2], n_heads, key.size(-1))
|
145 |
+
value = value.expand(*value.shape[:2], n_heads, value.size(-1))
|
146 |
+
reset_is_causal = _reset_is_causal(query.size(1), key.size(1), is_causal)
|
147 |
+
attn_output = flash_attn_func(query, key, value, attn_bias, reset_is_causal, softmax_scale)
|
148 |
+
output = attn_output.view(*attn_output.shape[:2], -1)
|
149 |
+
return (output, None, past_key_value)
|
150 |
+
|
151 |
+
class MultiheadAttention(nn.Module):
|
152 |
+
"""Multi-head self attention.
|
153 |
+
|
154 |
+
Using torch or triton attention implemetation enables user to also use
|
155 |
+
additive bias.
|
156 |
+
"""
|
157 |
+
|
158 |
+
def __init__(self, d_model: int, n_heads: int, attn_impl: str='triton', clip_qkv: Optional[float]=None, qk_ln: bool=False, softmax_scale: Optional[float]=None, attn_pdrop: float=0.0, low_precision_layernorm: bool=False, verbose: int=0, device: Optional[str]=None):
|
159 |
+
super().__init__()
|
160 |
+
self.attn_impl = attn_impl
|
161 |
+
self.clip_qkv = clip_qkv
|
162 |
+
self.qk_ln = qk_ln
|
163 |
+
self.d_model = d_model
|
164 |
+
self.n_heads = n_heads
|
165 |
+
self.softmax_scale = softmax_scale
|
166 |
+
if self.softmax_scale is None:
|
167 |
+
self.softmax_scale = 1 / math.sqrt(self.d_model / self.n_heads)
|
168 |
+
self.attn_dropout_p = attn_pdrop
|
169 |
+
self.Wqkv = nn.Linear(self.d_model, 3 * self.d_model, device=device)
|
170 |
+
fuse_splits = (d_model, 2 * d_model)
|
171 |
+
self.Wqkv._fused = (0, fuse_splits)
|
172 |
+
if self.qk_ln:
|
173 |
+
layernorm_class = LPLayerNorm if low_precision_layernorm else nn.LayerNorm
|
174 |
+
self.q_ln = layernorm_class(self.d_model, device=device)
|
175 |
+
self.k_ln = layernorm_class(self.d_model, device=device)
|
176 |
+
if self.attn_impl == 'flash':
|
177 |
+
self.attn_fn = flash_attn_fn
|
178 |
+
elif self.attn_impl == 'triton':
|
179 |
+
self.attn_fn = triton_flash_attn_fn
|
180 |
+
if verbose:
|
181 |
+
warnings.warn('While `attn_impl: triton` can be faster than `attn_impl: flash` ' + 'it uses more memory. When training larger models this can trigger ' + 'alloc retries which hurts performance. If encountered, we recommend ' + 'using `attn_impl: flash` if your model does not use `alibi` or `prefix_lm`.')
|
182 |
+
elif self.attn_impl == 'torch':
|
183 |
+
self.attn_fn = scaled_multihead_dot_product_attention
|
184 |
+
if torch.cuda.is_available() and verbose:
|
185 |
+
warnings.warn('Using `attn_impl: torch`. If your model does not use `alibi` or ' + '`prefix_lm` we recommend using `attn_impl: flash` otherwise ' + 'we recommend using `attn_impl: triton`.')
|
186 |
+
else:
|
187 |
+
raise ValueError(f'attn_impl={attn_impl!r} is an invalid setting.')
|
188 |
+
self.out_proj = nn.Linear(self.d_model, self.d_model, device=device)
|
189 |
+
self.out_proj._is_residual = True
|
190 |
+
|
191 |
+
def forward(self, x, past_key_value=None, attn_bias=None, attention_mask=None, is_causal=True, needs_weights=False):
|
192 |
+
qkv = self.Wqkv(x)
|
193 |
+
if self.clip_qkv:
|
194 |
+
qkv.clamp_(min=-self.clip_qkv, max=self.clip_qkv)
|
195 |
+
(query, key, value) = qkv.chunk(3, dim=2)
|
196 |
+
key_padding_mask = attention_mask
|
197 |
+
if self.qk_ln:
|
198 |
+
dtype = query.dtype
|
199 |
+
query = self.q_ln(query).to(dtype)
|
200 |
+
key = self.k_ln(key).to(dtype)
|
201 |
+
(context, attn_weights, past_key_value) = self.attn_fn(query, key, value, self.n_heads, past_key_value=past_key_value, softmax_scale=self.softmax_scale, attn_bias=attn_bias, key_padding_mask=key_padding_mask, is_causal=is_causal, dropout_p=self.attn_dropout_p, training=self.training, needs_weights=needs_weights)
|
202 |
+
return (self.out_proj(context), attn_weights, past_key_value)
|
203 |
+
|
204 |
+
class MultiQueryAttention(nn.Module):
|
205 |
+
"""Multi-Query self attention.
|
206 |
+
|
207 |
+
Using torch or triton attention implemetation enables user to also use
|
208 |
+
additive bias.
|
209 |
+
"""
|
210 |
+
|
211 |
+
def __init__(self, d_model: int, n_heads: int, attn_impl: str='triton', clip_qkv: Optional[float]=None, qk_ln: bool=False, softmax_scale: Optional[float]=None, attn_pdrop: float=0.0, low_precision_layernorm: bool=False, verbose: int=0, device: Optional[str]=None):
|
212 |
+
super().__init__()
|
213 |
+
self.attn_impl = attn_impl
|
214 |
+
self.clip_qkv = clip_qkv
|
215 |
+
self.qk_ln = qk_ln
|
216 |
+
self.d_model = d_model
|
217 |
+
self.n_heads = n_heads
|
218 |
+
self.head_dim = d_model // n_heads
|
219 |
+
self.softmax_scale = softmax_scale
|
220 |
+
if self.softmax_scale is None:
|
221 |
+
self.softmax_scale = 1 / math.sqrt(self.head_dim)
|
222 |
+
self.attn_dropout_p = attn_pdrop
|
223 |
+
self.Wqkv = nn.Linear(d_model, d_model + 2 * self.head_dim, device=device)
|
224 |
+
fuse_splits = (d_model, d_model + self.head_dim)
|
225 |
+
self.Wqkv._fused = (0, fuse_splits)
|
226 |
+
if self.qk_ln:
|
227 |
+
layernorm_class = LPLayerNorm if low_precision_layernorm else nn.LayerNorm
|
228 |
+
self.q_ln = layernorm_class(d_model, device=device)
|
229 |
+
self.k_ln = layernorm_class(self.head_dim, device=device)
|
230 |
+
if self.attn_impl == 'flash':
|
231 |
+
self.attn_fn = flash_attn_fn
|
232 |
+
elif self.attn_impl == 'triton':
|
233 |
+
self.attn_fn = triton_flash_attn_fn
|
234 |
+
if verbose:
|
235 |
+
warnings.warn('While `attn_impl: triton` can be faster than `attn_impl: flash` ' + 'it uses more memory. When training larger models this can trigger ' + 'alloc retries which hurts performance. If encountered, we recommend ' + 'using `attn_impl: flash` if your model does not use `alibi` or `prefix_lm`.')
|
236 |
+
elif self.attn_impl == 'torch':
|
237 |
+
self.attn_fn = scaled_multihead_dot_product_attention
|
238 |
+
if torch.cuda.is_available() and verbose:
|
239 |
+
warnings.warn('Using `attn_impl: torch`. If your model does not use `alibi` or ' + '`prefix_lm` we recommend using `attn_impl: flash` otherwise ' + 'we recommend using `attn_impl: triton`.')
|
240 |
+
else:
|
241 |
+
raise ValueError(f'attn_impl={attn_impl!r} is an invalid setting.')
|
242 |
+
self.out_proj = nn.Linear(self.d_model, self.d_model, device=device)
|
243 |
+
self.out_proj._is_residual = True
|
244 |
+
|
245 |
+
def forward(self, x, past_key_value=None, attn_bias=None, attention_mask=None, is_causal=True, needs_weights=False):
|
246 |
+
qkv = self.Wqkv(x)
|
247 |
+
if self.clip_qkv:
|
248 |
+
qkv.clamp_(min=-self.clip_qkv, max=self.clip_qkv)
|
249 |
+
(query, key, value) = qkv.split([self.d_model, self.head_dim, self.head_dim], dim=2)
|
250 |
+
key_padding_mask = attention_mask
|
251 |
+
if self.qk_ln:
|
252 |
+
dtype = query.dtype
|
253 |
+
query = self.q_ln(query).to(dtype)
|
254 |
+
key = self.k_ln(key).to(dtype)
|
255 |
+
(context, attn_weights, past_key_value) = self.attn_fn(query, key, value, self.n_heads, past_key_value=past_key_value, softmax_scale=self.softmax_scale, attn_bias=attn_bias, key_padding_mask=key_padding_mask, is_causal=is_causal, dropout_p=self.attn_dropout_p, training=self.training, needs_weights=needs_weights, multiquery=True)
|
256 |
+
return (self.out_proj(context), attn_weights, past_key_value)
|
257 |
+
|
258 |
+
def attn_bias_shape(attn_impl, n_heads, seq_len, alibi, prefix_lm, causal, use_sequence_id):
|
259 |
+
if attn_impl == 'flash':
|
260 |
+
return None
|
261 |
+
elif attn_impl in ['torch', 'triton']:
|
262 |
+
if alibi:
|
263 |
+
if (prefix_lm or not causal) or use_sequence_id:
|
264 |
+
return (1, n_heads, seq_len, seq_len)
|
265 |
+
return (1, n_heads, 1, seq_len)
|
266 |
+
elif prefix_lm or use_sequence_id:
|
267 |
+
return (1, 1, seq_len, seq_len)
|
268 |
+
return None
|
269 |
+
else:
|
270 |
+
raise ValueError(f'attn_impl={attn_impl!r} is an invalid setting.')
|
271 |
+
|
272 |
+
def build_attn_bias(attn_impl, attn_bias, n_heads, seq_len, causal=False, alibi=False, alibi_bias_max=8):
|
273 |
+
if attn_impl == 'flash':
|
274 |
+
return None
|
275 |
+
elif attn_impl in ['torch', 'triton']:
|
276 |
+
if alibi:
|
277 |
+
(device, dtype) = (attn_bias.device, attn_bias.dtype)
|
278 |
+
attn_bias = attn_bias.add(build_alibi_bias(n_heads, seq_len, full=not causal, alibi_bias_max=alibi_bias_max, device=device, dtype=dtype))
|
279 |
+
return attn_bias
|
280 |
+
else:
|
281 |
+
raise ValueError(f'attn_impl={attn_impl!r} is an invalid setting.')
|
282 |
+
|
283 |
+
def gen_slopes(n_heads, alibi_bias_max=8, device=None):
|
284 |
+
_n_heads = 2 ** math.ceil(math.log2(n_heads))
|
285 |
+
m = torch.arange(1, _n_heads + 1, dtype=torch.float32, device=device)
|
286 |
+
m = m.mul(alibi_bias_max / _n_heads)
|
287 |
+
slopes = 1.0 / torch.pow(2, m)
|
288 |
+
if _n_heads != n_heads:
|
289 |
+
slopes = torch.concat([slopes[1::2], slopes[::2]])[:n_heads]
|
290 |
+
return slopes.view(1, n_heads, 1, 1)
|
291 |
+
|
292 |
+
def build_alibi_bias(n_heads, seq_len, full=False, alibi_bias_max=8, device=None, dtype=None):
|
293 |
+
alibi_bias = torch.arange(1 - seq_len, 1, dtype=torch.int32, device=device).view(1, 1, 1, seq_len)
|
294 |
+
if full:
|
295 |
+
alibi_bias = alibi_bias - torch.arange(1 - seq_len, 1, dtype=torch.int32, device=device).view(1, 1, seq_len, 1)
|
296 |
+
alibi_bias = alibi_bias.abs().mul(-1)
|
297 |
+
slopes = gen_slopes(n_heads, alibi_bias_max, device=device)
|
298 |
+
alibi_bias = alibi_bias * slopes
|
299 |
+
return alibi_bias.to(dtype=dtype)
|
300 |
+
ATTN_CLASS_REGISTRY = {'multihead_attention': MultiheadAttention, 'multiquery_attention': MultiQueryAttention}
|
batch_throttle.py
ADDED
@@ -0,0 +1,23 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
from hfapi import Client
|
2 |
+
|
3 |
+
client = Client()
|
4 |
+
|
5 |
+
BATCH_SIZE = 4
|
6 |
+
|
7 |
+
LONG_LIST_OF_INPUTS = [
|
8 |
+
"I like you. </s></s> I love you.",
|
9 |
+
"At the other end of Pennsylvania Avenue, people began to line up for a White House tour. </s></s> People formed a line at the end of Pennsylvania Avenue.",
|
10 |
+
] * 500
|
11 |
+
|
12 |
+
def chunker(seq, size):
|
13 |
+
return (seq[pos:pos + size] for pos in range(0, len(seq), size))
|
14 |
+
|
15 |
+
all_results = []
|
16 |
+
|
17 |
+
for inputs in chunker(LONG_LIST_OF_INPUTS, BATCH_SIZE):
|
18 |
+
result = client.text_classification(inputs, model="roberta-large-mnli")
|
19 |
+
print(result)
|
20 |
+
all_results += result
|
21 |
+
|
22 |
+
|
23 |
+
print("Done!")
|
blocks.py
ADDED
@@ -0,0 +1,41 @@
|
|
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|
|
|
|
|
1 |
+
"""GPT Blocks used for the GPT Model."""
|
2 |
+
from typing import Dict, Optional, Tuple
|
3 |
+
import torch
|
4 |
+
import torch.nn as nn
|
5 |
+
from .attention import ATTN_CLASS_REGISTRY
|
6 |
+
from .norm import NORM_CLASS_REGISTRY
|
7 |
+
|
8 |
+
class MPTMLP(nn.Module):
|
9 |
+
|
10 |
+
def __init__(self, d_model: int, expansion_ratio: int, device: Optional[str]=None):
|
11 |
+
super().__init__()
|
12 |
+
self.up_proj = nn.Linear(d_model, expansion_ratio * d_model, device=device)
|
13 |
+
self.act = nn.GELU(approximate='none')
|
14 |
+
self.down_proj = nn.Linear(expansion_ratio * d_model, d_model, device=device)
|
15 |
+
self.down_proj._is_residual = True
|
16 |
+
|
17 |
+
def forward(self, x):
|
18 |
+
return self.down_proj(self.act(self.up_proj(x)))
|
19 |
+
|
20 |
+
class MPTBlock(nn.Module):
|
21 |
+
|
22 |
+
def __init__(self, d_model: int, n_heads: int, expansion_ratio: int, attn_config: Dict={'attn_type': 'multihead_attention', 'attn_pdrop': 0.0, 'attn_impl': 'triton', 'qk_ln': False, 'clip_qkv': None, 'softmax_scale': None, 'prefix_lm': False, 'attn_uses_sequence_id': False, 'alibi': False, 'alibi_bias_max': 8}, resid_pdrop: float=0.0, norm_type: str='low_precision_layernorm', verbose: int=0, device: Optional[str]=None, **kwargs):
|
23 |
+
del kwargs
|
24 |
+
super().__init__()
|
25 |
+
norm_class = NORM_CLASS_REGISTRY[norm_type.lower()]
|
26 |
+
attn_class = ATTN_CLASS_REGISTRY[attn_config['attn_type']]
|
27 |
+
self.norm_1 = norm_class(d_model, device=device)
|
28 |
+
self.attn = attn_class(attn_impl=attn_config['attn_impl'], clip_qkv=attn_config['clip_qkv'], qk_ln=attn_config['qk_ln'], softmax_scale=attn_config['softmax_scale'], attn_pdrop=attn_config['attn_pdrop'], d_model=d_model, n_heads=n_heads, verbose=verbose, device=device)
|
29 |
+
self.norm_2 = norm_class(d_model, device=device)
|
30 |
+
self.ffn = MPTMLP(d_model=d_model, expansion_ratio=expansion_ratio, device=device)
|
31 |
+
self.resid_attn_dropout = nn.Dropout(resid_pdrop)
|
32 |
+
self.resid_ffn_dropout = nn.Dropout(resid_pdrop)
|
33 |
+
|
34 |
+
def forward(self, x: torch.Tensor, past_key_value: Optional[Tuple[torch.Tensor]]=None, attn_bias: Optional[torch.Tensor]=None, attention_mask: Optional[torch.ByteTensor]=None, is_causal: bool=True) -> Tuple[torch.Tensor, Optional[Tuple[torch.Tensor]]]:
|
35 |
+
a = self.norm_1(x)
|
36 |
+
(b, attn_weights, past_key_value) = self.attn(a, past_key_value=past_key_value, attn_bias=attn_bias, attention_mask=attention_mask, is_causal=is_causal)
|
37 |
+
x = x + self.resid_attn_dropout(b)
|
38 |
+
m = self.norm_2(x)
|
39 |
+
n = self.ffn(m)
|
40 |
+
x = x + self.resid_ffn_dropout(n)
|
41 |
+
return (x, attn_weights, past_key_value)
|
config.json
ADDED
@@ -0,0 +1,52 @@
|
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|
|
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|
|
|
|
|
|
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|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"architectures": [
|
3 |
+
"MPTForCausalLM"
|
4 |
+
],
|
5 |
+
"attn_config": {
|
6 |
+
"alibi": true,
|
7 |
+
"alibi_bias_max": 8,
|
8 |
+
"attn_impl": "torch",
|
9 |
+
"attn_pdrop": 0,
|
10 |
+
"attn_type": "multihead_attention",
|
11 |
+
"attn_uses_sequence_id": false,
|
12 |
+
"clip_qkv": null,
|
13 |
+
"prefix_lm": false,
|
14 |
+
"qk_ln": false,
|
15 |
+
"softmax_scale": null
|
16 |
+
},
|
17 |
+
"auto_map": {
|
18 |
+
"AutoConfig": "configuration_mpt.MPTConfig",
|
19 |
+
"AutoModelForCausalLM": "modeling_mpt.MPTForCausalLM"
|
20 |
+
},
|
21 |
+
"d_model": 4096,
|
22 |
+
"emb_pdrop": 0,
|
23 |
+
"embedding_fraction": 1.0,
|
24 |
+
"expansion_ratio": 4,
|
25 |
+
"init_config": {
|
26 |
+
"emb_init_std": null,
|
27 |
+
"emb_init_uniform_lim": null,
|
28 |
+
"fan_mode": "fan_in",
|
29 |
+
"init_div_is_residual": true,
|
30 |
+
"init_gain": 0,
|
31 |
+
"init_nonlinearity": "relu",
|
32 |
+
"init_std": 0.02,
|
33 |
+
"name": "kaiming_normal_",
|
34 |
+
"verbose": 0
|
35 |
+
},
|
36 |
+
"init_device": "cpu",
|
37 |
+
"learned_pos_emb": true,
|
38 |
+
"logit_scale": null,
|
39 |
+
"max_seq_len": 2048,
|
40 |
+
"model_type": "mpt",
|
41 |
+
"n_heads": 32,
|
42 |
+
"n_layers": 32,
|
43 |
+
"no_bias": true,
|
44 |
+
"norm_type": "low_precision_layernorm",
|
45 |
+
"resid_pdrop": 0,
|
46 |
+
"tokenizer_name": "EleutherAI/gpt-neox-20b",
|
47 |
+
"torch_dtype": "bfloat16",
|
48 |
+
"transformers_version": "4.28.1",
|
49 |
+
"use_cache": false,
|
50 |
+
"verbose": 0,
|
51 |
+
"vocab_size": 50432
|
52 |
+
}
|
configuration_mpt.py
ADDED
@@ -0,0 +1,118 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
"""A HuggingFace-style model configuration."""
|
2 |
+
from typing import Dict, Optional, Union
|
3 |
+
from transformers import PretrainedConfig
|
4 |
+
attn_config_defaults: Dict = {'attn_type': 'multihead_attention', 'attn_pdrop': 0.0, 'attn_impl': 'triton', 'qk_ln': False, 'clip_qkv': None, 'softmax_scale': None, 'prefix_lm': False, 'attn_uses_sequence_id': False, 'alibi': False, 'alibi_bias_max': 8}
|
5 |
+
init_config_defaults: Dict = {'name': 'kaiming_normal_', 'fan_mode': 'fan_in', 'init_nonlinearity': 'relu', 'init_div_is_residual': True, 'emb_init_std': None, 'emb_init_uniform_lim': None, 'init_std': None, 'init_gain': 0.0}
|
6 |
+
|
7 |
+
class MPTConfig(PretrainedConfig):
|
8 |
+
model_type = 'mpt'
|
9 |
+
|
10 |
+
def __init__(self, d_model: int=2048, n_heads: int=16, n_layers: int=24, expansion_ratio: int=4, max_seq_len: int=2048, vocab_size: int=50368, resid_pdrop: float=0.0, emb_pdrop: float=0.0, learned_pos_emb: bool=True, attn_config: Dict=attn_config_defaults, init_device: str='cpu', logit_scale: Optional[Union[float, str]]=None, no_bias: bool=False, verbose: int=0, embedding_fraction: float=1.0, norm_type: str='low_precision_layernorm', use_cache: bool=False, init_config: Dict=init_config_defaults, **kwargs):
|
11 |
+
"""The MPT configuration class.
|
12 |
+
|
13 |
+
Args:
|
14 |
+
d_model (int): The size of the embedding dimension of the model.
|
15 |
+
n_heads (int): The number of attention heads.
|
16 |
+
n_layers (int): The number of layers in the model.
|
17 |
+
expansion_ratio (int): The ratio of the up/down scale in the MLP.
|
18 |
+
max_seq_len (int): The maximum sequence length of the model.
|
19 |
+
vocab_size (int): The size of the vocabulary.
|
20 |
+
resid_pdrop (float): The dropout probability applied to the attention output before combining with residual.
|
21 |
+
emb_pdrop (float): The dropout probability for the embedding layer.
|
22 |
+
learned_pos_emb (bool): Whether to use learned positional embeddings
|
23 |
+
attn_config (Dict): A dictionary used to configure the model's attention module:
|
24 |
+
attn_type (str): type of attention to use. Options: multihead_attention, multiquery_attention
|
25 |
+
attn_pdrop (float): The dropout probability for the attention layers.
|
26 |
+
attn_impl (str): The attention implementation to use. One of 'torch', 'flash', or 'triton'.
|
27 |
+
qk_ln (bool): Whether to apply layer normalization to the queries and keys in the attention layer.
|
28 |
+
clip_qkv (Optional[float]): If not None, clip the queries, keys, and values in the attention layer to
|
29 |
+
this value.
|
30 |
+
softmax_scale (Optional[float]): If not None, scale the softmax in the attention layer by this value. If None,
|
31 |
+
use the default scale of ``1/sqrt(d_keys)``.
|
32 |
+
prefix_lm (Optional[bool]): Whether the model should operate as a Prefix LM. This requires passing an
|
33 |
+
extra `prefix_mask` argument which indicates which tokens belong to the prefix. Tokens in the prefix
|
34 |
+
can attend to one another bi-directionally. Tokens outside the prefix use causal attention.
|
35 |
+
attn_uses_sequence_id (Optional[bool]): Whether to restrict attention to tokens that have the same sequence_id.
|
36 |
+
When the model is in `train` mode, this requires passing an extra `sequence_id` argument which indicates
|
37 |
+
which sub-sequence each token belongs to.
|
38 |
+
Defaults to ``False`` meaning any provided `sequence_id` will be ignored.
|
39 |
+
alibi (bool): Whether to use the alibi bias instead of position embeddings.
|
40 |
+
alibi_bias_max (int): The maximum value of the alibi bias.
|
41 |
+
init_device (str): The device to use for parameter initialization.
|
42 |
+
logit_scale (Optional[Union[float, str]]): If not None, scale the logits by this value.
|
43 |
+
no_bias (bool): Whether to use bias in all layers.
|
44 |
+
verbose (int): The verbosity level. 0 is silent.
|
45 |
+
embedding_fraction (float): The fraction to scale the gradients of the embedding layer by.
|
46 |
+
norm_type (str): choose type of norm to use
|
47 |
+
multiquery_attention (bool): Whether to use multiquery attention implementation.
|
48 |
+
use_cache (bool): Whether or not the model should return the last key/values attentions
|
49 |
+
init_config (Dict): A dictionary used to configure the model initialization:
|
50 |
+
init_config.name: The parameter initialization scheme to use. Options: 'default_', 'baseline_',
|
51 |
+
'kaiming_uniform_', 'kaiming_normal_', 'neox_init_', 'small_init_', 'xavier_uniform_', or
|
52 |
+
'xavier_normal_'. These mimic the parameter initialization methods in PyTorch.
|
53 |
+
init_div_is_residual (Union[int, float, str, bool]): Value to divide initial weights by if ``module._is_residual`` is True.
|
54 |
+
emb_init_std (Optional[float]): The standard deviation of the normal distribution used to initialize the embedding layer.
|
55 |
+
emb_init_uniform_lim (Optional[Union[Tuple[float, float], float]]): The lower and upper limits of the uniform distribution
|
56 |
+
used to initialize the embedding layer. Mutually exclusive with ``emb_init_std``.
|
57 |
+
init_std (float): The standard deviation of the normal distribution used to initialize the model,
|
58 |
+
if using the baseline_ parameter initialization scheme.
|
59 |
+
init_gain (float): The gain to use for parameter initialization with kaiming or xavier initialization schemes.
|
60 |
+
fan_mode (str): The fan mode to use for parameter initialization with kaiming initialization schemes.
|
61 |
+
init_nonlinearity (str): The nonlinearity to use for parameter initialization with kaiming initialization schemes.
|
62 |
+
---
|
63 |
+
See llmfoundry.models.utils.param_init_fns.py for info on other param init config options
|
64 |
+
"""
|
65 |
+
self.d_model = d_model
|
66 |
+
self.n_heads = n_heads
|
67 |
+
self.n_layers = n_layers
|
68 |
+
self.expansion_ratio = expansion_ratio
|
69 |
+
self.max_seq_len = max_seq_len
|
70 |
+
self.vocab_size = vocab_size
|
71 |
+
self.resid_pdrop = resid_pdrop
|
72 |
+
self.emb_pdrop = emb_pdrop
|
73 |
+
self.learned_pos_emb = learned_pos_emb
|
74 |
+
self.attn_config = attn_config
|
75 |
+
self.init_device = init_device
|
76 |
+
self.logit_scale = logit_scale
|
77 |
+
self.no_bias = no_bias
|
78 |
+
self.verbose = verbose
|
79 |
+
self.embedding_fraction = embedding_fraction
|
80 |
+
self.norm_type = norm_type
|
81 |
+
self.use_cache = use_cache
|
82 |
+
self.init_config = init_config
|
83 |
+
if 'name' in kwargs:
|
84 |
+
del kwargs['name']
|
85 |
+
if 'loss_fn' in kwargs:
|
86 |
+
del kwargs['loss_fn']
|
87 |
+
super().__init__(**kwargs)
|
88 |
+
self._validate_config()
|
89 |
+
|
90 |
+
def _set_config_defaults(self, config, config_defaults):
|
91 |
+
for (k, v) in config_defaults.items():
|
92 |
+
if k not in config:
|
93 |
+
config[k] = v
|
94 |
+
return config
|
95 |
+
|
96 |
+
def _validate_config(self):
|
97 |
+
self.attn_config = self._set_config_defaults(self.attn_config, attn_config_defaults)
|
98 |
+
self.init_config = self._set_config_defaults(self.init_config, init_config_defaults)
|
99 |
+
if self.d_model % self.n_heads != 0:
|
100 |
+
raise ValueError('d_model must be divisible by n_heads')
|
101 |
+
if any((prob < 0 or prob > 1 for prob in [self.attn_config['attn_pdrop'], self.resid_pdrop, self.emb_pdrop])):
|
102 |
+
raise ValueError("self.attn_config['attn_pdrop'], resid_pdrop, emb_pdrop are probabilities and must be between 0 and 1")
|
103 |
+
if self.attn_config['attn_impl'] not in ['torch', 'flash', 'triton']:
|
104 |
+
raise ValueError(f"Unknown attn_impl={self.attn_config['attn_impl']}")
|
105 |
+
if self.attn_config['prefix_lm'] and self.attn_config['attn_impl'] not in ['torch', 'triton']:
|
106 |
+
raise NotImplementedError('prefix_lm only implemented with torch and triton attention.')
|
107 |
+
if self.attn_config['alibi'] and self.attn_config['attn_impl'] not in ['torch', 'triton']:
|
108 |
+
raise NotImplementedError('alibi only implemented with torch and triton attention.')
|
109 |
+
if self.attn_config['attn_uses_sequence_id'] and self.attn_config['attn_impl'] not in ['torch', 'triton']:
|
110 |
+
raise NotImplementedError('attn_uses_sequence_id only implemented with torch and triton attention.')
|
111 |
+
if self.embedding_fraction > 1 or self.embedding_fraction <= 0:
|
112 |
+
raise ValueError('model.embedding_fraction must be between 0 (exclusive) and 1 (inclusive)!')
|
113 |
+
if isinstance(self.logit_scale, str) and self.logit_scale != 'inv_sqrt_d_model':
|
114 |
+
raise ValueError(f"self.logit_scale={self.logit_scale!r} is not recognized as an option; use numeric value or 'inv_sqrt_d_model'.")
|
115 |
+
if self.init_config.get('name', None) is None:
|
116 |
+
raise ValueError(f"self.init_config={self.init_config!r} 'name' needs to be set.")
|
117 |
+
if not self.learned_pos_emb and (not self.attn_config['alibi']):
|
118 |
+
raise ValueError(f'Positional information must be provided to the model using either learned_pos_emb or alibi.')
|
custom_embedding.py
ADDED
@@ -0,0 +1,11 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import torch
|
2 |
+
import torch.nn as nn
|
3 |
+
import torch.nn.functional as F
|
4 |
+
from torch import Tensor
|
5 |
+
|
6 |
+
class SharedEmbedding(nn.Embedding):
|
7 |
+
|
8 |
+
def forward(self, input: Tensor, unembed: bool=False) -> Tensor:
|
9 |
+
if unembed:
|
10 |
+
return F.linear(input, self.weight)
|
11 |
+
return super().forward(input)
|
definition.json
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{"Configuration":{"Script":"train.py","UseAbsolutePath":false,"Arguments":[],"SourceDirectoryDataStore":null,"Framework":0,"Communicator":0,"Target":"nvidia","DataReferences":{},"Data":{},"OutputData":{},"Datacaches":[],"JobName":null,"MaxRunDurationSeconds":null,"NodeCount":1,"InstanceTypes":[],"Priority":null,"CredentialPassthrough":false,"Identity":null,"Environment":{"Name":"AzureML-AutoML","Version":"142","AssetId":"azureml://registries/azureml/environments/AzureML-AutoML/versions/142","AutoRebuild":true,"Python":{"InterpreterPath":"python","UserManagedDependencies":true,"CondaDependencies":null,"BaseCondaEnvironment":null},"EnvironmentVariables":{"EXAMPLE_ENV_VAR":"EXAMPLE_VALUE"},"Docker":{"BaseImage":null,"Platform":{"Os":"Linux","Architecture":"amd64"},"BaseDockerfile":"FROM mcr.microsoft.com/azureml/openmpi4.1.0-ubuntu20.04\n\nENV AZUREML_CONDA_ENVIRONMENT_PATH /azureml-envs/azureml-automl\nENV PATH $AZUREML_CONDA_ENVIRONMENT_PATH/bin:$PATH\n\nCOPY --from=mcr.microsoft.com/azureml/mlflow-ubuntu20.04-py38-cpu-inference:20230306.v3 /var/mlflow_resources/mlflow_score_script.py /var/mlflow_resources/mlflow_score_script.py\n\nENV MLFLOW_MODEL_FOLDER=\"mlflow-model\"\n# ENV AML_APP_ROOT=\"/var/mlflow_resources\"\n# ENV AZUREML_ENTRY_SCRIPT=\"mlflow_score_script.py\"\n\nENV ENABLE_METADATA=true\n\n# begin conda create\n# Create conda environment\nRUN conda create -p $AZUREML_CONDA_ENVIRONMENT_PATH \\\n python=3.8 \\\n # begin conda dependencies\n pip=22.1.2 \\\n numpy~=1.22.3 \\\n py-cpuinfo=5.0.0 \\\n joblib=1.2.0 \\\n cloudpickle=1.6.0 \\\n scikit-learn=0.22.1 \\\n pandas~=1.1.5 \\\n py-xgboost=1.3.3 \\\n holidays=0.10.3 \\\n setuptools-git \\\n setuptools=65.5.1 \\\n wheel=0.38.1 \\\n pyopenssl=23.2.0 \\\n 'psutil>5.0.0,<6.0.0' \\\n # end conda dependencies\n -c conda-forge -c pytorch -c anaconda && \\\n conda run -p $AZUREML_CONDA_ENVIRONMENT_PATH && \\\n conda clean -a -y\n# end conda create\n\n# begin pip install\n# Install pip dependencies\nRUN pip install \\\n # begin pypi dependencies\n 'cryptography==41.0.0' \\\n 'azureml-core==1.52.0' \\\n 'azureml-mlflow==1.52.0' \\\n 'azureml-pipeline-core==1.52.0' \\\n 'azureml-telemetry==1.52.0' \\\n 'azureml-interpret==1.52.0' \\\n 'azureml-responsibleai==1.52.0' \\\n 'azureml-automl-core==1.52.0.post1' \\\n 'azureml-automl-runtime==1.52.0.post1' \\\n 'azureml-train-automl-client==1.52.0' \\\n 'azureml-train-automl-runtime==1.52.0' \\\n 'azureml-dataset-runtime==1.52.0' \\\n 'azureml-defaults==1.52.0' \\\n 'inference-schema' \\\n 'fbprophet==0.7.1' \\\n 'pystan==2.19.1.1' \\\n 'notebook==6.4.9' \\\n 'mltable>=1.0.0'\n # end pypi dependencies\n# end pip install","BaseImageRegistry":{"Address":null,"Username":null,"Password":null},"Enabled":false,"Arguments":[]},"Spark":{"Repositories":[],"Packages":[],"PrecachePackages":true},"InferencingStackVersion":null},"History":{"OutputCollection":true,"DirectoriesToWatch":["logs"],"EnableMLflowTracking":true},"Spark":{"Configuration":{"spark.app.name":"Azure ML Experiment","spark.yarn.maxAppAttempts":"1"}},"ParallelTask":{"MaxRetriesPerWorker":0,"WorkerCountPerNode":1,"TerminalExitCodes":null,"Configuration":{}},"BatchAi":{"NodeCount":0},"AmlCompute":{"Name":null,"VmSize":null,"RetainCluster":false,"ClusterMaxNodeCount":null},"AISuperComputer":{"InstanceType":"D2","FrameworkImage":null,"ImageVersion":null,"Location":null,"AISuperComputerStorageData":null,"Interactive":false,"ScalePolicy":null,"VirtualClusterArmId":null,"TensorboardLogDirectory":null,"SSHPublicKey":null,"SSHPublicKeys":null,"EnableAzmlInt":true,"Priority":"Medium","SLATier":"Standard","UserAlias":null},"KubernetesCompute":{"InstanceType":null},"Tensorflow":{"WorkerCount":1,"ParameterServerCount":1},"Mpi":{"ProcessCountPerNode":1},"PyTorch":{"CommunicationBackend":null,"ProcessCount":null},"Hdi":{"YarnDeployMode":2},"ContainerInstance":{"Region":null,"CpuCores":2.0,"MemoryGb":3.5},"ExposedPorts":null,"Docker":{"UseDocker":null,"SharedVolumes":null,"ShmSize":null,"Arguments":null},"Cmk8sCompute":{"Configuration":{}},"CommandReturnCodeConfig":{"ReturnCode":0,"SuccessfulReturnCodes":[]},"EnvironmentVariables":{"AUTOML_SDK_RESOURCE_URL":"https://aka.ms/automl-resources/"},"ApplicationEndpoints":{},"Parameters":[]},"Attribution":null,"TelemetryValues":null,"Overrides":null,"SnapshotId":null,"Snapshots":[],"SourceCodeDataReference":null,"ParentRunId":null,"DataContainerId":null,"RunType":null,"DisplayName":null,"EnvironmentAssetId":null,"Properties":{},"Tags":{},"AggregatedArtifactPath":null}
|
example.py
ADDED
@@ -0,0 +1,61 @@
|
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|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import hfapi
|
2 |
+
client = hfapi.Client()
|
3 |
+
|
4 |
+
print("""
|
5 |
+
|
6 |
+
```python
|
7 |
+
import hfapi
|
8 |
+
client = hfapi.Client()
|
9 |
+
```
|
10 |
+
|
11 |
+
""")
|
12 |
+
|
13 |
+
print("""```python
|
14 |
+
client.question_answering("Where does she live?", "She lives in Berlin.")
|
15 |
+
```
|
16 |
+
""")
|
17 |
+
|
18 |
+
print(">", client.question_answering("Where does she live?", "She lives in Berlin."))
|
19 |
+
|
20 |
+
print("""```python
|
21 |
+
client.text_generation("My name is Julien and I like to ")
|
22 |
+
```
|
23 |
+
""")
|
24 |
+
print("```")
|
25 |
+
print(">", client.text_generation("My name is Julien and I like to ", model="gpt2"))
|
26 |
+
print("```")
|
27 |
+
print()
|
28 |
+
|
29 |
+
print("""```python
|
30 |
+
client.summarization("The tower is 324 metres (1,063 ft) tall, about the same height as an 81-storey building, and the tallest structure in Paris. Its base is square, measuring 125 metres (410 ft) on each side. During its construction, the Eiffel Tower surpassed the Washington Monument to become the tallest man-made structure in the world, a title it held for 41 years until the Chrysler Building in New York City was finished in 1930. It was the first structure to reach a height of 300 metres. Due to the addition of a broadcasting aerial at the top of the tower in 1957, it is now taller than the Chrysler Building by 5.2 metres (17 ft). Excluding transmitters, the Eiffel Tower is the second tallest free-standing structure in France after the Millau Viaduct.")
|
31 |
+
```
|
32 |
+
""")
|
33 |
+
|
34 |
+
print(">", client.summarization("The tower is 324 metres (1,063 ft) tall, about the same height as an 81-storey building, and the tallest structure in Paris. Its base is square, measuring 125 metres (410 ft) on each side. During its construction, the Eiffel Tower surpassed the Washington Monument to become the tallest man-made structure in the world, a title it held for 41 years until the Chrysler Building in New York City was finished in 1930. It was the first structure to reach a height of 300 metres. Due to the addition of a broadcasting aerial at the top of the tower in 1957, it is now taller than the Chrysler Building by 5.2 metres (17 ft). Excluding transmitters, the Eiffel Tower is the second tallest free-standing structure in France after the Millau Viaduct."))
|
35 |
+
print()
|
36 |
+
|
37 |
+
print("""```python
|
38 |
+
client.fill_mask("Paris is the [MASK] of France."))
|
39 |
+
```
|
40 |
+
""")
|
41 |
+
|
42 |
+
print(">",client.fill_mask("Paris is the [MASK] of France."))
|
43 |
+
print()
|
44 |
+
|
45 |
+
|
46 |
+
print("""```python
|
47 |
+
client.text_classification("I hated the movie!")
|
48 |
+
```
|
49 |
+
""")
|
50 |
+
|
51 |
+
print(">", client.text_classification("I hated the movie!"))
|
52 |
+
print()
|
53 |
+
|
54 |
+
|
55 |
+
print("""```python
|
56 |
+
client.token_classification("My name is Sarah and I live in London")
|
57 |
+
```
|
58 |
+
""")
|
59 |
+
|
60 |
+
print(">", client.token_classification("My name is Sarah and I live in London"))
|
61 |
+
print()
|
executionlogs.txt
ADDED
@@ -0,0 +1,7 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
[2023-08-21 20:04:57Z] ReuseHash: f387a391-e047-4a7e-a68f-03143c74a99c6CHiqOaRogHJZ/gVU4iBFxTspPbrgWRgLEh8sUA3w04=, JobDefinitionToHash: {"Configuration":{"Command":"python finetune.py --apply_lora '$AZUREML_PARAMETER_apply_lora' --merge_lora_weights '$AZUREML_PARAMETER_merge_lora_weights' --lora_alpha '$AZUREML_PARAMETER_lora_alpha' --lora_r '$AZUREML_PARAMETER_lora_r' --lora_dropout '$AZUREML_PARAMETER_lora_dropout' --num_train_epochs '$AZUREML_PARAMETER_num_train_epochs' --max_steps '$AZUREML_PARAMETER_max_steps' --per_device_train_batch_size '$AZUREML_PARAMETER_per_device_train_batch_size' --per_device_eval_batch_size '$AZUREML_PARAMETER_per_device_eval_batch_size' --auto_find_batch_size '$AZUREML_PARAMETER_auto_find_batch_size' --optim '$AZUREML_PARAMETER_optim' --learning_rate '$AZUREML_PARAMETER_learning_rate' --warmup_steps '$AZUREML_PARAMETER_warmup_steps' --weight_decay '$AZUREML_PARAMETER_weight_decay' --adam_beta1 '$AZUREML_PARAMETER_adam_beta1' --adam_beta2 '$AZUREML_PARAMETER_adam_beta2' --adam_epsilon '$AZUREML_PARAMETER_adam_epsilon' --gradient_accumulation_steps '$AZUREML_PARAMETER_gradient_accumulation_steps' --lr_scheduler_type '$AZUREML_PARAMETER_lr_scheduler_type' --precision '$AZUREML_PARAMETER_precision' --seed '$AZUREML_PARAMETER_seed' --enable_full_determinism '$AZUREML_PARAMETER_enable_full_determinism' --dataloader_num_workers '$AZUREML_PARAMETER_dataloader_num_workers' --ignore_mismatched_sizes '$AZUREML_PARAMETER_ignore_mismatched_sizes' --max_grad_norm '$AZUREML_PARAMETER_max_grad_norm' --evaluation_strategy '$AZUREML_PARAMETER_evaluation_strategy' --evaluation_steps_interval '$AZUREML_PARAMETER_evaluation_steps_interval' --eval_steps '$AZUREML_PARAMETER_eval_steps' --logging_strategy '$AZUREML_PARAMETER_logging_strategy' --logging_steps '$AZUREML_PARAMETER_logging_steps' --metric_for_best_model '$AZUREML_PARAMETER_metric_for_best_model' --resume_from_checkpoint '$AZUREML_PARAMETER_resume_from_checkpoint' --save_total_limit '$AZUREML_PARAMETER_save_total_limit' --apply_early_stopping '$AZUREML_PARAMETER_apply_early_stopping' --apply_ort '$AZUREML_PARAMETER_apply_ort' --apply_deepspeed '$AZUREML_PARAMETER_apply_deepspeed' --model_selector_output '$AZUREML_DATAREFERENCE_model_selector_output' --preprocess_output '$AZUREML_DATAREFERENCE_preprocess_output' --pytorch_model_folder 'DatasetOutputConfig:pytorch_model_folder' --mlflow_model_folder 'DatasetOutputConfig:mlflow_model_folder'","UseAbsolutePath":false,"Framework":"PyTorch","Communicator":"Nccl","Target":"nvidia","InputAssets":{"preprocess_output":{"Asset":{"AssetId":"azureml://locations/westus3/workspaces/f387a391-e047-4a7e-a68f-03143c74a99c/data/azureml_3974939a-2b62-43c8-ada8-0cccce16ef2b_output_data_output_dir/versions/1","Type":"UriFolder"},"Mechanism":"Mount","EnvironmentVariableName":"AZURE_ML_INPUT_preprocess_output","Overwrite":true,"Options":{"IsEvalMode":"False","ReadWrite":"False","ForceFolder":"False"}},"model_selector_output":{"Asset":{"AssetId":"azureml://locations/westus3/workspaces/f387a391-e047-4a7e-a68f-03143c74a99c/data/azureml_d4677798-41bc-41cf-882c-b9c4dcc13695_output_data_output_dir/versions/1","Type":"UriFolder"},"Mechanism":"Mount","EnvironmentVariableName":"AZURE_ML_INPUT_model_selector_output","Overwrite":true,"Options":{"IsEvalMode":"False","ReadWrite":"False","ForceFolder":"False"}}},"OutputData":{"pytorch_model_folder":{"OutputLocation":{"Uri":{"Path":"azureml://datastores/workspaceblobstore/paths/azureml/${{name}}/pytorch_model_folder/","IsFile":false},"Type":"UriFolder"},"Mechanism":"Upload","AdditionalOptions":{"RegistrationOptions":{}},"EnvironmentVariableName":"AZURE_ML_OUTPUT_pytorch_model_folder"},"mlflow_model_folder":{"OutputLocation":{"Uri":{"Path":"azureml://datastores/workspaceblobstore/paths/azureml/${{name}}/mlflow_model_folder/","IsFile":false},"Type":"MLFlowModel"},"Mechanism":"Mount","AdditionalOptions":{"RegistrationOptions":{}},"EnvironmentVariableName":"AZURE_ML_OUTPUT_mlflow_model_folder"}},"NodeCount":1,"CredentialPassthrough":false,"Environment":{"Name":"azureml://registries/azureml/environments/acft-hf-nlp-gpu/versions/18","Version":"18"},"History":{"OutputCollection":true,"DirectoriesToWatch":["logs"],"EnableMLflowTracking":false},"Spark":{},"ParallelTask":{"MaxRetriesPerWorker":0,"WorkerCountPerNode":1},"BatchAi":{"NodeCount":0},"AmlCompute":{"RetainCluster":false,"ClusterMaxNodeCount":1},"AISuperComputer":{"InstanceType":"D2","ImageVersion":"pytorch-1.7.0","Interactive":false,"EnableAzmlInt":true,"SLATier":"Standard"},"KubernetesCompute":{},"Tensorflow":{"WorkerCount":0,"ParameterServerCount":0},"Mpi":{"ProcessCountPerNode":1},"PyTorch":{"CommunicationBackend":"Nccl","ProcessCount":0},"Hdi":{"YarnDeployMode":"None"},"ContainerInstance":{"CpuCores":2.0,"MemoryGb":3.5},"Docker":{"UseDocker":true,"SharedVolumes":true,"ShmSize":"2g"},"Cmk8sCompute":{},"GlobalJobDispatcher":{"MyResourceOnly":false,"LowPriorityVMTolerant":true},"CommandReturnCodeConfig":{"ReturnCode":"Zero"},"EnvironmentVariables":{"AZUREML_PARAMETER_Node_Count":"1","AZUREML_PARAMETER_apply_lora":"true","AZUREML_PARAMETER_merge_lora_weights":"false","AZUREML_PARAMETER_lora_alpha":"128","AZUREML_PARAMETER_lora_r":"8","AZUREML_PARAMETER_lora_dropout":"0","AZUREML_PARAMETER_num_train_epochs":"5","AZUREML_PARAMETER_max_steps":"-1","AZUREML_PARAMETER_per_device_train_batch_size":"1","AZUREML_PARAMETER_per_device_eval_batch_size":"1","AZUREML_PARAMETER_auto_find_batch_size":"false","AZUREML_PARAMETER_optim":"adamw_hf","AZUREML_PARAMETER_learning_rate":"0.00002","AZUREML_PARAMETER_warmup_steps":"20","AZUREML_PARAMETER_weight_decay":"0","AZUREML_PARAMETER_adam_beta1":"0.9","AZUREML_PARAMETER_adam_beta2":"0.999","AZUREML_PARAMETER_adam_epsilon":"1e-8","AZUREML_PARAMETER_gradient_accumulation_steps":"1","AZUREML_PARAMETER_lr_scheduler_type":"linear","AZUREML_PARAMETER_precision":"4","AZUREML_PARAMETER_seed":"42","AZUREML_PARAMETER_enable_full_determinism":"false","AZUREML_PARAMETER_dataloader_num_workers":"0","AZUREML_PARAMETER_ignore_mismatched_sizes":"true","AZUREML_PARAMETER_max_grad_norm":"1.0","AZUREML_PARAMETER_evaluation_strategy":"epoch","AZUREML_PARAMETER_evaluation_steps_interval":"0","AZUREML_PARAMETER_eval_steps":"500","AZUREML_PARAMETER_logging_strategy":"epoch","AZUREML_PARAMETER_logging_steps":"100","AZUREML_PARAMETER_metric_for_best_model":"loss","AZUREML_PARAMETER_resume_from_checkpoint":"false","AZUREML_PARAMETER_save_total_limit":"-1","AZUREML_PARAMETER_apply_early_stopping":"false","AZUREML_PARAMETER_early_stopping_patience":"","AZUREML_PARAMETER_early_stopping_threshold":"","AZUREML_PARAMETER_apply_deepspeed":"false","AZUREML_PARAMETER_apply_ort":"false"},"DataBricks":{"Workers":0,"MinimumWorkerCount":0,"MaxMumWorkerCount":0,"SparkVersion":"4.0.x-scala2.11","NodeTypeId":"Standard_D3_v2","TimeoutSeconds":0}},"Snapshots":[{"SnapshotAssetId":"azureml://registries/azureml/codes/9e2860b2-0620-472b-a333-e9381650f0a6/versions/1"}]}
|
2 |
+
[2023-08-21 20:04:57Z] Starting run in Execution Service
|
3 |
+
[2023-08-21 20:04:58Z] RunId:[d6ec0227-2b44-4155-bd63-c777454c1fd5] ParentRunId:[sub-cf48025a-2632-4164-aec4-03e3a1384025] ComputeTarget:[AmlCompute]
|
4 |
+
[2023-08-21 20:04:58Z] The latency of ESCloud submitting an ES job returning 'Submitted' status : 1854.9268ms
|
5 |
+
[2023-08-21 20:05:00Z] Job is in progress. Execution status: Starting.
|
6 |
+
[2023-08-21 20:05:00Z] Job is in progress. Execution status: Preparing.
|
7 |
+
[2023-08-21 20:05:00Z] Job is in progress. Execution status: Preparing.
|
flash_attn_triton.py
ADDED
@@ -0,0 +1,484 @@
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1 |
+
"""
|
2 |
+
Copied from https://github.com/HazyResearch/flash-attention/blob/eff9fe6b8076df59d64d7a3f464696738a3c7c24/flash_attn/flash_attn_triton.py
|
3 |
+
update imports to use 'triton_pre_mlir'
|
4 |
+
|
5 |
+
*Experimental* implementation of FlashAttention in Triton.
|
6 |
+
Tested with triton==2.0.0.dev20221202.
|
7 |
+
Triton 2.0 has a new backend (MLIR) but seems like it doesn't yet work for head dimensions
|
8 |
+
other than 64:
|
9 |
+
https://github.com/openai/triton/blob/d376020f90002757eea3ea9475d4f7cfc2ec5ead/python/triton/ops/flash_attention.py#L207
|
10 |
+
We'll update this implementation with the new Triton backend once this is fixed.
|
11 |
+
|
12 |
+
We use the FlashAttention implementation from Phil Tillet a starting point.
|
13 |
+
https://github.com/openai/triton/blob/master/python/tutorials/06-fused-attention.py
|
14 |
+
|
15 |
+
Changes:
|
16 |
+
- Implement both causal and non-causal attention.
|
17 |
+
- Implement both self-attention and cross-attention.
|
18 |
+
- Support arbitrary seqlens (not just multiples of 128), for both forward and backward.
|
19 |
+
- Support all head dimensions up to 128 (not just 16, 32, 64, 128), for both forward and backward.
|
20 |
+
- Support attention bias.
|
21 |
+
- Speed up the forward pass a bit, and only store the LSE instead of m and l.
|
22 |
+
- Make the backward for d=128 much faster by reducing register spilling.
|
23 |
+
- Optionally parallelize the backward pass across seqlen_k, to deal with the case of
|
24 |
+
small batch size * nheads.
|
25 |
+
|
26 |
+
Caution:
|
27 |
+
- This is an *experimental* implementation. The forward pass should be quite robust but
|
28 |
+
I'm not 100% sure that the backward pass doesn't have race conditions (due to the Triton compiler).
|
29 |
+
- This implementation has only been tested on A100.
|
30 |
+
- If you plan to use headdim other than 64 and 128, you should test for race conditions
|
31 |
+
(due to the Triton compiler), as done in tests/test_flash_attn.py
|
32 |
+
"test_flash_attn_triton_race_condition". I've tested and fixed many race conditions
|
33 |
+
for different head dimensions (40, 48, 64, 128, 80, 88, 96), but I'm still not 100% confident
|
34 |
+
that there are none left for other head dimensions.
|
35 |
+
|
36 |
+
Differences between this Triton version and the CUDA version:
|
37 |
+
- Triton version doesn't support dropout.
|
38 |
+
- Triton forward is generally faster than CUDA forward, while Triton backward is
|
39 |
+
generally slower than CUDA backward. Overall Triton forward + backward is slightly slower
|
40 |
+
than CUDA forward + backward.
|
41 |
+
- Triton version doesn't support different sequence lengths in a batch (i.e., RaggedTensor/NestedTensor).
|
42 |
+
- Triton version supports attention bias, while CUDA version doesn't.
|
43 |
+
"""
|
44 |
+
import math
|
45 |
+
import torch
|
46 |
+
import triton_pre_mlir as triton
|
47 |
+
import triton_pre_mlir.language as tl
|
48 |
+
|
49 |
+
@triton.heuristics({'EVEN_M': lambda args: args['seqlen_q'] % args['BLOCK_M'] == 0, 'EVEN_N': lambda args: args['seqlen_k'] % args['BLOCK_N'] == 0, 'EVEN_HEADDIM': lambda args: args['headdim'] == args['BLOCK_HEADDIM']})
|
50 |
+
@triton.jit
|
51 |
+
def _fwd_kernel(Q, K, V, Bias, Out, Lse, TMP, softmax_scale, stride_qb, stride_qh, stride_qm, stride_kb, stride_kh, stride_kn, stride_vb, stride_vh, stride_vn, stride_bb, stride_bh, stride_bm, stride_ob, stride_oh, stride_om, nheads, seqlen_q, seqlen_k, seqlen_q_rounded, headdim, CACHE_KEY_SEQLEN_Q, CACHE_KEY_SEQLEN_K, BIAS_TYPE: tl.constexpr, IS_CAUSAL: tl.constexpr, BLOCK_HEADDIM: tl.constexpr, EVEN_M: tl.constexpr, EVEN_N: tl.constexpr, EVEN_HEADDIM: tl.constexpr, BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr):
|
52 |
+
start_m = tl.program_id(0)
|
53 |
+
off_hb = tl.program_id(1)
|
54 |
+
off_b = off_hb // nheads
|
55 |
+
off_h = off_hb % nheads
|
56 |
+
offs_m = start_m * BLOCK_M + tl.arange(0, BLOCK_M)
|
57 |
+
offs_n = tl.arange(0, BLOCK_N)
|
58 |
+
offs_d = tl.arange(0, BLOCK_HEADDIM)
|
59 |
+
q_ptrs = Q + off_b * stride_qb + off_h * stride_qh + (offs_m[:, None] * stride_qm + offs_d[None, :])
|
60 |
+
k_ptrs = K + off_b * stride_kb + off_h * stride_kh + (offs_n[:, None] * stride_kn + offs_d[None, :])
|
61 |
+
v_ptrs = V + off_b * stride_vb + off_h * stride_vh + (offs_n[:, None] * stride_vn + offs_d[None, :])
|
62 |
+
if BIAS_TYPE == 'vector':
|
63 |
+
b_ptrs = Bias + off_b * stride_bb + off_h * stride_bh + offs_n
|
64 |
+
elif BIAS_TYPE == 'matrix':
|
65 |
+
b_ptrs = Bias + off_b * stride_bb + off_h * stride_bh + (offs_m[:, None] * stride_bm + offs_n[None, :])
|
66 |
+
t_ptrs = TMP + off_hb * seqlen_q_rounded + offs_m
|
67 |
+
lse_i = tl.zeros([BLOCK_M], dtype=tl.float32) - float('inf')
|
68 |
+
m_i = tl.zeros([BLOCK_M], dtype=tl.float32) - float('inf')
|
69 |
+
acc_o = tl.zeros([BLOCK_M, BLOCK_HEADDIM], dtype=tl.float32)
|
70 |
+
if EVEN_M & EVEN_N:
|
71 |
+
if EVEN_HEADDIM:
|
72 |
+
q = tl.load(q_ptrs)
|
73 |
+
else:
|
74 |
+
q = tl.load(q_ptrs, mask=offs_d[None, :] < headdim, other=0.0)
|
75 |
+
elif EVEN_HEADDIM:
|
76 |
+
q = tl.load(q_ptrs, mask=offs_m[:, None] < seqlen_q, other=0.0)
|
77 |
+
else:
|
78 |
+
q = tl.load(q_ptrs, mask=(offs_m[:, None] < seqlen_q) & (offs_d[None, :] < headdim), other=0.0)
|
79 |
+
end_n = seqlen_k if not IS_CAUSAL else tl.minimum((start_m + 1) * BLOCK_M, seqlen_k)
|
80 |
+
for start_n in range(0, end_n, BLOCK_N):
|
81 |
+
start_n = tl.multiple_of(start_n, BLOCK_N)
|
82 |
+
if EVEN_N & EVEN_M:
|
83 |
+
if EVEN_HEADDIM:
|
84 |
+
k = tl.load(k_ptrs + start_n * stride_kn)
|
85 |
+
else:
|
86 |
+
k = tl.load(k_ptrs + start_n * stride_kn, mask=offs_d[None, :] < headdim, other=0.0)
|
87 |
+
elif EVEN_HEADDIM:
|
88 |
+
k = tl.load(k_ptrs + start_n * stride_kn, mask=(start_n + offs_n)[:, None] < seqlen_k, other=0.0)
|
89 |
+
else:
|
90 |
+
k = tl.load(k_ptrs + start_n * stride_kn, mask=((start_n + offs_n)[:, None] < seqlen_k) & (offs_d[None, :] < headdim), other=0.0)
|
91 |
+
qk = tl.zeros([BLOCK_M, BLOCK_N], dtype=tl.float32)
|
92 |
+
qk += tl.dot(q, k, trans_b=True)
|
93 |
+
if not EVEN_N:
|
94 |
+
qk += tl.where((start_n + offs_n)[None, :] < seqlen_k, 0, float('-inf'))
|
95 |
+
if IS_CAUSAL:
|
96 |
+
qk += tl.where(offs_m[:, None] >= (start_n + offs_n)[None, :], 0, float('-inf'))
|
97 |
+
if BIAS_TYPE != 'none':
|
98 |
+
if BIAS_TYPE == 'vector':
|
99 |
+
if EVEN_N:
|
100 |
+
bias = tl.load(b_ptrs + start_n).to(tl.float32)
|
101 |
+
else:
|
102 |
+
bias = tl.load(b_ptrs + start_n, mask=start_n + offs_n < seqlen_k, other=0.0).to(tl.float32)
|
103 |
+
bias = bias[None, :]
|
104 |
+
elif BIAS_TYPE == 'matrix':
|
105 |
+
if EVEN_M & EVEN_N:
|
106 |
+
bias = tl.load(b_ptrs + start_n).to(tl.float32)
|
107 |
+
else:
|
108 |
+
bias = tl.load(b_ptrs + start_n, mask=(offs_m[:, None] < seqlen_q) & ((start_n + offs_n)[None, :] < seqlen_k), other=0.0).to(tl.float32)
|
109 |
+
qk = qk * softmax_scale + bias
|
110 |
+
m_ij = tl.maximum(tl.max(qk, 1), lse_i)
|
111 |
+
p = tl.exp(qk - m_ij[:, None])
|
112 |
+
else:
|
113 |
+
m_ij = tl.maximum(tl.max(qk, 1) * softmax_scale, lse_i)
|
114 |
+
p = tl.exp(qk * softmax_scale - m_ij[:, None])
|
115 |
+
l_ij = tl.sum(p, 1)
|
116 |
+
acc_o_scale = tl.exp(m_i - m_ij)
|
117 |
+
tl.store(t_ptrs, acc_o_scale)
|
118 |
+
acc_o_scale = tl.load(t_ptrs)
|
119 |
+
acc_o = acc_o * acc_o_scale[:, None]
|
120 |
+
if EVEN_N & EVEN_M:
|
121 |
+
if EVEN_HEADDIM:
|
122 |
+
v = tl.load(v_ptrs + start_n * stride_vn)
|
123 |
+
else:
|
124 |
+
v = tl.load(v_ptrs + start_n * stride_vn, mask=offs_d[None, :] < headdim, other=0.0)
|
125 |
+
elif EVEN_HEADDIM:
|
126 |
+
v = tl.load(v_ptrs + start_n * stride_vn, mask=(start_n + offs_n)[:, None] < seqlen_k, other=0.0)
|
127 |
+
else:
|
128 |
+
v = tl.load(v_ptrs + start_n * stride_vn, mask=((start_n + offs_n)[:, None] < seqlen_k) & (offs_d[None, :] < headdim), other=0.0)
|
129 |
+
p = p.to(v.dtype)
|
130 |
+
acc_o += tl.dot(p, v)
|
131 |
+
m_i = m_ij
|
132 |
+
l_i_new = tl.exp(lse_i - m_ij) + l_ij
|
133 |
+
lse_i = m_ij + tl.log(l_i_new)
|
134 |
+
o_scale = tl.exp(m_i - lse_i)
|
135 |
+
tl.store(t_ptrs, o_scale)
|
136 |
+
o_scale = tl.load(t_ptrs)
|
137 |
+
acc_o = acc_o * o_scale[:, None]
|
138 |
+
start_m = tl.program_id(0)
|
139 |
+
offs_m = start_m * BLOCK_M + tl.arange(0, BLOCK_M)
|
140 |
+
lse_ptrs = Lse + off_hb * seqlen_q_rounded + offs_m
|
141 |
+
tl.store(lse_ptrs, lse_i)
|
142 |
+
offs_d = tl.arange(0, BLOCK_HEADDIM)
|
143 |
+
out_ptrs = Out + off_b * stride_ob + off_h * stride_oh + (offs_m[:, None] * stride_om + offs_d[None, :])
|
144 |
+
if EVEN_M:
|
145 |
+
if EVEN_HEADDIM:
|
146 |
+
tl.store(out_ptrs, acc_o)
|
147 |
+
else:
|
148 |
+
tl.store(out_ptrs, acc_o, mask=offs_d[None, :] < headdim)
|
149 |
+
elif EVEN_HEADDIM:
|
150 |
+
tl.store(out_ptrs, acc_o, mask=offs_m[:, None] < seqlen_q)
|
151 |
+
else:
|
152 |
+
tl.store(out_ptrs, acc_o, mask=(offs_m[:, None] < seqlen_q) & (offs_d[None, :] < headdim))
|
153 |
+
|
154 |
+
@triton.jit
|
155 |
+
def _bwd_preprocess_do_o_dot(Out, DO, Delta, stride_ob, stride_oh, stride_om, stride_dob, stride_doh, stride_dom, nheads, seqlen_q, seqlen_q_rounded, headdim, BLOCK_M: tl.constexpr, BLOCK_HEADDIM: tl.constexpr):
|
156 |
+
start_m = tl.program_id(0)
|
157 |
+
off_hb = tl.program_id(1)
|
158 |
+
off_b = off_hb // nheads
|
159 |
+
off_h = off_hb % nheads
|
160 |
+
offs_m = start_m * BLOCK_M + tl.arange(0, BLOCK_M)
|
161 |
+
offs_d = tl.arange(0, BLOCK_HEADDIM)
|
162 |
+
o = tl.load(Out + off_b * stride_ob + off_h * stride_oh + offs_m[:, None] * stride_om + offs_d[None, :], mask=(offs_m[:, None] < seqlen_q) & (offs_d[None, :] < headdim), other=0.0).to(tl.float32)
|
163 |
+
do = tl.load(DO + off_b * stride_dob + off_h * stride_doh + offs_m[:, None] * stride_dom + offs_d[None, :], mask=(offs_m[:, None] < seqlen_q) & (offs_d[None, :] < headdim), other=0.0).to(tl.float32)
|
164 |
+
delta = tl.sum(o * do, axis=1)
|
165 |
+
tl.store(Delta + off_hb * seqlen_q_rounded + offs_m, delta)
|
166 |
+
|
167 |
+
@triton.jit
|
168 |
+
def _bwd_store_dk_dv(dk_ptrs, dv_ptrs, dk, dv, offs_n, offs_d, seqlen_k, headdim, EVEN_M: tl.constexpr, EVEN_N: tl.constexpr, EVEN_HEADDIM: tl.constexpr):
|
169 |
+
if EVEN_N & EVEN_M:
|
170 |
+
if EVEN_HEADDIM:
|
171 |
+
tl.store(dv_ptrs, dv)
|
172 |
+
tl.store(dk_ptrs, dk)
|
173 |
+
else:
|
174 |
+
tl.store(dv_ptrs, dv, mask=offs_d[None, :] < headdim)
|
175 |
+
tl.store(dk_ptrs, dk, mask=offs_d[None, :] < headdim)
|
176 |
+
elif EVEN_HEADDIM:
|
177 |
+
tl.store(dv_ptrs, dv, mask=offs_n[:, None] < seqlen_k)
|
178 |
+
tl.store(dk_ptrs, dk, mask=offs_n[:, None] < seqlen_k)
|
179 |
+
else:
|
180 |
+
tl.store(dv_ptrs, dv, mask=(offs_n[:, None] < seqlen_k) & (offs_d[None, :] < headdim))
|
181 |
+
tl.store(dk_ptrs, dk, mask=(offs_n[:, None] < seqlen_k) & (offs_d[None, :] < headdim))
|
182 |
+
|
183 |
+
@triton.jit
|
184 |
+
def _bwd_kernel_one_col_block(start_n, Q, K, V, Bias, DO, DQ, DK, DV, LSE, D, softmax_scale, stride_qm, stride_kn, stride_vn, stride_bm, stride_dom, stride_dqm, stride_dkn, stride_dvn, seqlen_q, seqlen_k, headdim, ATOMIC_ADD: tl.constexpr, BIAS_TYPE: tl.constexpr, IS_CAUSAL: tl.constexpr, BLOCK_HEADDIM: tl.constexpr, EVEN_M: tl.constexpr, EVEN_N: tl.constexpr, EVEN_HEADDIM: tl.constexpr, BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr):
|
185 |
+
begin_m = 0 if not IS_CAUSAL else start_n * BLOCK_N // BLOCK_M * BLOCK_M
|
186 |
+
offs_qm = begin_m + tl.arange(0, BLOCK_M)
|
187 |
+
offs_n = start_n * BLOCK_N + tl.arange(0, BLOCK_N)
|
188 |
+
offs_m = tl.arange(0, BLOCK_M)
|
189 |
+
offs_d = tl.arange(0, BLOCK_HEADDIM)
|
190 |
+
q_ptrs = Q + (offs_qm[:, None] * stride_qm + offs_d[None, :])
|
191 |
+
k_ptrs = K + (offs_n[:, None] * stride_kn + offs_d[None, :])
|
192 |
+
v_ptrs = V + (offs_n[:, None] * stride_vn + offs_d[None, :])
|
193 |
+
do_ptrs = DO + (offs_qm[:, None] * stride_dom + offs_d[None, :])
|
194 |
+
dq_ptrs = DQ + (offs_qm[:, None] * stride_dqm + offs_d[None, :])
|
195 |
+
if BIAS_TYPE == 'vector':
|
196 |
+
b_ptrs = Bias + offs_n
|
197 |
+
elif BIAS_TYPE == 'matrix':
|
198 |
+
b_ptrs = Bias + (offs_qm[:, None] * stride_bm + offs_n[None, :])
|
199 |
+
dv = tl.zeros([BLOCK_N, BLOCK_HEADDIM], dtype=tl.float32)
|
200 |
+
dk = tl.zeros([BLOCK_N, BLOCK_HEADDIM], dtype=tl.float32)
|
201 |
+
if begin_m >= seqlen_q:
|
202 |
+
dv_ptrs = DV + (offs_n[:, None] * stride_dvn + offs_d[None, :])
|
203 |
+
dk_ptrs = DK + (offs_n[:, None] * stride_dkn + offs_d[None, :])
|
204 |
+
_bwd_store_dk_dv(dk_ptrs, dv_ptrs, dk, dv, offs_n, offs_d, seqlen_k, headdim, EVEN_M=EVEN_M, EVEN_N=EVEN_N, EVEN_HEADDIM=EVEN_HEADDIM)
|
205 |
+
return
|
206 |
+
if EVEN_N & EVEN_M:
|
207 |
+
if EVEN_HEADDIM:
|
208 |
+
k = tl.load(k_ptrs)
|
209 |
+
v = tl.load(v_ptrs)
|
210 |
+
else:
|
211 |
+
k = tl.load(k_ptrs, mask=offs_d[None, :] < headdim, other=0.0)
|
212 |
+
v = tl.load(v_ptrs, mask=offs_d[None, :] < headdim, other=0.0)
|
213 |
+
elif EVEN_HEADDIM:
|
214 |
+
k = tl.load(k_ptrs, mask=offs_n[:, None] < seqlen_k, other=0.0)
|
215 |
+
v = tl.load(v_ptrs, mask=offs_n[:, None] < seqlen_k, other=0.0)
|
216 |
+
else:
|
217 |
+
k = tl.load(k_ptrs, mask=(offs_n[:, None] < seqlen_k) & (offs_d[None, :] < headdim), other=0.0)
|
218 |
+
v = tl.load(v_ptrs, mask=(offs_n[:, None] < seqlen_k) & (offs_d[None, :] < headdim), other=0.0)
|
219 |
+
num_block_m = tl.cdiv(seqlen_q, BLOCK_M)
|
220 |
+
for start_m in range(begin_m, num_block_m * BLOCK_M, BLOCK_M):
|
221 |
+
start_m = tl.multiple_of(start_m, BLOCK_M)
|
222 |
+
offs_m_curr = start_m + offs_m
|
223 |
+
if EVEN_M & EVEN_HEADDIM:
|
224 |
+
q = tl.load(q_ptrs)
|
225 |
+
elif EVEN_HEADDIM:
|
226 |
+
q = tl.load(q_ptrs, mask=offs_m_curr[:, None] < seqlen_q, other=0.0)
|
227 |
+
else:
|
228 |
+
q = tl.load(q_ptrs, mask=(offs_m_curr[:, None] < seqlen_q) & (offs_d[None, :] < headdim), other=0.0)
|
229 |
+
qk = tl.dot(q, k, trans_b=True)
|
230 |
+
if not EVEN_N:
|
231 |
+
qk = tl.where(offs_n[None, :] < seqlen_k, qk, float('-inf'))
|
232 |
+
if IS_CAUSAL:
|
233 |
+
qk = tl.where(offs_m_curr[:, None] >= offs_n[None, :], qk, float('-inf'))
|
234 |
+
if BIAS_TYPE != 'none':
|
235 |
+
tl.debug_barrier()
|
236 |
+
if BIAS_TYPE == 'vector':
|
237 |
+
if EVEN_N:
|
238 |
+
bias = tl.load(b_ptrs).to(tl.float32)
|
239 |
+
else:
|
240 |
+
bias = tl.load(b_ptrs, mask=offs_n < seqlen_k, other=0.0).to(tl.float32)
|
241 |
+
bias = bias[None, :]
|
242 |
+
elif BIAS_TYPE == 'matrix':
|
243 |
+
if EVEN_M & EVEN_N:
|
244 |
+
bias = tl.load(b_ptrs).to(tl.float32)
|
245 |
+
else:
|
246 |
+
bias = tl.load(b_ptrs, mask=(offs_m_curr[:, None] < seqlen_q) & (offs_n[None, :] < seqlen_k), other=0.0).to(tl.float32)
|
247 |
+
qk = qk * softmax_scale + bias
|
248 |
+
if not EVEN_M & EVEN_HEADDIM:
|
249 |
+
tl.debug_barrier()
|
250 |
+
lse_i = tl.load(LSE + offs_m_curr)
|
251 |
+
if BIAS_TYPE == 'none':
|
252 |
+
p = tl.exp(qk * softmax_scale - lse_i[:, None])
|
253 |
+
else:
|
254 |
+
p = tl.exp(qk - lse_i[:, None])
|
255 |
+
if EVEN_M & EVEN_HEADDIM:
|
256 |
+
do = tl.load(do_ptrs)
|
257 |
+
else:
|
258 |
+
do = tl.load(do_ptrs, mask=(offs_m_curr[:, None] < seqlen_q) & (offs_d[None, :] < headdim), other=0.0)
|
259 |
+
dv += tl.dot(p.to(do.dtype), do, trans_a=True)
|
260 |
+
if not EVEN_M & EVEN_HEADDIM:
|
261 |
+
tl.debug_barrier()
|
262 |
+
dp = tl.dot(do, v, trans_b=True)
|
263 |
+
if not EVEN_HEADDIM:
|
264 |
+
tl.debug_barrier()
|
265 |
+
Di = tl.load(D + offs_m_curr)
|
266 |
+
ds = (p * (dp - Di[:, None]) * softmax_scale).to(q.dtype)
|
267 |
+
dk += tl.dot(ds, q, trans_a=True)
|
268 |
+
if not EVEN_M & EVEN_HEADDIM:
|
269 |
+
tl.debug_barrier()
|
270 |
+
if not ATOMIC_ADD:
|
271 |
+
if EVEN_M & EVEN_HEADDIM:
|
272 |
+
dq = tl.load(dq_ptrs, eviction_policy='evict_last')
|
273 |
+
dq += tl.dot(ds, k)
|
274 |
+
tl.store(dq_ptrs, dq, eviction_policy='evict_last')
|
275 |
+
elif EVEN_HEADDIM:
|
276 |
+
dq = tl.load(dq_ptrs, mask=offs_m_curr[:, None] < seqlen_q, other=0.0, eviction_policy='evict_last')
|
277 |
+
dq += tl.dot(ds, k)
|
278 |
+
tl.store(dq_ptrs, dq, mask=offs_m_curr[:, None] < seqlen_q, eviction_policy='evict_last')
|
279 |
+
else:
|
280 |
+
dq = tl.load(dq_ptrs, mask=(offs_m_curr[:, None] < seqlen_q) & (offs_d[None, :] < headdim), other=0.0, eviction_policy='evict_last')
|
281 |
+
dq += tl.dot(ds, k)
|
282 |
+
tl.store(dq_ptrs, dq, mask=(offs_m_curr[:, None] < seqlen_q) & (offs_d[None, :] < headdim), eviction_policy='evict_last')
|
283 |
+
else:
|
284 |
+
dq = tl.dot(ds, k)
|
285 |
+
if EVEN_M & EVEN_HEADDIM:
|
286 |
+
tl.atomic_add(dq_ptrs, dq)
|
287 |
+
elif EVEN_HEADDIM:
|
288 |
+
tl.atomic_add(dq_ptrs, dq, mask=offs_m_curr[:, None] < seqlen_q)
|
289 |
+
else:
|
290 |
+
tl.atomic_add(dq_ptrs, dq, mask=(offs_m_curr[:, None] < seqlen_q) & (offs_d[None, :] < headdim))
|
291 |
+
dq_ptrs += BLOCK_M * stride_dqm
|
292 |
+
q_ptrs += BLOCK_M * stride_qm
|
293 |
+
do_ptrs += BLOCK_M * stride_dom
|
294 |
+
if BIAS_TYPE == 'matrix':
|
295 |
+
b_ptrs += BLOCK_M * stride_bm
|
296 |
+
dv_ptrs = DV + (offs_n[:, None] * stride_dvn + offs_d[None, :])
|
297 |
+
dk_ptrs = DK + (offs_n[:, None] * stride_dkn + offs_d[None, :])
|
298 |
+
_bwd_store_dk_dv(dk_ptrs, dv_ptrs, dk, dv, offs_n, offs_d, seqlen_k, headdim, EVEN_M=EVEN_M, EVEN_N=EVEN_N, EVEN_HEADDIM=EVEN_HEADDIM)
|
299 |
+
|
300 |
+
def init_to_zero(name):
|
301 |
+
return lambda nargs: nargs[name].zero_()
|
302 |
+
|
303 |
+
@triton.autotune(configs=[triton.Config({'BLOCK_M': 128, 'BLOCK_N': 128, 'SEQUENCE_PARALLEL': False}, num_warps=8, num_stages=1, pre_hook=init_to_zero('DQ')), triton.Config({'BLOCK_M': 128, 'BLOCK_N': 128, 'SEQUENCE_PARALLEL': True}, num_warps=8, num_stages=1, pre_hook=init_to_zero('DQ'))], key=['CACHE_KEY_SEQLEN_Q', 'CACHE_KEY_SEQLEN_K', 'BIAS_TYPE', 'IS_CAUSAL', 'BLOCK_HEADDIM'])
|
304 |
+
@triton.heuristics({'EVEN_M': lambda args: args['seqlen_q'] % args['BLOCK_M'] == 0, 'EVEN_N': lambda args: args['seqlen_k'] % args['BLOCK_N'] == 0, 'EVEN_HEADDIM': lambda args: args['headdim'] == args['BLOCK_HEADDIM']})
|
305 |
+
@triton.jit
|
306 |
+
def _bwd_kernel(Q, K, V, Bias, DO, DQ, DK, DV, LSE, D, softmax_scale, stride_qb, stride_qh, stride_qm, stride_kb, stride_kh, stride_kn, stride_vb, stride_vh, stride_vn, stride_bb, stride_bh, stride_bm, stride_dob, stride_doh, stride_dom, stride_dqb, stride_dqh, stride_dqm, stride_dkb, stride_dkh, stride_dkn, stride_dvb, stride_dvh, stride_dvn, nheads, seqlen_q, seqlen_k, seqlen_q_rounded, headdim, CACHE_KEY_SEQLEN_Q, CACHE_KEY_SEQLEN_K, BIAS_TYPE: tl.constexpr, IS_CAUSAL: tl.constexpr, BLOCK_HEADDIM: tl.constexpr, SEQUENCE_PARALLEL: tl.constexpr, EVEN_M: tl.constexpr, EVEN_N: tl.constexpr, EVEN_HEADDIM: tl.constexpr, BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr):
|
307 |
+
off_hb = tl.program_id(1)
|
308 |
+
off_b = off_hb // nheads
|
309 |
+
off_h = off_hb % nheads
|
310 |
+
Q += off_b * stride_qb + off_h * stride_qh
|
311 |
+
K += off_b * stride_kb + off_h * stride_kh
|
312 |
+
V += off_b * stride_vb + off_h * stride_vh
|
313 |
+
DO += off_b * stride_dob + off_h * stride_doh
|
314 |
+
DQ += off_b * stride_dqb + off_h * stride_dqh
|
315 |
+
DK += off_b * stride_dkb + off_h * stride_dkh
|
316 |
+
DV += off_b * stride_dvb + off_h * stride_dvh
|
317 |
+
if BIAS_TYPE != 'none':
|
318 |
+
Bias += off_b * stride_bb + off_h * stride_bh
|
319 |
+
D += off_hb * seqlen_q_rounded
|
320 |
+
LSE += off_hb * seqlen_q_rounded
|
321 |
+
if not SEQUENCE_PARALLEL:
|
322 |
+
num_block_n = tl.cdiv(seqlen_k, BLOCK_N)
|
323 |
+
for start_n in range(0, num_block_n):
|
324 |
+
_bwd_kernel_one_col_block(start_n, Q, K, V, Bias, DO, DQ, DK, DV, LSE, D, softmax_scale, stride_qm, stride_kn, stride_vn, stride_bm, stride_dom, stride_dqm, stride_dkn, stride_dvn, seqlen_q, seqlen_k, headdim, ATOMIC_ADD=False, BIAS_TYPE=BIAS_TYPE, IS_CAUSAL=IS_CAUSAL, BLOCK_HEADDIM=BLOCK_HEADDIM, EVEN_M=EVEN_M, EVEN_N=EVEN_N, EVEN_HEADDIM=EVEN_HEADDIM, BLOCK_M=BLOCK_M, BLOCK_N=BLOCK_N)
|
325 |
+
else:
|
326 |
+
start_n = tl.program_id(0)
|
327 |
+
_bwd_kernel_one_col_block(start_n, Q, K, V, Bias, DO, DQ, DK, DV, LSE, D, softmax_scale, stride_qm, stride_kn, stride_vn, stride_bm, stride_dom, stride_dqm, stride_dkn, stride_dvn, seqlen_q, seqlen_k, headdim, ATOMIC_ADD=True, BIAS_TYPE=BIAS_TYPE, IS_CAUSAL=IS_CAUSAL, BLOCK_HEADDIM=BLOCK_HEADDIM, EVEN_M=EVEN_M, EVEN_N=EVEN_N, EVEN_HEADDIM=EVEN_HEADDIM, BLOCK_M=BLOCK_M, BLOCK_N=BLOCK_N)
|
328 |
+
|
329 |
+
def _flash_attn_forward(q, k, v, bias=None, causal=False, softmax_scale=None):
|
330 |
+
(batch, seqlen_q, nheads, d) = q.shape
|
331 |
+
(_, seqlen_k, _, _) = k.shape
|
332 |
+
assert k.shape == (batch, seqlen_k, nheads, d)
|
333 |
+
assert v.shape == (batch, seqlen_k, nheads, d)
|
334 |
+
assert d <= 128, 'FlashAttention only support head dimensions up to 128'
|
335 |
+
assert q.dtype == k.dtype == v.dtype, 'All tensors must have the same type'
|
336 |
+
assert q.dtype in [torch.float16, torch.bfloat16], 'Only support fp16 and bf16'
|
337 |
+
assert q.is_cuda and k.is_cuda and v.is_cuda
|
338 |
+
softmax_scale = softmax_scale or 1.0 / math.sqrt(d)
|
339 |
+
has_bias = bias is not None
|
340 |
+
bias_type = 'none'
|
341 |
+
if has_bias:
|
342 |
+
assert bias.dtype in [q.dtype, torch.float]
|
343 |
+
assert bias.is_cuda
|
344 |
+
assert bias.dim() == 4
|
345 |
+
if bias.stride(-1) != 1:
|
346 |
+
bias = bias.contiguous()
|
347 |
+
if bias.shape[2:] == (1, seqlen_k):
|
348 |
+
bias_type = 'vector'
|
349 |
+
elif bias.shape[2:] == (seqlen_q, seqlen_k):
|
350 |
+
bias_type = 'matrix'
|
351 |
+
else:
|
352 |
+
raise RuntimeError('Last 2 dimensions of bias must be (1, seqlen_k) or (seqlen_q, seqlen_k)')
|
353 |
+
bias = bias.expand(batch, nheads, seqlen_q, seqlen_k)
|
354 |
+
bias_strides = (bias.stride(0), bias.stride(1), bias.stride(2)) if has_bias else (0, 0, 0)
|
355 |
+
seqlen_q_rounded = math.ceil(seqlen_q / 128) * 128
|
356 |
+
lse = torch.empty((batch, nheads, seqlen_q_rounded), device=q.device, dtype=torch.float32)
|
357 |
+
tmp = torch.empty((batch, nheads, seqlen_q_rounded), device=q.device, dtype=torch.float32)
|
358 |
+
o = torch.empty_like(q)
|
359 |
+
BLOCK_HEADDIM = max(triton.next_power_of_2(d), 16)
|
360 |
+
BLOCK = 128
|
361 |
+
num_warps = 4 if d <= 64 else 8
|
362 |
+
grid = lambda META: (triton.cdiv(seqlen_q, META['BLOCK_M']), batch * nheads)
|
363 |
+
_fwd_kernel[grid](q, k, v, bias, o, lse, tmp, softmax_scale, q.stride(0), q.stride(2), q.stride(1), k.stride(0), k.stride(2), k.stride(1), v.stride(0), v.stride(2), v.stride(1), *bias_strides, o.stride(0), o.stride(2), o.stride(1), nheads, seqlen_q, seqlen_k, seqlen_q_rounded, d, seqlen_q // 32, seqlen_k // 32, bias_type, causal, BLOCK_HEADDIM, BLOCK_M=BLOCK, BLOCK_N=BLOCK, num_warps=num_warps, num_stages=1)
|
364 |
+
return (o, lse, softmax_scale)
|
365 |
+
|
366 |
+
def _flash_attn_backward(do, q, k, v, o, lse, dq, dk, dv, bias=None, causal=False, softmax_scale=None):
|
367 |
+
if do.stride(-1) != 1:
|
368 |
+
do = do.contiguous()
|
369 |
+
(batch, seqlen_q, nheads, d) = q.shape
|
370 |
+
(_, seqlen_k, _, _) = k.shape
|
371 |
+
assert d <= 128
|
372 |
+
seqlen_q_rounded = math.ceil(seqlen_q / 128) * 128
|
373 |
+
assert lse.shape == (batch, nheads, seqlen_q_rounded)
|
374 |
+
assert q.stride(-1) == k.stride(-1) == v.stride(-1) == o.stride(-1) == 1
|
375 |
+
assert dq.stride(-1) == dk.stride(-1) == dv.stride(-1) == 1
|
376 |
+
softmax_scale = softmax_scale or 1.0 / math.sqrt(d)
|
377 |
+
dq_accum = torch.empty_like(q, dtype=torch.float32)
|
378 |
+
delta = torch.empty_like(lse)
|
379 |
+
BLOCK_HEADDIM = max(triton.next_power_of_2(d), 16)
|
380 |
+
grid = lambda META: (triton.cdiv(seqlen_q, META['BLOCK_M']), batch * nheads)
|
381 |
+
_bwd_preprocess_do_o_dot[grid](o, do, delta, o.stride(0), o.stride(2), o.stride(1), do.stride(0), do.stride(2), do.stride(1), nheads, seqlen_q, seqlen_q_rounded, d, BLOCK_M=128, BLOCK_HEADDIM=BLOCK_HEADDIM)
|
382 |
+
has_bias = bias is not None
|
383 |
+
bias_type = 'none'
|
384 |
+
if has_bias:
|
385 |
+
assert bias.dtype in [q.dtype, torch.float]
|
386 |
+
assert bias.is_cuda
|
387 |
+
assert bias.dim() == 4
|
388 |
+
assert bias.stride(-1) == 1
|
389 |
+
if bias.shape[2:] == (1, seqlen_k):
|
390 |
+
bias_type = 'vector'
|
391 |
+
elif bias.shape[2:] == (seqlen_q, seqlen_k):
|
392 |
+
bias_type = 'matrix'
|
393 |
+
else:
|
394 |
+
raise RuntimeError('Last 2 dimensions of bias must be (1, seqlen_k) or (seqlen_q, seqlen_k)')
|
395 |
+
bias = bias.expand(batch, nheads, seqlen_q, seqlen_k)
|
396 |
+
bias_strides = (bias.stride(0), bias.stride(1), bias.stride(2)) if has_bias else (0, 0, 0)
|
397 |
+
grid = lambda META: (triton.cdiv(seqlen_k, META['BLOCK_N']) if META['SEQUENCE_PARALLEL'] else 1, batch * nheads)
|
398 |
+
_bwd_kernel[grid](q, k, v, bias, do, dq_accum, dk, dv, lse, delta, softmax_scale, q.stride(0), q.stride(2), q.stride(1), k.stride(0), k.stride(2), k.stride(1), v.stride(0), v.stride(2), v.stride(1), *bias_strides, do.stride(0), do.stride(2), do.stride(1), dq_accum.stride(0), dq_accum.stride(2), dq_accum.stride(1), dk.stride(0), dk.stride(2), dk.stride(1), dv.stride(0), dv.stride(2), dv.stride(1), nheads, seqlen_q, seqlen_k, seqlen_q_rounded, d, seqlen_q // 32, seqlen_k // 32, bias_type, causal, BLOCK_HEADDIM)
|
399 |
+
dq.copy_(dq_accum)
|
400 |
+
|
401 |
+
class FlashAttnQKVPackedFunc(torch.autograd.Function):
|
402 |
+
|
403 |
+
@staticmethod
|
404 |
+
def forward(ctx, qkv, bias=None, causal=False, softmax_scale=None):
|
405 |
+
"""
|
406 |
+
qkv: (batch, seqlen, 3, nheads, headdim)
|
407 |
+
bias: optional, shape broadcastible to (batch, nheads, seqlen, seqlen).
|
408 |
+
For example, ALiBi mask for causal would have shape (1, nheads, 1, seqlen).
|
409 |
+
ALiBi mask for non-causal would have shape (1, nheads, seqlen, seqlen)
|
410 |
+
"""
|
411 |
+
if qkv.stride(-1) != 1:
|
412 |
+
qkv = qkv.contiguous()
|
413 |
+
(o, lse, ctx.softmax_scale) = _flash_attn_forward(qkv[:, :, 0], qkv[:, :, 1], qkv[:, :, 2], bias=bias, causal=causal, softmax_scale=softmax_scale)
|
414 |
+
ctx.save_for_backward(qkv, o, lse, bias)
|
415 |
+
ctx.causal = causal
|
416 |
+
return o
|
417 |
+
|
418 |
+
@staticmethod
|
419 |
+
def backward(ctx, do):
|
420 |
+
(qkv, o, lse, bias) = ctx.saved_tensors
|
421 |
+
assert not ctx.needs_input_grad[1], 'FlashAttention does not support bias gradient yet'
|
422 |
+
with torch.inference_mode():
|
423 |
+
dqkv = torch.empty_like(qkv)
|
424 |
+
_flash_attn_backward(do, qkv[:, :, 0], qkv[:, :, 1], qkv[:, :, 2], o, lse, dqkv[:, :, 0], dqkv[:, :, 1], dqkv[:, :, 2], bias=bias, causal=ctx.causal, softmax_scale=ctx.softmax_scale)
|
425 |
+
return (dqkv, None, None, None)
|
426 |
+
flash_attn_qkvpacked_func = FlashAttnQKVPackedFunc.apply
|
427 |
+
|
428 |
+
class FlashAttnKVPackedFunc(torch.autograd.Function):
|
429 |
+
|
430 |
+
@staticmethod
|
431 |
+
def forward(ctx, q, kv, bias=None, causal=False, softmax_scale=None):
|
432 |
+
"""
|
433 |
+
q: (batch, seqlen_q, nheads, headdim)
|
434 |
+
kv: (batch, seqlen_k, 2, nheads, headdim)
|
435 |
+
bias: optional, shape broadcastible to (batch, nheads, seqlen_q, seqlen_k).
|
436 |
+
For example, ALiBi mask for causal would have shape (1, nheads, 1, seqlen_k).
|
437 |
+
ALiBi mask for non-causal would have shape (1, nheads, seqlen_q, seqlen_k)
|
438 |
+
"""
|
439 |
+
(q, kv) = [x if x.stride(-1) == 1 else x.contiguous() for x in [q, kv]]
|
440 |
+
(o, lse, ctx.softmax_scale) = _flash_attn_forward(q, kv[:, :, 0], kv[:, :, 1], bias=bias, causal=causal, softmax_scale=softmax_scale)
|
441 |
+
ctx.save_for_backward(q, kv, o, lse, bias)
|
442 |
+
ctx.causal = causal
|
443 |
+
return o
|
444 |
+
|
445 |
+
@staticmethod
|
446 |
+
def backward(ctx, do):
|
447 |
+
(q, kv, o, lse, bias) = ctx.saved_tensors
|
448 |
+
if len(ctx.needs_input_grad) >= 3:
|
449 |
+
assert not ctx.needs_input_grad[2], 'FlashAttention does not support bias gradient yet'
|
450 |
+
with torch.inference_mode():
|
451 |
+
dq = torch.empty_like(q)
|
452 |
+
dkv = torch.empty_like(kv)
|
453 |
+
_flash_attn_backward(do, q, kv[:, :, 0], kv[:, :, 1], o, lse, dq, dkv[:, :, 0], dkv[:, :, 1], bias=bias, causal=ctx.causal, softmax_scale=ctx.softmax_scale)
|
454 |
+
return (dq, dkv, None, None, None)
|
455 |
+
flash_attn_kvpacked_func = FlashAttnKVPackedFunc.apply
|
456 |
+
|
457 |
+
class FlashAttnFunc(torch.autograd.Function):
|
458 |
+
|
459 |
+
@staticmethod
|
460 |
+
def forward(ctx, q, k, v, bias=None, causal=False, softmax_scale=None):
|
461 |
+
"""
|
462 |
+
q: (batch_size, seqlen_q, nheads, headdim)
|
463 |
+
k, v: (batch_size, seqlen_k, nheads, headdim)
|
464 |
+
bias: optional, shape broadcastible to (batch, nheads, seqlen_q, seqlen_k).
|
465 |
+
For example, ALiBi mask for causal would have shape (1, nheads, 1, seqlen_k).
|
466 |
+
ALiBi mask for non-causal would have shape (1, nheads, seqlen_q, seqlen_k)
|
467 |
+
"""
|
468 |
+
(q, k, v) = [x if x.stride(-1) == 1 else x.contiguous() for x in [q, k, v]]
|
469 |
+
(o, lse, ctx.softmax_scale) = _flash_attn_forward(q, k, v, bias=bias, causal=causal, softmax_scale=softmax_scale)
|
470 |
+
ctx.save_for_backward(q, k, v, o, lse, bias)
|
471 |
+
ctx.causal = causal
|
472 |
+
return o
|
473 |
+
|
474 |
+
@staticmethod
|
475 |
+
def backward(ctx, do):
|
476 |
+
(q, k, v, o, lse, bias) = ctx.saved_tensors
|
477 |
+
assert not ctx.needs_input_grad[3], 'FlashAttention does not support bias gradient yet'
|
478 |
+
with torch.inference_mode():
|
479 |
+
dq = torch.empty_like(q)
|
480 |
+
dk = torch.empty_like(k)
|
481 |
+
dv = torch.empty_like(v)
|
482 |
+
_flash_attn_backward(do, q, k, v, o, lse, dq, dk, dv, bias=bias, causal=ctx.causal, softmax_scale=ctx.softmax_scale)
|
483 |
+
return (dq, dk, dv, None, None, None)
|
484 |
+
flash_attn_func = FlashAttnFunc.apply
|
generation_config.json
ADDED
@@ -0,0 +1,6 @@
|
|
|
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|
|
|
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|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"_from_model_config": true,
|
3 |
+
"transformers_version": "4.28.1",
|
4 |
+
"eos_token_id": 0,
|
5 |
+
"use_cache": false
|
6 |
+
}
|
hf_prefixlm_converter.py
ADDED
@@ -0,0 +1,415 @@
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|
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|
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|
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|
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|
|
|
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|
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|
|
|
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|
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|
|
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|
|
|
|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
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|
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|
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|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
"""Converts Huggingface Causal LM to Prefix LM.
|
2 |
+
|
3 |
+
Conversion does lightweight surgery on a HuggingFace
|
4 |
+
Causal LM to convert it to a Prefix LM.
|
5 |
+
|
6 |
+
Prefix LMs accepts a `bidirectional_mask` input in `forward`
|
7 |
+
and treat the input prompt as the prefix in `generate`.
|
8 |
+
"""
|
9 |
+
import math
|
10 |
+
import warnings
|
11 |
+
from types import MethodType
|
12 |
+
from typing import Any, Dict, List, Optional, Tuple, Union
|
13 |
+
import torch
|
14 |
+
from transformers.models.bloom.modeling_bloom import BaseModelOutputWithPastAndCrossAttentions, BloomForCausalLM, BloomModel, CausalLMOutputWithCrossAttentions, CrossEntropyLoss
|
15 |
+
from transformers.models.bloom.modeling_bloom import _expand_mask as _expand_mask_bloom
|
16 |
+
from transformers.models.bloom.modeling_bloom import _make_causal_mask as _make_causal_mask_bloom
|
17 |
+
from transformers.models.bloom.modeling_bloom import logging
|
18 |
+
from transformers.models.gpt2.modeling_gpt2 import GPT2LMHeadModel
|
19 |
+
from transformers.models.gpt_neo.modeling_gpt_neo import GPTNeoForCausalLM
|
20 |
+
from transformers.models.gpt_neox.modeling_gpt_neox import GPTNeoXForCausalLM
|
21 |
+
from transformers.models.gptj.modeling_gptj import GPTJForCausalLM
|
22 |
+
from transformers.models.opt.modeling_opt import OPTForCausalLM
|
23 |
+
from transformers.models.opt.modeling_opt import _expand_mask as _expand_mask_opt
|
24 |
+
from transformers.models.opt.modeling_opt import _make_causal_mask as _make_causal_mask_opt
|
25 |
+
logger = logging.get_logger(__name__)
|
26 |
+
_SUPPORTED_GPT_MODELS = (GPT2LMHeadModel, GPTJForCausalLM, GPTNeoForCausalLM, GPTNeoXForCausalLM)
|
27 |
+
CAUSAL_GPT_TYPES = Union[GPT2LMHeadModel, GPTJForCausalLM, GPTNeoForCausalLM, GPTNeoXForCausalLM]
|
28 |
+
|
29 |
+
def _convert_gpt_causal_lm_to_prefix_lm(model: CAUSAL_GPT_TYPES) -> CAUSAL_GPT_TYPES:
|
30 |
+
"""Converts a GPT-style Causal LM to a Prefix LM.
|
31 |
+
|
32 |
+
Supported HuggingFace model classes:
|
33 |
+
- `GPT2LMHeadModel`
|
34 |
+
- `GPTNeoForCausalLM`
|
35 |
+
- `GPTNeoXForCausalLM`
|
36 |
+
- `GPTJForCausalLM`
|
37 |
+
|
38 |
+
See `convert_hf_causal_lm_to_prefix_lm` for more details.
|
39 |
+
"""
|
40 |
+
if hasattr(model, '_prefix_lm_converted'):
|
41 |
+
return model
|
42 |
+
assert isinstance(model, _SUPPORTED_GPT_MODELS)
|
43 |
+
assert model.config.add_cross_attention == False, 'Only supports GPT-style decoder-only models'
|
44 |
+
|
45 |
+
def _get_attn_modules(model: CAUSAL_GPT_TYPES) -> List[torch.nn.Module]:
|
46 |
+
"""Helper that gets a list of the model's attention modules.
|
47 |
+
|
48 |
+
Each module has a `bias` buffer used for causal masking. The Prefix LM
|
49 |
+
conversion adds logic to dynamically manipulate these biases to support
|
50 |
+
Prefix LM attention masking.
|
51 |
+
"""
|
52 |
+
attn_modules = []
|
53 |
+
if isinstance(model, GPTNeoXForCausalLM):
|
54 |
+
blocks = model.gpt_neox.layers
|
55 |
+
else:
|
56 |
+
blocks = model.transformer.h
|
57 |
+
for block in blocks:
|
58 |
+
if isinstance(model, GPTNeoForCausalLM):
|
59 |
+
if block.attn.attention_type != 'global':
|
60 |
+
continue
|
61 |
+
attn_module = block.attn.attention
|
62 |
+
elif isinstance(model, GPTNeoXForCausalLM):
|
63 |
+
attn_module = block.attention
|
64 |
+
else:
|
65 |
+
attn_module = block.attn
|
66 |
+
attn_modules.append(attn_module)
|
67 |
+
return attn_modules
|
68 |
+
setattr(model, '_original_forward', getattr(model, 'forward'))
|
69 |
+
setattr(model, '_original_generate', getattr(model, 'generate'))
|
70 |
+
|
71 |
+
def forward(self: CAUSAL_GPT_TYPES, input_ids: Optional[torch.LongTensor]=None, past_key_values: Optional[Tuple[Tuple[torch.Tensor]]]=None, attention_mask: Optional[torch.FloatTensor]=None, bidirectional_mask: Optional[torch.Tensor]=None, token_type_ids: Optional[torch.LongTensor]=None, position_ids: Optional[torch.LongTensor]=None, head_mask: Optional[torch.FloatTensor]=None, inputs_embeds: Optional[torch.FloatTensor]=None, labels: Optional[torch.LongTensor]=None, use_cache: Optional[bool]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, return_dict: Optional[bool]=None):
|
72 |
+
"""Wraps original forward to enable PrefixLM attention."""
|
73 |
+
|
74 |
+
def call_og_forward():
|
75 |
+
if isinstance(self, GPTNeoXForCausalLM):
|
76 |
+
return self._original_forward(input_ids=input_ids, past_key_values=past_key_values, attention_mask=attention_mask, head_mask=head_mask, inputs_embeds=inputs_embeds, labels=labels, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
|
77 |
+
else:
|
78 |
+
return self._original_forward(input_ids=input_ids, past_key_values=past_key_values, attention_mask=attention_mask, token_type_ids=token_type_ids, position_ids=position_ids, head_mask=head_mask, inputs_embeds=inputs_embeds, labels=labels, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
|
79 |
+
if bidirectional_mask is None:
|
80 |
+
return call_og_forward()
|
81 |
+
assert isinstance(bidirectional_mask, torch.Tensor)
|
82 |
+
attn_modules = _get_attn_modules(model)
|
83 |
+
(b, s) = bidirectional_mask.shape
|
84 |
+
max_length = attn_modules[0].bias.shape[-1]
|
85 |
+
if s > max_length:
|
86 |
+
raise ValueError(f'bidirectional_mask sequence length (={s}) exceeds the ' + f'max length allowed by the model ({max_length}).')
|
87 |
+
assert s <= max_length
|
88 |
+
if s < max_length:
|
89 |
+
pad = torch.zeros((int(b), int(max_length - s)), dtype=bidirectional_mask.dtype, device=bidirectional_mask.device)
|
90 |
+
bidirectional_mask = torch.cat([bidirectional_mask, pad], dim=1)
|
91 |
+
bidirectional = bidirectional_mask.unsqueeze(1).unsqueeze(1)
|
92 |
+
for attn_module in attn_modules:
|
93 |
+
attn_module.bias.data = torch.logical_or(attn_module.bias.data, bidirectional)
|
94 |
+
output = call_og_forward()
|
95 |
+
for attn_module in attn_modules:
|
96 |
+
attn_module.bias.data = torch.tril(attn_module.bias.data[0, 0])[None, None]
|
97 |
+
return output
|
98 |
+
|
99 |
+
def generate(self: CAUSAL_GPT_TYPES, *args: tuple, **kwargs: Dict[str, Any]):
|
100 |
+
"""Wraps original generate to enable PrefixLM attention."""
|
101 |
+
attn_modules = _get_attn_modules(model)
|
102 |
+
for attn_module in attn_modules:
|
103 |
+
attn_module.bias.data[:] = 1
|
104 |
+
output = self._original_generate(*args, **kwargs)
|
105 |
+
for attn_module in attn_modules:
|
106 |
+
attn_module.bias.data = torch.tril(attn_module.bias.data[0, 0])[None, None]
|
107 |
+
return output
|
108 |
+
setattr(model, 'forward', MethodType(forward, model))
|
109 |
+
setattr(model, 'generate', MethodType(generate, model))
|
110 |
+
setattr(model, '_prefix_lm_converted', True)
|
111 |
+
return model
|
112 |
+
|
113 |
+
def _convert_bloom_causal_lm_to_prefix_lm(model: BloomForCausalLM) -> BloomForCausalLM:
|
114 |
+
"""Converts a BLOOM Causal LM to a Prefix LM.
|
115 |
+
|
116 |
+
Supported HuggingFace model classes:
|
117 |
+
- `BloomForCausalLM`
|
118 |
+
|
119 |
+
See `convert_hf_causal_lm_to_prefix_lm` for more details.
|
120 |
+
"""
|
121 |
+
if hasattr(model, '_prefix_lm_converted'):
|
122 |
+
return model
|
123 |
+
assert isinstance(model, BloomForCausalLM)
|
124 |
+
assert model.config.add_cross_attention == False, 'Only supports BLOOM decoder-only models'
|
125 |
+
|
126 |
+
def _prepare_attn_mask(self: BloomModel, attention_mask: torch.Tensor, bidirectional_mask: Optional[torch.Tensor], input_shape: Tuple[int, int], past_key_values_length: int) -> torch.BoolTensor:
|
127 |
+
combined_attention_mask = None
|
128 |
+
device = attention_mask.device
|
129 |
+
(_, src_length) = input_shape
|
130 |
+
if src_length > 1:
|
131 |
+
combined_attention_mask = _make_causal_mask_bloom(input_shape, device=device, past_key_values_length=past_key_values_length)
|
132 |
+
if bidirectional_mask is not None:
|
133 |
+
assert attention_mask.shape == bidirectional_mask.shape
|
134 |
+
expanded_bidirectional_mask = _expand_mask_bloom(bidirectional_mask, tgt_length=src_length)
|
135 |
+
combined_attention_mask = torch.logical_and(combined_attention_mask, expanded_bidirectional_mask)
|
136 |
+
expanded_attn_mask = _expand_mask_bloom(attention_mask, tgt_length=src_length)
|
137 |
+
combined_attention_mask = expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask | combined_attention_mask
|
138 |
+
return combined_attention_mask
|
139 |
+
|
140 |
+
def _build_alibi_tensor(self: BloomModel, batch_size: int, query_length: int, key_length: int, dtype: torch.dtype, device: torch.device) -> torch.Tensor:
|
141 |
+
num_heads = self.config.n_head
|
142 |
+
closest_power_of_2 = 2 ** math.floor(math.log2(num_heads))
|
143 |
+
base = torch.tensor(2 ** (-2 ** (-(math.log2(closest_power_of_2) - 3))), device=device, dtype=torch.float32)
|
144 |
+
powers = torch.arange(1, 1 + closest_power_of_2, device=device, dtype=torch.int32)
|
145 |
+
slopes = torch.pow(base, powers)
|
146 |
+
if closest_power_of_2 != num_heads:
|
147 |
+
extra_base = torch.tensor(2 ** (-2 ** (-(math.log2(2 * closest_power_of_2) - 3))), device=device, dtype=torch.float32)
|
148 |
+
num_remaining_heads = min(closest_power_of_2, num_heads - closest_power_of_2)
|
149 |
+
extra_powers = torch.arange(1, 1 + 2 * num_remaining_heads, 2, device=device, dtype=torch.int32)
|
150 |
+
slopes = torch.cat([slopes, torch.pow(extra_base, extra_powers)], dim=0)
|
151 |
+
qa = torch.arange(query_length, device=device, dtype=torch.int32).view(-1, 1)
|
152 |
+
ka = torch.arange(key_length, device=device, dtype=torch.int32).view(1, -1)
|
153 |
+
diffs = qa - ka + key_length - query_length
|
154 |
+
diffs = -diffs.abs()
|
155 |
+
alibi = slopes.view(1, num_heads, 1, 1) * diffs.view(1, 1, query_length, key_length)
|
156 |
+
alibi = alibi.expand(batch_size, -1, -1, -1).reshape(-1, query_length, key_length)
|
157 |
+
return alibi.to(dtype)
|
158 |
+
KeyValueT = Tuple[torch.Tensor, torch.Tensor]
|
159 |
+
|
160 |
+
def forward(self: BloomModel, input_ids: Optional[torch.LongTensor]=None, past_key_values: Optional[Tuple[KeyValueT, ...]]=None, attention_mask: Optional[torch.Tensor]=None, bidirectional_mask: Optional[torch.Tensor]=None, head_mask: Optional[torch.LongTensor]=None, inputs_embeds: Optional[torch.LongTensor]=None, use_cache: Optional[bool]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, return_dict: Optional[bool]=None, **deprecated_arguments) -> Union[Tuple[torch.Tensor, ...], BaseModelOutputWithPastAndCrossAttentions]:
|
161 |
+
if deprecated_arguments.pop('position_ids', False) is not False:
|
162 |
+
warnings.warn('`position_ids` have no functionality in BLOOM and will be removed in v5.0.0. ' + 'You can safely ignore passing `position_ids`.', FutureWarning)
|
163 |
+
if len(deprecated_arguments) > 0:
|
164 |
+
raise ValueError(f'Got unexpected arguments: {deprecated_arguments}')
|
165 |
+
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
166 |
+
output_hidden_states = output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
167 |
+
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
168 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
169 |
+
if input_ids is not None and inputs_embeds is not None:
|
170 |
+
raise ValueError('You cannot specify both input_ids and inputs_embeds at the same time')
|
171 |
+
elif input_ids is not None:
|
172 |
+
(batch_size, seq_length) = input_ids.shape
|
173 |
+
elif inputs_embeds is not None:
|
174 |
+
(batch_size, seq_length, _) = inputs_embeds.shape
|
175 |
+
else:
|
176 |
+
raise ValueError('You have to specify either input_ids or inputs_embeds')
|
177 |
+
if past_key_values is None:
|
178 |
+
past_key_values = tuple([None] * len(self.h))
|
179 |
+
head_mask = self.get_head_mask(head_mask, self.config.n_layer)
|
180 |
+
if inputs_embeds is None:
|
181 |
+
inputs_embeds = self.word_embeddings(input_ids)
|
182 |
+
hidden_states = self.word_embeddings_layernorm(inputs_embeds)
|
183 |
+
presents = () if use_cache else None
|
184 |
+
all_self_attentions = () if output_attentions else None
|
185 |
+
all_hidden_states = () if output_hidden_states else None
|
186 |
+
seq_length_with_past = seq_length
|
187 |
+
past_key_values_length = 0
|
188 |
+
if past_key_values[0] is not None:
|
189 |
+
tmp = past_key_values[0][0]
|
190 |
+
past_key_values_length = tmp.shape[2]
|
191 |
+
seq_length_with_past = seq_length_with_past + past_key_values_length
|
192 |
+
if attention_mask is None:
|
193 |
+
attention_mask = torch.ones((batch_size, seq_length_with_past), device=hidden_states.device)
|
194 |
+
else:
|
195 |
+
attention_mask = attention_mask.to(hidden_states.device)
|
196 |
+
alibi = self._build_alibi_tensor(batch_size=batch_size, query_length=seq_length, key_length=seq_length_with_past, dtype=hidden_states.dtype, device=hidden_states.device)
|
197 |
+
causal_mask = self._prepare_attn_mask(attention_mask, bidirectional_mask, input_shape=(batch_size, seq_length), past_key_values_length=past_key_values_length)
|
198 |
+
for (i, (block, layer_past)) in enumerate(zip(self.h, past_key_values)):
|
199 |
+
if output_hidden_states:
|
200 |
+
hst = (hidden_states,)
|
201 |
+
all_hidden_states = all_hidden_states + hst
|
202 |
+
if self.gradient_checkpointing and self.training:
|
203 |
+
if use_cache:
|
204 |
+
logger.warning('`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`...')
|
205 |
+
use_cache = False
|
206 |
+
|
207 |
+
def create_custom_forward(module):
|
208 |
+
|
209 |
+
def custom_forward(*inputs):
|
210 |
+
return module(*inputs, use_cache=use_cache, output_attentions=output_attentions)
|
211 |
+
return custom_forward
|
212 |
+
outputs = torch.utils.checkpoint.checkpoint(create_custom_forward(block), hidden_states, alibi, causal_mask, head_mask[i])
|
213 |
+
else:
|
214 |
+
outputs = block(hidden_states, layer_past=layer_past, attention_mask=causal_mask, head_mask=head_mask[i], use_cache=use_cache, output_attentions=output_attentions, alibi=alibi)
|
215 |
+
hidden_states = outputs[0]
|
216 |
+
if use_cache is True:
|
217 |
+
presents = presents + (outputs[1],)
|
218 |
+
if output_attentions:
|
219 |
+
oa = (outputs[2 if use_cache else 1],)
|
220 |
+
all_self_attentions = all_self_attentions + oa
|
221 |
+
hidden_states = self.ln_f(hidden_states)
|
222 |
+
if output_hidden_states:
|
223 |
+
hst = (hidden_states,)
|
224 |
+
all_hidden_states = all_hidden_states + hst
|
225 |
+
if not return_dict:
|
226 |
+
return tuple((v for v in [hidden_states, presents, all_hidden_states, all_self_attentions] if v is not None))
|
227 |
+
return BaseModelOutputWithPastAndCrossAttentions(last_hidden_state=hidden_states, past_key_values=presents, hidden_states=all_hidden_states, attentions=all_self_attentions)
|
228 |
+
setattr(model.transformer, '_prepare_attn_mask', MethodType(_prepare_attn_mask, model.transformer))
|
229 |
+
setattr(model.transformer, '_build_alibi_tensor', MethodType(_build_alibi_tensor, model.transformer))
|
230 |
+
setattr(model.transformer, 'forward', MethodType(forward, model.transformer))
|
231 |
+
KeyValueT = Tuple[torch.Tensor, torch.Tensor]
|
232 |
+
|
233 |
+
def forward(self: BloomForCausalLM, input_ids: Optional[torch.LongTensor]=None, past_key_values: Optional[Tuple[KeyValueT, ...]]=None, attention_mask: Optional[torch.Tensor]=None, bidirectional_mask: Optional[torch.Tensor]=None, head_mask: Optional[torch.Tensor]=None, inputs_embeds: Optional[torch.Tensor]=None, labels: Optional[torch.Tensor]=None, use_cache: Optional[bool]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, return_dict: Optional[bool]=None, **deprecated_arguments) -> Union[Tuple[torch.Tensor], CausalLMOutputWithCrossAttentions]:
|
234 |
+
"""Replacement forward method for BloomCausalLM."""
|
235 |
+
if deprecated_arguments.pop('position_ids', False) is not False:
|
236 |
+
warnings.warn('`position_ids` have no functionality in BLOOM and will be removed ' + 'in v5.0.0. You can safely ignore passing `position_ids`.', FutureWarning)
|
237 |
+
if len(deprecated_arguments) > 0:
|
238 |
+
raise ValueError(f'Got unexpected arguments: {deprecated_arguments}')
|
239 |
+
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
240 |
+
transformer_outputs = self.transformer(input_ids, past_key_values=past_key_values, attention_mask=attention_mask, bidirectional_mask=bidirectional_mask, head_mask=head_mask, inputs_embeds=inputs_embeds, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
|
241 |
+
hidden_states = transformer_outputs[0]
|
242 |
+
lm_logits = self.lm_head(hidden_states)
|
243 |
+
loss = None
|
244 |
+
if labels is not None:
|
245 |
+
shift_logits = lm_logits[..., :-1, :].contiguous()
|
246 |
+
shift_labels = labels[..., 1:].contiguous()
|
247 |
+
(batch_size, seq_length, vocab_size) = shift_logits.shape
|
248 |
+
loss_fct = CrossEntropyLoss()
|
249 |
+
loss = loss_fct(shift_logits.view(batch_size * seq_length, vocab_size), shift_labels.view(batch_size * seq_length))
|
250 |
+
if not return_dict:
|
251 |
+
output = (lm_logits,) + transformer_outputs[1:]
|
252 |
+
return (loss,) + output if loss is not None else output
|
253 |
+
return CausalLMOutputWithCrossAttentions(loss=loss, logits=lm_logits, past_key_values=transformer_outputs.past_key_values, hidden_states=transformer_outputs.hidden_states, attentions=transformer_outputs.attentions)
|
254 |
+
|
255 |
+
def prepare_inputs_for_generation(self: BloomForCausalLM, input_ids: torch.LongTensor, past: Optional[torch.Tensor]=None, attention_mask: Optional[torch.Tensor]=None, **kwargs) -> dict:
|
256 |
+
if past:
|
257 |
+
input_ids = input_ids[:, -1].unsqueeze(-1)
|
258 |
+
bidirectional_mask = None
|
259 |
+
if past[0][0].shape[0] == input_ids.shape[0]:
|
260 |
+
past = self._convert_to_bloom_cache(past)
|
261 |
+
else:
|
262 |
+
bidirectional_mask = torch.ones_like(input_ids)
|
263 |
+
return {'input_ids': input_ids, 'past_key_values': past, 'use_cache': True, 'attention_mask': attention_mask, 'bidirectional_mask': bidirectional_mask}
|
264 |
+
setattr(model, 'forward', MethodType(forward, model))
|
265 |
+
setattr(model, 'prepare_inputs_for_generation', MethodType(prepare_inputs_for_generation, model))
|
266 |
+
setattr(model, '_prefix_lm_converted', True)
|
267 |
+
return model
|
268 |
+
|
269 |
+
def _convert_opt_causal_lm_to_prefix_lm(model: OPTForCausalLM) -> OPTForCausalLM:
|
270 |
+
"""Converts an OPT Causal LM to a Prefix LM.
|
271 |
+
|
272 |
+
Supported HuggingFace model classes:
|
273 |
+
- `OPTForCausalLM`
|
274 |
+
|
275 |
+
See `convert_hf_causal_lm_to_prefix_lm` for more details.
|
276 |
+
"""
|
277 |
+
if hasattr(model, '_prefix_lm_converted'):
|
278 |
+
return model
|
279 |
+
assert isinstance(model, OPTForCausalLM)
|
280 |
+
assert model.config.add_cross_attention == False, 'Only supports OPT decoder-only models'
|
281 |
+
setattr(model, '_original_forward', getattr(model, 'forward'))
|
282 |
+
setattr(model, '_original_generate', getattr(model, 'generate'))
|
283 |
+
model.model.decoder.bidirectional_mask = None
|
284 |
+
|
285 |
+
def _prepare_decoder_attention_mask(self, attention_mask, input_shape, inputs_embeds, past_key_values_length):
|
286 |
+
combined_attention_mask = None
|
287 |
+
if input_shape[-1] > 1:
|
288 |
+
if self.bidirectional_mask == 'g':
|
289 |
+
(bsz, src_length) = input_shape
|
290 |
+
combined_attention_mask = torch.zeros((bsz, 1, src_length, src_length + past_key_values_length), dtype=inputs_embeds.dtype, device=inputs_embeds.device)
|
291 |
+
else:
|
292 |
+
combined_attention_mask = _make_causal_mask_opt(input_shape, inputs_embeds.dtype, past_key_values_length=past_key_values_length).to(inputs_embeds.device)
|
293 |
+
if self.bidirectional_mask is not None:
|
294 |
+
assert attention_mask.shape == self.bidirectional_mask.shape
|
295 |
+
expanded_bidirectional_mask = _expand_mask_opt(self.bidirectional_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(inputs_embeds.device)
|
296 |
+
combined_attention_mask = torch.maximum(expanded_bidirectional_mask, combined_attention_mask)
|
297 |
+
if attention_mask is not None:
|
298 |
+
expanded_attn_mask = _expand_mask_opt(attention_mask, inputs_embeds.dtype, tgt_len=input_shape[-1]).to(inputs_embeds.device)
|
299 |
+
combined_attention_mask = expanded_attn_mask if combined_attention_mask is None else expanded_attn_mask + combined_attention_mask
|
300 |
+
return combined_attention_mask
|
301 |
+
setattr(model.model.decoder, '_prepare_decoder_attention_mask', MethodType(_prepare_decoder_attention_mask, model.model.decoder))
|
302 |
+
|
303 |
+
def forward(self: OPTForCausalLM, input_ids: Optional[torch.LongTensor]=None, attention_mask: Optional[torch.Tensor]=None, bidirectional_mask: Optional[torch.ByteTensor]=None, head_mask: Optional[torch.Tensor]=None, past_key_values: Optional[List[torch.FloatTensor]]=None, inputs_embeds: Optional[torch.FloatTensor]=None, labels: Optional[torch.LongTensor]=None, use_cache: Optional[bool]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, return_dict: Optional[bool]=None):
|
304 |
+
|
305 |
+
def call_og_forward():
|
306 |
+
return self._original_forward(input_ids=input_ids, attention_mask=attention_mask, head_mask=head_mask, past_key_values=past_key_values, inputs_embeds=inputs_embeds, labels=labels, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict)
|
307 |
+
if bidirectional_mask is None:
|
308 |
+
return call_og_forward()
|
309 |
+
self.model.decoder.bidirectional_mask = bidirectional_mask
|
310 |
+
try:
|
311 |
+
outputs = call_og_forward()
|
312 |
+
except:
|
313 |
+
self.model.decoder.bidirectional_mask = None
|
314 |
+
raise
|
315 |
+
self.model.decoder.bidirectional_mask = None
|
316 |
+
return outputs
|
317 |
+
|
318 |
+
def generate(self: OPTForCausalLM, *args: tuple, **kwargs: Dict[str, Any]):
|
319 |
+
"""Wraps original generate to enable PrefixLM-style attention."""
|
320 |
+
self.model.decoder.bidirectional_mask = 'g'
|
321 |
+
try:
|
322 |
+
output = self._original_generate(*args, **kwargs)
|
323 |
+
except:
|
324 |
+
self.model.decoder.bidirectional_mask = None
|
325 |
+
raise
|
326 |
+
self.model.decoder.bidirectional_mask = None
|
327 |
+
return output
|
328 |
+
setattr(model, 'forward', MethodType(forward, model))
|
329 |
+
setattr(model, 'generate', MethodType(generate, model))
|
330 |
+
setattr(model, '_prefix_lm_converted', True)
|
331 |
+
return model
|
332 |
+
_SUPPORTED_HF_MODELS = _SUPPORTED_GPT_MODELS + (BloomForCausalLM, OPTForCausalLM)
|
333 |
+
CAUSAL_LM_TYPES = Union[GPT2LMHeadModel, GPTJForCausalLM, GPTNeoForCausalLM, GPTNeoXForCausalLM, BloomForCausalLM, OPTForCausalLM]
|
334 |
+
|
335 |
+
def convert_hf_causal_lm_to_prefix_lm(model: CAUSAL_LM_TYPES) -> CAUSAL_LM_TYPES:
|
336 |
+
"""Converts a HuggingFace Causal LM to a Prefix LM.
|
337 |
+
|
338 |
+
Supported HuggingFace model classes:
|
339 |
+
- `GPT2LMHeadModel`
|
340 |
+
- `GPTNeoForCausalLM`
|
341 |
+
- `GPTNeoXForCausalLM`
|
342 |
+
- `GPTJForCausalLM`
|
343 |
+
- `BloomForCausalLM`
|
344 |
+
- `OPTForCausalLM`
|
345 |
+
|
346 |
+
Conversion to a Prefix LM is done by modifying the `forward` method, and possibly also the
|
347 |
+
`generate` method and/or select underlying methods depending on the model class.
|
348 |
+
|
349 |
+
These changes preserve the model API, but add a new input to `forward`: "bidirectional_mask".
|
350 |
+
|
351 |
+
Notes on training:
|
352 |
+
To actually train the converted model as a Prefix LM, training batches will need to indicate
|
353 |
+
the prefix/target structure by including `bidirectional_mask` as part of the batch inputs.
|
354 |
+
|
355 |
+
**This is not a standard input and requires custom layers either within or after your dataloader.**
|
356 |
+
|
357 |
+
In addition to adding `bidirectional_mask` to the batch, this custom code should modify `labels`
|
358 |
+
such that `batch['labels'][batch['bidirectional_mask'] == 1] == -100`.
|
359 |
+
That is, the prefix portion of the sequence should not generate any loss. Loss should only be
|
360 |
+
generated by the target portion of the sequence.
|
361 |
+
|
362 |
+
Notes on `GPTNeoForCausalLM`:
|
363 |
+
To simplify the implementation, "global" and "local" attention layers are handled differently.
|
364 |
+
For "global" layers, we handle conversion as described above. For "local" layers, which use a
|
365 |
+
causal attention mask within a restricted local window, we do not alter the masking.
|
366 |
+
|
367 |
+
Notes on `forward` method conversion:
|
368 |
+
After conversion, the `forward` method will handle a new input, `bidirectional_mask`,
|
369 |
+
which should be a [batch_size, seq_length] byte tensor, where 1 indicates token positions
|
370 |
+
belonging to the prefix (prefix tokens can attend to one another bidirectionally), and
|
371 |
+
0 indicates token positions belonging to the target.
|
372 |
+
|
373 |
+
The new `forward` method will incorporate `bidirectional_mask` (if supplied) into the existing
|
374 |
+
causal mask, call the original `forward` method, and (if the causal mask is a buffer) reset
|
375 |
+
the causal masks before returning the result.
|
376 |
+
|
377 |
+
Notes on `generate` method conversion:
|
378 |
+
After conversion, the `generate` method will have the same signature but will internally
|
379 |
+
convert all causal masks to be purely bidirectional, call the original `generate` method, and
|
380 |
+
(where appropriate) reset the causal masks before returning the result.
|
381 |
+
|
382 |
+
This works thanks to the logic of the HuggingFace `generate` API, which first encodes the token
|
383 |
+
"prompt" passed to `generate` (which is treated as the prefix) and then sequentially generates
|
384 |
+
each new token. Encodings are cached as generation happens, so all prefix tokens can attend to one
|
385 |
+
another (as expected in a Prefix LM) and generated tokens can only attend to prefix tokens and
|
386 |
+
previously-generated tokens (also as expected in a Prefix LM).
|
387 |
+
|
388 |
+
To preserve the API, the original methods are renamed to `_original_forward` and
|
389 |
+
`_original_generate`, and replaced with new `forward` and `generate` methods that wrap
|
390 |
+
them, respectively. Although implementation details vary by model class.
|
391 |
+
"""
|
392 |
+
if isinstance(model, _SUPPORTED_GPT_MODELS):
|
393 |
+
return _convert_gpt_causal_lm_to_prefix_lm(model)
|
394 |
+
elif isinstance(model, BloomForCausalLM):
|
395 |
+
return _convert_bloom_causal_lm_to_prefix_lm(model)
|
396 |
+
elif isinstance(model, OPTForCausalLM):
|
397 |
+
return _convert_opt_causal_lm_to_prefix_lm(model)
|
398 |
+
else:
|
399 |
+
raise TypeError(f'Cannot convert model to Prefix LM. ' + f'Model does not belong to set of supported HF models:' + f'\n{_SUPPORTED_HF_MODELS}')
|
400 |
+
|
401 |
+
def add_bidirectional_mask_if_missing(batch: Dict[str, Any]):
|
402 |
+
"""Attempts to add bidirectional_mask to batch if missing.
|
403 |
+
|
404 |
+
Raises:
|
405 |
+
KeyError if bidirectional_mask is missing and can't be inferred
|
406 |
+
"""
|
407 |
+
if 'bidirectional_mask' not in batch:
|
408 |
+
if batch.get('mode', None) == 'icl_task':
|
409 |
+
batch['bidirectional_mask'] = batch['attention_mask'].clone()
|
410 |
+
for (i, continuation_indices) in enumerate(batch['continuation_indices']):
|
411 |
+
batch['bidirectional_mask'][i, continuation_indices] = 0
|
412 |
+
elif 'labels' in batch and 'attention_mask' in batch:
|
413 |
+
batch['bidirectional_mask'] = torch.logical_and(torch.eq(batch['attention_mask'], 1), torch.eq(batch['labels'], -100)).type_as(batch['attention_mask'])
|
414 |
+
else:
|
415 |
+
raise KeyError('No bidirectional_mask in batch and not sure how to construct one.')
|
meta_init_context.py
ADDED
@@ -0,0 +1,94 @@
|
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|
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|
|
|
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|
|
|
|
|
|
|
|
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|
|
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|
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|
|
|
|
|
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|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
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|
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|
|
|
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|
|
|
|
|
|
|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
from contextlib import contextmanager
|
2 |
+
import torch
|
3 |
+
import torch.nn as nn
|
4 |
+
|
5 |
+
@contextmanager
|
6 |
+
def init_empty_weights(include_buffers: bool=False):
|
7 |
+
"""Meta initialization context manager.
|
8 |
+
|
9 |
+
A context manager under which models are initialized with all parameters
|
10 |
+
on the meta device, therefore creating an empty model. Useful when just
|
11 |
+
initializing the model would blow the available RAM.
|
12 |
+
|
13 |
+
Args:
|
14 |
+
include_buffers (`bool`, *optional*, defaults to `False`): Whether or
|
15 |
+
not to also put all buffers on the meta device while initializing.
|
16 |
+
|
17 |
+
Example:
|
18 |
+
```python
|
19 |
+
import torch.nn as nn
|
20 |
+
|
21 |
+
# Initialize a model with 100 billions parameters in no time and without using any RAM.
|
22 |
+
with init_empty_weights():
|
23 |
+
tst = nn.Sequential(*[nn.Linear(10000, 10000) for _ in range(1000)])
|
24 |
+
```
|
25 |
+
|
26 |
+
<Tip warning={true}>
|
27 |
+
|
28 |
+
Any model created under this context manager has no weights. As such you can't do something like
|
29 |
+
`model.to(some_device)` with it. To load weights inside your empty model, see [`load_checkpoint_and_dispatch`].
|
30 |
+
|
31 |
+
</Tip>
|
32 |
+
"""
|
33 |
+
with init_on_device(torch.device('meta'), include_buffers=include_buffers) as f:
|
34 |
+
yield f
|
35 |
+
|
36 |
+
@contextmanager
|
37 |
+
def init_on_device(device: torch.device, include_buffers: bool=False):
|
38 |
+
"""Device initialization context manager.
|
39 |
+
|
40 |
+
A context manager under which models are initialized with all parameters
|
41 |
+
on the specified device.
|
42 |
+
|
43 |
+
Args:
|
44 |
+
device (`torch.device`): Device to initialize all parameters on.
|
45 |
+
include_buffers (`bool`, *optional*, defaults to `False`): Whether or
|
46 |
+
not to also put all buffers on the meta device while initializing.
|
47 |
+
|
48 |
+
Example:
|
49 |
+
```python
|
50 |
+
import torch.nn as nn
|
51 |
+
|
52 |
+
with init_on_device(device=torch.device("cuda")):
|
53 |
+
tst = nn.Liner(100, 100) # on `cuda` device
|
54 |
+
```
|
55 |
+
"""
|
56 |
+
old_register_parameter = nn.Module.register_parameter
|
57 |
+
if include_buffers:
|
58 |
+
old_register_buffer = nn.Module.register_buffer
|
59 |
+
|
60 |
+
def register_empty_parameter(module, name, param):
|
61 |
+
old_register_parameter(module, name, param)
|
62 |
+
if param is not None:
|
63 |
+
param_cls = type(module._parameters[name])
|
64 |
+
kwargs = module._parameters[name].__dict__
|
65 |
+
module._parameters[name] = param_cls(module._parameters[name].to(device), **kwargs)
|
66 |
+
|
67 |
+
def register_empty_buffer(module, name, buffer):
|
68 |
+
old_register_buffer(module, name, buffer)
|
69 |
+
if buffer is not None:
|
70 |
+
module._buffers[name] = module._buffers[name].to(device)
|
71 |
+
if include_buffers:
|
72 |
+
tensor_constructors_to_patch = {torch_function_name: getattr(torch, torch_function_name) for torch_function_name in ['empty', 'zeros', 'ones', 'full']}
|
73 |
+
else:
|
74 |
+
tensor_constructors_to_patch = {}
|
75 |
+
|
76 |
+
def patch_tensor_constructor(fn):
|
77 |
+
|
78 |
+
def wrapper(*args, **kwargs):
|
79 |
+
kwargs['device'] = device
|
80 |
+
return fn(*args, **kwargs)
|
81 |
+
return wrapper
|
82 |
+
try:
|
83 |
+
nn.Module.register_parameter = register_empty_parameter
|
84 |
+
if include_buffers:
|
85 |
+
nn.Module.register_buffer = register_empty_buffer
|
86 |
+
for torch_function_name in tensor_constructors_to_patch.keys():
|
87 |
+
setattr(torch, torch_function_name, patch_tensor_constructor(getattr(torch, torch_function_name)))
|
88 |
+
yield
|
89 |
+
finally:
|
90 |
+
nn.Module.register_parameter = old_register_parameter
|
91 |
+
if include_buffers:
|
92 |
+
nn.Module.register_buffer = old_register_buffer
|
93 |
+
for (torch_function_name, old_torch_function) in tensor_constructors_to_patch.items():
|
94 |
+
setattr(torch, torch_function_name, old_torch_function)
|
modeling_mpt.py
ADDED
@@ -0,0 +1,323 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
|
|
|
|
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|
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|
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|
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|
|
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|
|
|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
"""A simple, flexible implementation of a GPT model.
|
2 |
+
|
3 |
+
Inspired by https://github.com/karpathy/minGPT/blob/master/mingpt/model.py
|
4 |
+
"""
|
5 |
+
import math
|
6 |
+
import warnings
|
7 |
+
from typing import List, Optional, Tuple, Union
|
8 |
+
import torch
|
9 |
+
import torch.nn as nn
|
10 |
+
import torch.nn.functional as F
|
11 |
+
from transformers import PreTrainedModel, PreTrainedTokenizer, PreTrainedTokenizerFast
|
12 |
+
from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast
|
13 |
+
from .attention import attn_bias_shape, build_attn_bias
|
14 |
+
from .blocks import MPTBlock
|
15 |
+
from .custom_embedding import SharedEmbedding
|
16 |
+
from .norm import NORM_CLASS_REGISTRY
|
17 |
+
from .configuration_mpt import MPTConfig
|
18 |
+
from .adapt_tokenizer import AutoTokenizerForMOD, adapt_tokenizer_for_denoising
|
19 |
+
from .hf_prefixlm_converter import add_bidirectional_mask_if_missing, convert_hf_causal_lm_to_prefix_lm
|
20 |
+
from .meta_init_context import init_empty_weights
|
21 |
+
from .param_init_fns import MODEL_INIT_REGISTRY, generic_param_init_fn_
|
22 |
+
try:
|
23 |
+
from .flash_attn_triton import flash_attn_func
|
24 |
+
except:
|
25 |
+
pass
|
26 |
+
Tokenizer = Union[PreTrainedTokenizer, PreTrainedTokenizerFast]
|
27 |
+
|
28 |
+
class MPTPreTrainedModel(PreTrainedModel):
|
29 |
+
config_class = MPTConfig
|
30 |
+
base_model_prefix = 'model'
|
31 |
+
_no_split_modules = ['MPTBlock']
|
32 |
+
|
33 |
+
class MPTModel(MPTPreTrainedModel):
|
34 |
+
|
35 |
+
def __init__(self, config: MPTConfig):
|
36 |
+
config._validate_config()
|
37 |
+
super().__init__(config)
|
38 |
+
self.attn_impl = config.attn_config['attn_impl']
|
39 |
+
self.prefix_lm = config.attn_config['prefix_lm']
|
40 |
+
self.attn_uses_sequence_id = config.attn_config['attn_uses_sequence_id']
|
41 |
+
self.alibi = config.attn_config['alibi']
|
42 |
+
self.alibi_bias_max = config.attn_config['alibi_bias_max']
|
43 |
+
if config.init_device == 'mixed':
|
44 |
+
if dist.get_local_rank() == 0:
|
45 |
+
config.init_device = 'cpu'
|
46 |
+
else:
|
47 |
+
config.init_device = 'meta'
|
48 |
+
if config.norm_type.lower() not in NORM_CLASS_REGISTRY.keys():
|
49 |
+
norm_options = ' | '.join(NORM_CLASS_REGISTRY.keys())
|
50 |
+
raise NotImplementedError(f'Requested norm type ({config.norm_type}) is not implemented within this repo (Options: {norm_options}).')
|
51 |
+
norm_class = NORM_CLASS_REGISTRY[config.norm_type.lower()]
|
52 |
+
self.embedding_fraction = config.embedding_fraction
|
53 |
+
self.wte = SharedEmbedding(config.vocab_size, config.d_model, device=config.init_device)
|
54 |
+
if not self.alibi:
|
55 |
+
self.wpe = torch.nn.Embedding(config.max_seq_len, config.d_model, device=config.init_device)
|
56 |
+
self.emb_drop = nn.Dropout(config.emb_pdrop)
|
57 |
+
self.blocks = nn.ModuleList([MPTBlock(device=config.init_device, **config.to_dict()) for _ in range(config.n_layers)])
|
58 |
+
self.norm_f = norm_class(config.d_model, device=config.init_device)
|
59 |
+
if config.init_device != 'meta':
|
60 |
+
print(f'You are using config.init_device={config.init_device!r}, but you can also use config.init_device="meta" with Composer + FSDP for fast initialization.')
|
61 |
+
self.apply(self.param_init_fn)
|
62 |
+
self.is_causal = not self.prefix_lm
|
63 |
+
self._attn_bias_initialized = False
|
64 |
+
self.attn_bias = None
|
65 |
+
self.attn_bias_shape = attn_bias_shape(self.attn_impl, config.n_heads, config.max_seq_len, self.alibi, prefix_lm=self.prefix_lm, causal=self.is_causal, use_sequence_id=self.attn_uses_sequence_id)
|
66 |
+
if config.no_bias:
|
67 |
+
for module in self.modules():
|
68 |
+
if hasattr(module, 'bias') and isinstance(module.bias, nn.Parameter):
|
69 |
+
if config.verbose:
|
70 |
+
warnings.warn(f'Removing bias ({module.bias}) from {module}.')
|
71 |
+
module.register_parameter('bias', None)
|
72 |
+
if config.verbose and config.verbose > 2:
|
73 |
+
print(self)
|
74 |
+
if 'verbose' not in self.config.init_config:
|
75 |
+
self.config.init_config['verbose'] = self.config.verbose
|
76 |
+
if self.config.init_config['verbose'] > 1:
|
77 |
+
init_fn_name = self.config.init_config['name']
|
78 |
+
warnings.warn(f'Using {init_fn_name} initialization.')
|
79 |
+
|
80 |
+
def get_input_embeddings(self):
|
81 |
+
return self.wte
|
82 |
+
|
83 |
+
def set_input_embeddings(self, value):
|
84 |
+
self.wte = value
|
85 |
+
|
86 |
+
@torch.no_grad()
|
87 |
+
def _attn_bias(self, device, dtype, attention_mask: Optional[torch.ByteTensor]=None, prefix_mask: Optional[torch.ByteTensor]=None, sequence_id: Optional[torch.LongTensor]=None):
|
88 |
+
if not self._attn_bias_initialized:
|
89 |
+
if self.attn_bias_shape:
|
90 |
+
self.attn_bias = torch.zeros(self.attn_bias_shape, device=device, dtype=dtype)
|
91 |
+
self.attn_bias = build_attn_bias(self.attn_impl, self.attn_bias, self.config.n_heads, self.config.max_seq_len, causal=self.is_causal, alibi=self.alibi, alibi_bias_max=self.alibi_bias_max)
|
92 |
+
self._attn_bias_initialized = True
|
93 |
+
if self.attn_impl == 'flash':
|
94 |
+
return (self.attn_bias, attention_mask)
|
95 |
+
if self.attn_bias is not None:
|
96 |
+
self.attn_bias = self.attn_bias.to(dtype=dtype, device=device)
|
97 |
+
attn_bias = self.attn_bias
|
98 |
+
if self.prefix_lm:
|
99 |
+
assert isinstance(attn_bias, torch.Tensor)
|
100 |
+
assert isinstance(prefix_mask, torch.Tensor)
|
101 |
+
attn_bias = self._apply_prefix_mask(attn_bias, prefix_mask)
|
102 |
+
if self.attn_uses_sequence_id and sequence_id is not None:
|
103 |
+
assert isinstance(attn_bias, torch.Tensor)
|
104 |
+
attn_bias = self._apply_sequence_id(attn_bias, sequence_id)
|
105 |
+
if attention_mask is not None:
|
106 |
+
s_k = attention_mask.shape[-1]
|
107 |
+
if attn_bias is None:
|
108 |
+
attn_bias = torch.zeros((1, 1, 1, s_k), device=device, dtype=dtype)
|
109 |
+
else:
|
110 |
+
_s_k = max(0, attn_bias.size(-1) - s_k)
|
111 |
+
attn_bias = attn_bias[:, :, :, _s_k:]
|
112 |
+
if prefix_mask is not None and attention_mask.shape != prefix_mask.shape:
|
113 |
+
raise ValueError(f'attention_mask shape={attention_mask.shape} ' + f'and prefix_mask shape={prefix_mask.shape} are not equal.')
|
114 |
+
min_val = torch.finfo(attn_bias.dtype).min
|
115 |
+
attn_bias = attn_bias.masked_fill(~attention_mask.view(-1, 1, 1, s_k), min_val)
|
116 |
+
return (attn_bias, None)
|
117 |
+
|
118 |
+
def _apply_prefix_mask(self, attn_bias: torch.Tensor, prefix_mask: torch.Tensor):
|
119 |
+
(s_k, s_q) = attn_bias.shape[-2:]
|
120 |
+
if s_k != self.config.max_seq_len or s_q != self.config.max_seq_len:
|
121 |
+
raise ValueError('attn_bias does not match the expected shape. ' + f'The last two dimensions should both be {self.config.max_length} ' + f'but are {s_k} and {s_q}.')
|
122 |
+
seq_len = prefix_mask.shape[-1]
|
123 |
+
if seq_len > self.config.max_seq_len:
|
124 |
+
raise ValueError(f'prefix_mask sequence length cannot exceed max_seq_len={self.config.max_seq_len}')
|
125 |
+
attn_bias = attn_bias[..., :seq_len, :seq_len]
|
126 |
+
causal = torch.tril(torch.ones((seq_len, seq_len), dtype=torch.bool, device=prefix_mask.device)).view(1, 1, seq_len, seq_len)
|
127 |
+
prefix = prefix_mask.view(-1, 1, 1, seq_len)
|
128 |
+
cannot_attend = ~torch.logical_or(causal, prefix.bool())
|
129 |
+
min_val = torch.finfo(attn_bias.dtype).min
|
130 |
+
attn_bias = attn_bias.masked_fill(cannot_attend, min_val)
|
131 |
+
return attn_bias
|
132 |
+
|
133 |
+
def _apply_sequence_id(self, attn_bias: torch.Tensor, sequence_id: torch.LongTensor):
|
134 |
+
seq_len = sequence_id.shape[-1]
|
135 |
+
if seq_len > self.config.max_seq_len:
|
136 |
+
raise ValueError(f'sequence_id sequence length cannot exceed max_seq_len={self.config.max_seq_len}')
|
137 |
+
attn_bias = attn_bias[..., :seq_len, :seq_len]
|
138 |
+
cannot_attend = torch.logical_not(torch.eq(sequence_id.view(-1, seq_len, 1), sequence_id.view(-1, 1, seq_len))).unsqueeze(1)
|
139 |
+
min_val = torch.finfo(attn_bias.dtype).min
|
140 |
+
attn_bias = attn_bias.masked_fill(cannot_attend, min_val)
|
141 |
+
return attn_bias
|
142 |
+
|
143 |
+
def forward(self, input_ids: torch.LongTensor, past_key_values: Optional[List[Tuple[torch.FloatTensor]]]=None, attention_mask: Optional[torch.ByteTensor]=None, prefix_mask: Optional[torch.ByteTensor]=None, sequence_id: Optional[torch.LongTensor]=None, return_dict: Optional[bool]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, use_cache: Optional[bool]=None, inputs_embeds: Optional[torch.Tensor]=None):
|
144 |
+
return_dict = return_dict if return_dict is not None else self.config.return_dict
|
145 |
+
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
146 |
+
if attention_mask is not None:
|
147 |
+
attention_mask = attention_mask.bool()
|
148 |
+
if prefix_mask is not None:
|
149 |
+
prefix_mask = prefix_mask.bool()
|
150 |
+
if not return_dict:
|
151 |
+
raise NotImplementedError('return_dict False is not implemented yet for MPT')
|
152 |
+
if output_attentions:
|
153 |
+
if self.attn_impl != 'torch':
|
154 |
+
raise NotImplementedError('output_attentions is not implemented for MPT when using attn_impl `flash` or `triton`.')
|
155 |
+
if attention_mask is not None and attention_mask[:, 0].sum() != attention_mask.shape[0] and self.training:
|
156 |
+
raise NotImplementedError('MPT does not support training with left padding.')
|
157 |
+
if self.prefix_lm and prefix_mask is None:
|
158 |
+
raise ValueError('prefix_mask is a required argument when MPT is configured with prefix_lm=True.')
|
159 |
+
if inputs_embeds is not None:
|
160 |
+
raise NotImplementedError('inputs_embeds is not implemented for MPT.')
|
161 |
+
if self.training:
|
162 |
+
if self.attn_uses_sequence_id and sequence_id is None:
|
163 |
+
raise ValueError('sequence_id is a required argument when MPT is configured with attn_uses_sequence_id=True ' + 'and the model is in train mode.')
|
164 |
+
elif self.attn_uses_sequence_id is False and sequence_id is not None:
|
165 |
+
warnings.warn('MPT received non-None input for `sequence_id` but is configured with attn_uses_sequence_id=False. ' + 'This input will be ignored. If you want the model to use `sequence_id`, set attn_uses_sequence_id to True.')
|
166 |
+
S = input_ids.size(1)
|
167 |
+
assert S <= self.config.max_seq_len, f'Cannot forward input with seq_len={S}, this model only supports seq_len<={self.config.max_seq_len}'
|
168 |
+
tok_emb = self.wte(input_ids)
|
169 |
+
if self.alibi:
|
170 |
+
x = tok_emb
|
171 |
+
else:
|
172 |
+
past_position = 0
|
173 |
+
if past_key_values is not None:
|
174 |
+
if len(past_key_values) != self.config.n_layers:
|
175 |
+
raise ValueError(f'past_key_values must provide a past_key_value for each attention ' + f'layer in the network (len(past_key_values)={len(past_key_values)!r}; self.config.n_layers={self.config.n_layers!r}).')
|
176 |
+
past_position = past_key_values[0][0].size(1)
|
177 |
+
if self.attn_impl == 'torch':
|
178 |
+
past_position = past_key_values[0][0].size(3)
|
179 |
+
if S + past_position > self.config.max_seq_len:
|
180 |
+
raise ValueError(f'Cannot forward input with past sequence length {past_position} and current sequence length {S + 1}, this model only supports total sequence length <= {self.config.max_seq_len}.')
|
181 |
+
pos = torch.arange(past_position, S + past_position, dtype=torch.long, device=input_ids.device).unsqueeze(0)
|
182 |
+
if attention_mask is not None:
|
183 |
+
pos = torch.clamp(pos - torch.cumsum((~attention_mask).to(torch.int32), dim=1)[:, past_position:], min=0)
|
184 |
+
pos_emb = self.wpe(pos)
|
185 |
+
x = tok_emb + pos_emb
|
186 |
+
if self.embedding_fraction == 1:
|
187 |
+
x = self.emb_drop(x)
|
188 |
+
else:
|
189 |
+
x_shrunk = x * self.embedding_fraction + x.detach() * (1 - self.embedding_fraction)
|
190 |
+
assert isinstance(self.emb_drop, nn.Module)
|
191 |
+
x = self.emb_drop(x_shrunk)
|
192 |
+
(attn_bias, attention_mask) = self._attn_bias(device=x.device, dtype=torch.float32, attention_mask=attention_mask, prefix_mask=prefix_mask, sequence_id=sequence_id)
|
193 |
+
if use_cache and past_key_values is None:
|
194 |
+
past_key_values = [() for _ in range(self.config.n_layers)]
|
195 |
+
all_hidden_states = () if output_hidden_states else None
|
196 |
+
all_self_attns = () if output_attentions else None
|
197 |
+
for (b_idx, block) in enumerate(self.blocks):
|
198 |
+
if output_hidden_states:
|
199 |
+
assert all_hidden_states is not None
|
200 |
+
all_hidden_states = all_hidden_states + (x,)
|
201 |
+
past_key_value = past_key_values[b_idx] if past_key_values is not None else None
|
202 |
+
(x, attn_weights, past_key_value) = block(x, past_key_value=past_key_value, attn_bias=attn_bias, attention_mask=attention_mask, is_causal=self.is_causal)
|
203 |
+
if past_key_values is not None:
|
204 |
+
past_key_values[b_idx] = past_key_value
|
205 |
+
if output_attentions:
|
206 |
+
assert all_self_attns is not None
|
207 |
+
all_self_attns = all_self_attns + (attn_weights,)
|
208 |
+
x = self.norm_f(x)
|
209 |
+
if output_hidden_states:
|
210 |
+
assert all_hidden_states is not None
|
211 |
+
all_hidden_states = all_hidden_states + (x,)
|
212 |
+
return BaseModelOutputWithPast(last_hidden_state=x, past_key_values=past_key_values, hidden_states=all_hidden_states, attentions=all_self_attns)
|
213 |
+
|
214 |
+
def param_init_fn(self, module):
|
215 |
+
init_fn_name = self.config.init_config['name']
|
216 |
+
MODEL_INIT_REGISTRY[init_fn_name](module=module, n_layers=self.config.n_layers, d_model=self.config.d_model, **self.config.init_config)
|
217 |
+
|
218 |
+
def fsdp_wrap_fn(self, module):
|
219 |
+
return isinstance(module, MPTBlock)
|
220 |
+
|
221 |
+
def activation_checkpointing_fn(self, module):
|
222 |
+
return isinstance(module, MPTBlock)
|
223 |
+
|
224 |
+
class MPTForCausalLM(MPTPreTrainedModel):
|
225 |
+
|
226 |
+
def __init__(self, config: MPTConfig):
|
227 |
+
super().__init__(config)
|
228 |
+
if not config.tie_word_embeddings:
|
229 |
+
raise ValueError('MPTForCausalLM only supports tied word embeddings')
|
230 |
+
print(f'Instantiating an MPTForCausalLM model from {__file__}')
|
231 |
+
self.transformer = MPTModel(config)
|
232 |
+
for child in self.transformer.children():
|
233 |
+
if isinstance(child, torch.nn.ModuleList):
|
234 |
+
continue
|
235 |
+
if isinstance(child, torch.nn.Module):
|
236 |
+
child._fsdp_wrap = True
|
237 |
+
self.logit_scale = None
|
238 |
+
if config.logit_scale is not None:
|
239 |
+
logit_scale = config.logit_scale
|
240 |
+
if isinstance(logit_scale, str):
|
241 |
+
if logit_scale == 'inv_sqrt_d_model':
|
242 |
+
logit_scale = 1 / math.sqrt(config.d_model)
|
243 |
+
else:
|
244 |
+
raise ValueError(f"logit_scale={logit_scale!r} is not recognized as an option; use numeric value or 'inv_sqrt_d_model'.")
|
245 |
+
self.logit_scale = logit_scale
|
246 |
+
|
247 |
+
def get_input_embeddings(self):
|
248 |
+
return self.transformer.wte
|
249 |
+
|
250 |
+
def set_input_embeddings(self, value):
|
251 |
+
self.transformer.wte = value
|
252 |
+
|
253 |
+
def get_output_embeddings(self):
|
254 |
+
return self.transformer.wte
|
255 |
+
|
256 |
+
def set_output_embeddings(self, new_embeddings):
|
257 |
+
self.transformer.wte = new_embeddings
|
258 |
+
|
259 |
+
def set_decoder(self, decoder):
|
260 |
+
self.transformer = decoder
|
261 |
+
|
262 |
+
def get_decoder(self):
|
263 |
+
return self.transformer
|
264 |
+
|
265 |
+
def forward(self, input_ids: torch.LongTensor, past_key_values: Optional[List[Tuple[torch.FloatTensor]]]=None, attention_mask: Optional[torch.ByteTensor]=None, prefix_mask: Optional[torch.ByteTensor]=None, sequence_id: Optional[torch.LongTensor]=None, labels: Optional[torch.LongTensor]=None, return_dict: Optional[bool]=None, output_attentions: Optional[bool]=None, output_hidden_states: Optional[bool]=None, use_cache: Optional[bool]=None, inputs_embeds: Optional[torch.FloatTensor]=None):
|
266 |
+
return_dict = return_dict if return_dict is not None else self.config.return_dict
|
267 |
+
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
268 |
+
if inputs_embeds is not None:
|
269 |
+
raise NotImplementedError('inputs_embeds has to be None (for hf/peft support).')
|
270 |
+
outputs = self.transformer(input_ids=input_ids, past_key_values=past_key_values, attention_mask=attention_mask, prefix_mask=prefix_mask, sequence_id=sequence_id, return_dict=return_dict, output_attentions=output_attentions, output_hidden_states=output_hidden_states, use_cache=use_cache)
|
271 |
+
logits = self.transformer.wte(outputs.last_hidden_state.to(self.transformer.wte.weight.device), True)
|
272 |
+
if self.logit_scale is not None:
|
273 |
+
if self.logit_scale == 0:
|
274 |
+
warnings.warn(f'Multiplying logits by self.logit_scale={self.logit_scale!r}. This will produce uniform (uninformative) outputs.')
|
275 |
+
logits *= self.logit_scale
|
276 |
+
loss = None
|
277 |
+
if labels is not None:
|
278 |
+
labels = torch.roll(labels, shifts=-1)
|
279 |
+
labels[:, -1] = -100
|
280 |
+
loss = F.cross_entropy(logits.view(-1, logits.size(-1)), labels.to(logits.device).view(-1))
|
281 |
+
return CausalLMOutputWithPast(loss=loss, logits=logits, past_key_values=outputs.past_key_values, hidden_states=outputs.hidden_states, attentions=outputs.attentions)
|
282 |
+
|
283 |
+
def param_init_fn(self, module):
|
284 |
+
init_fn_name = self.config.init_config['name']
|
285 |
+
MODEL_INIT_REGISTRY[init_fn_name](module=module, n_layers=self.config.n_layers, d_model=self.config.d_model, **self.config.init_config)
|
286 |
+
|
287 |
+
def fsdp_wrap_fn(self, module):
|
288 |
+
return isinstance(module, MPTBlock)
|
289 |
+
|
290 |
+
def activation_checkpointing_fn(self, module):
|
291 |
+
return isinstance(module, MPTBlock)
|
292 |
+
|
293 |
+
def prepare_inputs_for_generation(self, input_ids, past_key_values=None, inputs_embeds=None, **kwargs):
|
294 |
+
if inputs_embeds is not None:
|
295 |
+
raise NotImplementedError('inputs_embeds is not implemented for MPT yet')
|
296 |
+
attention_mask = kwargs['attention_mask'].bool()
|
297 |
+
if attention_mask[:, -1].sum() != attention_mask.shape[0]:
|
298 |
+
raise NotImplementedError('MPT does not support generation with right padding.')
|
299 |
+
if self.transformer.attn_uses_sequence_id and self.training:
|
300 |
+
sequence_id = torch.zeros_like(input_ids[:1])
|
301 |
+
else:
|
302 |
+
sequence_id = None
|
303 |
+
if past_key_values is not None:
|
304 |
+
input_ids = input_ids[:, -1].unsqueeze(-1)
|
305 |
+
if self.transformer.prefix_lm:
|
306 |
+
prefix_mask = torch.ones_like(attention_mask)
|
307 |
+
if kwargs.get('use_cache') == False:
|
308 |
+
raise NotImplementedError('MPT with prefix_lm=True does not support use_cache=False.')
|
309 |
+
else:
|
310 |
+
prefix_mask = None
|
311 |
+
return {'input_ids': input_ids, 'attention_mask': attention_mask, 'prefix_mask': prefix_mask, 'sequence_id': sequence_id, 'past_key_values': past_key_values, 'use_cache': kwargs.get('use_cache', True)}
|
312 |
+
|
313 |
+
@staticmethod
|
314 |
+
def _reorder_cache(past_key_values, beam_idx):
|
315 |
+
"""Used by HuggingFace generate when using beam search with kv-caching.
|
316 |
+
|
317 |
+
See https://github.com/huggingface/transformers/blob/3ec7a47664ebe40c40f4b722f6bb1cd30c3821ec/src/transformers/models/gpt2/modeling_gpt2.py#L1122-L1133
|
318 |
+
for an example in transformers.
|
319 |
+
"""
|
320 |
+
reordered_past = []
|
321 |
+
for layer_past in past_key_values:
|
322 |
+
reordered_past += [tuple((past_state.index_select(0, beam_idx) for past_state in layer_past))]
|
323 |
+
return reordered_past
|
norm.py
ADDED
@@ -0,0 +1,56 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import torch
|
2 |
+
|
3 |
+
def _cast_if_autocast_enabled(tensor):
|
4 |
+
if torch.is_autocast_enabled():
|
5 |
+
if tensor.device.type == 'cuda':
|
6 |
+
dtype = torch.get_autocast_gpu_dtype()
|
7 |
+
elif tensor.device.type == 'cpu':
|
8 |
+
dtype = torch.get_autocast_cpu_dtype()
|
9 |
+
else:
|
10 |
+
raise NotImplementedError()
|
11 |
+
return tensor.to(dtype=dtype)
|
12 |
+
return tensor
|
13 |
+
|
14 |
+
class LPLayerNorm(torch.nn.LayerNorm):
|
15 |
+
|
16 |
+
def __init__(self, normalized_shape, eps=1e-05, elementwise_affine=True, device=None, dtype=None):
|
17 |
+
super().__init__(normalized_shape=normalized_shape, eps=eps, elementwise_affine=elementwise_affine, device=device, dtype=dtype)
|
18 |
+
|
19 |
+
def forward(self, x):
|
20 |
+
module_device = x.device
|
21 |
+
downcast_x = _cast_if_autocast_enabled(x)
|
22 |
+
downcast_weight = _cast_if_autocast_enabled(self.weight) if self.weight is not None else self.weight
|
23 |
+
downcast_bias = _cast_if_autocast_enabled(self.bias) if self.bias is not None else self.bias
|
24 |
+
with torch.autocast(enabled=False, device_type=module_device.type):
|
25 |
+
return torch.nn.functional.layer_norm(downcast_x, self.normalized_shape, downcast_weight, downcast_bias, self.eps)
|
26 |
+
|
27 |
+
def rms_norm(x, weight=None, eps=1e-05):
|
28 |
+
output = x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + eps)
|
29 |
+
if weight is not None:
|
30 |
+
return output * weight
|
31 |
+
return output
|
32 |
+
|
33 |
+
class RMSNorm(torch.nn.Module):
|
34 |
+
|
35 |
+
def __init__(self, normalized_shape, eps=1e-05, weight=True, dtype=None, device=None):
|
36 |
+
super().__init__()
|
37 |
+
self.eps = eps
|
38 |
+
if weight:
|
39 |
+
self.weight = torch.nn.Parameter(torch.ones(normalized_shape, dtype=dtype, device=device))
|
40 |
+
else:
|
41 |
+
self.register_parameter('weight', None)
|
42 |
+
|
43 |
+
def forward(self, x):
|
44 |
+
return rms_norm(x.float(), self.weight, self.eps).to(dtype=x.dtype)
|
45 |
+
|
46 |
+
class LPRMSNorm(RMSNorm):
|
47 |
+
|
48 |
+
def __init__(self, normalized_shape, eps=1e-05, weight=True, dtype=None, device=None):
|
49 |
+
super().__init__(normalized_shape=normalized_shape, eps=eps, weight=weight, dtype=dtype, device=device)
|
50 |
+
|
51 |
+
def forward(self, x):
|
52 |
+
downcast_x = _cast_if_autocast_enabled(x)
|
53 |
+
downcast_weight = _cast_if_autocast_enabled(self.weight) if self.weight is not None else self.weight
|
54 |
+
with torch.autocast(enabled=False, device_type=x.device.type):
|
55 |
+
return rms_norm(downcast_x, downcast_weight, self.eps).to(dtype=x.dtype)
|
56 |
+
NORM_CLASS_REGISTRY = {'layernorm': torch.nn.LayerNorm, 'low_precision_layernorm': LPLayerNorm, 'rmsnorm': RMSNorm, 'low_precision_rmsnorm': LPRMSNorm}
|
param_init_fns.py
ADDED
@@ -0,0 +1,181 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import math
|
2 |
+
import warnings
|
3 |
+
from collections.abc import Sequence
|
4 |
+
from functools import partial
|
5 |
+
from typing import Optional, Tuple, Union
|
6 |
+
import torch
|
7 |
+
from torch import nn
|
8 |
+
from .norm import NORM_CLASS_REGISTRY
|
9 |
+
|
10 |
+
def torch_default_param_init_fn_(module: nn.Module, verbose: int=0, **kwargs):
|
11 |
+
del kwargs
|
12 |
+
if verbose > 1:
|
13 |
+
warnings.warn(f"Initializing network using module's reset_parameters attribute")
|
14 |
+
if hasattr(module, 'reset_parameters'):
|
15 |
+
module.reset_parameters()
|
16 |
+
|
17 |
+
def fused_init_helper_(module: nn.Module, init_fn_):
|
18 |
+
_fused = getattr(module, '_fused', None)
|
19 |
+
if _fused is None:
|
20 |
+
raise RuntimeError(f'Internal logic error')
|
21 |
+
(dim, splits) = _fused
|
22 |
+
splits = (0, *splits, module.weight.size(dim))
|
23 |
+
for (s, e) in zip(splits[:-1], splits[1:]):
|
24 |
+
slice_indices = [slice(None)] * module.weight.ndim
|
25 |
+
slice_indices[dim] = slice(s, e)
|
26 |
+
init_fn_(module.weight[slice_indices])
|
27 |
+
|
28 |
+
def generic_param_init_fn_(module: nn.Module, init_fn_, n_layers: int, d_model: Optional[int]=None, init_div_is_residual: Union[int, float, str, bool]=True, emb_init_std: Optional[float]=None, emb_init_uniform_lim: Optional[Union[Tuple[float, float], float]]=None, verbose: int=0, **kwargs):
|
29 |
+
del kwargs
|
30 |
+
if verbose > 1:
|
31 |
+
warnings.warn(f'If model has bias parameters they are initialized to 0.')
|
32 |
+
init_div_is_residual = init_div_is_residual
|
33 |
+
if init_div_is_residual is False:
|
34 |
+
div_is_residual = 1.0
|
35 |
+
elif init_div_is_residual is True:
|
36 |
+
div_is_residual = math.sqrt(2 * n_layers)
|
37 |
+
elif isinstance(init_div_is_residual, float) or isinstance(init_div_is_residual, int):
|
38 |
+
div_is_residual = init_div_is_residual
|
39 |
+
elif isinstance(init_div_is_residual, str) and init_div_is_residual.isnumeric():
|
40 |
+
div_is_residual = float(init_div_is_residual)
|
41 |
+
else:
|
42 |
+
div_is_residual = 1.0
|
43 |
+
raise ValueError(f'Expected init_div_is_residual to be boolean or numeric, got {init_div_is_residual}')
|
44 |
+
if init_div_is_residual is not False:
|
45 |
+
if verbose > 1:
|
46 |
+
warnings.warn(f'Initializing _is_residual layers then dividing them by {div_is_residual:.3f}. ' + f'Set `init_div_is_residual: false` in init config to disable this.')
|
47 |
+
if isinstance(module, nn.Linear):
|
48 |
+
if hasattr(module, '_fused'):
|
49 |
+
fused_init_helper_(module, init_fn_)
|
50 |
+
else:
|
51 |
+
init_fn_(module.weight)
|
52 |
+
if module.bias is not None:
|
53 |
+
torch.nn.init.zeros_(module.bias)
|
54 |
+
if init_div_is_residual is not False and getattr(module, '_is_residual', False):
|
55 |
+
with torch.no_grad():
|
56 |
+
module.weight.div_(div_is_residual)
|
57 |
+
elif isinstance(module, nn.Embedding):
|
58 |
+
if emb_init_std is not None:
|
59 |
+
std = emb_init_std
|
60 |
+
if std == 0:
|
61 |
+
warnings.warn(f'Embedding layer initialized to 0.')
|
62 |
+
emb_init_fn_ = partial(torch.nn.init.normal_, mean=0.0, std=std)
|
63 |
+
if verbose > 1:
|
64 |
+
warnings.warn(f'Embedding layer initialized using normal distribution with mean=0 and std={std!r}.')
|
65 |
+
elif emb_init_uniform_lim is not None:
|
66 |
+
lim = emb_init_uniform_lim
|
67 |
+
if isinstance(lim, Sequence):
|
68 |
+
if len(lim) > 2:
|
69 |
+
raise ValueError(f'Uniform init requires a min and a max limit. User input: {lim}.')
|
70 |
+
if lim[0] == lim[1]:
|
71 |
+
warnings.warn(f'Embedding layer initialized to {lim[0]}.')
|
72 |
+
else:
|
73 |
+
if lim == 0:
|
74 |
+
warnings.warn(f'Embedding layer initialized to 0.')
|
75 |
+
lim = [-lim, lim]
|
76 |
+
(a, b) = lim
|
77 |
+
emb_init_fn_ = partial(torch.nn.init.uniform_, a=a, b=b)
|
78 |
+
if verbose > 1:
|
79 |
+
warnings.warn(f'Embedding layer initialized using uniform distribution in range {lim}.')
|
80 |
+
else:
|
81 |
+
emb_init_fn_ = init_fn_
|
82 |
+
emb_init_fn_(module.weight)
|
83 |
+
elif isinstance(module, tuple(set(NORM_CLASS_REGISTRY.values()))):
|
84 |
+
if verbose > 1:
|
85 |
+
warnings.warn(f'Norm weights are set to 1. If norm layer has a bias it is initialized to 0.')
|
86 |
+
if hasattr(module, 'weight') and module.weight is not None:
|
87 |
+
torch.nn.init.ones_(module.weight)
|
88 |
+
if hasattr(module, 'bias') and module.bias is not None:
|
89 |
+
torch.nn.init.zeros_(module.bias)
|
90 |
+
elif isinstance(module, nn.MultiheadAttention):
|
91 |
+
if module._qkv_same_embed_dim:
|
92 |
+
assert module.in_proj_weight is not None
|
93 |
+
assert module.q_proj_weight is None and module.k_proj_weight is None and (module.v_proj_weight is None)
|
94 |
+
assert d_model is not None
|
95 |
+
_d = d_model
|
96 |
+
splits = (0, _d, 2 * _d, 3 * _d)
|
97 |
+
for (s, e) in zip(splits[:-1], splits[1:]):
|
98 |
+
init_fn_(module.in_proj_weight[s:e])
|
99 |
+
else:
|
100 |
+
assert module.q_proj_weight is not None and module.k_proj_weight is not None and (module.v_proj_weight is not None)
|
101 |
+
assert module.in_proj_weight is None
|
102 |
+
init_fn_(module.q_proj_weight)
|
103 |
+
init_fn_(module.k_proj_weight)
|
104 |
+
init_fn_(module.v_proj_weight)
|
105 |
+
if module.in_proj_bias is not None:
|
106 |
+
torch.nn.init.zeros_(module.in_proj_bias)
|
107 |
+
if module.bias_k is not None:
|
108 |
+
torch.nn.init.zeros_(module.bias_k)
|
109 |
+
if module.bias_v is not None:
|
110 |
+
torch.nn.init.zeros_(module.bias_v)
|
111 |
+
init_fn_(module.out_proj.weight)
|
112 |
+
if init_div_is_residual is not False and getattr(module.out_proj, '_is_residual', False):
|
113 |
+
with torch.no_grad():
|
114 |
+
module.out_proj.weight.div_(div_is_residual)
|
115 |
+
if module.out_proj.bias is not None:
|
116 |
+
torch.nn.init.zeros_(module.out_proj.bias)
|
117 |
+
else:
|
118 |
+
for _ in module.parameters(recurse=False):
|
119 |
+
raise NotImplementedError(f'{module.__class__.__name__} parameters are not initialized by param_init_fn.')
|
120 |
+
|
121 |
+
def _normal_init_(std, mean=0.0):
|
122 |
+
return partial(torch.nn.init.normal_, mean=mean, std=std)
|
123 |
+
|
124 |
+
def _normal_param_init_fn_(module: nn.Module, std: float, n_layers: int, d_model: Optional[int]=None, init_div_is_residual: Union[int, float, str, bool]=True, emb_init_std: Optional[float]=None, emb_init_uniform_lim: Optional[Union[Tuple[float, float], float]]=None, verbose: int=0, **kwargs):
|
125 |
+
del kwargs
|
126 |
+
init_fn_ = _normal_init_(std=std)
|
127 |
+
if verbose > 1:
|
128 |
+
warnings.warn(f'Using torch.nn.init.normal_ init fn mean=0.0, std={std}')
|
129 |
+
generic_param_init_fn_(module=module, init_fn_=init_fn_, d_model=d_model, n_layers=n_layers, init_div_is_residual=init_div_is_residual, emb_init_std=emb_init_std, emb_init_uniform_lim=emb_init_uniform_lim, verbose=verbose)
|
130 |
+
|
131 |
+
def baseline_param_init_fn_(module: nn.Module, init_std: float, n_layers: int, d_model: Optional[int]=None, init_div_is_residual: Union[int, float, str, bool]=True, emb_init_std: Optional[float]=None, emb_init_uniform_lim: Optional[Union[Tuple[float, float], float]]=None, verbose: int=0, **kwargs):
|
132 |
+
del kwargs
|
133 |
+
if init_std is None:
|
134 |
+
raise ValueError("You must set model.init_config['init_std'] to a float value to use the default initialization scheme.")
|
135 |
+
_normal_param_init_fn_(module=module, std=init_std, d_model=d_model, n_layers=n_layers, init_div_is_residual=init_div_is_residual, emb_init_std=emb_init_std, emb_init_uniform_lim=emb_init_uniform_lim, verbose=verbose)
|
136 |
+
|
137 |
+
def small_param_init_fn_(module: nn.Module, n_layers: int, d_model: int, init_div_is_residual: Union[int, float, str, bool]=True, emb_init_std: Optional[float]=None, emb_init_uniform_lim: Optional[Union[Tuple[float, float], float]]=None, verbose: int=0, **kwargs):
|
138 |
+
del kwargs
|
139 |
+
std = math.sqrt(2 / (5 * d_model))
|
140 |
+
_normal_param_init_fn_(module=module, std=std, d_model=d_model, n_layers=n_layers, init_div_is_residual=init_div_is_residual, emb_init_std=emb_init_std, emb_init_uniform_lim=emb_init_uniform_lim, verbose=verbose)
|
141 |
+
|
142 |
+
def neox_param_init_fn_(module: nn.Module, n_layers: int, d_model: int, emb_init_std: Optional[float]=None, emb_init_uniform_lim: Optional[Union[Tuple[float, float], float]]=None, verbose: int=0, **kwargs):
|
143 |
+
"""From section 2.3.1 of GPT-NeoX-20B:
|
144 |
+
|
145 |
+
An Open-Source AutoregressiveLanguage Model — Black et. al. (2022)
|
146 |
+
see https://github.com/EleutherAI/gpt-neox/blob/9610391ab319403cef079b438edd016a2443af54/megatron/model/init_functions.py#L151
|
147 |
+
and https://github.com/EleutherAI/gpt-neox/blob/main/megatron/model/transformer.py
|
148 |
+
"""
|
149 |
+
del kwargs
|
150 |
+
residual_div = n_layers / math.sqrt(10)
|
151 |
+
if verbose > 1:
|
152 |
+
warnings.warn(f'setting init_div_is_residual to {residual_div}')
|
153 |
+
small_param_init_fn_(module=module, d_model=d_model, n_layers=n_layers, init_div_is_residual=residual_div, emb_init_std=emb_init_std, emb_init_uniform_lim=emb_init_uniform_lim, verbose=verbose)
|
154 |
+
|
155 |
+
def kaiming_uniform_param_init_fn_(module: nn.Module, n_layers: int, d_model: Optional[int]=None, init_div_is_residual: Union[int, float, str, bool]=True, emb_init_std: Optional[float]=None, emb_init_uniform_lim: Optional[Union[Tuple[float, float], float]]=None, init_gain: float=0, fan_mode: str='fan_in', init_nonlinearity: str='leaky_relu', verbose: int=0, **kwargs):
|
156 |
+
del kwargs
|
157 |
+
if verbose > 1:
|
158 |
+
warnings.warn(f'Using nn.init.kaiming_uniform_ init fn with parameters: ' + f'a={init_gain}, mode={fan_mode}, nonlinearity={init_nonlinearity}')
|
159 |
+
kaiming_uniform_ = partial(nn.init.kaiming_uniform_, a=init_gain, mode=fan_mode, nonlinearity=init_nonlinearity)
|
160 |
+
generic_param_init_fn_(module=module, init_fn_=kaiming_uniform_, d_model=d_model, n_layers=n_layers, init_div_is_residual=init_div_is_residual, emb_init_std=emb_init_std, emb_init_uniform_lim=emb_init_uniform_lim, verbose=verbose)
|
161 |
+
|
162 |
+
def kaiming_normal_param_init_fn_(module: nn.Module, n_layers: int, d_model: Optional[int]=None, init_div_is_residual: Union[int, float, str, bool]=True, emb_init_std: Optional[float]=None, emb_init_uniform_lim: Optional[Union[Tuple[float, float], float]]=None, init_gain: float=0, fan_mode: str='fan_in', init_nonlinearity: str='leaky_relu', verbose: int=0, **kwargs):
|
163 |
+
del kwargs
|
164 |
+
if verbose > 1:
|
165 |
+
warnings.warn(f'Using nn.init.kaiming_normal_ init fn with parameters: ' + f'a={init_gain}, mode={fan_mode}, nonlinearity={init_nonlinearity}')
|
166 |
+
kaiming_normal_ = partial(torch.nn.init.kaiming_normal_, a=init_gain, mode=fan_mode, nonlinearity=init_nonlinearity)
|
167 |
+
generic_param_init_fn_(module=module, init_fn_=kaiming_normal_, d_model=d_model, n_layers=n_layers, init_div_is_residual=init_div_is_residual, emb_init_std=emb_init_std, emb_init_uniform_lim=emb_init_uniform_lim, verbose=verbose)
|
168 |
+
|
169 |
+
def xavier_uniform_param_init_fn_(module: nn.Module, n_layers: int, d_model: Optional[int]=None, init_div_is_residual: Union[int, float, str, bool]=True, emb_init_std: Optional[float]=None, emb_init_uniform_lim: Optional[Union[Tuple[float, float], float]]=None, init_gain: float=0, verbose: int=0, **kwargs):
|
170 |
+
del kwargs
|
171 |
+
xavier_uniform_ = partial(torch.nn.init.xavier_uniform_, gain=init_gain)
|
172 |
+
if verbose > 1:
|
173 |
+
warnings.warn(f'Using torch.nn.init.xavier_uniform_ init fn with parameters: ' + f'gain={init_gain}')
|
174 |
+
generic_param_init_fn_(module=module, init_fn_=xavier_uniform_, d_model=d_model, n_layers=n_layers, init_div_is_residual=init_div_is_residual, emb_init_std=emb_init_std, emb_init_uniform_lim=emb_init_uniform_lim, verbose=verbose)
|
175 |
+
|
176 |
+
def xavier_normal_param_init_fn_(module: nn.Module, n_layers: int, d_model: Optional[int]=None, init_div_is_residual: Union[int, float, str, bool]=True, emb_init_std: Optional[float]=None, emb_init_uniform_lim: Optional[Union[Tuple[float, float], float]]=None, init_gain: float=0, verbose: int=0, **kwargs):
|
177 |
+
xavier_normal_ = partial(torch.nn.init.xavier_normal_, gain=init_gain)
|
178 |
+
if verbose > 1:
|
179 |
+
warnings.warn(f'Using torch.nn.init.xavier_normal_ init fn with parameters: ' + f'gain={init_gain}')
|
180 |
+
generic_param_init_fn_(module=module, init_fn_=xavier_normal_, d_model=d_model, n_layers=n_layers, init_div_is_residual=init_div_is_residual, emb_init_std=emb_init_std, emb_init_uniform_lim=emb_init_uniform_lim, verbose=verbose)
|
181 |
+
MODEL_INIT_REGISTRY = {'default_': torch_default_param_init_fn_, 'baseline_': baseline_param_init_fn_, 'kaiming_uniform_': kaiming_uniform_param_init_fn_, 'kaiming_normal_': kaiming_normal_param_init_fn_, 'neox_init_': neox_param_init_fn_, 'small_init_': small_param_init_fn_, 'xavier_uniform_': xavier_uniform_param_init_fn_, 'xavier_normal_': xavier_normal_param_init_fn_}
|
pytorch_model.bin.index.json
ADDED
@@ -0,0 +1,201 @@
|
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|
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|
|
|
|
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|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"metadata": {
|
3 |
+
"total_size": 13298573312
|
4 |
+
},
|
5 |
+
"weight_map": {
|
6 |
+
"transformer.blocks.0.attn.Wqkv.weight": "pytorch_model-00001-of-00002.bin",
|
7 |
+
"transformer.blocks.0.attn.out_proj.weight": "pytorch_model-00001-of-00002.bin",
|
8 |
+
"transformer.blocks.0.ffn.down_proj.weight": "pytorch_model-00001-of-00002.bin",
|
9 |
+
"transformer.blocks.0.ffn.up_proj.weight": "pytorch_model-00001-of-00002.bin",
|
10 |
+
"transformer.blocks.0.norm_1.weight": "pytorch_model-00001-of-00002.bin",
|
11 |
+
"transformer.blocks.0.norm_2.weight": "pytorch_model-00001-of-00002.bin",
|
12 |
+
"transformer.blocks.1.attn.Wqkv.weight": "pytorch_model-00001-of-00002.bin",
|
13 |
+
"transformer.blocks.1.attn.out_proj.weight": "pytorch_model-00001-of-00002.bin",
|
14 |
+
"transformer.blocks.1.ffn.down_proj.weight": "pytorch_model-00001-of-00002.bin",
|
15 |
+
"transformer.blocks.1.ffn.up_proj.weight": "pytorch_model-00001-of-00002.bin",
|
16 |
+
"transformer.blocks.1.norm_1.weight": "pytorch_model-00001-of-00002.bin",
|
17 |
+
"transformer.blocks.1.norm_2.weight": "pytorch_model-00001-of-00002.bin",
|
18 |
+
"transformer.blocks.10.attn.Wqkv.weight": "pytorch_model-00001-of-00002.bin",
|
19 |
+
"transformer.blocks.10.attn.out_proj.weight": "pytorch_model-00001-of-00002.bin",
|
20 |
+
"transformer.blocks.10.ffn.down_proj.weight": "pytorch_model-00001-of-00002.bin",
|
21 |
+
"transformer.blocks.10.ffn.up_proj.weight": "pytorch_model-00001-of-00002.bin",
|
22 |
+
"transformer.blocks.10.norm_1.weight": "pytorch_model-00001-of-00002.bin",
|
23 |
+
"transformer.blocks.10.norm_2.weight": "pytorch_model-00001-of-00002.bin",
|
24 |
+
"transformer.blocks.11.attn.Wqkv.weight": "pytorch_model-00001-of-00002.bin",
|
25 |
+
"transformer.blocks.11.attn.out_proj.weight": "pytorch_model-00001-of-00002.bin",
|
26 |
+
"transformer.blocks.11.ffn.down_proj.weight": "pytorch_model-00001-of-00002.bin",
|
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|
201 |
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}
|
requirements.txt
ADDED
@@ -0,0 +1,2 @@
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+
einops==0.5.0
|
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+
triton-pre-mlir@git+https://github.com/vchiley/triton.git@triton_pre_mlir_sm90#subdirectory=python
|
score.py.txt
ADDED
@@ -0,0 +1,36 @@
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|
|
|
|
|
|
|
1 |
+
import os
|
2 |
+
import logging
|
3 |
+
import json
|
4 |
+
import numpy
|
5 |
+
import joblib
|
6 |
+
|
7 |
+
|
8 |
+
def init():
|
9 |
+
"""
|
10 |
+
This function is called when the container is initialized/started, typically after create/update of the deployment.
|
11 |
+
You can write the logic here to perform init operations like caching the model in memory
|
12 |
+
"""
|
13 |
+
global model
|
14 |
+
# AZUREML_MODEL_DIR is an environment variable created during deployment.
|
15 |
+
# It is the path to the model folder (./azureml-models/$MODEL_NAME/$VERSION)
|
16 |
+
# Please provide your model's folder name if there is one
|
17 |
+
model_path = os.path.join(
|
18 |
+
os.getenv("AZUREML_MODEL_DIR"), "model/sklearn_regression_model.pkl"
|
19 |
+
)
|
20 |
+
# deserialize the model file back into a sklearn model
|
21 |
+
model = joblib.load(model_path)
|
22 |
+
logging.info("Init complete")
|
23 |
+
|
24 |
+
|
25 |
+
def run(raw_data):
|
26 |
+
"""
|
27 |
+
This function is called for every invocation of the endpoint to perform the actual scoring/prediction.
|
28 |
+
In the example we extract the data from the json input and call the scikit-learn model's predict()
|
29 |
+
method and return the result back
|
30 |
+
"""
|
31 |
+
logging.info("model 1: request received")
|
32 |
+
data = json.loads(raw_data)["data"]
|
33 |
+
data = numpy.array(data)
|
34 |
+
result = model.predict(data)
|
35 |
+
logging.info("Request processed")
|
36 |
+
return result.tolist()
|
setup.py
ADDED
@@ -0,0 +1,10 @@
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|
1 |
+
#!/usr/bin/env python
|
2 |
+
|
3 |
+
|
4 |
+
from distutils.core import setup
|
5 |
+
|
6 |
+
setup(name='HF API',
|
7 |
+
version='0.1',
|
8 |
+
description='Hugging Face Python API',
|
9 |
+
packages=['hfapi']
|
10 |
+
)
|
special_tokens_map.json
ADDED
@@ -0,0 +1,5 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"bos_token": "<|endoftext|>",
|
3 |
+
"eos_token": "<|endoftext|>",
|
4 |
+
"unk_token": "<|endoftext|>"
|
5 |
+
}
|
stderrlogs.txt
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
[2023-08-21 20:05:01Z] Job failed, job RunId is d6ec0227-2b44-4155-bd63-c777454c1fd5. Error: {"Error":{"Code":"UserError","Severity":null,"Message":"Invalid PyTorch configuration due to the process count of 0 is less than the node count of 1","MessageFormat":"Invalid PyTorch configuration due to {reason}","MessageParameters":{"reason":"the process count of 0 is less than the node count of 1"},"ReferenceCode":null,"DetailsUri":null,"Target":null,"Details":[],"InnerError":{"Code":"PyTorchValidationDetails","InnerError":null},"DebugInfo":null,"AdditionalInfo":null},"Correlation":{"operation":"31997bfd4aa685a902c5677c3c555704","request":"72dae7b189154280"},"Environment":"westus3","Location":"westus3","Time":"2023-08-21T20:05:01.4084449+00:00","ComponentName":"GlobalJobDispatcher","statusCode":400}
|
stdoutlogs.txt
ADDED
File without changes
|
tokenizer.json
ADDED
The diff for this file is too large to render.
See raw diff
|
|
tokenizer_config.json
ADDED
@@ -0,0 +1,9 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"add_prefix_space": false,
|
3 |
+
"bos_token": "<|endoftext|>",
|
4 |
+
"clean_up_tokenization_spaces": true,
|
5 |
+
"eos_token": "<|endoftext|>",
|
6 |
+
"model_max_length": 2048,
|
7 |
+
"tokenizer_class": "GPTNeoXTokenizer",
|
8 |
+
"unk_token": "<|endoftext|>"
|
9 |
+
}
|
zalmati.ipynb
ADDED
@@ -0,0 +1,115 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
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|
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|
|
|
|
|
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|
|
|
|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
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|
|
|
|
|
|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
{
|
2 |
+
"cells": [
|
3 |
+
{
|
4 |
+
"cell_type": "code",
|
5 |
+
"execution_count": 1,
|
6 |
+
"id": "32d34b8d",
|
7 |
+
"metadata": {
|
8 |
+
"_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
|
9 |
+
"_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
|
10 |
+
"execution": {
|
11 |
+
"iopub.execute_input": "2023-08-13T11:25:15.729799Z",
|
12 |
+
"iopub.status.busy": "2023-08-13T11:25:15.729380Z",
|
13 |
+
"iopub.status.idle": "2023-08-13T11:25:15.776786Z",
|
14 |
+
"shell.execute_reply": "2023-08-13T11:25:15.775969Z"
|
15 |
+
},
|
16 |
+
"papermill": {
|
17 |
+
"duration": 0.053795,
|
18 |
+
"end_time": "2023-08-13T11:25:15.779385",
|
19 |
+
"exception": false,
|
20 |
+
"start_time": "2023-08-13T11:25:15.725590",
|
21 |
+
"status": "completed"
|
22 |
+
},
|
23 |
+
"tags": []
|
24 |
+
},
|
25 |
+
"outputs": [
|
26 |
+
{
|
27 |
+
"name": "stdout",
|
28 |
+
"output_type": "stream",
|
29 |
+
"text": [
|
30 |
+
"/kaggle/input/pdf-classification-ml-models-and-metrics/rna.csv\n",
|
31 |
+
"/kaggle/input/pdf-classification-ml-models-and-metrics/random_forest.csv\n",
|
32 |
+
"/kaggle/input/pdf-classification-ml-models-and-metrics/logistic_regression.csv\n",
|
33 |
+
"/kaggle/input/pdf-classification-ml-models-and-metrics/decision_tree.csv\n",
|
34 |
+
"/kaggle/input/pdf-classification-ml-models-and-metrics/support_vector_machine.csv\n",
|
35 |
+
"/kaggle/input/pdf-classification-ml-models-and-metrics/k-nearest_neighbors.csv\n",
|
36 |
+
"/kaggle/input/programminglanguagesdataset/LanguagesData.csv\n",
|
37 |
+
"/kaggle/input/tech-trend/Tech_trend.csv\n",
|
38 |
+
"/kaggle/input/tech-trend/tech_trend_QnA.pdf\n",
|
39 |
+
"/kaggle/input/mnist-allmat/mnist_all.mat\n",
|
40 |
+
"/kaggle/input/ai4code/pytorch/deberta-v3-large/4/spm.model\n",
|
41 |
+
"/kaggle/input/ai4code/pytorch/deberta-v3-large/4/config.json\n",
|
42 |
+
"/kaggle/input/ai4code/pytorch/deberta-v3-large/4/tokenizer_config.json\n",
|
43 |
+
"/kaggle/input/ai4code/pytorch/deberta-v3-large/4/deberta-v3-large_fold0.pth.tar\n",
|
44 |
+
"/kaggle/input/ai4code/pytorch/deberta-v3-large/4/utils.py\n",
|
45 |
+
"/kaggle/input/ai4code/pytorch/deberta-v3-large/4/data_processing.py\n",
|
46 |
+
"/kaggle/input/ai4code/pytorch/deberta-v3-large/4/models.py\n",
|
47 |
+
"/kaggle/input/ai4code/pytorch/deberta-v3-large/4/parameter.py\n",
|
48 |
+
"/kaggle/input/ai4code/pytorch/deberta-v3-large/4/special_tokens_map.json\n",
|
49 |
+
"/kaggle/input/ai4code/pytorch/deberta-v3-large/4/predict.py\n",
|
50 |
+
"/kaggle/input/ai4code/pytorch/deberta-v3-large/4/dataset.py\n",
|
51 |
+
"/kaggle/input/ai4code/pytorch/deberta-v3-large/4/added_tokens.json\n",
|
52 |
+
"/kaggle/input/hacker-news-openai-embeddings/story.csv\n",
|
53 |
+
"/kaggle/input/structured-query-language/DQL_select.sql\n",
|
54 |
+
"/kaggle/input/urldataset/urldata.csv\n",
|
55 |
+
"/kaggle/input/google-capstone/.RData\n",
|
56 |
+
"/kaggle/input/programming-language-data-set/programminglanguage.csv\n",
|
57 |
+
"/kaggle/input/binary-alpha-digits/binaryalphadigs.mat\n"
|
58 |
+
]
|
59 |
+
}
|
60 |
+
],
|
61 |
+
"source": [
|
62 |
+
"# This Python 3 environment comes with many helpful analytics libraries installed\n",
|
63 |
+
"# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
|
64 |
+
"# For example, here's several helpful packages to load\n",
|
65 |
+
"\n",
|
66 |
+
"import numpy as np # linear algebra\n",
|
67 |
+
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
|
68 |
+
"\n",
|
69 |
+
"# Input data files are available in the read-only \"../input/\" directory\n",
|
70 |
+
"# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
|
71 |
+
"\n",
|
72 |
+
"import os\n",
|
73 |
+
"for dirname, _, filenames in os.walk('/kaggle/input'):\n",
|
74 |
+
" for filename in filenames:\n",
|
75 |
+
" print(os.path.join(dirname, filename))\n",
|
76 |
+
"\n",
|
77 |
+
"# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
|
78 |
+
"# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
|
79 |
+
]
|
80 |
+
}
|
81 |
+
],
|
82 |
+
"metadata": {
|
83 |
+
"kernelspec": {
|
84 |
+
"display_name": "Python 3",
|
85 |
+
"language": "python",
|
86 |
+
"name": "python3"
|
87 |
+
},
|
88 |
+
"language_info": {
|
89 |
+
"codemirror_mode": {
|
90 |
+
"name": "ipython",
|
91 |
+
"version": 3
|
92 |
+
},
|
93 |
+
"file_extension": ".py",
|
94 |
+
"mimetype": "text/x-python",
|
95 |
+
"name": "python",
|
96 |
+
"nbconvert_exporter": "python",
|
97 |
+
"pygments_lexer": "ipython3",
|
98 |
+
"version": "3.10.12"
|
99 |
+
},
|
100 |
+
"papermill": {
|
101 |
+
"default_parameters": {},
|
102 |
+
"duration": 13.110633,
|
103 |
+
"end_time": "2023-08-13T11:25:16.704786",
|
104 |
+
"environment_variables": {},
|
105 |
+
"exception": null,
|
106 |
+
"input_path": "__notebook__.ipynb",
|
107 |
+
"output_path": "__notebook__.ipynb",
|
108 |
+
"parameters": {},
|
109 |
+
"start_time": "2023-08-13T11:25:03.594153",
|
110 |
+
"version": "2.4.0"
|
111 |
+
}
|
112 |
+
},
|
113 |
+
"nbformat": 4,
|
114 |
+
"nbformat_minor": 5
|
115 |
+
}
|
zalmati.pbids
ADDED
@@ -0,0 +1 @@
|
|
|
|
|
1 |
+
{"version":"1.0","connections":[{"details":{"protocol":"tds","address":{"server":"zalmati.database.windows.net","database":"zalmati"}},"mode":"DirectQuery"}]}
|
zalmati.pem
ADDED
@@ -0,0 +1,39 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
-----BEGIN RSA PRIVATE KEY-----
|
2 |
+
MIIG5gIBAAKCAYEA3SKTPPG41sLFatD2OBoSbJ9e0x0tiz4XREnXtqKiEfBOePnl
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3 |
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4 |
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5 |
+
Ar62eg+8kgERIiJFsMsHpICyKMATkAx+Y/jtsujQoK+45jv60xDDQoLVZ1Vdqqi1
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6 |
+
eTLmx0BrZeVYWaxOCMQ9X0Dz7dJJskwQeL2aUktn4LdTM9mtU0PJcP5uZ4ZQOv7v
|
7 |
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Reh8bm/hGOvpjnE8IJxCiqzXrqReDqq64FDkS98wBz++aMPzCCND3rR3c7kh1Bdy
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8 |
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ZjNy6uOXLxA0VzG6CYqL7f0fC3zBYbu3A9e9DiyfH4fAqwU1e/5xWv+8cgzTpJm3
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UMumRLRjXoJYIAM56giXLH7Is2oXXULViTienyNzzk45cnS5wMbzinlIpNp19jPW
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10 |
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PhaxIEYmievXbjH5AgMBAAECggGBANCnZSKmOnB6C2kEjq7U+vl/TywIZgbyqWWH
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7AzlsKBpfoxACxfGWl49m04Vxpnw+YOvdoOzgXkKW+z1615hgDWTP+2SznKm9Hk/
|
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g4idYUU9IG3L5NzhzrMadGHbz5wrWY109BsaBTwfMtGLyMBse8h0OmusA/Xd5HeL
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38 |
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W/CtOBpqxuFnUp2xiQJ6438kIZJfKkd86cl7N5rL4x113ku6DunV2a7H
|
39 |
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-----END RSA PRIVATE KEY-----
|