ColossalAI/examples/language/palm
digger yu 518b31c059
[docs] change placememt_policy to placement_policy (#3829)
* fix typo colossalai/autochunk auto_parallel amp

* fix typo colossalai/auto_parallel nn utils etc.

* fix typo colossalai/auto_parallel autochunk fx/passes  etc.

* fix typo docs/

* change placememt_policy to placement_policy in docs/ and examples/
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data
palm_pytorch
README.md
requirements.txt [example] add example requirement (#2345) 2023-01-06 09:03:29 +08:00
run.sh [CI] add test_ci.sh for palm, opt and gpt (#2475) 2023-01-16 14:44:29 +08:00
test_ci.sh [CI] add test_ci.sh for palm, opt and gpt (#2475) 2023-01-16 14:44:29 +08:00
train.py [docs] change placememt_policy to placement_policy (#3829) 2023-05-24 14:51:49 +08:00

README.md

PaLM - Pytorch

Implementation of the specific Transformer architecture from PaLM - Scaling Language Modeling with Pathways, in less than 200 lines of code.

This model is pretty much SOTA on everything language.

It obviously will not scale, but it is just for educational purposes. To elucidate the public how simple it all really is.

Install

$ pip install PaLM-pytorch

Usage

import torch
from palm_pytorch import PaLM

palm = PaLM(
    num_tokens = 20000,
    dim = 512,
    depth = 12,
    heads = 8,
    dim_head = 64,
)

tokens = torch.randint(0, 20000, (1, 2048))
logits = palm(tokens) # (1, 2048, 20000)

The PaLM 540B in the paper would be

palm = PaLM(
    num_tokens = 256000,
    dim = 18432,
    depth = 118,
    heads = 48,
    dim_head = 256
)

Test on Enwik8

$ python train.py

Todo

Citations

@article{chowdhery2022PaLM,
  title   = {PaLM: Scaling Language Modeling with Pathways},
  author  = {Chowdhery, Aakanksha et al},
  year    = {2022}
}