flamingo pytorch

? Flamingo - Pytorch

Implementasi Flamingo, pertanyaan visual beberapa gambar canggih yang menjawab jaring perhatian, di Pytorch. Ini akan mencakup resampler penerima (termasuk skema di mana kueri yang dipelajari menyumbangkan kunci/nilai yang harus diperhatikan, selain penyematan media), blok perhatian silang bertopeng khusus, dan akhirnya gerbang tanh di ujung perhatian silang + blok umpan maju yang sesuai

Presentasi Yannic Kilcher

Memasang

$ pip install flamingo-pytorch

Penggunaan

 import torch
from flamingo_pytorch import PerceiverResampler

perceive = PerceiverResampler (
    dim = 1024 ,
    depth = 2 ,
    dim_head = 64 ,
    heads = 8 ,
    num_latents = 64 ,    # the number of latents to shrink your media sequence to, perceiver style
    num_time_embeds = 4  # say you have 4 images maximum in your dialogue
)

medias = torch . randn ( 1 , 2 , 256 , 1024 ) # (batch, time, sequence length, dimension)
perceived = perceive ( medias ) # (1, 2, 64, 1024) - (batch, time, num latents, dimension)

Kemudian Anda memasukkan GatedCrossAttentionBlock pada interval yang berbeda dalam model bahasa raksasa Anda. Teks Anda kemudian akan memperhatikan media yang dirasakan dari atas

Cara yang disarankan untuk mendapatkan tensor boolean media_locations adalah dengan mengalokasikan id token khusus ke media, dan kemudian, di awal model bahasa besar Anda, lakukan media_locations = text_id == media_token_id

 import torch
from flamingo_pytorch import GatedCrossAttentionBlock

cross_attn = GatedCrossAttentionBlock (
    dim = 1024 ,
    dim_head = 64 ,
    heads = 8
)

text = torch . randn ( 1 , 512 , 1024 )
perceived = torch . randn ( 1 , 2 , 64 , 1024 )

media_locations = torch . randint ( 0 , 2 , ( 1 , 512 )). bool ()

text = cross_attn (
    text ,
    perceived ,
    media_locations = media_locations
)

Itu saja!

Hanya perhatian yang Anda butuhkan.

Contoh kerja lengkap dengan Flamingo + PaLM ???

Integrasi dengan PaLM

Pertama instal vit-pytorch untuk vision encoder

$ pip install vit-pytorch

Kemudian

 from vit_pytorch . vit import ViT
from vit_pytorch . extractor import Extractor

vit = ViT (
    image_size = 256 ,
    patch_size = 32 ,
    num_classes = 1000 ,
    dim = 1024 ,
    depth = 6 ,
    heads = 16 ,
    mlp_dim = 2048 ,
    dropout = 0.1 ,
    emb_dropout = 0.1
)

vit = Extractor ( vit , return_embeddings_only = True )

# first take your trained image encoder and wrap it in an adapter that returns the image embeddings
# here we use the ViT from the vit-pytorch library

import torch
from flamingo_pytorch import FlamingoPaLM

# a PaLM language model, the 540 billion parameter model from google that shows signs of general intelligence

flamingo_palm = FlamingoPaLM (
    num_tokens = 20000 ,          # number of tokens
    dim = 1024 ,                  # dimensions
    depth = 12 ,                  # depth
    heads = 8 ,                   # attention heads
    dim_head = 64 ,               # dimension per attention head
    img_encoder = vit ,           # plugin your image encoder (this can be optional if you pass in the image embeddings separately, but probably want to train end to end given the perceiver resampler)
    media_token_id = 3 ,          # the token id representing the [media] or [image]
    cross_attn_every = 3 ,        # how often to cross attend
    perceiver_num_latents = 64 ,  # perceiver number of latents, should be smaller than the sequence length of the image tokens
    perceiver_depth = 2          # perceiver resampler depth
)

# train your PaLM as usual

text = torch . randint ( 0 , 20000 , ( 2 , 512 ))

palm_logits = flamingo_palm ( text )

# after much training off the regular PaLM logits
# now you are ready to train Flamingo + PaLM
# by passing in images, it automatically freezes everything but the perceiver and cross attention blocks, as in the paper

dialogue = torch . randint ( 0 , 20000 , ( 4 , 512 ))
images = torch . randn ( 4 , 2 , 3 , 256 , 256 )

flamingo_logits = flamingo_palm ( dialogue , images )

# do your usual cross entropy loss

Sangat jelas ke mana arah semua ini jika Anda berpikir lebih dari sekedar gambar.

Lahirnya

Untuk kebenaran faktual, bayangkan saja di mana posisi sistem ini jika seseorang menggunakan model bahasa pengambilan yang canggih sebagai dasarnya.

Kutipan

 @article { Alayrac2022Flamingo ,
    title   = { Flamingo: a Visual Language Model for Few-Shot Learning } ,
    author  = { Jean-Baptiste Alayrac et al } ,
    year    = { 2022 }
}

 @inproceedings { Chowdhery2022PaLMSL ,
    title   = { PaLM: Scaling Language Modeling with Pathways } ,
    author  = {Aakanksha Chowdhery and Sharan Narang and Jacob Devlin and Maarten Bosma and Gaurav Mishra and Adam Roberts and Paul Barham and Hyung Won Chung and Charles Sutton and Sebastian Gehrmann and Parker Schuh and Kensen Shi and Sasha Tsvyashchenko and Joshua Maynez and Abhishek Rao and Parker Barnes and Yi Tay and Noam M. Shazeer and Vinodkumar Prabhakaran and Emily Reif and Nan Du and Benton C. Hutchinson and Reiner Pope and James Bradbury and Jacob Austin and Michael Isard and Guy Gur-Ari and Pengcheng Yin and Toju Duke and Anselm Levskaya and Sanjay Ghemawat and Sunipa Dev and Henryk Michalewski and Xavier Garc{'i}a and Vedant Misra and Kevin Robinson and Liam Fedus and Denny Zhou and Daphne Ippolito and David Luan and Hyeontaek Lim and Barret Zoph and Alexander Spiridonov and Ryan Sepassi and David Dohan and Shivani Agrawal and Mark Omernick and Andrew M. Dai and Thanumalayan Sankaranarayana Pillai and Marie Pellat and Aitor Lewkowycz and Erica Oliveira Moreira and Rewon Child and Oleksandr Polozov and Katherine Lee and Zongwei Zhou and Xuezhi Wang and Brennan Saeta and Mark Diaz and Orhan Firat and Michele Catasta and Jason Wei and Kathleen S. Meier-Hellstern and Douglas Eck and Jeff Dean and Slav Petrov and Noah Fiedel},
    year    = { 2022 }
}