Unofficial implementation of Perceiver IO: A General Architecture for Structured Inputs & Outputs

Related tags

Deep Learningpythonnlpmachine-learningdeep-learningpytorchtransformerself-attentionperceiverperceiverio
Overview

Perceiver IO

Unofficial implementation of Perceiver IO: A General Architecture for Structured Inputs & Outputs

Usage

import torch

from src.perceiver.decoders import PerceiverDecoder
from src.perceiver.encoder import PerceiverEncoder
from src.perceiver import PerceiverIO


num_latents = 128
latent_dim = 256
input_dim = 64

decoder_query_dim = 4


encoder = PerceiverEncoder(
    num_latents=num_latents,
    latent_dim=latent_dim,
    input_dim=input_dim,
    num_self_attn_per_block=8,
    num_blocks=1
)
decoder = PerceiverDecoder(
    latent_dim=latent_dim,
    query_dim=decoder_query_dim
)
perceiver = PerceiverIO(encoder, decoder)

inputs = torch.randn(2, 16, input_dim)
output_query = torch.randn(2, 3, decoder_query_dim)

perceiver(inputs, output_query)  # shape = (2, 3, 4)

List of implemented decoders

  • ProjectionDecoder
  • ClassificationDecoder
  • PerceiverDecoder

Example architectures:

Citation

@misc{jaegle2021perceiver,
    title   = {Perceiver IO: A General Architecture for Structured Inputs & Outputs},
    author  = {Andrew Jaegle and Sebastian Borgeaud and Jean-Baptiste Alayrac and Carl Doersch and Catalin Ionescu and David Ding and Skanda Koppula and Andrew Brock and Evan Shelhamer and Olivier Hénaff and Matthew M. Botvinick and Andrew Zisserman and Oriol Vinyals and João Carreira},
    year    = {2021},
    eprint  = {2107.14795},
    archivePrefix = {arXiv},
    primaryClass = {cs.LG}
}
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Comments
  • Issue related to LayerNorm

    Issue related to LayerNorm

    Hello, man. First of all thank for your effort a lot. I can see that It was taken your time quite much to write a clear code. How ever, I just have a small question about Cross Attention class:

            self.kv_layer_norm = nn.LayerNorm(kv_dim)
        self.q_layer_norm = nn.LayerNorm(q_dim)
        self.qkv_layer_norm = nn.LayerNorm(q_dim)

    When I integrated the repository to my program as the last layer . The outputs of these LayerNorm were always 0. When I removed these Norm layers, The code run pretty well but much worse than the simple method (let's say simply concatenate the inputs and queries). p/s: To be more specific, My queries and inputs were taken from 2 separated nets. Do you have any idea about it? Once again, thank you for your great work a lot.

    opened by NathanielNguyen11 7
  • Comparison with perceiver-pytorch?

    Comparison with perceiver-pytorch?

    How does this repository compare with https://github.com/lucidrains/perceiver-pytorch ?

    Would you have any interest in generalizing and integrating the two implementations together?

    opened by xloem 3
  • Bug in MultiHeadAttention

    Bug in MultiHeadAttention

    https://github.com/esceptico/perceiver-io/blob/6b6507334451f61eeb073665b62f00d26f331893/src/perceiver_io/attention.py#L74

    in the referenced line self.scale should be multiplied instead of the divide, since it's defined as self.scale = self.qk_head_dim ** -0.5. The correct expression should be attention = (q @ k.transpose(-2, -1) * self.scale)

    -Nilesh

    opened by nilesh2797 2
Releases(v0.1.4)
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