Implementation of Memformer, a Memory-augmented Transformer, in Pytorch

Related tags

Deep Learningmemorytransformersartificial-intelligenceattention-mechanism
Overview

Memformer - Pytorch

Implementation of Memformer, a Memory-augmented Transformer, in Pytorch. It includes memory slots, which are updated with attention, learned efficiently through Memory-Replay BackPropagation (MRBP) through time.

Install

$ pip install memformer

Usage

Full encoder / decoder, as in the paper

import torch
from memformer import Memformer

model = Memformer(
    dim = 512,
    enc_num_tokens = 256,
    enc_depth = 2,
    enc_heads = 8,
    enc_max_seq_len = 1024,
    dec_num_tokens = 256,
    dec_depth = 2,
    dec_heads = 8,
    dec_max_seq_len = 1024,
    num_memory_slots = 128
)

src_seg_1 = torch.randint(0, 256, (1, 1024))
src_seg_2 = torch.randint(0, 256, (1, 1024))
src_seg_3 = torch.randint(0, 256, (1, 1024))

tgt = torch.randint(0, 256, (1, 1024))

enc_out1, mems1,    _ = model(src_seg_1) # (1, 1024, 512), (1, 128, 512), _
enc_out2, mems2,    _ = model(src_seg_2, mems = mems1)
enc_out3, mems3, loss = model(src_seg_3, tgt, mems = mems2)

loss.backward()

Encoder only

import torch
from memformer import Memformer

model = Memformer(
    dim = 512,
    enc_num_tokens = 256,
    enc_heads = 8,
    enc_depth = 2,
    enc_max_seq_len = 1024,
    num_memory_slots = 128,
    num_mem_updates = 2,
    encoder_only = True       # only use encoder, in which output is encoded output
)

src1 = torch.randint(0, 256, (1, 1024))
src2 = torch.randint(0, 256, (1, 1024))

enc1, mems1 = model(src1) # (1, 1024, 512), (1, 128, 512)
enc2, mems2 = model(src2, mems = mems1)

Memory Replay Back-Propagation

import torch
from memformer import Memformer, memory_replay_backprop

model = Memformer(
    dim = 512,
    num_memory_slots = 128,
    enc_num_tokens = 256,
    enc_depth = 2,
    enc_max_seq_len = 1024,
    dec_num_tokens = 256,
    dec_depth = 2,
    dec_max_seq_len = 1024
).cuda()

seq = torch.randint(0, 256, (1, 8192)).cuda()
seq_mask = torch.ones_like(seq).bool().cuda()

tgt = torch.randint(0, 256, (1, 512)).cuda()
tgt_mask = torch.ones_like(tgt).bool().cuda()

# will automatically split the source sequence to 8 segments
memory_replay_backprop(
    model,
    src = seq,
    tgt = tgt,
    src_mask = seq_mask,
    tgt_mask = tgt_mask
)

Citations

@inproceedings{
    anonymous2021memformer,
    title={Memformer: The Memory-Augmented Transformer},
    author={Anonymous},
    booktitle={Submitted to International Conference on Learning Representations},
    year={2021},
    url={https://openreview.net/forum?id=_adSMszz_g9},
    note={under review}
}
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Comments
  • WIP - MemformerEncoder

    WIP - MemformerEncoder

    I´m always trying all your awesome work on transformers. My problem is NER on very large texts, with few examples.

    Memformer is the first one so far to converge faster and wield better accuracy than RNN encoders as LSTM, SRU and IndRNN It is ridiculously better than everything else I tested, congratulations @lucidrains 🥳

    I need to use the transformer as a Encoder in my pipeline, to feed a CRF layer. So I modified the code to accept an already embedded input, and to only do the Encode step.

    TODO:

    • [ ] Support Mask
    • [ ] Re-utilize code with Memformer class

    Is this within the scope of the project?

    opened by bratao 10
  • ETA on complete examples

    ETA on complete examples

    @lucidrains As I asked about the feedback-transformer, I was also wondering about this memformer implementation as I would love to try it. Any eta on any complete examples here? They will be much appreciated. Thanks.

    And similarly, I would love to see a simple example for custom line-by-line TXT datasets as well.

    Thank you again :)

    opened by asigalov61 0
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