gmp

g-mlp-pytorch

Implementation of gMLP, an all-MLP replacement for Transformers, in Pytorch

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gMLP - Pytorch

Implementation of gMLP, an all-MLP replacement for Transformers, in Pytorch

Install

$ pip install g-mlp-pytorch

Usage

For masked language modelling

import torch
from torch import nn
from g_mlp_pytorch import gMLP

model = gMLP(
    num_tokens = 20000,
    dim = 512,
    depth = 6,
    seq_len = 256,
    circulant_matrix = True,      # use circulant weight matrix for linear increase in parameters in respect to sequence length
    act = nn.Tanh()               # activation for spatial gate (defaults to identity)
)

x = torch.randint(0, 20000, (1, 256))
logits = model(x) # (1, 256, 20000)

For image classification

import torch
from g_mlp_pytorch import gMLPVision

model = gMLPVision(
    image_size = 256,
    patch_size = 16,
    num_classes = 1000,
    dim = 512,
    depth = 6
)

img = torch.randn(1, 3, 256, 256)
logits = model(img) # (1, 1000)

You can also add a tiny amount of attention (one-headed) to boost performance, as mentioned in the paper as aMLP, with the addition of one extra keyword attn_dim. This applies to both gMLPVision and gMLP

import torch
from g_mlp_pytorch import gMLPVision

model = gMLPVision(
    image_size = 256,
    patch_size = 16,
    num_classes = 1000,
    dim = 512,
    depth = 6,
    attn_dim = 64
)

img = torch.randn(1, 3, 256, 256)
pred = model(img) # (1, 1000)

Non-square images and patch sizes

import torch
from g_mlp_pytorch import gMLPVision

model = gMLPVision(
    image_size = (256, 128),
    patch_size = (16, 8),
    num_classes = 1000,
    dim = 512,
    depth = 6,
    attn_dim = 64
)

img = torch.randn(1, 3, 256, 128)
pred = model(img) # (1, 1000)

Experimental

A independent researcher proposes using a multi-headed approach for gMLPs in a blogpost on Zhihu. To do so, just set heads to be greater than 1

import torch
from torch import nn
from g_mlp_pytorch import gMLP

model = gMLP(
    num_tokens = 20000,
    dim = 512,
    depth = 6,
    seq_len = 256,
    causal = True,
    circulant_matrix = True,
    heads = 4 # 4 heads
)

x = torch.randint(0, 20000, (1, 256))
logits = model(x) # (1, 256, 20000)

Citations

@misc{liu2021pay,
    title   = {Pay Attention to MLPs}, 
    author  = {Hanxiao Liu and Zihang Dai and David R. So and Quoc V. Le},
    year    = {2021},
    eprint  = {2105.08050},
    archivePrefix = {arXiv},
    primaryClass = {cs.LG}
}
@software{peng_bo_2021_5196578,
    author       = {PENG Bo},
    title        = {BlinkDL/RWKV-LM: 0.01},
    month        = aug,
    year         = 2021,
    publisher    = {Zenodo},
    version      = {0.01},
    doi          = {10.5281/zenodo.5196578},
    url          = {https://doi.org/10.5281/zenodo.5196578%7D
}

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