lucidrains/vector-quantize-pytorch

import torch
from vector_quantize_pytorch import VectorQuantize

vq = VectorQuantize(
    dim = 256,
    codebook_size = 512,     # codebook size
    decay = 0.8,             # the exponential moving average decay, lower means the dictionary will change faster
    commitment_weight = 1.   # the weight on the commitment loss
)

x = torch.randn(1, 1024, 256)
quantized, indices, commit_loss = vq(x) # (1, 1024, 256), (1, 1024), (1)

import torch
from vector_quantize_pytorch import ResidualVQ

residual_vq = ResidualVQ(
    dim = 256,
    num_quantizers = 8,      # specify number of quantizers
    codebook_size = 1024,    # codebook size
)

x = torch.randn(1, 1024, 256)

quantized, indices, commit_loss = residual_vq(x)

# (1, 1024, 256), (1, 1024, 8), (1, 8)
# (batch, seq, dim), (batch, seq, quantizer), (batch, quantizer)

# if you need all the codes across the quantization layers, just pass return_all_codes = True

quantized, indices, commit_loss, all_codes = residual_vq(x, return_all_codes = True)

# *_, (8, 1, 1024, 256)
# all_codes - (quantizer, batch, seq, dim)

import torch
from vector_quantize_pytorch import ResidualVQ

residual_vq = ResidualVQ(
    dim = 256,
    num_quantizers = 8,
    codebook_size = 1024,
    stochastic_sample_codes = True,
    sample_codebook_temp = 0.1,         # temperature for stochastically sampling codes, 0 would be equivalent to non-stochastic
    shared_codebook = True              # whether to share the codebooks for all quantizers or not
)

x = torch.randn(1, 1024, 256)
quantized, indices, commit_loss = residual_vq(x)

# (1, 1024, 256), (8, 1, 1024), (8, 1)
# (batch, seq, dim), (quantizer, batch, seq), (quantizer, batch)

import torch
from vector_quantize_pytorch import GroupedResidualVQ

residual_vq = GroupedResidualVQ(
    dim = 256,
    num_quantizers = 8,      # specify number of quantizers
    groups = 2,
    codebook_size = 1024,    # codebook size
)

x = torch.randn(1, 1024, 256)

quantized, indices, commit_loss = residual_vq(x)

# (1, 1024, 256), (1, 1024, 8), (1, 8)
# (batch, seq, dim), (groups, batch, seq, quantizer), (groups, batch, quantizer)

import torch
from vector_quantize_pytorch import ResidualVQ

residual_vq = ResidualVQ(
    dim = 256,
    codebook_size = 256,
    num_quantizers = 4,
    kmeans_init = True,   # set to True
    kmeans_iters = 10     # number of kmeans iterations to calculate the centroids for the codebook on init
)

x = torch.randn(1, 1024, 256)
quantized, indices, commit_loss = residual_vq(x)

import torch
from vector_quantize_pytorch import VectorQuantize

vq = VectorQuantize(
    dim = 256,
    codebook_size = 256,
    codebook_dim = 16      # paper proposes setting this to 32 or as low as 8 to increase codebook usage
)

x = torch.randn(1, 1024, 256)
quantized, indices, commit_loss = vq(x)

import torch
from vector_quantize_pytorch import VectorQuantize

vq = VectorQuantize(
    dim = 256,
    codebook_size = 256,
    use_cosine_sim = True   # set this to True
)

x = torch.randn(1, 1024, 256)
quantized, indices, commit_loss = vq(x)

import torch
from vector_quantize_pytorch import VectorQuantize

vq = VectorQuantize(
    dim = 256,
    codebook_size = 512,
    threshold_ema_dead_code = 2  # should actively replace any codes that have an exponential moving average cluster size less than 2
)

x = torch.randn(1, 1024, 256)
quantized, indices, commit_loss = vq(x)

import torch
from vector_quantize_pytorch import VectorQuantize
vq = VectorQuantize(
    dim = 256,
    codebook_size = 256,
    accept_image_fmap = True,                   # set this true to be able to pass in an image feature map
    orthogonal_reg_weight = 10,                 # in paper, they recommended a value of 10
    orthogonal_reg_max_codes = 128,             # this would randomly sample from the codebook for the orthogonal regularization loss, for limiting memory usage
    orthogonal_reg_active_codes_only = False    # set this to True if you have a very large codebook, and would only like to enforce the loss on the activated codes per batch
)
img_fmap = torch.randn(1, 256, 32, 32)
quantized, indices, loss = vq(img_fmap) # (1, 256, 32, 32), (1, 32, 32), (1,)
# loss now contains the orthogonal regularization loss with the weight as assigned

import torch
from vector_quantize_pytorch import VectorQuantize

vq = VectorQuantize(
    dim = 256,
    codebook_dim = 32,                  # a number of papers have shown smaller codebook dimension to be acceptable
    heads = 8,                          # number of heads to vector quantize, codebook shared across all heads
    separate_codebook_per_head = True,  # whether to have a separate codebook per head. False would mean 1 shared codebook
    codebook_size = 8196,
    accept_image_fmap = True
)

img_fmap = torch.randn(1, 256, 32, 32)
quantized, indices, loss = vq(img_fmap) # (1, 256, 32, 32), (1, 32, 32, 8), (1,)

# indices shape - (batch, height, width, heads)

import torch
from vector_quantize_pytorch import RandomProjectionQuantizer

quantizer = RandomProjectionQuantizer(
    dim = 512,               # input dimensions
    num_codebooks = 16,      # in USM, they used up to 16 for 5% gain
    codebook_dim = 256,      # codebook dimension
    codebook_size = 1024     # codebook size
)

x = torch.randn(1, 1024, 512)
indices = quantizer(x) # (1, 1024, 16) - (batch, seq, num_codebooks)

import torch
from torch import nn
from vector_quantize_pytorch import VectorQuantize

# ddp related imports

import os
import torch.distributed as dist
import torch.multiprocessing as mp
from torch.nn.parallel import DistributedDataParallel as DDP

def setup(rank, world_size):
    os.environ['MASTER_ADDR'] = 'localhost'
    os.environ['MASTER_PORT'] = '12355'
    dist.init_process_group("gloo", rank=rank, world_size=world_size)

def cleanup():
    dist.destroy_process_group()

def start(rank, world_size):
    setup(rank, world_size)

    net = nn.Sequential(
        nn.Conv2d(256, 256, 1),
        VectorQuantize(
            dim = 256,
            codebook_size = 1024,
            accept_image_fmap = True,
            sync_codebook = True           # this needs to be set to True
        )
    ).cuda(rank)

    ddp_mp_model = DDP(net, device_ids = [rank])
    img_fmap = torch.randn(1, 256, 32, 32).cuda(rank)
    quantized, indices, loss = ddp_mp_model(img_fmap)

    cleanup()

if __name__ == '__main__':
    world_size = torch.cuda.device_count()
    assert world_size >= 2, f"requires at least 2 GPUs to run, but got {n_gpus}"
    mp.spawn(start, args=(world_size,), nprocs=world_size, join=True)