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"""
EVENTUALLY: Tune the batch sizes selected here for a good balance of speed and generality.
"""
import os
import torch
import numpy as np
from itertools import product
from colbert.infra.config import ColBERTConfig
from colbert.indexing.codecs.residual_embeddings import ResidualEmbeddings
from colbert.utils.utils import print_message
import pathlib
from torch.utils.cpp_extension import load
class ResidualCodec:
Embeddings = ResidualEmbeddings
def __init__(self, config, centroids, avg_residual=None, bucket_cutoffs=None, bucket_weights=None):
self.use_gpu = config.total_visible_gpus > 0
ResidualCodec.try_load_torch_extensions(self.use_gpu)
if self.use_gpu > 0:
self.centroids = centroids.cuda().half()
else:
self.centroids = centroids.float()
self.dim, self.nbits = config.dim, config.nbits
self.avg_residual = avg_residual
if torch.is_tensor(self.avg_residual):
if self.use_gpu:
self.avg_residual = self.avg_residual.cuda().half()
if torch.is_tensor(bucket_cutoffs):
if self.use_gpu:
bucket_cutoffs = bucket_cutoffs.cuda()
bucket_weights = bucket_weights.half().cuda()
self.bucket_cutoffs = bucket_cutoffs
self.bucket_weights = bucket_weights
if not self.use_gpu and self.bucket_weights is not None:
self.bucket_weights = self.bucket_weights.to(torch.float32)
self.arange_bits = torch.arange(0, self.nbits, device='cuda' if self.use_gpu else 'cpu', dtype=torch.uint8)
self.rank = config.rank
# We reverse the residual bits because arange_bits as
# currently constructed produces results with the reverse
# of the expected endianness
self.reversed_bit_map = []
mask = (1 << self.nbits) - 1
for i in range(256):
# The reversed byte
z = 0
for j in range(8, 0, -self.nbits):
# Extract a subsequence of length n bits
x = (i >> (j - self.nbits)) & mask
# Reverse the endianness of each bit subsequence (e.g. 10 -> 01)
y = 0
for k in range(self.nbits - 1, -1, -1):
y += ((x >> (self.nbits - k - 1)) & 1) * (2 ** k)
# Set the corresponding bits in the output byte
z |= y
if j > self.nbits:
z <<= self.nbits
self.reversed_bit_map.append(z)
self.reversed_bit_map = torch.tensor(self.reversed_bit_map).to(torch.uint8)
# A table of all possible lookup orders into bucket_weights
# given n bits per lookup
keys_per_byte = 8 // self.nbits
if self.bucket_weights is not None:
self.decompression_lookup_table = (
torch.tensor(
list(
product(
list(range(len(self.bucket_weights))),
repeat=keys_per_byte
)
)
)
.to(torch.uint8)
)
else:
self.decompression_lookup_table = None
if self.use_gpu:
self.reversed_bit_map = self.reversed_bit_map.cuda()
if self.decompression_lookup_table is not None:
self.decompression_lookup_table = self.decompression_lookup_table.cuda()
@classmethod
def try_load_torch_extensions(cls, use_gpu):
if hasattr(cls, "loaded_extensions") or not use_gpu:
return
print_message(f"Loading decompress_residuals_cpp extension (set COLBERT_LOAD_TORCH_EXTENSION_VERBOSE=True for more info)...")
decompress_residuals_cpp = load(
name="decompress_residuals_cpp",
sources=[
os.path.join(
pathlib.Path(__file__).parent.resolve(), "decompress_residuals.cpp"
),
os.path.join(
pathlib.Path(__file__).parent.resolve(), "decompress_residuals.cu"
),
],
verbose=os.getenv("COLBERT_LOAD_TORCH_EXTENSION_VERBOSE", "False") == "True",
)
cls.decompress_residuals = decompress_residuals_cpp.decompress_residuals_cpp
print_message(f"Loading packbits_cpp extension (set COLBERT_LOAD_TORCH_EXTENSION_VERBOSE=True for more info)...")
packbits_cpp = load(
name="packbits_cpp",
sources=[
os.path.join(
pathlib.Path(__file__).parent.resolve(), "packbits.cpp"
),
os.path.join(
pathlib.Path(__file__).parent.resolve(), "packbits.cu"
),
],
verbose=os.getenv("COLBERT_LOAD_TORCH_EXTENSION_VERBOSE", "False") == "True",
)
cls.packbits = packbits_cpp.packbits_cpp
cls.loaded_extensions = True
@classmethod
def load(cls, index_path):
config = ColBERTConfig.load_from_index(index_path)
centroids_path = os.path.join(index_path, 'centroids.pt')
avgresidual_path = os.path.join(index_path, 'avg_residual.pt')
buckets_path = os.path.join(index_path, 'buckets.pt')
centroids = torch.load(centroids_path, map_location='cpu')
avg_residual = torch.load(avgresidual_path, map_location='cpu')
bucket_cutoffs, bucket_weights = torch.load(buckets_path, map_location='cpu')
if avg_residual.dim() == 0:
avg_residual = avg_residual.item()
return cls(config=config, centroids=centroids, avg_residual=avg_residual, bucket_cutoffs=bucket_cutoffs, bucket_weights=bucket_weights)
def save(self, index_path):
assert self.avg_residual is not None
assert torch.is_tensor(self.bucket_cutoffs), self.bucket_cutoffs
assert torch.is_tensor(self.bucket_weights), self.bucket_weights
centroids_path = os.path.join(index_path, 'centroids.pt')
avgresidual_path = os.path.join(index_path, 'avg_residual.pt')
buckets_path = os.path.join(index_path, 'buckets.pt')
torch.save(self.centroids.half(), centroids_path)
torch.save((self.bucket_cutoffs, self.bucket_weights), buckets_path)
if torch.is_tensor(self.avg_residual):
torch.save(self.avg_residual, avgresidual_path)
else:
torch.save(torch.tensor([self.avg_residual]), avgresidual_path)
def compress(self, embs):
codes, residuals = [], []
for batch in embs.split(1 << 18):
if self.use_gpu:
batch = batch.cuda().half()
codes_ = self.compress_into_codes(batch, out_device=batch.device)
centroids_ = self.lookup_centroids(codes_, out_device=batch.device)
residuals_ = (batch - centroids_)
codes.append(codes_.cpu())
residuals.append(self.binarize(residuals_).cpu())
codes = torch.cat(codes)
residuals = torch.cat(residuals)
return ResidualCodec.Embeddings(codes, residuals)
def binarize(self, residuals):
residuals = torch.bucketize(residuals.float(), self.bucket_cutoffs).to(dtype=torch.uint8)
residuals = residuals.unsqueeze(-1).expand(*residuals.size(), self.nbits) # add a new nbits-wide dim
residuals = residuals >> self.arange_bits # divide by 2^bit for each bit position
residuals = residuals & 1 # apply mod 2 to binarize
assert self.dim % 8 == 0
assert self.dim % (self.nbits * 8) == 0, (self.dim, self.nbits)
if self.use_gpu:
residuals_packed = ResidualCodec.packbits(residuals.contiguous().flatten())
else:
residuals_packed = np.packbits(np.asarray(residuals.contiguous().flatten()))
residuals_packed = torch.as_tensor(residuals_packed, dtype=torch.uint8)
residuals_packed = residuals_packed.reshape(residuals.size(0), self.dim // 8 * self.nbits)
return residuals_packed
def compress_into_codes(self, embs, out_device):
"""
EVENTUALLY: Fusing the kernels or otherwise avoiding materalizing the entire matrix before max(dim=0)
seems like it would help here a lot.
"""
codes = []
bsize = (1 << 29) // self.centroids.size(0)
for batch in embs.split(bsize):
if self.use_gpu:
indices = (self.centroids @ batch.T.cuda().half()).max(dim=0).indices.to(device=out_device)
else:
indices = (self.centroids @ batch.T.cpu().float()).max(dim=0).indices.to(device=out_device)
codes.append(indices)
return torch.cat(codes)
def lookup_centroids(self, codes, out_device):
"""
Handles multi-dimensional codes too.
EVENTUALLY: The .split() below should happen on a flat view.
"""
centroids = []
for batch in codes.split(1 << 20):
if self.use_gpu:
centroids.append(self.centroids[batch.cuda().long()].to(device=out_device))
else:
centroids.append(self.centroids[batch.long()].to(device=out_device))
return torch.cat(centroids)
#@profile
def decompress(self, compressed_embs: Embeddings):
"""
We batch below even if the target device is CUDA to avoid large temporary buffers causing OOM.
"""
codes, residuals = compressed_embs.codes, compressed_embs.residuals
D = []
for codes_, residuals_ in zip(codes.split(1 << 15), residuals.split(1 << 15)):
if self.use_gpu:
codes_, residuals_ = codes_.cuda(), residuals_.cuda()
centroids_ = ResidualCodec.decompress_residuals(
residuals_,
self.bucket_weights,
self.reversed_bit_map,
self.decompression_lookup_table,
codes_,
self.centroids,
self.dim,
self.nbits,
).cuda()
else:
# TODO: Remove dead code
centroids_ = self.lookup_centroids(codes_, out_device='cpu')
residuals_ = self.reversed_bit_map[residuals_.long()]
residuals_ = self.decompression_lookup_table[residuals_.long()]
residuals_ = residuals_.reshape(residuals_.shape[0], -1)
residuals_ = self.bucket_weights[residuals_.long()]
centroids_.add_(residuals_)
if self.use_gpu:
D_ = torch.nn.functional.normalize(centroids_, p=2, dim=-1).half()
else:
D_ = torch.nn.functional.normalize(centroids_.to(torch.float32), p=2, dim=-1)
D.append(D_)
return torch.cat(D)
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