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45 lines
1.5 KiB
Python
45 lines
1.5 KiB
Python
import numpy as np
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import msgpack
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import math
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import torch
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import faiss
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import tqdm
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n_dims = 1152
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output_code_size = 64
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output_code_bits = 8
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output_codebook_size = 2**output_code_bits
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n_dims_per_code = n_dims // output_code_size
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dataset = np.fromfile("embeddings.bin", dtype=np.float16).reshape(-1, n_dims)[:100000].astype(np.float32)
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queryset = np.fromfile("query.bin", dtype=np.float16).reshape(-1, n_dims)[:100000].astype(np.float32)
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device = "cpu"
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def pq_assign(centroids, batch):
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quantized = torch.zeros_like(batch)
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# Assign to nearest centroid in each subspace
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for dmin in range(0, n_dims, n_dims_per_code):
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dmax = dmin + n_dims_per_code
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similarities = torch.matmul(batch[:, dmin:dmax], centroids[:, dmin:dmax].T)
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assignments = similarities.argmax(dim=1)
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quantized[:, dmin:dmax] = centroids[assignments, dmin:dmax]
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return quantized
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with open("opq.msgpack", "rb") as f:
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data = msgpack.unpack(f)
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centroids = torch.tensor(data["centroids"], device=device).reshape(2**output_code_bits, n_dims)
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projection = torch.tensor(data["transform"], device=device).reshape(n_dims, n_dims)
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vectors = torch.tensor(dataset, device=device)
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queries = torch.tensor(queryset, device=device)
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sample_size = 64
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qsample = pq_assign(centroids, vectors[:sample_size] @ projection)
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print(qsample)
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print(vectors[:sample_size])
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exact_results = vectors[:sample_size] @ queries[0]
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approx_results = qsample @ (projection @ queries[0])
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print(np.argsort(approx_results))
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print(np.argsort(exact_results))
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