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* Adding cosine distance - I didn't know we had that as a first level distance metric * Making our mkl and iomp linking game more rigorously defined for the ubuntus * Included latest as a path fragment twice on accident * libmkl_def.so is named something different when installed via the intel oneapi installer * Making a number of changes to homogenize our api (same parameters, minimize parameters as much as possible, etc) * Stashing this and going to work on the CICD stuff, it's driving me nuts * Fairly happy with the Python API now. Documentation needs another pass, the @Overloads in the .pyi files need to be addressed, and documentation checked again. The apps folder also needs updating to use fire instead of argparse * Updated build to not use tcmalloc for pybind, as well as fixed the pyproject.toml so that cibuildwheel can actually successfully build our project. * Making a change to in-mem-static for the new api and also adjusting the comment in in-mem-dynamic a bit, though... I probably shouldn't have
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,152 @@ | ||
| import diskannpy as dap | ||
| import numpy as np | ||
| import numpy.typing as npt | ||
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| import fire | ||
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| from contextlib import contextmanager | ||
| from time import perf_counter | ||
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| from typing import Tuple | ||
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| def _basic_setup( | ||
| dtype: str, | ||
| query_vectors_file: str | ||
| ) -> Tuple[dap.VectorDType, npt.NDArray[dap.VectorDType]]: | ||
| _dtype = dap.valid_dtype(dtype) | ||
| vectors_to_query = dap.vectors_from_binary(query_vectors_file, dtype=_dtype) | ||
| return _dtype, vectors_to_query | ||
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| def dynamic( | ||
| dtype: str, | ||
| index_vectors_file: str, | ||
| query_vectors_file: str, | ||
| build_complexity: int, | ||
| graph_degree: int, | ||
| K: int, | ||
| search_complexity: int, | ||
| num_insert_threads: int, | ||
| num_search_threads: int, | ||
| gt_file: str = "", | ||
| ): | ||
| _dtype, vectors_to_query = _basic_setup(dtype, query_vectors_file) | ||
| vectors_to_index = dap.vectors_from_binary(index_vectors_file, dtype=_dtype) | ||
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| npts, ndims = vectors_to_index.shape | ||
| index = dap.DynamicMemoryIndex( | ||
| "l2", _dtype, ndims, npts, build_complexity, graph_degree | ||
| ) | ||
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| tags = np.arange(1, npts+1, dtype=np.uintc) | ||
| timer = Timer() | ||
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| with timer.time("batch insert"): | ||
| index.batch_insert(vectors_to_index, tags, num_insert_threads) | ||
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| delete_tags = np.random.choice( | ||
| np.array(range(1, npts + 1, 1), dtype=np.uintc), | ||
| size=int(0.5 * npts), | ||
| replace=False | ||
| ) | ||
| with timer.time("mark deletion"): | ||
| for tag in delete_tags: | ||
| index.mark_deleted(tag) | ||
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| with timer.time("consolidation"): | ||
| index.consolidate_delete() | ||
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| deleted_data = vectors_to_index[delete_tags - 1, :] | ||
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| with timer.time("re-insertion"): | ||
| index.batch_insert(deleted_data, delete_tags, num_insert_threads) | ||
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| with timer.time("batch searched"): | ||
| tags, dists = index.batch_search(vectors_to_query, K, search_complexity, num_search_threads) | ||
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| # res_ids = tags - 1 | ||
| # if gt_file != "": | ||
| # recall = utils.calculate_recall_from_gt_file(K, res_ids, gt_file) | ||
| # print(f"recall@{K} is {recall}") | ||
|
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| def static( | ||
| dtype: str, | ||
| index_directory: str, | ||
| index_vectors_file: str, | ||
| query_vectors_file: str, | ||
| build_complexity: int, | ||
| graph_degree: int, | ||
| K: int, | ||
| search_complexity: int, | ||
| num_threads: int, | ||
| gt_file: str = "", | ||
| index_prefix: str = "ann" | ||
| ): | ||
| _dtype, vectors_to_query = _basic_setup(dtype, query_vectors_file) | ||
| timer = Timer() | ||
| with timer.time("build static index"): | ||
| # build index | ||
| dap.build_memory_index( | ||
| data=index_vectors_file, | ||
| metric="l2", | ||
| vector_dtype=_dtype, | ||
| index_directory=index_directory, | ||
| complexity=build_complexity, | ||
| graph_degree=graph_degree, | ||
| num_threads=num_threads, | ||
| index_prefix=index_prefix, | ||
| alpha=1.2, | ||
| use_pq_build=False, | ||
| num_pq_bytes=8, | ||
| use_opq=False, | ||
| ) | ||
|
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| with timer.time("load static index"): | ||
| # ready search object | ||
| index = dap.StaticMemoryIndex( | ||
| metric="l2", | ||
| vector_dtype=_dtype, | ||
| data_path=index_vectors_file, | ||
| index_directory=index_directory, | ||
| num_threads=num_threads, # this can be different at search time if you would like | ||
| initial_search_complexity=search_complexity, | ||
| index_prefix=index_prefix | ||
| ) | ||
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| ids, dists = index.batch_search(vectors_to_query, K, search_complexity, num_threads) | ||
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| # if gt_file != "": | ||
| # recall = utils.calculate_recall_from_gt_file(K, ids, gt_file) | ||
| # print(f"recall@{K} is {recall}") | ||
|
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| def dynamic_clustered(): | ||
| pass | ||
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| def generate_clusters(): | ||
| pass | ||
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| class Timer: | ||
| def __init__(self): | ||
| self._start = -1 | ||
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| @contextmanager | ||
| def time(self, message: str): | ||
| start = perf_counter() | ||
| if self._start == -1: | ||
| self._start = start | ||
| yield | ||
| now = perf_counter() | ||
| print(f"Operation {message} completed in {(now - start):.3f}s, total: {(now - self._start):.3f}s") | ||
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| if __name__ == "__main__": | ||
| fire.Fire({ | ||
| "in-mem-dynamic": dynamic, | ||
| "in-mem-static": static, | ||
| "in-mem-dynamic-clustered": dynamic_clustered, | ||
| "generate-clusters": generate_clusters | ||
| }, name="cli") |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,2 @@ | ||
| diskannpy | ||
| fire |
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