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a4f407f595
Slope threshold of 2.0 determined qualitatively on local Mac device Minor unused import and clean-up
83 lines
2.9 KiB
Python
83 lines
2.9 KiB
Python
# Standard Packages
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import numpy as np
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import psutil
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from scipy.stats import linregress
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import secrets
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# External Packages
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import pytest
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# Internal Packages
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from khoj.processor.embeddings import EmbeddingsModel
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from khoj.utils import helpers
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def test_get_from_null_dict():
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# null handling
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assert helpers.get_from_dict(dict()) == dict()
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assert helpers.get_from_dict(dict(), None) == None
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# key present in nested dictionary
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# 1-level dictionary
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assert helpers.get_from_dict({"a": 1, "b": 2}, "a") == 1
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assert helpers.get_from_dict({"a": 1, "b": 2}, "c") == None
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# 2-level dictionary
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assert helpers.get_from_dict({"a": {"a_a": 1}, "b": 2}, "a") == {"a_a": 1}
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assert helpers.get_from_dict({"a": {"a_a": 1}, "b": 2}, "a", "a_a") == 1
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# key not present in nested dictionary
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# 2-level_dictionary
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assert helpers.get_from_dict({"a": {"a_a": 1}, "b": 2}, "b", "b_a") == None
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def test_merge_dicts():
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# basic merge of dicts with non-overlapping keys
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assert helpers.merge_dicts(priority_dict={"a": 1}, default_dict={"b": 2}) == {"a": 1, "b": 2}
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# use default dict items when not present in priority dict
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assert helpers.merge_dicts(priority_dict={}, default_dict={"b": 2}) == {"b": 2}
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# do not override existing key in priority_dict with default dict
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assert helpers.merge_dicts(priority_dict={"a": 1}, default_dict={"a": 2}) == {"a": 1}
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def test_lru_cache():
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# Test initializing cache
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cache = helpers.LRU({"a": 1, "b": 2}, capacity=2)
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assert cache == {"a": 1, "b": 2}
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# Test capacity overflow
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cache["c"] = 3
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assert cache == {"b": 2, "c": 3}
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# Test delete least recently used item from LRU cache on capacity overflow
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cache["b"] # accessing 'b' makes it the most recently used item
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cache["d"] = 4 # so 'c' is deleted from the cache instead of 'b'
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assert cache == {"b": 2, "d": 4}
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@pytest.mark.skip(reason="Memory leak exists on GPU, MPS devices")
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def test_encode_docs_memory_leak():
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# Arrange
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iterations = 50
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batch_size = 20
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embeddings_model = EmbeddingsModel()
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memory_usage_trend = []
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# Act
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# Encode random strings repeatedly and record memory usage trend
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for iteration in range(iterations):
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random_docs = [" ".join(secrets.token_hex(5) for _ in range(10)) for _ in range(batch_size)]
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a = [embeddings_model.embed_documents(random_docs)]
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memory_usage_trend += [psutil.Process().memory_info().rss / (1024 * 1024)]
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print(f"{iteration:02d}, {memory_usage_trend[-1]:.2f}", flush=True)
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# Calculate slope of line fitting memory usage history
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memory_usage_trend = np.array(memory_usage_trend)
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slope, _, _, _, _ = linregress(np.arange(len(memory_usage_trend)), memory_usage_trend)
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# Assert
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# If slope is positive memory utilization is increasing
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# Positive threshold of 2, from observing memory usage trend on MPS vs CPU device
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assert slope < 2, f"Memory usage increasing at ~{slope:.2f} MB per iteration"
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