Build latest book artifacts / build (push) Canceled after 0s
dependency resolution / resolve (3.11) (push) Canceled after 0s
dependency resolution / resolve (3.13) (push) Canceled after 0s
deploy-pages / build (push) Canceled after 0s
deploy-pages / deploy (push) Canceled after 0s
i18n consistency check / check (push) Canceled after 0s
provider adoption tests / test (chapter2/context-compression) (push) Canceled after 0s
provider adoption tests / test (chapter2/prompt-injection) (push) Canceled after 0s
provider adoption tests / test (chapter2/system-hint) (push) Canceled after 0s
provider adoption tests / test (chapter3/log-sanitization) (push) Canceled after 0s
web-search-agent tests / test (push) Canceled after 0s
web-search-agent tests / agentbook (push) Canceled after 0s
21 lines
678 B
Python
21 lines
678 B
Python
import numpy as np
|
|
|
|
from run_attention_experiment import matrix_metrics, region_indices, resolve_layer
|
|
|
|
|
|
def test_resolve_negative_layer():
|
|
assert resolve_layer(-1, 28) == 27
|
|
assert resolve_layer(13, 28) == 13
|
|
|
|
|
|
def test_region_indices_separates_thinking_and_answer():
|
|
tokens = ["prompt", "<think>", "work", "</think>", "answer"]
|
|
assert region_indices(tokens, 1) == {"thinking": [1, 2, 3], "answer": [4]}
|
|
|
|
|
|
def test_matrix_metrics_detects_causal_triangle_and_sink():
|
|
matrix = np.asarray([[1.0, 0.0], [0.75, 0.25]])
|
|
metrics = matrix_metrics(matrix)
|
|
assert metrics["causal_upper_triangle_max"] == 0.0
|
|
assert metrics["attention_sink_mean"] == 0.875
|