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SikongJueluo
8b4d4c1b57
- Make cam_read_noise a pass-through module, removing all noise injection logic - Switch write noise to use noise_mask_bernoulli instead of noise_mask_grouped - Add state machine to cam_write_noise for mask generation timing - Remove noise_mask_grouped.sv (no longer needed) - Remove read noise parameters from cam_noisy and cam_top - Update simulation and benchmark code to reflect read noise removal - Sync documentation to reflect Phase 2 architecture
103 lines
3.8 KiB
Python
103 lines
3.8 KiB
Python
# -*- coding: utf-8 -*-
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"""
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参考模型（ref_model）的纯 Python 单元测试 — Phase 2 cleaned.
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本文件不涉及任何 RTL / Verilator 仿真，仅验证 Python 参考模型的正确性。
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Phase 2 后只保留 pure matching 函数；所有 grouped/read-noise helpers 已删除。
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测试覆盖：
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1. XNOR 评分语义 — 确认是「匹配位数」而非「汉明距离」
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2. Top-1 matching — 纯匹配，正确选出最高分索引
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3. Top-K matching — 返回排序后的行索引列表
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4. Top-K 排序规则 — 分数降序、平局行号升序
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5. Top-K k 超过行数时 clamp
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"""
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from __future__ import annotations
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from model.ref_model import (
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match_top1,
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match_topk,
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match_topk_from_scores,
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xnor_popcount_score,
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)
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import numpy as np
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# ==============================================================================
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# 测试 1：评分函数语义 — 确认是「XNOR 匹配位数」而非「汉明距离」
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# ==============================================================================
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def test_score_is_bit_match_popcount_not_hamming_distance():
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"""
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关键语义验证：xnor_popcount_score 计算的是匹配位的数量，不是汉明距离。
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示例：
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query = 0b1010
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stored = 0b1000
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XNOR = 0b1101 → popcount = 3（有 3 个位匹配）
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汉明距离 = 1 → 只有一个位不同
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为什么这个区分很重要：
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- 如果 RTL 或模型错误地使用了汉明距离作为分数，则：
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完全匹配的分数会是 0 而非 hash_bits（512）
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Top-K 排序会颠倒（分数低的反而排前面）
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- 这会导致整个 CAM 检索系统返回错误结果
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"""
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query = 0b1010
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stored = 0b1000
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assert xnor_popcount_score(query, stored, width=4) == 3
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# ==============================================================================
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# 测试 2：Top-1 matching
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# ==============================================================================
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def test_match_top1_selects_highest_xnor_score_with_row_index_tiebreak():
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"""Top-1 应选出 XNOR 分最高的行；平局时选最小行号。"""
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rows = [0b0000, 0b1111, 0b0011, 0b0101]
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query = 0b0000
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result = match_top1(query, rows, width=4)
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assert result.top1_index == 0
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assert result.top1_score == 4
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assert result.scores.tolist() == [4, 0, 2, 2]
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# ==============================================================================
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# 测试 3：Top-K matching
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# ==============================================================================
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def test_match_topk_scores_rows_by_xnor_popcount():
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"""match_topk 通过 xnor_popcount 计算分数，返回排序后的行索引和分数数组。"""
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rows = [0b0000, 0b1111, 0b0011, 0b0101]
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query = 0b0000
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indices, scores = match_topk(query, rows, width=4, k=3)
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assert scores.tolist() == [4, 0, 2, 2]
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assert indices == [0, 2, 3]
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# ==============================================================================
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# 测试 4：Top-K 排序 — 分数降序、平局行号升序
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# ==============================================================================
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def test_match_topk_from_scores_uses_score_desc_then_row_asc():
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"""Top-K 排序规则：分数越大越优先；分数相同时行号越小越优先。"""
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scores = np.array([7, 9, 9, 2, 7], dtype=np.int32)
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assert match_topk_from_scores(scores, 4) == [1, 2, 0, 4]
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# ==============================================================================
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# 测试 5：Top-K k 超过行数时 clamp
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# ==============================================================================
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def test_match_topk_clamps_k_to_row_count():
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"""当 k 超过实际行数时，返回所有行（按排序）。"""
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indices, scores = match_topk(0, [0, 1], width=1, k=5)
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assert scores.tolist() == [1, 0]
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assert indices == [0, 1]
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