Mini-Nav/hw/sim/tests/top/write_noise/test_write_noise.py

# -*- coding: utf-8 -*-
"""
CAM 写入噪声（Write Noise）集成测试 —— 专用配置。

本文件测试 WRITE_NOISE_EN=1, READ_NOISE_EN=0 配置下，
写入噪声模块的正确性。默认噪声率约 1%（NUM=1, DEN=100）。

=== 测试列表 ===

  1. default_noise_reproducible  — 固定种子 = 确定性噪声，两次运行结果一致
  2. exact_noise_model_match     — RTL 存储的哈希与 ref_model.py 的 PRNG 掩码逐位匹配
  3. zero_rate_noise             — 写入噪声模块连接但 RATE_NUM=0 → 无翻转
  4. full_rate_noise             — 100% 写入噪声率，与 Python 模型对比

=== 架构背景 ===

  写入噪声流水线位置：Write Noise → Banked Core Storage → Read Noise → Match Engine
  本测试覆盖完整的 cam_top 链路，写入噪声为唯一活跃噪声源。

=== Makefile 子目标 ===

  test-zero-rate  : make test-zero-rate  (WRITE_NOISE_RATE_NUM=0)
  test-full-rate  : make test-full-rate   (WRITE_NOISE_RATE_NUM=1, RATE_DEN=1, SIM_DEBUG)
"""

from __future__ import annotations

import cocotb
import numpy as np
from cocotb.clock import Clock
from cocotb.triggers import RisingEdge
from model.ref_model import (
    generate_write_flip_mask,
    match_top1,
    random_hashes,
)
from tests.top.utils import (
    collect_topk,
    dut_hash_bits,
    dut_num_rows,
    get_param,
    query_once,
    query_topk_once,
    reset_dut,
    wait_idle,
    write_row,
    write_rows,
)


# ═══════════════════════════════════════════════════════════════════════════════
# 测试 1：默认噪声 ~1%、可复现
# ── 固定种子 → 两次相同写入产生相同结果
# ═══════════════════════════════════════════════════════════════════════════════


@cocotb.test()
async def default_noise_reproducible(dut):
    """可复现性测试：相同种子、相同数据 → 两次独立运行结果一致。

    流程：
      1. 写入全部行，查询 row 50 → 记录 top1_index 和 top1_score
      2. 复位
      3. 再次写入相同数据，查询相同行 → 记录结果
      4. 断言两次结果完全一致

    如果结果不一致，说明 RTL 的 PRNG 状态没有正确复位，
    或存在跨运行的状态残留。
    """
    cocotb.start_soon(Clock(dut.clk, 10, unit="ns").start())
    await reset_dut(dut)

    num_rows = dut_num_rows(dut)
    hash_bits = dut_hash_bits(dut)
    rng = np.random.default_rng(42)
    rows = random_hashes(rng, num_rows, width=hash_bits)

    await write_rows(dut, rows)
    query = rows[min(50, num_rows - 1)]
    top1_index_1, top1_score_1, _ = await query_once(dut, query)

    await reset_dut(dut)

    await write_rows(dut, rows)
    top1_index_2, top1_score_2, _ = await query_once(dut, query)

    assert top1_index_1 == top1_index_2
    assert top1_score_1 == top1_score_2


# ═══════════════════════════════════════════════════════════════════════════════
# 测试 2：精确 RTL-vs-模型 PRNG 掩码匹配
# ── RTL 存储的哈希与 ref_model.py 生成的掩码逐位一致
# ═══════════════════════════════════════════════════════════════════════════════


@cocotb.test()
async def exact_noise_model_match(dut):
    """精确噪声模型匹配：RTL 的 PRNG 输出必须与 Python 参考模型逐位一致。

    测试方法：
      1. 用固定 RTL seed 和已知噪声参数，在 Python 中预计算每行的 flip 掩码
      2. 预期存储值 = 原始值 XOR flip_mask
      3. 写入原始值到 RTL，查询预期存储值
      4. 断言每行 score = HASH_BITS（完全匹配）

    这验证了 RTL 的 LFSR 实现与 Python 模型的 PRNG 使用相同的
    多项式、相同的位宽、相同的种子初始化序列。
    """
    if not hasattr(dut, "score_debug_flat"):
        dut._log.info("Skipping exact_noise_model_match: requires SIM_DEBUG.")
        return

    rtol = None
    atol = None
    cocotb.start_soon(Clock(dut.clk, 10, unit="ns").start())
    await reset_dut(dut)

    hash_bits = dut_hash_bits(dut)
    noise_bits = get_param(dut, "WRITE_NOISE_BITS", 8)
    rate_num = get_param(dut, "WRITE_NOISE_RATE_NUM", 1)
    rate_den = get_param(dut, "WRITE_NOISE_RATE_DEN", 100)

    n_test_rows = 4
    rng = np.random.default_rng(99)
    rows = random_hashes(rng, n_test_rows, width=hash_bits)

    RTL_SEED = 0xB504_F32D_B504_F32D
    prng_state = (RTL_SEED << 64) | RTL_SEED
    expected_stored = []
    for row in rows:
        flip, prng_state = generate_write_flip_mask(
            prng_state,
            hash_bits,
            noise_bits,
            rate_num,
            rate_den,
        )
        expected_stored.append(row ^ flip)

    for idx, val in enumerate(rows):
        await write_row(dut, idx, val)

    for idx, expected in enumerate(expected_stored):
        top1_index, top1_score, score_debug = await query_once(dut, expected)
        assert score_debug is not None, (
            "score_debug required for mask match verification"
        )
        assert int(score_debug[idx]) == hash_bits, (
            f"Row {idx}: expected stored hash to match model prediction, "
            f"score={score_debug[idx]} != {hash_bits}"
        )


# ═══════════════════════════════════════════════════════════════════════════════
# 测试 3：零噪声率（WRITE_NOISE_EN=1, RATE_NUM=0）
# ── 噪声模块已连接但翻转概率为 0 → 行为应与无噪声一致
# ═══════════════════════════════════════════════════════════════════════════════


@cocotb.test()
async def zero_rate_noise(dut):
    """零速率噪声：WRITE_NOISE_RATE_NUM=0 时不应有任何位被翻转。

    这是噪声模块的边界测试——验证 RATE_NUM=0 确实禁用了翻转，
    而非产生「默认速率」的噪声。
    """
    rate_num = get_param(dut, "WRITE_NOISE_RATE_NUM", 1)
    if rate_num != 0:
        dut._log.info(
            "Skipping zero_rate_noise: requires WRITE_NOISE_RATE_NUM=0."
        )
        return
    cocotb.start_soon(Clock(dut.clk, 10, unit="ns").start())
    await reset_dut(dut)

    num_rows = dut_num_rows(dut)
    hash_bits = dut_hash_bits(dut)
    rng = np.random.default_rng(1)
    rows = random_hashes(rng, num_rows, width=hash_bits)
    query_index = min(123, num_rows - 1)
    query = rows[query_index]

    await write_rows(dut, rows)
    top1_index, top1_score, score_debug = await query_once(dut, query)

    expected = match_top1(query, rows, width=hash_bits)

    assert top1_index == expected.top1_index
    assert top1_score == expected.top1_score
    assert top1_index == query_index
    assert top1_score == hash_bits

    if score_debug is not None:
        assert np.array_equal(score_debug, expected.scores)


# ═══════════════════════════════════════════════════════════════════════════════
# 测试 4：100% 噪声率（RATE_NUM=1, RATE_DEN=1）
# ── 每组都翻转 → 精确验证 PRNG 掩码生成
# ═══════════════════════════════════════════════════════════════════════════════


@cocotb.test()
async def full_rate_noise(dut):
    """完全速率噪声：每组 100% 翻转概率。

    使用固定 RTL seed (0xB504F32DB504F32D)，用 Python 模型预计算
    写入全 0 和全 1 行后应存储的哈希值，然后验证 RTL 实际存储的哈希
    与模型预测完全一致。

    这是最低容忍度的噪声测试——要求 score_debug_flat（SIM_DEBUG）
    且每行的分数必须精确等于 HASH_BITS。
    """
    rate_num = get_param(dut, "WRITE_NOISE_RATE_NUM", 1)
    rate_den = get_param(dut, "WRITE_NOISE_RATE_DEN", 100)
    if rate_num != 1 or rate_den != 1:
        dut._log.info(
            "Skipping full_rate_noise: requires WRITE_NOISE_RATE_NUM=1, RATE_DEN=1."
        )
        return
    if not hasattr(dut, "score_debug_flat"):
        dut._log.info(
            "Skipping full_rate_noise: requires SIM_DEBUG (score_debug_flat)."
        )
        return

    cocotb.start_soon(Clock(dut.clk, 10, unit="ns").start())
    await reset_dut(dut)

    hash_bits = dut_hash_bits(dut)
    num_rows = dut_num_rows(dut)
    noise_bits = get_param(dut, "WRITE_NOISE_BITS", 8)
    all_zero = 0
    all_one = (1 << hash_bits) - 1

    RTL_SEED = 0xB504_F32D_B504_F32D
    prng_state = (RTL_SEED << 64) | RTL_SEED

    flip0, prng_state = generate_write_flip_mask(
        prng_state,
        hash_bits,
        noise_bits,
        rate_num,
        rate_den,
    )
    expected_row0 = all_zero ^ flip0

    flip1, prng_state = generate_write_flip_mask(
        prng_state,
        hash_bits,
        noise_bits,
        rate_num,
        rate_den,
    )
    expected_row1 = all_one ^ flip1

    rows = [0] * num_rows
    rows[0] = all_zero
    rows[1] = all_one

    await write_rows(dut, rows)

    top1_index, top1_score, score_debug = await query_once(dut, expected_row0)
    assert score_debug is not None, "score_debug required for full_rate_noise"
    assert int(score_debug[0]) == hash_bits, (
        f"Row 0: expected exact match, score={score_debug[0]} != {hash_bits}"
    )

    top1_index, top1_score, score_debug = await query_once(dut, expected_row1)
    assert score_debug is not None
    assert int(score_debug[1]) == hash_bits, (
        f"Row 1: expected exact match, score={score_debug[1]} != {hash_bits}"
    )