feat(m1): attribute vocab + sampling (T1.2)

- ATTRIBUTE_VOCAB (16 AIOps channels w/ units)
- FREQ_BANDS (6 sampling resolutions)
- sample_attributes(n_channels, seed) -> unique-name, reproducible
- tests/test_attributes.py (8 tests, RED->GREEN)

Exit criteria: fields complete, count correct, seed-reproducible.

openspec/changes/ts-as-modality/tasks.md

@@ -6,7 +6,7 @@
 ## 1. M1 · 数据管线
 
 - [x] 1.1 项目脚手架：创建 `pyproject.toml`（torch/transformers/peft/accelerate/datasets/numpy/tqdm/pyyaml/pytest）、`src/tsmm/` 模块骨架、`configs/`、`scripts/`、`tests/`，并建 `data/`、`checkpoints/` 加 `.gitignore`（验证：`python -c "import tsmm"` 不报错；`pytest tests/` 可跑）
-- [ ] 1.2 属性词表与采样 `data/attributes.py`：定义变量名词表/单位/采样率档位，`sample_attributes(n_channels)` 返回结构化属性（验证：单测字段齐全、可复现）
+- [x] 1.2 属性词表与采样 `data/attributes.py`：定义变量名词表/单位/采样率档位，`sample_attributes(n_channels)` 返回结构化属性（验证：单测字段齐全、可复现）
 - [ ] 1.3 成分模型与时序合成 `data/synthesis.py`：`generate_series`（趋势+周期+基线+噪声）、`inject_anomaly`（尖刺/水平偏移/方差膨胀/缺失段/缓慢漂移，返回逐点 labels+段元数据）、`couple_event`（t 处阶跃+事件文本）、`add_missing`（NaN 占位）（验证：给定 seed 确定；异常段与 labels 一致；事件处有阶跃）
 - [ ] 1.4 指令与回答生成 `data/instruct.py`：6 类指令模板（描述/异常/根因/预测/比较/事件关联）+ Evol-Instruct 演化，`build_instruction` 产 JSON 区间/数值/文本回答（验证：异常类 JSON 可 `json.loads` 且段与 labels 吻合）
 - [ ] 1.5 离线生成脚本 `scripts/gen_synthetic.py`：多进程产 `align.jsonl`(50万)/`sft.jsonl`(10万)/`eval_synth.jsonl`(2k held-out)，支持 `--n/--out/--seed/--workers` + tqdm（验证：`--n 1000` 跑通，抽样 5 条 schema 合规）

src/tsmm/data/attributes.py

@@ -0,0 +1,77 @@
+"""Variable attribute vocabulary + attribute sampling (T1.2).
+
+Defines a curated vocabulary of AIOps-relevant channel *names* with their
+*units* and a set of *sampling frequency bands*. ``sample_attributes`` draws a
+reproducible, unique-name subset to describe the channels of a multivariate
+series — metadata that the instruction generator and the multimodal splicer
+will later turn into text tokens like ``[属性: CPU usage (%), 1s]``.
+"""
+
+from __future__ import annotations
+
+import random
+from typing import Any
+
+# ─── vocabulary ────────────────────────────────────────────────────────────
+# Each entry: name (human label), unit. Frequencies are drawn separately from
+# FREQ_BANDS so the same channel name can appear at different resolutions.
+ATTRIBUTE_VOCAB: list[dict[str, str]] = [
+    {"name": "CPU usage", "unit": "%"},
+    {"name": "Memory usage", "unit": "%"},
+    {"name": "Disk I/O", "unit": "MB/s"},
+    {"name": "Network inbound traffic", "unit": "Mbps"},
+    {"name": "Network outbound traffic", "unit": "Mbps"},
+    {"name": "Request rate", "unit": "req/s"},
+    {"name": "Request latency p50", "unit": "ms"},
+    {"name": "Request latency p99", "unit": "ms"},
+    {"name": "Error rate", "unit": "%"},
+    {"name": "Temperature", "unit": "°C"},
+    {"name": "Queue length", "unit": "tasks"},
+    {"name": "Active connections", "unit": "conn"},
+    {"name": "Cache hit ratio", "unit": "%"},
+    {"name": "GPU utilization", "unit": "%"},
+    {"name": "Battery level", "unit": "%"},
+    {"name": "Pod restart count", "unit": "count"},
+]
+
+# Sampling frequency bands (resolution at which the series is recorded).
+FREQ_BANDS: list[str] = ["1s", "5s", "10s", "30s", "1min", "5min"]
+
+
+def sample_attributes(
+    n_channels: int, *, seed: int | None = None
+) -> list[dict[str, Any]]:
+    """Sample ``n_channels`` distinct channel attribute descriptors.
+
+    Each descriptor is a dict with keys ``name``, ``unit`` (drawn from
+    :data:`ATTRIBUTE_VOCAB`) and ``freq`` (drawn from :data:`FREQ_BANDS`).
+    Channel names are guaranteed unique within the returned list.
+
+    Parameters
+    ----------
+    n_channels:
+        Number of channels to sample. Must be ``<= len(ATTRIBUTE_VOCAB)``.
+    seed:
+        RNG seed for reproducibility.
+
+    Raises
+    ------
+    ValueError
+        If ``n_channels`` exceeds the available distinct names.
+    """
+    if not isinstance(n_channels, int) or n_channels < 0:
+        raise ValueError(f"n_channels must be a non-negative int, got {n_channels!r}")
+    if n_channels > len(ATTRIBUTE_VOCAB):
+        raise ValueError(
+            f"requested {n_channels} channels but vocab only has "
+            f"{len(ATTRIBUTE_VOCAB)} distinct names"
+        )
+
+    rng = random.Random(seed)
+    vocab = list(ATTRIBUTE_VOCAB)
+    chosen = rng.sample(vocab, n_channels)
+    freqs = rng.choices(FREQ_BANDS, k=n_channels)
+    return [
+        {"name": c["name"], "unit": c["unit"], "freq": f}
+        for c, f in zip(chosen, freqs)
+    ]

tests/test_attributes.py

@@ -0,0 +1,62 @@
+"""Tests for attribute vocab + sampling (T1.2)."""
+import numpy as np
+
+from tsmm.data.attributes import sample_attributes, ATTRIBUTE_VOCAB, FREQ_BANDS
+
+
+# ─── field completeness ────────────────────────────────────────────────────
+def test_each_sampled_attribute_has_required_fields():
+    attrs = sample_attributes(n_channels=4, seed=0)
+    assert len(attrs) == 4
+    for a in attrs:
+        assert set(a.keys()) >= {"name", "unit", "freq"}
+        assert isinstance(a["name"], str) and a["name"]
+        assert isinstance(a["unit"], str) and a["unit"]
+        assert isinstance(a["freq"], str) and a["freq"]
+
+
+def test_names_are_unique_within_a_sample():
+    attrs = sample_attributes(n_channels=5, seed=1)
+    names = [a["name"] for a in attrs]
+    assert len(names) == len(set(names)), "channel names must be unique"
+
+
+# ─── correctness of count ──────────────────────────────────────────────────
+def test_count_matches_request():
+    for n in (1, 2, 8):
+        assert len(sample_attributes(n_channels=n, seed=0)) == n
+
+
+def test_raises_when_request_exceeds_vocab():
+    # vocab is finite; requesting more channels than distinct names must raise.
+    import pytest
+
+    too_many = len(ATTRIBUTE_VOCAB) + 1
+    with pytest.raises(ValueError):
+        sample_attributes(n_channels=too_many, seed=0)
+
+
+# ─── reproducibility ───────────────────────────────────────────────────────
+def test_same_seed_gives_same_result():
+    a = sample_attributes(n_channels=6, seed=42)
+    b = sample_attributes(n_channels=6, seed=42)
+    assert a == b
+
+
+def test_different_seed_gives_different_result():
+    a = sample_attributes(n_channels=6, seed=1)
+    b = sample_attributes(n_channels=6, seed=2)
+    assert a != b
+
+
+# ─── vocab sanity ──────────────────────────────────────────────────────────
+def test_attribute_vocab_is_non_empty():
+    assert len(ATTRIBUTE_VOCAB) >= 6  # CPU/mem/traffic/latency/temp/rps ...
+    for entry in ATTRIBUTE_VOCAB:
+        assert "name" in entry and "unit" in entry
+
+
+def test_freq_bands_are_documented():
+    assert len(FREQ_BANDS) >= 2
+    for band in FREQ_BANDS:
+        assert isinstance(band, str) and band  # e.g. "1s", "10s", "1min"