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38fc6ed | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 | # db/reader.py
"""
ๆฐๆฎๅบๆฅ่ฏขๆจกๅ
- ๆ่กๆฆๆฅ่ฏข
- ๆจกๅ่ฏฆๆ
ๆฅ่ฏข
- ๆญ็น็ปญไผ ็ถๆๆฅ่ฏข
"""
import sqlite3
import pandas as pd
from db.schema import get_connection, init_db
# โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
# ๆ่กๆฆ
# โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
def get_leaderboard(
conn: sqlite3.Connection,
prefix_filter: str = None, # ๅช็ๆไธช็ปไปถ๏ผNone=ๅ
จ้จ
layer_type: str = "standard",
limit: int = 50,
) -> pd.DataFrame:
"""
ๆ่กๆฆๆฅ่ฏข
ๆ wang_score ้ๅบๆๅ
"""
sql = """
SELECT
s.model_id,
s.prefix,
s.layer_type,
s.wang_score,
s.median_pearson_QK,
s.median_ssr_QK,
s.mean_ssr_QK,
s.median_cosU_QK,
s.median_cosU_QV,
s.median_cosV_QK,
s.median_cond_Q,
s.n_layers,
s.n_records,
s.updated_at,
-- ็ปไปถไฟกๆฏ
c.head_dim_min,
c.head_dim_max,
c.has_kv_shared,
c.has_global,
c.d_model
FROM model_summary s
LEFT JOIN components c
ON s.model_id = c.model_id AND s.prefix = c.prefix
WHERE s.layer_type = ?
"""
params = [layer_type]
if prefix_filter:
sql += " AND s.prefix LIKE ?"
params.append(f"%{prefix_filter}%")
sql += " ORDER BY s.wang_score DESC LIMIT ?"
params.append(limit)
cur = conn.cursor()
cur.execute(sql, params)
rows = cur.fetchall()
if not rows:
return pd.DataFrame()
cols = [d[0] for d in cur.description]
return pd.DataFrame([dict(zip(cols, row)) for row in rows])
# โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
# ๆจกๅ่ฏฆๆ
# โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
def get_model_summary(
conn: sqlite3.Connection,
model_id: str,
) -> pd.DataFrame:
"""่ทๅๆๆจกๅๆๆ็ปไปถ็ๆฑๆป็ป่ฎก"""
cur = conn.cursor()
cur.execute(
"""
SELECT * FROM model_summary
WHERE model_id = ?
ORDER BY prefix, layer_type
""",
(model_id,)
)
rows = cur.fetchall()
if not rows:
return pd.DataFrame()
cols = [d[0] for d in cur.description]
return pd.DataFrame([dict(zip(cols, row)) for row in rows])
def get_layer_metrics(
conn: sqlite3.Connection,
model_id: str,
prefix: str = None,
layer_type: str = None,
start_layer:int = None,
end_layer: int = None,
) -> pd.DataFrame:
"""
ๆฅ่ฏข้ๅคดๅๅงๆฐๆฎ
ๆฏๆๆ prefix / layer_type / ๅฑๅท่ๅด่ฟๆปค
"""
sql = "SELECT * FROM layer_head_metrics WHERE model_id = ?"
params = [model_id]
if prefix:
sql += " AND prefix = ?"
params.append(prefix)
if layer_type:
sql += " AND layer_type = ?"
params.append(layer_type)
if start_layer is not None:
sql += " AND layer >= ?"
params.append(start_layer)
if end_layer is not None:
sql += " AND layer <= ?"
params.append(end_layer)
sql += " ORDER BY prefix, layer, kv_head, q_head"
cur = conn.cursor()
cur.execute(sql, params)
rows = cur.fetchall()
if not rows:
return pd.DataFrame()
cols = [d[0] for d in cur.description]
return pd.DataFrame([dict(zip(cols, row)) for row in rows])
def get_analyzed_models(conn: sqlite3.Connection) -> pd.DataFrame:
"""่ทๅๆๆๅทฒๅๆๆจกๅๅ่กจ"""
cur = conn.cursor()
cur.execute(
"""
SELECT
m.model_id,
m.model_type,
m.analyzed_at,
m.analyze_sec,
COUNT(DISTINCT c.prefix) as n_components,
SUM(c.n_layers) as total_layers
FROM models m
LEFT JOIN components c ON m.model_id = c.model_id
GROUP BY m.model_id
ORDER BY m.analyzed_at DESC
"""
)
rows = cur.fetchall()
if not rows:
return pd.DataFrame()
cols = [d[0] for d in cur.description]
return pd.DataFrame([dict(zip(cols, row)) for row in rows])
# โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
# ๆญ็น็ปญไผ ็ถๆ
# โโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโโ
def get_resume_status(
conn: sqlite3.Connection,
model_id: str,
prefix: str,
) -> dict:
"""
ๆฅ่ฏขๆ (model_id, prefix) ็ๆญ็น็ปญไผ ็ถๆ
่ฟๅๅทฒๅฎๆ็ๅฑๅท้ๅๅ็ป่ฎกไฟกๆฏ
"""
cur = conn.cursor()
# ๅทฒๅฎๆ็ๅฑ
cur.execute(
"""
SELECT DISTINCT layer, COUNT(*) as n_heads
FROM layer_head_metrics
WHERE model_id = ? AND prefix = ?
GROUP BY layer
ORDER BY layer
""",
(model_id, prefix)
)
rows = cur.fetchall()
done_layers = {r[0]: r[1] for r in rows}
return {
"done_layers": set(done_layers.keys()),
"layer_detail": done_layers, # layer โ n_heads
"total_done": len(done_layers),
} |