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Browse files- examples/Model use +315 -303
- lynxkite-app/web/src/workspace/nodes/NodeParameter.tsx +108 -40
- lynxkite-graph-analytics/src/lynxkite_graph_analytics/core.py +14 -3
- lynxkite-graph-analytics/src/lynxkite_graph_analytics/lynxkite_ops.py +43 -13
- lynxkite-graph-analytics/src/lynxkite_graph_analytics/pytorch_model_ops.py +68 -41
examples/Model use
CHANGED
@@ -8,31 +8,31 @@
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@@ -1227,27 +1034,23 @@
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[
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|
1012 |
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|
1013 |
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|
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+
],
|
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"loss_inputs": [
|
1087 |
+
"Input__label_1_y",
|
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"Activation_2_x"
|
1089 |
],
|
1090 |
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|
1091 |
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"Activation_2_x"
|
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],
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1100 |
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1101 |
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1103 |
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"name": "bundle",
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1105 |
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"position": "left",
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"type": {
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1107 |
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"type": "<class 'lynxkite_graph_analytics.core.Bundle'>"
|
1108 |
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}
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}
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},
|
1111 |
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"name": "Define model",
|
1112 |
+
"outputs": {
|
1113 |
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"output": {
|
1114 |
+
"name": "output",
|
1115 |
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"position": "right",
|
1116 |
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"type": {
|
1117 |
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"type": "None"
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}
|
1119 |
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}
|
1120 |
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},
|
1121 |
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"params": {
|
1122 |
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"model_workspace": {
|
1123 |
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1124 |
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"name": "model_workspace",
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}
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},
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"save_as": {
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"default": "model",
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"name": "save_as",
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"type": {
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"type": "<class 'str'>"
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}
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"type": "basic"
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},
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"params": {
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"model_workspace": "Model definition",
|
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+
"save_as": "model"
|
1142 |
+
},
|
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+
"status": "done",
|
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"title": "Define model"
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},
|
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"dragHandle": ".bg-primary",
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"height": 537.0,
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"id": "Define model 1",
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"position": {
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"x": 795.0,
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"y": -45.0
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"width": 498.0
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},
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{
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"data": {
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"__execution_delay": 0.0,
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"collapsed": null,
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"display": {
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"dataframes": {
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"df": {
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1163 |
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"columns": [
|
1164 |
+
"x",
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+
"y"
|
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+
]
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},
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"df_test": {
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"columns": [
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"x",
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|
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]
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},
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"df_train": {
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"columns": [
|
1176 |
+
"x",
|
1177 |
+
"y"
|
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]
|
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}
|
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},
|
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"other": {
|
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"model": {
|
1183 |
+
"model": {
|
1184 |
+
"inputs": [
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1185 |
+
"Input__embedding_1_x"
|
1186 |
+
],
|
1187 |
+
"loss_inputs": [
|
1188 |
+
"Input__label_1_y",
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1189 |
+
"Activation_2_x"
|
1190 |
+
],
|
1191 |
+
"outputs": [
|
1192 |
+
"Activation_2_x"
|
1193 |
+
],
|
1194 |
+
"trained": true
|
1195 |
+
},
|
1196 |
+
"type": "model"
|
1197 |
+
}
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1198 |
+
},
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1199 |
+
"relations": []
|
1200 |
+
},
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1201 |
+
"error": null,
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1202 |
"meta": {
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1203 |
"inputs": {
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1204 |
"bundle": {
|
|
|
1231 |
"default": null,
|
1232 |
"name": "input_mapping",
|
1233 |
"type": {
|
1234 |
+
"type": "<class 'lynxkite_graph_analytics.lynxkite_ops.ModelTrainingInputMapping'>"
|
1235 |
}
|
1236 |
},
|
1237 |
+
"model_name": {
|
1238 |
+
"default": "model",
|
1239 |
+
"name": "model_name",
|
1240 |
"type": {
|
1241 |
"type": "<class 'str'>"
|
1242 |
}
|
1243 |
+
}
|
1244 |
+
},
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1245 |
+
"position": {
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1246 |
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"x": 666.0,
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1247 |
+
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1248 |
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},
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1249 |
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"type": "basic"
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1250 |
+
},
|
1251 |
+
"params": {
|
1252 |
+
"epochs": "1000",
|
1253 |
+
"input_mapping": "{\"map\":{\"Input__embedding_1_x\":{\"column\":\"x\",\"df\":\"df_train\"},\"Input__label_1_y\":{\"column\":\"y\",\"df\":\"df_train\"}}}",
|
1254 |
+
"model_name": "model"
|
1255 |
+
},
|
1256 |
+
"status": "done",
|
1257 |
+
"title": "Train model"
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1258 |
+
},
|
1259 |
+
"dragHandle": ".bg-primary",
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1260 |
+
"height": 604.0,
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1261 |
+
"id": "Train model 2",
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1262 |
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"position": {
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1263 |
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"x": 1399.5245787239226,
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1264 |
+
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1265 |
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"type": "basic",
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1267 |
+
"width": 586.0
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1268 |
+
},
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1269 |
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{
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1270 |
+
"data": {
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1271 |
+
"__execution_delay": 0.0,
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1272 |
+
"collapsed": null,
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1273 |
+
"display": {
|
1274 |
+
"dataframes": {
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1275 |
+
"df": {
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1276 |
+
"columns": [
|
1277 |
+
"x",
|
1278 |
+
"y"
|
1279 |
+
]
|
1280 |
},
|
1281 |
+
"df_test": {
|
1282 |
+
"columns": [
|
1283 |
+
"predicted",
|
1284 |
+
"x",
|
1285 |
+
"y"
|
1286 |
+
]
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1287 |
+
},
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1288 |
+
"df_train": {
|
1289 |
+
"columns": [
|
1290 |
+
"x",
|
1291 |
+
"y"
|
1292 |
+
]
|
1293 |
+
}
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1294 |
+
},
|
1295 |
+
"other": {
|
1296 |
+
"model": {
|
1297 |
+
"model": {
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1298 |
+
"inputs": [
|
1299 |
+
"Input__embedding_1_x"
|
1300 |
+
],
|
1301 |
+
"loss_inputs": [
|
1302 |
+
"Input__label_1_y",
|
1303 |
+
"Activation_2_x"
|
1304 |
+
],
|
1305 |
+
"outputs": [
|
1306 |
+
"Activation_2_x"
|
1307 |
+
],
|
1308 |
+
"trained": true
|
1309 |
+
},
|
1310 |
+
"type": "model"
|
1311 |
+
}
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1312 |
+
},
|
1313 |
+
"relations": []
|
1314 |
+
},
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1315 |
+
"error": null,
|
1316 |
+
"meta": {
|
1317 |
+
"inputs": {
|
1318 |
+
"bundle": {
|
1319 |
+
"name": "bundle",
|
1320 |
+
"position": "left",
|
1321 |
+
"type": {
|
1322 |
+
"type": "<class 'lynxkite_graph_analytics.core.Bundle'>"
|
1323 |
+
}
|
1324 |
+
}
|
1325 |
+
},
|
1326 |
+
"name": "Model inference",
|
1327 |
+
"outputs": {
|
1328 |
+
"output": {
|
1329 |
+
"name": "output",
|
1330 |
+
"position": "right",
|
1331 |
+
"type": {
|
1332 |
+
"type": "None"
|
1333 |
+
}
|
1334 |
+
}
|
1335 |
+
},
|
1336 |
+
"params": {
|
1337 |
+
"input_mapping": {
|
1338 |
+
"default": null,
|
1339 |
+
"name": "input_mapping",
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1340 |
+
"type": {
|
1341 |
+
"type": "<class 'lynxkite_graph_analytics.lynxkite_ops.ModelInferenceInputMapping'>"
|
1342 |
+
}
|
1343 |
+
},
|
1344 |
+
"model_name": {
|
1345 |
"default": "model",
|
1346 |
+
"name": "model_name",
|
1347 |
"type": {
|
1348 |
"type": "<class 'str'>"
|
1349 |
}
|
1350 |
+
},
|
1351 |
+
"output_mapping": {
|
1352 |
+
"default": null,
|
1353 |
+
"name": "output_mapping",
|
1354 |
+
"type": {
|
1355 |
+
"type": "<class 'lynxkite_graph_analytics.lynxkite_ops.ModelOutputMapping'>"
|
1356 |
+
}
|
1357 |
}
|
1358 |
},
|
1359 |
"position": {
|
1360 |
+
"x": 934.0,
|
1361 |
+
"y": 167.0
|
1362 |
},
|
1363 |
"type": "basic"
|
1364 |
},
|
1365 |
"params": {
|
1366 |
+
"input_mapping": "{\"map\":{\"Input__embedding_1_x\":{\"column\":\"x\",\"df\":\"df_test\"}}}",
|
1367 |
+
"model_name": "model",
|
1368 |
+
"output_mapping": "{\"map\":{\"Activation_2_x\":{\"column\":\"predicted\",\"df\":\"df_test\"}}}"
|
|
|
1369 |
},
|
1370 |
"status": "done",
|
1371 |
+
"title": "Model inference"
|
1372 |
},
|
1373 |
"dragHandle": ".bg-primary",
|
1374 |
+
"height": 893.0,
|
1375 |
+
"id": "Model inference 1",
|
1376 |
"position": {
|
1377 |
+
"x": 2181.718373860645,
|
1378 |
+
"y": -69.44701793295484
|
1379 |
},
|
1380 |
"type": "basic",
|
1381 |
+
"width": 529.0
|
1382 |
}
|
1383 |
]
|
1384 |
}
|
lynxkite-app/web/src/workspace/nodes/NodeParameter.tsx
CHANGED
@@ -2,17 +2,54 @@
|
|
2 |
import ArrowsHorizontal from "~icons/tabler/arrows-horizontal.jsx";
|
3 |
|
4 |
const BOOLEAN = "<class 'bool'>";
|
5 |
-
const
|
6 |
-
"<class 'lynxkite_graph_analytics.
|
|
|
|
|
|
|
|
|
7 |
function ParamName({ name }: { name: string }) {
|
8 |
return (
|
9 |
<span className="param-name bg-base-200">{name.replace(/_/g, " ")}</span>
|
10 |
);
|
11 |
}
|
12 |
|
13 |
-
function
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
14 |
function bindingsOfModel(m: any): string[] {
|
15 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
16 |
}
|
17 |
const bindings = new Set<string>();
|
18 |
const other = data?.display?.other ?? data?.display?.value?.other ?? {};
|
@@ -31,19 +68,19 @@ function getModelBindings(data: any): string[] {
|
|
31 |
function parseJsonOrEmpty(json: string): object {
|
32 |
try {
|
33 |
const j = JSON.parse(json);
|
34 |
-
if (typeof j === "object") {
|
35 |
return j;
|
36 |
}
|
37 |
} catch (e) {}
|
38 |
return {};
|
39 |
}
|
40 |
|
41 |
-
function ModelMapping({ value, onChange, data }: any) {
|
42 |
const v: any = parseJsonOrEmpty(value);
|
43 |
v.map ??= {};
|
44 |
const dfs =
|
45 |
data?.display?.dataframes ?? data?.display?.value?.dataframes ?? {};
|
46 |
-
const bindings = getModelBindings(data);
|
47 |
return (
|
48 |
<table className="model-mapping-param">
|
49 |
<tbody>
|
@@ -63,12 +100,12 @@ function ModelMapping({ value, onChange, data }: any) {
|
|
63 |
value={v.map?.[binding]?.df}
|
64 |
onChange={(evt) => {
|
65 |
const df = evt.currentTarget.value;
|
66 |
-
if (df === "
|
67 |
const map = { ...v.map, [binding]: undefined };
|
68 |
onChange(JSON.stringify({ map }));
|
69 |
} else {
|
70 |
const columnSpec = {
|
71 |
-
column: dfs[df][0],
|
72 |
...(v.map?.[binding] || {}),
|
73 |
df,
|
74 |
};
|
@@ -77,9 +114,7 @@ function ModelMapping({ value, onChange, data }: any) {
|
|
77 |
}
|
78 |
}}
|
79 |
>
|
80 |
-
<option key="
|
81 |
-
unbound
|
82 |
-
</option>
|
83 |
{Object.keys(dfs).map((df: string) => (
|
84 |
<option key={df} value={df}>
|
85 |
{df}
|
@@ -88,22 +123,39 @@ function ModelMapping({ value, onChange, data }: any) {
|
|
88 |
</select>
|
89 |
</td>
|
90 |
<td>
|
91 |
-
|
92 |
-
|
93 |
-
|
94 |
-
|
95 |
-
|
96 |
-
|
97 |
-
|
98 |
-
|
99 |
-
|
100 |
-
|
101 |
-
|
102 |
-
|
103 |
-
|
104 |
-
|
105 |
-
|
106 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
107 |
</td>
|
108 |
</tr>
|
109 |
))
|
@@ -178,25 +230,41 @@ export default function NodeParameter({
|
|
178 |
{name.replace(/_/g, " ")}
|
179 |
</label>
|
180 |
</div>
|
181 |
-
) : meta?.type?.type ===
|
182 |
<>
|
183 |
<ParamName name={name} />
|
184 |
-
<ModelMapping
|
|
|
|
|
|
|
|
|
|
|
185 |
</>
|
186 |
-
) : (
|
187 |
<>
|
188 |
<ParamName name={name} />
|
189 |
-
<
|
190 |
-
|
191 |
-
|
192 |
-
|
193 |
-
|
194 |
-
|
195 |
-
|
196 |
-
|
197 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
198 |
/>
|
199 |
</>
|
|
|
|
|
|
|
|
|
|
|
200 |
)}
|
201 |
</label>
|
202 |
);
|
|
|
2 |
import ArrowsHorizontal from "~icons/tabler/arrows-horizontal.jsx";
|
3 |
|
4 |
const BOOLEAN = "<class 'bool'>";
|
5 |
+
const MODEL_TRAINING_INPUT_MAPPING =
|
6 |
+
"<class 'lynxkite_graph_analytics.lynxkite_ops.ModelTrainingInputMapping'>";
|
7 |
+
const MODEL_INFERENCE_INPUT_MAPPING =
|
8 |
+
"<class 'lynxkite_graph_analytics.lynxkite_ops.ModelInferenceInputMapping'>";
|
9 |
+
const MODEL_OUTPUT_MAPPING =
|
10 |
+
"<class 'lynxkite_graph_analytics.lynxkite_ops.ModelOutputMapping'>";
|
11 |
function ParamName({ name }: { name: string }) {
|
12 |
return (
|
13 |
<span className="param-name bg-base-200">{name.replace(/_/g, " ")}</span>
|
14 |
);
|
15 |
}
|
16 |
|
17 |
+
function Input({
|
18 |
+
value,
|
19 |
+
onChange,
|
20 |
+
}: {
|
21 |
+
value: string;
|
22 |
+
onChange: (value: string, options?: { delay: number }) => void;
|
23 |
+
}) {
|
24 |
+
return (
|
25 |
+
<input
|
26 |
+
className="input input-bordered w-full"
|
27 |
+
value={value || ""}
|
28 |
+
onChange={(evt) => onChange(evt.currentTarget.value, { delay: 2 })}
|
29 |
+
onBlur={(evt) => onChange(evt.currentTarget.value, { delay: 0 })}
|
30 |
+
onKeyDown={(evt) =>
|
31 |
+
evt.code === "Enter" && onChange(evt.currentTarget.value, { delay: 0 })
|
32 |
+
}
|
33 |
+
/>
|
34 |
+
);
|
35 |
+
}
|
36 |
+
|
37 |
+
function getModelBindings(
|
38 |
+
data: any,
|
39 |
+
variant: "training input" | "inference input" | "output",
|
40 |
+
): string[] {
|
41 |
function bindingsOfModel(m: any): string[] {
|
42 |
+
switch (variant) {
|
43 |
+
case "training input":
|
44 |
+
return [
|
45 |
+
...m.inputs,
|
46 |
+
...m.loss_inputs.filter((i: string) => !m.outputs.includes(i)),
|
47 |
+
];
|
48 |
+
case "inference input":
|
49 |
+
return m.inputs;
|
50 |
+
case "output":
|
51 |
+
return m.outputs;
|
52 |
+
}
|
53 |
}
|
54 |
const bindings = new Set<string>();
|
55 |
const other = data?.display?.other ?? data?.display?.value?.other ?? {};
|
|
|
68 |
function parseJsonOrEmpty(json: string): object {
|
69 |
try {
|
70 |
const j = JSON.parse(json);
|
71 |
+
if (j !== null && typeof j === "object") {
|
72 |
return j;
|
73 |
}
|
74 |
} catch (e) {}
|
75 |
return {};
|
76 |
}
|
77 |
|
78 |
+
function ModelMapping({ value, onChange, data, variant }: any) {
|
79 |
const v: any = parseJsonOrEmpty(value);
|
80 |
v.map ??= {};
|
81 |
const dfs =
|
82 |
data?.display?.dataframes ?? data?.display?.value?.dataframes ?? {};
|
83 |
+
const bindings = getModelBindings(data, variant);
|
84 |
return (
|
85 |
<table className="model-mapping-param">
|
86 |
<tbody>
|
|
|
100 |
value={v.map?.[binding]?.df}
|
101 |
onChange={(evt) => {
|
102 |
const df = evt.currentTarget.value;
|
103 |
+
if (df === "") {
|
104 |
const map = { ...v.map, [binding]: undefined };
|
105 |
onChange(JSON.stringify({ map }));
|
106 |
} else {
|
107 |
const columnSpec = {
|
108 |
+
column: dfs[df].columns[0],
|
109 |
...(v.map?.[binding] || {}),
|
110 |
df,
|
111 |
};
|
|
|
114 |
}
|
115 |
}}
|
116 |
>
|
117 |
+
<option key="" value="" />
|
|
|
|
|
118 |
{Object.keys(dfs).map((df: string) => (
|
119 |
<option key={df} value={df}>
|
120 |
{df}
|
|
|
123 |
</select>
|
124 |
</td>
|
125 |
<td>
|
126 |
+
{variant === "output" ? (
|
127 |
+
<Input
|
128 |
+
value={v.map?.[binding]?.column}
|
129 |
+
onChange={(column, options) => {
|
130 |
+
const columnSpec = {
|
131 |
+
...(v.map?.[binding] || {}),
|
132 |
+
column,
|
133 |
+
};
|
134 |
+
const map = { ...v.map, [binding]: columnSpec };
|
135 |
+
onChange(JSON.stringify({ map }), options);
|
136 |
+
}}
|
137 |
+
/>
|
138 |
+
) : (
|
139 |
+
<select
|
140 |
+
className="select select-ghost"
|
141 |
+
value={v.map?.[binding]?.column}
|
142 |
+
onChange={(evt) => {
|
143 |
+
const column = evt.currentTarget.value;
|
144 |
+
const columnSpec = {
|
145 |
+
...(v.map?.[binding] || {}),
|
146 |
+
column,
|
147 |
+
};
|
148 |
+
const map = { ...v.map, [binding]: columnSpec };
|
149 |
+
onChange(JSON.stringify({ map }));
|
150 |
+
}}
|
151 |
+
>
|
152 |
+
{dfs[v.map?.[binding]?.df]?.columns.map((col: string) => (
|
153 |
+
<option key={col} value={col}>
|
154 |
+
{col}
|
155 |
+
</option>
|
156 |
+
))}
|
157 |
+
</select>
|
158 |
+
)}
|
159 |
</td>
|
160 |
</tr>
|
161 |
))
|
|
|
230 |
{name.replace(/_/g, " ")}
|
231 |
</label>
|
232 |
</div>
|
233 |
+
) : meta?.type?.type === MODEL_TRAINING_INPUT_MAPPING ? (
|
234 |
<>
|
235 |
<ParamName name={name} />
|
236 |
+
<ModelMapping
|
237 |
+
value={value}
|
238 |
+
data={data}
|
239 |
+
variant="training input"
|
240 |
+
onChange={onChange}
|
241 |
+
/>
|
242 |
</>
|
243 |
+
) : meta?.type?.type === MODEL_INFERENCE_INPUT_MAPPING ? (
|
244 |
<>
|
245 |
<ParamName name={name} />
|
246 |
+
<ModelMapping
|
247 |
+
value={value}
|
248 |
+
data={data}
|
249 |
+
variant="inference input"
|
250 |
+
onChange={onChange}
|
251 |
+
/>
|
252 |
+
</>
|
253 |
+
) : meta?.type?.type === MODEL_OUTPUT_MAPPING ? (
|
254 |
+
<>
|
255 |
+
<ParamName name={name} />
|
256 |
+
<ModelMapping
|
257 |
+
value={value}
|
258 |
+
data={data}
|
259 |
+
variant="output"
|
260 |
+
onChange={onChange}
|
261 |
/>
|
262 |
</>
|
263 |
+
) : (
|
264 |
+
<>
|
265 |
+
<ParamName name={name} />
|
266 |
+
<Input value={value} onChange={onChange} />
|
267 |
+
</>
|
268 |
)}
|
269 |
</label>
|
270 |
);
|
lynxkite-graph-analytics/src/lynxkite_graph_analytics/core.py
CHANGED
@@ -204,8 +204,8 @@ def _execute_node(node, ws, catalog, outputs):
|
|
204 |
for edge in ws.edges
|
205 |
if edge.target == node.id
|
206 |
}
|
|
|
207 |
try:
|
208 |
-
# Convert inputs types to match operation signature.
|
209 |
inputs = []
|
210 |
for p in op.inputs.values():
|
211 |
if p.name not in input_map:
|
@@ -219,13 +219,24 @@ def _execute_node(node, ws, catalog, outputs):
|
|
219 |
elif p.type == Bundle and isinstance(x, pd.DataFrame):
|
220 |
x = Bundle.from_df(x)
|
221 |
inputs.append(x)
|
222 |
-
result = op(*inputs, **params)
|
223 |
except Exception as e:
|
224 |
if os.environ.get("LYNXKITE_LOG_OP_ERRORS"):
|
225 |
traceback.print_exc()
|
226 |
node.publish_error(e)
|
227 |
return
|
228 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
229 |
node.publish_result(result)
|
230 |
|
231 |
|
|
|
204 |
for edge in ws.edges
|
205 |
if edge.target == node.id
|
206 |
}
|
207 |
+
# Convert inputs types to match operation signature.
|
208 |
try:
|
|
|
209 |
inputs = []
|
210 |
for p in op.inputs.values():
|
211 |
if p.name not in input_map:
|
|
|
219 |
elif p.type == Bundle and isinstance(x, pd.DataFrame):
|
220 |
x = Bundle.from_df(x)
|
221 |
inputs.append(x)
|
|
|
222 |
except Exception as e:
|
223 |
if os.environ.get("LYNXKITE_LOG_OP_ERRORS"):
|
224 |
traceback.print_exc()
|
225 |
node.publish_error(e)
|
226 |
return
|
227 |
+
# Execute op.
|
228 |
+
try:
|
229 |
+
result = op(*inputs, **params)
|
230 |
+
except Exception as e:
|
231 |
+
if os.environ.get("LYNXKITE_LOG_OP_ERRORS"):
|
232 |
+
traceback.print_exc()
|
233 |
+
result = ops.Result(error=str(e))
|
234 |
+
# On error, just output the first input. This helps reduce the errors on the frontend,
|
235 |
+
# and it lets boxes easily access things from their inputs on the UI, even in error state.
|
236 |
+
if inputs:
|
237 |
+
result.output = inputs[0]
|
238 |
+
if result.output is not None:
|
239 |
+
outputs[node.id] = result.output
|
240 |
node.publish_result(result)
|
241 |
|
242 |
|
lynxkite-graph-analytics/src/lynxkite_graph_analytics/lynxkite_ops.py
CHANGED
@@ -363,31 +363,60 @@ def biomedical_foundation_graph(*, filter_nodes: str):
|
|
363 |
return None
|
364 |
|
365 |
|
366 |
-
@op("
|
367 |
-
def
|
368 |
bundle: core.Bundle,
|
369 |
*,
|
370 |
model_workspace: str,
|
371 |
-
input_mapping: pytorch_model_ops.ModelMapping,
|
372 |
-
epochs: int = 1,
|
373 |
save_as: str = "model",
|
374 |
):
|
375 |
"""Trains the selected model on the selected dataset. Most training parameters are set in the model definition."""
|
376 |
assert model_workspace, "Model workspace is unset."
|
377 |
-
print(f"input_mapping: {input_mapping}")
|
378 |
ws = load_ws(model_workspace)
|
379 |
-
inputs
|
380 |
-
|
381 |
-
|
382 |
-
m = pytorch_model_ops.build_model(ws, inputs)
|
383 |
bundle = bundle.copy()
|
384 |
bundle.other[save_as] = m
|
385 |
-
|
386 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
387 |
t = tqdm(range(epochs), desc="Training model")
|
388 |
for _ in t:
|
389 |
loss = m.train(inputs)
|
390 |
t.set_postfix({"loss": loss})
|
|
|
|
|
|
|
391 |
return bundle
|
392 |
|
393 |
|
@@ -396,13 +425,14 @@ def model_inference(
|
|
396 |
bundle: core.Bundle,
|
397 |
*,
|
398 |
model_name: str = "model",
|
399 |
-
input_mapping:
|
400 |
-
output_mapping:
|
401 |
):
|
402 |
"""Executes a trained model."""
|
403 |
if input_mapping is None or output_mapping is None:
|
404 |
return ops.Result(bundle, error="Mapping is unset.")
|
405 |
m = bundle.other[model_name]
|
|
|
406 |
inputs = pytorch_model_ops.to_tensors(bundle, input_mapping)
|
407 |
outputs = m.inference(inputs)
|
408 |
bundle = bundle.copy()
|
|
|
363 |
return None
|
364 |
|
365 |
|
366 |
+
@op("Define model")
|
367 |
+
def define_model(
|
368 |
bundle: core.Bundle,
|
369 |
*,
|
370 |
model_workspace: str,
|
|
|
|
|
371 |
save_as: str = "model",
|
372 |
):
|
373 |
"""Trains the selected model on the selected dataset. Most training parameters are set in the model definition."""
|
374 |
assert model_workspace, "Model workspace is unset."
|
|
|
375 |
ws = load_ws(model_workspace)
|
376 |
+
# Build the model without inputs, to get its interface.
|
377 |
+
m = pytorch_model_ops.build_model(ws, {})
|
378 |
+
m.source_workspace = model_workspace
|
|
|
379 |
bundle = bundle.copy()
|
380 |
bundle.other[save_as] = m
|
381 |
+
return bundle
|
382 |
+
|
383 |
+
|
384 |
+
# These contain the same mapping, but they get different UIs.
|
385 |
+
# For inputs, you select existing columns. For outputs, you can create new columns.
|
386 |
+
class ModelInferenceInputMapping(pytorch_model_ops.ModelMapping):
|
387 |
+
pass
|
388 |
+
|
389 |
+
|
390 |
+
class ModelTrainingInputMapping(pytorch_model_ops.ModelMapping):
|
391 |
+
pass
|
392 |
+
|
393 |
+
|
394 |
+
class ModelOutputMapping(pytorch_model_ops.ModelMapping):
|
395 |
+
pass
|
396 |
+
|
397 |
+
|
398 |
+
@op("Train model")
|
399 |
+
def train_model(
|
400 |
+
bundle: core.Bundle,
|
401 |
+
*,
|
402 |
+
model_name: str = "model",
|
403 |
+
input_mapping: ModelTrainingInputMapping,
|
404 |
+
epochs: int = 1,
|
405 |
+
):
|
406 |
+
"""Trains the selected model on the selected dataset. Most training parameters are set in the model definition."""
|
407 |
+
m = bundle.other[model_name].copy()
|
408 |
+
inputs = pytorch_model_ops.to_tensors(bundle, input_mapping)
|
409 |
+
if not m.trained and m.source_workspace:
|
410 |
+
# Rebuild the model for the correct inputs.
|
411 |
+
ws = load_ws(m.source_workspace)
|
412 |
+
m = pytorch_model_ops.build_model(ws, inputs)
|
413 |
t = tqdm(range(epochs), desc="Training model")
|
414 |
for _ in t:
|
415 |
loss = m.train(inputs)
|
416 |
t.set_postfix({"loss": loss})
|
417 |
+
m.trained = True
|
418 |
+
bundle = bundle.copy()
|
419 |
+
bundle.other[model_name] = m
|
420 |
return bundle
|
421 |
|
422 |
|
|
|
425 |
bundle: core.Bundle,
|
426 |
*,
|
427 |
model_name: str = "model",
|
428 |
+
input_mapping: ModelInferenceInputMapping,
|
429 |
+
output_mapping: ModelOutputMapping,
|
430 |
):
|
431 |
"""Executes a trained model."""
|
432 |
if input_mapping is None or output_mapping is None:
|
433 |
return ops.Result(bundle, error="Mapping is unset.")
|
434 |
m = bundle.other[model_name]
|
435 |
+
assert m.trained, "The model is not trained."
|
436 |
inputs = pytorch_model_ops.to_tensors(bundle, input_mapping)
|
437 |
outputs = m.inference(inputs)
|
438 |
bundle = bundle.copy()
|
lynxkite-graph-analytics/src/lynxkite_graph_analytics/pytorch_model_ops.py
CHANGED
@@ -1,13 +1,15 @@
|
|
1 |
"""Boxes for defining PyTorch models."""
|
2 |
|
|
|
3 |
import graphlib
|
|
|
4 |
|
5 |
import pydantic
|
6 |
from lynxkite.core import ops, workspace
|
7 |
from lynxkite.core.ops import Parameter as P
|
8 |
import torch
|
9 |
import torch_geometric as pyg
|
10 |
-
|
11 |
from . import core
|
12 |
|
13 |
ENV = "PyTorch model"
|
@@ -125,9 +127,9 @@ ops.register_passive_op(
|
|
125 |
)
|
126 |
|
127 |
|
128 |
-
def _to_id(
|
129 |
"""Replaces all non-alphanumeric characters with underscores."""
|
130 |
-
return "".join(c if c.isalnum() else "_" for c in s)
|
131 |
|
132 |
|
133 |
class ColumnSpec(pydantic.BaseModel):
|
@@ -139,7 +141,7 @@ class ModelMapping(pydantic.BaseModel):
|
|
139 |
map: dict[str, ColumnSpec]
|
140 |
|
141 |
|
142 |
-
@dataclass
|
143 |
class ModelConfig:
|
144 |
model: torch.nn.Module
|
145 |
model_inputs: list[str]
|
@@ -147,6 +149,8 @@ class ModelConfig:
|
|
147 |
loss_inputs: list[str]
|
148 |
loss: torch.nn.Module
|
149 |
optimizer: torch.optim.Optimizer
|
|
|
|
|
150 |
|
151 |
def _forward(self, inputs: dict[str, torch.Tensor]) -> dict[str, torch.Tensor]:
|
152 |
model_inputs = [inputs[i] for i in self.model_inputs]
|
@@ -176,8 +180,8 @@ class ModelConfig:
|
|
176 |
|
177 |
def copy(self):
|
178 |
"""Returns a copy of the model."""
|
179 |
-
c =
|
180 |
-
c.model = self.model
|
181 |
return c
|
182 |
|
183 |
def default_display(self):
|
@@ -187,6 +191,7 @@ class ModelConfig:
|
|
187 |
"inputs": self.model_inputs,
|
188 |
"outputs": self.model_outputs,
|
189 |
"loss_inputs": self.loss_inputs,
|
|
|
190 |
},
|
191 |
}
|
192 |
|
@@ -206,13 +211,17 @@ def build_model(
|
|
206 |
assert len(optimizers) == 1, f"More than one optimizer found: {optimizers}"
|
207 |
[optimizer] = optimizers
|
208 |
dependencies = {n.id: [] for n in ws.nodes}
|
209 |
-
|
|
|
210 |
# TODO: Dissolve repeat boxes here.
|
211 |
for e in ws.edges:
|
212 |
dependencies[e.target].append(e.source)
|
213 |
-
|
214 |
(e.source, e.sourceHandle)
|
215 |
)
|
|
|
|
|
|
|
216 |
sizes = {}
|
217 |
for k, i in inputs.items():
|
218 |
sizes[k] = i.shape[-1]
|
@@ -221,8 +230,10 @@ def build_model(
|
|
221 |
loss_layers = []
|
222 |
in_loss = set()
|
223 |
cfg = {}
|
224 |
-
|
225 |
-
|
|
|
|
|
226 |
for node_id in ts.static_order():
|
227 |
node = nodes[node_id]
|
228 |
t = node.data.title
|
@@ -231,51 +242,62 @@ def build_model(
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for b in dependencies[node_id]:
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if b in in_loss:
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in_loss.add(node_id)
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ls = loss_layers if node_id in in_loss else layers
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-
nid = _to_id(node_id)
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match t:
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case "Linear":
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-
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i = _to_id(ib) + "_" + ih
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used_inputs.add(i)
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-
isize = sizes[i]
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osize = isize if p["output_dim"] == "same" else int(p["output_dim"])
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-
ls.append((torch.nn.Linear(isize, osize), f"{
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-
sizes[
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case "Activation":
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[(ib, ih)] = edges[node_id, "x"]
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i = _to_id(ib) + "_" + ih
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used_inputs.add(i)
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f = getattr(
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torch.nn.functional, p["type"].name.lower().replace(" ", "_")
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)
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ls.append((f, f"{
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sizes[
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case "MSE loss":
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-
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-
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-
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loss_inputs.add(yi)
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in_loss.add(node_id)
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loss_layers.append(
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(torch.nn.functional.mse_loss, f"{xi}, {yi} -> {nid}_loss")
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)
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cfg["model_inputs"] = list(
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cfg["model_outputs"] = list(
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cfg["loss_inputs"] = list(
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# Make sure the trained output is output from the last model layer.
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outputs = ", ".join(cfg["model_outputs"])
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layers.append((torch.nn.Identity(), f"{outputs} -> {outputs}"))
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# Create model.
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-
cfg["model"] = pyg.nn.Sequential(", ".join(
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# Make sure the loss is output from the last loss layer.
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-
[(lossb, lossh)] =
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lossi = _to_id(lossb
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loss_layers.append((torch.nn.Identity(), f"{lossi} -> loss"))
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# Create loss function.
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-
cfg["loss"] = pyg.nn.Sequential(", ".join(loss_inputs), loss_layers)
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assert not list(cfg["loss"].parameters()), (
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f"loss should have no parameters: {list(cfg['loss'].parameters())}"
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)
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@@ -287,9 +309,14 @@ def build_model(
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return ModelConfig(**cfg)
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-
def to_tensors(b: core.Bundle, m: ModelMapping) -> dict[str, torch.Tensor]:
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-
"""Converts a tensor to the correct type for PyTorch."""
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tensors = {}
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for k, v in m.map.items():
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return tensors
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1 |
"""Boxes for defining PyTorch models."""
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+
import copy
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import graphlib
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+
import types
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|
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import pydantic
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from lynxkite.core import ops, workspace
|
9 |
from lynxkite.core.ops import Parameter as P
|
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import torch
|
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import torch_geometric as pyg
|
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+
import dataclasses
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13 |
from . import core
|
14 |
|
15 |
ENV = "PyTorch model"
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127 |
)
|
128 |
|
129 |
|
130 |
+
def _to_id(*strings: str) -> str:
|
131 |
"""Replaces all non-alphanumeric characters with underscores."""
|
132 |
+
return "_".join("".join(c if c.isalnum() else "_" for c in s) for s in strings)
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133 |
|
134 |
|
135 |
class ColumnSpec(pydantic.BaseModel):
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|
141 |
map: dict[str, ColumnSpec]
|
142 |
|
143 |
|
144 |
+
@dataclasses.dataclass
|
145 |
class ModelConfig:
|
146 |
model: torch.nn.Module
|
147 |
model_inputs: list[str]
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|
149 |
loss_inputs: list[str]
|
150 |
loss: torch.nn.Module
|
151 |
optimizer: torch.optim.Optimizer
|
152 |
+
source_workspace: str | None = None
|
153 |
+
trained: bool = False
|
154 |
|
155 |
def _forward(self, inputs: dict[str, torch.Tensor]) -> dict[str, torch.Tensor]:
|
156 |
model_inputs = [inputs[i] for i in self.model_inputs]
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|
180 |
|
181 |
def copy(self):
|
182 |
"""Returns a copy of the model."""
|
183 |
+
c = dataclasses.replace(self)
|
184 |
+
c.model = copy.deepcopy(self.model)
|
185 |
return c
|
186 |
|
187 |
def default_display(self):
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|
191 |
"inputs": self.model_inputs,
|
192 |
"outputs": self.model_outputs,
|
193 |
"loss_inputs": self.loss_inputs,
|
194 |
+
"trained": self.trained,
|
195 |
},
|
196 |
}
|
197 |
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|
211 |
assert len(optimizers) == 1, f"More than one optimizer found: {optimizers}"
|
212 |
[optimizer] = optimizers
|
213 |
dependencies = {n.id: [] for n in ws.nodes}
|
214 |
+
in_edges = {}
|
215 |
+
out_edges = {}
|
216 |
# TODO: Dissolve repeat boxes here.
|
217 |
for e in ws.edges:
|
218 |
dependencies[e.target].append(e.source)
|
219 |
+
in_edges.setdefault(e.target, {}).setdefault(e.targetHandle, []).append(
|
220 |
(e.source, e.sourceHandle)
|
221 |
)
|
222 |
+
out_edges.setdefault(e.source, {}).setdefault(e.sourceHandle, []).append(
|
223 |
+
(e.target, e.targetHandle)
|
224 |
+
)
|
225 |
sizes = {}
|
226 |
for k, i in inputs.items():
|
227 |
sizes[k] = i.shape[-1]
|
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|
230 |
loss_layers = []
|
231 |
in_loss = set()
|
232 |
cfg = {}
|
233 |
+
used_in_model = set()
|
234 |
+
made_in_model = set()
|
235 |
+
used_in_loss = set()
|
236 |
+
made_in_loss = set()
|
237 |
for node_id in ts.static_order():
|
238 |
node = nodes[node_id]
|
239 |
t = node.data.title
|
|
|
242 |
for b in dependencies[node_id]:
|
243 |
if b in in_loss:
|
244 |
in_loss.add(node_id)
|
245 |
+
if "loss" in t:
|
246 |
+
in_loss.add(node_id)
|
247 |
+
inputs = {}
|
248 |
+
for n in in_edges.get(node_id, []):
|
249 |
+
for b, h in in_edges[node_id][n]:
|
250 |
+
i = _to_id(b, h)
|
251 |
+
inputs[n] = i
|
252 |
+
if node_id in in_loss:
|
253 |
+
used_in_loss.add(i)
|
254 |
+
else:
|
255 |
+
used_in_model.add(i)
|
256 |
+
outputs = {}
|
257 |
+
for out in out_edges.get(node_id, []):
|
258 |
+
i = _to_id(node_id, out)
|
259 |
+
outputs[out] = i
|
260 |
+
if inputs: # Nodes with no inputs are input nodes. Their outputs are not "made" by us.
|
261 |
+
if node_id in in_loss:
|
262 |
+
made_in_loss.add(i)
|
263 |
+
else:
|
264 |
+
made_in_model.add(i)
|
265 |
+
inputs = types.SimpleNamespace(**inputs)
|
266 |
+
outputs = types.SimpleNamespace(**outputs)
|
267 |
ls = loss_layers if node_id in in_loss else layers
|
|
|
268 |
match t:
|
269 |
case "Linear":
|
270 |
+
isize = sizes.get(inputs.x, 1)
|
|
|
|
|
|
|
271 |
osize = isize if p["output_dim"] == "same" else int(p["output_dim"])
|
272 |
+
ls.append((torch.nn.Linear(isize, osize), f"{inputs.x} -> {outputs.x}"))
|
273 |
+
sizes[outputs.x] = osize
|
274 |
case "Activation":
|
|
|
|
|
|
|
275 |
f = getattr(
|
276 |
torch.nn.functional, p["type"].name.lower().replace(" ", "_")
|
277 |
)
|
278 |
+
ls.append((f, f"{inputs.x} -> {outputs.x}"))
|
279 |
+
sizes[outputs.x] = sizes.get(inputs.x, 1)
|
280 |
case "MSE loss":
|
281 |
+
ls.append(
|
282 |
+
(
|
283 |
+
torch.nn.functional.mse_loss,
|
284 |
+
f"{inputs.x}, {inputs.y} -> {outputs.loss}",
|
285 |
+
)
|
|
|
|
|
|
|
|
|
286 |
)
|
287 |
+
cfg["model_inputs"] = list(used_in_model - made_in_model)
|
288 |
+
cfg["model_outputs"] = list(made_in_model & used_in_loss)
|
289 |
+
cfg["loss_inputs"] = list(used_in_loss - made_in_loss)
|
290 |
# Make sure the trained output is output from the last model layer.
|
291 |
outputs = ", ".join(cfg["model_outputs"])
|
292 |
layers.append((torch.nn.Identity(), f"{outputs} -> {outputs}"))
|
293 |
# Create model.
|
294 |
+
cfg["model"] = pyg.nn.Sequential(", ".join(cfg["model_inputs"]), layers)
|
295 |
# Make sure the loss is output from the last loss layer.
|
296 |
+
[(lossb, lossh)] = in_edges[optimizer]["loss"]
|
297 |
+
lossi = _to_id(lossb, lossh)
|
298 |
loss_layers.append((torch.nn.Identity(), f"{lossi} -> loss"))
|
299 |
# Create loss function.
|
300 |
+
cfg["loss"] = pyg.nn.Sequential(", ".join(cfg["loss_inputs"]), loss_layers)
|
301 |
assert not list(cfg["loss"].parameters()), (
|
302 |
f"loss should have no parameters: {list(cfg['loss'].parameters())}"
|
303 |
)
|
|
|
309 |
return ModelConfig(**cfg)
|
310 |
|
311 |
|
312 |
+
def to_tensors(b: core.Bundle, m: ModelMapping | None) -> dict[str, torch.Tensor]:
|
313 |
+
"""Converts a tensor to the correct type for PyTorch. Ignores missing mappings."""
|
314 |
+
if m is None:
|
315 |
+
return {}
|
316 |
tensors = {}
|
317 |
for k, v in m.map.items():
|
318 |
+
if v.df in b.dfs and v.column in b.dfs[v.df]:
|
319 |
+
tensors[k] = torch.tensor(
|
320 |
+
b.dfs[v.df][v.column].to_list(), dtype=torch.float32
|
321 |
+
)
|
322 |
return tensors
|