diff --git a/.github/workflows/test.yaml b/.github/workflows/test.yaml index f0bd414ebd7371d402b5d3f54089e92dedab52c8..7f5d334766005bbf321848137ac2f023646948d5 100644 --- a/.github/workflows/test.yaml +++ b/.github/workflows/test.yaml @@ -81,6 +81,7 @@ jobs: - name: Run Playwright tests run: | cd lynxkite-app/web + npm run build npm run test - uses: actions/upload-artifact@v4 diff --git a/Dockerfile b/Dockerfile index 953fd64a05f143211a2e247b850b72a06f943759..e87fa83062e248cbd0e30cda40abf8a9a219b0e5 100644 --- a/Dockerfile +++ b/Dockerfile @@ -5,12 +5,16 @@ USER node ENV HOME=/home/node PATH=/home/node/.local/bin:$PATH WORKDIR $HOME/app COPY --chown=node . $HOME/app -RUN uv venv && uv pip install \ +ENV GIT_SSH_COMMAND="ssh -i /run/secrets/LYNXSCRIBE_DEPLOY_KEY -o StrictHostKeyChecking=no" +RUN --mount=type=secret,id=LYNXSCRIBE_DEPLOY_KEY,mode=0444,required=true \ + uv venv && uv pip install \ -e lynxkite-core \ -e lynxkite-app \ -e lynxkite-graph-analytics \ -e lynxkite-bio \ - -e lynxkite-pillow-example + -e lynxkite-lynxscribe \ + -e lynxkite-pillow-example \ + chromadb openai WORKDIR $HOME/app/examples ENV PORT=7860 CMD ["uv", "run", "lynxkite"] diff --git a/biome.json b/biome.json index 36e6d4044fca6a8dcd976ddece803d3333463c67..8241ee4754cb6ed78c419474919d49a7a67ffd15 100644 --- a/biome.json +++ b/biome.json @@ -1,6 +1,10 @@ { + "files": { + "ignore": ["**/*.lynxkite.json"] + }, "formatter": { "ignore": ["**/node_modules/**", "**/dist/**"], + "lineWidth": 100, "indentStyle": "space" }, "linter": { @@ -20,6 +24,7 @@ "useKeyWithClickEvents": "off", "useValidAnchor": "off", "useButtonType": "off", + "noAutofocus": "off", "noNoninteractiveTabindex": "off" } } diff --git a/examples/AIMO b/examples/AIMO.lynxkite.json similarity index 100% rename from examples/AIMO rename to examples/AIMO.lynxkite.json diff --git a/examples/Airlines demo b/examples/Airlines demo.lynxkite.json similarity index 100% rename from examples/Airlines demo rename to examples/Airlines 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Could you please specify which G\u00e1bor you are inquiring about?\n\n- **G\u00e1bor Benedek**: Chief Innovation Officer & Co-founder at Lynx Analytics. He specializes in economic and business simulations, social network analysis, data mining, and predictive analytics. He has an academic background as a former Associate Professor at Corvinus University of Budapest and has founded several data-related companies.\n\n- **G\u00e1bor Kriv\u00e1chy**: Country Manager at Lynx Analytics in Hungary. He is an experienced technology executive with a background in system implementation, integration, and project management, particularly in SAP implementations.\n\nLet me know which G\u00e1bor's details you would like to learn more about!" - ] - ] - } - } - }, - "error": null, - "meta": { - "inputs": { - "input": { - "name": "input", - "position": "left", - "type": { - "type": "" - } - } - }, - "name": "View", - "outputs": {}, - "params": {}, - "type": "table_view" - }, - "params": {}, - "status": "done", - "title": "View" - }, - "dragHandle": ".bg-primary", - "height": 950.0, - "id": "View 1", - "parentId": null, - "position": { - "x": -754.9225960536905, - "y": -643.161064357758 - }, - "type": "table_view", - "width": 1256.0 - }, - { - "data": { - "display": null, - "error": null, - "meta": { - "inputs": {}, - "name": "Truncate history", - "outputs": { - "output": { - "name": "output", - "position": "top", - "type": { - "type": "None" - } - } - }, - "params": { - "max_tokens": { - 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0000000000000000000000000000000000000000..85bf8112ee1b6a4cdf9f9f7d6ea0594d353d5144 --- /dev/null +++ b/examples/fake_data.py @@ -0,0 +1,16 @@ +from lynxkite.core.ops import op +from faker import Faker +import pandas as pd + +faker = Faker() + + +@op("LynxKite Graph Analytics", "Fake data") +def fake(*, n=10): + df = pd.DataFrame( + { + "name": [faker.name() for _ in range(n)], + "address": [faker.address() for _ in range(n)], + } + ) + return df diff --git a/examples/requirements.txt b/examples/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..a07e975f8a0c963ead4ac69c33086266463ca91a --- /dev/null +++ b/examples/requirements.txt @@ -0,0 +1 @@ +Faker diff --git a/examples/sql b/examples/sql.lynxkite.json similarity index 100% rename from examples/sql rename to examples/sql.lynxkite.json diff --git a/examples/uploads/plus-one-dataset.parquet b/examples/uploads/plus-one-dataset.parquet new file mode 100644 index 0000000000000000000000000000000000000000..03eb0a488400bc0864067da24dca11ca43a021ea Binary files /dev/null and b/examples/uploads/plus-one-dataset.parquet differ diff --git a/examples/word2vec.py b/examples/word2vec.py new file mode 100644 index 0000000000000000000000000000000000000000..b12799d6e1204c67535ed64811152e809baf1186 --- /dev/null +++ b/examples/word2vec.py @@ -0,0 +1,26 @@ +from lynxkite.core.ops import op +import staticvectors +import pandas as pd + +ENV = "LynxKite Graph Analytics" + + +@op(ENV, "Word2vec for the top 1000 words", slow=True) +def word2vec_1000(): + model = staticvectors.StaticVectors("neuml/word2vec-quantized") + df = pd.read_csv( + "https://gist.githubusercontent.com/deekayen/4148741/raw/98d35708fa344717d8eee15d11987de6c8e26d7d/1-1000.txt", + names=["word"], + ) + df["embedding"] = model.embeddings(df.word.tolist()).tolist() + return df + + +@op(ENV, "Take first N") +def first_n(df: pd.DataFrame, *, n=10): + return df.head(n) + + +@op(ENV, "Sample N") +def sample_n(df: pd.DataFrame, *, n=10): + return df.sample(n) diff --git a/lynxkite-app/src/lynxkite_app/crdt.py b/lynxkite-app/src/lynxkite_app/crdt.py index d86096ecb584b8d6a402d41ef1597a953963fc1f..9b8e3a4b654a8005f3eb8aa7cf867d9bc30a6330 100644 --- a/lynxkite-app/src/lynxkite_app/crdt.py +++ b/lynxkite-app/src/lynxkite_app/crdt.py @@ -26,11 +26,11 @@ def ws_exception_handler(exception, log): return True -class WebsocketServer(pycrdt_websocket.WebsocketServer): +class WorkspaceWebsocketServer(pycrdt_websocket.WebsocketServer): async def init_room(self, name: str) -> pycrdt_websocket.YRoom: """Initialize a room for the workspace with the given name. - The workspace is loaded from "crdt_data" if it exists there, or from "data", or a new workspace is created. + The workspace is loaded from ".crdt" if it exists there, or from a JSON file, or a new workspace is created. """ crdt_path = pathlib.Path(".crdt") path = crdt_path / f"{name}.crdt" @@ -40,9 +40,7 @@ class WebsocketServer(pycrdt_websocket.WebsocketServer): ydoc["workspace"] = ws = pycrdt.Map() # Replay updates from the store. try: - for update, timestamp in [ - (item[0], item[-1]) async for item in ystore.read() - ]: + for update, timestamp in [(item[0], item[-1]) async for item in ystore.read()]: ydoc.apply_update(update) except pycrdt_websocket.ystore.YDocNotFound: pass @@ -84,14 +82,49 @@ class WebsocketServer(pycrdt_websocket.WebsocketServer): return room +class CodeWebsocketServer(WorkspaceWebsocketServer): + async def init_room(self, name: str) -> pycrdt_websocket.YRoom: + """Initialize a room for a text document with the given name.""" + crdt_path = pathlib.Path(".crdt") + path = crdt_path / f"{name}.crdt" + assert path.is_relative_to(crdt_path) + ystore = pycrdt_websocket.ystore.FileYStore(path) + ydoc = pycrdt.Doc() + ydoc["text"] = text = pycrdt.Text() + # Replay updates from the store. + try: + for update, timestamp in [(item[0], item[-1]) async for item in ystore.read()]: + ydoc.apply_update(update) + except pycrdt_websocket.ystore.YDocNotFound: + pass + if len(text) == 0: + if os.path.exists(name): + with open(name) as f: + text += f.read() + room = pycrdt_websocket.YRoom( + ystore=ystore, ydoc=ydoc, exception_handler=ws_exception_handler + ) + room.text = text + + def on_change(changes): + asyncio.create_task(code_changed(name, changes, text)) + + text.observe(on_change) + return room + + last_ws_input = None def clean_input(ws_pyd): for node in ws_pyd.nodes: node.data.display = None + node.data.input_metadata = None node.data.error = None node.data.status = workspace.NodeStatus.done + for p in list(node.data.params): + if p.startswith("_"): + del node.data.params[p] node.position.x = 0 node.position.y = 0 if node.model_extra: @@ -168,9 +201,12 @@ def try_to_load_workspace(ws: pycrdt.Map, name: str): """ if os.path.exists(name): ws_pyd = workspace.load(name) - # We treat the display field as a black box, since it is a large - # dictionary that is meant to change as a whole. - crdt_update(ws, ws_pyd.model_dump(), non_collaborative_fields={"display"}) + crdt_update( + ws, + ws_pyd.model_dump(), + # We treat some fields as black boxes. They are not edited on the frontend. + non_collaborative_fields={"display", "input_metadata"}, + ) last_known_versions = {} @@ -208,9 +244,7 @@ async def workspace_changed(name: str, changes: pycrdt.MapEvent, ws_crdt: pycrdt await execute(name, ws_crdt, ws_pyd) -async def execute( - name: str, ws_crdt: pycrdt.Map, ws_pyd: workspace.Workspace, delay: int = 0 -): +async def execute(name: str, ws_crdt: pycrdt.Map, ws_pyd: workspace.Workspace, delay: int = 0): """Execute the workspace and update the CRDT object with the results. Args: @@ -230,6 +264,7 @@ async def execute( assert path.is_relative_to(cwd), "Provided workspace path is invalid" # Save user changes before executing, in case the execution fails. workspace.save(ws_pyd, path) + ops.load_user_scripts(name) ws_pyd._crdt = ws_crdt with ws_crdt.doc.transaction(): for nc, np in zip(ws_crdt["nodes"], ws_pyd.nodes): @@ -243,14 +278,21 @@ async def execute( print(f"Finished running {name} in {ws_pyd.env}.") +async def code_changed(name: str, changes: pycrdt.TextEvent, text: pycrdt.Text): + # TODO: Make this more fancy? + with open(name, "w") as f: + f.write(str(text).strip() + "\n") + + @contextlib.asynccontextmanager async def lifespan(app): - global websocket_server - websocket_server = WebsocketServer( - auto_clean_rooms=False, - ) - async with websocket_server: - yield + global ws_websocket_server + global code_websocket_server + ws_websocket_server = WorkspaceWebsocketServer(auto_clean_rooms=False) + code_websocket_server = CodeWebsocketServer(auto_clean_rooms=False) + async with ws_websocket_server: + async with code_websocket_server: + yield print("closing websocket server") @@ -261,5 +303,12 @@ def sanitize_path(path): @router.websocket("/ws/crdt/{room_name}") async def crdt_websocket(websocket: fastapi.WebSocket, room_name: str): room_name = sanitize_path(room_name) - server = pycrdt_websocket.ASGIServer(websocket_server) + server = pycrdt_websocket.ASGIServer(ws_websocket_server) + await server({"path": room_name}, websocket._receive, websocket._send) + + +@router.websocket("/ws/code/crdt/{room_name}") +async def code_crdt_websocket(websocket: fastapi.WebSocket, room_name: str): + room_name = sanitize_path(room_name) + server = pycrdt_websocket.ASGIServer(code_websocket_server) await server({"path": room_name}, websocket._receive, websocket._send) diff --git a/lynxkite-app/src/lynxkite_app/main.py b/lynxkite-app/src/lynxkite_app/main.py index 6f5368ef3f88c2ac13195ca53ee57c48eda85c50..cb41dadbd491599250e74dc7763e37b251025873 100644 --- a/lynxkite-app/src/lynxkite_app/main.py +++ b/lynxkite-app/src/lynxkite_app/main.py @@ -26,6 +26,7 @@ def detect_plugins(): lynxkite_plugins = detect_plugins() +ops.save_catalogs("plugins loaded") app = fastapi.FastAPI(lifespan=crdt.lifespan) app.include_router(crdt.router) @@ -33,11 +34,9 @@ app.add_middleware(GZipMiddleware) @app.get("/api/catalog") -def get_catalog(): - return { - k: {op.name: op.model_dump() for op in v.values()} - for k, v in ops.CATALOGS.items() - } +def get_catalog(workspace: str): + ops.load_user_scripts(workspace) + return {k: {op.name: op.model_dump() for op in v.values()} for k, v in ops.CATALOGS.items()} class SaveRequest(workspace.BaseConfig): @@ -85,6 +84,15 @@ class DirectoryEntry(pydantic.BaseModel): type: str +def _get_path_type(path: pathlib.Path) -> str: + if path.is_dir(): + return "directory" + elif path.suffixes[-2:] == [".lynxkite", ".json"]: + return "workspace" + else: + return "file" + + @app.get("/api/dir/list") def list_dir(path: str): path = data_path / path @@ -93,7 +101,7 @@ def list_dir(path: str): [ DirectoryEntry( name=str(p.relative_to(data_path)), - type="directory" if p.is_dir() else "workspace", + type=_get_path_type(p), ) for p in path.iterdir() if not p.name.startswith(".") diff --git a/lynxkite-app/tests/test_main.py b/lynxkite-app/tests/test_main.py index c9d15c8b7752b8d443f822727a2c40d056b24551..74ebecad95c7a35fd027994237555dc3cbf20e18 100644 --- a/lynxkite-app/tests/test_main.py +++ b/lynxkite-app/tests/test_main.py @@ -22,7 +22,7 @@ def test_detect_plugins_with_plugins(): def test_get_catalog(): - response = client.get("/api/catalog") + response = client.get("/api/catalog?workspace=test") assert response.status_code == 200 @@ -37,6 +37,7 @@ def test_save_and_load(): "type": "basic", "data": { "display": None, + "input_metadata": None, "error": "Unknown operation.", "title": "Test node", "params": {"param1": "value"}, @@ -58,15 +59,22 @@ def test_save_and_load(): def test_list_dir(): test_dir = pathlib.Path() / str(uuid.uuid4()) test_dir.mkdir(parents=True, exist_ok=True) - test_file = test_dir / "test_file.txt" - test_file.touch() + dir = test_dir / "test_dir" + dir.mkdir(exist_ok=True) + file = test_dir / "test_file.txt" + file.touch() + ws = test_dir / "test_workspace.lynxkite.json" + ws.touch() response = client.get(f"/api/dir/list?path={str(test_dir)}") assert response.status_code == 200 - assert len(response.json()) == 1 - assert response.json()[0]["name"] == f"{test_dir}/test_file.txt" - assert response.json()[0]["type"] == "workspace" - test_file.unlink() - test_dir.rmdir() + assert response.json() == [ + {"name": f"{test_dir}/test_dir", "type": "directory"}, + {"name": f"{test_dir}/test_file.txt", "type": "file"}, + {"name": f"{test_dir}/test_workspace.lynxkite.json", "type": "workspace"}, + ] + file.unlink() + ws.unlink() + dir.rmdir() def test_make_dir(): diff --git a/lynxkite-app/web/eslint.config.js b/lynxkite-app/web/eslint.config.js index f6acaea802c9332a8c48507c09ac0ed023c0b67f..f2e6b5305244cf13b4f487b4f0fdc0713a42a41d 100644 --- a/lynxkite-app/web/eslint.config.js +++ b/lynxkite-app/web/eslint.config.js @@ -19,10 +19,7 @@ export default tseslint.config( }, rules: { ...reactHooks.configs.recommended.rules, - "react-refresh/only-export-components": [ - "warn", - { allowConstantExport: true }, - ], + "react-refresh/only-export-components": ["warn", { allowConstantExport: true }], }, }, ); diff --git a/lynxkite-app/web/index.html b/lynxkite-app/web/index.html index 27509497671372558a63c1aeb5d1af4fabd00c5c..4035409471ca0fa4aba169f71669c1e13b96be2c 100644 --- a/lynxkite-app/web/index.html +++ b/lynxkite-app/web/index.html @@ -4,7 +4,6 @@ - LynxKite 2025
diff --git a/lynxkite-app/web/package-lock.json b/lynxkite-app/web/package-lock.json index f3586f64dceb4f8f03276655eed7a92a2c06a76b..5006fe472db89f74e6fec51e755ffccdb7628d30 100644 --- a/lynxkite-app/web/package-lock.json +++ b/lynxkite-app/web/package-lock.json @@ -10,6 +10,7 @@ "dependencies": { "@esbuild/linux-x64": "^0.25.0", "@iconify-json/tabler": "^1.2.10", + "@monaco-editor/react": "^4.7.0", "@svgr/core": "^8.1.0", "@svgr/plugin-jsx": "^8.1.0", "@swc/core": "^1.10.1", @@ -28,6 +29,7 @@ "react-router-dom": "^7.0.2", "swr": "^2.2.5", "unplugin-icons": "^0.21.0", + "y-monaco": "^0.1.6", "y-websocket": "^2.0.4", "yjs": "^13.6.20" }, @@ -1105,6 +1107,29 @@ "integrity": "sha512-4JQNk+3mVzK3xh2rqd6RB4J46qUR19azEHBneZyTZM+c456qOrbbM/5xcR8huNCCcbVt7+UmizG6GuUvPvKUYg==", "license": "MIT" }, + "node_modules/@monaco-editor/loader": { + "version": "1.5.0", + "resolved": "https://registry.npmjs.org/@monaco-editor/loader/-/loader-1.5.0.tgz", + "integrity": "sha512-hKoGSM+7aAc7eRTRjpqAZucPmoNOC4UUbknb/VNoTkEIkCPhqV8LfbsgM1webRM7S/z21eHEx9Fkwx8Z/C/+Xw==", + "license": "MIT", + "dependencies": { + "state-local": "^1.0.6" + } + }, + "node_modules/@monaco-editor/react": { + "version": "4.7.0", + "resolved": "https://registry.npmjs.org/@monaco-editor/react/-/react-4.7.0.tgz", + "integrity": "sha512-cyzXQCtO47ydzxpQtCGSQGOC8Gk3ZUeBXFAxD+CWXYFo5OqZyZUonFl0DwUlTyAfRHntBfw2p3w4s9R6oe1eCA==", + "license": "MIT", + "dependencies": { + "@monaco-editor/loader": "^1.5.0" + }, + "peerDependencies": { + "monaco-editor": ">= 0.25.0 < 1", + "react": "^16.8.0 || ^17.0.0 || ^18.0.0 || ^19.0.0", + "react-dom": "^16.8.0 || ^17.0.0 || ^18.0.0 || ^19.0.0" + } + }, "node_modules/@nodelib/fs.scandir": { "version": "2.1.5", "resolved": "https://registry.npmjs.org/@nodelib/fs.scandir/-/fs.scandir-2.1.5.tgz", @@ -5436,6 +5461,13 @@ "ufo": "^1.5.4" } }, + "node_modules/monaco-editor": { + "version": "0.52.2", + "resolved": "https://registry.npmjs.org/monaco-editor/-/monaco-editor-0.52.2.tgz", + "integrity": "sha512-GEQWEZmfkOGLdd3XK8ryrfWz3AIP8YymVXiPHEdewrUq7mh0qrKrfHLNCXcbB6sTnMLnOZ3ztSiKcciFUkIJwQ==", + "license": "MIT", + "peer": true + }, "node_modules/ms": { "version": "2.1.3", "resolved": "https://registry.npmjs.org/ms/-/ms-2.1.3.tgz", @@ -6437,6 +6469,12 @@ "url": "https://github.com/sponsors/wooorm" } }, + "node_modules/state-local": { + "version": "1.0.7", + "resolved": "https://registry.npmjs.org/state-local/-/state-local-1.0.7.tgz", + "integrity": "sha512-HTEHMNieakEnoe33shBYcZ7NX83ACUjCu8c40iOGEZsngj9zRnkqS9j1pqQPXwobB0ZcVTk27REb7COQ0UR59w==", + "license": "MIT" + }, "node_modules/string_decoder": { "version": "1.3.0", "resolved": "https://registry.npmjs.org/string_decoder/-/string_decoder-1.3.0.tgz", @@ -7360,6 +7398,23 @@ "yjs": "^13.0.0" } }, + "node_modules/y-monaco": { + "version": "0.1.6", + "resolved": "https://registry.npmjs.org/y-monaco/-/y-monaco-0.1.6.tgz", + "integrity": "sha512-sYRywMmcylt+Nupl+11AvizD2am06ST8lkVbUXuaEmrtV6Tf+TD4rsEm6u9YGGowYue+Vfg1IJ97SUP2J+PVXg==", + "license": "MIT", + "dependencies": { + "lib0": "^0.2.43" + }, + "engines": { + "node": ">=12.0.0", + "npm": ">=6.0.0" + }, + "peerDependencies": { + "monaco-editor": ">=0.20.0", + "yjs": "^13.3.1" + } + }, "node_modules/y-protocols": { "version": "1.0.6", "resolved": "https://registry.npmjs.org/y-protocols/-/y-protocols-1.0.6.tgz", diff --git a/lynxkite-app/web/package.json b/lynxkite-app/web/package.json index 6bc7a50736c271492d99e77c598d0f3ddc52228b..a770941db057165d43815ee83bb8404e84fc3c1c 100644 --- a/lynxkite-app/web/package.json +++ b/lynxkite-app/web/package.json @@ -13,6 +13,7 @@ "dependencies": { "@esbuild/linux-x64": "^0.25.0", "@iconify-json/tabler": "^1.2.10", + "@monaco-editor/react": "^4.7.0", "@svgr/core": "^8.1.0", "@svgr/plugin-jsx": "^8.1.0", "@swc/core": "^1.10.1", @@ -31,6 +32,7 @@ "react-router-dom": "^7.0.2", "swr": "^2.2.5", "unplugin-icons": "^0.21.0", + "y-monaco": "^0.1.6", "y-websocket": "^2.0.4", "yjs": "^13.6.20" }, diff --git a/lynxkite-app/web/playwright.config.ts b/lynxkite-app/web/playwright.config.ts index b0d3e4c31adf4a28eaed19a16dd856d3364f4e5b..7b439d07a6a720684fc4ebe2ff2453c93a7b61c9 100644 --- a/lynxkite-app/web/playwright.config.ts +++ b/lynxkite-app/web/playwright.config.ts @@ -7,6 +7,7 @@ export default defineConfig({ /* Fail the build on CI if you accidentally left test.only in the source code. */ forbidOnly: !!process.env.CI, retries: process.env.CI ? 1 : 0, + maxFailures: 5, workers: 1, reporter: process.env.CI ? [["github"], ["html"]] : "html", use: { @@ -24,7 +25,7 @@ export default defineConfig({ ], webServer: { command: "cd ../../examples && lynxkite", - url: "http://127.0.0.1:8000", + port: 8000, reuseExistingServer: false, }, }); diff --git a/lynxkite-app/web/src/Code.tsx b/lynxkite-app/web/src/Code.tsx new file mode 100644 index 0000000000000000000000000000000000000000..a10256184e5796269693721152613be4e60c2c51 --- /dev/null +++ b/lynxkite-app/web/src/Code.tsx @@ -0,0 +1,101 @@ +// Full-page editor for code files. + +import Editor, { type Monaco } from "@monaco-editor/react"; +import type { editor } from "monaco-editor"; +import { useEffect, useRef } from "react"; +import { useParams } from "react-router"; +import { WebsocketProvider } from "y-websocket"; +import * as Y from "yjs"; +// @ts-ignore +import Atom from "~icons/tabler/atom.jsx"; +// @ts-ignore +import Backspace from "~icons/tabler/backspace.jsx"; +// @ts-ignore +import Close from "~icons/tabler/x.jsx"; +import favicon from "./assets/favicon.ico"; +import theme from "./code-theme.ts"; + +export default function Code() { + const { path } = useParams(); + const parentDir = path!.split("/").slice(0, -1).join("/"); + const yDocRef = useRef(); + const wsProviderRef = useRef(); + const monacoBindingRef = useRef(); + const yMonacoRef = useRef(); + const editorRef = useRef(); + useEffect(() => { + const loadMonaco = async () => { + // y-monaco is gigantic. The other Monaco packages are small. + yMonacoRef.current = await import("y-monaco"); + initCRDT(); + }; + loadMonaco(); + }, []); + function beforeMount(monaco: Monaco) { + monaco.editor.defineTheme("lynxkite", theme); + } + function onMount(_editor: editor.IStandaloneCodeEditor) { + editorRef.current = _editor; + initCRDT(); + } + function initCRDT() { + if (!yMonacoRef.current || !editorRef.current) return; + if (yDocRef.current) return; + yDocRef.current = new Y.Doc(); + const text = yDocRef.current.getText("text"); + const proto = location.protocol === "https:" ? "wss:" : "ws:"; + wsProviderRef.current = new WebsocketProvider( + `${proto}//${location.host}/ws/code/crdt`, + path!, + yDocRef.current, + ); + monacoBindingRef.current = new yMonacoRef.current.MonacoBinding( + text, + editorRef.current.getModel()!, + new Set([editorRef.current]), + wsProviderRef.current.awareness, + ); + } + useEffect(() => { + return () => { + yDocRef.current?.destroy(); + wsProviderRef.current?.destroy(); + monacoBindingRef.current?.destroy(); + }; + }); + return ( +
+
+ + + +
{path}
+ +
+ +
+ ); +} diff --git a/lynxkite-app/web/src/Directory.tsx b/lynxkite-app/web/src/Directory.tsx index 7e1115319e26b9548b24de12162b62c7cd16e313..f6c005b4e9fb3be77486193e38ab2a385ea07fd0 100644 --- a/lynxkite-app/web/src/Directory.tsx +++ b/lynxkite-app/web/src/Directory.tsx @@ -15,9 +15,46 @@ import FolderPlus from "~icons/tabler/folder-plus"; // @ts-ignore import Home from "~icons/tabler/home"; // @ts-ignore +import LayoutGrid from "~icons/tabler/layout-grid"; +// @ts-ignore +import LayoutGridAdd from "~icons/tabler/layout-grid-add"; +// @ts-ignore import Trash from "~icons/tabler/trash"; import logo from "./assets/logo.png"; +function EntryCreator(props: { + label: string; + icon: JSX.Element; + onCreate: (name: string) => void; +}) { + const [isCreating, setIsCreating] = useState(false); + return ( + <> + {isCreating ? ( +
{ + e.preventDefault(); + props.onCreate((e.target as HTMLFormElement).entryName.value.trim()); + }} + > + setIsCreating(false)} + placeholder={`${props.label} name`} + /> +
+ ) : ( + + )} + + ); +} + const fetcher = (url: string) => fetch(url).then((res) => res.json()); export default function () { @@ -27,68 +64,51 @@ export default function () { dedupingInterval: 0, }); const navigate = useNavigate(); - const [isCreatingDir, setIsCreatingDir] = useState(false); - const [isCreatingWorkspace, setIsCreatingWorkspace] = useState(false); function link(item: DirectoryEntry) { if (item.type === "directory") { return `/dir/${item.name}`; } - return `/edit/${item.name}`; + if (item.type === "workspace") { + return `/edit/${item.name}`; + } + return `/code/${item.name}`; } function shortName(item: DirectoryEntry) { - return item.name.split("/").pop(); + return item.name + .split("/") + .pop() + ?.replace(/[.]lynxkite[.]json$/, ""); } - function newName(list: DirectoryEntry[], baseName = "Untitled") { - let i = 0; - while (true) { - const name = `${baseName}${i ? ` ${i}` : ""}`; - if (!list.find((item) => item.name === name)) { - return name; - } - i++; - } + function newWorkspaceIn(path: string, workspaceName: string) { + const pathSlash = path ? `${path}/` : ""; + navigate(`/edit/${pathSlash}${workspaceName}.lynxkite.json`, { replace: true }); } - - function newWorkspaceIn( - path: string, - list: DirectoryEntry[], - workspaceName?: string, - ) { + function newCodeFile(path: string, name: string) { const pathSlash = path ? `${path}/` : ""; - const name = workspaceName || newName(list); - navigate(`/edit/${pathSlash}${name}`, { replace: true }); + navigate(`/code/${pathSlash}${name}`, { replace: true }); } - - async function newFolderIn( - path: string, - list: DirectoryEntry[], - folderName?: string, - ) { - const name = folderName || newName(list, "New Folder"); + async function newFolderIn(path: string, folderName: string) { const pathSlash = path ? `${path}/` : ""; - const res = await fetch("/api/dir/mkdir", { method: "POST", headers: { "Content-Type": "application/json" }, - body: JSON.stringify({ path: pathSlash + name }), + body: JSON.stringify({ path: pathSlash + folderName }), }); if (res.ok) { - navigate(`/dir/${pathSlash}${name}`); + navigate(`/dir/${pathSlash}${folderName}`); } else { alert("Failed to create folder."); } } async function deleteItem(item: DirectoryEntry) { - if (!window.confirm(`Are you sure you want to delete "${item.name}"?`)) - return; + if (!window.confirm(`Are you sure you want to delete "${item.name}"?`)) return; const pathSlash = path ? `${path}/` : ""; - const apiPath = - item.type === "directory" ? "/api/dir/delete" : "/api/delete"; + const apiPath = item.type === "directory" ? "/api/dir/delete" : "/api/delete"; await fetch(apiPath, { method: "POST", headers: { "Content-Type": "application/json" }, @@ -115,89 +135,66 @@ export default function () { {list.data && ( <>
-
- {isCreatingWorkspace && ( - // @ts-ignore -
{ - e.preventDefault(); - newWorkspaceIn( - path || "", - list.data, - ( - e.target as HTMLFormElement - ).workspaceName.value.trim(), - ); - }} - > - -
- )} - -
- -
- {isCreatingDir && ( - // @ts-ignore -
{ - e.preventDefault(); - newFolderIn( - path || "", - list.data, - (e.target as HTMLFormElement).folderName.value.trim(), - ); - }} - > - -
- )} - -
+ { + newWorkspaceIn(path || "", name); + }} + icon={} + label="New workspace" + /> + { + newCodeFile(path || "", name); + }} + icon={} + label="New code file" + /> + { + newFolderIn(path || "", name); + }} + icon={} + label="New folder" + />
- {path && ( + {path ? (
{" "} {path} + {path}
+ ) : ( + LynxKite 2000:MM )} - {list.data.map((item: DirectoryEntry) => ( -
- - {item.type === "directory" ? : } - {shortName(item)} - - -
- ))} + {list.data.map( + (item: DirectoryEntry) => + !shortName(item)?.startsWith("__") && ( +
+ + {item.type === "directory" ? ( + + ) : item.type === "workspace" ? ( + + ) : ( + + )} + {shortName(item)} + + +
+ ), + )} )} {" "} diff --git a/lynxkite-app/web/src/apiTypes.ts b/lynxkite-app/web/src/apiTypes.ts index 5bed31c557f4b610c9a8cf39c2d7ad2446e48c94..2f696b3f2f3013998034ad9519ccbf13ebfc0177 100644 --- a/lynxkite-app/web/src/apiTypes.ts +++ b/lynxkite-app/web/src/apiTypes.ts @@ -5,6 +5,8 @@ /* Do not modify it by hand - just update the pydantic models and then re-run the script */ +export type NodeStatus = "planned" | "active" | "done"; + export interface DirectoryEntry { name: string; type: string; @@ -40,8 +42,9 @@ export interface WorkspaceNodeData { [k: string]: unknown; }; display?: unknown; + input_metadata?: unknown; error?: string | null; - in_progress?: boolean; + status?: NodeStatus; [k: string]: unknown; } export interface Position { diff --git a/lynxkite-app/web/src/code-theme.ts b/lynxkite-app/web/src/code-theme.ts new file mode 100644 index 0000000000000000000000000000000000000000..f87b908baae0b2004a8bf3e9f4b576a817865f5e --- /dev/null +++ b/lynxkite-app/web/src/code-theme.ts @@ -0,0 +1,38 @@ +// A simple theme using the LynxKite colors. + +import type { editor } from "monaco-editor/esm/vs/editor/editor.api"; + +const theme: editor.IStandaloneThemeData = { + base: "vs-dark", + inherit: true, + rules: [ + { + foreground: "ff8800", + token: "keyword", + }, + { + foreground: "0088ff", + fontStyle: "italic", + token: "comment", + }, + { + foreground: "39bcf3", + token: "string", + }, + { + foreground: "ffc600", + token: "", + }, + ], + colors: { + "editor.foreground": "#FFFFFF", + "editor.background": "#002a4c", + "editor.selectionBackground": "#0050a4", + "editor.lineHighlightBackground": "#1f4662", + "editorCursor.foreground": "#ffc600", + "editorWhitespace.foreground": "#7f7f7fb2", + "editorIndentGuide.background": "#3b5364", + "editorIndentGuide.activeBackground": "#ffc600", + }, +}; +export default theme; diff --git a/lynxkite-app/web/src/index.css b/lynxkite-app/web/src/index.css index 9da6e892bff1e43ddedd36c4ae92a1d426d1845c..dabd851362f7a4184409c8e7c5cd48951fbb93ba 100644 --- a/lynxkite-app/web/src/index.css +++ b/lynxkite-app/web/src/index.css @@ -102,15 +102,14 @@ body { --status-color-1: oklch(75% 0.2 55); --status-color-2: oklch(75% 0.2 55); --status-color-3: oklch(75% 0.2 55); - transition: --status-color-1 0.3s, --status-color-2 0.3s, --status-color-3 - 0.3s; + transition: --status-color-1 0.3s, --status-color-2 0.3s, --status-color-3 0.3s; } .lynxkite-node .title.active { --status-color-1: oklch(75% 0.2 55); --status-color-2: oklch(90% 0.2 55); --status-color-3: oklch(75% 0.1 55); - /* animation: active-node-gradient-animation 2s ease-in-out infinite; */ + animation: active-node-gradient-animation 2s ease-in-out infinite; } .lynxkite-node .title.planned { @@ -256,6 +255,14 @@ body { cursor: pointer; } } + + .model-mapping-param { + border: 1px solid var(--fallback-bc, oklch(var(--bc) / 0.2)); + border-collapse: separate; + border-radius: 5px; + padding: 5px 10px; + width: 100%; + } } .params-expander { @@ -319,7 +326,14 @@ body { .actions { display: flex; justify-content: space-evenly; + align-items: center; + height: 50px; padding: 5px; + + form, + button { + flex: 1; + } } .actions a { @@ -527,3 +541,25 @@ body { .add-relationship-button:hover { background-color: #218838; } + +.yRemoteSelection { + background-color: rgb(250, 129, 0, 0.5); +} + +.yRemoteSelectionHead { + position: absolute; + border-left: #ff8800 solid 2px; + border-top: #ff8800 solid 2px; + border-bottom: #ff8800 solid 2px; + height: 100%; + box-sizing: border-box; +} + +.yRemoteSelectionHead::after { + position: absolute; + content: " "; + border: 3px solid #ff8800; + border-radius: 4px; + left: -4px; + top: -5px; +} diff --git a/lynxkite-app/web/src/main.tsx b/lynxkite-app/web/src/main.tsx index 9d9eb752e2d437853ec87ff4fe375ef898951971..af03ec06c6b5b70991c475082e183a52db2b3da6 100644 --- a/lynxkite-app/web/src/main.tsx +++ b/lynxkite-app/web/src/main.tsx @@ -3,6 +3,7 @@ import { createRoot } from "react-dom/client"; import "@xyflow/react/dist/style.css"; import "./index.css"; import { BrowserRouter, Route, Routes } from "react-router"; +import Code from "./Code.tsx"; import Directory from "./Directory.tsx"; import Workspace from "./workspace/Workspace.tsx"; @@ -14,6 +15,7 @@ createRoot(document.getElementById("root")!).render( } /> } /> } /> + } /> , diff --git a/lynxkite-app/web/src/workspace/NodeSearch.tsx b/lynxkite-app/web/src/workspace/NodeSearch.tsx index 967e22a5435f9de6ce144897fc8e1937d6a7cc78..b0d80114e6c03d2bc68f68392a2b8fb8f36cafc7 100644 --- a/lynxkite-app/web/src/workspace/NodeSearch.tsx +++ b/lynxkite-app/web/src/workspace/NodeSearch.tsx @@ -30,9 +30,7 @@ export default function (props: { boxes.sort((a, b) => a.item.name.localeCompare(b.item.name)); return boxes; }, [props.boxes]); - const hits: { item: OpsOp }[] = searchText - ? fuse.search(searchText) - : allOps; + const hits: { item: OpsOp }[] = searchText ? fuse.search(searchText) : allOps; const [selectedIndex, setSelectedIndex] = useState(0); useEffect(() => searchBox.current.focus()); function typed(text: string) { @@ -64,10 +62,7 @@ export default function (props: { } return ( -
+
{ @@ -69,11 +64,7 @@ function LynxKiteFlow() { setState(state); const doc = getYjsDoc(state); const proto = location.protocol === "https:" ? "wss:" : "ws:"; - const wsProvider = new WebsocketProvider( - `${proto}//${location.host}/ws/crdt`, - path!, - doc, - ); + const wsProvider = new WebsocketProvider(`${proto}//${location.host}/ws/crdt`, path!, doc); const onChange = (_update: any, origin: any, _doc: any, _tr: any) => { if (origin === wsProvider) { // An update from the CRDT. Apply it to the local state. @@ -165,7 +156,7 @@ function LynxKiteFlow() { const fetcher: Fetcher = (resource: string, init?: RequestInit) => fetch(resource, init).then((res) => res.json()); - const catalog = useSWR("/api/catalog", fetcher); + const catalog = useSWR(`/api/catalog?workspace=${path}`, fetcher); const [suppressSearchUntil, setSuppressSearchUntil] = useState(0); const [nodeSearchSettings, setNodeSearchSettings] = useState( undefined as @@ -190,11 +181,7 @@ function LynxKiteFlow() { useEffect(() => { const handleKeyDown = (event: KeyboardEvent) => { // Show the node search dialog on "/". - if ( - event.key === "/" && - !nodeSearchSettings && - !isTypingInFormElement() - ) { + if (event.key === "/" && !nodeSearchSettings && !isTypingInFormElement()) { event.preventDefault(); setNodeSearchSettings({ pos: { x: 100, y: 100 }, @@ -238,11 +225,7 @@ function LynxKiteFlow() { }, [catalog, state, nodeSearchSettings, suppressSearchUntil, closeNodeSearch], ); - function addNode( - node: Partial, - state: { workspace: Workspace }, - nodes: Node[], - ) { + function addNode(node: Partial, state: { workspace: Workspace }, nodes: Node[]) { const title = node.data?.title; let i = 1; node.id = `${title} ${i}`; @@ -260,9 +243,7 @@ function LynxKiteFlow() { data: { meta: meta, title: meta.name, - params: Object.fromEntries( - Object.values(meta.params).map((p) => [p.name, p.default]), - ), + params: Object.fromEntries(Object.values(meta.params).map((p) => [p.name, p.default])), }, }; return node; @@ -310,9 +291,7 @@ function LynxKiteFlow() { try { await axios.post("/api/upload", formData, { onUploadProgress: (progressEvent) => { - const percentCompleted = Math.round( - (100 * progressEvent.loaded) / progressEvent.total!, - ); + const percentCompleted = Math.round((100 * progressEvent.loaded) / progressEvent.total!); if (percentCompleted === 100) setMessage("Processing file..."); else setMessage(`Uploading ${percentCompleted}%`); }, @@ -345,7 +324,8 @@ function LynxKiteFlow() { -
{path}
+
{shortPath}
+ {shortPath}
-
+
1) { return ( - +
); } if (df.data.length) { @@ -60,9 +55,7 @@ export default function NodeWithGraphCreationView(props: any) { const display = props.data.display?.value; const tables = display?.dataframes || {}; const singleTable = tables && Object.keys(tables).length === 1; - const [relations, setRelations] = useState( - relationsToDict(display?.relations) || {}, - ); + const [relations, setRelations] = useState(relationsToDict(display?.relations) || {}); const singleRelation = relations && Object.keys(relations).length === 1; function setParam(name: string, newValue: any, opts: UpdateOptions) { reactFlow.updateNodeData(props.id, { @@ -211,9 +204,7 @@ export default function NodeWithGraphCreationView(props: any) { {tables[relation.source_table] && - tables[relation.df].columns.map((name: string) => ( - @@ -274,10 +265,7 @@ export default function NodeWithGraphCreationView(props: any) {
Relationships -
diff --git a/lynxkite-app/web/src/workspace/nodes/LynxKiteNode.tsx b/lynxkite-app/web/src/workspace/nodes/LynxKiteNode.tsx index 20758cc283135508919cd50d3e7734fa06730f1e..15e05e75b429adf7ffdeaf53780a02feed40332f 100644 --- a/lynxkite-app/web/src/workspace/nodes/LynxKiteNode.tsx +++ b/lynxkite-app/web/src/workspace/nodes/LynxKiteNode.tsx @@ -1,9 +1,4 @@ -import { - Handle, - NodeResizeControl, - type Position, - useReactFlow, -} from "@xyflow/react"; +import { Handle, NodeResizeControl, type Position, useReactFlow } from "@xyflow/react"; // @ts-ignore import ChevronDownRight from "~icons/tabler/chevron-down-right.jsx"; @@ -38,10 +33,8 @@ function getHandles(inputs: object, outputs: object) { } for (const e of handles) { e.offsetPercentage = (100 * (e.index + 1)) / (counts[e.position] + 1); - const simpleHorizontal = - counts.top === 0 && counts.bottom === 0 && handles.length <= 2; - const simpleVertical = - counts.left === 0 && counts.right === 0 && handles.length <= 2; + const simpleHorizontal = counts.top === 0 && counts.bottom === 0 && handles.length <= 2; + const simpleVertical = counts.left === 0 && counts.right === 0 && handles.length <= 2; e.showLabel = !simpleHorizontal && !simpleVertical; } return handles; @@ -71,10 +64,7 @@ export default function LynxKiteNode(props: LynxKiteNodeProps) { }} >
-
+
{data.title} {data.error && ⚠️} {expanded || ⋯} @@ -99,14 +89,11 @@ export default function LynxKiteNode(props: LynxKiteNodeProps) { type={handle.type} position={handle.position as Position} style={{ - [handleOffsetDirection[handle.position]]: - `${handle.offsetPercentage}% `, + [handleOffsetDirection[handle.position]]: `${handle.offsetPercentage}% `, }} > {handle.showLabel && ( - - {handle.name.replace(/_/g, " ")} - + {handle.name.replace(/_/g, " ")} )} ))} diff --git a/lynxkite-app/web/src/workspace/nodes/NodeGroupParameter.tsx b/lynxkite-app/web/src/workspace/nodes/NodeGroupParameter.tsx index 28564d40ead0e2644ff4af774ce0cd79d4ae05c0..89cbfb8e12373151c1bc6af52e090756f17664c7 100644 --- a/lynxkite-app/web/src/workspace/nodes/NodeGroupParameter.tsx +++ b/lynxkite-app/web/src/workspace/nodes/NodeGroupParameter.tsx @@ -24,6 +24,7 @@ interface GroupsType { interface NodeGroupParameterProps { meta: { selector: SelectorType; groups: GroupsType }; value: any; + data: any; setParam: (name: string, value: any, options?: { delay: number }) => void; deleteParam: (name: string, options?: { delay: number }) => void; } @@ -31,14 +32,13 @@ interface NodeGroupParameterProps { export default function NodeGroupParameter({ meta, value, + data, setParam, deleteParam, }: NodeGroupParameterProps) { const selector = meta.selector; const groups = meta.groups; - const [selectedValue, setSelectedValue] = useState( - value || selector.default, - ); + const [selectedValue, setSelectedValue] = useState(value || selector.default); const handleSelectorChange = (value: any, opts?: { delay: number }) => { setSelectedValue(value); @@ -47,9 +47,7 @@ export default function NodeGroupParameter({ useEffect(() => { // Clean possible previous parameters first - Object.values(groups).flatMap((group) => - group.map((entry) => deleteParam(entry.name)), - ); + Object.values(groups).flatMap((group) => group.map((entry) => deleteParam(entry.name))); for (const param of groups[selectedValue]) { setParam(param.name, param.default); } @@ -60,6 +58,7 @@ export default function NodeGroupParameter({ name={selector.name} key={selector.name} value={selectedValue} + data={data} meta={selector} onChange={handleSelectorChange} /> diff --git a/lynxkite-app/web/src/workspace/nodes/NodeParameter.tsx b/lynxkite-app/web/src/workspace/nodes/NodeParameter.tsx index 39b46006d248f36247cae85bcae8d5c84856aa2c..943360e0ea143717c339218a3203c8ad79955eda 100644 --- a/lynxkite-app/web/src/workspace/nodes/NodeParameter.tsx +++ b/lynxkite-app/web/src/workspace/nodes/NodeParameter.tsx @@ -1,8 +1,186 @@ -const BOOLEAN = ""; +import { useRef } from "react"; +// @ts-ignore +import ArrowsHorizontal from "~icons/tabler/arrows-horizontal.jsx"; +const BOOLEAN = ""; +const MODEL_TRAINING_INPUT_MAPPING = + ""; +const MODEL_INFERENCE_INPUT_MAPPING = + ""; +const MODEL_OUTPUT_MAPPING = ""; function ParamName({ name }: { name: string }) { + return {name.replace(/_/g, " ")}; +} + +function Input({ + value, + onChange, + inputRef, +}: { + value: string; + onChange: (value: string, options?: { delay: number }) => void; + inputRef?: React.Ref; +}) { + return ( + onChange(evt.currentTarget.value, { delay: 2 })} + onBlur={(evt) => onChange(evt.currentTarget.value, { delay: 0 })} + onKeyDown={(evt) => evt.code === "Enter" && onChange(evt.currentTarget.value, { delay: 0 })} + /> + ); +} + +type Bindings = { + [key: string]: { + df: string; + column: string; + }; +}; + +function getModelBindings( + data: any, + variant: "training input" | "inference input" | "output", +): string[] { + function bindingsOfModel(m: any): string[] { + switch (variant) { + case "training input": + return [...m.inputs, ...m.loss_inputs.filter((i: string) => !m.outputs.includes(i))]; + case "inference input": + return m.inputs; + case "output": + return m.outputs; + } + } + const bindings = new Set(); + const inputs = data?.input_metadata?.value ?? data?.input_metadata ?? []; + for (const input of inputs) { + const other = input.other ?? {}; + for (const e of Object.values(other) as any[]) { + if (e.type === "model") { + for (const b of bindingsOfModel(e.model)) { + bindings.add(b); + } + } + } + } + const list = [...bindings]; + list.sort(); + return list; +} + +function parseJsonOrEmpty(json: string): object { + try { + const j = JSON.parse(json); + if (j !== null && typeof j === "object") { + return j; + } + } catch (e) {} + return {}; +} + +function ModelMapping({ value, onChange, data, variant }: any) { + const dfsRef = useRef({} as { [binding: string]: HTMLSelectElement | null }); + const columnsRef = useRef( + {} as { [binding: string]: HTMLSelectElement | HTMLInputElement | null }, + ); + const v: any = parseJsonOrEmpty(value); + v.map ??= {}; + const dfs: { [df: string]: string[] } = {}; + const inputs = data?.input_metadata?.value ?? data?.input_metadata ?? []; + for (const input of inputs) { + if (!input.dataframes) continue; + const dataframes = input.dataframes as { + [df: string]: { columns: string[] }; + }; + for (const [df, { columns }] of Object.entries(dataframes)) { + dfs[df] = columns; + } + } + const bindings = getModelBindings(data, variant); + function getMap() { + const map: Bindings = {}; + for (const binding of bindings) { + const df = dfsRef.current[binding]?.value ?? ""; + const column = columnsRef.current[binding]?.value ?? ""; + if (df.length || column.length) { + map[binding] = { df, column }; + } + } + return map; + } return ( - {name.replace(/_/g, " ")} +
+ + {bindings.length > 0 ? ( + bindings.map((binding: string) => ( + + + + + + + )) + ) : ( + + + + )} + +
{binding} + + + + + {variant === "output" ? ( + { + columnsRef.current[binding] = el; + }} + value={v.map?.[binding]?.column} + onChange={(column, options) => { + const map = getMap(); + // At this point the has not been updated yet. We use the value from the event. + const df = dfsRef.current[binding]?.value ?? ""; + map[binding] ??= { df, column }; + map[binding].column = column; + onChange(JSON.stringify({ map }), options); + }} + /> + ) : ( + + )} +
no bindings
); } @@ -10,15 +188,11 @@ interface NodeParameterProps { name: string; value: any; meta: any; + data: any; onChange: (value: any, options?: { delay: number }) => void; } -export default function NodeParameter({ - name, - value, - meta, - onChange, -}: NodeParameterProps) { +export default function NodeParameter({ name, value, meta, data, onChange }: NodeParameterProps) { return ( // biome-ignore lint/a11y/noLabelWithoutControl: Most of the time there is a control. diff --git a/lynxkite-app/web/src/workspace/nodes/NodeWithImage.tsx b/lynxkite-app/web/src/workspace/nodes/NodeWithImage.tsx index d1755aac430a707df81ea8a14e8ec79cbdbc156a..1cd68215ed038ddf6d99deb2b827263d8b3444c7 100644 --- a/lynxkite-app/web/src/workspace/nodes/NodeWithImage.tsx +++ b/lynxkite-app/web/src/workspace/nodes/NodeWithImage.tsx @@ -3,9 +3,7 @@ import NodeWithParams from "./NodeWithParams"; const NodeWithImage = (props: any) => { return ( - {props.data.display && ( - Node Display - )} + {props.data.display && Node Display} ); }; diff --git a/lynxkite-app/web/src/workspace/nodes/NodeWithParams.tsx b/lynxkite-app/web/src/workspace/nodes/NodeWithParams.tsx index ef568a41f0cca30dd88a097722d470b4d78d4e5c..17b4b366f03133912ba2c9fadb4fb5b3312b0d86 100644 --- a/lynxkite-app/web/src/workspace/nodes/NodeWithParams.tsx +++ b/lynxkite-app/web/src/workspace/nodes/NodeWithParams.tsx @@ -35,11 +35,8 @@ function NodeWithParams(props: any) { return ( - {props.collapsed && ( -
setCollapsed(!collapsed)} - > + {props.collapsed && params.length > 0 && ( +
setCollapsed(!collapsed)}>
)} @@ -49,23 +46,21 @@ function NodeWithParams(props: any) { setParam(name, value, opts || {}) } - deleteParam={(name: string, opts?: UpdateOptions) => - deleteParam(name, opts || {}) - } + deleteParam={(name: string, opts?: UpdateOptions) => deleteParam(name, opts || {})} /> ) : ( - setParam(name, value, opts || {}) - } + onChange={(value: any, opts?: UpdateOptions) => setParam(name, value, opts || {})} /> ), )} diff --git a/lynxkite-app/web/src/workspace/nodes/NodeWithTableView.tsx b/lynxkite-app/web/src/workspace/nodes/NodeWithTableView.tsx index 7b311db28220cc7b07e4545134b9ea20dd935302..490ed239d31484fcbefb718faedbb467f4ee00aa 100644 --- a/lynxkite-app/web/src/workspace/nodes/NodeWithTableView.tsx +++ b/lynxkite-app/web/src/workspace/nodes/NodeWithTableView.tsx @@ -1,3 +1,4 @@ +import { useReactFlow } from "@xyflow/react"; import { useState } from "react"; import React from "react"; import Markdown from "react-markdown"; @@ -14,34 +15,41 @@ function toMD(v: any): string { return JSON.stringify(v); } +type OpenState = { [name: string]: boolean }; + export default function NodeWithTableView(props: any) { - const [open, setOpen] = useState({} as { [name: string]: boolean }); + const reactFlow = useReactFlow(); + const [open, setOpen] = useState((props.data?.params?._tables_open ?? {}) as OpenState); const display = props.data.display?.value; - const single = - display?.dataframes && Object.keys(display?.dataframes).length === 1; + const single = display?.dataframes && Object.keys(display?.dataframes).length === 1; const dfs = Object.entries(display?.dataframes || {}); dfs.sort(); + function setParam(name: string, newValue: any) { + reactFlow.updateNodeData(props.id, (prevData: any) => ({ + ...prevData, + params: { ...prevData.data.params, [name]: newValue }, + })); + } + function toggleTable(name: string) { + setOpen((prevOpen: OpenState) => { + const newOpen = { ...prevOpen, [name]: !prevOpen[name] }; + setParam("_tables_open", newOpen); + return newOpen; + }); + } return ( {display && [ dfs.map(([name, df]: [string, any]) => ( {!single && ( -
setOpen({ ...open, [name]: !open[name] })} - > +
toggleTable(name)}> {name}
)} {(single || open[name]) && (df.data.length > 1 ? ( - +
) : df.data.length ? (
{df.columns.map((c: string, i: number) => ( @@ -60,11 +68,7 @@ export default function NodeWithTableView(props: any) { )), Object.entries(display.others || {}).map(([name, o]) => ( <> -
setOpen({ ...open, [name]: !open[name] })} - > +
toggleTable(name)}> {name}
{open[name] && 
{(o as any).toString()}
} diff --git a/lynxkite-app/web/src/workspace/nodes/NodeWithVisualization.tsx b/lynxkite-app/web/src/workspace/nodes/NodeWithVisualization.tsx index 6c69792e5e0195f9e0501c74dab3f6f78744c9a5..ba9516b29bc2ceda74111d975346a75d3baf90ea 100644 --- a/lynxkite-app/web/src/workspace/nodes/NodeWithVisualization.tsx +++ b/lynxkite-app/web/src/workspace/nodes/NodeWithVisualization.tsx @@ -8,6 +8,10 @@ const NodeWithVisualization = (props: any) => { useEffect(() => { const opts = props.data?.display?.value; if (!opts || !chartsRef.current) return; + if (opts.tooltip?.formatter === "GET_THIRD_VALUE") { + // We can't pass a function from the backend, and can't get good tooltips otherwise. + opts.tooltip.formatter = (params: any) => params.value[2]; + } chartsInstanceRef.current = echarts.init(chartsRef.current, null, { renderer: "canvas", width: "auto", diff --git a/lynxkite-app/web/tests/directory.spec.ts b/lynxkite-app/web/tests/directory.spec.ts index 6177977bbfc7a698387fce18f9ae7dab3c4b7bc6..863633473ec181524761fd2dbce83fba6e938b08 100644 --- a/lynxkite-app/web/tests/directory.spec.ts +++ b/lynxkite-app/web/tests/directory.spec.ts @@ -14,13 +14,6 @@ test.describe("Directory operations", () => { splash = await Splash.open(page); }); - test("Create workspace with default name", async () => { - const workspace = await Workspace.empty(splash.page); - // Not checking for exact match, since there may be pre-existing "Untitled" workspaces - expect(workspace.name).toContain("Untitled"); - await workspace.close(); - }); - test("Create & delete workspace", async () => { const workspaceName = `TestWorkspace-${Date.now()}`; const workspace = await Workspace.empty(splash.page, workspaceName); @@ -40,11 +33,6 @@ test.describe("Directory operations", () => { await splash.deleteEntry(folderName); await expect(splash.getEntry(folderName)).not.toBeVisible(); }); - - test("Create folder with default name", async () => { - await splash.createFolder(); - await expect(splash.currentFolder()).toContainText("Untitled"); - }); }); test.describe diff --git a/lynxkite-app/web/tests/errors.spec.ts b/lynxkite-app/web/tests/errors.spec.ts index 3ba316ac034f5ea6e89e648cbd20d42bf08d6d14..9c725917df724be74f64fcee06495302867fec37 100644 --- a/lynxkite-app/web/tests/errors.spec.ts +++ b/lynxkite-app/web/tests/errors.spec.ts @@ -35,9 +35,7 @@ test("unknown operation", async () => { await graphBox.getByLabel("n", { exact: true }).fill("10"); await workspace.setEnv("LynxScribe"); const csvBox = workspace.getBox("NX › Scale-Free Graph 1"); - await expect(csvBox.locator(".error")).toHaveText( - 'Operation "NX › Scale-Free Graph" not found.', - ); + await expect(csvBox.locator(".error")).toHaveText('Operation "NX › Scale-Free Graph" not found.'); await workspace.setEnv("LynxKite Graph Analytics"); await expect(csvBox.locator(".error")).not.toBeVisible(); }); diff --git a/lynxkite-app/web/tests/graph_creation.spec.ts b/lynxkite-app/web/tests/graph_creation.spec.ts index ca562a54e9dea02b8a32ea3aed02e98e5b5aabfe..879b09bd98a67d84d29ff85c136766ab3cc50014 100644 --- a/lynxkite-app/web/tests/graph_creation.spec.ts +++ b/lynxkite-app/web/tests/graph_creation.spec.ts @@ -5,15 +5,9 @@ import { Splash, Workspace } from "./lynxkite"; let workspace: Workspace; test.beforeEach(async ({ browser }) => { - workspace = await Workspace.empty( - await browser.newPage(), - "graph_creation_spec_test", - ); + workspace = await Workspace.empty(await browser.newPage(), "graph_creation_spec_test"); await workspace.addBox("NX › Scale-Free Graph"); - await workspace - .getBox("NX › Scale-Free Graph 1") - .getByLabel("n", { exact: true }) - .fill("10"); + await workspace.getBox("NX › Scale-Free Graph 1").getByLabel("n", { exact: true }).fill("10"); await workspace.addBox("Create graph"); await workspace.connectBoxes("NX › Scale-Free Graph 1", "Create graph 1"); }); @@ -45,9 +39,7 @@ test("Tables are displayed in the Graph creation box", async () => { test("Adding and removing relationships", async () => { const graphBox = await workspace.getBox("Create graph 1"); - const addRelationshipButton = await graphBox.locator( - ".add-relationship-button", - ); + const addRelationshipButton = await graphBox.locator(".add-relationship-button"); await addRelationshipButton.click(); const formData: Record = { name: "relation_1", @@ -69,10 +61,9 @@ test("Adding and removing relationships", async () => { // check that the relationship has been saved in the backend await workspace.page.reload(); const graphBoxAfterReload = await workspace.getBox("Create graph 1"); - const relationHeader = await graphBoxAfterReload.locator( - ".graph-relations .df-head", - { hasText: "relation_1" }, - ); + const relationHeader = await graphBoxAfterReload.locator(".graph-relations .df-head", { + hasText: "relation_1", + }); await expect(relationHeader).toBeVisible(); await relationHeader.locator("button").click(); // Delete the relationship await expect(relationHeader).not.toBeVisible(); diff --git a/lynxkite-app/web/tests/import.spec.ts b/lynxkite-app/web/tests/import.spec.ts index e699728e5001fe3a06146d4a6ff447f42da0ac03..329b38614e4452bdfd37718f1589da98b333e620 100644 --- a/lynxkite-app/web/tests/import.spec.ts +++ b/lynxkite-app/web/tests/import.spec.ts @@ -7,10 +7,7 @@ import { Splash, Workspace } from "./lynxkite"; let workspace: Workspace; test.beforeEach(async ({ browser }) => { - workspace = await Workspace.empty( - await browser.newPage(), - "import_spec_test", - ); + workspace = await Workspace.empty(await browser.newPage(), "import_spec_test"); }); test.afterEach(async () => { @@ -22,11 +19,7 @@ test.afterEach(async () => { await splash.deleteEntry("import_spec_test"); }); -async function validateImport( - workspace: Workspace, - fileName: string, - fileFormat: string, -) { +async function validateImport(workspace: Workspace, fileName: string, fileFormat: string) { const __filename = fileURLToPath(import.meta.url); const __dirname = dirname(__filename); const filePath = join(__dirname, "data", fileName); diff --git a/lynxkite-app/web/tests/lynxkite.ts b/lynxkite-app/web/tests/lynxkite.ts index 04f8f4ae326ffa5e7ae11e3438ba0a58f21da612..20a2058df4f88f4e8ee5d280593da4e69854187d 100644 --- a/lynxkite-app/web/tests/lynxkite.ts +++ b/lynxkite-app/web/tests/lynxkite.ts @@ -18,7 +18,7 @@ export class Workspace { } // Starts with a brand new workspace. - static async empty(page: Page, workspaceName?: string): Promise { + static async empty(page: Page, workspaceName: string): Promise { const splash = await Splash.open(page); return await splash.createWorkspace(workspaceName); } @@ -63,10 +63,7 @@ export class Workspace { await this.page.locator(".ws-name").click(); await this.page.keyboard.press("/"); - await this.page - .locator(".node-search") - .getByText(boxName, { exact: true }) - .click(); + await this.page.locator(".node-search").getByText(boxName, { exact: true }).click(); await expect(this.getBoxes()).toHaveCount(allBoxes.length + 1); } @@ -107,9 +104,7 @@ export class Workspace { getBoxHandle(boxId: string, pos?: string) { if (pos) { - return this.page.locator( - `[data-id="${boxId}"] [data-handlepos="${pos}"]`, - ); + return this.page.locator(`[data-id="${boxId}"] [data-handlepos="${pos}"]`); } return this.page.getByTestId(boxId); } @@ -133,11 +128,9 @@ export class Workspace { } else if (offset) { // Without steps the movement is too fast and the box is not dragged. The more steps, // the better the movement is captured - await this.page.mouse.move( - boxCenterX + offset.offsetX, - boxCenterY + offset.offsetY, - { steps: 5 }, - ); + await this.page.mouse.move(boxCenterX + offset.offsetX, boxCenterY + offset.offsetY, { + steps: 5, + }); } await this.page.mouse.up(); } @@ -189,20 +182,12 @@ export class Splash { return this.page.locator(".entry").filter({ hasText: name }).first(); } - async createWorkspace(name?: string) { + async createWorkspace(name: string) { await this.page.getByRole("button", { name: "New workspace" }).click(); - await this.page.locator('input[name="workspaceName"]').click(); - let workspaceName: string; - if (name) { - workspaceName = name; - await this.page.locator('input[name="workspaceName"]').fill(name); - } else { - workspaceName = await this.page - .locator('input[name="workspaceName"]') - .inputValue(); - } - await this.page.locator('input[name="workspaceName"]').press("Enter"); - const ws = new Workspace(this.page, workspaceName); + const nameBox = this.page.locator('input[name="entryName"]'); + await nameBox.fill(name); + await nameBox.press("Enter"); + const ws = new Workspace(this.page, name); await ws.setEnv("LynxKite Graph Analytics"); return ws; } @@ -212,13 +197,11 @@ export class Splash { return new Workspace(this.page, name); } - async createFolder(folderName?: string) { + async createFolder(folderName: string) { await this.page.getByRole("button", { name: "New folder" }).click(); - await this.page.locator('input[name="folderName"]').click(); - if (folderName) { - await this.page.locator('input[name="folderName"]').fill(folderName); - } - await this.page.locator('input[name="folderName"]').press("Enter"); + const nameBox = this.page.locator('input[name="entryName"]'); + await nameBox.fill(folderName); + await nameBox.press("Enter"); } async deleteEntry(entryName: string) { diff --git a/lynxkite-app/web/tsconfig.json b/lynxkite-app/web/tsconfig.json index 1ffef600d959ec9e396d5a260bd3f5b927b2cef8..d32ff682003e0ff5d8a6e6bb3663d4c35a45b116 100644 --- a/lynxkite-app/web/tsconfig.json +++ b/lynxkite-app/web/tsconfig.json @@ -1,7 +1,4 @@ { "files": [], - "references": [ - { "path": "./tsconfig.app.json" }, - { "path": "./tsconfig.node.json" } - ] + "references": [{ "path": "./tsconfig.app.json" }, { "path": "./tsconfig.node.json" }] } diff --git a/lynxkite-app/web/vite.config.ts b/lynxkite-app/web/vite.config.ts index 9e94a9ecbd68f8e142bc84890c20b3f53710f4ab..69c5df557bd8fb98f97a7e9392770b98a2b92854 100644 --- a/lynxkite-app/web/vite.config.ts +++ b/lynxkite-app/web/vite.config.ts @@ -5,7 +5,7 @@ import { defineConfig } from "vite"; // https://vite.dev/config/ export default defineConfig({ build: { - chunkSizeWarningLimit: 2048, + chunkSizeWarningLimit: 3000, }, esbuild: { supported: { diff --git a/lynxkite-bio/src/lynxkite_bio/__init__.py b/lynxkite-bio/src/lynxkite_bio/__init__.py index e8aa15d383fdd933ae9529bce24711b3e82eac14..12c6fb19244c6974e867e29d4a2a47e08838514e 100644 --- a/lynxkite-bio/src/lynxkite_bio/__init__.py +++ b/lynxkite-bio/src/lynxkite_bio/__init__.py @@ -1,68 +1,4 @@ """An expansion for `lynxkite-graph-analytics` that provides algorithms for biological applications.""" -from lynxkite_graph_analytics import Bundle, RelationDefinition -from lynxkite.core import ops -import joblib -import numpy as np -import pandas as pd -import rdkit.Chem -import rdkit.Chem.rdFingerprintGenerator -import rdkit.Chem.Fingerprints.ClusterMols -import scipy - -mem = joblib.Memory("joblib-cache") -ENV = "LynxKite Graph Analytics" -op = ops.op_registration(ENV) - - -@op("Parse SMILES") -def parse_smiles(bundle: Bundle, *, table="df", smiles_column="SMILES", save_as="mols"): - """Parse SMILES strings into RDKit molecules.""" - df = bundle.dfs[table] - mols = [rdkit.Chem.MolFromSmiles(smiles) for smiles in df[smiles_column].dropna()] - mols = [mol for mol in mols if mol is not None] - bundle = bundle.copy() - bundle.dfs[table] = df.assign(**{save_as: mols}) - return bundle - - -def _get_similarity_matrix(mols): - mfpgen = rdkit.Chem.rdFingerprintGenerator.GetMorganGenerator(radius=2, fpSize=2048) - fps = [(0, mfpgen.GetFingerprint(mol)) for mol in mols] - similarity_matrix = rdkit.Chem.Fingerprints.ClusterMols.GetDistanceMatrix( - fps, metric=rdkit.Chem.DataStructs.TanimotoSimilarity, isSimilarity=1 - ) - return scipy.spatial.distance.squareform(similarity_matrix) - - -@op("Graph from molecule similarity") -def graph_from_similarity( - bundle: Bundle, *, table="df", mols_column="mols", average_degree=10 -): - """Creates edges for pairs of molecules that are the most similar.""" - df = bundle.dfs[table] - mols = df[mols_column] - similarity_matrix = _get_similarity_matrix(mols) - i_idx, j_idx = np.triu_indices_from(similarity_matrix, k=1) - sim_values = similarity_matrix[i_idx, j_idx] - N = int(average_degree * len(mols)) - top_n_idx = np.argsort(sim_values)[-N:] - top_n_pairs = [(i_idx[k], j_idx[k], sim_values[k]) for k in top_n_idx] - edges = pd.DataFrame(top_n_pairs, columns=["source", "target", "similarity"]) - nodes = df.copy() - nodes.index.name = "id" - bundle = Bundle( - dfs={"edges": edges, "nodes": nodes}, - relations=[ - RelationDefinition( - df="edges", - source_column="source", - target_column="target", - source_table="nodes", - target_table="nodes", - source_key="id", - target_key="id", - ) - ], - ) - return bundle +from . import nims # noqa (imported to trigger registration) +from . import rdkit # noqa (imported to trigger registration) diff --git a/lynxkite-bio/src/lynxkite_bio/nims.py b/lynxkite-bio/src/lynxkite_bio/nims.py new file mode 100644 index 0000000000000000000000000000000000000000..6bda4aeb7fec0b1002c8ed380fa0fc516d926930 --- /dev/null +++ b/lynxkite-bio/src/lynxkite_bio/nims.py @@ -0,0 +1,133 @@ +"""Wrappers for BioNeMo NIMs.""" + +from enum import Enum +from lynxkite_graph_analytics import Bundle +from lynxkite.core import ops +import joblib +import os + +NIM_URLS = os.environ.get("NIM_URLS", "http://localhost:8000").split(",") + +mem = joblib.Memory(".joblib-cache") +ENV = "LynxKite Graph Analytics" +op = ops.op_registration(ENV) + + +class MSASearchTypes(Enum): + ALPHAFOLD2 = "ALPHAFOLD2" + ESM2 = "ESM2" + + +class AlignmentFormats(Enum): + FASTA = "fasta" + A3M = "a3m" + STOCKHOLM = "stockholm" + CLUSTAL = "clustal" + PDB = "pdb" + PIR = "pir" + MSF = "msf" + TSV = "tsv" + + +@op("MSA-search") +@mem.cache +def msa_search( + bundle: Bundle, + *, + protein_table: str, + protein_column: str, + e_value: float = 0.0001, + iterations: int = 1, + search_type: MSASearchTypes = MSASearchTypes.ALPHAFOLD2, + output_alignment_formats: list[AlignmentFormats] = [ + AlignmentFormats.FASTA, + AlignmentFormats.A3M, + ], + databases: str = '["Uniref30_2302", "colabfold_envdb_202108", "PDB70_220313"]', +): + bundle = bundle.copy() + return bundle + + +@op("Query OpenFold2") +@mem.cache +def query_openfold2( + bundle: Bundle, + *, + protein_table: str, + protein_column: str, + alignment_table: str, + alignment_column: str, + use_templates: bool = False, + relaxed_prediction: bool = False, + databases: str = '["Uniref30_2302", "colabfold_envdb_202108", "PDB70_220313"]', +): + bundle = bundle.copy() + return bundle + + +@op("View molecules", view="visualization") +def view_molecules( + bundle: Bundle, + *, + molecule_table: str, + molecule_column: str, + color="spectrum", +): + return { + "series": [ + { + "type": "pie", + "radius": ["40%", "70%"], + "itemStyle": { + "borderRadius": 10, + "borderColor": "#fff", + "borderWidth": 2, + }, + "data": [ + {"value": 2, "name": "Hydrogen"}, + {"value": 1, "name": "Sulfur"}, + {"value": 4, "name": "Oxygen"}, + ], + } + ] + } + + +@op("Known drug") +def known_drug(*, drug_name: str): + return Bundle() + + +@op("Query GenMol") +@mem.cache +def query_genmol( + bundle: Bundle, + *, + molecule_table: str, + molecule_column: str, + num_molecules: int = 5, + temperature: float = 1.0, + noise: float = 0.2, + step_size: int = 4, + scoring: str = "QED", +): + bundle = bundle.copy() + return bundle + + +@op("Query DiffDock") +@mem.cache +def query_diffdock( + proteins: Bundle, + ligands: Bundle, + *, + protein_table: str, + protein_column: str, + ligand_table: str, + ligand_column: str, + num_poses=10, + time_divisions=20, + num_steps=18, +): + return proteins diff --git a/lynxkite-bio/src/lynxkite_bio/rdkit.py b/lynxkite-bio/src/lynxkite_bio/rdkit.py new file mode 100644 index 0000000000000000000000000000000000000000..ac6cf2d7aa635e7be2a8f2a282a48ad6015f6755 --- /dev/null +++ b/lynxkite-bio/src/lynxkite_bio/rdkit.py @@ -0,0 +1,66 @@ +"""An expansion for `lynxkite-graph-analytics` that provides algorithms for biological applications.""" + +from lynxkite_graph_analytics import Bundle, RelationDefinition +from lynxkite.core import ops +import joblib +import numpy as np +import pandas as pd +import rdkit.Chem +import rdkit.Chem.rdFingerprintGenerator +import rdkit.Chem.Fingerprints.ClusterMols +import scipy + +mem = joblib.Memory(".joblib-cache") +ENV = "LynxKite Graph Analytics" +op = ops.op_registration(ENV) + + +@op("Parse SMILES") +def parse_smiles(bundle: Bundle, *, table="df", smiles_column="SMILES", save_as="mols"): + """Parse SMILES strings into RDKit molecules.""" + df = bundle.dfs[table] + mols = [rdkit.Chem.MolFromSmiles(smiles) for smiles in df[smiles_column].dropna()] + mols = [mol for mol in mols if mol is not None] + bundle = bundle.copy() + bundle.dfs[table] = df.assign(**{save_as: mols}) + return bundle + + +def _get_similarity_matrix(mols): + mfpgen = rdkit.Chem.rdFingerprintGenerator.GetMorganGenerator(radius=2, fpSize=2048) + fps = [(0, mfpgen.GetFingerprint(mol)) for mol in mols] + similarity_matrix = rdkit.Chem.Fingerprints.ClusterMols.GetDistanceMatrix( + fps, metric=rdkit.Chem.DataStructs.TanimotoSimilarity, isSimilarity=1 + ) + return scipy.spatial.distance.squareform(similarity_matrix) + + +@op("Graph from molecule similarity") +def graph_from_similarity(bundle: Bundle, *, table="df", mols_column="mols", average_degree=10): + """Creates edges for pairs of molecules that are the most similar.""" + df = bundle.dfs[table] + mols = df[mols_column] + similarity_matrix = _get_similarity_matrix(mols) + i_idx, j_idx = np.triu_indices_from(similarity_matrix, k=1) + sim_values = similarity_matrix[i_idx, j_idx] + N = int(average_degree * len(mols)) + top_n_idx = np.argsort(sim_values)[-N:] + top_n_pairs = [(i_idx[k], j_idx[k], sim_values[k]) for k in top_n_idx] + edges = pd.DataFrame(top_n_pairs, columns=["source", "target", "similarity"]) + nodes = df.copy() + nodes.index.name = "id" + bundle = Bundle( + dfs={"edges": edges, "nodes": nodes}, + relations=[ + RelationDefinition( + df="edges", + source_column="source", + target_column="target", + source_table="nodes", + target_table="nodes", + source_key="id", + target_key="id", + ) + ], + ) + return bundle diff --git a/lynxkite-core/src/lynxkite/core/executors/one_by_one.py b/lynxkite-core/src/lynxkite/core/executors/one_by_one.py index a4079a120a2d0b762a0b3eb7fd25866f3d0e71ce..e6a450ef6a5ea9968ec4d750c999e44777a7f6b0 100644 --- a/lynxkite-core/src/lynxkite/core/executors/one_by_one.py +++ b/lynxkite-core/src/lynxkite/core/executors/one_by_one.py @@ -167,9 +167,9 @@ async def execute(ws: workspace.Workspace, catalog, cache=None): op = catalog[t.data.title] i = op.inputs[edge.targetHandle] if i.position in "top or bottom": - batch_inputs.setdefault( - (edge.target, edge.targetHandle), [] - ).extend(results) + batch_inputs.setdefault((edge.target, edge.targetHandle), []).extend( + results + ) else: tasks.setdefault(edge.target, []).extend(results) node.publish_result(result) diff --git a/lynxkite-core/src/lynxkite/core/ops.py b/lynxkite-core/src/lynxkite/core/ops.py index 023406649eb056bc83f8091bedb43551917fefd2..61a5fafb49e220e539e1082e68f2d4204b9e327f 100644 --- a/lynxkite-core/src/lynxkite/core/ops.py +++ b/lynxkite-core/src/lynxkite/core/ops.py @@ -1,9 +1,15 @@ """API for implementing LynxKite operations.""" from __future__ import annotations +import asyncio import enum import functools +import importlib import inspect +import pathlib +import subprocess +import traceback +import joblib import types import pydantic import typing @@ -13,8 +19,11 @@ from typing_extensions import Annotated if typing.TYPE_CHECKING: from . import workspace -CATALOGS = {} +Catalog = dict[str, "Op"] +Catalogs = dict[str, Catalog] +CATALOGS: Catalogs = {} EXECUTORS = {} +mem = joblib.Memory(".joblib-cache") typeof = type # We have some arguments called "type". @@ -61,7 +70,7 @@ class Parameter(BaseConfig): @staticmethod def options(name, options, default=None): e = enum.Enum(f"OptionsFor_{name}", options) - return Parameter.basic(name, e[default or options[0]], e) + return Parameter.basic(name, default or options[0], e) @staticmethod def collapsed(name, default, type=None): @@ -106,11 +115,13 @@ class Result: The `output` attribute is what will be used as input for other operations. The `display` attribute is used to send data to display on the UI. The value has to be JSON-serializable. + `input_metadata` is a list of JSON objects describing each input. """ output: typing.Any = None display: ReadOnlyJSON | None = None error: str | None = None + input_metadata: ReadOnlyJSON | None = None MULTI_INPUT = Input(name="multi", type="*") @@ -140,6 +151,11 @@ def _param_to_type(name, value, type): return None if value == "" else _param_to_type(name, value, type) case (type, types.NoneType): return None if value == "" else _param_to_type(name, value, type) + if isinstance(type, typeof) and issubclass(type, pydantic.BaseModel): + try: + return type.model_validate_json(value) + except pydantic.ValidationError: + return None return value @@ -154,9 +170,7 @@ class Op(BaseConfig): def __call__(self, *inputs, **params): # Convert parameters. - for p in params: - if p in self.params: - params[p] = _param_to_type(p, params[p], self.params[p].type) + params = self.convert_params(params) res = self.func(*inputs, **params) if not isinstance(res, Result): # Automatically wrap the result in a Result object, if it isn't already. @@ -172,12 +186,25 @@ class Op(BaseConfig): res.display = res.output return res + def convert_params(self, params): + """Returns the parameters converted to the expected type.""" + res = {} + for p in params: + if p in self.params: + res[p] = _param_to_type(p, params[p], self.params[p].type) + else: + res[p] = params[p] + return res + -def op(env: str, name: str, *, view="basic", outputs=None, params=None): +def op(env: str, name: str, *, view="basic", outputs=None, params=None, slow=False): """Decorator for defining an operation.""" def decorator(func): sig = inspect.signature(func) + if slow: + func = mem.cache(func) + func = _global_slow(func) # Positional arguments are inputs. inputs = { name: Input(name=name, type=param.annotation) @@ -193,9 +220,7 @@ def op(env: str, name: str, *, view="basic", outputs=None, params=None): if outputs: _outputs = {name: Output(name=name, type=None) for name in outputs} else: - _outputs = ( - {"output": Output(name="output", type=None)} if view == "basic" else {} - ) + _outputs = {"output": Output(name="output", type=None)} if view == "basic" else {} op = Op( func=func, name=name, @@ -249,12 +274,10 @@ def register_passive_op(env: str, name: str, inputs=[], outputs=["output"], para name=name, params={p.name: p for p in params}, inputs=dict( - (i, Input(name=i, type=None)) if isinstance(i, str) else (i.name, i) - for i in inputs + (i, Input(name=i, type=None)) if isinstance(i, str) else (i.name, i) for i in inputs ), outputs=dict( - (o, Output(name=o, type=None)) if isinstance(o, str) else (o.name, o) - for o in outputs + (o, Output(name=o, type=None)) if isinstance(o, str) else (o.name, o) for o in outputs ), ) CATALOGS.setdefault(env, {}) @@ -286,3 +309,64 @@ def op_registration(env: str): def passive_op_registration(env: str): """Returns a function that can be used to register operations without associated code.""" return functools.partial(register_passive_op, env) + + +def slow(func): + """Decorator for slow, blocking operations. Turns them into separate threads.""" + + @functools.wraps(func) + async def wrapper(*args, **kwargs): + return await asyncio.to_thread(func, *args, **kwargs) + + return wrapper + + +_global_slow = slow # For access inside op(). +CATALOGS_SNAPSHOTS: dict[str, Catalogs] = {} + + +def save_catalogs(snapshot_name: str): + CATALOGS_SNAPSHOTS[snapshot_name] = {k: dict(v) for k, v in CATALOGS.items()} + + +def load_catalogs(snapshot_name: str): + global CATALOGS + snap = CATALOGS_SNAPSHOTS[snapshot_name] + CATALOGS = {k: dict(v) for k, v in snap.items()} + + +def load_user_scripts(workspace: str): + """Reloads the *.py in the workspace's directory and higher-level directories.""" + if "plugins loaded" in CATALOGS_SNAPSHOTS: + load_catalogs("plugins loaded") + cwd = pathlib.Path() + path = cwd / workspace + assert path.is_relative_to(cwd), "Provided workspace path is invalid" + for p in path.parents: + print("checking user scripts in", p) + for f in p.glob("*.py"): + try: + run_user_script(f) + except Exception: + traceback.print_exc() + req = p / "requirements.txt" + if req.exists(): + try: + install_requirements(req) + except Exception: + traceback.print_exc() + if p == cwd: + break + + +def install_requirements(req: pathlib.Path): + cmd = ["uv", "pip", "install", "-r", str(req)] + print(f"Running {' '.join(cmd)}") + subprocess.check_call(cmd) + + +def run_user_script(script_path: pathlib.Path): + print(f"Running {script_path}...") + spec = importlib.util.spec_from_file_location(script_path.stem, str(script_path)) + module = importlib.util.module_from_spec(spec) + spec.loader.exec_module(module) diff --git a/lynxkite-core/src/lynxkite/core/workspace.py b/lynxkite-core/src/lynxkite/core/workspace.py index 6721b12e9eed69d4772d732e9d4993bfa285b296..e434fe1127114e859dbd500a87c83b0a6f70c0f7 100644 --- a/lynxkite-core/src/lynxkite/core/workspace.py +++ b/lynxkite-core/src/lynxkite/core/workspace.py @@ -32,6 +32,7 @@ class WorkspaceNodeData(BaseConfig): title: str params: dict display: Optional[object] = None + input_metadata: Optional[object] = None error: Optional[str] = None status: NodeStatus = NodeStatus.done # Also contains a "meta" field when going out. @@ -59,12 +60,14 @@ class WorkspaceNode(BaseConfig): def publish_result(self, result: ops.Result): """Sends the result to the frontend. Call this in an executor when the result is available.""" self.data.display = result.display + self.data.input_metadata = result.input_metadata self.data.error = result.error self.data.status = NodeStatus.done if hasattr(self, "_crdt"): with self._crdt.doc.transaction(): - self._crdt["data"]["display"] = result.display - self._crdt["data"]["error"] = result.error + self._crdt["data"]["display"] = self.data.display + self._crdt["data"]["input_metadata"] = self.data.input_metadata + self._crdt["data"]["error"] = self.data.error self._crdt["data"]["status"] = NodeStatus.done def publish_error(self, error: Exception | str | None): @@ -108,9 +111,7 @@ def save(ws: Workspace, path: str): if dirname: os.makedirs(dirname, exist_ok=True) # Create temp file in the same directory to make sure it's on the same filesystem. - with tempfile.NamedTemporaryFile( - "w", prefix=f".{basename}.", dir=dirname, delete=False - ) as f: + with tempfile.NamedTemporaryFile("w", prefix=f".{basename}.", dir=dirname, delete=False) as f: temp_name = f.name f.write(j) os.replace(temp_name, path) diff --git a/lynxkite-core/tests/test_ops.py b/lynxkite-core/tests/test_ops.py index 6fb5c8b0dc3b3bb73fca7d5f46ea430d15465856..cc895130a84274b7de1f3de2813faadd7d16a066 100644 --- a/lynxkite-core/tests/test_ops.py +++ b/lynxkite-core/tests/test_ops.py @@ -14,9 +14,7 @@ def test_op_decorator_no_params_no_types_default_positions(): "a": ops.Input(name="a", type=inspect._empty, position="left"), "b": ops.Input(name="b", type=inspect._empty, position="left"), } - assert add.__op__.outputs == { - "result": ops.Output(name="result", type=None, position="right") - } + assert add.__op__.outputs == {"result": ops.Output(name="result", type=None, position="right")} assert add.__op__.type == "basic" assert ops.CATALOGS["test"]["add"] == add.__op__ @@ -34,9 +32,7 @@ def test_op_decorator_custom_positions(): "a": ops.Input(name="a", type=inspect._empty, position="right"), "b": ops.Input(name="b", type=inspect._empty, position="top"), } - assert add.__op__.outputs == { - "result": ops.Output(name="result", type=None, position="bottom") - } + assert add.__op__.outputs == {"result": ops.Output(name="result", type=None, position="bottom")} assert add.__op__.type == "basic" assert ops.CATALOGS["test"]["add"] == add.__op__ @@ -76,9 +72,7 @@ def test_op_decorator_with_complex_types(): assert complex_op.__op__.inputs == { "color": ops.Input(name="color", type=Color, position="left"), "color_list": ops.Input(name="color_list", type=list[Color], position="left"), - "color_dict": ops.Input( - name="color_dict", type=dict[str, Color], position="left" - ), + "color_dict": ops.Input(name="color_dict", type=dict[str, Color], position="left"), } assert complex_op.__op__.type == "basic" assert complex_op.__op__.outputs == { diff --git a/lynxkite-graph-analytics/pyproject.toml b/lynxkite-graph-analytics/pyproject.toml index 95ddf6c1b1f94adb3822e5fc9668177cb46f7edf..77f4fc53ae68f4b96a2f01845b747bff13a18eb5 100644 --- a/lynxkite-graph-analytics/pyproject.toml +++ b/lynxkite-graph-analytics/pyproject.toml @@ -14,6 +14,9 @@ dependencies = [ "osmnx>=2.0.1", "pandas>=2.2.3", "polars[gpu]>=1.14.0", + "torch>=2.6.0", + "torch-geometric>=2.6.1", + "cuml-cu12>=25.2.1", ] [project.optional-dependencies] diff --git a/lynxkite-graph-analytics/src/lynxkite_graph_analytics/__init__.py b/lynxkite-graph-analytics/src/lynxkite_graph_analytics/__init__.py index b6a581fc5c149366dca3785dd4d07d5053025262..dee185d63de0c5639f4b56a9bcdb5bcc47f2ebab 100644 --- a/lynxkite-graph-analytics/src/lynxkite_graph_analytics/__init__.py +++ b/lynxkite-graph-analytics/src/lynxkite_graph_analytics/__init__.py @@ -13,7 +13,8 @@ pd.options.mode.copy_on_write = True # Prepare for Pandas 3.0. from .core import * # noqa (easier access for core classes) from . import lynxkite_ops # noqa (imported to trigger registration) from . import networkx_ops # noqa (imported to trigger registration) -from . import pytorch_model_ops # noqa (imported to trigger registration) +from . import pytorch # noqa (imported to trigger registration) +from . import ml_ops # noqa (imported to trigger registration) if os.environ.get("LYNXKITE_BIONEMO_INSTALLED", "").strip().lower() == "true": from . import bionemo_ops # noqa (imported to trigger registration) diff --git a/lynxkite-graph-analytics/src/lynxkite_graph_analytics/bionemo_ops.py b/lynxkite-graph-analytics/src/lynxkite_graph_analytics/bionemo_ops.py index 9222e650c581fc6e998b3def9b6396e25e79ce6d..1a93d812f79f512f9ccf914deeb841715fcf8929 100644 --- a/lynxkite-graph-analytics/src/lynxkite_graph_analytics/bionemo_ops.py +++ b/lynxkite-graph-analytics/src/lynxkite_graph_analytics/bionemo_ops.py @@ -40,7 +40,7 @@ from bionemo.scdl.io.single_cell_collection import SingleCellCollection import scanpy -mem = joblib.Memory("joblib-cache") +mem = joblib.Memory(".joblib-cache") op = ops.op_registration(core.ENV) DATA_PATH = Path("/workspace") @@ -89,9 +89,7 @@ def download_cellxgene_dataset( adata.write_h5ad(h5ad_outfile) with tempfile.TemporaryDirectory() as temp_dir: coll = SingleCellCollection(temp_dir) - coll.load_h5ad_multi( - h5ad_outfile.parent, max_workers=max_workers, use_processes=use_mp - ) + coll.load_h5ad_multi(h5ad_outfile.parent, max_workers=max_workers, use_processes=use_mp) coll.flatten(DATA_PATH / save_path, destroy_on_copy=True) return DATA_PATH / save_path @@ -148,9 +146,7 @@ def download_model(*, model_name: str) -> str: @op("BioNeMo > Infer") @mem.cache(verbose=1) -def infer( - dataset_path: str, model_path: str | None = None, *, results_path: str -) -> str: +def infer(dataset_path: str, model_path: str | None = None, *, results_path: str) -> str: """Infer on a dataset.""" # This import is slow, so we only import it when we need it. from bionemo.geneformer.scripts.infer_geneformer import infer_model @@ -175,10 +171,7 @@ def infer( @op("BioNeMo > Load results") def load_results(results_path: str): embeddings = ( - torch.load(f"{results_path}/predictions__rank_0.pt")["embeddings"] - .float() - .cpu() - .numpy() + torch.load(f"{results_path}/predictions__rank_0.pt")["embeddings"].float().cpu().numpy() ) return embeddings @@ -248,9 +241,7 @@ def run_benchmark(data, labels, *, use_pca: bool = False): ] ) else: - pipeline = Pipeline( - [("classifier", RandomForestClassifier(class_weight="balanced"))] - ) + pipeline = Pipeline([("classifier", RandomForestClassifier(class_weight="balanced"))]) # Set up StratifiedKFold to ensure each fold reflects the overall distribution of labels cv = StratifiedKFold(n_splits=5) @@ -258,9 +249,7 @@ def run_benchmark(data, labels, *, use_pca: bool = False): # Define the scoring functions scoring = { "accuracy": make_scorer(accuracy_score), - "precision": make_scorer( - precision_score, average="macro" - ), # 'macro' averages over classes + "precision": make_scorer(precision_score, average="macro"), # 'macro' averages over classes "recall": make_scorer(recall_score, average="macro"), "f1_score": make_scorer(f1_score, average="macro"), # 'roc_auc' requires probability or decision function; hence use multi_class if applicable @@ -298,9 +287,7 @@ def plot_confusion_matrix(benchmark_output, labels): # heatmap has the 0,0 at the bottom left corner num_rows = len(str_labels) heatmap_data = [ - [j, num_rows - i - 1, norm_cm[i][j]] - for i in range(len(labels)) - for j in range(len(labels)) + [j, num_rows - i - 1, norm_cm[i][j]] for i in range(len(labels)) for j in range(len(labels)) ] options = { @@ -332,9 +319,7 @@ def plot_confusion_matrix(benchmark_output, labels): "orient": "vertical", "right": 10, "top": "center", - "inRange": { - "color": ["#E0F7FA", "#81D4FA", "#29B6F6", "#0288D1", "#01579B"] - }, + "inRange": {"color": ["#E0F7FA", "#81D4FA", "#29B6F6", "#0288D1", "#01579B"]}, }, "series": [ { @@ -424,9 +409,7 @@ def accuracy_comparison(benchmark_output10m, benchmark_output100m): { "name": "Error Bars", "type": "errorbar", - "data": [ - [val - err, val + err] for val, err in zip(values, error_bars) - ], + "data": [[val - err, val + err] for val, err in zip(values, error_bars)], "itemStyle": {"color": "#1f77b4"}, }, ], @@ -509,9 +492,7 @@ def f1_comparison(benchmark_output10m, benchmark_output100m): { "name": "Error Bars", "type": "errorbar", - "data": [ - [val - err, val + err] for val, err in zip(values, error_bars) - ], + "data": [[val - err, val + err] for val, err in zip(values, error_bars)], "itemStyle": {"color": "#1f77b4"}, }, ], diff --git a/lynxkite-graph-analytics/src/lynxkite_graph_analytics/core.py b/lynxkite-graph-analytics/src/lynxkite_graph_analytics/core.py index db46115e27bebd09316d27951f2b9525c4cfe270..c5aa00cc17a4dd4ce4b1fed45c8b2b8dc31a1911 100644 --- a/lynxkite-graph-analytics/src/lynxkite_graph_analytics/core.py +++ b/lynxkite-graph-analytics/src/lynxkite_graph_analytics/core.py @@ -1,5 +1,6 @@ """Graph analytics executor and data types.""" +import inspect import os from lynxkite.core import ops, workspace import dataclasses @@ -19,12 +20,8 @@ class RelationDefinition: """Defines a set of edges.""" df: str # The DataFrame that contains the edges. - source_column: ( - str # The column in the edge DataFrame that contains the source node ID. - ) - target_column: ( - str # The column in the edge DataFrame that contains the target node ID. - ) + source_column: str # The column in the edge DataFrame that contains the source node ID. + target_column: str # The column in the edge DataFrame that contains the target node ID. source_table: str # The DataFrame that contains the source nodes. target_table: str # The DataFrame that contains the target nodes. source_key: str # The column in the source table that contains the node ID. @@ -42,7 +39,7 @@ class Bundle: dfs: dict[str, pd.DataFrame] = dataclasses.field(default_factory=dict) relations: list[RelationDefinition] = dataclasses.field(default_factory=list) - other: dict[str, typing.Any] = None + other: dict[str, typing.Any] = dataclasses.field(default_factory=dict) @classmethod def from_nx(cls, graph: nx.Graph): @@ -86,11 +83,7 @@ class Bundle: ( e["source"], e["target"], - { - k: e[k] - for k in edges.columns - if k not in ["source", "target"] - }, + {k: e[k] for k in edges.columns if k not in ["source", "target"]}, ) for e in edges.to_records() ] @@ -102,10 +95,11 @@ class Bundle: return Bundle( dfs=dict(self.dfs), relations=list(self.relations), - other=dict(self.other) if self.other else None, + other=dict(self.other), ) def to_dict(self, limit: int = 100): + """JSON-serializable representation of the bundle, including some data.""" return { "dataframes": { name: { @@ -115,7 +109,20 @@ class Bundle: for name, df in self.dfs.items() }, "relations": [dataclasses.asdict(relation) for relation in self.relations], - "other": self.other, + "other": {k: str(v) for k, v in self.other.items()}, + } + + def metadata(self): + """JSON-serializable information about the bundle, metadata only.""" + return { + "dataframes": { + name: { + "columns": sorted(str(c) for c in df.columns), + } + for name, df in self.dfs.items() + }, + "relations": [dataclasses.asdict(relation) for relation in self.relations], + "other": {k: getattr(v, "metadata", lambda: {})() for k, v in self.other.items()}, } @@ -137,8 +144,15 @@ def nx_node_attribute_func(name): def disambiguate_edges(ws: workspace.Workspace): """If an input plug is connected to multiple edges, keep only the last edge.""" + catalog = ops.CATALOGS[ws.env] + nodes = {node.id: node for node in ws.nodes} seen = set() for edge in reversed(ws.edges): + dst_node = nodes[edge.target] + op = catalog.get(dst_node.data.title) + if op.inputs[edge.targetHandle].type == list[Bundle]: + # Takes multiple bundles as an input. No need to disambiguate. + continue if (edge.target, edge.targetHandle) in seen: i = ws.edges.index(edge) del ws.edges[i] @@ -164,23 +178,28 @@ async def execute(ws: workspace.Workspace): # All inputs for this node are ready, we can compute the output. todo.remove(id) progress = True - _execute_node(node, ws, catalog, outputs) + await _execute_node(node, ws, catalog, outputs) -def _execute_node(node, ws, catalog, outputs): +async def await_if_needed(obj): + if inspect.isawaitable(obj): + obj = await obj + return obj + + +async def _execute_node(node, ws, catalog, outputs): params = {**node.data.params} op = catalog.get(node.data.title) if not op: node.publish_error("Operation not found in catalog") return node.publish_started() + # TODO: Handle multi-inputs. input_map = { - edge.targetHandle: outputs[edge.source] - for edge in ws.edges - if edge.target == node.id + edge.targetHandle: outputs[edge.source] for edge in ws.edges if edge.target == node.id } + # Convert inputs types to match operation signature. try: - # Convert inputs types to match operation signature. inputs = [] for p in op.inputs.values(): if p.name not in input_map: @@ -194,16 +213,31 @@ def _execute_node(node, ws, catalog, outputs): elif p.type == Bundle and isinstance(x, pd.DataFrame): x = Bundle.from_df(x) inputs.append(x) - result = op(*inputs, **params) except Exception as e: if os.environ.get("LYNXKITE_LOG_OP_ERRORS"): traceback.print_exc() node.publish_error(e) return - outputs[node.id] = result.output + # Execute op. + try: + result = op(*inputs, **params) + result.output = await await_if_needed(result.output) + except Exception as e: + if os.environ.get("LYNXKITE_LOG_OP_ERRORS"): + traceback.print_exc() + result = ops.Result(error=str(e)) + result.input_metadata = [_get_metadata(i) for i in inputs] + if result.output is not None: + outputs[node.id] = result.output node.publish_result(result) +def _get_metadata(x): + if hasattr(x, "metadata"): + return x.metadata() + return {} + + def df_for_frontend(df: pd.DataFrame, limit: int) -> pd.DataFrame: """Returns a DataFrame with values that are safe to send to the frontend.""" df = df[:limit] diff --git a/lynxkite-graph-analytics/src/lynxkite_graph_analytics/lynxkite_ops.py b/lynxkite-graph-analytics/src/lynxkite_graph_analytics/lynxkite_ops.py index 1027168963b639a68701b68ebb85d30fdc9653c9..1efe076770a4cea70a46ad660406c6744a74b3ae 100644 --- a/lynxkite-graph-analytics/src/lynxkite_graph_analytics/lynxkite_ops.py +++ b/lynxkite-graph-analytics/src/lynxkite_graph_analytics/lynxkite_ops.py @@ -5,6 +5,7 @@ import os import fsspec from lynxkite.core import ops from collections import deque + from . import core import grandcypher import joblib @@ -15,7 +16,7 @@ import polars as pl import json -mem = joblib.Memory("joblib-cache") +mem = joblib.Memory(".joblib-cache") op = ops.op_registration(core.ENV) @@ -31,9 +32,7 @@ class FileFormat(enum.StrEnum): params={ "file_format": ops.ParameterGroup( name="file_format", - selector=ops.Parameter( - name="file_format", type=FileFormat, default=FileFormat.csv - ), + selector=ops.Parameter(name="file_format", type=FileFormat, default=FileFormat.csv), groups={ "csv": [ ops.Parameter.basic("columns", type=str, default=""), @@ -41,9 +40,7 @@ class FileFormat(enum.StrEnum): ], "parquet": [], "json": [], - "excel": [ - ops.Parameter.basic("sheet_name", type=str, default="Sheet1") - ], + "excel": [ops.Parameter.basic("sheet_name", type=str, default="Sheet1")], }, default=FileFormat.csv, ), @@ -64,9 +61,7 @@ def import_file( """ if file_format == "csv": names = kwargs.pop("columns", "") - names = ( - pd.api.extensions.no_default if names == "" else names.split(",") - ) + names = pd.api.extensions.no_default if names == "" else names.split(",") sep = kwargs.pop("separator", "") sep = pd.api.extensions.no_default if sep == "" else sep df = pd.read_csv(file_path, names=names, sep=sep, **kwargs) @@ -87,23 +82,19 @@ def import_parquet(*, filename: str): return pd.read_parquet(filename) -@mem.cache @op("Import CSV") -def import_csv( - *, filename: str, columns: str = "", separator: str = "" -): +@mem.cache +def import_csv(*, filename: str, columns: str = "", separator: str = ""): """Imports a CSV file.""" return pd.read_csv( filename, - names=pd.api.extensions.no_default - if columns == "" - else columns.split(","), + names=pd.api.extensions.no_default if columns == "" else columns.split(","), sep=pd.api.extensions.no_default if separator == "" else separator, ) -@mem.cache @op("Import GraphML") +@mem.cache def import_graphml(*, filename: str): """Imports a GraphML file.""" files = fsspec.open_files(filename, compression="infer") @@ -114,8 +105,8 @@ def import_graphml(*, filename: str): raise ValueError(f"No .graphml file found at {filename}") -@mem.cache @op("Graph from OSM") +@mem.cache def import_osm(*, location: str): import osmnx as ox @@ -141,12 +132,7 @@ def sql(bundle: core.Bundle, *, query: ops.LongStr, save_as: str = "result"): if os.environ.get("NX_CUGRAPH_AUTOCONFIG", "").strip().lower() == "true": with pl.Config() as cfg: cfg.set_verbose(True) - res = ( - pl.SQLContext(bundle.dfs) - .execute(query) - .collect(engine="gpu") - .to_pandas() - ) + res = pl.SQLContext(bundle.dfs).execute(query).collect(engine="gpu").to_pandas() # TODO: Currently `collect()` moves the data from cuDF to Polars. Then we convert it to Pandas, # which (hopefully) puts it back into cuDF. Hopefully we will be able to keep it in cuDF. else: @@ -165,11 +151,11 @@ def cypher(bundle: core.Bundle, *, query: ops.LongStr, save_as: str = "result"): return bundle -@op("Organize bundle") -def organize_bundle(bundle: core.Bundle, *, code: ops.LongStr): +@op("Organize") +def organize(bundle: list[core.Bundle], *, code: ops.LongStr) -> core.Bundle: """Lets you rename/copy/delete DataFrames, and modify relations. - TODO: Use a declarative solution instead of Python code. Add UI. + TODO: Merge this with "Create graph". """ bundle = bundle.copy() exec(code, globals(), {"bundle": bundle}) @@ -207,9 +193,7 @@ def _map_color(value): colors = cmap.colors[: len(categories)] return [ "#{:02x}{:02x}{:02x}".format(int(r * 255), int(g * 255), int(b * 255)) - for r, g, b in [ - colors[min(len(colors) - 1, categories.get_loc(v))] for v in value - ] + for r, g, b in [colors[min(len(colors) - 1, categories.get_loc(v))] for v in value] ] @@ -246,14 +230,10 @@ def visualize_graph( curveness = 0 # Street maps are better with straight streets. break else: - pos = nx.spring_layout( - graph.to_nx(), iterations=max(1, int(10000 / len(nodes))) - ) + pos = nx.spring_layout(graph.to_nx(), iterations=max(1, int(10000 / len(nodes)))) curveness = 0.3 nodes = nodes.to_records() - edges = core.df_for_frontend( - graph.dfs["edges"].drop_duplicates(["source", "target"]), 10_000 - ) + edges = core.df_for_frontend(graph.dfs["edges"].drop_duplicates(["source", "target"]), 10_000) if color_edges_by: edges["color"] = _map_color(edges[color_edges_by]) edges = edges.to_records() @@ -287,9 +267,7 @@ def visualize_graph( "itemStyle": {"color": n.color} if color_nodes_by else {}, "label": {"show": label_by is not None}, "name": str(getattr(n, label_by, "")) if label_by else None, - "value": str(getattr(n, color_nodes_by, "")) - if color_nodes_by - else None, + "value": str(getattr(n, color_nodes_by, "")) if color_nodes_by else None, } for n in nodes ], @@ -298,9 +276,7 @@ def visualize_graph( "source": str(r.source), "target": str(r.target), "lineStyle": {"color": r.color} if color_edges_by else {}, - "value": str(getattr(r, color_edges_by, "")) - if color_edges_by - else None, + "value": str(getattr(r, color_edges_by, "")) if color_edges_by else None, } for r in edges ], @@ -311,7 +287,8 @@ def visualize_graph( @op("View tables", view="table_view") -def view_tables(bundle: core.Bundle, *, limit: int = 100): +def view_tables(bundle: core.Bundle, *, _tables_open: str = "", limit: int = 100): + _tables_open = _tables_open # The frontend uses this parameter to track which tables are open. return bundle.to_dict(limit=limit) @@ -338,7 +315,11 @@ def create_graph(bundle: core.Bundle, *, relations: str = None) -> core.Bundle: """ bundle = bundle.copy() if not (relations is None or relations.strip() == ""): - bundle.relations = [ - core.RelationDefinition(**r) for r in json.loads(relations).values() - ] + bundle.relations = [core.RelationDefinition(**r) for r in json.loads(relations).values()] return ops.Result(output=bundle, display=bundle.to_dict(limit=100)) + + +@op("Biomedical foundation graph (PLACEHOLDER)") +def biomedical_foundation_graph(*, filter_nodes: str): + """Loads the gigantic Lynx-maintained knowledge graph. Includes drugs, diseases, genes, proteins, etc.""" + return None diff --git a/lynxkite-graph-analytics/src/lynxkite_graph_analytics/ml_ops.py b/lynxkite-graph-analytics/src/lynxkite_graph_analytics/ml_ops.py new file mode 100644 index 0000000000000000000000000000000000000000..fd9dc91979282fe815fad38c025e000d18360a8a --- /dev/null +++ b/lynxkite-graph-analytics/src/lynxkite_graph_analytics/ml_ops.py @@ -0,0 +1,221 @@ +"""Operations for machine learning.""" + +import enum +import functools +import numpy as np +from . import core +from lynxkite.core import workspace +from .pytorch import pytorch_core +from lynxkite.core import ops +from tqdm import tqdm +import cuml +import joblib +import pandas as pd +import pathlib + + +mem = joblib.Memory(".joblib-cache") +op = ops.op_registration(core.ENV) + + +def load_ws(model_workspace: str): + cwd = pathlib.Path() + path = cwd / model_workspace + assert path.is_relative_to(cwd) + assert path.exists(), f"Workspace {path} does not exist" + ws = workspace.load(path) + return ws + + +@op("Define model") +def define_model( + bundle: core.Bundle, + *, + model_workspace: str, + save_as: str = "model", +): + """Trains the selected model on the selected dataset. Most training parameters are set in the model definition.""" + assert model_workspace, "Model workspace is unset." + ws = load_ws(model_workspace) + # Build the model without inputs, to get its interface. + m = pytorch_core.build_model(ws) + m.source_workspace = model_workspace + bundle = bundle.copy() + bundle.other[save_as] = m + return bundle + + +# These contain the same mapping, but they get different UIs. +# For inputs, you select existing columns. For outputs, you can create new columns. +class ModelInferenceInputMapping(pytorch_core.ModelMapping): + pass + + +class ModelTrainingInputMapping(pytorch_core.ModelMapping): + pass + + +class ModelOutputMapping(pytorch_core.ModelMapping): + pass + + +@op("Train model") +@ops.slow +def train_model( + bundle: core.Bundle, + *, + model_name: str = "model", + input_mapping: ModelTrainingInputMapping, + epochs: int = 1, +): + """Trains the selected model on the selected dataset. Most training parameters are set in the model definition.""" + m = bundle.other[model_name].copy() + inputs = pytorch_core.to_tensors(bundle, input_mapping) + t = tqdm(range(epochs), desc="Training model") + losses = [] + for _ in t: + loss = m.train(inputs) + t.set_postfix({"loss": loss}) + losses.append(loss) + m.trained = True + bundle = bundle.copy() + bundle.dfs["training"] = pd.DataFrame({"training_loss": losses}) + bundle.other[model_name] = m + return bundle + + +@op("Model inference") +@ops.slow +def model_inference( + bundle: core.Bundle, + *, + model_name: str = "model", + input_mapping: ModelInferenceInputMapping, + output_mapping: ModelOutputMapping, +): + """Executes a trained model.""" + if input_mapping is None or output_mapping is None: + return ops.Result(bundle, error="Mapping is unset.") + m = bundle.other[model_name] + assert m.trained, "The model is not trained." + inputs = pytorch_core.to_tensors(bundle, input_mapping) + outputs = m.inference(inputs) + bundle = bundle.copy() + copied = set() + for k, v in output_mapping.map.items(): + if not v.df or not v.column: + continue + if v.df not in copied: + bundle.dfs[v.df] = bundle.dfs[v.df].copy() + copied.add(v.df) + bundle.dfs[v.df][v.column] = outputs[k].detach().numpy().tolist() + return bundle + + +@op("Train/test split") +def train_test_split(bundle: core.Bundle, *, table_name: str, test_ratio: float = 0.1): + """Splits a dataframe in the bundle into separate "_train" and "_test" dataframes.""" + df = bundle.dfs[table_name] + test = df.sample(frac=test_ratio).reset_index() + train = df.drop(test.index).reset_index() + bundle = bundle.copy() + bundle.dfs[f"{table_name}_train"] = train + bundle.dfs[f"{table_name}_test"] = test + return bundle + + +@op("View loss", view="visualization") +def view_loss(bundle: core.Bundle): + loss = bundle.dfs["training"].training_loss.tolist() + v = { + "title": {"text": "Training loss"}, + "xAxis": {"type": "category"}, + "yAxis": {"type": "value"}, + "series": [{"data": loss, "type": "line"}], + } + return v + + +@op("Reduce dimensions") +def reduce_dimensions(bundle: core.Bundle): + return + + +VIRIDIS = [ + "#440154", + "#482777", + "#3E4989", + "#31688E", + "#26828E", + "#1F9E89", + "#35B779", + "#6CCE59", + "#B4DE2C", + "#FDE725", +] + + +class UMAPMetric(enum.Enum): + l1 = "l1" + cityblock = "cityblock" + taxicab = "taxicab" + manhattan = "manhattan" + euclidean = "euclidean" + l2 = "l2" + sqeuclidean = "sqeuclidean" + canberra = "canberra" + minkowski = "minkowski" + chebyshev = "chebyshev" + linf = "linf" + cosine = "cosine" + correlation = "correlation" + hellinger = "hellinger" + hamming = "hamming" + + +@op("View vectors", view="visualization") +def view_vectors( + bundle: core.Bundle, + *, + table_name: str = "nodes", + vector_column: str = "", + label_column: str = "", + n_neighbors: int = 15, + min_dist: float = 0.1, + metric: UMAPMetric = UMAPMetric.euclidean, +): + vec = np.stack(bundle.dfs[table_name][vector_column].to_numpy()) + umap = functools.partial( + cuml.manifold.umap.UMAP, + n_neighbors=n_neighbors, + min_dist=min_dist, + metric=metric.value, + ) + proj = umap(n_components=2).fit_transform(vec) + color = umap(n_components=1).fit_transform(vec) + data = [[*p.tolist(), "", c.item()] for p, c in zip(proj, color)] + if label_column: + for i, row in enumerate(bundle.dfs[table_name][label_column]): + data[i][2] = row + size = 100 / len(data) ** 0.4 + v = { + "title": { + "text": f"UMAP projection of {vector_column}", + }, + "visualMap": { + "min": color[:, 0].min().item(), + "max": color[:, 0].max().item(), + "right": 10, + "top": "center", + "calculable": True, + "dimension": 3, + "inRange": {"color": VIRIDIS}, + }, + "tooltip": {"trigger": "item", "formatter": "GET_THIRD_VALUE"} + if label_column + else {"show": False}, + "xAxis": [{"type": "value"}], + "yAxis": [{"type": "value"}], + "series": [{"type": "scatter", "symbolSize": size, "data": data}], + } + return v diff --git a/lynxkite-graph-analytics/src/lynxkite_graph_analytics/networkx_ops.py b/lynxkite-graph-analytics/src/lynxkite_graph_analytics/networkx_ops.py index d470ab7f5929f977f0f310ec7b1e4e2d5639e4f0..5320b721f8ca6dd1025a950323b1dd89ab2d42ed 100644 --- a/lynxkite-graph-analytics/src/lynxkite_graph_analytics/networkx_ops.py +++ b/lynxkite-graph-analytics/src/lynxkite_graph_analytics/networkx_ops.py @@ -210,9 +210,7 @@ def _get_params(func) -> dict | None: continue params[name] = ops.Parameter.basic( name=name, - default=str(param.default) - if type(param.default) in [str, int, float] - else None, + default=str(param.default) if type(param.default) in [str, int, float] else None, type=_type, ) return params diff --git a/lynxkite-graph-analytics/src/lynxkite_graph_analytics/pytorch/__init__.py b/lynxkite-graph-analytics/src/lynxkite_graph_analytics/pytorch/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..5e3686d31113ae7798a63ddb490349e183578067 --- /dev/null +++ b/lynxkite-graph-analytics/src/lynxkite_graph_analytics/pytorch/__init__.py @@ -0,0 +1,2 @@ +from . import pytorch_core # noqa +from . import pytorch_ops # noqa diff --git a/lynxkite-graph-analytics/src/lynxkite_graph_analytics/pytorch/pytorch_core.py b/lynxkite-graph-analytics/src/lynxkite_graph_analytics/pytorch/pytorch_core.py new file mode 100644 index 0000000000000000000000000000000000000000..17e677a92e6b9582d9b5c71ff599f29f04473b06 --- /dev/null +++ b/lynxkite-graph-analytics/src/lynxkite_graph_analytics/pytorch/pytorch_core.py @@ -0,0 +1,369 @@ +"""Infrastructure for defining PyTorch models.""" + +import copy +import graphlib + +import pydantic +from lynxkite.core import ops, workspace +import torch +import torch_geometric.nn as pyg_nn +import dataclasses +from .. import core + +ENV = "PyTorch model" + + +def op(name, **kwargs): + _op = ops.op(ENV, name, **kwargs) + + def decorator(func): + _op(func) + op = func.__op__ + for p in op.inputs.values(): + p.position = "bottom" + for p in op.outputs.values(): + p.position = "top" + return func + + return decorator + + +def reg(name, inputs=[], outputs=None, params=[]): + if outputs is None: + outputs = inputs + return ops.register_passive_op( + ENV, + name, + inputs=[ops.Input(name=name, position="bottom", type="tensor") for name in inputs], + outputs=[ops.Output(name=name, position="top", type="tensor") for name in outputs], + params=params, + ) + + +def _to_id(*strings: str) -> str: + """Replaces all non-alphanumeric characters with underscores.""" + return "_".join("".join(c if c.isalnum() else "_" for c in s) for s in strings) + + +@dataclasses.dataclass +class Layer: + """Temporary data structure used by ModelBuilder.""" + + module: torch.nn.Module + origin_id: str + inputs: list[str] + outputs: list[str] + + def for_sequential(self): + """The layer signature for pyg.nn.Sequential.""" + # "nothing" is used as a bogus input if an operation has no inputs. + # The module in turn needs to take one argument, but it will always be None. + inputs = ", ".join(self.inputs) or "nothing" + outputs = ", ".join(self.outputs) + return self.module, f"{inputs} -> {outputs}" + + +class ColumnSpec(pydantic.BaseModel): + df: str + column: str + + +class ModelMapping(pydantic.BaseModel): + map: dict[str, ColumnSpec] + + +@dataclasses.dataclass +class ModelConfig: + model: torch.nn.Module + model_inputs: list[str] + model_outputs: list[str] + loss_inputs: list[str] + loss: torch.nn.Module + optimizer_parameters: dict[str, any] + optimizer: torch.optim.Optimizer | None = None + source_workspace: str | None = None + trained: bool = False + + def __post_init__(self): + self._make_optimizer() + + def num_parameters(self) -> int: + return sum(p.numel() for p in self.model.parameters()) + + def _forward(self, inputs: dict[str, torch.Tensor]) -> dict[str, torch.Tensor]: + output = self.model(nothing=None, **inputs) + if not isinstance(output, tuple): + output = (output,) + values = {k: v for k, v in zip(self.model_outputs, output)} + return values + + def inference(self, inputs: dict[str, torch.Tensor]) -> dict[str, torch.Tensor]: + # TODO: Do multiple batches. + self.model.eval() + return self._forward(inputs) + + def train(self, inputs: dict[str, torch.Tensor]) -> float: + """Train the model for one epoch. Returns the loss.""" + # TODO: Do multiple batches. + self.model.train() + self.optimizer.zero_grad() + values = self._forward(inputs) + values.update(inputs) + loss = self.loss(nothing=None, **values) + loss.backward() + self.optimizer.step() + return loss.item() + + def _make_optimizer(self): + # We need to make a new optimizer when the model is copied. (It's tied to its parameters.) + p = self.optimizer_parameters + o = getattr(torch.optim, p["type"].name) + self.optimizer = o(self.model.parameters(), lr=p["lr"]) + + def copy(self): + """Returns a copy of the model.""" + c = dataclasses.replace( + self, + model=copy.deepcopy(self.model), + ) + c._make_optimizer() + c.optimizer.load_state_dict(self.optimizer.state_dict()) + return c + + def metadata(self): + return { + "type": "model", + "model": { + "inputs": self.model_inputs, + "outputs": self.model_outputs, + "loss_inputs": self.loss_inputs, + "trained": self.trained, + }, + } + + +def build_model(ws: workspace.Workspace) -> ModelConfig: + """Builds the model described in the workspace.""" + builder = ModelBuilder(ws) + return builder.build_model() + + +class ModelBuilder: + """The state shared between methods that are used to build the model.""" + + def __init__(self, ws: workspace.Workspace): + self.catalog = ops.CATALOGS[ENV] + optimizers = [] + self.nodes: dict[str, workspace.WorkspaceNode] = {} + repeats: list[str] = [] + for node in ws.nodes: + self.nodes[node.id] = node + if node.data.title == "Optimizer": + optimizers.append(node.id) + elif node.data.title == "Repeat": + repeats.append(node.id) + self.nodes[f"START {node.id}"] = node + self.nodes[f"END {node.id}"] = node + assert optimizers, "No optimizer found." + assert len(optimizers) == 1, f"More than one optimizer found: {optimizers}" + [self.optimizer] = optimizers + self.dependencies = {n: [] for n in self.nodes} + self.in_edges: dict[str, dict[str, list[tuple[str, str]]]] = {n: {} for n in self.nodes} + self.out_edges: dict[str, dict[str, list[tuple[str, str]]]] = {n: {} for n in self.nodes} + for e in ws.edges: + self.dependencies[e.target].append(e.source) + self.in_edges.setdefault(e.target, {}).setdefault(e.targetHandle, []).append( + (e.source, e.sourceHandle) + ) + self.out_edges.setdefault(e.source, {}).setdefault(e.sourceHandle, []).append( + (e.target, e.targetHandle) + ) + # Split repeat boxes into start and end, and insert them into the flow. + # TODO: Think about recursive repeats. + for repeat in repeats: + if not self.out_edges[repeat] or not self.in_edges[repeat]: + continue + start_id = f"START {repeat}" + end_id = f"END {repeat}" + # repeat -> first <- real_input + # ...becomes... + # real_input -> start -> first + first, firsth = self.out_edges[repeat]["output"][0] + [(real_input, real_inputh)] = [ + k for k in self.in_edges[first][firsth] if k != (repeat, "output") + ] + self.dependencies[first].remove(repeat) + self.dependencies[first].append(start_id) + self.dependencies[start_id] = [real_input] + self.out_edges[real_input][real_inputh] = [ + k if k != (first, firsth) else (start_id, "input") + for k in self.out_edges[real_input][real_inputh] + ] + self.in_edges[start_id] = {"input": [(real_input, real_inputh)]} + self.out_edges[start_id] = {"output": [(first, firsth)]} + self.in_edges[first][firsth] = [(start_id, "output")] + # repeat <- last -> real_output + # ...becomes... + # last -> end -> real_output + [(last, lasth)] = self.in_edges[repeat]["input"] + del self.dependencies[repeat] + self.dependencies[end_id] = [last] + real_edges = [e for e in self.out_edges[last][lasth] if e != (repeat, "input")] + self.out_edges[last][lasth] = [(end_id, "input")] + self.in_edges[end_id] = {"input": [(last, lasth)]} + self.out_edges[end_id] = {"output": []} # Populated below. + for real_output, real_outputh in real_edges: + self.dependencies[real_output].append(end_id) + self.in_edges[real_output][real_outputh] = [ + k if k != (last, lasth) else (end_id, "output") + for k in self.in_edges[real_output][real_outputh] + ] + self.out_edges[end_id]["output"].append((real_output, real_outputh)) + self.inv_dependencies = {n: [] for n in self.nodes} + for k, v in self.dependencies.items(): + for i in v: + self.inv_dependencies[i].append(k) + self.layers: list[Layer] = [] + # Clean up disconnected nodes. + disconnected = set() + for node_id in self.nodes: + op = self.catalog[self.nodes[node_id].data.title] + if len(self.in_edges[node_id]) != len(op.inputs): + disconnected.add(node_id) + disconnected |= self.all_upstream(node_id) + for node_id in disconnected: + del self.dependencies[node_id] + del self.in_edges[node_id] + del self.out_edges[node_id] + del self.inv_dependencies[node_id] + del self.nodes[node_id] + + def all_upstream(self, node: str) -> set[str]: + """Returns all nodes upstream of a node.""" + deps = set() + for dep in self.dependencies[node]: + deps.add(dep) + deps.update(self.all_upstream(dep)) + return deps + + def all_downstream(self, node: str) -> set[str]: + """Returns all nodes downstream of a node.""" + deps = set() + for dep in self.inv_dependencies[node]: + deps.add(dep) + deps.update(self.all_downstream(dep)) + return deps + + def run_node(self, node_id: str) -> None: + """Adds the layer(s) produced by this node to self.layers.""" + node = self.nodes[node_id] + t = node.data.title + op = self.catalog[t] + p = op.convert_params(node.data.params) + match t: + case "Repeat": + if node_id.startswith("END "): + repeat_id = node_id.removeprefix("END ") + start_id = f"START {repeat_id}" + [last_output] = self.in_edges[node_id]["input"] + after_start = self.all_downstream(start_id) + after_end = self.all_downstream(node_id) + before_end = self.all_upstream(node_id) + affected_nodes = after_start - after_end - {node_id} + repeated_nodes = after_start & before_end + assert affected_nodes == repeated_nodes, ( + f"edges leave repeated section '{repeat_id}':\n{affected_nodes - repeated_nodes}" + ) + repeated_layers = [e for e in self.layers if e.origin_id in repeated_nodes] + assert p["times"] >= 1, f"Cannot repeat {repeat_id} {p['times']} times." + for i in range(p["times"] - 1): + # Copy repeat section's output to repeat section's input. + self.layers.append( + Layer( + torch.nn.Identity(), + origin_id=node_id, + inputs=[_to_id(*last_output)], + outputs=[_to_id(start_id, "output")], + ) + ) + # Repeat the layers in the section. + for layer in repeated_layers: + if p["same_weights"]: + self.layers.append(layer) + else: + self.run_node(layer.origin_id) + self.layers.append(self.run_op(node_id, op, p)) + case "Optimizer" | "Input: tensor" | "Input: graph edges" | "Input: sequential": + return + case _: + self.layers.append(self.run_op(node_id, op, p)) + + def run_op(self, node_id: str, op: ops.Op, params) -> Layer: + """Returns the layer produced by this op.""" + inputs = [_to_id(*i) for n in op.inputs for i in self.in_edges[node_id][n]] + outputs = [_to_id(node_id, n) for n in op.outputs] + if op.func == ops.no_op: + module = torch.nn.Identity() + else: + module = op.func(*inputs, **params) + return Layer(module, node_id, inputs, outputs) + + def build_model(self) -> ModelConfig: + # Walk the graph in topological order. + ts = graphlib.TopologicalSorter(self.dependencies) + for node_id in ts.static_order(): + self.run_node(node_id) + return self.get_config() + + def get_config(self) -> ModelConfig: + # Split the design into model and loss. + model_nodes = set() + for node_id in self.nodes: + if self.nodes[node_id].data.title == "Output": + model_nodes.add(node_id) + model_nodes |= self.all_upstream(node_id) + assert model_nodes, "The model definition must have at least one Output node." + layers = [] + loss_layers = [] + for layer in self.layers: + if layer.origin_id in model_nodes: + layers.append(layer) + else: + loss_layers.append(layer) + used_in_model = set(input for layer in layers for input in layer.inputs) + used_in_loss = set(input for layer in loss_layers for input in layer.inputs) + made_in_model = set(output for layer in layers for output in layer.outputs) + made_in_loss = set(output for layer in loss_layers for output in layer.outputs) + layers = [layer.for_sequential() for layer in layers] + loss_layers = [layer.for_sequential() for layer in loss_layers] + cfg = {} + cfg["model_inputs"] = list(used_in_model - made_in_model) + cfg["model_outputs"] = list(made_in_model & used_in_loss) + cfg["loss_inputs"] = list(used_in_loss - made_in_loss) + # Make sure the trained output is output from the last model layer. + outputs = ", ".join(cfg["model_outputs"]) + layers.append((torch.nn.Identity(), f"{outputs} -> {outputs}")) + # Create model. + cfg["model"] = pyg_nn.Sequential(", ".join(cfg["model_inputs"]), layers) + # Make sure the loss is output from the last loss layer. + [(lossb, lossh)] = self.in_edges[self.optimizer]["loss"] + lossi = _to_id(lossb, lossh) + loss_layers.append((torch.nn.Identity(), f"{lossi} -> loss")) + # Create loss function. + cfg["loss"] = pyg_nn.Sequential(", ".join(cfg["loss_inputs"]), loss_layers) + assert not list(cfg["loss"].parameters()), f"loss should have no parameters: {loss_layers}" + # Create optimizer. + op = self.catalog["Optimizer"] + cfg["optimizer_parameters"] = op.convert_params(self.nodes[self.optimizer].data.params) + return ModelConfig(**cfg) + + +def to_tensors(b: core.Bundle, m: ModelMapping | None) -> dict[str, torch.Tensor]: + """Converts a tensor to the correct type for PyTorch. Ignores missing mappings.""" + if m is None: + return {} + tensors = {} + for k, v in m.map.items(): + if v.df in b.dfs and v.column in b.dfs[v.df]: + tensors[k] = torch.tensor(b.dfs[v.df][v.column].to_list(), dtype=torch.float32) + return tensors diff --git a/lynxkite-graph-analytics/src/lynxkite_graph_analytics/pytorch/pytorch_ops.py b/lynxkite-graph-analytics/src/lynxkite_graph_analytics/pytorch/pytorch_ops.py new file mode 100644 index 0000000000000000000000000000000000000000..d40f5fdfa557d9b4a0140c5ea64ef8f618204875 --- /dev/null +++ b/lynxkite-graph-analytics/src/lynxkite_graph_analytics/pytorch/pytorch_ops.py @@ -0,0 +1,183 @@ +"""Boxes for defining PyTorch models.""" + +import enum +from lynxkite.core import ops +from lynxkite.core.ops import Parameter as P +import torch +import torch_geometric.nn as pyg_nn +from .pytorch_core import op, reg, ENV + +reg("Input: tensor", outputs=["output"], params=[P.basic("name")]) +reg("Input: graph edges", outputs=["edges"]) +reg("Input: sequential", outputs=["y"]) +reg("Output", inputs=["x"], outputs=["x"], params=[P.basic("name")]) + + +@op("LSTM") +def lstm(x, *, input_size=1024, hidden_size=1024, dropout=0.0): + return torch.nn.LSTM(input_size, hidden_size, dropout=0.0) + + +reg( + "Neural ODE", + inputs=["x"], + params=[ + P.basic("relative_tolerance"), + P.basic("absolute_tolerance"), + P.options( + "method", + [ + "dopri8", + "dopri5", + "bosh3", + "fehlberg2", + "adaptive_heun", + "euler", + "midpoint", + "rk4", + "explicit_adams", + "implicit_adams", + ], + ), + ], +) + + +@op("Attention", outputs=["outputs", "weights"]) +def attention(query, key, value, *, embed_dim=1024, num_heads=1, dropout=0.0): + return torch.nn.MultiHeadAttention(embed_dim, num_heads, dropout=dropout, need_weights=True) + + +@op("LayerNorm", outputs=["outputs", "weights"]) +def layernorm(x, *, normalized_shape=""): + normalized_shape = [int(s.strip()) for s in normalized_shape.split(",")] + return torch.nn.LayerNorm(normalized_shape) + + +@op("Dropout", outputs=["outputs", "weights"]) +def dropout(x, *, p=0.0): + return torch.nn.Dropout(p) + + +@op("Linear") +def linear(x, *, output_dim=1024): + return pyg_nn.Linear(-1, output_dim) + + +class ActivationTypes(enum.Enum): + ReLU = "ReLU" + Leaky_ReLU = "Leaky ReLU" + Tanh = "Tanh" + Mish = "Mish" + + +@op("Activation") +def activation(x, *, type: ActivationTypes = ActivationTypes.ReLU): + return getattr(torch.nn.functional, type.name.lower().replace(" ", "_")) + + +@op("MSE loss") +def mse_loss(x, y): + return torch.nn.functional.mse_loss + + +@op("Constant vector") +def constant_vector(*, value=0, size=1): + return lambda _: torch.full((size,), value) + + +@op("Softmax") +def softmax(x, *, dim=1): + return torch.nn.Softmax(dim=dim) + + +@op("Concatenate") +def concatenate(a, b): + return lambda a, b: torch.concatenate(*torch.broadcast_tensors(a, b)) + + +reg( + "Graph conv", + inputs=["x", "edges"], + outputs=["x"], + params=[P.options("type", ["GCNConv", "GATConv", "GATv2Conv", "SAGEConv"])], +) + +reg("Triplet margin loss", inputs=["x", "x_pos", "x_neg"], outputs=["loss"]) +reg("Cross-entropy loss", inputs=["x", "y"], outputs=["loss"]) +reg( + "Optimizer", + inputs=["loss"], + outputs=[], + params=[ + P.options( + "type", + [ + "AdamW", + "Adafactor", + "Adagrad", + "SGD", + "Lion", + "Paged AdamW", + "Galore AdamW", + ], + ), + P.basic("lr", 0.001), + ], +) + +ops.register_passive_op( + ENV, + "Repeat", + inputs=[ops.Input(name="input", position="top", type="tensor")], + outputs=[ops.Output(name="output", position="bottom", type="tensor")], + params=[ + ops.Parameter.basic("times", 1, int), + ops.Parameter.basic("same_weights", False, bool), + ], +) + +ops.register_passive_op( + ENV, + "Recurrent chain", + inputs=[ops.Input(name="input", position="top", type="tensor")], + outputs=[ops.Output(name="output", position="bottom", type="tensor")], + params=[], +) + + +def _set_handle_positions(op): + op: ops.Op = op.__op__ + for v in op.outputs.values(): + v.position = "top" + for v in op.inputs.values(): + v.position = "bottom" + + +def _register_simple_pytorch_layer(func): + op = ops.op(ENV, func.__name__.title())(lambda input: func) + _set_handle_positions(op) + + +def _register_two_tensor_function(func): + op = ops.op(ENV, func.__name__.title())(lambda a, b: func) + _set_handle_positions(op) + + +SIMPLE_FUNCTIONS = [ + torch.sin, + torch.cos, + torch.log, + torch.exp, +] +TWO_TENSOR_FUNCTIONS = [ + torch.multiply, + torch.add, + torch.subtract, +] + + +for f in SIMPLE_FUNCTIONS: + _register_simple_pytorch_layer(f) +for f in TWO_TENSOR_FUNCTIONS: + _register_two_tensor_function(f) diff --git a/lynxkite-graph-analytics/src/lynxkite_graph_analytics/pytorch_model_ops.py b/lynxkite-graph-analytics/src/lynxkite_graph_analytics/pytorch_model_ops.py deleted file mode 100644 index e70dc0287005a89c268d44ee011a55f4431a206c..0000000000000000000000000000000000000000 --- a/lynxkite-graph-analytics/src/lynxkite_graph_analytics/pytorch_model_ops.py +++ /dev/null @@ -1,75 +0,0 @@ -"""Boxes for defining PyTorch models.""" - -from lynxkite.core import ops -from lynxkite.core.ops import Parameter as P - -ENV = "PyTorch model" - - -def reg(name, inputs=[], outputs=None, params=[]): - if outputs is None: - outputs = inputs - return ops.register_passive_op( - ENV, - name, - inputs=[ - ops.Input(name=name, position="bottom", type="tensor") for name in inputs - ], - outputs=[ - ops.Output(name=name, position="top", type="tensor") for name in outputs - ], - params=params, - ) - - -reg("Input: features", outputs=["x"]) -reg("Input: graph edges", outputs=["edges"]) -reg("Input: label", outputs=["y"]) -reg("Input: positive sample", outputs=["x_pos"]) -reg("Input: negative sample", outputs=["x_neg"]) - -reg("Attention", inputs=["q", "k", "v"], outputs=["x"]) -reg("LayerNorm", inputs=["x"]) -reg("Dropout", inputs=["x"], params=[P.basic("p", 0.5)]) -reg("Linear", inputs=["x"], params=[P.basic("output_dim", "same")]) -reg( - "Graph conv", - inputs=["x", "edges"], - outputs=["x"], - params=[P.options("type", ["GCNConv", "GATConv", "GATv2Conv", "SAGEConv"])], -) -reg( - "Activation", - inputs=["x"], - params=[P.options("type", ["ReLU", "LeakyReLU", "Tanh", "Mish"])], -) -reg("Supervised loss", inputs=["x", "y"], outputs=["loss"]) -reg("Triplet loss", inputs=["x", "x_pos", "x_neg"], outputs=["loss"]) -reg( - "Optimizer", - inputs=["loss"], - outputs=[], - params=[ - P.options( - "type", - [ - "AdamW", - "Adafactor", - "Adagrad", - "SGD", - "Lion", - "Paged AdamW", - "Galore AdamW", - ], - ), - P.basic("lr", 0.001), - ], -) - -ops.register_passive_op( - ENV, - "Repeat", - inputs=[ops.Input(name="input", position="top", type="tensor")], - outputs=[ops.Output(name="output", position="bottom", type="tensor")], - params=[ops.Parameter.basic("times", 1, int)], -) diff --git a/lynxkite-graph-analytics/tests/test_pytorch_model_ops.py b/lynxkite-graph-analytics/tests/test_pytorch_model_ops.py new file mode 100644 index 0000000000000000000000000000000000000000..587ad9329128cc97b3fd670f4f1f882f9157d751 --- /dev/null +++ b/lynxkite-graph-analytics/tests/test_pytorch_model_ops.py @@ -0,0 +1,122 @@ +from lynxkite.core import workspace +from lynxkite_graph_analytics.pytorch import pytorch_core +import torch +import pytest + + +def make_ws(env, nodes: dict[str, dict], edges: list[tuple[str, str]]): + ws = workspace.Workspace(env=env) + for id, data in nodes.items(): + title = data["title"] + del data["title"] + ws.nodes.append( + workspace.WorkspaceNode( + id=id, + type="basic", + data=workspace.WorkspaceNodeData(title=title, params=data), + position=workspace.Position( + x=data.get("x", 0), + y=data.get("y", 0), + ), + ) + ) + ws.edges = [ + workspace.WorkspaceEdge( + id=f"{source}->{target}", + source=source.split(":")[0], + target=target.split(":")[0], + sourceHandle=source.split(":")[1], + targetHandle=target.split(":")[1], + ) + for source, target in edges + ] + return ws + + +def summarize_layers(m: pytorch_core.ModelConfig) -> str: + return "".join(str(e)[0] for e in m.model) + + +def summarize_connections(m: pytorch_core.ModelConfig) -> str: + return " ".join( + "".join(n[0] for n in c.param_names) + "->" + "".join(n[0] for n in c.return_names) + for c in m.model._children + ) + + +async def test_build_model(): + ws = make_ws( + pytorch_core.ENV, + { + "input": {"title": "Input: tensor"}, + "lin": {"title": "Linear", "output_dim": 4}, + "act": {"title": "Activation", "type": "Leaky_ReLU"}, + "output": {"title": "Output"}, + "label": {"title": "Input: tensor"}, + "loss": {"title": "MSE loss"}, + "optim": {"title": "Optimizer", "type": "SGD", "lr": 0.1}, + }, + [ + ("input:output", "lin:x"), + ("lin:output", "act:x"), + ("act:output", "output:x"), + ("output:x", "loss:x"), + ("label:output", "loss:y"), + ("loss:output", "optim:loss"), + ], + ) + x = torch.rand(100, 4) + y = x + 1 + m = pytorch_core.build_model(ws) + for i in range(1000): + loss = m.train({"input_output": x, "label_output": y}) + assert loss < 0.1 + o = m.inference({"input_output": x[:1]}) + error = torch.nn.functional.mse_loss(o["output_x"], x[:1] + 1) + assert error < 0.1 + + +async def test_build_model_with_repeat(): + def repeated_ws(times): + return make_ws( + pytorch_core.ENV, + { + "input": {"title": "Input: tensor"}, + "lin": {"title": "Linear", "output_dim": 8}, + "act": {"title": "Activation", "type": "Leaky_ReLU"}, + "output": {"title": "Output"}, + "label": {"title": "Input: tensor"}, + "loss": {"title": "MSE loss"}, + "optim": {"title": "Optimizer", "type": "SGD", "lr": 0.1}, + "repeat": {"title": "Repeat", "times": times, "same_weights": False}, + }, + [ + ("input:output", "lin:x"), + ("lin:output", "act:x"), + ("act:output", "output:x"), + ("output:x", "loss:x"), + ("label:output", "loss:y"), + ("loss:output", "optim:loss"), + ("repeat:output", "lin:x"), + ("act:output", "repeat:input"), + ], + ) + + # 1 repetition + m = pytorch_core.build_model(repeated_ws(1)) + assert summarize_layers(m) == "ILS S->l l->a a->E E->o o->o" + + # 2 repetitions + m = pytorch_core.build_model(repeated_ws(2)) + assert summarize_layers(m) == "ILS S->l l->a a->S S->l l->a a->E E->o o->o" + + # 3 repetitions + m = pytorch_core.build_model(repeated_ws(3)) + assert summarize_layers(m) == "ILS S->l l->a a->S S->l l->a a->S S->l l->a a->E E->o o->o" + + +if __name__ == "__main__": + pytest.main() diff --git a/lynxkite-graph-analytics/uv.lock b/lynxkite-graph-analytics/uv.lock index b4fb75927cca52c0357ef1270f9e38bbe491119d..4ba0b9cfca0ed3f206f2b11152b101bbc784f12c 100644 --- a/lynxkite-graph-analytics/uv.lock +++ b/lynxkite-graph-analytics/uv.lock @@ -9,6 +9,110 @@ resolution-markers = [ "python_full_version >= '3.12' and platform_machine != 'aarch64' and platform_machine != 'x86_64'", ] +[[package]] +name = "aiohappyeyeballs" +version = "2.6.1" +source = { registry 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= "sha256:ac1bbe05fd2991f160ebce24ffbac5f6d11d83dc90891255885223d42b3cd931", size = 9630 }, +] diff --git a/lynxkite-lynxscribe/README.md b/lynxkite-lynxscribe/README.md index bf7d73667d0711bc546bd3586a9f5141b53f8b52..95e52eb7900d7e753cd28006052762f4818ee52a 100644 --- a/lynxkite-lynxscribe/README.md +++ b/lynxkite-lynxscribe/README.md @@ -16,11 +16,16 @@ Run tests with: uv run pytest ``` -The LLM agent flow examples use local models. +The LLM agent flow examples can use local models. ```bash uv pip install infinity-emb[all] infinity_emb v2 --model-id michaelfeil/bge-small-en-v1.5 uv pip install "sglang[all]>=0.4.2.post2" --find-links https://flashinfer.ai/whl/cu124/torch2.5/flashinfer/ python -m sglang.launch_server --model-path SultanR/SmolTulu-1.7b-Instruct --port 8080 +export LLM_BASE_URL='http://localhost:8080/v1' +export LLM_MODEL='SultanR/SmolTulu-1.7b-Instruct' +export EMBEDDING_BASE_URL='http://localhost:7997/' +export EMBEDDING_MODEL='michaelfeil/bge-small-en-v1.5' +lynxkite ``` diff --git a/lynxkite-lynxscribe/src/lynxkite_lynxscribe/llm_ops.py b/lynxkite-lynxscribe/src/lynxkite_lynxscribe/llm_ops.py index 3031d55cea92355664e178330b75dbccb33d7aed..aaa80db1481b2d4c5048979395f4b1a995128c94 100644 --- a/lynxkite-lynxscribe/src/lynxkite_lynxscribe/llm_ops.py +++ b/lynxkite-lynxscribe/src/lynxkite_lynxscribe/llm_ops.py @@ -6,6 +6,7 @@ as an "agentic logic flow". It might just get deleted. (This is why the dependencies are left hanging.) """ +import os from lynxkite.core import ops import enum import jinja2 @@ -20,13 +21,18 @@ LLM_CACHE = {} ENV = "LLM logic" one_by_one.register(ENV) op = ops.op_registration(ENV) +LLM_BASE_URL = os.environ.get("LLM_BASE_URL", None) +EMBEDDING_BASE_URL = os.environ.get("EMBEDDING_BASE_URL", None) +LLM_MODEL = os.environ.get("LLM_MODEL", "gpt-4o-mini-2024-07-18") +EMBEDDING_MODEL = os.environ.get("EMBEDDING_MODEL", "text-embedding-3-small") def chat(*args, **kwargs): import openai - chat_client = openai.OpenAI(base_url="http://localhost:8080/v1") - key = json.dumps({"method": "chat", "args": args, "kwargs": kwargs}) + chat_client = openai.OpenAI(base_url=LLM_BASE_URL) + kwargs.setdefault("model", LLM_MODEL) + key = json.dumps({"method": "chat", "base_url": LLM_BASE_URL, "args": args, "kwargs": kwargs}) if key not in LLM_CACHE: completion = chat_client.chat.completions.create(*args, **kwargs) LLM_CACHE[key] = [c.message.content for c in completion.choices] @@ -36,8 +42,16 @@ def chat(*args, **kwargs): def embedding(*args, **kwargs): import openai - embedding_client = openai.OpenAI(base_url="http://localhost:7997/") - key = json.dumps({"method": "embedding", "args": args, "kwargs": kwargs}) + embedding_client = openai.OpenAI(base_url=EMBEDDING_BASE_URL) + kwargs.setdefault("model", EMBEDDING_MODEL) + key = json.dumps( + { + "method": "embedding", + "base_url": EMBEDDING_BASE_URL, + "args": args, + "kwargs": kwargs, + } + ) if key not in LLM_CACHE: res = embedding_client.embeddings.create(*args, **kwargs) [data] = res.data @@ -105,17 +119,14 @@ def add_neighbors(nodes, edges, item): @op("Create prompt") def create_prompt(input, *, save_as="prompt", template: ops.LongStr): - assert template, ( - "Please specify the template. Refer to columns using the Jinja2 syntax." - ) + assert template, "Please specify the template. Refer to columns using the Jinja2 syntax." t = jinja.from_string(template) prompt = t.render(**input) return {**input, save_as: prompt} @op("Ask LLM") -def ask_llm(input, *, model: str, accepted_regex: str = None, max_tokens: int = 100): - assert model, "Please specify the model." +def ask_llm(input, *, accepted_regex: str = None, max_tokens: int = 100): assert "prompt" in input, "Please create the prompt first." options = {} if accepted_regex: @@ -123,7 +134,6 @@ def ask_llm(input, *, model: str, accepted_regex: str = None, max_tokens: int = "regex": accepted_regex, } results = chat( - model=model, max_tokens=max_tokens, messages=[ {"role": "user", "content": input["prompt"]}, @@ -212,10 +222,9 @@ def rag( results = [db[int(r)] for r in results["ids"][0]] return {**input, "rag": results, "_collection": collection} if engine == RagEngine.Custom: - model = "michaelfeil/bge-small-en-v1.5" chat = input[input_field] - embeddings = [embedding(input=[r[db_field]], model=model) for r in db] - q = embedding(input=[chat], model=model) + embeddings = [embedding(input=[r[db_field]]) for r in db] + q = embedding(input=[chat]) def cosine_similarity(a, b): return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b)) diff --git a/ruff.toml b/ruff.toml new file mode 100644 index 0000000000000000000000000000000000000000..71de868235494b222a9d12a06aa937ea71a98a4c --- /dev/null +++ b/ruff.toml @@ -0,0 +1 @@ +line-length = 100