refactor: use tanstack start for frontend; make a more neuronpedia-like ui

server/app.py

@@ -1,7 +1,7 @@
-import io
 import json
 import os
-from functools import lru_cache
+import threading
+from functools import lru_cache, wraps
 from typing import Any, Generator, Optional
 
 import msgpack
@@ -31,33 +31,41 @@
 
 app.add_middleware(GZipMiddleware, minimum_size=1000)
 
-print(os.environ.get("MONGO_URI", "mongodb://localhost:27017/"))
-print(os.environ.get("MONGO_DB", "mechinterp"))
 client = MongoClient(MongoDBConfig())
 sae_series = os.environ.get("SAE_SERIES", "default")
 tokenizer_only = os.environ.get("TOKENIZER_ONLY", "false").lower() == "true"
-if tokenizer_only:
-    print("WARNING: Tokenizer only mode is enabled, some features may not be available")
 
-# Remove global caches in favor of LRU cache
-# sae_cache: dict[str, SparseAutoEncoder] = {}
-# lm_cache: dict[str, LanguageModel] = {}
-# dataset_cache: dict[tuple[str, int, int], Dataset] = {}
 
+def synchronized(func):
+    """Decorator to ensure sequential execution of a function based on parameters.
 
-@lru_cache(maxsize=8)
-def get_model(name: str) -> LanguageModel:
-    """Load and cache a language model.
+    Different parameters can be acquired in parallel, but the same parameters
+    will be executed sequentially.
+    """
+    locks: dict[frozenset[tuple[str, Any]], threading.Lock] = {}
+    global_lock = threading.Lock()
 
-    Args:
-        name: Name of the model to load
+    @wraps(func)
+    def wrapper(*args, **kwargs):
+        assert len(args) == 0, "Positional arguments are not supported"
+        key = frozenset(kwargs.items())
 
-    Returns:
-        LanguageModel: The loaded model
+        # The lock creation is locked by the global lock to avoid race conditions on locks.
+        with global_lock:
+            if key not in locks:
+                locks[key] = threading.Lock()
+            lock = locks[key]
 
-    Raises:
-        ValueError: If the model is not found
-    """
+        with lock:
+            return func(*args, **kwargs)
+
+    return wrapper
+
+
+@lru_cache(maxsize=8)
+@synchronized
+def get_model(*, name: str) -> LanguageModel:
+    """Load and cache a language model."""
     cfg = client.get_model_cfg(name)
     if cfg is None:
         raise ValueError(f"Model {name} not found")
@@ -66,38 +74,18 @@ def get_model(name: str) -> LanguageModel:
 
 
 @lru_cache(maxsize=16)
-def get_dataset(name: str, shard_idx: int = 0, n_shards: int = 1) -> Dataset:
-    """Load and cache a dataset shard.
-
-    Args:
-        name: Name of the dataset
-        shard_idx: Index of the shard to load
-        n_shards: Total number of shards
-
-    Returns:
-        Dataset: The loaded dataset shard
-
-    Raises:
-        AssertionError: If the dataset is not found
-    """
+@synchronized
+def get_dataset(*, name: str, shard_idx: int = 0, n_shards: int = 1) -> Dataset:
+    """Load and cache a dataset shard."""
     cfg = client.get_dataset_cfg(name)
     assert cfg is not None, f"Dataset {name} not found"
     return load_dataset_shard(cfg, shard_idx, n_shards)
 
 
 @lru_cache(maxsize=8)
-def get_sae(name: str) -> SparseAutoEncoder:
-    """Load and cache a sparse autoencoder.
-
-    Args:
-        name: Name of the SAE to load
-
-    Returns:
-        SparseAutoEncoder: The loaded SAE
-
-    Raises:
-        AssertionError: If the SAE is not found
-    """
+@synchronized
+def get_sae(*, name: str) -> SparseAutoEncoder:
+    """Load and cache a sparse autoencoder."""
     path = client.get_sae_path(name, sae_series)
     assert path is not None, f"SAE {name} not found"
     cfg = SAEConfig.from_pretrained(path)
@@ -161,29 +149,6 @@ def list_dictionaries():
     return client.list_saes(sae_series=sae_series, has_analyses=True)
 
 
-@app.get("/images/{dataset_name}")
-def get_image(dataset_name: str, context_idx: int, image_idx: int, shard_idx: int = 0, n_shards: int = 1):
-    assert transforms is not None, "torchvision not found, image processing will be disabled"
-    dataset = get_dataset(dataset_name, shard_idx, n_shards)
-    data = dataset[int(context_idx)]
-
-    image_key = next((key for key in ["image", "images"] if key in data), None)
-    if image_key is None:
-        return Response(content="Image not found", status_code=404)
-
-    if len(data[image_key]) <= image_idx:
-        return Response(content="Image not found", status_code=404)
-
-    image_tensor = data[image_key][image_idx]
-
-    # Convert tensor to PIL Image and then to bytes
-    image = transforms.ToPILImage()(image_tensor.to(torch.uint8))
-    img_byte_arr = io.BytesIO()
-    image.save(img_byte_arr, format="PNG")
-
-    return Response(content=img_byte_arr.getvalue(), media_type="image/png")
-
-
 @app.get("/dictionaries/{name}/metrics")
 def get_available_metrics(name: str):
     """Get available metrics for a dictionary.
@@ -311,9 +276,9 @@ def process_sample(
         Returns:
             dict: Processed sample data
         """  # Get model and dataset
-        model = get_model(model_name)
+        model = get_model(name=model_name)
         # model = None
-        data = get_dataset(dataset_name, shard_idx, n_shards)[context_idx.item()]
+        data = get_dataset(name=dataset_name, shard_idx=shard_idx, n_shards=n_shards)[context_idx.item()]
 
         # Get origins for the features
         origins = model.trace({k: [v] for k, v in data.items()})[0]
@@ -477,84 +442,6 @@ def process_sparse_feature_acts(
     )
 
 
-# @app.post("/dictionaries/{name}/cache_features")
-# def cache_features(
-#     name: str,
-#     features: list[dict[str, Any]] = Body(..., embed=True),
-#     output_dir: str = Body(...),
-# ):
-#     """Batch-fetch and persist feature payloads for offline reuse.
-
-#     Args:
-#         name: Dictionary/SAE name.
-#         features: List of feature specs currently on screen. Each item should contain
-#             - feature_id: int
-#             - layer: int
-#             - is_lorsa: bool
-#             - analysis_name: Optional[str] (overrides auto selection)
-#         output_dir: Directory on the server filesystem to write files into.
-
-#     Returns:
-#         Dict with count and directory path.
-#     """
-#     os.makedirs(output_dir, exist_ok=True)
-
-#     saved = 0
-#     for f in features:
-#         feature_id = int(f["feature_id"])  # may raise KeyError which FastAPI will surface
-#         layer = int(f["layer"])  # required for formatting analysis name
-#         is_lorsa = bool(f.get("is_lorsa", False))
-#         analysis_name_override = f.get("analysis_name")
-
-#         # Determine analysis name for this feature
-#         formatted_analysis_name: str | None = None
-#         if analysis_name_override is not None:
-#             formatted_analysis_name = analysis_name_override
-#         else:
-#             try:
-#                 base_name = (
-#                     client.get_lorsa_analysis_name(name, sae_series)
-#                     if is_lorsa
-#                     else client.get_clt_analysis_name(name, sae_series)
-#                 )
-#             except AttributeError:
-#                 base_name = None
-#             if base_name is None:
-#                 feat = client.get_random_alive_feature(sae_name=name, sae_series=sae_series)
-#                 if feat is None:
-#                     return Response(content=f"Dictionary {name} not found", status_code=404)
-#                 available = [a.name for a in feat.analyses]
-#                 preferred = [a for a in available if ("lorsa" in a) == is_lorsa]
-#                 base_name = preferred[0] if preferred else available[0]
-#             formatted_analysis_name = base_name.replace("{}", str(layer))
-
-#         # Reuse existing single-feature endpoint logic. Align with frontend usage where
-#         # the path 'name' is the formatted analysis name used by GET /dictionaries/{name}/features/{id}.
-#         res = get_feature(name=formatted_analysis_name, feature_index=feature_id, feature_analysis_name=None)
-#         if isinstance(res, Response) and res.status_code != 200:
-#             # Skip but continue
-#             continue
-
-#         payload = res.body if isinstance(res, Response) else res
-#         # Write as msgpack for fidelity and also a JSON alongside for convenience
-#         base = os.path.join(output_dir, f"layer{layer}__feature{feature_id}__{formatted_analysis_name}.msgpack")
-#         with open(base, "wb") as fbin:
-#             fbin.write(payload)
-#         try:
-#             decoded = msgpack.unpackb(payload, raw=False)
-#             json_path = base.replace(".msgpack", ".json")
-#             # make_serializable handles tensors/np arrays
-#             import json as _json
-
-#             with open(json_path, "w") as fj:
-#                 _json.dump(make_serializable(decoded), fj)
-#         except Exception:
-#             pass
-#         saved += 1
-
-#     return {"saved": saved, "output_dir": output_dir}
-
-
 @app.get("/dictionaries/{name}")
 def get_dictionary(name: str):
     # Get feature activation times
@@ -610,6 +497,38 @@ def get_analyses(name: str):
     return analyses
 
 
+@app.post("/dictionaries/{name}/features/{feature_index}/infer")
+def infer_feature(name: str, feature_index: int, text: str):
+    """Infer feature activations for a given text."""
+    model_name = client.get_sae_model_name(name, sae_series)
+    assert model_name is not None, f"SAE {name} not found or no model name is associated with it"
+    model = get_model(name=model_name)
+    sae = get_sae(name=name)
+
+    activations = model.to_activations({"text": [text]}, hook_points=sae.cfg.associated_hook_points)
+    activations = sae.normalize_activations(activations)
+    x, encoder_kwargs, _ = sae.prepare_input(activations)
+    feature_acts = sae.encode(x, **encoder_kwargs)[0, :, feature_index]
+
+    feature_acts = feature_acts.to_sparse()
+
+    origins = model.trace({"text": [text]})[0]
+
+    return Response(
+        content=msgpack.packb(
+            make_serializable(
+                {
+                    "text": text,
+                    "origins": origins,
+                    "feature_acts_indices": feature_acts.indices()[0],
+                    "feature_acts_values": feature_acts.values(),
+                }
+            )
+        ),
+        media_type="application/x-msgpack",
+    )
+
+
 @app.post("/dictionaries/{name}/features/{feature_index}/bookmark")
 def add_bookmark(name: str, feature_index: int):
     """Add a bookmark for a feature.

src/lm_saes/database.py

@@ -190,9 +190,9 @@
         if isinstance(data, ObjectId) and self.fs.exists(data):
             self.fs.delete(data)
 
-    def create_sae(self, name: str, series: str, path: str, cfg: BaseSAEConfig):
+    def create_sae(self, name: str, series: str, path: str, cfg: BaseSAEConfig, model_name: str | None = None):
         inserted_id = self.sae_collection.insert_one(
-            {"name": name, "series": series, "path": path, "cfg": cfg.model_dump()}
+            {"name": name, "series": series, "path": path, "cfg": cfg.model_dump(), "model_name": model_name}
         ).inserted_id
         self.feature_collection.insert_many(
             [{"sae_name": name, "sae_series": series, "index": i} for i in range(cfg.d_sae)]
@@ -363,6 +363,9 @@
             return None
         return sae["path"]
 
+    def get_sae_model_name(self, sae_name: str, sae_series: str) -> Optional[str]:
+        return self.sae_collection.find_one({"name": sae_name, "series": sae_series})["model_name"]
+
     def add_dataset(self, name: str, cfg: DatasetConfig):
         self.dataset_collection.update_one({"name": name}, {"$set": {"cfg": cfg.model_dump()}}, upsert=True)
 

ui-ssr/.cta.json

@@ -0,0 +1,21 @@
+{
+  "projectName": "ui-ssr",
+  "mode": "file-router",
+  "typescript": true,
+  "tailwind": true,
+  "packageManager": "bun",
+  "addOnOptions": {},
+  "git": true,
+  "version": 1,
+  "framework": "react-cra",
+  "chosenAddOns": [
+    "eslint",
+    "nitro",
+    "start",
+    "compiler",
+    "shadcn",
+    "table",
+    "store",
+    "tanstack-query"
+  ]
+}

ui-ssr/.cursorrules

@@ -0,0 +1,7 @@
+# shadcn instructions
+
+Use the latest version of Shadcn to install new components, like this command to add a button component:
+
+```bash
+pnpx shadcn@latest add button
+```

ui-ssr/.gitignore

@@ -0,0 +1,11 @@
+node_modules
+.DS_Store
+dist
+dist-ssr
+*.local
+.env
+.nitro
+.tanstack
+.wrangler
+.output
+.vinxi

ui-ssr/.prettierignore

@@ -0,0 +1,3 @@
+package-lock.json
+pnpm-lock.yaml
+yarn.lock

ui-ssr/.vscode/settings.json

@@ -0,0 +1,11 @@
+{
+  "files.watcherExclude": {
+    "**/routeTree.gen.ts": true
+  },
+  "search.exclude": {
+    "**/routeTree.gen.ts": true
+  },
+  "files.readonlyInclude": {
+    "**/routeTree.gen.ts": true
+  }
+}