Edu logit lens

package.json

@@ -0,0 +1 @@
+{"dependencies": {}}

scripts/api.sh

@@ -1,4 +1,4 @@
 #!/bin/bash
 
 cd workbench
-uvicorn _api.main:app --host 0.0.0.0 --port 8000 --reload
+python -m uvicorn _api.main:app --host 0.0.0.0 --port 8000 --reload

workbench/_api/main.py

@@ -1,13 +1,17 @@
-from fastapi import FastAPI
+from fastapi import FastAPI, Request
 from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import JSONResponse
 import logging
 import os
+import traceback
 import anyio
 
-from .routes import lens, patch, models, logit_lens, activation_patching
+from .routes import lens, patch, models, logit_lens, activation_patching, causal_mediation
 from .state import AppState
 
-from dotenv import load_dotenv; load_dotenv()
+from pathlib import Path
+from dotenv import load_dotenv
+load_dotenv(Path(__file__).resolve().parent / ".env")
 
 # Configure logging
 logging.basicConfig(
@@ -59,9 +63,19 @@ def fastapi_app():
     app.include_router(lens, prefix="/lens")
     app.include_router(logit_lens, prefix="/logit_lens")
     app.include_router(activation_patching, prefix="/activation_patching")
+    app.include_router(causal_mediation, prefix="/causal_mediation", tags=["causal_mediation"])
     app.include_router(patch, prefix="/patch")
     app.include_router(models, prefix="/models")
 
+    @app.exception_handler(Exception)
+    async def global_exception_handler(request: Request, exc: Exception):
+        tb = traceback.format_exception(type(exc), exc, exc.__traceback__)
+        logging.error(f"Unhandled exception: {''.join(tb)}")
+        return JSONResponse(
+            status_code=500,
+            content={"detail": str(exc), "traceback": ''.join(tb[-3:])},
+        )
+
     app.state.m = AppState()
 
     return app

workbench/_api/routes/__init__.py

@@ -3,9 +3,17 @@
 from .models import router as models
 from .logit_lens import router as logit_lens
 from .activation_patching import router as activation_patching
+from .causal_mediation import router as causal_mediation
 
 from nnsight import ndif
 import nnsightful
 ndif.register(nnsightful)
 
-__all__ = ["lens", "patch", "models", "logit_lens", "activation_patching"]
+__all__ = [
+    "lens",
+    "patch",
+    "models",
+    "logit_lens",
+    "activation_patching",
+    "causal_mediation",
+]

workbench/_api/routes/causal_mediation.py

@@ -0,0 +1,214 @@
+from __future__ import annotations
+
+from typing import Any
+
+import torch
+from fastapi import APIRouter, Depends
+from pydantic import BaseModel
+
+from ..auth import require_user_email
+from ..data_models import NDIFResponse
+from ..state import AppState, get_state
+
+from nnsightful.types import LogitLensData
+
+router = APIRouter()
+
+
+class CausalMediationRequest(BaseModel):
+    model: str
+    src_prompt: str
+    tgt_prompt: str
+    src_token_pos: int
+    src_layer: int
+    tgt_token_pos: int
+    tgt_layer: int
+    topk: int = 5
+    include_entropy: bool = True
+
+
+class CausalMediationResponse(NDIFResponse):
+    """Identical shape to LogitLensResponse so the frontend can reuse the
+    existing logit-lens transform/renderer."""
+    data: LogitLensData | None = None
+
+
+def _format_lens(
+    logits: torch.Tensor,
+    tokenizer,
+    model_name: str,
+    input_tokens: list[str],
+    n_layers: int,
+    *,
+    top_k: int = 5,
+    include_entropy: bool = True,
+) -> dict[str, Any]:
+    """Inlined mirror of the local `format()` inside
+    `nnsightful.tools.logit_lens._run`. Turns a [L, T, V] logits tensor into
+    the dict shape consumed by `LogitLensData(**...)`.
+
+    Why: `LogitLensTool` doesn't override `_format`, so calling
+    `logit_lens_tool._format(...)` falls through to the abstract `Tool._format`
+    in `nnsightful/tools/_base.py` whose body is `...` — i.e. returns `None`.
+    """
+    layers = list(range(n_layers))
+    positions = list(range(len(input_tokens)))
+
+    if include_entropy:
+        log_p = torch.nn.functional.log_softmax(logits, dim=-1)
+        p = log_p.exp()
+        entropy = torch.round(-(p * log_p).sum(dim=-1), decimals=3).tolist()
+    else:
+        entropy = None
+
+    probs = torch.nn.functional.softmax(logits, dim=-1)
+    logits.to("cpu")  # free memory
+
+    _, top_indices = torch.topk(probs, k=top_k, dim=-1)
+
+    topks = [
+        [tokenizer.batch_decode(torch.tensor(pos).unsqueeze(dim=1)) for pos in layer]
+        for layer in top_indices.tolist()
+    ]
+
+    unique_indices = [
+        torch.unique(top_indices[:, pi, :].flatten(), sorted=False).tolist()
+        for pi in range(top_indices.shape[1])
+    ]
+    probs = probs.permute(1, 2, 0)
+    trajectories = [
+        {
+            tokenizer.decode(token): torch.round(probs[pos_idx][token], decimals=3).tolist()
+            for token in pos
+        }
+        for pos_idx, pos in enumerate(unique_indices)
+    ]
+
+    return {
+        "meta": {"version": 2, "timestamp": "3h", "model": model_name},
+        "layers": layers,
+        "input": input_tokens,
+        "tracked": trajectories,
+        "topk": topks,
+        "entropy": entropy,
+        "positions": positions,
+    }
+
+
+def _run_causal_mediation(
+    model,
+    src_prompt: str,
+    tgt_prompt: str,
+    src_token_pos: int,
+    src_layer: int,
+    tgt_token_pos: int,
+    tgt_layer: int,
+    *,
+    remote: bool = False,
+    backend=None,
+) -> dict[str, Any]:
+    """Capture the source residual at (src_layer, src_token_pos), patch it
+    into the target prompt at (tgt_layer, tgt_token_pos), then run a logit
+    lens over the *patched* forward pass.
+
+    Patching pattern mirrors `nnsightful.tools.activation_patching._run`:
+    a slice-assign on `model.layers_output[i]` is sufficient to register the
+    intervention and have it propagate through subsequent layers — no
+    explicit `model.layers[i].output = (...)` reassignment is needed.
+    """
+    n_layers = model.num_layers
+
+    with torch.no_grad():
+        with model.session(remote=remote, backend=backend):
+            # 1) Source pass — capture the residual at (src_layer, src_token_pos).
+            with model.trace(src_prompt):
+                src_hidden = model.layers_output[src_layer][0, src_token_pos].save()
+
+            # 2) Target pass — at tgt_layer, slice-assign the source residual
+            #    into the target's tgt_token_pos. project_on_vocab at every
+            #    layer gives us a logit-lens grid over the patched pass.
+            with model.trace(tgt_prompt):
+                per_layer_logits = []
+                for i in range(n_layers):
+                    hs = model.layers_output[i]
+                    if i == tgt_layer:
+                        hs[0, tgt_token_pos][:] = src_hidden
+                    per_layer_logits.append(model.project_on_vocab(hs))
+                # Stack to a single [L, T, V] tensor so backend() returns a
+                # known shape on the remote path (one saved key: "logits").
+                logits = torch.cat(per_layer_logits, dim=0).save()
+
+    if remote and backend is not None:
+        return {"job_id": backend.job_id}
+
+    return {"logits": logits}
+
+
+@router.post("/start", response_model=CausalMediationResponse)
+async def start_causal_mediation(
+    req: CausalMediationRequest,
+    state: AppState = Depends(get_state),
+    user_email: str = Depends(require_user_email),
+):
+    model = state[req.model]
+    backend = state.make_backend(model=model)
+
+    raw = _run_causal_mediation(
+        model,
+        req.src_prompt,
+        req.tgt_prompt,
+        req.src_token_pos,
+        req.src_layer,
+        req.tgt_token_pos,
+        req.tgt_layer,
+        remote=state.remote,
+        backend=backend,
+    )
+
+    if "job_id" in raw:
+        return {"job_id": raw["job_id"]}
+
+    input_tokens = [
+        str(model.tokenizer.decode(token))
+        for token in model.tokenizer.encode(req.tgt_prompt)
+    ]
+    data = _format_lens(
+        raw["logits"],
+        tokenizer=model.tokenizer,
+        model_name=req.model,
+        input_tokens=input_tokens,
+        n_layers=model.num_layers,
+        top_k=req.topk,
+        include_entropy=req.include_entropy,
+    )
+    return {"data": data}
+
+
+@router.post("/results/{job_id}", response_model=CausalMediationResponse)
+async def collect_causal_mediation(
+    job_id: str,
+    req: CausalMediationRequest,
+    state: AppState = Depends(get_state),
+    user_email: str = Depends(require_user_email),
+):
+    backend = state.make_backend(job_id=job_id)
+    results = backend()
+
+    model = state[req.model]
+    tokenizer = model.tokenizer
+    input_tokens = [
+        str(tokenizer.decode(token))
+        for token in tokenizer.encode(req.tgt_prompt)
+    ]
+
+    data = _format_lens(
+        results["logits"],
+        tokenizer=tokenizer,
+        model_name=req.model,
+        input_tokens=input_tokens,
+        n_layers=model.num_layers,
+        top_k=req.topk,
+        include_entropy=req.include_entropy,
+    )
+
+    return {"data": data}

workbench/_web/instrumentation.ts

@@ -6,7 +6,7 @@ export function register() {
 
 export const onRequestError = async (err: Error, request: NextRequest, context: NextResponse) => {
     if (process.env.NEXT_RUNTIME === "nodejs") {
-        const { getPostHogServer } = require("./src/lib/posthog-server");
+        const { getPostHogServer } = await import("./src/lib/posthog-server");
         const posthog = await getPostHogServer();
 
         // Only capture exception if PostHog is enabled

workbench/_web/next.config.js

@@ -8,7 +8,7 @@ const __dirname = path.dirname(fileURLToPath(import.meta.url));
 
 const nextConfig = {
     reactStrictMode: true,
-    transpilePackages: ["nnsightful"],
+    transpilePackages: ["nnsightful", "edulogitlens"],
     turbopack: {
         // Expand root so Turbopack can resolve the symlinked nnsightful package
         // which lives at ../../nnsightful (outside the default _web/ root)