more_nnsight.git

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 # more-nnsight
-
-This library adds one abstraction on top of NNSight: `SavedActivation`, a
-container that holds activation slices keyed by their model path and token
-position.
-
-NNSight already lets you save and patch individual activations. The problem
-starts when you need several at once — across layers, token positions, or
-prompts. You end up juggling loose tensors and remembering which one came from
-where. NNSight also requires that you access modules in layer-major order
-within a trace: you cannot touch layer 2, then layer 5, then go back to layer
-2. `SavedActivation` keeps activations together, enforces canonical ordering
-internally, and gives you batch, arithmetic, and patching operations over the
-whole set.
-
-## API
-
-```python
-from more_nnsight import SavedActivation, save_activations, updates
-```
-
-Call `save_activations` inside an NNSight trace to capture activations. Paths
-follow the model's attribute structure; the final bracket is the token position.
-
-```python
-with model.trace() as tracer:
-    with tracer.invoke(prompts):
-        saved = save_activations(model, [
-            "model.transformer.h[2].output[10]",
-            "model.transformer.h[3].output[-1]",
-        ])
-```
-
-Use `[:]` to expand over a `ModuleList` — `"model.transformer.h[:].output[10]"`
-becomes one key per layer. Each saved tensor has shape `(batch, hidden)`.
-
-Values come back by string or by attribute traversal:
-
-```python
-saved.get("model.transformer.h[2].output[10]")
-saved.values.transformer.h[2].output[10]
-saved.keys()  # list of saved path strings
-```
-
-You can slice the batch dimension, take the mean, or do arithmetic across all
-saved paths at once:
-
-```python
-saved.slice[0:3]                                   # first three batch rows
-saved.mean()                                        # (1, hidden) per path
-direction = positive.mean() - negative.mean()       # +, -, scalar * all work
-```
-
-`subset` narrows to specific paths; `union` merges two disjoint sets:
-
-```python
-focused = saved.subset(["model.transformer.h[2].output[10]"])
-combined = patch_a.union(patch_b)
-```
-
-To write stored activations into a later forward pass, use `apply`:
-
-```python
-with model.trace() as tracer:
-    with tracer.invoke(corrupted_prompts):
-        saved.apply(model)
-```
-
-When the replacement depends on the live activation, use `updates` instead.
-It is a generator that walks paths in layer-major order and yields three values
-per path: `key` (the path string), `current` (the activation from the current
-forward pass at that path and token position), and `update` (a callback that
-writes a new tensor back to the same location). At each step, you call
-`update(new_value)` with whatever you want to write. This lets you express
-per-layer logic — for example, adding a scaled steering vector at one layer
-while replacing outright at another:
-
-```python
-with model.trace() as tracer:
-    with tracer.invoke(prompts):
-        for key, current, update in updates(model, saved.keys()):
-            if key == "model.transformer.h[2].output[-1]":
-                update(current + 2.0 * saved.get(key))
-            else:
-                update(saved.get(key))
-```
-
-You can also build a `SavedActivation` directly from tensors you already have:
-
-```python
-patch = SavedActivation.from_pairs(
-    ("model.transformer.h[2].output[10]", tensor_a),
-    ("model.transformer.h[3].output[9]", tensor_b),
-)
-```
-
-## Typical workflows
-
-**Activation patching** — save from a clean run, replay into a corrupted run:
-
-```python
-with model.trace() as tracer:
-    with tracer.invoke([clean_prompt]):
-        patch = save_activations(model, ["model.transformer.h[2].output[9]"])
-
-with model.trace() as tracer:
-    with tracer.invoke([corrupted_prompt]):
-        patch.apply(model)
-        logits = model.lm_head.output.save()
-```
-
-**Steering** — compute a direction from contrastive prompts, add it to a
-neutral run:
-
-```python
-prompts = positive_prompts + negative_prompts + [neutral_prompt]
-path = "model.transformer.h[5].output[-1]"
-
-with model.trace() as tracer:
-    with tracer.invoke(prompts):
-        saved = save_activations(model, [path])
-        pos = saved.slice[0:3].mean()
-        neg = saved.slice[3:6].mean()
-        neutral = saved.slice[6]
-
-steered = neutral + 1.5 * (pos - neg)
-```