pyvene-interventions

pyvene: Causal Interventions for Neural Networks

pyvene is Stanford NLP's library for performing causal interventions on PyTorch models. It provides a declarative, dict-based framework for activation patching, causal tracing, and interchange intervention training - making intervention experiments reproducible and shareable.

GitHub: stanfordnlp/pyvene (840+ stars) Paper: pyvene: A Library for Understanding and Improving PyTorch Models via Interventions (NAACL 2024)

When to Use pyvene

Use pyvene when you need to:

• Perform causal tracing (ROME-style localization)
• Run activation patching experiments
• Conduct interchange intervention training (IIT)
• Test causal hypotheses about model components
• Share/reproduce intervention experiments via HuggingFace
• Work with any PyTorch architecture (not just transformers)

Consider alternatives when:

• You need exploratory activation analysis → Use TransformerLens
• You want to train/analyze SAEs → Use SAELens
• You need remote execution on massive models → Use nnsight
• You want lower-level control → Use nnsight

Installation

pip install pyvene

Standard import:

import pyvene as pv

Core Concepts

IntervenableModel

The main class that wraps any PyTorch model with intervention capabilities:

import pyvene as pv
from transformers import AutoModelForCausalLM, AutoTokenizer

# Load base model
model = AutoModelForCausalLM.from_pretrained("gpt2")
tokenizer = AutoTokenizer.from_pretrained("gpt2")

# Define intervention configuration
config = pv.IntervenableConfig(
    representations=[
        pv.RepresentationConfig(
            layer=8,
            component="block_output",
            intervention_type=pv.VanillaIntervention,
        )
    ]
)

# Create intervenable model
intervenable = pv.IntervenableModel(config, model)

Intervention Types

| Type | Description | Use Case | |------|-------------|----------| | VanillaIntervention | Swap activations between runs | Activation patching | | AdditionIntervention | Add activations to base run | Steering, ablation | | SubtractionIntervention | Subtract activations | Ablation | | ZeroIntervention | Zero out activations | Component knockout | | RotatedSpaceIntervention | DAS trainable intervention | Causal discovery | | CollectIntervention | Collect activations | Probing, analysis |

Component Targets

# Available components to intervene on
components = [
    "block_input",      # Input to transformer block
    "block_output",     # Output of transformer block
    "mlp_input",        # Input to MLP
    "mlp_output",       # Output of MLP
    "mlp_activation",   # MLP hidden activations
    "attention_input",  # Input to attention
    "attention_output", # Output of attention
    "attention_value_output",  # Attention value vectors
    "query_output",     # Query vectors
    "key_output",       # Key vectors
    "value_output",     # Value vectors
    "head_attention_value_output",  # Per-head values
]

Workflow 1: Causal Tracing (ROME-style)

Locate where factual associations are stored by corrupting inputs and restoring activations.

Step-by-Step

import pyvene as pv
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained("gpt2-xl")
tokenizer = AutoTokenizer.from_pretrained("gpt2-xl")

# 1. Define clean and corrupted inputs
clean_prompt = "The Space Needle is in downtown"
corrupted_prompt = "The ##### ###### ## ## ########"  # Noise

clean_tokens = tokenizer(clean_prompt, return_tensors="pt")
corrupted_tokens = tokenizer(corrupted_prompt, return_tensors="pt")

# 2. Get clean activations (source)
with torch.no_grad():
    clean_outputs = model(**clean_tokens, output_hidden_states=True)
    clean_states = clean_outputs.hidden_states

# 3. Define restoration intervention
def run_causal_trace(layer, position):
    """Restore clean activation at specific layer and position."""
    config = pv.IntervenableConfig(
        representations=[
            pv.RepresentationConfig(
                layer=layer,
                component="block_output",
                intervention_type=pv.VanillaIntervention,
                unit="pos",
                max_number_of_units=1,
            )
        ]
    )

    intervenable = pv.IntervenableModel(config, model)

    # Run with intervention
    _, patched_outputs = intervenable(
        base=corrupted_tokens,
        sources=[clean_tokens],
        unit_locations={"sources->base": ([[[position]]], [[[position]]])},
        output_original_output=True,
    )

    # Return probability of correct token
    probs = torch.softmax(patched_outputs.logits[0, -1], dim=-1)
    seattle_token = tokenizer.encode(" Seattle")[0]
    return probs[seattle_token].item()

# 4. Sweep over layers and positions
n_layers = model.config.n_layer
seq_len = clean_tokens["input_ids"].shape[1]

results = torch.zeros(n_layers, seq_len)
for layer in range(n_layers):
    for pos in range(seq_len):
        results[layer, pos] = run_causal_trace(layer, pos)

# 5. Visualize (layer x position heatmap)
# High values indicate causal importance

Checklist

• [ ] Prepare clean prompt with target factual association
• [ ] Create corrupted version (noise or counterfactual)
• [ ] Define intervention config for each (layer, position)
• [ ] Run patching sweep
• [ ] Identify causal hotspots in heatmap

Workflow 2: Activation Patching for Circuit Analysis

Test which components are necessary for a specific behavior.

Step-by-Step

import pyvene as pv
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained("gpt2")
tokenizer = AutoTokenizer.from_pretrained("gpt2")

# IOI task setup
clean_prompt = "When John and Mary went to the store, Mary gave a bottle to"
corrupted_prompt = "When John and Mary went to the store, John gave a bottle to"

clean_tokens = tokenizer(clean_prompt, return_tensors="pt")
corrupted_tokens = tokenizer(corrupted_prompt, return_tensors="pt")

john_token = tokenizer.encode(" John")[0]
mary_token = tokenizer.encode(" Mary")[0]

def logit_diff(logits):
    """IO - S logit difference."""
    return logits[0, -1, john_token] - logits[0, -1, mary_token]

# Patch attention output at each layer
def patch_attention(layer):
    config = pv.IntervenableConfig(
        representations=[
            pv.RepresentationConfig(
                layer=layer,
                component="attention_output",
                intervention_type=pv.VanillaIntervention,
            )
        ]
    )

    intervenable = pv.IntervenableModel(config, model)

    _, patched_outputs = intervenable(
        base=corrupted_tokens,
        sources=[clean_tokens],
    )

    return logit_diff(patched_outputs.logits).item()

# Find which layers matter
results = []
for layer in range(model.config.n_layer):
    diff = patch_attention(layer)
    results.append(diff)
    print(f"Layer {layer}: logit diff = {diff:.3f}")

Workflow 3: Interchange Intervention Training (IIT)

Train interventions to discover causal structure.

Step-by-Step

import pyvene as pv
from transformers import AutoModelForCausalLM
import torch

model = AutoModelForCausalLM.from_pretrained("gpt2")

# 1. Define trainable intervention
config = pv.IntervenableConfig(
    representations=[
        pv.RepresentationConfig(
            layer=6,
            component="block_output",
            intervention_type=pv.RotatedSpaceIntervention,  # Trainable
            low_rank_dimension=64,  # Learn 64-dim subspace
        )
    ]
)

intervenable = pv.IntervenableModel(config, model)

# 2. Set up training
optimizer = torch.optim.Adam(
    intervenable.get_trainable_parameters(),
    lr=1e-4
)

# 3. Training loop (simplified)
for base_input, source