graphpatch 0.1.0

graphpatch is a library for activation patching on PyTorch neural network models.

graphpatch

graphpatch is a library for activation patching on PyTorch neural network models. You use it by first wrapping your model in a PatchableGraph and then running operations in a context created by PatchableGraph.patch():

model = GPT2LMHeadModel.from_pretrained(
   "gpt2-xl",
   device_map="auto",
   load_in_8bit=True,
   torch_dtype=torch.float16
)
tokenizer = AutoTokenizer.from_pretrained("gpt2-xl")
inputs = tokenizer(
   "The Eiffel Tower, located in", return_tensors="pt", padding=False
).to(torch.device("cuda"))
# Note that all arguments after the first are forwarded as example inputs
# to the model during compilation; use_cache and return_dict are arguments
# to GPT2LMHeadModel, not graphpatch-specific.
pg = PatchableGraph(model, **inputs, use_cache=False, return_dict=False)
# Applies two patches to the multiplication result within the activation function
# of the MLP in the 18th transformer layer. ProbePatch records the last observed value
# at the given node, while ZeroPatch zeroes out the value seen by downstream computations.
with pg.patch("transformer.h_17.mlp.act.mul_3": [probe := ProbePatch(), ZeroPatch()]):
   output = pg(**inputs)
# Patches are applied in order. probe.activation holds the value prior
# to ZeroPatch zeroing it out.
print(probe.activation)

graphpatch can patch (or record) any intermediate Tensor value without manual modification of the underlying model’s code. See full documentation here.

Requirements

graphpatch requires torch>=2 as it uses torch.compile() to build the computational graph it uses for activation patching. As of torch>=2.1.0, Python 3.8–3.11 are supported. torch==2.0.* do not support compilation on Python 3.11; you will get an exception if you try to use graphpatch on such a configuration.

graphpatch automatically supports models loaded with features supplied by accelerate and bitsandbytes. For example, you can easily use graphpatch on multiple GPU's and with quantized inference:

model = LlamaForCausalLM.from_pretrained(
   model_path, device_map="auto", load_in_8bit=True, torch_dtype=torch.float16
)
pg = PatchableGraph(model, **example_inputs)

Installation

graphpatch is available on PyPI, and can be installed via pip:

pip install graphpatch

Optionally, you can install graphpatch with the "transformers" extra to select known compatible versions of transformers, accelerate, bitsandbytes, and miscellaneous optional requirements of these packages to quickly get started with multi-GPU and quantized inference on real-world models:

pip install graphpatch[transformers]

Demos

See the demos for some practical usage examples.

Documentation

See the full documentation on Read the Docs.