llamppl 0.2.0

Probabilistic programming with Large Language Models.

LLaMPPL

LLaMPPL is a research prototype for language model probabilistic programming: specifying language generation tasks by writing probabilistic programs that combine calls to LLMs, symbolic program logic, and probabilistic conditioning. To solve these tasks, LLaMPPL uses a specialized sequential Monte Carlo inference algorithm. This technique, SMC steering, is described in our recent workshop abstract.

This library was formerly known as hfppl.

Installation

If you just want to try out LLaMPPL, check out our demo notebook on Colab, which performs a simple constrained generation task using GPT-2. (Larger models may require more RAM or GPU resources than Colab's free version provides.)

To get started on your own machine, you can install this library from PyPI:

pip install llamppl

Local installation

For local development, clone this repository and run pip install -e ".[dev,examples]" to install llamppl and its development dependencies.

git clone https://github.com/genlm/llamppl
cd llamppl
pip install -e ".[dev,examples]"

Then, try running an example. Note that this will cause the weights of a HuggingFace model to be downloaded.

python examples/hard_constraints.py

If everything is working, you should see the model generate political news using words that are at most five letters long (e.g., "Dr. Jill Biden may still be a year away from the White House but she is set to make her first trip to the U.N. today.").

Modeling with LLaMPPL

A LLaMPPL program is a subclass of the llamppl.Model class.

from llamppl import Model, LMContext, CachedCausalLM

# A LLaMPPL model subclasses the Model class
class MyModel(Model):

    # The __init__ method is used to process arguments
    # and initialize instance variables.
    def __init__(self, lm, prompt, forbidden_letter):
        super().__init__()

        # A stateful context object for the LLM, initialized with the prompt
        self.context = LMContext(lm, prompt)
        self.eos_token = lm.tokenizer.eos_token_id

        # The forbidden letter
        self.forbidden_tokens = set(i for (i, v) in enumerate(lm.vocab)
                                      if forbidden_letter in v)

    # The step method is used to perform a single 'step' of generation.
    # This might be a single token, a single phrase, or any other division.
    # Here, we generate one token at a time.
    async def step(self):
        # Condition on the next token *not* being a forbidden token.
        await self.observe(self.context.mask_dist(self.forbidden_tokens), False)

        # Sample the next token from the LLM -- automatically extends `self.context`.
        token = await self.sample(self.context.next_token())

        # Check for EOS or end of sentence
        if token.token_id == self.eos_token or str(token) in ['.', '!', '?']:
            # Finish generation
            self.finish()

    # To improve performance, a hint that `self.forbidden_tokens` is immutable
    def immutable_properties(self):
        return set(['forbidden_tokens'])

The Model class provides a number of useful methods for specifying a LLaMPPL program:

• self.sample(dist[, proposal]) samples from the given distribution. Providing a proposal does not modify the task description, but can improve inference. Here, for example, we use a proposal that pre-emptively avoids the forbidden letter.
• self.condition(cond) conditions on the given Boolean expression.
• self.finish() indicates that generation is complete.
• self.observe(dist, obs) performs a form of 'soft conditioning' on the given distribution. It is equivalent to (but more efficient than) sampling a value v from dist and then immediately running condition(v == obs).

To run inference, we use the smc_steer or smc_standard methods:

import asyncio
from llamppl import smc_steer

# Initialize the language model
lm = CachedCausalLM.from_pretrained("meta-llama/Llama-2-7b-hf")

# Create a model instance
model = MyModel(lm, "The weather today is expected to be", "e")

# Run inference
particles = asyncio.run(smc_steer(model, 5, 3)) # number of particles N, and beam factor K

Sample output:

sunny.
sunny and cool.
34° (81°F) in Chicago with winds at 5mph.
34° (81°F) in Chicago with winds at 2-9 mph.
hot and humid with a possibility of rain, which is not uncommon for this part of Mississippi.

Further documentation can be found at https://genlm.github.io/llamppl.