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HazyResearch / manifest

Prompt programming with FMs.
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Manifest

How to make prompt programming with Foundation Models a little easier.

Table of Contents

Install

Install:

pip install manifest-ml

Install with diffusion support:

pip install manifest-ml[diffusers]

Install with HuggingFace local model support:

pip install manifest-ml[api]

Dev Install:

git clone git@github.com:HazyResearch/manifest.git
cd manifest
make dev

Getting Started

Running is simple to get started. If using OpenAI, set export OPENAI_API_KEY=<OPENAIKEY> (or pass key in through variable client_connection) then run

from manifest import Manifest

# Start a manifest session to OpenAI - default `engine=text-davinci-003`
manifest = Manifest(
    client_name = "openai",
)
manifest.run("Why is the grass green?")

Examples

We have example notebook and python scripts located at examples. These show how to use different models, model types (i.e. text, diffusers, or embedding models), and async running.

Manifest Components

Manifest is meant to be a very light weight package to help with prompt design and iteration. Three key design decisions of Manifest are

Models

Manifest provides model clients for OpenAI, AI21, Cohere, Together, and HuggingFace (see below for how to use locally hosted HuggingFace models). You can toggle between the models by changing client_name and client_connection. For example, if a HuggingFace model is loaded locally, run

manifest = Manifest(
    client_name = "huggingface",
    client_connection = "http://127.0.0.1:5000",
)

If you want to use Cohere, run

manifest = Manifest(
    client_name = "cohere",
    client_connection = <COHERE_API_KEY>,
)

You can also just set export COHERE_API_KEY=<COHERE_API_KEY> and not use client_connection.

If you want to use AI21 Labs, run

manifest = Manifest(
    client_name = "ai21",
    client_connection = <AI21_API_KEY>,
)

You can see the model details and possible model inputs to run() via

print(manifest.client_pool.get_current_client().get_model_params())
print(manifest.client_pool.get_current_client().get_model_inputs())

Global Cache

We support having queries and results stored in a global cache that can be shared across users. We treat inputs and outputs as key value pairs and support SQLite or Redis backends. To start with global caching using SQLite, run

manifest = Manifest(
    client_name = "openai",
    cache_name = "sqlite",
    cache_connection = "mycache.sqlite",
)

The cache will be saved in mycache.sqlite.

We also support Redis backend.

manifest = Manifest(
    client_name = "openai",
    cache_name = "redis",
    cache_connection = "localhost:6379"
)

As a hint, if you want to get Redis running, see the docker run command below under development.

Running Queries

Once you have a session open, you can write and develop prompts.

result = manifest.run("Hello, my name is Laurel")

You can also run over multiple examples if supported by the client.

results = manifest.run(["Where are the cats?", "Where are the dogs?"])

We support async queries as well via

import asyncio
results = asyncio.run(manifest.arun_batch(["Where are the cats?", "Where are the dogs?"]))

If something doesn't go right, you can also ask to get a raw manifest Response.

result_object = manifest.run(["Where are the cats?", "Where are the dogs?"], return_response=True)
print(result_object.get_request_obj())
print(result_object.is_cached())
print(result_object.get_response_obj())

By default, we do not truncate results based on a stop token. You can change this by either passing a new stop token to a Manifest session or to a run.

result = manifest.run(prompt, "Laurel", stop_token="and")

If you want to change default parameters to a model, we pass those as kwargs to the client.

result = manifest.run(prompt, "Laurel", max_tokens=50)

Streaming Queries

Manifest also supports streaming the model response back, assuming it's supported by the underlying client. When calling run, pass stream=True to get a streaming iterator in response.

result_iterator = manifest.run("Tell me a story. Once upon a time", max_tokens=100, stream=True)
for res_text in result_iterator:
    print(res_text)

Streaming responses are only supported for single string queries (not batch mode) for text completion models.

Model Pools

Manifest supports querying multiple models with different schedulers. This is very much a work in progress effort, but Manifest will round robin select (or randomly select) the clients you want. You can use the same client multiple times with different connection strings (e.g. different API keys), or you can mix and match. The only requirement is that all clients are the same request type. I.e. you can't have a pool of generation models and embedding models.

To query between a local model and OpenAI,

from manifest.connections.client_pool import ClientConnection
from manifest import Manifest

client_connection1 = ClientConnection(
    client_name="huggingface",
    client_connection="http://127.0.0.1:5000",
)
client_connection2 = ClientConnection(client_name="openai", engine="text-ada-001")
manifest = Manifest(
    client_pool=[client_connection1, client_connection2],
    cache_name="sqlite",
    client_connection=sqlite_cache,
)
manifest.run(...)

The speed benefit comes in with async batched runs. When calling arun_batch with a list of prompts, Manifest supports a chunk_size param. This will break the prompts into chunk_size chunks to spread across the client pool. By default chunk_size is -1 which means only one client will get all the prompts to run asynchronously. You must set chunk_size > 1 to distribute across the pool. There is a further batch_size param which control the individual client batch_size to send to the model.

responses = asyncio.run(manifest.arun_batch(prompts, max_tokens=30, chunk_size=20))

Other Models

Local Huggingface Models

To use a HuggingFace generative model, in manifest/api we have a Flask application that hosts the models for you.

In a separate terminal or Tmux/Screen session, to load 6B parameters models, run

python3 -m manifest.api.app \
    --model_type huggingface \
    --model_name_or_path EleutherAI/gpt-j-6B \
    --device 0

You will see the Flask session start and output a URL http://127.0.0.1:5000. Pass this in to Manifest. If you want to use a different port, set the FLASK_PORT environment variable.

manifest = Manifest(
    client_name = "huggingface",
    client_connection = "http://127.0.0.1:5000",
)

If you have a custom model you trained, pass the model path to --model_name_or_path.

To help load larger models, we also support using parallelize() from HF, accelerate, bitsandbytes, and deepspeed. You will need to install these packages first via pip install manifest-ml[api]. We list the commands to load larger models below.

Chat Models

Manifest has specific support for executing against chat models in the more standard "system" / "user" dialogue. To pass in a dialogue history to Manifest, use the run command with a list of dictionary inputs with role and content keys using an associated chat model such as openaichat.

manifest = Manifest(client_name="openaichat")
dialogue = [
    {"role": "system", "content": "You are a helpful assistant who also responds in rhymes"},
    {"role": "user", "content": "What is the date?"},
]
res = manifest.run(dialogue, max_tokens=100)

Embedding Models

Manifest also supports getting embeddings from models and available APIs. We do this all through changing the client_name argument. You still use run and abatch_run.

To use OpenAI's embedding models, simply run

manifest = Manifest(client_name="openaiembedding")
embedding_as_np = manifest.run("Get me an embedding for a bunny")

As explained above, you can load local HuggingFace models that give you embeddings, too. If you want to use a standard generative model, load the model as above use use client_name="huggingfaceembedding". If you want to use a standard embedding model, like those from SentenceTransformers, load your local model via

python3 -m manifest.api.app \
    --model_type sentence_transformers \
    --model_name_or_path all-mpnet-base-v2 \
    --device 0

Road Map

Here's what's coming up next

Development

Before submitting a PR, run

export REDIS_PORT="6379"  # or whatever PORT local redis is running for those tests
cd <REDIS_PATH>
docker run -d -p 127.0.0.1:${REDIS_PORT}:6379 -v `pwd`:`pwd` -w `pwd` --name manifest_redis_test redis
make test

Cite

Please cite Manifest if you used it for any publications. Thanks!!

@misc{orr2022manifest,
  author = {Orr, Laurel},
  title = {Manifest},
  year = {2022},
  publisher = {GitHub},
  howpublished = {\url{https://github.com/HazyResearch/manifest}},
}