GenGPT3.jl

GPT-3 as a generative function in Gen.jl, implemented by wrapping the OpenAI API in Gen's interface.

Usage

Install both Gen and this package via the Julia Pkg REPL:

add Gen
add GenGPT3

Add your OpenAI API key as an environment variable named OPENAI_API_KEY. You can follow this guide, or set ENV["OPENAI_API_KEY"] to the value of your API key in the Julia REPL.

Now you can construct GPT-3 as a generative function, and call GFI functions on it:

using Gen, GenGPT3

# Construct GPT3GenerativeFunction
gpt3 = GPT3GF(model="davinci-002", max_tokens=256)

# Untraced execution 
prompt = "What is the tallest mountain on Mars?"
output = gpt3(prompt)

# Traced execution
trace = simulate(gpt3, (prompt,))

# Constrained generation
constraints = choicemap((:output, "Olympus Mons."))
trace, weight = generate(gpt3, (prompt,), constraints)

Configuration

The constructor for a GPT3GenerativeFunction (or GPT3GF for short), can be used to configure a variety of options, documented below:

GPT3GenerativeFunction(;
    model = "davinci-002",
    temperature = 1.0,
    max_tokens = 1024,
    stop = nothing,
    encoding = GenGPT3.MODEL_ENCODINGS[model],
    api_key_lookup = () -> ENV["OPENAI_API_KEY"],
    organization_lookup = () -> ENV["OPENAI_ORGANIZATION"]
)

Constructs GPT-3 as a generative function, where sampling and scoring of completions are performed via calls to the OpenAI API.

The generative function takes in a prompt as an (optional) argument, then samples and returns a completion. This represents a distribution over strings (up to max_tokens long) which end in a stop sequence. The completion is stored in the :output address of the resulting trace.

Arguments

• model::String: The pretrained model to query. Defaults to "davinci-002".
• temperature::Float64 = 1.0: The softmax temperature. Values between 0.0 and 2.0 are allowed. Higher temperatures increase randomness. Note that if this is not set to 1.0, then the resulting log probabilities will no longer be normalized.
• max_tokens::Int = 1024: The maximum number of output tokens generated (including the stop sequence).
• encoding::String = GenGPT3.MODEL_ENCODINGS[model]: Tokenizer encoding for the model. For most models, this is "cl100k_base".
• stop::Union{String,Nothing} = nothing: The stop sequence as a string. Defaults to the <|endoftext|> token if not specified. If specified, then the model will be prevented from generating any <|endoftext|> tokens (to avoid multiple termination possibilities).
• api_key_lookup::Function: A zero-argument function that returns the OpenAI API key. Defaults to looking up the "OPENAI_API_KEY" environment variable.
• organization_lookup::Function: A zero-argument function that returns the OpenAI organization ID to use. Defaults to the "OPENAI_ORGANIZATION" environment variable, if specified.

Variants

GenGPT3.jl also provides support for batched LLM calls with MultiGPT3GF and a "mixture-of-prompts" generative function called GPT3Mixture. MultiGPT3GF reduces the latency of making multiple API calls, and GPT3Mixture (which uses MultiGPT3GF under the hood) can be used to marginalize over uncertainty about the prompt.

Support for batched importance sampling of LLM generations is provided by GPT3ImportanceSampler, which can be configured to have separate model and proposal LLM generative functions.

Utilities

Utilities for converting between strings and tokens are also included as part of this package (using functionality provided by BytePairEncoding.jl and TextEncodeBase.jl):

julia> tokens = GenGPT3.tokenize("cl100k_base", "What is the tallest mountain on Mars?")
["What", " is", " the", " tallest", " mountain", " on", " Mars", "?"]

julia> ids = GenGPT3.encode("cl100k_base", tokens)
[3923, 374, 279, 82717, 16700, 389, 21725, 30]

julia> text = GenGPT3.id_detokenize("cl100k_base", ids)
"What is the tallest mountain on Mars?"

julia> ids = GenGPT3.id_tokenize("cl100k_base", text)
[3923, 374, 279, 82717, 16700, 389, 21725, 30]

julia> tokens = GenGPT3.decode("cl100k_base", ids)
["What", " is", " the", " tallest", " mountain", " on", " Mars", "?"]

Support for calling the OpenAI Embeddings API is also provided:

julia> embedder = GenGPT3.Embedder(model="text-embedding-ada-002");

julia> embedder("What is the tallest mountain on Mars?")
1536-element Vector{Float64}:
  0.009133313
 -0.0017141271
 -0.010894737
  ⋮
 -0.0018739601
 -0.013621684
 -0.037864104