Auto Generation

flaml.autogen is a subpackage for automating generation tasks. It uses flaml.tune to find good hyperparameter configurations under budget constraints.
Such optimization has several benefits:

Maximize the utility out of using expensive foundation models.
Reduce the inference cost by using cheaper models or configurations which achieve equal or better performance.

Choices to Optimize

The cost of using foundation models for text generation is typically measured in terms of the number of tokens in the input and output combined. From the perspective of an application builder using foundation models, the use case is to maximize the utility of the generated text under an inference budget constraint (e.g., measured by the average dollar cost needed to solve a coding problem). This can be achieved by optimizing the hyperparameters of the inference,
which can significantly affect both the utility and the cost of the generated text.

The tunable hyperparameters include:

model - this is a required input, specifying the model ID to use.
prompt - the input prompt to the model, which provides the context for the text generation task.
max_tokens - the maximum number of tokens (words or word pieces) to generate in the output.
temperature - a value between 0 and 1 that controls the randomness of the generated text. A higher temperature will result in more random and diverse text, while a lower temperature will result in more predictable text.
top_p - a value between 0 and 1 that controls the sampling probability mass for each token generation. A lower top_p value will make it more likely to generate text based on the most likely tokens, while a higher value will allow the model to explore a wider range of possible tokens.
n - the number of responses to generate for a given prompt. Generating multiple responses can provide more diverse and potentially more useful output, but it also increases the cost of the request.
stop - a list of strings that, when encountered in the generated text, will cause the generation to stop. This can be used to control the length or the validity of the output.
presence_penalty, frequency_penalty - values that control the relative importance of the presence and frequency of certain words or phrases in the generated text.
best_of - the number of responses to generate server-side when selecting the "best" (the one with the highest log probability per token) response for a given prompt.

The cost and utility of text generation are intertwined with the joint effect of these hyperparameters.
There are also complex interactions among subsets of the hyperparameters. For example,
the temperature and top_p are not recommended to be altered from their default values together because they both control the randomness of the generated text, and changing both at the same time can result in conflicting effects; n and best_of are rarely tuned together because if the application can process multiple outputs, filtering on the server side causes unnecessary information loss; both n and max_tokens will affect the total number of tokens generated, which in turn will affect the cost of the request.
These interactions and trade-offs make it difficult to manually determine the optimal hyperparameter settings for a given text generation task.

Tune Hyperparameters

The tuning can be performed with the following information:

Validation data.
Evaluation function.
Metric to optimize.
Search space.
Budgets: inference and optimization respectively.

Validation data

Collect a diverse set of instances. They can be stored in an iterable of dicts. For example, each instance dict can contain "problem" as a key and the description str of a math problem as the value; and "solution" as a key and the solution str as the value.

Evaluation function

The evaluation function should take a list of responses, and other keyword arguments corresponding to the keys in each validation data instance as input, and output a dict of metrics. For example,

def success_metrics(responses: List[str], problem: str, solution: str) -> Dict:
    # select a response from the list of responses
    # check whether the answer is correct
    return {"success": True or False}

flaml.autogen offers some example evaluation functions for common tasks such as code generation and math problem solving.

Metric to optimize

The metric to optimize is usually an aggregated metric over all the tuning data instances. For example, users can specify "success" as the metric and "max" as the optimization mode. By default, the aggregation function is taking the average. Users can provide a customized aggregation function if needed.

Search space

Users can specify the (optional) search range for each hyperparameter.

model. Either a constant str, or multiple choices specified by flaml.tune.choice.
prompt. Either a str or a list of strs, of the prompt templates.
Each prompt template will be formatted with each data instance. For example, the prompt template can be:
"{problem} Solve the problem carefully. Simplify your answer as much as possible. Put the final answer in \boxed{{}}."
And {problem} will be replaced by the "problem" field of each data instance.
max_tokens, n, best_of. They can be constants, or specified by flaml.tune.randint, flaml.tune.qrandint, flaml.tune.lograndint or flaml.qlograndint. By default, max_tokens is searched in [50, 1000); n is searched in [1, 100); and best_of is fixed to 1.
stop. It can be a str or a list of strs, or a list of lists of strs or None. Default is None.
temperature or top_p. One of them can be specified as a constant or by flaml.tune.uniform or flaml.tune.loguniform etc.
Please don't provide both. By default, each configuration will choose either a temperature or a top_p in [0, 1] uniformly.
presence_penalty, frequency_penalty. They can be constants or specified by flaml.tune.uniform etc. Not tuned by default.

Budgets

One can specify an inference budget and an optimization budget.
The inference budget refers to the average inference cost per data instance.
The optimization budget refers to the total budget allowed in the tuning process. Both are measured by dollars and follow the price per 1000 tokens.

Perform tuning

Now, you can use flaml.oai.Completion.tune for tuning. For example,

from flaml import oai

config, analysis = oai.Completion.tune(
    data=tune_data,
    metric="success",
    mode="max",
    eval_func=eval_func,
    inference_budget=0.05,
    optimization_budget=3,
    num_samples=-1,
)

num_samples is the number of configurations to sample. -1 means unlimited (until optimization budget is exhausted).
The returned config contains the optimized configuration and analysis contains an ExperimentAnalysis object for all the tried configurations and results.

Perform inference with the tuned config

One can use flaml.oai.Completion.create to performance inference. It materializes a prompt using a given context. For example,

response = oai.Completion.create(problme=problem, **config)
responses = oai.Completion.extract_text(response)
# Extract a list of str responses

flaml.oai.Completion is compatible with both openai.Completion and openai.ChatCompletion. So models such as "text-davinci-003", "gpt-3.5-turbo" and "gpt-4" can share a common API. When only tuning the chat-based models, flaml.oai.ChatCompletion can be used.

flaml.oai.Completion also offers some additional utilities including a test function to conveniently evaluate the configuration over test data, a cost function to calculate the cost of an API call, and caching and error handling. It also supports both OpenAI API and Azure OpenAI API.

Interested in trying it yourself? Please check the following notebook examples:

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