m-promptune 0.2.8

DSPy acceleration

M PROMPTUNE

Accelerates DSPy

Uses batch Expected Improvement to suggest multiple prompts to run in parallel. The mipro_optimizer_v2.py can parallelize but only at the the data evaluation level. The qEI sampler allows parallelization at the prompt level. Running prompts is definitely an IO bound process and you should definitely be running more threads than cores.

Semantic Based

Tree Parzen Estimator does't really handle covariance between categorical variables. Instead of using Tree Parzen Estimator with a categorical distribution, qEI uses a Gaussian Process fit on embeddings of the possible combinations. This applies Bayesian Optimization at the semantic level in parallel.

Forked Repository

Until my changes are merged with DSPy, I created a forked repository that allows a sampler to be passed when initializing mipro_sampler_v2.

It is backwards compatible with DSPy and the same in every other way.

git git+https://github.com/sign-of-fourier/dspy.git      

Install this sampler that uses batch Expected Improvment.

pip install m-promptune

qEI.Sampler(instruction_candidates, demo_candidates, max_space_size, n_batches, batch_size, min_cold_start)

• instruction candidates: generated internally by the MIMPROv2 sampler.
• demonstration_candidates: also generated internally.
• max_space_size: when sampling, the maximum number of points to consider.
• n_batches: when batching, the number of batches. In this context, a batch is a group of suggestions.
• batch_size: number in each batch
• min_cold_start: run with random selections until you get to this point

This example from DSPy optimizes instruction, few shot combinations.

You will need tokens from Open AI and RapidAPI.

import os
import dspy
from mpromptune import qEI
from dspy.datasets.gsm8k import GSM8K, gsm8k_metric
from dspy.teleprompt import MIPROv2

os.environ['OPEN_AI_KEY']='Your token here'
os.environ['X_RAPIDAPI_KEY']='Your token here'

Using example data (Grade school math 8K). n_jobs controls parallelization at the prompt level.

gsm8k = GSM8K()
optimized_program = teleprompter.compile(
    dspy.ChainOfThought("question -> answer"),
    trainset=gsm8k.train,
    n_jobs=2,
    sampler=qEI.qEISampler()
)

Specify the number of threads for parallelizing at the data evaluation level.

lm = dspy.LM('openai/gpt-4o-mini', api_key=os.environ['OPEN_AI_KEY'])
dspy.configure(lm=lm)
teleprompter = MIPROv2(
    metric=gsm8k_metric,
    auto="light",
    num_threads=2
)

Contact me for questions [e-mail]