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import cv2
import numpy as np
import IPython
import os
import openai
import pandas as pd
import json
import subprocess
# create dataset by loading the python file
def format_prompt(task_name):
instruction_text = open('misc/finetune_instructions_prompt.txt').read()
instruction_text = instruction_text.replace("TASK_NAME_TEMPLATE", task_name)
prompt_text = "\n Instructions: " + instruction_text + "\n\n###\n\n"
return prompt_text
def format_completion(task_name, descriptions, code):
completion_text = f" \nDescriptions: \n ```{task_name}: {descriptions} \n\n###\n\n"
completion_text += "Implementation: \n ```python\n" + code + "<|endoftext|>"
return completion_text
# test if using the finetuned model can generate better task coed than the base model
# https://platform.openai.com/docs/guides/fine-tuning
data_path = 'prompts/data'
def load_offline_memory():
"""get the current task descriptions, assets, and code"""
base_task_path = os.path.join(data_path, "base_tasks.json")
base_asset_path = os.path.join(data_path, "base_assets.json")
base_task_code_path = os.path.join(data_path, "base_task_codes.json")
base_tasks = json.load(open(base_task_path))
base_assets = json.load(open(base_asset_path))
base_task_codes = json.load(open(base_task_code_path))
generated_task_path = os.path.join(data_path, "generated_tasks.json")
generated_asset_path = os.path.join(data_path, "generated_assets.json")
generated_task_code_path = os.path.join(data_path, "generated_task_codes.json")
# print("original base task num:", len(base_tasks))
base_tasks.update(json.load(open(generated_task_path)))
# base_assets.update(json.load(open(generated_asset_path)))
for task in json.load(open(generated_task_code_path)):
if task not in base_task_codes:
base_task_codes.append(task)
# print("current base task num:", len(base_tasks))
return base_tasks, base_assets, base_task_codes
code_buffer = {}
base_tasks, base_assets, base_task_codes = load_offline_memory()
TOTAL_DATASET_TOKENS = 0
added_tasks = []
df = pd.DataFrame()
for task_file in base_task_codes:
## TODO(lirui): consider adding more structure here.
task_name = task_file[:-3].replace("_", "-")
if task_name in added_tasks:
continue
if task_name not in base_tasks:
print(f"{task_name} missing")
continue
added_tasks.append(task_name)
task_description = base_tasks[task_name]
if os.path.exists("cliport/tasks/" + task_file):
task_code = open("cliport/tasks/" + task_file).read()
# the generated cliport task path
elif os.path.exists("cliport/generated_tasks/" + task_file):
task_code = open("cliport/generated_tasks/" + task_file).read()
prompt = format_prompt(task_name)
completion = format_completion(task_name, task_description, task_code)
# rough estimates
TOTAL_DATASET_TOKENS += len(prompt) / 4
TOTAL_DATASET_TOKENS += len(completion) / 4
new_row = { 'prompt': prompt,
'completion': completion}
new_row = pd.DataFrame([new_row])
df = pd.concat([df, new_row], axis=0, ignore_index=True)
df.to_csv("misc/finetune_data.csv", index=False)
print("======================================")
print("estimate number of tokens:", TOTAL_DATASET_TOKENS)
print("estimate price for davinci:", TOTAL_DATASET_TOKENS / 1000 * 0.03)
print("total number of instructions:", len(df))
print("======================================")
# actual finetuning
## prepared_data.csv --> prepared_data_prepared.json
subprocess.run('openai tools fine_tunes.prepare_data --file misc/finetune_data.csv --quiet'.split())
print("now you can run \n openai api fine_tunes.create --training_file output/finetune_data_prepared.jsonl --model davinci --suffix 'GenSim'")
# Model Training Usage
# Ada $0.0004 / 1K tokens $0.0016 / 1K tokens
# Curie $0.0030 / 1K tokens $0.0120 / 1K tokens
# Davinci $0.0300 / 1K tokens $0.1200 / 1K tokens
# ## Start fine-tuning
# openai api fine_tunes.create --training_file output/finetune_data_prepared.jsonl --model davinci --suffix "GenSim"
# subprocess.run('openai api fine_tunes.create --training_file output/finetune_data_prepared.jsonl --model davinci --suffix "GenSim"'.split())
# Tracking Finetune Status
# openai api fine_tunes.follow -i
# openai api fine_tunes.get -i
# openai wandb sync |