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import argparse
import json
import multiprocessing
import os
import os.path as osp
import shutil
import sys
import time
from datetime import datetime
import openai
import torch
from aider.coders import Coder
from aider.io import InputOutput
from aider.models import Model
from ai_scientist.generate_ideas import check_idea_novelty, generate_ideas
from ai_scientist.llm import allchoices
from ai_scientist.perform_experiments import perform_experiments
from ai_scientist.perform_review import load_paper, perform_improvement, perform_review
from ai_scientist.perform_writeup import generate_latex, perform_writeup
NUM_REFLECTIONS = 3
def print_time():
print(datetime.now().strftime("%Y-%m-%d %H:%M:%S"))
def parse_arguments():
parser = argparse.ArgumentParser(description="Run AI scientist experiments")
parser.add_argument(
"--skip-idea-generation",
action="store_true",
help="Skip idea generation and load existing ideas",
)
parser.add_argument(
"--skip-novelty-check",
action="store_true",
help="Skip novelty check and use existing ideas",
)
# add type of experiment (nanoGPT, Boston, etc.)
parser.add_argument(
"--experiment",
type=str,
default="nanoGPT_lite",
help="Experiment to run AI Scientist on.",
)
parser.add_argument(
"--model",
type=str,
default="Qwen/Qwen2.5-72B-Instruct",
choices=allchoices,
help="Model to use for AI Scientist.",
)
parser.add_argument(
"--writeup",
type=str,
default="latex",
choices=["latex"],
help="What format to use for writeup",
)
parser.add_argument(
"--parallel",
type=int,
default=0,
help="Number of parallel processes to run. 0 for sequential execution.",
)
parser.add_argument(
"--improvement",
action="store_true",
help="Improve based on reviews.",
)
parser.add_argument(
"--gpus",
type=str,
default=None,
help="Comma-separated list of GPU IDs to use (e.g., '0,1,2'). If not specified, all available GPUs will be used.",
)
parser.add_argument(
"--num-ideas",
type=int,
default=2,
help="Number of ideas to generate",
)
return parser.parse_args()
def get_available_gpus(gpu_ids=None):
if gpu_ids is not None:
return [int(gpu_id) for gpu_id in gpu_ids.split(",")]
return list(range(torch.cuda.device_count()))
def worker(
queue,
base_dir,
results_dir,
model,
client,
client_model,
writeup,
improvement,
gpu_id,
):
os.environ["CUDA_VISIBLE_DEVICES"] = str(gpu_id)
print(f"Worker {gpu_id} started.")
while True:
idea = queue.get()
if idea is None:
break
success = do_idea(
base_dir,
results_dir,
idea,
model,
client,
client_model,
writeup,
improvement,
log_file=True,
)
print(f"Completed idea: {idea['Name']}, Success: {success}")
print(f"Worker {gpu_id} finished.")
def do_idea(
base_dir,
results_dir,
idea,
model,
client,
client_model,
writeup,
improvement,
log_file=False,
):
## CREATE PROJECT FOLDER
timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
idea_name = f"{timestamp}_{idea['Name']}"
folder_name = osp.join(results_dir, idea_name)
assert not osp.exists(folder_name), f"Folder {folder_name} already exists."
destination_dir = folder_name
shutil.copytree(base_dir, destination_dir, dirs_exist_ok=True)
with open(osp.join(base_dir, "run_0", "final_info.json"), "r") as f:
baseline_results = json.load(f)
baseline_results = {k: v["means"] for k, v in baseline_results.items()}
exp_file = osp.join(folder_name, "experiment.py")
vis_file = osp.join(folder_name, "plot.py")
notes = osp.join(folder_name, "notes.txt")
with open(notes, "w") as f:
f.write(f"# Title: {idea['Title']}\n")
f.write(f"# Experiment description: {idea['Experiment']}\n")
f.write(f"## Run 0: Baseline\n")
f.write(f"Results: {baseline_results}\n")
f.write(f"Description: Baseline results.\n")
if log_file:
original_stdout = sys.stdout
original_stderr = sys.stderr
log_path = osp.join(folder_name, "log.txt")
log = open(log_path, "a")
sys.stdout = log
sys.stderr = log
try:
print_time()
print(f"*Starting idea: {idea_name}*")
## PERFORM EXPERIMENTS
fnames = [exp_file, vis_file, notes]
io = InputOutput(
yes=True, chat_history_file=f"{folder_name}/{idea_name}_aider.txt"
)
if model == "hybrid":
main_model = Model("claude-3-5-sonnet-20240620")
elif model == "deepseek-coder-v2-0724":
main_model = Model("deepseek-ai/DeepSeek-V2.5")
elif model == "llama3.1-405b":
main_model = Model("openrouter/meta-llama/llama-3.1-405b-instruct")
# ----------------------------------------------------
elif args.model == "Qwen/Qwen2.5-72B-Instruct":
print("aider model chosen")
# main_model = Model("fireworks_ai/accounts/fireworks/models/qwen2-72b-instruct")
# main_model = Model("openai/Qwen2.5-72B-Instruct")
main_model = Model("friendli/Qwen2.5-72B-Instruct")
elif model == "hyperbolic/meta-llama/Meta-Llama-3.1-70B-Instruct":
main_model = Model("hyperbolic/meta-llama/Meta-Llama-3.1-70B-Instruct")
# ----------------------------------------------------
else:
main_model = Model(model)
coder = Coder.create(
main_model=main_model,
fnames=fnames,
io=io,
stream=False,
use_git=False,
edit_format="diff",
)
print_time()
print(f"*Starting Experiments*")
try:
success = perform_experiments(idea, folder_name, coder, baseline_results)
except Exception as e:
print(f"Error during experiments: {e}")
print(f"Experiments failed for idea {idea_name}")
return False
if not success:
print(f"Experiments failed for idea {idea_name}")
return False
print_time()
print(f"*Starting Writeup*")
## PERFORM WRITEUP
if writeup == "latex":
writeup_file = osp.join(folder_name, "latex", "template.tex")
fnames = [exp_file, writeup_file, notes]
if model == "deepseek-coder-v2-0724":
main_model = Model("deepseek-ai/DeepSeek-V2.5")
elif model == "llama3.1-405b":
main_model = Model("openrouter/meta-llama/llama-3.1-405b-instruct")
# ----------------------------------------------------
elif args.model == "Qwen/Qwen2.5-72B-Instruct":
print("aider model chosen")
# main_model = Model("fireworks_ai/accounts/fireworks/models/qwen2-72b-instruct")
main_model = Model("openai/Qwen/Qwen2.5-72B-Instruct")
elif model == "hyperbolic/meta-llama/Meta-Llama-3.1-70B-Instruct":
main_model = Model("hyperbolic/meta-llama/Meta-Llama-3.1-70B-Instruct")
# ----------------------------------------------------
else:
main_model = Model(model)
coder = Coder.create(
main_model=main_model,
fnames=fnames,
io=io,
stream=False,
use_git=False,
edit_format="diff",
)
try:
perform_writeup(idea, folder_name, coder, client, client_model)
except Exception as e:
print(f"Failed to perform writeup: {e}")
return False
print("Done writeup")
else:
raise ValueError(f"Writeup format {writeup} not supported.")
print_time()
print(f"*Starting Review*")
## REVIEW PAPER
if writeup == "latex":
try:
paper_text = load_paper(f"{folder_name}/{idea['Name']}.pdf")
if model == "gpt-4o-2024-05-13":
main_model = Model(model)
review = perform_review(
paper_text,
model=main_model,
client=openai.OpenAI(),
num_reflections=5,
num_fs_examples=1,
num_reviews_ensemble=5,
temperature=0.1,
)
elif model.startswith("ollama"):
# Use Ollama API for review generation
review = perform_review(
paper_text,
model=model.split("/")[-1],
client=openai.OpenAI(
api_key="ollama", base_url="http://localhost:11434/v1"
),
num_reflections=5,
num_fs_examples=1,
num_reviews_ensemble=5,
temperature=0.1,
)
# Store the review in separate review.txt file
with open(osp.join(folder_name, "review.txt"), "w") as f:
f.write(json.dumps(review, indent=4))
except Exception as e:
print(f"Failed to perform review: {e}")
return False
## IMPROVE WRITEUP
if writeup == "latex" and improvement:
print_time()
print(f"*Starting Improvement*")
try:
perform_improvement(review, coder)
generate_latex(
coder, folder_name, f"{folder_name}/{idea['Name']}_improved.pdf"
)
paper_text = load_paper(f"{folder_name}/{idea['Name']}_improved.pdf")
if model == "gpt-4o-2024-05-13":
main_model = Model(model)
review = perform_review(
paper_text,
model=main_model,
client=openai.OpenAI(),
num_reflections=5,
num_fs_examples=1,
num_reviews_ensemble=5,
temperature=0.1,
)
elif model.startswith("ollama"):
# Use Ollama API for review generation
review = perform_review(
paper_text,
model=model.split("/")[-1],
client=openai.OpenAI(
api_key="ollama", base_url="http://localhost:11434/v1"
),
num_reflections=5,
num_fs_examples=1,
num_reviews_ensemble=5,
temperature=0.1,
)
# Store the review in separate review.txt file
with open(osp.join(folder_name, "review_improved.txt"), "w") as f:
f.write(json.dumps(review))
except Exception as e:
print(f"Failed to perform improvement: {e}")
return False
return True
except Exception as e:
print(f"Failed to evaluate idea {idea_name}: {str(e)}")
return False
finally:
print("FINISHED IDEA")
if log_file:
sys.stdout = original_stdout
sys.stderr = original_stderr
log.close()
if __name__ == "__main__":
import traceback
try:
args = parse_arguments()
# Check available GPUs and adjust parallel processes if necessary
available_gpus = get_available_gpus(args.gpus)
if args.parallel > len(available_gpus):
print(
f"Warning: Requested {args.parallel} parallel processes, but only {len(available_gpus)} GPUs available. Adjusting to {len(available_gpus)}."
)
args.parallel = len(available_gpus)
print(f"Using GPUs: {available_gpus}")
# Create client
if args.model == "claude-3-5-sonnet-20240620":
import anthropic
print(f"Using Anthropic API with model {args.model}.")
client_model = "claude-3-5-sonnet-20240620"
client = anthropic.Anthropic()
elif args.model.startswith("bedrock") and "claude" in args.model:
import anthropic
# Expects: bedrock/<MODEL_ID>
client_model = args.model.split("/")[-1]
print(f"Using Amazon Bedrock with model {client_model}.")
client = anthropic.AnthropicBedrock(
aws_access_key=os.getenv("AWS_ACCESS_KEY_ID"),
aws_secret_key=os.getenv("AWS_SECRET_ACCESS_KEY"),
aws_region=os.getenv("AWS_REGION_NAME"),
)
elif args.model.startswith("vertex_ai") and "claude" in args.model:
import anthropic
# Expects: vertex_ai/<MODEL_ID>
client_model = args.model.split("/")[-1]
print(f"Using Vertex AI with model {client_model}.")
client = anthropic.AnthropicVertex()
elif args.model == "gpt-4o-2024-05-13":
import openai
print(f"Using OpenAI API with model {args.model}.")
client_model = "gpt-4o-2024-05-13"
client = openai.OpenAI()
# ----------------------------------------------------
elif args.model == "Qwen/Qwen2.5-72B-Instruct":
# elif args.model.startswith("hyperbolic"):
print(f"Welcome to the PARADISE of debug <launch_scientist.py> {args.model}.")
import openai
import os
# client_model = args.model[11:]
client_model = args.model
client = openai.OpenAI(
api_key=os.environ["OPENAI_API_KEY"], base_url="https://api.hyperbolic.xyz/v1"
)
# ----------------------------------------------------
elif args.model.startswith("ollama"):
import openai
print(f"Using Ollama with {args.model}.")
client_model = args.model.split("/")[-1]
client = openai.OpenAI(api_key="ollama", base_url="http://localhost:11434/v1")
else:
raise ValueError(f"Model {args.model} not supported.")
base_dir = osp.join("templates", args.experiment)
results_dir = osp.join("results", args.experiment)
ideas = generate_ideas(
base_dir,
client=client,
model=client_model,
skip_generation=args.skip_idea_generation,
max_num_generations=args.num_ideas,
num_reflections=NUM_REFLECTIONS,
)
ideas = check_idea_novelty(
ideas,
base_dir=base_dir,
client=client,
model=client_model,
)
with open(osp.join(base_dir, "ideas.json"), "w") as f:
json.dump(ideas, f, indent=4)
novel_ideas = [idea for idea in ideas if idea["novel"]]
# novel_ideas = list(reversed(novel_ideas))
if args.parallel > 0:
print(f"Running {args.parallel} parallel processes")
queue = multiprocessing.Queue()
for idea in novel_ideas:
queue.put(idea)
processes = []
for i in range(args.parallel):
gpu_id = available_gpus[i % len(available_gpus)]
p = multiprocessing.Process(
target=worker,
args=(
queue,
base_dir,
results_dir,
args.model,
client,
client_model,
args.writeup,
args.improvement,
gpu_id,
),
)
p.start()
time.sleep(150)
processes.append(p)
# Signal workers to exit
for _ in range(args.parallel):
queue.put(None)
for p in processes:
p.join()
print("All parallel processes completed.")
else:
for idea in novel_ideas:
print(f"Processing idea: {idea['Name']}")
try:
success = do_idea(
base_dir,
results_dir,
idea,
args.model,
client,
client_model,
args.writeup,
args.improvement,
)
print(f"Completed idea: {idea['Name']}, Success: {success}")
except Exception as e:
print(f"Failed to evaluate idea {idea['Name']}: {str(e)}")
print("All ideas evaluated.")
except Exception as e:
print("error aya re baba")
traceback.print_exc() |