362 lines
11 KiB
Plaintext
362 lines
11 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "4a6ab9a2-28a2-445d-8512-a0dc8d1b54e9",
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"metadata": {},
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"source": [
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"# Code Generator\n",
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"\n",
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"The requirement: use a Frontier model to generate high performance C++ code from Python code\n",
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"\n",
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"- Try adding Gemini to the Closed Source mix\n",
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"- Try more open-source models such as CodeLlama and StarCoder, and see if you can get CodeGemma to work\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "e610bf56-a46e-4aff-8de1-ab49d62b1ad3",
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"metadata": {},
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"outputs": [],
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"source": [
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"# imports\n",
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"\n",
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"import os\n",
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"import io\n",
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"import sys\n",
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"from dotenv import load_dotenv\n",
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"from openai import OpenAI\n",
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"# import google.generativeai\n",
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"from google import genai\n",
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"from google.genai import types\n",
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"import anthropic\n",
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"import ollama\n",
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"from IPython.display import Markdown, display, update_display\n",
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"import gradio as gr"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "4f672e1c-87e9-4865-b760-370fa605e614",
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"metadata": {},
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"outputs": [],
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"source": [
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"# environment\n",
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"\n",
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"load_dotenv(override=True)\n",
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"os.environ['OPENAI_API_KEY'] = os.getenv('OPENAI_API_KEY', 'your-key-if-not-using-env')\n",
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"os.environ['ANTHROPIC_API_KEY'] = os.getenv('ANTHROPIC_API_KEY', 'your-key-if-not-using-env')\n",
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"os.environ['GOOGLE_API_KEY'] = os.getenv('GOOGLE_API_KEY', 'your-key-if-not-using-env')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "8aa149ed-9298-4d69-8fe2-8f5de0f667da",
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"metadata": {},
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"outputs": [],
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"source": [
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"# initialize\n",
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"\n",
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"openai = OpenAI()\n",
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"claude = anthropic.Anthropic()\n",
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"# google.generativeai.configure()\n",
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"client = genai.Client()\n",
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"\n",
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"\n",
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"OPENAI_MODEL = \"gpt-4o\"\n",
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"CLAUDE_MODEL = \"claude-sonnet-4-20250514\"\n",
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"GEMINI_MODEL = 'gemini-2.5-flash'\n",
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"LLAMA_MODEL = \"llama3.2\"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "6896636f-923e-4a2c-9d6c-fac07828a201",
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"metadata": {},
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"outputs": [],
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"source": [
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"system_message = \"You are an assistant that reimplements Python code in high performance C++ for an M1 Mac. \"\n",
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"system_message += \"Respond only with C++ code; use comments sparingly and do not provide any explanation other than occasional comments. \"\n",
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"system_message += \"The C++ response needs to produce an identical output in the fastest possible time. \"\n",
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"system_message += \"Do not include Markdown formatting (```), language tags (cpp), or extra text. \""
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "8e7b3546-57aa-4c29-bc5d-f211970d04eb",
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"metadata": {},
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"outputs": [],
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"source": [
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"def user_prompt_for(python):\n",
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" user_prompt = \"Rewrite this Python code in C++ with the fastest possible implementation that produces identical output in the least time. \"\n",
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" user_prompt += \"Respond only with C++ code; do not explain your work other than a few comments. \"\n",
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" user_prompt += \"\"\"Your response must not contain neither the leading \"```cpp\" nor the trailer: \"```\" strings! \"\"\"\n",
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" user_prompt += \"Pay attention to number types to ensure no int overflows. Remember to #include all necessary C++ packages such as iomanip.\\n\\n\"\n",
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" user_prompt += python\n",
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" return user_prompt"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "c6190659-f54c-4951-bef4-4960f8e51cc4",
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"metadata": {},
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"outputs": [],
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"source": [
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"def messages_for(python):\n",
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" return [\n",
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" {\"role\": \"system\", \"content\": system_message},\n",
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" {\"role\": \"user\", \"content\": user_prompt_for(python)}\n",
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" ]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "71e1ba8c-5b05-4726-a9f3-8d8c6257350b",
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"metadata": {},
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"outputs": [],
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"source": [
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"# write to a file called optimized.cpp\n",
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"\n",
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"def write_output(cpp):\n",
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" with open(\"optimized.cpp\", \"w\") as f:\n",
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" f.write(cpp)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "a1cbb778-fa57-43de-b04b-ed523f396c38",
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"metadata": {},
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"outputs": [],
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"source": [
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"pi = \"\"\"\n",
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"import time\n",
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"\n",
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"def calculate(iterations, param1, param2):\n",
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" result = 1.0\n",
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" for i in range(1, iterations+1):\n",
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" j = i * param1 - param2\n",
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" result -= (1/j)\n",
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" j = i * param1 + param2\n",
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" result += (1/j)\n",
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" return result\n",
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"\n",
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"start_time = time.time()\n",
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"result = calculate(100_000_000, 4, 1) * 4\n",
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"end_time = time.time()\n",
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"\n",
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"print(f\"Result: {result:.12f}\")\n",
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"print(f\"Execution Time: {(end_time - start_time):.6f} seconds\")\n",
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"\"\"\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "c3b497b3-f569-420e-b92e-fb0f49957ce0",
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"metadata": {},
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"outputs": [],
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"source": [
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"python_hard = \"\"\"# Be careful to support large number sizes\n",
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"\n",
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"def lcg(seed, a=1664525, c=1013904223, m=2**32):\n",
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" value = seed\n",
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" while True:\n",
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" value = (a * value + c) % m\n",
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" yield value\n",
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"\n",
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"def max_subarray_sum(n, seed, min_val, max_val):\n",
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" lcg_gen = lcg(seed)\n",
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" random_numbers = [next(lcg_gen) % (max_val - min_val + 1) + min_val for _ in range(n)]\n",
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" max_sum = float('-inf')\n",
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" for i in range(n):\n",
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" current_sum = 0\n",
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" for j in range(i, n):\n",
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" current_sum += random_numbers[j]\n",
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" if current_sum > max_sum:\n",
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" max_sum = current_sum\n",
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" return max_sum\n",
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"\n",
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"def total_max_subarray_sum(n, initial_seed, min_val, max_val):\n",
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" total_sum = 0\n",
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" lcg_gen = lcg(initial_seed)\n",
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" for _ in range(20):\n",
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" seed = next(lcg_gen)\n",
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" total_sum += max_subarray_sum(n, seed, min_val, max_val)\n",
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" return total_sum\n",
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"\n",
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"# Parameters\n",
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"n = 10000 # Number of random numbers\n",
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"initial_seed = 42 # Initial seed for the LCG\n",
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"min_val = -10 # Minimum value of random numbers\n",
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"max_val = 10 # Maximum value of random numbers\n",
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"\n",
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"# Timing the function\n",
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"import time\n",
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"start_time = time.time()\n",
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"result = total_max_subarray_sum(n, initial_seed, min_val, max_val)\n",
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"end_time = time.time()\n",
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"\n",
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"print(\"Total Maximum Subarray Sum (20 runs):\", result)\n",
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"print(\"Execution Time: {:.6f} seconds\".format(end_time - start_time))\n",
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"\"\"\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "0be9f47d-5213-4700-b0e2-d444c7c738c0",
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"metadata": {},
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"outputs": [],
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"source": [
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"def stream_gpt(python):\n",
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" stream = openai.chat.completions.create(model=OPENAI_MODEL, messages=messages_for(python), stream=True)\n",
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" reply = \"\"\n",
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" for chunk in stream:\n",
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" fragment = chunk.choices[0].delta.content or \"\"\n",
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" reply += fragment\n",
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" yield reply"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "8669f56b-8314-4582-a167-78842caea131",
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"metadata": {},
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"outputs": [],
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"source": [
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"def stream_claude(python):\n",
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" result = claude.messages.stream(\n",
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" model=CLAUDE_MODEL,\n",
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" max_tokens=2000,\n",
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" system=system_message,\n",
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" messages=[{\"role\": \"user\", \"content\": user_prompt_for(python)}],\n",
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" )\n",
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" reply = \"\"\n",
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" with result as stream:\n",
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" for text in stream.text_stream:\n",
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" reply += text\n",
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" yield reply"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "97205162",
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"metadata": {},
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"outputs": [],
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"source": [
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"def stream_gemini(python):\n",
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" response = client.models.generate_content_stream(\n",
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" model=GEMINI_MODEL,\n",
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" config=types.GenerateContentConfig(\n",
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" system_instruction=system_message),\n",
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" contents=user_prompt_for(python)\n",
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" )\n",
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"\n",
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" reply = \"\"\n",
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" for chunk in response:\n",
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" fragment = chunk.text or \"\"\n",
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" reply += fragment\n",
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" yield reply\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "4f94b13e",
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"metadata": {},
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"outputs": [],
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"source": [
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"def stream_llama_local(python):\n",
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" stream = ollama.chat(\n",
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" model='llama3.2',\n",
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" messages=messages_for(python),\n",
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" stream=True,\n",
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" )\n",
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"\n",
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" reply = \"\"\n",
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" # Iterate through the streamed chunks and print the content\n",
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" for chunk in stream:\n",
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" #print(chunk['message']['content'], end='', flush=True)\n",
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" if 'content' in chunk['message']:\n",
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" fragment = chunk['message']['content']\n",
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" reply += fragment\n",
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" yield reply\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "2f1ae8f5-16c8-40a0-aa18-63b617df078d",
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"metadata": {},
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"outputs": [],
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"source": [
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"def optimize(python, model):\n",
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" if model==\"GPT\":\n",
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" result = stream_gpt(python)\n",
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" elif model==\"Claude\":\n",
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" result = stream_claude(python)\n",
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" elif model==\"Gemini\":\n",
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" result = stream_gemini(python)\n",
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" elif model==\"Llama\":\n",
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" result = stream_llama_local(python)\n",
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"\n",
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" else:\n",
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" raise ValueError(\"Unknown model\")\n",
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" for stream_so_far in result:\n",
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" yield stream_so_far"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "f1ddb38e-6b0a-4c37-baa4-ace0b7de887a",
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"metadata": {},
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"outputs": [],
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"source": [
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"with gr.Blocks() as ui:\n",
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" with gr.Row():\n",
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" python = gr.Textbox(label=\"Python code:\", lines=10, value=python_hard)\n",
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" cpp = gr.Textbox(label=\"C++ code, copy it here: https://www.programiz.com/cpp-programming/online-compiler/\", lines=10)\n",
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" with gr.Row():\n",
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" model = gr.Dropdown([\"GPT\", \"Claude\", \"Gemini\", \"Llama\"], label=\"Select model\", value=\"GPT\")\n",
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" convert = gr.Button(\"Convert code\")\n",
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"\n",
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" convert.click(optimize, inputs=[python, model], outputs=[cpp])\n",
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"\n",
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"ui.launch(inbrowser=True)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.13"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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