{ "cells": [ { "cell_type": "markdown", "id": "4a6ab9a2-28a2-445d-8512-a0dc8d1b54e9", "metadata": {}, "source": [ "# Code Generator\n", "\n", "The requirement: use an Open Source model to generate high performance C++ code from Python code\n", "\n", "To replicate this, you'll need to set up a HuggingFace endpoint as I do in the video. It's simple to do, and it's quite satisfying to see the results!\n", "\n", "It's also an important part of your learning; this is the first example of deploying an open source model to be behind an API. We'll return to this in Week 8, but this should plant a seed in your mind for what's involved in moving open source models into production." ] }, { "cell_type": "code", "execution_count": null, "id": "e610bf56-a46e-4aff-8de1-ab49d62b1ad3", "metadata": {}, "outputs": [], "source": [ "# imports\n", "\n", "import os\n", "import io\n", "import sys\n", "import json\n", "import requests\n", "from dotenv import load_dotenv\n", "from openai import OpenAI\n", "import google.generativeai\n", "import anthropic\n", "from IPython.display import Markdown, display, update_display\n", "import gradio as gr\n", "import subprocess" ] }, { "cell_type": "code", "execution_count": null, "id": "4f672e1c-87e9-4865-b760-370fa605e614", "metadata": {}, "outputs": [], "source": [ "# environment\n", "\n", "load_dotenv()\n", "os.environ['OPENAI_API_KEY'] = os.getenv('OPENAI_API_KEY', 'your-key-if-not-using-env')\n", "os.environ['ANTHROPIC_API_KEY'] = os.getenv('ANTHROPIC_API_KEY', 'your-key-if-not-using-env')\n", "os.environ['HF_TOKEN'] = os.getenv('HF_TOKEN', 'your-key-if-not-using-env')" ] }, { "cell_type": "code", "execution_count": null, "id": "8aa149ed-9298-4d69-8fe2-8f5de0f667da", "metadata": {}, "outputs": [], "source": [ "# initialize\n", "\n", "openai = OpenAI()\n", "claude = anthropic.Anthropic()\n", "OPENAI_MODEL = \"gpt-4o\"\n", "CLAUDE_MODEL = \"claude-3-5-sonnet-20240620\"" ] }, { "cell_type": "code", "execution_count": null, "id": "6896636f-923e-4a2c-9d6c-fac07828a201", "metadata": {}, "outputs": [], "source": [ "system_message = \"You are an assistant that reimplements Python code in high performance C++ for an M1 Mac. \"\n", "system_message += \"Respond only with C++ code; use comments sparingly and do not provide any explanation other than occasional comments. \"\n", "system_message += \"The C++ response needs to produce an identical output in the fastest possible time. Keep implementations of random number generators identical so that results match exactly.\"" ] }, { "cell_type": "code", "execution_count": null, "id": "8e7b3546-57aa-4c29-bc5d-f211970d04eb", "metadata": {}, "outputs": [], "source": [ "def user_prompt_for(python):\n", " user_prompt = \"Rewrite this Python code in C++ with the fastest possible implementation that produces identical output in the least time. \"\n", " user_prompt += \"Respond only with C++ code; do not explain your work other than a few comments. \"\n", " 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", " user_prompt += python\n", " return user_prompt" ] }, { "cell_type": "code", "execution_count": null, "id": "c6190659-f54c-4951-bef4-4960f8e51cc4", "metadata": {}, "outputs": [], "source": [ "def messages_for(python):\n", " return [\n", " {\"role\": \"system\", \"content\": system_message},\n", " {\"role\": \"user\", \"content\": user_prompt_for(python)}\n", " ]" ] }, { "cell_type": "code", "execution_count": null, "id": "71e1ba8c-5b05-4726-a9f3-8d8c6257350b", "metadata": {}, "outputs": [], "source": [ "# write to a file called optimized.cpp\n", "\n", "def write_output(cpp):\n", " code = cpp.replace(\"```cpp\",\"\").replace(\"```\",\"\")\n", " with open(\"optimized.cpp\", \"w\") as f:\n", " f.write(code)" ] }, { "cell_type": "code", "execution_count": null, "id": "e7d2fea8-74c6-4421-8f1e-0e76d5b201b9", "metadata": {}, "outputs": [], "source": [ "def optimize_gpt(python): \n", " stream = openai.chat.completions.create(model=OPENAI_MODEL, messages=messages_for(python), stream=True)\n", " reply = \"\"\n", " for chunk in stream:\n", " fragment = chunk.choices[0].delta.content or \"\"\n", " reply += fragment\n", " print(fragment, end='', flush=True)\n", " write_output(reply)" ] }, { "cell_type": "code", "execution_count": null, "id": "7cd84ad8-d55c-4fe0-9eeb-1895c95c4a9d", "metadata": {}, "outputs": [], "source": [ "def optimize_claude(python):\n", " result = claude.messages.stream(\n", " model=CLAUDE_MODEL,\n", " max_tokens=2000,\n", " system=system_message,\n", " messages=[{\"role\": \"user\", \"content\": user_prompt_for(python)}],\n", " )\n", " reply = \"\"\n", " with result as stream:\n", " for text in stream.text_stream:\n", " reply += text\n", " print(text, end=\"\", flush=True)\n", " write_output(reply)" ] }, { "cell_type": "code", "execution_count": null, "id": "a1cbb778-fa57-43de-b04b-ed523f396c38", "metadata": {}, "outputs": [], "source": [ "pi = \"\"\"\n", "import time\n", "\n", "def calculate(iterations, param1, param2):\n", " result = 1.0\n", " for i in range(1, iterations+1):\n", " j = i * param1 - param2\n", " result -= (1/j)\n", " j = i * param1 + param2\n", " result += (1/j)\n", " return result\n", "\n", "start_time = time.time()\n", "result = calculate(100_000_000, 4, 1) * 4\n", "end_time = time.time()\n", "\n", "print(f\"Result: {result:.12f}\")\n", "print(f\"Execution Time: {(end_time - start_time):.6f} seconds\")\n", "\"\"\"" ] }, { "cell_type": "code", "execution_count": null, "id": "7fe1cd4b-d2c5-4303-afed-2115a3fef200", "metadata": {}, "outputs": [], "source": [ "exec(pi)" ] }, { "cell_type": "code", "execution_count": null, "id": "105db6f9-343c-491d-8e44-3a5328b81719", "metadata": {}, "outputs": [], "source": [ "optimize_gpt(pi)" ] }, { "cell_type": "code", "execution_count": null, "id": "bf26ee95-0c77-491d-9a91-579a1e96a8a3", "metadata": {}, "outputs": [], "source": [ "exec(pi)" ] }, { "cell_type": "code", "execution_count": null, "id": "4194e40c-04ab-4940-9d64-b4ad37c5bb40", "metadata": {}, "outputs": [], "source": [ "!clang++ -O3 -std=c++17 -march=armv8.3-a -o optimized optimized.cpp\n", "!./optimized" ] }, { "cell_type": "code", "execution_count": null, "id": "983a11fe-e24d-4c65-8269-9802c5ef3ae6", "metadata": {}, "outputs": [], "source": [ "optimize_claude(pi)" ] }, { "cell_type": "code", "execution_count": null, "id": "d5a766f9-3d23-4bb4-a1d4-88ec44b61ddf", "metadata": {}, "outputs": [], "source": [ "!clang++ -O3 -std=c++17 -march=armv8.3-a -o optimized optimized.cpp\n", "!./optimized" ] }, { "cell_type": "code", "execution_count": null, "id": "c3b497b3-f569-420e-b92e-fb0f49957ce0", "metadata": {}, "outputs": [], "source": [ "python_hard = \"\"\"\n", "def lcg(seed, a=1664525, c=1013904223, m=2**32):\n", " value = seed\n", " while True:\n", " value = (a * value + c) % m\n", " yield value\n", " \n", "def max_subarray_sum(n, seed, min_val, max_val):\n", " lcg_gen = lcg(seed)\n", " random_numbers = [next(lcg_gen) % (max_val - min_val + 1) + min_val for _ in range(n)]\n", " max_sum = float('-inf')\n", " for i in range(n):\n", " current_sum = 0\n", " for j in range(i, n):\n", " current_sum += random_numbers[j]\n", " if current_sum > max_sum:\n", " max_sum = current_sum\n", " return max_sum\n", "\n", "def total_max_subarray_sum(n, initial_seed, min_val, max_val):\n", " total_sum = 0\n", " lcg_gen = lcg(initial_seed)\n", " for _ in range(20):\n", " seed = next(lcg_gen)\n", " total_sum += max_subarray_sum(n, seed, min_val, max_val)\n", " return total_sum\n", "\n", "# Parameters\n", "n = 10000 # Number of random numbers\n", "initial_seed = 42 # Initial seed for the LCG\n", "min_val = -10 # Minimum value of random numbers\n", "max_val = 10 # Maximum value of random numbers\n", "\n", "# Timing the function\n", "import time\n", "start_time = time.time()\n", "result = total_max_subarray_sum(n, initial_seed, min_val, max_val)\n", "end_time = time.time()\n", "\n", "print(\"Total Maximum Subarray Sum (20 runs):\", result)\n", "print(\"Execution Time: {:.6f} seconds\".format(end_time - start_time))\n", "\"\"\"" ] }, { "cell_type": "code", "execution_count": null, "id": "dab5e4bc-276c-4555-bd4c-12c699d5e899", "metadata": {}, "outputs": [], "source": [ "exec(python_hard)" ] }, { "cell_type": "code", "execution_count": null, "id": "e8d24ed5-2c15-4f55-80e7-13a3952b3cb8", "metadata": {}, "outputs": [], "source": [ "optimize_gpt(python_hard)" ] }, { "cell_type": "code", "execution_count": null, "id": "e0b3d073-88a2-40b2-831c-6f0c345c256f", "metadata": {}, "outputs": [], "source": [ "!clang++ -O3 -std=c++17 -march=armv8.3-a -o optimized optimized.cpp\n", "!./optimized" ] }, { "cell_type": "code", "execution_count": null, "id": "e9305446-1d0c-4b51-866a-b8c1e299bf5c", "metadata": {}, "outputs": [], "source": [ "optimize_claude(python_hard)" ] }, { "cell_type": "code", "execution_count": null, "id": "0c181036-8193-4fdd-aef3-fc513b218d43", "metadata": {}, "outputs": [], "source": [ "!clang++ -O3 -std=c++17 -march=armv8.3-a -o optimized optimized.cpp\n", "!./optimized" ] }, { "cell_type": "code", "execution_count": null, "id": "0be9f47d-5213-4700-b0e2-d444c7c738c0", "metadata": {}, "outputs": [], "source": [ "def stream_gpt(python): \n", " stream = openai.chat.completions.create(model=OPENAI_MODEL, messages=messages_for(python), stream=True)\n", " reply = \"\"\n", " for chunk in stream:\n", " fragment = chunk.choices[0].delta.content or \"\"\n", " reply += fragment\n", " yield reply.replace('```cpp\\n','').replace('```','')" ] }, { "cell_type": "code", "execution_count": null, "id": "8669f56b-8314-4582-a167-78842caea131", "metadata": {}, "outputs": [], "source": [ "def stream_claude(python):\n", " result = claude.messages.stream(\n", " model=CLAUDE_MODEL,\n", " max_tokens=2000,\n", " system=system_message,\n", " messages=[{\"role\": \"user\", \"content\": user_prompt_for(python)}],\n", " )\n", " reply = \"\"\n", " with result as stream:\n", " for text in stream.text_stream:\n", " reply += text\n", " yield reply.replace('```cpp\\n','').replace('```','')" ] }, { "cell_type": "code", "execution_count": null, "id": "2f1ae8f5-16c8-40a0-aa18-63b617df078d", "metadata": {}, "outputs": [], "source": [ "def optimize(python, model):\n", " if model==\"GPT\":\n", " result = stream_gpt(python)\n", " elif model==\"Claude\":\n", " result = stream_claude(python)\n", " else:\n", " raise ValueError(\"Unknown model\")\n", " for stream_so_far in result:\n", " yield stream_so_far " ] }, { "cell_type": "code", "execution_count": null, "id": "f1ddb38e-6b0a-4c37-baa4-ace0b7de887a", "metadata": {}, "outputs": [], "source": [ "with gr.Blocks() as ui:\n", " with gr.Row():\n", " python = gr.Textbox(label=\"Python code:\", lines=10, value=python_hard)\n", " cpp = gr.Textbox(label=\"C++ code:\", lines=10)\n", " with gr.Row():\n", " model = gr.Dropdown([\"GPT\", \"Claude\"], label=\"Select model\", value=\"GPT\")\n", " convert = gr.Button(\"Convert code\")\n", "\n", " convert.click(optimize, inputs=[python, model], outputs=[cpp])\n", "\n", "ui.launch(inbrowser=True)" ] }, { "cell_type": "code", "execution_count": null, "id": "19bf2bff-a822-4009-a539-f003b1651383", "metadata": {}, "outputs": [], "source": [ "def execute_python(code):\n", " try:\n", " output = io.StringIO()\n", " sys.stdout = output\n", " exec(code)\n", " finally:\n", " sys.stdout = sys.__stdout__\n", " return output.getvalue()" ] }, { "cell_type": "code", "execution_count": null, "id": "77f3ab5d-fcfb-4d3f-8728-9cacbf833ea6", "metadata": {}, "outputs": [], "source": [ "def execute_cpp(code):\n", " write_output(code)\n", " try:\n", " compile_cmd = [\"clang++\", \"-Ofast\", \"-std=c++17\", \"-march=armv8.5-a\", \"-mtune=apple-m1\", \"-mcpu=apple-m1\", \"-o\", \"optimized\", \"optimized.cpp\"]\n", " compile_result = subprocess.run(compile_cmd, check=True, text=True, capture_output=True)\n", " run_cmd = [\"./optimized\"]\n", " run_result = subprocess.run(run_cmd, check=True, text=True, capture_output=True)\n", " return run_result.stdout\n", " except subprocess.CalledProcessError as e:\n", " return f\"An error occurred:\\n{e.stderr}\"" ] }, { "cell_type": "code", "execution_count": null, "id": "9a2274f1-d03b-42c0-8dcc-4ce159b18442", "metadata": {}, "outputs": [], "source": [ "css = \"\"\"\n", ".python {background-color: #306998;}\n", ".cpp {background-color: #050;}\n", "\"\"\"" ] }, { "cell_type": "code", "execution_count": null, "id": "f1303932-160c-424b-97a8-d28c816721b2", "metadata": {}, "outputs": [], "source": [ "with gr.Blocks(css=css) as ui:\n", " gr.Markdown(\"## Convert code from Python to C++\")\n", " with gr.Row():\n", " python = gr.Textbox(label=\"Python code:\", value=python_hard, lines=10)\n", " cpp = gr.Textbox(label=\"C++ code:\", lines=10)\n", " with gr.Row():\n", " model = gr.Dropdown([\"GPT\", \"Claude\"], label=\"Select model\", value=\"GPT\")\n", " with gr.Row():\n", " convert = gr.Button(\"Convert code\")\n", " with gr.Row():\n", " python_run = gr.Button(\"Run Python\")\n", " cpp_run = gr.Button(\"Run C++\")\n", " with gr.Row():\n", " python_out = gr.TextArea(label=\"Python result:\", elem_classes=[\"python\"])\n", " cpp_out = gr.TextArea(label=\"C++ result:\", elem_classes=[\"cpp\"])\n", "\n", " convert.click(optimize, inputs=[python, model], outputs=[cpp])\n", " python_run.click(execute_python, inputs=[python], outputs=[python_out])\n", " cpp_run.click(execute_cpp, inputs=[cpp], outputs=[cpp_out])\n", "\n", "ui.launch(inbrowser=True)" ] }, { "cell_type": "code", "execution_count": null, "id": "bb8c5b4e-ec51-4f21-b3f8-6aa94fede86d", "metadata": {}, "outputs": [], "source": [ "from huggingface_hub import login, InferenceClient\n", "from transformers import AutoTokenizer" ] }, { "cell_type": "code", "execution_count": null, "id": "13347633-4606-4e38-9927-80c39e65c1f1", "metadata": {}, "outputs": [], "source": [ "hf_token = os.environ['HF_TOKEN']\n", "login(hf_token, add_to_git_credential=True)" ] }, { "cell_type": "code", "execution_count": null, "id": "ef60a4df-6267-4ebd-8eed-dcb917af0a5e", "metadata": {}, "outputs": [], "source": [ "code_qwen = \"Qwen/CodeQwen1.5-7B-Chat\"\n", "code_gemma = \"google/codegemma-7b-it\"\n", "CODE_QWEN_URL = \"https://h1vdol7jxhje3mpn.us-east-1.aws.endpoints.huggingface.cloud\"\n", "CODE_GEMMA_URL = \"https://c5hggiyqachmgnqg.us-east-1.aws.endpoints.huggingface.cloud\"" ] }, { "cell_type": "code", "execution_count": null, "id": "695ce389-a903-4533-a2f1-cd9e2a6af8f2", "metadata": {}, "outputs": [], "source": [ "tokenizer = AutoTokenizer.from_pretrained(code_qwen)\n", "messages = messages_for(pi)\n", "text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)" ] }, { "cell_type": "code", "execution_count": null, "id": "d4548e96-0b32-4793-bdd6-1b072c2f26ab", "metadata": {}, "outputs": [], "source": [ "print(text)" ] }, { "cell_type": "code", "execution_count": null, "id": "bb2a126b-09e7-4966-bc97-0ef5c2cc7896", "metadata": {}, "outputs": [], "source": [ "client = InferenceClient(CODE_QWEN_URL, token=hf_token)\n", "stream = client.text_generation(text, stream=True, details=True, max_new_tokens=3000)\n", "for r in stream:\n", " print(r.token.text, end = \"\")" ] }, { "cell_type": "code", "execution_count": null, "id": "127a52e5-ad85-42b7-a0f5-9afda5efe090", "metadata": {}, "outputs": [], "source": [ "def stream_code_quen(python):\n", " tokenizer = AutoTokenizer.from_pretrained(code_qwen)\n", " messages = messages_for(python)\n", " text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)\n", " client = InferenceClient(CODE_QWEN_URL, token=hf_token)\n", " stream = client.text_generation(text, stream=True, details=True, max_new_tokens=3000)\n", " result = \"\"\n", " for r in stream:\n", " result += r.token.text\n", " yield result " ] }, { "cell_type": "code", "execution_count": null, "id": "a82387d1-7651-4923-995b-fe18356fcaa6", "metadata": {}, "outputs": [], "source": [ "def optimize(python, model):\n", " if model==\"GPT\":\n", " result = stream_gpt(python)\n", " elif model==\"Claude\":\n", " result = stream_claude(python)\n", " elif model==\"CodeQwen\":\n", " result = stream_code_qwen(python)\n", " else:\n", " raise ValueError(\"Unknown model\")\n", " for stream_so_far in result:\n", " yield stream_so_far " ] }, { "cell_type": "code", "execution_count": null, "id": "f9ca2e6f-60c1-4e5f-b570-63c75b2d189b", "metadata": {}, "outputs": [], "source": [ "with gr.Blocks(css=css) as ui:\n", " gr.Markdown(\"## Convert code from Python to C++\")\n", " with gr.Row():\n", " python = gr.Textbox(label=\"Python code:\", value=python_hard, lines=10)\n", " cpp = gr.Textbox(label=\"C++ code:\", lines=10)\n", " with gr.Row():\n", " model = gr.Dropdown([\"GPT\", \"Claude\", \"CodeQwen\"], label=\"Select model\", value=\"GPT\")\n", " with gr.Row():\n", " convert = gr.Button(\"Convert code\")\n", " with gr.Row():\n", " python_run = gr.Button(\"Run Python\")\n", " cpp_run = gr.Button(\"Run C++\")\n", " with gr.Row():\n", " python_out = gr.TextArea(label=\"Python result:\", elem_classes=[\"python\"])\n", " cpp_out = gr.TextArea(label=\"C++ result:\", elem_classes=[\"cpp\"])\n", "\n", " convert.click(optimize, inputs=[python, model], outputs=[cpp])\n", " python_run.click(execute_python, inputs=[python], outputs=[python_out])\n", " cpp_run.click(execute_cpp, inputs=[cpp], outputs=[cpp_out])\n", "\n", "ui.launch(inbrowser=True)" ] }, { "cell_type": "code", "execution_count": null, "id": "f12bfe23-135b-45a7-8c6d-0c27d68b0a82", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.10" } }, "nbformat": 4, "nbformat_minor": 5 }