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{ |
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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 an Open Source model to generate high performance C++ code from Python code\n", |
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"\n", |
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"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", |
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"\n", |
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"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.\n", |
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"\n", |
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"Added the use of inference providers that was introduced recently by Hugging Face to convert the code.\n", |
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"Improved the user prompt to include algorithic efficeiny and performance optimization.\n", |
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"\n", |
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"Note: C++ commands work on windows environment." |
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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": 44, |
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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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"import json\n", |
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"import requests\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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"import anthropic\n", |
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"from IPython.display import Markdown, display, update_display\n", |
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"import gradio as gr\n", |
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"import subprocess" |
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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": 45, |
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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()\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['HF_TOKEN'] = os.getenv('HF_TOKEN', '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": 46, |
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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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"OPENAI_MODEL = \"gpt-4o\"\n", |
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"CLAUDE_MODEL = \"claude-3-5-sonnet-20240620\"" |
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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": 47, |
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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 Windows intel i7. \"\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. Keep implementations of random number generators identical so that results match exactly.\"" |
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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": 72, |
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"id": "70583432-e851-40d1-a219-2fb32b830dc8", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"#updated the original prompt to include algorithic efficeiny and performance optimization\n", |
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"def user_prompt_for(python: str) -> str:\n", |
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" user_prompt = (\n", |
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" \"First, analyze the given Python code to understand its core purpose and algorithmic approach. \"\n", |
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" \"Then, implement a C++ solution that achieves the same output while prioritizing:\\n\"\n", |
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" \"1. **Algorithmic Efficiency** - Optimize time and space complexity, even if it means using a different approach.\\n\"\n", |
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" \"2. **Numerical Correctness** - Prevent integer overflows, use appropriate data types (`int64_t`, `uint64_t`, `double`, etc.), \"\n", |
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" \"and ensure correct handling of edge cases.\\n\"\n", |
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" \"3. **Performance Optimization** - Utilize C++-specific features (e.g., `std::vector` with preallocation, SIMD optimizations, cache-friendly structures).\\n\\n\"\n", |
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" \n", |
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" \"### **Important Notes:**\\n\"\n", |
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" \"- Use `int64_t` instead of `int` where necessary to prevent overflows.\\n\"\n", |
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" \"- Ensure random number generation in C++ matches Python's output as closely as possible.\\n\"\n", |
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" \"- Avoid undefined behavior, such as bit shifts that exceed type width (`1UL << 32` is incorrect for `uint32_t`).\\n\"\n", |
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" \"- Comment on key optimizations and complexity improvements in the C++ code.\\n\\n\"\n", |
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" \n", |
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" \"### **Expected Response:**\\n\"\n", |
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" \"Respond **only with C++ code**, including all necessary headers and ensuring the output matches the Python version exactly.\\n\\n\"\n", |
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" \n", |
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" \"Here's the Python code to analyze and optimize:\\n\\n\"\n", |
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" + python\n", |
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" )\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": 49, |
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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": 50, |
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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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" code = cpp.replace(\"```cpp\",\"\").replace(\"```\",\"\")\n", |
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" with open(\"optimized.cpp\", \"w\") as f:\n", |
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" f.write(code)" |
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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": 51, |
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"id": "e7d2fea8-74c6-4421-8f1e-0e76d5b201b9", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"def optimize_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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" print(fragment, end='', flush=True)\n", |
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" write_output(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": 52, |
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"id": "7cd84ad8-d55c-4fe0-9eeb-1895c95c4a9d", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"def optimize_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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" print(text, end=\"\", flush=True)\n", |
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" write_output(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": 53, |
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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": 59, |
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"id": "7fe1cd4b-d2c5-4303-afed-2115a3fef200", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"exec(pi)" |
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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": "105db6f9-343c-491d-8e44-3a5328b81719", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"optimize_gpt(pi)" |
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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": "bf26ee95-0c77-491d-9a91-579a1e96a8a3", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"exec(pi)" |
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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": "4194e40c-04ab-4940-9d64-b4ad37c5bb40", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"!g++ -O3 -std=c++17 -march=native -o optimized optimized.cpp\n", |
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"!optimized.exe" |
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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": "983a11fe-e24d-4c65-8269-9802c5ef3ae6", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"optimize_claude(pi)" |
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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": "d5a766f9-3d23-4bb4-a1d4-88ec44b61ddf", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"!g++ -O3 -std=c++17 -march=native -o optimized optimized.cpp\n", |
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"!optimized.exe" |
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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": 54, |
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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", |
||||
"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": 23, |
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"id": "dab5e4bc-276c-4555-bd4c-12c699d5e899", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"exec(python_hard)" |
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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": "e8d24ed5-2c15-4f55-80e7-13a3952b3cb8", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"optimize_gpt(python_hard)" |
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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": "e0b3d073-88a2-40b2-831c-6f0c345c256f", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"!g++ -O3 -std=c++17 -march=native -o optimized optimized.cpp\n", |
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"!optimized.exe" |
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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": "e9305446-1d0c-4b51-866a-b8c1e299bf5c", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"optimize_claude(python_hard)" |
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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": "0c181036-8193-4fdd-aef3-fc513b218d43", |
||||
"metadata": {}, |
||||
"outputs": [], |
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"source": [ |
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"!g++ -O3 -std=c++17 -march=native -o optimized optimized.cpp\n", |
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"!optimized.exe" |
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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": 55, |
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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", |
||||
" 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": 56, |
||||
"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": 57, |
||||
"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 " |
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] |
||||
}, |
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{ |
||||
"cell_type": "code", |
||||
"execution_count": 24, |
||||
"id": "f1ddb38e-6b0a-4c37-baa4-ace0b7de887a", |
||||
"metadata": {}, |
||||
"outputs": [ |
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{ |
||||
"data": { |
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"text/html": [ |
||||
"<div><iframe src=\"http://127.0.0.1:7862/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>" |
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], |
||||
"text/plain": [ |
||||
"<IPython.core.display.HTML object>" |
||||
] |
||||
}, |
||||
"metadata": {}, |
||||
"output_type": "display_data" |
||||
}, |
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{ |
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"data": { |
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"text/plain": [] |
||||
}, |
||||
"execution_count": 24, |
||||
"metadata": {}, |
||||
"output_type": "execute_result" |
||||
} |
||||
], |
||||
"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": 58, |
||||
"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": 59, |
||||
"id": "77f3ab5d-fcfb-4d3f-8728-9cacbf833ea6", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def execute_cpp(code):\n", |
||||
" write_output(code)\n", |
||||
" try:\n", |
||||
" compile_result = subprocess.run(compiler_cmd[2], check=True, text=True, capture_output=True)\n", |
||||
" run_cmd = [\"optimized.exe\"]\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": 60, |
||||
"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": 28, |
||||
"id": "f1303932-160c-424b-97a8-d28c816721b2", |
||||
"metadata": {}, |
||||
"outputs": [ |
||||
{ |
||||
"data": { |
||||
"text/html": [ |
||||
"<div><iframe src=\"http://127.0.0.1:7863/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>" |
||||
], |
||||
"text/plain": [ |
||||
"<IPython.core.display.HTML object>" |
||||
] |
||||
}, |
||||
"metadata": {}, |
||||
"output_type": "display_data" |
||||
}, |
||||
{ |
||||
"data": { |
||||
"text/plain": [] |
||||
}, |
||||
"execution_count": 28, |
||||
"metadata": {}, |
||||
"output_type": "execute_result" |
||||
} |
||||
], |
||||
"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": 61, |
||||
"id": "bb8c5b4e-ec51-4f21-b3f8-6aa94fede86d", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"from huggingface_hub import login, InferenceClient" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 62, |
||||
"id": "13347633-4606-4e38-9927-80c39e65c1f1", |
||||
"metadata": {}, |
||||
"outputs": [ |
||||
{ |
||||
"name": "stderr", |
||||
"output_type": "stream", |
||||
"text": [ |
||||
"Note: Environment variable`HF_TOKEN` is set and is the current active token independently from the token you've just configured.\n" |
||||
] |
||||
} |
||||
], |
||||
"source": [ |
||||
"hf_token = os.environ['HF_TOKEN']\n", |
||||
"login(hf_token)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 63, |
||||
"id": "ef60a4df-6267-4ebd-8eed-dcb917af0a5e", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"code_qwen = \"Qwen/CodeQwen1.5-7B-Chat\"\n", |
||||
"code_gemma = \"google/codegemma-7b-it\"\n", |
||||
"messages=messages_for(pi)" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 64, |
||||
"id": "3825d77a-03c6-42b2-89bc-ccbcb1585740", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"client = InferenceClient(\n", |
||||
"\tprovider=\"sambanova\",\n", |
||||
"\tapi_key=hf_token\n", |
||||
")\n", |
||||
"stream = client.chat.completions.create(\n", |
||||
"\tmodel=\"Qwen/Qwen2.5-Coder-32B-Instruct\", \n", |
||||
"\tmessages=messages, \n", |
||||
"\tmax_tokens=500,\n", |
||||
"\tstream=True\n", |
||||
")\n", |
||||
"\n", |
||||
"for chunk in stream:\n", |
||||
" print(chunk.choices[0].delta.content, end=\"\")" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 65, |
||||
"id": "cc0c3e9c-2572-41d1-a476-6eae96b20695", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"# using inference providers\n", |
||||
"def stream_code_qwen(python):\n", |
||||
" messages = messages_for(python)\n", |
||||
" client = InferenceClient(\n", |
||||
" \tprovider=\"sambanova\",\n", |
||||
" \tapi_key=hf_token\n", |
||||
" )\n", |
||||
" stream = client.chat.completions.create(\n", |
||||
" \tmodel=\"Qwen/Qwen2.5-Coder-32B-Instruct\", \n", |
||||
" \tmessages=messages, \n", |
||||
" \tmax_tokens=500,\n", |
||||
" \tstream=True\n", |
||||
" )\n", |
||||
" result = \"\"\n", |
||||
" for chunk in stream:\n", |
||||
" if chunk.choices and chunk.choices[0].delta.content:\n", |
||||
" result += chunk.choices[0].delta.content\n", |
||||
" yield result" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 66, |
||||
"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": 67, |
||||
"id": "4ba311ec-c16a-4fe0-946b-4b940704cf65", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def select_sample_program(sample_program):\n", |
||||
" if sample_program==\"pi\":\n", |
||||
" return pi\n", |
||||
" elif sample_program==\"python_hard\":\n", |
||||
" return python_hard\n", |
||||
" else:\n", |
||||
" return \"Type your Python program here\"" |
||||
] |
||||
}, |
||||
{ |
||||
"cell_type": "code", |
||||
"execution_count": 68, |
||||
"id": "06148e88-501b-4686-a41d-c3be528d8e6f", |
||||
"metadata": {}, |
||||
"outputs": [], |
||||
"source": [ |
||||
"def execute_cpp(code):\n", |
||||
" write_output(code)\n", |
||||
" try:\n", |
||||
" compile_cmd = [\"g++\", \"-Ofast\", \"-std=c++17\", \"-march=native\", \"-mtune=intel\", \"-o\", \"optimized\", \"optimized.cpp\"]\n", |
||||
" compile_result = subprocess.run(compile_cmd, check=True, text=True, capture_output=True)\n", |
||||
" run_cmd = [\"optimized.exe\"]\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": 71, |
||||
"id": "f9ca2e6f-60c1-4e5f-b570-63c75b2d189b", |
||||
"metadata": { |
||||
"scrolled": true |
||||
}, |
||||
"outputs": [ |
||||
{ |
||||
"data": { |
||||
"text/html": [ |
||||
"<div><iframe src=\"http://127.0.0.1:7867/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>" |
||||
], |
||||
"text/plain": [ |
||||
"<IPython.core.display.HTML object>" |
||||
] |
||||
}, |
||||
"metadata": {}, |
||||
"output_type": "display_data" |
||||
}, |
||||
{ |
||||
"data": { |
||||
"text/plain": [] |
||||
}, |
||||
"execution_count": 71, |
||||
"metadata": {}, |
||||
"output_type": "execute_result" |
||||
}, |
||||
{ |
||||
"name": "stderr", |
||||
"output_type": "stream", |
||||
"text": [ |
||||
"Traceback (most recent call last):\n", |
||||
" File \"C:\\Users\\danym\\anaconda3\\envs\\llms\\Lib\\site-packages\\huggingface_hub\\utils\\_http.py\", line 406, in hf_raise_for_status\n", |
||||
" response.raise_for_status()\n", |
||||
" File \"C:\\Users\\danym\\anaconda3\\envs\\llms\\Lib\\site-packages\\requests\\models.py\", line 1024, in raise_for_status\n", |
||||
" raise HTTPError(http_error_msg, response=self)\n", |
||||
"requests.exceptions.HTTPError: 402 Client Error: Payment Required for url: https://huggingface.co/api/inference-proxy/sambanova/v1/chat/completions\n", |
||||
"\n", |
||||
"The above exception was the direct cause of the following exception:\n", |
||||
"\n", |
||||
"Traceback (most recent call last):\n", |
||||
" File \"C:\\Users\\danym\\anaconda3\\envs\\llms\\Lib\\site-packages\\gradio\\queueing.py\", line 625, in process_events\n", |
||||
" response = await route_utils.call_process_api(\n", |
||||
" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", |
||||
" File \"C:\\Users\\danym\\anaconda3\\envs\\llms\\Lib\\site-packages\\gradio\\route_utils.py\", line 322, in call_process_api\n", |
||||
" output = await app.get_blocks().process_api(\n", |
||||
" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", |
||||
" File \"C:\\Users\\danym\\anaconda3\\envs\\llms\\Lib\\site-packages\\gradio\\blocks.py\", line 2088, in process_api\n", |
||||
" result = await self.call_function(\n", |
||||
" ^^^^^^^^^^^^^^^^^^^^^^^^^\n", |
||||
" File \"C:\\Users\\danym\\anaconda3\\envs\\llms\\Lib\\site-packages\\gradio\\blocks.py\", line 1647, in call_function\n", |
||||
" prediction = await utils.async_iteration(iterator)\n", |
||||
" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", |
||||
" File \"C:\\Users\\danym\\anaconda3\\envs\\llms\\Lib\\site-packages\\gradio\\utils.py\", line 728, in async_iteration\n", |
||||
" return await anext(iterator)\n", |
||||
" ^^^^^^^^^^^^^^^^^^^^^\n", |
||||
" File \"C:\\Users\\danym\\anaconda3\\envs\\llms\\Lib\\site-packages\\gradio\\utils.py\", line 722, in __anext__\n", |
||||
" return await anyio.to_thread.run_sync(\n", |
||||
" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", |
||||
" File \"C:\\Users\\danym\\anaconda3\\envs\\llms\\Lib\\site-packages\\anyio\\to_thread.py\", line 56, in run_sync\n", |
||||
" return await get_async_backend().run_sync_in_worker_thread(\n", |
||||
" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", |
||||
" File \"C:\\Users\\danym\\anaconda3\\envs\\llms\\Lib\\site-packages\\anyio\\_backends\\_asyncio.py\", line 2505, in run_sync_in_worker_thread\n", |
||||
" return await future\n", |
||||
" ^^^^^^^^^^^^\n", |
||||
" File \"C:\\Users\\danym\\anaconda3\\envs\\llms\\Lib\\site-packages\\anyio\\_backends\\_asyncio.py\", line 1005, in run\n", |
||||
" result = context.run(func, *args)\n", |
||||
" ^^^^^^^^^^^^^^^^^^^^^^^^\n", |
||||
" File \"C:\\Users\\danym\\anaconda3\\envs\\llms\\Lib\\site-packages\\gradio\\utils.py\", line 705, in run_sync_iterator_async\n", |
||||
" return next(iterator)\n", |
||||
" ^^^^^^^^^^^^^^\n", |
||||
" File \"C:\\Users\\danym\\anaconda3\\envs\\llms\\Lib\\site-packages\\gradio\\utils.py\", line 866, in gen_wrapper\n", |
||||
" response = next(iterator)\n", |
||||
" ^^^^^^^^^^^^^^\n", |
||||
" File \"C:\\Users\\danym\\AppData\\Local\\Temp\\ipykernel_16896\\2223836700.py\", line 10, in optimize\n", |
||||
" for stream_so_far in result:\n", |
||||
" File \"C:\\Users\\danym\\AppData\\Local\\Temp\\ipykernel_16896\\2217507934.py\", line 8, in stream_code_qwen\n", |
||||
" stream = client.chat.completions.create(\n", |
||||
" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", |
||||
" File \"C:\\Users\\danym\\anaconda3\\envs\\llms\\Lib\\site-packages\\huggingface_hub\\inference\\_client.py\", line 970, in chat_completion\n", |
||||
" data = self._inner_post(request_parameters, stream=stream)\n", |
||||
" ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", |
||||
" File \"C:\\Users\\danym\\anaconda3\\envs\\llms\\Lib\\site-packages\\huggingface_hub\\inference\\_client.py\", line 327, in _inner_post\n", |
||||
" hf_raise_for_status(response)\n", |
||||
" File \"C:\\Users\\danym\\anaconda3\\envs\\llms\\Lib\\site-packages\\huggingface_hub\\utils\\_http.py\", line 477, in hf_raise_for_status\n", |
||||
" raise _format(HfHubHTTPError, str(e), response) from e\n", |
||||
"huggingface_hub.errors.HfHubHTTPError: 402 Client Error: Payment Required for url: https://huggingface.co/api/inference-proxy/sambanova/v1/chat/completions (Request ID: Root=1-67af964d-18ce264b79019ea460d62fd1;041b0bf3-9206-4a8a-aa61-f493ff9b1f8a)\n", |
||||
"\n", |
||||
"You have exceeded your monthly included credits for Inference Endpoints. Subscribe to PRO to get 20x more monthly allowance.\n" |
||||
] |
||||
} |
||||
], |
||||
"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", |
||||
" with gr.Column():\n", |
||||
" sample_program = gr.Radio([\"pi\", \"python_hard\"], label=\"Sample program\", value=\"python_hard\")\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", |
||||
" sample_program.change(select_sample_program, inputs=[sample_program], outputs=[python])\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": "9d0ad093-425b-488e-8c3f-67f729dd9c06", |
||||
"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": "3.11.11" |
||||
} |
||||
}, |
||||
"nbformat": 4, |
||||
"nbformat_minor": 5 |
||||
} |
Loading…
Reference in new issue