From e8ab36c86e4a4436400e170d75c53972d3e04fa6 Mon Sep 17 00:00:00 2001 From: iammarshall Date: Thu, 6 Feb 2025 18:03:36 +0000 Subject: [PATCH] add hf inference provider option as community contribution to week4 day4 base file --- ...seek_and_hf_inference_provider_added.ipynb | 1513 +++++++++++++++++ 1 file changed, 1513 insertions(+) create mode 100644 week4/community-contributions/day4_extra_deepseek_and_hf_inference_provider_added.ipynb diff --git a/week4/community-contributions/day4_extra_deepseek_and_hf_inference_provider_added.ipynb b/week4/community-contributions/day4_extra_deepseek_and_hf_inference_provider_added.ipynb new file mode 100644 index 0000000..39b22fc --- /dev/null +++ b/week4/community-contributions/day4_extra_deepseek_and_hf_inference_provider_added.ipynb @@ -0,0 +1,1513 @@ +{ + "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": "markdown", + "id": "22e1567b-33fd-49e7-866e-4b635d15715a", + "metadata": {}, + "source": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
\n", + " \n", + " \n", + "

Important - Pause Endpoints when not in use

\n", + " \n", + " If you do decide to use HuggingFace endpoints for this project, you should stop or pause the endpoints when you are done to avoid accruing unnecessary running cost. The costs are very low as long as you only run the endpoint when you're using it. Navigate to the HuggingFace endpoint UI here, open your endpoint, and click Pause to put it on pause so you no longer pay for it. \n", + "Many thanks to student John L. for raising this.\n", + "

\n", + "In week 8 we will use Modal instead of HuggingFace endpoints; with Modal you only pay for the time that you use it and you should get free credits.\n", + "
\n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "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": 2, + "id": "4f672e1c-87e9-4865-b760-370fa605e614", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Successfully loaded token beginning hf_gI\n" + ] + } + ], + "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')\n", + "\n", + "if os.environ['HF_TOKEN'] == 'your-key-if-not-using-env':\n", + " print(\"Failed to load token, is it inside your .env file?\")\n", + "else:\n", + " print(f\"Successfully loaded token beginning {os.environ['HF_TOKEN'][:5]}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "8aa149ed-9298-4d69-8fe2-8f5de0f667da", + "metadata": {}, + "outputs": [], + "source": [ + "# initialize\n", + "\n", + "openai = OpenAI()\n", + "claude = anthropic.Anthropic()\n", + "code_qwen_inference_hf = OpenAI(base_url=\"https://api-inference.huggingface.co/v1/\", api_key=os.environ['HF_TOKEN'])\n", + "OPENAI_MODEL = \"gpt-4o\"\n", + "CLAUDE_MODEL = \"claude-3-5-sonnet-20240620\"\n", + "CODE_QWEN_MODEL = \"deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B\" #\"Qwen/CodeQwen1.5-7B-Chat\"" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "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": 5, + "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": 6, + "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": 7, + "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": 8, + "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": 9, + "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": 39, + "id": "8047295c-ec4a-489c-8dad-79db73a2dfca", + "metadata": {}, + "outputs": [], + "source": [ + "def optimize_code_qwen(python): \n", + " stream = code_qwen_inference_hf.chat.completions.create(model=CODE_QWEN_MODEL, messages=messages_for(python), max_tokens=500, 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": 11, + "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": 12, + "id": "7fe1cd4b-d2c5-4303-afed-2115a3fef200", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Result: 3.141592658589\n", + "Execution Time: 12.479274 seconds\n" + ] + } + ], + "source": [ + "exec(pi)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "105db6f9-343c-491d-8e44-3a5328b81719", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "```cpp\n", + "#include \n", + "#include \n", + "#include \n", + "\n", + "double calculate(int iterations, int param1, int param2) {\n", + " double result = 1.0;\n", + " for (int i = 1; i <= iterations; ++i) {\n", + " int j = i * param1 - param2;\n", + " result -= 1.0 / j;\n", + " j = i * param1 + param2;\n", + " result += 1.0 / j;\n", + " }\n", + " return result;\n", + "}\n", + "\n", + "int main() {\n", + " auto start_time = std::chrono::high_resolution_clock::now();\n", + " \n", + " double result = calculate(100000000, 4, 1) * 4;\n", + " \n", + " auto end_time = std::chrono::high_resolution_clock::now();\n", + " std::chrono::duration elapsed = end_time - start_time;\n", + " \n", + " std::cout << std::fixed << std::setprecision(12) \n", + " << \"Result: \" << result << std::endl;\n", + " std::cout << \"Execution Time: \" << elapsed.count() << \" seconds\" << std::endl;\n", + " \n", + " return 0;\n", + "}\n", + "```" + ] + } + ], + "source": [ + "optimize_gpt(pi)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "bf26ee95-0c77-491d-9a91-579a1e96a8a3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Result: 3.141592658589\n", + "Execution Time: 11.925580 seconds\n" + ] + } + ], + "source": [ + "exec(pi)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "4194e40c-04ab-4940-9d64-b4ad37c5bb40", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Result: 3.141592658589\n", + "Execution Time: 0.320651458000 seconds\n" + ] + } + ], + "source": [ + "!clang++ -O3 -std=c++17 -march=armv8.3-a -o optimized optimized.cpp\n", + "!./optimized" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "983a11fe-e24d-4c65-8269-9802c5ef3ae6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "#include \n", + "omanip>e lude (i) * param1 - param2;\n", + "1.0 / j);esult -= (\n", + "_cast(i) * param1 + param2;\n", + "1.0 / j);esult += (\n", + " }\n", + " return result;\n", + "}\n", + "\n", + "() {main\n", + "d::chrono::high_resolution_clock::now();\n", + " \n", + "(100'000'000, 4, 1) * 4;ulate\n", + " \n", + "chrono::high_resolution_clock::now();\n", + "ono::duration_cast(end_time - start_time);\n", + "\n", + "d::setprecision(12);::fixed << st\n", + " << \"Result: \" << result << std::endl;\n", + "Execution Time: \" << duration.count() / 1e6 << \" seconds\" << std::endl;\n", + "\n", + "0; return \n", + "}" + ] + } + ], + "source": [ + "optimize_claude(pi)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "d5a766f9-3d23-4bb4-a1d4-88ec44b61ddf", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Result: 3.141592658589\n", + "Execution Time: 0.317956000000 seconds\n" + ] + } + ], + "source": [ + "!clang++ -O3 -std=c++17 -march=armv8.3-a -o optimized optimized.cpp\n", + "!./optimized" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "c3b497b3-f569-420e-b92e-fb0f49957ce0", + "metadata": {}, + "outputs": [], + "source": [ + "python_hard = \"\"\"# Be careful to support large number sizes\n", + "\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": 19, + "id": "dab5e4bc-276c-4555-bd4c-12c699d5e899", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total Maximum Subarray Sum (20 runs): 10980\n", + "Execution Time: 38.106860 seconds\n" + ] + } + ], + "source": [ + "exec(python_hard)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "e8d24ed5-2c15-4f55-80e7-13a3952b3cb8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "```cpp\n", + "#include \n", + "#include \n", + "#include \n", + "#include \n", + "#include \n", + "\n", + " Linear Congruential Generator (LCG)\n", + "class LCG {\n", + "public:\n", + " LCG(uint32_t seed, uint32_t a = 1664525, uint32_t c = 1013904223, uint32_t m = 1U << 32)\n", + " : value(seed), a(a), c(c), m(m) {}\n", + "\n", + " uint32_t next() {\n", + " value = (a * value + c) % m;\n", + " return value;\n", + " }\n", + "\n", + "private:\n", + " uint32_t value, a, c, m;\n", + "};\n", + "\n", + " Finds the max subarray sum in an array\n", + "int32_t max_subarray_sum(int n, uint32_t seed, int min_val, int max_val) {\n", + " LCG lcg(seed);\n", + " int range = max_val - min_val + 1;\n", + " std::vector random_numbers(n);\n", + "\n", + " for (int i = 0; i < n; ++i) {\n", + " random_numbers[i] = lcg.next() % range + min_val;\n", + " }\n", + "\n", + " int32_t max_sum = std::numeric_limits::min();\n", + " for (int i = 0; i < n; ++i) {\n", + " int32_t current_sum = 0;\n", + " for (int j = i; j < n; ++j) {\n", + " current_sum += random_numbers[j];\n", + " if (current_sum > max_sum) {\n", + " max_sum = current_sum;\n", + " }\n", + " }\n", + " }\n", + " return max_sum;\n", + "}\n", + "\n", + " Computes the total max subarray sum for 20 runs\n", + "int64_t total_max_subarray_sum(int n, uint32_t initial_seed, int min_val, int max_val) {\n", + " int64_t total_sum = 0;\n", + " LCG lcg(initial_seed);\n", + "\n", + " for (int i = 0; i < 20; ++i) {\n", + " uint32_t seed = lcg.next();\n", + " total_sum += max_subarray_sum(n, seed, min_val, max_val);\n", + " }\n", + " return total_sum;\n", + "}\n", + "\n", + "int main() {\n", + " int n = 10000;\n", + " uint32_t initial_seed = 42;\n", + " int min_val = -10;\n", + " int max_val = 10;\n", + " \n", + " auto start_time = std::chrono::high_resolution_clock::now();\n", + " int64_t result = total_max_subarray_sum(n, initial_seed, min_val, max_val);\n", + " auto end_time = std::chrono::high_resolution_clock::now();\n", + "\n", + " std::chrono::duration execution_time = end_time - start_time;\n", + "\n", + " std::cout << \"Total Maximum Subarray Sum (20 runs): \" << result << std::endl;\n", + " std::cout << \"Execution Time: \" << execution_time.count() << \" seconds\" << std::endl;\n", + " \n", + " return 0;\n", + "}\n", + "```" + ] + } + ], + "source": [ + "optimize_gpt(python_hard)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "e0b3d073-88a2-40b2-831c-6f0c345c256f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1moptimized.cpp:11:87: \u001b[0m\u001b[0;1;35mwarning: \u001b[0m\u001b[1mshift count >= width of type [-Wshift-count-overflow]\u001b[0m\n", + " 11 | LCG(uint32_t seed, uint32_t a = 1664525, uint32_t c = 1013904223, uint32_t m = 1U << 32)\u001b[0m\n", + " | \u001b[0;1;32m ^ ~~\n", + "1 warning generated.\n", + "Total Maximum Subarray Sum (20 runs): 0\n", + "Execution Time: 0.553671 seconds\n" + ] + } + ], + "source": [ + "!clang++ -O3 -std=c++17 -march=armv8.3-a -o optimized optimized.cpp\n", + "!./optimized" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "e9305446-1d0c-4b51-866a-b8c1e299bf5c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "#include \n", + "#include \n", + "#include \n", + " \n", + "omanip>e random_numbers(n);\n", + " (int i = 0; i < n; ++i) {\n", + "[i] = lcg.next() % (max_val - min_val + 1) + min_val;\n", + " }\n", + "\n", + "_limits::min();meric\n", + " int64_t current_sum = 0;\n", + "0; int64_t min_sum = \n", + "\n", + "0; i < n; ++i) { \n", + "i]; current_sum += random_numbers[\n", + "max_sum, current_sum - min_sum);\n", + " min(min_sum, current_sum);\n", + " }\n", + "\n", + " return max_sum;\n", + "}\n", + "\n", + "(int n, uint64_t initial_seed, int min_val, int max_val) {\n", + "_sum = 0;_t total\n", + "G lcg(initial_seed);\n", + " < 20; ++i) {i = 0; i\n", + "uint64_t seed = lcg.next();\n", + "array_sum(n, seed, min_val, max_val);\n", + " }\n", + " return total_sum;\n", + "}\n", + "\n", + " main() {\n", + "000;int n = 10\n", + "seed = 42;_t initial_\n", + "val = -10;n_\n", + "val = 10;ax_\n", + "\n", + "time = high_resolution_clock::now();\n", + "max_subarray_sum(n, initial_seed, min_val, max_val);\n", + "resolution_clock::now();_\n", + "\n", + "(end_time - start_time);\n", + "\n", + " Subarray Sum (20 runs): \" << result << endl;\n", + "< fixed << setprecision(6) << duration.count() / 1e6 << \" seconds\" << endl;\n", + "\n", + " return 0;\n", + "}" + ] + } + ], + "source": [ + "optimize_claude(python_hard)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "0c181036-8193-4fdd-aef3-fc513b218d43", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Total Maximum Subarray Sum (20 runs): 10980\n", + "Execution Time: 0.001604 seconds\n" + ] + } + ], + "source": [ + "!clang++ -O3 -std=c++17 -march=armv8.3-a -o optimized optimized.cpp\n", + "!./optimized" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "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": 25, + "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": 40, + "id": "fcb30458-1023-41d7-bc9a-7e95d5e782aa", + "metadata": {}, + "outputs": [], + "source": [ + "def stream_code_qwen(python): \n", + " stream = code_qwen_inference_hf.chat.completions.create(model=CODE_QWEN_MODEL, messages=messages_for(python), max_tokens=500, 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": 41, + "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", + " elif model==\"Code Qwen\":\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": 42, + "id": "f1ddb38e-6b0a-4c37-baa4-ace0b7de887a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [] + }, + "execution_count": 42, + "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\", \"Code Qwen\"], 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": 29, + "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": 30, + "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\"]\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": 31, + "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": 32, + "id": "f1303932-160c-424b-97a8-d28c816721b2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "* Running on local URL: http://127.0.0.1:7865\n", + "\n", + "To create a public link, set `share=True` in `launch()`.\n" + ] + }, + { + "data": { + "text/html": [ + "
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "Traceback (most recent call last):\n", + " File \"/opt/anaconda3/envs/llms/lib/python3.11/site-packages/gradio/queueing.py\", line 625, in process_events\n", + " response = await route_utils.call_process_api(\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/opt/anaconda3/envs/llms/lib/python3.11/site-packages/gradio/route_utils.py\", line 322, in call_process_api\n", + " output = await app.get_blocks().process_api(\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/opt/anaconda3/envs/llms/lib/python3.11/site-packages/gradio/blocks.py\", line 2051, in process_api\n", + " result = await self.call_function(\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/opt/anaconda3/envs/llms/lib/python3.11/site-packages/gradio/blocks.py\", line 1610, in call_function\n", + " prediction = await utils.async_iteration(iterator)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/opt/anaconda3/envs/llms/lib/python3.11/site-packages/gradio/utils.py\", line 728, in async_iteration\n", + " return await anext(iterator)\n", + " ^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/opt/anaconda3/envs/llms/lib/python3.11/site-packages/gradio/utils.py\", line 722, in __anext__\n", + " return await anyio.to_thread.run_sync(\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/opt/anaconda3/envs/llms/lib/python3.11/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 \"/opt/anaconda3/envs/llms/lib/python3.11/site-packages/anyio/_backends/_asyncio.py\", line 2505, in run_sync_in_worker_thread\n", + " return await future\n", + " ^^^^^^^^^^^^\n", + " File \"/opt/anaconda3/envs/llms/lib/python3.11/site-packages/anyio/_backends/_asyncio.py\", line 1005, in run\n", + " result = context.run(func, *args)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/opt/anaconda3/envs/llms/lib/python3.11/site-packages/gradio/utils.py\", line 705, in run_sync_iterator_async\n", + " return next(iterator)\n", + " ^^^^^^^^^^^^^^\n", + " File \"/opt/anaconda3/envs/llms/lib/python3.11/site-packages/gradio/utils.py\", line 866, in gen_wrapper\n", + " response = next(iterator)\n", + " ^^^^^^^^^^^^^^\n", + " File \"/var/folders/jp/lj6x3d515f92pn0n_tm5wqzr0000gn/T/ipykernel_87995/3588058922.py\", line 10, in optimize\n", + " for stream_so_far in result:\n", + " File \"/var/folders/jp/lj6x3d515f92pn0n_tm5wqzr0000gn/T/ipykernel_87995/3037730415.py\", line 2, in stream_code_qwen\n", + " stream = code_qwen_inference_hf.chat.completions.create(model=CODE_QWEN_MODEL, messages=messages_for(python), stream=True)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/opt/anaconda3/envs/llms/lib/python3.11/site-packages/openai/_utils/_utils.py\", line 279, in wrapper\n", + " return func(*args, **kwargs)\n", + " ^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/opt/anaconda3/envs/llms/lib/python3.11/site-packages/openai/resources/chat/completions.py\", line 863, in create\n", + " return self._post(\n", + " ^^^^^^^^^^^\n", + " File \"/opt/anaconda3/envs/llms/lib/python3.11/site-packages/openai/_base_client.py\", line 1283, in post\n", + " return cast(ResponseT, self.request(cast_to, opts, stream=stream, stream_cls=stream_cls))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/opt/anaconda3/envs/llms/lib/python3.11/site-packages/openai/_base_client.py\", line 960, in request\n", + " return self._request(\n", + " ^^^^^^^^^^^^^^\n", + " File \"/opt/anaconda3/envs/llms/lib/python3.11/site-packages/openai/_base_client.py\", line 1049, in _request\n", + " return self._retry_request(\n", + " ^^^^^^^^^^^^^^^^^^^^\n", + " File \"/opt/anaconda3/envs/llms/lib/python3.11/site-packages/openai/_base_client.py\", line 1098, in _retry_request\n", + " return self._request(\n", + " ^^^^^^^^^^^^^^\n", + " File \"/opt/anaconda3/envs/llms/lib/python3.11/site-packages/openai/_base_client.py\", line 1049, in _request\n", + " return self._retry_request(\n", + " ^^^^^^^^^^^^^^^^^^^^\n", + " File \"/opt/anaconda3/envs/llms/lib/python3.11/site-packages/openai/_base_client.py\", line 1098, in _retry_request\n", + " return self._request(\n", + " ^^^^^^^^^^^^^^\n", + " File \"/opt/anaconda3/envs/llms/lib/python3.11/site-packages/openai/_base_client.py\", line 1064, in _request\n", + " raise self._make_status_error_from_response(err.response) from None\n", + "openai.InternalServerError: Error code: 503 - {'error': 'Model deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B is currently loading', 'estimated_time': 142.1670379638672}\n", + "Traceback (most recent call last):\n", + " File \"/opt/anaconda3/envs/llms/lib/python3.11/site-packages/gradio/queueing.py\", line 625, in process_events\n", + " response = await route_utils.call_process_api(\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/opt/anaconda3/envs/llms/lib/python3.11/site-packages/gradio/route_utils.py\", line 322, in call_process_api\n", + " output = await app.get_blocks().process_api(\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/opt/anaconda3/envs/llms/lib/python3.11/site-packages/gradio/blocks.py\", line 2051, in process_api\n", + " result = await self.call_function(\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/opt/anaconda3/envs/llms/lib/python3.11/site-packages/gradio/blocks.py\", line 1610, in call_function\n", + " prediction = await utils.async_iteration(iterator)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/opt/anaconda3/envs/llms/lib/python3.11/site-packages/gradio/utils.py\", line 728, in async_iteration\n", + " return await anext(iterator)\n", + " ^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/opt/anaconda3/envs/llms/lib/python3.11/site-packages/gradio/utils.py\", line 722, in __anext__\n", + " return await anyio.to_thread.run_sync(\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/opt/anaconda3/envs/llms/lib/python3.11/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 \"/opt/anaconda3/envs/llms/lib/python3.11/site-packages/anyio/_backends/_asyncio.py\", line 2505, in run_sync_in_worker_thread\n", + " return await future\n", + " ^^^^^^^^^^^^\n", + " File \"/opt/anaconda3/envs/llms/lib/python3.11/site-packages/anyio/_backends/_asyncio.py\", line 1005, in run\n", + " result = context.run(func, *args)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/opt/anaconda3/envs/llms/lib/python3.11/site-packages/gradio/utils.py\", line 705, in run_sync_iterator_async\n", + " return next(iterator)\n", + " ^^^^^^^^^^^^^^\n", + " File \"/opt/anaconda3/envs/llms/lib/python3.11/site-packages/gradio/utils.py\", line 866, in gen_wrapper\n", + " response = next(iterator)\n", + " ^^^^^^^^^^^^^^\n", + " File \"/var/folders/jp/lj6x3d515f92pn0n_tm5wqzr0000gn/T/ipykernel_87995/3588058922.py\", line 10, in optimize\n", + " for stream_so_far in result:\n", + " File \"/var/folders/jp/lj6x3d515f92pn0n_tm5wqzr0000gn/T/ipykernel_87995/3037730415.py\", line 2, in stream_code_qwen\n", + " stream = code_qwen_inference_hf.chat.completions.create(model=CODE_QWEN_MODEL, messages=messages_for(python), stream=True)\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/opt/anaconda3/envs/llms/lib/python3.11/site-packages/openai/_utils/_utils.py\", line 279, in wrapper\n", + " return func(*args, **kwargs)\n", + " ^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/opt/anaconda3/envs/llms/lib/python3.11/site-packages/openai/resources/chat/completions.py\", line 863, in create\n", + " return self._post(\n", + " ^^^^^^^^^^^\n", + " File \"/opt/anaconda3/envs/llms/lib/python3.11/site-packages/openai/_base_client.py\", line 1283, in post\n", + " return cast(ResponseT, self.request(cast_to, opts, stream=stream, stream_cls=stream_cls))\n", + " ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n", + " File \"/opt/anaconda3/envs/llms/lib/python3.11/site-packages/openai/_base_client.py\", line 960, in request\n", + " return self._request(\n", + " ^^^^^^^^^^^^^^\n", + " File \"/opt/anaconda3/envs/llms/lib/python3.11/site-packages/openai/_base_client.py\", line 1049, in _request\n", + " return self._retry_request(\n", + " ^^^^^^^^^^^^^^^^^^^^\n", + " File \"/opt/anaconda3/envs/llms/lib/python3.11/site-packages/openai/_base_client.py\", line 1098, in _retry_request\n", + " return self._request(\n", + " ^^^^^^^^^^^^^^\n", + " File \"/opt/anaconda3/envs/llms/lib/python3.11/site-packages/openai/_base_client.py\", line 1049, in _request\n", + " return self._retry_request(\n", + " ^^^^^^^^^^^^^^^^^^^^\n", + " File \"/opt/anaconda3/envs/llms/lib/python3.11/site-packages/openai/_base_client.py\", line 1098, in _retry_request\n", + " return self._request(\n", + " ^^^^^^^^^^^^^^\n", + " File \"/opt/anaconda3/envs/llms/lib/python3.11/site-packages/openai/_base_client.py\", line 1064, in _request\n", + " raise self._make_status_error_from_response(err.response) from None\n", + "openai.InternalServerError: Error code: 503 - {'error': 'Model deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B is currently loading', 'estimated_time': 142.1670379638672}\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", + " model = gr.Dropdown([\"GPT\", \"Claude\", \"Code Qwen\"], 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": "markdown", + "id": "2922afc6-eb62-4a28-b3e7-ef0b6d2fc24a", + "metadata": {}, + "source": [ + "# New code" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "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": 34, + "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, add_to_git_credential=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "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": 36, + "id": "695ce389-a903-4533-a2f1-cd9e2a6af8f2", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "691c42df67f94725afdb01ed86ac0bf4", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "tokenizer_config.json: 0%| | 0.00/972 [00:00system\n", + "You are an assistant that reimplements Python code in high performance C++ for an M1 Mac. Respond only with C++ code; use comments sparingly and do not provide any explanation other than occasional comments. 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.<|im_end|>\n", + "<|im_start|>user\n", + "Rewrite this Python code in C++ with the fastest possible implementation that produces identical output in the least time. Respond only with C++ code; do not explain your work other than a few comments. Pay attention to number types to ensure no int overflows. Remember to #include all necessary C++ packages such as iomanip.\n", + "\n", + "\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", + "<|im_end|>\n", + "<|im_start|>assistant\n", + "\n" + ] + } + ], + "source": [ + "print(text)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "bb2a126b-09e7-4966-bc97-0ef5c2cc7896", + "metadata": {}, + "outputs": [ + { + "ename": "HfHubHTTPError", + "evalue": "(Request ID: HaGzFE)\n\n403 Forbidden: None.\nCannot access content at: https://h1vdol7jxhje3mpn.us-east-1.aws.endpoints.huggingface.cloud/.\nMake sure your token has the correct permissions.\nForbidden: You don't have the required permissions to complete this action, missing permissions: inference.endpoints.infer.write", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mHTTPError\u001b[0m Traceback (most recent call last)", + "File \u001b[0;32m/opt/anaconda3/envs/llms/lib/python3.11/site-packages/huggingface_hub/utils/_http.py:406\u001b[0m, in \u001b[0;36mhf_raise_for_status\u001b[0;34m(response, endpoint_name)\u001b[0m\n\u001b[1;32m 405\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 406\u001b[0m \u001b[43mresponse\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mraise_for_status\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 407\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m HTTPError \u001b[38;5;28;01mas\u001b[39;00m e:\n", + "File \u001b[0;32m/opt/anaconda3/envs/llms/lib/python3.11/site-packages/requests/models.py:1024\u001b[0m, in \u001b[0;36mResponse.raise_for_status\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1023\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m http_error_msg:\n\u001b[0;32m-> 1024\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m HTTPError(http_error_msg, response\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m)\n", + "\u001b[0;31mHTTPError\u001b[0m: 403 Client Error: Forbidden for url: https://h1vdol7jxhje3mpn.us-east-1.aws.endpoints.huggingface.cloud/", + "\nThe above exception was the direct cause of the following exception:\n", + "\u001b[0;31mHfHubHTTPError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[38], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m client \u001b[38;5;241m=\u001b[39m InferenceClient(CODE_QWEN_URL, token\u001b[38;5;241m=\u001b[39mhf_token)\n\u001b[0;32m----> 2\u001b[0m stream \u001b[38;5;241m=\u001b[39m \u001b[43mclient\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtext_generation\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtext\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdetails\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmax_new_tokens\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m3000\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m r \u001b[38;5;129;01min\u001b[39;00m stream:\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28mprint\u001b[39m(r\u001b[38;5;241m.\u001b[39mtoken\u001b[38;5;241m.\u001b[39mtext, end \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "File \u001b[0;32m/opt/anaconda3/envs/llms/lib/python3.11/site-packages/huggingface_hub/inference/_client.py:2332\u001b[0m, in \u001b[0;36mInferenceClient.text_generation\u001b[0;34m(self, prompt, details, stream, model, adapter_id, best_of, decoder_input_details, do_sample, frequency_penalty, grammar, max_new_tokens, repetition_penalty, return_full_text, seed, stop, stop_sequences, temperature, top_k, top_n_tokens, top_p, truncate, typical_p, watermark)\u001b[0m\n\u001b[1;32m 2307\u001b[0m _set_unsupported_text_generation_kwargs(model, unused_params)\n\u001b[1;32m 2308\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtext_generation( \u001b[38;5;66;03m# type: ignore\u001b[39;00m\n\u001b[1;32m 2309\u001b[0m prompt\u001b[38;5;241m=\u001b[39mprompt,\n\u001b[1;32m 2310\u001b[0m details\u001b[38;5;241m=\u001b[39mdetails,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 2330\u001b[0m watermark\u001b[38;5;241m=\u001b[39mwatermark,\n\u001b[1;32m 2331\u001b[0m )\n\u001b[0;32m-> 2332\u001b[0m \u001b[43mraise_text_generation_error\u001b[49m\u001b[43m(\u001b[49m\u001b[43me\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2334\u001b[0m \u001b[38;5;66;03m# Parse output\u001b[39;00m\n\u001b[1;32m 2335\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m stream:\n", + "File \u001b[0;32m/opt/anaconda3/envs/llms/lib/python3.11/site-packages/huggingface_hub/inference/_common.py:466\u001b[0m, in \u001b[0;36mraise_text_generation_error\u001b[0;34m(http_error)\u001b[0m\n\u001b[1;32m 463\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m exception \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mhttp_error\u001b[39;00m\n\u001b[1;32m 465\u001b[0m \u001b[38;5;66;03m# Otherwise, fallback to default error\u001b[39;00m\n\u001b[0;32m--> 466\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m http_error\n", + "File \u001b[0;32m/opt/anaconda3/envs/llms/lib/python3.11/site-packages/huggingface_hub/inference/_client.py:2302\u001b[0m, in \u001b[0;36mInferenceClient.text_generation\u001b[0;34m(self, prompt, details, stream, model, adapter_id, best_of, decoder_input_details, do_sample, frequency_penalty, grammar, max_new_tokens, repetition_penalty, return_full_text, seed, stop, stop_sequences, temperature, top_k, top_n_tokens, top_p, truncate, typical_p, watermark)\u001b[0m\n\u001b[1;32m 2300\u001b[0m \u001b[38;5;66;03m# Handle errors separately for more precise error messages\u001b[39;00m\n\u001b[1;32m 2301\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 2302\u001b[0m bytes_output \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpost\u001b[49m\u001b[43m(\u001b[49m\u001b[43mjson\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpayload\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtask\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtext-generation\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;66;03m# type: ignore\u001b[39;00m\n\u001b[1;32m 2303\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m HTTPError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m 2304\u001b[0m match \u001b[38;5;241m=\u001b[39m MODEL_KWARGS_NOT_USED_REGEX\u001b[38;5;241m.\u001b[39msearch(\u001b[38;5;28mstr\u001b[39m(e))\n", + "File \u001b[0;32m/opt/anaconda3/envs/llms/lib/python3.11/site-packages/huggingface_hub/inference/_client.py:296\u001b[0m, in \u001b[0;36mInferenceClient.post\u001b[0;34m(self, json, data, model, task, stream)\u001b[0m\n\u001b[1;32m 293\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m InferenceTimeoutError(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInference call timed out: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00murl\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01merror\u001b[39;00m \u001b[38;5;66;03m# type: ignore\u001b[39;00m\n\u001b[1;32m 295\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 296\u001b[0m \u001b[43mhf_raise_for_status\u001b[49m\u001b[43m(\u001b[49m\u001b[43mresponse\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 297\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m response\u001b[38;5;241m.\u001b[39miter_lines() \u001b[38;5;28;01mif\u001b[39;00m stream \u001b[38;5;28;01melse\u001b[39;00m response\u001b[38;5;241m.\u001b[39mcontent\n\u001b[1;32m 298\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m HTTPError \u001b[38;5;28;01mas\u001b[39;00m error:\n", + "File \u001b[0;32m/opt/anaconda3/envs/llms/lib/python3.11/site-packages/huggingface_hub/utils/_http.py:468\u001b[0m, in \u001b[0;36mhf_raise_for_status\u001b[0;34m(response, endpoint_name)\u001b[0m\n\u001b[1;32m 462\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m response\u001b[38;5;241m.\u001b[39mstatus_code \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m403\u001b[39m:\n\u001b[1;32m 463\u001b[0m message \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m 464\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;132;01m{\u001b[39;00mresponse\u001b[38;5;241m.\u001b[39mstatus_code\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m Forbidden: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00merror_message\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 465\u001b[0m \u001b[38;5;241m+\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mCannot access content at: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mresponse\u001b[38;5;241m.\u001b[39murl\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 466\u001b[0m \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mMake sure your token has the correct permissions.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 467\u001b[0m )\n\u001b[0;32m--> 468\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m _format(HfHubHTTPError, message, response) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01me\u001b[39;00m\n\u001b[1;32m 470\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m response\u001b[38;5;241m.\u001b[39mstatus_code \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m416\u001b[39m:\n\u001b[1;32m 471\u001b[0m range_header \u001b[38;5;241m=\u001b[39m response\u001b[38;5;241m.\u001b[39mrequest\u001b[38;5;241m.\u001b[39mheaders\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRange\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "\u001b[0;31mHfHubHTTPError\u001b[0m: (Request ID: HaGzFE)\n\n403 Forbidden: None.\nCannot access content at: https://h1vdol7jxhje3mpn.us-east-1.aws.endpoints.huggingface.cloud/.\nMake sure your token has the correct permissions.\nForbidden: You don't have the required permissions to complete this action, missing permissions: inference.endpoints.infer.write" + ] + } + ], + "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": "1c032ad4", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "127a52e5-ad85-42b7-a0f5-9afda5efe090", + "metadata": {}, + "outputs": [], + "source": [ + "def stream_code_qwen(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 \n", + " " + ] + }, + { + "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": "markdown", + "id": "4b0a6a97-5b8a-4a9b-8ee0-7561e0ced673", + "metadata": {}, + "source": [ + "\n", + " \n", + " \n", + " \n", + " \n", + "
\n", + " \n", + " \n", + "

Thank you to @CloudLlama for an amazing contribution

\n", + " \n", + " A student has contributed a chunk of code to improve this, in the next 2 cells. You can now select which Python porgram to run,\n", + " and a compiler is automatically selected that will work on PC, Windows and Mac. Massive thank you @CloudLlama!\n", + " \n", + "
" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "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": null, + "id": "e42286bc-085c-45dc-b101-234308e58269", + "metadata": {}, + "outputs": [], + "source": [ + "import platform\n", + "\n", + "VISUAL_STUDIO_2022_TOOLS = \"C:\\\\Program Files\\\\Microsoft Visual Studio\\\\2022\\\\Community\\\\Common7\\Tools\\\\VsDevCmd.bat\"\n", + "VISUAL_STUDIO_2019_TOOLS = \"C:\\\\Program Files (x86)\\\\Microsoft Visual Studio\\\\2019\\\\BuildTools\\\\Common7\\\\Tools\\\\VsDevCmd.bat\"\n", + "\n", + "simple_cpp = \"\"\"\n", + "#include \n", + "\n", + "int main() {\n", + " std::cout << \"Hello\";\n", + " return 0;\n", + "}\n", + "\"\"\"\n", + "\n", + "def run_cmd(command_to_run):\n", + " try:\n", + " run_result = subprocess.run(command_to_run, check=True, text=True, capture_output=True)\n", + " return run_result.stdout if run_result.stdout else \"SUCCESS\"\n", + " except:\n", + " return \"\"\n", + "\n", + "def c_compiler_cmd(filename_base):\n", + " my_platform = platform.system()\n", + " my_compiler = []\n", + "\n", + " try:\n", + " with open(\"simple.cpp\", \"w\") as f:\n", + " f.write(simple_cpp)\n", + " \n", + " if my_platform == \"Windows\":\n", + " if os.path.isfile(VISUAL_STUDIO_2022_TOOLS):\n", + " if os.path.isfile(\"./simple.exe\"):\n", + " os.remove(\"./simple.exe\")\n", + " compile_cmd = [\"cmd\", \"/c\", VISUAL_STUDIO_2022_TOOLS, \"&\", \"cl\", \"simple.cpp\"]\n", + " if run_cmd(compile_cmd):\n", + " if run_cmd([\"./simple.exe\"]) == \"Hello\":\n", + " my_compiler = [\"Windows\", \"Visual Studio 2022\", [\"cmd\", \"/c\", VISUAL_STUDIO_2022_TOOLS, \"&\", \"cl\", f\"{filename_base}.cpp\"]]\n", + " \n", + " if not my_compiler:\n", + " if os.path.isfile(VISUAL_STUDIO_2019_TOOLS):\n", + " if os.path.isfile(\"./simple.exe\"):\n", + " os.remove(\"./simple.exe\")\n", + " compile_cmd = [\"cmd\", \"/c\", VISUAL_STUDIO_2019_TOOLS, \"&\", \"cl\", \"simple.cpp\"]\n", + " if run_cmd(compile_cmd):\n", + " if run_cmd([\"./simple.exe\"]) == \"Hello\":\n", + " my_compiler = [\"Windows\", \"Visual Studio 2019\", [\"cmd\", \"/c\", VISUAL_STUDIO_2019_TOOLS, \"&\", \"cl\", f\"{filename_base}.cpp\"]]\n", + " \n", + " if not my_compiler:\n", + " my_compiler=[my_platform, \"Unavailable\", []]\n", + " \n", + " elif my_platform == \"Linux\":\n", + " if os.path.isfile(\"./simple\"):\n", + " os.remove(\"./simple\")\n", + " compile_cmd = [\"g++\", \"simple.cpp\", \"-o\", \"simple\"]\n", + " if run_cmd(compile_cmd):\n", + " if run_cmd([\"./simple\"]) == \"Hello\":\n", + " my_compiler = [\"Linux\", \"GCC (g++)\", [\"g++\", f\"{filename_base}.cpp\", \"-o\", f\"{filename_base}\" ]]\n", + " \n", + " if not my_compiler:\n", + " if os.path.isfile(\"./simple\"):\n", + " os.remove(\"./simple\")\n", + " compile_cmd = [\"clang++\", \"simple.cpp\", \"-o\", \"simple\"]\n", + " if run_cmd(compile_cmd):\n", + " if run_cmd([\"./simple\"]) == \"Hello\":\n", + " my_compiler = [\"Linux\", \"Clang++\", [\"clang++\", f\"{filename_base}.cpp\", \"-o\", f\"{filename_base}\"]]\n", + " \n", + " if not my_compiler:\n", + " my_compiler=[my_platform, \"Unavailable\", []]\n", + " \n", + " elif my_platform == \"Darwin\":\n", + " if os.path.isfile(\"./simple\"):\n", + " os.remove(\"./simple\")\n", + " compile_cmd = [\"clang++\", \"-Ofast\", \"-std=c++17\", \"-march=armv8.5-a\", \"-mtune=apple-m1\", \"-mcpu=apple-m1\", \"-o\", \"simple\", \"simple.cpp\"]\n", + " if run_cmd(compile_cmd):\n", + " if run_cmd([\"./simple\"]) == \"Hello\":\n", + " my_compiler = [\"Macintosh\", \"Clang++\", [\"clang++\", \"-Ofast\", \"-std=c++17\", \"-march=armv8.5-a\", \"-mtune=apple-m1\", \"-mcpu=apple-m1\", \"-o\", f\"{filename_base}\", f\"{filename_base}.cpp\"]]\n", + " \n", + " if not my_compiler:\n", + " my_compiler=[my_platform, \"Unavailable\", []]\n", + " except:\n", + " my_compiler=[my_platform, \"Unavailable\", []]\n", + " \n", + " if my_compiler:\n", + " return my_compiler\n", + " else:\n", + " return [\"Unknown\", \"Unavailable\", []]\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f9ca2e6f-60c1-4e5f-b570-63c75b2d189b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "* Running on local URL: http://127.0.0.1:7866\n", + "\n", + "To create a public link, set `share=True` in `launch()`.\n" + ] + } + ], + "source": [ + "compiler_cmd = c_compiler_cmd(\"optimized\")\n", + "\n", + "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.Column():\n", + " architecture = gr.Radio([compiler_cmd[0]], label=\"Architecture\", interactive=False, value=compiler_cmd[0])\n", + " compiler = gr.Radio([compiler_cmd[1]], label=\"Compiler\", interactive=False, value=compiler_cmd[1])\n", + " with gr.Row():\n", + " convert = gr.Button(\"Convert code\")\n", + " with gr.Row():\n", + " python_run = gr.Button(\"Run Python\")\n", + " if not compiler_cmd[1] == \"Unavailable\":\n", + " cpp_run = gr.Button(\"Run C++\")\n", + " else:\n", + " cpp_run = gr.Button(\"No compiler to run C++\", interactive=False)\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)" + ] + } + ], + "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 +}