mirror of
https://github.com/csunny/DB-GPT.git
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update
This commit is contained in:
csunny
parent
e6fbd037ef
commit
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/home/magic/miniconda3/envs/gpt_env/lib/python3.9/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
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" from .autonotebook import tqdm as notebook_tqdm\n"
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]
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}
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],
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"source": [
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"from llama_index import SimpleDirectoryReader, LangchainEmbedding, GPTListIndex, GPTSimpleVectorIndex, PromptHelper\n",
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"from langchain.embeddings.huggingface import HuggingFaceEmbeddings\n",
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"from llama_index import LLMPredictor\n",
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"import torch\n",
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"from langchain.llms.base import LLM\n",
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"from transformers import pipeline\n",
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"\n",
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"import os\n",
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"os.environ[\"PYTORCH_CUDA_ALLOC_CONF\"] = \"max_split_size_mb:512\"\n",
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"device = \"cuda\" if torch.cuda.is_available() else \"cpu\""
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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": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Loading checkpoint shards: 100%|██████████| 2/2 [00:34<00:00, 17.15s/it]\n"
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]
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},
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{
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"ename": "OutOfMemoryError",
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"evalue": "CUDA out of memory. Tried to allocate 86.00 MiB (GPU 0; 23.62 GiB total capacity; 22.63 GiB already allocated; 37.56 MiB free; 22.63 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mOutOfMemoryError\u001b[0m Traceback (most recent call last)",
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"Cell \u001b[0;32mIn[2], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[39mclass\u001b[39;00m \u001b[39mFlanLLM\u001b[39;00m(LLM):\n\u001b[1;32m 2\u001b[0m model_name \u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39m../../vicuna-7b\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m 3\u001b[0m pipeline \u001b[39m=\u001b[39m pipeline(\u001b[39m\"\u001b[39m\u001b[39mtext-generation\u001b[39m\u001b[39m\"\u001b[39m, model\u001b[39m=\u001b[39mmodel_name, device\u001b[39m=\u001b[39m\u001b[39m0\u001b[39m, model_kwargs\u001b[39m=\u001b[39m{\n\u001b[1;32m 4\u001b[0m \u001b[39m\"\u001b[39m\u001b[39mtorch_dtype\u001b[39m\u001b[39m\"\u001b[39m: torch\u001b[39m.\u001b[39mbfloat16\n\u001b[1;32m 5\u001b[0m })\n",
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"File \u001b[0;32m~/miniconda3/envs/gpt_env/lib/python3.9/site-packages/pydantic/main.py:221\u001b[0m, in \u001b[0;36mpydantic.main.ModelMetaclass.__new__\u001b[0;34m()\u001b[0m\n",
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"File \u001b[0;32m~/miniconda3/envs/gpt_env/lib/python3.9/site-packages/pydantic/fields.py:506\u001b[0m, in \u001b[0;36mpydantic.fields.ModelField.infer\u001b[0;34m()\u001b[0m\n",
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"File \u001b[0;32m~/miniconda3/envs/gpt_env/lib/python3.9/site-packages/pydantic/fields.py:436\u001b[0m, in \u001b[0;36mpydantic.fields.ModelField.__init__\u001b[0;34m()\u001b[0m\n",
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"File \u001b[0;32m~/miniconda3/envs/gpt_env/lib/python3.9/site-packages/pydantic/fields.py:546\u001b[0m, in \u001b[0;36mpydantic.fields.ModelField.prepare\u001b[0;34m()\u001b[0m\n",
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"File \u001b[0;32m~/miniconda3/envs/gpt_env/lib/python3.9/site-packages/pydantic/fields.py:570\u001b[0m, in \u001b[0;36mpydantic.fields.ModelField._set_default_and_type\u001b[0;34m()\u001b[0m\n",
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"File \u001b[0;32m~/miniconda3/envs/gpt_env/lib/python3.9/site-packages/pydantic/fields.py:439\u001b[0m, in \u001b[0;36mpydantic.fields.ModelField.get_default\u001b[0;34m()\u001b[0m\n",
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"File \u001b[0;32m~/miniconda3/envs/gpt_env/lib/python3.9/site-packages/pydantic/utils.py:693\u001b[0m, in \u001b[0;36mpydantic.utils.smart_deepcopy\u001b[0;34m()\u001b[0m\n",
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"File \u001b[0;32m~/miniconda3/envs/gpt_env/lib/python3.9/copy.py:172\u001b[0m, in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m 170\u001b[0m y \u001b[39m=\u001b[39m x\n\u001b[1;32m 171\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 172\u001b[0m y \u001b[39m=\u001b[39m _reconstruct(x, memo, \u001b[39m*\u001b[39;49mrv)\n\u001b[1;32m 174\u001b[0m \u001b[39m# If is its own copy, don't memoize.\u001b[39;00m\n\u001b[1;32m 175\u001b[0m \u001b[39mif\u001b[39;00m y \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m x:\n",
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"File \u001b[0;32m~/miniconda3/envs/gpt_env/lib/python3.9/copy.py:270\u001b[0m, in \u001b[0;36m_reconstruct\u001b[0;34m(x, memo, func, args, state, listiter, dictiter, deepcopy)\u001b[0m\n\u001b[1;32m 268\u001b[0m \u001b[39mif\u001b[39;00m state \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m 269\u001b[0m \u001b[39mif\u001b[39;00m deep:\n\u001b[0;32m--> 270\u001b[0m state \u001b[39m=\u001b[39m deepcopy(state, memo)\n\u001b[1;32m 271\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mhasattr\u001b[39m(y, \u001b[39m'\u001b[39m\u001b[39m__setstate__\u001b[39m\u001b[39m'\u001b[39m):\n\u001b[1;32m 272\u001b[0m y\u001b[39m.\u001b[39m__setstate__(state)\n",
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"File \u001b[0;32m~/miniconda3/envs/gpt_env/lib/python3.9/copy.py:146\u001b[0m, in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m 144\u001b[0m copier \u001b[39m=\u001b[39m _deepcopy_dispatch\u001b[39m.\u001b[39mget(\u001b[39mcls\u001b[39m)\n\u001b[1;32m 145\u001b[0m \u001b[39mif\u001b[39;00m copier \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m--> 146\u001b[0m y \u001b[39m=\u001b[39m copier(x, memo)\n\u001b[1;32m 147\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m 148\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39missubclass\u001b[39m(\u001b[39mcls\u001b[39m, \u001b[39mtype\u001b[39m):\n",
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"File \u001b[0;32m~/miniconda3/envs/gpt_env/lib/python3.9/copy.py:230\u001b[0m, in \u001b[0;36m_deepcopy_dict\u001b[0;34m(x, memo, deepcopy)\u001b[0m\n\u001b[1;32m 228\u001b[0m memo[\u001b[39mid\u001b[39m(x)] \u001b[39m=\u001b[39m y\n\u001b[1;32m 229\u001b[0m \u001b[39mfor\u001b[39;00m key, value \u001b[39min\u001b[39;00m x\u001b[39m.\u001b[39mitems():\n\u001b[0;32m--> 230\u001b[0m y[deepcopy(key, memo)] \u001b[39m=\u001b[39m deepcopy(value, memo)\n\u001b[1;32m 231\u001b[0m \u001b[39mreturn\u001b[39;00m y\n",
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"File \u001b[0;32m~/miniconda3/envs/gpt_env/lib/python3.9/copy.py:172\u001b[0m, in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m 170\u001b[0m y \u001b[39m=\u001b[39m x\n\u001b[1;32m 171\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 172\u001b[0m y \u001b[39m=\u001b[39m _reconstruct(x, memo, \u001b[39m*\u001b[39;49mrv)\n\u001b[1;32m 174\u001b[0m \u001b[39m# If is its own copy, don't memoize.\u001b[39;00m\n\u001b[1;32m 175\u001b[0m \u001b[39mif\u001b[39;00m y \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m x:\n",
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"File \u001b[0;32m~/miniconda3/envs/gpt_env/lib/python3.9/copy.py:270\u001b[0m, in \u001b[0;36m_reconstruct\u001b[0;34m(x, memo, func, args, state, listiter, dictiter, deepcopy)\u001b[0m\n\u001b[1;32m 268\u001b[0m \u001b[39mif\u001b[39;00m state \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m 269\u001b[0m \u001b[39mif\u001b[39;00m deep:\n\u001b[0;32m--> 270\u001b[0m state \u001b[39m=\u001b[39m deepcopy(state, memo)\n\u001b[1;32m 271\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mhasattr\u001b[39m(y, \u001b[39m'\u001b[39m\u001b[39m__setstate__\u001b[39m\u001b[39m'\u001b[39m):\n\u001b[1;32m 272\u001b[0m y\u001b[39m.\u001b[39m__setstate__(state)\n",
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"File \u001b[0;32m~/miniconda3/envs/gpt_env/lib/python3.9/copy.py:146\u001b[0m, in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m 144\u001b[0m copier \u001b[39m=\u001b[39m _deepcopy_dispatch\u001b[39m.\u001b[39mget(\u001b[39mcls\u001b[39m)\n\u001b[1;32m 145\u001b[0m \u001b[39mif\u001b[39;00m copier \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m--> 146\u001b[0m y \u001b[39m=\u001b[39m copier(x, memo)\n\u001b[1;32m 147\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m 148\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39missubclass\u001b[39m(\u001b[39mcls\u001b[39m, \u001b[39mtype\u001b[39m):\n",
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"File \u001b[0;32m~/miniconda3/envs/gpt_env/lib/python3.9/copy.py:230\u001b[0m, in \u001b[0;36m_deepcopy_dict\u001b[0;34m(x, memo, deepcopy)\u001b[0m\n\u001b[1;32m 228\u001b[0m memo[\u001b[39mid\u001b[39m(x)] \u001b[39m=\u001b[39m y\n\u001b[1;32m 229\u001b[0m \u001b[39mfor\u001b[39;00m key, value \u001b[39min\u001b[39;00m x\u001b[39m.\u001b[39mitems():\n\u001b[0;32m--> 230\u001b[0m y[deepcopy(key, memo)] \u001b[39m=\u001b[39m deepcopy(value, memo)\n\u001b[1;32m 231\u001b[0m \u001b[39mreturn\u001b[39;00m y\n",
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" \u001b[0;31m[... skipping similar frames: deepcopy at line 172 (1 times)]\u001b[0m\n",
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"File \u001b[0;32m~/miniconda3/envs/gpt_env/lib/python3.9/copy.py:296\u001b[0m, in \u001b[0;36m_reconstruct\u001b[0;34m(x, memo, func, args, state, listiter, dictiter, deepcopy)\u001b[0m\n\u001b[1;32m 294\u001b[0m \u001b[39mfor\u001b[39;00m key, value \u001b[39min\u001b[39;00m dictiter:\n\u001b[1;32m 295\u001b[0m key \u001b[39m=\u001b[39m deepcopy(key, memo)\n\u001b[0;32m--> 296\u001b[0m value \u001b[39m=\u001b[39m deepcopy(value, memo)\n\u001b[1;32m 297\u001b[0m y[key] \u001b[39m=\u001b[39m value\n\u001b[1;32m 298\u001b[0m \u001b[39melse\u001b[39;00m:\n",
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" \u001b[0;31m[... skipping similar frames: deepcopy at line 172 (2 times), _deepcopy_dict at line 230 (1 times), _reconstruct at line 270 (1 times), deepcopy at line 146 (1 times)]\u001b[0m\n",
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"File \u001b[0;32m~/miniconda3/envs/gpt_env/lib/python3.9/copy.py:296\u001b[0m, in \u001b[0;36m_reconstruct\u001b[0;34m(x, memo, func, args, state, listiter, dictiter, deepcopy)\u001b[0m\n\u001b[1;32m 294\u001b[0m \u001b[39mfor\u001b[39;00m key, value \u001b[39min\u001b[39;00m dictiter:\n\u001b[1;32m 295\u001b[0m key \u001b[39m=\u001b[39m deepcopy(key, memo)\n\u001b[0;32m--> 296\u001b[0m value \u001b[39m=\u001b[39m deepcopy(value, memo)\n\u001b[1;32m 297\u001b[0m y[key] \u001b[39m=\u001b[39m value\n\u001b[1;32m 298\u001b[0m \u001b[39melse\u001b[39;00m:\n",
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" \u001b[0;31m[... skipping similar frames: deepcopy at line 172 (7 times), _deepcopy_dict at line 230 (3 times), _reconstruct at line 270 (3 times), _reconstruct at line 296 (3 times), deepcopy at line 146 (3 times)]\u001b[0m\n",
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"File \u001b[0;32m~/miniconda3/envs/gpt_env/lib/python3.9/copy.py:270\u001b[0m, in \u001b[0;36m_reconstruct\u001b[0;34m(x, memo, func, args, state, listiter, dictiter, deepcopy)\u001b[0m\n\u001b[1;32m 268\u001b[0m \u001b[39mif\u001b[39;00m state \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m 269\u001b[0m \u001b[39mif\u001b[39;00m deep:\n\u001b[0;32m--> 270\u001b[0m state \u001b[39m=\u001b[39m deepcopy(state, memo)\n\u001b[1;32m 271\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mhasattr\u001b[39m(y, \u001b[39m'\u001b[39m\u001b[39m__setstate__\u001b[39m\u001b[39m'\u001b[39m):\n\u001b[1;32m 272\u001b[0m y\u001b[39m.\u001b[39m__setstate__(state)\n",
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"File \u001b[0;32m~/miniconda3/envs/gpt_env/lib/python3.9/copy.py:146\u001b[0m, in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m 144\u001b[0m copier \u001b[39m=\u001b[39m _deepcopy_dispatch\u001b[39m.\u001b[39mget(\u001b[39mcls\u001b[39m)\n\u001b[1;32m 145\u001b[0m \u001b[39mif\u001b[39;00m copier \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m--> 146\u001b[0m y \u001b[39m=\u001b[39m copier(x, memo)\n\u001b[1;32m 147\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m 148\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39missubclass\u001b[39m(\u001b[39mcls\u001b[39m, \u001b[39mtype\u001b[39m):\n",
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"File \u001b[0;32m~/miniconda3/envs/gpt_env/lib/python3.9/copy.py:230\u001b[0m, in \u001b[0;36m_deepcopy_dict\u001b[0;34m(x, memo, deepcopy)\u001b[0m\n\u001b[1;32m 228\u001b[0m memo[\u001b[39mid\u001b[39m(x)] \u001b[39m=\u001b[39m y\n\u001b[1;32m 229\u001b[0m \u001b[39mfor\u001b[39;00m key, value \u001b[39min\u001b[39;00m x\u001b[39m.\u001b[39mitems():\n\u001b[0;32m--> 230\u001b[0m y[deepcopy(key, memo)] \u001b[39m=\u001b[39m deepcopy(value, memo)\n\u001b[1;32m 231\u001b[0m \u001b[39mreturn\u001b[39;00m y\n",
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"File \u001b[0;32m~/miniconda3/envs/gpt_env/lib/python3.9/copy.py:172\u001b[0m, in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m 170\u001b[0m y \u001b[39m=\u001b[39m x\n\u001b[1;32m 171\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m--> 172\u001b[0m y \u001b[39m=\u001b[39m _reconstruct(x, memo, \u001b[39m*\u001b[39;49mrv)\n\u001b[1;32m 174\u001b[0m \u001b[39m# If is its own copy, don't memoize.\u001b[39;00m\n\u001b[1;32m 175\u001b[0m \u001b[39mif\u001b[39;00m y \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m x:\n",
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"File \u001b[0;32m~/miniconda3/envs/gpt_env/lib/python3.9/copy.py:296\u001b[0m, in \u001b[0;36m_reconstruct\u001b[0;34m(x, memo, func, args, state, listiter, dictiter, deepcopy)\u001b[0m\n\u001b[1;32m 294\u001b[0m \u001b[39mfor\u001b[39;00m key, value \u001b[39min\u001b[39;00m dictiter:\n\u001b[1;32m 295\u001b[0m key \u001b[39m=\u001b[39m deepcopy(key, memo)\n\u001b[0;32m--> 296\u001b[0m value \u001b[39m=\u001b[39m deepcopy(value, memo)\n\u001b[1;32m 297\u001b[0m y[key] \u001b[39m=\u001b[39m value\n\u001b[1;32m 298\u001b[0m \u001b[39melse\u001b[39;00m:\n",
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"File \u001b[0;32m~/miniconda3/envs/gpt_env/lib/python3.9/copy.py:153\u001b[0m, in \u001b[0;36mdeepcopy\u001b[0;34m(x, memo, _nil)\u001b[0m\n\u001b[1;32m 151\u001b[0m copier \u001b[39m=\u001b[39m \u001b[39mgetattr\u001b[39m(x, \u001b[39m\"\u001b[39m\u001b[39m__deepcopy__\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39mNone\u001b[39;00m)\n\u001b[1;32m 152\u001b[0m \u001b[39mif\u001b[39;00m copier \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m--> 153\u001b[0m y \u001b[39m=\u001b[39m copier(memo)\n\u001b[1;32m 154\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m 155\u001b[0m reductor \u001b[39m=\u001b[39m dispatch_table\u001b[39m.\u001b[39mget(\u001b[39mcls\u001b[39m)\n",
|
||||
"File \u001b[0;32m~/miniconda3/envs/gpt_env/lib/python3.9/site-packages/torch/nn/parameter.py:55\u001b[0m, in \u001b[0;36mParameter.__deepcopy__\u001b[0;34m(self, memo)\u001b[0m\n\u001b[1;32m 53\u001b[0m \u001b[39mreturn\u001b[39;00m memo[\u001b[39mid\u001b[39m(\u001b[39mself\u001b[39m)]\n\u001b[1;32m 54\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m---> 55\u001b[0m result \u001b[39m=\u001b[39m \u001b[39mtype\u001b[39m(\u001b[39mself\u001b[39m)(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mdata\u001b[39m.\u001b[39;49mclone(memory_format\u001b[39m=\u001b[39;49mtorch\u001b[39m.\u001b[39;49mpreserve_format), \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mrequires_grad)\n\u001b[1;32m 56\u001b[0m memo[\u001b[39mid\u001b[39m(\u001b[39mself\u001b[39m)] \u001b[39m=\u001b[39m result\n\u001b[1;32m 57\u001b[0m \u001b[39mreturn\u001b[39;00m result\n",
|
||||
"\u001b[0;31mOutOfMemoryError\u001b[0m: CUDA out of memory. Tried to allocate 86.00 MiB (GPU 0; 23.62 GiB total capacity; 22.63 GiB already allocated; 37.56 MiB free; 22.63 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation. See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"class FlanLLM(LLM):\n",
|
||||
" model_name = \"../../vicuna-7b\"\n",
|
||||
" pipeline = pipeline(\"text-generation\", model=model_name, device=0, model_kwargs={\n",
|
||||
" \"torch_dtype\": torch.bfloat16\n",
|
||||
" })\n",
|
||||
"\n",
|
||||
" def _call(self, prompt, stop=None):\n",
|
||||
" return self.pipeline(prompt, max_length=9999)[0][\"generated_text\"]\n",
|
||||
"\n",
|
||||
" def _identifying_params(self):\n",
|
||||
" return {\"name_of_model\": self.model_name}\n",
|
||||
"\n",
|
||||
" def _llm_type(self):\n",
|
||||
" return \"custome\"\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"llm_predictor = LLMPredictor(llm=FlanLLM())\n",
|
||||
"hfemb = HuggingFaceEmbeddings()\n",
|
||||
"embed_model = LangchainEmbedding(hfemb)"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "gpt_env",
|
||||
"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": "3.9.16"
|
||||
},
|
||||
"orig_nbformat": 4
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
Loading…
Reference in New Issue
Block a user