01 - Finetune_FLAN-T5-CNN_SAMSum_LoRA.ipynb

{"cells":[{"cell_type":"markdown","metadata":{"id":"btCIbk4TH_RX"},"source":["# Google FLAN-T5-Base Finetuned with LoRA for Chat & Dialogue Summarization"]},{"cell_type":"markdown","metadata":{"id":"r6_EGHP6qyqk"},"source":["This project used the following blog as a reference: https://www.philschmid.de/fine-tune-flan-t5-peft\n","\n","Instead of finetuning flan-t5-base as is, I chose to further finetune a finetuned model \"braindao/flan-t5-cnn\" on chat & dialogue SAMSum dataset. \"braindao/flan-t5-cnn\" had been already finetuned on the CNN Dailymail dataset."]},{"cell_type":"markdown","metadata":{"id":"GOW3EoNFHiok"},"source":["## Dependencies"]},{"cell_type":"code","execution_count":1,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":37236,"status":"ok","timestamp":1682210008274,"user":{"displayName":"Seulkee Lee","userId":"14463507896336644914"},"user_tz":420},"id":"k6ALc6zKGNAL","outputId":"a017d3af-2d29-422b-9a74-75333fa7fd52"},"outputs":[{"name":"stdout","output_type":"stream","text":["Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting git+https://github.com/huggingface/peft.git\n","  Cloning https://github.com/huggingface/peft.git to /tmp/pip-req-build-i8gnczmm\n","  Running command git clone --filter=blob:none --quiet https://github.com/huggingface/peft.git /tmp/pip-req-build-i8gnczmm\n","  Resolved https://github.com/huggingface/peft.git to commit 2822398fbe896f25d4dac5e468624dc5fd65a51b\n","  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n","  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n","  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n","Requirement already satisfied: transformers in /usr/local/lib/python3.8/dist-packages (from peft==0.3.0.dev0) (4.27.4)\n","Collecting accelerate\n","  Downloading accelerate-0.18.0-py3-none-any.whl (215 kB)\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m215.3/215.3 KB\u001b[0m \u001b[31m5.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hRequirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.8/dist-packages (from peft==0.3.0.dev0) (23.0)\n","Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.8/dist-packages (from peft==0.3.0.dev0) (1.22.4)\n","Requirement already satisfied: psutil in /usr/local/lib/python3.8/dist-packages (from peft==0.3.0.dev0) (5.4.8)\n","Requirement already satisfied: pyyaml in /usr/local/lib/python3.8/dist-packages (from peft==0.3.0.dev0) (6.0)\n","Requirement already satisfied: torch>=1.13.0 in /usr/local/lib/python3.8/dist-packages (from peft==0.3.0.dev0) (1.13.1+cu116)\n","Requirement already satisfied: typing-extensions in /usr/local/lib/python3.8/dist-packages (from torch>=1.13.0->peft==0.3.0.dev0) (4.5.0)\n","Requirement already satisfied: filelock in /usr/local/lib/python3.8/dist-packages (from transformers->peft==0.3.0.dev0) (3.9.0)\n","Requirement already satisfied: huggingface-hub<1.0,>=0.11.0 in /usr/local/lib/python3.8/dist-packages (from transformers->peft==0.3.0.dev0) (0.13.4)\n","Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.8/dist-packages (from transformers->peft==0.3.0.dev0) (2022.6.2)\n","Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.8/dist-packages (from transformers->peft==0.3.0.dev0) (4.64.1)\n","Requirement already satisfied: tokenizers!=0.11.3,<0.14,>=0.11.1 in /usr/local/lib/python3.8/dist-packages (from transformers->peft==0.3.0.dev0) (0.13.3)\n","Requirement already satisfied: requests in /usr/local/lib/python3.8/dist-packages (from transformers->peft==0.3.0.dev0) (2.25.1)\n","Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.8/dist-packages (from requests->transformers->peft==0.3.0.dev0) (2.10)\n","Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.8/dist-packages (from requests->transformers->peft==0.3.0.dev0) (1.26.14)\n","Requirement already satisfied: chardet<5,>=3.0.2 in /usr/local/lib/python3.8/dist-packages (from requests->transformers->peft==0.3.0.dev0) (4.0.0)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.8/dist-packages (from requests->transformers->peft==0.3.0.dev0) (2022.12.7)\n","Building wheels for collected packages: peft\n","  Building wheel for peft (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n","  Created wheel for peft: filename=peft-0.3.0.dev0-py3-none-any.whl size=50501 sha256=32dea0e97755e3463015c07458f227de260079f827afebe749c05e5c02f747bb\n","  Stored in directory: /tmp/pip-ephem-wheel-cache-pvro6130/wheels/95/fe/57/a484616f9bd99820cb946c7c3d2b1b492423b504356b0797dd\n","Successfully built peft\n","Installing collected packages: accelerate, peft\n","Successfully installed accelerate-0.18.0 peft-0.3.0.dev0\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m6.8/6.8 MB\u001b[0m \u001b[31m48.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m462.8/462.8 KB\u001b[0m \u001b[31m29.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m212.8/212.8 KB\u001b[0m \u001b[31m17.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m76.3/76.3 MB\u001b[0m \u001b[31m20.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m66.4/66.4 KB\u001b[0m \u001b[31m2.9 MB/s\u001b[0m eta 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py7zr\n"]},{"cell_type":"code","execution_count":2,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":387,"referenced_widgets":["2f973fc9496e4076b291164dc40c9c47","cc30a01bfd9c438cb8459e85a2b68a37","d08c34fba9964d00af4e3dfc6799d596","24ff225c42f64139ae7024c1a80c5e00","bea9f3f00c8040fa9b78566be770e434","85d14cd3ca8a4932b47a35a265104755","04367785afc54d74bc6c3cf53fd69dba","a87163e3e06b46c5aa64cb57a065f106","0dd2872cf940421cafb6b61772e830f5","5437aca23d1e4242ad7dbcf80a15c82f","6538d1a3b5cb473398ff3368bb85aa81","5c9744ebafd044d88edb4e90b06acbd1","e768ae5951c44d6db328a95dfecb95ca","999f67946aa14c6da5b42219d6fbc73d","c30736aac9a84c65a5d2525b6c5957c6","89ec271045a848fdaa41ff068c9d1de6","fac07f158b9d4123b89a4e1e1dce1ae3"]},"executionInfo":{"elapsed":448,"status":"ok","timestamp":1682210022983,"user":{"displayName":"Seulkee Lee","userId":"14463507896336644914"},"user_tz":420},"id":"-ugIBrgtMzlB","outputId":"b120c339-2869-4fc6-9540-513c238c3ac1"},"outputs":[{"name":"stdout","output_type":"stream","text":["Token is valid.\n","Your token has been saved in your configured git credential helpers (store).\n","Your token has been saved to /root/.cache/huggingface/token\n","Login successful\n"]}],"source":["from huggingface_hub import notebook_login\n","\n","notebook_login()"]},{"cell_type":"markdown","metadata":{"id":"RHw93NFDHnPG"},"source":["## Dataset Preprocessing"]},{"cell_type":"markdown","metadata":{"id":"AuSHVZiBIpXq"},"source":["### Load Dataset SAMSum"]},{"cell_type":"code","execution_count":3,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":268,"referenced_widgets":["d8cb7e4626aa41bb98df60b6da40db7b","96aaf4ba31f64ae28b1f66f12b999e40","74f63dc80e4d49bea59069978d365d87","4931e4e0596c4dc1bd6fad2f484c2950","40fbf5a57a6740efbd9ec23e4f94daa0","b1d271a3f1714efc829b8801e4acb082","60e16c977cce4976a4ffa029f0625238","90c2d43f276c4fefa4b336deab8d7c79","82b7965348714af2b07c10afd1904f6d","76b3fec725f34543aa5b368a30f4e858","c5762f2ef0b449c0a94496a8b0ba202e","c5e5348c98004fd896894ed39a460b94","0031a59b28e247a48615dff03a11b8c9","6f2455c862284e9893018b97f8842752","4f6d9ea97f5b49cb862a365f7e218c6d","28537e68f6c645dabeca79f36f640ab1","32d3c703e756476d9f89139f539f8140","fb710ffdc0af4ab6a9492cf396570296","d03b5d3daaf8465fa6f0beb26e9673b1","ccca6c9d79cd4148abbe2abf3f532ed7","23fb27f76e8e46029c7ee161acb62cab","503286535e144abfb73a2bfb7b9929cf","4748c3f769c64b69bbac53018d223a1b","018fe51f08754a28bb7b22a8ceea7326","d01fb490d08949f4bb9b50ab37b342a1","2955df8d653f47b195f29aea193d892a","93054a3f1392408096c4ed171e69d2ef","951d7685b3494eee8898c0cb785657c8","0ed752867e4b4a94bdaf34210aa06ac6","5f1e385a2d8541d3b745f48c64057051","a45b496de37c4d859d8b031d856608e8","dea782f322644d50bff025675e731a4e","3164d816adb8474685442d280b540d22","2b51a932338c4695bb26f8e33a73d278","5ca975cef74541e0a2edd162be6b6ef1","3845fc23e07943c088f6d7f64541c940","4f45477678384b3b9f9b542de1e2dc33","e50e571f50f141cb85b4667ceca422a7","0d5e29dcc7c34144b8201bded0209e1e","e979529405384f3cb635b4b54e347c77","a3e86084405049ebbefacaa545572dbd","7775187077b64c4ba0c7c32a5257025e","0047b7f87514442b984e6a2b726b7589","734e63839b814d0f8d33211339259517","a50be96193f54d8b9b6b28b09d5c57f5","8351feb4b5b34a899e1e5a3b656921f5","7672fdb01f404864a1e22721e2cba1a1","042220dcd5ff4e2481639284f21fe808","3212ce8fc35040a7bfb8ce68172148f8","48942b374b2840caa8728540651a818d","6f988426010e4cb7aff098db1853824f","bd4a50bcd51f489195f9e38b70c88707","d442d45412cb4a4fbdd56f0d3f3ef209","3e17241464324dd492c7cfe07c86bf60","c7c40fefdde9470aa905dd10d908ac92","6ed5edfbf7ff46b2abfc720682cd026a","e851fd0042934f9f8e66c6028936f659","9369c07037ab469c9a994fd9434ed30d","46e6bca837d540a1bdfc2bf8f47878b8","9657edf729b94ea1a7128fd99e658c2a","88330c88aef54e9eb8ea83308c9b43de","2aaa389c2b0348b1b9c91adb0ca9b85c","b23e180f7f14437a80dd61af73c0dbb8","f0aade0534a24b4f9b2804e754e0ffa0","40659a755bad4671be184754700ef232","b0c922a8e912438f9aeada0878e0f24d","eb5acae3fe6f4fd580201d7b8f73a3e6","67a7b6569dc64bd78da29bf1ded642fb","d659d032721746a0850b91d28a2796f6","ca3172d67bfe4a988892925488d8d8c2","5a8546859506459aa3bc156c093d43d5","c467d081e22f4c56b1088ab6361204d0","33f846bb407b44a59ba2099c89f4e3b4","86b787b3a037457789eaa37c68b49742","22a758489bb443deaff0bde119e98820","a7467455d8974e2f8b7abf2fa148199a","63f2adff5f834ef7aad361f9c4ce7578","f35253243ae04e868c11d461f6d96ff4","b04e6be0ae6b4512970e7d12bc36a24e","b6076c0f1cfd4e20811d276151fd9fd0","5136ab6bd30946bc86d103689fff757a","62c4d522368c4c9c8d9fb31de7e9b517","2eb26e06234645b3ad9dbe4c6bf26f77","c8be41f5e726407caac864d014f738ba","2ff53c5112a74b08b7b4bdb7216785ee","eb7ffbc967fa4d4db25fa52347aaeda2","cdc1d716e491490db96986100477bb8b","b6df9a6da7f6453c9afa7aed75202f92"]},"executionInfo":{"elapsed":4800,"status":"ok","timestamp":1682210041431,"user":{"displayName":"Seulkee Lee","userId":"14463507896336644914"},"user_tz":420},"id":"Gg0yS2urHmpn","outputId":"7fb9557f-9124-4aec-8c98-3fa1f67d4cd8"},"outputs":[{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"d8cb7e4626aa41bb98df60b6da40db7b","version_major":2,"version_minor":0},"text/plain":["Downloading builder script:   0%|          | 0.00/3.36k [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"c5e5348c98004fd896894ed39a460b94","version_major":2,"version_minor":0},"text/plain":["Downloading metadata:   0%|          | 0.00/1.58k [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"4748c3f769c64b69bbac53018d223a1b","version_major":2,"version_minor":0},"text/plain":["Downloading readme:   0%|          | 0.00/7.04k [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["Downloading and preparing dataset samsum/samsum to /root/.cache/huggingface/datasets/samsum/samsum/0.0.0/f1d7c6b7353e6de335d444e424dc002ef70d1277109031327bc9cc6af5d3d46e...\n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"2b51a932338c4695bb26f8e33a73d278","version_major":2,"version_minor":0},"text/plain":["Downloading data:   0%|          | 0.00/2.94M [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"a50be96193f54d8b9b6b28b09d5c57f5","version_major":2,"version_minor":0},"text/plain":["Generating train split:   0%|          | 0/14732 [00:00<?, ? examples/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"6ed5edfbf7ff46b2abfc720682cd026a","version_major":2,"version_minor":0},"text/plain":["Generating test split:   0%|          | 0/819 [00:00<?, ? examples/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"eb5acae3fe6f4fd580201d7b8f73a3e6","version_major":2,"version_minor":0},"text/plain":["Generating validation split:   0%|          | 0/818 [00:00<?, ? examples/s]"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["Dataset samsum downloaded and prepared to /root/.cache/huggingface/datasets/samsum/samsum/0.0.0/f1d7c6b7353e6de335d444e424dc002ef70d1277109031327bc9cc6af5d3d46e. Subsequent calls will reuse this data.\n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"f35253243ae04e868c11d461f6d96ff4","version_major":2,"version_minor":0},"text/plain":["  0%|          | 0/3 [00:00<?, ?it/s]"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["Train dataset size: 14732\n","Test dataset size: 819\n"]}],"source":["from datasets import load_dataset\n","\n","# Load dataset from the hub\n","dataset = load_dataset(\"samsum\")\n","\n","print(f\"Train dataset size: {len(dataset['train'])}\")\n","print(f\"Test dataset size: {len(dataset['test'])}\")\n","\n","# Train dataset size: 14732\n","# Test dataset size: 819"]},{"cell_type":"markdown","metadata":{"id":"B6kaDBDBH3pH"},"source":["### Initiate Tokenizer for FLAN-T5"]},{"cell_type":"code","execution_count":4,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":145,"referenced_widgets":["f5d573ed2b3b4cff8050a293fe044ca1","555ffb46a7f649a6be5e8d7c4634d3e6","bf8304f7db08448aa248d77fdec485f2","b08841dfe6ff49a5972d7b5d9691e8d7","7882ea06f1cb469f8e8620dbd18f5e84","e303c3c5563a418f8198887bf8b63db1","50ef2caffeb1432bab6035e737120dec","f2183c0ff6844af8bd4bf434960f11e8","46879f38da2d476db3f4997eee395b67","42529647ce134c63a79daa8081f969ea","2f75ab0a66704120a92c92c1ba598056","fa068354345b4352b67a103f7eb83822","ac99cd7571a047ec9b2adf51961fc2df","485d3ce45ed84291a5eedae20d72c870","16762f0a38654b478e9dc95af1edc9cf","8b03ea89a3bd41d0bda0f0a52f0114c3","2350a09dedde46498987a39f9e0a63d6","786d96bd693e41dab161e7d834107c9a","ac72b7caae984bebb1a499c84d914345","b139c46bf50e493a82d49edfebe19a52","e10f9a1f2eca44c6b6156e0ffa23bf28","1336a4af104c45f1a07ed85b6f85935b","64536ed583854145a864bc7316ee603f","52e8172f44ed4609af1ac97006f51e8e","c07449e6e9eb4163b435862bb5ca9fd6","49bb80b316de4541b5a1628662c1c2c3","d1e8bbc71c22480a9a5686d1232fa06e","0e5c2a43b6d340d9a8b04944d74c7b0c","f1c58b53141a42d79790f75c30d470ae","fb6eec07ffc643e79545681e2927c5b0","f7d784e77d444fa3a83a7b01785f894a","a9b972f0ece340a3977afed8e71ea2b3","c46d7a0fedc241ea90b666cfd02419c7","5ae6d6b558df4b5cbcf6611bf8b3eb22","dc3c862679284544ac62cf7b02b57639","a6a1247e336d494991a7882b570ba4e9","7147aa24557a4226bcfae7696d86aa24","cfd927a15df84fc4889801683e57fa3b","acd23f7d636445c5b9089d0d926f7388","77550330b95545eb9412990257eb52ff","8bb9389b0f7c44ac94500b07b634a806","df5800e63ce8492fb5d892edd1bf9fe3","4f63324609ed497dba149ba30cc1841b","844809be71bd43b4b6e2b9f0ad6664de"]},"executionInfo":{"elapsed":1958,"status":"ok","timestamp":1682210055402,"user":{"displayName":"Seulkee Lee","userId":"14463507896336644914"},"user_tz":420},"id":"sa3r_daSHmsc","outputId":"1f323df9-1bb3-4499-b6d2-ad1857320434"},"outputs":[{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"f5d573ed2b3b4cff8050a293fe044ca1","version_major":2,"version_minor":0},"text/plain":["Downloading (…)okenizer_config.json:   0%|          | 0.00/2.50k [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"fa068354345b4352b67a103f7eb83822","version_major":2,"version_minor":0},"text/plain":["Downloading spiece.model:   0%|          | 0.00/792k [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"64536ed583854145a864bc7316ee603f","version_major":2,"version_minor":0},"text/plain":["Downloading (…)/main/tokenizer.json:   0%|          | 0.00/2.42M [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"5ae6d6b558df4b5cbcf6611bf8b3eb22","version_major":2,"version_minor":0},"text/plain":["Downloading (…)cial_tokens_map.json:   0%|          | 0.00/2.20k [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"}],"source":["from transformers import AutoTokenizer, AutoModelForSeq2SeqLM\n","\n","model_id=\"braindao/flan-t5-cnn\"\n","\n","# Load tokenizer of FLAN-t5\n","tokenizer = AutoTokenizer.from_pretrained(model_id)\n"]},{"cell_type":"markdown","metadata":{"id":"V4rWIEBBIyRN"},"source":["### Preprocess"]},{"cell_type":"code","execution_count":5,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":117,"referenced_widgets":["7c8d76d5860e4fd9b1d848fe1ca03363","5369dde947ed4ce582267d16e14b69f2","d0cf949ba3cf4e96abc83a64df8bb284","29518c5277144bbb89a54bf4d568d919","c122de6577ac45ac83a8bdca83631f08","fff71db540a740c29196e856b5238700","03a55fc4dfd541b59e7e861a62754167","110394fd349e4e86b783bece6f7a8b12","886dd839738a4c878f9f4d7ee688659a","7a33c0fab81e44fe8f284ee0b4fc9b19","4ec58bd60a70488faa9aeefb6582c976","34a9e913729a426a9ffb24822a1df47a","cabcf585f1e745fb83b7e7453fbad1ba","9742470155664e0091f34175270b72f6","539ba1cb870e4998801781ca7195b995","2731229244034030a47e725c63bcbc9c","3e94ab644ee0417f89adcd68867176ca","a7f2b520445e4f71a7e79f969a0de620","6efafa1968524476afd8c470b139e41a","d5e609ae43494386bf8fb52444aa6dbe","fe644d903dc4461ea29760b6d074dc09","8213d6bb23f14f329c0ca4d41903e5cb"]},"executionInfo":{"elapsed":3760,"status":"ok","timestamp":1682210062724,"user":{"displayName":"Seulkee Lee","userId":"14463507896336644914"},"user_tz":420},"id":"9KgoBITpHmze","outputId":"71ebe6a0-89c6-4965-a5d2-929a47b4a2a6"},"outputs":[{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"7c8d76d5860e4fd9b1d848fe1ca03363","version_major":2,"version_minor":0},"text/plain":["  0%|          | 0/16 [00:00<?, ?ba/s]"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["Max source length: 255\n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"34a9e913729a426a9ffb24822a1df47a","version_major":2,"version_minor":0},"text/plain":["  0%|          | 0/16 [00:00<?, ?ba/s]"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["Max target length: 50\n"]}],"source":["from datasets import concatenate_datasets\n","import numpy as np\n","# The maximum total input sequence length after tokenization.\n","# Sequences longer than this will be truncated, sequences shorter will be padded.\n","tokenized_inputs = concatenate_datasets([dataset[\"train\"], dataset[\"test\"]]).map(lambda x: tokenizer(x[\"dialogue\"], truncation=True), batched=True, remove_columns=[\"dialogue\", \"summary\"])\n","input_lenghts = [len(x) for x in tokenized_inputs[\"input_ids\"]]\n","# take 85 percentile of max length for better utilization\n","max_source_length = int(np.percentile(input_lenghts, 85))\n","print(f\"Max source length: {max_source_length}\")\n","\n","# The maximum total sequence length for target text after tokenization.\n","# Sequences longer than this will be truncated, sequences shorter will be padded.\"\n","tokenized_targets = concatenate_datasets([dataset[\"train\"], dataset[\"test\"]]).map(lambda x: tokenizer(x[\"summary\"], truncation=True), batched=True, remove_columns=[\"dialogue\", \"summary\"])\n","target_lenghts = [len(x) for x in tokenized_targets[\"input_ids\"]]\n","# take 90 percentile of max length for better utilization\n","max_target_length = int(np.percentile(target_lenghts, 90))\n","print(f\"Max target length: {max_target_length}\")"]},{"cell_type":"code","execution_count":6,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":131,"referenced_widgets":["4639e0811bf54fe9a968279d4edb22b8","17652ccf04b54e31a8f19588c2373c3f","d509696c680d4670bc13556fa52272ce","bdf95b284e95444296b6218c43a11517","98c7237146de41faad36f729658e7c35","20368da4c33242b9b50174706351fbda","2924d29d91d347ac9583cff5bb30f5a6","5f7050e2514f4290a883821375bc72c7","4a72f1abe58f4da3bc889be91d533f02","11c04c5b10264770a9fc669a1b46b8ab","264c40143d6a4281aa41320d5d622d38","0f67d9273f7b48aaa19a0be07c390e59","4bac02f08ff54f8aa718442ce43d60c3","3242f1b1805e42bc9c1af95440e8d77c","fff31a2f4a764e97a6c7c87e2368b111","213b5872673341179f8aae48da5d71db","018fc485bbde4f9eb3ac786f05de790a","37871542fccd4225a73c7e0b53b218fc","08cfb2a77c5a4659a4a23f23073479b9","8c9ba8a950354aecaf1266eae9153d93","cea1a9b425f040a19f7ef56e0f64518a","cd4ecbd1dbd642db8953c282d4077e27","49d32c8b305c4ffa993b0147a63e15bb","a1b37926ce56455baf0bce64d161ca5e","e4ac7df44d884259982df62ae55411c4","b664dac15e654bd0ad704c4efc42d33d","0ebd3f66641b4445a776dbd796dd8b41","02da4799bf424f3582623647e5fefb75","b5891c47433f4a879833b937b77fdfee","928c065f37414ad983b6e555163d318a","2e8caa5a4e774715825309c9b57be6b9","27a403fbe2254ab3b15364db08ab023d","c12fa9b97512402ab26dd9d4f76ee2b8","f9428afd4a574b2787b1973c2f176adc","017fbcfc4bf547d5bd909bc1d3343d77","0b459465390e4ffc9fbf0d8edbc27832","6ad83ca99cf04b81b0973414de5f9027","36683d8354ab4912b05751e6341bf2c9","0a4a31e33657468db72263d84494a197","e8424a6f7a5f4919baf3289e5e875390","d456395d740547559d1aaf233f18e966","2c6031bd753842f7991aa4e3aa0ed3ef","150d45e349cb4f189f3531c7b1f3dd00","5989d362f5214c7e9eeacdbde1af307f","76d55978339c4eb280ddbdce9d201607","29b26bf0b885459b9e3f23d7b8d26f6c","b5e1e13461a246b5b6c2c9a215321bd3","1466e88eca5c4abc9ba008323667eeec","3ed1f395de124674baf1b746808b870a","4cdcc1f7c79c48399075f7d786f481e3","94fc76aa09b34de0ba1d2e150d8881d5","6327412e4c034ef285c04e260cc872ef","12023dc97ac74d9ea8044cadec751200","a1b15965e687416b81d8233120c72be8","96a7b71a1bc24f0a85beab6f095a9a38"]},"executionInfo":{"elapsed":5100,"status":"ok","timestamp":1682210119482,"user":{"displayName":"Seulkee Lee","userId":"14463507896336644914"},"user_tz":420},"id":"0ewtQbEFHm1n","outputId":"8c96d5c0-a075-476f-8f7d-480016889bf1"},"outputs":[{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"4639e0811bf54fe9a968279d4edb22b8","version_major":2,"version_minor":0},"text/plain":["  0%|          | 0/15 [00:00<?, ?ba/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"0f67d9273f7b48aaa19a0be07c390e59","version_major":2,"version_minor":0},"text/plain":["  0%|          | 0/1 [00:00<?, ?ba/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"49d32c8b305c4ffa993b0147a63e15bb","version_major":2,"version_minor":0},"text/plain":["  0%|          | 0/1 [00:00<?, ?ba/s]"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["Keys of tokenized dataset: ['input_ids', 'attention_mask', 'labels']\n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"f9428afd4a574b2787b1973c2f176adc","version_major":2,"version_minor":0},"text/plain":["Saving the dataset (0/1 shards):   0%|          | 0/14732 [00:00<?, ? examples/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"76d55978339c4eb280ddbdce9d201607","version_major":2,"version_minor":0},"text/plain":["Saving the dataset (0/1 shards):   0%|          | 0/819 [00:00<?, ? examples/s]"]},"metadata":{},"output_type":"display_data"}],"source":["def preprocess_function(sample, padding=\"max_length\"):\n","    # add prefix to the input for t5\n","    inputs = [\"summarize: \" + item for item in sample[\"dialogue\"]]\n","\n","    # tokenize inputs\n","    model_inputs = tokenizer(inputs, max_length=max_source_length, padding=padding, truncation=True)\n","\n","    # Tokenize targets with the `text_target` keyword argument\n","    labels = tokenizer(text_target=sample[\"summary\"], max_length=max_target_length, padding=padding, truncation=True)\n","\n","    # If we are padding here, replace all tokenizer.pad_token_id in the labels by -100 when we want to ignore\n","    # padding in the loss.\n","    if padding == \"max_length\":\n","        labels[\"input_ids\"] = [\n","            [(l if l != tokenizer.pad_token_id else -100) for l in label] for label in labels[\"input_ids\"]\n","        ]\n","\n","    model_inputs[\"labels\"] = labels[\"input_ids\"]\n","    return model_inputs\n","\n","tokenized_dataset = dataset.map(preprocess_function, batched=True, remove_columns=[\"dialogue\", \"summary\", \"id\"])\n","print(f\"Keys of tokenized dataset: {list(tokenized_dataset['train'].features)}\")\n","\n","# save datasets to disk for later easy loading\n","tokenized_dataset[\"train\"].save_to_disk(\"data-sam/train\")\n","tokenized_dataset[\"test\"].save_to_disk(\"data-sam/eval\")\n"]},{"cell_type":"markdown","metadata":{"id":"mz5tdnSiKBZk"},"source":["## Fine-Tune T5 with PEFT LoRA and bnb int-8"]},{"cell_type":"code","execution_count":7,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":417,"referenced_widgets":["685dcf26956b47bd9e27ab0f9fcf8508","267c1938b455474fb89c1a425376c4e7","848e506f340b4c36b3610416218f62f2","fe7e3f33aa094312af6526fe94d08de4","307f0426147b4f56a940ee0bd4dd65ed","73b8cb10751c4d7bb97da85ad4c198d5","21fba13ae6cf4547a96ee14d0eec2984","56257903e39844dcb5c6d3fe71be43ac","b80dcd361a8447b2b19c88d180ec5166","899cfee5529f4fd8871249eb38a0bb0f","a07dc0da354e49eeaf59b3658865b971","515c0769610047a69dd6fb1d3db929dd","6adb3f54c63b49aabf43b88fb84f9f31","ca007b4cb286493484ee6c1332166f7a","21f68dc52fe74703b9354a97a3cbb038","f9b19312c5c84abda26d58b32f135ab1","3b217243410443d680ca32888a1d7c95","745960c948cb495093222a0be2b4f5f0","c473b119789146128d84d6dfcadff0a8","e7eaa643506e469ebc61ee9499986f3b","f77f0c0e20fa459db7b9366d5b933aee","bc908b50510d4d359b4097b4ec6f6d8e","0ed434ac886b4f91b4d940be8f8956c9","0581505fa4ba4b56b20ff62a671bc9e1","fcd6133b01fb49c68adb63ed915cb953","4056eaca542c49c6ad2b72a3c51f5334","ac5700b19aa34764916995ffb9a94dad","9a752695473a43949402e42edac6f602","d892dc8568884567a9d15cdca05a7975","821a73561afc4b7db376b6807a8278a2","6883b67e8aa8495f8d9a95f70024e409","5d1ff8abd7ac40f59c366addd5e26eeb","4401782355e249d0a12905932de7ef41"]},"executionInfo":{"elapsed":32330,"status":"ok","timestamp":1682210166636,"user":{"displayName":"Seulkee Lee","userId":"14463507896336644914"},"user_tz":420},"id":"mUnbXEu-I7TU","outputId":"79f65db8-6e86-4b4c-97a6-e0904f96c9a4"},"outputs":[{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"685dcf26956b47bd9e27ab0f9fcf8508","version_major":2,"version_minor":0},"text/plain":["Downloading (…)lve/main/config.json:   0%|          | 0.00/1.53k [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"name":"stderr","output_type":"stream","text":["Overriding torch_dtype=None with `torch_dtype=torch.float16` due to requirements of `bitsandbytes` to enable model loading in mixed int8. Either pass torch_dtype=torch.float16 or don't pass this argument at all to remove this warning.\n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"515c0769610047a69dd6fb1d3db929dd","version_major":2,"version_minor":0},"text/plain":["Downloading pytorch_model.bin:   0%|          | 0.00/990M [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["\n","===================================BUG REPORT===================================\n","Welcome to bitsandbytes. For bug reports, please submit your error trace to: https://github.com/TimDettmers/bitsandbytes/issues\n","================================================================================\n","CUDA_SETUP: WARNING! libcudart.so not found in any environmental path. Searching /usr/local/cuda/lib64...\n","CUDA SETUP: CUDA runtime path found: /usr/local/cuda/lib64/libcudart.so\n","CUDA SETUP: Highest compute capability among GPUs detected: 8.0\n","CUDA SETUP: Detected CUDA version 118\n","CUDA SETUP: Loading binary /usr/local/lib/python3.8/dist-packages/bitsandbytes/libbitsandbytes_cuda118.so...\n"]},{"name":"stderr","output_type":"stream","text":["/usr/local/lib/python3.8/dist-packages/bitsandbytes/cuda_setup/main.py:136: UserWarning: /usr/lib64-nvidia did not contain libcudart.so as expected! Searching further paths...\n","  warn(msg)\n","/usr/local/lib/python3.8/dist-packages/bitsandbytes/cuda_setup/main.py:136: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('/env/python')}\n","  warn(msg)\n","/usr/local/lib/python3.8/dist-packages/bitsandbytes/cuda_setup/main.py:136: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('//ipykernel.pylab.backend_inline'), PosixPath('module')}\n","  warn(msg)\n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"0ed434ac886b4f91b4d940be8f8956c9","version_major":2,"version_minor":0},"text/plain":["Downloading (…)neration_config.json:   0%|          | 0.00/147 [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"}],"source":["from transformers import AutoModelForSeq2SeqLM\n","\n","# huggingface hub model id\n","# model_id = \"philschmid/flan-t5-xxl-sharded-fp16\"\n","\n","# load model from the hub\n","model = AutoModelForSeq2SeqLM.from_pretrained(model_id, load_in_8bit=True, device_map=\"auto\") # load_in_8bit=True, \n"]},{"cell_type":"markdown","metadata":{"id":"EM_OsjmVK6Bx"},"source":["### Prepare the model for the LoRA int-8 training using peft."]},{"cell_type":"code","execution_count":8,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":569,"status":"ok","timestamp":1682210172191,"user":{"displayName":"Seulkee Lee","userId":"14463507896336644914"},"user_tz":420},"id":"IwGkyrbSI7V7","outputId":"8265fef7-d04e-46c6-dda5-d5cd5c1558ed"},"outputs":[{"name":"stdout","output_type":"stream","text":["trainable params: 1769472 || all params: 249347328 || trainable%: 0.7096414524241463\n"]}],"source":["from peft import LoraConfig, get_peft_model, prepare_model_for_int8_training, TaskType\n","\n","# Define LoRA Config\n","lora_config = LoraConfig(\n"," r=16,\n"," lora_alpha=32,\n"," target_modules=[\"q\", \"v\"], # 'None' to let peft figure out the target modules for the type of model\n"," lora_dropout=0.05,\n"," bias=\"none\",\n"," task_type=TaskType.SEQ_2_SEQ_LM\n",")\n","# prepare int-8 model for training\n","model = prepare_model_for_int8_training(model) \n","\n","# add LoRA adaptor\n","model = get_peft_model(model, lora_config)\n","model.print_trainable_parameters()\n","\n","# trainable params: 18874368 || all params: 11154206720 || trainable%: 0.16921300163961817\n"]},{"cell_type":"markdown","metadata":{"id":"kSGqLGr5LRl4"},"source":["### Create a DataCollator that will take care of padding inputs and labels. \n","Use the DataCollatorForSeq2Seq from the HF Transformers library."]},{"cell_type":"code","execution_count":9,"metadata":{"executionInfo":{"elapsed":4092,"status":"ok","timestamp":1682210195765,"user":{"displayName":"Seulkee Lee","userId":"14463507896336644914"},"user_tz":420},"id":"RUqms8VeI7Yh"},"outputs":[],"source":["from transformers import DataCollatorForSeq2Seq\n","\n","# we want to ignore tokenizer pad token in the loss\n","label_pad_token_id = -100\n","# Data collator\n","data_collator = DataCollatorForSeq2Seq(\n","    tokenizer,\n","    model=model,\n","    label_pad_token_id=label_pad_token_id,\n","    pad_to_multiple_of=8\n",")\n"]},{"cell_type":"markdown","metadata":{"id":"ghcjYslmLnjm"},"source":["### Define the hyperparameters (TrainingArguments) for training"]},{"cell_type":"code","execution_count":10,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":8283,"status":"ok","timestamp":1682210287283,"user":{"displayName":"Seulkee Lee","userId":"14463507896336644914"},"user_tz":420},"id":"IfceO_koLjrG","outputId":"beb4d77c-c1af-4b9a-f2bb-ca9d28825710"},"outputs":[{"name":"stderr","output_type":"stream","text":["Cloning https://huggingface.co/sooolee/flan-t5-base-cnn-samsum-lora into local empty directory.\n","WARNING:huggingface_hub.repository:Cloning https://huggingface.co/sooolee/flan-t5-base-cnn-samsum-lora into local empty directory.\n"]}],"source":["from transformers import Seq2SeqTrainer, Seq2SeqTrainingArguments\n","\n","# output_dir=\"lora-bart-large-cnn-ss\"\n","\n","output_dir=\"flan-t5-base-cnn-samsum-lora\"\n","\n","# Define training args\n","training_args = Seq2SeqTrainingArguments(\n","    output_dir=output_dir,\n","\tauto_find_batch_size=True,\n","    learning_rate=1e-3, # higher learning rate\n","    num_train_epochs=5,\n","    logging_dir=f\"{output_dir}/logs\",\n","    logging_strategy=\"steps\",\n","    logging_steps=500,\n","    save_strategy=\"no\",\n","    push_to_hub=True,\n","    report_to=\"tensorboard\",\n",")\n","\n","# Create Trainer instance\n","trainer = Seq2SeqTrainer(\n","    model=model,\n","    args=training_args,\n","    data_collator=data_collator,\n","    train_dataset=tokenized_dataset[\"train\"],\n",")\n","model.config.use_cache = False  # silence the warnings. Please re-enable for inference!\n"]},{"cell_type":"markdown","metadata":{"id":"70ObEZ3qMJcW"},"source":["### Train"]},{"cell_type":"code","execution_count":11,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":802},"executionInfo":{"elapsed":6202565,"status":"ok","timestamp":1682216493358,"user":{"displayName":"Seulkee Lee","userId":"14463507896336644914"},"user_tz":420},"id":"kBkDCq7xLjtQ","outputId":"7200a700-a310-4161-b66e-8f0475c74d0b"},"outputs":[{"name":"stderr","output_type":"stream","text":["/usr/local/lib/python3.8/dist-packages/transformers/optimization.py:391: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n","  warnings.warn(\n","You're using a T5TokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n","/usr/local/lib/python3.8/dist-packages/bitsandbytes/autograd/_functions.py:298: UserWarning: MatMul8bitLt: inputs will be cast from torch.float32 to float16 during quantization\n","  warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n"]},{"data":{"text/html":["\n","    <div>\n","      \n","      <progress value='9210' max='9210' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [9210/9210 1:43:15, Epoch 5/5]\n","    </div>\n","    <table border=\"1\" class=\"dataframe\">\n","  <thead>\n"," <tr style=\"text-align: left;\">\n","      <th>Step</th>\n","      <th>Training Loss</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <td>500</td>\n","      <td>1.599700</td>\n","    </tr>\n","    <tr>\n","      <td>1000</td>\n","      <td>1.586200</td>\n","    </tr>\n","    <tr>\n","      <td>1500</td>\n","      <td>1.574200</td>\n","    </tr>\n","    <tr>\n","      <td>2000</td>\n","      <td>1.549500</td>\n","    </tr>\n","    <tr>\n","      <td>2500</td>\n","      <td>1.511100</td>\n","    </tr>\n","    <tr>\n","      <td>3000</td>\n","      <td>1.512200</td>\n","    </tr>\n","    <tr>\n","      <td>3500</td>\n","      <td>1.531800</td>\n","    </tr>\n","    <tr>\n","      <td>4000</td>\n","      <td>1.492100</td>\n","    </tr>\n","    <tr>\n","      <td>4500</td>\n","      <td>1.455200</td>\n","    </tr>\n","    <tr>\n","      <td>5000</td>\n","      <td>1.463800</td>\n","    </tr>\n","    <tr>\n","      <td>5500</td>\n","      <td>1.470700</td>\n","    </tr>\n","    <tr>\n","      <td>6000</td>\n","      <td>1.404900</td>\n","    </tr>\n","    <tr>\n","      <td>6500</td>\n","      <td>1.426900</td>\n","    </tr>\n","    <tr>\n","      <td>7000</td>\n","      <td>1.412300</td>\n","    </tr>\n","    <tr>\n","      <td>7500</td>\n","      <td>1.414100</td>\n","    </tr>\n","    <tr>\n","      <td>8000</td>\n","      <td>1.384000</td>\n","    </tr>\n","    <tr>\n","      <td>8500</td>\n","      <td>1.364300</td>\n","    </tr>\n","    <tr>\n","      <td>9000</td>\n","      <td>1.367700</td>\n","    </tr>\n","  </tbody>\n","</table><p>"],"text/plain":["<IPython.core.display.HTML object>"]},"metadata":{},"output_type":"display_data"},{"data":{"text/plain":["TrainOutput(global_step=9210, training_loss=1.470960930607865, metrics={'train_runtime': 6202.2785, 'train_samples_per_second': 11.876, 'train_steps_per_second': 1.485, 'total_flos': 2.541981446701056e+16, 'train_loss': 1.470960930607865, 'epoch': 5.0})"]},"execution_count":11,"metadata":{},"output_type":"execute_result"}],"source":["# train model\n","trainer.train()"]},{"cell_type":"code","execution_count":12,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":478,"referenced_widgets":["3faa8ba37d6343eca8c4b198823a9ebb","97059de3ec2646679dfb70d38119d1f9","e606635bd54a4216959232a5e5faf939","a903c52a4f98415e9d59b38e817fa58e","b8413585d1fb438583b79326031408cf","fb9ff1a716994aceac0a7cef79eaeecc","7b7858a4df804f438770e66272d26840","fb4033e1980b4844b4136c836f79ae72","71029025b948404888e73240a42b8812","f196c10739084dd6b221afe5db06c902","0d22454c7a5f4de2831d06a2a6b9aff7","b744cc481c62435981c23b198e614af9","edb11fc0fa184702808e88db3f55695c","a96ead7fac4b42959a0bccb3d9c147be","cd04a51f39524fc4b4dddf2acda47f7f","a8d920ce34cc48cdb7f92a04f8b6d9fe","e163971814a6407daabe4165efbbe070","30b0dd4dac804319856ba24444a3194f","caae47d2b3f642aba6679f30c64f4a4f","a2ccd30cf4a1462281312681ba0f3ac3","451428e62ec34bbcbdb64fad4a0a89a6","6518dac9a20c4cc7b7a875181d3e9e13","c197756ec9bb4b34ad9967bc4298509a","349b313229d3496e8409da3d2468605f","f82cd3e297d84815a37c90ba9a4365a3","076e364a99a94e249f391e97e839cfed","7e5ca536f4e14458a73e83e64ba99440","172641c4aec348da8e01ec6050ca72db","afdbccf023904a66a6cfc70e163090a5","cd3765683cb0485f83f6541eb9f42c8c","fe5847aba98b4db993e1c34356ebe241","6034d531d43d45ad9db65b60e06362b4","4e69b80e802245c8a542e4b87df05246","1069c7f24bf440989a436993c6714b93","99875d2d1a5c4657b029eee87e7dc673","ffa8279a2d07482ba362e5f53e6dbbb4","3e3f629f47724efc8da687b4da2c1f5d","3e3c841adc5a48d68cabf882c37c3421","c977946a57e84dea9a9af19b407c4d71","80b9516787e6462285cbbabc3e67a291","ab8bdacf144549fa9dd649be7e4ebba5","944d2be5413c47b68de1124c3264913d","0fa8c58ba8a24c058e06f435c42a6c92","cfddbdfddc7e4f20a422c6d6279d8674"]},"executionInfo":{"elapsed":32462,"status":"ok","timestamp":1682217548124,"user":{"displayName":"Seulkee Lee","userId":"14463507896336644914"},"user_tz":420},"id":"tlkLQYV0-V3c","outputId":"7c128da9-4e85-4e9a-ab61-17e194fe9e9c"},"outputs":[{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"3faa8ba37d6343eca8c4b198823a9ebb","version_major":2,"version_minor":0},"text/plain":["Upload file pytorch_model.bin:   0%|          | 1.00/398M [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"b744cc481c62435981c23b198e614af9","version_major":2,"version_minor":0},"text/plain":["Upload file logs/events.out.tfevents.1682210290.b43466669fb9.201.0:   0%|          | 1.00/7.92k [00:00<?, ?B/s…"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"c197756ec9bb4b34ad9967bc4298509a","version_major":2,"version_minor":0},"text/plain":["Upload file training_args.bin:   0%|          | 1.00/3.62k [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"1069c7f24bf440989a436993c6714b93","version_major":2,"version_minor":0},"text/plain":["Upload file logs/1682210291.0099092/events.out.tfevents.1682210291.b43466669fb9.201.1:   0%|          | 1.00/5…"]},"metadata":{},"output_type":"display_data"},{"name":"stderr","output_type":"stream","text":["To https://huggingface.co/sooolee/flan-t5-base-cnn-samsum-lora\n","   2052386..31abdbc  main -> main\n","\n","WARNING:huggingface_hub.repository:To https://huggingface.co/sooolee/flan-t5-base-cnn-samsum-lora\n","   2052386..31abdbc  main -> main\n","\n","To https://huggingface.co/sooolee/flan-t5-base-cnn-samsum-lora\n","   31abdbc..3d9c1d2  main -> main\n","\n","WARNING:huggingface_hub.repository:To https://huggingface.co/sooolee/flan-t5-base-cnn-samsum-lora\n","   31abdbc..3d9c1d2  main -> main\n","\n"]},{"data":{"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"},"text/plain":["'https://huggingface.co/sooolee/flan-t5-base-cnn-samsum-lora/commit/31abdbc133303a69978e5f55f6a2542bac8c1b85'"]},"execution_count":12,"metadata":{},"output_type":"execute_result"}],"source":["trainer.push_to_hub()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"HyMlu3qYUSHk"},"outputs":[],"source":["# model.push_to_hub(\"flan-t5-base-cnn-samsum-lora\")\n","# tokenizer.push_to_hub(\"flan-t5-base-cnn-samsum-lora\")"]},{"cell_type":"code","execution_count":13,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":119,"status":"ok","timestamp":1682217743143,"user":{"displayName":"Seulkee Lee","userId":"14463507896336644914"},"user_tz":420},"id":"VzJ88z0bLjv0","outputId":"e5f12a3a-6f3d-41eb-9db1-e1ed4dbe7e63"},"outputs":[{"data":{"text/plain":["('flan-cnn-sam-results/tokenizer_config.json',\n"," 'flan-cnn-sam-results/special_tokens_map.json',\n"," 'flan-cnn-sam-results/spiece.model',\n"," 'flan-cnn-sam-results/added_tokens.json',\n"," 'flan-cnn-sam-results/tokenizer.json')"]},"execution_count":13,"metadata":{},"output_type":"execute_result"}],"source":["# Save our LoRA model & tokenizer results in local drive.\n","peft_model_id=\"flan-cnn-sam-results\"\n","trainer.model.save_pretrained(peft_model_id)\n","tokenizer.save_pretrained(peft_model_id)\n","\n","# if you want to save the base model to call\n","# trainer.model.base_model.save_pretrained(peft_model_id)"]},{"cell_type":"markdown","metadata":{"id":"eAglifgLMqhD"},"source":["## Evaluate & Run Inferences\n"]},{"cell_type":"code","execution_count":18,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":2655,"status":"ok","timestamp":1682217967095,"user":{"displayName":"Seulkee Lee","userId":"14463507896336644914"},"user_tz":420},"id":"DEUuR35aMpzd","outputId":"b6e739b5-3967-4f22-8e7e-58675fbd13b9"},"outputs":[{"name":"stderr","output_type":"stream","text":["Overriding torch_dtype=None with `torch_dtype=torch.float16` due to requirements of `bitsandbytes` to enable model loading in mixed int8. Either pass torch_dtype=torch.float16 or don't pass this argument at all to remove this warning.\n"]},{"name":"stdout","output_type":"stream","text":["Peft model loaded\n"]}],"source":["import torch\n","from peft import PeftModel, PeftConfig\n","from transformers import AutoModelForSeq2SeqLM, AutoTokenizer\n","\n","# Load peft config for pre-trained checkpoint etc.\n","peft_model_id = \"flan-cnn-sam-results\"\n","config = PeftConfig.from_pretrained(peft_model_id)\n","\n","# load base LLM model and tokenizer\n","model = AutoModelForSeq2SeqLM.from_pretrained(config.base_model_name_or_path, load_in_8bit=True, device_map='auto') # load_in_8bit=True,\n","tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)\n","\n","# Load the Lora model\n","model = PeftModel.from_pretrained(model, peft_model_id, device_map='auto')\n","model.eval()\n","\n","print(\"Peft model loaded\")\n"]},{"cell_type":"code","execution_count":20,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":513,"referenced_widgets":["cf10ca53ecdb4043b08b739233a062c5","1e83f3525db543f3a1944b7d40d86242","fee7720053eb47bbb6ed821048ddc304","20a479bb55cf4424b6a9f996ba7cfa74","268f38e27f3347baa0e724551a709935","156543ed30054b809bee322576196a8b","22d184a082774cfc8c4f0487a3340314","de9dd9b96676417d85637b75c54d17f2","80e8b048423f4fa8921cbb8bc27ac329","6cf28798167b4ed583bce2d8b63d24d4","812cc2597df949d9832fcc2101b4ca60"]},"executionInfo":{"elapsed":2555,"status":"ok","timestamp":1682218126490,"user":{"displayName":"Seulkee Lee","userId":"14463507896336644914"},"user_tz":420},"id":"WB95xzvQNB7u","outputId":"04d74900-04e9-4b81-d20c-9293be20733b"},"outputs":[{"name":"stderr","output_type":"stream","text":["WARNING:datasets.builder:Found cached dataset samsum (/root/.cache/huggingface/datasets/samsum/samsum/0.0.0/f1d7c6b7353e6de335d444e424dc002ef70d1277109031327bc9cc6af5d3d46e)\n"]},{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"cf10ca53ecdb4043b08b739233a062c5","version_major":2,"version_minor":0},"text/plain":["  0%|          | 0/3 [00:00<?, ?it/s]"]},"metadata":{},"output_type":"display_data"},{"name":"stdout","output_type":"stream","text":["input sentence: Abby: Have you talked to Miro?\r\n","Dylan: No, not really, I've never had an opportunity\r\n","Brandon: me neither, but he seems a nice guy\r\n","Brenda: you met him yesterday at the party?\r\n","Abby: yes, he's so interesting\r\n","Abby: told me the story of his father coming from Albania to the US in the early 1990s\r\n","Dylan: really, I had no idea he is Albanian\r\n","Abby: he is, he speaks only Albanian with his parents\r\n","Dylan: fascinating, where does he come from in Albania?\r\n","Abby: from the seacoast\r\n","Abby: Duress I believe, he told me they are not from Tirana\r\n","Dylan: what else did he tell you?\r\n","Abby: That they left kind of illegally\r\n","Abby: it was a big mess and extreme poverty everywhere\r\n","Abby: then suddenly the border was open and they just left \r\n","Abby: people were boarding available ships, whatever, just to get out of there\r\n","Abby: he showed me some pictures, like <file_photo>\r\n","Dylan: insane\r\n","Abby: yes, and his father was among the people\r\n","Dylan: scary but interesting\r\n","Abby: very!\n","------------------------------------------------------------\n","summary:\n","Miro is an Albanian, who came to the US in the early 1990s from the\n"]}],"source":["# Load the dataset again with a random sample to try the summarization\n","\n","from datasets import load_dataset\n","from random import randrange\n","\n","\n","# Load dataset from the hub and get a sample\n","dataset = load_dataset(\"samsum\")\n","sample = dataset['test'][randrange(len(dataset[\"test\"]))]\n","\n","input_ids = tokenizer(sample[\"dialogue\"], return_tensors=\"pt\", truncation=True).input_ids.cuda()\n","\n","# with torch.inference_mode():\n","outputs = model.generate(input_ids=input_ids, do_sample=True, top_p=0.9), #max_new_tokens=60,\n","print(f\"input sentence: {sample['dialogue']}\\n{'---'* 20}\")\n","\n","print(f\"summary:\\n{tokenizer.batch_decode(outputs[0].detach().cpu().numpy(), skip_special_tokens=True)[0]}\")\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"-eQ7DyoHWLVR"},"outputs":[],"source":["## To make an inference with your own data / contents\n","\n","# contents = \"blah blah blah\"\n","# inputs = tokenizer(contents, return_tensors=\"pt\")\n","\n","# with torch.no_grad():\n","#     outputs = model.generate(input_ids=inputs[\"input_ids\"].to(\"cuda\"), max_new_tokens=60, do_sample=True, top_p=0.9)\n","#     print(tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True)[0])"]},{"cell_type":"markdown","metadata":{"id":"F0ovIhI9Ncq8"},"source":["### Metric\n","Evaluate the inferences against the test set of processed dataset from samsum. We need to use and create some utilities to generate the summaries and group them together. The most commonly used metrics to evaluate summarization task is **rogue_score** short for Recall-Oriented Understudy for Gisting Evaluation). This metric does not behave like the standard accuracy: it will compare a generated summary against a set of reference summaries."]},{"cell_type":"code","execution_count":21,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":138,"referenced_widgets":["dd06f666089341d395bd39c32d4e920e","7457ed4962f04478aa59833e2a37c89c","38d5a714252f42f7bca776ba29d30d9a","607b092877fd4bb8a401e30b66c3f7c6","94d403c74ec74fcf9332bcfd9634c8f0","b1648546dff344e89b76e96f46eb2f3c","9f28d637f3e04ed7b8555db2d8d764f6","bd050861d7c54635b5d61e41f409c6e3","9f735b9ee70c44ce91ad7c95dd1cca7d","9cdb80e8773a412697ff6919a2c99fe6","92f374a5d4e940289c7affc12172e482"]},"executionInfo":{"elapsed":2140859,"status":"ok","timestamp":1682220278268,"user":{"displayName":"Seulkee Lee","userId":"14463507896336644914"},"user_tz":420},"id":"ipsq9UhINcEJ","outputId":"c26cc7bf-855a-4169-82d6-ab32aff96e77"},"outputs":[{"data":{"application/vnd.jupyter.widget-view+json":{"model_id":"dd06f666089341d395bd39c32d4e920e","version_major":2,"version_minor":0},"text/plain":["Downloading builder script:   0%|          | 0.00/6.27k [00:00<?, ?B/s]"]},"metadata":{},"output_type":"display_data"},{"name":"stderr","output_type":"stream","text":["100%|██████████| 819/819 [35:36<00:00,  2.61s/it]\n"]},{"name":"stdout","output_type":"stream","text":["Rogue1: 46.819522%\n","rouge2: 20.898074%\n","rougeL: 37.300937%\n","rougeLsum: 37.271341%\n"]}],"source":["import evaluate\n","import numpy as np\n","from datasets import load_from_disk\n","from tqdm import tqdm\n","\n","# Metric\n","metric = evaluate.load(\"rouge\")\n","\n","def evaluate_peft_model(sample, max_target_length=50):\n","    # generate summary\n","    outputs = model.generate(input_ids=sample[\"input_ids\"].unsqueeze(0).cuda(), do_sample=True, top_p=0.9, max_new_tokens=max_target_length)\n","    prediction = tokenizer.decode(outputs[0].detach().cpu().numpy(), skip_special_tokens=True)\n","    # decode eval sample\n","    # Replace -100 in the labels as we can't decode them.\n","    labels = np.where(sample['labels'] != -100, sample['labels'], tokenizer.pad_token_id)\n","    labels = tokenizer.decode(labels, skip_special_tokens=True)\n","\n","    # Some simple post-processing\n","    return prediction, labels\n","\n","# load test dataset from distk\n","test_dataset = load_from_disk(\"data-sam/eval/\").with_format(\"torch\")\n","\n","# run predictions\n","# this can take ~45 minutes\n","predictions, references = [] , []\n","for sample in tqdm(test_dataset):\n","    p,l = evaluate_peft_model(sample)\n","    predictions.append(p)\n","    references.append(l)\n","\n","# compute metric\n","rogue = metric.compute(predictions=predictions, references=references, use_stemmer=True)\n","\n","# print results\n","print(f\"Rogue1: {rogue['rouge1']* 100:2f}%\")\n","print(f\"rouge2: {rogue['rouge2']* 100:2f}%\")\n","print(f\"rougeL: {rogue['rougeL']* 100:2f}%\")\n","print(f\"rougeLsum: {rogue['rougeLsum']* 100:2f}%\")\n"]}],"metadata":{"colab":{"authorship_tag":"ABX9TyP/IuoczT4Q/d2Cx+cTspYE","provenance":[{"file_id":"1TpuZchOBGl2RsPK3KmoKZlIbMB6sSf58","timestamp":1682144720706}]},"kernelspec":{"display_name":"Python 3.9.6 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