02 - Fine-Tune BART-Large-CNN on SAMSum with LoRA.ipynb

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BART-Large-CNN Fine-Tuning with LoRA for Chat & Dialogue Summarization"],"metadata":{"id":"btCIbk4TH_RX"}},{"cell_type":"markdown","source":["https://www.philschmid.de/fine-tune-flan-t5-peft"],"metadata":{"id":"r6_EGHP6qyqk"}},{"cell_type":"code","source":["!nvidia-smi"],"metadata":{"id":"BQgh_WR2Sn43"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## Dependencies"],"metadata":{"id":"GOW3EoNFHiok"}},{"cell_type":"code","execution_count":1,"metadata":{"id":"k6ALc6zKGNAL","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1682243116928,"user_tz":420,"elapsed":21661,"user":{"displayName":"Seulkee Lee","userId":"14463507896336644914"}},"outputId":"3a036423-2d53-4dbd-8d52-4e4fd4b9bbba"},"outputs":[{"output_type":"stream","name":"stdout","text":["  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"]}],"source":["# install Hugging Face Libraries\n","!pip install -q git+https://github.com/huggingface/peft.git\n","!pip install -q \"transformers==4.27.2\" \"datasets==2.9.0\" \"accelerate==0.17.1\" \"evaluate==0.4.0\" \"bitsandbytes==0.37.1\" loralib --upgrade --quiet\n","# install additional dependencies needed for training\n","!pip install -q rouge-score tensorboard py7zr\n"]},{"cell_type":"code","source":["from huggingface_hub import notebook_login\n","\n","notebook_login()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":387,"referenced_widgets":["c5c78a5aab4c4b85a3575ecccb56ba27","0d0b99387310430ea8c07b17da0d45b5","3033664871d442e5bf4764f719a87398","7ff4f99d0cb849018f1e35dcdc565eb7","bcad9afc253a4241b5942d787a6f1fcc","1402a86637264eaaaee34b61f2acecf9","5c037edec5b847c0a557de8a914d3814","1e6ca71545f2405d909107d84680a29b","8c164d270c0047e8b26cd3e1c106ab38","b212d3a925954b758998289f8428def4","b19d68084b6f4a2294b8f03bad25eb3d","8f62f944892444498030fdb7a24d7c00","fff9226c0c444cfa849893723cf87d57","5f4f305abcb9401a822532f5a0ce6946","b08225bda036481a860d7a7b129c66b9","ae75aeb609f54d8a9cb84ae7eb5a8616","d76a21fe94004bfa99a56d496998d565"]},"id":"-ugIBrgtMzlB","executionInfo":{"status":"ok","timestamp":1682243118928,"user_tz":420,"elapsed":239,"user":{"displayName":"Seulkee Lee","userId":"14463507896336644914"}},"outputId":"2bac0b64-edef-484f-c87a-ca81aa3f0bcc"},"execution_count":2,"outputs":[{"output_type":"stream","name":"stdout","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"]}]},{"cell_type":"markdown","source":["## Dataset Preprocessing"],"metadata":{"id":"RHw93NFDHnPG"}},{"cell_type":"markdown","source":["### Load Dataset SAMSum"],"metadata":{"id":"AuSHVZiBIpXq"}},{"cell_type":"code","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"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":122,"referenced_widgets":["6d9e40f360e04f6e9dc5219ad5103b39","be145fd7bb9644a3888f8188a7fe4f10","06b2e0e776f34b06b5397ac3f5fb9994","a9bfe2cfecc74523b93c2f0084f86bfc","d574617d1015425eba19b5a169f0e545","5f73e9f262184190bbbfb6ca6c8d2115","2bfc9ea5aac24fa996965358b6682e89","eaee22a5aa89462c8e42b45d186e4005","44139740dce2430093dec5bc262f80e5","be585b61c632438f84af0273cff4c910","76f4c1eda1ff443a933fee42303c75d9"]},"id":"Gg0yS2urHmpn","executionInfo":{"status":"ok","timestamp":1682243247689,"user_tz":420,"elapsed":2110,"user":{"displayName":"Seulkee Lee","userId":"14463507896336644914"}},"outputId":"c37f09c8-5834-4535-d850-da409ef9b7ed"},"execution_count":3,"outputs":[{"output_type":"stream","name":"stderr","text":["WARNING:datasets.builder:Found cached dataset samsum (/root/.cache/huggingface/datasets/samsum/samsum/0.0.0/f1d7c6b7353e6de335d444e424dc002ef70d1277109031327bc9cc6af5d3d46e)\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/3 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"6d9e40f360e04f6e9dc5219ad5103b39"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Train dataset size: 14732\n","Test dataset size: 819\n"]}]},{"cell_type":"markdown","source":["### Initiate Tokenizer"],"metadata":{"id":"B6kaDBDBH3pH"}},{"cell_type":"code","source":["from transformers import AutoTokenizer, AutoModelForSeq2SeqLM\n","\n","model_id = \"facebook/bart-large-cnn\"\n","\n","# Load tokenizer of BART\n","tokenizer = AutoTokenizer.from_pretrained(model_id)\n"],"metadata":{"id":"sa3r_daSHmsc","executionInfo":{"status":"ok","timestamp":1682243254403,"user_tz":420,"elapsed":2070,"user":{"displayName":"Seulkee Lee","userId":"14463507896336644914"}},"colab":{"base_uri":"https://localhost:8080/","height":145,"referenced_widgets":["d7311c4ac0ae4a30a2695c80c3ad415e","dfdf32c4c1a04673b91e2ae7d155fc24","ffbf9ad1c5854169be14eaafb24b69a9","9c71d4ff305c438ba6ae01a75fa2706f","8f8995d08d9b4d89a1ed2020819e8b91","3ca8a51d10c5478282ffe8528938e4a1","9fc4a90442254ff983b0b60287507010","a925c39d60574b6391dc5d791f39be1a","f819dbd348b340f1b8113d386164b1b1","d68504c70f8a403ba197af8156eb5293","3e5fbe9bf1744c9084de54e85e58db06","4371c4ccc39d46f09590405b46756e53","312836d5c6df42ebbb4cb6a46bb61151","9a7227f4d0a34d148a94518f300e3a7c","c42f8107694f4dde9a99d1cb7f4edbcf","cb8691c7ca9a427893336fccc454fad7","b28e4361fb2449b1bfd8211a15fe95d2","f83e3c6df1594aceaa45efc5cf2b51e1","9728f48e0af34d82aa7b3629d8a52aaf","4611dd3b9b324a8bb21448f32a35d473","10813aa71f2f472eb371150d6721804e","55e00f45522242af928abddad123394d","d15a7d4313254e75b5976318f9d7071a","2bb86e54134c4c288ddffea6de36c560","e0697956a0ef461c9b2d0954b12d32aa","d2783557646248e88dcd49281315024e","9183371a52c041b6b5e2c759db3030cf","c0d905fa3b3c4ebe9b1005e51dab489a","0c88dd89314348fd95ee07ebe2e4dc63","916f91a2786a434f8b8f0087878e26c8","74a8d8b989bc429e98a5450c13872d7f","21f7f62f36654156900c6b33e1ea32b2","9dbd4ccb21c4496b831af613f5ad2e77","2fe088c52aed4ac686cc81446d50cd40","f1477372feb848db8e9cfcd9cd3029db","6275a00d0a2446c6a4942814a0e18ab7","d5127dcf7faa40ca89ffc2dcab5177d6","5538428ea2c94d50894da3d81de68de7","033479ce67204f44a1ae792f2c9e35f2","b322e0bd417a435788bd8d2b1407ff15","1be26709e1cc41ac9f48be320468ad8c","38633f81436f4991aadf31aad0cfd973","896d9a259b064f3c808a15670424c5b7","e4305dc2eb964406ae7f3b447637a1be"]},"outputId":"2023a318-4a41-4b54-ffda-ddc652af46e1"},"execution_count":4,"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading (…)lve/main/config.json:   0%|          | 0.00/1.58k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"d7311c4ac0ae4a30a2695c80c3ad415e"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading (…)olve/main/vocab.json:   0%|          | 0.00/899k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"4371c4ccc39d46f09590405b46756e53"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading (…)olve/main/merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"d15a7d4313254e75b5976318f9d7071a"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading (…)/main/tokenizer.json:   0%|          | 0.00/1.36M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"2fe088c52aed4ac686cc81446d50cd40"}},"metadata":{}}]},{"cell_type":"markdown","source":["### Preprocess"],"metadata":{"id":"V4rWIEBBIyRN"}},{"cell_type":"code","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","\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","\n","print(f\"Max target length: {max_target_length}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":117,"referenced_widgets":["0275a9b452754f9cacb179852f5fb91b","8dd50d92d249403cb19615857bc62a2d","2fc67cb88db74a02a15b5b3b63dd4234","0eb24428c14c40baac7f982a31bfa15a","7264da254154489e87df89a75bfbd228","193d132b33d14a26ab1a515adf80aab1","c280528d43874a98aab6298c42ef746f","4fb278214f3c4990be8fb94fc5d7d72f","5e2ed249ce72400e941d2cf66d667bd4","472db5c9ab9d453aa88ce4e8aad07de9","2ee47edfcc1a4aee909a09e081cb0d27","c4e78757071947ee8fc52572ad0335af","7c79ef5fe7d747e69b7f83e4554fb7c8","9438ddb24d704508a968da3f434d1412","14c53b63425045d09dd0c6d2a808b7a8","5fb56088b4674fbeab519e3594b8bbf7","1dea1009ef4242d8850ddd1e13feeff1","e2bf212e56bc470aba77a4ca7a0a175e","45b80e1124cc43aa8059f55dfa2dfb20","05a4aea9359041e08c3ccb93c34b08a3","ec72e1df4796428ba273fdf227d58eb5","e3906e922ec74634a4a1da863110e220"]},"id":"9KgoBITpHmze","executionInfo":{"status":"ok","timestamp":1682243273082,"user_tz":420,"elapsed":3666,"user":{"displayName":"Seulkee Lee","userId":"14463507896336644914"}},"outputId":"2b6307e5-b31d-407f-bd6b-886152264336"},"execution_count":5,"outputs":[{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/16 [00:00<?, ?ba/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"0275a9b452754f9cacb179852f5fb91b"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Max source length: 270\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/16 [00:00<?, ?ba/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"c4e78757071947ee8fc52572ad0335af"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Max target length: 47\n"]}]},{"cell_type":"code","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(\"bart-sam-data/train\")\n","tokenized_dataset[\"test\"].save_to_disk(\"bart-sam-data/eval\")\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":195,"referenced_widgets":["48762e763b3e4ed5a1d67b28138b18bf","0312565fbcb64aee8e9b8c9c14ba92b1","2168cec5602e492bb20b2fa3fb1aacaa","d5097fff93dc41ea89a33a3bc1190678","775d858d74db430bb78acb4290bce000","9137a119e5874462afc22a6b811a835a","aadba8d2dc2d41d2a467581fc4a2a723","b82e2c8297224193a9cb4366a1de3e90","6c490742551f4c0c93cf367506b7e0d1","555d00429cbc4f028bded5375a8d8810","9c0b5ed0c53c4d3889c1b9a2f16e7438","13884cd9105843cdb54cd1af6b553d73","c505a2e286ec47a1b3a69bcf7c5792cf","bc62d08913c048d9a9729fda0aedf96b","8e11fb44d49242a884d74def6a516de0","1de497f139d14d69ad0959bf2d0caf1f","557c88c83e864c81ac46e1ba354ce3f6","114e9fc409e94d3ca44428e0e5d99c9a","bcaeabac8c9b4f79bab031f7c192860e","65137272ec3f4a41872878336b93b145","1eba97bd1756424fb58207f991b9346f","88fac28a1d794140bdc2c5a73072b78d","b8a5122e04d34d8880a1aafc8ffb39fa","e42ea7b5cc2940af9652520990fadba2","9a2936eaebf44e22893772f897d38b31","513e4d7c2ff04c8fa26f85d0dd6c0bd8","90e3c48571f7465784031dee07187655","454446404bd64266afc29220493d82b2","1fb84c58ac584e25a9ab0e152bf215f7","fe1b37088aeb4559b677583a3dda4a48","1ef8fde78960427bad1ff35d2b8cfc1e","b0c63522fb6a40d787f3e1a64733db11","e227178e1b2341fab00492d5ed6b3474","8dd97d9a0d924d26aca70a48c99535a9","fea59a3ec32f426d8f8d48a7ba1a5cea","5471719eb91f478aa40e44309752690e","35d721d8040a447bbb79387f20aab188","159a4fa0159f46c9ba9115b5f4f15e0a","d89563a6c231446e86c3674d3a481d3e","7218339c805446b1853d97844c3d40f2","f798c31806e74b8780b7b41c3c9e1ce0","9561b82ddced4700aa6515cd2e6693ca","f4b4e5b2dbb548df9d742b2afe70680b","4a4b4c4f5b9b4c1fab2ed5aef6f86e47","b0ba18c6c3cb4d48ade6dd237180be57","d6d53019c6d94273b43624f1d2fc8b7a","d734105955c94ec7a56addcf8085e53f","d716111be6c4487ba6cbf6b883bf04d4","1724b9fc0a9f4841bfdfea3044aabb3c","d0db175cce77469e9e12ca3a52a1fae8","929bdfa87c2245fda516d2c78f28bfd8","161999b34c754dfb8fdb1b4edecae743","9f04c67e91ba40fbb5b58445b63ea76e","7bd548d6d1724669b609788ecab1659d","2db749d180dc437b94f868e0f5755813"]},"id":"0ewtQbEFHm1n","executionInfo":{"status":"ok","timestamp":1682243323193,"user_tz":420,"elapsed":5158,"user":{"displayName":"Seulkee Lee","userId":"14463507896336644914"}},"outputId":"ef0a7c8f-c70f-426b-fd8d-1c8e8410e5d4"},"execution_count":6,"outputs":[{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/15 [00:00<?, ?ba/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"48762e763b3e4ed5a1d67b28138b18bf"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/1 [00:00<?, ?ba/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"13884cd9105843cdb54cd1af6b553d73"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/1 [00:00<?, ?ba/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"b8a5122e04d34d8880a1aafc8ffb39fa"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["Keys of tokenized dataset: ['input_ids', 'attention_mask', 'labels']\n"]},{"output_type":"display_data","data":{"text/plain":["Saving the dataset (0/1 shards):   0%|          | 0/14732 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"8dd97d9a0d924d26aca70a48c99535a9"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Saving the dataset (0/1 shards):   0%|          | 0/819 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"b0ba18c6c3cb4d48ade6dd237180be57"}},"metadata":{}}]},{"cell_type":"markdown","source":["## Fine-Tune the Model with LoRA using PEFT"],"metadata":{"id":"mz5tdnSiKBZk"}},{"cell_type":"code","source":["from transformers import AutoModelForSeq2SeqLM\n","\n","# load model from the hub\n","model = AutoModelForSeq2SeqLM.from_pretrained(model_id, device_map=\"auto\") # load_in_8bit=True, \n"],"metadata":{"id":"mUnbXEu-I7TU","executionInfo":{"status":"ok","timestamp":1682243353390,"user_tz":420,"elapsed":19404,"user":{"displayName":"Seulkee Lee","userId":"14463507896336644914"}},"colab":{"base_uri":"https://localhost:8080/","height":81,"referenced_widgets":["21a2e60439784ed3a19cd030a49d9442","de7ee0e40517425caa0c1a9813ae763e","07008cd53a964289a51a7be789952866","710cd4b459244c4da53e2be2f7992204","9b63bbce6dc142f6aed91f62c67118ac","66d10cec633c4cfc817eb47ba610144d","02632bf39ba545a9b4b890ce8672cf2c","0d7272d0c4ed4fda83d9e6349efdf1e0","6f3fd246b4d44cac934ff7b5a2dc7a55","d259e53ab718416c8f1d8cc00aeaeb16","a7cf30e5920845339b2724a75a65149b","204cf1bb0ce44828848f900dde84bd1f","0f4fddc665104b748df06e72835cb53a","614c5bd7e6ce4dc9a98ba6d2b0a60c8d","5cd051e8403c494f91fc8bba9fdc8460","74e799ee82da42f9b2c4051bd9bcb654","bc3607b3a843411dab76c807771eb956","e4264ab0a550425f9fc330e99900896b","e08004afa12543888eccfff53ad57cdf","e2ff19cef24543a2b37b2628140f3644","30faf2576b8c4d959f141a40dca2c79c","fcb15dce3e4846d3915deb1361c86513"]},"outputId":"5c5a859b-7e68-4bc8-db80-1ae28dcdc0eb"},"execution_count":7,"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading pytorch_model.bin:   0%|          | 0.00/1.63G [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"21a2e60439784ed3a19cd030a49d9442"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading (…)neration_config.json:   0%|          | 0.00/363 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"204cf1bb0ce44828848f900dde84bd1f"}},"metadata":{}}]},{"cell_type":"code","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=8, #16\n"," lora_alpha=32,\n"," target_modules=None, # [\"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) # --> Doesn't work for BART.\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"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"IwGkyrbSI7V7","executionInfo":{"status":"ok","timestamp":1682243358686,"user_tz":420,"elapsed":1342,"user":{"displayName":"Seulkee Lee","userId":"14463507896336644914"}},"outputId":"015334d2-c347-4164-e1e0-9196f608c8a3"},"execution_count":8,"outputs":[{"output_type":"stream","name":"stdout","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"]},{"output_type":"stream","name":"stderr","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('module'), PosixPath('//ipykernel.pylab.backend_inline')}\n","  warn(msg)\n"]},{"output_type":"stream","name":"stdout","text":["trainable params: 1179648 || all params: 510410752 || trainable%: 0.23111738837351137\n"]}]},{"cell_type":"markdown","source":["### Create a DataCollator that will take care of padding inputs and labels. Use the DataCollatorForSeq2Seq from the HF Transformers library."],"metadata":{"id":"kSGqLGr5LRl4"}},{"cell_type":"code","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"],"metadata":{"id":"RUqms8VeI7Yh","executionInfo":{"status":"ok","timestamp":1682243372195,"user_tz":420,"elapsed":3697,"user":{"displayName":"Seulkee Lee","userId":"14463507896336644914"}}},"execution_count":9,"outputs":[]},{"cell_type":"markdown","source":["### Define the hyperparameters (TrainingArguments) for training"],"metadata":{"id":"ghcjYslmLnjm"}},{"cell_type":"code","source":["from transformers import Seq2SeqTrainer, Seq2SeqTrainingArguments\n","\n","# output_dir=\"lora-bart-large-cnn-ss\"\n","\n","output_dir=\"bart-large-cnn-finetuned-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=5e-4, # 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","    tokenizer=tokenizer,\n",")\n","model.config.use_cache = False  # silence the warnings. Please re-enable for inference!\n"],"metadata":{"id":"IfceO_koLjrG","executionInfo":{"status":"ok","timestamp":1682243808040,"user_tz":420,"elapsed":422939,"user":{"displayName":"Seulkee Lee","userId":"14463507896336644914"}},"colab":{"base_uri":"https://localhost:8080/","height":497,"referenced_widgets":["980a92f86e0a4955b2c0dfab3b91e7ad","96909a1f2bfd41a2b4d904c5301562f8","acd3d6e5196447dc9d2cfb186c4fa1bb","911413710efd4c99b279e8744f4c9b30","43b70d82ea7345a08b006df6f93b7c4f","b427ca1397a641389be75de54b4eeb76","d03324a084434ee988605e0a89d8b973","fb6faca3ac0e466488613a3b08cecf72","56348025ca514d81ba3ee14b55c86f76","7ea3364d6377428b97c025535baf6d78","e3b512834e7344c3b731a7ab18f6d159","ff5e245c710a4095a5b8819bad325731","923dad8d00b64e50b0eb1fa613f497ce","da1f91dec82147f6b9705f91ddcb896d","9969d1873803451591b689f63362350b","3c65551698de454c9a1748a2de4a89f0","2547448c9c184d7295d8c43f90350e6d","c0842837112a47d489851e0302ade928","3bb497c9a95d4c8380f2ce06f7d5b32f","0ea1e1eca1c044b896a439af197fd56d","e3ef132e2e2e4acbbb9ccc44353a67d4","53a03c0a4b1c41f48ae8a9eca8467467","43717c4b68c749a5aac631f9b4cffe7c","d27e4abf5cdc4f28aeec32512fd0ebbe","af4e431b67b247828fd421ed3176104b","debb50c4619d4bb3a9fda88a1473dedc","89b0dcdfb4514ed2bf354e1547c36e80","a27421de0be843fe8aa41043f82f9cdd","d71e455814974820bd31a365c94d66b9","a58007336f5b4c559cc654411c0c534c","d158a7b4f16d4d05aa2c369606a07c63","19318e4fb12d4f64bb846ea826363a1a","5c0201898c8a4c90a9d4a8b0612dcff9","560608ea811b40d1bb120b3d8a1c27dc","1ad641bcb3414f2ab2d5a8b2b5b96ec2","1f2a35c5204540c083a63d5a7f391fdc","d976c9cabc83418298dd5445d87e04bb","f483a85eb14a461f8bec16cdc6447548","bd607d73d1ef4e829fb0a99daa807b70","9cf3102739c94ebaabdc180372584eb9","bb5792fe2b31451bb842282d90cb5231","5ecfca3a38644eb1bfa6124603e264c7","a8d219dcb5194b9eb0f4714884f12fe3","573c3259bdd8475692b0f2d84f697aaa","5c1930ae654a47f89f24013b5eb01ad7","067a85d14fb4465e89f97c4fce945d21","1e617ea4e8db4a4e8e976f4aa5766dd4","705af1519f10400799fa8787c7c4cf02","3e7ec6f8110f4e76b637c256294a7030","c7e56fd4083847c6a13fa26f0c0d1fc5","c48c013154674aec84d0f1b1f5061e9c","e0ef2fbf840341a2aa6d91537808f2d9","7d9737b99790491d863f41bcdd8f3efd","621e61e5f10344cfb24e07f4d668a837","9925630ed3c248c286da4b0b0c3fda03","1771e128ba2f497d9dc8e23c153e7456","f0a014b848fd4152be1a3020a068e750","a607079545564aefb5939e1717cbce70","e71815a46f284160a5de698d1bcf85cf","d0f4c2c7dd1c4d1ba0a66c628d40ce9e","eee64eacd0b94ad09d6b5f8538952185","b879070683394cf395484ba5c957effd","b59cda54fd4740f3ba7427864224a782","5f5d53b924044985b89215c7681e4b6e","4f3b07e70aa04cbda39a6bf57be9d1c0","0a7a0d14e0ca47f1a6f0b2c770bc7fa4","8735b3e3236a4a5596fa403682e935ad","2f9889d711944fcbad8bc38cab8b0f59","08cb9d8fb0b246e482608a8b95b0de04","5eda1f1362a44a95a9383d0a4ef4d903","97615f20b1444acba0e1cfffea0c6cdd","ea07f77a8daf41fea661679220d95952","48471dd492644a0dbfcffee7d33479eb","2d734a3d6cfb47abaac656be1420a04f","cf31a906ee4949648673d4d8b2688e0d","96cb45ddba9544dcb3eb0db61245d750","2e729daab6244b2eaea54a1c6a33a21b","82a18851795b43a2b12be36b575becaa","d724ca3afa3e47fa9a8100183848c23c","3f1e0ea0a0ba4ba882fc5cc28b3d8ed1","7d0e8d8194cb46ef9119d9de00ea28b5","f258df2f9c79435b883f41ca70dd1715","dbd5af16cb434521a6783c17da24fa24","5de96822cffc4f749a7d3a62d1d1cd76","82697f3c1dec456f82b9b62fb256cde6","937d603d8b1240258ba4adf7418e2868","755af26f835042129815644cee82f4b4","fe84b9efd38d4337b93fd0d02c5ff383"]},"outputId":"a0f248bf-3f15-4d73-f0ea-71e2970b9ed5"},"execution_count":10,"outputs":[{"output_type":"stream","name":"stderr","text":["Cloning https://huggingface.co/sooolee/bart-large-cnn-finetuned-samsum-lora into local empty directory.\n","WARNING:huggingface_hub.repository:Cloning https://huggingface.co/sooolee/bart-large-cnn-finetuned-samsum-lora into local empty directory.\n"]},{"output_type":"display_data","data":{"text/plain":["Download file pytorch_model.bin:   0%|          | 8.00k/1.90G [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"980a92f86e0a4955b2c0dfab3b91e7ad"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Download file logs/events.out.tfevents.1682217341.5cfe7eb1d133.1082.2: 100%|##########| 8.08k/8.08k [00:00<?, …"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"ff5e245c710a4095a5b8819bad325731"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Download file training_args.bin: 100%|##########| 3.62k/3.62k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"43717c4b68c749a5aac631f9b4cffe7c"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Clean file logs/events.out.tfevents.1682217341.5cfe7eb1d133.1082.2:  12%|#2        | 1.00k/8.08k [00:00<?, ?B/…"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"560608ea811b40d1bb120b3d8a1c27dc"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Download file logs/1682217341.1537435/events.out.tfevents.1682217341.5cfe7eb1d133.1082.3: 100%|##########| 5.9…"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"5c1930ae654a47f89f24013b5eb01ad7"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Clean file training_args.bin:  28%|##7       | 1.00k/3.62k [00:00<?, 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model\n","trainer.train()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":766},"id":"kBkDCq7xLjtQ","outputId":"f9b34750-ae8b-4cfb-b393-1498d8276704","executionInfo":{"status":"ok","timestamp":1682246021401,"user_tz":420,"elapsed":2202361,"user":{"displayName":"Seulkee Lee","userId":"14463507896336644914"}}},"execution_count":11,"outputs":[{"output_type":"stream","name":"stderr","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 BartTokenizerFast 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"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","    <div>\n","      \n","      <progress value='9210' max='9210' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [9210/9210 36:38, 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>1.531700</td>\n","    </tr>\n","    <tr>\n","      <td>1000</td>\n","      <td>1.463100</td>\n","    </tr>\n","    <tr>\n","      <td>1500</td>\n","      <td>1.439900</td>\n","    </tr>\n","    <tr>\n","      <td>2000</td>\n","      <td>1.404100</td>\n","    </tr>\n","    <tr>\n","      <td>2500</td>\n","      <td>1.329300</td>\n","    </tr>\n","    <tr>\n","      <td>3000</td>\n","      <td>1.340200</td>\n","    </tr>\n","    <tr>\n","      <td>3500</td>\n","      <td>1.351400</td>\n","    </tr>\n","    <tr>\n","      <td>4000</td>\n","      <td>1.290500</td>\n","    </tr>\n","    <tr>\n","      <td>4500</td>\n","      <td>1.247000</td>\n","    </tr>\n","    <tr>\n","      <td>5000</td>\n","      <td>1.266000</td>\n","    </tr>\n","    <tr>\n","      <td>5500</td>\n","      <td>1.266500</td>\n","    </tr>\n","    <tr>\n","      <td>6000</td>\n","      <td>1.178700</td>\n","    </tr>\n","    <tr>\n","      <td>6500</td>\n","      <td>1.193500</td>\n","    </tr>\n","    <tr>\n","      <td>7000</td>\n","      <td>1.186600</td>\n","    </tr>\n","    <tr>\n","      <td>7500</td>\n","      <td>1.182600</td>\n","    </tr>\n","    <tr>\n","      <td>8000</td>\n","      <td>1.139100</td>\n","    </tr>\n","    <tr>\n","      <td>8500</td>\n","      <td>1.123500</td>\n","    </tr>\n","    <tr>\n","      <td>9000</td>\n","      <td>1.128900</td>\n","    </tr>\n","  </tbody>\n","</table><p>"]},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":["TrainOutput(global_step=9210, training_loss=1.2776606109320923, metrics={'train_runtime': 2202.1521, 'train_samples_per_second': 33.449, 'train_steps_per_second': 4.182, 'total_flos': 4.254324236746752e+16, 'train_loss': 1.2776606109320923, 'epoch': 5.0})"]},"metadata":{},"execution_count":11}]},{"cell_type":"code","source":["trainer.push_to_hub()"],"metadata":{"id":"Iyp4KE7lw5GI"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["model.push_to_hub(\"bart-large-cnn-finetuned-samsum-lora\")\n","tokenizer.push_to_hub(\"bart-large-cnn-finetuned-samsum-lora\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":152,"referenced_widgets":["7b26d4c926ff4fdfbce3947d23f283b9","f92523f09c254b8e8247c565034caccc","699f11e6ea844d27b848ae68dec0e8b4","37d422d2c7d74933ad763961e1fe7594","617c1c12a0a54b0faa6f1c0c86a73015","7fdcdbe031cb4b20b9122a3ae6bba17f","c0fe860e9b91485fb5aae87b0e16e53d","63dde3f23faf45448af78b7abd4280a7","3a94fa46b77b4bff956bf8844dcb2c2c","c533791afc014dc6b1bbe01b8ce12e12","2df9cd74091f43958c106f306abc852e","fa6fce319eab4d83993d1254d9e76ab7","fc8fdd8c11ec402c8265c353a684212c","4092ffd68e504c8ebdd6f72b5e06aea9","42f7e1f1cad44eb4b94fbe558ad89430","3b4d0094b86c40adb015d4ab932b0746","96a3fb08fdd249c8ab7ef1c1c547cb15","dea9dcb514c34daa980ae8c6bdb7c9d5","b447a5400ac24e948641d9d73cce42d1","461b2d57b5324d50ace83ec076b99742","13522a04a31446228a3ac91fdff73c52","cd9e029036b543d89d28eb32da9cbf2c"]},"id":"7G-lonarUcmo","executionInfo":{"status":"ok","timestamp":1682246213827,"user_tz":420,"elapsed":3337,"user":{"displayName":"Seulkee Lee","userId":"14463507896336644914"}},"outputId":"afdddf99-d16b-49c2-9d06-a0e8c4ff09e5"},"execution_count":14,"outputs":[{"output_type":"display_data","data":{"text/plain":["adapter_model.bin:   0%|          | 0.00/4.77M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"7b26d4c926ff4fdfbce3947d23f283b9"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Upload 1 LFS files:   0%|          | 0/1 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"fa6fce319eab4d83993d1254d9e76ab7"}},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":["CommitInfo(commit_url='https://huggingface.co/sooolee/bart-large-cnn-finetuned-samsum-lora/commit/025bd6a492e258dc88c43e53e1f191d444d0e563', commit_message='Upload tokenizer', commit_description='', oid='025bd6a492e258dc88c43e53e1f191d444d0e563', pr_url=None, pr_revision=None, pr_num=None)"]},"metadata":{},"execution_count":14}]},{"cell_type":"code","source":["# Save our LoRA model & tokenizer results\n","peft_model_id=\"bart-results\"\n","trainer.model.save_pretrained(peft_model_id)\n","tokenizer.save_pretrained(peft_model_id)\n","# if you want to save the base model to call\n","# trainer.model.base_model.save_pretrained(peft_model_id)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"VzJ88z0bLjv0","executionInfo":{"status":"ok","timestamp":1682246279049,"user_tz":420,"elapsed":149,"user":{"displayName":"Seulkee Lee","userId":"14463507896336644914"}},"outputId":"6ea19f07-a71e-472c-b9fb-fb55764206f5"},"execution_count":15,"outputs":[{"output_type":"execute_result","data":{"text/plain":["('bart-results/tokenizer_config.json',\n"," 'bart-results/special_tokens_map.json',\n"," 'bart-results/vocab.json',\n"," 'bart-results/merges.txt',\n"," 'bart-results/added_tokens.json',\n"," 'bart-results/tokenizer.json')"]},"metadata":{},"execution_count":15}]},{"cell_type":"markdown","source":["## Evaluate & Run Inference\n"],"metadata":{"id":"eAglifgLMqhD"}},{"cell_type":"code","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 = \"bart-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, 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"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"DEUuR35aMpzd","executionInfo":{"status":"ok","timestamp":1682246366915,"user_tz":420,"elapsed":4915,"user":{"displayName":"Seulkee Lee","userId":"14463507896336644914"}},"outputId":"4b4e9379-419f-4361-f350-5834248522ee"},"execution_count":16,"outputs":[{"output_type":"stream","name":"stdout","text":["Peft model loaded\n"]}]},{"cell_type":"code","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","# with torch.inference_mode():\n","outputs = model.generate(input_ids=input_ids, do_sample=True, top_p=0.9), # max_new_tokens=10,\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"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":424,"referenced_widgets":["d2def313a0b54fb794c9bb62f27a4a58","f23c190c80544b8382dbc5b23077b53f","60727498baaa48458e4d7da9eaf354a3","1ddf67313073453c8e4ac22f7514edf7","0359be24314740649166e01c742e1842","13b2438e8df144afb5bd113d1f0cf9ec","31134d4582524611abd54b06b534c397","f2ef19a958f04f8b88369e6af0637329","9f747484e7cb4b3d8362928f43c147e8","003c5c20708245848f9cdd5cfe8cf717","5399723087c5424c98219cbe27115024"]},"id":"WB95xzvQNB7u","executionInfo":{"status":"ok","timestamp":1682247048895,"user_tz":420,"elapsed":2841,"user":{"displayName":"Seulkee Lee","userId":"14463507896336644914"}},"outputId":"73fda63c-02a8-41ca-f71d-f66f6cdb66bc"},"execution_count":22,"outputs":[{"output_type":"stream","name":"stderr","text":["WARNING:datasets.builder:Found cached dataset samsum (/root/.cache/huggingface/datasets/samsum/samsum/0.0.0/f1d7c6b7353e6de335d444e424dc002ef70d1277109031327bc9cc6af5d3d46e)\n"]},{"output_type":"display_data","data":{"text/plain":["  0%|          | 0/3 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"d2def313a0b54fb794c9bb62f27a4a58"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["input sentence: Robin: Btw, do you know anything about that poker platform?\n","Jacks: Which one? The Bulls Eye one?\n","Robin: Yeah\n","Jacks: I played on it several times, it's good!\n","Robin: I'm trying to read up on the game\n","Jacks: It takes time, but once you get into it it's fun\n","Jacks: I made a few bucks on it\n","Robin: Nice\n","Robin: Any well worth tricks to share? 😂\n","Jacks: Well it's more about strategy mate\n","Jacks: I can send you a few links to some good sites\n","Jacks: Check this out\n","Jacks: <file_other>\n","Jacks: <file_other>\n","Jacks: <file_other>\n","Robin: Cheers! I'll take a look\n","------------------------------------------------------------\n","summary:\n","Jacks played poker on the Bulls Eye platform several times and he made a few bucks on it. Robin is trying to read up on the game. He will take a look at the links to some good poker sites.    Jack can send  \n"]}]},{"cell_type":"code","source":["# contents = \"blah blah blah\"\n","# inputs = tokenizer(\"summarize: \" + contents, return_tensors=\"pt\")\n","\n","# with torch.no_grad():\n","#     outputs = model.generate(input_ids=inputs[\"input_ids\"].to(\"cuda\"), max_new_tokens=10)\n","#     print(tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True)[0])"],"metadata":{"id":"mvI6eJFEXVlT"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["text = \"okay so hi everyone so this is going to  be the repeat stream of uh my deep speed  tutorial which is a distributed systems  library for uh making training larger  models a lot easier  and so a lot of this comes down to like  a whole bunch of bag of tricks that i  thought this library does a really good  job of covering and discussing like what  they are  like how they work and why they're  important and so i'm just going to give  you like all the background you'll need  to pretty much understand all of these  tricks and hopefully sort of train  larger models yourself and go beyond  like just like a single  v100 or a single a single machine setup  um so yeah so i guess like  without further ado we can just get  started right so really like if you come  here like the first claim that they're  making is like look we can train 10  times larger models 10 times faster with  minimal code change  and the idea behind the minimal code  change is really really important  because ultimately you just want to be  able to say like have your model  definition for example in a high level  framework like pytorch or tensorflow  so you'll have your init function your  void function and then when you say  model.fit or model.train or whatever  uh these things should just work across  multiple machines  and it's as far as you're concerned like  this is totally  uh you know like happening without your  knowledge uh but of course making it  debuggable and stuff is a lot easier  it's just that it's possible for you to  start doing this stuff  uh without spending too much time  becoming an expert in distributed  systems  uh so really this so so there's a couple  of a couple of important ideas here  uh the first one is that like okay well  we can they can train over larger models  making these things really memory  efficient so there's a couple of tricks  related to  another people called zero which  essentially gives you different ways of  sharding  uh models across multiple devices and  across the cpu and i'll talk about this  stuff in a second  it also talks about uh scaling to  extremely long sequence links so when  you think of something like transformers  well for the most part the default ones  scale to up to 128 sequences you can do  more by let's say if you have uh like  you want to work over a  like sequence of lengths 256 you could  concatenate two 128 representations and  you know just use that as a  representation for something that's a  sequence length 356.\"\n","inputs = tokenizer(text, return_tensors=\"pt\", truncation=True)\n","with torch.no_grad():\n","    outputs = model.generate(input_ids=inputs[\"input_ids\"].to(\"cuda\"), max_new_tokens=57, do_sample=True, top_p=0.9)\n","    print(f\"summary:\\n{tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True)}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Z-MKMWYfQAZ_","executionInfo":{"status":"ok","timestamp":1682249521233,"user_tz":420,"elapsed":2381,"user":{"displayName":"Seulkee Lee","userId":"14463507896336644914"}},"outputId":"ce575189-58ce-4d44-b9ef-17596ea1a57d"},"execution_count":38,"outputs":[{"output_type":"stream","name":"stdout","text":["summary:\n","['This is a repeat stream of my deep speed tutorial on a distributed systems library for making training larger models easier. There are some tricks to make it easier to train larger models with minimal code changes. Zero gives you ways of …. The library also.  ']\n"]}]},{"cell_type":"code","source":["text = \"okay so hi everyone so this is going to  be the repeat stream of uh my deep speed  tutorial which is a distributed systems  library for uh making training larger  models a lot easier  and so a lot of this comes down to like  a whole bunch of bag of tricks that i  thought this library does a really good  job of covering and discussing like what  they are  like how they work and why they're  important and so i'm just going to give  you like all the background you'll need  to pretty much understand all of these  tricks and hopefully sort of train  larger models yourself and go beyond  like just like a single  v100 or a single a single machine setup  um so yeah so i guess like  without further ado we can just get  started right so really like if you come  here like the first claim that they're  making is like look we can train 10  times larger models 10 times faster with  minimal code change  and the idea behind the minimal code  change is really really important  because ultimately you just want to be  able to say like have your model  definition for example in a high level  framework like pytorch or tensorflow  so you'll have your init function your  void function and then when you say  model.fit or model.train or whatever  uh these things should just work across  multiple machines  and it's as far as you're concerned like  this is totally  uh you know like happening without your  knowledge uh but of course making it  debuggable and stuff is a lot easier  it's just that it's possible for you to  start doing this stuff  uh without spending too much time  becoming an expert in distributed  systems  uh so really this so so there's a couple  of a couple of important ideas here  uh the first one is that like okay well  we can they can train over larger models  making these things really memory  efficient so there's a couple of tricks  related to  another people called zero which  essentially gives you different ways of  sharding  uh models across multiple devices and  across the cpu and i'll talk about this  stuff in a second  it also talks about uh scaling to  extremely long sequence links so when  you think of something like transformers  well for the most part the default ones  scale to up to 128 sequences you can do  more by let's say if you have uh like  you want to work over a  like sequence of lengths 256 you could  concatenate two 128 representations and  you know just use that as a  representation for something that's a  sequence length 356.\"\n","inputs = tokenizer(text, return_tensors=\"pt\", truncation=True)\n","with torch.no_grad():\n","    outputs = model.generate(input_ids=inputs[\"input_ids\"].to(\"cuda\"))\n","    print(f\"summary:\\n{tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True)[0]}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"FbLmQSMr3b1X","executionInfo":{"status":"ok","timestamp":1682249427391,"user_tz":420,"elapsed":2046,"user":{"displayName":"Seulkee Lee","userId":"14463507896336644914"}},"outputId":"0cfd3e74-b5ca-4c6d-fae2-1dd637f2b2e3"},"execution_count":35,"outputs":[{"output_type":"stream","name":"stdout","text":["summary:\n","This is a repeat stream of my deep speed tutorial on a distributed systems library for making training larger models easier. The library makes it possible to train 10 times larger models 10 times faster with minimal code change. Zero gives the …    etc.  \n"]}]},{"cell_type":"markdown","source":["Take a closer look and evaluate it against the test set of processed dataset from samsum. Therefore 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."],"metadata":{"id":"F0ovIhI9Ncq8"}},{"cell_type":"code","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=55):\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(\"bart-sam-data/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","\n","# Rogue1: 50.386161%\n","# rouge2: 24.842412%\n","# rougeL: 41.370130%\n","# rougeLsum: 41.394230%\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ipsq9UhINcEJ","executionInfo":{"status":"ok","timestamp":1682248927266,"user_tz":420,"elapsed":1688395,"user":{"displayName":"Seulkee Lee","userId":"14463507896336644914"}},"outputId":"c48f89f3-0f1c-470a-f0ac-891d4d54e3db"},"execution_count":26,"outputs":[{"output_type":"stream","name":"stderr","text":["100%|██████████| 819/819 [28:03<00:00,  2.06s/it]\n"]},{"output_type":"stream","name":"stdout","text":["Rogue1: 43.115465%\n","rouge2: 21.563061%\n","rougeL: 33.409979%\n","rougeLsum: 33.414162%\n"]}]}]}