sft_phi3_2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "578786b8-092a-4de8-9955-4e87da557639",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: peft in /opt/conda/lib/python3.10/site-packages (0.11.1)\n",
      "Requirement already satisfied: numpy>=1.17 in /opt/conda/lib/python3.10/site-packages (from peft) (1.26.3)\n",
      "Requirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.10/site-packages (from peft) (23.1)\n",
      "Requirement already satisfied: psutil in /opt/conda/lib/python3.10/site-packages (from peft) (5.9.0)\n",
      "Requirement already satisfied: pyyaml in /opt/conda/lib/python3.10/site-packages (from peft) (6.0.1)\n",
      "Requirement already satisfied: torch>=1.13.0 in /opt/conda/lib/python3.10/site-packages (from peft) (2.2.0)\n",
      "Requirement already satisfied: transformers in /opt/conda/lib/python3.10/site-packages (from peft) (4.42.3)\n",
      "Requirement already satisfied: tqdm in /opt/conda/lib/python3.10/site-packages (from peft) (4.66.4)\n",
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      "Requirement already satisfied: safetensors in /opt/conda/lib/python3.10/site-packages (from peft) (0.4.3)\n",
      "Requirement already satisfied: huggingface-hub>=0.17.0 in /opt/conda/lib/python3.10/site-packages (from peft) (0.23.4)\n",
      "Requirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from huggingface-hub>=0.17.0->peft) (3.13.1)\n",
      "Requirement already satisfied: fsspec>=2023.5.0 in /opt/conda/lib/python3.10/site-packages (from huggingface-hub>=0.17.0->peft) (2023.12.2)\n",
      "Requirement already satisfied: requests in /opt/conda/lib/python3.10/site-packages (from huggingface-hub>=0.17.0->peft) (2.32.3)\n",
      "Requirement already satisfied: typing-extensions>=3.7.4.3 in /opt/conda/lib/python3.10/site-packages (from huggingface-hub>=0.17.0->peft) (4.9.0)\n",
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      "Requirement already satisfied: jinja2 in /opt/conda/lib/python3.10/site-packages (from torch>=1.13.0->peft) (3.1.2)\n",
      "Requirement already satisfied: regex!=2019.12.17 in /opt/conda/lib/python3.10/site-packages (from transformers->peft) (2024.5.15)\n",
      "Requirement already satisfied: tokenizers<0.20,>=0.19 in /opt/conda/lib/python3.10/site-packages (from transformers->peft) (0.19.1)\n",
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      "Requirement already satisfied: urllib3<3,>=1.21.1 in /opt/conda/lib/python3.10/site-packages (from requests->huggingface-hub>=0.17.0->peft) (1.26.18)\n",
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      "Requirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->torch>=1.13.0->peft) (1.3.0)\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "#!pip install huggingface_hub torch transformers datasets trl \n",
    "#!pip install flash-attn --no-build-isolation\n",
    "!pip install --upgrade peft"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "4a74bec4-4bf0-47be-802a-046073da573e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "import logging\n",
    "\n",
    "import datasets\n",
    "from datasets import load_dataset\n",
    "from peft import LoraConfig\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import transformers\n",
    "from trl import SFTTrainer, SFTConfig\n",
    "from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments, BitsAndBytesConfig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d2e94000-8097-4f09-a8dc-506801bb9f12",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8c9a7b9e2bb5463c979a2659118c4912",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from huggingface_hub import HfApi, notebook_login\n",
    "\n",
    "notebook_login()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8b994f91-1973-405d-8275-07948eadab99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CommitInfo(commit_url='https://huggingface.co/datasets/Granther/assorted-notebooks-bin/commit/daccf7107bc7eac6717dea879fde2ba3a51cccf4', commit_message='Upload sft_phi3_2.ipynb with huggingface_hub', commit_description='', oid='daccf7107bc7eac6717dea879fde2ba3a51cccf4', pr_url=None, pr_revision=None, pr_num=None)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "api = HfApi()\n",
    "\n",
    "quant_repo = \"Granther/assorted-notebooks-bin\"\n",
    "\n",
    "api.upload_file(\n",
    "    path_or_fileobj=\"sft_phi3_2.ipynb\",\n",
    "    path_in_repo=\"sft_phi3_2.ipynb\",\n",
    "    repo_id=quant_repo,\n",
    "    repo_type=\"dataset\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8a9bc6f8-4a1e-42d8-897d-5225e1b5011a",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_id = (\"wikitext\", \"wikitext-103-raw-v1\")\n",
    "dataset_id = \"HuggingFaceH4/ultrachat_200k\"\n",
    "\n",
    "dataset = load_dataset(dataset_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f3b226eb-b159-4533-bd33-2746181a80b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "training_config = {\n",
    "    \"bf16\": True,\n",
    "    \"do_eval\": False,\n",
    "    \"do_train\": True, # defualts to False, not sure where this fits\n",
    "    \"learning_rate\": 5.0e-06,\n",
    "    \"log_level\": \"info\",\n",
    "    \"logging_steps\": 20,\n",
    "    \"logging_strategy\": \"steps\",\n",
    "    \"lr_scheduler_type\": \"cosine\",\n",
    "    \"num_train_epochs\": 1,\n",
    "    \"max_steps\": -1,\n",
    "    \"output_dir\": \"./checkpoint_dir\", # model predictions and checkpoints\n",
    "    \"overwrite_output_dir\": True,\n",
    "    \"per_device_eval_batch_size\": 4,\n",
    "    \"per_device_train_batch_size\": 4,\n",
    "    \"remove_unused_columns\": True,\n",
    "    \"save_steps\": 100,\n",
    "    \"save_total_limit\": 1,\n",
    "    \"seed\": 0,\n",
    "    \"gradient_checkpointing\": True,\n",
    "    \"gradient_checkpointing_kwargs\":{\"use_reentrant\": False},\n",
    "    \"gradient_accumulation_steps\": 1, # number of steps to accumulate before beckprop\n",
    "    \"warmup_ratio\": 0.2,\n",
    "    \"packing\": False,\n",
    "    \"max_seq_length\": 2048,\n",
    "    \"dataset_text_field\": \"text\",\n",
    "    }\n",
    "\n",
    "peft_config = {\n",
    "    \"r\": 16, # default values VV\n",
    "    \"lora_alpha\": 32,\n",
    "    \"lora_dropout\": 0.05,\n",
    "    \"bias\": \"none\",\n",
    "    \"task_type\": \"CAUSAL_LM\",\n",
    "    \"target_modules\": \"all-linear\",\n",
    "    \"modules_to_save\": None,\n",
    "}\n",
    "\n",
    "train_conf = SFTConfig(**training_config)\n",
    "#train_conf = TrainingArguments(**training_config)\n",
    "peft_conf = LoraConfig(**peft_config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "20c9d834-50fe-4495-b003-7d80495c8439",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "35f0c3abec7c4220b2cbd89ecc0671c0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
     ]
    }
   ],
   "source": [
    "checkpoint_path = \"microsoft/Phi-3-mini-128k-instruct\"\n",
    "model_kwargs = dict(\n",
    "    use_cache=False,\n",
    "    trust_remote_code=True,\n",
    "    attn_implementation=\"flash_attention_2\",  # loading the model with flash-attenstion support\n",
    "    torch_dtype=torch.bfloat16,\n",
    "    device_map=\"auto\"\n",
    ")\n",
    "\n",
    "model = AutoModelForCausalLM.from_pretrained(checkpoint_path, **model_kwargs)\n",
    "tokenizer = AutoTokenizer.from_pretrained(checkpoint_path, truncation=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d684252c-2151-4601-8ebb-398bd3a63f00",
   "metadata": {},
   "outputs": [],
   "source": [
    "tokenizer.model_max_length = 2048\n",
    "#tokenizer.pad_token = tokenizer.unk_token  # use unk rather than eos token to prevent endless generation\n",
    "#tokenizer.pad_token_id = tokenizer.convert_tokens_to_ids(tokenizer.pad_token)\n",
    "tokenizer.padding_side = 'right'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "75869100-99f7-49c7-a9d3-7a3950dd7d72",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def preproc(examples, tokenizer):\n",
    "    messages = examples['messages']\n",
    "    examples['text'] = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=False) #, return_dict=True)\n",
    "    return examples\n",
    "\n",
    "train_dataset = dataset['train_sft']\n",
    "test_dataset = dataset['test_sft']\n",
    "\n",
    "train_dataset = train_dataset.map(preproc,\n",
    "                            fn_kwargs={'tokenizer':tokenizer},\n",
    "                            num_proc=24,\n",
    "                            #batched=True,\n",
    "                            remove_columns=list(train_dataset.features)).select(range(1000))\n",
    "\n",
    "test_dataset = test_dataset.map(preproc,\n",
    "                            fn_kwargs={'tokenizer':tokenizer},\n",
    "                            num_proc=24,\n",
    "                            #batched=True,\n",
    "                            remove_columns=list(test_dataset.features))#[10000:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "56cd1b31-6f7e-4c7d-8524-b12cf94b9c9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5d79f04152484f9494e389b264fc7176",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/1000 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using auto half precision backend\n"
     ]
    }
   ],
   "source": [
    "trainer = SFTTrainer(\n",
    "    model=model,\n",
    "    args=train_conf,\n",
    "    peft_config=peft_conf,\n",
    "    train_dataset=train_dataset,\n",
    "    #eval_dataset=test_dataset,\n",
    "    # max_seq_length=tokenizer.model_max_length,\n",
    "    # dataset_text_field=\"text\",\n",
    "    tokenizer=tokenizer,\n",
    "    # packing=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d8e6b669-1717-429a-9c43-3c02adb8a3d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_result = trainer.train()\n",
    "metrics = train_result.metrics\n",
    "trainer.save_state()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4d4207fc-1578-4591-a480-467fd2a5855b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'train_runtime': 506.2204,\n",
       " 'train_samples_per_second': 1.975,\n",
       " 'train_steps_per_second': 0.494,\n",
       " 'total_flos': 4.041582948790272e+16,\n",
       " 'train_loss': 1.1037534561157227,\n",
       " 'epoch': 1.0}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f92339ec-0448-40d2-9458-6242e35b9bdc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "575ddc622079468cb4ca994ed76dc2f4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from peft import PeftConfig, PeftModel \n",
    "\n",
    "checkpoint_path = \"microsoft/Phi-3-mini-128k-instruct\"\n",
    "adapter_path = \"./checkpoint_dir/checkpoint-250\"\n",
    "\n",
    "model_kwargs = dict(\n",
    "    use_cache=False,\n",
    "    trust_remote_code=True,\n",
    "    attn_implementation=\"flash_attention_2\",  # loading the model with flash-attenstion support\n",
    "    torch_dtype=torch.bfloat16,\n",
    "    device_map=\"auto\"\n",
    ")\n",
    "\n",
    "model = AutoModelForCausalLM.from_pretrained(checkpoint_path, **model_kwargs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f0cf458d-8b4f-4ff9-bd60-bbe510416cea",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
     ]
    }
   ],
   "source": [
    "model = PeftModel.from_pretrained(model, adapter_path)\n",
    "tokenizer = AutoTokenizer.from_pretrained(checkpoint_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "8e0b4443-cc10-47ca-a476-055fd915c1b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "cats = None\n",
    "kittens = None\n",
    "paris = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "b5ada882-b7d2-46c5-ba5b-54fab2556832",
   "metadata": {},
   "outputs": [],
   "source": [
    "input_text = [\n",
    "    {'role': 'user', 'content': \"Cats are\"},\n",
    "    #{'role': 'system', 'content': \"I'm going \"}\n",
    "]\n",
    "\n",
    "input = \"Tell me about paris\"\n",
    "\n",
    "input = tokenizer(input, return_tensors='pt')\n",
    "\n",
    "def generate_out(input, len=100):\n",
    "    return model.generate(\n",
    "        input['input_ids'].to('cuda'),\n",
    "        max_length=len,  # Maximum length of the generated text\n",
    "        num_return_sequences=1,  # Number of sequences to generate\n",
    "    )\n",
    "output = generate_out(input)[0].to(torch.float32)\n",
    "\n",
    "paris = output\n",
    "\n",
    "#loss(output, output)\n",
    "#input = tokenizer.apply_chat_template(input_text, tokenize=False, add_generation_prompt=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "8f39d54c-7ec8-4e1a-9db3-83157eae8f7d",
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "loop of ufunc does not support argument 0 of type builtin_function_or_method which has no callable exp method",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;31mAttributeError\u001b[0m: 'builtin_function_or_method' object has no attribute 'exp'",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[92], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mnumpy\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mnp\u001b[39;00m\n\u001b[0;32m----> 3\u001b[0m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexp\u001b[49m\u001b[43m(\u001b[49m\u001b[43mloss\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcats\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mparis\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mitem\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[0;31mTypeError\u001b[0m: loop of ufunc does not support argument 0 of type builtin_function_or_method which has no callable exp method"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "np.exp(loss(cats, paris).item)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "d440d74d-2b5d-48e0-ad86-f355a25a6c16",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([  274,  1446,   526,   451,   278,   871, 15006,   393,   508,   367,\n",
       "        15201,   491,   445,  4195, 29889,   360, 12099, 29892, 27127,  1169,\n",
       "        29892,   322,  1584, 17952,   508,   884,  8812,   515,   445, 17135,\n",
       "        29889,    13,    13,  1576, 25828,  4835,   310,   285,  5570,   454,\n",
       "         2679, 29747, 24424,   297, 20309,   508, 13100,  8679,   373,   278,\n",
       "         7408,   310,   278, 17135, 29889,   512,   278,  4688, 22950, 29892,\n",
       "          274,  1446,  1122,  1510,   694, 18906,   310,  4486,  2264, 29889,\n",
       "         2398, 29892,   408,   278, 17135,  6728,   267, 29892,   896,  1122,\n",
       "         4953,   454,   386,  1191,   293, 29892, 14074,  1009,   623,   300,\n",
       "          568, 29892,   322,  2693,  2381,   324,  2435,   301,   962,   561],\n",
       "       device='cuda:0')"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "8f557898-32bb-4cc9-b9de-cb80bb4ee21e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([4, 3, 0])"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = torch.tensor([123], dtype=torch.long)\n",
    "\n",
    "#loss(x,x)\n",
    "\n",
    "input = torch.randn(3, 5, requires_grad=True)\n",
    "target = torch.empty(3, dtype=torch.long).random_(5)\n",
    "\n",
    "target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "139e9973-003a-484f-95f8-42428dd436f5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cats are not the only animals that can be affected by this condition. Dogs, rabbits, and even birds can also suffer from this disease.\n",
      "\n",
      "The symptoms of feline leukemia virus infection can vary depending on the stage of the disease. In the early stages, cats may show no signs of illness. However, as the disease progresses, they may become lethargic, lose their appetite, and develop swollen lymph\n"
     ]
    }
   ],
   "source": [
    "generated_text = tokenizer.decode(output[0], skip_special_tokens=True)\n",
    "\n",
    "print(generated_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "cda57a53-98f7-45eb-84c1-9ef8360926a4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Attempting to cast a BatchEncoding to type torch.float16. This is not supported.\n",
      "Attempting to cast a BatchEncoding to type torch.float16. This is not supported.\n"
     ]
    },
    {
     "ename": "RuntimeError",
     "evalue": "Expected floating point type for target with class probabilities, got Long",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[54], line 6\u001b[0m\n\u001b[1;32m      3\u001b[0m i \u001b[38;5;241m=\u001b[39m tokenizer(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mHello\u001b[39m\u001b[38;5;124m\"\u001b[39m, return_tensors\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpt\u001b[39m\u001b[38;5;124m'\u001b[39m)\u001b[38;5;241m.\u001b[39mto(torch\u001b[38;5;241m.\u001b[39mfloat16)\n\u001b[1;32m      4\u001b[0m lab \u001b[38;5;241m=\u001b[39m tokenizer(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mHella\u001b[39m\u001b[38;5;124m\"\u001b[39m, return_tensors\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpt\u001b[39m\u001b[38;5;124m'\u001b[39m)\u001b[38;5;241m.\u001b[39mto(torch\u001b[38;5;241m.\u001b[39mfloat16)\n\u001b[0;32m----> 6\u001b[0m \u001b[43mloss\u001b[49m\u001b[43m(\u001b[49m\u001b[43mgenerate_out\u001b[49m\u001b[43m(\u001b[49m\u001b[43mi\u001b[49m\u001b[43m,\u001b[49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m10\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mgenerate_out\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlab\u001b[49m\u001b[43m,\u001b[49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m10\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py:1511\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1509\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1510\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1511\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py:1520\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1515\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1516\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1517\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1518\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1519\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1520\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1522\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1523\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
      "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/torch/nn/modules/loss.py:1179\u001b[0m, in \u001b[0;36mCrossEntropyLoss.forward\u001b[0;34m(self, input, target)\u001b[0m\n\u001b[1;32m   1178\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28minput\u001b[39m: Tensor, target: Tensor) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Tensor:\n\u001b[0;32m-> 1179\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mF\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcross_entropy\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtarget\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mweight\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mweight\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1180\u001b[0m \u001b[43m                           \u001b[49m\u001b[43mignore_index\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mignore_index\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mreduction\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreduction\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1181\u001b[0m \u001b[43m                           \u001b[49m\u001b[43mlabel_smoothing\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlabel_smoothing\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m/opt/conda/lib/python3.10/site-packages/torch/nn/functional.py:3059\u001b[0m, in \u001b[0;36mcross_entropy\u001b[0;34m(input, target, weight, size_average, ignore_index, reduce, reduction, label_smoothing)\u001b[0m\n\u001b[1;32m   3057\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m size_average \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mor\u001b[39;00m reduce \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m   3058\u001b[0m     reduction \u001b[38;5;241m=\u001b[39m _Reduction\u001b[38;5;241m.\u001b[39mlegacy_get_string(size_average, reduce)\n\u001b[0;32m-> 3059\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_C\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_nn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcross_entropy_loss\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtarget\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mweight\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m_Reduction\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_enum\u001b[49m\u001b[43m(\u001b[49m\u001b[43mreduction\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mignore_index\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlabel_smoothing\u001b[49m\u001b[43m)\u001b[49m\n",
      "\u001b[0;31mRuntimeError\u001b[0m: Expected floating point type for target with class probabilities, got Long"
     ]
    }
   ],
   "source": [
    "loss = nn.CrossEntropyLoss()\n",
    "\n",
    "i = tokenizer(\"Hello\", return_tensors='pt').to(torch.float16)\n",
    "lab = tokenizer(\"Hella\", return_tensors='pt').to(torch.float16\n",
    "\n",
    "loss(generate_out(i,len=10), generate_out(lab,len=10))\n",
    "\n",
    "#loss(i, lab)\n",
    "\n",
    "                                    "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f99adf3c-aefb-4c22-a8b1-e8f79ef0b9d8",
   "metadata": {},
   "source": [
    "### Test perplexity using MMLU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "287155f7-6808-4419-9b12-8756e1746e28",
   "metadata": {},
   "outputs": [],
   "source": [
    "prompt = \"When a diver points a flashlight upward toward the surface of the water at an angle 20° from the normal, the beam of light\"\n",
    "subject = \"conceptual_physics\"\n",
    "answer = \"passes into the air above\"\n",
    "\n",
    "input_text = []\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7bdb1138-7da5-44dc-9549-df7d40ec68e1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2024-07-04:16:55:06,590 INFO     [__main__.py:272] Verbosity set to INFO\n",
      "2024-07-04:16:55:11,201 INFO     [__main__.py:369] Selected Tasks: ['hellaswag']\n",
      "2024-07-04:16:55:11,202 INFO     [evaluator.py:152] Setting random seed to 0 | Setting numpy seed to 1234 | Setting torch manual seed to 1234\n",
      "2024-07-04:16:55:11,202 INFO     [evaluator.py:189] Initializing hf model, with arguments: {'pretrained': 'EleutherAI/gpt-j-6B'}\n",
      "2024-07-04:16:55:11,241 INFO     [huggingface.py:170] Using device 'cuda:0'\n",
      "Downloading builder script: 100%|██████████| 4.36k/4.36k [00:00<00:00, 19.3MB/s]\n",
      "Downloading metadata: 100%|████████████████| 2.53k/2.53k [00:00<00:00, 17.6MB/s]\n",
      "Downloading readme: 100%|██████████████████| 6.84k/6.84k [00:00<00:00, 24.9MB/s]\n",
      "Downloading data: 47.5MB [00:00, 137MB/s]                                       \n",
      "Downloading data: 11.8MB [00:00, 92.0MB/s]                                      \n",
      "Downloading data: 12.2MB [00:00, 93.1MB/s]                                      \n",
      "Generating train split: 100%|███| 39905/39905 [00:02<00:00, 17573.67 examples/s]\n",
      "Generating test split: 100%|████| 10003/10003 [00:00<00:00, 17738.73 examples/s]\n",
      "Generating validation split: 100%|█| 10042/10042 [00:00<00:00, 17489.21 examples\n",
      "Map: 100%|██████████████████████| 39905/39905 [00:03<00:00, 10310.72 examples/s]\n",
      "Map: 100%|███████████████████████| 10042/10042 [00:01<00:00, 9523.34 examples/s]\n",
      "2024-07-04:16:56:20,566 INFO     [evaluator.py:261] Setting fewshot random generator seed to 1234\n",
      "2024-07-04:16:56:20,567 INFO     [task.py:411] Building contexts for hellaswag on rank 0...\n",
      "100%|███████████████████████████████████| 10042/10042 [00:02<00:00, 3806.50it/s]\n",
      "2024-07-04:16:56:23,924 INFO     [evaluator.py:438] Running loglikelihood requests\n",
      "Running loglikelihood requests:   0%|                 | 0/40168 [00:00<?, ?it/s]Traceback (most recent call last):\n",
      "  File \"/opt/conda/bin/lm_eval\", line 8, in <module>\n",
      "    sys.exit(cli_evaluate())\n",
      "  File \"/lm-evaluation-harness/lm_eval/__main__.py\", line 375, in cli_evaluate\n",
      "    results = evaluator.simple_evaluate(\n",
      "  File \"/lm-evaluation-harness/lm_eval/utils.py\", line 395, in _wrapper\n",
      "    return fn(*args, **kwargs)\n",
      "  File \"/lm-evaluation-harness/lm_eval/evaluator.py\", line 277, in simple_evaluate\n",
      "    results = evaluate(\n",
      "  File \"/lm-evaluation-harness/lm_eval/utils.py\", line 395, in _wrapper\n",
      "    return fn(*args, **kwargs)\n",
      "  File \"/lm-evaluation-harness/lm_eval/evaluator.py\", line 449, in evaluate\n",
      "    resps = getattr(lm, reqtype)(cloned_reqs)\n",
      "  File \"/lm-evaluation-harness/lm_eval/api/model.py\", line 371, in loglikelihood\n",
      "    return self._loglikelihood_tokens(new_reqs, disable_tqdm=disable_tqdm)\n",
      "  File \"/lm-evaluation-harness/lm_eval/models/huggingface.py\", line 1086, in _loglikelihood_tokens\n",
      "    self._model_call(batched_inps, **call_kwargs), dim=-1\n",
      "  File \"/lm-evaluation-harness/lm_eval/models/huggingface.py\", line 801, in _model_call\n",
      "    return self.model(inps).logits\n",
      "  File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1511, in _wrapped_call_impl\n",
      "    return self._call_impl(*args, **kwargs)\n",
      "  File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1520, in _call_impl\n",
      "    return forward_call(*args, **kwargs)\n",
      "  File \"/opt/conda/lib/python3.10/site-packages/transformers/models/gptj/modeling_gptj.py\", line 1124, in forward\n",
      "    transformer_outputs = self.transformer(\n",
      "  File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1511, in _wrapped_call_impl\n",
      "    return self._call_impl(*args, **kwargs)\n",
      "  File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1520, in _call_impl\n",
      "    return forward_call(*args, **kwargs)\n",
      "  File \"/opt/conda/lib/python3.10/site-packages/transformers/models/gptj/modeling_gptj.py\", line 950, in forward\n",
      "    outputs = block(\n",
      "  File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1511, in _wrapped_call_impl\n",
      "    return self._call_impl(*args, **kwargs)\n",
      "  File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1520, in _call_impl\n",
      "    return forward_call(*args, **kwargs)\n",
      "  File \"/opt/conda/lib/python3.10/site-packages/transformers/models/gptj/modeling_gptj.py\", line 590, in forward\n",
      "    feed_forward_hidden_states = self.mlp(hidden_states)\n",
      "  File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1511, in _wrapped_call_impl\n",
      "    return self._call_impl(*args, **kwargs)\n",
      "  File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1520, in _call_impl\n",
      "    return forward_call(*args, **kwargs)\n",
      "  File \"/opt/conda/lib/python3.10/site-packages/transformers/models/gptj/modeling_gptj.py\", line 552, in forward\n",
      "    hidden_states = self.act(hidden_states)\n",
      "  File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1511, in _wrapped_call_impl\n",
      "    return self._call_impl(*args, **kwargs)\n",
      "  File \"/opt/conda/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1520, in _call_impl\n",
      "    return forward_call(*args, **kwargs)\n",
      "  File \"/opt/conda/lib/python3.10/site-packages/transformers/activations.py\", line 56, in forward\n",
      "    return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi) * (input + 0.044715 * torch.pow(input, 3.0))))\n",
      "torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate 76.00 MiB. GPU 0 has a total capacity of 23.65 GiB of which 27.81 MiB is free. Process 1711577 has 23.62 GiB memory in use. Of the allocated memory 23.07 GiB is allocated by PyTorch, and 103.80 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation.  See documentation for Memory Management  (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)\n",
      "Running loglikelihood requests:   0%|                 | 0/40168 [00:00<?, ?it/s]\n"
     ]
    }
   ],
   "source": [
    "!lm_eval --model hf \\\n",
    "    --model_args pretrained=EleutherAI/gpt-j-6B \\\n",
    "    --tasks hellaswag \\\n",
    "    --device cuda:0 \\\n",
    "    --batch_size 8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a9e753f6-4272-4305-bbfc-2eceeb5c4532",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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