DPO_modified.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "FYNbRRXb3wF1",
    "outputId": "9a853320-c2a7-4200-fec8-2a2edb927ddd"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting datasets\n",
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      "Collecting xxhash (from datasets)\n",
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      "  Downloading multiprocess-0.70.16-py310-none-any.whl.metadata (7.2 kB)\n",
      "Collecting fsspec<=2024.9.0,>=2023.1.0 (from fsspec[http]<=2024.9.0,>=2023.1.0->datasets)\n",
      "  Downloading fsspec-2024.9.0-py3-none-any.whl.metadata (11 kB)\n",
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      "Requirement already satisfied: propcache>=0.2.0 in /usr/local/lib/python3.10/dist-packages (from yarl<2.0,>=1.12.0->aiohttp->datasets) (0.2.0)\n",
      "Downloading datasets-3.1.0-py3-none-any.whl (480 kB)\n",
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      "\u001b[?25hDownloading xxhash-3.5.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (194 kB)\n",
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      "\u001b[?25hInstalling collected packages: xxhash, fsspec, dill, multiprocess, datasets\n",
      "  Attempting uninstall: fsspec\n",
      "    Found existing installation: fsspec 2024.10.0\n",
      "    Uninstalling fsspec-2024.10.0:\n",
      "      Successfully uninstalled fsspec-2024.10.0\n",
      "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
      "gcsfs 2024.10.0 requires fsspec==2024.10.0, but you have fsspec 2024.9.0 which is incompatible.\u001b[0m\u001b[31m\n",
      "\u001b[0mSuccessfully installed datasets-3.1.0 dill-0.3.8 fsspec-2024.9.0 multiprocess-0.70.16 xxhash-3.5.0\n"
     ]
    }
   ],
   "source": [
    "!pip install datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "bNQBgre2HfCJ",
    "outputId": "ca675ae7-c394-431c-83d6-967332a6e653"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\u001b[34m\u001b[1mwandb\u001b[0m: Using wandb-core as the SDK backend. Please refer to https://wandb.me/wandb-core for more information.\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: W&B API key is configured. Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m If you're specifying your api key in code, ensure this code is not shared publicly.\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: \u001b[33mWARNING\u001b[0m Consider setting the WANDB_API_KEY environment variable, or running `wandb login` from the command line.\n",
      "\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n"
     ]
    }
   ],
   "source": [
    "import wandb\n",
    "wandb.login(key=\"2bcea92e3d3f472ffce763d1296a2ff409c4662a\")\n",
    "from tqdm import tqdm\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "OsbyCOmPGpMg"
   },
   "outputs": [],
   "source": [
    "import argparse\n",
    "import random\n",
    "import numpy as np\n",
    "from functools import partial\n",
    "from torch.cuda.amp import autocast, GradScaler\n",
    "\n",
    "\n",
    "import torch\n",
    "torch.cuda.empty_cache()\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from torch.optim import AdamW\n",
    "\n",
    "from torch.utils.data import DataLoader\n",
    "from datasets import load_dataset\n",
    "!export PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True\n",
    "torch.backends.cuda.matmul.allow_fp16_reduced_precision_reduction = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "Mbwo9K7gGbql"
   },
   "outputs": [],
   "source": [
    "def seed_everything(seed=2003):\n",
    "    torch.manual_seed(seed)\n",
    "    torch.cuda.manual_seed_all(seed)\n",
    "    np.random.seed(seed)\n",
    "    random.seed(seed)\n",
    "    torch.backends.cudnn.deterministic = True\n",
    "\n",
    "def calculate_DPO_loss(model_prefered_logprob, model_disprefered_logprob,\n",
    "                       ref_prefered_logprob, ref_disprefered_logprob,\n",
    "                       beta=0.5):\n",
    "\n",
    "    prefered_relative_logprob = model_prefered_logprob - ref_prefered_logprob\n",
    "    disprefered_relative_logprob = model_disprefered_logprob - ref_disprefered_logprob\n",
    "\n",
    "    reward_accuracies = (prefered_relative_logprob > disprefered_relative_logprob).float().mean(dim=-1)\n",
    "    reward_margins = (prefered_relative_logprob - disprefered_relative_logprob).mean(dim=-1)\n",
    "\n",
    "    loss = -F.logsigmoid(beta * (prefered_relative_logprob - disprefered_relative_logprob)).mean(dim=-1)\n",
    "\n",
    "    return loss, prefered_relative_logprob.mean(dim=-1), disprefered_relative_logprob.mean(dim=-1), reward_accuracies, reward_margins\n",
    "\n",
    "def get_log_prob(logits, labels):\n",
    "    log_probs = F.log_softmax(logits, dim=-1)\n",
    "    return torch.gather(log_probs, -1, labels.unsqueeze(-1)).squeeze(-1).mean(-1)\n",
    "\n",
    "def collate_fn(batch, tokenizer, max_length, device):\n",
    "    prompts = ['Instruct: ' + item['prompt'] + '\\n' for item in batch]\n",
    "    chosen_responses = ['Output: ' + item['chosen'] for item in batch]\n",
    "    rejected_responses = ['Output: ' + item['rejected'] for item in batch]\n",
    "\n",
    "    prompt_ids = tokenizer.batch_encode_plus(prompts, padding=True, return_tensors=\"pt\", max_length=max_length, truncation=True)['input_ids'].to(device)\n",
    "    prefered_ids = tokenizer.batch_encode_plus(chosen_responses, padding=True, return_tensors=\"pt\", max_length=max_length, truncation=True)['input_ids'].to(device)\n",
    "    disprefered_ids = tokenizer.batch_encode_plus(rejected_responses, padding=True, return_tensors=\"pt\", max_length=max_length, truncation=True)['input_ids'].to(device)\n",
    "\n",
    "    prompt_prefered_ids = torch.cat([prompt_ids, prefered_ids], dim=-1)\n",
    "    prompt_disprefered_ids = torch.cat([prompt_ids, disprefered_ids], dim=-1)\n",
    "\n",
    "    prompt_prefered_mask = torch.cat([torch.ones_like(prompt_ids), torch.zeros_like(prefered_ids)], dim=-1)\n",
    "    prompt_disprefered_mask = torch.cat([torch.ones_like(prompt_ids), torch.zeros_like(disprefered_ids)], dim=-1)\n",
    "\n",
    "    return {'prompt_prefered_ids': prompt_prefered_ids,\n",
    "            'prompt_disprefered_ids': prompt_disprefered_ids,\n",
    "            'prompt_prefered_mask': prompt_prefered_mask,\n",
    "            'prompt_disprefered_mask': prompt_disprefered_mask}\n",
    "\n",
    "def train(model, ref_model, tokenizer, optimizer, train_dataloader, epochs=1, beta=0.1):\n",
    "    model.train()\n",
    "    ref_model.eval()\n",
    "    model, train_dataloader = accelerator.prepare(model, train_dataloader)\n",
    "    batch_count = 0\n",
    "    for batch in train_dataloader:\n",
    "       # Check and print tensor devices for batch tensors\n",
    "      batch_count+=1\n",
    "      print(f\"Batch {batch_count} of {len(train_dataloader)}\")\n",
    "\n",
    "      # Move model tensors to the GPU (handled by Accelerator)\n",
    "      prompt_prefered_ids = batch['prompt_prefered_ids']\n",
    "      prompt_disprefered_ids = batch['prompt_disprefered_ids']\n",
    "      prompt_prefered_mask = batch['prompt_prefered_mask']\n",
    "      prompt_disprefered_mask = batch['prompt_disprefered_mask']\n",
    "\n",
    "\n",
    "\n",
    "      # Forward pass with main model (on GPU)\n",
    "      model_prefered_log_prob = get_log_prob(model(prompt_prefered_ids.clone(), attention_mask=prompt_prefered_mask.clone()).logits.to(device), prompt_prefered_ids.clone())\n",
    "      model_disprefered_log_prob = get_log_prob(model(prompt_disprefered_ids.clone(), attention_mask=prompt_disprefered_mask.clone()).logits.to(device), prompt_disprefered_ids.clone())\n",
    "      print(\"Before loss calculation:\")\n",
    "\n",
    "      # Move tensors to CPU for ref_model forward pass if required\n",
    "      prompt_prefered_ids_cpu = prompt_prefered_ids.to(\"cpu\")\n",
    "      prompt_disprefered_ids_cpu = prompt_disprefered_ids.to(\"cpu\")\n",
    "      prompt_prefered_mask_cpu = prompt_prefered_mask.to(\"cpu\")\n",
    "      prompt_disprefered_mask_cpu = prompt_disprefered_mask.to(\"cpu\")\n",
    "\n",
    "      # Forward pass with ref_model (on CPU)\n",
    "      with torch.no_grad():\n",
    "          ref_prefered_log_prob = get_log_prob(\n",
    "              ref_model(prompt_prefered_ids_cpu.clone(), attention_mask=prompt_prefered_mask_cpu.clone()).logits,\n",
    "              prompt_prefered_ids_cpu.clone()\n",
    "          )\n",
    "          ref_disprefered_log_prob = get_log_prob(\n",
    "              ref_model(prompt_disprefered_ids_cpu.clone(), attention_mask=prompt_disprefered_mask_cpu.clone()).logits,\n",
    "              prompt_disprefered_ids_cpu.clone()\n",
    "          )\n",
    "\n",
    "      # Move ref_model outputs to GPU for loss calculation\n",
    "      ref_prefered_log_prob = ref_prefered_log_prob.to(device)\n",
    "      ref_disprefered_log_prob = ref_disprefered_log_prob.to(device)\n",
    "\n",
    "\n",
    "\n",
    "      # Calculate DPO loss on GPU\n",
    "      loss, prefered_relative_logprob, disprefered_relative_logprob, reward_accuracies, reward_margins = calculate_DPO_loss(\n",
    "          model_prefered_log_prob, model_disprefered_log_prob,\n",
    "          ref_prefered_log_prob, ref_disprefered_log_prob,\n",
    "          beta=beta\n",
    "      )\n",
    "\n",
    "    # Backpropagation and optimization\n",
    "    torch.autograd.set_detect_anomaly(True)\n",
    "    torch.autograd.set_detect_anomaly(True)\n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "    optimizer.zero_grad()\n",
    "    del loss, prefered_relative_logprob, disprefered_relative_logprob, reward_accuracies, reward_margins\n",
    "    del model_prefered_log_prob, model_disprefered_log_prob, ref_prefered_log_prob, ref_disprefered_log_prob\n",
    "    del prompt_prefered_ids, prompt_disprefered_ids, prompt_prefered_mask, prompt_disprefered_mask\n",
    "\n",
    "    torch.cuda.empty_cache()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "VSbaDATW7ry4"
   },
   "outputs": [],
   "source": [
    "%%capture\n",
    "# Installs Unsloth, Xformers (Flash Attention) and all other packages!\n",
    "!pip install \"unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git\"\n",
    "!pip install --no-deps xformers \"trl<0.9.0\" peft accelerate bitsandbytes\n",
    "!pip install --upgrade transformers accelerate bitsandbytes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
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    "id": "zGCeceiWE8RX",
    "outputId": "ad282d32-cc17-475f-e3c4-8000fa953a05"
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n",
      "==((====))==  Unsloth 2024.11.5: Fast Mistral patching. Transformers = 4.46.2.\n",
      "   \\\\   /|    GPU: NVIDIA A100-SXM4-40GB. Max memory: 39.564 GB. Platform = Linux.\n",
      "O^O/ \\_/ \\    Pytorch: 2.5.0+cu121. CUDA = 8.0. CUDA Toolkit = 12.1.\n",
      "\\        /    Bfloat16 = TRUE. FA [Xformers = 0.0.28.post3. FA2 = False]\n",
      " \"-____-\"     Free Apache license: http://github.com/unslothai/unsloth\n",
      "Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!\n"
     ]
    },
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      ]
     },
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    {
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      ]
     },
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    },
    {
     "data": {
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from unsloth import FastLanguageModel\n",
    "import torch\n",
    "max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!\n",
    "dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+\n",
    "load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.\n",
    "\n",
    "# 4bit pre quantized models we support for 4x faster downloading + no OOMs.\n",
    "fourbit_models = [\n",
    "    \"unsloth/mistral-7b-v0.3-bnb-4bit\",      # New Mistral v3 2x faster!\n",
    "    \"unsloth/mistral-7b-instruct-v0.3-bnb-4bit\",\n",
    "    \"unsloth/llama-3-8b-bnb-4bit\",           # Llama-3 15 trillion tokens model 2x faster!\n",
    "    \"unsloth/llama-3-8b-Instruct-bnb-4bit\",\n",
    "    \"unsloth/llama-3-70b-bnb-4bit\",\n",
    "    \"unsloth/Phi-3-mini-4k-instruct\",        # Phi-3 2x faster!\n",
    "    \"unsloth/Phi-3-medium-4k-instruct\",\n",
    "    \"unsloth/mistral-7b-bnb-4bit\",\n",
    "    \"unsloth/gemma-7b-bnb-4bit\",             # Gemma 2.2x faster!\n",
    "] # More models at https://huggingface.co/unsloth\n",
    "\n",
    "model, tokenizer = FastLanguageModel.from_pretrained(\n",
    "    model_name = \"unsloth/Phi-3-mini-4k-instruct\",\n",
    "    max_seq_length = max_seq_length,\n",
    "    dtype = dtype,\n",
    "    load_in_4bit = load_in_4bit,\n",
    "    # token = \"hf_...\", # use one if using gated models like meta-llama/Llama-2-7b-hf\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "NjYY8mMMJVKL"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000,
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    "id": "4kjPl1UW3dWX",
    "outputId": "27606f6d-be43-4484-940d-245e225fe16d"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:accelerate.big_modeling:You shouldn't move a model that is dispatched using accelerate hooks.\n",
      "WARNING:accelerate.big_modeling:You shouldn't move a model that is dispatched using accelerate hooks.\n"
     ]
    },
    {
     "data": {
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       "model_id": "1346f8383d2e41628044fcccfe13ee21",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7e34ef149c20417f93c6a9d1ec82715e",
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       "version_minor": 0
      },
      "text/plain": [
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      ]
     },
     "metadata": {},
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    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2187a32951954d298f319096895068b6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating train split:   0%|          | 0/1016 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Batch 1 of 50\n",
      "Before loss calculation:\n",
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      "Batch 50 of 50\n",
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     ]
    },
    {
     "ename": "RuntimeError",
     "evalue": "one of the variables needed for gradient computation has been modified by an inplace operation: [torch.cuda.FloatTensor [1, 236, 3072]], which is output 0 of MulBackward0, is at version 1; expected version 0 instead. Hint: the backtrace further above shows the operation that failed to compute its gradient. The variable in question was changed in there or anywhere later. Good luck!",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-7-e4a779157984>\u001b[0m in \u001b[0;36m<cell line: 13>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0mtrain_dataloader\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataLoader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcollate_fn\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpartial\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcollate_fn\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtokenizer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtokenizer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_length\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m512\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdevice\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m \u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mref_model\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtokenizer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moptimizer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrain_dataloader\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mepochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mbeta\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m<ipython-input-4-59eb6be03823>\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(model, ref_model, tokenizer, optimizer, train_dataloader, epochs, beta)\u001b[0m\n\u001b[1;32m     99\u001b[0m     \u001b[0;31m# Backpropagation and optimization\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    100\u001b[0m     \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mautograd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_detect_anomaly\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 101\u001b[0;31m     \u001b[0mloss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbackward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    102\u001b[0m     \u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    103\u001b[0m     \u001b[0moptimizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mzero_grad\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/_tensor.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(self, gradient, retain_graph, create_graph, inputs)\u001b[0m\n\u001b[1;32m    579\u001b[0m                 \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    580\u001b[0m             )\n\u001b[0;32m--> 581\u001b[0;31m         torch.autograd.backward(\n\u001b[0m\u001b[1;32m    582\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgradient\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mretain_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcreate_graph\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    583\u001b[0m         )\n",
      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/autograd/__init__.py\u001b[0m in \u001b[0;36mbackward\u001b[0;34m(tensors, grad_tensors, retain_graph, create_graph, grad_variables, inputs)\u001b[0m\n\u001b[1;32m    345\u001b[0m     \u001b[0;31m# some Python versions print out the first line of a multi-line function\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    346\u001b[0m     \u001b[0;31m# calls in the traceback and some print out the last line\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 347\u001b[0;31m     _engine_run_backward(\n\u001b[0m\u001b[1;32m    348\u001b[0m         \u001b[0mtensors\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    349\u001b[0m         \u001b[0mgrad_tensors_\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/autograd/graph.py\u001b[0m in \u001b[0;36m_engine_run_backward\u001b[0;34m(t_outputs, *args, **kwargs)\u001b[0m\n\u001b[1;32m    823\u001b[0m         \u001b[0munregister_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_register_logging_hooks_on_whole_graph\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt_outputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    824\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 825\u001b[0;31m         return Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass\n\u001b[0m\u001b[1;32m    826\u001b[0m             \u001b[0mt_outputs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    827\u001b[0m         )  # Calls into the C++ engine to run the backward pass\n",
      "\u001b[0;31mRuntimeError\u001b[0m: one of the variables needed for gradient computation has been modified by an inplace operation: [torch.cuda.FloatTensor [1, 236, 3072]], which is output 0 of MulBackward0, is at version 1; expected version 0 instead. Hint: the backtrace further above shows the operation that failed to compute its gradient. The variable in question was changed in there or anywhere later. Good luck!"
     ]
    }
   ],
   "source": [
    "from accelerate import Accelerator\n",
    "accelerator = Accelerator()\n",
    "device = accelerator.device\n",
    "#device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "tokenizer.pad_token = tokenizer.eos_token\n",
    "model = model.to(device)\n",
    "ref_model = model.to(device)\n",
    "optimizer = AdamW(model.parameters(), lr=1e-6)\n",
    "\n",
    "dataset = load_dataset(\"jondurbin/truthy-dpo-v0.1\", split=\"train[:50]\")\n",
    "train_dataloader = torch.utils.data.DataLoader(dataset, batch_size=1, shuffle=True, collate_fn=partial(collate_fn, tokenizer=tokenizer, max_length=512, device=device))\n",
    "\n",
    "train(model, ref_model, tokenizer, optimizer, train_dataloader, epochs=10, beta=0.1)"
   ]
  }
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