{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0dd1298de3c84ea3ab8ed31b2a0b2888",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import torch\n",
    "from multiprocessing import set_start_method\n",
    "from transformers import Blip2Processor, Blip2ForConditionalGeneration\n",
    "from datasets import load_dataset\n",
    "\n",
    "# Load BLIP-2 model and processor\n",
    "processor = Blip2Processor.from_pretrained(\"Salesforce/blip2-opt-2.7b\")\n",
    "model = Blip2ForConditionalGeneration.from_pretrained(\"Salesforce/blip2-opt-2.7b\", torch_dtype=torch.float16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gpu_computation(batch, rank):\n",
    "    device = f\"cuda:{(rank or 0) % torch.cuda.device_count()}\"\n",
    "    model.to(device)\n",
    "    inputs = processor(images=batch[\"image\"], return_tensors=\"pt\").to(device, torch.float16)\n",
    "\n",
    "    with torch.no_grad():\n",
    "        generated_ids = model.generate(**inputs, max_length=51)\n",
    "    \n",
    "    batch[\"caption\"] = processor.batch_decode(generated_ids, skip_special_tokens=True)\n",
    "    return batch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "61fe62d696904a7c894bd2c6f082b426",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/10 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import multiprocessing\n",
    "\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    # Check if start method is already set\n",
    "    try:\n",
    "        multiprocessing.get_start_method()\n",
    "    except RuntimeError:\n",
    "        multiprocessing.set_start_method(\"spawn\")\n",
    "\n",
    "    # Load your dataset\n",
    "    dataset = load_dataset(\"visual-layer/oxford-iiit-pet-vl-enriched\", split=\"train\")\n",
    "    dataset = dataset.select(range(10))\n",
    "\n",
    "    updated_dataset = dataset.map(\n",
    "        gpu_computation,\n",
    "        batched=True,\n",
    "        batch_size=4,  # Adjust based on your GPU memory\n",
    "        with_rank=True,\n",
    "        num_proc=torch.cuda.device_count(),  # one process per GPU\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['a cat walking on grass\\n',\n",
       " 'a white dog playing with a ball\\n',\n",
       " 'a dog sitting in the grass\\n',\n",
       " 'a dog laying in the grass\\n',\n",
       " 'a dog standing in the snow\\n',\n",
       " 'a dog laying in the grass\\n',\n",
       " 'a dog laying on a brick sidewalk\\n',\n",
       " 'a man holding a black dog\\n',\n",
       " 'a large dog standing in the grass\\n',\n",
       " 'a pug dog with its tongue out standing on a tiled floor\\n']"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "updated_dataset['caption']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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