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{
"cells": [
{
"cell_type": "markdown",
"id": "d0b72877",
"metadata": {},
"source": [
"# Pre-encoding a dataset for DALLE·mini"
]
},
{
"cell_type": "markdown",
"id": "ba7b31e6",
"metadata": {},
"source": [
"This notebook shows how to pre-encode images to token sequences using JAX, VQGAN and a dataset in the [`webdataset` format](https://webdataset.github.io/webdataset/).\n",
"\n",
"Adapt it to your own dataset and image encoder.\n",
"\n",
"At the end you should have a dataset of pairs:\n",
"* a caption defined as a string\n",
"* an encoded image defined as a list of int."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3b59489e",
"metadata": {},
"outputs": [],
"source": [
"from tqdm.notebook import tqdm\n",
"\n",
"import torchvision.transforms as T\n",
"\n",
"import webdataset as wds\n",
"\n",
"import jax\n",
"import braceexpand\n",
"from pathlib import Path"
]
},
{
"cell_type": "markdown",
"id": "c7c4c1e6",
"metadata": {},
"source": [
"## Configuration Parameters"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "1265dbfe",
"metadata": {},
"outputs": [],
"source": [
"shards = \"my_images/shard-{0000..0008}.tar\" # defined using braceexpand format as used by webdataset\n",
"encoded_output = Path(\"encoded_data\") # where we will save our encoded data\n",
"\n",
"VQGAN_REPO, VQGAN_COMMIT_ID = (\n",
" \"dalle-mini/vqgan_imagenet_f16_16384\",\n",
" \"85eb5d3b51a1c62a0cc8f4ccdee9882c0d0bd384\",\n",
")\n",
"\n",
"# good defaults for a TPU v3-8\n",
"batch_size = 128 # Per device\n",
"num_workers = 8 # For parallel processing\n",
"total_bs = batch_size * jax.device_count() # You can use a smaller size while testing\n",
"save_frequency = 128 # Number of batches to create a new file (180MB for f16 and 720MB for f8 per file)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "cd956ec6-7d98-4d4d-a454-f80fe857eadd",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['XXX/shard-0000.tar',\n",
" 'XXX/shard-0001.tar',\n",
" 'XXX/shard-0002.tar',\n",
" 'XXX/shard-0003.tar',\n",
" 'XXX/shard-0004.tar',\n",
" 'XXX/shard-0005.tar',\n",
" 'XXX/shard-0006.tar',\n",
" 'XXX/shard-0007.tar',\n",
" 'XXX/shard-0008.tar']"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"shards = list(\n",
" braceexpand.braceexpand(shards)\n",
") # better display for tqdm with known length"
]
},
{
"cell_type": "markdown",
"id": "75dba8e2",
"metadata": {},
"source": [
"## Load data"
]
},
{
"cell_type": "markdown",
"id": "a1e8fb95",
"metadata": {},
"source": [
"We load data using `webdataset`."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "9ef5de9e",
"metadata": {},
"outputs": [],
"source": [
"ds = (\n",
" wds.WebDataset(shards, handler=wds.warn_and_continue)\n",
" .decode(\"rgb\", handler=wds.warn_and_continue)\n",
" .to_tuple(\"jpg\", \"txt\") # assumes image is in `jpg` and caption in `txt`\n",
" .batched(total_bs) # load in batch per worker (faster)\n",
")"
]
},
{
"cell_type": "markdown",
"id": "90981824",
"metadata": {},
"source": [
"Note:\n",
"* you can also shuffle shards and items using `shardshuffle` and `shuffle` if necessary.\n",
"* you may need to resize images in your pipeline (with `map_dict` for example), we assume they are already set to 256x256.\n",
"* you can also filter out some items using `select`."
]
},
{
"cell_type": "markdown",
"id": "129c377d",
"metadata": {},
"source": [
"We can now inspect our data."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8cac98cb",
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"%%time\n",
"images, captions = next(iter(ds))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "cd268fbf",
"metadata": {},
"outputs": [],
"source": [
"images.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5acfc4d8",
"metadata": {},
"outputs": [],
"source": [
"captions[:10]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c24693c0",
"metadata": {},
"outputs": [],
"source": [
"T.ToPILImage()(images[0].permute(2, 0, 1))"
]
},
{
"cell_type": "markdown",
"id": "3059ffb1",
"metadata": {},
"source": [
"Finally we create our dataloader."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c227c551",
"metadata": {},
"outputs": [],
"source": [
"dl = (\n",
" wds.WebLoader(ds, batch_size=None, num_workers=8).unbatched().batched(total_bs)\n",
") # avoid partial batch at the end of each worker"
]
},
{
"cell_type": "markdown",
"id": "a354472b",
"metadata": {},
"source": [
"## Image encoder\n",
"\n",
"We'll use a VQGAN trained with Taming Transformers and converted to a JAX model."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "47a8b818",
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"from vqgan_jax.modeling_flax_vqgan import VQModel\n",
"from flax.jax_utils import replicate\n",
"\n",
"vqgan = VQModel.from_pretrained(\"flax-community/vqgan_f16_16384\")\n",
"vqgan_params = replicate(vqgan.params)"
]
},
{
"cell_type": "markdown",
"id": "62ad01c3",
"metadata": {},
"source": [
"## Encoding"
]
},
{
"cell_type": "markdown",
"id": "20357f74",
"metadata": {},
"source": [
"Encoding is really simple using `shard` to automatically distribute batches across devices and `pmap`."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "322a4619",
"metadata": {},
"outputs": [],
"source": [
"from flax.training.common_utils import shard\n",
"from functools import partial\n",
"\n",
"\n",
"@partial(jax.pmap, axis_name=\"batch\")\n",
"def p_encode(batch, params):\n",
" # Not sure if we should `replicate` params, does not seem to have any effect\n",
" _, indices = vqgan.encode(batch, params=params)\n",
" return indices"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ff6c10d4",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"\n",
"\n",
"def encode_dataset(dataloader, output_dir, save_frequency):\n",
" output_dir.mkdir(parents=True, exist_ok=True)\n",
" all_captions = []\n",
" all_encoding = []\n",
" n_file = 1\n",
" for idx, (images, captions) in enumerate(tqdm(dataloader)):\n",
" images = images.numpy()\n",
" n = len(images) // 8 * 8\n",
" if n != len(images):\n",
" # get the max number of images we can (multiple of 8)\n",
" print(f\"Different sizes {n} vs {len(images)}\")\n",
" images = images[:n]\n",
" captions = captions[:n]\n",
" if not len(captions):\n",
" print(f\"No images/captions in batch...\")\n",
" continue\n",
" images = shard(images)\n",
" encoded = p_encode(images, vqgan_params)\n",
" encoded = encoded.reshape(-1, encoded.shape[-1])\n",
" all_captions.extend(captions)\n",
" all_encoding.extend(encoded.tolist())\n",
"\n",
" # save files\n",
" if (idx + 1) % save_frequency == 0:\n",
" print(f\"Saving file {n_file}\")\n",
" batch_df = pd.DataFrame.from_dict(\n",
" {\"caption\": all_captions, \"encoding\": all_encoding}\n",
" )\n",
" batch_df.to_parquet(f\"{output_dir}/{n_file:03d}.parquet\")\n",
" all_captions = []\n",
" all_encoding = []\n",
" n_file += 1\n",
"\n",
" if len(all_captions):\n",
" print(f\"Saving final file {n_file}\")\n",
" batch_df = pd.DataFrame.from_dict(\n",
" {\"caption\": all_captions, \"encoding\": all_encoding}\n",
" )\n",
" batch_df.to_parquet(f\"{output_dir}/{n_file:03d}.parquet\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7704863d",
"metadata": {},
"outputs": [],
"source": [
"encode_dataset(dl, output_dir=encoded_output, save_frequency=save_frequency)"
]
},
{
"cell_type": "markdown",
"id": "8953dd84",
"metadata": {},
"source": [
"----"
]
}
],
"metadata": {
"interpreter": {
"hash": "db471c52d602b4f5f40ecaf278e88ccfef85c29d0a1a07185b0d51fc7acf4e26"
},
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.7"
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"nbformat": 4,
"nbformat_minor": 5
}
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