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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"id": "4ff2a984-b8b2-4a69-89cf-0d16da2393c8",
"metadata": {},
"outputs": [],
"source": [
"import tempfile\n",
"from functools import partial\n",
"import random\n",
"import numpy as np\n",
"from PIL import Image\n",
"from tqdm.notebook import tqdm\n",
"import jax\n",
"import jax.numpy as jnp\n",
"from flax.training.common_utils import shard, shard_prng_key\n",
"from flax.jax_utils import replicate\n",
"import wandb\n",
"from dalle_mini.model import CustomFlaxBartForConditionalGeneration\n",
"from vqgan_jax.modeling_flax_vqgan import VQModel\n",
"from transformers import BartTokenizer, CLIPProcessor, FlaxCLIPModel\n",
"from dalle_mini.text import TextNormalizer"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "92f4557c-fd7f-4edc-81c2-de0b0a10c270",
"metadata": {},
"outputs": [],
"source": [
"run_ids = [\"63otg87g\"]\n",
"ENTITY, PROJECT = \"dalle-mini\", \"dalle-mini\" # used only for training run\n",
"VQGAN_REPO, VQGAN_COMMIT_ID = (\n",
" \"dalle-mini/vqgan_imagenet_f16_16384\",\n",
" \"e93a26e7707683d349bf5d5c41c5b0ef69b677a9\",\n",
")\n",
"latest_only = True # log only latest or all versions\n",
"suffix = \"\" # mainly for duplicate inference runs with a deleted version\n",
"add_clip_32 = False"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "71f27b96-7e6c-4472-a2e4-e99a8fb67a72",
"metadata": {},
"outputs": [],
"source": [
"# model.generate parameters - Not used yet\n",
"gen_top_k = None\n",
"gen_top_p = None\n",
"temperature = None"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "93b2e24b-f0e5-4abe-a3ec-0aa834cc3bf3",
"metadata": {},
"outputs": [],
"source": [
"batch_size = 8\n",
"num_images = 128\n",
"top_k = 8\n",
"text_normalizer = TextNormalizer()\n",
"padding_item = \"NONE\"\n",
"seed = random.randint(0, 2 ** 32 - 1)\n",
"key = jax.random.PRNGKey(seed)\n",
"api = wandb.Api()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c6a878fa-4bf5-4978-abb5-e235841d765b",
"metadata": {},
"outputs": [],
"source": [
"vqgan = VQModel.from_pretrained(VQGAN_REPO, revision=VQGAN_COMMIT_ID)\n",
"vqgan_params = replicate(vqgan.params)\n",
"\n",
"clip16 = FlaxCLIPModel.from_pretrained(\"openai/clip-vit-base-patch16\")\n",
"processor16 = CLIPProcessor.from_pretrained(\"openai/clip-vit-base-patch16\")\n",
"clip16_params = replicate(clip16.params)\n",
"\n",
"if add_clip_32:\n",
" clip32 = FlaxCLIPModel.from_pretrained(\"openai/clip-vit-base-patch32\")\n",
" processor32 = CLIPProcessor.from_pretrained(\"openai/clip-vit-base-patch32\")\n",
" clip32_params = replicate(clip32.params)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a500dd07-dbc3-477d-80d4-2b73a3b83ef3",
"metadata": {},
"outputs": [],
"source": [
"@partial(jax.pmap, axis_name=\"batch\")\n",
"def p_decode(indices, params):\n",
" return vqgan.decode_code(indices, params=params)\n",
"\n",
"\n",
"@partial(jax.pmap, axis_name=\"batch\")\n",
"def p_clip16(inputs, params):\n",
" logits = clip16(params=params, **inputs).logits_per_image\n",
" return logits\n",
"\n",
"\n",
"if add_clip_32:\n",
"\n",
" @partial(jax.pmap, axis_name=\"batch\")\n",
" def p_clip32(inputs, params):\n",
" logits = clip32(params=params, **inputs).logits_per_image\n",
" return logits"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e57797ab-0b3a-4490-be58-03d8d1c23fe9",
"metadata": {},
"outputs": [],
"source": [
"with open(\"samples.txt\", encoding=\"utf8\") as f:\n",
" samples = [l.strip() for l in f.readlines()]\n",
" # make list multiple of batch_size by adding elements\n",
" samples_to_add = [padding_item] * (-len(samples) % batch_size)\n",
" samples.extend(samples_to_add)\n",
" # reshape\n",
" samples = [samples[i : i + batch_size] for i in range(0, len(samples), batch_size)]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f3e02d9d-4ee1-49e7-a7bc-4d8b139e9614",
"metadata": {},
"outputs": [],
"source": [
"def get_artifact_versions(run_id, latest_only=False):\n",
" try:\n",
" if latest_only:\n",
" return [\n",
" api.artifact(\n",
" type=\"bart_model\", name=f\"{ENTITY}/{PROJECT}/model-{run_id}:latest\"\n",
" )\n",
" ]\n",
" else:\n",
" return api.artifact_versions(\n",
" type_name=\"bart_model\",\n",
" name=f\"{ENTITY}/{PROJECT}/model-{run_id}\",\n",
" per_page=10000,\n",
" )\n",
" except:\n",
" return []"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f0d7ed17-7abb-4a31-ab3c-a12b9039a570",
"metadata": {},
"outputs": [],
"source": [
"def get_training_config(run_id):\n",
" training_run = api.run(f\"{ENTITY}/{PROJECT}/{run_id}\")\n",
" config = training_run.config\n",
" return config"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "7e784a43-626d-4e8d-9e47-a23775b2f35f",
"metadata": {},
"outputs": [],
"source": [
"# retrieve inference run details\n",
"def get_last_inference_version(run_id):\n",
" try:\n",
" inference_run = api.run(f\"dalle-mini/dalle-mini/{run_id}-clip16{suffix}\")\n",
" return inference_run.summary.get(\"version\", None)\n",
" except:\n",
" return None"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d1cc9993-1bfc-4ec6-a004-c056189c42ac",
"metadata": {},
"outputs": [],
"source": [
"# compile functions - needed only once per run\n",
"def pmap_model_function(model):\n",
" @partial(jax.pmap, axis_name=\"batch\")\n",
" def _generate(tokenized_prompt, key, params):\n",
" return model.generate(\n",
" **tokenized_prompt,\n",
" do_sample=True,\n",
" num_beams=1,\n",
" prng_key=key,\n",
" params=params,\n",
" top_k=gen_top_k,\n",
" top_p=gen_top_p\n",
" )\n",
"\n",
" return _generate"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "23b2444c-67a9-44d7-abd1-187ed83a9431",
"metadata": {},
"outputs": [],
"source": [
"run_id = run_ids[0]\n",
"# TODO: loop over runs"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "bba70f33-af8b-4eb3-9973-7be672301a0b",
"metadata": {},
"outputs": [],
"source": [
"artifact_versions = get_artifact_versions(run_id, latest_only)\n",
"last_inference_version = get_last_inference_version(run_id)\n",
"training_config = get_training_config(run_id)\n",
"run = None\n",
"p_generate = None\n",
"model_files = [\n",
" \"config.json\",\n",
" \"flax_model.msgpack\",\n",
" \"merges.txt\",\n",
" \"special_tokens_map.json\",\n",
" \"tokenizer.json\",\n",
" \"tokenizer_config.json\",\n",
" \"vocab.json\",\n",
"]\n",
"for artifact in artifact_versions:\n",
" print(f\"Processing artifact: {artifact.name}\")\n",
" version = int(artifact.version[1:])\n",
" results16, results32 = [], []\n",
" columns = [\"Caption\"] + [f\"Image {i+1}\" for i in range(top_k)]\n",
"\n",
" if latest_only:\n",
" assert last_inference_version is None or version > last_inference_version\n",
" else:\n",
" if last_inference_version is None:\n",
" # we should start from v0\n",
" assert version == 0\n",
" elif version <= last_inference_version:\n",
" print(\n",
" f\"v{version} has already been logged (versions logged up to v{last_inference_version}\"\n",
" )\n",
" else:\n",
" # check we are logging the correct version\n",
" assert version == last_inference_version + 1\n",
"\n",
" # start/resume corresponding run\n",
" if run is None:\n",
" run = wandb.init(\n",
" job_type=\"inference\",\n",
" entity=\"dalle-mini\",\n",
" project=\"dalle-mini\",\n",
" config=training_config,\n",
" id=f\"{run_id}-clip16{suffix}\",\n",
" resume=\"allow\",\n",
" )\n",
"\n",
" # work in temporary directory\n",
" with tempfile.TemporaryDirectory() as tmp:\n",
"\n",
" # download model files\n",
" artifact = run.use_artifact(artifact)\n",
" for f in model_files:\n",
" artifact.get_path(f).download(tmp)\n",
"\n",
" # load tokenizer and model\n",
" tokenizer = BartTokenizer.from_pretrained(tmp)\n",
" model = CustomFlaxBartForConditionalGeneration.from_pretrained(tmp)\n",
" model_params = replicate(model.params)\n",
"\n",
" # pmap model function needs to happen only once per model config\n",
" if p_generate is None:\n",
" p_generate = pmap_model_function(model)\n",
"\n",
" # process one batch of captions\n",
" for batch in tqdm(samples):\n",
" processed_prompts = (\n",
" [text_normalizer(x) for x in batch]\n",
" if model.config.normalize_text\n",
" else list(batch)\n",
" )\n",
"\n",
" # repeat the prompts to distribute over each device and tokenize\n",
" processed_prompts = processed_prompts * jax.device_count()\n",
" tokenized_prompt = tokenizer(\n",
" processed_prompts,\n",
" return_tensors=\"jax\",\n",
" padding=\"max_length\",\n",
" truncation=True,\n",
" max_length=128,\n",
" ).data\n",
" tokenized_prompt = shard(tokenized_prompt)\n",
"\n",
" # generate images\n",
" images = []\n",
" pbar = tqdm(\n",
" range(num_images // jax.device_count()),\n",
" desc=\"Generating Images\",\n",
" leave=True,\n",
" )\n",
" for i in pbar:\n",
" key, subkey = jax.random.split(key)\n",
" encoded_images = p_generate(\n",
" tokenized_prompt, shard_prng_key(subkey), model_params\n",
" )\n",
" encoded_images = encoded_images.sequences[..., 1:]\n",
" decoded_images = p_decode(encoded_images, vqgan_params)\n",
" decoded_images = decoded_images.clip(0.0, 1.0).reshape(\n",
" (-1, 256, 256, 3)\n",
" )\n",
" for img in decoded_images:\n",
" images.append(\n",
" Image.fromarray(np.asarray(img * 255, dtype=np.uint8))\n",
" )\n",
"\n",
" def add_clip_results(results, processor, p_clip, clip_params):\n",
" clip_inputs = processor(\n",
" text=batch,\n",
" images=images,\n",
" return_tensors=\"np\",\n",
" padding=\"max_length\",\n",
" max_length=77,\n",
" truncation=True,\n",
" ).data\n",
" # each shard will have one prompt, images need to be reorganized to be associated to the correct shard\n",
" images_per_prompt_indices = np.asarray(\n",
" range(0, len(images), batch_size)\n",
" )\n",
" clip_inputs[\"pixel_values\"] = jnp.concatenate(\n",
" list(\n",
" clip_inputs[\"pixel_values\"][images_per_prompt_indices + i]\n",
" for i in range(batch_size)\n",
" )\n",
" )\n",
" clip_inputs = shard(clip_inputs)\n",
" logits = p_clip(clip_inputs, clip_params)\n",
" logits = logits.reshape(-1, num_images)\n",
" top_scores = logits.argsort()[:, -top_k:][..., ::-1]\n",
" logits = jax.device_get(logits)\n",
" # add to results table\n",
" for i, (idx, scores, sample) in enumerate(\n",
" zip(top_scores, logits, batch)\n",
" ):\n",
" if sample == padding_item:\n",
" continue\n",
" cur_images = [images[x] for x in images_per_prompt_indices + i]\n",
" top_images = [\n",
" wandb.Image(cur_images[x], caption=f\"Score: {scores[x]:.2f}\")\n",
" for x in idx\n",
" ]\n",
" results.append([sample] + top_images)\n",
"\n",
" # get clip scores\n",
" pbar.set_description(\"Calculating CLIP 16 scores\")\n",
" add_clip_results(results16, processor16, p_clip16, clip16_params)\n",
"\n",
" # get clip 32 scores\n",
" if add_clip_32:\n",
" pbar.set_description(\"Calculating CLIP 32 scores\")\n",
" add_clip_results(results32, processor32, p_clip32, clip32_params)\n",
"\n",
" pbar.close()\n",
"\n",
" # log results\n",
" table = wandb.Table(columns=columns, data=results16)\n",
" run.log({\"Samples\": table, \"version\": version})\n",
" wandb.finish()\n",
"\n",
" if add_clip_32:\n",
" run = wandb.init(\n",
" job_type=\"inference\",\n",
" entity=\"dalle-mini\",\n",
" project=\"dalle-mini\",\n",
" config=training_config,\n",
" id=f\"{run_id}-clip32{suffix}\",\n",
" resume=\"allow\",\n",
" )\n",
" table = wandb.Table(columns=columns, data=results32)\n",
" run.log({\"Samples\": table, \"version\": version})\n",
" wandb.finish()\n",
" run = None # ensure we don't log on this run"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "415d3f54-7226-43de-9eea-4283a948dc93",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"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"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|