Spaces:
Running
Running
feat: allow latest version only
Browse files- dev/inference/wandb-backend.ipynb +154 -358
dev/inference/wandb-backend.ipynb
CHANGED
@@ -32,9 +32,25 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"
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"VQGAN_REPO, VQGAN_COMMIT_ID = 'dalle-mini/vqgan_imagenet_f16_16384', None\n",
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"normalize_text = True"
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{
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@@ -104,18 +120,6 @@
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" samples = [samples[i:i+batch_size] for i in range(0, len(samples), batch_size)]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "3ffb1d09-bd1c-4f57-9ae5-3eda6f7d3a08",
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"metadata": {},
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"outputs": [],
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"source": [
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"# TODO: iterate on runs\n",
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"wandb_run = wandb_runs[0]\n",
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"model_pmapped = False"
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]
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},
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"cell_type": "code",
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"execution_count": null,
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@@ -123,12 +127,14 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"def get_artifact_versions(run_id):\n",
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" try:\n",
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"
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" except:\n",
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"
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" return versions"
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},
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{
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@@ -139,7 +145,7 @@
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"outputs": [],
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"source": [
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"def get_training_config(run_id):\n",
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" training_run = api.run(f'
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" config = training_run.config\n",
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" return config"
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]
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@@ -155,7 +161,7 @@
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"def get_last_inference_version(run_id):\n",
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" try:\n",
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" inference_run = api.run(f'dalle-mini/dalle-mini/inference-{run_id}')\n",
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" return inference_run.summary.get('
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" except:\n",
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" return None"
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]
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@@ -186,68 +192,142 @@
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "
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"metadata": {},
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"outputs": [],
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"source": [
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"
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"
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" last_inference_version = get_last_inference_version(run_id)\n",
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" training_config = get_training_config(run_id)\n",
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" run = None\n",
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" p_generate = None\n",
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" model_files = ['config.json', 'flax_model.msgpack', 'merges.txt', 'special_tokens_map.json', 'tokenizer.json', 'tokenizer_config.json', 'vocab.json']\n",
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" for artifact in artifact_versions:\n",
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" print(f'Processing artifact: {artifact.name}')\n",
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" version = int(artifact.version[1:])\n",
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" if last_version_inference is None:\n",
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" # we should start from v0\n",
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" assert version == 0\n",
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" elif version <= last_version_inference:\n",
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" print(f'v{version} has already been logged (versions logged up to v{last_version_inference}')\n",
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" else:\n",
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" # check we are logging the correct version\n",
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" assert version == last_version_inference + 1\n",
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" \n",
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" # start/resume corresponding run\n",
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" if run is None:\n",
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" run = wandb.init(job_type='inference', config=config, id=f'inference-{wandb_run}', resume='allow')\n",
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" \n",
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" # work in temporary directory\n",
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" with tempfile.TemporaryDirectory() as tmp:\n",
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" \n",
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" # download model files\n",
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" artifact = run.use_artifact(artifact)\n",
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" for f in model_files:\n",
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" artifact.get_path(f).download(tmp)\n",
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" \n",
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" # load tokenizer and model\n",
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" tokenizer = BartTokenizer.from_pretrained(tmp)\n",
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" model = CustomFlaxBartForConditionalGeneration.from_pretrained(tmp)\n",
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" model_params = replicate(model.params)\n",
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" \n",
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" # pmap model function needs to happen only once per model config\n",
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" if p_generate is None:\n",
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" p_generate = pmap_model_function(model)\n",
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" \n",
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" for batch in tqdm(samples):\n",
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" prompts = [x['Caption'] for x in batch]\n",
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" processed_prompts = [text_normalizer(x) for x in prompts] if normalize_text else prompts\n",
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" \n",
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"\n",
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" \n",
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" \n",
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" "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "
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"metadata": {},
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"outputs": [
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{
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@@ -257,296 +337,12 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"def log_runs(runs):\n",
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" for run in tqdm(runs):\n",
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" log_run(run)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "7a24b903-777b-4e3d-817c-00ed613a7021",
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"metadata": {},
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"outputs": [],
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"source": [
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"# TODO: loop over samples\n",
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"batch = samples[0]\n",
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"prompts = [x['Caption'] for x in batch]\n",
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"processed_prompts = [text_normalizer(x) for x in prompts] if normalize_text else prompts"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "d77aa785-dc05-4070-aba2-aa007524d20b",
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"metadata": {},
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"outputs": [],
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"source": [
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"processed_prompts"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "95db38fb-8948-4814-98ae-c172ca7c6d0a",
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"metadata": {},
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"outputs": [],
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"source": [
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"repeated_prompts = processed_prompts * jax.device_count()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "e948ba9e-3700-4e87-926f-580a10f3e5cd",
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"metadata": {},
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"outputs": [],
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"source": [
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"tokenized_prompt = tokenizer(repeated_prompts, return_tensors='jax', padding='max_length', truncation=True, max_length=128).data\n",
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"tokenized_prompt = shard(tokenized_prompt)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "30d96812-fc17-4acf-bb64-5fdb8d0cd313",
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"metadata": {},
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"outputs": [],
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"source": [
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"tokenized_prompt['input_ids'].shape"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "92ea034b-2649-4d18-ab6d-877ed04ae5c4",
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"metadata": {},
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"outputs": [],
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"source": [
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"images = []\n",
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"for i in range(num_images // jax.device_count()):\n",
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" key, subkey = jax.random.split(key, 2)\n",
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" \n",
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" encoded_images = p_generate(tokenized_prompt, shard_prng_key(subkey), model_params)\n",
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" encoded_images = encoded_images.sequences[..., 1:]\n",
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" \n",
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" decoded_images = p_decode(encoded_images, vqgan_params)\n",
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" decoded_images = decoded_images.clip(0., 1.).reshape((-1, 256, 256, 3))\n",
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" \n",
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" for img in decoded_images:\n",
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" images.append(Image.fromarray(np.asarray(img * 255, dtype=np.uint8)))\n",
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" "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "84d52f30-44c9-4a74-9992-fb2578f19b90",
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"metadata": {},
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"outputs": [],
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"source": [
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"len(images)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "beb594f9-5b91-47fe-98bd-41e68c6b1d73",
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"metadata": {},
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"outputs": [],
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"source": [
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"images[0]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "bb135190-64e5-44af-b416-e688b034da44",
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"metadata": {},
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"outputs": [],
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"source": [
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"images[1]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "d78a0d92-72c2-4f82-a6ab-b3f5865dd863",
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"metadata": {},
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"outputs": [],
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"source": [
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"clip_inputs = processor(text=prompts, images=images, return_tensors='np', padding='max_length', max_length=77, truncation=True).data"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "89ff78a6-bfa4-44d9-ad66-07a4a68b4352",
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"metadata": {},
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"outputs": [],
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"source": [
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"# each shard will have one prompt\n",
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"clip_inputs['input_ids'].shape"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "2cda8984-049c-4c87-96ad-7b0412750656",
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"metadata": {},
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"outputs": [],
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"source": [
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"# each shard needs to have the images corresponding to a specific prompt\n",
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"clip_inputs['pixel_values'].shape"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "0a044e8f-be29-404b-b6c7-8f2395c5efc6",
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"metadata": {},
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"outputs": [],
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"source": [
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"images_per_prompt_indices = np.asarray(range(0, len(images), batch_size))\n",
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"images_per_prompt_indices"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "7a6c61b3-12e0-45d8-b39a-830288324d3d",
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "7318e67e-4214-46f9-bf60-6d139d4bd00f",
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"metadata": {},
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"outputs": [],
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"source": [
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"# reorder so each shard will have correct images\n",
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"clip_inputs['pixel_values'] = jnp.concatenate(list(clip_inputs['pixel_values'][images_per_prompt_indices + i] for i in range(batch_size)))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "90c949a2-8e2a-4905-b6d4-92038f1704b8",
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"metadata": {},
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"outputs": [],
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"source": [
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"clip_inputs = shard(clip_inputs)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "58fa836e-5ebb-45e7-af77-ab10646dfbfb",
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"metadata": {},
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"outputs": [],
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"source": [
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"logits = p_clip(clip_inputs)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "fd7a3f91-3a1f-4a0a-8b3e-3c926cd367fb",
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"metadata": {},
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"outputs": [],
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"source": [
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"logits.shape"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "fa406db7-0a21-4e4b-9890-4c7aece4280c",
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"metadata": {},
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"outputs": [],
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"source": [
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"logits = logits.reshape(-1, num_images)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "9c359a8c-2c27-4e68-8775-371857397723",
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"metadata": {},
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"source": [
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"cell_type": "code",
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"execution_count": null,
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"id": "a56b9f28-dd91-4382-bc47-11e89fda1254",
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"metadata": {},
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"outputs": [],
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"source": [
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"logits"
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]
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "0bed8167-0a6d-46c1-badf-8bdc20b93c31",
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-
"metadata": {},
|
498 |
-
"outputs": [],
|
499 |
-
"source": [
|
500 |
-
"top_idx = logits.argsort()[:, -top_k:][..., ::-1]"
|
501 |
-
]
|
502 |
-
},
|
503 |
-
{
|
504 |
-
"cell_type": "code",
|
505 |
-
"execution_count": null,
|
506 |
-
"id": "188c5333-6b8c-4a17-8cc8-15651c77ef99",
|
507 |
-
"metadata": {},
|
508 |
-
"outputs": [],
|
509 |
-
"source": [
|
510 |
-
"len(images)"
|
511 |
-
]
|
512 |
-
},
|
513 |
-
{
|
514 |
-
"cell_type": "code",
|
515 |
-
"execution_count": null,
|
516 |
-
"id": "babd22b3-e773-467d-8bbb-f0323f57a44b",
|
517 |
-
"metadata": {},
|
518 |
-
"outputs": [],
|
519 |
-
"source": [
|
520 |
-
"results = []\n",
|
521 |
-
"columns = ['Caption', 'Theme'] + [f'Image {i+1}' for i in range(top_k)] + [f'Score {i+1}' for i in range(top_k)]\n",
|
522 |
-
"logits = jax.device_get(logits)"
|
523 |
-
]
|
524 |
-
},
|
525 |
-
{
|
526 |
-
"cell_type": "code",
|
527 |
-
"execution_count": null,
|
528 |
-
"id": "75976c9f-dea5-48e3-8920-55a1bbfd91c2",
|
529 |
-
"metadata": {},
|
530 |
-
"outputs": [],
|
531 |
-
"source": [
|
532 |
-
"for i, (idx, scores, sample) in enumerate(zip(top_idx, logits, batch)):\n",
|
533 |
-
" if sample['Caption'] == padding_item: continue\n",
|
534 |
-
" cur_images = [images[x] for x in images_per_prompt_indices + i]\n",
|
535 |
-
" top_images = [wandb.Image(cur_images[x]) for x in idx]\n",
|
536 |
-
" top_scores = [scores[x] for x in idx]\n",
|
537 |
-
" results.append([sample['Caption'], sample['Theme']] + top_images + top_scores)"
|
538 |
-
]
|
539 |
-
},
|
540 |
-
{
|
541 |
-
"cell_type": "code",
|
542 |
-
"execution_count": null,
|
543 |
-
"id": "4bf40461-99d3-4d36-b7cc-e0129a3c9053",
|
544 |
-
"metadata": {},
|
545 |
-
"outputs": [],
|
546 |
-
"source": [
|
547 |
-
"table = wandb.Table(columns=columns, data=results)"
|
548 |
-
]
|
549 |
-
},
|
550 |
{
|
551 |
"cell_type": "code",
|
552 |
"execution_count": null,
|
|
|
32 |
"metadata": {},
|
33 |
"outputs": [],
|
34 |
"source": [
|
35 |
+
"run_ids = ['rjf3rycy']\n",
|
36 |
+
"ENTITY, PROJECT = 'dalle-mini', 'dalle-mini' # used only for training run\n",
|
37 |
"VQGAN_REPO, VQGAN_COMMIT_ID = 'dalle-mini/vqgan_imagenet_f16_16384', None\n",
|
38 |
+
"normalize_text = True\n",
|
39 |
+
"latest_only = False # log only latest or all versions"
|
40 |
+
]
|
41 |
+
},
|
42 |
+
{
|
43 |
+
"cell_type": "code",
|
44 |
+
"execution_count": null,
|
45 |
+
"id": "23e00271-941c-4e1b-b6a9-107a1b77324d",
|
46 |
+
"metadata": {},
|
47 |
+
"outputs": [],
|
48 |
+
"source": [
|
49 |
+
"run_ids = ['4oh3u7ca']\n",
|
50 |
+
"ENTITY, PROJECT = 'wandb', 'hf-flax-dalle-mini'\n",
|
51 |
+
"VQGAN_REPO, VQGAN_COMMIT_ID = 'dalle-mini/vqgan_imagenet_f16_16384', None\n",
|
52 |
+
"normalize_text = False\n",
|
53 |
+
"latest_only = True # log only latest or all versions"
|
54 |
]
|
55 |
},
|
56 |
{
|
|
|
120 |
" samples = [samples[i:i+batch_size] for i in range(0, len(samples), batch_size)]"
|
121 |
]
|
122 |
},
|
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|
123 |
{
|
124 |
"cell_type": "code",
|
125 |
"execution_count": null,
|
|
|
127 |
"metadata": {},
|
128 |
"outputs": [],
|
129 |
"source": [
|
130 |
+
"def get_artifact_versions(run_id, latest_only=False):\n",
|
131 |
" try:\n",
|
132 |
+
" if latest_only:\n",
|
133 |
+
" return [api.artifact(type='bart_model', name=f'{ENTITY}/{PROJECT}/model-{run_id}:latest')]\n",
|
134 |
+
" else:\n",
|
135 |
+
" return api.artifact_versions(type_name='bart_model', name=f'{ENTITY}/{PROJECT}/model-{run_id}', per_page=10000)\n",
|
136 |
" except:\n",
|
137 |
+
" return []"
|
|
|
138 |
]
|
139 |
},
|
140 |
{
|
|
|
145 |
"outputs": [],
|
146 |
"source": [
|
147 |
"def get_training_config(run_id):\n",
|
148 |
+
" training_run = api.run(f'{ENTITY}/{PROJECT}/{run_id}')\n",
|
149 |
" config = training_run.config\n",
|
150 |
" return config"
|
151 |
]
|
|
|
161 |
"def get_last_inference_version(run_id):\n",
|
162 |
" try:\n",
|
163 |
" inference_run = api.run(f'dalle-mini/dalle-mini/inference-{run_id}')\n",
|
164 |
+
" return inference_run.summary.get('version', None)\n",
|
165 |
" except:\n",
|
166 |
" return None"
|
167 |
]
|
|
|
192 |
{
|
193 |
"cell_type": "code",
|
194 |
"execution_count": null,
|
195 |
+
"id": "23b2444c-67a9-44d7-abd1-187ed83a9431",
|
196 |
"metadata": {},
|
197 |
"outputs": [],
|
198 |
"source": [
|
199 |
+
"run_id = run_ids[0]\n",
|
200 |
+
"# TODO: turn everything into a class"
|
|
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|
201 |
]
|
202 |
},
|
203 |
{
|
204 |
"cell_type": "code",
|
205 |
"execution_count": null,
|
206 |
+
"id": "bba70f33-af8b-4eb3-9973-7be672301a0b",
|
207 |
"metadata": {},
|
208 |
+
"outputs": [
|
209 |
+
{
|
210 |
+
"name": "stdout",
|
211 |
+
"output_type": "stream",
|
212 |
+
"text": [
|
213 |
+
"Processing artifact: model-4oh3u7ca:v54\n"
|
214 |
+
]
|
215 |
+
},
|
216 |
+
{
|
217 |
+
"name": "stderr",
|
218 |
+
"output_type": "stream",
|
219 |
+
"text": [
|
220 |
+
"\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mborisd13\u001b[0m (use `wandb login --relogin` to force relogin)\n"
|
221 |
+
]
|
222 |
+
},
|
223 |
+
{
|
224 |
+
"data": {
|
225 |
+
"text/html": [
|
226 |
+
"\n",
|
227 |
+
" Syncing run <strong><a href=\"https://wandb.ai/dalle-mini/dalle-mini/runs/inference-4oh3u7ca\" target=\"_blank\">inference-4oh3u7ca</a></strong> to <a href=\"https://wandb.ai/dalle-mini/dalle-mini\" target=\"_blank\">Weights & Biases</a> (<a href=\"https://docs.wandb.com/integrations/jupyter.html\" target=\"_blank\">docs</a>).<br/>\n",
|
228 |
+
"\n",
|
229 |
+
" "
|
230 |
+
],
|
231 |
+
"text/plain": [
|
232 |
+
"<IPython.core.display.HTML object>"
|
233 |
+
]
|
234 |
+
},
|
235 |
+
"metadata": {},
|
236 |
+
"output_type": "display_data"
|
237 |
+
}
|
238 |
+
],
|
239 |
+
"source": [
|
240 |
+
"artifact_versions = get_artifact_versions(run_id, latest_only)\n",
|
241 |
+
"last_inference_version = get_last_inference_version(run_id)\n",
|
242 |
+
"training_config = get_training_config(run_id)\n",
|
243 |
+
"run = None\n",
|
244 |
+
"p_generate = None\n",
|
245 |
+
"model_files = ['config.json', 'flax_model.msgpack', 'merges.txt', 'special_tokens_map.json', 'tokenizer.json', 'tokenizer_config.json', 'vocab.json']\n",
|
246 |
+
"for artifact in artifact_versions:\n",
|
247 |
+
" print(f'Processing artifact: {artifact.name}')\n",
|
248 |
+
" version = int(artifact.version[1:])\n",
|
249 |
+
" results = []\n",
|
250 |
+
" columns = ['Caption', 'Theme'] + [f'Image {i+1}' for i in range(top_k)] + [f'Score {i+1}' for i in range(top_k)]\n",
|
251 |
+
" \n",
|
252 |
+
" if latest_only:\n",
|
253 |
+
" assert last_inference_version is None or version > last_inference_version\n",
|
254 |
+
" else:\n",
|
255 |
+
" if last_inference_version is None:\n",
|
256 |
+
" # we should start from v0\n",
|
257 |
+
" assert version == 0\n",
|
258 |
+
" elif version <= last_inference_version:\n",
|
259 |
+
" print(f'v{version} has already been logged (versions logged up to v{last_inference_version}')\n",
|
260 |
+
" else:\n",
|
261 |
+
" # check we are logging the correct version\n",
|
262 |
+
" assert version == last_inference_version + 1\n",
|
263 |
+
"\n",
|
264 |
+
" # start/resume corresponding run\n",
|
265 |
+
" if run is None:\n",
|
266 |
+
" run = wandb.init(job_type='inference', entity='dalle-mini', project='dalle-mini', config=training_config, id=f'inference-{run_id}', resume='allow')\n",
|
267 |
+
"\n",
|
268 |
+
" # work in temporary directory\n",
|
269 |
+
" with tempfile.TemporaryDirectory() as tmp:\n",
|
270 |
+
"\n",
|
271 |
+
" # download model files\n",
|
272 |
+
" artifact = run.use_artifact(artifact)\n",
|
273 |
+
" for f in model_files:\n",
|
274 |
+
" artifact.get_path(f).download(tmp)\n",
|
275 |
+
"\n",
|
276 |
+
" # load tokenizer and model\n",
|
277 |
+
" tokenizer = BartTokenizer.from_pretrained(tmp)\n",
|
278 |
+
" model = CustomFlaxBartForConditionalGeneration.from_pretrained(tmp)\n",
|
279 |
+
" model_params = replicate(model.params)\n",
|
280 |
+
"\n",
|
281 |
+
" # pmap model function needs to happen only once per model config\n",
|
282 |
+
" if p_generate is None:\n",
|
283 |
+
" p_generate = pmap_model_function(model)\n",
|
284 |
+
"\n",
|
285 |
+
" # process one batch of captions\n",
|
286 |
+
" for batch in tqdm(samples):\n",
|
287 |
+
" prompts = [x['Caption'] for x in batch]\n",
|
288 |
+
" processed_prompts = [text_normalizer(x) for x in prompts] if normalize_text else prompts\n",
|
289 |
+
"\n",
|
290 |
+
" # repeat the prompts to distribute over each device and tokenize\n",
|
291 |
+
" processed_prompts = processed_prompts * jax.device_count()\n",
|
292 |
+
" tokenized_prompt = tokenizer(processed_prompts, return_tensors='jax', padding='max_length', truncation=True, max_length=128).data\n",
|
293 |
+
" tokenized_prompt = shard(tokenized_prompt)\n",
|
294 |
+
"\n",
|
295 |
+
" # generate images\n",
|
296 |
+
" print('Generating images')\n",
|
297 |
+
" images = []\n",
|
298 |
+
" for i in tqdm(range(num_images // jax.device_count())):\n",
|
299 |
+
" key, subkey = jax.random.split(key)\n",
|
300 |
+
" encoded_images = p_generate(tokenized_prompt, shard_prng_key(subkey), model_params)\n",
|
301 |
+
" encoded_images = encoded_images.sequences[..., 1:]\n",
|
302 |
+
" decoded_images = p_decode(encoded_images, vqgan_params)\n",
|
303 |
+
" decoded_images = decoded_images.clip(0., 1.).reshape((-1, 256, 256, 3))\n",
|
304 |
+
" for img in decoded_images:\n",
|
305 |
+
" images.append(Image.fromarray(np.asarray(img * 255, dtype=np.uint8)))\n",
|
306 |
+
"\n",
|
307 |
+
" # get clip scores\n",
|
308 |
+
" print('Calculating CLIP scores')\n",
|
309 |
+
" clip_inputs = processor(text=prompts, images=images, return_tensors='np', padding='max_length', max_length=77, truncation=True).data\n",
|
310 |
+
" # each shard will have one prompt, images need to be reorganized to be associated to the correct shard\n",
|
311 |
+
" images_per_prompt_indices = np.asarray(range(0, len(images), batch_size))\n",
|
312 |
+
" clip_inputs['pixel_values'] = jnp.concatenate(list(clip_inputs['pixel_values'][images_per_prompt_indices + i] for i in range(batch_size)))\n",
|
313 |
+
" clip_inputs = shard(clip_inputs)\n",
|
314 |
+
" logits = p_clip(clip_inputs)\n",
|
315 |
+
" logits = logits.reshape(-1, num_images)\n",
|
316 |
+
" top_scores = logits.argsort()[:, -top_k:][..., ::-1]\n",
|
317 |
+
" logits = jax.device_get(logits)\n",
|
318 |
+
"\n",
|
319 |
+
" # add to results table\n",
|
320 |
+
" for i, (idx, scores, sample) in enumerate(zip(top_scores, logits, batch)):\n",
|
321 |
+
" if sample['Caption'] == padding_item: continue\n",
|
322 |
+
" cur_images = [images[x] for x in images_per_prompt_indices + i]\n",
|
323 |
+
" top_images = [wandb.Image(cur_images[x]) for x in idx]\n",
|
324 |
+
" top_scores = [scores[x] for x in idx]\n",
|
325 |
+
" results.append([sample['Caption'], sample['Theme']] + top_images + top_scores)\n",
|
326 |
+
"\n",
|
327 |
+
" # log results\n",
|
328 |
+
" table = wandb.Table(columns=columns, data=results)\n",
|
329 |
+
" run.log({'Samples': table, 'version': version})\n",
|
330 |
+
" wandb.finish()"
|
331 |
]
|
332 |
},
|
333 |
{
|
|
|
337 |
"metadata": {},
|
338 |
"outputs": [],
|
339 |
"source": [
|
340 |
+
"# TODO: not implemented\n",
|
341 |
"def log_runs(runs):\n",
|
342 |
" for run in tqdm(runs):\n",
|
343 |
" log_run(run)"
|
344 |
]
|
345 |
},
|
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346 |
{
|
347 |
"cell_type": "code",
|
348 |
"execution_count": null,
|