test merge (#9)

* Update Hex-LLM container URI.

PiperOrigin-RevId: 705656670

* Peft docker fix (GoogleCloudPlatform#3751)

* Updated util files to fix peft docker

* fix imports

* fix imports fileutils.py

* Add fast deployment section to Llama 3.1 deployment notebook

PiperOrigin-RevId: 705931336

* mediapipe Object detection notebook bug fix and re-formatting

PiperOrigin-RevId: 706673143

* Update yolov8 model to use model and endpoint dictionary.

PiperOrigin-RevId: 706750375

* Add H100 80 GB config for Llama 3

PiperOrigin-RevId: 706888765

---------

Co-authored-by: Vertex MG Team <[email protected]>
Co-authored-by: sageof6path <[email protected]>
Co-authored-by: Changyu Zhu <[email protected]>

community-content/vertex_model_garden/model_oss/util/commons.py

@@ -1,6 +1,6 @@
 """Common utility lib for prediction on images."""
 
-from typing import Any, Dict, List
+from typing import Any, Dict, List, Tuple
 
 import numpy as np
 from PIL import Image
@@ -10,6 +10,28 @@
 from util import image_format_converter
 
 
+def convert_list_to_label_map(
+    input_list: List[str],
+) -> Tuple[Dict[str, Dict[int, str]], List[int]]:
+  """Converts a list of labels to a dictionary and numerical encoding.
+
+  Args:
+    input_list: A list of strings representing class labels.
+
+  Returns:
+    A tuple containing:
+      label_map: A dictionary mapping unique labels to integer indices.
+      encoded_list: A list of integers corresponding to the labels in the input
+      list.
+  """
+  unique_labels = set(input_list)
+  label_map_reverse = {label: idx for idx, label in enumerate(unique_labels)}
+  label_map = {idx: label for idx, label in enumerate(unique_labels)}
+  encoded_list = [label_map_reverse[label] for label in input_list]
+
+  return {"label_map": label_map}, encoded_list
+
+
 def get_prediction_instances(image: Image.Image) -> List[Dict[str, Any]]:
   """Gets prediction instances.
 
@@ -40,24 +62,25 @@ def get_label_map(label_map_yaml_filepath: str) -> Dict[str, Any]:
 
 
 def get_object_detection_endpoint_predictions(
-    detection_endpoint: ...,
+    detector_endpoint: ...,
     input_image: np.ndarray,
     detection_thresh: float = 0.2,
 ) -> np.ndarray:
   """Gets endpoint predictions.
 
   Args:
-      detection_endpoint: image object detection endpoint.
+      detector_endpoint: image object detection endpoint.
       input_image: Input image.
       detection_thresh: Detection threshold.
 
   Returns:
       Object detection predictions from endpoints.
   """
   height, width, _ = input_image.shape
-  predictions = detection_endpoint.predict(
+  predictions = detector_endpoint.predict(
       get_prediction_instances(Image.fromarray(input_image))
   ).predictions
+
   detection_scores = np.array(predictions[0]["detection_scores"])
   detection_classes = np.array(predictions[0]["detection_classes"])
   detection_boxes = np.array(
@@ -66,6 +89,29 @@ def get_object_detection_endpoint_predictions(
           for b in predictions[0]["detection_boxes"]
       ]
   )
+  return merge_boxes_and_classes(
+      detection_scores, detection_boxes, detection_classes, detection_thresh
+  )
+
+
+def merge_boxes_and_classes(
+    detection_scores: np.ndarray,
+    detection_boxes: np.ndarray,
+    detection_classes: np.ndarray,
+    detection_thresh: float = 0.2,
+) -> np.ndarray:
+  """Merges prediction boxes and classes.
+
+  Args:
+      detection_scores: array of detection scores.
+      detection_boxes: array of detection boxes.
+      detection_classes: array of detection classes.
+      detection_thresh: float indicating the detection threshold.
+
+  Returns:
+      preds_merge_cls: a numpy array containing the detection boxes, scores and
+      classes.
+  """
   thresh_indices = [
       x for x, val in enumerate(detection_scores) if val > detection_thresh
   ]
@@ -76,4 +122,5 @@ def get_object_detection_endpoint_predictions(
   preds_merge_cls = np.column_stack(
       (preds_merge_conf, detection_classes[thresh_indices])
   )
+
   return preds_merge_cls

community-content/vertex_model_garden/model_oss/util/constants.py

@@ -36,6 +36,12 @@
 
 # Reported hyperparameter tuning metric tag.
 HP_METRIC_TAG = 'model_performance'
+HP_LOSS_TAG = 'model_loss'
+
+# Reported places.
+REPORT_TO_NONE = 'none'
+REPORT_TO_WANDB = 'wandb'
+REPORT_TO_TENSORBOARD = 'tensorboard'
 
 # HPT trial prefix.
 TRIAL_PREFIX = 'trial_'
@@ -45,7 +51,7 @@
 ML_USE_VALIDATION = 'validation'
 ML_USE_TEST = 'test'
 
-# COCO json keys
+# COCO json keys.
 COCO_JSON_ANNOTATIONS = 'annotations'
 COCO_JSON_ANNOTATION_IMAGE_ID = 'image_id'
 COCO_JSON_ANNOTATION_CATEGORY_ID = 'category_id'
@@ -60,36 +66,88 @@
 COCO_JSON_IMAGE_COCO_URL = 'coco_url'
 COCO_ANNOTATION_BBOX = 'bbox'
 
-# GCS prefixes
+# GCS prefixes.
 GCS_URI_PREFIX = 'gs://'
 GCSFUSE_URI_PREFIX = '/gcs/'
 
 LOCAL_EVALUATION_RESULT_DIR = '/tmp/evaluation_result_dir'
 LOCAL_MODEL_DIR = '/tmp/model_dir'
+LOCAL_LORA_DIR = '/tmp/lora_dir'
 LOCAL_BASE_MODEL_DIR = '/tmp/base_model_dir'
 LOCAL_DATA_DIR = '/tmp/data'
+LOCAL_OUTPUT_DIR = '/tmp/output_dir'
+LOCAL_PREDICTION_RESULT_DIR = '/tmp/prediction_result_dir'
+SHARED_MEM_DIR = '/dev/shm'
 
 # Huggingface files.
 HF_MODEL_WEIGHTS_SUFFIX = '.bin'
 
 # PEFT finetuning constants.
+TEXT_TO_IMAGE = 'text-to-image'
 TEXT_TO_IMAGE_LORA = 'text-to-image-lora'
+TEXT_TO_IMAGE_DREAMBOOTH = 'text-to-image-dreambooth'
+TEXT_TO_IMAGE_DREAMBOOTH_LORA = 'text-to-image-dreambooth-lora'
+TEXT_TO_IMAGE_DREAMBOOTH_LORA_SDXL = 'text-to-image-dreambooth-lora-sdxl'
 SEQUENCE_CLASSIFICATION_LORA = 'sequence-classification-lora'
-CAUSAL_LANGUAGE_MODELING_LORA = 'causal-language-modeling-lora'
+MERGE_CAUSAL_LANGUAGE_MODEL_LORA = 'merge-causal-language-model-lora'
+QUANTIZE_MODEL = 'quantize-model'
 INSTRUCT_LORA = 'instruct-lora'
-CAUSAL_LANGUAGE_MODELING_LORA_TARGET_MODULES = [
-  "q_proj",
-  "v_proj",
-]
-INSTRUCT_LORA_TARGET_MODULES = [
-    "query_key_value",
-    "dense",
-    "dense_h_to_4h",
-    "dense_4h_to_h",
-]
+VALIDATE_DATASET_WITH_TEMPLATE = 'validate-dataset-with-template'
+DEFAULT_TEXT_COLUMN_IN_DATASET = 'quote'
+DEFAULT_TEXT_COLUMN_IN_QUANTIZATION_DATASET = 'text'
+DEFAULT_INSTRUCT_COLUMN_IN_DATASET = 'text'
+
+FINAL_CHECKPOINT_DIRNAME = 'checkpoint-final'
+
+# ImageBind inference constants.
+FEATURE_EMBEDDING_GENERATION = 'feature-embedding-generation'
+ZERO_SHOT_CLASSIFICATION = 'zero-shot-classification'
 
 # Precision modes for loading model weights.
+PRECISION_MODE_2 = '2bit'
+PRECISION_MODE_3 = '3bit'
 PRECISION_MODE_4 = '4bit'
 PRECISION_MODE_8 = '8bit'
+PRECISION_MODE_FP8 = 'float8'  # to use fbgemm_fp8 quantization
 PRECISION_MODE_16 = 'float16'
+PRECISION_MODE_16B = 'bfloat16'
 PRECISION_MODE_32 = 'float32'
+
+# Quantization modes.
+GPTQ = 'gptq'
+AWQ = 'awq'
+
+# AWQ versions.
+GEMM = 'GEMM'
+GEMV = 'GEMV'
+
+# Environment variable keys.
+PRIVATE_BUCKET_ENV_KEY = 'AIP_PRIVATE_BUCKET_NAME'
+
+# Kfp pipeline constants.
+TFVISION_TRAIN_OUTPUT_ARTIFACT_NAME = 'checkpoint_dir'
+
+# Vertex IOD type.
+AUTOML = 'AUTOML'
+MODEL_GARDEN = 'MODEL_GARDEN'
+
+# LRU Disk Cache constants.
+MD5_HASHMAP_FILENAME = 'md5_hashmap.json'
+
+# Prediction request keys.
+PREDICT_INSTANCE_KEY = 'instances'
+PREDICT_INSTANCE_IMAGE_KEY = 'image'
+PREDICT_INSTANCE_POSE_IMAGE_KEY = 'pose_image'
+PREDICT_INSTANCE_TEXT_KEY = 'text'
+PREDICT_INSTANCE_PROMPT_KEY = 'prompt'
+
+PREDICT_PARAMETERS_KEY = 'parameters'
+PREDICT_PARAMETERS_NUM_INFERENCE_STEPS_KEY = 'num_inference_steps'
+PREDICT_PARAMETERS_HEIGHT_KEY = 'height'
+PREDICT_PARAMETERS_WIDTH_KEY = 'width'
+PREDICT_PARAMETERS_GUIDANCE_SCALE_KEY = 'guidance_scale'
+PREDICT_PARAMETERS_NEGATIVE_PROMPT_KEY = 'negative_prompt'
+PREDICT_PARAMETERS_LORA_ID_KEY = 'lora_id'
+PREDICT_PARAMETERS_IGNORE_LORA_CACHE_KEY = 'ignore_lora_cache'
+
+PREDICT_OUTPUT_KEY = 'output'