Update CODEOWNERS

PiperOrigin-RevId: 744731980

.github/dependabot.yml

@@ -0,0 +1,10 @@
+version: 2
+updates:
+
+  # Ignore model garden dockerfiles:
+  - package-ecosystem: "npm"
+    directory: "/community-content/vertex_model_garden"
+    schedule:
+      interval: "monthly"
+    ignore:
+      - dependency-name: "*"

.github/workflows/linter/Dockerfile

@@ -4,7 +4,7 @@
 # 2. To lint specific notebooks:
 # docker run -v ${PWD}:/setup/app gcr.io/python-docs-samples-tests/notebook_linter:latest notebooks/1.ipynb notebooks/2.ipynb
 
-FROM python:3.12
+FROM python:3.13
 
 WORKDIR setup
 

.github/workflows/linter/requirements.txt

@@ -2,9 +2,9 @@ git+https://github.com/tensorflow/docs
 ipython
 jupyter
 nbconvert
-black==24.4.2
-pyupgrade==3.16.0
-isort==5.13.2
-flake8==7.1.0
-nbqa==1.8.5
+black==25.1.0
+pyupgrade==3.19.1
+isort==6.0.1
+flake8==7.2.0
+nbqa==1.9.1
 

.github/workflows/linter/run_linter.sh

@@ -58,7 +58,7 @@ done
 # Only check notebooks in test folders modified in this pull request.
 # Note: Use process substitution to persist the data in the array
 if [ ${#notebooks[@]} -eq 0 ]; then
-    echo "Checking for changed notebooked using git"
+    echo "Checking for changed notebooks using git"
     while read -r file || [ -n "$line" ]; do
         notebooks+=("$file")
     done < <(git diff --name-only main... | grep '\.ipynb$')

community-content/CODEOWNERS

@@ -20,6 +20,7 @@
 /vertex_model_garden/model_oss/movinet @KCFindstr
 /vertex_model_garden/model_oss/data_converter @KCFindstr
 /vertex_model_garden/model_oss/peft @weigary
+/vertex_model_garden/model_oss/peft/templates @rayandasoriya
 /vertex_model_garden/model_oss/lm-evaluation-harness @kathyyu-google
 /vertex_model_garden/model_oss/tfvision @dstnluong-google
 /vertex_model_garden/model_oss/fvlm @minwoo33park

community-content/pytorch_image_classification_distributed_data_parallel_training_with_vertex_sdk/trainer/Dockerfile

@@ -1,16 +1,40 @@
-FROM pytorch/pytorch:1.8.1-cuda11.1-cudnn8-runtime
+# Stage 1: Build Environment
+FROM pytorch/pytorch:1.8.1-cuda11.1-cudnn8-runtime AS builder
 
+# Install necessary tools and dependencies
 RUN apt-get update && \
     apt-get install -y curl gnupg && \
     echo "deb [signed-by=/usr/share/keyrings/cloud.google.gpg] http://packages.cloud.google.com/apt cloud-sdk main" | tee -a /etc/apt/sources.list.d/google-cloud-sdk.list && \
-    curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | apt-key --keyring /usr/share/keyrings/cloud.google.gpg  add - && \
+    curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | apt-key --keyring /usr/share/keyrings/cloud.google.gpg add - && \
     apt-get update -y && \
-    apt-get install google-cloud-sdk -y
+    apt-get install -y google-cloud-sdk
 
+# Copy application code
 COPY . /trainer
 
+# Set working directory
 WORKDIR /trainer
 
-RUN pip install -r requirements.txt
+# Install Python dependencies
+RUN pip install --no-cache-dir -r requirements.txt
 
-ENTRYPOINT ["python", "-m", "task"]
+# Stage 2: Runtime Environment
+FROM pytorch/pytorch:1.8.1-cuda11.1-cudnn8-runtime
+
+# Install Google Cloud SDK
+RUN apt-get update && \
+    apt-get install -y curl gnupg && \
+    echo "deb [signed-by=/usr/share/keyrings/cloud.google.gpg] http://packages.cloud.google.com/apt cloud-sdk main" | tee -a /etc/apt/sources.list.d/google-cloud-sdk.list && \
+    curl https://packages.cloud.google.com/apt/doc/apt-key.gpg | apt-key --keyring /usr/share/keyrings/cloud.google.gpg add - && \
+    apt-get update -y && \
+    apt-get install -y google-cloud-sdk && \
+    apt-get clean && rm -rf /var/lib/apt/lists/*
+
+# Copy from the builder stage
+COPY --from=builder /trainer /trainer
+
+# Set working directory
+WORKDIR /trainer
+
+# Set the entry point
+ENTRYPOINT ["python", "-m", "task"]

community-content/pytorch_image_classification_distributed_data_parallel_training_with_vertex_sdk/trainer/requirements.txt

@@ -1,3 +1,3 @@
-torch==1.13.1
+torch==2.2.0
 torchvision==0.9.1
 tensorboard==2.5.0

community-content/tf_agents_bandits_movie_recommendation_with_kfp_and_vertex_sdk/mlops_pipeline_tf_agents_bandits_movie_recommendation/src/logger/requirements.txt

@@ -1,4 +1,4 @@
 google-cloud-bigquery==2.20.0
-tensorflow==2.7.2
+tensorflow==2.12.1
 pillow==10.3.0
 tf-agents==0.8.0

community-content/tf_agents_bandits_movie_recommendation_with_kfp_and_vertex_sdk/mlops_pipeline_tf_agents_bandits_movie_recommendation/src/prediction_container/requirements.txt

@@ -1,4 +1,4 @@
 google-cloud-pubsub==2.5.0
 pillow==10.3.0
 tf-agents==0.8.0
-tensorflow==2.7.2
+tensorflow==2.12.1

community-content/tf_agents_bandits_movie_recommendation_with_kfp_and_vertex_sdk/mlops_pipeline_tf_agents_bandits_movie_recommendation/src/simulator/requirements.txt

@@ -1,5 +1,5 @@
 dataclasses==0.6
 google-cloud-aiplatform==1.8.1
-tensorflow==2.7.2
+tensorflow==2.12.1
 pillow==10.3.0
 tf-agents==0.8.0

community-content/tf_keras_image_classification_distributed_multi_worker_with_vertex_sdk/trainer/requirements.txt

@@ -1 +1 @@
-tensorflow==2.7.2
+tensorflow==2.12.1

community-content/tf_keras_text_classification_distributed_single_worker_gpus_with_gcloud_local_run_and_vertex_sdk/trainer/requirements.txt

@@ -1 +1 @@
-tensorflow==2.7.2
+tensorflow==2.12.1

community-content/vertex_cpr_samples/sklearn/predictor_Classifier.py

@@ -0,0 +1,33 @@
+import numpy as np
+import os
+import pickle
+
+from google.cloud.aiplatform.constants import prediction
+from google.cloud.aiplatform.utils import prediction_utils
+from google.cloud.aiplatform.prediction.predictor import Predictor
+from sklearn.datasets import load_breast_cancer
+from sklearn.linear_model import RidgeClassifier
+
+class LinearRegressionPredictor(Predictor):
+
+    def __init__(self):
+        return
+
+    def load(self, artifacts_uri: str) -> None:
+        prediction_utils.download_model_artifacts(artifacts_uri)
+        if os.path.exists(prediction.MODEL_FILENAME_PKL):
+            self._model = pickle.load(open(prediction.MODEL_FILENAME_PKL, "rb"))
+        else:
+            self._model = RidgeClassifier()
+        X, y = load_breast_cancer(return_X_y=True)
+        self._model.fit(X, y)
+
+    def preprocess(self, prediction_input: dict) -> np.ndarray:
+        instances = prediction_input["instances"]
+        return np.asarray(instances)
+
+    def predict(self, instances: np.ndarray) -> np.ndarray:
+        return self._model.predict(instances)
+
+    def postprocess(self, prediction_results: np.ndarray) -> dict:
+        return {"predictions": prediction_results.tolist()}

community-content/vertex_cpr_samples/sklearn/predictor_LinearRegression.py

@@ -0,0 +1,33 @@
+import numpy as np
+import os
+import pickle
+
+from google.cloud.aiplatform.constants import prediction
+from google.cloud.aiplatform.utils import prediction_utils
+from google.cloud.aiplatform.prediction.predictor import Predictor
+from sklearn.datasets import make_blobs
+from sklearn.linear_model import LinearRegression 
+
+class LinearRegressionPredictor(Predictor):
+
+    def __init__(self):
+        return
+
+    def load(self, artifacts_uri: str) -> None:
+        prediction_utils.download_model_artifacts(artifacts_uri)
+        if os.path.exists(prediction.MODEL_FILENAME_PKL):
+            self._model = pickle.load(open(prediction.MODEL_FILENAME_PKL, "rb"))
+        else:
+            self._model = LogisticRegression()
+        X, y = make_blobs(n_samples=100, centers=2, n_features=2, random_state=1)
+        self._model.fit(X, y)
+
+    def preprocess(self, prediction_input: dict) -> np.ndarray:
+        instances = prediction_input["instances"]
+        return np.asarray(instances)
+
+    def predict(self, instances: np.ndarray) -> np.ndarray:
+        return self._model.predict_proba(instances)
+
+    def postprocess(self, prediction_results: np.ndarray) -> dict:
+        return {"predictions": prediction_results.tolist()}

community-content/vertex_cpr_samples/sklearn/predictor_SGDClassifier.py

@@ -0,0 +1,33 @@
+import numpy as np
+import os
+import pickle
+
+from google.cloud.aiplatform.constants import prediction
+from google.cloud.aiplatform.utils import prediction_utils
+from google.cloud.aiplatform.prediction.predictor import Predictor
+from sklearn.linear_model import SGDClassifier
+
+class SGDClassifierPredictor(Predictor):
+
+    def __init__(self):
+        return
+
+    def load(self, artifacts_uri: str) -> None:
+        prediction_utils.download_model_artifacts(artifacts_uri)
+        if os.path.exists(prediction.MODEL_FILENAME_PKL):
+            self._model = pickle.load(open(prediction.MODEL_FILENAME_PKL, "rb"))
+        else:
+            self._model = SGDClassifier(max_iter=5)
+        X = [[0., 0.], [1., 1.]]
+        y = [0, 1]
+        self._model.fit(X, y)
+
+    def preprocess(self, prediction_input: dict) -> np.ndarray:
+        instances = prediction_input["instances"]
+        return np.asarray(instances)
+
+    def predict(self, instances: np.ndarray) -> np.ndarray:
+        return self._model.predict(instances)
+
+    def postprocess(self, prediction_results: np.ndarray) -> dict:
+        return {"predictions": prediction_results.tolist()}

community-content/vertex_cpr_samples/torch/predictor_resnet.py

@@ -0,0 +1,34 @@
+import os
+import torch
+
+from google.cloud.aiplatform.utils import prediction_utils
+from google.cloud.aiplatform.prediction.predictor import Predictor
+from torchvision.models import detection, resnet50, ResNet50_Weights
+from typing import Dict, List
+
+class ResNetPredictor(Predictor):
+
+    def __init__(self):
+        return
+
+    def load(self, artifacts_uri: str) -> None:
+        prediction_utils.download_model_artifacts(artifacts_uri)
+        if os.path.exists("model.pth.tar"):
+            self.model = detection.fasterrcnn_resnet50_fpn(pretrained=True)
+            stat_dic = torch.load("model.pth.tar")
+            self.model.load_state_dict(stat_dic['state_dict'])
+        else:
+            weights = ResNet50_Weights.DEFAULT
+            self.model = resnet50(weights=weights)
+        self.model.eval()
+
+    def preprocess(self, prediction_input: dict) -> torch.Tensor:
+        instances = prediction_input["instances"]
+        return torch.Tensor(instances)
+
+    @torch.inference_mode()
+    def predict(self, instances: torch.Tensor) -> List[str]:
+        return self._model(instances)
+
+    def postprocess(self, prediction_results: List[str]) -> Dict:
+        return {"predictions": prediction_results}

community-content/vertex_cpr_samples/xgboost/predictor_Classifier.py

@@ -0,0 +1,37 @@
+import os
+import numpy as np
+import pickle
+import xgboost as xgb
+
+from google.cloud.aiplatform.constants import prediction
+from google.cloud.aiplatform.utils import prediction_utils
+from google.cloud.aiplatform.prediction.predictor import Predictor
+from sklearn.datasets import make_blobs
+from xgboost import XGBClassifier
+
+
+class ClassifierPredictor(Predictor):
+
+    def __init__(self):
+        return
+
+    def load(self, artifacts_uri: str) -> None:
+        prediction_utils.download_model_artifacts(artifacts_uri)
+        if os.path.exists(prediction.MODEL_FILENAME_PKL):
+            booster = pickle.load(open(prediction.MODEL_FILENAME_PKL, "rb"))
+        else:
+            X, y = make_blobs(n_samples=100, centers=2, n_features=2, random_state=1)
+            model = XGBClassifier()
+            model.fit(X, y)
+            booster = model.get_booster()
+        self._booster = booster
+
+    def preprocess(self, prediction_input: dict) -> xgb.DMatrix:
+        instances = prediction_input["instances"]
+        return xgb.DMatrix(instances)
+
+    def predict(self, instances: xgb.DMatrix) -> np.ndarray:
+        return self._booster.predict(instances)
+
+    def postprocess(self, prediction_results: np.ndarray) -> dict:
+        return {"predictions": prediction_results.tolist()}

community-content/vertex_cpr_samples/xgboost/predictor_xgbranker.py

@@ -0,0 +1,41 @@
+import os
+import numpy as np
+import pandas as pd
+import pickle
+import xgboost as xgb
+
+from google.cloud.aiplatform.constants import prediction
+from google.cloud.aiplatform.utils import prediction_utils
+from google.cloud.aiplatform.prediction.predictor import Predictor
+
+class XGBRankerPredictor(Predictor):
+
+    def __init__(self):
+        return
+
+    def load(self, artifacts_uri: str) -> None:
+        prediction_utils.download_model_artifacts(artifacts_uri)
+        if os.path.exists(prediction.MODEL_FILENAME_PKL):
+            booster = pickle.load(open(prediction.MODEL_FILENAME_PKL, "rb"))
+            self._booster = booster
+        else:
+            N = 500
+            dates = pd.date_range(start='2023-01-01', end='2023-01-12', periods=N)
+            X = pd.DataFrame(np.random.randn(N, 5), columns=list('ABCDE'), index=dates)
+            y = pd.Series(np.random.randint(0, 10, size=N), index=dates, name='label')
+            group = X.groupby(dates + pd.offsets.MonthEnd(0)).size()
+            sample_weight = pd.Series(np.arange(len(group)), index=group.index)
+            model = xgb.XGBRanker(objective='rank:pairwise', max_depth=3, learning_rate=0.1, booster='gbtree', tree_method='hist', n_jobs=4, n_estimators=50, enable_categorical=False, random_state=42)
+            model.fit(X=X, y=y, group=group, sample_weight=sample_weight, verbose=True)
+            booster = model.get_booster()
+            self._booster = booster
+
+    def preprocess(self, prediction_input: dict) -> xgb.DMatrix:
+        instances = prediction_input["instances"]
+        return xgb.DMatrix(instances)
+
+    def predict(self, instances: xgb.DMatrix) -> np.ndarray:
+        return self._booster.predict(instances, output_margin=False, ntree_limit=0)
+
+    def postprocess(self, prediction_results: np.ndarray) -> dict:
+        return {"predictions": prediction_results.tolist()}

community-content/vertex_model_garden/model_oss/cloudnerf/requirements.txt

@@ -1,6 +1,6 @@
 --find-links https://download.pytorch.org/whl/torch_stable.html
 
-torch==2.0.1+cu118
+torch==2.2.0
 numpy==1.26.1
 absl_py==2.0.0
 accelerate==0.24.0

community-content/vertex_model_garden/model_oss/notebook_util/common_util.py

@@ -6,8 +6,10 @@ import io
 import json
 import os
 import subprocess
+import time
 from typing import Any, Dict, Sequence
 
+from google import auth
 from google.cloud import storage
 import matplotlib.pyplot as plt
 import numpy as np
@@ -85,7 +87,9 @@ def get_job_name_with_datetime(prefix: str) -> str:
   Returns:
     A job name.
   """
-  return prefix + datetime.datetime.now().strftime("_%Y%m%d_%H%M%S")
+  now = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
+  job_name = f"{prefix}-{now}".replace("_", "-")
+  return job_name
 
 
 def create_job_name(prefix: str) -> str:
@@ -99,7 +103,7 @@ def create_job_name(prefix: str) -> str:
   """
   user = os.environ.get("USER")
   now = datetime.datetime.now().strftime("%Y%m%d_%H%M%S")
-  job_name = f"{prefix}-{user}-{now}"
+  job_name = f"{prefix}-{user}-{now}".replace("_", "-")
   return job_name
 
 
@@ -228,23 +232,23 @@ def download_image(url: str) -> str:
     base64 encoded image.
   """
   response = requests.get(url)
-  return Image.open(io.BytesIO(response.content))
+  return Image.open(io.BytesIO(response.content))  # pytype: disable=bad-return-type  # pillow-102-upgrade
 
 
 def resize_image(image: Any, new_width: int = 1000) -> Any:
-    """Resizes an image to a certain width.
+  """Resizes an image to a certain width.
 
-    Args:
-      image: The image which has to be resized.
-      new_width: New width of the image.
+  Args:
+    image: The image which has to be resized.
+    new_width: New width of the image.
 
-    Returns:
-      New resized image.
-    """
-    width, height = image.size
-    new_height = int(height * new_width / width)
-    new_img = image.resize((new_width, new_height))
-    return new_img
+  Returns:
+    New resized image.
+  """
+  width, height = image.size
+  new_height = int(height * new_width / width)
+  new_img = image.resize((new_width, new_height))
+  return new_img
 
 
 def load_img(path: str) -> Any:
@@ -323,6 +327,138 @@ def get_prediction_instances(test_filepath: str, new_width: int = -1) -> Any:
   return instances
 
 
+def vqa_predict(
+    endpoint: Any,
+    question_prompts: Sequence[str],
+    image: Any,
+    language_code: str = "en",
+    new_width: int = 1000,
+) -> Sequence[str]:
+  """Predicts the answer to a question about an image using an Endpoint."""
+  # Resize and convert image to base64 string.
+  resized_image = resize_image(image, new_width)
+  resized_image_base64 = image_to_base64(resized_image)
+
+  instances = []
+  if question_prompts:
+    # Format question prompt
+    question_prompt_format = "answer {} {}\n"
+    for question_prompt in question_prompts:
+      if question_prompt:
+        instances.append({
+            "prompt": question_prompt_format.format(
+                language_code, question_prompt
+            ),
+            "image": resized_image_base64,
+        })
+  else:
+    instances.append({
+        "image": resized_image_base64,
+    })
+
+  response = endpoint.predict(instances=instances)
+  return [pred.get("response") for pred in response.predictions]
+
+
+def caption_predict(
+    endpoint: Any,
+    language_code: str,
+    image: Any,
+    caption_prompt: bool = False,
+    new_width: int = 1000,
+) -> str:
+  """Predicts a caption for a given image using an Endpoint."""
+  # Resize and convert image to base64 string.
+  resized_image = resize_image(image, new_width)
+  resized_image_base64 = image_to_base64(resized_image)
+
+  instance = {"image": resized_image_base64}
+
+  if caption_prompt:
+    # Format caption prompt
+    caption_prompt_format = "caption {}\n"
+    instance["prompt"] = caption_prompt_format.format(language_code)
+
+  instances = [instance]
+  response = endpoint.predict(instances=instances)
+  return response.predictions[0].get("response")
+
+
+def ocr_predict(
+    endpoint: Any,
+    ocr_prompt: str,
+    image: Any,
+    new_width: int = 1000,
+) -> str:
+  """Extracts text from a given image using an Endpoint."""
+  # Resize and convert image to base64 string.
+  resized_image = resize_image(image, new_width)
+  resized_image_base64 = image_to_base64(resized_image)
+
+  instance = {"image": resized_image_base64}
+  if ocr_prompt:
+    instance["prompt"] = ocr_prompt
+  instances = [instance]
+
+  response = endpoint.predict(instances=instances)
+  return response.predictions[0].get("response")
+
+
+def detect_predict(
+    endpoint: Any,
+    detect_prompt: str,
+    image: Any,
+    new_width: int = 1000,
+) -> str:
+  """Predicts the answer to a question about an image using an Endpoint."""
+  # Resize and convert image to base64 string.
+  resized_image = resize_image(image, new_width)
+  resized_image_base64 = image_to_base64(resized_image)
+
+  instance = {"image": resized_image_base64}
+  if detect_prompt:
+    instance["prompt"] = detect_prompt
+  instances = [instance]
+
+  response = endpoint.predict(instances=instances)
+  return response.predictions[0].get("response")
+
+
+def copy_model_artifacts(
+    model_id: str,
+    model_source: str,
+    model_destination: str,
+) -> None:
+  """Copies model artifacts from model_source to model_destination.
+
+  model_source and model_destination should be GCS path.
+
+  Args:
+    model_id: The model id.
+    model_source: The source of the model artifact.
+    model_destination: The destination of the model artifact.
+  """
+  if not model_source.startswith(GCS_URI_PREFIX):
+    raise ValueError(
+        f"{model_source} is not a GCS path starting with {GCS_URI_PREFIX}."
+    )
+  if not model_destination.startswith(GCS_URI_PREFIX):
+    raise ValueError(
+        f"{model_destination} is not a GCS path starting with {GCS_URI_PREFIX}."
+    )
+  model_source = f"{model_source}/{model_id}"
+  model_destination = f"{model_destination}/{model_id}"
+  print("Copying model artifact from ", model_source, " to ", model_destination)
+  subprocess.check_output([
+      "gcloud",
+      "storage",
+      "cp",
+      "-r",
+      model_source,
+      model_destination,
+  ])
+
+
 def get_quota(project_id: str, region: str, resource_id: str) -> int:
   """Returns the quota for a resource in a region.
 
@@ -373,42 +509,76 @@ def get_quota(project_id: str, region: str, resource_id: str) -> int:
   return -1
 
 
-def get_resource_id(accelerator_type: str, is_for_training: bool) -> str:
+def get_resource_id(
+    accelerator_type: str,
+    is_for_training: bool,
+    is_restricted_image: bool = False,
+    is_dynamic_workload_scheduler: bool = False,
+) -> str:
   """Returns the resource id for a given accelerator type and the use case.
 
   Args:
     accelerator_type: The accelerator type.
     is_for_training: Whether the resource is used for training. Set false for
       serving use case.
+    is_restricted_image: Whether the image is hosted in `vertex-ai-restricted`.
+    is_dynamic_workload_scheduler: Whether the resource is used with Dynamic
+      Workload Scheduler.
 
   Returns:
     The resource id.
   """
-  training_accelerator_map = {
-      "NVIDIA_TESLA_V100": "custom_model_training_nvidia_v100_gpus",
-      "NVIDIA_L4": "custom_model_training_nvidia_l4_gpus",
-      "NVIDIA_TESLA_A100": "custom_model_training_nvidia_a100_gpus",
-      "NVIDIA_A100_80GB": "custom_model_training_nvidia_a100_80gb_gpus",
-      "NVIDIA_TESLA_T4": "custom_model_training_nvidia_t4_gpus",
-      "TPU_V5e": "custom_model_training_tpu_v5e",
-      "TPU_V3": "custom_model_training_tpu_v3",
+  accelerator_suffix_map = {
+      "NVIDIA_TESLA_V100": "nvidia_v100_gpus",
+      "NVIDIA_TESLA_P100": "nvidia_p100_gpus",
+      "NVIDIA_L4": "nvidia_l4_gpus",
+      "NVIDIA_TESLA_A100": "nvidia_a100_gpus",
+      "NVIDIA_A100_80GB": "nvidia_a100_80gb_gpus",
+      "NVIDIA_H100_80GB": "nvidia_h100_gpus",
+      "NVIDIA_H100_MEGA_80GB": "nvidia_h100_mega_gpus",
+      "NVIDIA_TESLA_T4": "nvidia_t4_gpus",
+      "TPU_V5e": "tpu_v5e",
+      "TPU_V3": "tpu_v3",
+  }
+  default_training_accelerator_map = {
+      key: f"custom_model_training_{accelerator_suffix_map[key]}"
+      for key in accelerator_suffix_map
+  }
+  dws_training_accelerator_map = {
+      key: f"custom_model_training_preemptible_{accelerator_suffix_map[key]}"
+      for key in accelerator_suffix_map
+  }
+  restricted_image_training_accelerator_map = {
+      "NVIDIA_A100_80GB": "restricted_image_training_nvidia_a100_80gb_gpus",
   }
   serving_accelerator_map = {
-      "NVIDIA_TESLA_V100": "custom_model_serving_nvidia_v100_gpus",
-      "NVIDIA_L4": "custom_model_serving_nvidia_l4_gpus",
-      "NVIDIA_TESLA_A100": "custom_model_serving_nvidia_a100_gpus",
-      "NVIDIA_A100_80GB": "custom_model_serving_nvidia_a100_80gb_gpus",
-      "NVIDIA_TESLA_T4": "custom_model_serving_nvidia_t4_gpus",
-      "TPU_V5e": "custom_model_serving_tpu_v5e",
+      key: f"custom_model_serving_{accelerator_suffix_map[key]}"
+      for key in accelerator_suffix_map
   }
+
   if is_for_training:
+    if is_restricted_image and is_dynamic_workload_scheduler:
+      raise ValueError(
+          "Dynamic Workload Scheduler does not work for restricted image"
+          " training."
+      )
+    training_accelerator_map = (
+        restricted_image_training_accelerator_map
+        if is_restricted_image
+        else default_training_accelerator_map
+    )
     if accelerator_type in training_accelerator_map:
-      return training_accelerator_map[accelerator_type]
+      if is_dynamic_workload_scheduler:
+        return dws_training_accelerator_map[accelerator_type]
+      else:
+        return training_accelerator_map[accelerator_type]
     else:
       raise ValueError(
           f"Could not find accelerator type: {accelerator_type} for training."
       )
   else:
+    if is_dynamic_workload_scheduler:
+      raise ValueError("Dynamic Workload Scheduler does not work for serving.")
     if accelerator_type in serving_accelerator_map:
       return serving_accelerator_map[accelerator_type]
     else:
@@ -423,9 +593,16 @@ def check_quota(
     accelerator_type: str,
     accelerator_count: int,
     is_for_training: bool,
+    is_restricted_image: bool = False,
+    is_dynamic_workload_scheduler: bool = False,
 ):
   """Checks if the project and the region has the required quota."""
-  resource_id = get_resource_id(accelerator_type, is_for_training)
+  resource_id = get_resource_id(
+      accelerator_type,
+      is_for_training=is_for_training,
+      is_restricted_image=is_restricted_image,
+      is_dynamic_workload_scheduler=is_dynamic_workload_scheduler,
+  )
   quota = get_quota(project_id, region, resource_id)
   quota_request_instruction = (
       "Either use "
@@ -445,3 +622,76 @@ def check_quota(
         f"Quota not enough for {resource_id} in {region}: {quota} <"
         f" {accelerator_count}. {quota_request_instruction}"
     )
+
+
+def get_deploy_source() -> str:
+  """Gets deploy_source string based on running environment."""
+  vertex_product = os.environ.get("VERTEX_PRODUCT", "")
+  match vertex_product:
+    case "COLAB_ENTERPRISE":
+      return "notebook_colab_enterprise"
+    case "WORKBENCH_INSTANCE":
+      return "notebook_workbench"
+    case _:
+      # Legacy workbench, legacy colab, or other custom environments.
+      return "notebook_environment_unspecified"
+
+
+def _is_operation_done(op_name: str, region: str) -> bool:
+  """Checks if the operation is done.
+
+  Args:
+    op_name: The name of the operation to poll.
+    region: The region of the operation.
+
+  Returns:
+    True if the operation is done, False otherwise.
+
+  Raises:
+    ValueError: If the operation failed.
+  """
+  creds, _ = auth.default()
+  auth_req = auth.transport.requests.Request()
+  creds.refresh(auth_req)
+  headers = {
+      "Authorization": f"Bearer {creds.token}",
+  }
+  url = f"https://{region}-aiplatform.googleapis.com/ui/{op_name}"
+  response = requests.get(url, headers=headers)
+  operation_data = response.json()
+  if "error" in operation_data:
+    raise ValueError(f"Operation failed: {operation_data['error']}")
+  return operation_data.get("done", False)
+
+
+def poll_and_wait(
+    op_name: str, region: str, total_wait: int, interval: int = 60
+) -> None:
+  """Polls the operation and waits for it to complete.
+
+  Args:
+    op_name: The name of the operation to poll.
+    region: The region of the operation.
+    total_wait: The total wait time in seconds.
+    interval: The interval between each poll in seconds.
+
+  Raises:
+    TimeoutError: If the operation times out.
+  """
+  start_time = time.time()
+  while True:
+    if _is_operation_done(op_name, region):
+      break
+    time_elapsed = time.time() - start_time
+    if time_elapsed > total_wait:
+      raise TimeoutError(
+          f"Operation timed out after {int(time_elapsed)} seconds."
+      )
+    print(
+        "\rStill waiting for operation... Elapsed time in seconds:"
+        f" {int(time_elapsed):<6}",
+        end="",
+        flush=True,
+    )
+    time.sleep(interval)
+

community-content/vertex_model_garden/model_oss/notebook_util/dataset_validation_util.py

@@ -0,0 +1,543 @@
+"""Functions for dataset validation.
+
+This tool is used to validate the dataset against the given template.
+"""
+
+import json
+import multiprocessing
+import os
+import subprocess
+from typing import Any, Callable, Dict, Union
+from absl import logging
+import accelerate
+import datasets
+import transformers
+
+GCS_URI_PREFIX = "gs://"
+GCSFUSE_URI_PREFIX = "/gcs/"
+LOCAL_BASE_MODEL_DIR = "/tmp/base_model_dir"
+LOCAL_TEMPLATE_DIR = "/tmp/template_dir"
+_TEMPLATE_DIRNAME = "templates"
+_VERTEX_AI_SAMPLES_GITHUB_REPO_NAME = "vertex-ai-samples"
+_VERTEX_AI_SAMPLES_GITHUB_TEMPLATE_DIR = (
+    "community-content/vertex_model_garden/model_oss/peft/train/vmg/templates"
+)
+_MODELS_REQUIRING_PAD_TOKEN = ("llama", "falcon", "mistral", "mixtral")
+_MODELS_REQUIRING_EOS_TOEKN = ("gemma-2b", "gemma-7b")
+_DESCRIPTION_KEY = "description"
+_SOURCE_KEY = "source"
+_PROMPT_INPUT_KEY = "prompt_input"
+_PROMPT_NO_INPUT_KEY = "prompt_no_input"
+_RESPONSE_SEPARATOR = "response_separator"
+_INSTRUCTION_SEPARATOR = "instruction_separator"
+_CHAT_TEMPLATE_KEY = "chat_template"
+_KNOWN_KEYS = (
+    _DESCRIPTION_KEY,
+    _SOURCE_KEY,
+    _PROMPT_INPUT_KEY,
+    _PROMPT_NO_INPUT_KEY,
+    _RESPONSE_SEPARATOR,
+    _INSTRUCTION_SEPARATOR,
+    _CHAT_TEMPLATE_KEY,
+)
+
+
+def is_gcs_path(input_path: str) -> bool:
+  """Checks if the input path is a Google Cloud Storage (GCS) path.
+
+  Args:
+      input_path: The input path to be checked.
+
+  Returns:
+      True if the input path is a GCS path, False otherwise.
+  """
+  return input_path is not None and input_path.startswith(GCS_URI_PREFIX)
+
+
+def force_gcs_fuse_path(gcs_uri: str) -> str:
+  """Converts gs:// uris to their /gcs/ equivalents. No-op for other uris.
+
+  Args:
+    gcs_uri: The GCS URI to convert.
+
+  Returns:
+    The converted GCS URI.
+  """
+  if is_gcs_path(gcs_uri):
+    return GCSFUSE_URI_PREFIX + gcs_uri[len(GCS_URI_PREFIX) :]
+  else:
+    return gcs_uri
+
+
+def download_gcs_uri_to_local(
+    gcs_uri: str, destination_dir: str = LOCAL_BASE_MODEL_DIR
+) -> str:
+  """Downloads GCS URI to local.
+
+  If GCS URI is a directory, gs://some/folder is downloaded to
+  /destination_dir/folder. If GCS URI is a file, gs://some/file is downloaded to
+  /destination_dir/file.
+
+  Args:
+    gcs_uri: GCS URI to download.
+    destination_dir: Local directory directory.
+
+  Returns:
+    Local path to target folder/file.
+  """
+  target = os.path.join(
+      destination_dir,
+      os.path.basename(os.path.normpath(gcs_uri)),
+  )
+  if os.path.exists(target):
+    logging.info("File %s already exists.", target)
+    return target
+  if accelerate.PartialState().is_local_main_process:
+    logging.info(
+        "Downloading file(s) from %s to %s...", gcs_uri, destination_dir
+    )
+    if not os.path.exists(destination_dir):
+      os.mkdir(destination_dir)
+    subprocess.check_output([
+        "gsutil",
+        "-m",
+        "cp",
+        "-r",
+        gcs_uri,
+        destination_dir,
+    ])
+    logging.info("Downloaded file(s) from %s to %s.", gcs_uri, destination_dir)
+  # Make sure ALL processes process to next step after data downloading is done.
+  # It matters for the main process to wait for other processes as well.
+  accelerate.PartialState().wait_for_everyone()
+  return target
+
+
+def get_template(template_path: str) -> Dict[str, str]:
+  """Gets the template dictionary given the file path.
+
+  Args:
+    template_path: Path to the template file.
+
+  Returns:
+    A dictionary of the template.
+
+  Raises:
+    ValueError: If the template file does not exist or contains unknown keys.
+  """
+  if is_gcs_path(template_path):
+    template_path = force_gcs_fuse_path(template_path)
+  elif not os.path.isfile(template_path):
+    template_path = os.path.join(
+        os.path.dirname(__file__),
+        _TEMPLATE_DIRNAME,
+        template_path + ".json",
+    )
+  if not os.path.isfile(template_path):
+    raise ValueError(f"Template file {template_path} does not exist.")
+  with open(template_path, "r") as f:
+    template_json: dict[str, str] = json.load(f)
+    for key in template_json:
+      if key not in _KNOWN_KEYS:
+        raise ValueError(f"Unknown key {key} in template {template_path}.")
+    return template_json
+
+
+def get_response_separator(template_json: Dict[str, str]) -> Union[str, None]:
+  return template_json.get(_RESPONSE_SEPARATOR, None)
+
+
+def get_instruction_separator(
+    template_json: Dict[str, str],
+) -> Union[str, None]:
+  return template_json.get(_INSTRUCTION_SEPARATOR, None)
+
+
+def _format_template_fn(
+    template: str,
+    input_column: str,
+    tokenizer: transformers.PreTrainedTokenizer | None = None,
+) -> Callable[[Dict[str, str]], Dict[str, str]]:
+  """Formats a dataset example according to a template.
+
+  Args:
+    template: Name of the JSON template file under `templates/` or GCS path to
+      the template file.
+    input_column: The input column in the dataset to be used or updated by the
+      template. If it does not exist, the template's `prompt_no_input` will be
+      used, and the input_column will be created.
+    tokenizer: The tokenizer to use for chat_template templates.
+
+  Returns:
+    A function that formats data according to the template.
+  """
+  template_json = get_template(template)
+
+  if _CHAT_TEMPLATE_KEY not in template_json:
+
+    def format_fn(example: Dict[str, str]) -> Dict[str, str]:
+      format_dict = {key: value for key, value in example.items()}
+      if format_dict.get(input_column):
+        format_str = template_json[_PROMPT_INPUT_KEY]
+      elif _PROMPT_NO_INPUT_KEY in template_json:
+        format_str = template_json[_PROMPT_NO_INPUT_KEY]
+      else:
+        raise KeyError(
+            f"The template {os.path.basename(template)} does not contain"
+            f" {_PROMPT_INPUT_KEY} or {_PROMPT_NO_INPUT_KEY} key."
+        )
+      try:
+        return {input_column: format_str.format(**format_dict)}
+      except KeyError as e:
+        raise KeyError(
+            f"The template {os.path.basename(template)} contains a key {e} in"
+            f" {_PROMPT_INPUT_KEY} or {_PROMPT_NO_INPUT_KEY} that does not"
+            " exist in the dataset example. The dataset example looks like"
+            f" {format_dict}."
+        ) from e
+
+    return format_fn
+  elif (
+      _PROMPT_INPUT_KEY in template_json
+      or _PROMPT_NO_INPUT_KEY in template_json
+  ):
+    raise ValueError(
+        f"chat_template templates do not support {_PROMPT_INPUT_KEY} or"
+        f" {_PROMPT_NO_INPUT_KEY} templates."
+    )
+  else:
+    if tokenizer is None:
+      raise ValueError("A tokenizer is required for chat_template templates.")
+    # Assign HuggingFace jinja template.
+    tokenizer.chat_template = template_json[_CHAT_TEMPLATE_KEY]
+
+    def format_fn(example: Dict[str, str]) -> Dict[str, str]:
+      try:
+        return {
+            input_column: tokenizer.apply_chat_template(
+                example[input_column],
+                tokenize=False,
+                add_generation_prompt=False,
+            )
+        }
+      except KeyError as e:
+        raise KeyError(
+            f"The template {os.path.basename(template)} contains a key {e} in"
+            f" {_CHAT_TEMPLATE_KEY} that does not exist in the dataset example."
+        ) from e
+
+    return format_fn
+
+
+def _get_split_string(
+    split: str,
+    dataset_percent: int | None = None,
+    dataset_k_rows: int | None = None,
+) -> str:
+  """Gets the formatted split string for the dataset.
+
+  This is used to format the split string as per
+  https://huggingface.co/docs/datasets/v2.21.0/loading#slice-splits. Also, this
+  function will only be used to load the partial dataset for validating the
+  dataset against the template.
+
+  Args:
+    split: Split of the dataset.
+    dataset_percent: The percentage of the dataset to load.
+    dataset_k_rows: The top k sequences to load from the dataset.
+
+  Returns:
+    A formatted split string.
+  """
+  # Validate the dataset_percent and dataset_k_rows values.
+  if dataset_percent and dataset_k_rows:
+    raise ValueError(
+        "You can set either validate_percentage_of_dataset or"
+        " validate_k_rows_of_dataset, but not both."
+    )
+
+  if dataset_percent:
+    logging.info("Loading %d percent of the dataset...", dataset_percent)
+    return f"{split}[:{dataset_percent}%]"
+
+  if dataset_k_rows:
+    logging.info("Loading top %d rows of the dataset...", dataset_k_rows)
+    return f"{split}[:{dataset_k_rows}]"
+
+  return split
+
+
+def _github_template_path(template: str) -> str:
+  """Generates the path to the template in the Vertex AI Samples GitHub repo.
+
+  Args:
+    template: Name of the template.
+
+  Returns:
+    The path to the template in the Vertex AI Samples GitHub repo.
+  """
+  # vertex-ai-samples directory may lie under separate directory depending on
+  # the scratch_dir parameter in the notebook execution environment.
+  vertex_ai_samples_abs_path = os.getcwd().split(
+      _VERTEX_AI_SAMPLES_GITHUB_REPO_NAME
+  )[0]
+  return os.path.join(
+      vertex_ai_samples_abs_path,
+      _VERTEX_AI_SAMPLES_GITHUB_REPO_NAME,
+      _VERTEX_AI_SAMPLES_GITHUB_TEMPLATE_DIR,
+      template + ".json",
+  )
+
+
+def _get_dataset(
+    dataset_name: str,
+    split: str,
+    num_proc: int | None = None,
+) -> datasets.DatasetDict:
+  """Gets a dataset.
+
+  Args:
+    dataset_name: Name of the dataset or path to a custom dataset.
+    split: Split of the dataset.
+    num_proc: Number of processors to use.
+
+  Returns:
+    A dataset.
+  """
+  dataset_name = force_gcs_fuse_path(dataset_name)
+  if os.path.isfile(dataset_name):
+    # Custom dataset.
+    return datasets.load_dataset(
+        "json",
+        data_files=[dataset_name],
+        split=split,
+        num_proc=num_proc,
+    )
+  # HF dataset.
+  return datasets.load_dataset(dataset_name, split=split, num_proc=num_proc)
+
+
+def should_add_pad_token(model_id: str) -> bool:
+  """Returns whether the model requires adding a special pad token.
+
+  Args:
+    model_id: The name of the model.
+
+  Returns:
+    True if the model requires adding a special pad token, False otherwise.
+  """
+  return any(s.lower() in model_id.lower() for s in _MODELS_REQUIRING_PAD_TOKEN)
+
+
+def should_add_eos_token(model_id: str) -> bool:
+  """Returns whether the model requires adding a special eos token.
+
+  Args:
+    model_id: The name of the model.
+
+  Returns:
+    True if the model requires adding a special eos token, False otherwise.
+  """
+  return any(m in model_id for m in _MODELS_REQUIRING_EOS_TOEKN)
+
+
+def load_tokenizer(
+    pretrained_model_id: str,
+    padding_side: str | None = None,
+    access_token: str | None = None,
+) -> transformers.AutoTokenizer:
+  """Loads tokenizer based on `pretrained_model_id`.
+
+  Args:
+    pretrained_model_id: The name of the pretrained model.
+    padding_side: The side to pad the input on.
+    access_token: The access token to use for the tokenizer.
+
+  Returns:
+    The tokenizer.
+  """
+  tokenizer_kwargs = {}
+  if should_add_eos_token(pretrained_model_id):
+    tokenizer_kwargs["add_eos_token"] = True
+  if padding_side:
+    tokenizer_kwargs["padding_side"] = padding_side
+
+  with accelerate.PartialState().local_main_process_first():
+    tokenizer = transformers.AutoTokenizer.from_pretrained(
+        pretrained_model_id,
+        trust_remote_code=False,
+        use_fast=True,
+        token=access_token,
+        **tokenizer_kwargs,
+    )
+
+  if should_add_pad_token(pretrained_model_id):
+    tokenizer.add_special_tokens({"pad_token": "[PAD]"})
+
+  return tokenizer
+
+
+def get_filtered_dataset(
+    dataset: Any,
+    input_column: str,
+    max_seq_length: int,
+    tokenizer: transformers.PreTrainedTokenizer,
+) -> Any:
+  """Returns the dataset by removing examples that are longer than max_seq_length.
+
+  Args:
+    dataset: The dataset to filter.
+    input_column: The input column in the dataset to be used.
+    max_seq_length: The maximum sequence length.
+    tokenizer: The tokenizer.
+  """
+  actual_dataset_length = len(dataset)
+  filtered_dataset = dataset.filter(
+      lambda x: len(tokenizer(x[input_column])["input_ids"]) <= max_seq_length
+  )
+  filtered_dataset_length = len(filtered_dataset)
+  if actual_dataset_length != filtered_dataset_length:
+    examples_removed_percent = (
+        (actual_dataset_length - filtered_dataset_length)
+        * 100
+        / actual_dataset_length
+    )
+    logging.info(
+        "(%.2f%%) of examples token length is <= max-seq-length(%d); (%.2f%%) >"
+        " max-seq-length. Filtering out %d example(s) which are longer than"
+        " max-seq-length.",
+        100 - examples_removed_percent,
+        max_seq_length,
+        examples_removed_percent,
+        actual_dataset_length - filtered_dataset_length,
+    )
+
+  return filtered_dataset
+
+
+def load_dataset_with_template(
+    dataset_name: str,
+    split: str,
+    input_column: str,
+    template: str = None,
+    tokenizer: transformers.PreTrainedTokenizer | None = None,
+) -> Any:
+  """Loads dataset with templates.
+
+  Args:
+    dataset_name: Name of the dataset or path to a custom dataset.
+    split: Split of the dataset.
+    input_column: The input column in the dataset to be used or updaded by the
+      template. If it does not exist, the template's `prompt_no_input` will be
+      used, and the input_column will be created.
+    template: Name of the JSON template file under `templates/` or GCS path to
+      the template file.
+    tokenizer: The tokenizer to use for chat_template templates.
+
+  Returns:
+    A dataset compatible with the template.
+  """
+  dataset = _get_dataset(dataset_name, split=split)
+  if template:
+    dataset = dataset.map(
+        _format_template_fn(
+            template,
+            input_column=input_column,
+            tokenizer=tokenizer,
+        )
+    )
+
+  return dataset
+
+
+def validate_dataset_with_template(
+    dataset_name: str,
+    split: str,
+    input_column: str,
+    template: str,
+    tokenizer: transformers.PreTrainedTokenizer | None = None,
+    max_seq_length: int | None = None,
+    use_multiprocessing: bool = False,
+    validate_percentage_of_dataset: int | None = None,
+    validate_k_rows_of_dataset: int | None = None,
+) -> Any:
+  """Validates dataset with templates.
+
+  This function will be used to load the dataset and validate it against the
+  template. In case of validation, we also allow the users to load the dataset
+  partially by allowing them to read x% or top k rows of the dataset. To
+  validate the dataset, the template file must be available in the GCS bucket
+  and the dataset must be available either in the GCS bucket or Hugging Face.
+
+  Args:
+    dataset_name: Name of the dataset or path to a custom dataset.
+    split: Split of the dataset.
+    input_column: The input column in the dataset to be used or updaded by the
+      template. If it does not exist, the template's `prompt_no_input` will be
+      used, and the input_column will be created.
+    template: Name of the JSON template file under `templates/` or GCS path to
+      the template file.
+    tokenizer: The tokenizer to use for chat_template templates.
+    max_seq_length: The maximum sequence length.
+    use_multiprocessing: If True, it will use multiprocessing to load the
+      dataset.
+    validate_percentage_of_dataset: The percentage of the dataset to load.
+    validate_k_rows_of_dataset: The top k sequences to load from the dataset.
+
+  Returns:
+    None if the validation is successful, otherwise returns the error message.
+  """
+  if not template:
+    raise ValueError("template is required for validate_dataset.")
+
+  if not dataset_name:
+    raise ValueError("dataset_name is empty.")
+
+  if not split:
+    raise ValueError("split is empty.")
+
+  split = _get_split_string(
+      split,
+      validate_percentage_of_dataset,
+      validate_k_rows_of_dataset,
+  )
+
+  num_proc = multiprocessing.cpu_count() if use_multiprocessing else 1
+
+  # gcsfuse cannot be used from the notebook runtime env. Hence, we have
+  # to download dataset and template from gcs to local.
+  if is_gcs_path(dataset_name):
+    dataset_name = download_gcs_uri_to_local(dataset_name, LOCAL_BASE_MODEL_DIR)
+
+  if is_gcs_path(template):
+    template_path = download_gcs_uri_to_local(template, LOCAL_TEMPLATE_DIR)
+  elif os.path.isfile(_github_template_path(template)):
+    template_path = _github_template_path(template)
+  else:
+    raise ValueError(
+        f"Template file {template} does not exist. To validate the"
+        " dataset, please provide a valid GCS path for the template or a valid"
+        " template name from"
+        f" https://github.com/GoogleCloudPlatform/{_VERTEX_AI_SAMPLES_GITHUB_REPO_NAME}/tree/main/{_VERTEX_AI_SAMPLES_GITHUB_TEMPLATE_DIR}."
+    )
+
+  dataset = _get_dataset(dataset_name, split, num_proc).map(
+      _format_template_fn(
+          template_path,
+          input_column=input_column,
+          tokenizer=tokenizer,
+      )
+  )
+
+  if tokenizer is not None:
+    get_filtered_dataset(
+        dataset=dataset,
+        input_column=input_column,
+        max_seq_length=max_seq_length,
+        tokenizer=tokenizer,
+    )
+  print(
+      "Dataset {} is compatible with the {} template.".format(
+          os.path.basename(dataset_name), os.path.basename(template)
+      )
+  )

community-content/vertex_model_garden/model_oss/peft/causal_language_modeling_lora.py

@@ -1,142 +0,0 @@
-"""Causal language modeling with LoRA models."""
-
-# pylint: disable=g-importing-member
-
-from datasets import load_dataset
-from peft import get_peft_model
-from peft import LoraConfig
-import torch
-from torch import nn
-import transformers
-from transformers import AutoModelForCausalLM
-from transformers import AutoTokenizer
-from transformers import BitsAndBytesConfig
-from transformers import TrainingArguments
-from util import constants
-
-
-def finetune_causal_language_modeling(
-    pretrained_model_id: str,
-    dataset_name: str,
-    output_dir: str,
-    precision_mode: str = None,
-    lora_rank: int = 16,
-    lora_alpha: int = 32,
-    lora_dropout: float = 0.05,
-    warmup_steps: int = 10,
-    max_steps: int = 10,
-    learning_rate: float = 2e-4,
-    local_pretrained_model_id: str = None,
-) -> None:
-  """Finetunes causal language modelings."""
-  if precision_mode == constants.PRECISION_MODE_32:
-    model = AutoModelForCausalLM.from_pretrained(
-        local_pretrained_model_id
-        if local_pretrained_model_id
-        else pretrained_model_id,
-        torch_dtype=torch.float32,
-        device_map="auto",
-    )
-  elif precision_mode == constants.PRECISION_MODE_16:
-    model = AutoModelForCausalLM.from_pretrained(
-        local_pretrained_model_id
-        if local_pretrained_model_id
-        else pretrained_model_id,
-        torch_dtype=torch.bfloat16,
-        device_map="auto",
-    )
-  elif precision_mode == constants.PRECISION_MODE_8:
-    quantization_config = BitsAndBytesConfig(
-        load_in_8bit=True, int8_threshold=0
-    )
-    model = AutoModelForCausalLM.from_pretrained(
-        local_pretrained_model_id
-        if local_pretrained_model_id
-        else pretrained_model_id,
-        torch_dtype=torch.float16,
-        device_map="auto",
-        quantization_config=quantization_config,
-    )
-  else:
-    quantization_config = BitsAndBytesConfig(
-        load_in_4bit=True,
-        bnb_4bit_quant_type="nf4",
-        bnb_4bit_compute_dtype=torch.bfloat16,
-    )
-    model = AutoModelForCausalLM.from_pretrained(
-        local_pretrained_model_id
-        if local_pretrained_model_id
-        else pretrained_model_id,
-        device_map="auto",
-        torch_dtype=torch.bfloat16,
-        quantization_config=quantization_config,
-    )
-
-  tokenizer = AutoTokenizer.from_pretrained(
-      local_pretrained_model_id
-      if local_pretrained_model_id
-      else pretrained_model_id
-  )
-  if "llama" in pretrained_model_id:
-    tokenizer.pad_token = "[PAD]"
-
-  for param in model.parameters():
-    # Freezes the model - train adapters later.
-    param.requires_grad = False
-    if param.ndim == 1:
-      # Casts the small parameters (e.g. layernorm) to fp32 for stability.
-      param.data = param.data.to(torch.float32)
-
-  # Reduces the number of stored activations.
-  model.gradient_checkpointing_enable()
-  model.enable_input_require_grads()
-
-  class CastOutputToFloat(nn.Sequential):
-
-    def forward(self, x):
-      return super().forward(x).to(torch.float32)
-
-  model.lm_head = CastOutputToFloat(model.lm_head)
-
-  config = LoraConfig(
-      r=lora_rank,
-      lora_alpha=lora_alpha,
-      target_modules=["q_proj", "v_proj"],
-      lora_dropout=lora_dropout,
-      bias="none",
-      task_type="CAUSAL_LM",
-  )
-
-  model = get_peft_model(model, config)
-  model.print_trainable_parameters()
-
-  data = load_dataset(dataset_name)
-  data = data.map(
-      lambda samples: tokenizer(samples["quote"]),
-      batched=True,
-  )
-
-  trainer = transformers.Trainer(
-      model=model,
-      train_dataset=data["train"],
-      args=TrainingArguments(
-          per_device_train_batch_size=4,
-          gradient_accumulation_steps=4,
-          warmup_steps=warmup_steps,
-          max_steps=max_steps,
-          learning_rate=learning_rate,
-          fp16=True,
-          logging_steps=1,
-          output_dir=output_dir,
-          ddp_find_unused_parameters=False,
-      ),
-      data_collator=transformers.DataCollatorForLanguageModeling(
-          tokenizer,
-          mlm=False,
-      ),
-  )
-  # Silence the warnings. Please re-enable for inference!
-  model.config.use_cache = False
-  trainer.train()
-
-  model.save_pretrained(output_dir)

community-content/vertex_model_garden/model_oss/peft/dockerfile/code_llama_convert.Dockerfile

@@ -0,0 +1,28 @@
+# Base on pytorch-cuda image.
+FROM pytorch/pytorch:2.0.0-cuda11.7-cudnn8-devel
+
+# Install tools.
+ENV DEBIAN_FRONTEND=noninteractive
+RUN apt-get update
+RUN apt-get install -y --no-install-recommends apt-utils
+RUN apt-get install -y --no-install-recommends curl
+RUN apt-get install -y --no-install-recommends wget
+RUN apt-get install -y --no-install-recommends git
+
+# Install libraries.
+ENV PIP_ROOT_USER_ACTION=ignore
+RUN python3 -m pip install --upgrade pip
+RUN pip install tokenizers==0.13.3
+RUN pip install accelerate==0.21.0
+RUN pip install sentencepiece==0.1.99
+RUN pip install datasets==2.14.4
+RUN pip install protobuf==4.24.1
+
+# Install transformers
+RUN git clone https://github.com/huggingface/transformers.git
+WORKDIR transformers
+# Pin the commit to add-code-llama 08/25/2023
+RUN git reset --hard 015f8e110d270a0ad42de4ae5b98198d69eb1964
+RUN pip install -e .
+
+ENTRYPOINT ["python","src/transformers/models/llama/convert_llama_weights_to_hf.py"]

community-content/vertex_model_garden/model_oss/peft/dockerfile/conversion.Dockerfile

@@ -0,0 +1,22 @@
+# Dockerfile for Language Model Conversion.
+#
+# To build:
+# docker build -f model_oss/peft/dockerfile/conversion.Dockerfile . -t ${YOUR_IMAGE_TAG}
+#
+# To push to gcr:
+# docker tag ${YOUR_IMAGE_TAG} gcr.io/${YOUR_PROJECT}/${YOUR_IMAGE_TAG}
+# docker push gcr.io/${YOUR_PROJECT}/${YOUR_IMAGE_TAG}
+
+FROM tensorflow/build:2.14-python3.8
+
+RUN git clone https://github.com/facebookresearch/llama-recipes.git && \
+    cd llama-recipes && \
+    pip install -r requirements.txt && \
+    pip freeze | grep transformers && \
+    git clone https://github.com/huggingface/transformers.git && \
+    cd transformers && \
+    pip install protobuf
+
+WORKDIR /llama-recipes/transformers
+
+ENTRYPOINT ["python","src/transformers/models/llama/convert_llama_weights_to_hf.py"]

community-content/vertex_model_garden/model_oss/peft/dockerfile/serve.Dockerfile

@@ -7,39 +7,40 @@
 # docker tag ${YOUR_IMAGE_TAG} gcr.io/${YOUR_PROJECT}/${YOUR_IMAGE_TAG}
 # docker push gcr.io/${YOUR_PROJECT}/${YOUR_IMAGE_TAG}
 
-FROM pytorch/torchserve:0.7.0-gpu
+FROM pytorch/torchserve:0.11.0-gpu
 
 USER root
 
-ENV infer_port=7080
-ENV mng_port=7081
-ENV model_name="peft_serving"
+ENV INFER_PORT=7080
+ENV MNG_PORT=7081
+ENV MODEL="peft_serving"
 ENV PATH="/home/model-server/:${PATH}"
 
-RUN apt-get update && apt-get install -y --no-install-recommends \
+RUN apt-get update && apt-get -y upgrade && apt-get install -y --no-install-recommends \
         curl \
         wget \
         vim \
         git \
         git-lfs
 RUN git lfs install
+RUN apt-get autoremove -y
 
 # Install libraries.
 ENV PIP_ROOT_USER_ACTION=ignore
 RUN python3 -m pip install --upgrade pip
-RUN pip install --upgrade torch==2.0.1
+RUN pip install --upgrade torch==2.0.1 --index-url https://download.pytorch.org/whl/cu118
 RUN pip install torchvision==0.15.2
 RUN pip install tokenizers==0.13.3
 RUN pip install accelerate==0.21.0
 RUN pip install sentencepiece==0.1.99
 RUN pip install grpcio-status==1.33.2
 RUN pip install protobuf==3.19.6
-RUN python3 -m pip install --no-cache-dir git+https://github.com/huggingface/peft.git
+RUN pip install peft==0.5.0
 RUN pip install datasets==2.14.4
-RUN pip install triton==2.0.0.dev20221120
+RUN pip install triton==3.0.0
 RUN pip install xformers==0.0.20
-RUN pip install google-cloud-storage==2.7.0
-RUN pip install absl-py==1.4.0
+RUN pip install google-cloud-storage
+RUN pip install absl-py
 RUN pip install scipy==1.10.1
 RUN pip install evaluate==0.4.0
 RUN pip install scikit-learn==1.2.2
@@ -47,52 +48,43 @@ RUN pip install loralib==0.1.1
 RUN pip install bitsandbytes==0.39.0
 RUN pip install trl==0.4.4
 RUN pip install einops==0.6.1
-
-# Install diffusers from source.
-RUN git clone --depth 1 --branch v0.16.1 https://github.com/huggingface/diffusers.git
-WORKDIR diffusers
-RUN pip install -e .
-WORKDIR /home/model-server
-
-# Install transformers from source.
-RUN git clone --depth 1 --branch v4.31.0 https://github.com/huggingface/transformers.git
-# The patch is used to change the transformers loading model behavior:
-# 1) For models on Huggingface hub: if the model has multiple shards, each shard
-#    will be downloaded separately and get deleted after loading to GPU.
-# 2) For models on local disk: if a model bin file is actually a text file
-#    recording a GCS path, the model file will be downloaded and get deleted
-#    after loading to GPU.
-COPY model_oss/peft/hf_transformers_lazy_download.patch /home/model-server/hf_transformers_lazy_download.patch
-WORKDIR transformers
-RUN git apply /home/model-server/hf_transformers_lazy_download.patch
-RUN pip install -e .
-WORKDIR /home/model-server
+RUN pip install optimum==1.13.2
+RUN pip install auto-gptq==0.4.2
+RUN pip install https://github.com/casper-hansen/AutoAWQ/releases/download/v0.1.7/autoawq-0.1.7+cu118-cp39-cp39-linux_x86_64.whl
+RUN pip install diffusers==0.27.2
+RUN pip install tiktoken==0.6.0
+RUn pip install git+https://github.com/huggingface/transformers.git@76fa17c1663a0efeca7208c20579833365584889
+RUN pip install pynvml==11.4.0
+RUN pip install -i https://test.pypi.org/simple/ bitsandbytes
 
 # Copy license.
+WORKDIR /home/model-server
 RUN wget https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/LICENSE
 
 # Copy model artifacts.
 COPY model_oss/peft/handler.py /home/model-server/handler.py
 COPY model_oss/peft/config.properties /home/model-server/config.properties
 COPY model_oss/util/ /home/model-server/util/
+COPY model_oss/util/pytorch_startup_prober.sh /model_garden/scripts/pytorch_startup_prober.sh
 ENV PYTHONPATH /home/model-server/
 
 # Expose ports.
-EXPOSE ${infer_port}
-EXPOSE ${mng_port}
+EXPOSE ${INFER_PORT}
+EXPOSE ${MNG_PORT}
 
 # Set environments.
 ENV TASK "causal-language-modeling-lora"
-ENV MODEL_ID "openlm-research/open_llama_7b"
+ENV BASE_MODEL_ID ""
+ENV MODEL_ID ""
 ENV PRECISION_LOADING_MODE "float16"
 ENV FINETUNED_LORA_MODEL_PATH ""
-
+ENV TRUST_REMOTE_CODE ""
 
 # Archive model artifacts and dependencies.
 # Do not set --model-file and --serialized-file because model and checkpoint
 # will be dynamically loaded in handler.py.
 RUN torch-model-archiver \
---model-name=${model_name} \
+--model-name=${MODEL} \
 --version=1.0 \
 --handler=/home/model-server/handler.py \
 --runtime=python3 \
@@ -103,5 +95,5 @@ RUN torch-model-archiver \
 # Run Torchserve HTTP serve to respond to prediction requests.
 CMD ["torchserve", "--start", \
      "--ts-config", "/home/model-server/config.properties", \
-     "--models", "${model_name}=${model_name}.mar", \
+     "--models", "${MODEL}=${MODEL}.mar", \
      "--model-store", "/home/model-server/model-store"]

community-content/vertex_model_garden/model_oss/peft/dockerfile/train.Dockerfile

@@ -1,111 +0,0 @@
-# Dockerfile for PEFT Training.
-#
-# To build:
-# docker build -f model_oss/peft/dockerfile/train.Dockerfile . -t ${YOUR_IMAGE_TAG}
-#
-# To push to gcr:
-# docker tag ${YOUR_IMAGE_TAG} gcr.io/${YOUR_PROJECT}/${YOUR_IMAGE_TAG}
-# docker push gcr.io/${YOUR_PROJECT}/${YOUR_IMAGE_TAG}
-
-# Builds GPU docker image of PyTorch
-# Uses multi-staged approach to reduce size
-# Stage 1
-# Use base conda image to reduce time
-FROM continuumio/miniconda3:latest AS compile-image
-# Specify py version
-ENV PYTHON_VERSION=3.8
-# Install apt libs - copied from https://github.com/huggingface/accelerate/blob/main/docker/accelerate-gpu/Dockerfile
-RUN apt-get update && \
-    apt-get install -y curl git wget software-properties-common git-lfs && \
-    apt-get clean && \
-    rm -rf /var/lib/apt/lists*
-
-# Install audio-related libraries
-RUN apt-get update && \
-    apt install -y ffmpeg
-
-RUN apt install -y libsndfile1-dev
-RUN git lfs install
-
-# Create our conda env - copied from https://github.com/huggingface/accelerate/blob/main/docker/accelerate-gpu/Dockerfile
-RUN conda create --name peft python=${PYTHON_VERSION} ipython jupyter pip
-RUN python3 -m pip install --no-cache-dir --upgrade pip
-
-# Below is copied from https://github.com/huggingface/accelerate/blob/main/docker/accelerate-gpu/Dockerfile
-# We don't install pytorch here yet since CUDA isn't available
-# instead we use the direct torch wheel
-ENV PATH /opt/conda/envs/peft/bin:$PATH
-# Activate our bash shell
-RUN chsh -s /bin/bash
-SHELL ["/bin/bash", "-c"]
-# Activate the conda env and install transformers + accelerate from source
-RUN source activate peft
-RUN python3 -m pip install --no-cache-dir git+https://github.com/huggingface/transformers
-RUN python3 -m pip install --no-cache-dir git+https://github.com/huggingface/accelerate
-RUN python3 -m pip install --no-cache-dir git+https://github.com/huggingface/peft#egg=peft[test]
-RUN python3 -m pip install --no-cache-dir bitsandbytes
-
-# Stage 2
-FROM nvidia/cuda:11.2.2-cudnn8-devel-ubuntu20.04 AS build-image
-COPY --from=compile-image /opt/conda /opt/conda
-ENV PATH /opt/conda/bin:$PATH
-
-# Install apt libs
-RUN apt-get update && \
-    apt-get install -y curl git wget vim && \
-    apt-get clean && \
-    rm -rf /var/lib/apt/lists*
-
-# Copy license.
-RUN wget https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/LICENSE
-
-RUN echo "source activate peft" >> ~/.profile
-
-# Install libraries.
-RUN pip install --upgrade torch==2.0.1
-RUN pip install torchvision==0.15.2
-RUN pip install git+https://github.com/huggingface/transformers@de9255de27abfcae4a1f816b904915f0b1e23cd9
-RUN pip install transformers -U
-RUN pip install accelerate==0.21.0
-RUN pip install sentencepiece==0.1.99
-RUN pip install grpcio-status==1.33.2
-RUN pip install protobuf==3.19.6
-RUN python3 -m pip install --no-cache-dir git+https://github.com/huggingface/peft.git
-RUN pip install datasets==2.9.0
-RUN pip install triton==2.0.0.dev20221120
-RUN pip install xformers==0.0.20
-RUN pip install Jinja2==3.1.2
-RUN pip install ftfy==6.1.1
-RUN pip install cloudml-hypertune==0.1.0.dev6
-RUN pip install tensorboard==2.12.0
-RUN pip install scipy==1.10.1
-RUN pip install evaluate==0.4.0
-RUN pip install scikit-learn==1.2.2
-RUN pip install loralib==0.1.1
-RUN pip install bitsandbytes==0.39.0
-RUN pip install trl==0.4.4
-RUN pip install einops==0.6.1
-RUN pip install google-cloud-storage==2.7.0
-
-RUN git clone --depth 1 --branch v0.16.1 https://github.com/huggingface/diffusers.git
-WORKDIR diffusers
-RUN pip install -e .
-
-# Switch to diffusers examples folder.
-WORKDIR examples
-
-# NOTE: use 'sed' to modify train_text_to_image_lora.py to
-#       fix the bug for accelerator.
-RUN sed -i \
-"s#logging_dir=logging_dir#project_dir=logging_dir#g" \
-text_to_image/train_text_to_image_lora.py
-
-# Config accelerate.
-RUN mkdir -p ./vertex_vision_model_garden_peft/
-COPY model_oss/peft/train.sh ./vertex_vision_model_garden_peft/train.sh
-COPY model_oss/peft/*.py     ./vertex_vision_model_garden_peft/
-COPY model_oss/util /diffusers/examples/util
-ENV PYTHONPATH /diffusers/examples/
-
-# Generate accelerate config at the beginning of docker run.
-ENTRYPOINT ["python3", "vertex_vision_model_garden_peft/main.py"]

community-content/vertex_model_garden/model_oss/peft/handler.py

@@ -72,15 +72,39 @@ class PeftHandler(BaseHandler):
         "PRECISION_LOADING_MODE", constants.PRECISION_MODE_16
     )
     self.task = os.environ.get("TASK", CAUSAL_LANGUAGE_MODELING_LORA)
-    self.base_model_id = os.environ.get("BASE_MODEL_ID", None)
-    self.model_id = self.base_model_id
-    if not self.base_model_id:
-      self.model_id = os.environ.get("MODEL_ID", "")
+    trust_remote_code = os.environ.get("TRUST_REMOTE_CODE", None)
+    if trust_remote_code == "false":
+      self.trust_remote_code = False
+    else:
+      self.trust_remote_code = True
+
+    # If present, the path of the model in the container.
+    aip_storage_dir = os.environ.get("AIP_STORAGE_DIR", None)
+
+    # If present, the URI of the model in a google owned GCS bucket.
+    aip_storage_uri = os.environ.get("AIP_STORAGE_URI", None)
+
+    model_id = os.environ.get("MODEL_ID", None)
+    base_model_id = os.environ.get("BASE_MODEL_ID", None)
+
+    self.model_id = None
+    if aip_storage_dir:
+      self.model_id = aip_storage_dir
+      logging.info(f"Loaded base model from AIP_STORAGE_DIR: {self.model_id}.")
+    elif aip_storage_uri:
+      self.model_id = aip_storage_uri
+      logging.info(f"Loaded base model from AIP_STORAGE_URI: {self.model_id}.")
+    elif model_id:
+      self.model_id = model_id
+      logging.info(f"Loaded base model from MODEL_ID: {self.model_id}.")
+    elif base_model_id:
+      # Note: BASE_MODEL_ID has been unified with MODEL_ID.
+      # MODEL_ID should be used whenever possible.
+      self.model_id = base_model_id
+      logging.info(f"Loaded base model from BASE_MODEL_ID: {self.model_id}.")
+
     self.quantization = os.environ.get("QUANTIZATION", None)
-    logging.info(f"Load base model id from MODEL_ID:{self.model_id}.")
-    if not self.model_id:
-      self.model_id = os.environ.get("AIP_STORAGE_URI", "")
-      logging.info(f"Load base model id from AIP_STORAGE_URI: {self.model_id}.")
+
     if not self.model_id:
       raise ValueError("Base model id is must be set.")
     if fileutils.is_gcs_path(self.model_id):
@@ -101,8 +125,7 @@ class PeftHandler(BaseHandler):
 
     logging.info(
         f"Using task:{self.task}, base model:{self.model_id}, lora model:"
-        f" {self.finetuned_lora_model_path}, and precision"
-        f" {self.precision_mode}."
+        f" {self.finetuned_lora_model_path}, precision {self.precision_mode}."
     )
 
     self.pipeline = None
@@ -145,11 +168,18 @@ class PeftHandler(BaseHandler):
     elif (
         self.task == CAUSAL_LANGUAGE_MODELING_LORA or self.task == INSTRUCT_LORA
     ):
-      tokenizer = AutoTokenizer.from_pretrained(self.model_id)
+      tokenizer = AutoTokenizer.from_pretrained(
+          self.model_id,
+          trust_remote_code=self.trust_remote_code,
+      )
+
       logging.debug("Initialized the tokenizer.")
       if self.task == CAUSAL_LANGUAGE_MODELING_LORA:
         if self.quantization == constants.AWQ:
-          model = AutoAWQForCausalLM.from_quantized(self.model_id)
+          model = AutoAWQForCausalLM.from_quantized(
+              self.model_id,
+              trust_remote_code=self.trust_remote_code,
+          )
         elif self.quantization == constants.GPTQ or not self.quantization:
           if self.precision_mode == constants.PRECISION_MODE_32:
             model = AutoModelForCausalLM.from_pretrained(
@@ -157,6 +187,7 @@ class PeftHandler(BaseHandler):
                 return_dict=True,
                 torch_dtype=torch.float32,
                 device_map="auto",
+                trust_remote_code=self.trust_remote_code,
             )
           elif self.precision_mode == constants.PRECISION_MODE_16B:
             model = AutoModelForCausalLM.from_pretrained(
@@ -164,6 +195,7 @@ class PeftHandler(BaseHandler):
                 return_dict=True,
                 torch_dtype=torch.bfloat16,
                 device_map="auto",
+                trust_remote_code=self.trust_remote_code,
             )
           elif self.precision_mode == constants.PRECISION_MODE_16:
             model = AutoModelForCausalLM.from_pretrained(
@@ -171,6 +203,7 @@ class PeftHandler(BaseHandler):
                 return_dict=True,
                 torch_dtype=torch.float16,
                 device_map="auto",
+                trust_remote_code=self.trust_remote_code,
             )
           elif self.precision_mode == constants.PRECISION_MODE_8:
             quantization_config = BitsAndBytesConfig(
@@ -182,6 +215,7 @@ class PeftHandler(BaseHandler):
                 torch_dtype=torch.float16,
                 device_map="auto",
                 quantization_config=quantization_config,
+                trust_remote_code=self.trust_remote_code,
             )
           else:
             quantization_config = BitsAndBytesConfig(
@@ -195,6 +229,7 @@ class PeftHandler(BaseHandler):
                 device_map="auto",
                 torch_dtype=torch.bfloat16,
                 quantization_config=quantization_config,
+                trust_remote_code=self.trust_remote_code,
             )
         else:
           raise ValueError(f"Invalid QUANTIZATION value: {self.quantization}")
@@ -203,14 +238,14 @@ class PeftHandler(BaseHandler):
           model = AutoModelForCausalLM.from_pretrained(
               self.model_id,
               torch_dtype=torch.bfloat16,
-              trust_remote_code=True,
+              trust_remote_code=self.trust_remote_code,
               device_map="auto",
           )
         except:  # pylint: disable=bare-except
           model = AutoModelForCausalLM.from_pretrained(
               self.model_id,
               torch_dtype=torch.bfloat16,
-              trust_remote_code=True,
+              trust_remote_code=self.trust_remote_code,
               device_map="auto",
           )
       logging.debug("Initialized the base model.")
@@ -329,4 +364,4 @@ class PeftHandler(BaseHandler):
     return f"Prompt:\n{prompt.strip()}\nOutput:\n{output}"
 
 
-# pylint: enable=logging-fstring-interpolation
+# pylint: enable=logging-fstring-interpolation

community-content/vertex_model_garden/model_oss/peft/hf_transformers_lazy_download.patch

@@ -1,131 +0,0 @@
-diff --git a/src/transformers/modeling_utils.py b/src/transformers/modeling_utils.py
-index 45459ed..32527f4 100644
---- a/src/transformers/modeling_utils.py
-+++ b/src/transformers/modeling_utils.py
-@@ -32,6 +32,8 @@ import torch
- from packaging import version
- from torch import Tensor, nn
- from torch.nn import CrossEntropyLoss
-+from huggingface_hub import hf_hub_download
-+from google.cloud import storage
-
- from .activations import get_activation
- from .configuration_utils import PretrainedConfig
-@@ -442,6 +444,29 @@ def load_state_dict(checkpoint_file: Union[str, os.PathLike]):
-     """
-     Reads a PyTorch checkpoint file, returning properly formatted errors if they arise.
-     """
-+    delete_download = False
-+    tmp_dir = "/tmp/model"
-+    os.makedirs(tmp_dir, exist_ok=True)
-+    if isinstance(checkpoint_file, dict):
-+        # Download model file from huggingface
-+        print(f"==> Download model from HF: {checkpoint_file}")
-+        checkpoint_file = hf_hub_download(
-+            local_dir=tmp_dir, local_dir_use_symlinks=False, force_download=True, resume_download=True, **checkpoint_file)
-+        delete_download = True
-+    else:
-+        with open(checkpoint_file, "rb") as f:
-+            is_gcs_file = (f.read(2) == b"gs")
-+        if is_gcs_file:
-+            # Download model file from GCS
-+            with open(checkpoint_file, "r") as f:
-+                gcs_file = f.read()
-+            checkpoint_file = os.path.join(tmp_dir, gcs_file.split("/")[-1])
-+            print(f"==> Download model from GCS: {gcs_file} to: {checkpoint_file}")
-+            client = storage.Client()
-+            with open(checkpoint_file, 'wb') as f:
-+                client.download_blob_to_file(gcs_file, f)
-+            delete_download = True
-+
-     if checkpoint_file.endswith(".safetensors") and is_safetensors_available():
-         # Check format of the archive
-         with safe_open(checkpoint_file, framework="pt") as f:
-@@ -455,9 +480,9 @@ def load_state_dict(checkpoint_file: Union[str, os.PathLike]):
-             raise NotImplementedError(
-                 f"Conversion from a {metadata['format']} safetensors archive to PyTorch is not implemented yet."
-             )
--        return safe_load_file(checkpoint_file)
-+        state_dict = safe_load_file(checkpoint_file)
-     try:
--        return torch.load(checkpoint_file, map_location="cpu")
-+        state_dict = torch.load(checkpoint_file, map_location="cpu")
-     except Exception as e:
-         try:
-             with open(checkpoint_file) as f:
-@@ -478,6 +503,10 @@ def load_state_dict(checkpoint_file: Union[str, os.PathLike]):
-                 f"at '{checkpoint_file}'. "
-                 "If you tried to load a PyTorch model from a TF 2.0 checkpoint, please set from_tf=True."
-             )
-+    if delete_download:
-+        print(f"==> Delete downloaded model: {checkpoint_file}")
-+        os.remove(checkpoint_file)
-+    return state_dict
-
-
- def set_initialized_submodules(model, state_dict_keys):
-@@ -3179,7 +3208,10 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin, PushToHubMix
-             return mismatched_keys
-
-         if resolved_archive_file is not None:
--            folder = os.path.sep.join(resolved_archive_file[0].split(os.path.sep)[:-1])
-+            if isinstance(resolved_archive_file, str):
-+                folder = os.path.sep.join(resolved_archive_file[0].split(os.path.sep)[:-1])
-+            else:
-+                folder = None
-         else:
-             folder = None
-         if device_map is not None and is_safetensors:
-diff --git a/src/transformers/utils/hub.py b/src/transformers/utils/hub.py
-index ffed743..4b15770 100644
---- a/src/transformers/utils/hub.py
-+++ b/src/transformers/utils/hub.py
-@@ -414,20 +414,34 @@ def cached_file(
-     user_agent = http_user_agent(user_agent)
-     try:
-         # Load from URL or cache if already cached
--        resolved_file = hf_hub_download(
--            path_or_repo_id,
--            filename,
--            subfolder=None if len(subfolder) == 0 else subfolder,
--            repo_type=repo_type,
--            revision=revision,
--            cache_dir=cache_dir,
--            user_agent=user_agent,
--            force_download=force_download,
--            proxies=proxies,
--            resume_download=resume_download,
--            use_auth_token=use_auth_token,
--            local_files_only=local_files_only,
--        )
-+        if filename.endswith(".bin"):
-+            # NOTE: To save disk we do not download bin file eagerly. Do not support safetensors.
-+            resolved_file = dict(
-+                repo_id=path_or_repo_id,
-+                filename=filename,
-+                subfolder=None if len(subfolder) == 0 else subfolder,
-+                repo_type=repo_type,
-+                revision=revision,
-+                user_agent=user_agent,
-+                proxies=proxies,
-+                use_auth_token=use_auth_token,
-+            )
-+            print(f"--> Apply lazy download to bin file: {resolved_file}")
-+        else:
-+            resolved_file = hf_hub_download(
-+                path_or_repo_id,
-+                filename,
-+                subfolder=None if len(subfolder) == 0 else subfolder,
-+                repo_type=repo_type,
-+                revision=revision,
-+                cache_dir=cache_dir,
-+                user_agent=user_agent,
-+                force_download=force_download,
-+                proxies=proxies,
-+                resume_download=resume_download,
-+                use_auth_token=use_auth_token,
-+                local_files_only=local_files_only,
-+            )
-
-     except RepositoryNotFoundError:
-         raise EnvironmentError(

community-content/vertex_model_garden/model_oss/peft/instruct_lora.py

@@ -1,97 +0,0 @@
-"""Instruct/Chat with LoRA models."""
-
-# pylint: disable=g-importing-member
-from datasets import load_dataset
-from peft import LoraConfig
-import torch
-from transformers import AutoModelForCausalLM
-from transformers import AutoTokenizer
-from transformers import BitsAndBytesConfig
-from transformers import TrainingArguments
-from trl import SFTTrainer
-
-
-def finetune_instruct(
-    pretrained_model_id: str,
-    dataset_name: str,
-    output_dir: str,
-    lora_rank: int = 64,
-    lora_alpha: int = 16,
-    lora_dropout: float = 0.1,
-    warmup_ratio: int = 0.03,
-    max_steps: int = 10,
-    max_seq_length: int = 512,
-    learning_rate: float = 2e-4,
-) -> None:
-  """Finetunes instruct."""
-  dataset = load_dataset(dataset_name, split="train")
-
-  bnb_config = BitsAndBytesConfig(
-      load_in_4bit=True,
-      bnb_4bit_quant_type="nf4",
-      bnb_4bit_compute_dtype=torch.float16,
-  )
-
-  model = AutoModelForCausalLM.from_pretrained(
-      pretrained_model_id,
-      quantization_config=bnb_config,
-      trust_remote_code=True,
-  )
-  model.config.use_cache = False
-
-  tokenizer = AutoTokenizer.from_pretrained(
-      pretrained_model_id, trust_remote_code=True
-  )
-  tokenizer.pad_token = tokenizer.eos_token
-
-  peft_config = LoraConfig(
-      lora_alpha=lora_alpha,
-      lora_dropout=lora_dropout,
-      r=lora_rank,
-      bias="none",
-      task_type="CAUSAL_LM",
-      target_modules=[
-          "query_key_value",
-          "dense",
-          "dense_h_to_4h",
-          "dense_4h_to_h",
-      ],
-  )
-
-  per_device_train_batch_size = 4
-  gradient_accumulation_steps = 4
-  optim = "paged_adamw_32bit"
-  save_steps = 10
-  logging_steps = 10
-  max_grad_norm = 0.3
-  lr_scheduler_type = "constant"
-
-  training_arguments = TrainingArguments(
-      output_dir=output_dir,
-      per_device_train_batch_size=per_device_train_batch_size,
-      gradient_accumulation_steps=gradient_accumulation_steps,
-      optim=optim,
-      save_steps=save_steps,
-      logging_steps=logging_steps,
-      learning_rate=learning_rate,
-      fp16=True,
-      max_grad_norm=max_grad_norm,
-      max_steps=max_steps,
-      warmup_ratio=warmup_ratio,
-      group_by_length=True,
-      lr_scheduler_type=lr_scheduler_type,
-  )
-
-  trainer = SFTTrainer(
-      model=model,
-      train_dataset=dataset,
-      peft_config=peft_config,
-      dataset_text_field="text",
-      max_seq_length=max_seq_length,
-      tokenizer=tokenizer,
-      args=training_arguments,
-  )
-  for name, module in trainer.model.named_modules():
-    if "norm" in name:
-      module = module.to(torch.float32)
-  trainer.train()

community-content/vertex_model_garden/model_oss/peft/main.py

@@ -1,177 +0,0 @@
-"""Main function to start PEFT finetuning."""
-import subprocess
-
-from absl import app
-from absl import flags
-from absl import logging
-
-from peft import causal_language_modeling_lora
-from peft import instruct_lora
-from peft import sequence_classification_lora
-from util import constants
-from util import fileutils
-
-_TASK = flags.DEFINE_string(
-    'task',
-    constants.CAUSAL_LANGUAGE_MODELING_LORA,
-    'The supported PEFT tasks.',
-)
-
-_PRETRAINED_MODEL_ID = flags.DEFINE_string(
-    'pretrained_model_id',
-    None,
-    'The pretrained model id. Supported models can be causal language modeling'
-    ' models from https://github.com/huggingface/peft/tree/main.',
-    required=True,
-)
-
-_DATASET_NAME = flags.DEFINE_string(
-    'dataset_name',
-    None,
-    'The dataset name in huggingface.',
-    required=True,
-)
-
-_OUTPUT_DIR = flags.DEFINE_string(
-    'output_dir',
-    None,
-    'The output directory.',
-    required=True,
-)
-
-_PRECISION_MODE = flags.DEFINE_string(
-    'precision_mode',
-    constants.PRECISION_MODE_16,
-    'Supported finetuning precision_modes are `{}` and `{}`.'.format(
-        constants.PRECISION_MODE_8, constants.PRECISION_MODE_16
-    ),
-)
-
-_LORA_RANK = flags.DEFINE_integer(
-    'lora_rank',
-    16,
-    'The rank of the update matrices, expressed in int. Lower rank results in'
-    ' smaller update matrices with fewer trainable parameters, referring to'
-    ' https://huggingface.co/docs/peft/conceptual_guides/lora.',
-)
-
-_LORA_ALPHA = flags.DEFINE_integer(
-    'lora_alpha',
-    32,
-    'LoRA scaling factor, referring to'
-    ' https://huggingface.co/docs/peft/conceptual_guides/lora.',
-)
-
-_LORA_DROPOUT = flags.DEFINE_float(
-    'lora_dropout',
-    0.05,
-    'dropout probability of the LoRA layers, referring to'
-    ' https://huggingface.co/docs/peft/task_guides/token-classification-lora.',
-)
-
-_WARMUP_STEPS = flags.DEFINE_integer(
-    'warmup_steps',
-    10,
-    'Number of steps for the warmup in the learning rate scheduler.',
-)
-
-_WARMUP_RATIO = flags.DEFINE_float(
-    'warmup_ratio',
-    0.03,
-    'The warmup ratio in the learning rate scheduler.',
-)
-
-_MAX_STEPS = flags.DEFINE_integer(
-    'max_steps',
-    10,
-    'Total number of training steps.',
-)
-
-_MAX_SEQ_LENGTH = flags.DEFINE_integer(
-    'max_seq_length',
-    512,
-    'The maximum sequence length.',
-)
-
-_NUM_EPOCHS = flags.DEFINE_integer(
-    'num_epochs',
-    20,
-    'The number of training epochs.',
-)
-
-_BATCH_SIZE = flags.DEFINE_integer(
-    'batch_size',
-    32,
-    'The batch size.',
-)
-
-_LEARNING_RATE = flags.DEFINE_float(
-    'learning_rate',
-    2e-4,
-    'The learning rate after the potential warmup period.',
-)
-
-
-def main(_) -> None:
-  task = _TASK.value
-  pretrained_model_id = _PRETRAINED_MODEL_ID.value
-  local_pretrained_model_id = None
-  if pretrained_model_id.startswith(constants.GCS_URI_PREFIX):
-    logging.info(
-        'Start to copy pretrained models locally: %s.', pretrained_model_id
-    )
-    fileutils.download_gcs_dir_to_local(
-        pretrained_model_id, constants.LOCAL_BASE_MODEL_DIR
-    )
-    local_pretrained_model_id = constants.LOCAL_BASE_MODEL_DIR
-    logging.info(
-        'Finished copying pretrained models locally to: %s.',
-        local_pretrained_model_id,
-    )
-  if task == constants.TEXT_TO_IMAGE_LORA:
-    subprocess.run(['/bin/bash', 'train.sh'], check=True)
-  elif task == constants.SEQUENCE_CLASSIFICATION_LORA:
-    sequence_classification_lora.finetune_sequence_classification(
-        pretrained_model_id=pretrained_model_id,
-        dataset_name=_DATASET_NAME.value,
-        output_dir=_OUTPUT_DIR.value,
-        lora_rank=_LORA_RANK.value,
-        lora_alpha=_LORA_ALPHA.value,
-        lora_dropout=_LORA_DROPOUT.value,
-        num_epochs=_NUM_EPOCHS.value,
-        batch_size=_BATCH_SIZE.value,
-        learning_rate=_LEARNING_RATE.value,
-    )
-  elif task == constants.CAUSAL_LANGUAGE_MODELING_LORA:
-    causal_language_modeling_lora.finetune_causal_language_modeling(
-        pretrained_model_id=pretrained_model_id,
-        dataset_name=_DATASET_NAME.value,
-        output_dir=_OUTPUT_DIR.value,
-        precision_mode=_PRECISION_MODE.value,
-        lora_rank=_LORA_RANK.value,
-        lora_alpha=_LORA_ALPHA.value,
-        lora_dropout=_LORA_DROPOUT.value,
-        warmup_steps=_WARMUP_STEPS.value,
-        max_steps=_MAX_STEPS.value,
-        learning_rate=_LEARNING_RATE.value,
-        local_pretrained_model_id=local_pretrained_model_id,
-    )
-  elif task == constants.INSTRUCT_LORA:
-    instruct_lora.finetune_instruct(
-        pretrained_model_id=pretrained_model_id,
-        dataset_name=_DATASET_NAME.value,
-        output_dir=_OUTPUT_DIR.value,
-        lora_rank=_LORA_RANK.value,
-        lora_alpha=_LORA_ALPHA.value,
-        lora_dropout=_LORA_DROPOUT.value,
-        warmup_ratio=_WARMUP_RATIO.value,
-        max_steps=_MAX_STEPS.value,
-        max_seq_length=_MAX_SEQ_LENGTH.value,
-        learning_rate=_LEARNING_RATE.value,
-    )
-  else:
-    raise ValueError('The task {} is not supported.'.format(task))
-
-
-if __name__ == '__main__':
-  app.run(main)

community-content/vertex_model_garden/model_oss/peft/train.sh

@@ -1,6 +0,0 @@
-#!/bin/bash
-
-# Setup accelerate config before running trainer.
-python -c "from accelerate.utils import write_basic_config; write_basic_config(mixed_precision='fp16')"
-
-accelerate launch "$@"

community-content/vertex_model_garden/model_oss/peft/train/axolotl/dockerfile/train.Dockerfile

@@ -0,0 +1,16 @@
+# Dockerfile for axolotl training.
+#
+# To build:
+# docker build -f model_oss/peft/train/axolotol/dockerfile/train.Dockerfile . -t ${YOUR_IMAGE_TAG}
+#
+# To push to gcr:
+# docker tag ${YOUR_IMAGE_TAG} gcr.io/${YOUR_PROJECT}/${YOUR_IMAGE_TAG}
+# docker push gcr.io/${YOUR_PROJECT}/${YOUR_IMAGE_TAG}
+
+FROM winglian/axolotl:main-latest
+
+RUN mkdir -p ./vertex_vision_model_garden/
+
+COPY model_oss/peft/train/axolotl/*.py ./vertex_vision_model_garden/
+
+ENTRYPOINT ["python3", "./vertex_vision_model_garden/train_entrypoint.py"]

community-content/vertex_model_garden/model_oss/peft/train/axolotl/scripts/build_train_docker.sh

@@ -0,0 +1,20 @@
+#!/bin/bash
+
+# Run copybara first:
+# cloud/ml/applications/vision/model_garden/copybara/run_copybara_local.sh
+# Run docker build:
+# cloud/ml/applications/vision/model_garden/model_oss/peft/train/axolotl/scripts/build_train_docker.sh
+
+set -x
+
+COPYBARA_DIR="/tmp/train_docker/"
+
+pushd "${COPYBARA_DIR}"
+
+PROJECT="cloud-nas-260507"
+IMAGE_TAG="gcr.io/${PROJECT}/axolotl-train:${USER}-test"
+
+docker build -f model_oss/peft/train/axolotl/dockerfile/train.Dockerfile . -t "${IMAGE_TAG}"
+docker push "${IMAGE_TAG}"
+
+popd

community-content/vertex_model_garden/model_oss/peft/train/axolotl/train_entrypoint.py

@@ -0,0 +1,88 @@
+"""Entrypoint for axolotl train docker."""
+
+import argparse
+import json
+import os
+import subprocess
+
+
+def _get_multi_node_flags(cluster_spec: str) -> list[str]:
+  """Returns the multi-node flags."""
+  print(f'CLUSTER_SPEC: {cluster_spec}')
+
+  cluster_data = json.loads(cluster_spec)
+
+  # Get primary node info
+  primary_node = cluster_data['cluster']['workerpool0'][0]
+  print(f'primary node: {primary_node}')
+  primary_node_addr, primary_node_port = primary_node.split(':')
+  print(f'primary node address: {primary_node_addr}')
+  print(f'primary node port: {primary_node_port}')
+
+  # Determine node rank of this machine
+  workerpool = cluster_data['task']['type']
+  if workerpool == 'workerpool0':
+    node_rank = 0
+  else:
+    node_rank = cluster_data['task']['index'] + 1
+  print(f'node rank: {node_rank}')
+
+  # Calculate total nodes
+  num_worker_nodes = len(cluster_data['cluster']['workerpool1'])
+  num_nodes = num_worker_nodes + 1  # Add 1 for the primary node
+  print(f'num nodes: {num_nodes}')
+
+  return [
+      f'--machine_rank={node_rank}',
+      f'--num_machines={num_nodes}',
+      f'--main_process_ip={primary_node_addr}',
+      f'--main_process_port={primary_node_port}',
+      '--max_restarts=0',
+      '--monitor_interval=120',
+      '--dynamo_backend=no',
+  ]
+
+
+def main() -> None:
+  parser = argparse.ArgumentParser()
+  parser.add_argument('--config_file')
+  parser.add_argument('--huggingface_access_token')
+  args, unknown = parser.parse_known_args()
+
+  accelerate_flags = []
+
+  if args.config_file:
+    accelerate_flags.append(f'--config_file={args.config_file}')
+
+  if cluster_spec := os.getenv('CLUSTER_SPEC', default=None):
+    print('========== Launch on cloud multi nodes ==========')
+    accelerate_flags.extend(_get_multi_node_flags(cluster_spec))
+
+  cmd = (
+      [
+          'accelerate',
+          'launch',
+      ]
+      + accelerate_flags
+      + [
+          '-m',
+          'axolotl.cli.train',
+      ]
+      + unknown
+  )
+  print(f'{cmd=}', flush=True)
+
+  env = os.environ.copy()
+
+  if args.huggingface_access_token:
+    env['HF_TOKEN'] = args.huggingface_access_token
+
+  subprocess.run(
+      cmd,
+      check=True,
+      env=env,
+  )
+
+
+if __name__ == '__main__':
+  main()

community-content/vertex_model_garden/model_oss/peft/train/test_utils/test_util.py

@@ -0,0 +1,440 @@
+"""Test util class."""
+
+import copy
+import dataclasses
+import datetime
+import os
+import signal
+import subprocess
+import sys
+from absl import flags
+from absl import logging
+from absl.testing import parameterized
+import command_builder
+import frozendict
+import torch
+
+_DOCKER_URI = flags.DEFINE_string('docker_uri', None, 'docker image uri')
+
+_DRY_RUN = flags.DEFINE_bool('dry_run', False, 'dry-run the commands')
+
+_LOCAL_INPUT_DIR = flags.DEFINE_string(
+    'local_input_dir',
+    os.path.expanduser('~/test_input'),
+    'local directory for storing input data.',
+)
+
+_LOCAL_OUTPUT_DIR = flags.DEFINE_string(
+    'local_output_dir',
+    '/tmp',
+    'local directory for storing test output.',
+)
+
+
+_GCS_INPUT_DIR = flags.DEFINE_string(
+    'gcs_input_dir',
+    'gs://peft-docker-test',
+    'GCS directory that stores model checkpoint, dataset and etc.',
+)
+
+_GCS_OUTPUT_DIR = flags.DEFINE_string(
+    'gcs_output_dir',
+    'gs://peft-docker-test/output',
+    'GCS directory that stores test output.',
+)
+
+_GCS_TESTDATA_DIR = 'peft-train-image-test'
+
+_THROUGHPUT_TEST_EXCEPTIONS = frozendict.frozendict({
+    ('bm_deepspeed_zero3_8gpu_gemma-2-9b-it_4bit.txt', '12.0'): float('inf'),
+    ('bm_fsdp_8gpu_llama3.1-70b-hf_4bit.txt', '20.0'): float('inf'),
+    ('bm_deepspeed_zero2_8gpu_gemma-2-2b-it_bfloat16.txt', '12.0'): 20.0,
+    ('bm_deepspeed_zero3_8gpu_gemma-2-2b-it_4bit.txt', '4.0'): 20.0,
+    ('bm_deepspeed_zero3_8gpu_gemma-2-27b-it_4bit.txt', '4.0'): 20.0,
+})
+
+
+@dataclasses.dataclass
+class BenchmarkStats:
+  """Class to store the benchmark result.
+
+  Attributes:
+    peak_mem: peak memory in GB.
+    throughput: throughput in tokens/sec.
+  """
+
+  peak_mem: float
+  throughput: float
+
+
+class TestBase(parameterized.TestCase):
+  """Test base class that defines how to run commands."""
+
+  def setUp(self):
+    super().setUp()
+
+    # Create a copy of the environment variables
+    self.old_env_var = copy.deepcopy(os.environ)
+    if _DOCKER_URI.value:
+      self.command_builder = command_builder.DockerCommandBuilder(
+          _DOCKER_URI.value
+      )
+    else:
+      self.command_builder = command_builder.PythonCommandBuilder()
+
+    self.command_builder.add_mount_map(
+        os.path.expanduser('~'), os.path.expanduser('~')
+    )
+    self.command_builder.add_mount_map(
+        self.local_input_dir(), self.local_input_dir()
+    )
+
+    self.task_cmd_builder = None
+
+  def tearDown(self):
+    super().tearDown()
+    # Restore the original environment variables
+    os.environ.clear()
+    os.environ.update(self.old_env_var)
+
+  def cmd(self):
+    return self.command_builder.build_cmd() + self.task_cmd_builder.build_cmd()
+
+  def run_cmd(self) -> int:
+    logging.info('running command: \n%s', ' \\\n'.join(self.cmd()))
+    if _DRY_RUN.value:
+      return 0
+
+    p = subprocess.Popen(self.cmd(), stdout=sys.stdout, stderr=sys.stderr)
+    try:
+      unused_output, unused_error = p.communicate()
+      return p.returncode
+    except KeyboardInterrupt:
+      p.send_signal(signal.SIGINT)
+      return 0
+
+  def gcs_output_dir(self):
+    return _GCS_OUTPUT_DIR.value
+
+  def local_output_dir(self):
+    return _LOCAL_OUTPUT_DIR.value
+
+  def local_input_dir(self):
+    return _LOCAL_INPUT_DIR.value
+
+
+def get_timestamp():
+  return datetime.datetime.now(datetime.timezone.utc).strftime(
+      '%Y%m%d_%H%M%S%Z'
+  )
+
+
+def download_from_gcs(gcs_uri: str, local_dir: str):
+  if not os.path.exists(local_dir):
+    os.mkdir(local_dir)
+  subprocess.check_output([
+      'gcloud',
+      'storage',
+      'cp',
+      '-r',
+      gcs_uri,
+      local_dir,
+  ])
+
+
+def get_test_data_path(name: str, download: bool = True) -> str:
+  """Gets test data path.
+
+  Args:
+    name: name of the test data
+    download: if True, then download data from GCS and returns its local path.
+
+  Returns:
+    test data path.
+  """
+  if not download:
+    return os.path.join(_GCS_INPUT_DIR.value, name)
+
+  local_data = os.path.join(_LOCAL_INPUT_DIR.value, name)
+  if not os.path.exists(local_data):
+    download_from_gcs(
+        os.path.join(_GCS_INPUT_DIR.value, name), _LOCAL_INPUT_DIR.value
+    )
+
+  return local_data
+
+
+def get_pretrained_model_name_or_path(model_id: str) -> str:
+  # If `model_id` contains `/`, it is assumed to be HF model or model from GCS.
+  if '/' in model_id:
+    return model_id
+
+  return get_test_data_path(model_id, download=True)
+
+
+def is_gpu_h100():
+  """Checks if the GPU is H100."""
+  return 'H100' in torch.cuda.get_device_name()
+
+
+def is_gpu_a100():
+  """Checks if the GPU is A100."""
+  return 'A100' in torch.cuda.get_device_name()
+
+
+def _get_formatted_string(max_seq_length: int) -> str:
+  """Returns the formatted string for max_seq_length.
+
+  Args:
+    max_seq_length: max sequence length to get the formatted string.
+
+  Returns:
+    formatted string for max_seq_length.
+  """
+  return f'{max_seq_length/1024.0:.1f}'
+
+
+def get_benchmark_results(
+    benchmark_file_path: str, max_seq_length: int
+) -> BenchmarkStats:
+  """Gets benchmark results from the benchmark file.
+
+  Args:
+    benchmark_file_path: path to the benchmark file.
+    max_seq_length: max sequence length to get the benchmark results.
+
+  Returns:
+    peak_mem: peak memory in GB.
+    throughput: throughput in tokens/sec.
+  """
+  formatted_max_seq_length = _get_formatted_string(max_seq_length)
+  peak_mem, throughput = None, None
+  with open(benchmark_file_path, 'r') as f:
+    for line in f:
+      if line.startswith(formatted_max_seq_length):
+        metrics = line.split('|')
+        try:
+          peak_mem = float(metrics[1].strip())
+        except ValueError:
+          pass
+        try:
+          throughput = float(metrics[2].strip())
+        except ValueError:
+          pass
+        break
+    else:
+      logging.error(
+          'No metrics found for max_seq_length %s in %s',
+          formatted_max_seq_length,
+          benchmark_file_path,
+      )
+  return BenchmarkStats(peak_mem, throughput)
+
+
+def print_benchmark_file(file_path: str) -> None:
+  """Prints the contents of the file.
+
+  Args:
+    file_path: path to the file.
+  """
+  with open(file_path, 'r') as f:
+    for line in f:
+      logging.info(line.strip())
+
+
+def print_benchmark_results(
+    benchmark_file_path: str, benchmark_type: str
+) -> None:
+  """Prints the benchmark results.
+
+  Args:
+    benchmark_file_path: path to the benchmark file.
+    benchmark_type: type of the benchmark.
+  """
+  benchmark_filename = os.path.basename(benchmark_file_path)
+  logging.info('--------------------------------------------------------------')
+  logging.info('%s benchmark for %s', benchmark_type, benchmark_filename)
+  logging.info('--------------------------------------------------------------')
+  print_benchmark_file(benchmark_file_path)
+
+
+def _calculate_percent_change(
+    actual_value: float, expected_value: float
+) -> float:
+  """Calculates the percent change between the actual and expected values.
+
+  Args:
+    actual_value: actual value to compare.
+    expected_value: expected value to compare.
+
+  Returns:
+    percent change between the actual and expected values.
+  """
+  return ((actual_value - expected_value) / expected_value) * 100.0
+
+
+def compare_benchmark_results(
+    expected_benchmark_file_path: str,
+    actual_benchmark_file_path: str,
+    allowed_threshold: float,
+    max_seq_length: int,
+) -> bool:
+  """Compares if the benchmark results are the similar.
+
+  Args:
+    expected_benchmark_file_path: path to the expected benchmark file.
+    actual_benchmark_file_path: path to the actual benchmark file.
+    allowed_threshold: allowed percent range of the benchmark results.
+    max_seq_length: max sequence length to get the benchmark results.
+
+  Returns:
+    True if the benchmark results are the similar, False otherwise.
+  """
+  benchmark_filename = os.path.basename(expected_benchmark_file_path)
+  expected_results = get_benchmark_results(
+      expected_benchmark_file_path, max_seq_length
+  )
+  expected_peak_mem, expected_throughput = (
+      expected_results.peak_mem,
+      expected_results.throughput,
+  )
+  actual_results = get_benchmark_results(
+      actual_benchmark_file_path, max_seq_length
+  )
+  actual_peak_mem, actual_throughput = (
+      actual_results.peak_mem,
+      actual_results.throughput,
+  )
+  formatted_max_seq_length = _get_formatted_string(max_seq_length)
+
+  # Case 1: both peak mem and throughput are None(ideally due to OOM)
+  if expected_peak_mem is None and actual_peak_mem is None:
+    logging.info(
+        'Both peak mem and throughput are None for max_seq_length %d.',
+        max_seq_length,
+    )
+    return True
+
+  check_oom_exception = _THROUGHPUT_TEST_EXCEPTIONS.get(
+      (benchmark_filename, formatted_max_seq_length), 0.0
+  ) == float('inf')
+  # Case 2: When something strated to fail recently, or something which failed
+  # before but is working now.
+  if expected_peak_mem is None and actual_peak_mem is not None:
+    if check_oom_exception:
+      return True
+    logging.error(
+        'One of the failing benchmarks in %s is passing now for max_seq_length'
+        ' %d. The expected peak mem and throughput are None, but the actual'
+        ' peak mem is %f and actual throughput is %f',
+        benchmark_filename,
+        max_seq_length,
+        actual_peak_mem,
+        actual_throughput,
+    )
+    return False
+  if actual_peak_mem is None and expected_peak_mem is not None:
+    if check_oom_exception:
+      return True
+    logging.error(
+        'One of the passing benchmarks in %s is failing now for max_seq_length'
+        ' %d. The actual peak mem and throughput are None, but the expected'
+        ' peak mem is %f and expected throughput is %f',
+        benchmark_filename,
+        max_seq_length,
+        expected_peak_mem,
+        expected_throughput,
+    )
+    return False
+  # Case 3: When both actual peak mem and throughput lies within the range
+  # of their respective expected values.
+  mem_percent_change = _calculate_percent_change(
+      actual_peak_mem, expected_peak_mem
+  )
+  throughput_percent_change = _calculate_percent_change(
+      actual_throughput, expected_throughput
+  )
+  allowed_threshold = _THROUGHPUT_TEST_EXCEPTIONS.get(
+      (benchmark_filename, formatted_max_seq_length), allowed_threshold
+  )
+
+  if abs(mem_percent_change) > allowed_threshold:
+    logging.error(
+        'The peak memory is changing by more than %f%% for max_seq_length %d.'
+        ' Expected: %f, Actual: %f',
+        allowed_threshold,
+        max_seq_length,
+        expected_peak_mem,
+        actual_peak_mem,
+    )
+    return False
+  if abs(throughput_percent_change) > allowed_threshold:
+    logging.error(
+        'The throughput is changing by more than %f%% for max_seq_length %d.'
+        ' Expected throughput: %f, Actual throughput: %f',
+        allowed_threshold,
+        max_seq_length,
+        expected_throughput,
+        actual_throughput,
+    )
+    return False
+
+  return True
+
+
+def check_benchmark_results(
+    actual_benchmark_file_path: str,
+    model_family: str,
+    allowed_threshold: float,
+    max_seq_length: int,
+) -> bool:
+  """Checks the benchmark result between the actual and expected benchmark files.
+
+  Args:
+    actual_benchmark_file_path: path to the actual benchmark file.
+    model_family: family of the model.
+    allowed_threshold: allowed range of the benchmark results in percent.
+    max_seq_length: max sequence length to get the benchmark results.
+
+  Returns:
+    True if the benchmark results are the similar, False otherwise.
+  """
+  benchmark_filename = os.path.basename(actual_benchmark_file_path)
+  get_test_data_path(_GCS_TESTDATA_DIR)
+  expected_benchmark_file_path = os.path.join(
+      _LOCAL_INPUT_DIR.value,
+      _GCS_TESTDATA_DIR,
+      model_family,
+      benchmark_filename,
+  )
+  print_benchmark_results(expected_benchmark_file_path, 'Expected')
+  print_benchmark_results(actual_benchmark_file_path, 'Actual')
+
+  return compare_benchmark_results(
+      expected_benchmark_file_path,
+      actual_benchmark_file_path,
+      allowed_threshold,
+      max_seq_length,
+  )
+
+
+def list_gcs_directories(bucket: str, directory: str) -> list[str]:
+  """Lists GCS files."""
+  output = subprocess.check_output([
+      'gcloud',
+      'storage',
+      'ls',
+      f'gs://{bucket}/{directory}',
+  ])
+  return output.decode('utf-8').splitlines()
+
+
+def delete_gcs_object(gcs_directory: str):
+  """Deletes GCS object."""
+  subprocess.check_output([
+      'gcloud',
+      'storage',
+      'rm',
+      '-r',
+      f'{gcs_directory}',
+  ])

community-content/vertex_model_garden/model_oss/peft/train/vmg/callbacks.py

@@ -0,0 +1,124 @@
+"""Different trainer callbacks for PEFT Trainer."""
+
+from collections.abc import MutableMapping
+import math
+import time
+
+from absl import logging
+import accelerate
+from transformers import TrainingArguments
+from transformers.trainer_callback import TrainerCallback
+from transformers.trainer_callback import TrainerControl
+from transformers.trainer_callback import TrainerState
+
+from vertex_vision_model_garden_peft.train.vmg import utils
+
+
+class TrainerStatsCallback(TrainerCallback):
+  """Trainer callback to report trainer stats."""
+
+  def __init__(self, max_seq_length, filename=None):
+    self._max_seq_length = max_seq_length
+    self._filename = filename
+
+    self._partial_state = accelerate.PartialState()
+    self._start_time = float('nan')
+    self._prev_time = float('nan')
+    self._peak_mem = 0.0
+    self._avg_throughput = 0.0
+
+  def on_log(
+      self,
+      args: TrainingArguments,
+      state: TrainerState,
+      control: TrainerControl,
+      logs: MutableMapping[str, float] | None = None,
+      **kwargs,
+  ) -> None:
+    """Calculates perplexity from train loss.
+
+    Args:
+      args: Arguments passed to the trainer.
+      state: State of the trainer.
+      control: Control of the trainer.
+      logs: A dict of logs from the training loop.
+      **kwargs: Additional keyword arguments, not used in this callback.
+    """
+    del kwargs  # Unused.
+    if self._partial_state.is_main_process:
+      train_loss = logs.get('loss') if logs is not None else None
+      if train_loss is not None:
+        perplexity = round(float(math.exp(train_loss)), 4)
+        logs['perplexity'] = perplexity
+
+  def on_step_end(
+      self,
+      args: TrainingArguments,
+      state: TrainerState,
+      control: TrainerControl,
+      **kwargs,
+  ):
+    if self._partial_state.is_main_process:
+      if state.global_step == 1:
+        self._prev_time = time.time()
+        self._prev_num_token = state.num_input_tokens_seen
+        throughput = 0.0
+      else:
+        cur_time = time.time()
+        cur_num_token = state.num_input_tokens_seen
+        throughput = (cur_num_token - self._prev_num_token) / (
+            cur_time - self._prev_time
+        )
+        self._prev_time = cur_time
+        self._prev_num_token = cur_num_token
+        self._avg_throughput += (throughput - self._avg_throughput) / (
+            state.global_step - 1
+        )
+
+      gpu_stats = utils.gpu_stats()
+      self._peak_mem = max(gpu_stats.total_mem, self._peak_mem)
+      logging.info(
+          'on_step_end: Throughput: %.2f token/s. %s, %s',
+          throughput,
+          utils.gpu_stats_str(gpu_stats),
+          utils.cpu_stats_str(),
+      )
+
+  def on_train_begin(
+      self,
+      args: TrainingArguments,
+      state: TrainerState,
+      control: TrainerControl,
+      **kwargs,
+  ):
+    if self._partial_state.is_main_process:
+      self._start_time = time.time()
+      logging.info(
+          'on_train_begin: %s, %s',
+          utils.gpu_stats_str(),
+          utils.cpu_stats_str(),
+      )
+
+  def on_train_end(
+      self,
+      args: TrainingArguments,
+      state: TrainerState,
+      control: TrainerControl,
+      **kwargs,
+  ):
+    if self._partial_state.is_main_process:
+      train_time = time.time() - self._start_time
+      throughput = state.num_input_tokens_seen / train_time
+      logging.info(
+          'training time %.2f s, throughput (including overhead, e.g., ckpt'
+          ' saving): %.2f token/s, peak_mem: %.2f GB',
+          train_time,
+          throughput,
+          self._peak_mem,
+      )
+      if self._filename:
+        with open(self._filename, 'a') as out_f:
+          out_f.write(
+              f'{self._max_seq_length/1024.0:.1f} | {self._peak_mem:.2f} |'
+              f' {self._avg_throughput:.2f}\n'
+          )

community-content/vertex_model_garden/model_oss/peft/train/vmg/configs/custom_loglikelihood.yaml

@@ -0,0 +1,18 @@
+group:
+- vertex
+task: custom_loglikelihood
+dataset_path: json
+dataset_name: null
+output_type: loglikelihood
+training_split: null
+validation_split: null
+test_split: test
+doc_to_text: "Request: {{prompt}}\nResponse:"
+doc_to_target: " {{ground_truth}}"
+metric_list:
+- metric: perplexity
+  aggregation: perplexity
+  higher_is_better: false
+- metric: acc
+  aggregation: mean
+  higher_is_better: true

community-content/vertex_model_garden/model_oss/peft/train/vmg/configs/ddp_4gpu.yaml

@@ -0,0 +1,17 @@
+compute_environment: LOCAL_MACHINE
+debug: false
+distributed_type: MULTI_GPU
+downcast_bf16: 'no'
+enable_cpu_affinity: false
+gpu_ids: all
+machine_rank: 0
+main_training_function: main
+mixed_precision: fp16
+num_machines: 1
+num_processes: 4
+rdzv_backend: static
+same_network: true
+tpu_env: []
+tpu_use_cluster: false
+tpu_use_sudo: false
+use_cpu: false

community-content/vertex_model_garden/model_oss/peft/train/vmg/configs/ddp_8gpu.yaml

@@ -0,0 +1,17 @@
+compute_environment: LOCAL_MACHINE
+debug: false
+distributed_type: MULTI_GPU
+downcast_bf16: 'no'
+enable_cpu_affinity: false
+gpu_ids: all
+machine_rank: 0
+main_training_function: main
+mixed_precision: fp16
+num_machines: 1
+num_processes: 8
+rdzv_backend: static
+same_network: true
+tpu_env: []
+tpu_use_cluster: false
+tpu_use_sudo: false
+use_cpu: false

community-content/vertex_model_garden/model_oss/peft/train/vmg/configs/deepspeed_zero2_4gpu.yaml

@@ -0,0 +1,17 @@
+compute_environment: LOCAL_MACHINE
+debug: false
+deepspeed_config:
+  deepspeed_config_file: /diffusers/examples/vertex_vision_model_garden_peft/zero2.json
+  zero3_init_flag: true
+distributed_type: DEEPSPEED
+downcast_bf16: 'no'
+machine_rank: 0
+main_training_function: main
+num_machines: 1
+num_processes: 4
+rdzv_backend: static
+same_network: true
+tpu_env: []
+tpu_use_cluster: false
+tpu_use_sudo: false
+use_cpu: false

community-content/vertex_model_garden/model_oss/peft/train/vmg/configs/deepspeed_zero2_8gpu.yaml

@@ -0,0 +1,17 @@
+compute_environment: LOCAL_MACHINE
+debug: false
+deepspeed_config:
+  deepspeed_config_file: /diffusers/examples/vertex_vision_model_garden_peft/zero2.json
+  zero3_init_flag: true
+distributed_type: DEEPSPEED
+downcast_bf16: 'no'
+machine_rank: 0
+main_training_function: main
+num_machines: 1
+num_processes: 8
+rdzv_backend: static
+same_network: true
+tpu_env: []
+tpu_use_cluster: false
+tpu_use_sudo: false
+use_cpu: false

community-content/vertex_model_garden/model_oss/peft/train/vmg/configs/deepspeed_zero3_4gpu.yaml

@@ -0,0 +1,17 @@
+compute_environment: LOCAL_MACHINE
+debug: false
+deepspeed_config:
+  deepspeed_config_file: /diffusers/examples/vertex_vision_model_garden_peft/zero3.json
+  zero3_init_flag: true
+distributed_type: DEEPSPEED
+downcast_bf16: 'no'
+machine_rank: 0
+main_training_function: main
+num_machines: 1
+num_processes: 4
+rdzv_backend: static
+same_network: true
+tpu_env: []
+tpu_use_cluster: false
+tpu_use_sudo: false
+use_cpu: false

community-content/vertex_model_garden/model_oss/peft/train/vmg/configs/deepspeed_zero3_8gpu.yaml

@@ -0,0 +1,17 @@
+compute_environment: LOCAL_MACHINE
+debug: false
+deepspeed_config:
+  deepspeed_config_file: /diffusers/examples/vertex_vision_model_garden_peft/zero3.json
+  zero3_init_flag: true
+distributed_type: DEEPSPEED
+downcast_bf16: 'no'
+machine_rank: 0
+main_training_function: main
+num_machines: 1
+num_processes: 8
+rdzv_backend: static
+same_network: true
+tpu_env: []
+tpu_use_cluster: false
+tpu_use_sudo: false
+use_cpu: false

community-content/vertex_model_garden/model_oss/peft/train/vmg/configs/gemma2_fsdp_8gpu.yaml

@@ -0,0 +1,28 @@
+compute_environment: LOCAL_MACHINE
+debug: false
+distributed_type: FSDP
+downcast_bf16: 'no'
+enable_cpu_affinity: false
+fsdp_config:
+  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
+  fsdp_transformer_layer_cls_to_wrap: Gemma2DecoderLayer
+  fsdp_backward_prefetch: NO_PREFETCH
+  fsdp_cpu_ram_efficient_loading: true
+  fsdp_forward_prefetch: false
+  fsdp_offload_params: true
+  fsdp_sharding_strategy: FULL_SHARD
+  fsdp_state_dict_type: SHARDED_STATE_DICT
+  fsdp_sync_module_states: true
+  fsdp_use_orig_params: false
+  fsdp_activation_checkpointing: false
+main_training_function: main
+mixed_precision: bf16
+machine_rank: 0
+num_machines: 1
+num_processes: 8
+rdzv_backend: static
+same_network: true
+tpu_env: []
+tpu_use_cluster: false
+tpu_use_sudo: false
+use_cpu: false

community-content/vertex_model_garden/model_oss/peft/train/vmg/configs/llama_fsdp_128gpu.yaml

@@ -0,0 +1,28 @@
+compute_environment: LOCAL_MACHINE
+debug: false
+distributed_type: FSDP
+downcast_bf16: 'no'
+enable_cpu_affinity: false
+fsdp_config:
+  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
+  fsdp_transformer_layer_cls_to_wrap: LlamaDecoderLayer
+  fsdp_backward_prefetch: NO_PREFETCH
+  fsdp_cpu_ram_efficient_loading: true
+  fsdp_forward_prefetch: false
+  fsdp_offload_params: true
+  fsdp_sharding_strategy: FULL_SHARD
+  fsdp_state_dict_type: SHARDED_STATE_DICT
+  fsdp_sync_module_states: true
+  fsdp_use_orig_params: false
+  fsdp_activation_checkpointing: false
+main_training_function: main
+mixed_precision: bf16
+machine_rank: 0
+num_machines: 16
+num_processes: 128
+rdzv_backend: static
+same_network: true
+tpu_env: []
+tpu_use_cluster: false
+tpu_use_sudo: false
+use_cpu: false

community-content/vertex_model_garden/model_oss/peft/train/vmg/configs/llama_fsdp_16gpu.yaml

@@ -0,0 +1,28 @@
+compute_environment: LOCAL_MACHINE
+debug: false
+distributed_type: FSDP
+downcast_bf16: 'no'
+enable_cpu_affinity: false
+fsdp_config:
+  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
+  fsdp_transformer_layer_cls_to_wrap: LlamaDecoderLayer
+  fsdp_backward_prefetch: NO_PREFETCH
+  fsdp_cpu_ram_efficient_loading: true
+  fsdp_forward_prefetch: false
+  fsdp_offload_params: true
+  fsdp_sharding_strategy: FULL_SHARD
+  fsdp_state_dict_type: SHARDED_STATE_DICT
+  fsdp_sync_module_states: true
+  fsdp_use_orig_params: false
+  fsdp_activation_checkpointing: false
+main_training_function: main
+mixed_precision: bf16
+machine_rank: 0
+num_machines: 2
+num_processes: 16
+rdzv_backend: static
+same_network: true
+tpu_env: []
+tpu_use_cluster: false
+tpu_use_sudo: false
+use_cpu: false

community-content/vertex_model_garden/model_oss/peft/train/vmg/configs/llama_fsdp_24gpu.yaml

@@ -0,0 +1,28 @@
+compute_environment: LOCAL_MACHINE
+debug: false
+distributed_type: FSDP
+downcast_bf16: 'no'
+enable_cpu_affinity: false
+fsdp_config:
+  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
+  fsdp_transformer_layer_cls_to_wrap: LlamaDecoderLayer
+  fsdp_backward_prefetch: NO_PREFETCH
+  fsdp_cpu_ram_efficient_loading: true
+  fsdp_forward_prefetch: false
+  fsdp_offload_params: true
+  fsdp_sharding_strategy: FULL_SHARD
+  fsdp_state_dict_type: SHARDED_STATE_DICT
+  fsdp_sync_module_states: true
+  fsdp_use_orig_params: false
+  fsdp_activation_checkpointing: false
+main_training_function: main
+mixed_precision: bf16
+machine_rank: 0
+num_machines: 3
+num_processes: 24
+rdzv_backend: static
+same_network: true
+tpu_env: []
+tpu_use_cluster: false
+tpu_use_sudo: false
+use_cpu: false

community-content/vertex_model_garden/model_oss/peft/train/vmg/configs/llama_fsdp_32gpu.yaml

@@ -0,0 +1,28 @@
+compute_environment: LOCAL_MACHINE
+debug: false
+distributed_type: FSDP
+downcast_bf16: 'no'
+enable_cpu_affinity: false
+fsdp_config:
+  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
+  fsdp_transformer_layer_cls_to_wrap: LlamaDecoderLayer
+  fsdp_backward_prefetch: NO_PREFETCH
+  fsdp_cpu_ram_efficient_loading: true
+  fsdp_forward_prefetch: false
+  fsdp_offload_params: true
+  fsdp_sharding_strategy: FULL_SHARD
+  fsdp_state_dict_type: SHARDED_STATE_DICT
+  fsdp_sync_module_states: true
+  fsdp_use_orig_params: false
+  fsdp_activation_checkpointing: false
+main_training_function: main
+mixed_precision: bf16
+machine_rank: 0
+num_machines: 4
+num_processes: 32
+rdzv_backend: static
+same_network: true
+tpu_env: []
+tpu_use_cluster: false
+tpu_use_sudo: false
+use_cpu: false

community-content/vertex_model_garden/model_oss/peft/train/vmg/configs/llama_fsdp_8gpu.yaml

@@ -0,0 +1,28 @@
+compute_environment: LOCAL_MACHINE
+debug: false
+distributed_type: FSDP
+downcast_bf16: 'no'
+enable_cpu_affinity: false
+fsdp_config:
+  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
+  fsdp_transformer_layer_cls_to_wrap: LlamaDecoderLayer
+  fsdp_backward_prefetch: NO_PREFETCH
+  fsdp_cpu_ram_efficient_loading: true
+  fsdp_forward_prefetch: false
+  fsdp_offload_params: true
+  fsdp_sharding_strategy: FULL_SHARD
+  fsdp_state_dict_type: SHARDED_STATE_DICT
+  fsdp_sync_module_states: true
+  fsdp_use_orig_params: false
+  fsdp_activation_checkpointing: false
+main_training_function: main
+mixed_precision: bf16
+machine_rank: 0
+num_machines: 1
+num_processes: 8
+rdzv_backend: static
+same_network: true
+tpu_env: []
+tpu_use_cluster: false
+tpu_use_sudo: false
+use_cpu: false

community-content/vertex_model_garden/model_oss/peft/train/vmg/configs/llama_hsdp_16gpu.yaml

@@ -0,0 +1,28 @@
+compute_environment: LOCAL_MACHINE
+debug: false
+distributed_type: FSDP
+downcast_bf16: 'no'
+enable_cpu_affinity: false
+fsdp_config:
+  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
+  fsdp_transformer_layer_cls_to_wrap: LlamaDecoderLayer
+  fsdp_backward_prefetch: NO_PREFETCH
+  fsdp_cpu_ram_efficient_loading: true
+  fsdp_forward_prefetch: false
+  fsdp_offload_params: true
+  fsdp_sharding_strategy: HYBRID_SHARD
+  fsdp_state_dict_type: SHARDED_STATE_DICT
+  fsdp_sync_module_states: true
+  fsdp_use_orig_params: false
+  fsdp_activation_checkpointing: false
+main_training_function: main
+mixed_precision: bf16
+machine_rank: 0
+num_machines: 2
+num_processes: 16
+rdzv_backend: static
+same_network: true
+tpu_env: []
+tpu_use_cluster: false
+tpu_use_sudo: false
+use_cpu: false

community-content/vertex_model_garden/model_oss/peft/train/vmg/configs/llama_hsdp_24gpu.yaml

@@ -0,0 +1,28 @@
+compute_environment: LOCAL_MACHINE
+debug: false
+distributed_type: FSDP
+downcast_bf16: 'no'
+enable_cpu_affinity: false
+fsdp_config:
+  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
+  fsdp_transformer_layer_cls_to_wrap: LlamaDecoderLayer
+  fsdp_backward_prefetch: NO_PREFETCH
+  fsdp_cpu_ram_efficient_loading: true
+  fsdp_forward_prefetch: false
+  fsdp_offload_params: true
+  fsdp_sharding_strategy: HYBRID_SHARD
+  fsdp_state_dict_type: SHARDED_STATE_DICT
+  fsdp_sync_module_states: true
+  fsdp_use_orig_params: false
+  fsdp_activation_checkpointing: false
+main_training_function: main
+mixed_precision: bf16
+machine_rank: 0
+num_machines: 3
+num_processes: 24
+rdzv_backend: static
+same_network: true
+tpu_env: []
+tpu_use_cluster: false
+tpu_use_sudo: false
+use_cpu: false

community-content/vertex_model_garden/model_oss/peft/train/vmg/configs/llama_hsdp_32gpu.yaml

@@ -0,0 +1,28 @@
+compute_environment: LOCAL_MACHINE
+debug: false
+distributed_type: FSDP
+downcast_bf16: 'no'
+enable_cpu_affinity: false
+fsdp_config:
+  fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
+  fsdp_transformer_layer_cls_to_wrap: LlamaDecoderLayer
+  fsdp_backward_prefetch: NO_PREFETCH
+  fsdp_cpu_ram_efficient_loading: true
+  fsdp_forward_prefetch: false
+  fsdp_offload_params: true
+  fsdp_sharding_strategy: HYBRID_SHARD
+  fsdp_state_dict_type: SHARDED_STATE_DICT
+  fsdp_sync_module_states: true
+  fsdp_use_orig_params: false
+  fsdp_activation_checkpointing: false
+main_training_function: main
+mixed_precision: bf16
+machine_rank: 0
+num_machines: 4
+num_processes: 32
+rdzv_backend: static
+same_network: true
+tpu_env: []
+tpu_use_cluster: false
+tpu_use_sudo: false
+use_cpu: false

community-content/vertex_model_garden/model_oss/peft/train/vmg/configs/zero2.json

@@ -0,0 +1,24 @@
+{
+  "zero_optimization": {
+    "stage": 2,
+    "contiguous_gradients": false,
+    "overlap_comm": false
+  },
+  "bf16": {
+    "enabled": "auto"
+  },
+  "fp16": {
+    "enabled": "auto",
+    "auto_cast": false,
+    "loss_scale": 0,
+    "initial_scale_power": 32,
+    "loss_scale_window": 1000,
+    "hysteresis": 2,
+    "min_loss_scale": 1
+  },
+  "gradient_accumulation_steps": "auto",
+  "gradient_clipping": "auto",
+  "train_batch_size": "auto",
+  "train_micro_batch_size_per_gpu": "auto",
+  "wall_clock_breakdown": false
+}

community-content/vertex_model_garden/model_oss/peft/train/vmg/configs/zero3.json

@@ -0,0 +1,31 @@
+{
+  "zero_optimization": {
+    "stage": 3,
+    "overlap_comm": false,
+    "contiguous_gradients": false,
+    "sub_group_size": 0,
+    "reduce_bucket_size": "auto",
+    "stage3_prefetch_bucket_size": "auto",
+    "stage3_param_persistence_threshold": "auto",
+    "stage3_max_live_parameters": 0,
+    "stage3_max_reuse_distance": 0,
+    "stage3_gather_16bit_weights_on_model_save": true
+  },
+  "bf16": {
+    "enabled": "auto"
+  },
+  "fp16": {
+    "enabled": "auto",
+    "auto_cast": false,
+    "loss_scale": 0,
+    "initial_scale_power": 32,
+    "loss_scale_window": 1000,
+    "hysteresis": 2,
+    "min_loss_scale": 1
+  },
+  "gradient_accumulation_steps": "auto",
+  "gradient_clipping": "auto",
+  "train_batch_size": "auto",
+  "train_micro_batch_size_per_gpu": "auto",
+  "wall_clock_breakdown": false
+}

community-content/vertex_model_garden/model_oss/peft/train/vmg/dockerfile/merge_env.yaml

@@ -0,0 +1,43 @@
+# Doc about format of conda environment file
+# https://conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#create-env-file-manually
+name: merge
+channels:
+- nodefaults
+- conda-forge
+dependencies:
+- _libgcc_mutex=0.1=conda_forge
+- _openmp_mutex=4.5=2_gnu
+- bzip2=1.0.8=h4bc722e_7
+- ca-certificates=2024.7.4=hbcca054_0
+- ld_impl_linux-64=2.40=hf3520f5_7
+- libffi=3.4.2=h7f98852_5
+- libgcc-ng=14.1.0=h77fa898_0
+- libgomp=14.1.0=h77fa898_0
+- libnsl=2.0.1=hd590300_0
+- libsqlite=3.46.0=hde9e2c9_0
+- libuuid=2.38.1=h0b41bf4_0
+- libxcrypt=4.4.36=hd590300_1
+- libzlib=1.3.1=h4ab18f5_1
+- ncurses=6.5=h59595ed_0
+- openssl=3.3.1=h4bc722e_2
+- pip=24.2=pyhd8ed1ab_0
+- python=3.10.14=hd12c33a_0_cpython
+- readline=8.2=h8228510_1
+- setuptools=72.1.0=pyhd8ed1ab_0
+- tk=8.6.13=noxft_h4845f30_101
+- tzdata=2024a=h0c530f3_0
+- wheel=0.44.0=pyhd8ed1ab_0
+- xz=5.2.6=h166bdaf_0
+- pip:
+  - --extra-index-url https://download.pytorch.org/whl/cu121
+  - absl-py==2.1.0
+  - accelerate==0.34.2  # Needed for fp8
+  - datasets==2.19.2
+  - fbgemm-gpu==0.8.0+cu121  # Needed for fp8
+  - kfp==2.5.0
+  - peft==0.12.0
+  - protobuf==3.20.3
+  - pynvml==11.5.3
+  - torch==2.4.0+cu121  # Needed for fp8
+  - transformers==4.47.1
+  - trl==0.11.2

community-content/vertex_model_garden/model_oss/peft/train/vmg/dockerfile/requirements.txt

@@ -0,0 +1,31 @@
+# Doc about format of requirement file
+# https://pip.pypa.io/en/stable/reference/requirements-file-format
+
+--extra-index-url https://download.pytorch.org/whl/cu118
+--extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/
+
+# keep sorted
+accelerate==0.34.2
+auto_gptq==0.7.1+cu118
+autoawq==0.2.8
+bitsandbytes==0.43.2
+cloudml-hypertune==0.1.0.dev6
+datasets==2.20.0
+deepspeed==0.15.2
+diffusers==0.25.1
+evaluate==0.4.3
+fsspec==2024.3.1
+gcsfs==2024.3.1
+ninja==1.11.1 # Needed to avoid `ninja 1.11.1.1 is not supported on this platform` error
+nltk==3.9.1
+optimum==1.17.1
+peft==0.12.0
+pynvml==11.5.3
+rouge_score==0.1.2
+torch==2.2.2+cu118
+torchvision==0.17.2+cu118
+transformers==4.47.1
+trl==0.11.2
+wandb==0.17.1
+ydata-profiling==4.7.0 # Upgrade the version from 4.6.0 to 4.7.0 to fix the old `pydantic` package error.
+psutil==6.0.0

community-content/vertex_model_garden/model_oss/peft/train/vmg/dockerfile/train.Dockerfile

@@ -0,0 +1,81 @@
+# Dockerfile for PEFT Training.
+#
+# To build:
+# docker build -f model_oss/peft/dockerfile/train.Dockerfile . -t ${YOUR_IMAGE_TAG}
+#
+# To push to gcr:
+# docker tag ${YOUR_IMAGE_TAG} gcr.io/${YOUR_PROJECT}/${YOUR_IMAGE_TAG}
+# docker push gcr.io/${YOUR_PROJECT}/${YOUR_IMAGE_TAG}
+
+# Picked from https://cloud.google.com/deep-learning-containers/docs/choosing-container#pytorch
+FROM us-docker.pkg.dev/deeplearning-platform-release/gcr.io/pytorch-cu121.2-2.py310:m123
+RUN apt-get update && \
+    apt-get upgrade -y && \
+    apt-get install -y curl git wget software-properties-common vim libaio-dev && \
+    apt-get clean && \
+    rm -rf /var/lib/apt/lists*
+
+# Copy license.
+RUN wget https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/LICENSE
+
+
+# Install libraries.
+ENV PIP_ROOT_USER_ACTION=ignore
+RUN pip install --upgrade pip
+
+# Remove packages that are not needed and are causing conflicts.
+# dataproc_jupyter_plugin was installed as a part of pytorch-cu121.2-2.py310
+# container which we don't need. It depends on ibis-framework and bigframes.
+# The package and its dependencies request lower versions of pyarrow/pydantic
+# than deepspeed/datasets. So, dataproc_jupyter_plugin conflicts with
+# deepspeed/datasets.
+RUN pip uninstall -y dataproc_jupyter_plugin ibis-framework bigframes
+
+# Prefer to install with requirement file as much as possible for reasons
+# described in b/355034754.
+COPY model_oss/peft/train/vmg/dockerfile/requirements.txt /tmp/requirements.txt
+RUN pip install -r /tmp/requirements.txt
+
+# flash-attn cannot be installed with the requirement file approach above
+# because of the `no-build-isolation` requirement.
+#
+# It is OK to install it after other packages FOR NOW because it only has
+# limited dependencies. And there's no concern about it overwriting previously
+# installed packages.
+# https://github.com/Dao-AILab/flash-attention/blob/v2.6.3/setup.py#L523
+RUN pip install flash-attn==2.6.3 --no-build-isolation
+
+# Install `diffusers` library as editable and in root folder (/) on purpose.
+RUN git clone --depth 1 --branch v0.25.1 https://github.com/huggingface/diffusers.git
+# Remove `diffusers` (NOTE that the dependency libraries are kept).
+RUN pip uninstall -y diffusers
+# Using `--no-deps` option to make sure previously installed packages are not
+# overwritten.
+RUN pip install --no-deps -e /diffusers
+
+# Make sure there's no inconsistent pip libraries.
+RUN pip check
+
+# Install merge related packages in a separate env.
+COPY model_oss/peft/train/vmg/dockerfile/merge_env.yaml /tmp/merge_env.yaml
+RUN conda env create -n merge --yes --file /tmp/merge_env.yaml
+RUN conda init
+
+# Switch to diffusers examples folder.
+WORKDIR /diffusers/examples
+
+RUN mkdir -p ./vertex_vision_model_garden_peft/
+COPY model_oss/peft/train/vmg/configs/* ./vertex_vision_model_garden_peft/
+COPY model_oss/peft/train/vmg/*.py ./vertex_vision_model_garden_peft/train/vmg/
+COPY model_oss/peft/train/vmg/templates /diffusers/examples/util/templates
+COPY model_oss/util /diffusers/examples/util
+COPY model_oss/notebook_util/dataset_validation_util.py /diffusers/examples/util
+COPY model_oss/peft/train/vmg/tests/*.py ./vertex_vision_model_garden_peft/tests/
+COPY model_oss/peft/train/test_utils/test_util.py ./vertex_vision_model_garden_peft/tests/
+
+RUN chmod a+rwX -R /diffusers/examples/
+ENV PYTHONPATH /diffusers/examples/
+# Must disable torch XLA, otherwise runtime uses CPU even if GPU exists.
+ENV USE_TORCH_XLA 0
+
+ENTRYPOINT ["python3", "./vertex_vision_model_garden_peft/train/vmg/train_entrypoint.py"]

community-content/vertex_model_garden/model_oss/peft/train/vmg/eval_lib.py

@@ -0,0 +1,286 @@
+"""Library for running evaluations during training."""
+
+from collections.abc import Callable, Mapping, MutableMapping, Sequence
+import dataclasses
+import string
+from typing import Type
+
+from absl import logging
+import evaluate
+import numpy as np
+import torch
+import transformers
+
+from util import dataset_validation_util
+from util import constants
+
+
+_STRING_TRANSLATOR = str.maketrans("", "", string.punctuation)
+
+_GREATER_IS_BETTER_MAP = {
+    "loss": False,
+    "perplexity": False,
+    "bleu": True,
+    "google_bleu": True,
+    "rouge1": True,
+    "rouge2": True,
+    "rougeL": True,
+    "rougeLsum": True,
+}
+
+
+@dataclasses.dataclass(frozen=True)
+class EvalConfig:
+  """Configuration for running evaluations during training.
+
+  Attributes:
+    steps: The number of steps to run evaluation.
+    tasks: The list of tasks to run evaluation on.
+    per_device_batch_size: The per device batch size for evaluation.
+    num_fewshot: The number of few-shot examples to use for evaluation.
+    limit: The maximum number of examples to evaluate.
+    metric_name: The name of the metric to compute.
+    tokenize_dataset: Whether to tokenize the dataset.
+    dataset_path: The path to the dataset.
+    split: The split of the dataset to evaluate.
+    template: The template to use for the dataset.
+    column: The column name of the dataset.
+    metric_for_best_model: The metric to use for loading the best model.
+  """
+
+  steps: int
+  per_device_batch_size: int
+  num_fewshot: int | None
+  limit: float | None
+  metric_name: Sequence[str]
+  tokenize_dataset: bool
+  dataset_path: str = ""
+  split: str = "test"
+  template: str = ""
+  column: str = constants.DEFAULT_TRAIN_COLUMN
+  metric_for_best_model: str | None = None
+
+
+def create_trainer(
+    cls: Type[transformers.Trainer],
+    eval_config: EvalConfig | None,
+    tokenizer: transformers.PreTrainedTokenizerBase | None,
+    args: transformers.TrainingArguments,
+    **kwargs,
+) -> transformers.Trainer:
+  """Creates a trainer. If eval config is provided, injects evaluation loop.
+
+  Args:
+    cls: The trainer class.
+    eval_config: The evaluation config.
+    tokenizer: The tokenizer.
+    args: The training arguments.
+    **kwargs: The keyword arguments.
+
+  Returns:
+    A trainer.
+  """
+  if not eval_config:
+    return cls(args=args, **kwargs)
+
+  args.eval_strategy = "steps"
+  args.eval_steps = eval_config.steps
+  args.per_device_eval_batch_size = eval_config.per_device_batch_size
+  args.metric_for_best_model = eval_config.metric_for_best_model
+  args.greater_is_better = _GREATER_IS_BETTER_MAP.get(
+      eval_config.metric_for_best_model, None
+  )
+  args.save_strategy = (
+      transformers.trainer_utils.SaveStrategy.STEPS
+      if eval_config.metric_for_best_model is None
+      else transformers.trainer_utils.SaveStrategy.BEST
+  )
+
+  kwargs["tokenizer"] = tokenizer
+
+  try:
+    eval_dataset = dataset_validation_util.load_dataset_with_template(
+        dataset_name=eval_config.dataset_path,
+        split=eval_config.split,
+        input_column=eval_config.column,
+        template=eval_config.template,
+        tokenizer=tokenizer,
+    )
+    if eval_config.limit is not None:
+      if eval_config.limit >= 1:
+        limit = int(eval_config.limit)
+      else:
+        limit = int(eval_config.limit * len(eval_dataset))
+      eval_dataset = eval_dataset.select(range(limit))
+    if tokenizer is not None:
+      eval_dataset = dataset_validation_util.get_filtered_dataset(
+          dataset=eval_dataset,
+          input_column=eval_config.column,
+          max_seq_length=kwargs["max_seq_length"],
+          tokenizer=tokenizer,
+      )
+    if eval_config.tokenize_dataset:
+      eval_dataset = eval_dataset.map(
+          lambda samples: tokenizer(samples[eval_config.column])
+      )
+    kwargs["eval_dataset"] = eval_dataset
+  except (OSError, ValueError, IndexError) as e:
+    logging.warning(
+        "Failed to load eval dataset %s. Evaluation will be skipped.\n%s",
+        eval_config.dataset_path,
+        e,
+    )
+    del args.evaluation_strategy
+    del args.eval_steps
+    del args.per_device_eval_batch_size
+  return cls(args=args, **kwargs)
+
+
+def _cleanup_text(text: str) -> str:
+  """Cleans up the prediction and references text.
+
+  Args:
+    text: The text to clean up.
+
+  Returns:
+    Cleaned up text.
+  """
+  text = text.translate(_STRING_TRANSLATOR)
+  text = text.strip()
+  text = " ".join(text.split())
+  return text.lower()
+
+
+def create_compute_metrics(
+    tokenizer: transformers.PreTrainedTokenizerBase,
+    eval_metrics: Mapping[str, evaluate.EvaluationModule],
+) -> Callable[[transformers.EvalPrediction], MutableMapping[str, float]]:
+  """Creates a compute_metrics function using Hugging Face evaluate library.
+
+  Args:
+    tokenizer: The tokenizer for decoding predictions.
+    eval_metrics: The eval metrics to compute.
+
+  Returns:
+    Function that computes comprehensive metrics.
+  """
+
+  def _preprocess_data(
+      predictions: np.ndarray, labels: np.ndarray
+  ) -> tuple[Sequence[str], Sequence[str]]:
+    """Preprocesses predictions and lavels before evaluation.
+
+    Args:
+      predictions: The predictions to preprocess.
+      labels: The labels to preprocess.
+
+    Returns:
+      A tuple (preprocessed predictions, labels).
+    """
+    # Handle padding and special tokens.
+    predictions = np.where(
+        predictions != -100, predictions, tokenizer.pad_token_id
+    )
+    labels = np.where(labels != -100, labels, tokenizer.pad_token_id)
+
+    # Decode to text.
+    pred_texts = tokenizer.batch_decode(predictions, skip_special_tokens=True)
+    label_texts = tokenizer.batch_decode(labels, skip_special_tokens=True)
+
+    # Clean up text.
+    cleaned_pred_texts = [_cleanup_text(text) for text in pred_texts]
+    cleaned_label_texts = [_cleanup_text(text) for text in label_texts]
+
+    return cleaned_pred_texts, cleaned_label_texts
+
+  def _compute_metrics_with_tokenizer(
+      eval_pred: transformers.EvalPrediction,
+  ) -> MutableMapping[str, float]:
+    """Computes metrics using Hugging Face evaluate library.
+
+    Args:
+      eval_pred: The evaluation prediction.
+
+    Returns:
+      A dictionary of metrics.
+    """
+    predictions, perplexities = eval_pred.predictions
+    labels = eval_pred.label_ids
+
+    pred_texts, label_texts = _preprocess_data(predictions, labels)
+
+    metrics = {}
+
+    for eval_metric, computed_eval_metric in eval_metrics.items():
+      match eval_metric:
+        case "perplexity":
+          # We don't use the perplexity from HF Evaluate since it loads the
+          # model again. This causes an increase in the GPU utilization and
+          # hence an OOM. Due to this, we compute the perplexity ourselves
+          # using the eval_loss over the unmasked tokens in
+          # preprocess_logits_for_metrics fn.
+          metrics[eval_metric] = np.mean(perplexities)
+        case "bleu" | "google_bleu":
+          num_valid_labels = len(list(filter(None, label_texts)))
+          if num_valid_labels:
+            eval_score = computed_eval_metric.compute(
+                predictions=pred_texts,
+                references=[[text] for text in label_texts],
+            )
+            metrics[eval_metric] = eval_score[eval_metric]
+          else:
+            metrics[eval_metric] = 0.0
+        case "rouge1" | "rouge2" | "rougeL" | "rougeLsum":
+          rouge_scores = computed_eval_metric.compute(
+              predictions=pred_texts,
+              references=label_texts,
+              use_stemmer=True,
+          )
+          metrics[eval_metric] = rouge_scores[eval_metric]
+
+    pred_lengths = [len(pred.split()) for pred in pred_texts]
+    label_lengths = [len(label.split()) for label in label_texts]
+
+    metrics["gen_len"] = np.mean(pred_lengths)
+    metrics["ref_len"] = np.mean(label_lengths)
+    metrics["length_ratio"] = np.mean(
+        [len(p) / len(r) if r else 0 for p, r in zip(pred_texts, label_texts)]
+    )
+
+    # Round all metrics to 4 decimal places.
+    return {k: round(float(v), 4) for k, v in metrics.items()}
+
+  return _compute_metrics_with_tokenizer
+
+
+def preprocess_logits_for_metrics(
+    logits: torch.Tensor, labels: torch.Tensor
+) -> tuple[torch.Tensor, torch.Tensor]:
+  """Preprocesses the logits before caching them for eval metric calculation.
+
+  Args:
+    logits: Logits predicted by the model.
+    labels: Ground truth labels.
+
+  Returns:
+    A tuple (pred_ids, perplexities).
+  """
+  # Calculate prediction IDs.
+  pred_ids = logits.argmax(dim=-1)
+
+  # This step shifts the logits and labels to align them correctly, where we are
+  # predicting the next token in a sequence. The last logit doesn't have a
+  # corresponding label, and the first label doesn't have a preceding logit to
+  # predict it. This calculation of perplexity is inspired from
+  # https://github.com/huggingface/evaluate/blob/main/metrics/perplexity/perplexity.py.
+  shift_logits = logits[..., :-1, :].contiguous()
+  shift_labels = labels[..., 1:].contiguous()
+  attn_mask = shift_labels != -100
+  loss_fct = torch.nn.CrossEntropyLoss(reduction="none")
+
+  perplexities = torch.exp(
+      (loss_fct(shift_logits.transpose(1, 2), shift_labels) * attn_mask).sum(1)
+      / attn_mask.sum(1)
+  )
+
+  return (pred_ids, perplexities)

community-content/vertex_model_garden/model_oss/peft/train/vmg/gcs_syncer.py

@@ -0,0 +1,86 @@
+"""Sync local directory to GCS directory using rsync."""
+
+from collections.abc import Sequence
+import multiprocessing
+import os
+import subprocess
+import time
+
+from absl import logging
+
+_GCS_COMMAND_RETRIES = 3
+_RSYNC_RETRY_INTERVAL_SECS = 30
+
+
+def _rsync_local_to_gcs(local_dir: str, gcs_dir: str) -> None:
+  """Syncs the local directory to GCS.
+
+  Args:
+    local_dir: The local directory to sync.
+    gcs_dir: The GCS directory to sync to.
+  """
+  if not os.listdir(local_dir):
+    logging.info("Not rsyncing to GCS since %s is empty.", local_dir)
+    return
+
+  logging.info("Rsyncing %s <--> %s...", local_dir, gcs_dir)
+  cmd = [
+      "gcloud",
+      "storage",
+      "rsync",
+      "-r",
+      "--delete-unmatched-destination-objects",
+  ]
+  cmd.extend([local_dir, gcs_dir])
+
+  attempt = 0
+  while attempt < _GCS_COMMAND_RETRIES:
+    try:
+      subprocess.check_output(cmd)
+      break
+    except subprocess.CalledProcessError as e:
+      attempt += 1
+      if attempt < _GCS_COMMAND_RETRIES:
+        logging.exception(
+            "Attempt %d: Command failed: %s. Retrying in %d seconds...",
+            attempt,
+            e,
+            _RSYNC_RETRY_INTERVAL_SECS,
+        )
+        time.sleep(_RSYNC_RETRY_INTERVAL_SECS)
+      else:
+        logging.exception(
+            "Command failed after %d attempts: %s.", e, _GCS_COMMAND_RETRIES
+        )
+
+  logging.info("%s rsynced to %s.", local_dir, gcs_dir)
+
+
+def start_gcs_rsync(
+    dirs_to_sync: Sequence[tuple[str, str]],
+    mp_queue: multiprocessing.Queue,
+    gcs_rsync_interval_secs: int,
+) -> None:
+  """Starts a rsync process to sync local directories to GCS directories.
+
+  Args:
+    dirs_to_sync: A list of tuples, where each tuple contains local directory
+      which will be synced to GCS. For example: [('/tmp/local_dir_1',
+      'gs://bucket/gcs_dir_1'), ('/tmp/local_dir_2', 'gs://bucket/gcs_dir_2')]
+    mp_queue: The multiprocessing queue to check if the training is finished.
+    gcs_rsync_interval_secs: Integer, interval in seconds to run gcs rsync.
+  """
+  while True:
+    for local_dir, gcs_dir in dirs_to_sync:
+      _rsync_local_to_gcs(local_dir, gcs_dir)
+    if not mp_queue.empty():
+      break
+    time.sleep(gcs_rsync_interval_secs)
+
+  # Sync up the directory one more time to avoid a race condition.
+  # There can be a case when we are doing an rsync and receive a signal that
+  # the training has been done. The final checkpoint will be skipped in such
+  # case. So we do a final sync to make sure that the all directories
+  # are synced.
+  for local_dir, gcs_dir in dirs_to_sync:
+    _rsync_local_to_gcs(local_dir, gcs_dir)

community-content/vertex_model_garden/model_oss/peft/train/vmg/instruct_lora.py

@@ -0,0 +1,907 @@
+"""Instruct/Chat with LoRA models."""
+
+from collections.abc import Callable, Mapping, Sequence
+import dataclasses
+import datetime
+import json
+import os
+from typing import Any
+import warnings
+
+from absl import app
+from absl import flags
+from absl import logging
+from accelerate import DistributedType
+from accelerate import PartialState
+import bitsandbytes as bnb
+import evaluate
+from peft import get_peft_model
+from peft import LoraConfig
+import torch
+import transformers
+import trl
+import wandb
+
+from util import dataset_validation_util
+from vertex_vision_model_garden_peft.train.vmg import callbacks
+from vertex_vision_model_garden_peft.train.vmg import eval_lib
+from vertex_vision_model_garden_peft.train.vmg import utils
+from util import constants
+from util import fileutils
+
+
+_PRETRAINED_MODEL_NAME_OR_PATH = flags.DEFINE_string(
+    'pretrained_model_name_or_path',
+    None,
+    'The pretrained model name or path. Supported models can be causal language'
+    ' modeling models from https://github.com/huggingface/peft/tree/main. Note,'
+    ' there might be different paddings for different models. This tool assumes'
+    ' the pretrained_model_name_or_path contains model name, and then choose'
+    ' proper padding methods. e.g. it must contain `llama` for `Llama2'
+    ' models`.',
+    required=True,
+)
+
+_HUGGINGFACE_ACCESS_TOKEN = flags.DEFINE_string(
+    'huggingface_access_token',
+    None,
+    'The access token for loading huggingface gated models.',
+)
+
+_TRAIN_DATASET = flags.DEFINE_string(
+    'train_dataset',
+    None,
+    'The training dataset name in huggingface or path.',
+)
+
+_OUTPUT_DIR = flags.DEFINE_string(
+    'output_dir',
+    None,
+    'The output directory.',
+)
+
+_LOGGING_OUTPUT_DIR = flags.DEFINE_string(
+    'logging_output_dir',
+    '',
+    'The logging output directory, which defaults to same as output_dir.',
+)
+
+_PRECISION_MODE = flags.DEFINE_enum(
+    'precision_mode',
+    constants.PRECISION_MODE_16,
+    [
+        constants.PRECISION_MODE_4,
+        constants.PRECISION_MODE_8,
+        constants.PRECISION_MODE_16,
+        constants.PRECISION_MODE_16B,
+        constants.PRECISION_MODE_32,
+    ],
+    'Precision to load model weights for finetuning.',
+)
+
+_LORA_RANK = flags.DEFINE_integer(
+    'lora_rank',
+    16,
+    'The rank of the update matrices, expressed in int. Lower rank results in'
+    ' smaller update matrices with fewer trainable parameters, referring to'
+    ' https://huggingface.co/docs/peft/conceptual_guides/lora.',
+)
+
+_LORA_ALPHA = flags.DEFINE_integer(
+    'lora_alpha',
+    32,
+    'LoRA scaling factor, referring to'
+    ' https://huggingface.co/docs/peft/conceptual_guides/lora.',
+)
+
+_LORA_DROPOUT = flags.DEFINE_float(
+    'lora_dropout',
+    0.05,
+    'dropout probability of the LoRA layers, referring to'
+    ' https://huggingface.co/docs/peft/task_guides/token-classification-lora.',
+)
+
+_WARMUP_STEPS = flags.DEFINE_integer(
+    'warmup_steps',
+    10,
+    'Number of steps for the warmup in the learning rate scheduler.',
+)
+
+_WARMUP_RATIO = flags.DEFINE_float(
+    'warmup_ratio',
+    0.03,
+    'The warmup ratio in the learning rate scheduler.',
+)
+
+_WEIGHT_DECAY = flags.DEFINE_float(
+    'weight_decay',
+    0.001,
+    'The weight decay in the learning rate scheduler.',
+)
+
+_NUM_TRAIN_EPOCHS = flags.DEFINE_float(
+    'num_train_epochs',
+    None,
+    'The number of training epochs. Only used for'
+    ' "sequence-classification-lora" with an integer value and for'
+    ' "instruct-lora" with a float value allowed.',
+)
+
+_MAX_STEPS = flags.DEFINE_integer(
+    'max_steps',
+    None,
+    'Total number of training steps. Overrides num_train_epochs if set. Only'
+    ' used for "instruct-lora."',
+)
+
+_MAX_SEQ_LENGTH = flags.DEFINE_integer(
+    'max_seq_length',
+    512,
+    'The maximum sequence length.',
+)
+
+_LEARNING_RATE = flags.DEFINE_float(
+    'learning_rate',
+    2e-4,
+    'The learning rate after the potential warmup period.',
+)
+
+_TRAIN_COLUMN = flags.DEFINE_string(
+    'train_column',
+    constants.DEFAULT_TRAIN_COLUMN,
+    'The instruct column in dataset.',
+)
+
+_REPORT_TO = flags.DEFINE_string(
+    'report_to',
+    constants.REPORT_TO_NONE,
+    'Where logging is reported to, which can be tensorboard or none.',
+)
+
+_PER_DEVICE_TRAIN_BATCH_SIZE = flags.DEFINE_integer(
+    'per_device_train_batch_size',
+    4,
+    'The per device train batch size.',
+)
+
+_GRADIENT_ACCUMULATION_STEPS = flags.DEFINE_integer(
+    'gradient_accumulation_steps',
+    4,
+    'The gradient accumulation steps.',
+)
+
+_GRADIENT_CHECKPOINTING = flags.DEFINE_boolean(
+    'gradient_checkpointing',
+    False,
+    'Whether to enable gradient checkpointing.',
+)
+
+_ENABLE_PEFT = flags.DEFINE_boolean(
+    'enable_peft',
+    True,
+    'Whether to enable peft.',
+)
+_TRAIN_TEMPLATE = flags.DEFINE_string(
+    'train_template',
+    None,
+    'Template for formatting language model training data. Must be a filename'
+    ' under `templates` folder, without `.json` extension, e.g. `alpaca`, or a'
+    ' Cloud Storage URI to a JSON file.',
+)
+
+_OPTIMIZER = flags.DEFINE_string(
+    'optimizer',
+    'adamw_torch',
+    'The optimizer.',
+)
+
+_LR_SCHEDULER_TYPE = flags.DEFINE_string(
+    'lr_scheduler_type',
+    'cosine',
+    'The learning rate scheduler type.',
+)
+
+_SAVE_STEPS = flags.DEFINE_integer(
+    'save_steps',
+    10,
+    'The save steps.',
+)
+
+_LOGGING_STEPS = flags.DEFINE_integer(
+    'logging_steps',
+    10,
+    'The logging steps.',
+)
+
+_EVAL_STEPS = flags.DEFINE_integer(
+    'eval_steps',
+    10,
+    'The number of training steps between evaluations.',
+)
+
+_TRAIN_SPLIT = flags.DEFINE_string(
+    'train_split',
+    'train',
+    'The train split name.',
+)
+
+_PER_DEVICE_EVAL_BATCH_SIZE = flags.DEFINE_integer(
+    'per_device_eval_batch_size',
+    1,
+    'The per device batch size for model evaluation.',
+)
+
+_EVAL_NUM_FEWSHOT = flags.DEFINE_integer(
+    'eval_num_fewshot',
+    None,
+    'Run N-shot language model evaluation. Not implemented in `builtin_eval`.',
+)
+
+_EVAL_LIMIT = flags.DEFINE_float(
+    'eval_limit',
+    None,
+    'Limit the number of examples per task. If <1, limit is a percentage of the'
+    ' total number of examples.',
+)
+
+_EVAL_METRIC_NAME = flags.DEFINE_list(
+    'eval_metric_name',
+    ['loss'],
+    'A comma-separated list of metric names to aggregate during model'
+    ' evaluation. The supported metrics are: '
+    + ', '.join(constants.SUPPORTED_EVAL_METRICS),
+)
+
+_EVAL_DATASET = flags.DEFINE_string(
+    'eval_dataset',
+    None,
+    'Overrides the default evaluation dataset path. In `builtin_eval` mode,'
+    ' this can be any Hugging Face dataset name or path.',
+)
+
+# We set the default eval split as `test`, based on observation from
+# https://huggingface.co/datasets/timdettmers/openassistant-guanaco/viewer/default/test.
+_EVAL_SPLIT = flags.DEFINE_string(
+    'eval_split',
+    'test',
+    'Eval split name in the eval dataset for `builtin_eval`.',
+)
+
+_EVAL_TEMPLATE = flags.DEFINE_string(
+    'eval_template',
+    None,
+    'Template for formatting language model evaluation data for `builtin_eval`.'
+    ' Must be a filename under `templates` folder, without `.json` extension,'
+    ' e.g. `alpaca`, or a Cloud Storage URI to a JSON file.',
+)
+
+_EVAL_COLUMN = flags.DEFINE_string(
+    'eval_column',
+    None,
+    'Eval column name in the eval dataset for `builtin_eval`.',
+)
+
+_METRIC_FOR_BEST_MODEL = flags.DEFINE_string(
+    'metric_for_best_model',
+    None,
+    'If set, the best model is saved at the end of training based on the'
+    ' metric',
+)
+
+_TRAIN_PRECISION = flags.DEFINE_enum(
+    'train_precision',
+    constants.PRECISION_MODE_16B,
+    [
+        constants.PRECISION_MODE_16,
+        constants.PRECISION_MODE_16B,
+        constants.PRECISION_MODE_32,
+    ],
+    'Precision to train the model.',
+)
+
+_EXAMPLE_PACKING = flags.DEFINE_boolean(
+    'example_packing',
+    False,
+    'Enables example packing during training, which uses '
+    '`ConstantLengthDataset` under the hood.',
+)
+
+_INPUT_MASKING = flags.DEFINE_boolean(
+    'input_masking',
+    False,
+    'If set, it uses DataCollatorForCompletionOnlyLM to train the model on the'
+    ' generated prompts only, i.e., masking out the input',
+)
+
+_ATTN_IMPLEMENTATION = flags.DEFINE_string(
+    'attn_implementation',
+    None,
+    'Attention implementation, can be `eager`, `sdpa` or `flash_attention_2`',
+)
+
+_MAX_GRAD_NORM = flags.DEFINE_float(
+    'max_grad_norm',
+    0.3,
+    'Maximum gradient norm used for gradient clipping',
+)
+
+_WARNINGS_FILTER = flags.DEFINE_string(
+    'warnings_filter',
+    'ignore',
+    'Warning filter as defined in '
+    'https://docs.python.org/3/library/warnings.html#the-warnings-filter',
+)
+
+_LOGGER_LEVEL = flags.DEFINE_string(
+    'logger_level',
+    'passive',
+    'logging level passed to TrainingArguments. Note that this is for python'
+    ' logging module, NOT the one from absl',
+)
+
+_BENCHMARK_OUT_FILE = flags.DEFINE_string(
+    'benchmark_out_file', None, 'file path for writing benchmark result'
+)
+
+
+_NCCL_TIMEOUT = flags.DEFINE_integer(
+    'nccl_timeout', 6000, 'nccl timeout in seconds'
+)
+
+_TUNING_DATA_STATS_FILE = flags.DEFINE_string(
+    'tuning_data_stats_file', None, 'file path for writing tuning data stats.'
+)
+
+_TARGET_MODULES = flags.DEFINE_list(
+    'target_modules', None, 'The names of the modules to apply LoRA adapter to.'
+)
+
+_MAX_GPU_MEMORY_FRACTION = flags.DEFINE_float(
+    'max_gpu_memory_fraction',
+    '0.9',
+    'Maximum GPU memory a caching allocator is allowed to use per GPU.',
+)
+
+
+@flags.multi_flags_validator(
+    [
+        _INPUT_MASKING.name,
+        _EXAMPLE_PACKING.name,
+    ],
+    message='`example_packing=True` does not work with `input_masking=True`',
+)
+def check_example_packing(flags_dict: Mapping[str, Any]) -> bool:
+  """Check to make sure example packing is enabled properly.
+
+  Args:
+    flags_dict: Dictionary containing flags to check.
+
+  Returns:
+    If `example_packing` is set properly.
+  """
+  if flags_dict[_INPUT_MASKING.name] and flags_dict[_EXAMPLE_PACKING.name]:
+    return False
+  return True
+
+
+@flags.multi_flags_validator(
+    [
+        _INPUT_MASKING.name,
+        _TRAIN_TEMPLATE.name,
+    ],
+    message='`train_template` should be provided if using `input_masking=True`',
+)
+def check_input_masking(flags_dict: Mapping[str, Any]) -> bool:
+  """Check to make sure input_masking is enabled properly.
+
+  Args:
+    flags_dict: Dictionary containing flags to check.
+
+  Returns:
+    If `input_masking` is set properly
+  """
+  if (
+      flags_dict[_INPUT_MASKING.name]
+      and flags_dict[_TRAIN_TEMPLATE.name] is None
+  ):
+    return False
+  return True
+
+
+@flags.multi_flags_validator(
+    [
+        _EVAL_DATASET.name,
+        _EVAL_METRIC_NAME.name,
+    ],
+    message=(
+        '`eval_metric_name` should be a valid metric name and present when'
+        ' eval_dataset is provided.'
+    ),
+)
+def _validate_eval_metrics(flags_dict: Mapping[str, Any]) -> bool:
+  """Validates the eval metric name.
+
+  Args:
+    flags_dict: Dictionary containing flags to check.
+
+  Returns:
+    If the eval metrics are valid.
+  """
+  if flags_dict[_EVAL_DATASET.name] is None:
+    return True
+  eval_metrics = flags_dict[_EVAL_METRIC_NAME.name]
+  for eval_metric in eval_metrics:
+    if eval_metric not in constants.SUPPORTED_EVAL_METRICS:
+      raise flags.ValidationError(f'Invalid eval metric: {eval_metric}')
+  if 'perplexity' in eval_metrics and 'loss' not in eval_metrics:
+    _EVAL_METRIC_NAME.value.append('loss')
+    logging.warning(
+        'Adding `loss` to eval_metric_name because `perplexity` is present.'
+    )
+  return True
+
+
+@flags.multi_flags_validator(
+    [
+        _METRIC_FOR_BEST_MODEL.name,
+        _EVAL_METRIC_NAME.name,
+    ],
+    message='`metric_for_best_model` should be in `eval_metric_name`.',
+)
+def _validate_metric_for_best_model(flags_dict: Mapping[str, Any]) -> bool:
+  """Validates the metric for best model.
+
+  Args:
+    flags_dict: Dictionary containing flags to check.
+
+  Returns:
+    If the metric for best model is valid.
+  """
+  if flags_dict[_METRIC_FOR_BEST_MODEL.name] is None:
+    return True
+
+  metric_for_best_model = flags_dict[_METRIC_FOR_BEST_MODEL.name]
+  eval_metric_name = flags_dict[_EVAL_METRIC_NAME.name]
+
+  if metric_for_best_model not in eval_metric_name:
+    raise flags.ValidationError(
+        'Invalid metric for picking the best model:'
+        f' {metric_for_best_model}. The metric should be one'
+        f' of the {eval_metric_name}.'
+    )
+  return True
+
+
+# References:
+# Huggingface SFT trainer example:
+# https://github.com/huggingface/trl/blob/main/examples/scripts/sft_trainer.py.
+# Huggingface sagemaker example:
+# https://github.com/huggingface/notebooks/blob/main/sagemaker/28_train_llms_with_qlora/scripts/run_clm.py.
+
+
+def _calculate_hf_eval_metrics(
+    tokenizer: transformers.PreTrainedTokenizerBase,
+    eval_config: eval_lib.EvalConfig | None,
+) -> tuple[
+    Callable[[transformers.EvalPrediction], Mapping[str, float]], torch.Tensor
+]:
+  """Calculates the HF evaluation metrics.
+
+  Args:
+    tokenizer: The tokenizer to use for evaluation.
+    eval_config: The evaluation config to use.
+
+  Returns:
+    The compute metrics and preprocess logits for metrics.
+  """
+  if eval_config is None:
+    return None, None
+  hf_eval_metrics = {}
+  for metric in eval_config.metric_name:
+    if metric in constants.SUPPORTED_HF_EVAL_METRICS:
+      if metric in constants.ROUGE_VARIANTS:
+        hf_eval_metrics[metric] = evaluate.load('rouge')
+      else:
+        hf_eval_metrics[metric] = evaluate.load(metric)
+
+  if not hf_eval_metrics:
+    return None, None
+  return (
+      eval_lib.create_compute_metrics(tokenizer, hf_eval_metrics),
+      eval_lib.preprocess_logits_for_metrics,
+  )
+
+
+# Copied from https://github.com/artidoro/qlora/blob/main/qlora.py.
+def find_all_linear_names(
+    model: transformers.AutoModelForCausalLM, precision_mode: str
+) -> Sequence[str]:
+  """Finds all linear module names."""
+  if precision_mode == constants.PRECISION_MODE_4:
+    cls = bnb.nn.Linear4bit
+  elif precision_mode == constants.PRECISION_MODE_8:
+    cls = bnb.nn.Linear8bitLt
+  else:
+    cls = torch.nn.Linear
+  lora_module_names = set()
+  for name, module in model.named_modules():
+    if isinstance(module, cls):
+      names = name.split('.')
+      lora_module_names.add(names[0] if len(names) == 1 else names[-1])
+  if 'lm_head' in lora_module_names:  # needed for 16-bit
+    lora_module_names.remove('lm_head')
+  return list(lora_module_names)
+
+
+def finetune_instruct(
+    pretrained_model_name_or_path: str,
+    train_dataset: str,
+    output_dir: str,
+    logging_output_dir: str,
+    lora_rank: int = 64,
+    lora_alpha: int = 16,
+    lora_dropout: float = 0.1,
+    warmup_ratio: int = 0.03,
+    num_train_epochs: float | None = None,
+    max_steps: int | None = None,
+    warmup_steps: int = 10,
+    max_seq_length: int = 512,
+    learning_rate: float = 2e-4,
+    precision_mode: str = None,
+    train_column: str = constants.DEFAULT_TRAIN_COLUMN,
+    per_device_train_batch_size: int = 4,
+    gradient_accumulation_steps: int = 4,
+    optim: str = 'paged_adamw_32bit',
+    weight_decay: float = 0.001,
+    gradient_checkpointing: bool = False,
+    enable_peft: bool = True,
+    train_template: str = None,
+    lr_scheduler_type: str = 'constant',
+    save_steps: int = 10,
+    logging_steps: int = 10,
+    train_split: str = 'train',
+    eval_config: eval_lib.EvalConfig | None = None,
+    report_to: str = constants.REPORT_TO_NONE,
+    access_token: str | None = None,
+    train_precision: str = constants.PRECISION_MODE_16B,
+    example_packing: bool = False,
+    attn_implementation: str | None = None,
+    max_grad_norm: float = 0.3,
+    input_masking: bool = False,
+    logger_level: str = 'passive',
+    benchmark_out_file: str | None = None,
+    tuning_data_stats_file: str | None = None,
+    target_modules: str | None = None,
+) -> None:
+  """Finetunes instruct."""
+  logging.info(
+      'on entering instruct_lora, %s,\n%s',
+      utils.gpu_stats_str(),
+      utils.cpu_stats_str(),
+  )
+  gradient_checkpointing_kwargs = {}
+  # DDP provides limited support with the reentrant variant of gradient
+  # checkpoint [1]. Below is an indirect way of checking whether DDP will be
+  # used. It is "indirect" because there are complex logic under the hood of
+  # `SFTTrainer` and since those are not public API, they might change as we
+  # update the library.
+  if PartialState().distributed_type == DistributedType.MULTI_GPU:
+    gradient_checkpointing_kwargs['use_reentrant'] = False
+
+  tokenizer = dataset_validation_util.load_tokenizer(
+      pretrained_model_name_or_path,
+      'right',
+      access_token=access_token,
+  )
+
+  train_dataset_with_template = (
+      dataset_validation_util.load_dataset_with_template(
+          train_dataset,
+          split=train_split,
+          input_column=train_column,
+          template=train_template,
+          tokenizer=tokenizer,
+      )
+  )
+  train_dataset_with_template = dataset_validation_util.get_filtered_dataset(
+      dataset=train_dataset_with_template,
+      input_column=train_column,
+      max_seq_length=max_seq_length,
+      tokenizer=tokenizer,
+  )
+
+  if tuning_data_stats_file:
+    with PartialState().main_process_first():
+      effective_batch_size = (
+          per_device_train_batch_size
+          * gradient_accumulation_steps
+          * PartialState().num_processes
+      )
+      logging.info(
+          'getting tuning data stats with effective batch size %s',
+          effective_batch_size,
+      )
+      train_dataset_stats = utils.get_dataset_stats(
+          train_dataset_with_template,
+          tokenizer,
+          train_column,
+          effective_batch_size,
+      )
+      logging.info('stats: %s', train_dataset_stats)
+      tuning_data_stats_file = dataset_validation_util.force_gcs_fuse_path(
+          tuning_data_stats_file
+      )
+      with open(tuning_data_stats_file, 'w') as out_f:
+        json.dump(dataclasses.asdict(train_dataset_stats), out_f)
+
+  model = utils.load_model(
+      pretrained_model_name_or_path=pretrained_model_name_or_path,
+      tokenizer=tokenizer,
+      precision_mode=precision_mode,
+      gradient_checkpointing=gradient_checkpointing,
+      access_token=access_token,
+      attn_implementation=attn_implementation,
+      train_precision=train_precision,
+  )
+
+  if enable_peft:
+    if target_modules is None:
+      target_modules = find_all_linear_names(
+          model, precision_mode=precision_mode
+      )
+    logging.info('applying lora adapters to modules: %s', target_modules)
+    peft_config = LoraConfig(
+        lora_alpha=lora_alpha,
+        lora_dropout=lora_dropout,
+        r=lora_rank,
+        bias='none',
+        task_type='CAUSAL_LM',
+        target_modules=target_modules,
+    )
+    # If we pass in `peft_config` to SFTTrainer, it does a lot of magic under
+    # the hood, e.g., calling `prepare_model_for_kbit_training` before calling
+    # `get_peft_model`, which may revert other changes we did before. That's why
+    # we are calling `get_peft_model` explicitly here.
+    model = get_peft_model(model, peft_config)
+
+    # This is to work-around mix-precision training. This issue is not fixed as
+    # of transformers==4.41.2.
+    # See b/332760883#comment30 for more details.
+    if precision_mode in (
+        constants.PRECISION_MODE_16,
+        constants.PRECISION_MODE_16B,
+    ):
+      for param in filter(lambda p: p.requires_grad, model.parameters()):
+        param.data = param.data.to(torch.float32)
+
+  if not logging_output_dir:
+    logging_output_dir = output_dir
+
+  # To use singleton PartialState() without re-initializing it. See
+  # b/357970482#comment3
+  accelerator_config = {'use_configured_state': True}
+
+  training_arguments = transformers.TrainingArguments(
+      report_to=report_to,
+      output_dir=output_dir,
+      per_device_train_batch_size=per_device_train_batch_size,
+      gradient_accumulation_steps=gradient_accumulation_steps,
+      optim=optim,
+      save_steps=save_steps,
+      save_total_limit=3,
+      logging_dir=os.path.join(logging_output_dir, 'logs'),
+      logging_steps=logging_steps,
+      learning_rate=learning_rate,
+      fp16=(train_precision == constants.PRECISION_MODE_16),
+      bf16=(train_precision == constants.PRECISION_MODE_16B),
+      max_grad_norm=max_grad_norm,
+      num_train_epochs=num_train_epochs if num_train_epochs else -1,
+      max_steps=max_steps if max_steps else -1,
+      warmup_ratio=warmup_ratio,
+      warmup_steps=warmup_steps,
+      group_by_length=False,
+      lr_scheduler_type=lr_scheduler_type,
+      gradient_checkpointing=gradient_checkpointing,
+      gradient_checkpointing_kwargs=gradient_checkpointing_kwargs,
+      weight_decay=weight_decay,
+      log_level=logger_level,
+      accelerator_config=accelerator_config,
+      include_num_input_tokens_seen=True,
+  )
+  trainer_kwargs = {}
+  if input_masking and train_template:
+    template_json = dataset_validation_util.get_template(
+        template_path=train_template
+    )
+    instruction_sep = dataset_validation_util.get_instruction_separator(
+        template_json
+    )
+    response_sep = dataset_validation_util.get_response_separator(template_json)
+    if not response_sep:
+      raise ValueError(
+          '`response_separator` must be provided to use'
+          ' `DataCollatorForCompletionOnlyLM`'
+      )
+
+    trainer_kwargs['data_collator'] = trl.DataCollatorForCompletionOnlyLM(
+        instruction_template=instruction_sep,
+        response_template=response_sep,
+        tokenizer=tokenizer,
+    )
+    logging.info('using DataCollatorForCompletionOnlyLM')
+
+  trainer_stats_callback = callbacks.TrainerStatsCallback(
+      max_seq_length, benchmark_out_file
+  )
+  compute_metrics, preprocess_logits = _calculate_hf_eval_metrics(
+      tokenizer, eval_config
+  )
+
+  trainer = eval_lib.create_trainer(
+      cls=trl.SFTTrainer,
+      eval_config=eval_config,
+      model=model,
+      train_dataset=train_dataset_with_template,
+      dataset_text_field=train_column,
+      max_seq_length=max_seq_length,
+      tokenizer=tokenizer,
+      args=training_arguments,
+      packing=example_packing,
+      callbacks=[trainer_stats_callback],
+      compute_metrics=compute_metrics,
+      preprocess_logits_for_metrics=preprocess_logits,
+      **trainer_kwargs,
+  )
+
+  # `eval_lib.create_trainer` might modify the training args. Printing here
+  # should capture what will be used by the trainer.
+  if PartialState().is_main_process:
+    logging.info('training args: %s', trainer.args)
+
+  if enable_peft:
+    trainer.model.print_trainable_parameters()
+
+  if trainer.is_fsdp_enabled:
+    logging.info('Trainer running with FSDP.')
+  elif trainer.is_deepspeed_enabled:
+    logging.info('Trainer running with DeepSpeed.')
+  else:
+    logging.info('Trainer running without parallelism.')
+
+  trainer.train()
+
+  # Always save the final checkpoint.
+  final_checkpoint = utils.get_final_checkpoint_path(output_dir)
+  logging.info('The final checkpoint is: %s.', final_checkpoint)
+
+  if trainer.is_fsdp_enabled:
+    trainer.accelerator.state.fsdp_plugin.set_state_dict_type('FULL_STATE_DICT')
+    # This method saves the sharded weights like `accelerator.save_state`, see
+    # https://huggingface.co/docs/accelerate/en/usage_guides/fsdp#saving-and-loading
+    trainer.save_model(output_dir)
+    model = trainer.model
+    state_dict = trainer.accelerator.get_state_dict(model)
+    # To aggregate the weights from all the devices, we need to use
+    # `state_dict=state_dict`.
+    model.save_pretrained(
+        final_checkpoint,
+        state_dict=state_dict,
+        is_main_process=PartialState().is_main_process,
+        save_embedding_layers=False,  # Only pad token is added. See go/lora-adapter-pad-token #pylint: disable=line-too-long
+    )
+  else:
+    trainer.model.save_pretrained(
+        final_checkpoint,
+        is_main_process=PartialState().is_main_process,
+        save_embedding_layers=False,  # Only pad token is added. See go/lora-adapter-pad-token #pylint: disable=line-too-long
+    )
+
+  if eval_config is not None and trainer.eval_dataset is not None:
+    metrics = trainer.evaluate(metric_key_prefix='eval')
+    # Both `log_metrics` and `save_metrics` are multiple process safe.
+    # https://github.com/huggingface/transformers/blob/v4.38.2/src/transformers/trainer_pt_utils.py#L911 #pylint: disable=line-too-long
+    # https://github.com/huggingface/transformers/blob/v4.38.2/src/transformers/trainer_pt_utils.py#L1001 #pylint: disable=line-too-long
+    trainer.log_metrics('eval', metrics)
+    trainer.save_metrics('eval', metrics)
+
+  if not enable_peft:
+    tokenizer.save_pretrained(
+        final_checkpoint, is_main_process=PartialState().is_main_process
+    )
+
+
+def main(unused_argv: Sequence[str]) -> None:
+  # This needs to be called before any other PartialState() calls.
+  utils.init_partial_state(
+      timeout=datetime.timedelta(seconds=_NCCL_TIMEOUT.value)
+  )
+
+  torch.cuda.set_per_process_memory_fraction(
+      _MAX_GPU_MEMORY_FRACTION.value, device=PartialState().local_process_index
+  )
+
+  utils.print_library_versions()
+  warnings.simplefilter(_WARNINGS_FILTER.value)
+
+  pretrained_model_name_or_path = fileutils.force_gcs_path(
+      _PRETRAINED_MODEL_NAME_OR_PATH.value
+  )
+  if dataset_validation_util.is_gcs_path(pretrained_model_name_or_path):
+    pretrained_model_name_or_path = (
+        dataset_validation_util.download_gcs_uri_to_local(
+            pretrained_model_name_or_path
+        )
+    )
+
+  # GCS Fuse does not sync flushed files if not closed. See b/361771727.
+  logging_output_dir = fileutils.force_gcs_path(_LOGGING_OUTPUT_DIR.value)
+
+  # Creates evaluation config.
+  if _EVAL_DATASET.value:
+    eval_config = eval_lib.EvalConfig(
+        per_device_batch_size=_PER_DEVICE_EVAL_BATCH_SIZE.value,
+        num_fewshot=_EVAL_NUM_FEWSHOT.value,
+        limit=_EVAL_LIMIT.value,
+        metric_name=_EVAL_METRIC_NAME.value,
+        steps=_EVAL_STEPS.value,
+        dataset_path=dataset_validation_util.force_gcs_fuse_path(
+            _EVAL_DATASET.value
+        ),
+        split=_EVAL_SPLIT.value,
+        template=_EVAL_TEMPLATE.value,
+        column=_EVAL_COLUMN.value,
+        tokenize_dataset=False,
+        metric_for_best_model=_METRIC_FOR_BEST_MODEL.value,
+    )
+  else:
+    eval_config = None
+
+  if _REPORT_TO.value == constants.REPORT_TO_WANDB:
+    wandb.login()
+
+  finetune_instruct(
+      pretrained_model_name_or_path=pretrained_model_name_or_path,
+      train_dataset=_TRAIN_DATASET.value,
+      output_dir=_OUTPUT_DIR.value,
+      logging_output_dir=logging_output_dir,
+      precision_mode=_PRECISION_MODE.value,
+      lora_rank=_LORA_RANK.value,
+      lora_alpha=_LORA_ALPHA.value,
+      lora_dropout=_LORA_DROPOUT.value,
+      warmup_ratio=_WARMUP_RATIO.value,
+      num_train_epochs=_NUM_TRAIN_EPOCHS.value,
+      warmup_steps=_WARMUP_STEPS.value,
+      max_steps=_MAX_STEPS.value,
+      max_seq_length=_MAX_SEQ_LENGTH.value,
+      learning_rate=_LEARNING_RATE.value,
+      train_column=_TRAIN_COLUMN.value,
+      per_device_train_batch_size=_PER_DEVICE_TRAIN_BATCH_SIZE.value,
+      optim=_OPTIMIZER.value,
+      weight_decay=_WEIGHT_DECAY.value,
+      gradient_accumulation_steps=_GRADIENT_ACCUMULATION_STEPS.value,
+      gradient_checkpointing=_GRADIENT_CHECKPOINTING.value,
+      enable_peft=_ENABLE_PEFT.value,
+      train_template=_TRAIN_TEMPLATE.value,
+      lr_scheduler_type=_LR_SCHEDULER_TYPE.value,
+      save_steps=_SAVE_STEPS.value,
+      logging_steps=_LOGGING_STEPS.value,
+      train_split=_TRAIN_SPLIT.value,
+      eval_config=eval_config,
+      report_to=_REPORT_TO.value,
+      access_token=_HUGGINGFACE_ACCESS_TOKEN.value,
+      train_precision=_TRAIN_PRECISION.value,
+      example_packing=_EXAMPLE_PACKING.value,
+      attn_implementation=_ATTN_IMPLEMENTATION.value,
+      max_grad_norm=_MAX_GRAD_NORM.value,
+      input_masking=_INPUT_MASKING.value,
+      logger_level=_LOGGER_LEVEL.value,
+      benchmark_out_file=_BENCHMARK_OUT_FILE.value,
+      tuning_data_stats_file=_TUNING_DATA_STATS_FILE.value,
+      target_modules=_TARGET_MODULES.value,
+  )
+  # Frees the model from GPU.
+  utils.force_gc()
+
+
+if __name__ == '__main__':
+  app.run(main)

community-content/vertex_model_garden/model_oss/peft/train/vmg/merge_causal_language_model_lora.py

@@ -0,0 +1,99 @@
+"""Script to merge PEFT adapter with base model."""
+
+from collections.abc import Mapping, Sequence
+from typing import Any
+
+from absl import app
+from absl import flags
+
+from util import dataset_validation_util
+from vertex_vision_model_garden_peft.train.vmg import utils
+from util import constants
+from util import fileutils
+
+
+_PRETRAINED_MODEL_NAME_OR_PATH = flags.DEFINE_string(
+    'pretrained_model_name_or_path',
+    None,
+    'The pretrained model id. Supported models can be causal language modeling'
+    ' models from https://github.com/huggingface/peft/tree/main. Note, there'
+    ' might be different paddings for different models. This tool assumes the'
+    ' pretrained_model_name_or_path contains model name, and then choose proper'
+    ' padding methods. e.g. it must contain `llama` for `Llama2 models`.',
+    required=True,
+)
+
+_MERGE_BASE_AND_LORA_OUTPUT_DIR = flags.DEFINE_string(
+    'merge_base_and_lora_output_dir',
+    None,
+    'The directory to store the merged model with the base and lora adapter.',
+)
+
+_MERGE_MODEL_PRECISION_MODE = flags.DEFINE_enum(
+    'merge_model_precision_mode',
+    constants.PRECISION_MODE_16,
+    [
+        constants.PRECISION_MODE_4,
+        constants.PRECISION_MODE_8,
+        constants.PRECISION_MODE_FP8,
+        constants.PRECISION_MODE_16,
+        constants.PRECISION_MODE_16B,
+        constants.PRECISION_MODE_32,
+    ],
+    'Merging model precision mode.',
+)
+
+_FINETUNED_LORA_MODEL_DIR = flags.DEFINE_string(
+    'finetuned_lora_model_dir',
+    None,
+    'The directory storing finetuned LoRA model weights.',
+)
+
+_HUGGINGFACE_ACCESS_TOKEN = flags.DEFINE_string(
+    'huggingface_access_token',
+    None,
+    'The access token for loading huggingface gated models.',
+)
+
+
+@flags.multi_flags_validator(
+    [
+        _PRETRAINED_MODEL_NAME_OR_PATH.name,
+        _FINETUNED_LORA_MODEL_DIR.name,
+        _MERGE_BASE_AND_LORA_OUTPUT_DIR.name,
+    ],
+)
+def check_merge_lora_model_flags(flags_dict: Mapping[str, Any]) -> bool:
+  """Check if required flags are set on merge model LoRA task.
+
+  Args:
+    flags_dict: Dictionary containing task and flags to check.
+
+  Returns:
+    If required flags are not None.
+  """
+  return all(map(lambda x: x is not None, flags_dict.values()))
+
+
+def main(unused_argv: Sequence[str]) -> None:
+  pretrained_model_name_or_path = fileutils.force_gcs_path(
+      _PRETRAINED_MODEL_NAME_OR_PATH.value
+  )
+  if dataset_validation_util.is_gcs_path(pretrained_model_name_or_path):
+    pretrained_model_name_or_path = (
+        dataset_validation_util.download_gcs_uri_to_local(
+            pretrained_model_name_or_path
+        )
+    )
+
+  utils.merge_causal_language_model_with_lora(
+      pretrained_model_name_or_path=pretrained_model_name_or_path,
+      precision_mode=_MERGE_MODEL_PRECISION_MODE.value,
+      finetuned_lora_model_dir=_FINETUNED_LORA_MODEL_DIR.value,
+      merged_model_output_dir=_MERGE_BASE_AND_LORA_OUTPUT_DIR.value,
+      access_token=_HUGGINGFACE_ACCESS_TOKEN.value,
+  )
+
+
+if __name__ == '__main__':
+  app.run(main)

community-content/vertex_model_garden/model_oss/peft/train/vmg/scripts/build_train_docker.sh

@@ -0,0 +1,21 @@
+#!/bin/bash
+
+# Run copybara first:
+# cloud/ml/applications/vision/model_garden/copybara/run_copybara_local.sh
+# Run docker build:
+# cloud/ml/applications/vision/model_garden/model_oss/peft/train/vmg/scripts/build_train_docker.sh
+
+set -x
+set -e
+
+COPYBARA_DIR="/tmp/train_docker/"
+
+pushd "${COPYBARA_DIR}"
+
+PROJECT="cloud-nas-260507"
+IMAGE_TAG="gcr.io/${PROJECT}/pytorch-peft-train:${USER}-test"
+
+docker build -f model_oss/peft/train/vmg/dockerfile/train.Dockerfile . -t "${IMAGE_TAG}"
+docker push "${IMAGE_TAG}"
+
+popd

community-content/vertex_model_garden/model_oss/peft/train/vmg/sequence_classification_lora.py

@@ -1,7 +1,9 @@
 """Sequence classification with LoRA models."""
 
-# pylint: disable=g-importing-member
+from typing import Sequence
 
+from absl import app
+from absl import flags
 from datasets import load_dataset
 import evaluate
 from peft import get_peft_model
@@ -14,15 +16,81 @@ from transformers import AutoModelForSequenceClassification
 from transformers import AutoTokenizer
 from transformers import get_linear_schedule_with_warmup
 
+from util import dataset_validation_util
+
+
+_PRETRAINED_MODEL_NAME_OR_PATH = flags.DEFINE_string(
+    "pretrained_model_name_or_path",
+    None,
+    "The pretrained model name or path. Supported models can be causal language"
+    " modeling models from https://github.com/huggingface/peft/tree/main. Note,"
+    " there might be different paddings for different models. This tool assumes"
+    " the pretrained_model_name_or_path contains model name, and then choose"
+    " proper padding methods. e.g. it must contain `llama` for `Llama2"
+    " models`.",
+)
+
+_OUTPUT_DIR = flags.DEFINE_string(
+    "output_dir",
+    None,
+    "The output directory.",
+)
+
+_DATASET_NAME = flags.DEFINE_string(
+    "dataset_name",
+    None,
+    "The dataset name in huggingface.",
+)
+
+_LORA_RANK = flags.DEFINE_integer(
+    "lora_rank",
+    16,
+    "The rank of the update matrices, expressed in int. Lower rank results in"
+    " smaller update matrices with fewer trainable parameters, referring to"
+    " https://huggingface.co/docs/peft/conceptual_guides/lora.",
+)
+
+_LORA_ALPHA = flags.DEFINE_integer(
+    "lora_alpha",
+    32,
+    "LoRA scaling factor, referring to"
+    " https://huggingface.co/docs/peft/conceptual_guides/lora.",
+)
+
+_LORA_DROPOUT = flags.DEFINE_float(
+    "lora_dropout",
+    0.05,
+    "dropout probability of the LoRA layers, referring to"
+    " https://huggingface.co/docs/peft/task_guides/token-classification-lora.",
+)
+
+_NUM_TRAIN_EPOCHS = flags.DEFINE_integer(
+    "num_train_epochs",
+    None,
+    "The number of training epochs.",
+)
+
+_BATCH_SIZE = flags.DEFINE_integer(
+    "batch_size",
+    32,
+    "The batch size.",
+)
+
+_LEARNING_RATE = flags.DEFINE_float(
+    "learning_rate",
+    2e-4,
+    "The learning rate after the potential warmup period.",
+)
+
 
 def finetune_sequence_classification(
-    pretrained_model_id: str,
+    pretrained_model_name_or_path: str,
     dataset_name: str,
     output_dir: str,
     lora_rank: int = 8,
     lora_alpha: int = 16,
     lora_dropout: float = 0.1,
-    num_epochs: int = 20,
+    num_train_epochs: int = 20,
     batch_size: int = 32,
     learning_rate: float = 3e-4,
 ) -> None:
@@ -37,13 +105,13 @@ def finetune_sequence_classification(
       lora_alpha=lora_alpha,
       lora_dropout=lora_dropout,
   )
-  if any(k in pretrained_model_id for k in ("gpt", "opt", "bloom")):
+  if any(k in pretrained_model_name_or_path for k in ("gpt", "opt", "bloom")):
     padding_side = "left"
   else:
     padding_side = "right"
 
   tokenizer = AutoTokenizer.from_pretrained(
-      pretrained_model_id, padding_side=padding_side
+      pretrained_model_name_or_path, padding_side=padding_side
   )
   if getattr(tokenizer, "pad_token_id") is None:
     tokenizer.pad_token_id = tokenizer.eos_token_id
@@ -89,7 +157,7 @@ def finetune_sequence_classification(
   )
 
   model = AutoModelForSequenceClassification.from_pretrained(
-      pretrained_model_id, return_dict=True
+      pretrained_model_name_or_path, return_dict=True
   )
   model = get_peft_model(model, peft_config)
   model.print_trainable_parameters()
@@ -99,12 +167,12 @@ def finetune_sequence_classification(
   # Instantiate scheduler
   lr_scheduler = get_linear_schedule_with_warmup(
       optimizer=optimizer,
-      num_warmup_steps=0.06 * (len(train_dataloader) * num_epochs),
-      num_training_steps=(len(train_dataloader) * num_epochs),
+      num_warmup_steps=0.06 * (len(train_dataloader) * num_train_epochs),
+      num_training_steps=(len(train_dataloader) * num_train_epochs),
   )
 
   model.to(device)
-  for epoch in range(num_epochs):
+  for epoch in range(num_train_epochs):
     model.train()
     for _, batch in enumerate(tqdm(train_dataloader)):
       batch.to(device)
@@ -131,3 +199,34 @@ def finetune_sequence_classification(
     print(f"epoch {epoch}:", eval_metric)
 
   model.save_pretrained(output_dir)
+
+
+def main(unused_argv: Sequence[str]) -> None:
+  if dataset_validation_util.is_gcs_path(_PRETRAINED_MODEL_NAME_OR_PATH.value):
+    pretrained_model_name_or_path = (
+        dataset_validation_util.download_gcs_uri_to_local(
+            _PRETRAINED_MODEL_NAME_OR_PATH.value
+        )
+    )
+  else:
+    pretrained_model_name_or_path = _PRETRAINED_MODEL_NAME_OR_PATH.value
+  pretrained_model_path = dataset_validation_util.force_gcs_fuse_path(
+      pretrained_model_name_or_path
+  )
+  output_dir = dataset_validation_util.force_gcs_fuse_path(_OUTPUT_DIR.value)
+
+  finetune_sequence_classification(
+      pretrained_model_name_or_path=pretrained_model_path,
+      dataset_name=_DATASET_NAME.value,
+      output_dir=output_dir,
+      lora_rank=_LORA_RANK.value,
+      lora_alpha=_LORA_ALPHA.value,
+      lora_dropout=_LORA_DROPOUT.value,
+      num_train_epochs=int(_NUM_TRAIN_EPOCHS.value),
+      batch_size=_BATCH_SIZE.value,
+      learning_rate=_LEARNING_RATE.value,
+  )
+
+
+if __name__ == "__main__":
+  app.run(main)

community-content/vertex_model_garden/model_oss/peft/train/vmg/templates/README.md

@@ -0,0 +1,98 @@
+# Vertex Model Garden Training Dataset Template
+
+## Overview
+
+Vertex Model Garden training provides templates for streamlined preprocessing of
+datasets. Although datasets often have intricate structures, the supported LLM
+models accept only flat strings. A template facilitates parsing a dataset and
+preprocessing it to be compatible with the model.
+
+When fine-tuning a pretrained model, it is advisable to maintain the same format
+as the original training data. A template helps replicate the format, ensuring
+consistency and potentially enhancing the fine-tuning process.
+
+Both multi-turn messages and single instruction-response pairs are supported.
+Multi-turn messages are accommodated using a more general `chat_template` field,
+whereas simple instruction-response pair datasets are supported through the
+`prompt_input` field.
+
+A template is a JSON file consisting of string key-value pairs. Refer to the
+following for the definitions of the supported fields.
+
+## Template field documentation
+
+**description**: An explanation of the template.
+
+**source**: Information about the origin of the template.
+
+**chat_template**: A
+[jinja template](https://jinja.palletsprojects.com/en/3.1.x/templates/) that can
+be used to parse a chat dataset. This is the same format as
+[HF chat templates](https://huggingface.co/docs/transformers/main/en/chat_templating).
+To create a chat_template, use the `messages` variable to be filled with the
+sample. The flag `--instruct_column_in_dataset` identifies which column will be
+passed to the `messages` variable in the chat_template. This field is mutually
+exclusive with `prompt_input` and `prompt_no_input`.
+
+**prompt_input**: A string template that is used when value for the input column
+exists in the sample. It should be able to be formatted with the
+[str.format](https://docs.python.org/3/library/stdtypes.html#str.format) method.
+The input column is specified with the flag `--instruct_column_in_dataset`. Used
+for instruction dataset. This field is mutually exclusive with `chat_template`.
+
+**prompt_no_input**: A string template that is used when value for the input
+column does not exist in the sample. It should be able to be formatted with the
+[str.format](https://docs.python.org/3/library/stdtypes.html#str.format) method.
+The input column is specified with the flag `--instruct_column_in_dataset`. Used
+for instruction dataset. This field is mutually exclusive with `chat_template`.
+
+**instruction_separator**: A unique string used to indicate the start of the
+instructions. If not specified, every token after response_separator will be
+treated as a response, and every token before the first response_separator will
+be treated as instruction.
+
+**response_separator**: A unique string used to indicate the start of the
+response. This field is required if `--completion_only` flag is set to `True`.
+
+## Example templates
+
+-   See the list of all supported templates [here](https://github.com/GoogleCloudPlatform/vertex-ai-samples/tree/main/community-content/vertex_model_garden/model_oss/peft/train/vmg/templates).
+-   For an example with `chat_template` see the JSON template below.
+
+```
+{
+  "description": "Chat template used by Llama 3.",
+  "source": "https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct/blob/a5a71a7527eac1d651bb145436c72026887fb68e/tokenizer_config.json#L2053",
+  "chat_template": "{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' %}{% if loop.index0 == 0 %}{% set content = bos_token + content %}{% endif %}{{ content }}{% endfor %}{% if add_generation_prompt %}{{ '<|start_header_id|>assistant<|end_header_id|>\n\n' }}{% endif %}",
+  "instruction_separator": "<|start_header_id|>user<|end_header_id|>\n\n",
+  "response_separator": "<|start_header_id|>assistant<|end_header_id|>\n\n"
+}
+```
+
+-   For an example with `prompt_input` see the JSON template below. In this case
+    the flag `--instruct_column_in_dataset=text` should be set, and there must
+    be a column named `text` in the dataset.
+
+```
+{
+  "description": "Template for openassistant-guanaco dataset.",
+  "source": "https://huggingface.co/datasets/timdettmers/openassistant-guanaco",
+  "prompt_input": "{text}",
+  "instruction_separator": "### Human:",
+  "response_separator": "### Assistant:"
+}
+```
+
+-   For an example with `prompt_no_input` see the JSON template below. In this
+    case the flag `--instruct_column_in_dataset=input` should be set, and there
+    must be columns named `input` and `instruction` in the dataset.
+
+```
+{
+  "description": "Template used by Alpaca-LoRA.",
+  "source": "https://github.com/tloen/alpaca-lora/blob/main/templates/alpaca.json",
+  "prompt_input": "Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Input:\n{input}\n\n### Response:\n",
+  "prompt_no_input": "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Response:\n",
+  "response_separator": "### Response:"
+}
+```

community-content/vertex_model_garden/model_oss/peft/train/vmg/templates/alpaca.json

@@ -0,0 +1,7 @@
+{
+  "description": "Template used by Alpaca-LoRA.",
+  "source": "https://github.com/tloen/alpaca-lora/blob/main/templates/alpaca.json",
+  "prompt_input": "Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Input:\n{input}\n\n### Response:\n",
+  "prompt_no_input": "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Response:\n",
+  "response_separator": "### Response:"
+}

community-content/vertex_model_garden/model_oss/peft/train/vmg/templates/alpaca_short.json

@@ -0,0 +1,7 @@
+{
+  "description": "A shorter template to experiment with.",
+  "source": "https://github.com/tloen/alpaca-lora/blob/main/templates/alpaca_short.json",
+  "prompt_input": "### Instruction:\n{instruction}\n\n### Input:\n{input}\n\n### Response:\n",
+  "prompt_no_input": "### Instruction:\n{instruction}\n\n### Response:\n",
+  "response_separator": "### Response:"
+}

community-content/vertex_model_garden/model_oss/peft/train/vmg/templates/gemma.json

@@ -0,0 +1,7 @@
+{
+  "description": "Chat template used by Gemma. 'assistant' role is replaced by 'model'",
+  "source": "https://huggingface.co/google/gemma-1.1-2b-it/blob/bf4924f313df5166dee1467161e886e55f2eb4d4/tokenizer_config.json#L1507",
+  "chat_template": "{{ bos_token }}{% if messages[0]['role'] == 'system' %}{{ raise_exception('System role not supported') }}{% endif %}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if (message['role'] == 'assistant') %}{% set role = 'model' %}{% else %}{% set role = message['role'] %}{% endif %}{{ '<start_of_turn>' + role + '\n' + message['content'] | trim + '<end_of_turn>\n' }}{% endfor %}{% if add_generation_prompt %}{{'<start_of_turn>model\n'}}{% endif %}",
+  "instruction_separator": "<start_of_turn>user\n",
+  "response_separator": "<start_of_turn>model\n"
+}

community-content/vertex_model_garden/model_oss/peft/train/vmg/templates/llama3-text-bison.json

@@ -0,0 +1,7 @@
+{
+  "description": "Template used by Llama 3, accepting text-bison format.",
+  "source": "https://cloud.google.com/vertex-ai/generative-ai/docs/models/tune-text-models-supervised#dataset-format",
+  "prompt_input": "\n\n<|start_header_id|>user<|end_header_id|>\n\n{input_text}<|eot_id|>\n\n<|start_header_id|>assistant<|end_header_id|>\n\n{output_text}<|eot_id|>",
+  "instruction_separator": "<|start_header_id|>user<|end_header_id|>\n\n",
+  "response_separator": "<|start_header_id|>assistant<|end_header_id|>\n\n"
+}

community-content/vertex_model_garden/model_oss/peft/train/vmg/templates/llama3.json

@@ -0,0 +1,7 @@
+{
+  "description": "Chat template used by Llama 3.",
+  "source": "https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct/blob/a5a71a7527eac1d651bb145436c72026887fb68e/tokenizer_config.json#L2053",
+  "chat_template": "{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '\n\n<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' %}{% if loop.index0 == 0 %}{% set content = bos_token + content %}{% endif %}{{ content }}{% endfor %}{% if add_generation_prompt %}{{ '\n\n<|start_header_id|>assistant<|end_header_id|>\n\n' }}{% endif %}",
+  "instruction_separator": "<|start_header_id|>user<|end_header_id|>\n\n",
+  "response_separator": "<|start_header_id|>assistant<|end_header_id|>\n\n"
+}

community-content/vertex_model_garden/model_oss/peft/train/vmg/templates/mistral.json

@@ -0,0 +1,7 @@
+{
+  "description": "Chat template used by Mistral.",
+  "source": "https://github.com/OpenAccess-AI-Collective/axolotl/blob/main/src/axolotl/utils/chat_templates.py",
+  "chat_template": "{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if message['role'] == 'user' %}{{ '[INST] ' + message['content'] + ' [/INST]' }}{% elif message['role'] == 'assistant' %}{{ message['content'] + eos_token}}{% else %}{{ raise_exception('Only user and assistant roles are supported!') }}{% endif %}{% endfor %}",
+  "instruction_separator": "[INST]",
+  "response_separator": "[/INST]"
+}

community-content/vertex_model_garden/model_oss/peft/train/vmg/templates/openai-chat.json

@@ -0,0 +1,7 @@
+{
+  "description": "Template used by openai chat.",
+  "source": "https://platform.openai.com/docs/api-reference/fine-tuning/chat-input",
+  "chat_template": "{% set loop_messages = messages %}{% set content = '' %}{% for message in loop_messages %}{% set content = content ~ '\n\n<|start_header_id|>' ~ message.role ~ '<|end_header_id|>\n\n' %}{% if message.content is string %}{% set content = content ~ message.content|trim ~ '<|eot_id|>' %}{% else %}{% set content = content ~ message.content|join(' ', attribute='text')|trim ~ '<|eot_id|>' %}{% endif %}{% if loop.index0 == 0 %}{% set content = bos_token ~ content %}{% endif %}{{ content }}{% endfor %}{% if add_generation_prompt %}{{ '<|start_header_id|>assistant<|end_header_id|>\n\n' }}{% endif %}",
+  "instruction_separator": "<|start_header_id|>user<|end_header_id|>\n\n",
+  "response_separator": "<|start_header_id|>assistant<|end_header_id|>\n\n"
+}

community-content/vertex_model_garden/model_oss/peft/train/vmg/templates/openai-completion.json

@@ -0,0 +1,7 @@
+{
+  "description": "Template used by openai completion.",
+  "source": "https://platform.openai.com/docs/api-reference/fine-tuning/completions-input",
+  "prompt_input": "\n\n<|start_header_id|>user<|end_header_id|>\n\n{prompt}<|eot_id|>\n\n<|start_header_id|>assistant<|end_header_id|>\n\n{completion}<|eot_id|>",
+  "instruction_separator": "<|start_header_id|>user<|end_header_id|>\n\n",
+  "response_separator": "<|start_header_id|>assistant<|end_header_id|>\n\n"
+}

community-content/vertex_model_garden/model_oss/peft/train/vmg/templates/openassistant-guanaco.json

@@ -0,0 +1,7 @@
+{
+  "description": "Template for openassistant-guanaco dataset.",
+  "source": "https://huggingface.co/datasets/timdettmers/openassistant-guanaco",
+  "prompt_input": "{text}",
+  "instruction_separator": "### Human:",
+  "response_separator": "### Assistant:"
+}

community-content/vertex_model_garden/model_oss/peft/train/vmg/templates/vertex-chat.json

@@ -0,0 +1,7 @@
+{
+  "description": "Template used for chat based models.",
+  "source": "https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct/blob/a5a71a7527eac1d651bb145436c72026887fb68e/tokenizer_config.json#L2053",
+  "chat_template": "{% set loop_messages = messages %}{% for message in loop_messages %}{% set content = '\n\n<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' %}{% if loop.index0 == 0 %}{% set content = bos_token + content %}{% endif %}{{ content }}{% endfor %}{% if add_generation_prompt %}{{ '\n\n<|start_header_id|>model<|end_header_id|>\n\n' }}{% endif %}",
+  "instruction_separator": "<|start_header_id|>user<|end_header_id|>\n\n",
+  "response_separator": "<|start_header_id|>model<|end_header_id|>\n\n"
+}

community-content/vertex_model_garden/model_oss/peft/train/vmg/tests/command_builder.py

@@ -0,0 +1,87 @@
+"""Class that bundles docker related flags."""
+
+import getpass
+import os
+import pwd
+
+
+class CommandBuilder:
+  """Base class for building commands."""
+
+  def __init__(self):
+    self._defaults = []
+    self._env_vars = {}
+
+  def add_env_var(self, var: str, val: str) -> None:
+    """Add environment variable to the command.
+
+    Args:
+      var: environment variable name.
+      val: environment variable value.
+    """
+    self._env_vars[var] = val
+
+  def add_mount_map(self, host_path, docker_path):
+    pass
+
+
+class DockerCommandBuilder(CommandBuilder):
+  """Bundle docker related flags."""
+
+  def __init__(self, docker_uri: str, shm_size: str = '128gb'):
+    super().__init__()
+    self._docker_uri = [docker_uri]
+    self.privilege_mode = []
+
+    self._defaults = [
+        'docker',
+        'run',
+        '--gpus=all',
+        '--net=host',
+        '--rm',
+        f'--shm-size={shm_size}',
+    ]
+
+    self._mount_maps = []
+    user = getpass.getuser()
+    # username ends with `_google_com` is managed by ldap and does not have a
+    # corresponding entry in /etc/passwd or /etc/group file. We cannot enable
+    # non-root docker user with below method.
+    if not user.endswith('_google_com'):
+      uid = os.getuid()
+      gid = pwd.getpwuid(uid).pw_gid
+      self._defaults += [
+          f'--user={uid}:{gid}',
+          '--volume=/etc/group:/etc/group:ro',
+          '--volume=/etc/passwd:/etc/passwd:ro',
+      ]
+
+  def add_mount_map(self, host_path, docker_path):
+    self._mount_maps.append(f'--volume={host_path}:{docker_path}')
+
+  def add_privilege_mode(self):
+    self.privilege_mode = ['--privileged']
+
+  def build_cmd(self) -> str:
+    return (
+        self._defaults
+        + [f'--env={var}={val}' for var, val in self._env_vars.items()]
+        + self._mount_maps
+        + self.privilege_mode
+        + self._docker_uri
+    )
+
+
+class PythonCommandBuilder(CommandBuilder):
+  """Bundle Python test command related flags."""
+
+  def __init__(self):
+    super().__init__()
+    self._defaults = [
+        'python3',
+        './vertex_vision_model_garden_peft/train/vmg/train_entrypoint.py',
+    ]
+
+  def build_cmd(self) -> str:
+    os.environ.update(self._env_vars)
+    return self._defaults

community-content/vertex_model_garden/model_oss/peft/train/vmg/tests/instruct_lora_command_builder.py

@@ -0,0 +1,453 @@
+# pylint: disable=W,C,R
+
+# DO NOT MODIFY: this file is auto-generated
+# See go/vmg-oss-peft-tests#command-builder-genpy
+
+
+class InstructLoraCommandBuilder:
+
+  def __init__(self):
+    self._config_file = None
+    self._task = None
+    self._gcs_rsync_interval_secs = None
+    self._pretrained_model_name_or_path = None
+    self._train_dataset = None
+    self._train_split = None
+    self._train_template = None
+    self._train_column = None
+    self._output_dir = None
+    self._merge_base_and_lora_output_dir = None
+    self._logging_output_dir = None
+    self._per_device_train_batch_size = None
+    self._gradient_accumulation_steps = None
+    self._lora_rank = None
+    self._lora_alpha = None
+    self._lora_dropout = None
+    self._max_steps = None
+    self._num_train_epochs = None
+    self._max_seq_length = None
+    self._learning_rate = None
+    self._lr_scheduler_type = None
+    self._precision_mode = None
+    self._train_precision = None
+    self._gradient_checkpointing = None
+    self._example_packing = None
+    self._attn_implementation = None
+    self._optimizer = None
+    self._warmup_ratio = None
+    self._report_to = None
+    self._save_steps = None
+    self._logging_steps = None
+    self._huggingface_access_token = None
+    self._eval_dataset = None
+    self._eval_column = None
+    self._eval_template = None
+    self._eval_split = None
+    self._eval_steps = None
+    self._eval_metric_name = None
+    self._metric_for_best_model = None
+    self._input_masking = None
+    self._max_grad_norm = None
+    self._logger_level = None
+    self._benchmark_out_file = None
+    self._tuning_data_stats_file = None
+    self._enable_peft = None
+    self._merge_model_precision_mode = None
+    self._target_modules = None
+    self._unnamed_args = None
+
+  @property
+  def config_file(self):
+    return self._config_file
+
+  @config_file.setter
+  def config_file(self, val: str):
+    self._config_file = val
+
+  @property
+  def task(self):
+    return self._task
+
+  @task.setter
+  def task(self, val: str):
+    self._task = val
+
+  @property
+  def gcs_rsync_interval_secs(self):
+    return self._gcs_rsync_interval_secs
+
+  @gcs_rsync_interval_secs.setter
+  def gcs_rsync_interval_secs(self, val: str):
+    self._gcs_rsync_interval_secs = val
+
+  @property
+  def pretrained_model_name_or_path(self):
+    return self._pretrained_model_name_or_path
+
+  @pretrained_model_name_or_path.setter
+  def pretrained_model_name_or_path(self, val: str):
+    self._pretrained_model_name_or_path = val
+
+  @property
+  def train_dataset(self):
+    return self._train_dataset
+
+  @train_dataset.setter
+  def train_dataset(self, val: str):
+    self._train_dataset = val
+
+  @property
+  def train_split(self):
+    return self._train_split
+
+  @train_split.setter
+  def train_split(self, val: str):
+    self._train_split = val
+
+  @property
+  def train_template(self):
+    return self._train_template
+
+  @train_template.setter
+  def train_template(self, val: str):
+    self._train_template = val
+
+  @property
+  def train_column(self):
+    return self._train_column
+
+  @train_column.setter
+  def train_column(self, val: str):
+    self._train_column = val
+
+  @property
+  def ckpt_dir(self):
+    return self._output_dir
+
+  @ckpt_dir.setter
+  def ckpt_dir(self, val: str):
+    self._output_dir = val
+
+  @property
+  def merged_model_dir(self):
+    return self._merge_base_and_lora_output_dir
+
+  @merged_model_dir.setter
+  def merged_model_dir(self, val: str):
+    self._merge_base_and_lora_output_dir = val
+
+  @property
+  def logging_dir(self):
+    return self._logging_output_dir
+
+  @logging_dir.setter
+  def logging_dir(self, val: str):
+    self._logging_output_dir = val
+
+  @property
+  def per_device_batch_size(self):
+    return self._per_device_train_batch_size
+
+  @per_device_batch_size.setter
+  def per_device_batch_size(self, val: int):
+    self._per_device_train_batch_size = val
+
+  @property
+  def gradient_accumulation_steps(self):
+    return self._gradient_accumulation_steps
+
+  @gradient_accumulation_steps.setter
+  def gradient_accumulation_steps(self, val: int):
+    self._gradient_accumulation_steps = val
+
+  @property
+  def lora_rank(self):
+    return self._lora_rank
+
+  @lora_rank.setter
+  def lora_rank(self, val: int):
+    self._lora_rank = val
+
+  @property
+  def lora_alpha(self):
+    return self._lora_alpha
+
+  @lora_alpha.setter
+  def lora_alpha(self, val: int):
+    self._lora_alpha = val
+
+  @property
+  def lora_dropout(self):
+    return self._lora_dropout
+
+  @lora_dropout.setter
+  def lora_dropout(self, val: float):
+    self._lora_dropout = val
+
+  @property
+  def max_steps(self):
+    return self._max_steps
+
+  @max_steps.setter
+  def max_steps(self, val: int):
+    self._max_steps = val
+
+  @property
+  def num_train_epochs(self):
+    return self._num_train_epochs
+
+  @num_train_epochs.setter
+  def num_train_epochs(self, val: float):
+    self._num_train_epochs = val
+
+  @property
+  def max_seq_length(self):
+    return self._max_seq_length
+
+  @max_seq_length.setter
+  def max_seq_length(self, val: int):
+    self._max_seq_length = val
+
+  @property
+  def learning_rate(self):
+    return self._learning_rate
+
+  @learning_rate.setter
+  def learning_rate(self, val: float):
+    self._learning_rate = val
+
+  @property
+  def lr_scheduler_type(self):
+    return self._lr_scheduler_type
+
+  @lr_scheduler_type.setter
+  def lr_scheduler_type(self, val: str):
+    self._lr_scheduler_type = val
+
+  @property
+  def load_precision(self):
+    return self._precision_mode
+
+  @load_precision.setter
+  def load_precision(self, val: str):
+    self._precision_mode = val
+
+  @property
+  def train_precision(self):
+    return self._train_precision
+
+  @train_precision.setter
+  def train_precision(self, val: str):
+    self._train_precision = val
+
+  @property
+  def gradient_checkpointing(self):
+    return self._gradient_checkpointing
+
+  @gradient_checkpointing.setter
+  def gradient_checkpointing(self, val: bool):
+    self._gradient_checkpointing = val
+
+  @property
+  def example_packing(self):
+    return self._example_packing
+
+  @example_packing.setter
+  def example_packing(self, val: bool):
+    self._example_packing = val
+
+  @property
+  def attn_implementation(self):
+    return self._attn_implementation
+
+  @attn_implementation.setter
+  def attn_implementation(self, val: str):
+    self._attn_implementation = val
+
+  @property
+  def optimizer(self):
+    return self._optimizer
+
+  @optimizer.setter
+  def optimizer(self, val: str):
+    self._optimizer = val
+
+  @property
+  def warmup_ratio(self):
+    return self._warmup_ratio
+
+  @warmup_ratio.setter
+  def warmup_ratio(self, val: float):
+    self._warmup_ratio = val
+
+  @property
+  def report_to(self):
+    return self._report_to
+
+  @report_to.setter
+  def report_to(self, val: str):
+    self._report_to = val
+
+  @property
+  def save_steps(self):
+    return self._save_steps
+
+  @save_steps.setter
+  def save_steps(self, val: int):
+    self._save_steps = val
+
+  @property
+  def logging_steps(self):
+    return self._logging_steps
+
+  @logging_steps.setter
+  def logging_steps(self, val: int):
+    self._logging_steps = val
+
+  @property
+  def huggingface_access_token(self):
+    return self._huggingface_access_token
+
+  @huggingface_access_token.setter
+  def huggingface_access_token(self, val: str):
+    self._huggingface_access_token = val
+
+  @property
+  def eval_dataset(self):
+    return self._eval_dataset
+
+  @eval_dataset.setter
+  def eval_dataset(self, val: str):
+    self._eval_dataset = val
+
+  @property
+  def eval_column(self):
+    return self._eval_column
+
+  @eval_column.setter
+  def eval_column(self, val: str):
+    self._eval_column = val
+
+  @property
+  def eval_template(self):
+    return self._eval_template
+
+  @eval_template.setter
+  def eval_template(self, val: str):
+    self._eval_template = val
+
+  @property
+  def eval_split(self):
+    return self._eval_split
+
+  @eval_split.setter
+  def eval_split(self, val: str):
+    self._eval_split = val
+
+  @property
+  def eval_steps(self):
+    return self._eval_steps
+
+  @eval_steps.setter
+  def eval_steps(self, val: int):
+    self._eval_steps = val
+
+  @property
+  def eval_metric_name(self):
+    return self._eval_metric_name
+
+  @eval_metric_name.setter
+  def eval_metric_name(self, val: str):
+    self._eval_metric_name = val
+
+  @property
+  def metric_for_best_model(self):
+    return self._metric_for_best_model
+
+  @metric_for_best_model.setter
+  def metric_for_best_model(self, val: str):
+    self._metric_for_best_model = val
+
+  @property
+  def input_masking(self):
+    return self._input_masking
+
+  @input_masking.setter
+  def input_masking(self, val: bool):
+    self._input_masking = val
+
+  @property
+  def max_grad_norm(self):
+    return self._max_grad_norm
+
+  @max_grad_norm.setter
+  def max_grad_norm(self, val: float):
+    self._max_grad_norm = val
+
+  @property
+  def logger_level(self):
+    return self._logger_level
+
+  @logger_level.setter
+  def logger_level(self, val: str):
+    self._logger_level = val
+
+  @property
+  def benchmark_out_file(self):
+    return self._benchmark_out_file
+
+  @benchmark_out_file.setter
+  def benchmark_out_file(self, val: str):
+    self._benchmark_out_file = val
+
+  @property
+  def tuning_data_stats_file(self):
+    return self._tuning_data_stats_file
+
+  @tuning_data_stats_file.setter
+  def tuning_data_stats_file(self, val: str):
+    self._tuning_data_stats_file = val
+
+  @property
+  def enable_peft(self):
+    return self._enable_peft
+
+  @enable_peft.setter
+  def enable_peft(self, val: bool):
+    self._enable_peft = val
+
+  @property
+  def merge_model_precision_mode(self):
+    return self._merge_model_precision_mode
+
+  @merge_model_precision_mode.setter
+  def merge_model_precision_mode(self, val: str):
+    self._merge_model_precision_mode = val
+
+  @property
+  def target_modules(self):
+    return self._target_modules
+
+  @target_modules.setter
+  def target_modules(self, val: str):
+    self._target_modules = val
+
+  @property
+  def unnamed_args(self):
+    return self._unnamed_args
+
+  @unnamed_args.setter
+  def unnamed_args(self, val: list):
+    self._unnamed_args = val
+
+  def build_cmd(self) -> list[str]:
+    cmd = []
+    args = ''
+    for k, v in self.__dict__.items():
+      if k == '_unnamed_args' and v is not None:
+        args += ' '.join(v)
+        continue
+      if v is not None:
+        cmd.append(f'--{k[1:]}={v}')
+    cmd.append(f'{args}')
+    return cmd

community-content/vertex_model_garden/model_oss/peft/train/vmg/tests/quantize_model_command_builder.py

@@ -0,0 +1,142 @@
+# pylint: disable=W,C,R
+
+# DO NOT MODIFY: this file is auto-generated
+# See go/vmg-oss-peft-tests#command-builder-genpy
+
+
+class QuantizeModelCommandBuilder:
+
+  def __init__(self):
+    self._task = None
+    self._pretrained_model_name_or_path = None
+    self._quantization_method = None
+    self._quantization_precision_mode = None
+    self._quantization_dataset_name = None
+    self._text_column_in_quantization_dataset = None
+    self._quantization_output_dir = None
+    self._device_map = None
+    self._max_memory = None
+    self._group_size = None
+    self._desc_act = None
+    self._damp_percent = None
+    self._cache_examples_on_gpu = None
+    self._awq_version = None
+
+  @property
+  def task(self):
+    return self._task
+
+  @task.setter
+  def task(self, val: str):
+    self._task = val
+
+  @property
+  def pretrained_model_name_or_path(self):
+    return self._pretrained_model_name_or_path
+
+  @pretrained_model_name_or_path.setter
+  def pretrained_model_name_or_path(self, val: str):
+    self._pretrained_model_name_or_path = val
+
+  @property
+  def quantization_method(self):
+    return self._quantization_method
+
+  @quantization_method.setter
+  def quantization_method(self, val: str):
+    self._quantization_method = val
+
+  @property
+  def quantization_precision_mode(self):
+    return self._quantization_precision_mode
+
+  @quantization_precision_mode.setter
+  def quantization_precision_mode(self, val: str):
+    self._quantization_precision_mode = val
+
+  @property
+  def quantization_dataset_name(self):
+    return self._quantization_dataset_name
+
+  @quantization_dataset_name.setter
+  def quantization_dataset_name(self, val: str):
+    self._quantization_dataset_name = val
+
+  @property
+  def text_column_in_quantization_dataset(self):
+    return self._text_column_in_quantization_dataset
+
+  @text_column_in_quantization_dataset.setter
+  def text_column_in_quantization_dataset(self, val: str):
+    self._text_column_in_quantization_dataset = val
+
+  @property
+  def quantization_output_dir(self):
+    return self._quantization_output_dir
+
+  @quantization_output_dir.setter
+  def quantization_output_dir(self, val: str):
+    self._quantization_output_dir = val
+
+  @property
+  def device_map(self):
+    return self._device_map
+
+  @device_map.setter
+  def device_map(self, val: str):
+    self._device_map = val
+
+  @property
+  def max_memory(self):
+    return self._max_memory
+
+  @max_memory.setter
+  def max_memory(self, val: str):
+    self._max_memory = val
+
+  @property
+  def group_size(self):
+    return self._group_size
+
+  @group_size.setter
+  def group_size(self, val: int):
+    self._group_size = val
+
+  @property
+  def desc_act(self):
+    return self._desc_act
+
+  @desc_act.setter
+  def desc_act(self, val: bool):
+    self._desc_act = val
+
+  @property
+  def damp_percent(self):
+    return self._damp_percent
+
+  @damp_percent.setter
+  def damp_percent(self, val: float):
+    self._damp_percent = val
+
+  @property
+  def cache_examples_on_gpu(self):
+    return self._cache_examples_on_gpu
+
+  @cache_examples_on_gpu.setter
+  def cache_examples_on_gpu(self, val: bool):
+    self._cache_examples_on_gpu = val
+
+  @property
+  def awq_version(self):
+    return self._awq_version
+
+  @awq_version.setter
+  def awq_version(self, val: str):
+    self._awq_version = val
+
+  def build_cmd(self) -> str:
+    cmd = []
+    for k, v in self.__dict__.items():
+      if v is not None:
+        cmd.append(f'--{k[1:]}={v}')
+    return cmd

community-content/vertex_model_garden/model_oss/peft/train/vmg/tests/run_tests.py

@@ -0,0 +1,61 @@
+"""Run the tests from docker command line."""
+
+import subprocess
+import sys
+from typing import Sequence
+
+from absl import app
+from absl import flags
+
+
+_ALLOWED_TEST_FILE_PATHS = (
+    "test_instruct_lora_adapters",
+    "test_instruct_lora_features",
+    "test_instruct_lora_throughput",
+    "test_instruct_lora_trained_model_quality",
+    "test_validate_dataset_with_template",
+)
+
+_TEST_FILE_PATH = flags.DEFINE_multi_enum(
+    "test_file_path",
+    None,
+    _ALLOWED_TEST_FILE_PATHS + ("all",),
+    "The test file path.",
+    required=True,
+)
+
+_IS_AUTOMATED_TEST = flags.DEFINE_bool(
+    "is_automated_test",
+    True,
+    "Whether the test is an automated test.",
+)
+
+
+def main(argv: Sequence[str]) -> None:
+  if len(argv) > 1:
+    raise app.UsageError("Too many command-line arguments.")
+  test_file_path = _TEST_FILE_PATH.value
+  if "all" in test_file_path:
+    test_file_path = _ALLOWED_TEST_FILE_PATHS
+
+  for test_file in test_file_path:
+    cmd = [
+        "python3",
+        f"vertex_vision_model_garden_peft/tests/{test_file}.py",
+    ]
+    if (
+        test_file == "test_instruct_lora_throughput"
+        and _IS_AUTOMATED_TEST.value
+    ):
+      subprocess.run(
+          cmd + ["--", "-k", "peft_train_image_automated_test"],
+          stdout=sys.stdout,
+          stderr=sys.stdout,
+          check=True,
+      )
+    else:
+      subprocess.run(cmd, stdout=sys.stdout, stderr=sys.stdout, check=True)
+
+
+if __name__ == "__main__":
+  app.run(main)

community-content/vertex_model_garden/model_oss/peft/train/vmg/tests/test_instruct_lora_adapters.py

@@ -0,0 +1,106 @@
+# pylint: disable=missing-function-docstring
+# pylint: disable=missing-class-docstring
+"""Tests adapters of PEFT train docker."""
+
+import inspect
+import os
+import time
+
+from absl.testing import absltest
+from absl.testing import parameterized
+import instruct_lora_command_builder as task_cmd_builder
+from safetensors import safe_open
+import test_util
+
+
+class AdapterTest(test_util.TestBase):
+
+  # Needs to be accessible outside docker to check artifacts.
+  _TEST_OUTPUT_DIR = os.path.expanduser('~/output')
+  _MODULES_NEED_TO_BE_EXCLUDED_IN_ADAPTER = ['lm_head', 'embed_tokens']
+
+  @classmethod
+  def setUpClass(cls):
+    super().setUpClass()
+    cls.test_suite_output_dir = os.path.join(
+        cls._TEST_OUTPUT_DIR,
+        os.path.splitext(os.path.basename(__file__))[0],
+        cls.__class__.__name__,
+    )
+
+  def setUp(self):
+    super().setUp()
+
+    self.command_builder.add_env_var('CUDA_VISIBLE_DEVICES', '0,1,2,3,4,5,6,7')
+
+    self.task_cmd_builder = task_cmd_builder.InstructLoraCommandBuilder()
+    self.task_cmd_builder.task = 'instruct-lora'
+    self.task_cmd_builder.per_device_batch_size = 1
+    self.task_cmd_builder.train_dataset = test_util.get_test_data_path(
+        'peft_train_sample.jsonl'
+    )
+    self.task_cmd_builder.train_split = 'train'
+    self.task_cmd_builder.train_column = 'input_text'
+    self.task_cmd_builder.train_template = 'llama3-text-bison'
+    self.task_cmd_builder.gradient_accumulation_steps = 1
+    self.task_cmd_builder.lora_rank = 16
+    self.task_cmd_builder.lora_alpha = 32
+    self.task_cmd_builder.lora_dropout = 0.05
+    self.task_cmd_builder.max_steps = 1
+    self.task_cmd_builder.max_seq_length = 256
+    self.task_cmd_builder.load_precision = '4bit'
+    self.task_cmd_builder.gradient_checkpointing = True
+    self.task_cmd_builder.attn_implementation = 'flash_attention_2'
+    self.task_cmd_builder.save_steps = 10
+    self.task_cmd_builder.max_steps = 3
+    self.task_cmd_builder.config_file = (
+        'vertex_vision_model_garden_peft/llama_fsdp_8gpu.yaml'
+    )
+
+  def setup_output_dir(self, testcase_name: str):
+    testcase_output_dir = os.path.join(
+        self.test_suite_output_dir, testcase_name, test_util.get_timestamp()
+    )
+    self.task_cmd_builder.ckpt_dir = os.path.join(
+        testcase_output_dir, 'adapter'
+    )
+    self.task_cmd_builder.logging_dir = os.path.join(
+        testcase_output_dir, 'logs'
+    )
+
+  def check_adapter_for_bad_modules(self, adapter_path):
+    unwanted_modules = set()
+    with safe_open(adapter_path, framework='pt', device='cpu') as f:
+      for key in f.keys():
+        for module in self._MODULES_NEED_TO_BE_EXCLUDED_IN_ADAPTER:
+          if module in key:
+            unwanted_modules.add(key)
+    assert (
+        not unwanted_modules
+    ), f'Adapter includes unwanted modules: {unwanted_modules}'
+
+  @parameterized.named_parameters(
+      ('llama3.1-8b', 'llama3.1-8b-hf'),
+      ('llama3.1-70b', 'llama3.1-70b-hf'),
+      ('llama2-7b', 'llama2-7b-hf'),
+  )
+  def test_llama_adapters(self, model_name):
+    test_function_name = inspect.stack()[0][3]
+    self.setup_output_dir(f'{test_function_name}-{model_name}')
+    self.task_cmd_builder.pretrained_model_name_or_path = (
+        test_util.get_pretrained_model_name_or_path(model_name)
+    )
+    start_time = time.time()
+    self.assertEqual(self.run_cmd(), 0)
+    end_time = time.time()
+    self.assertLess(end_time - start_time, 1000)
+
+    adapter = os.path.join(
+        self.task_cmd_builder.ckpt_dir,
+        'checkpoint-final/adapter_model.safetensors',
+    )
+    self.check_adapter_for_bad_modules(adapter)
+
+
+if __name__ == '__main__':
+  absltest.main()

community-content/vertex_model_garden/model_oss/peft/train/vmg/tests/test_instruct_lora_features.py

@@ -0,0 +1,367 @@
+# pylint: disable=missing-function-docstring
+# pylint: disable=missing-class-docstring
+"""Tests various features of PEFT train docker."""
+
+import os
+import time
+import unittest
+from absl.testing import absltest
+from absl.testing import parameterized
+import instruct_lora_command_builder as task_cmd_builder
+import test_util
+
+
+class EvalConfigTest(test_util.TestBase):
+
+  def setUp(self):
+    super().setUp()
+
+    self.task_cmd_builder = task_cmd_builder.InstructLoraCommandBuilder()
+    self.task_cmd_builder.pretrained_model_name_or_path = (
+        test_util.get_pretrained_model_name_or_path('llama3.1-8b-hf')
+    )
+    self.task_cmd_builder.config_file = (
+        'vertex_vision_model_garden_peft/llama_fsdp_8gpu.yaml'
+    )
+    self.task_cmd_builder.task = 'instruct-lora'
+    self.task_cmd_builder.per_device_batch_size = 1
+    self.task_cmd_builder.gradient_accumulation_steps = 1
+    self.task_cmd_builder.lora_rank = 16
+    self.task_cmd_builder.lora_alpha = 32
+    self.task_cmd_builder.lora_dropout = 0.05
+    self.task_cmd_builder.learning_rate = 5e-5
+    self.task_cmd_builder.warmup_ratio = 0.01
+    self.task_cmd_builder.max_steps = 10
+    self.task_cmd_builder.save_steps = 1000
+    self.task_cmd_builder.logging_steps = 1
+    self.task_cmd_builder.gradient_checkpointing = True
+    self.task_cmd_builder.attn_implementation = 'flash_attention_2'
+    self.task_cmd_builder.example_packing = True
+    self.task_cmd_builder.train_dataset = 'mlabonne/guanaco-llama2'
+    self.task_cmd_builder.train_split = 'train'
+    self.task_cmd_builder.train_column = 'text'
+    self.task_cmd_builder.train_template = 'openassistant-guanaco'
+    self.task_cmd_builder.ckpt_dir = '/tmp/adapter'
+    self.task_cmd_builder.logging_dir = '/tmp/logs'
+    self.task_cmd_builder.eval_steps = 10
+    self.task_cmd_builder.eval_dataset = 'mlabonne/guanaco-llama2'
+    self.task_cmd_builder.eval_split = 'test'
+    self.task_cmd_builder.eval_column = 'text'
+    self.task_cmd_builder.eval_template = 'openassistant-guanaco'
+
+  @parameterized.named_parameters(
+      ('all_eval_metric', 'loss,perplexity,bleu,google_bleu,rouge1', 0),
+      ('invalid_metric', 'invalid_metric', 1),
+      ('only_loss', 'loss', 0),
+      ('perplexity_without_loss', 'perplexity,bleu', 0),
+      ('unsupported_eval_metric', 'f1', 1),
+  )
+  def test_hf_eval_metrics(self, eval_metric_name, expected_return_code):
+    self.task_cmd_builder.eval_metric_name = eval_metric_name
+    self.assertEqual(self.run_cmd(), expected_return_code)
+
+  @parameterized.named_parameters(
+      ('valid_best_model_metric', 'loss,perplexity', 'perplexity', 0),
+      ('only_loss', None, 'loss', 0),
+      ('invalid_best_model_metric', 'loss', 'invalid_metric', 1),
+  )
+  def test_metric_for_best_model(
+      self, eval_metric_name, metric_for_best_model, expected_return_code
+  ):
+    self.task_cmd_builder.eval_metric_name = eval_metric_name
+    self.task_cmd_builder.metric_for_best_model = metric_for_best_model
+    self.assertEqual(self.run_cmd(), expected_return_code)
+
+
+class GcsUploadDownloadTest(test_util.TestBase):
+
+  def setUp(self):
+    super().setUp()
+
+    self.command_builder.add_env_var('CUDA_VISIBLE_DEVICES', '0')
+
+    self.task_cmd_builder = task_cmd_builder.InstructLoraCommandBuilder()
+    self.task_cmd_builder.task = 'instruct-lora'
+    self.task_cmd_builder.per_device_batch_size = 1
+    self.task_cmd_builder.train_dataset = test_util.get_test_data_path(
+        'peft_train_sample.jsonl'
+    )
+    self.task_cmd_builder.train_split = 'train'
+    self.task_cmd_builder.train_column = 'input_text'
+    self.task_cmd_builder.train_template = 'llama3-text-bison'
+    self.task_cmd_builder.gradient_accumulation_steps = 1
+    self.task_cmd_builder.lora_rank = 16
+    self.task_cmd_builder.lora_alpha = 32
+    self.task_cmd_builder.lora_dropout = 0.05
+    self.task_cmd_builder.max_steps = 1
+    self.task_cmd_builder.max_seq_length = 256
+    self.task_cmd_builder.load_precision = '4bit'
+    self.task_cmd_builder.gradient_checkpointing = True
+    self.task_cmd_builder.attn_implementation = 'flash_attention_2'
+    self.task_cmd_builder.ckpt_dir = '/tmp'
+
+  @parameterized.named_parameters(
+      (
+          'llama3_8b_gcs',
+          'gs://vertex-model-garden-public-us/llama3/llama3-8b-hf',
+      ),
+      ('llama2_7b_hf', 'NousResearch/Llama-2-7b-hf'),
+  )
+  def test_model_download_single_process(self, pretrained_model_name_or_path):
+    self.task_cmd_builder.pretrained_model_name_or_path = (
+        test_util.get_pretrained_model_name_or_path(
+            pretrained_model_name_or_path
+        )
+    )
+    start_time = time.time()
+    self.assertEqual(self.run_cmd(), 0)
+    end_time = time.time()
+    self.assertLess(end_time - start_time, 5 * 60.0)
+
+  @parameterized.named_parameters(
+      (
+          'llama3_8b_gcs',
+          'gs://vertex-model-garden-public-us/llama3/llama3-8b-hf',
+      ),
+      ('llama2_7b_hf', 'NousResearch/Llama-2-7b-hf'),
+  )
+  def test_model_download_multi_process(self, pretrained_model_name_or_path):
+    self.task_cmd_builder.pretrained_model_name_or_path = (
+        test_util.get_pretrained_model_name_or_path(
+            pretrained_model_name_or_path
+        )
+    )
+    self.task_cmd_builder.config_file = (
+        'vertex_vision_model_garden_peft/llama_fsdp_8gpu.yaml'
+    )
+
+    self.command_builder.add_env_var('CUDA_VISIBLE_DEVICES', '0,1,2,3,4,5,6,7')
+
+    start_time = time.time()
+    self.assertEqual(self.run_cmd(), 0)
+    end_time = time.time()
+    self.assertLess(end_time - start_time, 5 * 60.0)
+
+  def test_8b_model_download(self):
+    self.task_cmd_builder.pretrained_model_name_or_path = (
+        'gs://vertex-model-garden-public-us/llama3/llama3-8b-hf'
+    )
+    start_time = time.time()
+    self.assertEqual(self.run_cmd(), 0)
+    end_time = time.time()
+    self.assertLess(end_time - start_time, 10 * 60.0)
+
+  @parameterized.named_parameters(
+      ('merged-without-upload', '/tmp/merged'),
+      ('merged-and-upload-to-gcs', 'gs://vmg-test-ttl-1y/tests/merged'),
+  )
+  def test_model_merge(self, output_dir):
+    self.task_cmd_builder.pretrained_model_name_or_path = (
+        test_util.get_pretrained_model_name_or_path('llama3.1-8b-hf')
+    )
+
+    ckpt_dir = os.path.join(
+        output_dir,
+        f'output-{test_util.get_timestamp()}',
+    )
+    self.task_cmd_builder.ckpt_dir = ckpt_dir
+    self.task_cmd_builder.merged_model_dir = os.path.join(ckpt_dir, 'merged')
+    self.task_cmd_builder.logging_dir = '/tmp/logging'
+
+    start_time = time.time()
+    self.assertEqual(self.run_cmd(), 0)
+    end_time = time.time()
+    self.assertLess(end_time - start_time, 5 * 60.0)
+
+  @unittest.skipIf(
+      not test_util.is_gpu_h100(),
+      'Skipping because this test is only for H100',
+  )
+  def test_model_fp8_conversion(self):
+    self.task_cmd_builder.pretrained_model_name_or_path = (
+        test_util.get_pretrained_model_name_or_path('llama3.1-8b-hf')
+    )
+
+    ckpt_dir = f'/tmp/output/output-{test_util.get_timestamp()}'
+    self.task_cmd_builder.ckpt_dir = ckpt_dir
+    self.task_cmd_builder.merged_model_dir = os.path.join(ckpt_dir, 'merged')
+    self.task_cmd_builder.logging_dir = os.path.join(ckpt_dir, 'logging')
+    self.task_cmd_builder.merge_model_precision_mode = 'float8'
+
+    start_time = time.time()
+    self.assertEqual(self.run_cmd(), 0)
+    end_time = time.time()
+    self.assertLess(end_time - start_time, 5 * 60.0)
+
+  @parameterized.named_parameters(
+      ('merged-without-upload', '/tmp/merged'),
+      ('merged-and-upload-to-gcs', 'gs://vmg-test-ttl-1y/tests/merged'),
+  )
+  def test_model_merge_and_upload_deepspeed(self, merged_model_dir):
+    self.task_cmd_builder.pretrained_model_name_or_path = (
+        test_util.get_pretrained_model_name_or_path('llama3.1-8b-hf')
+    )
+    self.task_cmd_builder.config_file = (
+        'vertex_vision_model_garden_peft/deepspeed_zero2_8gpu.yaml'
+    )
+    self.task_cmd_builder.merged_model_dir = os.path.join(
+        merged_model_dir, f'merged-{test_util.get_timestamp()}'
+    )
+
+    self.command_builder.add_env_var('CUDA_VISIBLE_DEVICES', '0,1,2,3,4,5,6,7')
+    start_time = time.time()
+    self.assertEqual(self.run_cmd(), 0)
+    end_time = time.time()
+    self.assertLess(end_time - start_time, 5 * 60.0)
+
+  @parameterized.named_parameters(
+      ('save-only-last', 10),
+      ('save-multiple-times', 1),
+  )
+  def test_llama3_8b_save_and_merge_8_gpus_fsdp(self, save_steps):
+    self.task_cmd_builder.pretrained_model_name_or_path = (
+        test_util.get_pretrained_model_name_or_path('llama3.1-8b-hf')
+    )
+    self.task_cmd_builder.save_steps = save_steps
+    self.task_cmd_builder.max_steps = 3
+    self.task_cmd_builder.config_file = (
+        'vertex_vision_model_garden_peft/llama_fsdp_8gpu.yaml'
+    )
+    self.task_cmd_builder.merged_model_dir = '/tmp/merged'
+
+    self.command_builder.add_env_var('CUDA_VISIBLE_DEVICES', '0,1,2,3,4,5,6,7')
+    start_time = time.time()
+    self.assertEqual(self.run_cmd(), 0)
+    end_time = time.time()
+    self.assertLess(end_time - start_time, 9 * 60.0)
+
+
+class TemplateAndDataStatsTest(test_util.TestBase):
+
+  def setUp(self):
+    super().setUp()
+
+    self.command_builder.add_env_var('CUDA_VISIBLE_DEVICES', '0')
+
+    self.task_cmd_builder = task_cmd_builder.InstructLoraCommandBuilder()
+    self.task_cmd_builder.task = 'instruct-lora'
+    self.task_cmd_builder.per_device_batch_size = 1
+    self.task_cmd_builder.gradient_accumulation_steps = 1
+    self.task_cmd_builder.lora_rank = 16
+    self.task_cmd_builder.lora_alpha = 32
+    self.task_cmd_builder.lora_dropout = 0.05
+    self.task_cmd_builder.max_steps = 1
+    self.task_cmd_builder.max_seq_length = 256
+    self.task_cmd_builder.load_precision = '4bit'
+    self.task_cmd_builder.gradient_checkpointing = True
+    self.task_cmd_builder.attn_implementation = 'flash_attention_2'
+    self.task_cmd_builder.ckpt_dir = '/tmp'
+
+  @parameterized.named_parameters(
+      ('multi-chat-string-content', 'openai-multi-chat-example-data.jsonl'),
+      (
+          'multi-chat-array-content',
+          'openai-multi-chat-example-data-array-content.jsonl',
+      ),
+  )
+  def test_openai_chat_template(self, example_dataset):
+    self.task_cmd_builder.pretrained_model_name_or_path = (
+        test_util.get_pretrained_model_name_or_path('llama3.1-8b-hf')
+    )
+    self.task_cmd_builder.train_dataset = test_util.get_test_data_path(
+        example_dataset
+    )
+    self.task_cmd_builder.train_split = 'train'
+    self.task_cmd_builder.train_column = 'messages'
+    self.task_cmd_builder.train_template = 'openai-chat'
+
+    self.assertEqual(self.run_cmd(), 0)
+
+  def test_openai_completion_template(self):
+    self.task_cmd_builder.pretrained_model_name_or_path = (
+        test_util.get_pretrained_model_name_or_path('llama3.1-8b-hf')
+    )
+    self.task_cmd_builder.train_dataset = test_util.get_test_data_path(
+        'openai-completion-example-data.jsonl'
+    )
+    self.task_cmd_builder.train_split = 'train'
+    self.task_cmd_builder.train_column = 'prompt'
+    self.task_cmd_builder.train_template = 'openai-completion'
+
+    self.assertEqual(self.run_cmd(), 0)
+
+  def test_data_stats_chat_template(self):
+    self.task_cmd_builder.pretrained_model_name_or_path = (
+        test_util.get_pretrained_model_name_or_path('llama3.1-8b-hf')
+    )
+    self.task_cmd_builder.config_file = (
+        'vertex_vision_model_garden_peft/llama_fsdp_8gpu.yaml'
+    )
+    self.task_cmd_builder.train_dataset = test_util.get_test_data_path(
+        'openai-multi-chat-example-data.jsonl'
+    )
+    self.task_cmd_builder.train_split = 'train'
+    self.task_cmd_builder.train_column = 'messages'
+    self.task_cmd_builder.train_template = 'llama3'
+    self.task_cmd_builder.tuning_data_stats_file = '/tmp/data-stats.json'
+    self.command_builder.add_env_var('CUDA_VISIBLE_DEVICES', '0,1,2,3,4,5,6,7')
+
+    self.assertEqual(self.run_cmd(), 0)
+
+  def test_data_stats_completion_template(self):
+    self.task_cmd_builder.pretrained_model_name_or_path = (
+        test_util.get_pretrained_model_name_or_path('llama3.1-8b-hf')
+    )
+    self.task_cmd_builder.config_file = (
+        'vertex_vision_model_garden_peft/llama_fsdp_8gpu.yaml'
+    )
+    self.task_cmd_builder.train_dataset = test_util.get_test_data_path(
+        'openai-completion-example-data.jsonl'
+    )
+    self.task_cmd_builder.train_split = 'train'
+    self.task_cmd_builder.train_column = 'prompt'
+    self.task_cmd_builder.train_template = 'openai-completion'
+    self.task_cmd_builder.tuning_data_stats_file = '/tmp/data-stats.json'
+
+    self.command_builder.add_env_var('CUDA_VISIBLE_DEVICES', '0,1,2,3,4,5,6,7')
+    self.assertEqual(self.run_cmd(), 0)
+
+
+class TargetModulesTest(test_util.TestBase):
+
+  def setUp(self):
+    super().setUp()
+
+    self.command_builder.add_env_var('CUDA_VISIBLE_DEVICES', '0')
+
+    self.task_cmd_builder = task_cmd_builder.InstructLoraCommandBuilder()
+    self.task_cmd_builder.task = 'instruct-lora'
+    self.task_cmd_builder.per_device_batch_size = 1
+    self.task_cmd_builder.train_dataset = test_util.get_test_data_path(
+        'peft_train_sample.jsonl'
+    )
+    self.task_cmd_builder.train_split = 'train'
+    self.task_cmd_builder.train_column = 'input_text'
+    self.task_cmd_builder.train_template = 'llama3-text-bison'
+    self.task_cmd_builder.gradient_accumulation_steps = 1
+    self.task_cmd_builder.lora_rank = 16
+    self.task_cmd_builder.lora_alpha = 32
+    self.task_cmd_builder.lora_dropout = 0.05
+    self.task_cmd_builder.max_steps = 1
+    self.task_cmd_builder.max_seq_length = 256
+    self.task_cmd_builder.load_precision = '4bit'
+    self.task_cmd_builder.gradient_checkpointing = True
+    self.task_cmd_builder.attn_implementation = 'flash_attention_2'
+    self.task_cmd_builder.ckpt_dir = '/tmp'
+
+  def test_target_modules(self):
+    self.task_cmd_builder.pretrained_model_name_or_path = (
+        test_util.get_pretrained_model_name_or_path('llama3.1-8b-hf')
+    )
+    self.task_cmd_builder.target_modules = 'q_proj, v_proj, k_proj'
+
+    self.assertEqual(self.run_cmd(), 0)
+
+
+if __name__ == '__main__':
+  absltest.main()

community-content/vertex_model_garden/model_oss/peft/train/vmg/tests/test_instruct_lora_throughput.py

@@ -0,0 +1,263 @@
+# pylint: disable=missing-function-docstring
+# pylint: disable=missing-class-docstring
+"""Tests to check training throughput and GPU memory consumption."""
+
+import os
+import pathlib
+
+from absl.testing import absltest
+from absl.testing import parameterized
+import instruct_lora_command_builder as task_cmd_builder
+import test_util
+
+
+class TrainerThroughputTest(test_util.TestBase):
+
+  _TEST_OUTPUT_DIR = os.path.expanduser('~/throughput_tests')
+
+  @classmethod
+  def setUpClass(cls):
+    super().setUpClass()
+    cls.test_suite_output_dir = os.path.join(
+        cls._TEST_OUTPUT_DIR, os.path.splitext(os.path.basename(__file__))[0]
+    )
+    if not os.path.isdir(cls.test_suite_output_dir):
+      pathlib.Path(cls.test_suite_output_dir).mkdir(parents=True)
+
+  def setUp(self):
+    super().setUp()
+
+    self.task_cmd_builder = task_cmd_builder.InstructLoraCommandBuilder()
+    self.task_cmd_builder.task = 'instruct-lora'
+    self.task_cmd_builder.per_device_batch_size = 1
+    self.task_cmd_builder.gradient_accumulation_steps = 1
+    self.task_cmd_builder.lora_rank = 16
+    self.task_cmd_builder.lora_alpha = 32
+    self.task_cmd_builder.lora_dropout = 0.05
+    self.task_cmd_builder.learning_rate = 5e-5
+    self.task_cmd_builder.warmup_ratio = 0.01
+    self.task_cmd_builder.max_steps = 10
+    self.task_cmd_builder.save_steps = 1000
+    self.task_cmd_builder.logging_steps = 1
+    self.task_cmd_builder.gradient_checkpointing = True
+    self.task_cmd_builder.attn_implementation = 'flash_attention_2'
+    self.task_cmd_builder.example_packing = True
+    self.task_cmd_builder.train_dataset = 'mlabonne/guanaco-llama2-1k'
+    self.task_cmd_builder.train_split = 'train'
+    self.task_cmd_builder.train_column = 'text'
+    self.task_cmd_builder.train_template = 'openassistant-guanaco'
+    self.task_cmd_builder.ckpt_dir = '/tmp/adapter'
+    self.task_cmd_builder.logging_dir = '/tmp/logs'
+
+  def run_cmd_and_handle_failure(self):
+    ret = self.run_cmd()
+    if ret != 0:
+      with open(self.task_cmd_builder.benchmark_out_file, 'a') as f:
+        max_seq_length = self.task_cmd_builder.max_seq_length
+        f.write(f'{max_seq_length/1024.0:.1f} | failed | n/a\n')
+    return ret
+
+  @parameterized.product(
+      model_name=[
+          'llama3.1-8b-hf',
+          'llama3.1-70b-hf',
+          'Mistral-7B-v0.1',
+          'Mixtral-8x7B-v0.1',
+          'gemma-2-9b-it',
+      ],
+      precision=['4bit', '8bit', 'bfloat16'],
+      max_seq_length=list(range(4 * 1024, 24 * 1024 + 1, 4 * 1024)),
+  )
+  def test_model_single_gpu(self, model_name, precision, max_seq_length):
+    self.task_cmd_builder.pretrained_model_name_or_path = (
+        test_util.get_pretrained_model_name_or_path(model_name)
+    )
+    self.task_cmd_builder.max_seq_length = max_seq_length
+    self.task_cmd_builder.load_precision = precision
+    self.task_cmd_builder.benchmark_out_file = os.path.join(
+        self.test_suite_output_dir, f'bm_{model_name}_{precision}.txt'
+    )
+
+    self.command_builder.add_env_var('CUDA_VISIBLE_DEVICES', '0')
+
+    self.assertEqual(self.run_cmd_and_handle_failure(), 0)
+
+  @parameterized.product(
+      model_name=[
+          'llama3.1-8b-hf',
+          'llama3.1-70b-hf',
+          'Mistral-7B-v0.1',
+          'Mixtral-8x7B-v0.1',
+          'gemma-2-9b-it',
+      ],
+      precision=['4bit', '8bit', 'bfloat16'],
+      max_seq_length=list(range(4 * 1024, 24 * 1024 + 1, 4 * 1024)),
+      num_gpus=[8],
+      config=['deepspeed_zero2'],
+  )
+  def test_model_multi_gpu_deepspeed(
+      self, model_name, precision, max_seq_length, num_gpus, config
+  ):
+    self.assertTrue(num_gpus == 4 or num_gpus == 8)
+    self.task_cmd_builder.pretrained_model_name_or_path = (
+        test_util.get_pretrained_model_name_or_path(model_name)
+    )
+    self.task_cmd_builder.max_seq_length = max_seq_length
+    self.task_cmd_builder.load_precision = precision
+    self.task_cmd_builder.benchmark_out_file = os.path.join(
+        self.test_suite_output_dir,
+        f'bm_{config}_{num_gpus}gpu_{model_name}_{precision}.txt',
+    )
+
+    self.task_cmd_builder.config_file = (
+        f'vertex_vision_model_garden_peft/{config}_{num_gpus}gpu.yaml'
+    )
+    self.command_builder.add_env_var(
+        'CUDA_VISIBLE_DEVICES', ','.join([str(x) for x in range(0, num_gpus)])
+    )
+
+    self.assertEqual(self.run_cmd_and_handle_failure(), 0)
+
+  @parameterized.product(
+      model_name=['llama3.1-8b-hf', 'llama3.1-70b-hf'],
+      precision=['4bit', '8bit', 'bfloat16'],
+      max_seq_length=list(range(4 * 1024, 24 * 1024 + 1, 4 * 1024)),
+      num_gpus=[8],
+  )
+  def test_model_multi_gpu_fsdp_lora(
+      self, model_name, precision, max_seq_length, num_gpus
+  ):
+    self.task_cmd_builder.pretrained_model_name_or_path = (
+        test_util.get_pretrained_model_name_or_path(model_name)
+    )
+    self.task_cmd_builder.max_seq_length = max_seq_length
+    self.task_cmd_builder.load_precision = precision
+    self.task_cmd_builder.benchmark_out_file = os.path.join(
+        self.test_suite_output_dir,
+        f'bm_fsdp_{num_gpus}gpu_{model_name}_{precision}.txt',
+    )
+    self.task_cmd_builder.config_file = (
+        'vertex_vision_model_garden_peft/llama_fsdp_8gpu.yaml'
+    )
+
+    self.command_builder.add_env_var(
+        'CUDA_VISIBLE_DEVICES', ','.join([str(x) for x in range(0, num_gpus)])
+    )
+
+    self.assertEqual(self.run_cmd_and_handle_failure(), 0)
+
+  @parameterized.product(
+      model_name=['llama3.1-8b-hf', 'llama3.1-70b-hf'],
+      precision=['4bit', 'bfloat16'],
+      max_seq_length=list(range(4 * 1024, 24 * 1024 + 1, 4 * 1024)),
+      config=['deepspeed_zero2', 'fsdp'],
+  )
+  def test_peft_train_image_automated_test_llama(
+      self, model_name, precision, max_seq_length, config
+  ):
+    num_gpus = 8
+    self.task_cmd_builder.pretrained_model_name_or_path = (
+        test_util.get_pretrained_model_name_or_path(model_name)
+    )
+    self.task_cmd_builder.max_seq_length = max_seq_length
+    self.task_cmd_builder.load_precision = precision
+    benchmark_out_file = os.path.join(
+        self.test_suite_output_dir,
+        f'bm_{config}_{num_gpus}gpu_{model_name}_{precision}.txt',
+    )
+    self.task_cmd_builder.benchmark_out_file = benchmark_out_file
+    if config == 'fsdp':
+      self.task_cmd_builder.config_file = (
+          f'vertex_vision_model_garden_peft/llama_{config}_{num_gpus}gpu.yaml'
+      )
+    else:
+      self.task_cmd_builder.config_file = (
+          f'vertex_vision_model_garden_peft/{config}_{num_gpus}gpu.yaml'
+      )
+
+    self.command_builder.add_env_var(
+        'CUDA_VISIBLE_DEVICES', ','.join([str(x) for x in range(0, num_gpus)])
+    )
+    self.run_cmd_and_handle_failure()
+    if test_util.is_gpu_h100():
+      self.assertEqual(
+          test_util.check_benchmark_results(
+              benchmark_out_file, 'llama', 10.0, max_seq_length
+          ),
+          True,
+      )
+
+  @parameterized.product(
+      model_name=['gemma-2-2b-it', 'gemma-2-9b-it', 'gemma-2-27b-it'],
+      precision=['4bit', 'bfloat16'],
+      max_seq_length=list(range(4 * 1024, 24 * 1024 + 1, 4 * 1024)),
+      config=['deepspeed_zero2', 'deepspeed_zero3', 'fsdp'],
+  )
+  def test_peft_train_image_automated_test_gemma(
+      self, model_name, precision, max_seq_length, config
+  ):
+    num_gpus = 8
+    self.task_cmd_builder.pretrained_model_name_or_path = (
+        test_util.get_pretrained_model_name_or_path(model_name)
+    )
+    self.task_cmd_builder.attn_implementation = 'eager'
+    self.task_cmd_builder.max_seq_length = max_seq_length
+    self.task_cmd_builder.load_precision = precision
+    benchmark_out_file = os.path.join(
+        self.test_suite_output_dir,
+        f'bm_{config}_{num_gpus}gpu_{model_name}_{precision}.txt',
+    )
+    self.task_cmd_builder.benchmark_out_file = benchmark_out_file
+    if config == 'fsdp':
+      self.task_cmd_builder.config_file = (
+          f'vertex_vision_model_garden_peft/gemma2_{config}_{num_gpus}gpu.yaml'
+      )
+    else:
+      self.task_cmd_builder.config_file = (
+          f'vertex_vision_model_garden_peft/{config}_{num_gpus}gpu.yaml'
+      )
+
+    self.command_builder.add_env_var(
+        'CUDA_VISIBLE_DEVICES', ','.join([str(x) for x in range(0, num_gpus)])
+    )
+    self.run_cmd_and_handle_failure()
+    if test_util.is_gpu_h100():
+      self.assertEqual(
+          test_util.check_benchmark_results(
+              benchmark_out_file, 'gemma', 10.0, max_seq_length
+          ),
+          True,
+      )
+
+  @parameterized.product(
+      model_name=['llama3.1-8b-hf', 'llama3.1-70b-hf'],
+      precision=['bfloat16'],
+      max_seq_length=list(range(4 * 1024, 24 * 1024 + 1, 4 * 1024)),
+      num_gpus=[8],
+  )
+  def test_model_multi_gpu_fsdp_full_finetuning(
+      self, model_name, precision, max_seq_length, num_gpus
+  ):
+    self.task_cmd_builder.pretrained_model_name_or_path = (
+        test_util.get_pretrained_model_name_or_path(model_name)
+    )
+    self.task_cmd_builder.max_seq_length = max_seq_length
+    self.task_cmd_builder.load_precision = precision
+    self.task_cmd_builder.benchmark_out_file = os.path.join(
+        self.test_suite_output_dir,
+        f'bm_fsdp_full_finetuning_{num_gpus}gpu_{model_name}_{precision}.txt',
+    )
+    self.task_cmd_builder.config_file = (
+        'vertex_vision_model_garden_peft/llama_fsdp_8gpu.yaml'
+    )
+    self.task_cmd_builder.enable_peft = False
+
+    self.command_builder.add_env_var(
+        'CUDA_VISIBLE_DEVICES', ','.join([str(x) for x in range(0, num_gpus)])
+    )
+
+    self.assertEqual(self.run_cmd_and_handle_failure(), 0)
+
+
+if __name__ == '__main__':
+  absltest.main()

community-content/vertex_model_garden/model_oss/peft/train/vmg/tests/test_instruct_lora_trained_model_quality.py

@@ -0,0 +1,145 @@
+# pylint: disable=missing-function-docstring
+# pylint: disable=missing-class-docstring
+"""Tests to make sure trained model achieves decent quality.
+
+Right now, the metric is loss decreasing and we'll eyeball the TB graphs.
+"""
+
+import os
+
+from absl.testing import absltest
+from absl.testing import parameterized
+import instruct_lora_command_builder as task_cmd_builder
+import test_util
+
+
+class TrainedModelQualityTest(test_util.TestBase):
+
+  _TEST_OUTPUT_DIR = os.path.expanduser('~/output')
+
+  @classmethod
+  def setUpClass(cls):
+    super().setUpClass()
+    cls.test_suite_output_dir = os.path.join(
+        cls._TEST_OUTPUT_DIR,
+        os.path.splitext(os.path.basename(__file__))[0],
+        cls.__class__.__name__,
+    )
+
+  def setUp(self):
+    super().setUp()
+
+    self.task_cmd_builder = task_cmd_builder.InstructLoraCommandBuilder()
+    self.task_cmd_builder.task = 'instruct-lora'
+    self.task_cmd_builder.eval_metric_name = 'loss'
+    self.task_cmd_builder.per_device_batch_size = 1
+    self.task_cmd_builder.gradient_accumulation_steps = 8
+    self.task_cmd_builder.lora_rank = 16
+    self.task_cmd_builder.lora_alpha = 32
+    self.task_cmd_builder.lora_dropout = 0.05
+    self.task_cmd_builder.learning_rate = 5e-5
+    self.task_cmd_builder.num_train_epochs = 2.0
+    self.task_cmd_builder.warmup_ratio = 0.01
+    self.task_cmd_builder.max_steps = -1
+    self.task_cmd_builder.save_steps = 10
+    self.task_cmd_builder.eval_steps = 10
+    self.task_cmd_builder.max_seq_length = 4096
+    self.task_cmd_builder.load_precision = '4bit'
+    self.task_cmd_builder.gradient_checkpointing = True
+    self.task_cmd_builder.input_masking = True
+    self.task_cmd_builder.attn_implementation = 'flash_attention_2'
+    self.task_cmd_builder.report_to = 'tensorboard'
+
+  def setup_output_dir(self, testcase_name: str):
+    testcase_output_dir = os.path.join(
+        self.test_suite_output_dir, testcase_name
+    )
+    self.task_cmd_builder.ckpt_dir = os.path.join(
+        testcase_output_dir, 'adapter'
+    )
+    self.task_cmd_builder.logging_dir = os.path.join(
+        testcase_output_dir, 'logs'
+    )
+    self.task_cmd_builder.merged_model_dir = os.path.join(
+        testcase_output_dir, 'merged'
+    )
+
+  @parameterized.named_parameters(
+      ('llama3-8b', 'llama3-8b-hf'),
+      ('llama3.1-8b', 'llama3.1-8b-hf'),
+  )
+  def test_8b_model_deepspeed(self, model_name):
+    self.setup_output_dir(f'test_deepspeed_{model_name}')
+    self.task_cmd_builder.pretrained_model_name_or_path = (
+        test_util.get_pretrained_model_name_or_path(model_name)
+    )
+    self.task_cmd_builder.train_dataset = test_util.get_test_data_path(
+        'peft_train_sample.jsonl'
+    )
+    self.task_cmd_builder.train_split = 'train'
+    self.task_cmd_builder.train_column = 'input_text'
+    self.task_cmd_builder.train_template = 'llama3-text-bison'
+    self.task_cmd_builder.eval_dataset = test_util.get_test_data_path(
+        'peft_eval_sample.jsonl'
+    )
+    self.task_cmd_builder.eval_split = 'train'
+    self.task_cmd_builder.eval_column = self.task_cmd_builder.train_column
+    self.task_cmd_builder.eval_template = self.task_cmd_builder.train_template
+
+    self.command_builder.add_env_var('CUDA_VISIBLE_DEVICES', '0')
+
+    self.assertEqual(self.run_cmd(), 0)
+
+  @parameterized.named_parameters(
+      ('llama3-70b', 'llama3-70b-hf'),
+      ('llama3.1-70b', 'llama3.1-70b-hf'),
+  )
+  def test_70b_model_deepspeed(self, model_name):
+    self.setup_output_dir(f'test_deepspeed_{model_name}')
+    self.task_cmd_builder.pretrained_model_name_or_path = (
+        test_util.get_pretrained_model_name_or_path(model_name)
+    )
+    self.task_cmd_builder.config_file = (
+        'vertex_vision_model_garden_peft/deepspeed_zero2_8gpu.yaml'
+    )
+    self.task_cmd_builder.train_dataset = 'timdettmers/openassistant-guanaco'
+    self.task_cmd_builder.train_split = 'train'
+    self.task_cmd_builder.train_column = 'text'
+    self.task_cmd_builder.train_template = 'openassistant-guanaco'
+    self.task_cmd_builder.eval_dataset = self.task_cmd_builder.train_dataset
+    self.task_cmd_builder.eval_split = 'test'
+    self.task_cmd_builder.eval_column = self.task_cmd_builder.train_column
+    self.task_cmd_builder.eval_template = self.task_cmd_builder.train_template
+
+    self.command_builder.add_env_var('CUDA_VISIBLE_DEVICES', '0,1,2,3,4,5,6,7')
+
+    self.assertEqual(self.run_cmd(), 0)
+
+  @parameterized.named_parameters(
+      ('llama3-70b', 'llama3-70b-hf'),
+      ('llama3.1-70b', 'llama3.1-70b-hf'),
+  )
+  def test_70b_model_fsdp(self, model_name):
+    self.setup_output_dir(f'test_fsdp_{model_name}')
+    self.task_cmd_builder.pretrained_model_name_or_path = (
+        test_util.get_pretrained_model_name_or_path(model_name)
+    )
+    self.task_cmd_builder.config_file = (
+        'vertex_vision_model_garden_peft/llama_fsdp_8gpu.yaml'
+    )
+    self.task_cmd_builder.train_dataset = 'timdettmers/openassistant-guanaco'
+    self.task_cmd_builder.train_split = 'train'
+    self.task_cmd_builder.train_column = 'text'
+    self.task_cmd_builder.train_template = 'openassistant-guanaco'
+    self.task_cmd_builder.eval_dataset = self.task_cmd_builder.train_dataset
+    self.task_cmd_builder.eval_split = 'test'
+    self.task_cmd_builder.eval_column = self.task_cmd_builder.train_column
+    self.task_cmd_builder.eval_template = self.task_cmd_builder.train_template
+
+    self.command_builder.add_env_var('CUDA_VISIBLE_DEVICES', '0,1,2,3,4,5,6,7')
+
+    self.assertEqual(self.run_cmd(), 0)
+
+
+if __name__ == '__main__':
+  absltest.main()

community-content/vertex_model_garden/model_oss/peft/train/vmg/tests/test_validate_dataset_with_template.py

@@ -0,0 +1,353 @@
+"""Tests validate the dataset with template task in PEFT docker."""
+
+from absl.testing import absltest
+from absl.testing import parameterized
+import test_util
+import validate_dataset_with_template_command_builder as task_cmd_builder
+
+
+class ValidateDatasetWithTemplateTest(test_util.TestBase):
+  """Test the validate dataset with template task in PEFT docker."""
+
+  def setUp(self):
+    super().setUp()
+
+    self.task_cmd_builder = (
+        task_cmd_builder.ValidateDatasetWithTemplateCommandBuilder()
+    )
+    self.task_cmd_builder.task = "validate-dataset-with-template"
+
+  @parameterized.named_parameters(
+      dict(
+          testcase_name="valid_rows",
+          validate_top_k_rows=100,
+          expected_result=0,
+      ),
+      dict(
+          testcase_name="negative_rows",
+          validate_top_k_rows=-10,
+          expected_result=0,
+      ),
+      dict(
+          testcase_name="out_of_range_rows",
+          validate_top_k_rows=100000,
+          expected_result=0,
+      ),
+  )
+  def test_validate_dataset_with_template_top_k_rows(
+      self,
+      validate_top_k_rows,
+      expected_result,
+  ):
+    self.task_cmd_builder.dataset_name = "timdettmers/openassistant-guanaco"
+    self.task_cmd_builder.train_split = "train"
+    self.task_cmd_builder.train_column = "text"
+    self.task_cmd_builder.template = (
+        "gs://cloud-nas-260507-tmp-20240724/openassistant-guanaco.json"
+    )
+    self.task_cmd_builder.validate_percentage_of_dataset = None
+    self.task_cmd_builder.validate_k_rows_of_dataset = validate_top_k_rows
+    self.task_cmd_builder.use_multiprocessing = True
+    result = self.run_cmd()
+    self.assertEqual(result, expected_result)
+
+  @parameterized.named_parameters(
+      dict(
+          testcase_name="valid_positive_x_percent",
+          validate_percentage_of_dataset=10,
+          expected_result=0,
+      ),
+      dict(
+          testcase_name="valid_negative_x_percent",
+          validate_percentage_of_dataset=-10,
+          expected_result=0,
+      ),
+      dict(
+          testcase_name="invalid_positive_x_percent",
+          validate_percentage_of_dataset=110,
+          expected_result=1,
+      ),
+      dict(
+          testcase_name="invalid_negative_x_percent",
+          validate_percentage_of_dataset=-110,
+          expected_result=1,
+      ),
+  )
+  def test_validate_dataset_with_template_x_percent(
+      self,
+      validate_percentage_of_dataset,
+      expected_result,
+  ):
+    self.task_cmd_builder.dataset_name = "timdettmers/openassistant-guanaco"
+    self.task_cmd_builder.train_split = "train"
+    self.task_cmd_builder.train_column = "text"
+    self.task_cmd_builder.template = (
+        "gs://cloud-nas-260507-tmp-20240724/openassistant-guanaco.json"
+    )
+    self.task_cmd_builder.validate_percentage_of_dataset = (
+        validate_percentage_of_dataset
+    )
+    self.task_cmd_builder.validate_k_rows_of_dataset = None
+    self.task_cmd_builder.use_multiprocessing = True
+    result = self.run_cmd()
+    self.assertEqual(result, expected_result)
+
+  @parameterized.named_parameters(
+      dict(
+          testcase_name="small_max_seq_length",
+          max_seq_length=10,
+      ),
+      dict(
+          testcase_name="large_max_seq_length",
+          max_seq_length=1024,
+      ),
+  )
+  def test_validate_dataset_with_template_max_seq_length(
+      self,
+      max_seq_length,
+  ):
+    self.task_cmd_builder.dataset_name = "timdettmers/openassistant-guanaco"
+    self.task_cmd_builder.train_split = "train"
+    self.task_cmd_builder.train_column = "text"
+    self.task_cmd_builder.template = (
+        "gs://cloud-nas-260507-tmp-20240724/openassistant-guanaco.json"
+    )
+    self.task_cmd_builder.max_seq_length = max_seq_length
+    self.task_cmd_builder.validate_k_rows_of_dataset = None
+    self.task_cmd_builder.use_multiprocessing = True
+    result = self.run_cmd()
+    self.assertEqual(result, 0)
+
+  @parameterized.named_parameters(
+      dict(
+          testcase_name="invalid_default_input_column",
+          dataset_name="timdettmers/openassistant-guanaco",
+          split="train",
+          input_column="",
+          template=(
+              "gs://cloud-nas-260507-tmp-20240724/openassistant-guanaco.json"
+          ),
+          validate_percentage_of_dataset=None,
+          validate_top_k_rows=None,
+          use_multiprocessing=True,
+          expected_result=1,
+      ),
+      dict(
+          testcase_name="invalid_percentage",
+          dataset_name="timdettmers/openassistant-guanaco",
+          split="train",
+          input_column="text",
+          template=(
+              "gs://cloud-nas-260507-tmp-20240724/openassistant-guanaco.json"
+          ),
+          validate_percentage_of_dataset=110,
+          validate_top_k_rows=None,
+          use_multiprocessing=True,
+          expected_result=1,
+      ),
+      dict(
+          testcase_name="negative_percentage",
+          dataset_name="timdettmers/openassistant-guanaco",
+          split="train",
+          input_column="text",
+          template=(
+              "gs://cloud-nas-260507-tmp-20240724/openassistant-guanaco.json"
+          ),
+          validate_percentage_of_dataset=-110,
+          validate_top_k_rows=None,
+          use_multiprocessing=True,
+          expected_result=1,
+      ),
+      dict(
+          testcase_name="empty_dataset",
+          dataset_name="",
+          split="train",
+          input_column="text",
+          template=(
+              "gs://cloud-nas-260507-tmp-20240724/openassistant-guanaco.json"
+          ),
+          validate_percentage_of_dataset=None,
+          validate_top_k_rows=None,
+          use_multiprocessing=True,
+          expected_result=1,
+      ),
+      dict(
+          testcase_name="empty_split",
+          dataset_name="timdettmers/openassistant-guanaco",
+          split="",
+          input_column="text",
+          template=(
+              "gs://cloud-nas-260507-tmp-20240724/openassistant-guanaco.json"
+          ),
+          validate_percentage_of_dataset=None,
+          validate_top_k_rows=None,
+          use_multiprocessing=True,
+          expected_result=1,
+      ),
+      dict(
+          testcase_name="empty_template",
+          dataset_name="timdettmers/openassistant-guanaco",
+          split="train",
+          input_column="text",
+          template="",
+          validate_percentage_of_dataset=None,
+          validate_top_k_rows=None,
+          use_multiprocessing=True,
+          expected_result=1,
+      ),
+      dict(
+          testcase_name="wrong_gcs_template",
+          dataset_name="gs://cloud-nas-260507-tmp-20240724/model-evaluation/peft_train_sample.jsonl",
+          split="train",
+          input_column="text",
+          template="gs://cloud-nas-260507-tmp-20240724/sample-template.json",
+          validate_percentage_of_dataset=None,
+          validate_top_k_rows=None,
+          use_multiprocessing=True,
+          expected_result=1,
+      ),
+      dict(
+          testcase_name="wrong_gcs_dataset_name",
+          dataset_name="gs://cloud-nas-260507-tmp-20240724/model-evaluation/peft-train_sample.jsonl",
+          split="train",
+          input_column="text",
+          template="gs://cloud-nas-260507-tmp-20240724/sample_template.json",
+          validate_percentage_of_dataset=None,
+          validate_top_k_rows=None,
+          use_multiprocessing=True,
+          expected_result=1,
+      ),
+  )
+  def test_validate_dataset_with_template_invalid_input(
+      self,
+      dataset_name,
+      split,
+      input_column,
+      template,
+      validate_percentage_of_dataset,
+      validate_top_k_rows,
+      use_multiprocessing,
+      expected_result,
+  ):
+    self.task_cmd_builder.dataset_name = dataset_name
+    self.task_cmd_builder.train_split = split
+    self.task_cmd_builder.train_column = input_column
+    self.task_cmd_builder.template = template
+    self.task_cmd_builder.validate_percentage_of_dataset = (
+        validate_percentage_of_dataset
+    )
+    self.task_cmd_builder.validate_k_rows_of_dataset = validate_top_k_rows
+    self.task_cmd_builder.use_multiprocessing = use_multiprocessing
+    result = self.run_cmd()
+    self.assertEqual(result, expected_result)
+
+  @parameterized.named_parameters(
+      dict(
+          testcase_name="full_hf_dataset_with_multiprocessing",
+          dataset_name="timdettmers/openassistant-guanaco",
+          template=(
+              "gs://cloud-nas-260507-tmp-20240724/openassistant-guanaco.json"
+          ),
+          validate_percentage_of_dataset=None,
+          validate_top_k_rows=None,
+          use_multiprocessing=True,
+          expected_result=0,
+      ),
+      dict(
+          testcase_name="full_gcs_dataset_with_multiprocessing",
+          dataset_name="gs://cloud-nas-260507-tmp-20240724/model-evaluation/peft_train_sample.jsonl",
+          template="gs://cloud-nas-260507-tmp-20240724/sample_template.json",
+          validate_percentage_of_dataset=None,
+          validate_top_k_rows=None,
+          use_multiprocessing=True,
+          expected_result=0,
+      ),
+      dict(
+          testcase_name="half_dataset_with_multiprocessing",
+          dataset_name="timdettmers/openassistant-guanaco",
+          template=(
+              "gs://cloud-nas-260507-tmp-20240724/openassistant-guanaco.json"
+          ),
+          validate_percentage_of_dataset=50,
+          validate_top_k_rows=None,
+          use_multiprocessing=True,
+          expected_result=0,
+      ),
+      dict(
+          testcase_name="top_100_rows_with_multiprocessing",
+          dataset_name="timdettmers/openassistant-guanaco",
+          template=(
+              "gs://cloud-nas-260507-tmp-20240724/openassistant-guanaco.json"
+          ),
+          validate_percentage_of_dataset=None,
+          validate_top_k_rows=100,
+          use_multiprocessing=True,
+          expected_result=0,
+      ),
+      dict(
+          testcase_name="full_gcs_dataset_without_multiprocessing",
+          dataset_name="gs://cloud-nas-260507-tmp-20240724/model-evaluation/peft_train_sample.jsonl",
+          template="gs://cloud-nas-260507-tmp-20240724/sample_template.json",
+          validate_percentage_of_dataset=None,
+          validate_top_k_rows=None,
+          use_multiprocessing=False,
+          expected_result=0,
+      ),
+      dict(
+          testcase_name="full_hf_dataset_without_multiprocessing",
+          dataset_name="timdettmers/openassistant-guanaco",
+          template=(
+              "gs://cloud-nas-260507-tmp-20240724/openassistant-guanaco.json"
+          ),
+          validate_percentage_of_dataset=None,
+          validate_top_k_rows=None,
+          use_multiprocessing=False,
+          expected_result=0,
+      ),
+      dict(
+          testcase_name="half_dataset_without_multiprocessing",
+          dataset_name="timdettmers/openassistant-guanaco",
+          template=(
+              "gs://cloud-nas-260507-tmp-20240724/openassistant-guanaco.json"
+          ),
+          validate_percentage_of_dataset=50,
+          validate_top_k_rows=None,
+          use_multiprocessing=False,
+          expected_result=0,
+      ),
+      dict(
+          testcase_name="top_100_rows_without_multiprocessing",
+          dataset_name="timdettmers/openassistant-guanaco",
+          template=(
+              "gs://cloud-nas-260507-tmp-20240724/openassistant-guanaco.json"
+          ),
+          validate_percentage_of_dataset=None,
+          validate_top_k_rows=100,
+          use_multiprocessing=True,
+          expected_result=0,
+      ),
+  )
+  def test_validate_dataset_with_template_multiprocessing_option(
+      self,
+      dataset_name,
+      template,
+      validate_percentage_of_dataset,
+      validate_top_k_rows,
+      use_multiprocessing,
+      expected_result,
+  ):
+    self.task_cmd_builder.dataset_name = dataset_name
+    self.task_cmd_builder.train_split = "train"
+    self.task_cmd_builder.train_column = "text"
+    self.task_cmd_builder.template = template
+    self.task_cmd_builder.validate_percentage_of_dataset = (
+        validate_percentage_of_dataset
+    )
+    self.task_cmd_builder.validate_k_rows_of_dataset = validate_top_k_rows
+    self.task_cmd_builder.use_multiprocessing = use_multiprocessing
+    result = self.run_cmd()
+    self.assertEqual(result, expected_result)
+
+
+if __name__ == "__main__":
+  absltest.main()

community-content/vertex_model_garden/model_oss/peft/train/vmg/tests/validate_dataset_with_template_command_builder.py

@@ -0,0 +1,97 @@
+# pylint: disable=W,C,R
+
+# DO NOT MODIFY: this file is auto-generated
+# See go/vmg-oss-peft-tests#command-builder-genpy
+
+
+class ValidateDatasetWithTemplateCommandBuilder:
+
+  def __init__(self):
+    self._task = None
+    self._template = None
+    self._dataset_name = None
+    self._train_split = None
+    self._train_column = None
+    self._max_seq_length = None
+    self._use_multiprocessing = None
+    self._validate_k_rows_of_dataset = None
+    self._validate_percentage_of_dataset = None
+
+  @property
+  def task(self):
+    return self._task
+
+  @task.setter
+  def task(self, val: str):
+    self._task = val
+
+  @property
+  def template(self):
+    return self._template
+
+  @template.setter
+  def template(self, val: str):
+    self._template = val
+
+  @property
+  def dataset_name(self):
+    return self._dataset_name
+
+  @dataset_name.setter
+  def dataset_name(self, val: str):
+    self._dataset_name = val
+
+  @property
+  def train_split(self):
+    return self._train_split
+
+  @train_split.setter
+  def train_split(self, val: str):
+    self._train_split = val
+
+  @property
+  def train_column(self):
+    return self._train_column
+
+  @train_column.setter
+  def train_column(self, val: str):
+    self._train_column = val
+
+  @property
+  def max_seq_length(self):
+    return self._max_seq_length
+
+  @max_seq_length.setter
+  def max_seq_length(self, val: int):
+    self._max_seq_length = val
+
+  @property
+  def use_multiprocessing(self):
+    return self._use_multiprocessing
+
+  @use_multiprocessing.setter
+  def use_multiprocessing(self, val: bool):
+    self._use_multiprocessing = val
+
+  @property
+  def validate_k_rows_of_dataset(self):
+    return self._validate_k_rows_of_dataset
+
+  @validate_k_rows_of_dataset.setter
+  def validate_k_rows_of_dataset(self, val: int):
+    self._validate_k_rows_of_dataset = val
+
+  @property
+  def validate_percentage_of_dataset(self):
+    return self._validate_percentage_of_dataset
+
+  @validate_percentage_of_dataset.setter
+  def validate_percentage_of_dataset(self, val: int):
+    self._validate_percentage_of_dataset = val
+
+  def build_cmd(self) -> str:
+    cmd = []
+    for k, v in self.__dict__.items():
+      if v is not None:
+        cmd.append(f'--{k[1:]}={v}')
+    return cmd

community-content/vertex_model_garden/model_oss/peft/train/vmg/train_entrypoint.py

@@ -0,0 +1,369 @@
+"""Entrypoint for peft train docker.
+
+Dispatches to different scripts based on `task` type.
+
+For task type in `_TASK_TO_SCRIPT`, if `--config_file` is specified, the script
+will dispatch the call to `accelerate`, which is friendly for multi-GPU
+environment. Otherwise, `python3` is used.
+"""
+
+import argparse
+from collections.abc import MutableSequence, Sequence
+import json
+import multiprocessing
+import os
+import subprocess
+import sys
+from absl import app
+from absl import flags
+from absl import logging
+from util import dataset_validation_util
+from vertex_vision_model_garden_peft.train.vmg import gcs_syncer
+from vertex_vision_model_garden_peft.train.vmg import utils
+from util import constants
+from util import fileutils
+from util import hypertune_utils
+
+
+_TEXT_TO_IMAGE_TASKS_SCRIPTS = {
+    constants.TEXT_TO_IMAGE: 'text_to_image/train_text_to_image.py',
+    constants.TEXT_TO_IMAGE_LORA: 'text_to_image/train_text_to_image_lora.py',
+    constants.TEXT_TO_IMAGE_DREAMBOOTH: 'dreambooth/train_dreambooth.py',
+    constants.TEXT_TO_IMAGE_DREAMBOOTH_LORA: (
+        'dreambooth/train_dreambooth_lora.py'
+    ),
+    constants.TEXT_TO_IMAGE_DREAMBOOTH_LORA_SDXL: (
+        'dreambooth/train_dreambooth_lora_sdxl.py'
+    ),
+}
+
+_TASK_TO_SCRIPT = {
+    constants.INSTRUCT_LORA: (
+        'vertex_vision_model_garden_peft/train/vmg/instruct_lora.py'
+    ),
+    constants.MERGE_CAUSAL_LANGUAGE_MODEL_LORA: 'vertex_vision_model_garden_peft/train/vmg/merge_causal_language_model_lora.py',
+    constants.SEQUENCE_CLASSIFICATION_LORA: 'vertex_vision_model_garden_peft/train/vmg/sequence_classification_lora.py',
+    constants.VALIDATE_DATASET_WITH_TEMPLATE: 'vertex_vision_model_garden_peft/train/vmg/validate_dataset_with_template.py',
+    constants.RUN_TESTS: 'vertex_vision_model_garden_peft/tests/run_tests.py',
+}
+
+
+def launch_script_cmd(
+    script: str,
+    config_file: str | None,
+    accelerate_args: argparse.Namespace = argparse.Namespace(),
+) -> MutableSequence[str]:
+  """Returns the command to launch the script."""
+  if config_file:
+    cmd = [
+        'accelerate',
+        'launch',
+        '--config_file={}'.format(config_file),
+    ]
+  else:
+    cmd = ['python3']
+
+  _append_args_to_command_in_place(accelerate_args, cmd)
+  cmd.append(script)
+
+  return cmd
+
+
+def _get_accelerate_args() -> argparse.Namespace:
+  """Returns the accelerate args."""
+  # For the format of the cluster spec, see
+  # https://cloud.google.com/vertex-ai/docs/training/distributed-training#cluster-spec-format # pylint: disable=line-too-long
+  cluster_spec = os.getenv('CLUSTER_SPEC', default=None)
+  if not cluster_spec:
+    return argparse.Namespace()
+  logging.info('CLUSTER_SPEC: %s', cluster_spec)
+
+  cluster_data = json.loads(cluster_spec)
+  if (
+      'workerpool1' not in cluster_data['cluster']
+      or not cluster_data['cluster']['workerpool1']
+  ):
+    return argparse.Namespace()
+
+  # Get primary node info
+  primary_node = cluster_data['cluster']['workerpool0'][0]
+  logging.info('primary node: %s', primary_node)
+  primary_node_addr, primary_node_port = primary_node.split(':')
+  logging.info('primary node address: %s', primary_node_addr)
+  logging.info('primary node port: %s', primary_node_port)
+
+  # Determine node rank of this machine
+  workerpool = cluster_data['task']['type']
+  if workerpool == 'workerpool0':
+    node_rank = 0
+  elif workerpool == 'workerpool1':
+    # Add 1 for the primary node, since `index` is the index of workerpool1.
+    node_rank = cluster_data['task']['index'] + 1
+  else:
+    raise ValueError(
+        'Only workerpool0 and workerpool1 are supported. Unknown workerpool:'
+        f' {workerpool}'
+    )
+  logging.info('node rank: %s', node_rank)
+
+  # Calculate total nodes
+  num_worker_nodes = len(cluster_data['cluster']['workerpool1'])
+  num_nodes = num_worker_nodes + 1  # Add 1 for the primary node
+  logging.info('num nodes: %s', num_nodes)
+
+  accelerate_args = argparse.Namespace()
+  accelerate_args.machine_rank = node_rank
+  accelerate_args.num_machines = num_nodes
+  accelerate_args.main_process_ip = primary_node_addr
+  accelerate_args.main_process_port = primary_node_port
+  accelerate_args.max_restarts = 0
+  accelerate_args.monitor_interval = 120
+
+  return accelerate_args
+
+
+def _append_args_to_command_in_place(
+    args: argparse.Namespace, command: MutableSequence[str]
+):
+  for key, value in vars(args).items():
+    # If not specified, skip.
+    if value is not None:
+      command.append(f'--{key}={value}')
+
+
+def _is_gcs_or_gcsfuse_path(path: str) -> bool:
+  """Returns if the path is a GCS or gcsfuse path.
+
+  Args:
+    path: The path to check.
+
+  Returns:
+    True if the path is a GCS or gcsfuse path.
+  """
+  return path.startswith(
+      (constants.GCS_URI_PREFIX, constants.GCSFUSE_URI_PREFIX)
+  )
+
+
+def _manage_training_path(path: str, node_rank: int) -> tuple[str, str]:
+  """Returns local dir and GCS location for the given path if the given path is a GCS or gcsfuse path.
+
+  It will also create a local directory if it does not exist. Othereise, it
+  returns the same path.
+
+  Args:
+    path: The local or GCS path to manage.
+    node_rank: The node rank to be appended to the GCS path.
+
+  Returns:
+    The local and GCS paths.
+  """
+  local_dir = path
+  gcs_dir = path
+  if _is_gcs_or_gcsfuse_path(path):
+    local_dir = os.path.join(
+        constants.LOCAL_OUTPUT_DIR,
+        dataset_validation_util.force_gcs_fuse_path(path)[1:],
+    )
+    gcs_dir = fileutils.force_gcs_path(path)
+    os.makedirs(local_dir, exist_ok=True)
+  return local_dir, os.path.join(gcs_dir, f'node-{node_rank}')
+
+
+def _get_train_and_maybe_merge_cmd_and_dirs_to_sync(
+    task_type: str, config_file: str, unknown: Sequence[str]
+) -> Sequence[Sequence[str]]:
+  """Returns the training and merge command(if applicable) and dirs to sync.
+
+  Args:
+    task_type: The task type.
+    config_file: The accelerate config file path.
+    unknown: The unknown args which are not recognised by the parser.
+
+  Returns:
+    The bash commands to execute and the directories to sync.
+  """
+  dirs_to_sync = []
+  # Only populated when multi-node is used.
+  accelerate_args = _get_accelerate_args()
+  node_rank = getattr(accelerate_args, 'machine_rank', 0)
+  training_cmd = launch_script_cmd(
+      _TASK_TO_SCRIPT[task_type],
+      config_file,
+      accelerate_args=accelerate_args,
+  )
+
+  # Training only flag.
+  train_parser = argparse.ArgumentParser()
+  train_parser.add_argument('--output_dir', required=True)
+  training_args, unknown = train_parser.parse_known_args(unknown)
+  # Checks for `hypertune_utils._ENVIRONMENT_VARIABLE_FOR_TRIAL_ID` env var and
+  # appends the trial id if it exists.
+  training_args.output_dir = hypertune_utils.maybe_append_trial_id(
+      dataset_validation_util.force_gcs_fuse_path(training_args.output_dir)
+  )
+
+  local_output_dir, gcs_output_dir = _manage_training_path(
+      training_args.output_dir, node_rank
+  )
+  training_args.output_dir = local_output_dir
+  if _is_gcs_or_gcsfuse_path(gcs_output_dir):
+    dirs_to_sync.append((local_output_dir, gcs_output_dir))
+
+  # Merge only flags.
+  merge_parser = argparse.ArgumentParser()
+  merge_parser.add_argument('--merge_model_precision_mode')
+  merge_parser.add_argument('--merge_base_and_lora_output_dir')
+  merge_args, unknown = merge_parser.parse_known_args(unknown)
+
+  if merge_args.merge_base_and_lora_output_dir:
+    merge_local_dir, merge_gcs_dir = _manage_training_path(
+        merge_args.merge_base_and_lora_output_dir, node_rank
+    )
+    merge_args.merge_base_and_lora_output_dir = merge_local_dir
+    if _is_gcs_or_gcsfuse_path(merge_gcs_dir):
+      dirs_to_sync.append((merge_local_dir, merge_gcs_dir))
+
+  # Common flags shared by merging and training.
+  common_parser = argparse.ArgumentParser()
+  common_parser.add_argument('--pretrained_model_name_or_path', required=True)
+  common_parser.add_argument('--huggingface_access_token')
+  common_args, remaining = common_parser.parse_known_args(unknown)
+
+  # Add flags for training.
+  _append_args_to_command_in_place(training_args, training_cmd)
+  _append_args_to_command_in_place(common_args, training_cmd)
+  training_cmd.extend(remaining)  # Remaining args are passed to training cmd.
+  commands = [training_cmd]
+
+  # Only the main node runs merging.
+  if merge_args.merge_base_and_lora_output_dir and node_rank == 0:
+    lora_dir = utils.get_final_checkpoint_path(training_args.output_dir)
+    lora_local_dir, lora_gcs_dir = _manage_training_path(lora_dir, node_rank)
+    if _is_gcs_or_gcsfuse_path(lora_gcs_dir):
+      dirs_to_sync.append((lora_local_dir, lora_gcs_dir))
+
+    merge_cmd = [
+        'WORLD_SIZE=1',  # To ignore other nodes in multi-node setting.
+        'python3',
+        _TASK_TO_SCRIPT[constants.MERGE_CAUSAL_LANGUAGE_MODEL_LORA],
+        f'--finetuned_lora_model_dir={lora_local_dir}',
+    ]
+    _append_args_to_command_in_place(merge_args, merge_cmd)
+    _append_args_to_command_in_place(common_args, merge_cmd)
+
+    # Run in a conda environment.
+    conda_run_cmd = [
+        '/bin/bash',
+        '-c',
+        f'conda run -n merge {" ".join(merge_cmd)}',
+    ]
+    commands.append(conda_run_cmd)
+
+  return commands, dirs_to_sync
+
+
+def _setup_gcs_rsync(
+    dirs_to_sync: Sequence[tuple[str, str]],
+    mp_queue: multiprocessing.Queue,
+    gcs_rsync_interval_secs: int,
+) -> multiprocessing.Process:
+  """Sets up the GCS rsync process.
+
+  Args:
+    dirs_to_sync: The absolute directory paths which will be synced to GCS.
+    mp_queue: The multiprocessing queue to check if the training is finished.
+    gcs_rsync_interval_secs: Integer, interval in seconds to run gcs rsync.
+
+  Returns:
+    The GCS rsync process.
+  """
+  rsync_process = multiprocessing.Process(
+      target=gcs_syncer.start_gcs_rsync,
+      args=(dirs_to_sync, mp_queue, gcs_rsync_interval_secs),
+  )
+  rsync_process.start()
+  return rsync_process
+
+
+def _cleanup_gcs_rsync(
+    rsync_process: multiprocessing.Process, mp_queue: multiprocessing.Queue
+) -> None:
+  """Cleans up the GCS rsync process.
+
+  Args:
+    rsync_process: The GCS rsync process.
+    mp_queue: The multiprocessing queue.
+  """
+  mp_queue.put('training finished')
+  rsync_process.join()
+  if rsync_process.exitcode == 0:
+    logging.info('Artifacts have been uploaded to GCS.')
+  else:
+    logging.error(
+        'GCS rsync process failed with exit code %d.', rsync_process.exitcode
+    )
+
+
+def main(unused_argv: Sequence[str]) -> None:
+  parser = argparse.ArgumentParser()
+  parser.add_argument('--config_file')
+  parser.add_argument('--task')
+  parser.add_argument('--gcs_rsync_interval_secs', type=int, default=60)
+  args, unknown = parser.parse_known_args()
+
+  task = args.task
+  dirs_to_sync = None
+
+  if task in _TEXT_TO_IMAGE_TASKS_SCRIPTS:
+    # Setup accelerate config before running trainer.
+    config_gen_cmd = [
+        'python',
+        '-c',
+        (
+            'from accelerate.utils import write_basic_config;'
+            ' write_basic_config(mixed_precision="fp16")'
+        ),
+    ]
+    task_cmd = [
+        'accelerate',
+        'launch',
+        _TEXT_TO_IMAGE_TASKS_SCRIPTS[task],
+    ] + list(map(dataset_validation_util.force_gcs_fuse_path, unknown))
+    commands = [config_gen_cmd, task_cmd]
+  elif task in [constants.INSTRUCT_LORA]:
+    commands, dirs_to_sync = _get_train_and_maybe_merge_cmd_and_dirs_to_sync(
+        task_type=task, config_file=args.config_file, unknown=unknown
+    )
+  else:
+    assert task in _TASK_TO_SCRIPT
+    cmd = launch_script_cmd(_TASK_TO_SCRIPT[task], args.config_file)
+    cmd.extend(unknown)
+    commands = [cmd]
+
+  rsync_process = None
+  mp_queue = multiprocessing.Queue(maxsize=1)
+  if dirs_to_sync:
+    rsync_process = _setup_gcs_rsync(
+        dirs_to_sync, mp_queue, args.gcs_rsync_interval_secs
+    )
+
+  for cmd in commands:
+    logging.info('launching task=%s with cmd: \n%s', task, ' \\\n'.join(cmd))
+    # Both absl logging and python's logging module writes to stderr by default.
+    # Redirect output to stdout on purpose, such that log entries do not get
+    # marked as `Error` in Cloud's Log Explorer.
+    try:
+      subprocess.run(cmd, stdout=sys.stdout, stderr=sys.stdout, check=True)
+    except subprocess.CalledProcessError as e:
+      if rsync_process is not None and rsync_process.is_alive():
+        logging.info('Terminating GCS rsync process.')
+        rsync_process.terminate()
+      raise e
+  if rsync_process is not None:
+    _cleanup_gcs_rsync(rsync_process, mp_queue)
+
+
+if __name__ == '__main__':
+  logging.get_absl_handler().python_handler.stream = sys.stdout
+  app.run(main, flags_parser=lambda _args: flags.FLAGS(_args, known_only=True))

community-content/vertex_model_garden/model_oss/peft/train/vmg/utils.py

@@ -0,0 +1,524 @@
+"""Common libraries for PEFT."""
+
+from collections.abc import Mapping, Sequence
+import dataclasses
+import datetime
+import gc
+import os
+from typing import Any
+
+from absl import logging
+import accelerate
+from accelerate import DistributedType
+from accelerate import PartialState
+import numpy as np
+import peft
+from peft import PeftModel
+from peft import prepare_model_for_kbit_training
+import psutil
+import pynvml
+import torch
+import transformers
+from transformers import AutoModelForCausalLM
+from transformers import AutoTokenizer
+from transformers import BitsAndBytesConfig
+from transformers import FbgemmFp8Config
+import trl
+
+from util import dataset_validation_util
+from util import constants
+
+
+_LLAMA_3_1_405B_MODEL_ID = "Meta-Llama-3.1-405B"
+_LOCAL_MERGED_MODEL_DIR = "/tmp/merged_model"
+_GEMMA2_MODEL = "gemma-2"
+
+
+def load_model(
+    pretrained_model_name_or_path: str,
+    tokenizer: AutoTokenizer,
+    precision_mode: str = None,
+    gradient_checkpointing: bool = False,
+    gradient_checkpointing_kwargs: Mapping[str, Any] | None = None,
+    access_token: str | None = None,
+    attn_implementation: str | None = None,
+    train_precision: str | None = None,
+    device_map: str | None = None,
+    is_training: bool = True,
+) -> AutoModelForCausalLM:
+  """Loads models from the local dir if specified or from huggingface."""
+  # The `distributed_type` we got through `PartialState` is incorrect for FSDP.
+  # And that's why `Accelerator` is used here.
+  # See b/357138252 for more details.
+  accelerator = accelerate.Accelerator()
+  logging.info("using distributed_type %s", accelerator.distributed_type)
+
+  if device_map is None:
+    if accelerator.distributed_type == DistributedType.MULTI_GPU:
+      # https://github.com/artidoro/qlora/issues/186#issuecomment-1943045599
+      # and b/342038175.
+      device_map = {"": accelerator.process_index}
+    elif accelerator.distributed_type == DistributedType.DEEPSPEED:
+      # Deepspeed Zero3 does not allow setting device_map.
+      # https://github.com/huggingface/transformers/blob/v4.38.2/src/transformers/modeling_utils.py#L2941-L2943
+      device_map = None
+    elif accelerator.distributed_type == DistributedType.FSDP:
+      if precision_mode in [
+          constants.PRECISION_MODE_4,
+          constants.PRECISION_MODE_8,
+      ]:
+        device_map = trl.get_kbit_device_map()
+      else:
+        device_map = None
+    elif (
+        accelerator.distributed_type == DistributedType.NO
+        and torch.cuda.device_count() > 1
+    ):
+      # Setting device map to None to avoid using model parallelism (MP) when
+      # there are multiple GPUs, which can have very inefficient GPU utilization
+      # (b/342252819). This setting should trigger torch's nn.DataParallel
+      # instead, which has better GPU utilization.
+      device_map = None
+    else:
+      device_map = "auto"
+  logging.info("using device_map %s", device_map)
+
+  if train_precision == constants.PRECISION_MODE_32:
+    train_dtype = torch.float32
+  elif train_precision == constants.PRECISION_MODE_16:
+    train_dtype = torch.float16
+  elif train_precision == constants.PRECISION_MODE_16B:
+    train_dtype = torch.bfloat16
+  else:
+    train_dtype = "auto"
+
+  quantization_config = None
+  # Note: use_cache is False when enable gradient checkpointing.
+  if precision_mode == constants.PRECISION_MODE_32:
+    torch_dtype = torch.float32
+  elif precision_mode == constants.PRECISION_MODE_16:
+    torch_dtype = torch.float16
+  elif precision_mode == constants.PRECISION_MODE_16B:
+    torch_dtype = torch.bfloat16
+  elif precision_mode == constants.PRECISION_MODE_8:
+    quantization_config = BitsAndBytesConfig(
+        load_in_8bit=True, int8_threshold=0
+    )
+    torch_dtype = train_dtype
+  elif precision_mode == constants.PRECISION_MODE_4:
+    quantization_config = BitsAndBytesConfig(
+        load_in_4bit=True,
+        bnb_4bit_use_double_quant=True,
+        bnb_4bit_quant_type="nf4",
+        bnb_4bit_compute_dtype=train_dtype,
+    )
+    # `bnb_4bit_quant_storage` must be set when using FSDP.
+    # https://huggingface.co/docs/bitsandbytes/main/en/fsdp_qlora
+    if accelerator.distributed_type == DistributedType.FSDP:
+      quantization_config.bnb_4bit_quant_storage = train_dtype
+    torch_dtype = train_dtype
+  else:
+    raise ValueError(f"Invalid precision mode: {precision_mode}")
+  logging.info("using torch_type=%s", torch_dtype)
+
+  model_kwargs = {
+      "use_cache": not gradient_checkpointing,
+      "device_map": device_map,
+      "torch_dtype": torch_dtype,
+      "quantization_config": quantization_config,
+      "trust_remote_code": True,
+      "token": access_token,
+      "attn_implementation": attn_implementation,
+  }
+  if _GEMMA2_MODEL in pretrained_model_name_or_path:
+    # The cache_implementation for Gemma 2 is set to hybrid by default. This
+    # param is only supported by Gemma 2. The default 'hybrid' value causes an
+    # issue when use_cache is set to False. So we have to use 'None' in such
+    # cases.
+    # https://github.com/huggingface/transformers/commit/238b13478df209ab534f2195a397dc64a3930883
+    model_kwargs["cache_implementation"] = (
+        None if gradient_checkpointing else "hybrid"
+    )
+
+  model = AutoModelForCausalLM.from_pretrained(
+      pretrained_model_name_or_path, **model_kwargs
+  )
+
+  if precision_mode in (constants.PRECISION_MODE_4, constants.PRECISION_MODE_8):
+    model = prepare_model_for_kbit_training(
+        model,
+        use_gradient_checkpointing=gradient_checkpointing,
+        gradient_checkpointing_kwargs=gradient_checkpointing_kwargs,
+    )
+
+  if gradient_checkpointing:
+    model.gradient_checkpointing_enable(
+        gradient_checkpointing_kwargs=gradient_checkpointing_kwargs
+    )
+
+  # Flash attention only supports fp16 or bf16 [1].
+  # prepare_model_for_kbit_training will force cast some layers to float32 [2]
+  #
+  # [1]: https://github.com/Dao-AILab/flash-attention/issues/882
+  # [2]: https://github.com/huggingface/peft/blob/v0.10.0/src/peft/utils/other.py#L79-L81 # pylint: disable=line-too-long
+  if attn_implementation == "flash_attention_2" and precision_mode in (
+      constants.PRECISION_MODE_4,
+      constants.PRECISION_MODE_8,
+  ):
+    for _, param in model.named_parameters():
+      if param.dtype == torch.float32:
+        param.data = param.data.to(torch_dtype)
+
+  if is_training:
+    # KV cache is useless during training
+    # https://stackoverflow.com/a/77408076
+    model.config.use_cache = False
+
+  if dataset_validation_util.should_add_pad_token(
+      pretrained_model_name_or_path
+  ):
+    model.resize_token_embeddings(len(tokenizer), mean_resizing=False)
+    if is_training:
+      # The following is needed since we added a new token that needs to be
+      # learned.
+      # https://github.com/QwenLM/Qwen/issues/405#issuecomment-1751680291
+      model.enable_input_require_grads()
+
+  return model
+
+
+def _merge_causal_language_model_with_lora_internal(
+    pretrained_model_name_or_path: str,
+    merge_precision_mode: str,
+    finetuned_lora_model_dir: str,
+    merged_model_output_dir: str,
+    access_token: str | None = None,
+) -> None:
+  """Internal function to merges the base model with the lora adapter."""
+  logging.info("loading tokenizer...")
+  tokenizer = dataset_validation_util.load_tokenizer(
+      pretrained_model_name_or_path
+  )
+
+  # Note: merging peft adapter requires loading model in 16 bits, so merging
+  # is done on CPU on purpose in case one GPU cannot hold the base model.
+  logging.info("loading model %s...", pretrained_model_name_or_path)
+  device_map = "cpu"
+  model = load_model(
+      pretrained_model_name_or_path=pretrained_model_name_or_path,
+      tokenizer=tokenizer,
+      precision_mode=merge_precision_mode,
+      access_token=access_token,
+      device_map=device_map,
+      is_training=False,
+  )
+
+  logging.info("loading LoRA model...")
+  model = PeftModel.from_pretrained(
+      model, finetuned_lora_model_dir, device_map=device_map
+  )
+
+  logging.info("merging base model with finetuned LoRA model...")
+  model = model.merge_and_unload()
+
+  logging.info("saving model to %s...", merged_model_output_dir)
+  model.save_pretrained(
+      merged_model_output_dir,
+      safe_serialization=False,
+      is_main_process=PartialState().is_main_process,
+  )
+
+  logging.info("saving tokenizer to %s...", merged_model_output_dir)
+  tokenizer.save_pretrained(
+      merged_model_output_dir,
+      is_main_process=PartialState().is_main_process,
+  )
+
+
+def merge_causal_language_model_with_lora(
+    pretrained_model_name_or_path: str,
+    precision_mode: str,
+    finetuned_lora_model_dir: str,
+    merged_model_output_dir: str,
+    access_token: str | None = None,
+) -> None:
+  """Merges the base model with the lora adapter."""
+
+  # Set merge related variables.
+  if precision_mode == constants.PRECISION_MODE_FP8:
+    # Merge as FP16. FP8 requires conversion after merge.
+    merge_precision_mode = constants.PRECISION_MODE_16
+    local_merged_model_dir = _LOCAL_MERGED_MODEL_DIR
+  else:
+    merge_precision_mode = precision_mode
+    local_merged_model_dir = merged_model_output_dir
+
+  if PartialState().is_main_process:
+    logging.info("Starting merging job...")
+    _merge_causal_language_model_with_lora_internal(
+        pretrained_model_name_or_path=pretrained_model_name_or_path,
+        merge_precision_mode=merge_precision_mode,
+        finetuned_lora_model_dir=finetuned_lora_model_dir,
+        merged_model_output_dir=local_merged_model_dir,
+        access_token=access_token,
+    )
+    logging.info("merging job is done")
+
+  # Wait for all processes to sync here.
+  PartialState().wait_for_everyone()
+
+  if precision_mode == constants.PRECISION_MODE_FP8:
+    convert_model_to_fp8(
+        pretrained_model_name_or_path=pretrained_model_name_or_path,
+        merged_model_output_dir=local_merged_model_dir,
+        quantized_model_output_dir=merged_model_output_dir,
+        access_token=access_token,
+    )
+
+
+def convert_model_to_fp8(
+    pretrained_model_name_or_path: str,
+    merged_model_output_dir: str,
+    quantized_model_output_dir: str,
+    access_token: str | None = None,
+) -> None:
+  """Converts the model to fp8.
+
+  Args:
+    pretrained_model_name_or_path: Original base model name or path.
+    merged_model_output_dir: Path to directory containing the merged model.
+    quantized_model_output_dir: Path to directory to save the quantized model.
+    access_token: Access token for accessing the model.
+  """
+  if PartialState().is_main_process:
+    quantization_config = FbgemmFp8Config(
+        modules_to_not_convert=_maybe_get_modules_to_not_convert_by_model_id(
+            pretrained_model_name_or_path
+        )
+    )
+    quantized_model = AutoModelForCausalLM.from_pretrained(
+        merged_model_output_dir,
+        device_map="cpu",
+        quantization_config=quantization_config,
+        trust_remote_code=False,
+        token=access_token,
+    )
+    tokenizer = AutoTokenizer.from_pretrained(merged_model_output_dir)
+
+    quantized_model.save_pretrained(quantized_model_output_dir)
+    tokenizer.save_pretrained(quantized_model_output_dir)
+  PartialState().wait_for_everyone()
+
+
+@dataclasses.dataclass
+class TuningDataStats:
+  tuning_dataset_example_count: int
+  total_billable_token_count: int
+  tuning_step_count: int
+
+
+def get_dataset_stats(
+    dataset: Any,
+    tokenizer: transformers.PreTrainedTokenizer,
+    column: str,
+    effective_batch_size: int,
+) -> TuningDataStats:
+  """Calculates dataset statistics, e.g., total number of tokens."""
+  tokenized_dataset = dataset.map(lambda x: tokenizer(x[column]))
+  inputs = tokenized_dataset["input_ids"]
+  tuning_dataset_example_count = int(len(inputs))
+  total_billable_token_count = int(np.sum([len(ex) for ex in inputs]))
+  tuning_step_count = (
+      tuning_dataset_example_count + effective_batch_size - 1
+  ) // effective_batch_size
+  return TuningDataStats(
+      tuning_dataset_example_count,
+      total_billable_token_count,
+      tuning_step_count,
+  )
+
+
+def force_gc():
+  """Collects garbage immediately to release unused CPU/GPU resources."""
+  gc.collect()
+  torch.cuda.empty_cache()
+
+
+@dataclasses.dataclass
+class GpuStats:
+  """Holds information about GPU usage stats.
+
+  For memory related, see
+  https://pytorch.org/docs/stable/notes/cuda.html#cuda-memory-management
+  """
+
+  # total memory
+  total_mem: float
+  # memory occupied.
+  occupied: float
+  # memory reserved, but not used.
+  unused: float
+  # nvidia-smi usually reports more memory usages than pytorch (for driver,
+  # kernel and etc). `smi_diff` tracks this difference.
+  smi_diff: float
+  # Gpu utilization.
+  util: float
+
+  # Allows unpacking operation like
+  # total_mem, occupied, unused, smi_diff, util = GpuStats(...)
+  # See https://stackoverflow.com/a/70753113
+  def __iter__(self):
+    return iter(dataclasses.astuple(self))
+
+
+def gpu_stats() -> GpuStats:
+  """Reports GPU memory usage and utilization."""
+  # See https://pytorch.org/docs/stable/notes/cuda.html#memory-management
+  bytes_per_gb = 1024.0**3
+  device = torch.cuda.current_device()
+  occupied = torch.cuda.memory_allocated(device) / bytes_per_gb
+  reserved = torch.cuda.memory_reserved(device) / bytes_per_gb
+  unused = reserved - occupied
+
+  def smi_mem(device):
+    try:
+      pynvml.nvmlInit()
+      handle = pynvml.nvmlDeviceGetHandleByIndex(device)
+      info = pynvml.nvmlDeviceGetMemoryInfo(handle)
+      return info.used / bytes_per_gb
+    except pynvml.NVMLError:
+      return 0.0
+
+  mem_used_smi = smi_mem(device)
+  smi_diff = mem_used_smi - reserved
+
+  util = torch.cuda.utilization(device)
+  return GpuStats(mem_used_smi, occupied, unused, smi_diff, util)
+
+
+def gpu_stats_str(stats: GpuStats | None = None) -> str:
+  if stats is None:
+    stats = gpu_stats()
+  total, occupied, unused, smi_diff, util = stats
+  return (
+      f"GPU memory: {total:.2f}({occupied=:.2f}, {unused=:.2f},"
+      f" {smi_diff=:.2f}) GB. Utilization: {util:.2f}%"
+  )
+
+
+@dataclasses.dataclass
+class CpuStats:
+  """Holds information about CPU usage stats."""
+
+  # Total CPU virtual memory i.e. virtual memory allocated + unallocated.
+  total_virtual_mem: float
+  # CPU virtual memory available for use.
+  unallocated_virtual_mem: float
+  # CPU virtual memory already used.
+  allocated_virtual_mem: float
+  # Total CPU swap memory i.e. swap memory allocated + unallocated.
+  total_swap_mem: float
+  # CPU swap memory available for use.
+  unallocated_swap_mem: float
+  # CPU swap memory already used.
+  allocated_swap_mem: float
+  # CPU utilization percentage.
+  utilization: float
+
+
+def cpu_stats() -> CpuStats:
+  """Reports CPU memory usage and utilization."""
+
+  # https://psutil.readthedocs.io/en/latest/#memory
+  gb = 1024.0**3
+  vmem = psutil.virtual_memory()
+  vmem_total = vmem.total / gb
+  vmem_available = vmem.available / gb
+  vmem_used = vmem_total - vmem_available
+  smem = psutil.swap_memory()
+  swap_total = smem.total / gb
+  swap_free = smem.free / gb
+  swap_used = smem.used / gb
+  # https://psutil.readthedocs.io/en/latest/#psutil.cpu_percent
+  cpu_util = psutil.cpu_percent(interval=1e-6)
+  return CpuStats(
+      total_virtual_mem=vmem_total,
+      unallocated_virtual_mem=vmem_available,
+      allocated_virtual_mem=vmem_used,
+      total_swap_mem=swap_total,
+      unallocated_swap_mem=swap_free,
+      allocated_swap_mem=swap_used,
+      utilization=cpu_util,
+  )
+
+
+def cpu_stats_str(stats: CpuStats | None = None) -> str:
+  """Returns a string representation of the CPU stats."""
+
+  if stats is None:
+    stats = cpu_stats()
+  total, occupied, unused = (
+      stats.total_virtual_mem,
+      stats.allocated_virtual_mem,
+      stats.unallocated_virtual_mem,
+  )
+  virtual_mem = (
+      f"CPU virtual memory: {total:.2f}({occupied=:.2f}, {unused=:.2f}) GB"
+  )
+  total, occupied, unused = (
+      stats.total_swap_mem,
+      stats.allocated_swap_mem,
+      stats.unallocated_swap_mem,
+  )
+  swap_mem = f"CPU swap memory: {total:.2f}({occupied=:.2f}, {unused=:.2f}) GB"
+  percent = stats.utilization
+  return f"{virtual_mem} {swap_mem} CPU Utilization: {percent:.2f}%"
+
+
+def init_partial_state(
+    timeout: datetime.timedelta = datetime.timedelta(seconds=600),
+) -> None:
+  """Initializes the partial state with timeout."""
+  # This needs to be called before any other PartialState() calls, and
+  # TrainingArguments needs `use_configured_state`. See b/357970482#comment3
+  # for more details.
+  PartialState(timeout=timeout)
+
+
+def print_library_versions():
+  if PartialState().is_main_process:
+    logging.info("======================")
+    logging.info("library versions")
+    logging.info("======================")
+    logging.info("accelerate: %s", accelerate.__version__)
+    logging.info("peft: %s", peft.__version__)
+    logging.info("transformers: %s", transformers.__version__)
+    logging.info("trl: %s", trl.__version__)
+  PartialState().wait_for_everyone()
+
+
+def get_final_checkpoint_path(output_dir: str) -> str:
+  """Returns the final checkpoint path."""
+  return os.path.join(output_dir, constants.FINAL_CHECKPOINT_DIRNAME)
+
+
+def _maybe_get_modules_to_not_convert_by_model_id(
+    pretrained_model_name_or_path: str,
+) -> Sequence[str] | None:
+  """Returns the modules to not convert for the model."""
+  if _LLAMA_3_1_405B_MODEL_ID in pretrained_model_name_or_path:
+    return _get_llama_3_1_405b_modules_to_not_convert()
+  else:
+    return None
+
+
+def _get_llama_3_1_405b_modules_to_not_convert() -> Sequence[str]:
+  """Returns the modules to not convert for Llama 3.1 405B model."""
+  modules_to_not_convert = ["lm_head"]
+  for idx in range(126):
+    for proj_name in ["k_proj", "o_proj", "q_proj", "v_proj"]:
+      modules_to_not_convert.append(f"model.layers.{idx}.self_attn.{proj_name}")
+  for proj_name in ["down_proj", "gate_proj", "up_proj"]:
+    modules_to_not_convert.append(f"model.layers.0.mlp.{proj_name}")
+    modules_to_not_convert.append(f"model.layers.125.mlp.{proj_name}")
+  return tuple(modules_to_not_convert)

community-content/vertex_model_garden/model_oss/peft/train/vmg/validate_dataset_with_template.py

@@ -0,0 +1,84 @@
+"""Validate the dataset with the template."""
+
+from typing import Sequence
+
+from absl import app
+from absl import flags
+
+from util import dataset_validation_util
+from vertex_vision_model_garden_peft.train.vmg import utils
+from util import constants
+
+
+_DATASET_NAME = flags.DEFINE_string(
+    'dataset_name',
+    None,
+    'The dataset name in huggingface.',
+    required=True,
+)
+
+_TRAIN_SPLIT = flags.DEFINE_string(
+    'train_split',
+    'train',
+    'The train split name.',
+)
+
+_TRAIN_COLUMN = flags.DEFINE_string(
+    'train_column',
+    constants.DEFAULT_TRAIN_COLUMN,
+    'The instruct column in dataset.',
+)
+
+_TEMPLATE = flags.DEFINE_string(
+    'template',
+    None,
+    'Template for formatting language model training data. Must be a filename'
+    ' under `templates` folder, without `.json` extension, e.g. `alpaca`, or a'
+    ' Cloud Storage URI to a JSON file.',
+    required=True,
+)
+
+_MAX_SEQ_LENGTH = flags.DEFINE_integer(
+    'max_seq_length',
+    None,
+    'The maximum sequence length.',
+)
+
+_VALIDATE_PERCENTAGE_OF_DATASET = flags.DEFINE_integer(
+    'validate_percentage_of_dataset',
+    None,
+    'The percentage of the dataset to validate with the template. If set to'
+    ' -1, it loads the full dataset.',
+)
+
+_VALIDATE_K_ROWS_OF_DATASET = flags.DEFINE_integer(
+    'validate_k_rows_of_dataset',
+    None,
+    'The top k rows of the dataset to validate with the template. If set to -1,'
+    ' it loads the full dataset.',
+)
+
+_USE_MULTIPROCESSING = flags.DEFINE_boolean(
+    'use_multiprocessing',
+    False,
+    'Whether to use multiprocessing for loading the dataset.',
+)
+
+
+def main(unused_argv: Sequence[str]) -> None:
+  utils.print_library_versions()
+
+  dataset_validation_util.validate_dataset_with_template(
+      dataset_name=_DATASET_NAME.value,
+      split=_TRAIN_SPLIT.value,
+      input_column=_TRAIN_COLUMN.value,
+      template=_TEMPLATE.value,
+      max_seq_length=_MAX_SEQ_LENGTH.value,
+      use_multiprocessing=_USE_MULTIPROCESSING.value,
+      validate_percentage_of_dataset=_VALIDATE_PERCENTAGE_OF_DATASET.value,
+      validate_k_rows_of_dataset=_VALIDATE_K_ROWS_OF_DATASET.value,
+  )
+
+
+if __name__ == '__main__':
+  app.run(main)

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 @@ import yaml
 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.
 
@@ -40,14 +62,14 @@ 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.
 
@@ -55,9 +77,10 @@ def get_object_detection_endpoint_predictions(
       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 @@ BEST_CKPT_METRIC_COMP = 'higher'
 
 # 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_TRAINING = 'training'
 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'
@@ -60,26 +66,91 @@ COCO_JSON_IMAGE_HEIGHT = 'height'
 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'
 INSTRUCT_LORA = 'instruct-lora'
+VALIDATE_DATASET_WITH_TEMPLATE = 'validate-dataset-with-template'
+RUN_TESTS = 'test'
+DEFAULT_TEXT_COLUMN_IN_DATASET = 'quote'
+DEFAULT_TRAIN_COLUMN = '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'
+
+ROUGE_VARIANTS = ('rouge1', 'rouge2', 'rougeL', 'rougeLsum')
+
+# Supported HF evaluation metrics.
+SUPPORTED_HF_EVAL_METRICS = (
+    'perplexity',
+    'bleu',
+    'google_bleu',
+) + ROUGE_VARIANTS
+
+# Supported evaluation metrics.
+SUPPORTED_EVAL_METRICS = ('loss',) + SUPPORTED_HF_EVAL_METRICS
+
+# 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'

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

@@ -1,10 +1,10 @@
 """Fileutil lib to copy files between gcs and local."""
 
-import glob
+import fnmatch
 import os
 import pathlib
 import shutil
-from typing import Tuple
+from typing import List, Optional, Tuple
 import uuid
 
 from absl import logging
@@ -13,6 +13,17 @@ from google.cloud import storage
 from util import constants
 
 
+_GCS_CLIENT = None
+
+
+def _get_gcs_client() -> storage.Client:
+  """Gets the default GCS client."""
+  global _GCS_CLIENT
+  if _GCS_CLIENT is None:
+    _GCS_CLIENT = storage.Client()
+  return _GCS_CLIENT
+
+
 def generate_tmp_path(extension: str = '') -> str:
   """Generates a temporary file path with UUID.
 
@@ -36,6 +47,33 @@ def force_gcs_fuse_path(gcs_uri: str) -> str:
     return gcs_uri
 
 
+def force_gcs_path(uri: str) -> str:
+  """Converts /gcs/ uris to their gs:// equivalents. No-op for other uris."""
+  if uri.startswith(constants.GCSFUSE_URI_PREFIX):
+    return uri.replace(
+        constants.GCSFUSE_URI_PREFIX, constants.GCS_URI_PREFIX, 1
+    )
+  else:
+    return uri
+
+
+def download_gcs_file_to_memory(gcs_uri: str) -> bytes:
+  """Downloads a gcs file to in memory.
+
+  Args:
+    gcs_uri: A string of GCS uri.
+
+  Returns:
+    The content of the gcs file in byte format.
+  """
+  bucket = gcs_uri.split('/')[2]
+  file_path = gcs_uri[len(constants.GCS_URI_PREFIX + bucket + '/') :]
+  client = _get_gcs_client()
+  bucket = client.bucket(bucket)
+  blob = bucket.blob(file_path)
+  return blob.download_as_bytes()
+
+
 def download_gcs_file_to_local_dir(gcs_uri: str, local_dir: str):
   """Download a gcs file to a local dir.
 
@@ -62,15 +100,47 @@ def download_gcs_file_to_local(gcs_uri: str, local_path: str):
     raise ValueError(
         f'{gcs_uri} is not a GCS path starting with {constants.GCS_URI_PREFIX}.'
     )
-  client = storage.Client()
+  client = _get_gcs_client()
   os.makedirs(os.path.dirname(local_path), exist_ok=True)
   with open(local_path, 'wb') as f:
     client.download_blob_to_file(gcs_uri, f)
 
 
+def download_gcs_file_list_to_local(
+    gcs_uri_list: List[str], local_dir: str
+) -> List[str]:
+  """Downloads a list of GCS files to a local directory.
+
+  Args:
+    gcs_uri_list: A list of GCS file paths.
+    local_dir: Local directory in which the GCS files are saved.
+
+  Returns:
+    The local file paths corresponding to the input GCS file paths.
+
+  Raises:
+    ValueError: An input file path is not a GCS path.
+  """
+  local_paths = []
+  for gcs_uri in gcs_uri_list:
+    if not is_gcs_path(gcs_uri):
+      raise ValueError(
+          f'{gcs_uri} is not a GCS path starting with'
+          f' {constants.GCS_URI_PREFIX}.'
+      )
+    local_path = os.path.join(local_dir, gcs_uri.replace('gs://', ''))
+    download_gcs_file_to_local(gcs_uri, local_path)
+    local_paths.append(local_path)
+  return local_paths
+
+
 def download_gcs_dir_to_local(
-    gcs_dir: str, local_dir: str, skip_hf_model_bin: bool = False
-):
+    gcs_dir: str,
+    local_dir: str,
+    skip_hf_model_bin: bool = False,
+    allow_patterns: Optional[List[str]] = None,
+    log: bool = True,
+) -> None:
   """Downloads files in a GCS directory to a local directory.
 
   For example:
@@ -78,16 +148,21 @@ def download_gcs_dir_to_local(
     gs://bucket/foo/a -> /tmp/bar/a
     gs://bucket/foo/b/c -> /tmp/bar/b/c
 
-  Arguments:
+  Args:
     gcs_dir: A string of directory path on GCS.
     local_dir: A string of local directory path.
     skip_hf_model_bin: True to skip downloading HF model bin files.
+    allow_patterns: A list of allowed patterns. If provided, only files matching
+      one or more patterns are downloaded.
+    log: True to log each downloaded file.
   """
   if not is_gcs_path(gcs_dir):
     raise ValueError(f'{gcs_dir} is not a GCS path starting with gs://.')
   bucket_name = gcs_dir.split('/')[2]
-  prefix = gcs_dir[len(constants.GCS_URI_PREFIX + bucket_name) :].strip('/')
-  client = storage.Client()
+  prefix = (
+      gcs_dir[len(constants.GCS_URI_PREFIX + bucket_name) :].strip('/') + '/'
+  )
+  client = _get_gcs_client()
   blobs = client.list_blobs(bucket_name, prefix=prefix)
   for blob in blobs:
     if blob.name[-1] == '/':
@@ -95,43 +170,63 @@ def download_gcs_dir_to_local(
     file_path = blob.name[len(prefix) :].strip('/')
     local_file_path = os.path.join(local_dir, file_path)
     os.makedirs(os.path.dirname(local_file_path), exist_ok=True)
+    if allow_patterns and all(
+        [not fnmatch.fnmatch(file_path, p) for p in allow_patterns]
+    ):
+      continue
     if (
         file_path.endswith(constants.HF_MODEL_WEIGHTS_SUFFIX)
         and skip_hf_model_bin
     ):
-      logging.info('Skip downloading model bin %s', file_path)
+      if log:
+        logging.info('Skip downloading model bin %s', file_path)
       with open(local_file_path, 'w') as f:
-        f.write(f'{constants.GCS_URI_PREFIX}{bucket_name}/{prefix}/{file_path}')
+        f.write(f'{constants.GCS_URI_PREFIX}{bucket_name}/{prefix}{file_path}')
     else:
-      logging.info('Downloading %s to %s', file_path, local_file_path)
+      if log:
+        logging.info('Downloading %s to %s', file_path, local_file_path)
       blob.download_to_filename(local_file_path)
 
 
+def _get_relative_paths(base_dir: str) -> List[str]:
+  """Gets relative paths of all files in a local base directory."""
+  path = pathlib.Path(base_dir)
+  relative_paths = []
+  for local_file in path.rglob('*'):
+    if os.path.isfile(local_file):
+      relative_path = os.path.relpath(local_file, base_dir)
+      relative_paths.append(relative_path)
+  return relative_paths
+
+
+def _upload_local_files_to_gcs(
+    relative_paths: List[str], local_dir: str, gcs_dir: str
+):
+  """Uploads local files to gcs."""
+  bucket_name = gcs_dir.split('/')[2]
+  blob_dir = '/'.join(gcs_dir.split('/')[3:])
+  client = _get_gcs_client()
+  bucket = client.bucket(bucket_name)
+  for relative_path in relative_paths:
+    blob = bucket.blob(os.path.join(blob_dir, relative_path))
+    blob.upload_from_filename(os.path.join(local_dir, relative_path))
+
+
 def upload_local_dir_to_gcs(local_dir: str, gcs_dir: str):
   """Uploads local dir to gcs.
 
   For example:
     upload_local_dir_to_gcs(/tmp/bar, gs://bucket/foo)
-    gs://bucket/foo/a -> /tmp/bar/a
-    gs://bucket/foo/b/c -> /tmp/bar/b/c
+    /tmp/bar/a -> gs://bucket/foo/a
+    /tmp/bar/b/c -> gs://bucket/foo/b/c
 
   Arguments:
     local_dir: A string of local directory path.
     gcs_dir: A string of directory path on GCS.
   """
-  bucket_name = gcs_dir.split('/')[2]
-  blob_dir = '/'.join(gcs_dir.split('/')[3:])
-  client = storage.Client()
-  bucket = client.bucket(bucket_name)
-  for local_file in glob.glob(local_dir + '/**'):
-    if os.path.isfile(local_file):
-      logging.info(
-          'Uploading %s to %s',
-          local_file,
-          os.path.join(constants.GCS_URI_PREFIX, bucket_name, blob_dir),
-      )
-      blob = bucket.blob(os.path.join(blob_dir, os.path.basename(local_file)))
-      blob.upload_from_filename(local_file)
+  # Relative paths of all files in local_dir.
+  relative_paths = _get_relative_paths(local_dir)
+  _upload_local_files_to_gcs(relative_paths, local_dir, gcs_dir)
 
 
 def upload_file_to_gcs_path(
@@ -155,7 +250,7 @@ def upload_file_to_gcs_path(
   if not source_path_obj.exists():
     raise RuntimeError(f'Source path does not exist: {source_path}')
 
-  storage_client = storage.Client()
+  storage_client = _get_gcs_client()
   source_file_path = source_path
   destination_file_uri = destination_uri
   logging.info('Uploading "%s" to "%s"', source_file_path, destination_file_uri)
@@ -174,7 +269,9 @@ def is_gcs_path(input_path: str) -> bool:
   Returns:
       True if the input path is a GCS path, False otherwise.
   """
-  return input_path.startswith(constants.GCS_URI_PREFIX)
+  return input_path is not None and input_path.startswith(
+      constants.GCS_URI_PREFIX
+  )
 
 
 def release_text_assets(
@@ -232,13 +329,10 @@ def download_video_from_gcs_to_local(video_file_path: str) -> Tuple[str, str]:
   """
   _, local_video_file_name = os.path.split(video_file_path)
   file_extension = os.path.splitext(video_file_path)[1]
-  if file_extension:
-    remote_video_file_name = local_video_file_name.replace(
-        file_extension, '_overlay.mp4'
-    )
-  else:
-    remote_video_file_name = local_video_file_name + '_overlay.mp4'
-  local_file_path = generate_tmp_path(file_extension)
+  remote_video_file_name = local_video_file_name.replace(
+      file_extension, '_overlay.mp4'
+  )
+  local_file_path = generate_tmp_path(os.path.splitext(video_file_path)[1])
   logging.info('Downloading %s to %s...', video_file_path, local_file_path)
   download_gcs_file_to_local(video_file_path, local_file_path)
   return local_file_path, remote_video_file_name
@@ -254,10 +348,7 @@ def get_output_video_file(video_output_file_path: str) -> str:
     str: Local video output file path.
   """
   file_extension = os.path.splitext(video_output_file_path)[1]
-  if file_extension:
-    out_local_video_file_name = video_output_file_path.replace(
-        file_extension, '_overlay' + file_extension
-    )
-  else:
-    out_local_video_file_name = video_output_file_path + '_overlay'
+  out_local_video_file_name = video_output_file_path.replace(
+      file_extension, '_overlay' + file_extension
+  )
   return out_local_video_file_name

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

@@ -20,3 +20,11 @@ def get_trial_id_from_environment() -> str:
         _ENVIRONMENT_VARIABLE_FOR_TRIAL_ID,
     )
   return os.environ.get(_ENVIRONMENT_VARIABLE_FOR_TRIAL_ID, '0')
+
+
+def maybe_append_trial_id(path: str) -> str:
+  """Appends trial_N to path if running in a Hyperparameter Tuning Job."""
+  trial_id = os.environ.get(_ENVIRONMENT_VARIABLE_FOR_TRIAL_ID)
+  if trial_id is None:
+    return path
+  return os.path.join(path, f'trial_{trial_id}')

community-content/vertex_pipeline_kfpv2/customjob.py

@@ -0,0 +1,41 @@
+from kfp.v2 import dsl
+
+@dsl.component(base_image='python:3.8',packages_to_install=['google-cloud-aiplatform==1.36.0'])
+def customjob(
+    project_id: str,
+    location: str,
+    staging_bucket: str,
+    experiment: str,
+    job_name: str,
+    script_path: str,
+    container_uri: str,
+    machine_type: str,
+):
+    import os
+    from google.cloud import aiplatform
+
+    aiplatform.init(
+        project=project_id,
+        location=location,
+        staging_bucket=staging_bucket,
+        experiment=experiment,
+    )
+    job = aiplatform.CustomJob.from_local_script(
+        display_name=job_name,
+        script_path=os.path.join(os.getcwd(), script_path),
+        container_uri=container_uri,
+        machine_type=machine_type,
+    )
+    job.run()
+
+@dsl.pipeline(name='run-customjob')
+def pipeline_customjob():
+    customjob("990000000009", "us-west1", "gs://staging-bucket/customjob",
+        "run-experiment", "custom-job", "customjob.py",
+        "gcr.io/path/to/model_name:latest", "n1-standard-4")
+
+if __name__ == "__main__":
+    from kfp.v2 import compiler
+    compiler.Compiler().compile(
+        pipeline_func=pipeline_customjob,
+        package_path='customjob.json')

community-content/vertex_pipeline_kfpv2/evaluate-model.py

@@ -0,0 +1,34 @@
+from kfp.v2 import dsl
+
+@dsl.component(base_image='python:3.8',packages_to_install=['google-cloud-aiplatform==1.36.0'])
+def evaluate_model(
+    model_name: str,
+    project_id: str,
+    location: str,
+    data_uris: str,
+):
+    from google.cloud import aiplatform
+
+    aiplatform.init(
+        project=project_id,
+        location=location,
+    )
+    experiment_model = aiplatform.get_experiment_model(model_name)
+    lr_model = experiment_model.load_model()
+    evaluate_job = lr_model.evaluate(
+        prediction_type="regression",
+        target_field_name="type",
+        data_source_uris=[data_uris],
+        staging_bucket="gs://model-bucket/evaluation",
+    )
+    evaluate_job.wait()
+
+@dsl.pipeline(name='model-evaluation')
+def pipeline_evaluation():
+    evaluate_model("lr-model", "990000000009", "us-west1", "gs://path/to/evaluation_dataset.csv")
+
+if __name__ == "__main__":
+    from kfp.v2 import compiler
+    compiler.Compiler().compile(
+        pipeline_func=pipeline_evaluation,
+        package_path='evaluate_model.json')

community-content/vertex_pipeline_kfpv2/pipelinejob.py

@@ -0,0 +1,43 @@
+from kfp.v2 import dsl
+
+@dsl.component(base_image='python:3.8',packages_to_install=['google-cloud-aiplatform==1.36.0'])
+def pipelineJob(
+    project_id: str,
+    location: str,
+    display_name: str,
+    json_file: str,
+    pipeline_root: str,
+):
+    import os
+    from google.cloud import aiplatform
+
+    aiplatform.init(
+        project=project_id,
+        location=location,
+    )
+
+    job = aiplatform.PipelineJob(
+        display_name=display_name,
+        template_path=json_file,
+        pipeline_root=pipeline_root,
+        enable_caching=False,
+    ).run()
+
+    job.delete()
+
+
+@dsl.pipeline(name='pipelineJobs')
+def pipeline_run_jobs():
+    # 1. create endpoint
+    pipelineJob("990000000009", "us-west1", "Pipeline-create endpoint",
+        "create_endpoint.json", "gs://pipeline-root-bucket/pipelines")
+        
+    # 2. deploy model to endpoint
+    pipelineJob("990000000009", "us-west1", "Pipeline-deploy model",
+        "deploy_model.json", "gs://pipeline-root-bucket/pipelines")
+
+if __name__ == "__main__":
+    from kfp.v2 import compiler
+    compiler.Compiler().compile(
+        pipeline_func=pipeline_run_jobs,
+        package_path='pipelineJobs.json')

notebooks/community/CODEOWNERS

@@ -71,6 +71,7 @@
 /notebooks/community/model_garden/model_garden_pytorch_stable_diffusion_inpainting.ipynb @xiangxu-google
 /notebooks/community/model_garden/model_garden_pytorch_stable_diffusion_xl_1_0.ipynb @bingatgoogle
 /notebooks/community/model_garden/model_garden_pytorch_stable_diffusion_xl_lcm.ipynb @weigary
+/notebooks/community/model_garden/model_garden_pytorch_qwen2_deployment.ipynb @rayandasoriya
 /notebooks/community/model_garden/model_garden_pytorch_stable_diffusion_xl_lightning.ipynb @xcchen1
 /notebooks/community/model_garden/model_garden_pytorch_stable_diffusion_xl_lora.ipynb @weigary
 /notebooks/community/model_garden/model_garden_pytorch_stable_diffusion_xl_turbo.ipynb @weigary
@@ -97,6 +98,7 @@
 /notebooks/community/model_garden/model_garden_pytorch_text_to_video.ipynb @KCFindstr
 /notebooks/community/generative_ai/text_embedding_api_cloud_next_new_models.ipynb @xqr-g
 /notebooks/community/generative_ai/text_embedding_api_semantic_search_with_scann.ipynb @henrytansetiawan
+/notebooks/community/generative_ai/backoff_and_retry_for_LLMs.ipynb @pemujo
 /notebooks/community/bigquery_ml_inference/bq_ml_with_vision_translation_nlp.ipynb @deaconsmith
 /notebooks/community/model_garden/model_garden_keras_stable_diffusion.ipynb @genquan9
 /notebooks/community/model_garden/model_garden_keras_yolov8.ipynb @@dstnluong-google
@@ -139,6 +141,7 @@
 /notebooks/community/model_garden/model_garden_gemma_deployment_on_gke.ipynb @vilobhmm
 /notebooks/community/model_garden/model_garden_gemma_deployment_on_vertex.ipynb @kathyyu-google
 /notebooks/community/model_garden/model_garden_gemma2_deployment_on_vertex.ipynb @kathyyu-google
+/notebooks/community/model_garden/model_garden_gemma2_finetuning_on_vertex.ipynb @rayandasoriya
 /notebooks/community/model_garden/model_garden_gemma_finetuning_on_vertex.ipynb @KCFindstr
 /notebooks/community/model_garden/model_garden_pytorch_gemma_peft_finetuning_hf.ipynb @KCFindstr
 /notebooks/community/model_garden/model_garden_pytorch_stable_diffusion_deployment_1_5.ipynb @weigary
@@ -152,3 +155,4 @@
 /notebooks/community/model_garden/synthetic_data_generation_using_llama3_1.ipynb @xiangxu-google
 /notebooks/community/model_garden/model_garden_autosxs_evaluation_llama3_1.ipynb @inardini
 /notebooks/community/model_garden/model_garden_openai_api_llama3_1.ipynb @inardini
+/notebooks/community/model_garden/model_garden_phi3_deployment.ipynb @yashikagandhi

notebooks/community/generative_ai/backoff_and_retry_for_LLMs.ipynb

@@ -0,0 +1,663 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "63c7b05c4717"
+      },
+      "source": [
+        "<a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/generative_ai/backoff_and_retry_for_LLMs.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "id": "670bbc2007a2"
+      },
+      "outputs": [],
+      "source": [
+        "# Copyright 2024 Google LLC\n",
+        "#\n",
+        "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "#     https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "890ac0f4e121"
+      },
+      "source": [
+        "# Backoff and retry for LLM\n",
+        "\n",
+        "<table align=\"left\">\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/generative_ai/backoff_and_retry_for_LLMs.ipynb\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/colab-logo-32px.png\" alt=\"Google Colaboratory logo\"><br> Open in Colab\n",
+        "    </a>\n",
+        "  </td>\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/colab/import/https:%2F%2Fraw.githubusercontent.com%2FGoogleCloudPlatform%2Fvertex-ai-samples%2Fmain%2Fnotebooks%2Fcommunity%2Fgenerative_ai%2Fbackoff_and_retry_for_LLMs.ipynb\">\n",
+        "      <img width=\"32px\" src=\"https://cloud.google.com/ml-engine/images/colab-enterprise-logo-32px.png\" alt=\"Google Cloud Colab Enterprise logo\"><br> Open in Colab Enterprise\n",
+        "    </a>\n",
+        "  </td>    \n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://console.cloud.google.com/vertex-ai/workbench/deploy-notebook?download_url=https://raw.githubusercontent.com/GoogleCloudPlatform/vertex-ai-samples/main/notebooks/community/generative_ai/backoff_and_retry_for_LLMs.ipynb\">\n",
+        "      <img src=\"https://lh3.googleusercontent.com/UiNooY4LUgW_oTvpsNhPpQzsstV5W8F7rYgxgGBD85cWJoLmrOzhVs_ksK_vgx40SHs7jCqkTkCk=e14-rj-sc0xffffff-h130-w32\" alt=\"Vertex AI logo\"><br> Open in Workbench\n",
+        "    </a>\n",
+        "  </td>\n",
+        "  <td style=\"text-align: center\">\n",
+        "    <a href=\"https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/generative_ai/backoff_and_retry_for_LLMs.ipynb\">\n",
+        "      <img src=\"https://cloud.google.com/ml-engine/images/github-logo-32px.png\" alt=\"GitHub logo\"><br> View on GitHub\n",
+        "    </a>\n",
+        "  </td>\n",
+        "</table>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "81ce710a5836"
+      },
+      "source": [
+        "NOTE: This notebook has been tested in the following environment:\n",
+        "\n",
+        "Python version = 3.10"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ff76ac47eb9b"
+      },
+      "source": [
+        "## Overview\n",
+        "\n",
+        "This notebook demonstrates how sending large amounts of traffic to Gemini-1.5-Pro can cause \"429 Quota Exceeded Errors\" and how implementing a backoff-and-retry strategy can help complete jobs without interrupting operations.\n",
+        "\n",
+        "This notebook provides examples for the blog post: [Don't let resource exhaustion leave your users hanging: A guide to handling 429 errors](https://cloud.google.com/blog/products/ai-machine-learning/learn-how-to-handle-429-resource-exhaustion-errors-in-your-llms?e=48754805)\n",
+        "\n",
+        "This tutorial uses the following Google Cloud ML service:\n",
+        "\n",
+        "- Vertex LLM SDK\n",
+        "\n",
+        "The steps performed include:\n",
+        "\n",
+        "- Installation and imports\n",
+        "- Asynchronously calling the Gemini model\n",
+        "- Using the Tenacity retry decorator to implement backoff and retry"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4e3e949c0bdd"
+      },
+      "source": [
+        "### Costs\n",
+        "\n",
+        "This tutorial uses billable components of Google Cloud:\n",
+        "\n",
+        "* Vertex AI\n",
+        "\n",
+        "Learn about [Vertex AI pricing](https://cloud.google.com/vertex-ai/pricing),\n",
+        "and use the [Pricing Calculator](https://cloud.google.com/products/calculator/)\n",
+        "to generate a cost estimate based on your projected usage.\n",
+        "\n",
+        "**This notebook sends large amount of tokens to Gemini for inference, reduce the number of attempts or use smaller video to reduce costs.**"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "f0316df526f8"
+      },
+      "source": [
+        "## Get started"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "FyyMdUeAJIVv"
+      },
+      "source": [
+        "## Install Vertex AI SDK for Python and other required packages\n",
+        "\n",
+        "Install the following packages required to execute this notebook.\n",
+        "\n",
+        "**Remember to restart the runtime after installation.**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 16,
+      "metadata": {
+        "id": "snBUuUamoJPz"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install --upgrade --quiet google-cloud-aiplatform tenacity google-cloud-storage"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "WX3CHZitmSJM"
+      },
+      "source": [
+        "### Restart runtime (Colab only)\n",
+        "To use the newly installed packages, you must restart the runtime on Google Colab."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "f09b4dff629a"
+      },
+      "outputs": [],
+      "source": [
+        "import sys\n",
+        "\n",
+        "if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "    import IPython\n",
+        "\n",
+        "    app = IPython.Application.instance()\n",
+        "    app.kernel.do_shutdown(True)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "SbmM4z7FOBpM"
+      },
+      "source": [
+        "<div class=\"alert alert-block alert-warning\">\n",
+        "<b>⚠️ The kernel is going to restart. Wait until it's finished before continuing to the next step.  ⚠️</b>\n",
+        "</div>\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "sBCra4QMA2wR"
+      },
+      "source": [
+        "### Authenticate your Google Cloud account\n",
+        "\n",
+        "Depending on your Jupyter environment, you may have to manually authenticate. Follow the relevant instructions below."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "74ccc9e52986"
+      },
+      "source": [
+        "**1. Vertex AI Workbench**\n",
+        "* Do nothing as you are already authenticated."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "de775a3773ba"
+      },
+      "source": [
+        "**2. Local JupyterLab instance, uncomment and run:**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "254614fa0c46"
+      },
+      "outputs": [],
+      "source": [
+        "# ! gcloud auth login"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ef21552ccea8"
+      },
+      "source": [
+        "**3. Colab, uncomment and run:**"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "603adbbf0532"
+      },
+      "outputs": [],
+      "source": [
+        "# import sys\n",
+        "\n",
+        "# if \"google.colab\" in sys.modules:\n",
+        "\n",
+        "#     from google.colab import auth\n",
+        "\n",
+        "#     auth.authenticate_user()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dae340cb-0583-4e7e-a562-6817ee4d7f6d"
+      },
+      "source": [
+        "### Import libraries"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "id": "412d00f1-08db-4880-8ced-52a9583757b8"
+      },
+      "outputs": [],
+      "source": [
+        "import asyncio\n",
+        "import time\n",
+        "\n",
+        "import nest_asyncio\n",
+        "import vertexai\n",
+        "\n",
+        "nest_asyncio.apply()\n",
+        "from google.cloud import storage\n",
+        "from tenacity import retry, wait_random_exponential\n",
+        "from vertexai.generative_models import GenerationConfig, GenerativeModel, Part"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "DF4l8DTdWgPY"
+      },
+      "source": [
+        "### Set Google Cloud project information and initialize Vertex AI SDK for Python\n",
+        "\n",
+        "To get started using Vertex AI, you must have an existing Google Cloud project and [enable the Vertex AI API](https://console.cloud.google.com/flows/enableapi?apiid=aiplatform.googleapis.com). Learn more about [setting up a project and a development environment](https://cloud.google.com/vertex-ai/docs/start/cloud-environment)."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "id": "3EdtdqnoldX4"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Updated property [core/project].\n"
+          ]
+        }
+      ],
+      "source": [
+        "PROJECT_ID = \"[your-project-id]\"  # @param {type:\"string\"}\n",
+        "DEFAUL_MODEL_NAME = \"gemini-1.5-pro-001\"  # @param {type:\"string\"}\n",
+        "REGION = \"us-central1\"  # @param {type:\"string\"}\n",
+        "\n",
+        "\n",
+        "# Initiate Vertex AI\n",
+        "vertexai.init(project=PROJECT_ID, location=REGION)\n",
+        "config = GenerationConfig(temperature=0.5, max_output_tokens=512)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "f50f22f3-ec85-463e-b6fe-5c8e6b80b07b"
+      },
+      "source": [
+        "### Helper functions"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "id": "b18a366df00b"
+      },
+      "outputs": [],
+      "source": [
+        "def get_images_uri_from_bucket(bucket_name, prefix, delimiter=None):\n",
+        "    \"\"\"Lists all the images with extension '.jpg', 'jpeg' or 'png' in the bucket that begin with the prefix (folder).\"\"\"\n",
+        "    storage_client = storage.Client()\n",
+        "    blobs = storage_client.list_blobs(bucket_name, prefix=prefix, delimiter=delimiter)\n",
+        "    images = [\n",
+        "        f\"gs://{bucket_name}/{blob.name}\"\n",
+        "        for blob in blobs\n",
+        "        if blob.name.endswith(tuple([\".jpg\", \"jpeg\", \"png\"]))\n",
+        "    ]\n",
+        "    return images\n",
+        "\n",
+        "\n",
+        "async def async_ask_gemini(contents, model_name=DEFAUL_MODEL_NAME):\n",
+        "    # This basic function calls Gemini asynchronously without a retry logic\n",
+        "    multimodal_model = GenerativeModel(model_name)\n",
+        "    response = await multimodal_model.generate_content_async(\n",
+        "        contents=contents, generation_config=config\n",
+        "    )\n",
+        "    return response.text\n",
+        "\n",
+        "\n",
+        "@retry(wait=wait_random_exponential(multiplier=1, max=60))\n",
+        "async def retry_async_ask_gemini(contents, model_name=DEFAUL_MODEL_NAME):\n",
+        "    \"\"\"This is the same code as the async_ask_gemini function but implements a retry logic using tenacity decorator.\n",
+        "    wait_random_exponential(multiplier=1, max=60) means that it will\n",
+        "    Retry “Randomly wait up to 2^x * 1 seconds between each retry until the range reaches 60 seconds, then randomly up to 60 seconds afterwards.\n",
+        "    \"\"\"\n",
+        "\n",
+        "    multimodal_model = GenerativeModel(model_name)\n",
+        "    response = await multimodal_model.generate_content_async(\n",
+        "        contents=contents, generation_config=config\n",
+        "    )\n",
+        "    return response.text\n",
+        "\n",
+        "\n",
+        "async def load_test_gemini(function, model_name, attempts=5):\n",
+        "    failed_attempts = 0\n",
+        "    print(f\"Testing with model: {model_name} and function: {function.__name__}\")\n",
+        "    for i in range(attempts):\n",
+        "        try:\n",
+        "            time_start = time.time()\n",
+        "            get_gemini_responses = [\n",
+        "                function(\n",
+        "                    [\n",
+        "                        prompt,\n",
+        "                        video_part,\n",
+        "                        Part.from_uri(image_uri, mime_type=\"image/jpeg\"),\n",
+        "                    ],\n",
+        "                    model_name=MODEL_NAME,\n",
+        "                )\n",
+        "                for image_uri in images_list\n",
+        "            ]\n",
+        "            async_poems = await asyncio.gather(*get_gemini_responses)\n",
+        "            time_taken = time.time() - time_start\n",
+        "            print(f\"{len(async_poems)} Poems written in {time_taken:.0f} seconds\")\n",
+        "        except Exception as error:\n",
+        "            failed_attempts += 1\n",
+        "            print(\"An error occurred:\", error)\n",
+        "\n",
+        "    print(\n",
+        "        f\"{failed_attempts} out of {attempts} failed\"\n",
+        "    ) if failed_attempts > 0 else print(f\"All {attempts} attempts succeded\")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "c01755042c4b"
+      },
+      "source": [
+        "### Getting images and videos used for testing"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "id": "204228ea941e"
+      },
+      "outputs": [],
+      "source": [
+        "# The images and video used for this test are stored in a public GCS bucket: \"cloud-samples-data\"\n",
+        "bucket_name = \"cloud-samples-data\"\n",
+        "image_prefix = \"generative-ai/image/\"\n",
+        "images_list = get_images_uri_from_bucket(bucket_name, image_prefix, delimiter=\"/\")\n",
+        "\n",
+        "prompt = \"Get the elements from the image, get all the animals from the video, print all the animals and elements found on a numbered list, and then write a poem about them\\n\"\n",
+        "small_video_uri = \"gs://cloud-samples-data/generative-ai/video/animals.mp4\"\n",
+        "large_video_uri = (\n",
+        "    \"gs://cloud-samples-data/generative-ai/video/behind_the_scenes_pixel.mp4\"\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4f08049c98e5"
+      },
+      "source": [
+        "## Load testing Gemini "
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "91ffee55f2d7"
+      },
+      "source": [
+        "### Test without retry and default quota for Gemini-1.5-pro-001 of 60 QPM\n",
+        "\n",
+        "4 out of 5 tests fail due to 429 Quota exceeded"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "id": "7add4399f0a9"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Testing with model: gemini-1.5-pro-001 and function: async_ask_gemini\n",
+            "72 Poems written in 23 seconds\n",
+            "An error occurred: 429 Quota exceeded for aiplatform.googleapis.com/generate_content_requests_per_minute_per_project_per_base_model with base model: gemini-1.5-pro. Please submit a quota increase request. https://cloud.google.com/vertex-ai/docs/generative-ai/quotas-genai.\n",
+            "An error occurred: 429 Quota exceeded for aiplatform.googleapis.com/generate_content_requests_per_minute_per_project_per_base_model with base model: gemini-1.5-pro. Please submit a quota increase request. https://cloud.google.com/vertex-ai/docs/generative-ai/quotas-genai.\n",
+            "An error occurred: 429 Quota exceeded for aiplatform.googleapis.com/generate_content_requests_per_minute_per_project_per_base_model with base model: gemini-1.5-pro. Please submit a quota increase request. https://cloud.google.com/vertex-ai/docs/generative-ai/quotas-genai.\n",
+            "An error occurred: 429 Quota exceeded for aiplatform.googleapis.com/generate_content_input_tokens_per_minute_per_base_model with base model: gemini-1.5-pro. Please submit a quota increase request. https://cloud.google.com/vertex-ai/docs/generative-ai/quotas-genai.\n",
+            "4 out of 5 failed\n"
+          ]
+        }
+      ],
+      "source": [
+        "video_part = Part.from_uri(small_video_uri, mime_type=\"video/mp4\")\n",
+        "MODEL_NAME = \"gemini-1.5-pro-001\"\n",
+        "# Uncomment line below to re-run the test. Beware of costs since it will make multiple calls to Gemini\n",
+        "# await (load_test_gemini(async_ask_gemini, MODEL_NAME, attempts=5))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4121c249d591"
+      },
+      "source": [
+        "### Re-testing with backoff and retry mechanism enabled \n",
+        "\n",
+        "All tests finallize correctly"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "id": "2338b49fd72d"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Testing with model: gemini-1.5-pro-001 and function: retry_async_ask_gemini\n",
+            "72 Poems written in 21 seconds\n",
+            "72 Poems written in 167 seconds\n",
+            "72 Poems written in 18 seconds\n",
+            "72 Poems written in 149 seconds\n",
+            "72 Poems written in 22 seconds\n",
+            "All 5 attempts succeded\n"
+          ]
+        }
+      ],
+      "source": [
+        "MODEL_NAME = \"gemini-1.5-pro-001\"\n",
+        "# Uncomment line below to re-run the test. Beware of costs since it will make multiple calls to Gemini\n",
+        "# await (load_test_gemini(retry_async_ask_gemini, MODEL_NAME, attempts=5))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "d26a20615065"
+      },
+      "source": [
+        "### Testing without retry but with Dynamic Shared Quota using Gemini-1.5-pro-002 \n",
+        "\n",
+        "All 5 attempts succeded with a small video as input"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "id": "37cd3facf381"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Testing with model: gemini-1.5-pro-002 and function: async_ask_gemini\n",
+            "72 Poems written in 23 seconds\n",
+            "72 Poems written in 21 seconds\n",
+            "72 Poems written in 19 seconds\n",
+            "72 Poems written in 17 seconds\n",
+            "72 Poems written in 22 seconds\n",
+            "All 5 attempts succeded\n"
+          ]
+        }
+      ],
+      "source": [
+        "video_part = Part.from_uri(small_video_uri, mime_type=\"video/mp4\")\n",
+        "MODEL_NAME = \"gemini-1.5-pro-002\"\n",
+        "# Uncomment line below to re-run the test. Beware of costs since it will make multiple calls to Gemini\n",
+        "# await (load_test_gemini(async_ask_gemini, MODEL_NAME, attempts=5))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "a5fa686b7c52"
+      },
+      "source": [
+        "### Re-testing Dynamic Shared quota with larger video\n",
+        "\n",
+        "Without backoff and retry, testing Gemini-1.5-pro-002 with larger context window caused all tests to fail with 429 reason code."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "id": "d241524f5071"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Testing with model: gemini-1.5-pro-002 and function: async_ask_gemini\n",
+            "An error occurred: 429 Resource exhausted. Please try again later. Please refer to https://cloud.google.com/vertex-ai/generative-ai/docs/quotas#error-code-429 for more details.\n",
+            "An error occurred: 429 Resource exhausted. Please try again later. Please refer to https://cloud.google.com/vertex-ai/generative-ai/docs/quotas#error-code-429 for more details.\n",
+            "An error occurred: 429 Resource exhausted. Please try again later. Please refer to https://cloud.google.com/vertex-ai/generative-ai/docs/quotas#error-code-429 for more details.\n",
+            "An error occurred: 429 Resource exhausted. Please try again later. Please refer to https://cloud.google.com/vertex-ai/generative-ai/docs/quotas#error-code-429 for more details.\n",
+            "An error occurred: 429 Resource exhausted. Please try again later. Please refer to https://cloud.google.com/vertex-ai/generative-ai/docs/quotas#error-code-429 for more details.\n",
+            "5 out of 5 failed\n"
+          ]
+        }
+      ],
+      "source": [
+        "# Larger video  used to increase token input size\n",
+        "video_part = Part.from_uri(large_video_uri, mime_type=\"video/mp4\")\n",
+        "MODEL_NAME = \"gemini-1.5-pro-002\"\n",
+        "# Uncomment line below to re-run the test. Beware of costs since it will make multiple calls to Gemini\n",
+        "# await (load_test_gemini(async_ask_gemini, MODEL_NAME, attempts=5))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "999a6cd90e5d"
+      },
+      "source": [
+        "### Adding Backoff and Retry to Dynamic Shared Quota Testing\n",
+        "\n",
+        "Adding backoff and retry mechanisms significantly increased inference time, but all tests completed successfully even with much larger context window.\n",
+        "\n",
+        "Provisioned Throughput should be used to guarantee the capacity and therefore reduce latency.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "metadata": {
+        "id": "91c4d04ab9b4"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Testing with model: gemini-1.5-pro-002 and function: retry_async_ask_gemini\n",
+            "72 Poems written in 188 seconds\n",
+            "72 Poems written in 205 seconds\n",
+            "72 Poems written in 216 seconds\n",
+            "All 3 attempts succeded\n"
+          ]
+        }
+      ],
+      "source": [
+        "video_part = Part.from_uri(large_video_uri, mime_type=\"video/mp4\")\n",
+        "MODEL_NAME = \"gemini-1.5-pro-002\"\n",
+        "# Uncomment line below to re-run the test. Beware of costs since it will make multiple calls to Gemini\n",
+        "# await (load_test_gemini(retry_async_ask_gemini, MODEL_NAME, attempts=3))"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "9cabeb585f56"
+      },
+      "source": [
+        "## Summary\n",
+        "\n",
+        "These basic tests demonstrate how Dynamic Shared Quota reduces the frequency of \"429 Resource Exhausted\" errors.  The results highlight the importance of always using backoff and retry mechanisms when calling LLMs, regardless of the model version.  Combining this with Provisioned Throughput further enhances reliability by guaranteeing capacity."
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "name": "backoff_and_retry_for_LLMs.ipynb",
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}

notebooks/community/migration/UJ7 AutoML for natural language with Vertex AI Text Entity Extraction.ipynb

@@ -109,27 +109,27 @@
         "id": "3Sq3sGfdt89E"
       },
       "source": [
-        "## Before you begin\r\n",
-        "\r\n",
-        "### GPU run-time\r\n",
-        "\r\n",
-        "*Make sure you're running this notebook in a GPU runtime if you have that option. In Colab, select* **Runtime > Change Runtime Type > GPU**\r\n",
-        "\r\n",
-        "### Set up your GCP project\r\n",
-        "\r\n",
-        "**The following steps are required, regardless of your notebook environment.**\r\n",
-        "\r\n",
-        "1. [Select or create a GCP project](https://console.cloud.google.com/cloud-resource-manager). When you first create an account, you get a $300 free credit towards your compute/storage costs.\r\n",
-        "\r\n",
-        "2. [Make sure that billing is enabled for your project.](https://cloud.google.com/billing/docs/how-to/modify-project)\r\n",
-        "\r\n",
-        "3. [Enable the Vertex APIs and Compute Engine APIs.](https://console.cloud.google.com/flows/enableapi?apiid=ml.googleapis.com,compute_component)\r\n",
-        "\r\n",
-        "4. [Google Cloud SDK](https://cloud.google.com/sdk) is already installed in Google Cloud Notebooks.\r\n",
-        "\r\n",
-        "5. Enter your project ID in the cell below. Then run the  cell to make sure the\r\n",
-        "Cloud SDK uses the right project for all the commands in this notebook.\r\n",
-        "\r\n",
+        "## Before you begin\n",
+        "\n",
+        "### GPU run-time\n",
+        "\n",
+        "*Make sure you're running this notebook in a GPU runtime if you have that option. In Colab, select* **Runtime > Change Runtime Type > GPU**\n",
+        "\n",
+        "### Set up your GCP project\n",
+        "\n",
+        "**The following steps are required, regardless of your notebook environment.**\n",
+        "\n",
+        "1. [Select or create a GCP project](https://console.cloud.google.com/cloud-resource-manager). When you first create an account, you get a $300 free credit towards your compute/storage costs.\n",
+        "\n",
+        "2. [Make sure that billing is enabled for your project.](https://cloud.google.com/billing/docs/how-to/modify-project)\n",
+        "\n",
+        "3. [Enable the Vertex APIs and Compute Engine APIs.](https://console.cloud.google.com/flows/enableapi?apiid=ml.googleapis.com,compute_component)\n",
+        "\n",
+        "4. [Google Cloud SDK](https://cloud.google.com/sdk) is already installed in Google Cloud Notebooks.\n",
+        "\n",
+        "5. Enter your project ID in the cell below. Then run the  cell to make sure the\n",
+        "Cloud SDK uses the right project for all the commands in this notebook.\n",
+        "\n",
         "**Note**: Jupyter runs lines prefixed with `!` as shell commands, and it interpolates Python variables prefixed with `$` into these commands."
       ]
     },
@@ -240,11 +240,11 @@
         "id": "9zpjPUOhvRQz"
       },
       "source": [
-        "### Authenticate your GCP account\r\n",
-        "\r\n",
-        "**If you are using Google Cloud Notebooks**, your environment is already\r\n",
-        "authenticated. Skip this step.\r\n",
-        "\r\n",
+        "### Authenticate your GCP account\n",
+        "\n",
+        "**If you are using Google Cloud Notebooks**, your environment is already\n",
+        "authenticated. Skip this step.\n",
+        "\n",
         "*Note: If you are on an Vertex notebook and run the cell, the cell knows to skip executing the authentication steps.*"
       ]
     },
@@ -1459,8 +1459,7 @@
         "id": "gM-YixlLmDy9"
       },
       "source": [
-        "### Make a batch prediction file\r\n",
-        "\r\n"
+        "### Make a batch prediction file\n"
       ]
     },
     {
@@ -1489,19 +1488,6 @@
         "! gsutil cat $gcs_test_item"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "sPupiwqN_jAB"
-      },
-      "source": [
-        "*Example output*:\n",
-        "```\n",
-        "{\"content\": \"gs://migration-ucaip-trainingaip-20210301154552/test.txt\", \"mime_type\": \"text/plain\"}\n",
-        "Molecular basis of hexosaminidase A deficiency and pseudodeficiency in the Berks County Pennsylvania Dutch.\\tFollowing the birth of two infants with Tay-Sachs disease ( TSD ) , a non-Jewish , Pennsylvania Dutch kindred was screened for TSD carriers using the biochemical assay . A high frequency of individuals who appeared to be TSD heterozygotes was detected ( Kelly et al . , 1975 ) . Clinical and biochemical evidence suggested that the increased carrier frequency was due to at least two altered alleles for the hexosaminidase A alpha-subunit . We now report two mutant alleles in this Pennsylvania Dutch kindred , and one polymorphism . One allele , reported originally in a French TSD patient ( Akli et al . , 1991 ) , is a GT-- > AT transition at the donor splice-site of intron 9 . The second , a C-- > T transition at nucleotide 739 ( Arg247Trp ) , has been shown by Triggs-Raine et al . ( 1992 ) to be a clinically benign \" pseudodeficient \" allele associated with reduced enzyme activity against artificial substrate . Finally , a polymorphism [ G-- > A ( 759 ) ] , which leaves valine at codon 253 unchanged , is described\n",
-        "```\n"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -1557,45 +1543,6 @@
         ")"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "sPupiwqN_jAB"
-      },
-      "source": [
-        "*Example output*:\n",
-        "```\n",
-        "{\n",
-        "  \"parent\": \"projects/migration-ucaip-training/locations/us-central1\",\n",
-        "  \"batchPredictionJob\": {\n",
-        "    \"displayName\": \"ten_20210301154552\",\n",
-        "    \"model\": \"projects/116273516712/locations/us-central1/models/4400738115568795648\",\n",
-        "    \"inputConfig\": {\n",
-        "      \"instancesFormat\": \"jsonl\",\n",
-        "      \"gcsSource\": {\n",
-        "        \"uris\": [\n",
-        "          \"gs://migration-ucaip-trainingaip-20210301154552/test.jsonl\"\n",
-        "        ]\n",
-        "      }\n",
-        "    },\n",
-        "    \"outputConfig\": {\n",
-        "      \"predictionsFormat\": \"jsonl\",\n",
-        "      \"gcsDestination\": {\n",
-        "        \"outputUriPrefix\": \"gs://migration-ucaip-trainingaip-20210301154552/batch_output/\"\n",
-        "      }\n",
-        "    },\n",
-        "    \"dedicatedResources\": {\n",
-        "      \"machineSpec\": {\n",
-        "        \"machineType\": \"n1-standard-2\"\n",
-        "      },\n",
-        "      \"startingReplicaCount\": 1,\n",
-        "      \"maxReplicaCount\": 1\n",
-        "    }\n",
-        "  }\n",
-        "}\n",
-        "```\n"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -1638,42 +1585,6 @@
         "print(MessageToJson(request.__dict__[\"_pb\"]))"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "sPupiwqN_jAB"
-      },
-      "source": [
-        "*Example output*:\n",
-        "```\n",
-        "{\n",
-        "  \"name\": \"projects/116273516712/locations/us-central1/batchPredictionJobs/3588251799200464896\",\n",
-        "  \"displayName\": \"ten_20210301154552\",\n",
-        "  \"model\": \"projects/116273516712/locations/us-central1/models/4400738115568795648\",\n",
-        "  \"inputConfig\": {\n",
-        "    \"instancesFormat\": \"jsonl\",\n",
-        "    \"gcsSource\": {\n",
-        "      \"uris\": [\n",
-        "        \"gs://migration-ucaip-trainingaip-20210301154552/test.jsonl\"\n",
-        "      ]\n",
-        "    }\n",
-        "  },\n",
-        "  \"outputConfig\": {\n",
-        "    \"predictionsFormat\": \"jsonl\",\n",
-        "    \"gcsDestination\": {\n",
-        "      \"outputUriPrefix\": \"gs://migration-ucaip-trainingaip-20210301154552/batch_output/\"\n",
-        "    }\n",
-        "  },\n",
-        "  \"state\": \"JOB_STATE_PENDING\",\n",
-        "  \"completionStats\": {\n",
-        "    \"incompleteCount\": \"-1\"\n",
-        "  },\n",
-        "  \"createTime\": \"2021-03-01T17:59:42.777083Z\",\n",
-        "  \"updateTime\": \"2021-03-01T17:59:42.777083Z\"\n",
-        "}\n",
-        "```\n"
-      ]
-    },
     {
       "cell_type": "code",
       "execution_count": null,
@@ -1739,42 +1650,6 @@
         "print(MessageToJson(request.__dict__[\"_pb\"]))"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "sPupiwqN_jAB"
-      },
-      "source": [
-        "*Example output*:\n",
-        "```\n",
-        "{\n",
-        "  \"name\": \"projects/116273516712/locations/us-central1/batchPredictionJobs/3588251799200464896\",\n",
-        "  \"displayName\": \"ten_20210301154552\",\n",
-        "  \"model\": \"projects/116273516712/locations/us-central1/models/4400738115568795648\",\n",
-        "  \"inputConfig\": {\n",
-        "    \"instancesFormat\": \"jsonl\",\n",
-        "    \"gcsSource\": {\n",
-        "      \"uris\": [\n",
-        "        \"gs://migration-ucaip-trainingaip-20210301154552/test.jsonl\"\n",
-        "      ]\n",
-        "    }\n",
-        "  },\n",
-        "  \"outputConfig\": {\n",
-        "    \"predictionsFormat\": \"jsonl\",\n",
-        "    \"gcsDestination\": {\n",
-        "      \"outputUriPrefix\": \"gs://migration-ucaip-trainingaip-20210301154552/batch_output/\"\n",
-        "    }\n",
-        "  },\n",
-        "  \"state\": \"JOB_STATE_PENDING\",\n",
-        "  \"completionStats\": {\n",
-        "    \"incompleteCount\": \"-1\"\n",
-        "  },\n",
-        "  \"createTime\": \"2021-03-01T17:59:42.777083Z\",\n",
-        "  \"updateTime\": \"2021-03-01T17:59:42.777083Z\"\n",
-        "}\n",
-        "```\n"
-      ]
-    },
     {
       "cell_type": "code",
       "execution_count": null,
@@ -1798,19 +1673,6 @@
         "    time.sleep(60)"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "trainingpipelines_create:migration,new,response,icn"
-      },
-      "source": [
-        "*Example output*:\n",
-        "```\n",
-        "gs://migration-ucaip-trainingaip-20210301154552/batch_output/prediction-ten_20210301154552-2021-03-01T17:59:42.638222Z/predictions_00001.jsonl\n",
-        "{\"instance\":{\"content\":\"gs://migration-ucaip-trainingaip-20210301154552/test.txt\",\"mimeType\":\"text/plain\"},\"prediction\":{\"ids\":[\"7806436899697983488\",\"7806436899697983488\",\"7806436899697983488\",\"4347672385877442560\",\"4347672385877442560\",\"4347672385877442560\"],\"displayNames\":[\"SpecificDisease\",\"SpecificDisease\",\"SpecificDisease\",\"Modifier\",\"Modifier\",\"Modifier\"],\"textSegmentStartOffsets\":[\"149\",\"19\",\"169\",\"236\",\"688\",\"330\"],\"textSegmentEndOffsets\":[\"165\",\"45\",\"171\",\"238\",\"690\",\"332\"],\"confidences\":[0.99957836,0.9995628,0.9995044,0.9993287,0.9993144,0.99927235]}}\n",
-        "```\n"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -2338,11 +2200,11 @@
         "id": "bQ-VVaSxJjkd"
       },
       "source": [
-        "# Cleaning up\r\n",
-        "\r\n",
-        "To clean up all GCP resources used in this project, you can [delete the GCP\r\n",
-        "project](https://cloud.google.com/resource-manager/docs/creating-managing-projects#shutting_down_projects) you used for the tutorial.\r\n",
-        "\r\n",
+        "# Cleaning up\n",
+        "\n",
+        "To clean up all GCP resources used in this project, you can [delete the GCP\n",
+        "project](https://cloud.google.com/resource-manager/docs/creating-managing-projects#shutting_down_projects) you used for the tutorial.\n",
+        "\n",
         "Otherwise, you can delete the individual resources you created in this tutorial."
       ]
     },
@@ -2404,7 +2266,7 @@
   ],
   "metadata": {
     "colab": {
-      "name": "UJ7 unified AutoML for natural language with Vertex AI Text Entity Extraction.ipynb",
+      "name": "UJ7 AutoML for natural language with Vertex AI Text Entity Extraction.ipynb",
       "toc_visible": true
     },
     "kernelspec": {

notebooks/community/model_garden/benchmarking_reports/jax_vit_benchmarking_report.md

@@ -0,0 +1,253 @@
+# ViT PyTorch vs JAX training benchmarks on Vertex AI Training Platform
+
+Lav Rai, Software Engineer, Google Cloud
+
+Xiang Xu, Software Engineer, Google Cloud
+
+Andreas Steiner, Software Engineer, Google DeepMind
+
+Tao Wang, Software Engineer, Google DeepMind
+
+Alexander Kolesnikov, Research Engineer, Google DeepMind
+
+## Introduction
+
+Many repositories now offer both PyTorch and JAX versions of a model. For
+example, [Hugging Face offers many models such as GPT2, BERT][1]
+etc. Other examples are [OpenLLaMa][2] and [ViT][3]
+models which were first developed in JAX and then their corresponding PyTorch
+versions were made available. **Given both the PyTorch and JAX options for a
+model, it may not be obvious as to which option to choose**. To make such a
+decision, it is important for one to know about the training cost, effectiveness
+and efficiency for each choice.
+
+Apart from the framework choice, the other choice that one faces on Vertex AI
+training platform is the type and count of the accelerators. Although the
+[Vertex AI pricing table][4] lists the price per hour for each
+machine, **one may not know beforehand about the training speed of JAX and
+PyTorch frameworks for different types and count of the accelerators**.
+
+If one has access to some training benchmark numbers for the same model
+under (a) PyTorch and JAX frameworks and (b) for different types and count of
+the accelerators, then it will be easier for them to make a cost effective
+decision. Such a benchmark will also aid the developers in identifying strength
+and weakness of different choices and then figure out recipes to remove those
+weaknesses if possible.
+
+This blog uses the ViT [classification models][5] of varying sizes
+to benchmark the training performance of PyTorch and JAX versions on the Vertex
+AI Platform under different machine configurations. The goal is to:
+
+- Benchmark OSS ViT training for both PyTorch and JAX frameworks.
+- Benchmark OSS ViT L16, H14, g14, and G14 models.
+- Benchmark OSS ViT PyTorch training with A100 GPUs.
+- Benchmark OSS ViT JAX training with A100 GPUs and TPU V3 accelerators.
+
+## Benchmarking setup
+
+This section lays out the benchmarking set up for the [PyTorch][6] and [JAX][7]
+frameworks and provides a reasoning for choosing those settings.
+
+### PyTorch GPU
+
+#### Machine configuration
+
+We run training jobs on [Vertex AI Custom Training][8] using 1
+single node with 8 A100-40GB GPUs.
+
+- Machine type: [a2-highgpu-8g][9]
+- Machine count: 1
+- Accelerator type: [NVIDIA_TESLA_A100 (40GB)][10]
+- Accelerator count: 8
+
+#### Modeling
+
+We benchmark 4 variants of ViT model in different sizes:
+
+- [ViT-L16, 300M params][11]
+- [ViT-H14, 630M params][12]
+- [ViT-g14, 1B params][13]
+- [ViT-G14, 1.8B params][14]
+
+We use the Huggingface [transformers library][15] for ViT L16 and
+H14 variants, and the [TIMM library][16] for ViT g14 and G14
+variants.
+
+#### Dataset
+
+We run training against the [cifar10][17] dataset with 50K training
+images and 10K test images. To factor out network communication overhead for
+data loading, we copy the whole dataset to the local disk then load data from
+the local disk during training.
+
+#### Training parameters
+
+- Trainer
+  - We use [PyTorch Lightning][18] as the trainer for the
+    boilerplate data loading and train loop coding.
+- Precision
+  - Float16
+- Input resolution
+  - 224 x 224
+- Strategy
+  - We use [DDP][19] for models which can be entirely loaded to one
+    GPU, use [Deepspeed-ZeRO][20] otherwise:
+    - ViT-L16: DDP
+    - ViT-H14: DDP
+    - ViT-g14: DDP
+    - ViT-G14: Deepspeed-ZeRO stage-3
+- Batch size
+  - We use the max batch size as power of 2 without CUDA OOM for each model:
+    - ViT-L16: 64 per GPU
+    - ViT-H14: 16 per GPU
+    - ViT-g14: 16 per GPU
+    - ViT-G14: 32 per GPU
+- Compilation
+  - We apply [torch.compile][21] to model whenever it's applicable:
+    - ViT-L16: torch.compile
+    - ViT-H14: torch.compile
+    - ViT-g14: torch.compile
+    - ViT-G14: N/A
+
+### JAX TPU and GPU
+
+#### Machine configuration
+
+All the TPU and GPU training jobs are run on [Vertex AI Custom
+Training][8]. The following machine configurations were used for the
+TPU and GPU experiments:
+
+**Note**: TPU V3 POD requires multi-host supporting training code. For example,
+a 32 core POD runs on 4 hosts with each host using 8 cores.
+
+**Note**: 8 A100 are similar to TPU V3 32 cores in terms of [Vertex AI
+pricing][4].
+
+**Note**: [Each TPU v3 chip has 2 cores which can use 32 GB high-bandwidth
+memory][22] (16 GB per core) so total memory for 32 cores is 16x32 =
+512 GB. Therefore for the same price, TPUs offer more memory than 8 A100-40GB
+GPUs.
+
+#### Modeling
+
+We decided to use an OSS code repository for model implementation. Using an OSS
+repository helps anyone to independently verify the benchmarking results and
+also relate to the results well. For JAX, we selected the
+[Big Vision][23] code repository.
+
+Same as the PyTorch modeling, we benchmark 4 variants of ViT model in different
+sizes:
+
+- [ViT-L16, 300M params][24]
+- [ViT-H14, 630M params][24]
+- [ViT-g14, 1B params][24]
+- [ViT-G14, 1.8B params][24]
+
+**Note**: The [Big Vision code repo][23] has not made the
+checkpoints publicly available for the models larger than the ViT-L16. Therefore
+for the rest of the three variants, the experiments only used random
+initialization for benchmarking the training speed.
+
+#### Dataset
+
+We use training against the [cifar10 TensorFlow dataset][25] with
+50K training images and 10K test images. This dataset is the same as the one
+used for PyTorch experiments except that it is loaded as a TensorFlow dataset.
+Similar to the PyTorch experiments, we copy the whole dataset to the docker
+image to factor out network communication overhead for data loading.
+
+#### Training parameters
+
+- Precision
+  - "bfloat16" setting was used.
+- Input resolution
+  - 224 x 224 after resize (to 448x448) and random crop (to 224x224) before
+    training.
+    - This resolution for training was the same as the PyTorch settings.
+- Strategy
+  - Used DDP for all models except ViT-G14. ViT-G14 used the FSDP strategy.
+- Batch size
+  - We use the max batch size as power of 2 without OOM for each model. The
+    [Benchmarking results][26] section shows the final
+    batch size for each experiment.
+  - Once a maximum batch-size for TPU V3 8 cores was determined, we just scaled
+    it linearly for 32 cores.
+  - Once a maximum batch-size for 1 A100 GPU was determined, we just scaled it
+    linearly for 8 A100 GPUs.
+- Compilation
+  - [jax.jit() compilation][27] is used in JAX codes for efficient
+    execution in XLA.
+- GPU related flags
+  - The following flags are set in the dockerfile for the GPU runs.
+    - Note: _xla_gpu_enable_pipelined_collectives_ is set to false for the
+      ViT-G14 FSDP run.
+
+### Evaluation metric
+
+For both the PyTorch and JAX experiments, the following evaluation metrics are
+collected:
+
+- Throughput: Images-per-second observed for training.
+- Cost: The training-cost-per-epoch (USD).
+
+**Note**: The above metrics are not biased against any framework or machine
+configurations. In addition, these metrics will help one decide the most
+efficient training configurations on Vertex AI.
+
+## Benchmarking results
+
+The lowest cost experiment for each model is marked in **bold** in the last
+column.
+
+![vit_benchmarking_table](images/vit_benchmarking_table.png)
+
+The following bar charts summarize the performance visually:
+
+![vit_training_time](images/vit_training_time.png)
+
+![vit_training_cost](images/vit_training_cost.png)
+
+The following section provides observations and conclusions for these results.
+
+## Observation and Conclusions
+
+- Training with JAX TPU V3 POD with 32 cores costs 33% less than the PyTorch GPU
+  8 A100-40GBs runs.
+- Training with JAX GPU 8 A100-40GBs costs 23% less than the PyTorch GPU 8
+  A100-40GBs runs.
+- JAX TPU V3 POD with 32 cores was 4x faster and slightly more cost-effective
+  than the JAX TPU V3 8 core run for the ViT-large model. This indicates that it
+  might be better to use more cores. The JAX TPU V3 speed scales very well with
+  the number of cores.
+- Cloud TPU VM training speed numbers were the same as the Vertex AI for
+  TPU V3 8 cores. The dataset was copied to the docker in both the cases.
+- The training-cost-per-epoch increases with the model size irrespective of the
+  framework.
+
+[1]: https://github.com/huggingface/transformers/blob/main/examples/research_projects/jax-projects/README.md#quickstart-flax-and-jax-in-transformers
+[2]: https://github.com/openlm-research/open_llama
+[3]: https://github.com/google-research/vision_transformer
+[4]: https://cloud.google.com/vertex-ai/pricing#custom-trained_models
+[5]: https://arxiv.org/abs/2010.11929
+[6]: #pytorch-gpu
+[7]: #jax-tpu-and-gpu
+[8]: https://cloud.google.com/vertex-ai/docs/training/overview
+[9]: https://cloud.google.com/vertex-ai/docs/training/configure-compute#machine-types
+[10]: https://cloud.google.com/vertex-ai/docs/training/configure-compute#specifying_gpus
+[11]: https://huggingface.co/google/vit-large-patch16-224-in21k
+[12]: https://huggingface.co/google/vit-huge-patch14-224-in21k
+[13]: https://github.com/huggingface/pytorch-image-models/blob/v0.9.2/timm/models/vision_transformer.py#L1308
+[14]: https://github.com/huggingface/pytorch-image-models/blob/v0.9.2/timm/models/vision_transformer.py#L1312
+[15]: https://huggingface.co/docs/transformers/main/model_doc/vit#transformers.ViTModel
+[16]: https://github.com/huggingface/pytorch-image-models
+[17]: https://huggingface.co/datasets/cifar10
+[18]: https://lightning.ai/docs/pytorch/stable/
+[19]: https://pytorch.org/docs/stable/notes/ddp.html
+[20]: https://www.deepspeed.ai/tutorials/zero/
+[21]: https://pytorch.org/tutorials/intermediate/torch_compile_tutorial.html
+[22]: https://cloud.google.com/tpu/docs/system-architecture-tpu-vm#tpu_v3
+[23]: https://github.com/google-research/big_vision
+[24]: https://screenshot.googleplex.com/BximJgxsgvBVu38
+[25]: https://www.tensorflow.org/datasets/catalog/cifar10
+[26]: #benchmarking-results
+[27]: https://jax.readthedocs.io/en/latest/jax-101/02-jitting.html

notebooks/community/model_garden/benchmarking_reports/pytorch_openllama_hyperparameter_tuning_benchmark_report.md

@@ -0,0 +1,188 @@
+# Benchmark report on hyperparameter tuning the OpenLLaMA models on Google Cloud Vertex Model Garden
+
+Changyu Zhu, Software Engineer, Google Cloud
+
+Dustin Luong, Software Engineer, Google Cloud
+
+Gary Wei, Software Engineer, Google Cloud
+
+Genquan Duan, Software Engineer, Google Cloud
+
+## Introduction
+
+Fine-tuning of LLMs can be non-trivial to find an optimal configuration of
+machine types, training parameters, and other hyperparameters that achieves a
+good balance between cost efficiency and model performance. To facilitate users
+in conducting tuning experiments, this report benchmarks fine-tuning OpenLLaMA
+models with [Vertex AI Hyperparameter Tuning Service](https://cloud.google.com/vertex-ai/docs/training/hyperparameter-tuning-overview), demonstrating both efficiency
+and effectiveness. Similar hyperparameter tuning techniques can apply to other models as well.
+
+## Key takeaways
+
+- **The hyperparameter tuning service finds good parameters**: The best model found by the hyperparameter tuning service has an average improvement of around 4% in accuracy in *ARC*, *HellaSwag*, and *TruthfulQA* datasets, while only tuning the learning rate.
+
+- **Hyperparameter tuning works with QLoRA on limited resources**: 4bit QLoRA is sufficient for hyperparameter tuning to find a set of good parameters. In this way, all OpenLLaMA models can run on 1 single `NVIDIA_L4` GPU. It is also possible to train for more steps on the good parameters discovered by hyperparameter tuning, avoiding the waste of computing resources on fine-tuning with suboptimal hyperparameters.
+
+- **Hyperparameter tuning is cost-effective**: While `NVIDIA_L4` is slower than `NVIDIA_TESLA_V100`, it costs less and avoids the overhead of multi-GPU training since it has more GPU memory. Finding a good 3B/7B/13B OpenLLaMA model costs $28.5671, $47.8016, and $87.9208, respectively.
+
+## Benchmarking setup
+
+This section describes the experiment setup of the hyperparameter tuning experiments. The default tuning parameters are:
+
+### Machine configuration
+
+- Machine type: g2-standard-8
+- Machine count: 1
+- Accelerator type: NVIDIA_L4
+- Accelerator count: 1
+
+### Modeling
+
+We benchmark all 3 OpenLLaMA models:
+
+- [open_llama_3b](https://huggingface.co/openlm-research/open_llama_3b)
+- [open_llama_7b](https://huggingface.co/openlm-research/open_llama_7b)
+- [open_llama_13b](https://huggingface.co/openlm-research/open_llama_13b)
+
+We use the Huggingface [PEFT](https://github.com/huggingface/peft) library for fine-tuning.
+
+### Training dataset
+
+We use the dataset [timdettmers/openassistant-guanaco](https://huggingface.co/datasets/timdettmers/openassistant-guanaco) loaded directly via Huggingface.
+
+### Training parameters
+
+The set of training parameters used during benchmarking:
+
+- Batch size: 4
+- Precision mode: 4bit QLoRA
+- LoRA rank: 32
+- LoRA alpha: 64
+- Max sequence length: 512
+- Max train steps: 1000
+
+### Evaluation dataset
+
+We use the [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) library injected into the training loop for evaluation. The hyperparameter tuning job will pick the model according to the evaluation metrics.
+
+- Eval task: [ARC Challenge](https://huggingface.co/datasets/ai2_arc)
+- Eval metric: acc_norm
+- Max eval examples: 10000
+
+### Standalone evaluation dataset
+
+After finding the best model with Vertex hyperparameter tuning service, we run standalone evaluations with the model on the following datasets:
+
+- [ARC Challenge](https://huggingface.co/datasets/ai2_arc)
+- [HellaSwag](https://huggingface.co/datasets/Rowan/hellaswag)
+- [TruthfulQA](https://huggingface.co/datasets/EleutherAI/truthful_qa_mc)
+
+### Hyperparameter tuning
+
+We only tune the learning rate hyperparameter. It is considered a floating point value in the continuous range [1e-5, 1e-4]. We run 8 trials in total, with a parallelism of 1 or 2.
+
+### Code example
+
+The following code example launches an example hyperparameter tuning job of OpenLLaMA 7B model.
+
+```py
+from google.cloud import aiplatform
+from google.cloud.aiplatform import hyperparameter_tuning as hpt
+
+
+TRAIN_DOCKER_URI = 'us-docker.pkg.dev/vertex-ai/vertex-vision-model-garden-dockers/pytorch-peft-train:20231130_0936_RC00'
+output_dir = "gs://path/to/output/dir"
+base_model_id = "openlm-research/open_llama_7b"
+dataset_name = "timdettmers/openassistant-guanaco"
+hpt_precision_mode = "4bit"
+machine_type = "g2-standard-8"
+accelerator_type = "NVIDIA_L4"
+accelerator_count = 1
+eval_task = "arc_challenge"
+eval_metric_name = "acc_norm"
+max_steps = 1000
+eval_limit = 10000
+
+flags = {
+    "learning_rate": 1e-5,
+    "precision_mode": hpt_precision_mode,
+    "task": "instruct-lora",
+    "pretrained_model_id": base_model_id,
+    "output_dir": output_dir,
+    "warmup_steps": 10,
+    "max_steps": max_steps,
+    "lora_rank": 32,
+    "lora_alpha": 64,
+    "lora_dropout": 0.05,
+    "dataset_name": dataset_name,
+    "eval_steps": max_steps + 1,  # Only evaluates at the end.
+    "eval_tasks": eval_task,
+    "eval_limit": eval_limit,
+    "eval_metric_name": eval_metric_name,
+}
+worker_pool_specs = [
+    {
+        "machine_spec": {
+            "machine_type": machine_type,
+            "accelerator_type": accelerator_type,
+            "accelerator_count": accelerator_count,
+        },
+        "replica_count": 1,
+        "container_spec": {
+            "image_uri": TRAIN_DOCKER_URI,
+            "args": ["--{}={}".format(k, v) for k, v in flags.items()],
+        },
+    }
+]
+metric_spec = {"model_performance": "maximize"}
+parameter_spec = {
+    "learning_rate": hpt.DoubleParameterSpec(
+        min=1e-5, max=1e-4, scale="linear"
+    ),
+}
+
+train_job = aiplatform.CustomJob(
+    display_name=job_name,
+    worker_pool_specs=worker_pool_specs,
+    staging_bucket=STAGING_BUCKET,
+)
+
+train_hpt_job = aiplatform.HyperparameterTuningJob(
+    display_name=f"{job_name}_hpt",
+    custom_job=train_job,
+    metric_spec=metric_spec,
+    parameter_spec=parameter_spec,
+    max_trial_count=8,
+    parallel_trial_count=2,
+)
+
+train_hpt_job.run()
+```
+
+## Benchmark results
+
+### Fine-tuning cost
+
+The fine-tuning cost is calculated from `us-central1` pricing and may be subject to changes.
+
+| Model         | Train time | Trials | Parallel Trials | Hourly cost | Cost     | Eval acc_norm (ARC-Challenge) |
+|---------------|------------|--------|-----------------|-------------|----------|-------------------------------|
+| OpenLLaMA 3B  | 16 hrs     | 8      | 2               | $1.7072     | $28.5671 | 39.9%                         |
+| OpenLLaMA 7B  | 28 hrs     | 8      | 2               | $1.7072     | $47.8016 | 45.8%                         |
+| OpenLLaMA 13B | 103 hrs    | 8      | 1               | $0.8536     | $87.9208 | 47.6%                         |
+
+### Fine-tuning performance
+
+Here are the evaluation results of the best model found by hyperparameter tuning, compared with the baseline model. The column `Eval acc_norm` is calculated during training, which is always lower than that during standalone evaluation, because the model is loaded and evaluated at a lower precision (4bit during training / float16 during standalone evaluation).
+
+| Model         | Eval acc_norm (ARC-Challenge) | ARC    | hellaswag | Truthfulqa_mc | ∆ARC   | ∆Hellaswag | ∆Truthfulqa_mc | ∆Average |
+|---------------|-------------------------------|--------|-----------|---------------|--------|------------|----------------|----------|
+| OpenLLaMA 3B  | 39.9%                         | 41.47% | 69.97%    | 38.31%        | +1.62% | +7.32%     | +3.34%         | +4.09%   |
+| OpenLLaMA 7B  | 45.8%                         | 49.83% | 75.53%    | 41.53%        | +2.82% | +3.55%     | +6.68%         | +4.35%   |
+| OpenLLaMA 13B | 47.6%                         | 52.20% | 78.90%    | 44.27%        | +1.01% | +3.67%     | +6.19%         | +3.62%   |
+
+## Related documents
+
+1. [Benchmark report on fine tuning the OpenLLaMA 7B model on Google Cloud Vertex Model Garden
+](
+https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/community-content/vertex_model_garden/benchmarking_reports/pytorch_openllama_7b_finetune_benchmark_report.md)

notebooks/community/model_garden/benchmarking_reports/stable_diffusion_v1-5_benchmarking_report.md

@@ -0,0 +1,218 @@
+# Benchmark Stable Diffusion v1-5 Fine Tuning and Serving With Google Cloud Vertex Model Garden
+
+Dustin Luong, Software Engineer, Google Cloud
+Gary Wei, Software Engineer, Google Cloud
+Changyu Zhu, Software Engineer, Google Cloud
+Genquan Duan, Software Engineer, Google Cloud
+
+## Introduction
+[The public notebook][1] shows the full examples of fine tuning and serving of Stable diffusion v1-5. [The github repo][2] contains examples of building training and serving dockers for Google Cloud Vertex Model Garden. This report benchmarks Stable diffusion v1-5 fine tuning and serving in Google Cloud Vertex AI, showing both efficiencies and effectiveness.
+
+### Benchmark Highlights
+- Fine tuning
+  - Stable diffusion v1-5 with LoRA and Gradient checkpointing only requires ~10G GPU memory. Larger batch sizes, or larger resolutions require more GPU memories, but not does not change much for different LoRA ranks.
+  - The fine tuning speed is fast in ~11 minutes for 1k steps, and costs less than $1 in 1 A100. The fine tuning speed increases with batch sizes, decreases with resolution, but is not affected much by LoRA ranks.
+  - LoRA tunes a few percent (only 0.1% with LoRA rank=8) of all parameters, and the tuned models are very small (only 3.1MB with LoRA rank=8).
+  - Dreambooth+LoRA and Dreambooth can achieve similar performances, but Dreambooth LoRA can require much less GPU.
+  - Increasing batch size, reducing training steps, and increasing learning rate can result in models with the same performance for less cost.
+- Inference
+  - The optimized serving docker pytorch-peft-serve can speed up inference by 2x than current pytorch-diffuser-serve, and support both base models and fine tuned lora models.
+  - The optimized serving docker pytorch-peft-serve can generate 4 512*512 images in 4.1 seconds on 1 V100 and 1.7 seconds on 1 A100.
+
+Benchmark details are below.
+
+## Fine Tuning Benchmarks
+
+### Experiment Setup
+We mainly compare two tuning algorithms:
+- parameter efficient finetuning based on [dreambooth][3] and [LoRA][4] (shorten as Dreambooth+LoRA below)
+- full parameter fine tuning based on [dreambooth][3] (shorten as Dreambooth below)
+
+And then  report benchmark results on GPU memories, tuning parameters, tuning speeds, costs and accuracy, using the public oxford flowers dataset: [train][5] and [test][6], where the column blip_caption as texts, and column image as images. We also benchmark subject and prompt fidelity using the [dataset][7] from the Dreambooth paper.
+
+The default tuning parameters during benchmark are:
+- Hardware: 1 A100 40G
+- batch size: 4
+- lora_rank: 8
+- resolution: 512
+- max_train_steps: 10
+- use_lora: False
+- gradient_checkpointing: False
+
+```
+# Examples to start finetuning dockers.
+MODEL_NAME="runwayml/stable-diffusion-v1-5"
+OUTPUT_DIR=<OUTPUT_DIR>
+INSTANCE_DATA_DIR=<INSTANCE_DATA_DIR>
+INSTANCE_PROMPT=<INSTANCE_PROMPT>
+IMAGE="us-docker.pkg.dev/vertex-ai/vertex-vision-model-garden-dockers/pytorch-peft-train"
+docker run \
+  --runtime=nvidia -e NVIDIA_VISIBLE_DEVICES=0 \
+  --rm --name "test_gpu" \
+  -it ${IMAGE} \
+  --task=text-to-image-dreambooth-lora-peft \
+  --pretrained_model_name_or_path=$MODEL_NAME \
+  --resolution=512 \
+  --instance_data_dir=$INSTANCE_DATA_DIR \
+  --instance_prompt=$INSTANCE_PROMPT \
+  --train_batch_size=4 \
+  --max_train_steps=10 \
+  --output_dir=${OUTPUT_DIR} \
+  --use_lora \
+  --lora_r=8 \
+  --gradient_checkpointing
+```
+
+### GPU Memories
+Many various factors will impact GPU memory usages. In this benchmark, we mainly benchmark with different finetuning algorithms, batch sizes, lora rank, resolution, and then recommended max batch size on different GPUs.
+
+
+![sd_v1-5_peak_gpu_algorithm](images/stable_diffusion_v1-5_benchmarking_report/sd_v1-5_peak_gpu_algorithm.png)
+
+![sd_v1-5_peak_gpu_batch_size](images/stable_diffusion_v1-5_benchmarking_report/sd_v1-5_peak_gpu_batch_size.png)
+
+![sd_v1-5_peak_gpu_lora_rank](images/stable_diffusion_v1-5_benchmarking_report/sd_v1-5_peak_gpu_lora_rank.png)
+
+![sd_v1-5_peak_gpu_resolution](images/stable_diffusion_v1-5_benchmarking_report/sd_v1-5_peak_gpu_resolution.png)
+
+- LoRA tuning reduced about 47% peak RAM and 42% peak VRAM for GPU memory, compared to full parameter fine tuning.
+- Gradient checkpointing decreases about 1% peak RAM and 31% peak VRAM for GPU memory further, compared without gradient checkpointing.
+- The GPU memory does not change much for different LoRA ranks.
+- Larger batch sizes require more GPU memories.
+- Larger resolutions require more GPU memories.
+- Dreambooth+LoRA+Gradient_Checkpointing can support max batch size as 32, or max resolution as 2048, but Dreambooth can only support max batch size as 8, or max resolution as 1024.
+
+### Fine Tuning Parameters
+This section shows the percentage of trainable parameters, and tuned model sizes.
+
+- LoRA tunes quite a few percent (only 0.1% with LoRA rank=8) of all parameters, and the tuned models are very small (only 3.1MB with LoRA rank=8).
+
+| LoRA Rank | Trainable parameters | Total parameters | Trainable Parameter Percentage | Fine tuned model size (MB) |
+|---|---|---|---|---|
+| 4 | 398592 | 859919556 | 0.05% | 1.57 |
+|8 | 797184 | 860318148 | 0.09% | 3.09 |
+| 16 | 1594368| 861115332| 0.19%| 6.13|
+| 32| 3188736| 862709700| 0.37%| 12.21|
+### Fine Tuning Speed And Costs
+Fine tuning speeds and costs are affected by many different factors, such as batch size, tuning parameters, image resolutions, GPUs, and datasets. In order to make the report easy to understand, we set the following values in this section:
+- Hardware: 1 A100 40G
+- use_lora: True
+- gradient_checkpointing: True
+
+![sd_v1-5_training_speed_batch_size](images/stable_diffusion_v1-5_benchmarking_report/sd_v1-5_training_speed_batch_size.png)
+
+![sd_v1-5_training_speed_lora_rank](images/stable_diffusion_v1-5_benchmarking_report/sd_v1-5_training_speed_lora_rank.png)
+
+![sd_v1-5_training_speed_resolution](images/stable_diffusion_v1-5_benchmarking_report/sd_v1-5_training_speed_resolution.png)
+
+![sd_v1-5_training_cost_max_steps](images/stable_diffusion_v1-5_benchmarking_report/sd_v1-5_training_cost_max_steps.png)
+
+- The fine tuning speed increases with batch sizes, decreases with resolution, but is not affected much by LoRA ranks.
+- The fine tuning speed is about 11 minutes for 1k steps, and costs less than $1 in 1 A100.
+
+### Fine Tuning Quality
+In this benchmark, we mainly benchmark Dreambooth and Dreambooth+LoRA to compare fine tuning quality. We compare [subject fidelity scored (DINO)][8], how well the subject is represented in the generated images, and [prompt fidelity scores (CoCa)][9], how well the generated images match the given prompt, for a single subject, a [dog][10] from the dataset released with the original Dreambooth paper. In practice, we recommend saving checkpoints periodically and inspecting validation prompts visually. We fine tuned the unet without fine tuning the text encoder and used the following hyperparameters:
+
+Dreambooth
+- Learning rate: 5e-6
+- Batch size: 1
+
+Dreambooth+LoRA
+- Learning rate: 1e-4
+- Batch size: 1
+
+![sd_v1-5_finetuning_quality_subject_fidelity](images/stable_diffusion_v1-5_benchmarking_report/sd_v1-5_finetuning_quality_subject_fidelity.png)
+
+![sd_v1-5_finetuning_quality_prompt_fidelity](images/stable_diffusion_v1-5_benchmarking_report/sd_v1-5_finetuning_quality_prompt_fidelity.png)
+
+- Fine tuning with Dreambooth or Dreambooth+LoRA can result in models with comparable performance. The base model produced images of the class rather than the instance.
+- Dreambooth+LoRA is able to achieve the same subject fidelity score as Dreambooth if trained for more epochs.
+- Increasing the number of training steps results in better subject fidelity but at the cost of prompt fidelity.
+
+### Suggested Max Batch Sizes By Resolutions
+We benchmarked and suggested max batch sizes by resolutions on 1 A100 and 1 V100 as below. This is with LoRA and gradient checkpointing enabled.
+
+![sd_v1-5_batch_size_by_resolution](images/stable_diffusion_v1-5_benchmarking_report/sd_v1-5_batch_size_by_resolution.png)
+
+### Fine Tuning Cost Optimization
+Increasing batch size allows for more images to be considered at each training step for fine tuning. This allows models to be trained in fewer training steps. In this benchmark, we aim to show how batch size can be increased to reduce training costs while still preserving subject and prompt fidelity.
+
+Since the training dataset consists of 5 images, we train with a batch size of 5 and reduce the number of training steps from 400 to 80. Doing so results in a model that has not learned the subject since we’ve decreased the number of training steps. Conceptually, the model is taking a more precise step at each iteration, but it is taking fewer steps. To compensate for this, we increased the learning rate from 5e-6 and observed the best results at 1e-5 for full parameter finetuning.
+
+![sd_v1-5_subject_fidelity_batch_size_5_learning_rate](images/stable_diffusion_v1-5_benchmarking_report/sd_v1-5_subject_fidelity_batch_size_5_learning_rate.png)
+
+![sd_v1-5_prompt_fidelity_batch_size_5_learning_rate](images/stable_diffusion_v1-5_benchmarking_report/sd_v1-5_prompt_fidelity_batch_size_5_learning_rate.png)
+
+Comparing cost of training the “best” model for batch size 1 vs. batch size 5
+
+![sd_v1-5_cost_batch_size](images/stable_diffusion_v1-5_benchmarking_report/sd_v1-5_cost_batch_size.png)
+
+
+| Train method| Training parameters| Sample image| CoCa (prompt fidelity)| DINO (subject fidelity) | Cost of training on A100 |
+|---|---|---|---|---|---|
+| dreambooth| dreambooth, num_train_steps=400, batch_size=1, lr=5e-6| ![dog1](images/stable_diffusion_v1-5_benchmarking_report/sd_v1-5_dog1.png) | 0.12215| 0.76531| $0.26 |
+| dreambooth | dreambooth, num_train_steps=80, batch_size=5,lr=1e-5| ![dog2](images/stable_diffusion_v1-5_benchmarking_report/sd_v1-5_dog2.png)| 0.12644| 0.74697 | $0.15 |
+| dreambooth-lora| num_train_steps=500, batch_size=1, lr=1e-4, gc|![dog3](images/stable_diffusion_v1-5_benchmarking_report/sd_v1-5_dog3.png)| 0.12856| 0.78148 | $0.26|
+| dreambooth-lora | num_train_steps=50, batch_size=5, lr=1e-3, gc | ![dog4](images/stable_diffusion_v1-5_benchmarking_report/sd_v1-5_dog4.png) | 0.12566 | 0.75479 | $0.09 |
+
+
+
+A followup question is that since finetuning can be run on a single GPU, should finetuning be run on 1 V100 or A100?
+
+Setup:
+- num_train_steps=800 / batch_size
+- Resolution=512
+
+![sd_v1-5_cost_training_method_batch_size](images/stable_diffusion_v1-5_benchmarking_report/sd_v1-5_cost_training_method_batch_size.png)
+- Although V100 has a lower $/hr cost than an A100, the same training setup takes longer. Even given the longer training time, the cost on V100 is still lower.
+- Dreambooth+LoRA enables training with larger batch sizes, however, larger batch sizes will not necessarily mean faster training time.
+- It is possible to fine tune with 1 V100 on 512 resolution with Dreambooth+LoRA.
+- Dreambooth fine tuning must be run on 1 A100 at 512 resolution.
+
+## Inference Benchmarks
+We provide two serving dockers in vertex model garden for stable diffusion:
+- pytorch-diffuser-serve:
+  - us-docker.pkg.dev/vertex-ai/vertex-vision-model-garden-dockers/pytorch-diffusers-serve
+  - This serving docker only serves base stable diffusion models and does not contain any optimizations yet.
+- pytorch-peft-serve:
+  - us-docker.pkg.dev/vertex-ai/vertex-vision-model-garden-dockers/pytorch-peft-serve
+  - This serving docker can serve base stable diffusion models, and base stable diffusion models with fine tuned lora models, and contains optimization for serving.
+
+We run the two serving dockers on T4/V100/A100 to generate 4 512*512 images, and compare the inference speed without network considerations as:
+
+![sd_v1-5_inference_speed_gpu](images/stable_diffusion_v1-5_benchmarking_report/sd_v1-5_inference_speed_gpu.png)
+The speed up of optimized pytorch-peft-serve is about 2x than current pytorch-diffuser-serve.
+
+### Serving cost comparison
+
+Pytorch-diffuser-serve (without any optimizations)
+
+| GPU type|  Time required to generate 4 512x512 images | Machine unit price ($ / hour) | Cost per image ($) |
+|---|---|---|---|
+| T4 | 28.6 | 0.4025| 0.00080 |
+| V100 | 8.8 | 2.852| 0.00174|
+| A100 | 4.2 | 4.2245 | 0.00123 |
+
+Pytorch-peft-serve (with optimizations)
+
+| GPU type | Time required to generate 4 512x512 images | Machine unit price ($ / hour) | Cost per image ($) |
+|--- |---|---|---|
+| T4 | 12.6 | 0.4025 | 0.00035 |
+| V100 | 4.1 | 2.852 | 0.00081 |
+|  A100 | 1.7 | 4.2245 | 0.00050 |
+
+- The optimized pytorch-peft-serve has approximately half the price per image, compared with the un-optimized pytorch-diffuser-serve.
+- Serving the model with a T4 is most cost effective, however, serving with an A100 still has the best throughput and fastest predictions.
+
+
+
+[1]: https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/model_garden/model_garden_pytorch_stable_diffusion.ipynb
+[2]: https://github.com/GoogleCloudPlatform/vertex-ai-samples/tree/main/community-content/vertex_model_garden/model_oss
+[3]: https://arxiv.org/abs/2208.12242
+[4]: https://arxiv.org/abs/2106.09685
+[5]: https://huggingface.co/datasets/Multimodal-Fatima/OxfordFlowers_train
+[6]: https://huggingface.co/datasets/Multimodal-Fatima/OxfordFlowers_test_facebook_opt_6.7b_Attributes_ns_6149
+[7]: https://github.com/google/dreambooth
+[8]: https://arxiv.org/abs/2104.14294
+[9]: https://arxiv.org/abs/2205.01917
+[10]: https://github.com/google/dreambooth/tree/main/dataset/dog6