The main changes:

- `get_deployment_pod_name` now extract the app selector to query the pods
- remove dependency to service, using instead pod port instead
- adds a `POD_PORT` as template variable to allow to pass the port from the UI

PiperOrigin-RevId: 771205143

.cloud-build/execute_changed_notebooks_helper.py

@@ -238,7 +238,7 @@ def _get_notebook_python_version(notebook_path: str) -> str:
 
             # Look for the python version specification pattern
             re_match = re.search(
-                "python version = (\d+\.\d+)", markdown, flags=re.IGNORECASE
+                r"python version = (\d+\.\d+)", markdown, flags=re.IGNORECASE
             )
             if re_match:
                 # get the version number

.cloud-build/test_notebook_vm.txt

@@ -1,5 +1,3 @@
-notebooks/official/vizier/gapic-vizier-multi-objective-optimization.ipynb
-notebooks/official/pipelines/lightweight_functions_component_io_kfp.ipynb
 notebooks/official/ml_metadata/sdk-metric-parameter-tracking-for-locally-trained-models.ipynb
 notebooks/official/custom/custom-tabular-bq-managed-dataset.ipynb
 .cloud-build/tests/python_version_test.ipynb

.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

@@ -1,13 +1,16 @@
 """Common util functions for notebook."""
 
 import base64
+from collections.abc import Sequence
 import datetime
 import io
 import json
 import os
 import subprocess
-from typing import Any, Dict, Sequence
+import time
+from typing import Any
 
+from google import auth
 from google.cloud import storage
 import matplotlib.pyplot as plt
 import numpy as np
@@ -85,7 +88,9 @@ 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 +104,7 @@ 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 +233,23 @@ 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:
@@ -279,7 +284,7 @@ def decode_image(
   return image
 
 
-def get_label_map(label_map_yaml_filepath: str) -> Dict[int, str]:
+def get_label_map(label_map_yaml_filepath: str) -> dict[int, str]:
   """Returns class id to label mapping given a filepath to the label map.
 
   Args:
@@ -323,6 +328,150 @@ 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,
+    use_dedicated_endpoint: bool = False,
+) -> 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, use_dedicated_endpoint=use_dedicated_endpoint
+  )
+  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,
+    use_dedicated_endpoint: bool = False,
+) -> 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, use_dedicated_endpoint=use_dedicated_endpoint
+  )
+  return response.predictions[0].get("response")
+
+
+def ocr_predict(
+    endpoint: Any,
+    ocr_prompt: str,
+    image: Any,
+    new_width: int = 1000,
+    use_dedicated_endpoint: bool = False,
+) -> 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, use_dedicated_endpoint=use_dedicated_endpoint
+  )
+  return response.predictions[0].get("response")
+
+
+def detect_predict(
+    endpoint: Any,
+    detect_prompt: str,
+    image: Any,
+    new_width: int = 1000,
+    use_dedicated_endpoint: bool = False,
+) -> 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, use_dedicated_endpoint=use_dedicated_endpoint
+  )
+  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.
 
@@ -361,6 +510,17 @@ def get_quota(project_id: str, region: str, resource_id: str) -> int:
   ):
     return -1
   all_regions_data = quota_data[0]["consumerQuotaLimits"][0]["quotaBuckets"]
+
+  # If the quota data does not have dimensions, it is global quota. However,
+  # global quota may be overridden by regional quota. So we need to check the
+  # global quota first.
+  global_quota = -1
+  if (
+      all_regions_data
+      and "dimensions" not in all_regions_data[0]
+      and "effectiveLimit" in all_regions_data[0]
+  ):
+    global_quota = int(all_regions_data[0]["effectiveLimit"])
   for region_data in all_regions_data:
     if (
         region_data.get("dimensions")
@@ -370,47 +530,91 @@ def get_quota(project_id: str, region: str, resource_id: str) -> int:
         return int(region_data["effectiveLimit"])
       else:
         return 0
-  return -1
+  return global_quota
 
 
-def get_resource_id(accelerator_type: str, is_for_training: bool) -> str:
+def get_resource_id(
+    accelerator_type: str,
+    is_for_training: bool,
+    is_spot: bool = False,
+    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_spot: Whether the resource is used with Spot.
+    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_H200_141GB": "nvidia_h200_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",
+  }
+  spot_serving_accelerator_map = {
+      key: f"custom_model_serving_preemptible_{accelerator_suffix_map[key]}"
+      for key in accelerator_suffix_map
   }
   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 accelerator_type in serving_accelerator_map:
-      return serving_accelerator_map[accelerator_type]
+    if is_dynamic_workload_scheduler:
+      raise ValueError("Dynamic Workload Scheduler does not work for serving.")
+    accelerator_map = (
+        spot_serving_accelerator_map if is_spot else serving_accelerator_map
+    )
+    if accelerator_type in accelerator_map:
+      return accelerator_map[accelerator_type]
     else:
       raise ValueError(
           f"Could not find accelerator type: {accelerator_type} for serving."
@@ -423,9 +627,31 @@ def check_quota(
     accelerator_type: str,
     accelerator_count: int,
     is_for_training: bool,
-):
-  """Checks if the project and the region has the required quota."""
-  resource_id = get_resource_id(accelerator_type, is_for_training)
+    is_spot: bool = False,
+    is_restricted_image: bool = False,
+    is_dynamic_workload_scheduler: bool = False,
+) -> None:
+  """Checks if the project and the region has the required quota.
+
+  Args:
+    project_id: The project id.
+    region: The region.
+    accelerator_type: The accelerator type.
+    accelerator_count: The number of accelerators to check quota for.
+    is_for_training: Whether the resource is used for training. Set false for
+      serving use case.
+    is_spot: Whether the resource is used with Spot.
+    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.
+  """
+  resource_id = get_resource_id(
+      accelerator_type,
+      is_for_training=is_for_training,
+      is_spot=is_spot,
+      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 +671,76 @@ 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 +1,570 @@
+"""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, Tuple, 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,
+    check_path_exists: bool = True,
+) -> 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.
+    check_path_exists: Whether to check if the path exists.
+
+  Returns:
+    Local path to target folder/file.
+  """
+  target = os.path.join(
+      destination_dir,
+      os.path.basename(os.path.normpath(gcs_uri)),
+  )
+  if check_path_exists and 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 format_dataset(
+    dataset: datasets.Dataset,
+    input_column: str,
+    template: str = None,
+    tokenizer: transformers.PreTrainedTokenizer | None = None,
+) -> datasets.Dataset:
+  """Takes a raw dataset and formats it using a template and tokenizer.
+
+  Args:
+    dataset: The raw (unprocessed) dataset to format.
+    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.
+  """
+  return dataset.map(
+      _format_template_fn(
+          template,
+          input_column=input_column,
+          tokenizer=tokenizer,
+      )
+  )
+
+
+def load_dataset_with_template(
+    dataset_name: str,
+    split: str,
+    input_column: str,
+    template: str = None,
+    tokenizer: transformers.PreTrainedTokenizer | None = None,
+) -> Tuple[Any, 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:
+    The raw dataset and the dataset compatible with the template.
+  """
+  raw = _get_dataset(dataset_name, split=split)
+  if template:
+    templated = format_dataset(raw, input_column, template, tokenizer)
+  else:
+    templated = None
+
+  return raw, templated
+
+
+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 = format_dataset(
+      _get_dataset(dataset_name, split, num_proc),
+      input_column,
+      template_path,
+      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/sequence_classification_lora.py

@@ -1,133 +0,0 @@
-"""Sequence classification with LoRA models."""
-
-# pylint: disable=g-importing-member
-
-from datasets import load_dataset
-import evaluate
-from peft import get_peft_model
-from peft import LoraConfig
-import torch
-from torch.optim import AdamW
-from torch.utils.data import DataLoader
-from tqdm import tqdm
-from transformers import AutoModelForSequenceClassification
-from transformers import AutoTokenizer
-from transformers import get_linear_schedule_with_warmup
-
-
-def finetune_sequence_classification(
-    pretrained_model_id: str,
-    dataset_name: str,
-    output_dir: str,
-    lora_rank: int = 8,
-    lora_alpha: int = 16,
-    lora_dropout: float = 0.1,
-    num_epochs: int = 20,
-    batch_size: int = 32,
-    learning_rate: float = 3e-4,
-) -> None:
-  """Finetunes sequence classification."""
-  task = "mrpc"
-  device = "cuda"
-
-  peft_config = LoraConfig(
-      task_type="SEQ_CLS",
-      inference_mode=False,
-      r=lora_rank,
-      lora_alpha=lora_alpha,
-      lora_dropout=lora_dropout,
-  )
-  if any(k in pretrained_model_id for k in ("gpt", "opt", "bloom")):
-    padding_side = "left"
-  else:
-    padding_side = "right"
-
-  tokenizer = AutoTokenizer.from_pretrained(
-      pretrained_model_id, padding_side=padding_side
-  )
-  if getattr(tokenizer, "pad_token_id") is None:
-    tokenizer.pad_token_id = tokenizer.eos_token_id
-
-  datasets = load_dataset(dataset_name, task)
-  metric = evaluate.load(dataset_name, task)
-
-  def tokenize_function(examples):
-    # max_length=None => use the model max length (it's actually the default)
-    outputs = tokenizer(
-        examples["sentence1"],
-        examples["sentence2"],
-        truncation=True,
-        max_length=None,
-    )
-    return outputs
-
-  tokenized_datasets = datasets.map(
-      tokenize_function,
-      batched=True,
-      remove_columns=["idx", "sentence1", "sentence2"],
-  )
-
-  # We also rename the 'label' column to 'labels' which is the expected name for
-  # labels by the models of the transformers library.
-  tokenized_datasets = tokenized_datasets.rename_column("label", "labels")
-
-  def collate_fn(examples):
-    return tokenizer.pad(examples, padding="longest", return_tensors="pt")
-
-  # Instantiate dataloaders.
-  train_dataloader = DataLoader(
-      tokenized_datasets["train"],
-      shuffle=True,
-      collate_fn=collate_fn,
-      batch_size=batch_size,
-  )
-  eval_dataloader = DataLoader(
-      tokenized_datasets["validation"],
-      shuffle=False,
-      collate_fn=collate_fn,
-      batch_size=batch_size,
-  )
-
-  model = AutoModelForSequenceClassification.from_pretrained(
-      pretrained_model_id, return_dict=True
-  )
-  model = get_peft_model(model, peft_config)
-  model.print_trainable_parameters()
-
-  optimizer = AdamW(params=model.parameters(), lr=learning_rate)
-
-  # 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),
-  )
-
-  model.to(device)
-  for epoch in range(num_epochs):
-    model.train()
-    for _, batch in enumerate(tqdm(train_dataloader)):
-      batch.to(device)
-      outputs = model(**batch)
-      loss = outputs.loss
-      loss.backward()
-      optimizer.step()
-      lr_scheduler.step()
-      optimizer.zero_grad()
-
-    model.eval()
-    for _, batch in enumerate(tqdm(eval_dataloader)):
-      batch.to(device)
-      with torch.no_grad():
-        outputs = model(**batch)
-      predictions = outputs.logits.argmax(dim=-1)
-      references = batch["labels"]
-      metric.add_batch(
-          predictions=predictions,
-          references=references,
-      )
-
-    eval_metric = metric.compute()
-    print(f"epoch {epoch}:", eval_metric)
-
-  model.save_pretrained(output_dir)

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/command_builder.py

@@ -0,0 +1,95 @@
+"""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.entrypoint = []
+
+    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 add_entrypoint(self, entrypoint: list[str]):
+    self.entrypoint = entrypoint
+
+  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
+        + self.entrypoint
+    )
+
+
+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
+
+  def add_entrypoint(self, entrypoint: list[str]):
+    self._defaults = entrypoint

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

@@ -0,0 +1,471 @@
+"""Test util class."""
+
+import copy
+import dataclasses
+import datetime
+import inspect
+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 immutabledict
+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://vmg-tuning-docker-test',
+    'GCS directory that stores model checkpoint, dataset and etc.',
+)
+
+_GCS_OUTPUT_DIR = flags.DEFINE_string(
+    'gcs_output_dir',
+    'gs://vmg-tuning-docker-test/output',
+    'GCS directory that stores test output.',
+)
+
+_GCS_TESTDATA_DIR = 'peft-train-image-test'
+
+_THROUGHPUT_TEST_EXCEPTIONS = immutabledict.immutabledict({
+    ('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:
+    return run_cmd(self.cmd(), output_file=None)
+
+  def gcs_output_dir(self):
+    return _GCS_OUTPUT_DIR.value
+
+  def local_input_dir(self):
+    """Returns local input dir in host/docker."""
+    return _LOCAL_INPUT_DIR.value
+
+  def local_output_dir(self):
+    """Returns local output dir in host/docker."""
+    return _LOCAL_OUTPUT_DIR.value
+
+  def get_testcase_name(self):
+    """Returns the function name at the calling site."""
+    # https://docs.python.org/3/library/inspect.html#inspect.FrameInfo
+    cur_frame = inspect.currentframe()
+    # https://stackoverflow.com/a/17366561
+    return cur_frame.f_back.f_code.co_name
+
+
+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):
+    # If `name` is a file in sub-folders, then create the sub-folders under
+    # `_LOCAL_INPUT_DIR`.
+    local_data_dir = os.path.dirname(local_data)
+    if not os.path.exists(local_data_dir):
+      os.makedirs(local_data_dir)
+
+    download_from_gcs(os.path.join(_GCS_INPUT_DIR.value, name), local_data_dir)
+
+  return local_data
+
+
+def run_cmd(cmd: list[str], output_file: str = None) -> int:
+  """Runs the command and returns the return code.
+
+  Args:
+    cmd: The command to run.
+    output_file: The file to write the output to.
+
+  Returns:
+    The return code of the command.
+  """
+  logging.info('running command: \n%s', ' \\\n'.join(cmd))
+  if _DRY_RUN.value:
+    return 0
+  stdout = sys.stdout if output_file is None else open(output_file, 'w')
+  p = subprocess.Popen(cmd, stdout=stdout, stderr=sys.stderr)
+  try:
+    unused_output, unused_error = p.communicate()
+    return_code = p.returncode
+  except KeyboardInterrupt:
+    p.send_signal(signal.SIGINT)
+    return_code = 0
+  finally:
+    if output_file is not None:
+      stdout.close()
+  return return_code
+
+
+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/util/cluster_spec.py

@@ -0,0 +1,79 @@
+"""Get cluster info from environment variables."""
+
+import dataclasses
+import json
+import os
+
+from absl import logging
+
+
+@dataclasses.dataclass
+class ClusterInfo:
+  """Contains information about the cluster.
+
+  Attributes:
+    primary_node_addr: The address of the primary node.
+    primary_node_port: The port of the primary node.
+    node_rank: The rank of the node.
+    num_nodes: The number of nodes in the cluster.
+  """
+
+  primary_node_addr: str | None = None
+  primary_node_port: str | None = None
+  node_rank: int = 0
+  num_nodes: int = 1
+
+  # Allows unpacking operation like
+  # primary_node_addr, primary_node_port, _, _ = ClusterInfo()
+  # See https://stackoverflow.com/a/70753113
+  def __iter__(self):
+    return iter(dataclasses.astuple(self))
+
+
+def get_cluster_spec() -> ClusterInfo:
+  """Parses CLUSTER_SPEC environment variable and returns the cluster info.
+
+  Returns:
+    A ClusterInfo object.
+  """
+  cluster_spec = os.getenv('CLUSTER_SPEC', None)
+
+  # If CLUSTER_SPEC is not set, use individual vars to construct cluster info.
+  if not cluster_spec:
+    cluster_info = ClusterInfo(
+        primary_node_addr=os.getenv('MASTER_ADDR', None),
+        primary_node_port=os.getenv('MASTER_PORT', None),
+        node_rank=int(os.getenv('RANK', '0')),
+        num_nodes=int(os.getenv('NNODES', '1')),
+    )
+    return cluster_info
+
+  cluster_data = json.loads(cluster_spec)
+  # 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_nodes = 1  # For the primary node.
+  if 'workerpool1' in cluster_data['cluster']:
+    num_nodes += len(cluster_data['cluster']['workerpool1'])
+  logging.info('num nodes: %s', num_nodes)
+
+  return ClusterInfo(primary_node_addr, primary_node_port, node_rank, num_nodes)

community-content/vertex_model_garden/model_oss/peft/train/util/common_util.py

@@ -0,0 +1,24 @@
+"""Utility functions."""
+
+import logging
+import subprocess
+import sys
+import time
+
+
+def run_cmd(cmd: list[str]) -> float:
+  """Runs the command and logs the output.
+
+  Args:
+    cmd: The command to run.
+
+  Returns:
+    The time it took to run the command.
+  """
+  cmd_str = ' \\\n'.join(cmd)
+  logging.info('launching cmd: \n%s', cmd_str)
+  start_time = time.time()
+  subprocess.run(cmd, stdout=sys.stdout, stderr=sys.stdout, check=True)
+  elapsed_time = round(time.time() - start_time, 2)
+  logging.info('Command %s finished in %0.2f seconds.', cmd_str, elapsed_time)
+  return elapsed_time

community-content/vertex_model_garden/model_oss/peft/train/util/dataset_stats.py

@@ -0,0 +1,197 @@
+"""Calculate dataset statistics like token, example and character counts."""
+
+from collections.abc import Mapping, Sequence
+import dataclasses
+import json
+from typing import Any
+import datasets
+import numpy as np
+import transformers
+from util import dataset_validation_util
+
+_MAX_NUM_DATASET_SAMPLES = 6
+
+
+@dataclasses.dataclass
+class SupervisedTuningDatasetBucket:
+  """Represents a histogram bucket for tuning dataset distribution stats."""
+
+  count: float = 0
+  left: float = 0
+  right: float = 0
+
+
+@dataclasses.dataclass
+class SupervisedTuningDatasetDistribution:
+  """Represents a histogram with summary statistics for tuning dataset distribution stats."""
+
+  sum: int = 0
+  billable_sum: int = 0
+  min: float = 0
+  max: float = 0
+  mean: float = 0
+  median: float = 0
+  p5: float = 0
+  p95: float = 0
+  buckets: list[SupervisedTuningDatasetBucket] = dataclasses.field(
+      default_factory=list
+  )
+
+
+# Represents detailed tuning dataset statistics.
+@dataclasses.dataclass
+class SupervisedTuningDataStats:
+  """Represents detailed tuning dataset stats."""
+
+  tuning_dataset_example_count: int = 0
+  total_tuning_character_count: int = 0
+  total_billable_token_count: int = 0
+  tuning_step_count: int = 0
+  # Represents a histogram and some summary statistics of the number of input
+  # tokens across examples.
+  user_input_token_distribution: SupervisedTuningDatasetDistribution | None = (
+      None
+  )
+  # Represents a histogram and some summary statistics for the number of output
+  # tokens across examples.
+  user_output_token_distribution: SupervisedTuningDatasetDistribution | None = (
+      None
+  )
+  # Represents the number of "messages" (a single-turn conversation will have a
+  # single message) across examples.
+  user_message_per_example_distribution: (
+      SupervisedTuningDatasetDistribution | None
+  ) = None
+  user_dataset_examples: list[str] = dataclasses.field(default_factory=list)
+
+
+def get_dataset_stats(
+    *,
+    raw: Any,
+    templated: Any,
+    template: str,
+    tokenizer: transformers.PreTrainedTokenizer,
+    column: str,
+    effective_batch_size: int,
+) -> Mapping[str, Any]:
+  """Calculates dataset statistics for managed fine-tuning, e.g., total number of tokens."""
+  tokenized_dataset = templated.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]))
+  total_tuning_character_count = int(
+      np.sum([len(ex[column]) for ex in templated])
+  )
+  tuning_step_count = (
+      tuning_dataset_example_count + effective_batch_size - 1
+  ) // effective_batch_size
+
+  # Assume that data is represented as ChatCompletions or Vertex Text-Bison
+  # formats to extract per-example input/output tokens.
+  user_inputs = []
+  user_outputs = []
+  user_input_messages_counts = []
+
+  for ex in raw:
+    if "messages" in ex:
+      messages = ex["messages"]
+      if messages:
+        # For ChatCompletions assume the last turn (i.e. the instruction
+        # response) is the expected output.
+        user_inputs.append({**ex, "messages": messages[:-1]})
+        user_outputs.append({**ex, "messages": messages[-1:]})
+        # Exclude everything but the last message for the number of input
+        # messages.
+        user_input_messages_counts.append(len(messages[:-1]))
+    elif "input_text" in ex:
+      # For Vertex Text-Bison, the `output_text` field is the expected output.
+      user_inputs.append({**ex, "output_text": ""})
+      user_outputs.append(
+          {**ex, "input_text": ex["output_text"], "output_text": ""}
+      )
+      # Vertex Text-Bison goes from input -> output; i.e. there is only a single
+      # input "message".
+      user_input_messages_counts.append(1)
+
+  def calc_histogram(
+      counts: Sequence[int],
+  ) -> SupervisedTuningDatasetDistribution:
+    mean = np.mean(counts)
+    median = np.median(counts).item()
+    max_count = np.max(counts).item()
+    min_count = np.min(counts).item()
+    count_sum = np.sum(counts).item()
+    p5 = np.percentile(counts, 0.05).item()
+    p95 = np.percentile(counts, 0.95).item()
+    hist, bin_edges = np.histogram(counts, bins=10)
+
+    return SupervisedTuningDatasetDistribution(
+        sum=count_sum,
+        billable_sum=count_sum,
+        min=min_count,
+        max=max_count,
+        mean=mean,
+        median=median,
+        p5=p5,
+        p95=p95,
+        buckets=[
+            SupervisedTuningDatasetBucket(
+                count=hist[i].item(),
+                left=bin_edges[i].item(),
+                right=bin_edges[i + 1].item(),
+            )
+            for i in range(len(hist))
+        ],
+    )
+
+  # Tokenize input and output messages separately to generate separate summary
+  # statistics about them.
+  user_input_token_distribution = None
+  if user_inputs:
+    user_input_dataset = dataset_validation_util.format_dataset(
+        datasets.Dataset.from_list(user_inputs), column, template, tokenizer
+    )
+    user_input_tokenized_dataset = user_input_dataset.map(
+        lambda x: tokenizer(x[column])
+    )
+    user_input_tokens = user_input_tokenized_dataset["input_ids"]
+    user_input_token_counts = np.array([len(ex) for ex in user_input_tokens])
+    user_input_token_distribution = calc_histogram(user_input_token_counts)
+
+  user_output_token_distribution = None
+  if user_outputs:
+    user_output_dataset = dataset_validation_util.format_dataset(
+        datasets.Dataset.from_list(user_outputs), column, template, tokenizer
+    )
+    user_output_tokenized_dataset = user_output_dataset.map(
+        lambda x: tokenizer(x[column])
+    )
+    user_output_tokens = user_output_tokenized_dataset["input_ids"]
+    user_output_token_counts = np.array([len(ex) for ex in user_output_tokens])
+    user_output_token_distribution = calc_histogram(user_output_token_counts)
+
+  user_messages_per_example_distribution = None
+  if user_input_messages_counts:
+    user_input_messages_counts = np.array(user_input_messages_counts)
+    user_messages_per_example_distribution = calc_histogram(
+        user_input_messages_counts
+    )
+
+  user_dataset_examples = [
+      json.dumps(ex)
+      for ex in raw.shuffle().select(
+          range(min(len(raw), _MAX_NUM_DATASET_SAMPLES))
+      )
+  ]
+
+  dataset_stats = SupervisedTuningDataStats(
+      tuning_dataset_example_count=tuning_dataset_example_count,
+      total_tuning_character_count=total_tuning_character_count,
+      total_billable_token_count=total_billable_token_count,
+      tuning_step_count=tuning_step_count,
+      user_input_token_distribution=user_input_token_distribution,
+      user_output_token_distribution=user_output_token_distribution,
+      user_message_per_example_distribution=user_messages_per_example_distribution,
+      user_dataset_examples=user_dataset_examples,
+  )
+  return dataclasses.asdict(dataset_stats)

community-content/vertex_model_garden/model_oss/peft/train/util/device_stats.py

@@ -0,0 +1,140 @@
+"""Util functions for reporting device (GPU, CPU) stats."""
+
+import dataclasses
+
+import psutil
+import pynvml
+import torch
+
+
+@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
+  """
+
+  # device id
+  device_id: int
+  # memory reserved.
+  reserved: 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
+  # device_id, reserved, 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(device, reserved, occupied, unused, smi_diff, util)
+
+
+def gpu_stats_str(stats: GpuStats | None = None) -> str:
+  if stats is None:
+    stats = gpu_stats()
+  device, reserved, occupied, unused, smi_diff, util = stats
+  return (
+      f"GPU ({device=}) memory: {reserved:.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}%"

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

@@ -0,0 +1,126 @@
+"""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 util import device_stats
+
+
+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 = device_stats.gpu_stats()
+      self._peak_mem = max(
+          gpu_stats.reserved + gpu_stats.smi_diff, self._peak_mem
+      )
+      logging.info(
+          'on_step_end: Throughput: %.2f token/s. %s, %s',
+          throughput,
+          device_stats.gpu_stats_str(gpu_stats),
+          device_stats.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',
+          device_stats.gpu_stats_str(),
+          device_stats.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/qwen2_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: Qwen2DecoderLayer
+  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/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 +1,32 @@
+# 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
+immutabledict==4.2.1
+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 +1,83 @@
+# 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/peft/train/util/*.py /diffusers/examples/util/
+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/
+COPY model_oss/peft/train/test_utils/command_builder.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 +1,284 @@
+"""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.
+    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
+  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/instruct_lora.py

@@ -0,0 +1,905 @@
+"""Instruct/Chat with LoRA models."""
+
+from collections.abc import Callable, Mapping, Sequence
+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 util import dataset_stats
+from util import device_stats
+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_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,
+    'The Hugging Face dataset name or path to use for evaluation.',
+)
+
+# 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.',
+)
+
+_EVAL_TEMPLATE = flags.DEFINE_string(
+    'eval_template',
+    None,
+    'Template for formatting language model evaluation data.'
+    ' 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.',
+)
+
+_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',
+      device_stats.gpu_stats_str(),
+      device_stats.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, 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 = dataset_stats.get_dataset_stats(
+          raw=train_dataset,
+          templated=train_dataset_with_template,
+          template=train_template,
+          tokenizer=tokenizer,
+          column=train_column,
+          effective_batch_size=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(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)
+    adapter_for_eval_dir = os.path.join(output_dir, 'adapter_for_eval')
+    logging.info('saving adapter for evaluation to %s...', adapter_for_eval_dir)
+    peft_config.save_pretrained(adapter_for_eval_dir)
+    # 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,
+        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 +1,108 @@
+"""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_16B,
+    [
+        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
+        )
+    )
+
+  finetuned_lora_model_dir = fileutils.force_gcs_path(
+      _FINETUNED_LORA_MODEL_DIR.value
+  )
+  if dataset_validation_util.is_gcs_path(finetuned_lora_model_dir):
+    finetuned_lora_model_dir = (
+        dataset_validation_util.download_gcs_uri_to_local(
+            finetuned_lora_model_dir
+        )
+    )
+  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,
+      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/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/qwen2_5.json

@@ -0,0 +1,7 @@
+{
+  "description": "Chat template used by Qwen 2.5.",
+  "source": "https://huggingface.co/Qwen/Qwen2.5-72B-Instruct/blob/main/tokenizer_config.json#L198",
+  "chat_template": "{%- if tools %}\n    {{- '<|im_start|>system\\n' }}\n    {%- if messages[0]['role'] == 'system' %}\n        {{- messages[0]['content'] }}\n    {%- else %}\n        {{- 'You are Qwen, created by Alibaba Cloud. You are a helpful assistant.' }}\n    {%- endif %}\n    {{- \"\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within <tools></tools> XML tags:\\n<tools>\" }}\n    {%- for tool in tools %}\n        {{- \"\\n\" }}\n        {{- tool | tojson }}\n    {%- endfor %}\n    {{- \"\\n</tools>\\n\\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\\n<tool_call>\\n{\\\"name\\\": <function-name>, \\\"arguments\\\": <args-json-object>}\\n</tool_call><|im_end|>\\n\" }}\n{%- else %}\n    {%- if messages[0]['role'] == 'system' %}\n        {{- '<|im_start|>system\\n' + messages[0]['content'] + '<|im_end|>\\n' }}\n    {%- else %}\n        {{- '<|im_start|>system\\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\\n' }}\n    {%- endif %}\n{%- endif %}\n{%- for message in messages %}\n    {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) or (message.role == \"assistant\" and not message.tool_calls) %}\n        {{- '<|im_start|>' + message.role + '\\n' + message.content + '<|im_end|>' + '\\n' }}\n    {%- elif message.role == \"assistant\" %}\n        {{- '<|im_start|>' + message.role }}\n        {%- if message.content %}\n            {{- '\\n' + message.content }}\n        {%- endif %}\n        {%- for tool_call in message.tool_calls %}\n            {%- if tool_call.function is defined %}\n                {%- set tool_call = tool_call.function %}\n            {%- endif %}\n            {{- '\\n<tool_call>\\n{\"name\": \"' }}\n            {{- tool_call.name }}\n            {{- '\", \"arguments\": ' }}\n            {{- tool_call.arguments | tojson }}\n            {{- '}\\n</tool_call>' }}\n        {%- endfor %}\n        {{- '<|im_end|>\\n' }}\n    {%- elif message.role == \"tool\" %}\n        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != \"tool\") %}\n            {{- '<|im_start|>user' }}\n        {%- endif %}\n        {{- '\\n<tool_response>\\n' }}\n        {{- message.content }}\n        {{- '\\n</tool_response>' }}\n        {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n            {{- '<|im_end|>\\n' }}\n        {%- endif %}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|im_start|>assistant\\n' }}\n{%- endif %}\n",
+  "instruction_separator": "<|im_start|>user\n",
+  "response_separator": "<|im_start|>assistant\n"
+}

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/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 +1,276 @@
+# 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',
+          'Qwen2.5-32B-Instruct',
+      ],
+      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',
+          'Qwen2.5-32B-Instruct',
+      ],
+      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',
+          'Qwen2.5-32B-Instruct',
+      ],
+      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',
+    )
+    if 'llama' in model_name.lower():
+      self.task_cmd_builder.config_file = (
+          'vertex_vision_model_garden_peft/llama2_fsdp_8gpu.yaml'
+      )
+    elif 'qwen' in model_name.lower():
+      self.task_cmd_builder.config_file = (
+          'vertex_vision_model_garden_peft/qwen2_fsdp_8gpu.yaml'
+      )
+    else:
+      self.fail(f'Unsupported model: {model_name}')
+
+    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 +1,209 @@
+# 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)
+
+  @parameterized.named_parameters(
+      ('Qwen2.5-32B-Instruct', 'Qwen2.5-32B-Instruct'),
+  )
+  def test_qwen_model_deepspeed(self, model_name):
+    self.setup_output_dir(f'test_deepspeed_{model_name}')
+    self.task_cmd_builder.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 = test_util.get_test_data_path(
+        'llama-tuning-test/opposite-examples-train.jsonl'
+    )
+    self.task_cmd_builder.train_split = 'train'
+    self.task_cmd_builder.train_column = 'messages'
+    self.task_cmd_builder.train_template = 'qwen2_5'
+    self.task_cmd_builder.eval_dataset = test_util.get_test_data_path(
+        'llama-tuning-test/opposite-examples-eval.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', '0,1,2,3,4,5,6,7')
+    # Note(lavrai): The following parameters are needed for the opposite-word
+    # dataset to converge properly.
+    self.task_cmd_builder.gradient_accumulation_steps = 1
+    self.task_cmd_builder.num_train_epochs = 10.0
+    self.task_cmd_builder.logging_steps = 1
+
+    self.assertEqual(self.run_cmd(), 0)
+
+  @parameterized.named_parameters(
+      ('Qwen2.5-32B-Instruct', 'Qwen2.5-32B-Instruct'),
+  )
+  def test_qwen_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/qwen2_fsdp_8gpu.yaml'
+    )
+    self.task_cmd_builder.train_dataset = test_util.get_test_data_path(
+        'llama-tuning-test/opposite-examples-train.jsonl'
+    )
+    self.task_cmd_builder.train_split = 'train'
+    self.task_cmd_builder.train_column = 'messages'
+    self.task_cmd_builder.train_template = 'qwen2_5'
+    self.task_cmd_builder.eval_dataset = test_util.get_test_data_path(
+        'llama-tuning-test/opposite-examples-eval.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', '0,1,2,3,4,5,6,7')
+    # Note(lavrai): The following parameters are needed for the opposite-word
+    # dataset to converge properly.
+    self.task_cmd_builder.gradient_accumulation_steps = 1
+    self.task_cmd_builder.num_train_epochs = 10.0
+    self.task_cmd_builder.logging_steps = 1
+
+    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 +1,292 @@
+"""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 multiprocessing
+import subprocess
+import sys
+from absl import app
+from absl import flags
+from absl import logging
+from util import dataset_validation_util
+from util import cluster_spec
+from vertex_vision_model_garden_peft.train.vmg import utils
+from util import constants
+from util import gcs_syncer
+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.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."""
+  primary_node_addr, primary_node_port, node_rank, num_nodes = (
+      cluster_spec.get_cluster_spec()
+  )
+  accelerate_args = argparse.Namespace()
+  if num_nodes > 1:
+    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 _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 = gcs_syncer.manage_sync_path(
+      training_args.output_dir, node_rank
+  )
+  training_args.output_dir = local_output_dir
+  if gcs_syncer.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 = gcs_syncer.manage_sync_path(
+        merge_args.merge_base_and_lora_output_dir, None
+    )
+    merge_args.merge_base_and_lora_output_dir = merge_local_dir
+    if gcs_syncer.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 = gcs_syncer.manage_sync_path(
+        lora_dir, node_rank
+    )
+    if gcs_syncer.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 _get_merge_cmd_and_dirs_to_sync(
+    task_type: str, config_file: str, unknown: Sequence[str]
+) -> Sequence[Sequence[str]]:
+  """Returns the merge command 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.
+  """
+  # Merge only flags.
+  merge_parser = argparse.ArgumentParser()
+  merge_parser.add_argument('--merge_base_and_lora_output_dir')
+  merge_args, unknown = merge_parser.parse_known_args(unknown)
+
+  dirs_to_sync = []
+  if merge_args.merge_base_and_lora_output_dir:
+    merge_local_dir, merge_gcs_dir = gcs_syncer.manage_sync_path(
+        merge_args.merge_base_and_lora_output_dir, None
+    )
+    merge_args.merge_base_and_lora_output_dir = merge_local_dir
+    if gcs_syncer.is_gcs_or_gcsfuse_path(merge_gcs_dir):
+      dirs_to_sync.append((merge_local_dir, merge_gcs_dir))
+
+  cmd = launch_script_cmd(_TASK_TO_SCRIPT[task_type], config_file)
+  _append_args_to_command_in_place(merge_args, cmd)
+  cmd.extend(unknown)
+  return [cmd], dirs_to_sync
+
+
+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
+    )
+  elif task in [constants.MERGE_CAUSAL_LANGUAGE_MODEL_LORA]:
+    commands, dirs_to_sync = _get_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 = gcs_syncer.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:
+    gcs_syncer.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 +1,360 @@
+"""Common libraries for PEFT."""
+
+from collections.abc import Mapping, Sequence
+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 peft
+from peft import PeftModel
+from peft import prepare_model_for_kbit_training
+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": False,
+      "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()
+
+
+def force_gc():
+  """Collects garbage immediately to release unused CPU/GPU resources."""
+  gc.collect()
+  torch.cuda.empty_cache()
+
+
+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 +1,13 @@
 """Fileutil lib to copy files between gcs and local."""
 
-import glob
+import filecmp
+import fnmatch
 import os
 import pathlib
 import shutil
-from typing import Tuple
+import subprocess
+import time
+from typing import List, Optional, Tuple
 import uuid
 
 from absl import logging
@@ -13,6 +16,17 @@ 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 +50,123 @@ 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 is_file_available(
+    file_path: str, retry_interval_secs: int = 60, timeout_secs: int = 3600
+) -> bool:
+  """Checks and waits for a file to be available in GCS.
+
+  Args:
+    file_path: The file path to check.
+    retry_interval_secs: The interval in seconds to check the file.
+    timeout_secs: The timeout in seconds to wait for the file.
+
+  Returns:
+    True if the file is available, False otherwise.
+  """
+  start_time = time.time()
+  while True:
+    try:
+      file_check_cmd = ['gcloud', 'storage', 'ls', file_path]
+      result = subprocess.run(
+          file_check_cmd, capture_output=True, text=True, check=True
+      )
+      if file_path in result.stdout:
+        logging.info('File %s exists.', file_path)
+        return True
+    except subprocess.CalledProcessError as e:
+      elapsed_time = time.time() - start_time
+      if elapsed_time > timeout_secs:
+        logging.info(
+            "Timeout: File '%s' not found after %d seconds. Error: %s",
+            file_path,
+            elapsed_time,
+            e,
+        )
+        return False
+
+      logging.info(
+          "File '%s' not found yet. Checking again in %d seconds. Error: %s",
+          file_path,
+          retry_interval_secs,
+          e,
+      )
+      time.sleep(retry_interval_secs)
+
+
+def compare_dirs(
+    local_dir: str,
+    gcsfuse_dir: str,
+    retry_interval_secs: int = 30,
+    timeout_secs: int = 3600,
+) -> bool:
+  """Compares two directories and returns True if they are the same.
+
+  Args:
+    local_dir: The local directory.
+    gcsfuse_dir: The gcsfuse directory.
+    retry_interval_secs: The interval in seconds to check the directories.
+    timeout_secs: The timeout in seconds to wait for the directories.
+
+  Returns:
+    True if the directories are the same, False otherwise.
+  """
+  start_time = time.time()
+  while True:
+    if os.path.exists(local_dir) and os.path.exists(gcsfuse_dir):
+      comparison = filecmp.dircmp(local_dir, gcsfuse_dir)
+      if (
+          not comparison.left_only
+          and not comparison.right_only
+          and not comparison.diff_files
+      ):
+        return True
+    elapsed_time = time.time() - start_time
+    if elapsed_time > timeout_secs:
+      logging.info(
+          "Timeout: Directories '%s' and '%s' do not match after %d seconds.",
+          local_dir,
+          gcsfuse_dir,
+          elapsed_time,
+      )
+      return False
+
+    logging.info(
+        "Directories '%s' and '%s' do not match yet. Checking again in %d"
+        ' seconds.',
+        local_dir,
+        gcsfuse_dir,
+        retry_interval_secs,
+    )
+    time.sleep(retry_interval_secs)
+
+
+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 +193,47 @@ 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 +241,21 @@ 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 +263,63 @@ 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 +343,7 @@ 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 +362,9 @@ 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 +422,10 @@ 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 +441,19 @@ 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
+
+
+def delete_local_file(local_file_path: str) -> None:
+  """Deletes a local file."""
+  if os.path.exists(local_file_path):
+    os.remove(local_file_path)
+
+
+def delete_local_dir(local_dir: str) -> None:
+  """Deletes a local directory recursively."""
+  if os.path.exists(local_dir):
+    shutil.rmtree(local_dir)

community-content/vertex_model_garden/model_oss/util/gcs_download_launcher.sh

@@ -0,0 +1,119 @@
+#!/bin/bash
+#
+# This launcher downloads model files from GCS to local model directory before
+# launching the actual command.
+#
+# If GCS URI is passed as an environment variable, set GCS_URI_ENV_KEY to the
+# environment variable name.
+# If GCS URI is passed as an argument, set GCS_URI_ARG_KEY to the argument name.
+# The argument must be in the format of '--$GCS_URI_ARG_KEY=gs://*'. Do not
+# separate argument name and value with spaces.
+# This script will also try reading from AIP_STORAGE_URI or AIP_STORAGE_DIR.
+# Note that AIP_STORAGE_DIR is expected to be a local path, so it bypasses the
+# download process.
+#
+# Input priority: AIP_STORAGE_DIR > AIP_STORAGE_URI > GCS_URI_ENV_KEY > GCS_URI_ARG_KEY.
+# Will output the local model directory to GCS_URI_ENV_KEY and GCS_URI_ARG_KEY
+# if they are set. Both will be updated if both set.
+#
+# Requires google-cloud-sdk as a dependency (for gcloud storage CLI).
+
+set -e
+
+readonly LOCAL_MODEL_DIR=${LOCAL_MODEL_DIR:-"/tmp/model_dir"}
+readonly LOCAL_ARGS_FILE=${LOCAL_ARGS_FILE:-"/tmp/args.txt"}
+
+update_model_id() {
+  if [[ ! -z "$GCS_URI_ENV_KEY" ]]; then
+    echo "Updating env var $GCS_URI_ENV_KEY to $AIP_STORAGE_DIR."
+    export "$GCS_URI_ENV_KEY"="$AIP_STORAGE_DIR"
+  fi
+
+  if [[ ! -z "$GCS_URI_ARG_KEY" ]]; then
+    echo "Updating args $GCS_URI_ARG_KEY to $AIP_STORAGE_DIR."
+    updated=0
+    for (( i=1; i <= $#; i++)); do
+      arg="${!i}"
+      if [[ "$arg" == "--$GCS_URI_ARG_KEY="* ]]; then
+        echo "Found $arg, updating to $AIP_STORAGE_DIR."
+        set -- "${@:1:(($i-1))}" "--$GCS_URI_ARG_KEY=$AIP_STORAGE_DIR" "${@:$(($i+1))}";
+        updated=1
+        break
+      fi
+    done
+    if [[ $updated -eq 0 ]]; then
+      echo "Appending args $GCS_URI_ARG_KEY to $AIP_STORAGE_DIR."
+      set -- "$@" "--$GCS_URI_ARG_KEY=$AIP_STORAGE_DIR";
+    fi
+  fi
+  echo "$*" > "$LOCAL_ARGS_FILE"
+}
+
+maybe_download_model() {
+  if [[ -z "$GCS_URI_ENV_KEY" ]] && [[ -z "$GCS_URI_ARG_KEY" ]]; then
+    echo "Internal error: Required GCS_URI_ENV_KEY or GCS_URI_ARG_KEY."
+    exit 1
+  fi
+
+  echo "$*" > "$LOCAL_ARGS_FILE"
+  gcs_uri=""
+  if [[ ! -z "$AIP_STORAGE_DIR" ]]; then
+    # AIP_STORAGE_DIR is expected to be a local path.
+    echo "AIP_STORAGE_DIR set, proceeding to run the launcher."
+    update_model_id "$@"
+    return
+  elif [[ $AIP_STORAGE_URI == gs://* ]]; then
+    # Check AIP_STORAGE_URI environment variable.
+    echo "AIP_STORAGE_URI set and starts with 'gs://', proceeding to download from GCS."
+    gcs_uri="$AIP_STORAGE_URI"
+  elif [[ ! -z "$GCS_URI_ENV_KEY" ]] && [[ ${!GCS_URI_ENV_KEY} == gs://* ]]; then
+    # Check custom environment variable.
+    echo "Custom environment variable ${GCS_URI_ENV_KEY} set and starts with 'gs://', proceeding to download from GCS."
+    gcs_uri="${!GCS_URI_ENV_KEY}"
+  elif [[ ! -z "$GCS_URI_ARG_KEY" ]]; then
+    # Check custom args.
+    for arg in "$@"; do
+      if [[ "$arg" == "--$GCS_URI_ARG_KEY=gs://"* ]]; then
+        gcs_uri="${arg#*=}"
+        echo "Custom args ${GCS_URI_ARG_KEY} set and starts with 'gs://', proceeding to download from GCS."
+        break
+      elif [[ "$arg" == "--$GCS_URI_ARG_KEY" ]]; then
+        echo "Found $GCS_URI_ARG_KEY, but it's not in the format of '--$GCS_URI_ARG_KEY=gs://*'."
+        echo "Ensure the value of $GCS_URI_ARG_KEY is within the same arg, separated by '='."
+        exit 1
+      fi
+    done
+  fi
+
+  if [[ -z "$gcs_uri" ]]; then
+    echo "No GCS URI found, proceeding to run the launcher."
+    return
+  fi
+
+  # Remove trailing '/' if any.
+  gcs_uri="${gcs_uri%%/}"
+  export AIP_STORAGE_DIR="$LOCAL_MODEL_DIR/${gcs_uri##gs://}"
+
+  # Create the target directory.
+  mkdir -p "$AIP_STORAGE_DIR"
+  echo "Downloading model from ${gcs_uri} to ${AIP_STORAGE_DIR}."
+
+  # Use gcloud storage CLI to copy the content from GCS to the target directory.
+  if gcloud storage cp -r "$gcs_uri/*" "$AIP_STORAGE_DIR"; then
+    echo "Model downloaded successfully to ${AIP_STORAGE_DIR}."
+    update_model_id "$@"
+  else
+    echo "Failed to download model from GCS."
+    exit 1
+  fi
+}
+
+run_local_command() {
+  command=$(cat "$LOCAL_ARGS_FILE")
+  rm -f "$LOCAL_ARGS_FILE"
+  echo "Launch command: $command"
+  eval "$command"
+}
+
+maybe_download_model "$@"
+run_local_command

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

@@ -0,0 +1,176 @@
+"""Sync local directory to GCS directory using rsync."""
+
+import multiprocessing
+import os
+import subprocess
+import time
+from typing import Optional, Sequence, Tuple
+
+from absl import logging
+
+from util import constants
+from util import fileutils
+
+_GCS_COMMAND_RETRIES = 3
+_RSYNC_RETRY_INTERVAL_SECS = 30
+
+
+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_sync_path(
+    path: str, node_rank: Optional[int] = None
+) -> 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. Otherwise, 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,
+        fileutils.force_gcs_fuse_path(path)[1:],
+    )
+    gcs_dir = fileutils.force_gcs_path(path)
+  if not os.path.exists(local_dir):
+    os.makedirs(local_dir, exist_ok=True)
+
+  if node_rank is None:
+    return local_dir, gcs_dir
+  return local_dir, os.path.join(gcs_dir, f"node-{node_rank}")
+
+
+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=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("finish rsync process")
+  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 _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/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}')