From d7bdbc814d5134b051a879f02d59f20a6dbe5e3b Mon Sep 17 00:00:00 2001 From: Niels Rogge Date: Wed, 22 Mar 2023 09:48:01 +0100 Subject: [PATCH 01/38] First draft --- .../clip_downloader_component/Dockerfile | 21 - .../clip_downloader_component/README.MD | 38 - .../clip_downloader_component/__init__.py | 0 .../clip_downloader_component/component.yaml | 59 -- .../requirements.txt | 2 - .../clip_downloader_component/src/__init__.py | 0 .../clip_downloader_component/src/main.py | 251 ------ .../src/utils/__init__.py | 0 .../src/utils/image_downloader.py | 223 ----- .../clip_retrieval_component/Dockerfile | 14 - .../clip_retrieval_component/README.MD | 104 --- .../clip_retrieval_component/__init__.py | 0 .../clip_retrieval_component/component.yaml | 58 -- .../docs/benchmark_clip_retrieval.png | Bin 23118 -> 0 bytes .../retrieved_images_centroid_example.png | Bin 557092 -> 0 bytes .../docs/retrieved_images_knn_example.jpg | Bin 832943 -> 0 bytes .../docs/santa_captioned.jpg | Bin 40175 -> 0 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examples/pipelines/finetune_stable_diffusion/config/general_config.py delete mode 100644 examples/pipelines/finetune_stable_diffusion/pipelines_config/dataset_creation_config.py delete mode 100644 examples/pipelines/finetune_stable_diffusion/pipelines_config/sd_finetuning_config.py delete mode 100644 examples/pipelines/finetune_stable_diffusion/sd_finetuning_pipeline.py diff --git a/examples/pipelines/finetune_stable_diffusion/components/clip_downloader_component/Dockerfile b/examples/pipelines/finetune_stable_diffusion/components/clip_downloader_component/Dockerfile deleted file mode 100644 index a89965608..000000000 --- a/examples/pipelines/finetune_stable_diffusion/components/clip_downloader_component/Dockerfile +++ /dev/null @@ -1,21 +0,0 @@ -FROM europe-west1-docker.pkg.dev/storied-landing-366912/storied-landing-366912-default-repository/mlpipelines/kubeflow/components/base_component:latest - -ARG DEBIAN_FRONTEND=noninteractive - -# add required packages from universe repository -# Install opencv dependices -RUN apt-get update \ - && apt-get install ffmpeg libsm6 libxext6 libopencv-dev -y - -# Set the working directory to the source folder -WORKDIR /src - -# Copy over src-files of the component -COPY requirements.txt . - -# Install packages -RUN pip3 install -r requirements.txt - -COPY src /src - -ENTRYPOINT ["python", "main.py"] \ No newline at end of file diff --git a/examples/pipelines/finetune_stable_diffusion/components/clip_downloader_component/README.MD b/examples/pipelines/finetune_stable_diffusion/components/clip_downloader_component/README.MD deleted file mode 100644 index 0709ab7a8..000000000 --- a/examples/pipelines/finetune_stable_diffusion/components/clip_downloader_component/README.MD +++ /dev/null @@ -1,38 +0,0 @@ -# clip_downloader_component - -### Description - -This component downloads the images from the list of URLs retrieved from the -[`clip_retireval_components`](../clip_retrieval_component) and -creates the corresponding datasets. It uses the -[img2dataset library](https://github.com/rom1504/img2dataset) -for efficient and fast image download (multi-thread approach). The images are also filtered (based -on size -and area), resized and converted during download. - -The components extends the data manifest with two additional namespaces: `knn` and `centroid` to -denote -the datasets of those images. - -### **Inputs/Outputs** - -See [`component.yaml`](component.yaml) for a more detailed description on all the input/output -parameters. - -### **Practical considerations** - -* This component can be considered to perform multiple tasks at once -> image downloading, - filtering, - formatting and resizing. -* Not all the urls can be downloaded, some urls are broken or some images are filtered out - depending on size (in practice only around ~75% of the urls are valid). - At the end of the execution run, a detailed log report to get detailed information on the download - job execution - and why some urls failed to download. -* The images are resized to the expected values of stable diffusion (512x512). Since some images are - not squared, - the resizing might distort the images. The current resize is set to `border` which resizes the - images while adding - a border to the images. You might want to change - this [parameter](https://github.com/rom1504/img2dataset#:~:text=resize_mode%20The%20way%20to%20resize%20pictures%2C%20can%20be%20no%2C%20border%20or%20keep_ratio%20(default%20border)) - depending on your use case. \ No newline at end of file diff --git a/examples/pipelines/finetune_stable_diffusion/components/clip_downloader_component/__init__.py b/examples/pipelines/finetune_stable_diffusion/components/clip_downloader_component/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/examples/pipelines/finetune_stable_diffusion/components/clip_downloader_component/component.yaml b/examples/pipelines/finetune_stable_diffusion/components/clip_downloader_component/component.yaml deleted file mode 100644 index 3715d2203..000000000 --- a/examples/pipelines/finetune_stable_diffusion/components/clip_downloader_component/component.yaml +++ /dev/null @@ -1,59 +0,0 @@ -name: clip_downloader_component -description: A component that takes a the parquet files generated from clip retrieval, downloads them and uploads them to storage. It also includes deduplication, resizing and filtering steps. -inputs: - - name: run_id - description: The run id of the pipeline - type: String - - name: artifact_bucket - description: The GCS bucket used to store the artifact - type: String - - name: component_name - description: the name of the component (used to create gcs artefact path) - type: String - - name: project_id - description: The id of the gcp-project - type: String - - name: image_resize - description: the size to resize the image - type: Integer - - name: timeout - description: maximum time (in seconds to wait) when trying to download an image - type: Integer - - name: min_image_size - description: minimum size of the image to download (default 0) - type: Integer - - name: max_image_area - description: maximum area of the image to download (default inf) - type: Integer - - name: data_manifest_path - description: The previous component manifest path - type: String - - name: parquet_path_clip_centroid_retrieval - description: The path to the parquet file containing the urls from centroid retrieval - type: String - - name: parquet_path_clip_knn_retrieval - description: The path to the parquet file containing the urls from knn retrieval - type: String - -outputs: - - name: data_manifest_path_clip_downloader_component - description: the current component manifest path - -implementation: - container: - image: europe-west1-docker.pkg.dev/storied-landing-366912/storied-landing-366912-default-repository/mlpipelines/kubeflow/components/clip_downloader_component:latest - command: [ - python3, main.py, - --run-id, { inputValue: run_id }, - --artifact-bucket, { inputValue: artifact_bucket }, - --component-name, { inputValue: component_name }, - --project-id, { inputValue: project_id, }, - --image-resize, { inputValue: image_resize }, - --timeout, { inputValue: timeout }, - --min-image-size, { inputValue: min_image_size }, - --max-image-area, { inputValue: max_image_area }, - --data-manifest-path, { inputPath: data_manifest_path }, - --parquet-path-clip-knn-retrieval, { inputPath: parquet_path_clip_knn_retrieval }, - --parquet-path-clip-centroid-retrieval, { inputPath: parquet_path_clip_centroid_retrieval }, - --data-manifest-path-clip-downloader-component, { outputPath: data_manifest_path_clip_downloader_component }, - ] diff --git a/examples/pipelines/finetune_stable_diffusion/components/clip_downloader_component/requirements.txt b/examples/pipelines/finetune_stable_diffusion/components/clip_downloader_component/requirements.txt deleted file mode 100644 index cb6e365c8..000000000 --- a/examples/pipelines/finetune_stable_diffusion/components/clip_downloader_component/requirements.txt +++ /dev/null @@ -1,2 +0,0 @@ -img2dataset==1.36.0 -tqdm==4.64.1 \ No newline at end of file diff --git a/examples/pipelines/finetune_stable_diffusion/components/clip_downloader_component/src/__init__.py b/examples/pipelines/finetune_stable_diffusion/components/clip_downloader_component/src/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/examples/pipelines/finetune_stable_diffusion/components/clip_downloader_component/src/main.py b/examples/pipelines/finetune_stable_diffusion/components/clip_downloader_component/src/main.py deleted file mode 100644 index 9823910e2..000000000 --- a/examples/pipelines/finetune_stable_diffusion/components/clip_downloader_component/src/main.py +++ /dev/null @@ -1,251 +0,0 @@ -""" -This file is the entrypoint of the component. It will parse all arguments -and give them to the actual core of the component. -""" - -import os -import tempfile -import argparse -import logging -import json -from pathlib import Path -from datetime import datetime - -from google.cloud import storage -from tqdm import tqdm - -# pylint: disable=import-error -from utils.image_downloader import ImageDownloaderInput, ImageDownloader -from helpers.logger import get_logger -from helpers import storage_helpers, parquet_helpers -from helpers.manifest_helpers import DataManifest - - -def parse_args(): - """Parse component arguments""" - - parser = argparse.ArgumentParser() - parser.add_argument('--run-id', - type=str, - required=True, - help='The run id of the pipeline') - parser.add_argument('--artifact-bucket', - type=str, - required=True, - help='The GCS bucket used to store the artifacts') - parser.add_argument('--component-name', - type=str, - required=True, - help='The name of the component') - parser.add_argument('--project-id', - type=str, - required=True, - help='The id of the gcp-project') - parser.add_argument('--image-resize', - type=str, - required=True, - help='the size to resize the image') - parser.add_argument('--timeout', - type=int, - required=True, - help='maximum time (in seconds to wait) when trying to download an image') - parser.add_argument('--min-image-size', - type=int, - required=True, - help='Minimum size of the image to download (default 0)') - parser.add_argument('--max-image-area', - type=int, - required=True, - help='maximum area of the image to download (default inf)') - parser.add_argument('--data-manifest-path', - type=str, - required=True, - help='The previous component manifest path') - parser.add_argument('--parquet-path-clip-knn-retrieval', - type=str, - required=True, - help='The path to the parquet file containing the urls from knn retrieval') - parser.add_argument('--parquet-path-clip-centroid-retrieval', - type=str, - required=True, - help='The path to the parquet file containing the urls from centroid ' - 'retrieval') - parser.add_argument('--data-manifest-path-clip-downloader-component', - type=str, - required=True, - help='Path to the local file containing the gcs path where the output' - ' has been stored') - - return parser.parse_args() - - -# pylint: disable=too-many-locals, too-many-arguments, too-many-statements -def clip_downloader_component(run_id: str, - artifact_bucket: str, - component_name: str, - project_id: str, - image_resize: int, - timeout: int, - min_image_size: int, - max_image_area: int, - data_manifest_path: str, - parquet_path_clip_knn_retrieval: str, - parquet_path_clip_centroid_retrieval: str, - data_manifest_path_clip_downloader_component: str) -> None: - """ - Args: - run_id (str): The run id of the pipeline - artifact_bucket (str): The GCS bucket used to store the artifact - component_name (str): the name of the component (used to create gcs artefact path) - project_id (str): The id of the gcp-project - image_resize (int): the size to resize the image - timeout (int): maximum time (in seconds to wait) when trying to download an image - min_image_size (int): minimum size of the image to download (default 0) - max_image_area (int): maximum area of the image to download (default inf) - data_manifest_path (str): The previous component manifest path - parquet_path_clip_centroid_retrieval (str): The path to the parquet file containing the urls - from centroid retrieval - parquet_path_clip_knn_retrieval (str): The path to the parquet file containing the urls from - knn retrieval - data_manifest_path_clip_downloader_component (str): the current component manifest path - """ - logger = get_logger(name=__name__, level=logging.INFO) - logger.info('Started job...') - - # Initialize storage client - storage_client = storage.Client(project=project_id) - - with tempfile.TemporaryDirectory() as tmp_dir: - logger.info('created temporary directory %s', tmp_dir) - - # Initialize GCS custom artifact path - component_artifact_dir = run_id.rpartition('-')[0] - artifact_bucket_blob_path = f"custom_artifact/{component_artifact_dir}/{component_name}" - logger.info("custom artifact will be uploaded to %s", - f'gs://{artifact_bucket}/{artifact_bucket_blob_path}') - - # Initialize local directory - tmp_image_dir_path = os.path.join(tmp_dir, 'img_dir') - knn_dataset_parquet_path = os.path.join(tmp_dir, 'knn_dataset.parquet') - centroid_dataset_parquet_path = os.path.join(tmp_dir, 'centroid.parquet') - knn_image_dir_path = os.path.join(tmp_dir, 'knn_images') - centroid_image_dir_path = os.path.join(tmp_image_dir_path, 'centroid_images') - os.makedirs(knn_image_dir_path, exist_ok=True) - os.makedirs(centroid_image_dir_path, exist_ok=True) - - # Initialize gcs paths - downloaded_image_blob = os.path.join(artifact_bucket_blob_path, 'clip_retrieval_images') - knn_parquet_blob = os.path.join(artifact_bucket_blob_path, 'knn_dataset.parquet') - centroid_parquet_blob = os.path.join(artifact_bucket_blob_path, 'centroid_dataset.parquet') - index_parquet_blob = os.path.join(artifact_bucket_blob_path, 'index.parquet') - - # Load manifest - with open(data_manifest_path) as f: - manifest_load = json.load(f) - data_manifest = DataManifest.from_dict(manifest_load) - - # Load clip retrieval parquet files - with open(parquet_path_clip_knn_retrieval) as f: - parquet_path_clip_knn_retrieval = f.readline() - with open(parquet_path_clip_centroid_retrieval) as f: - parquet_path_clip_centroid_retrieval = f.readline() - - # Copy clip retrieval parquet files (containing clip ids and urls) to local directory - parquet_clip_knn_retrieval_tmp_path = storage_helpers.download_file_from_bucket( - storage_client, parquet_path_clip_knn_retrieval, tmp_dir) - parquet_clip_centroid_retrieval_tmp_path = storage_helpers.download_file_from_bucket( - storage_client, parquet_path_clip_centroid_retrieval, tmp_dir) - # Copy index to local directory - index_parquet_path = storage_helpers.download_file_from_bucket( - storage_client, data_manifest.index, tmp_dir) - - # Remove duplicate images based on ids. This duplication may have occurred since querying - # for the centroid and knn was done separately. - logger.info('Removing duplicate entries from centroid and KNN.') - parquet_helpers.remove_common_duplicates( - dataset_to_filter_path=parquet_clip_knn_retrieval_tmp_path, - reference_dataset_path=parquet_clip_centroid_retrieval_tmp_path, - duplicate_columns_name='id', - tmp_path=tmp_dir) - logger.info('Duplicates removed') - - # Fetch and download images - knn_downloader_input = \ - ImageDownloaderInput(source_url=parquet_clip_knn_retrieval_tmp_path, - dest_images=knn_image_dir_path, - dest_dataset_parquet=knn_dataset_parquet_path, - namespace='knn') - centroid_downloader_input = \ - ImageDownloaderInput(source_url=parquet_clip_centroid_retrieval_tmp_path, - dest_images=centroid_image_dir_path, - dest_dataset_parquet=centroid_dataset_parquet_path, - namespace='centroid') - - img_downloader = ImageDownloader(timeout=timeout, - image_resize=image_resize, - min_image_size=min_image_size, - max_image_area=max_image_area) - - for img_downloader_tuple in tqdm([knn_downloader_input, centroid_downloader_input]): - logger.info("Stating downloader job for %s dataset", img_downloader_tuple.namespace) - img_downloader.run(image_downloader_tuple=img_downloader_tuple, - tmp_dir=tmp_dir, - dest_gcs_uri=f"gs://{artifact_bucket}/{downloaded_image_blob}", - index_path=index_parquet_path) - logger.info('Image download job for %s dataset is complete', - img_downloader_tuple.namespace) - # Print job stat dict that shows how many download job succeeded/failed - # TODO: integrate output of job stat dict with the dataset metadata in the manifest - logger.info('job stat dict: %s', img_downloader.get_job_stat_dict()) - - # Upload parquet files - storage_helpers.upload_file_to_bucket(storage_client=storage_client, - file_to_upload_path=index_parquet_path, - bucket_name=artifact_bucket, - blob_path=index_parquet_blob) - storage_helpers.upload_file_to_bucket(storage_client=storage_client, - file_to_upload_path=centroid_dataset_parquet_path, - bucket_name=artifact_bucket, - blob_path=centroid_parquet_blob) - storage_helpers.upload_file_to_bucket(storage_client=storage_client, - file_to_upload_path=knn_dataset_parquet_path, - bucket_name=artifact_bucket, - blob_path=knn_parquet_blob) - - # Update manifest - logger.info('Updating data manifest job') - data_manifest.dataset_id = f"{run_id}_{component_name}" - data_manifest.metadata.branch = "" # TODO: Fill from docker build env var - data_manifest.metadata.commit_hash = "" # TODO: Fill from docker build env var - data_manifest.metadata.creation_date = datetime.now().strftime("%d-%m-%Y_%H-%M-%S") - data_manifest.metadata.run_id = run_id - data_manifest.index = f"gs://{artifact_bucket}/{index_parquet_blob}" - data_manifest.associated_data.dataset['centroid'] = \ - f"gs://{artifact_bucket}/{centroid_parquet_blob}" - data_manifest.associated_data.dataset['knn'] = \ - f"gs://{artifact_bucket}/{knn_parquet_blob}" - logger.info('Manifest file updated') - - Path(data_manifest_path_clip_downloader_component).parent.mkdir(parents=True, exist_ok=True) - Path(data_manifest_path_clip_downloader_component).write_text(data_manifest.to_json()) - logger.info('Manifest file written to %s', data_manifest_path_clip_downloader_component) - logger.info('Job completed.') - - -if __name__ == '__main__': - args = parse_args() - clip_downloader_component \ - (run_id=args.run_id, - artifact_bucket=args.artifact_bucket, - component_name=args.component_name, - project_id=args.project_id, - image_resize=args.image_resize, - timeout=args.timeout, - min_image_size=args.min_image_size, - max_image_area=args.max_image_area, - data_manifest_path=args.data_manifest_path, - parquet_path_clip_knn_retrieval= - args.parquet_path_clip_knn_retrieval, - parquet_path_clip_centroid_retrieval=args.parquet_path_clip_centroid_retrieval, - data_manifest_path_clip_downloader_component= - args.data_manifest_path_clip_downloader_component) diff --git a/examples/pipelines/finetune_stable_diffusion/components/clip_downloader_component/src/utils/__init__.py b/examples/pipelines/finetune_stable_diffusion/components/clip_downloader_component/src/utils/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/examples/pipelines/finetune_stable_diffusion/components/clip_downloader_component/src/utils/image_downloader.py b/examples/pipelines/finetune_stable_diffusion/components/clip_downloader_component/src/utils/image_downloader.py deleted file mode 100644 index dea1c4f6d..000000000 --- a/examples/pipelines/finetune_stable_diffusion/components/clip_downloader_component/src/utils/image_downloader.py +++ /dev/null @@ -1,223 +0,0 @@ -"""Image downloader utils""" -import subprocess # nosec -import os -import json -import logging -from collections import namedtuple -from typing import Dict - -# pylint: disable=import-error -from helpers.logger import get_logger -from helpers import parquet_helpers, storage_helpers, io_helpers - -logger = get_logger(name=__name__, level=logging.INFO) - -ImageDownloaderInput = namedtuple( - 'ImageDownloaderInput', - ['source_url', 'dest_images', 'dest_dataset_parquet', 'namespace']) - - -class ImageDownloader: - """ - Class for the image downloader that downloads the images from a parquet url files. The class - also contains functionalities for writing new parquet and indicies files and uploading the - downloaded files to GCS - """ - - def __init__(self, timeout: int, image_resize: int, min_image_size: int, max_image_area: int): - """ - Image downloader - Args: - timeout (int): maximum time (in seconds to wait) when trying to download an image - image_resize (int): the size to resize the image - min_image_size (int): minimum size of the image to download - (considers the min of width and height) - max_image_area (int): The maximum area (nr of pixels) of the images to download - - """ - self.timeout = timeout - self.image_resize = image_resize - self.min_image_size = min_image_size - self.max_image_area = max_image_area - # status dict field contains detailed error logs of failed download images and their counts - self.global_stat_dict = { - "count": 0, - "successes": 0, - "failed_to_download": 0, - "failed_to_resize": 0, - "status_dict": {}, - } - - def _start_download(self, src_parquet_file: str, dest_img_tmp_path: str): - """ - Function that starts downloading the images - Args: - src_parquet_file (str): the source parquet files where the image urls are located - dest_img_tmp_path (str): the root folder where the images will be downloaded - (will contain different image shards) - """ - logger.info('Starting image download job') - subprocess.call(['img2dataset', # nosec - '--url_list', src_parquet_file, - '--output_folder', dest_img_tmp_path, - '--thread_count', '128', - '--processes_count', '8', - '--output_format', 'files', - '--encode_format', 'jpg', - '--timeout', str(self.timeout), - '--image_size', str(self.image_resize), - '--resize_mode', 'border', - '--input_format', 'parquet', - '--min_image_size', str(self.min_image_size), - '--max_image_area', str(self.max_image_area), - '--url_col', 'url', - '--distributor', 'multiprocessing', - '--number_sample_per_shard', '1000']) - logger.info('Download job complete') - - def _update_download_job_stats(self, dest_img_tmp_path: str): - """ - Function that updates that download job statics path. 'img2dataset` creates multiple json - file with stats for each download partition it creates - Args: - dest_img_tmp_path (str): the root folder where the images will be downloaded - (will contain different image shards) - """ - logger.info('Updating global stat dict') - - def _update_status_dict(): - for status_key, status_value in stat_dict['status_dict'].items(): - if status_key in self.global_stat_dict['status_dict']: - self.global_stat_dict['status_dict'][status_key] += status_value - else: - self.global_stat_dict['status_dict'][status_key] = status_value - - # multiple stat files are created for each shard - stats_files = [file for file in os.listdir(dest_img_tmp_path) if "stats.json" in file] - for stat_file in stats_files: - with open(os.path.join(dest_img_tmp_path, stat_file)) as file: - stat_dict = json.load(file) - for key in self.global_stat_dict: - # Add detailed error logs - if key == 'status_dict' and 'status_dict' in stat_dict: - _update_status_dict() - # add global status logs - else: - self.global_stat_dict[key] += stat_dict[key] - - logger.info('Update complete') - - @staticmethod - def _rename_downloaded_images(dest_img_tmp_path: str, namespace: str, index_path: str, - tmp_dir: str): - """ - Function that renames the downloaded images by appending a namespace to them. The new image - ids are appended to the index parquet file. - Args: - dest_img_tmp_path (str): the root folder where the images will be downloaded - (will contain different image shards) - namespace (str): the namespace prefix to append to the name of uploaded files - index_path (str): the path to the index parquet file - tmp_dir (str): the path where to write the temporary parquet files - """ - logger.info('Renaming downloaded images in directory %s with namespace "%s" as prefix', - dest_img_tmp_path, namespace) - - def _img_rename_loop(): - sub_img_folders = [folder for folder in os.listdir(dest_img_tmp_path) if - os.path.isdir(os.path.join(dest_img_tmp_path, folder))] - for sub_img_folder in sub_img_folders: - sub_img_dir = os.path.join(dest_img_tmp_path, sub_img_folder) - for file in os.listdir(sub_img_dir): - if file.endswith('.jpg'): - new_file_name = f"{namespace}_{io_helpers.get_file_name(file)}" - src = os.path.join(*[dest_img_tmp_path, sub_img_folder, file]) - dest = os.path.join( - *[dest_img_tmp_path, sub_img_folder, f"{new_file_name}.jpg"]) - os.rename(src, dest) - yield new_file_name - - parquet_helpers.append_to_parquet_file( - parquet_path=index_path, - data_iterable=_img_rename_loop(), - tmp_path=tmp_dir) - - logger.info('Renaming task complete') - - @staticmethod - def _write_image_metadata(dest_img_tmp_path: str, dest_gcs_uri: str, dataset_path: str, - upload_file_txt: str): - """ - Function that writes the parquet dataset (containing) metadata of newly downloaded images. - Thos function also writes the url of images into a gcs text file to upload the images. - Args: - dest_img_tmp_path (str): the root folder where the images will be downloaded - (will contain different image shards - dest_gcs_uri (str): the destination where to write images - dataset_path (str): the path to the dataset parquet path - upload_file_txt (str): the path where to write the urls of files to upload - """ - logger.info('Writing metadata for images in directory %s to %s', dest_img_tmp_path, - dataset_path) - - def _write_metadata_loop(): - sub_img_folders = [folder for folder in os.listdir(dest_img_tmp_path) if - os.path.isdir(os.path.join(dest_img_tmp_path, folder))] - with open(upload_file_txt, "w") as upload_file: - for sub_img_folder in sub_img_folders: - sub_img_dir = os.path.join(dest_img_tmp_path, sub_img_folder) - for file in os.listdir(sub_img_dir): - if file.endswith('.jpg'): - file_id = io_helpers.get_file_name(file) - local_uri = os.path.join(*[dest_img_tmp_path, sub_img_folder, file]) - file_size = os.path.getsize(local_uri) - file_uri = f"{dest_gcs_uri}/{file}" - upload_file.write(local_uri + "\n") - yield file_uri, file_id, file_size, "jpg" - - parquet_helpers.write_dataset_parquet( - dataset_parquet_path=dataset_path, - data_iterable_producer=_write_metadata_loop) - - logger.info('Metadata written') - - def run(self, image_downloader_tuple: ImageDownloaderInput, tmp_dir: str, dest_gcs_uri: str, - index_path: str): - """ - Start and image download loop that downloads the images, uploads them to GCS under - a new name space and updates the parquet files - Args: - image_downloader_tuple (ImageDownloaderInput): tuple containing attributes for the image - download functionality - tmp_dir (str): the path where to write the temporary files - dest_gcs_uri (str): the destination where to write the images - index_path (str): the path to the index parquet file - """ - - source_url, dest_images, dest_parquet, namespace = image_downloader_tuple.source_url, \ - image_downloader_tuple.dest_images, image_downloader_tuple.dest_dataset_parquet, \ - image_downloader_tuple.namespace - upload_file_txt = os.path.join(tmp_dir, f"gcs_upload_{namespace}.txt") - - self._start_download(source_url, dest_images) - self._update_download_job_stats(dest_images) - self._rename_downloaded_images(dest_img_tmp_path=dest_images, - namespace=namespace, - index_path=index_path, - tmp_dir=tmp_dir) - self._write_image_metadata(dest_img_tmp_path=dest_images, - dest_gcs_uri=dest_gcs_uri, - dataset_path=dest_parquet, - upload_file_txt=upload_file_txt) - logger.info('Uploading downloaded images to %s', dest_gcs_uri) - storage_helpers.copy_files_bulk(upload_file_txt, dest_gcs_uri) - logger.info('Upload complete') - - def get_job_stat_dict(self) -> Dict[str, int]: - """ - Function that returns the global job state dict which contains information about the run - job statistics - Returns: - Dict[str, int]: the download global job statistics - """ - return self.global_stat_dict diff --git a/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/Dockerfile b/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/Dockerfile deleted file mode 100644 index 611ccfb16..000000000 --- a/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/Dockerfile +++ /dev/null @@ -1,14 +0,0 @@ -FROM europe-west1-docker.pkg.dev/storied-landing-366912/storied-landing-366912-default-repository/mlpipelines/kubeflow/components/base_component:latest - -# Set the working directory to the source folder -WORKDIR /src - -# Copy over src-files of the component -COPY requirements.txt . - -# Install packages -RUN pip3 install -r requirements.txt - -COPY src /src - -ENTRYPOINT ["python", "main.py"] \ No newline at end of file diff --git a/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/README.MD b/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/README.MD deleted file mode 100644 index 2f290b3d9..000000000 --- a/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/README.MD +++ /dev/null @@ -1,104 +0,0 @@ -# clip_retrieval_component - -### Description - -This component retrieves images from the LAION dataset using a clip -retrieval system. The images are retrieved using an efficient index built from the previously -extracted embeddings that enables for fast and accurate -querying against a large database. - -Checkout this [link](https://github.com/rom1504/clip-retrieval) for information on clip retrieval. -You can also -test out clip retrieval in -this [demo](https://rom1504.github.io/clip-retrieval/?back=https%3A%2F%2Fknn5.laion.ai&index=laion5B&useMclip=false). -The output of this component is a list of URLs containing the link to the retrieved image. - -Two current strategies are implemented for the retrieval of the images: - -1) **Knn strategy**: In this approach, an index is built from each embedding of the input dataset. - This allows to retrieve images of similar **context** and **style** to the input image. The image - below shows an example of the seed image and the retrieved images: - -seed_image - - -![knn](docs/retrieved_images_knn_example.jpg) - -2) **Centroid strategy**: In this approach, the image embeddings for all the images are **averaged - out** and - the image are retrieved from the index of that embedding. This allows to retrieve images of - similar **style** - but with varied contexts compared to the seed images. The average embedding can be interpreted as - the average representation of a certain image style/domain in an embedding space. - -The image below shows an example of images retrieved from the centroid of a collection of varied -Clip Art images. - -![knn](docs/retrieved_images_centroid_example.png) - -### **Benchmarks** - -The graph below demonstrates the number of images received with clip retrieval and the time it takes -to receive those images. The numbers on the graph are the requested number of images. - -knn - -One important thing to not out is that the number of received images is around a **30%** of the -number of -requested images. The reason for that is because the clip retrieval also takes care of deduplication -of the returned images based on their embeddings -(a lot of images in the LAION-5B are similar since they are scraped from different data sources). -This -deduplication is pairwise operations with time complexity O(n2). - -In short, it takes around **7 hours** to retrieve **~350k** images with an initial request of **1 -million images.** - -### **Inputs/Outputs** - -The Laion dataset is downloaded to the internal storage of the computed instance and queried from -there. - -Two important two parameters to specify here are the number of images to return through both knn and -centroid strategy: - -* `nb_images_knn`: the number of images to retrieve from **each embedding** of the seed images. -* `nb_images_centroid`: the number of images to retrieve from the **average embedding** of the seed - images. - -As a concrete example, if we have 1,000 images in our seed images and assuming that the amount -of images retrieved are around 30% of the requested images. Then to create a dataset of around 500k -images with -a balanced amount of images between both strategies (250k from each): - -_nb_images_knn = 250,000 / (1000*0.3) = ~ 830_ - -_nb_images_centroid = 250,000 / 0.3 = ~ 830,000_ - -Notice that the number of images retrieved from the centroid will be much larger that the one -the `knn` -approach and hence it will take much longer time overall (see benchmark above). - -See [`component.yaml`](component.yaml) for a more detailed description on all the input/output -parameters. - -### **Practical considerations** - -* Keep in mind that the total amount of image urls that will be retrieved will still be reduced by - around 25% - since many of the urls might be broken or the images might not pass the filters specified in the - [`clip_downloader_component`](../clip_downloader_component). -* Clip retrieval can also work with text or image input, this component currently only implements - the - embedding approach but can easily be changed to accomodate for the other methods. -* This component takes a long time to run since the LAION dataset need to be downloaded first from - GCS (total size is around 2.5 TB, it can take around 2-3 hours to download) - and the querying itself also takes a signficant amount of time. -* Since the knn indices and the metadata (URL and captions) are very large files that need to be queried - efficiently, - memory mapping to allow to map data on SSDs into virtual memory and query data that scales beyond - RAM capacity. - This requires the using GKE nodepools with NVMe drives and provisioning them to this component - during the pipeline run. - More information on NVMe SSDs the context of GKE can be - found [here](https://cloud.google.com/kubernetes-engine/docs/how-to/persistent-volumes/local-ssd). diff --git a/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/__init__.py b/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/component.yaml b/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/component.yaml deleted file mode 100644 index 44c3a5040..000000000 --- a/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/component.yaml +++ /dev/null @@ -1,58 +0,0 @@ -name: clip_retrieval_component -description: A component that takes a dataset manifest and returns an output data manifest with an extended dataset - by retrieving similar images from the laion dataset using different retrieval strategies (knn, centroid) -inputs: - - name: run_id - description: The run id of the pipeline - type: String - - name: artifact_bucket - description: The GCS bucket used to store the artifact - type: String - - name: component_name - description: the name of the component (used to create gcs artefact path) - type: String - - name: project_id - description: The id of the gcp-project - type: String - - name: laion_index_url - description: contains the indices of the metadata. Those indices need to be transformed in case you decide to use only a subset of the dataset - type: String - - name: laion_metadata_url - description: url to the metadata of laion dataset metadata (arrow format). It can either contain a subset of the laion 5b metadata (e.g. laion-en) or all of the metadata - type: String - - name: nb_images_knn - description: The number of images to return via the knn strategy (per image) - type: Integer - - name: nb_images_centroid - description: The number of images to return via the centroid strategy - type: Integer - - name: data_manifest_path - description: The previous component manifest path - type: String - -outputs: - - name: data_manifest_path_clip_retrieval_component - description: Path to the local file containing the gcs path where the output has been stored - - name: parquet_path_clip_centroid_retrieval - description: The path to the parquet file containing the urls from centroid retrieval - - name: parquet_path_clip_knn_retrieval - description: The path to the parquet file containing the urls from knn retrieval - -implementation: - container: - image: europe-west1-docker.pkg.dev/storied-landing-366912/storied-landing-366912-default-repository/mlpipelines/kubeflow/components/clip_retrieval_component:latest - command: [ - python3, main.py, - --run-id, { inputValue: run_id }, - --artifact-bucket, { inputValue: artifact_bucket }, - --component-name, { inputValue: component_name }, - --project-id, { inputValue: project_id, }, - --laion-index-url, { inputValue: laion_index_url }, - --laion-metadata-url, { inputValue: laion_metadata_url }, - --nb-images-knn, { inputValue: nb_images_knn }, - --nb-images-centroid, { inputValue: nb_images_centroid }, - --data-manifest-path, { inputPath: data_manifest_path }, - --data-manifest-path-clip-retrieval-component, { outputPath: data_manifest_path_clip_retrieval_component }, - --parquet-path-clip-centroid-retrieval, { outputPath: parquet_path_clip_centroid_retrieval }, - --parquet-path-clip-knn-retrieval, { outputPath: parquet_path_clip_knn_retrieval }, - ] diff --git 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-clip-retrieval==2.34.2 -numpy>=1.19.5,<2 -pandas==1.3.5 -tqdm==4.64.1 -Pillow==9.3.0 \ No newline at end of file diff --git a/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/src/__init__.py b/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/src/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/src/main.py b/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/src/main.py deleted file mode 100644 index 3edb53340..000000000 --- a/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/src/main.py +++ /dev/null @@ -1,267 +0,0 @@ -""" -This file is the entrypoint of the component. It will parse all arguments -and give them to the actual core of the component. -""" - -import os -import tempfile -import argparse -import logging -import json -import time -from pathlib import Path -from datetime import datetime - -import numpy as np -from tqdm import tqdm -from google.cloud import storage - -# pylint: disable=import-error -from helpers.logger import get_logger -from helpers import storage_helpers, parquet_helpers -from helpers.manifest_helpers import DataManifest -from utils.embedding_utils import get_average_embedding -from utils.knn_service import ClipRetrievalLaion5B - - -def parse_args(): - """Parse component arguments""" - - parser = argparse.ArgumentParser() - parser.add_argument('--run-id', - type=str, - required=True, - help='The run id of the pipeline') - parser.add_argument('--artifact-bucket', - type=str, - required=True, - help='The GCS bucket used to store the artifacts') - parser.add_argument('--component-name', - type=str, - required=True, - help='The name of the component') - parser.add_argument('--project-id', - type=str, - required=True, - help='The id of the gcp-project') - parser.add_argument('--laion-index-url', - type=str, - required=True, - help='contains the indices of the metadata. Those indices need to be ' - 'transformed in case you decide to use only a subset of the dataset') - parser.add_argument('--laion-metadata-url', - type=str, - required=True, - help='The id of the gcp-project') - parser.add_argument('--nb-images-knn', - type=int, - required=True, - help='The number of images to return via KNN method') - parser.add_argument('--nb-images-centroid', - type=int, - required=True, - help='The number of images to return via the centroid method') - parser.add_argument('--data-manifest-path', - type=str, - required=True, - help='The previous component manifest path') - parser.add_argument('--data-manifest-path-clip-retrieval-component', - type=str, - required=True, - help='The path to the output manifest file') - parser.add_argument('--parquet-path-clip-knn-retrieval', - type=str, - required=True, - help='The path to the parquet file containing the urls from knn retrieval') - parser.add_argument('--parquet-path-clip-centroid-retrieval', - type=str, - required=True, - help='The path to the parquet file containing the urls from centroid ' - 'retrieval') - - return parser.parse_args() - - -# pylint: disable=too-many-locals, too-many-arguments, too-many-statements -def clip_retrieval_component(run_id: str, - artifact_bucket: str, - component_name: str, - project_id: str, - laion_index_url: str, - laion_metadata_url: str, - nb_images_knn: int, - nb_images_centroid: int, - data_manifest_path: str, - data_manifest_path_clip_retrieval_component: str, - parquet_path_clip_knn_retrieval: str, - parquet_path_clip_centroid_retrieval: str) -> None: - """ - Args: - run_id (str): The run id of the pipeline - artifact_bucket (str): The GCS bucket used to store the artifact - component_name (str): the name of the component (used to create gcs artefact path) - project_id (str): The id of the gcp-project - laion_index_url (str): contains the indices of the metadata. Those indices need to be - transformed in case you decide to use only a subset of the dataset - laion_metadata_url (str): url to the metadata of laion dataset metadata (arrow format). It - can either contain a subset of the laion 5b metadata (e.g. laion-en) or all of the metadata - nb_images_knn (int): The number of images to return via knn method (per image) - nb_images_centroid (int): The number of images to return via the centroid method - data_manifest_path (str): The previous component manifest path - data_manifest_path_clip_retrieval_component (str): the current component output path - parquet_path_clip_centroid_retrieval (str): The path to the parquet file containing the urls - from centroid retrieval - parquet_path_clip_knn_retrieval (str): The path to the parquet file containing the urls - from knn retrieval - """ - logger = get_logger(name=__name__, level=logging.INFO) - logger.info('Started job...') - - # Initialize storage client - storage_client = storage.Client(project=project_id) - - # This component uses local SSD mounting to enable faster querying of the laion dataset. The - # local SSDs are mounted in the cache directory - laion_index_folder = os.path.join('/cache', 'laion_dataset') - laion_metadata_folder = os.path.join(laion_index_folder, 'metadata') - laion_indices_folder = os.path.join(laion_index_folder, 'image.index') - os.makedirs(laion_metadata_folder, exist_ok=True) - os.makedirs(laion_indices_folder, exist_ok=True) - - # Download laion indices and metadata from storage - start_indices = time.time() - storage_helpers.copy_folder_bulk(laion_index_url, laion_indices_folder) - logger.info('Laion index download complete: it took %s minutes to download the laion indices', - round((time.time() - start_indices) / 60)) - start_metadata = time.time() - storage_helpers.copy_folder_bulk(laion_metadata_url, laion_metadata_folder) - logger.info( - 'Laion metadata download complete: it took %s minutes to download the laion metadata', - round((time.time() - start_metadata) / 60)) - - with tempfile.TemporaryDirectory() as tmp_dir: - logger.info('created temporary directory %s', tmp_dir) - - # Initialize GCS custom artifact path - component_artifact_dir = run_id.rpartition('-')[0] - artifact_bucket_blob_path = f"custom_artifact/{component_artifact_dir}/{component_name}" - logger.info("custom artifact will be uploaded to %s", - f'gs://{artifact_bucket}/{artifact_bucket_blob_path}') - - tmp_embedding_dir_path = os.path.join(tmp_dir, 'embed_dir') - os.makedirs(tmp_embedding_dir_path, exist_ok=True) - laion_indices_folder = os.path.join(tmp_dir, 'index_file.json') - knn_retrieval_path = os.path.join(tmp_dir, 'clip_knn.parquet') - centroid_retrieval_path = os.path.join(tmp_dir, 'clip_centroid.parquet') - knn_retrieval_blob_path = os.path.join(artifact_bucket_blob_path, 'clip_knn.parquet') - centroid_retrieval_blob_path = os.path.join(artifact_bucket_blob_path, - 'clip_centroid.parquet') - - # Read manifest - with open(data_manifest_path) as f: - manifest_load = json.load(f) - data_manifest = DataManifest.from_dict(manifest_load) - embedding_path = data_manifest.associated_data.embedding['cf'] - - # Copy embeddings to local directory - storage_helpers.copy_folder_bulk(f"{embedding_path}/*", tmp_embedding_dir_path) - nb_src_images = len(os.listdir(tmp_embedding_dir_path)) - logging.info('Total number of source images (embedding): %s', nb_src_images) - centroid_embedding = get_average_embedding(tmp_embedding_dir_path) - - logger.info('A total of %s urls will be requested using the centroid approach.', - nb_images_centroid) - logger.info('A total of %s urls will be requested per individual image (total of %s images)' - ' using the knn approach.', nb_images_knn) - - # Setup KNN service - clip_retrieval_runner = ClipRetrievalLaion5B( - laion_index_path=laion_indices_folder, - laion_index_folder=laion_index_folder) - knn_service = clip_retrieval_runner.setup_knn_service() - - logger.info('Starting centroid clip retrieval') - - # Run clip retrieval with centroid approach - results_centroid = clip_retrieval_runner.run_query( - knn_service=knn_service, - query={'embedding_query': centroid_embedding}, - nb_images_request=nb_images_centroid, - deduplicate=True, - benchmark=True) - logger.info('Centroid clip retrieval complete') - - parquet_helpers.write_clip_retrieval_parquet( - clip_retrieval_parquet_path=centroid_retrieval_path, - data_iterable_producer=clip_retrieval_runner.clip_results_producer, - clip_results=results_centroid) - del results_centroid - - # Run clip retrieval with KNN approach - logger.info('Starting knn clip retrieval') - results_knn = [] - for embedding_file in tqdm(os.listdir(tmp_embedding_dir_path)): - embedding = np.load(os.path.join(tmp_embedding_dir_path, embedding_file)) - results = clip_retrieval_runner.run_query(knn_service=knn_service, - query={'embedding_query': embedding}, - nb_images_request=nb_images_knn, - deduplicate=True, - benchmark=True) - results_knn.extend(results) - logger.info('KNN clip retrieval complete') - parquet_helpers.write_clip_retrieval_parquet( - clip_retrieval_parquet_path=knn_retrieval_path, - data_iterable_producer=clip_retrieval_runner.clip_results_producer, - clip_results=results_knn) - del results_knn - - # Upload parquet files to bucket - storage_helpers.upload_file_to_bucket(storage_client=storage_client, - file_to_upload_path=centroid_retrieval_path, - bucket_name=artifact_bucket, - blob_path=centroid_retrieval_blob_path) - - storage_helpers.upload_file_to_bucket(storage_client=storage_client, - file_to_upload_path=knn_retrieval_path, - bucket_name=artifact_bucket, - blob_path=knn_retrieval_blob_path) - - # Update manifest - data_manifest.dataset_id = f"{run_id}_{component_name}" - data_manifest.metadata.branch = "" # TODO: Fill from docker build env var - data_manifest.metadata.commit_hash = "" # TODO: Fill from docker build env var - data_manifest.metadata.creation_date = datetime.now().strftime("%d-%m-%Y_%H-%M-%S") - data_manifest.metadata.run_id = run_id - - logger.info('Manifest file created and updated') - - # Write manifest to outputPath - Path(data_manifest_path_clip_retrieval_component).parent.mkdir(parents=True, exist_ok=True) - Path(parquet_path_clip_knn_retrieval).parent.mkdir(parents=True, exist_ok=True) - Path(parquet_path_clip_centroid_retrieval).parent.mkdir(parents=True, exist_ok=True) - # Write Parquet url files to outputPath - Path(data_manifest_path_clip_retrieval_component).write_text(data_manifest.to_json()) - Path(parquet_path_clip_knn_retrieval).write_text( - f'gs://{artifact_bucket}/{knn_retrieval_blob_path}') - Path(parquet_path_clip_centroid_retrieval).write_text( - f'gs://{artifact_bucket}/{centroid_retrieval_blob_path}') - logger.info('Manifest file written to %s', data_manifest_path_clip_retrieval_component) - logger.info('Job completed.') - - -if __name__ == '__main__': - args = parse_args() - clip_retrieval_component \ - (run_id=args.run_id, - artifact_bucket=args.artifact_bucket, - component_name=args.component_name, - project_id=args.project_id, - laion_index_url=args.laion_index_url, - laion_metadata_url=args.laion_metadata_url, - nb_images_knn=args.nb_images_knn, - nb_images_centroid=args.nb_images_centroid, - data_manifest_path=args.data_manifest_path, - data_manifest_path_clip_retrieval_component= - args.data_manifest_path_clip_retrieval_component, - parquet_path_clip_centroid_retrieval=args.parquet_path_clip_centroid_retrieval, - parquet_path_clip_knn_retrieval=args.parquet_path_clip_knn_retrieval) diff --git a/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/src/utils/__init__.py b/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/src/utils/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/src/utils/embedding_utils.py b/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/src/utils/embedding_utils.py deleted file mode 100644 index 3c51707af..000000000 --- a/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/src/utils/embedding_utils.py +++ /dev/null @@ -1,19 +0,0 @@ -"""Clip embedding util functions""" -import os - -import numpy as np - - -def get_average_embedding(embedding_dir: str) -> np.array: - """ - Function that returns the average embedding from a selection of embeddings - Args: - embedding_dir (str): the directory where the embeddings are located - Returns: - np.array: the average embedding - """ - - embeddings = [np.load(os.path.join(embedding_dir, embedding_file)) for embedding_file in - os.listdir(embedding_dir)] - avg_embedding = np.array(embeddings).mean(axis=0) - return avg_embedding diff --git a/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/src/utils/knn_service.py b/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/src/utils/knn_service.py deleted file mode 100644 index 7ad80db53..000000000 --- a/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/src/utils/knn_service.py +++ /dev/null @@ -1,156 +0,0 @@ -"""Clip retrieval script""" -import json -import io -import base64 -import warnings -import logging -from typing import Dict, List - -# pylint: disable=import-error -from PIL import Image -from clip_retrieval.clip_back import load_clip_indices, KnnService, ClipOptions - -from helpers.logger import get_logger -from utils.timer import CatchTime - -LOGGER = get_logger(name=__name__, level=logging.INFO) -warnings.filterwarnings("ignore", category=FutureWarning) - - -class ClipRetrievalLaion5B: - """Class that initializes and starts a clip retrieval job hosted locally using the laion5B - dataset (either full dataset or subset)""" - - def __init__(self, laion_index_path: str, laion_index_folder: str): - """ - Parameter initialization - Args: - laion_index_path (str): the path to the laion json index path containing job run specs - laion_index_folder (str): the path to the laion index folder. This folder must contain - two subfolders: - -'metadata': contains the laion dataset metadata (arrow format). This folder can - either contain a subset of the laion 5b metadata (e.g. laion-en) or all of the - metadata - -'image.index' contains the indices of the metadata. Those indices need to be - transformed in case you decide to use only a subset of the dataset (more info here: - https://github.com/rom1504/clip-retrieval/tree/ - a206f7908ed213030e950655335940a77af1e4ea/clip_retrieval/clip_back_prepro) - """ - self.laion_index_path = laion_index_path - self.laion_index_folder = laion_index_folder - self.timer = CatchTime() - - def _write_laion_index_path(self): - """Write the json index path containing the job run spec""" - laion_job_dict = { - "laion5B": { - "indice_folder": self.laion_index_folder, - "provide_safety_model": False, - "enable_faiss_memory_mapping": True, - "use_arrow": True, - "enable_hdf5": False, - "reorder_metadata_by_ivf_index": False, - "columns_to_return": ['id', 'url'], - "clip_model": "ViT-L/14", - "enable_mclip_option": False - } - } - - # Serializing json - json_object = json.dumps(laion_job_dict, indent=4) - - # Writing to indices_paths.json - with open(self.laion_index_path, "w") as outfile: - outfile.write(json_object) - - def setup_knn_service(self) -> KnnService: - """ - Function that sets up the knn service - Returns: - KNNService: the knn service client - """ - self._write_laion_index_path() - # Load clip indices with helper method - clip_resources = load_clip_indices( - indices_paths=self.laion_index_path, - clip_options=ClipOptions( - indice_folder="", - clip_model="ViT-L/14", - enable_hdf5=False, - enable_faiss_memory_mapping=True, - columns_to_return=None, - reorder_metadata_by_ivf_index=False, - enable_mclip_option=True, - use_jit=True, - use_arrow=False, - provide_safety_model=False, - provide_violence_detector=False, - provide_aesthetic_embeddings=True, - ) - ) - # Setup KNN service object with clip resources - knn_service = KnnService(clip_resources=clip_resources) - return knn_service - - # pylint: disable=too-many-arguments - def run_query(self, nb_images_request: int, query: Dict[str, str], knn_service: KnnService, - deduplicate: bool = True, benchmark: bool = True) -> List[Dict[str, str]]: - """ - Function that runs query on the KNN faiss backend - Args: - nb_images_request (int): the number of image to request from the KNN service - query (Dict[str, str]): the query to request from the KNN service - knn_service (KNNService): the KNN service client - deduplicate (bool): whether to deduplicate based on embeddings - benchmark (bool): enable to benchmark knn service - Returns: - List[Dict[str, str]]: the query results - """ - LOGGER.info("Running query") - - if benchmark is False: - self.timer.disable() - else: - self.timer.enable() - self.timer.reset() - - self.timer(f"Query for {nb_images_request} samples") - - with self.timer: - query_inputs = {'modality': "image", - 'indice_name': "laion5B", - 'num_images': int(nb_images_request), - 'deduplicate': deduplicate, - 'num_result_ids': int(nb_images_request)} - - if 'image_file' in query: - image = Image.open(query['image_file']) - buffer = io.BytesIO() - image.save(buffer, format="JPEG") - img_str = base64.b64encode(buffer.getvalue()) - query_inputs['image_input'] = img_str - elif 'image_url' in query: - query_inputs['image_url_input'] = query['image_url'] - elif 'text_query' in query: - query_inputs['text_input'] = query['text_query'] - elif 'embedding_query' in query: - query_inputs['embedding_input'] = query['embedding_query'] - else: - raise Exception("No text, image or embedding query found") - - results = knn_service.query(**query_inputs) - LOGGER.info('Query job complete') - LOGGER.info('A total of %s results were returned', len(results)) - return results - - @staticmethod - def clip_results_producer(clip_results: List[Dict[str, str]]) -> tuple: - """ - Laion results producer - Args: - clip_results (List[Dict[str, str]]): the clip retrieval results - Returns: - tuple: clip retrieval results - """ - for result in clip_results: - yield result['id'], result['url'] diff --git a/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/src/utils/timer.py b/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/src/utils/timer.py deleted file mode 100644 index 290776cc6..000000000 --- a/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/src/utils/timer.py +++ /dev/null @@ -1,53 +0,0 @@ -# pylint: disable-all -"""Timer helper function for benchmarking query time """ -import time -import logging - -import pandas as pd - -from helpers.logger import get_logger - -LOGGER = get_logger(name=__name__, level=logging.INFO) - - -class CatchTime: - """Helper class for timing and benchmarking pieces of code. - The benchmarks are saved in a list.""" - - def __init__(self): - self.benchmarks = [] - self.name = None - self.enabled = False - - def __enter__(self): - if self.enabled: - if self.name is None: - raise Exception("Name has to be set") - self.begin = time.time() - return self - - def __exit__(self, type, value, traceback): - if self.enabled: - total = time.time() - self.begin - self.benchmarks.append({'name': self.name, 'time': total}) - LOGGER.info(f"{self.name}:\t{total:.2f}s") - - def __call__(self, name): - self.name = name - - def reset(self): - self.name = None - self.benchmarks = [] - - def show_times(self): - df = pd.DataFrame(self.benchmarks) - return df - - def enable(self): - self.enabled = True - - def disable(self): - self.enabled = False - - def is_enabled(self): - return self.enabled diff --git a/examples/pipelines/finetune_stable_diffusion/components/dataset_loader_component/requirements.txt b/examples/pipelines/finetune_stable_diffusion/components/dataset_loader_component/requirements.txt index e69de29bb..59f405946 100644 --- a/examples/pipelines/finetune_stable_diffusion/components/dataset_loader_component/requirements.txt +++ b/examples/pipelines/finetune_stable_diffusion/components/dataset_loader_component/requirements.txt @@ -0,0 +1,2 @@ +https://github.com/ml6team/express.git +gcsfs \ No newline at end of file diff --git a/examples/pipelines/finetune_stable_diffusion/components/dataset_loader_component/src/main.py b/examples/pipelines/finetune_stable_diffusion/components/dataset_loader_component/src/main.py index 78f2d3042..408660d31 100644 --- a/examples/pipelines/finetune_stable_diffusion/components/dataset_loader_component/src/main.py +++ b/examples/pipelines/finetune_stable_diffusion/components/dataset_loader_component/src/main.py @@ -1,169 +1,38 @@ """ -This file is the entrypoint of the component. It will parse all arguments -and give them to the actual core of the component. +This component loads a seed dataset from the cloud and creates the initial manifest. """ -import os -import argparse -import logging -import tempfile -from pathlib import Path -from datetime import datetime +from typing import Optional, Union, Dict -from google.cloud import storage +from datasets import load_from_disk -# pylint: disable=import-error -from helpers import storage_helpers, parquet_helpers -from helpers.logger import get_logger -from helpers.manifest_helpers import DataManifest +from express.components.hf_datasets_components import HFDatasetsLoaderComponent, HFDatasetsDatasetDraft +from express.logger import configure_logging -def parse_args(): - """Parse component arguments""" +class SeedDatasetLoader(HFDatasetsLoaderComponent): + """Class that inherits from Hugging Face data loading """ - parser = argparse.ArgumentParser() - parser.add_argument('--run-id', - type=str, - required=True, - help='The run id of the pipeline') - parser.add_argument('--artifact-bucket', - type=str, - required=True, - help='The GCS bucket used to store the artifacts') - parser.add_argument('--component-name', - type=str, - required=True, - help='The name of the component') - parser.add_argument('--project-id', - type=str, - required=True, - help='The id of the gcp-project') - parser.add_argument('--source-dataset-bucket', - type=str, - required=True, - help='The GCS bucket containing the dataset to load') - parser.add_argument('--source-dataset-blob', - type=str, - required=True, - help='The GCS blob withing the specified bucket containing the dataset' - ' to load') - parser.add_argument('--namespace', - type=str, - required=True, - help='The dataset namespace (abbreviation for data source)') - parser.add_argument('--data-manifest-path', - type=str, - required=True, - help='The data manifest output artifact') - return parser.parse_args() + @classmethod + def load(cls, extra_args: Optional[ + Dict[str, Union[str, int, float, bool]]] = None) -> HFDatasetsDatasetDraft: + """ + An example function showcasing the data loader component using Express functionalities + Args: + extra_args (Optional[Dict[str, Union[str, int, float, bool]]): optional args to pass to + the function (e.g. seed data source) + Returns: + HFDatasetsDatasetDraft: a dataset draft that creates a plan for an output manifest + """ + configure_logging() + + # 1) Create data source + storage_options={"project": extra_args["project_id"]} + dataset.save_to_disk("s3://my-private-datasets/imdb/train", storage_options=storage_options) + print(len(dataset)) -# pylint: disable=too-many-locals, too-many-arguments -def dataset_loader_component(run_id: str, - artifact_bucket: str, - component_name: str, - project_id: str, - source_dataset_bucket: str, - source_dataset_blob: str, - namespace: str, - data_manifest_path: str) -> None: - """ - A component that takes an images dataset as input and initializes a data manifest - and writes it to an output file. - Args: - run_id (str): the run id of the pipeline - artifact_bucket (str): The GCS bucket used to store the artifacts - component_name (str): the name of the component (used to create gcs artefact path) - project_id (str): The id of the gcp-project - source_dataset_bucket (str): The GCS bucket containing the dataset to load - source_dataset_blob (str): The GCS blob withing the specified bucket containing the dataset - to load - namespace (str): The dataset namespace (abbreviation for data source) - data_manifest_path (str): the path to write the manifest - """ - logger = get_logger(name=__name__, level=logging.INFO) - logger.info('Started job...') - with tempfile.TemporaryDirectory() as tmp_dir: - logger.info('created temporary directory %s', tmp_dir) - - # Initialize storage client - storage_client = storage.Client(project=project_id) - - # Initialize GCS temporary storage paths - # Parse to match the directory convention from MINIO (-) - component_artifact_dir = run_id.rpartition('-')[0] - artifact_bucket_blob_path = f"custom_artifact/{component_artifact_dir}/{component_name}" - - logger.info("custom artifact will be uploaded to %s", - f'gs://{artifact_bucket}/{artifact_bucket_blob_path}') - - dataset_parquet_tmp_path = os.path.join(tmp_dir, 'dataset.parquet') - dataset_parquet_blob_path = os.path.join(artifact_bucket_blob_path, 'dataset.parquet') - index_parquet_tmp_path = os.path.join(tmp_dir, 'index.parquet') - index_parquet_blob_path = os.path.join(artifact_bucket_blob_path, 'index.parquet') - Path(dataset_parquet_tmp_path).parent.mkdir(parents=True, exist_ok=True) - Path(index_parquet_tmp_path).parent.mkdir(parents=True, exist_ok=True) - - logger.info('GCS and temporary paths initialized') - - # Write parquet index file - parquet_helpers.write_index_parquet(index_parquet_path=index_parquet_tmp_path, - data_iterable_producer=storage_helpers.get_blob_id, - storage_client=storage_client, - bucket_name=source_dataset_bucket, - prefix=source_dataset_blob, - id_prefix=namespace) - # Write parquet dataset - parquet_helpers.write_dataset_parquet \ - (dataset_parquet_path=dataset_parquet_tmp_path, - data_iterable_producer=storage_helpers.get_blob_metadata, - storage_client=storage_client, - bucket_name=source_dataset_bucket, - prefix=source_dataset_blob, - id_prefix=namespace) - - logger.info('Parquet manifest files updated') - - # Upload the parquet - storage_helpers.upload_file_to_bucket(storage_client=storage_client, - file_to_upload_path=dataset_parquet_tmp_path, - bucket_name=artifact_bucket, - blob_path=dataset_parquet_blob_path) - storage_helpers.upload_file_to_bucket(storage_client=storage_client, - file_to_upload_path=index_parquet_tmp_path, - bucket_name=artifact_bucket, - blob_path=index_parquet_blob_path) - logger.info('Parquet manifest files uploaded to GCS') - - data_manifest = DataManifest() - data_manifest.dataset_id = f"{run_id}_{component_name}" - data_manifest.index = f'gs://{artifact_bucket}/{index_parquet_blob_path}' - data_manifest.associated_data.dataset[namespace] = \ - f'gs://{artifact_bucket}/{dataset_parquet_blob_path}' - data_manifest.metadata.branch = "" # TODO: Fill from docker build env var - data_manifest.metadata.commit_hash = "" # TODO: Fill from docker build env var - data_manifest.metadata.creation_date = datetime.now().strftime("%d-%m-%Y_%H-%M-%S") - data_manifest.metadata.run_id = run_id - - logger.info('Manifest file created and updated') - - # Write manifest to outputPath - Path(data_manifest_path).parent.mkdir(parents=True, exist_ok=True) - Path(data_manifest_path).write_text(data_manifest.to_json()) - - logger.info('Manifest file written to %s', data_manifest_path) - - # Clean up temporary storage - logger.info('Files removed from temporary storage.') - logger.info('Job completed.') + return dataset if __name__ == '__main__': - args = parse_args() - dataset_loader_component(run_id=args.run_id, - artifact_bucket=args.artifact_bucket, - component_name=args.component_name, - project_id=args.project_id, - source_dataset_bucket=args.source_dataset_bucket, - source_dataset_blob=args.source_dataset_blob, - namespace=args.namespace, - data_manifest_path=args.data_manifest_path) + SeedDatasetLoader.run() \ No newline at end of file diff --git a/examples/pipelines/finetune_stable_diffusion/components/image_caption_component/Dockerfile b/examples/pipelines/finetune_stable_diffusion/components/image_caption_component/Dockerfile deleted file mode 100644 index e9c0e9543..000000000 --- a/examples/pipelines/finetune_stable_diffusion/components/image_caption_component/Dockerfile +++ /dev/null @@ -1,28 +0,0 @@ -FROM europe-west1-docker.pkg.dev/storied-landing-366912/storied-landing-366912-default-repository/mlpipelines/kubeflow/components/base_component:latest - -ARG DEBIAN_FRONTEND=noninteractive -ENV COMMIT_SHA_BLIP=3a29b7410476bf5f2ba0955827390eb6ea1f4f9d - -## System dependencies -RUN apt-get update && \ - apt-get upgrade -y && \ - apt-get install git wget -y - -# Set the working directory to the source folder -WORKDIR /src - -## Download blip model -RUN wget https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model*_base_caption.pth - -# Clone BLIP repo (no package version available) -RUN git clone https://github.com/salesforce/BLIP.git && \ - cd BLIP && \ - git reset --hard ${COMMIT_SHA_BLIP} - -# Install packages -COPY requirements.txt . -RUN pip3 install -r requirements.txt - -COPY src /src - -ENTRYPOINT ["python", "main.py"] \ No newline at end of file diff --git a/examples/pipelines/finetune_stable_diffusion/components/image_caption_component/README.MD b/examples/pipelines/finetune_stable_diffusion/components/image_caption_component/README.MD deleted file mode 100644 index 76e6b15a4..000000000 --- a/examples/pipelines/finetune_stable_diffusion/components/image_caption_component/README.MD +++ /dev/null @@ -1,26 +0,0 @@ -# image_caption_component - -### Description - -This component uses a captioning model ([BLIP](https://github.com/salesforce/BLIP)) -to caption the final filtered images for training. - -### **Inputs/Outputs** - -See [`component.yaml`](component.yaml) for a more detailed description on all the input/output parameters. - -### **Practical considerations** -* In general, it is [common practice](https://huggingface.co/datasets/lambdalabs/pokemon-blip-captions) -to generate relatively short descriptions when finetuning stable diffusion. - - -* Captions that are long generally take longer to produce but are more descriptive. However, -it is worth considering first the use case you want to employ. -In general, shorter captions tend to be less prone to errors. As an example, here is an image with generated captions with: - - - * Long captions _(`min_length`=10, `max_length`=30)_: "the image of a santa claus standing with his legs out in the air with his hand on" - - * Short caption _(`min_length`=20, `max_length`=40)_: "An image of a santa claus cartoon character" - -png_artifacts diff --git a/examples/pipelines/finetune_stable_diffusion/components/image_caption_component/__init__.py b/examples/pipelines/finetune_stable_diffusion/components/image_caption_component/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/examples/pipelines/finetune_stable_diffusion/components/image_caption_component/component.yaml b/examples/pipelines/finetune_stable_diffusion/components/image_caption_component/component.yaml deleted file mode 100644 index 190edc8da..000000000 --- a/examples/pipelines/finetune_stable_diffusion/components/image_caption_component/component.yaml +++ /dev/null @@ -1,52 +0,0 @@ -name: image_caption_component -description: A component that takes a data manifest as input and captions images that are not - captioned. The output manifest will contain references to the full captioned dataset. -inputs: - - name: run_id - description: The run id of the pipeline - type: String - - name: artifact_bucket - description: The GCS bucket used to store the artifact - type: String - - name: component_name - description: the name of the component (used to create gcs artefact path) - type: String - - name: project_id - description: The id of the gcp-project - type: String - - name: min_length - description: The minimum caption length - type: Integer - - name: max_length - description: The maximum caption length - type: Integer - - name: batch_size - description: The batch size of images to pass to the blip model - type: Integer - - name: beams - description: The blip beam parameters - type: Integer - - name: data_manifest_path - description: The previous component manifest path - type: String - -outputs: - - name: data_manifest_path_caption_component - description: Path to the local file containing the gcs path where the output has been stored - -implementation: - container: - image: europe-west1-docker.pkg.dev/storied-landing-366912/storied-landing-366912-default-repository/mlpipelines/kubeflow/components/image_caption_component:latest - command: [ - python3, main.py, - --run-id, { inputValue: run_id }, - --artifact-bucket, { inputValue: artifact_bucket }, - --component-name, { inputValue: component_name }, - --project-id, { inputValue: project_id }, - --min-length, { inputValue: min_length }, - --max-length, { inputValue: max_length }, - --batch-size, { inputValue: batch_size }, - --beams, { inputValue: beams }, - --data-manifest-path, { inputPath: data_manifest_path }, - --data-manifest-path-caption-component, { outputPath: data_manifest_path_caption_component }, - ] \ No newline at end of file diff --git a/examples/pipelines/finetune_stable_diffusion/components/image_caption_component/docs/santa_captioned.jpg 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a/examples/pipelines/finetune_stable_diffusion/components/image_caption_component/requirements.txt b/examples/pipelines/finetune_stable_diffusion/components/image_caption_component/requirements.txt deleted file mode 100644 index 986e3d106..000000000 --- a/examples/pipelines/finetune_stable_diffusion/components/image_caption_component/requirements.txt +++ /dev/null @@ -1,9 +0,0 @@ -fairscale==0.4.4 -ftfy==6.1.1 -Pillow==9.3.0 -regex==2022.10.31 -torchvision==0.13.0 -PyYAML==5.4.1 -transformers==4.15.0 -timm==0.4.12 -tqdm==4.64.1 \ No newline at end of file diff --git a/examples/pipelines/finetune_stable_diffusion/components/image_caption_component/src/__init__.py b/examples/pipelines/finetune_stable_diffusion/components/image_caption_component/src/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/examples/pipelines/finetune_stable_diffusion/components/image_caption_component/src/main.py b/examples/pipelines/finetune_stable_diffusion/components/image_caption_component/src/main.py deleted file mode 100644 index c5fac2b21..000000000 --- a/examples/pipelines/finetune_stable_diffusion/components/image_caption_component/src/main.py +++ /dev/null @@ -1,217 +0,0 @@ -""" -This file is the entrypoint of the component. It will parse all arguments -and give them to the actual core of the component. -""" -import os -import tempfile -import argparse -import logging -import json -from pathlib import Path -from datetime import datetime - -import pyarrow.compute as pc -from google.cloud import storage - -# pylint: disable=import-error -from helpers.logger import get_logger -from helpers import storage_helpers, parquet_helpers, kfp_helpers -from helpers.manifest_helpers import DataManifest -from utils.image_captioning import KfpPipelineImageCaptioner -from utils.blip_model import BlipModel - - -def parse_args(): - """Parse component arguments""" - - parser = argparse.ArgumentParser() - parser.add_argument('--run-id', - type=str, - required=True, - help='The run id of the pipeline') - parser.add_argument('--artifact-bucket', - type=str, - required=True, - help='The GCS bucket used to store the artifacts') - parser.add_argument('--component-name', - type=str, - required=True, - help='The name of the component') - parser.add_argument('--project-id', - type=str, - required=True, - help='The id of the gcp-project') - parser.add_argument('--min-length', - type=int, - required=True, - help='The minimum caption length') - parser.add_argument('--max-length', - type=int, - required=True, - help='The maximum caption length') - parser.add_argument('--beams', - type=int, - required=True, - help='The blip beam parameters') - parser.add_argument('--batch-size', - type=int, - required=True, - help='The batch size to pass to the captioning component') - parser.add_argument('--data-manifest-path', - type=str, - required=True, - help='The previous component manifest path') - parser.add_argument('--data-manifest-path-caption-component', - type=str, - required=True, - help='The path to the output manifest file') - - return parser.parse_args() - - -# pylint: disable=too-many-locals, too-many-arguments -def dataset_caption_component(run_id: str, - artifact_bucket: str, - component_name: str, - project_id: str, - min_length: int, - max_length: int, - batch_size: int, - beams: int, - data_manifest_path: str, - data_manifest_path_caption_component: str) -> None: - """ - A component that takes an images dataset as input and initializes a data manifest - and writes it to an output file. - Args: - run_id (str): the run id of the pipeline - artifact_bucket (str): The GCS bucket used to store the artifacts - component_name (str): the name of the component (used to create gcs artefact path) - project_id (str): The id of the gcp-project - min_length (str): The minimum caption length - max_length (str): the maximum caption length - batch_size (int): The batch size of images to pass to the blip model - beams (int): The blip beam parameters - data_manifest_path (str): The previous component manifest path - data_manifest_path_caption_component (str): the path to the output manifest file - """ - logger = get_logger(name=__name__, level=logging.INFO) - logger.info('Started job...') - - # Show CUDA availability - kfp_helpers.get_cuda_availability() - - # Initialize storage client - storage_client = storage.Client(project=project_id) - - with tempfile.TemporaryDirectory() as tmp_dir: - logger.info('created temporary directory %s', tmp_dir) - - # Initialize GCS custom artifact path - component_artifact_dir = run_id.rpartition('-')[0] - artifact_bucket_blob_path = f"custom_artifact/{component_artifact_dir}/{component_name}" - logger.info("custom artifact will be uploaded to %s", - f'gs://{artifact_bucket}/{artifact_bucket_blob_path}') - - tmp_img_dir_path = os.path.join(tmp_dir, 'img_dir') - os.makedirs(tmp_img_dir_path, exist_ok=True) - - # Read manifest - with open(data_manifest_path) as f: - manifest_load = json.load(f) - data_manifest = DataManifest.from_dict(manifest_load) - # Get index and dataset parquet gcs paths - index_parquet_prev_gcs_path = data_manifest.index - datasets_parquet_dict = data_manifest.associated_data.dataset - - # Download index files - index_parquet_prev_tmp_path = storage_helpers.download_file_from_bucket( - storage_client, index_parquet_prev_gcs_path, tmp_dir) - - # Download namespace datasets - dataset_parquet_path_dict = {} - for namespace, dataset_gcs_path in datasets_parquet_dict.items(): - dataset_parquet_tmp_path = storage_helpers.download_file_from_bucket( - storage_client, dataset_gcs_path, tmp_dir) - dataset_parquet_path_dict[namespace] = dataset_parquet_tmp_path - - # Initialize Blip model - blip_model = BlipModel(model_path='model*_base_caption.pth') - - # Get index_ids - index_ids_images_to_caption = parquet_helpers.get_column_list_from_parquet( - parquet_scanner_or_path=index_parquet_prev_tmp_path, - column_name='index') - - # Construct parquet filters and filter based on the criteria - filters = (pc.field("file_id").isin(index_ids_images_to_caption)) - - # Caption images for all the available namespaces - for namespace, dataset_parquet_tmp_path in dataset_parquet_path_dict.items(): - logger.info('Starting caption and upload job for for dataset with namespace "%s"', - namespace) - - parquet_dataset_scanner = parquet_helpers.filter_parquet_file( - file_path=dataset_parquet_tmp_path, - filters=filters, - batch_size=batch_size) - - # Caption images and store them in a parquet file - kfp_image_captioner = KfpPipelineImageCaptioner( - blip_model=blip_model, - parquet_dataset=parquet_dataset_scanner, - tmp_img_path=tmp_img_dir_path, - tmp_dir=tmp_dir, - namespace=namespace) - - kfp_image_captioner.start(min_length=min_length, - max_length=max_length, - beams=beams) - - captions_parquet_tmp_path = kfp_image_captioner.get_caption_path() - - # Upload caption parquet file to bucket bucket - captions_parquet_blob_path = os.path.join(artifact_bucket_blob_path, - f'captions_{namespace}.parquet') - storage_helpers.upload_file_to_bucket(storage_client=storage_client, - file_to_upload_path=captions_parquet_tmp_path, - bucket_name=artifact_bucket, - blob_path=captions_parquet_blob_path) - logger.info('Caption and upload job for dataset with namespace "%s" is complete', - namespace) - - # Update manifest - data_manifest.associated_data.caption[namespace] = \ - f"gs://{artifact_bucket}/{captions_parquet_blob_path}" - - # Update manifest - data_manifest.dataset_id = f"{run_id}_{component_name}" - data_manifest.metadata.branch = "" # TODO: Fill from docker build env var - data_manifest.metadata.commit_hash = "" # TODO: Fill from docker build env var - data_manifest.metadata.creation_date = datetime.now().strftime("%d-%m-%Y_%H-%M-%S") - data_manifest.metadata.run_id = run_id - - logger.info('Manifest file updated') - - # Write manifest to outputPath - Path(data_manifest_path_caption_component).parent.mkdir(parents=True, exist_ok=True) - Path(data_manifest_path_caption_component).write_text(data_manifest.to_json()) - - logger.info('Manifest file written to %s', data_manifest_path_caption_component) - - logger.info('Job completed.') - - -if __name__ == '__main__': - args = parse_args() - dataset_caption_component \ - (run_id=args.run_id, - artifact_bucket=args.artifact_bucket, - component_name=args.component_name, - project_id=args.project_id, - min_length=args.min_length, - max_length=args.max_length, - batch_size=args.batch_size, - beams=args.beams, - data_manifest_path=args.data_manifest_path, - data_manifest_path_caption_component=args.data_manifest_path_caption_component) diff --git a/examples/pipelines/finetune_stable_diffusion/components/image_caption_component/src/utils/__init__.py b/examples/pipelines/finetune_stable_diffusion/components/image_caption_component/src/utils/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/examples/pipelines/finetune_stable_diffusion/components/image_caption_component/src/utils/blip_model.py b/examples/pipelines/finetune_stable_diffusion/components/image_caption_component/src/utils/blip_model.py deleted file mode 100644 index 30dcbaf55..000000000 --- a/examples/pipelines/finetune_stable_diffusion/components/image_caption_component/src/utils/blip_model.py +++ /dev/null @@ -1,103 +0,0 @@ -""" -General class for Blip model -""" -import logging -import sys -from typing import List - -import torch -from PIL import Image -from torchvision import transforms -from torchvision.transforms.functional import InterpolationMode - -# pylint: disable=import-error, wrong-import-position -sys.path.insert(0, "/src/BLIP") -from BLIP.models.blip import blip_decoder -from helpers.logger import get_logger - -logger = get_logger(name=__name__, level=logging.INFO) - - -# pylint: disable=too-few-public-methods -class BlipModel: - """Blip Model class""" - - def __init__(self, model_path: str, image_size=384): - """ - Function that initialize the blip model - Args: - model_path (str): the path to the blip model - image_size (str): the image size to resize the image before captioning - """ - self.device = "cuda" if torch.cuda.is_available() else "cpu" - logger.info('BLIP model initialized with %s device', self.device) - self.med_config = "./BLIP/configs/med_config.json" - self.image_size = image_size - self.model = self._init_model(model_path=model_path, device=self.device) - # Normalization values taken from - # https://github.com/salesforce/BLIP/blob/d10be550b2974e17ea72e74edc7948c9e5eab884/predict.py - self.image_transformer = transforms.Compose([ - transforms.Resize((image_size, image_size), interpolation=InterpolationMode.BICUBIC), - transforms.ToTensor(), - transforms.Normalize((0.48145466, 0.4578275, 0.40821073), - (0.26862954, 0.26130258, 0.27577711)) - ]) - - def _init_model(self, model_path: str, device: str) -> blip_decoder: - """ - Initialize the blip model - Args: - model_path (str): the path to the blip model - device (str): the device to initialize the model to - Returns: - blip_decoder: the blip model - """ - model = blip_decoder(pretrained=model_path, image_size=self.image_size, vit='base', - med_config=self.med_config) - model.eval() - model = model.to(device) - - return model - - def _preprocess_images(self, raw_image: Image) -> torch.Tensor: - """ - Function that preprocesses the image before captioning - Args: - raw_image (Image): the image to caption - Returns: - torch.Tensor: a tensor of preprocessed image - """ - image = self.image_transformer(raw_image).unsqueeze(0) - - return image - - # pylint: disable=too-many-arguments - def caption_images(self, image_paths: List[str], min_length: int, - max_length: int, beams: int) -> List[str]: - """ - Main function to caption the image - Args: - image_paths (List[str]): a list of image paths to caption - min_length (int): the minimum caption length - max_length (int): the maximum caption length - beams (int): parameters to increase the caption per image quality but required more - compute time - Returns: - List[str]: a list of image captions - """ - image_tensors = [] - - with torch.no_grad(): - for image_path in image_paths: - image = Image.open(image_path).convert('RGB') - image = self._preprocess_images(image) - image_tensors.append(image) - - # pylint: disable=no-member - image_tensors = torch.cat(image_tensors, dim=0).to(self.device) - captions = self.model.generate(image_tensors, - sample=True, - num_beams=beams, - max_length=max_length, - min_length=min_length) - return captions diff --git a/examples/pipelines/finetune_stable_diffusion/components/image_caption_component/src/utils/image_captioning.py b/examples/pipelines/finetune_stable_diffusion/components/image_caption_component/src/utils/image_captioning.py deleted file mode 100644 index e1a495a30..000000000 --- a/examples/pipelines/finetune_stable_diffusion/components/image_caption_component/src/utils/image_captioning.py +++ /dev/null @@ -1,127 +0,0 @@ -"""Image captioning""" -import os -import logging -from typing import Iterable, List, Tuple - -from tqdm import tqdm -from PIL import Image -from pyarrow.dataset import Scanner - -# pylint: disable=import-error -from helpers import storage_helpers, parquet_helpers -from helpers.logger import get_logger -from utils.blip_model import BlipModel - -LOGGER = get_logger(name=__name__, level=logging.INFO) - -# Set to allow pillow to process images of different sizes -Image.MAX_IMAGE_PIXELS = None - - -# pylint: disable=too-many-instance-attributes, too-few-public-methods -class KfpPipelineImageCaptioner: - """kfp image captioner """ - - # pylint: disable=too-many-arguments - def __init__(self, blip_model: BlipModel, parquet_dataset: Scanner, tmp_img_path: str, - tmp_dir: str, namespace: str): - """ - Class that structures the kfp images conversion loop - Args: - blip_model (BlipModel): the blip model - parquet_dataset (Scanner): the scanned parquet dataset - tmp_img_path (str) the temporary path used to store the downloaded images - tmp_dir (str): temporary directory where to store - namespace (str): the dataset namespace of the images to caption - """ - self.blip_model = blip_model - self.parquet_dataset = parquet_dataset - self.tmp_img_path = tmp_img_path - self.caption_parquet_path = os.path.join(tmp_dir, f'captions_{namespace}.parquet') - self.download_list_file_path = os.path.join(tmp_dir, f'download_gcs_{namespace}.txt') - - def _write_gcs_file_lists(self): - """ - Function that writes the gcs download and upload list files for bulk download/upload - """ - with open(self.download_list_file_path, "w") as download_list_file: - for batch in self.parquet_dataset.to_batches(): - for row in batch.to_pylist(): - file_uri = row['file_uri'] - download_list_file.write(file_uri + "\n") - - LOGGER.info("GCS download file list written to %s", self.download_list_file_path) - - def _download_images_to_caption(self): - """Function that download the images to caption locally""" - LOGGER.info("Downloading images to caption") - storage_helpers.copy_files_bulk(self.download_list_file_path, - self.tmp_img_path) - LOGGER.info("The images to be captioned were successfully downloaded to %s", - self.tmp_img_path) - - def _write_parquet_captions(self, min_length: int, max_length: int, beams: int): - """ - Function that captions the images with BLIP and ranked them with CLIP - Args: - min_length (int): the minimum caption length - max_length (int): the maximum caption length - beams (int): parameters to increase the caption per image quality but required more - compute time - """ - - def _img_caption_loop() -> Iterable[Tuple[str, str, List[str]]]: - - LOGGER.info("Starting image captioning") - for batch in self.parquet_dataset.to_batches(): - # pyarrow's batch_size approximates the number of batches to return - # Batches may be smaller if there aren’t enough rows in the file and can return zero - # in some occasions - if len(batch) > 0: - file_uris, file_ids, local_paths = [], [], [] - for row in tqdm(batch.to_pylist()): - file_uri, file_id = row['file_uri'], row['file_id'] - file_name = os.path.basename(file_uri) - local_file_path = os.path.join(self.tmp_img_path, file_name) - file_ids.append(file_id) - file_uris.append(file_uri) - local_paths.append(local_file_path) - # caption images - captions = self.blip_model.caption_images( - image_paths=local_paths, - min_length=min_length, - max_length=max_length, - beams=beams) - for file_id, file_uri, caption in zip(file_ids, file_uris, captions): - # TODO: decide whether the caption field in caption parquet should be a str - # or a list (many caption per image) - yield file_id, file_uri, [caption] - - parquet_helpers.write_captions_parquet( - caption_parquet_path=self.caption_parquet_path, - data_iterable_producer=_img_caption_loop - ) - - LOGGER.info("Updated dataset parquet file written to %s", self.caption_parquet_path) - LOGGER.info("Image conversion completed") - - def start(self, min_length: int, max_length: int, beams: int): - """ - Function that starts the image captioning loop - Args: - min_length (int): the minimum caption length - max_length (int): the maximum caption length - beams (int): parameters to increase the caption per image quality but required more - compute time - """ - self._write_gcs_file_lists() - self._download_images_to_caption() - self._write_parquet_captions(min_length=min_length, max_length=max_length, beams=beams) - - def get_caption_path(self) -> str: - """ - Function that returns the image caption path - Returns: - str: the image caption path - """ - return self.caption_parquet_path diff --git a/examples/pipelines/finetune_stable_diffusion/components/image_conversion_component/Dockerfile b/examples/pipelines/finetune_stable_diffusion/components/image_conversion_component/Dockerfile deleted file mode 100644 index 541bbe2e2..000000000 --- a/examples/pipelines/finetune_stable_diffusion/components/image_conversion_component/Dockerfile +++ /dev/null @@ -1,18 +0,0 @@ -FROM europe-west1-docker.pkg.dev/storied-landing-366912/storied-landing-366912-default-repository/mlpipelines/kubeflow/components/base_component:latest - -# Set the working directory to the source folder -WORKDIR /src - -# System dependencies -RUN apt-get update && \ - apt-get upgrade -y && \ - apt-get install libpango1.0-0 libcairo2 libpq-dev -y - -# Install packages -COPY requirements.txt . -RUN pip3 install -r requirements.txt - -# Copy over src-files of the component -COPY src /src - -ENTRYPOINT ["python", "main.py"] \ No newline at end of file diff --git a/examples/pipelines/finetune_stable_diffusion/components/image_conversion_component/README.MD b/examples/pipelines/finetune_stable_diffusion/components/image_conversion_component/README.MD deleted file mode 100644 index 6f2ee6a1c..000000000 --- a/examples/pipelines/finetune_stable_diffusion/components/image_conversion_component/README.MD +++ /dev/null @@ -1,38 +0,0 @@ -# image_conversion_component - -### Description - -This component converts images from different selected formats -(currently only `png` and `svg`) to `jpg` images. This step is necessary since other images formats -are not suitable for training ML model on and often contain artifacts that can be eliminated -during conversion. - -The following image illustrates on the examples of a `png` image (left) when converted to `jgp` during (right) with **normal -conversion**: -```commandline -from PIL import Image - -im = Image.open("image.png") -rgb_im = im.convert('RGB') -``` - -![png_artifacts](docs/png_normal_conversion.png) - -You can see that there are some black edge artifacts around the target object. This can also introduce -subsequent artifacts when inference/finetuning on those images since this is the method of conversion that -occurs when `png` image are passed as arrays to a model and converted to Tensors. - -For that reason, a [custom function](src/utils/img_conversion.py) is implemented within that component to apply a conversion method -that gets rid of those artifacts. - -### **Inputs/Outputs** -The component accepts as input different parameters, mainly the `file_extensions` parameters which -indicates which image formats to filter. A new `dataset.parquet` file is written with updated reference -to the path of the converted images. - -See [`component.yaml`](component.yaml) for a more detailed description on all the input/output parameters. - -### **Practical considerations** - -* `svg` images can be converted and stretched to any desired formats. A good compromise of image quality and file size -is generally `1024x1024` (no risk of distortion if the image is not square sine the conversion just adds a border to compensate) \ No newline at end of file diff --git a/examples/pipelines/finetune_stable_diffusion/components/image_conversion_component/__init__.py b/examples/pipelines/finetune_stable_diffusion/components/image_conversion_component/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/examples/pipelines/finetune_stable_diffusion/components/image_conversion_component/component.yaml b/examples/pipelines/finetune_stable_diffusion/components/image_conversion_component/component.yaml deleted file mode 100644 index 97906d593..000000000 --- a/examples/pipelines/finetune_stable_diffusion/components/image_conversion_component/component.yaml +++ /dev/null @@ -1,48 +0,0 @@ -name: image_conversion_component -description: A component that takes a data manifest as input and converts image formats to 'jpeg' - which is a preferred format for subsequent preprocessing and training steps -inputs: - - name: run_id - description: The run id of the pipeline - type: String - - name: artifact_bucket - description: The GCS bucket used to store the artifact - type: String - - name: component_name - description: the name of the component (used to create gcs artefact path) - type: String - - name: project_id - description: The id of the gcp-project - type: String - - name: svg_image_width - description: The desired width to scale the converted SVG image to - type: Integer - - name: svg_image_height - description: The desired height to scale the converted SVG image to - type: Integer - - name: file_extensions - description: A list containing the image formats to convert - type: JsonArray - - name: data_manifest_path - description: The previous component manifest path - type: String - -outputs: - - name: data_manifest_path_image_conversion_component - description: Path to the local file containing the gcs path where the output has been stored - -implementation: - container: - image: europe-west1-docker.pkg.dev/storied-landing-366912/storied-landing-366912-default-repository/mlpipelines/kubeflow/components/image_conversion_component:latest - command: [ - python3, main.py, - --run-id, { inputValue: run_id }, - --artifact-bucket, { inputValue: artifact_bucket }, - --component-name, { inputValue: component_name }, - --project-id, { inputValue: project_id }, - --file-extensions, { inputValue: file_extensions }, - --svg-image-width, { inputValue: svg_image_width }, - --svg-image-height, { inputValue: svg_image_height }, - --data-manifest-path, { inputPath: data_manifest_path }, - --data-manifest-path-image-conversion-component, { outputPath: data_manifest_path_image_conversion_component }, - ] \ No newline at end of file diff --git a/examples/pipelines/finetune_stable_diffusion/components/image_conversion_component/docs/png_normal_conversion.png 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zZ9pYUWAcYYShZ9P^HJ0&XAMg`*>W@`Tx&OR9HYjK`A-YqR{O%_sIT-?u=iE8M$Lbr~ifM^=gim_7wb-N4|*f z(gtt|FE=Or-`&VRme$Xbif5mT)VBQ6;QzUh@&99FkD^rhF|OY0$xpRy*!-$;<^Say zthazxG5>H&YWfHnZ2=`P^KAS@94dc~VF-7&p8Is5HXJoT$6_tdKK}S7fe^mYI_|Ei xsj2zuP5XL!M;kA0+5G&AXY}1jo-wo{Y5RFA+&0+oAHdgUK diff --git a/examples/pipelines/finetune_stable_diffusion/components/image_conversion_component/requirements.txt b/examples/pipelines/finetune_stable_diffusion/components/image_conversion_component/requirements.txt deleted file mode 100644 index 7d35b5e8c..000000000 --- a/examples/pipelines/finetune_stable_diffusion/components/image_conversion_component/requirements.txt +++ /dev/null @@ -1,5 +0,0 @@ -CairoSVG==2.5.2 -Pillow==9.3.0 -pyvips==2.2.1 -tqdm==4.64.1 - diff --git a/examples/pipelines/finetune_stable_diffusion/components/image_conversion_component/src/__init__.py b/examples/pipelines/finetune_stable_diffusion/components/image_conversion_component/src/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/examples/pipelines/finetune_stable_diffusion/components/image_conversion_component/src/main.py b/examples/pipelines/finetune_stable_diffusion/components/image_conversion_component/src/main.py deleted file mode 100644 index c4cefa39d..000000000 --- a/examples/pipelines/finetune_stable_diffusion/components/image_conversion_component/src/main.py +++ /dev/null @@ -1,199 +0,0 @@ -""" -This file is the entrypoint of the component. It will parse all arguments -and give them to the actual core of the component. -""" -import os -import json -import argparse -import logging -import tempfile -from pathlib import Path -from datetime import datetime - -import pyarrow.dataset as ds -from google.cloud import storage - -# pylint: disable=import-error -from helpers import storage_helpers, parquet_helpers, kfp_helpers -from helpers.logger import get_logger -from helpers.manifest_helpers import DataManifest -from utils.img_conversion import KfpPipelineImageConverter - - -def parse_args(): - """Parse component arguments""" - parser = argparse.ArgumentParser() - - parser.add_argument('--run-id', - type=str, - required=True, - help='The run id of the pipeline') - parser.add_argument('--artifact-bucket', - type=str, - required=True, - help='The GCS bucket used to store the artifacts') - parser.add_argument('--component-name', - type=str, - required=True, - help='The name of the component') - parser.add_argument('--project-id', - type=str, - required=True, - help='The id of the gcp-project') - parser.add_argument('--file-extensions', - type=list, - required=True, - help='A list containing the file extensions to convert') - parser.add_argument('--svg-image-width', - type=list, - required=True, - help='The desired width to scale the converted SVG image to') - parser.add_argument('--svg-image-height', - type=list, - required=True, - help='The desired height to scale the converted SVG image to') - parser.add_argument('--data-manifest-path', - type=str, - required=True, - help='The previous component manifest path') - parser.add_argument('--data-manifest-path-image-conversion-component', - type=str, - required=True, - help='The path to the output manifest file') - - return parser.parse_args() - - -# pylint: disable=too-many-locals, too-many-arguments -def dataset_image_conversion_component(run_id: str, - artifact_bucket: str, - component_name: str, - project_id: str, - file_extensions: list, - data_manifest_path: str, - svg_image_width: int, - svg_image_height: int, - data_manifest_path_image_conversion_component: str) -> None: - """ - A component that takes a data manifest as input and converts image formats to 'jpeg' which is - a preferred format for subsequent preprocessing and training steps - Args: - run_id (str): The run id of the pipeline - artifact_bucket (str): The GCS bucket used to store the artifacts - component_name (str): The name of the component (used to create gcs artefact path) - project_id (str): The id of the gcp-project - file_extensions (list): A list containing the file extensions to convert - data_manifest_path (str): The previous component manifest path - svg_image_width (int): the desired width to scale the converted SVG image to - svg_image_height (int): the desired width to scale the converted SVG image to - data_manifest_path_image_conversion_component (str): the path to the output manifest file - """ - # Initialize logger - logger = get_logger(name=__name__, level=logging.INFO) - logger.info('Started job...') - - # Parse list variables - file_extensions = kfp_helpers.parse_kfp_list(file_extensions) - - # Initialize storage client - storage_client = storage.Client(project=project_id) - - with tempfile.TemporaryDirectory() as tmp_dir: - logger.info('created temporary directory %s', tmp_dir) - - # Initialize GCS custom artifact path - component_artifact_dir = run_id.rpartition('-')[0] - artifact_bucket_blob_path = f"custom_artifact/{component_artifact_dir}/{component_name}" - dataset_parquet_blob_path = os.path.join(artifact_bucket_blob_path, 'dataset.parquet') - converted_images_blob_path = os.path.join(artifact_bucket_blob_path, 'converted_images') - converted_images_gcs_uri = f'gs://{artifact_bucket}/{converted_images_blob_path}' - logger.info("custom artifact will be uploaded to %s", - f'gs://{artifact_bucket}/{artifact_bucket_blob_path}') - - # Initialize temporary directories and file paths - tmp_img_dir_path = os.path.join(tmp_dir, 'img_dir') - os.makedirs(tmp_img_dir_path, exist_ok=True) - dataset_parquet_tmp_path = os.path.join(tmp_dir, 'dataset.parquet') - Path(dataset_parquet_tmp_path).parent.mkdir(parents=True, exist_ok=True) - - logger.info('GCS and temporary paths initialized') - - # Read manifest from previous component - with open(data_manifest_path) as f: - manifest_load = json.load(f) - data_manifest = DataManifest.from_dict(manifest_load) - - # Get index and dataset parquet gcs paths - index_parquet_prev_gcs_path = data_manifest.index - # TODO: replace harcoded namespace with string or list input - dataset_parquet_prev_gcs_path = data_manifest.associated_data.dataset['cf'] - - # Download parquet fies locally - index_parquet_prev_tmp_path = storage_helpers.download_file_from_bucket( - storage_client, index_parquet_prev_gcs_path, tmp_dir) - dataset_parquet_prev_tmp_path = storage_helpers.download_file_from_bucket( - storage_client, dataset_parquet_prev_gcs_path, tmp_dir) - - # Get index_ids - index_ids = parquet_helpers.get_column_list_from_parquet( - parquet_scanner_or_path=index_parquet_prev_tmp_path, - column_name='index') - - # Initialize parquet dataset - parquet_dataset = ds.dataset(dataset_parquet_prev_tmp_path) \ - .scanner(columns=['file_extension', 'file_id', 'file_uri', 'file_size']) - - # Start image conversion - image_converter = KfpPipelineImageConverter( - parquet_dataset=parquet_dataset, - updated_parquet_dataset_path=dataset_parquet_tmp_path, - destination_gcs_uri=converted_images_gcs_uri, - index_ids=index_ids, - tmp_img_path=tmp_img_dir_path, - file_extensions=file_extensions, - download_list_file_path=os.path.join(tmp_dir, 'download_gcs.txt'), - upload_list_file_path=os.path.join(tmp_dir, 'upload_gcs.txt')) - image_converter.start(svg_image_height=svg_image_height, svg_image_width=svg_image_width) - - # Upload newly created parquet file to gcs - storage_helpers.upload_file_to_bucket(storage_client=storage_client, - file_to_upload_path=dataset_parquet_tmp_path, - bucket_name=artifact_bucket, - blob_path=dataset_parquet_blob_path) - - # Update manifest - data_manifest.dataset_id = f"{run_id}_{component_name}" - # TODO: replace harcoded namespace with string or list input - # TODO: make sure we read all the namespace ('cf', 'laion') - data_manifest.associated_data.dataset['cf'] = \ - f"gs://{artifact_bucket}/{dataset_parquet_blob_path}" - data_manifest.metadata.branch = "" # TODO: Fill from docker build env var - data_manifest.metadata.commit_hash = "" # TODO: Fill from docker build env var - data_manifest.metadata.creation_date = datetime.now().strftime("%d-%m-%Y_%H-%M-%S") - data_manifest.metadata.run_id = run_id - - logger.info('Manifest file created and updated') - - # Write manifest to outputPath - Path(data_manifest_path_image_conversion_component) \ - .parent.mkdir(parents=True, exist_ok=True) - Path(data_manifest_path_image_conversion_component).write_text(data_manifest.to_json()) - - logger.info('Manifest file written to %s', data_manifest_path_image_conversion_component) - - logger.info('Job completed.') - - -if __name__ == '__main__': - args = parse_args() - dataset_image_conversion_component( - run_id=args.run_id, - artifact_bucket=args.artifact_bucket, - component_name=args.component_name, - project_id=args.project_id, - file_extensions=args.file_extensions, - svg_image_width=args.svg_image_width, - svg_image_height=args.svg_image_height, - data_manifest_path=args.data_manifest_path, - data_manifest_path_image_conversion_component=args. - data_manifest_path_image_conversion_component) diff --git a/examples/pipelines/finetune_stable_diffusion/components/image_conversion_component/src/utils/__init__.py b/examples/pipelines/finetune_stable_diffusion/components/image_conversion_component/src/utils/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/examples/pipelines/finetune_stable_diffusion/components/image_conversion_component/src/utils/img_conversion.py b/examples/pipelines/finetune_stable_diffusion/components/image_conversion_component/src/utils/img_conversion.py deleted file mode 100644 index 0794c0b7c..000000000 --- a/examples/pipelines/finetune_stable_diffusion/components/image_conversion_component/src/utils/img_conversion.py +++ /dev/null @@ -1,200 +0,0 @@ -""" -Image conversion utils -""" - -import io -import os -import logging -from typing import Union - -import cairosvg -import pyarrow.dataset as ds -from tqdm import tqdm -from PIL import Image as img -from PIL.Image import Image - -# # pylint: disable=import-error -from helpers import storage_helpers, parquet_helpers, io_helpers -from helpers.logger import get_logger - -# Set to allow pillow to process images of different sizes -img.MAX_IMAGE_PIXELS = None - -LOGGER = get_logger(name=__name__, level=logging.INFO) - - -def convert_png_to_jpeg(image_source: Union[str, Image]) -> Image: - """ - Function that converts images from png to jpg - Args: - image_source (Union[str,Image]): png image source or Pillow Image object - Returns: - Image: Image in jpeg format - """ - - if isinstance(image_source, str): - image_to_convert = img.open(image_source).convert("RGBA") - else: - image_to_convert = image_source - converted_image = img.new("RGB", image_to_convert.size, (255, 255, 255)) - converted_image.paste(image_to_convert, mask=image_to_convert) - - return converted_image - - -def convert_svg_to_jpeg(img_path: str, output_width: int, output_height: int) -> Image: - """ - Function that converts images from svg to jpg - Args: - img_path (str): the folder path where the source images are located - output_width (int): the desired output width of the svg image - output_height (int): the desired output height of the svg image - Returns: - Image: Image in jpeg format - """ - # SVG has to be converted to png first before converting to jpg - png_bytes = cairosvg.svg2png(url=img_path, output_width=output_width, - output_height=output_height) - converted_image = img.open(io.BytesIO(png_bytes)) - converted_image = convert_png_to_jpeg(converted_image) - - return converted_image - - -# pylint: disable=too-many-instance-attributes, too-few-public-methods -class KfpPipelineImageConverter: - """kfp images conversion """ - - # pylint: disable=too-many-arguments - def __init__(self, parquet_dataset: ds.dataset, updated_parquet_dataset_path: str, - destination_gcs_uri: str, tmp_img_path: str, file_extensions: list, - index_ids: list, download_list_file_path: str, - upload_list_file_path: str): - """ - Class that structures the kfp images conversion loop - Args: - parquet_dataset (pq.ParquetDataset): the parquet dataset containing the list of images - to convert - updated_parquet_dataset_path (str): the path to write the updated parquet path after - conversion - destination_gcs_uri (str): the gcs uri to copy the files to. - It can indicate a bucket (e.g. gs://my-bucket) or a bucket with a blob path - (e.g gs://my-bucket/my-blob-path/) - tmp_img_path (str) the temporary path used to store the downloaded and converted images - file_extensions (list): a list containing the image extensions that need to be converted - index_ids (list): list of index ids that contain the images of interest - download_list_file_path (str): path to file list containing one-per-line list of GCS - URIs to download - upload_list_file_path (str): path to file list containing one-per-line list of local - file paths to upload - """ - self.parquet_dataset = parquet_dataset - self.updated_parquet_dataset_path = updated_parquet_dataset_path - self.destination_gcs_uri = destination_gcs_uri - self.tmp_img_path = tmp_img_path - self.file_extensions = file_extensions - self.index_ids = index_ids - self.download_list_file_path = download_list_file_path - self.upload_list_file_path = upload_list_file_path - - def _write_gcs_file_lists(self): - """ - Function that writes the gcs download and upload list files for bulk download/upload - """ - with open(self.download_list_file_path, "w") as download_list_file, \ - open(self.upload_list_file_path, "w") as upload_list_file: - for batch in self.parquet_dataset.to_batches(): - for row in batch.to_pylist(): - file_extension, file_id, file_uri = row['file_extension'], row['file_id'], row[ - 'file_uri'] - if file_id in self.index_ids and file_extension in self.file_extensions: - converted_img_name = f"{io_helpers.get_file_name(file_uri)}.jpg" - local_file_path = os.path.join(self.tmp_img_path, converted_img_name) - download_list_file.write(file_uri + "\n") - upload_list_file.write(local_file_path + "\n") - - LOGGER.info("GCS download file list written to %s", self.download_list_file_path) - LOGGER.info("GCS upload file list written to %s", self.upload_list_file_path) - - def _download_imgs_to_convert(self): - """Function that download the images to be converted locally""" - LOGGER.info("Downloading images to convert") - storage_helpers.copy_files_bulk(self.download_list_file_path, - self.tmp_img_path) - LOGGER.info("The images to be converted were successfully downloaded to %s", - self.tmp_img_path) - - def _upload_converted_imgs(self): - """Function that uploads the images to gcs """ - - LOGGER.info("Uploading converted images") - storage_helpers.copy_files_bulk(self.upload_list_file_path, - self.destination_gcs_uri) - LOGGER.info("The converted images were uploaded to %s", self.destination_gcs_uri) - - def _convert_images_to_jpeg(self, svg_image_width: int, svg_image_height: int): - """ - Function that converts the images to jpeg format and saves them locally. The updated - metadata is then written to new updated the parquet dataset file. - Args: - svg_image_width (int): the desired width to scale the converted SVG image to - svg_image_height (int): the desired height to scale the converted SVG image to - """ - - LOGGER.info("Image conversion started") - - def _img_conversion_loop(**kwargs): - """Function that loops through the images and converts them if they are located - in the index ids and if they match the specified image formats that need to be - converted""" - for batch in tqdm(self.parquet_dataset.to_batches()): - for row in tqdm(batch.to_pylist()): - file_extension, file_id, file_uri, file_size = \ - row['file_extension'], row['file_id'], row['file_uri'], row['file_size'] - - if file_id in self.index_ids and file_extension in self.file_extensions: - img_name = os.path.basename(file_uri) - local_file_path = os.path.join(self.tmp_img_path, img_name) - # convert image - if file_extension.lower() == 'png': - image = convert_png_to_jpeg(local_file_path) - elif file_extension.lower() == 'svg': - image = convert_svg_to_jpeg(local_file_path, kwargs['svg_image_width'], - kwargs['svg_image_height']) - else: - raise NotImplementedError( - f"Conversion to jpg for format {file_extension} is" - f"not implemented. Available conversions are" - f" 'png_to_jpeg' and 'svg_to_jpeg'") - - # Store img_temp_save_path image locally - file_extension = "jpg" - img_name = f"{io_helpers.get_file_name(file_uri)}.{file_extension}" - img_temp_save_path = os.path.join(self.tmp_img_path, img_name) - image.save(img_temp_save_path) - file_size = os.path.getsize(img_temp_save_path) - file_uri = f"{self.destination_gcs_uri}/{img_name}" - - yield file_uri, file_id, file_size, file_extension - - parquet_helpers.write_dataset_parquet( - dataset_parquet_path=self.updated_parquet_dataset_path, - data_iterable_producer=_img_conversion_loop, - svg_image_height=svg_image_height, - svg_image_width=svg_image_width) - - LOGGER.info("Updated dataset parquet file written to %s", - self.updated_parquet_dataset_path) - LOGGER.info("Image conversion completed") - - def start(self, svg_image_width: int, svg_image_height: int): - """ - Function that executes the kfp image conversion steps - Args: - svg_image_width (int): the desired width to scale the converted SVG image to - svg_image_height (int): the desired height to scale the converted SVG image to - """ - self._write_gcs_file_lists() - self._download_imgs_to_convert() - self._convert_images_to_jpeg(svg_image_width, svg_image_height) - self._upload_converted_imgs() diff --git a/examples/pipelines/finetune_stable_diffusion/components/image_embedding_component/Dockerfile b/examples/pipelines/finetune_stable_diffusion/components/image_embedding_component/Dockerfile deleted file mode 100644 index dc88da1b2..000000000 --- a/examples/pipelines/finetune_stable_diffusion/components/image_embedding_component/Dockerfile +++ /dev/null @@ -1,27 +0,0 @@ -FROM europe-west1-docker.pkg.dev/storied-landing-366912/storied-landing-366912-default-repository/mlpipelines/kubeflow/components/base_component:latest - -ARG DEBIAN_FRONTEND=noninteractive -ENV COMMIT_SHA_CLIP=d50d76daa670286dd6cacf3bcd80b5e4823fc8e1 - -## System dependencies -RUN apt-get update && \ - apt-get upgrade -y && \ - apt-get install git wget -y - -# Set the working directory to the source folder -WORKDIR /src - -## Download model -# (Download Link:https://github.com/openai/CLIP/issues/199) -RUN wget https://openaipublic.azureedge.net/clip/models/b8cca3fd41ae0c99ba7e8951adf17d267cdb84cd88be6f7c2e0eca1737a03836/ViT-L-14.pt - -# Install CLIP -RUN pip3 install git+https://github.com/openai/CLIP.git@${COMMIT_SHA_CLIP} - -# Install packages -COPY requirements.txt . -RUN pip3 install -r requirements.txt - -COPY src /src - -ENTRYPOINT ["python", "main.py"] \ No newline at end of file diff --git a/examples/pipelines/finetune_stable_diffusion/components/image_embedding_component/README.MD b/examples/pipelines/finetune_stable_diffusion/components/image_embedding_component/README.MD deleted file mode 100644 index 2dec621d7..000000000 --- a/examples/pipelines/finetune_stable_diffusion/components/image_embedding_component/README.MD +++ /dev/null @@ -1,23 +0,0 @@ -# image_embedding_component - -### Description - -This component extracts [image embeddings](https://rom1504.medium.com/image-embeddings-ed1b194d113e) -from the converted images using a [CLIP model](https://www.google.com/search?q=clip+embeddings&oq=clip+embeddings&aqs=chrome..69i57j0i22i30j69i60j69i64l2j69i60j69i64j69i60.6764j0j7&sourceid=chrome&ie=UTF-8). - -Since image embeddings are good at capturing the features of the image in a compact and useful way, they -will be used in the next steps to retrieve images similar to our seed images. - -The CLIP model is downloaded once inside the component during build time. The images are then embedded -and the embeddings are then saved as numpy files `.npy` files and uploaded to GCS. The data manifest is then updated with the reference to the embeddings path. - -### **Inputs/Outputs** - -See [`component.yaml`](component.yaml) for a more detailed description on all the input/output parameters. - -### **Practical considerations** - -* There exists many variants for the CLIP model, the current variant that is used is the `Vit-L/14`. The reason -for that is that the embeddings produced from this variant are the same ones that are used for building the indices for the -[LAION-5B dataset](https://laion.ai/blog/laion-5b/#:~:text=Pre%2DComputed%20Embeddings). - diff --git a/examples/pipelines/finetune_stable_diffusion/components/image_embedding_component/__init__.py b/examples/pipelines/finetune_stable_diffusion/components/image_embedding_component/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/examples/pipelines/finetune_stable_diffusion/components/image_embedding_component/component.yaml b/examples/pipelines/finetune_stable_diffusion/components/image_embedding_component/component.yaml deleted file mode 100644 index ccc4bb6d8..000000000 --- a/examples/pipelines/finetune_stable_diffusion/components/image_embedding_component/component.yaml +++ /dev/null @@ -1,40 +0,0 @@ -name: image_embedding_component -description: A component that takes a data manifest as input and embeds images with a CLIP model. - The output manifest will contain references to the blob path where the embeddings are stored. -inputs: - - name: run_id - description: The run id of the pipeline - type: String - - name: artifact_bucket - description: The GCS bucket used to store the artifact - type: String - - name: component_name - description: the name of the component (used to create gcs artefact path) - type: String - - name: project_id - description: The id of the gcp-project - type: String - - name: batch_size - description: The number of images to batch before embedding - type: Integer - - name: data_manifest_path - description: The previous component manifest path - type: Stringc - -outputs: - - name: data_manifest_path_embedding_component - description: Path to the local file containing the gcs path where the output has been stored - -implementation: - container: - image: europe-west1-docker.pkg.dev/storied-landing-366912/storied-landing-366912-default-repository/mlpipelines/kubeflow/components/image_embedding_component:latest - command: [ - python3, main.py, - --run-id, { inputValue: run_id }, - --artifact-bucket, { inputValue: artifact_bucket }, - --component-name, { inputValue: component_name }, - --project-id, { inputValue: project_id }, - --batch-size, { inputValue: batch_size }, - --data-manifest-path, { inputPath: data_manifest_path }, - --data-manifest-path-embedding-component, { outputPath: data_manifest_path_embedding_component }, - ] \ No newline at end of file diff --git a/examples/pipelines/finetune_stable_diffusion/components/image_embedding_component/requirements.txt b/examples/pipelines/finetune_stable_diffusion/components/image_embedding_component/requirements.txt deleted file mode 100644 index a4ebd6025..000000000 --- a/examples/pipelines/finetune_stable_diffusion/components/image_embedding_component/requirements.txt +++ /dev/null @@ -1,9 +0,0 @@ -fairscale==0.4.4 -ftfy==6.1.1 -Pillow==9.3.0 -regex==2022.10.31 -torchvision==0.13.0 -transformers==4.15.0 -tqdm==4.64.1 - - diff --git a/examples/pipelines/finetune_stable_diffusion/components/image_embedding_component/src/__init__.py b/examples/pipelines/finetune_stable_diffusion/components/image_embedding_component/src/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/examples/pipelines/finetune_stable_diffusion/components/image_embedding_component/src/main.py b/examples/pipelines/finetune_stable_diffusion/components/image_embedding_component/src/main.py deleted file mode 100644 index 2e314938e..000000000 --- a/examples/pipelines/finetune_stable_diffusion/components/image_embedding_component/src/main.py +++ /dev/null @@ -1,173 +0,0 @@ -""" -This file is the entrypoint of the component. It will parse all arguments -and give them to the actual core of the component. -""" -import os -import tempfile -import argparse -import logging -import json -from pathlib import Path -from datetime import datetime - -import pyarrow.compute as pc -from google.cloud import storage - -# pylint: disable=import-error -from helpers.logger import get_logger -from helpers import storage_helpers, parquet_helpers, kfp_helpers -from helpers.manifest_helpers import DataManifest -from utils.image_embedding import KfpPipelineImageEmbedder - - -def parse_args(): - """Parse component arguments""" - - parser = argparse.ArgumentParser() - parser.add_argument('--run-id', - type=str, - required=True, - help='The run id of the pipeline') - parser.add_argument('--artifact-bucket', - type=str, - required=True, - help='The GCS bucket used to store the artifacts') - parser.add_argument('--component-name', - type=str, - required=True, - help='The name of the component') - parser.add_argument('--project-id', - type=str, - required=True, - help='The id of the gcp-project') - parser.add_argument('--batch-size', - type=int, - required=True, - help='The number of images to batch before embedding') - parser.add_argument('--data-manifest-path', - type=str, - required=True, - help='The previous component manifest path') - parser.add_argument('--data-manifest-path-embedding-component', - type=str, - required=True, - help='The path to the output manifest file') - - return parser.parse_args() - - -# pylint: disable=too-many-locals, too-many-arguments -def image_embedding_component(run_id: str, - artifact_bucket: str, - component_name: str, - project_id: str, - batch_size: int, - data_manifest_path: str, - data_manifest_path_embedding_component: str) -> None: - """ - A component that takes an images dataset as input and generated image embeddings out of them - Args: - run_id (str): the run id of the pipeline - artifact_bucket (str): The GCS bucket used to store the artifacts - component_name (str): the name of the component (used to create gcs artefact path) - project_id (str): The id of the gcp-project - batch_size (int): the number of images to batch before embedding - data_manifest_path (str): The previous component manifest path - data_manifest_path_embedding_component (str): the path to the output manifest file - """ - logger = get_logger(name=__name__, level=logging.INFO) - logger.info('Started job...') - - # Show CUDA availability - kfp_helpers.get_cuda_availability() - - # Initialize storage client - storage_client = storage.Client(project=project_id) - - with tempfile.TemporaryDirectory() as tmp_dir: - logger.info('created temporary directory %s', tmp_dir) - - # Initialize GCS custom artifact path - component_artifact_dir = run_id.rpartition('-')[0] - artifact_bucket_blob_path = f"custom_artifact/{component_artifact_dir}/{component_name}" - embedding_blob_path = f"gs://{artifact_bucket}/{artifact_bucket_blob_path}/embeddings" - logger.info("custom artifact will be uploaded to %s", - f'gs://{artifact_bucket}/{artifact_bucket_blob_path}') - - tmp_img_dir_path = os.path.join(tmp_dir, 'img_dir') - tmp_embedding_dir_path = os.path.join(tmp_dir, 'embeddings_dir') - os.makedirs(tmp_img_dir_path, exist_ok=True) - os.makedirs(tmp_embedding_dir_path, exist_ok=True) - dataset_id_parquet_tmp_path = os.path.join(tmp_dir, 'dataset.parquet') - Path(dataset_id_parquet_tmp_path).parent.mkdir(parents=True, exist_ok=True) - - # Read manifest - with open(data_manifest_path) as f: - manifest_load = json.load(f) - data_manifest = DataManifest.from_dict(manifest_load) - - # Get index and dataset parquet gcs paths - index_parquet_prev_gcs_path = data_manifest.index - # TODO: replace harcoded namespace - dataset_parquet_prev_gcs_path = data_manifest.associated_data.dataset['cf'] - - # Download parquet files locally - index_parquet_prev_tmp_path = storage_helpers.download_file_from_bucket( - storage_client, index_parquet_prev_gcs_path, tmp_dir) - dataset_parquet_prev_tmp_path = storage_helpers.download_file_from_bucket( - storage_client, dataset_parquet_prev_gcs_path, tmp_dir) - - # Get index_ids - index_ids_images_to_embed = parquet_helpers.get_column_list_from_parquet( - parquet_scanner_or_path=index_parquet_prev_tmp_path, - column_name='index') - - # Construct parquet filters and filter based on the criteria - filters = (pc.field("file_id").isin(index_ids_images_to_embed)) - - filtered_dataset_scanner = parquet_helpers.filter_parquet_file( - file_path=dataset_parquet_prev_tmp_path, - filters=filters, - batch_size=batch_size) - - # Caption images and store them in a parquet file - kfp_image_embedder = KfpPipelineImageEmbedder( - parquet_dataset=filtered_dataset_scanner, - embedding_blob_path=embedding_blob_path, - tmp_img_path=tmp_img_dir_path, - tmp_embedding_path=tmp_embedding_dir_path, - download_list_file_path=os.path.join(tmp_dir, 'download_gcs.txt') - ) - - kfp_image_embedder.start() - - # Update manifest - data_manifest.dataset_id = f"{run_id}_{component_name}" - data_manifest.metadata.branch = "" # TODO: Fill from docker build env var - data_manifest.metadata.commit_hash = "" # TODO: Fill from docker build env var - # TODO: replace harcoded namespace with string or list input - data_manifest.associated_data.embedding['cf'] = embedding_blob_path - data_manifest.metadata.creation_date = datetime.now().strftime("%d-%m-%Y_%H-%M-%S") - data_manifest.metadata.run_id = run_id - - logger.info('Manifest file created and updated') - - # Write manifest to outputPath - Path(data_manifest_path_embedding_component).parent.mkdir(parents=True, exist_ok=True) - Path(data_manifest_path_embedding_component).write_text(data_manifest.to_json()) - - logger.info('Manifest file written to %s', data_manifest_path_embedding_component) - - logger.info('Job completed.') - - -if __name__ == '__main__': - args = parse_args() - image_embedding_component \ - (run_id=args.run_id, - artifact_bucket=args.artifact_bucket, - component_name=args.component_name, - project_id=args.project_id, - batch_size=args.batch_size, - data_manifest_path=args.data_manifest_path, - data_manifest_path_embedding_component=args.data_manifest_path_embedding_component) diff --git a/examples/pipelines/finetune_stable_diffusion/components/image_embedding_component/src/utils/__init__.py b/examples/pipelines/finetune_stable_diffusion/components/image_embedding_component/src/utils/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/examples/pipelines/finetune_stable_diffusion/components/image_embedding_component/src/utils/image_embedding.py b/examples/pipelines/finetune_stable_diffusion/components/image_embedding_component/src/utils/image_embedding.py deleted file mode 100644 index 5f6860fd8..000000000 --- a/examples/pipelines/finetune_stable_diffusion/components/image_embedding_component/src/utils/image_embedding.py +++ /dev/null @@ -1,117 +0,0 @@ -"""Image embedding""" -import os -import logging -from typing import List - -import torch -import numpy as np -from tqdm import tqdm -from PIL import Image -from pyarrow.dataset import Scanner - -# pylint: disable=import-error -import clip -from helpers import storage_helpers, io_helpers -from helpers.logger import get_logger - -LOGGER = get_logger(name=__name__, level=logging.INFO) - -# Set to allow pillow to process images of different sizes -Image.MAX_IMAGE_PIXELS = None - - -# pylint: disable=too-many-instance-attributes, too-few-public-methods -class KfpPipelineImageEmbedder: - """kfp image embedder """ - - # pylint: disable=too-many-arguments - def __init__(self, parquet_dataset: Scanner, embedding_blob_path: str, - tmp_img_path: str, tmp_embedding_path: str, download_list_file_path: str): - """ - Class that structures the kfp images conversion loop - Args: - embedding_blob_path (str): the blob path where the image embeddings will be stored - parquet_dataset (Scanner): the scanned parquet dataset - tmp_img_path (str) the temporary path used to store the downloaded images - tmp_embedding_path (str): the temporary path to save the image embeddings - download_list_file_path (str): path to file list containing one-per-line list of GCS - URIs to download - """ - self.device = "cuda" if torch.cuda.is_available() else "cpu" - LOGGER.info('CLIP model initialized with %s device', self.device) - self.clip_model_vit, self.clip_preprocess_vit = clip.load('ViT-L-14', - device=self.device) - self.parquet_dataset = parquet_dataset - self.embedding_blob_path = embedding_blob_path - self.tmp_img_path = tmp_img_path - self.tmp_embedding_path = tmp_embedding_path - self.download_list_file_path = download_list_file_path - self.device = "cuda" if torch.cuda.is_available() else "cpu" - - def _write_gcs_file_lists(self): - """ - Function that writes the gcs download and upload list files for bulk download/upload - """ - with open(self.download_list_file_path, "w") as download_list_file: - for batch in self.parquet_dataset.to_batches(): - for row in batch.to_pylist(): - file_uri = row['file_uri'] - download_list_file.write(file_uri + "\n") - - LOGGER.info("GCS download file list written to %s", self.download_list_file_path) - - def _download_images_to_embed(self): - """Function that download the images to caption locally""" - LOGGER.info("Downloading images to caption") - storage_helpers.copy_files_bulk(self.download_list_file_path, - self.tmp_img_path) - LOGGER.info("The images to be captioned were successfully downloaded to %s", - self.tmp_img_path) - - def _write_embeddings(self, embeddings: np.array, file_ids: List[str]): - """ - Function that saves the image embeddings as a numpy array - Args: - embeddings (np.array): the embeddings array - file_ids (List[str]): the list of file ids associated with the embeddings - """ - for embedding_array, file_id in zip(embeddings, file_ids): - np.save(os.path.join(self.tmp_embedding_path, f"{file_id}.npy"), embedding_array) - - def _embed_images(self): - """ - Function that embeds the images with CLIP - """ - LOGGER.info("Starting image embedding with CLIP") - - for batch in self.parquet_dataset.to_batches(): - # pyarrow's batch_size approximates the number of batches to return - # Batches may be smaller if there aren’t enough rows in the file and can return zero - # in some occasions - if len(batch) > 0: - img_list, file_ids = [], [] - for row in tqdm(batch.to_pylist()): - file_uri, file_id = row['file_uri'], row['file_id'] - file_name = io_helpers.get_file_name(file_uri, return_extension=True) - local_file_path = os.path.join(self.tmp_img_path, file_name) - img_list.append(self.clip_preprocess_vit(Image.open(local_file_path))) - file_ids.append(file_id) - # pylint: disable=no-member - img_stack = torch.tensor(np.stack(img_list), device=self.device) - embeddings = self.clip_model_vit.encode_image(img_stack).cpu().detach().numpy() - self._write_embeddings(embeddings, file_ids) - - LOGGER.info("Image embedding completed") - - def _upload_image_embeddings(self): - """Function that uploads the image embeddings from local disk to gcs""" - storage_helpers.copy_folder_bulk(self.tmp_embedding_path, self.embedding_blob_path) - - def start(self): - """ - Function that starts the image embedding loop - """ - self._write_gcs_file_lists() - self._download_images_to_embed() - self._embed_images() - self._upload_image_embeddings() diff --git a/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/Dockerfile b/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/Dockerfile deleted file mode 100644 index 611ccfb16..000000000 --- a/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/Dockerfile +++ /dev/null @@ -1,14 +0,0 @@ -FROM europe-west1-docker.pkg.dev/storied-landing-366912/storied-landing-366912-default-repository/mlpipelines/kubeflow/components/base_component:latest - -# Set the working directory to the source folder -WORKDIR /src - -# Copy over src-files of the component -COPY requirements.txt . - -# Install packages -RUN pip3 install -r requirements.txt - -COPY src /src - -ENTRYPOINT ["python", "main.py"] \ No newline at end of file diff --git a/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/README.MD b/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/README.MD deleted file mode 100644 index afe998d6b..000000000 --- a/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/README.MD +++ /dev/null @@ -1,20 +0,0 @@ -# dataset_filter_component - -### Description - -This component is used to filter the images based on the metadata -attributes to only keep images of a certain size and format. - -### **Inputs/Outputs** -The component accepts as input different filtering parameters of image size and formats to keep. The filtering -is done against the `dataset.parquet` which contains the relevant metadata used for filtering. -The **index.parquet** of the dataset source is then updated to include only the images that passed the filter. - -See [`component.yaml`](component.yaml) for a more detailed description on all the input/output parameters. - -### **Practical considerations** - -* The reason for only allowing `png` and `svg` (as well as `jpg`) is that the [`image_conversion_component`](../image_conversion_component) -currently only supports converting those formats to `jpg`. If you have a different data format that you want to include, -make sure to add a method to convert to `jpg` in the `image_conversion_component` and add that format to the `image_formats` so that -images of those formats won't get filtered. \ No newline at end of file diff --git a/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/__init__.py b/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/component.yaml b/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/component.yaml deleted file mode 100644 index 9d56b051c..000000000 --- a/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/component.yaml +++ /dev/null @@ -1,49 +0,0 @@ -name: image_filter_component -description: A component that takes a data manifest as input and filters images based on a set of - criteria. The output manifest will contain the same dataset parquet reference and a modified - index id reference. -inputs: - - name: run_id - description: The run id of the pipeline - type: String - - name: artifact_bucket - description: The GCS bucket used to store the artifact - type: String - - name: component_name - description: the name of the component (used to create gcs artefact path) - type: String - - name: project_id - description: The id of the gcp-project - type: String - - name: min_file_size - description: The minimum size of an image (filter) - type: Integer - - name: max_file_size - description: The maximum size of an image (filter) - type: Integer - - name: image_formats - description: The image formats to keep (filter) - type: JsonArray - - name: data_manifest_path - description: The previous component manifest path - type: String - -outputs: - - name: data_manifest_path_filter_component - description: Path to the local file containing the gcs path where the output has been stored - -implementation: - container: - image: europe-west1-docker.pkg.dev/storied-landing-366912/storied-landing-366912-default-repository/mlpipelines/kubeflow/components/image_filter_component:latest - command: [ - python3, main.py, - --run-id, { inputValue: run_id }, - --artifact-bucket, { inputValue: artifact_bucket }, - --component-name, { inputValue: component_name }, - --project-id, { inputValue: project_id }, - --min-file-size, { inputValue: min_file_size }, - --max-file-size, { inputValue: max_file_size }, - --image-formats, { inputValue: image_formats }, - --data-manifest-path, { inputPath: data_manifest_path }, - --data-manifest-path-filter-component, { outputPath: data_manifest_path_filter_component }, - ] \ No newline at end of file diff --git a/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/requirements.txt b/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/requirements.txt deleted file mode 100644 index 8b1378917..000000000 --- a/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/requirements.txt +++ /dev/null @@ -1 +0,0 @@ - diff --git a/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/src/__init__.py b/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/src/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/src/main.py b/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/src/main.py deleted file mode 100644 index 285978b4a..000000000 --- a/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/src/main.py +++ /dev/null @@ -1,199 +0,0 @@ -""" -This file is the entrypoint of the component. It will parse all arguments -and give them to the actual core of the component. -""" -import os -import argparse -import logging -import json -import tempfile -from pathlib import Path -from datetime import datetime - -import pyarrow.compute as pc -from google.cloud import storage - -# pylint: disable=import-error -from helpers.logger import get_logger -from helpers import storage_helpers, parquet_helpers, kfp_helpers -from helpers.manifest_helpers import DataManifest - - -def parse_args(): - """Parse component arguments""" - - parser = argparse.ArgumentParser() - parser.add_argument('--run-id', - type=str, - required=True, - help='The run id of the pipeline') - parser.add_argument('--artifact-bucket', - type=str, - required=True, - help='The GCS bucket used to store the artifacts') - parser.add_argument('--component-name', - type=str, - required=True, - help='The name of the component') - parser.add_argument('--project-id', - type=str, - required=True, - help='The id of the gcp-project') - parser.add_argument('--min-file-size', - type=int, - required=True, - help='The minimum size of an image (filter)') - parser.add_argument('--max-file-size', - type=int, - required=True, - help='The maximum size of an image (filter)') - parser.add_argument('--image-formats', - type=list, - required=True, - help='The image formats to keep (filter)') - parser.add_argument('--data-manifest-path', - type=str, - required=True, - help='The previous component manifest path') - parser.add_argument('--data-manifest-path-filter-component', - type=str, - required=True, - help='The path to the output manifest file') - - return parser.parse_args() - - -# pylint: disable=too-many-locals, too-many-arguments -def image_filter_component(run_id: str, - artifact_bucket: str, - component_name: str, - project_id: str, - min_file_size: int, - max_file_size: int, - image_formats: list, - data_manifest_path: str, - data_manifest_path_filter_component: str) -> None: - """ - A component that takes a data manifest as an input and filters it according to metadata related - information. - Args: - run_id (str): The run id of the pipeline - artifact_bucket (str): The GCS bucket used to store the artifacts - component_name (str): The name of the component (used to create gcs artefact path) - project_id (str): The id of the gcp-project - min_file_size (int): The minimum size of an image (filter) - max_file_size (int): The maximum size of an image (filter) - image_formats (list): The image formats to keep (filter) - data_manifest_path (str): The previous component manifest path - data_manifest_path_filter_component (str): the path to the output manifest file - """ - - logger = get_logger(name=__name__, level=logging.INFO) - logger.info('Started job...') - - with tempfile.TemporaryDirectory() as tmp_dir: - logger.info('created temporary directory %s', tmp_dir) - # Parse list variables - image_formats = kfp_helpers.parse_kfp_list(image_formats) - - # Initialize storage client - storage_client = storage.Client(project=project_id) - - # Initialize GCS custom artifact path - component_artifact_dir = run_id.rpartition('-')[0] - artifact_bucket_blob_path = f"custom_artifact/{component_artifact_dir}/{component_name}" - index_parquet_blob_path = os.path.join(artifact_bucket_blob_path, 'index.parquet') - logger.info("custom artifact will be uploaded to %s", - f'gs://{artifact_bucket}/{artifact_bucket_blob_path}') - - index_parquet_tmp_path = os.path.join(tmp_dir, 'index.parquet') - Path(index_parquet_tmp_path).parent.mkdir(parents=True, exist_ok=True) - - # Read manifest from previous component - with open(data_manifest_path) as f: - manifest_load = json.load(f) - data_manifest = DataManifest.from_dict(manifest_load) - - # Get index and dataset parquet gcs paths - index_parquet_prev_gcs_path = data_manifest.index - # TODO: replace harcoded namespace with string or list input - dataset_parquet_prev_gcs_path = data_manifest.associated_data.dataset['cf'] - - # Download parquet fies locally - index_parquet_prev_tmp_path = storage_helpers.download_file_from_bucket( - storage_client, index_parquet_prev_gcs_path, tmp_dir) - dataset_parquet_prev_tmp_path = storage_helpers.download_file_from_bucket( - storage_client, dataset_parquet_prev_gcs_path, tmp_dir) - - # Get indices - index_before_filtering = parquet_helpers.get_column_list_from_parquet( - parquet_scanner_or_path=index_parquet_prev_tmp_path, - column_name='index') - # Construct parquet filters and filter based on the criteria - filters = (pc.field("file_id").isin(index_before_filtering)) & \ - (pc.field("file_size") > pc.scalar(min_file_size)) & \ - (pc.field("file_size") < pc.scalar(max_file_size)) & \ - (pc.field("file_extension").isin(image_formats)) - - filtered_dataset_scanner = parquet_helpers.filter_parquet_file( - file_path=dataset_parquet_prev_tmp_path, - filters=filters) - - # Write new index ids parquet file and upload it to gcs - index_after_filtering = parquet_helpers.get_column_list_from_parquet( - parquet_scanner_or_path=filtered_dataset_scanner, - column_name='file_id') - - parquet_helpers.write_index_parquet( - index_parquet_path=index_parquet_tmp_path, - data_iterable_producer=lambda id_iterable: (id_element for id_element in id_iterable), - id_iterable=index_after_filtering) - - storage_helpers.upload_file_to_bucket(storage_client=storage_client, - file_to_upload_path=index_parquet_tmp_path, - bucket_name=artifact_bucket, - blob_path=index_parquet_blob_path) - - # Estimate the total number of filtered images - nb_images_before_filtering = len(index_before_filtering) - nb_images_after_filtering = parquet_helpers.get_nb_rows_from_parquet(index_parquet_tmp_path) - nb_filtered_image = nb_images_before_filtering - nb_images_after_filtering - percentage_filtered_images = round( - 100 * (nb_filtered_image / nb_images_before_filtering), 2) - - logger.info( - "The original number of images was %s. A total of %s images were filtered (%s%%)", - nb_images_before_filtering, nb_filtered_image, percentage_filtered_images) - - # Update manifest - data_manifest.dataset_id = f"{run_id}_{component_name}" - data_manifest.index = f"gs://{artifact_bucket}/{index_parquet_blob_path}" - data_manifest.metadata.branch = "" # TODO: Fill from docker build env var - data_manifest.metadata.commit_hash = "" # TODO: Fill from docker build env var - data_manifest.metadata.creation_date = datetime.now().strftime("%d-%m-%Y_%H-%M-%S") - - logger.info('Manifest file updated') - - # Write manifest to outputPath - Path(data_manifest_path_filter_component).parent.mkdir(parents=True, exist_ok=True) - Path(data_manifest_path_filter_component).write_text(data_manifest.to_json()) - - logger.info('Manifest file written to %s', data_manifest_path_filter_component) - - # Clean up temporary storage - logger.info('Files removed from temporary storage.') - logger.info('Job completed.') - - -if __name__ == '__main__': - args = parse_args() - image_filter_component( - run_id=args.run_id, - artifact_bucket=args.artifact_bucket, - component_name=args.component_name, - project_id=args.project_id, - max_file_size=args.max_file_size, - min_file_size=args.min_file_size, - image_formats=args.image_formats, - data_manifest_path=args.data_manifest_path, - data_manifest_path_filter_component=args.data_manifest_path_filter_component) diff --git a/examples/pipelines/finetune_stable_diffusion/components/sd_finetuning_component/Dockerfile b/examples/pipelines/finetune_stable_diffusion/components/sd_finetuning_component/Dockerfile deleted file mode 100644 index dc4eb54ad..000000000 --- a/examples/pipelines/finetune_stable_diffusion/components/sd_finetuning_component/Dockerfile +++ /dev/null @@ -1,23 +0,0 @@ -FROM europe-west1-docker.pkg.dev/storied-landing-366912/storied-landing-366912-default-repository/mlpipelines/kubeflow/components/base_component:latest - -ARG DEBIAN_FRONTEND=noninteractive - -## System dependencies -RUN apt-get update && \ - apt-get upgrade -y && \ - apt-get install git curl wget -y - -# Set the working directory to the source folder -WORKDIR /src - -# Install packages -COPY requirements.txt . -RUN pip3 install -r requirements.txt -# Specifc to A100 GPU https://github.com/pytorch/pytorch/issues/57922 -# (installations included here due to error in pylint when trying to find links from pytorch.org) -RUN pip3 install torch==1.12.0+cu116 -f https://download.pytorch.org/whl/torch_stable.html -RUN pip3 install torchvision==0.13.0+cu116 -f https://download.pytorch.org/whl/torch_stable.html - -COPY src /src - -ENTRYPOINT ["python", "main.py"] \ No newline at end of file diff --git a/examples/pipelines/finetune_stable_diffusion/components/sd_finetuning_component/README.MD b/examples/pipelines/finetune_stable_diffusion/components/sd_finetuning_component/README.MD deleted file mode 100644 index 53bd4a1e1..000000000 --- a/examples/pipelines/finetune_stable_diffusion/components/sd_finetuning_component/README.MD +++ /dev/null @@ -1,26 +0,0 @@ -# sd_finetuning_component - -### Description - -This component takes as input the -final data manifest that is output by the dataset -creation pipeline. The data -manifest keeps reference of all the necessary metadata is required for the next step such as the -reference to the filtered images and captions. The component prepares the dataset for training -according to the -required [format](https://huggingface.co/docs/datasets/image_dataset#:~:text=in%20load_dataset.-,Image%20captioning,-Image%20captioning%20datasets) -and starts the finetuning jobs. -### **Inputs/Outputs** - -See [`component.yaml`](component.yaml) for a more detailed description on all the input/output parameters. - -### **Practical considerations** -* The script uses the official [finetuning script](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image.py) -provided by diffusers and is launched with a subprocess call. Check the following -[docs](https://github.com/huggingface/diffusers/tree/main/examples/text_to_image) for more information on finetuning. -* Finetuning stable diffusion models requires at least 30 GB of GPU memory. It is currently recommended to -use at least 2 A100 GPUs for finetuning. Make sure to configure `accelerate_config.yaml` if you want to change -the number of GPUs used for training. -* It is difficult to pinpoint exactly how many steps are needed to obtain a good finetunable model. The best approach -is to create checkpoints throughout training and visualize the progression of the generation with a fixed prompt and seed. -* A good starting point is to finetune for 2 epochs (2 full passes over the dataset). \ No newline at end of file diff --git a/examples/pipelines/finetune_stable_diffusion/components/sd_finetuning_component/__init__.py b/examples/pipelines/finetune_stable_diffusion/components/sd_finetuning_component/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/examples/pipelines/finetune_stable_diffusion/components/sd_finetuning_component/component.yaml b/examples/pipelines/finetune_stable_diffusion/components/sd_finetuning_component/component.yaml deleted file mode 100644 index 63eb2e02d..000000000 --- a/examples/pipelines/finetune_stable_diffusion/components/sd_finetuning_component/component.yaml +++ /dev/null @@ -1,122 +0,0 @@ -name: sd_finetuning_component -description: A component that takes a data manifest as input and downloads the appropriate dataset for finetuning -inputs: - - name: run_id - description: The run id of the pipeline - type: String - - name: artifact_bucket - description: The GCS bucket used to store the artifact - type: String - - name: component_name - description: the name of the component (used to create gcs artefact path) - type: String - - name: project_id - description: The id of the gcp-project - type: String - - name: data_manifest_gcs_path - description: the path to the data manifest that contains information on the training set - type: String - - name: pretrained_model_gcs_path - description: The gcs path where the base model to finetune is located - type: String - - name: resume_from_checkpoint - description: Whether training should be resumed from a previous checkpoint. - Use a path saved in `pretrained_model_gcs_path` by `--checkpointing_steps`, - or `"latest"` to automatically select the last available checkpoint. - type: String - optional: True - - name: seed - description: the name of the component (used to create gcs artefact path) - type: Integer - - name: resolution - description: The resolution for input images, all the images in the train/validation - will be resized to this resolution - type: Integer - - name: train_batch_size - description: Batch size (per device) for the training dataloader - type: Integer - - name: num_train_epochs - description: Total number of epochs to perform - type: Integer - - name: max_train_steps - description: Total number of training steps to perform. If provided overrides `num_train_epochs` - type: Integer - optional: True - - name: checkpointing_steps - description: Save a checkpoint of the training state every X updates. These - checkpoints are only suitable for resuming training using - `--resume_from_checkpoint`. - type: Integer - optional: True - - name: gradient_accumulation_steps - description: The number of updates steps to accumulate before performing a backward/update pass - type: Integer - - name: gradient_checkpointing - description: Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass - type: String - - name: learning_rate - description: Initial learning rate (after the potential warmup period) to use. - type: Float - - name: scale_lr - description: Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size - type: String - - name: lr_scheduler - description: The scheduler type to use. Choose between ["linear", "cosine", - cosine_with_restarts", "polynomial", "constant","constant_with_warmup"] - type: String - - name: lr_warmup_steps - description: Scale the learning rate by the number of GPUs, gradient accumulation - steps, and batch size - type: Integer - - name: use_ema - description: Whether to use EMA model - type: String - - name: mixed_precision - description: Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). - Bf16 requires PyTorch >=1.10.and an Nvidia Ampere GPU. Default to the value of accelerate - config of the current system or the flag passed with the accelerate.launch` command. - Use this argument to override the accelerate config - type: String - - name: center_crop - description: whether to center crop images before resizing to resolution (if not set, random - crop will be used) - type: String - - name: random_flip - description: whether to randomly flip images horizontally - type: String - - -outputs: - - name: model_gcs_path_finetuning_component - description: The path where the model output and checkpoints are saved - -implementation: - container: - image: europe-west1-docker.pkg.dev/storied-landing-366912/storied-landing-366912-default-repository/mlpipelines/kubeflow/components/sd_finetuning_component:latest - command: [ - python3, main.py, - --run-id, { inputValue: run_id }, - --artifact-bucket, { inputValue: artifact_bucket }, - --component-name, { inputValue: component_name }, - --project-id, { inputValue: project_id }, - --data-manifest-gcs-path, { inputValue: data_manifest_gcs_path }, - --pretrained-model-gcs-path, { inputValue: pretrained_model_gcs_path }, - --resume-from-checkpoint, { inputValue: resume_from_checkpoint }, - --seed, { inputValue: seed }, - --resolution, { inputValue: resolution }, - --train-batch-size, { inputValue: train_batch_size }, - --num-train-epochs, { inputValue: num_train_epochs }, - --max-train-steps, { inputValue: max_train_steps }, - --checkpointing-steps, { inputValue: checkpointing_steps }, - --gradient-accumulation-steps, { inputValue: gradient_accumulation_steps }, - --gradient-checkpointing, { inputValue: gradient_checkpointing }, - --learning-rate, { inputValue: learning_rate }, - --scale-lr, { inputValue: scale_lr }, - --lr-scheduler, { inputValue: lr_scheduler }, - --lr-warmup-steps, { inputValue: lr_warmup_steps }, - --use-ema, { inputValue: use_ema }, - --mixed-precision, { inputValue: mixed_precision }, - --center-crop, { inputValue: center_crop }, - --random-flip, { inputValue: random_flip }, - --model-gcs-path-finetuning-component, { outputPath: model_gcs_path_finetuning_component }, - ] diff --git a/examples/pipelines/finetune_stable_diffusion/components/sd_finetuning_component/requirements.txt b/examples/pipelines/finetune_stable_diffusion/components/sd_finetuning_component/requirements.txt deleted file mode 100644 index 38c6470a3..000000000 --- a/examples/pipelines/finetune_stable_diffusion/components/sd_finetuning_component/requirements.txt +++ /dev/null @@ -1,14 +0,0 @@ -accelerate==0.13.2 -dataclasses-json==0.5.7 -datasets==2.7.1 -diffusers==0.11.0 -markupsafe==2.0.1 -APScheduler==3.9.1.post1 -ftfy==6.1.1 -jsonlines==3.1.0 -modelcards==0.1.6 -pandas==1.3.5 -Pillow==9.3.0 -sorcery==0.2.2 -tensorboard==2.10.1 -transformers==4.21.0 \ No newline at end of file diff --git a/examples/pipelines/finetune_stable_diffusion/components/sd_finetuning_component/src/__init__.py b/examples/pipelines/finetune_stable_diffusion/components/sd_finetuning_component/src/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/examples/pipelines/finetune_stable_diffusion/components/sd_finetuning_component/src/accelerate_config.yaml b/examples/pipelines/finetune_stable_diffusion/components/sd_finetuning_component/src/accelerate_config.yaml deleted file mode 100644 index d7233a1f8..000000000 --- a/examples/pipelines/finetune_stable_diffusion/components/sd_finetuning_component/src/accelerate_config.yaml +++ /dev/null @@ -1,16 +0,0 @@ -compute_environment: LOCAL_MACHINE -deepspeed_config: {} -distributed_type: MULTI_GPU -downcast_bf16: 'no' -fsdp_config: {} -gpu_ids: all -machine_rank: 0 -main_process_ip: null -main_process_port: null -main_training_function: main -mixed_precision: fp16 -num_machines: 1 -num_processes: 2 -rdzv_backend: static -same_network: true -use_cpu: false diff --git a/examples/pipelines/finetune_stable_diffusion/components/sd_finetuning_component/src/main.py b/examples/pipelines/finetune_stable_diffusion/components/sd_finetuning_component/src/main.py deleted file mode 100644 index a63264994..000000000 --- a/examples/pipelines/finetune_stable_diffusion/components/sd_finetuning_component/src/main.py +++ /dev/null @@ -1,399 +0,0 @@ -""" -This file is the entrypoint of the component. It will parse all arguments -and give them to the actual core of the component. -""" -import os -import subprocess # nosec -import tempfile -import argparse -import logging -import tarfile -from pathlib import Path -from typing import Union - -from apscheduler.schedulers.background import BackgroundScheduler -# pylint: disable=import-error -import pyarrow.compute as pc -from google.cloud import storage -from sorcery import dict_of - -from helpers.logger import get_logger -from helpers import storage_helpers, kfp_helpers, parquet_helpers -from helpers.manifest_helpers import DataManifest -from utils.dataset_utils import SDDatasetCreator -from utils import training_utils - - -def optional_int(passed_arg: any): - """Custom function to handle optional integers in the argument parser""" - return None if not passed_arg else int(passed_arg) - - -def parse_args(): - """Parse component arguments""" - - parser = argparse.ArgumentParser() - parser.add_argument('--run-id', - type=str, - required=True, - help='The run id of the pipeline') - parser.add_argument('--artifact-bucket', - type=str, - required=True, - help='The GCS bucket used to store the artifacts') - parser.add_argument('--component-name', - type=str, - required=True, - help='The name of the component') - parser.add_argument('--project-id', - type=str, - required=True, - help='The id of the gcp-project') - parser.add_argument('--data-manifest-gcs-path', - type=str, - required=True, - help='the path to the data manifest that contains information on the ' - 'training set') - parser.add_argument('--pretrained-model-gcs-path', - type=str, - required=True, - help='Model identifier from hugginface.com/models') - parser.add_argument('--resume-from-checkpoint', - type=str, - required=False, - help="Whether training should be resumed from a previous checkpoint. Use a" - "path saved in `pretrained_model_gcs_path` by `--checkpointing_steps`" - "or `latest` to automatically select the last available checkpoint.") - parser.add_argument('--resolution', - type=int, - required=True, - help='The resolution for input images, all the images in the ' - 'train/validation dataset will be resized to this resolution.') - parser.add_argument('--seed', - type=int, - required=True, - help='A seed for reproducible training.') - parser.add_argument('--train-batch-size', - type=int, - required=True, - help='Batch size (per device) for the training dataloader') - parser.add_argument('--num-train-epochs', - type=int, - required=True, - help='Total number of training epochs to perform') - parser.add_argument('--max-train-steps', - type=optional_int, - required=False, - help='Total number of training steps to perform. If provided,' - ' overrides num_train_epochs') - parser.add_argument('--checkpointing-steps', - type=optional_int, - required=False, - help='Save a checkpoint of the training state every X updates.' - ' These checkpoints are only suitable for resuming training' - ' using `--resume_from_checkpoint`.') - parser.add_argument('--gradient-accumulation-steps', - type=int, - required=True, - help='The number of updates steps to accumulate before performing a ' - 'backward/update pass') - parser.add_argument('--gradient-checkpointing', - type=str, - required=False, - help='Whether or not to use gradient checkpointing to save memory at' - 'the expense of slower backward pass') - parser.add_argument('--learning-rate', - type=float, - required=True, - help='Initial learning rate (after the potential warmup period) to use.') - parser.add_argument('--scale-lr', - type=str, - required=False, - help='Scale the learning rate by the number of GPUs, gradient accumulation' - ' steps, and batch size') - parser.add_argument('--lr-scheduler', - type=str, - required=True, - help='The scheduler type to use. Choose between ["linear", "cosine",' - ' "cosine_with_restarts", "polynomial", "constant",' - ' "constant_with_warmup"]') - parser.add_argument('--lr-warmup-steps', - type=int, - required=True, - help='Number of steps for the warmup in the lr scheduler') - parser.add_argument('--use-ema', - type=str, - required=False, - help='Whether to use EMA model') - parser.add_argument('--mixed-precision', - type=str, - required=True, - help="Whether to use mixed precision. Choose between fp16 and bf16 " - "(bfloat16). Bf16 requires PyTorch >=1.10.and an Nvidia Ampere GPU." - "Default to the value of accelerate config of the current system or" - "the flag passed with the `accelerate.launch` command. Use this " - "argument to override the accelerate config") - parser.add_argument('--center-crop', - type=str, - required=False, - help='whether to center crop images before resizing to resolution ' - '(if not set,random crop will be used)') - parser.add_argument('--random-flip', - type=str, - required=False, - help='whether to randomly flip the images horizontally') - parser.add_argument('--model-gcs-path-finetuning-component', - type=str, - required=True, - help='The path where the finetuned model and its checkpoints are saved') - - return parser.parse_args() - - -# pylint: disable=too-many-locals, too-many-arguments, too-many-statements -def sd_finetuning_component(run_id: str, - artifact_bucket: str, - component_name: str, - project_id: str, - data_manifest_gcs_path: str, - pretrained_model_gcs_path: str, - resume_from_checkpoint: Union[str, None], - seed: int, - resolution: int, - train_batch_size: int, - num_train_epochs: int, - max_train_steps: int, - checkpointing_steps: Union[int, None], - gradient_accumulation_steps: int, - gradient_checkpointing: Union[str, None], - learning_rate: float, - scale_lr: Union[str, None], - lr_scheduler: str, - lr_warmup_steps: int, - use_ema: Union[str, None], - mixed_precision: str, - center_crop: Union[str, None], - random_flip: Union[str, None], - model_gcs_path_finetuning_component: str) -> None: - """ - A component for finetuning a stable diffusion model on a custom dataset - Args: - run_id (str): the run id of the pipeline - artifact_bucket (str): The GCS bucket used to store the artifacts - component_name (str): the name of the component (used to create gcs artefact path) - project_id (str): The id of the gcp-project - data_manifest_gcs_path (str): the path to the data manifest that contains information on the - training set - pretrained_model_gcs_path (str): Model identifier from huggingface.co/models or gcs - path to a model - resume_from_checkpoint (Union[str, None]): Whether training should be resumed from a - previous checkpoint. Use a path saved in `pretrained_model_gcs_path` by - `--checkpointing_steps`, or `"latest"` to automatically select the last available - checkpoint. - seed (int): A seed for reproducible training. - resolution (int): The resolution for input images, all the images in the train/validation - dataset will be resized to this resolution - train_batch_size (int): Batch size (per device) for the training dataloader - num_train_epochs (int): Total number of epochs to perform - max_train_steps (int): Total number of training steps to perform. If provided overrides - `num_train_epochs` - checkpointing_steps (int): Save a checkpoint of the training state every X updates. These - checkpoints are only suitable for resuming training using `--resume_from_checkpoint`. - gradient_accumulation_steps (int): The number of updates steps to accumulate before - performing a backward/update pass - gradient_checkpointing (str): Whether to use gradient checkpointing to save memory - at the expense of slower backward pass - learning_rate (float): Initial learning rate (after the potential warmup period) to use. - scale_lr (str): Scale the learning rate by the number of GPUs, gradient accumulation steps, - and batch size - lr_warmup_steps (int): Scale the learning rate by the number of GPUs, gradient accumulation - steps, and batch size - lr_scheduler (str): The scheduler type to use. Choose between ["linear", "cosine", - cosine_with_restarts", "polynomial", "constant","constant_with_warmup"] - use_ema (str): Whether to use EMA model - mixed_precision (str): Whether to use mixed precision. Choose between fp16 and bf16 - (bfloat16). Bf16 requires PyTorch >=1.10.and an Nvidia Ampere GPU. - Default to the value of accelerate config of the current system or the flag passed with the - `accelerate.launch` command. Use this argument to override the `accelerate` config - center_crop (str): whether to center crop images before resizing to resolution (if not set, - random crop will be used) - random_flip (str): whether to randomly flip images horizontally - model_gcs_path_finetuning_component (str): The path where the finetuned model and its - checkpoints are saved - """ - - logger = get_logger(name=__name__, level=logging.INFO) - logger.info('Started job...') - - # Show CUDA availability - kfp_helpers.get_cuda_availability() - - # Initialize storage client - storage_client = storage.Client(project=project_id) - - with tempfile.TemporaryDirectory() as tmp_dir: - # Download and load data manifest - manifest_file_path = storage_helpers.download_file_from_bucket(storage_client, - data_manifest_gcs_path, - tmp_dir) - file = tarfile.open(manifest_file_path, "r:gz") - manifest_dict = file.extractfile(file.getmembers()[0]).read().decode("utf-8") - data_manifest = DataManifest.from_json(manifest_dict) - - # Get relevant metadata information - # Used to identify the dataset creation run associated with the training - dataset_creation_run_id = data_manifest.metadata.run_id - index_parquet_gcs_path = data_manifest.index - datasets_parquet_dict = data_manifest.associated_data.dataset - captions_parquet_dict = data_manifest.associated_data.caption - - # Initialize GCS custom artifact path - component_artifact_dir = run_id.rpartition('-')[0] - artifact_bucket_blob_path = f"custom_artifact/{component_artifact_dir}/{component_name}" - logger.info("custom artifact will be uploaded to %s", - f'gs://{artifact_bucket}/{artifact_bucket_blob_path}') - - # Initialize additional directories for temporary files - imgs_tmp_path = os.path.join(tmp_dir, 'img_dir') - models_tmp_path = os.path.join(tmp_dir, 'models') - model_to_finetune_name = os.path.basename(pretrained_model_gcs_path) - finetuned_model_name = f"{model_to_finetune_name}-tmp_dir-{dataset_creation_run_id}" - finetuned_model_path = os.path.join(models_tmp_path, finetuned_model_name) - model_to_finetune_path = os.path.join(models_tmp_path, model_to_finetune_name) - finetuned_model_gcs_uri = f"gs://{artifact_bucket}/{artifact_bucket_blob_path}/" \ - f"{finetuned_model_name}" - os.makedirs(imgs_tmp_path, exist_ok=True) - os.makedirs(finetuned_model_path, exist_ok=True) - os.makedirs(model_to_finetune_path, exist_ok=True) - - # Download index files locally - index_id_path = storage_helpers.download_file_from_bucket( - storage_client, index_parquet_gcs_path, tmp_dir) - - # Get index list - index_list = parquet_helpers.get_column_list_from_parquet(index_id_path, - column_name='index') - filters = (pc.field("file_id").isin(index_list)) - - sd_dataset_creator = SDDatasetCreator(tmp_path=tmp_dir, - img_path=imgs_tmp_path) - - for namespace in datasets_parquet_dict: - # Download - dataset_parquet_tmp_path = storage_helpers.download_file_from_bucket( - storage_client, datasets_parquet_dict[namespace], tmp_dir) - caption_parquet_tmp_path = storage_helpers.download_file_from_bucket( - storage_client, captions_parquet_dict[namespace], tmp_dir) - filtered_dataset = parquet_helpers.filter_parquet_file( - file_path=dataset_parquet_tmp_path, - filters=filters - ) - filtered_captions = parquet_helpers.filter_parquet_file( - file_path=caption_parquet_tmp_path, - filters=filters - ) - sd_dataset_creator.write_dataset(dataset_scanner=filtered_dataset, - captions_scanner=filtered_captions, - namespace=namespace) - - # Download model to finetune (without checkpoints) - training_utils.download_model_without_checkpoints(pretrained_model_gcs_path, - finetuned_model_path) - - # Fetch specified checkpoint and download it to the local model path - checkpoint_prefix = None - if resume_from_checkpoint: - # training script expects checkpoint to be located in the `output_dir` where the model - # was previously trained -> download the checkpoints there - checkpoint_prefix = training_utils.download_checkpoint_to_resume_from( - resume_from_checkpoint=resume_from_checkpoint, - pretrained_model_gcs_path=pretrained_model_gcs_path, - finetuned_model_path=model_to_finetune_path) - - # Initialize Daemon to upload model and checkpoints while training - scheduler = BackgroundScheduler() - scheduler.add_job(func=training_utils.sync_training_checkpoints, - trigger='interval', - args=[model_to_finetune_path, finetuned_model_gcs_uri], - minutes=10) - scheduler.start() - - # Specify training command (Subprocess requires integer values to be piped as string, - # the argument parser of training script will convert them again to integers/floats) - training_cmd = ['accelerate', 'launch', - '--config_file', 'accelerate_config.yaml', - '--mixed_precision', mixed_precision, - 'train_text_to_image.py', - '--seed', str(seed), - '--pretrained_model_name_or_path', finetuned_model_path, - '--dataset_name', imgs_tmp_path, - '--resolution', str(resolution), - '--train_batch_size', str(train_batch_size), - '--num_train_epochs', str(num_train_epochs), - '--gradient_accumulation_steps', str(gradient_accumulation_steps), - '--learning_rate', str(learning_rate), - '--lr_scheduler', lr_scheduler, - '--lr_warmup_steps', str(lr_warmup_steps), - '--output_dir', model_to_finetune_path - ] - - # Add arguments defined with 'store_true' action if they are set to "True" - optional_args = dict_of(random_flip, center_crop, use_ema, gradient_checkpointing, scale_lr) - training_cmd.extend([f'--{arg}' for arg, value in optional_args.items() if value]) - - # Add optional string/int/bool arguments - if max_train_steps: - training_cmd.extend(['--max_train_steps', str(max_train_steps)]) - if checkpointing_steps: - training_cmd.extend(['--checkpointing_steps', str(checkpointing_steps)]) - if checkpoint_prefix: - training_cmd.extend(['--resume_from_checkpoint', checkpoint_prefix]) - - logger.info(f'Starting training: {training_cmd}') - subprocess.run(training_cmd, check=True) # nosec - - logger.info('Training complete, waiting for model and checkpoint sync to upload the latest' - ' model state and shutting down gracefully ...') - scheduler.shutdown() - - # Run one last synchronization task to ensure that all files were uploaded partially (avoid - # issues when scheduler is uploading partial files as they are being written) - logger.info('Running final sync job') - subprocess.run(['gsutil', '-m', 'rsync', '-r', finetuned_model_path, # nosec - finetuned_model_gcs_uri], check=True) - - # Write manifest to outputPath - Path(model_gcs_path_finetuning_component).parent.mkdir(parents=True, exist_ok=True) - Path(model_gcs_path_finetuning_component).write_text(finetuned_model_gcs_uri) - - logger.info('Job completed.') - - -if __name__ == '__main__': - args = parse_args() - sd_finetuning_component( - run_id=args.run_id, - artifact_bucket=args.artifact_bucket, - component_name=args.component_name, - project_id=args.project_id, - data_manifest_gcs_path=args.data_manifest_gcs_path, - pretrained_model_gcs_path=args.pretrained_model_gcs_path, - resume_from_checkpoint=args.resume_from_checkpoint, - seed=args.seed, - resolution=args.resolution, - train_batch_size=args.train_batch_size, - num_train_epochs=args.num_train_epochs, - max_train_steps=args.max_train_steps, - checkpointing_steps=args.checkpointing_steps, - gradient_accumulation_steps=args.gradient_accumulation_steps, - gradient_checkpointing=args.gradient_checkpointing, - learning_rate=args.learning_rate, - scale_lr=args.scale_lr, - lr_scheduler=args.lr_scheduler, - lr_warmup_steps=args.lr_warmup_steps, - use_ema=args.use_ema, - mixed_precision=args.mixed_precision, - center_crop=args.center_crop, - random_flip=args.random_flip, - model_gcs_path_finetuning_component=args.model_gcs_path_finetuning_component - ) diff --git a/examples/pipelines/finetune_stable_diffusion/components/sd_finetuning_component/src/train_text_to_image.py b/examples/pipelines/finetune_stable_diffusion/components/sd_finetuning_component/src/train_text_to_image.py deleted file mode 100644 index e822b5e72..000000000 --- a/examples/pipelines/finetune_stable_diffusion/components/sd_finetuning_component/src/train_text_to_image.py +++ /dev/null @@ -1,819 +0,0 @@ -"""Stable diffusion finetuning. Cloned from commit: f7154f859ce4534227962ccd190eaf428ef0abc8 -Cloning from a commit since the finetuning script contains updates not included in the latest -# diffusers release (04/01/2023)""" -# pylint: skip-file -import argparse -import copy -import logging -import math -import os -import random -from pathlib import Path -from typing import Iterable, Optional - -import numpy as np -import PIL.Image -import torch -import torch.nn.functional as F -import torch.utils.checkpoint - -import datasets -import diffusers -import transformers -from accelerate import Accelerator -from accelerate.logging import get_logger -from accelerate.utils import set_seed -from datasets import load_dataset -from diffusers import AutoencoderKL, DDPMScheduler, StableDiffusionPipeline, UNet2DConditionModel -from diffusers.optimization import get_scheduler -from diffusers.utils import check_min_version -from diffusers.utils.import_utils import is_xformers_available -from huggingface_hub import HfFolder, Repository, whoami -from torchvision import transforms -from tqdm.auto import tqdm -from transformers import CLIPTextModel, CLIPTokenizer - -# Will error if the minimal version of diffusers is not installed. Remove at your own risks. -check_min_version("0.10.0.dev0") - -PIL.Image.MAX_IMAGE_PIXELS = None - -logger = get_logger(__name__) - - -def parse_args(): - parser = argparse.ArgumentParser(description="Simple example of a training script.") - parser.add_argument( - "--pretrained_model_name_or_path", - type=str, - default=None, - required=True, - help="Path to pretrained model or Model identifier from huggingface.co/models or gcs path to a model.", - ) - parser.add_argument( - "--revision", - type=str, - default=None, - required=False, - help="Revision of pretrained model identifier from huggingface.co/models.", - ) - parser.add_argument( - "--dataset_name", - type=str, - default=None, - help=( - "The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private," - " dataset). It can also be a path pointing to a local copy of a dataset in your filesystem," - " or to a folder containing files that 🤗 Datasets can understand." - ), - ) - parser.add_argument( - "--dataset_config_name", - type=str, - default=None, - help="The config of the Dataset, leave as None if there's only one config.", - ) - parser.add_argument( - "--train_data_dir", - type=str, - default=None, - help=( - "A folder containing the training data. Folder contents must follow the structure described in" - " https://huggingface.co/docs/datasets/image_dataset#imagefolder. In particular, a `metadata.jsonl` file" - " must exist to provide the captions for the images. Ignored if `dataset_name` is specified." - ), - ) - parser.add_argument( - "--image_column", type=str, default="image", - help="The column of the dataset containing an image." - ) - parser.add_argument( - "--caption_column", - type=str, - default="text", - help="The column of the dataset containing a caption or a list of captions.", - ) - parser.add_argument( - "--max_train_samples", - type=int, - default=None, - help=( - "For debugging purposes or quicker training, truncate the number of training examples to this " - "value if set." - ), - ) - parser.add_argument( - "--output_dir", - type=str, - default="sd-model-finetuned", - help="The output directory where the model predictions and checkpoints will be written.", - ) - parser.add_argument( - "--cache_dir", - type=str, - default=None, - help="The directory where the downloaded models and datasets will be stored.", - ) - parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.") - parser.add_argument( - "--resolution", - type=int, - default=512, - help=( - "The resolution for input images, all the images in the train/validation dataset will be resized to this" - " resolution" - ), - ) - parser.add_argument( - "--center_crop", - action="store_true", - help="Whether to center crop images before resizing to resolution (if not set, random crop will be used)", - ) - parser.add_argument( - "--random_flip", - action="store_true", - help="whether to randomly flip images horizontally", - ) - parser.add_argument( - "--train_batch_size", type=int, default=16, - help="Batch size (per device) for the training dataloader." - ) - parser.add_argument("--num_train_epochs", type=int, default=100) - parser.add_argument( - "--max_train_steps", - type=int, - default=None, - help="Total number of training steps to perform. If provided, overrides num_train_epochs.", - ) - parser.add_argument( - "--gradient_accumulation_steps", - type=int, - default=1, - help="Number of updates steps to accumulate before performing a backward/update pass.", - ) - parser.add_argument( - "--gradient_checkpointing", - action="store_true", - help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.", - ) - parser.add_argument( - "--learning_rate", - type=float, - default=1e-4, - help="Initial learning rate (after the potential warmup period) to use.", - ) - parser.add_argument( - "--scale_lr", - action="store_true", - default=False, - help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.", - ) - parser.add_argument( - "--lr_scheduler", - type=str, - default="constant", - help=( - 'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",' - ' "constant", "constant_with_warmup"]' - ), - ) - parser.add_argument( - "--lr_warmup_steps", type=int, default=500, - help="Number of steps for the warmup in the lr scheduler." - ) - parser.add_argument( - "--use_8bit_adam", action="store_true", - help="Whether or not to use 8-bit Adam from bitsandbytes." - ) - parser.add_argument( - "--allow_tf32", - action="store_true", - help=( - "Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see" - " https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices" - ), - ) - parser.add_argument("--use_ema", action="store_true", help="Whether to use EMA model.") - parser.add_argument( - "--non_ema_revision", - type=str, - default=None, - required=False, - help=( - "Revision of pretrained non-ema model identifier. Must be a branch, tag or git identifier of the local or" - " remote repository specified with --pretrained_model_name_or_path." - ), - ) - parser.add_argument("--adam_beta1", type=float, default=0.9, - help="The beta1 parameter for the Adam optimizer.") - parser.add_argument("--adam_beta2", type=float, default=0.999, - help="The beta2 parameter for the Adam optimizer.") - parser.add_argument("--adam_weight_decay", type=float, default=1e-2, - help="Weight decay to use.") - parser.add_argument("--adam_epsilon", type=float, default=1e-08, - help="Epsilon value for the Adam optimizer") - parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.") - parser.add_argument("--push_to_hub", action="store_true", - help="Whether or not to push the model to the Hub.") - parser.add_argument("--hub_token", type=str, default=None, - help="The token to use to push to the Model Hub.") - parser.add_argument( - "--hub_model_id", - type=str, - default=None, - help="The name of the repository to keep in sync with the local `output_dir`.", - ) - parser.add_argument( - "--logging_dir", - type=str, - default="logs", - help=( - "[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to" - " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***." - ), - ) - parser.add_argument( - "--mixed_precision", - type=str, - default=None, - choices=["no", "fp16", "bf16"], - help=( - "Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=" - " 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the" - " flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config." - ), - ) - parser.add_argument( - "--report_to", - type=str, - default="tensorboard", - help=( - 'The integration to report the results and logs to. Supported platforms are `"tensorboard"`' - ' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.' - ), - ) - parser.add_argument("--local_rank", type=int, default=-1, - help="For distributed training: local_rank") - parser.add_argument( - "--checkpointing_steps", - type=int, - default=500, - help=( - "Save a checkpoint of the training state every X updates. These checkpoints are only suitable for resuming" - " training using `--resume_from_checkpoint`." - ), - ) - parser.add_argument( - "--resume_from_checkpoint", - type=str, - default=None, - help=( - "Whether training should be resumed from a previous checkpoint. Use a path saved by" - ' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.' - ), - ) - parser.add_argument( - "--enable_xformers_memory_efficient_attention", action="store_true", - help="Whether or not to use xformers." - ) - - args = parser.parse_args() - env_local_rank = int(os.environ.get("LOCAL_RANK", -1)) - if env_local_rank != -1 and env_local_rank != args.local_rank: - args.local_rank = env_local_rank - - # Sanity checks - if args.dataset_name is None and args.train_data_dir is None: - raise ValueError("Need either a dataset name or a training folder.") - - # default to using the same revision for the non-ema model if not specified - if args.non_ema_revision is None: - args.non_ema_revision = args.revision - - return args - - -def get_full_repo_name(model_id: str, organization: Optional[str] = None, - token: Optional[str] = None): - if token is None: - token = HfFolder.get_token() - if organization is None: - username = whoami(token)["name"] - return f"{username}/{model_id}" - else: - return f"{organization}/{model_id}" - - -dataset_name_mapping = { - "lambdalabs/pokemon-blip-captions": ("image", "text"), -} - - -# Adapted from torch-ema https://github.com/fadel/pytorch_ema/blob/master/torch_ema/ema.py#L14 -class EMAModel: - """ - Exponential Moving Average of models weights - """ - - def __init__(self, parameters: Iterable[torch.nn.Parameter], decay=0.9999): - parameters = list(parameters) - self.shadow_params = [p.clone().detach() for p in parameters] - - self.collected_params = None - - self.decay = decay - self.optimization_step = 0 - - @torch.no_grad() - def step(self, parameters): - parameters = list(parameters) - - self.optimization_step += 1 - - # Compute the decay factor for the exponential moving average. - value = (1 + self.optimization_step) / (10 + self.optimization_step) - one_minus_decay = 1 - min(self.decay, value) - - for s_param, param in zip(self.shadow_params, parameters): - if param.requires_grad: - s_param.sub_(one_minus_decay * (s_param - param)) - else: - s_param.copy_(param) - - torch.cuda.empty_cache() - - def copy_to(self, parameters: Iterable[torch.nn.Parameter]) -> None: - """ - Copy current averaged parameters into given collection of parameters. - Args: - parameters: Iterable of `torch.nn.Parameter`; the parameters to be - updated with the stored moving averages. If `None`, the - parameters with which this `ExponentialMovingAverage` was - initialized will be used. - """ - parameters = list(parameters) - for s_param, param in zip(self.shadow_params, parameters): - param.data.copy_(s_param.data) - - def to(self, device=None, dtype=None) -> None: - r"""Move internal buffers of the ExponentialMovingAverage to `device`. - Args: - device: like `device` argument to `torch.Tensor.to` - """ - # .to() on the tensors handles None correctly - self.shadow_params = [ - p.to(device=device, dtype=dtype) if p.is_floating_point() else p.to(device=device) - for p in self.shadow_params - ] - - def state_dict(self) -> dict: - r""" - Returns the state of the ExponentialMovingAverage as a dict. - This method is used by accelerate during checkpointing to save the ema state dict. - """ - # Following PyTorch conventions, references to tensors are returned: - # "returns a reference to the state and not its copy!" - - # https://pytorch.org/tutorials/beginner/saving_loading_models.html#what-is-a-state-dict - return { - "decay": self.decay, - "optimization_step": self.optimization_step, - "shadow_params": self.shadow_params, - "collected_params": self.collected_params, - } - - def load_state_dict(self, state_dict: dict) -> None: - r""" - Loads the ExponentialMovingAverage state. - This method is used by accelerate during checkpointing to save the ema state dict. - Args: - state_dict (dict): EMA state. Should be an object returned - from a call to :meth:`state_dict`. - """ - # deepcopy, to be consistent with module API - state_dict = copy.deepcopy(state_dict) - - self.decay = state_dict["decay"] - if self.decay < 0.0 or self.decay > 1.0: - raise ValueError("Decay must be between 0 and 1") - - self.optimization_step = state_dict["optimization_step"] - if not isinstance(self.optimization_step, int): - raise ValueError("Invalid optimization_step") - - self.shadow_params = state_dict["shadow_params"] - if not isinstance(self.shadow_params, list): - raise ValueError("shadow_params must be a list") - if not all(isinstance(p, torch.Tensor) for p in self.shadow_params): - raise ValueError("shadow_params must all be Tensors") - - self.collected_params = state_dict["collected_params"] - if self.collected_params is not None: - if not isinstance(self.collected_params, list): - raise ValueError("collected_params must be a list") - if not all(isinstance(p, torch.Tensor) for p in self.collected_params): - raise ValueError("collected_params must all be Tensors") - if len(self.collected_params) != len(self.shadow_params): - raise ValueError("collected_params and shadow_params must have the same length") - - -def main(): - args = parse_args() - logging_dir = os.path.join(args.output_dir, args.logging_dir) - - accelerator = Accelerator( - gradient_accumulation_steps=args.gradient_accumulation_steps, - mixed_precision=args.mixed_precision, - log_with=args.report_to, - logging_dir=logging_dir, - ) - - # Make one log on every process with the configuration for debugging. - logging.basicConfig( - format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", - datefmt="%m/%d/%Y %H:%M:%S", - level=logging.INFO, - ) - logger.info(accelerator.state, main_process_only=False) - if accelerator.is_local_main_process: - datasets.utils.logging.set_verbosity_warning() - transformers.utils.logging.set_verbosity_warning() - diffusers.utils.logging.set_verbosity_info() - else: - datasets.utils.logging.set_verbosity_error() - transformers.utils.logging.set_verbosity_error() - diffusers.utils.logging.set_verbosity_error() - - # If passed along, set the training seed now. - if args.seed is not None: - set_seed(args.seed) - - # Handle the repository creation - if accelerator.is_main_process: - if args.push_to_hub: - if args.hub_model_id is None: - repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token) - else: - repo_name = args.hub_model_id - repo = Repository(args.output_dir, clone_from=repo_name) - - with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore: - if "step_*" not in gitignore: - gitignore.write("step_*\n") - if "epoch_*" not in gitignore: - gitignore.write("epoch_*\n") - elif args.output_dir is not None: - os.makedirs(args.output_dir, exist_ok=True) - - # Load scheduler, tokenizer and models. - noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, - subfolder="scheduler") - tokenizer = CLIPTokenizer.from_pretrained( - args.pretrained_model_name_or_path, subfolder="tokenizer", revision=args.revision - ) - text_encoder = CLIPTextModel.from_pretrained( - args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision - ) - vae = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae", - revision=args.revision) - unet = UNet2DConditionModel.from_pretrained( - args.pretrained_model_name_or_path, subfolder="unet", revision=args.non_ema_revision - ) - - # Freeze vae and text_encoder - vae.requires_grad_(False) - text_encoder.requires_grad_(False) - - # Create EMA for the unet. - if args.use_ema: - ema_unet = UNet2DConditionModel.from_pretrained( - args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision - ) - ema_unet = EMAModel(ema_unet.parameters()) - - if args.enable_xformers_memory_efficient_attention: - if is_xformers_available(): - unet.enable_xformers_memory_efficient_attention() - else: - raise ValueError("xformers is not available. Make sure it is installed correctly") - - if args.gradient_checkpointing: - unet.enable_gradient_checkpointing() - - # Enable TF32 for faster training on Ampere GPUs, - # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices - if args.allow_tf32: - torch.backends.cuda.matmul.allow_tf32 = True - - if args.scale_lr: - args.learning_rate = ( - args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes - ) - - # Initialize the optimizer - if args.use_8bit_adam: - try: - import bitsandbytes as bnb - except ImportError: - raise ImportError( - "Please install bitsandbytes to use 8-bit Adam. You can do so by running `pip install bitsandbytes`" - ) - - optimizer_cls = bnb.optim.AdamW8bit - else: - optimizer_cls = torch.optim.AdamW - - optimizer = optimizer_cls( - unet.parameters(), - lr=args.learning_rate, - betas=(args.adam_beta1, args.adam_beta2), - weight_decay=args.adam_weight_decay, - eps=args.adam_epsilon, - ) - - # Get the datasets: you can either provide your own training and evaluation files (see below) - # or specify a Dataset from the hub (the dataset will be downloaded automatically from the datasets Hub). - - # In distributed training, the load_dataset function guarantees that only one local process can concurrently - # download the dataset. - if args.dataset_name is not None: - # Downloading and loading a dataset from the hub. - dataset = load_dataset( - args.dataset_name, - args.dataset_config_name, - cache_dir=args.cache_dir, - ) - else: - data_files = {} - if args.train_data_dir is not None: - data_files["train"] = os.path.join(args.train_data_dir, "**") - dataset = load_dataset( - "imagefolder", - data_files=data_files, - cache_dir=args.cache_dir, - ) - # See more about loading custom images at - # https://huggingface.co/docs/datasets/v2.4.0/en/image_load#imagefolder - - # Preprocessing the datasets. - # We need to tokenize inputs and targets. - column_names = dataset["train"].column_names - - # 6. Get the column names for input/target. - dataset_columns = dataset_name_mapping.get(args.dataset_name, None) - if args.image_column is None: - image_column = dataset_columns[0] if dataset_columns is not None else column_names[0] - else: - image_column = args.image_column - if image_column not in column_names: - raise ValueError( - f"--image_column' value '{args.image_column}' needs to be one of: {', '.join(column_names)}" - ) - if args.caption_column is None: - caption_column = dataset_columns[1] if dataset_columns is not None else column_names[1] - else: - caption_column = args.caption_column - if caption_column not in column_names: - raise ValueError( - f"--caption_column' value '{args.caption_column}' needs to be one of: {', '.join(column_names)}" - ) - - # Preprocessing the datasets. - # We need to tokenize input captions and transform the images. - def tokenize_captions(examples, is_train=True): - captions = [] - for caption in examples[caption_column]: - if isinstance(caption, str): - captions.append(caption) - elif isinstance(caption, (list, np.ndarray)): - # take a random caption if there are multiple - captions.append(random.choice(caption) if is_train else caption[0]) - else: - raise ValueError( - f"Caption column `{caption_column}` should contain either strings or lists of strings." - ) - inputs = tokenizer( - captions, max_length=tokenizer.model_max_length, padding="max_length", truncation=True, - return_tensors="pt" - ) - return inputs.input_ids - - # Preprocessing the datasets. - train_transforms = transforms.Compose( - [ - transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR), - transforms.CenterCrop(args.resolution) if args.center_crop else transforms.RandomCrop( - args.resolution), - transforms.RandomHorizontalFlip() if args.random_flip else transforms.Lambda( - lambda x: x), - transforms.ToTensor(), - transforms.Normalize([0.5], [0.5]), - ] - ) - - def preprocess_train(examples): - images = [image.convert("RGB") for image in examples[image_column]] - examples["pixel_values"] = [train_transforms(image) for image in images] - examples["input_ids"] = tokenize_captions(examples) - return examples - - with accelerator.main_process_first(): - if args.max_train_samples is not None: - dataset["train"] = dataset["train"].shuffle(seed=args.seed).select( - range(args.max_train_samples)) - # Set the training transforms - train_dataset = dataset["train"].with_transform(preprocess_train) - - def collate_fn(examples): - pixel_values = torch.stack([example["pixel_values"] for example in examples]) - pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float() - input_ids = torch.stack([example["input_ids"] for example in examples]) - return {"pixel_values": pixel_values, "input_ids": input_ids} - - # DataLoaders creation: - train_dataloader = torch.utils.data.DataLoader( - train_dataset, shuffle=True, collate_fn=collate_fn, batch_size=args.train_batch_size - ) - - # Scheduler and math around the number of training steps. - overrode_max_train_steps = False - num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) - if args.max_train_steps is None: - args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch - overrode_max_train_steps = True - - lr_scheduler = get_scheduler( - args.lr_scheduler, - optimizer=optimizer, - num_warmup_steps=args.lr_warmup_steps * args.gradient_accumulation_steps, - num_training_steps=args.max_train_steps * args.gradient_accumulation_steps, - ) - - # Prepare everything with our `accelerator`. - unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare( - unet, optimizer, train_dataloader, lr_scheduler - ) - if args.use_ema: - accelerator.register_for_checkpointing(ema_unet) - - # For mixed precision training we cast the text_encoder and vae weights to half-precision - # as these models are only used for inference, keeping weights in full precision is not required. - weight_dtype = torch.float32 - if accelerator.mixed_precision == "fp16": - weight_dtype = torch.float16 - elif accelerator.mixed_precision == "bf16": - weight_dtype = torch.bfloat16 - - # Move text_encode and vae to gpu and cast to weight_dtype - text_encoder.to(accelerator.device, dtype=weight_dtype) - vae.to(accelerator.device, dtype=weight_dtype) - if args.use_ema: - ema_unet.to(accelerator.device) - - # We need to recalculate our total training steps as the size of the training dataloader may have changed. - num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) - if overrode_max_train_steps: - args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch - # Afterwards we recalculate our number of training epochs - args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch) - - # We need to initialize the trackers we use, and also store our configuration. - # The trackers initializes automatically on the main process. - if accelerator.is_main_process: - accelerator.init_trackers("text2image-fine-tune", config=vars(args)) - - # Train! - total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps - - logger.info("***** Running training *****") - logger.info(f" Num examples = {len(train_dataset)}") - logger.info(f" Num Epochs = {args.num_train_epochs}") - logger.info(f" Instantaneous batch size per device = {args.train_batch_size}") - logger.info( - f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}") - logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}") - logger.info(f" Total optimization steps = {args.max_train_steps}") - global_step = 0 - first_epoch = 0 - - # Potentially load in the weights and states from a previous save - if args.resume_from_checkpoint: - if args.resume_from_checkpoint != "latest": - path = os.path.basename(args.resume_from_checkpoint) - else: - # Get the most recent checkpoint - dirs = os.listdir(args.output_dir) - dirs = [d for d in dirs if d.startswith("checkpoint")] - dirs = sorted(dirs, key=lambda x: int(x.split("-")[1])) - path = dirs[-1] - accelerator.print(f"Resuming from checkpoint {path}") - accelerator.load_state(os.path.join(args.output_dir, path)) - global_step = int(path.split("-")[1]) - - first_epoch = global_step // num_update_steps_per_epoch - resume_step = global_step % num_update_steps_per_epoch - - # Only show the progress bar once on each machine. - progress_bar = tqdm(range(global_step, args.max_train_steps), - disable=not accelerator.is_local_main_process) - progress_bar.set_description("Steps") - - for epoch in range(first_epoch, args.num_train_epochs): - unet.train() - train_loss = 0.0 - for step, batch in enumerate(train_dataloader): - # Skip steps until we reach the resumed step - if args.resume_from_checkpoint and epoch == first_epoch and step < resume_step: - if step % args.gradient_accumulation_steps == 0: - progress_bar.update(1) - continue - - with accelerator.accumulate(unet): - # Convert images to latent space - latents = vae.encode(batch["pixel_values"].to(weight_dtype)).latent_dist.sample() - latents = latents * 0.18215 - - # Sample noise that we'll add to the latents - noise = torch.randn_like(latents) - bsz = latents.shape[0] - # Sample a random timestep for each image - timesteps = torch.randint(0, noise_scheduler.num_train_timesteps, (bsz,), - device=latents.device) - timesteps = timesteps.long() - - # Add noise to the latents according to the noise magnitude at each timestep - # (this is the forward diffusion process) - noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps) - - # Get the text embedding for conditioning - encoder_hidden_states = text_encoder(batch["input_ids"])[0] - - # Get the target for loss depending on the prediction type - if noise_scheduler.config.prediction_type == "epsilon": - target = noise - elif noise_scheduler.config.prediction_type == "v_prediction": - target = noise_scheduler.get_velocity(latents, noise, timesteps) - else: - raise ValueError( - f"Unknown prediction type {noise_scheduler.config.prediction_type}") - - # Predict the noise residual and compute loss - model_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample - loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean") - - # Gather the losses across all processes for logging (if we use distributed training). - avg_loss = accelerator.gather(loss.repeat(args.train_batch_size)).mean() - train_loss += avg_loss.item() / args.gradient_accumulation_steps - - # Backpropagate - accelerator.backward(loss) - if accelerator.sync_gradients: - accelerator.clip_grad_norm_(unet.parameters(), args.max_grad_norm) - optimizer.step() - lr_scheduler.step() - optimizer.zero_grad() - - # Checks if the accelerator has performed an optimization step behind the scenes - if accelerator.sync_gradients: - if args.use_ema: - ema_unet.step(unet.parameters()) - progress_bar.update(1) - global_step += 1 - accelerator.log({"train_loss": train_loss}, step=global_step) - train_loss = 0.0 - - if global_step % args.checkpointing_steps == 0: - if accelerator.is_main_process: - save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}") - accelerator.save_state(save_path) - logger.info(f"Saved state to {save_path}") - - logs = {"step_loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]} - progress_bar.set_postfix(**logs) - - if global_step >= args.max_train_steps: - break - - # Create the pipeline using the trained modules and save it. - accelerator.wait_for_everyone() - if accelerator.is_main_process: - unet = accelerator.unwrap_model(unet) - if args.use_ema: - ema_unet.copy_to(unet.parameters()) - - pipeline = StableDiffusionPipeline.from_pretrained( - args.pretrained_model_name_or_path, - text_encoder=text_encoder, - vae=vae, - unet=unet, - revision=args.revision, - ) - pipeline.save_pretrained(args.output_dir) - - if args.push_to_hub: - repo.push_to_hub(commit_message="End of training", blocking=False, auto_lfs_prune=True) - - accelerator.end_training() - - -if __name__ == "__main__": - main() diff --git a/examples/pipelines/finetune_stable_diffusion/components/sd_finetuning_component/src/utils/dataset_utils.py b/examples/pipelines/finetune_stable_diffusion/components/sd_finetuning_component/src/utils/dataset_utils.py deleted file mode 100644 index 379fcd05f..000000000 --- a/examples/pipelines/finetune_stable_diffusion/components/sd_finetuning_component/src/utils/dataset_utils.py +++ /dev/null @@ -1,99 +0,0 @@ -""" -Common huggingface utils -""" -import logging -import os - -# pylint: disable=import-error -from pyarrow.dataset import Scanner -import jsonlines - -from helpers import io_helpers, storage_helpers -from helpers.logger import get_logger - -logger = get_logger(name=__name__, level=logging.INFO) - - -# pylint: disable=too-few-public-methods -class SDDatasetCreator: - """ - Class that creates the dataset for SD - """ - - # pylint: disable=too-many-arguments - def __init__(self, tmp_path: str, img_path: str): - """ - Initialize parameters and dataset - Args: - tmp_path (str): path where to store temporary files - img_path (str): the path where to download the images to finetune - """ - self.tmp_path = tmp_path - self.img_path = img_path - self.metadata_file_path = os.path.join(self.img_path, 'metadata.jsonl') - - def _download_images_to_finetune(self, dataset_scanner: Scanner, - download_list_file_path: str) -> None: - """ - Function that writes downloads the images to finetune on locally - Args: - dataset_scanner (Scanner): a scanner of the dataset - download_list_file_path (str): the text files containing the list of uri to download - """ - - with open(download_list_file_path, "w") as download_list_file: - for batch in dataset_scanner.to_batches(): - files_uri_list = batch.to_pydict()['file_uri'] - download_list_file.write('\n'.join(files_uri_list)) - - storage_helpers.copy_files_bulk(download_list_file_path, self.img_path) - logger.info("Images to downloaded were downloaded to %s", self.img_path) - - def _write_hf_metadata(self, captions_scanner: Scanner) -> None: - """ - Function that writes the huggingface metadata file - Reference: - https://huggingface.co/docs/datasets/v2.4.0/en/image_load#imagefolder-with-metadata: - ~:text=in%20load_dataset.-,Image%20captioning,-Image%20captioning%20datasets - Args: - captions_scanner (Scanner): the captions scanner - """ - # We join the captions at this stage since it might create duplicates if there are - # multiple captions per image, we do not want to write them to the text containing the - # gcs images to download (avoid downloading the same image multiple times) - - with jsonlines.open(self.metadata_file_path, 'a') as writer: - for table_chunk in captions_scanner.to_batches(): - table_dict = table_chunk.to_pydict() - # Return file name - table_dict['file_uri'] = \ - [io_helpers.get_file_name(file_uri, return_extension=True) for file_uri in - table_dict['file_uri']] - # change format to match the expected value of HF metadata dataset - # {"file_name":, "text": } - table_dict['file_name'] = table_dict.pop('file_uri') - table_dict['text'] = table_dict.pop('file_captions') - # Filter dict to keep only field of interest to create the dataset metadata file - table_dict = {key: table_dict[key] for key in ['file_name', 'text']} - # Change dict of lists to list of dicts to write them to json new lines in batch - table_dict = [dict(zip(table_dict, t)) for t in zip(*table_dict.values())] - # Unpack caption list to have separate rows per caption (required HF format to have - # image/caption pair) - table_dict = [{"file_name": row['file_name'], "text": caption} for row in table_dict - for caption in row['text']] - # pylint: disable=no-member - writer.write_all(table_dict) - - logger.info("Huggingface metadata file written to %s", self.metadata_file_path) - - def write_dataset(self, dataset_scanner: Scanner, captions_scanner: Scanner, namespace: str): - """Create the dataset by downloading the images and creating the huggingface dataset - metadata file - Args: - dataset_scanner (Scanner): the filtered dataset scanner - captions_scanner (Scanner): the filtered captions scanner - namespace (str): the dataset namespace - """ - download_list_file_path = os.path.join(self.tmp_path, f"download_list_gcs_{namespace}.txt") - self._download_images_to_finetune(dataset_scanner, download_list_file_path) - self._write_hf_metadata(captions_scanner) diff --git a/examples/pipelines/finetune_stable_diffusion/components/sd_finetuning_component/src/utils/training_utils.py b/examples/pipelines/finetune_stable_diffusion/components/sd_finetuning_component/src/utils/training_utils.py deleted file mode 100644 index b1a157c32..000000000 --- a/examples/pipelines/finetune_stable_diffusion/components/sd_finetuning_component/src/utils/training_utils.py +++ /dev/null @@ -1,107 +0,0 @@ -"""Checkpoint utils""" -import os -import subprocess # nosec -import logging -from shutil import rmtree -from typing import List - -from helpers.logger import get_logger -from helpers import storage_helpers - -logger = get_logger(name=__name__, level=logging.INFO) - - -def sorted_checkpoints(gcs_blobs: List[str]) -> List[str]: - """ - Function to sort training checkpoints - Args: - gcs_blobs(List[str]): list of gcs blobs containing checkpoints - Returns: - List[str]: a list of available checkpoints in descending order - (e.g. [checkpoint-1000, checkpoint-500, ...]) - """ - available_checkpoints = [] - for p in gcs_blobs: - if not p: - continue - p = p.strip('/').split('/')[-1] - if p.startswith('checkpoint-'): - available_checkpoints.append(p) - available_checkpoints.sort(key=lambda p: int(p.split('checkpoint-')[1]), reverse=True) - return available_checkpoints - - -def download_checkpoint_to_resume_from(resume_from_checkpoint: str, pretrained_model_gcs_path: str, - finetuned_model_path: str) -> str: - """ - Function that downloads the checkpoint to resume from to a local gcs directory - Args: - resume_from_checkpoint (str): Whether training should be resumed from a previous - checkpoint. Use a path saved in `pretrained_model_gcs_path` by `--checkpointing_steps`, - or `"latest"` to automatically select the last available checkpoint. - pretrained_model_gcs_path (str): the gcs path of the pretrained model where the - checkpoints are created - finetuned_model_path (str): the path to the finetuned model where the checkpoints are - created - Returns: - str: the checkpoint prefix (e.g. 'checkpoint-500') to resume from - """ - - if resume_from_checkpoint == "latest": - possible_checkpoints = \ - subprocess.run(["gsutil", "ls", pretrained_model_gcs_path], # nosec - capture_output=True, check=True).stdout.decode().split('\n') - checkpoint_prefix = \ - sorted_checkpoints(possible_checkpoints)[0] - else: - checkpoint_prefix = resume_from_checkpoint - - logger.info( - f"Downloading checkpoint {resume_from_checkpoint} from {pretrained_model_gcs_path}.") - downloaded_checkpoint_path = os.path.join(finetuned_model_path, resume_from_checkpoint) - os.makedirs(downloaded_checkpoint_path, exist_ok=True) - storage_helpers.copy_folder_bulk( - os.path.join(pretrained_model_gcs_path, resume_from_checkpoint), - downloaded_checkpoint_path) - - return checkpoint_prefix - - -def sync_training_checkpoints(finetuned_model_path: str, finetuned_model_gcs_uri: str): - """ - Function that sync local created checkpoints during training with gcs - Args: - finetuned_model_path (str): the path to the finetuned model where the checkpoints are - created - finetuned_model_gcs_uri (str): the gcs uri where the model will be uploaded - """ - if os.path.exists(finetuned_model_path): - logger.info(f"Syncing checkpoints to {finetuned_model_gcs_uri}. " - f"Folder contents: {os.listdir(finetuned_model_path)}") - subprocess.run(['gsutil', '-m', 'rsync', '-r', finetuned_model_path, # nosec - finetuned_model_gcs_uri]) - - # Clean up older checkpoints. They'll remain available in cloud storage, but we want to - # avoid running out of ephemeral storage. - available_checkpoints = sorted_checkpoints(os.listdir(finetuned_model_path)) - for to_delete in available_checkpoints[1:]: - logger.info(f"Local cleanup of old checkpoint: {to_delete}") - rmtree(os.path.join(finetuned_model_path, to_delete), onerror=logger.info) - else: - logger.info( - f"Checked {finetuned_model_path} for checkpoints, but path doesn't exist yet.") - - -def download_model_without_checkpoints(pretrained_model_gcs_path: str, finetuned_model_path: str): - """ - Function that copies a pretrained model (without checkpoints) - Args: - pretrained_model_gcs_path (str): the gcs path to the pretrained model which may contain - checkpoints - finetuned_model_path (str): the local path to the finetuned model where to download the - model - """ - subprocess.run(['gsutil', '-m', 'rsync', '-r', '-x', 'checkpoint*', # nosec - pretrained_model_gcs_path, finetuned_model_path]) - logger.info( - f"Model downloaded from '{pretrained_model_gcs_path}' to '{finetuned_model_path}'.") diff --git a/examples/pipelines/finetune_stable_diffusion/config/dataset_loader_config.py b/examples/pipelines/finetune_stable_diffusion/config/dataset_loader_config.py new file mode 100644 index 000000000..763efc713 --- /dev/null +++ b/examples/pipelines/finetune_stable_diffusion/config/dataset_loader_config.py @@ -0,0 +1,14 @@ +from dataclasses import dataclass + +@dataclass +class DatasetLoaderConfig: + """ + Configs for the dataset loader component + Params: + BUCKET_NAME (str): The GCS bucket containing the initial dataset to load + BLOB_NAME (str): the zone of the k8 cluster hosting KFP + NAMESPACE (str): The dataset namespace (abbreviation for data source) + """ + BUCKET_NAME = "express-datasets" + BLOB_NAME = "initial-clean-cut-dataset" + NAMESPACE = "cf" \ No newline at end of file diff --git a/examples/pipelines/finetune_stable_diffusion/config/general_config.py b/examples/pipelines/finetune_stable_diffusion/config/general_config.py new file mode 100644 index 000000000..f54635e3e --- /dev/null +++ b/examples/pipelines/finetune_stable_diffusion/config/general_config.py @@ -0,0 +1,33 @@ +"""General config""" + +import os + +from dataclasses import dataclass + +@dataclass +class GeneralConfig: + """ + General configs + Params: + GCP_PROJECT_ID (str): GCP project ID + DATASET_NAME (str): name of the Hugging Face dataset + ENV (str): the project run environment (sbx, dev, prd) + """ + GCP_PROJECT_ID = "soy-audio-379412" + ENV = os.environ.get('ENV', 'dev') + + +@dataclass +class KubeflowConfig(GeneralConfig): + """ + Configs for the Kubeflow cluster + Params: + ARTIFACT_BUCKET (str): the GCS bucket used to store the artifacts + CLUSTER_NAME (str): the name of the k8 cluster hosting KFP + CLUSTER_ZONE (str): the zone of the k8 cluster hosting KFP + HOST (str): the kfp host url + """ + ARTIFACT_BUCKET = f"{GeneralConfig.GCP_PROJECT_ID}_kfp-artifacts" + CLUSTER_NAME = "kfp-express" + CLUSTER_ZONE = "europe-west4-a" + HOST = "https://472c61c751ab9be9-dot-europe-west1.pipelines.googleusercontent.com" diff --git a/examples/pipelines/finetune_stable_diffusion/dataset_creation_pipeline.py b/examples/pipelines/finetune_stable_diffusion/dataset_creation_pipeline.py index 4b6b290a8..1ded3877c 100644 --- a/examples/pipelines/finetune_stable_diffusion/dataset_creation_pipeline.py +++ b/examples/pipelines/finetune_stable_diffusion/dataset_creation_pipeline.py @@ -1,42 +1,24 @@ """Pipeline used to create a stable diffusion dataset from a set of given images. This is done by using clip retrieval on the LAION dataset""" # pylint: disable=import-error -import sys -import os -import logging - -sys.path.insert(0, os.path.abspath('..')) - -from config import GeneralConfig, KubeflowConfig -from pipelines_config.dataset_creation_config import DatasetLoaderConfig, ImageFilterConfig, \ - ImageConversionConfig, ImageEmbeddingConfig, ImageCaptionConfig, ClipRetrievalConfig, \ - ClipDownloaderConfig -from express.pipeline_utils import compile_and_upload_pipeline -from express.logger import configure_logging +import json from kfp import components as comp from kfp import dsl -from kubernetes import client as k8s_client -configure_logging() +from config.general_config import GeneralConfig, KubeflowConfig +from config.dataset_loader_config import DatasetLoaderConfig +from express.kfp_utils import compile_and_upload_pipeline -LOGGER = logging.getLogger(__name__) +# Load Components +run_id = '{{workflow.name}}' +artifact_bucket = KubeflowConfig.ARTIFACT_BUCKET -# Load Component -dataset_loader_component = comp.load_component( - 'components/dataset_loader_component/component.yaml') -image_filter_component = comp.load_component( - 'components/image_filter_component/component.yaml') -image_conversion_component = comp.load_component( - 'components/image_conversion_component/component.yaml') -image_embedding_component = comp.load_component( - 'components/image_embedding_component/component.yaml') -clip_retrieval_component = comp.load_component( - 'components/clip_retrieval_component/component.yaml') -clip_downloader_component = comp.load_component( - 'components/clip_downloader_component/component.yaml') -image_caption_component = comp.load_component( - 'components/image_caption_component/component.yaml') +dataset_loader_op = comp.load_component('components/dataset_loader_component/component.yaml') +dataset_loader_extra_args = {"project_id": GeneralConfig.GCP_PROJECT_ID, "bucket": DatasetLoaderConfig.BUCKET_NAME} +dataset_loader_metadata_args = {"run_id": run_id, "component_name": dataset_loader_op.__name__, "artifact_bucket": artifact_bucket} +dataset_loader_extra_args = json.dumps(dataset_loader_extra_args) +dataset_loader_metadata_args = json.dumps(dataset_loader_metadata_args) # Pipeline @@ -46,186 +28,13 @@ 'similar images as outputs' ) # pylint: disable=too-many-arguments, too-many-locals -def sd_dataset_creator_pipeline( - source_dataset_bucket: str = DatasetLoaderConfig.SOURCE_DATASET_BUCKET, - project_id: str = GeneralConfig.GCP_PROJECT_ID, - source_dataset_blob: str = DatasetLoaderConfig.SOURCE_DATASET_BLOB, - namespace: str = DatasetLoaderConfig.NAMESPACE, - max_file_size: int = ImageFilterConfig.MAX_FILE_SIZE, - min_file_size: int = ImageFilterConfig.MIN_FILE_SIZE, - image_formats: list = ImageFilterConfig.IMAGE_FORMATS, - file_extensions: list = ImageConversionConfig.FILE_EXTENSIONS, - svg_image_width: int = ImageConversionConfig.SVG_IMAGE_WIDTH, - svg_image_height: int = ImageConversionConfig.SVG_IMAGE_HEIGHT, - clip_batch_size: int = ImageEmbeddingConfig.BATCH_SIZE, - laion_index_url: str = ClipRetrievalConfig.LAION_INDEX_URL, - laion_metadata_url: str = ClipRetrievalConfig.LAION_METADATA_URL, - nb_images_knn: int = ClipRetrievalConfig.NB_IMAGES_KNN, - nb_images_centroid: int = ClipRetrievalConfig.NB_IMAGES_CENTROID, - image_resize: int = ClipDownloaderConfig.IMAGE_RESIZE, - timeout: int = ClipDownloaderConfig.TIMEOUT, - min_image_size: int = ClipDownloaderConfig.MIN_IMAGE_SIZE, - max_image_area: int = ClipDownloaderConfig.MAX_IMAGE_AREA, - min_length: int = ImageCaptionConfig.MIN_LENGTH, - max_length: int = ImageCaptionConfig.MAX_LENGTH, - blip_batch_size: int = ImageCaptionConfig.BATCH_SIZE, - beams: int = ImageCaptionConfig.BEAMS +def sd_dataset_creator_pipeline(dataset_loader_extra_args: str = dataset_loader_extra_args, + dataset_loader_metadata_args: str = dataset_loader_metadata_args, ): - """ - Pipeline that takes example images as input and returns an expanded dataset of - similar images as outputs - Args: - # General - project_id (str): project ID string - # Dataset loader component - source_dataset_bucket (str): The GCS bucket containing the dataset to load - source_dataset_blob (str): The GCS blob withing the specified bucket containing the - dataset to load - namespace (str): The dataset namespace (abbreviation for data source) - # Dataset filter component - max_file_size (int): The maximum size of an image (filter) - min_file_size (int): The minimum size of an image (filter) - image_formats (list): The image formats to keep (filter) - # Dataset conversion component - file_extensions (list): The list of image file extensions to convert - svg_image_width (int): the desired width to scale the converted SVG image to - svg_image_height (int): the desired width to scale the converted SVG image to - # Dataset embedding component - clip_batch_size (int): the bath size used to batch the images before embedding - # Clip retrieval component - laion_index_url (str): contains the indices of the metadata. Those indices need to be - transformed in case you decide to use only a subset of the dataset - laion_metadata_url (str): url to the metadata of laion dataset metadata (arrow format). - It can either contain a subset of the laion 5b metadata (e.g. laion-en) or all of the - metadata - nb_images_knn (int): The number of images to return with knn method (per image) - nb_images_centroid (int): The number of images to return with centroid method - # Clip downloader component - image_resize (int): the size to resize the image - timeout (int): maximum time (in seconds to wait) when trying to download an image - min_image_size (int): minimum size of the image to download - (considers the min of width and height) - max_image_area (int): The maximum area (nr of pixels) of the images to download - # Dataset caption component - min_length (str): The minimum caption length to generate - max_length (str): the maximum caption length to generate - blip_batch_size (int): the batch size of the images to caption - beams (int): The blip beam parameters - """ - # pylint: disable=not-callable,unused-variable - run_id = '{{pod.name}}' - artifact_bucket = KubeflowConfig.ARTIFACT_BUCKET - - # Define necessary volume mounts (local ssd) - local_ssd_volume = dsl.PipelineVolume(volume=k8s_client.V1Volume( - name="scratch-volume", - empty_dir=k8s_client.V1EmptyDirVolumeSource())) - - # Define components - dataset_loader_task = dataset_loader_component( - run_id=run_id, - artifact_bucket=artifact_bucket, - component_name=dataset_loader_component.__name__, - project_id=project_id, - source_dataset_bucket=source_dataset_bucket, - source_dataset_blob=source_dataset_blob, - namespace=namespace).set_display_name('Load Images') - - image_filter_task = image_filter_component( - run_id=run_id, - artifact_bucket=artifact_bucket, - component_name=image_filter_component.__name__, - project_id=project_id, - max_file_size=max_file_size, - min_file_size=min_file_size, - image_formats=image_formats, - data_manifest_path=dataset_loader_task.outputs['data_manifest_path']) \ - .set_display_name('Filter Images') - - image_conversion_task = image_conversion_component( - run_id=run_id, - artifact_bucket=artifact_bucket, - component_name=image_conversion_component.__name__, - project_id=project_id, - file_extensions=file_extensions, - svg_image_width=svg_image_width, - svg_image_height=svg_image_height, - data_manifest_path=image_filter_task.outputs['data_manifest_path_filter_component']) \ - .set_display_name('Convert Image Format') \ - .add_node_selector_constraint('node_pool', 'burst-zone') \ - .add_toleration( - k8s_client.V1Toleration(effect='NoSchedule', key='reserved-pool', operator='Equal', - value='true')) - - image_embedding_task = image_embedding_component( - run_id=run_id, - artifact_bucket=artifact_bucket, - component_name=image_embedding_component.__name__, - project_id=project_id, - batch_size=clip_batch_size, - data_manifest_path=image_conversion_task.outputs[ - 'data_manifest_path_image_conversion_component']) \ - .set_display_name('Embed Images') \ - .set_gpu_limit(1) \ - .add_node_selector_constraint('node_pool', 'model-inference-pool') \ - .add_toleration( - k8s_client.V1Toleration(effect='NoSchedule', key='reserved-pool', operator='Equal', - value='true')) - - clip_retrieval_task = clip_retrieval_component( - run_id=run_id, - artifact_bucket=artifact_bucket, - component_name=clip_retrieval_component.__name__, - project_id=project_id, - laion_index_url=laion_index_url, - laion_metadata_url=laion_metadata_url, - nb_images_knn=nb_images_knn, - nb_images_centroid=nb_images_centroid, - data_manifest_path=image_embedding_task.outputs[ - 'data_manifest_path_embedding_component']) \ - .set_display_name('Clip retrieval') \ - .set_ephemeral_storage_request('2T') \ - .add_pvolumes({'/cache': local_ssd_volume}) \ - .add_node_selector_constraint('node_pool', 'nvme-pool') - - clip_downloader_task = clip_downloader_component( - run_id=run_id, - artifact_bucket=artifact_bucket, - component_name=clip_downloader_component.__name__, - project_id=project_id, - image_resize=image_resize, - timeout=timeout, - min_image_size=min_image_size, - max_image_area=max_image_area, - data_manifest_path=clip_retrieval_task.outputs[ - 'data_manifest_path_clip_retrieval_component'], - parquet_path_clip_knn_retrieval=clip_retrieval_task.outputs[ - 'parquet_path_clip_knn_retrieval'], - parquet_path_clip_centroid_retrieval=clip_retrieval_task.outputs[ - 'parquet_path_clip_centroid_retrieval']) \ - .set_display_name('Clip Image downloader') \ - .add_node_selector_constraint('node_pool', 'burst-zone') \ - .add_toleration(k8s_client.V1Toleration - (effect='NoSchedule', key='reserved-pool', operator='Equal', - value='true')) - - image_caption_task = image_caption_component( - run_id=run_id, - artifact_bucket=artifact_bucket, - component_name=image_caption_component.__name__, - project_id=project_id, - min_length=min_length, - max_length=max_length, - batch_size=blip_batch_size, - beams=beams, - data_manifest_path=clip_downloader_task.outputs[ - 'data_manifest_path_clip_downloader_component']) \ - .set_display_name('Caption Images') \ - .set_gpu_limit(1) \ - .add_node_selector_constraint('node_pool', 'model-inference-pool') \ - .add_toleration( - k8s_client.V1Toleration(effect='NoSchedule', key='reserved-pool', operator='Equal', - value='true')) + # Component 1 + dataset_loader_task = dataset_loader_op(extra_args=dataset_loader_extra_args, + metadata_args=dataset_loader_metadata_args, + ).set_display_name('Load initial images') if __name__ == '__main__': diff --git a/examples/pipelines/finetune_stable_diffusion/pipelines_config/dataset_creation_config.py b/examples/pipelines/finetune_stable_diffusion/pipelines_config/dataset_creation_config.py deleted file mode 100644 index 454df0619..000000000 --- a/examples/pipelines/finetune_stable_diffusion/pipelines_config/dataset_creation_config.py +++ /dev/null @@ -1,109 +0,0 @@ -"""Dataset creation pipeline config""" - -from dataclasses import dataclass - - -@dataclass -class DatasetLoaderConfig: - """ - Configs for the dataset loader component - Params: - SOURCE_DATASET_BUCKET (str): The GCS bucket containing the dataset to load - SOURCE_DATASET_BLOB (str): the zone of the k8 cluster hosting KFP - NAMESPACE (str): The dataset namespace (abbreviation for data source) - """ - SOURCE_DATASET_BUCKET = "express-datasets" - SOURCE_DATASET_BLOB = "initial-clean-cut-dataset" - NAMESPACE = "cf" - - -@dataclass -class ImageFilterConfig: - """ - Configs for the dataset filter component - Params: - MIN_FILE_SIZE (str): The minimum size of an image in bytes (filter) - MAX_FILE_SIZE (str): The maximum size of an image in bytes (filter) - IMAGE_FORMATS (list): The image formats to keep, any other formats that are not included - will be filtered from the dataset - """ - MIN_FILE_SIZE = 100_000 # 100kb - MAX_FILE_SIZE = 5_000_000 # 5mb - # Image formats notation from 'type' in field in GCS - IMAGE_FORMATS = ['jpeg', 'jpg', 'png', 'svg'] - - -@dataclass -class ImageConversionConfig: - """ - Configs for dataset image converter component - Params: - FILE_EXTENSIONS (list): The list of image file extensions to convert from - SVG_IMAGE_WIDTH (int): the desired width to scale the converted SVG image to - SVG_IMAGE_HEIGHT (int): the desired width to scale the converted SVG image to - """ - FILE_EXTENSIONS = ['png', 'svg'] - SVG_IMAGE_WIDTH = 1024 - SVG_IMAGE_HEIGHT = 1024 - - -@dataclass -class ImageEmbeddingConfig: - """ - Configs for dataset image embedding component - Params: - BATCH_SIZE (int): the batch size used to batch the images before embedding - """ - BATCH_SIZE = 8 - - -@dataclass -class ClipRetrievalConfig: - """ - Configs for dataset image converter component - Params: - LAION_INDEX_URL(str): contains the indices of the metadata. Those indices need to be - transformed in case you decide to use only a subset of the dataset - LAION_METADATA_URL (str): url to the metadata of laion dataset metadata (arrow format). It - can either contain a subset of the laion 5b metadata (e.g. laion-en) or all of the metadata - NB_IMAGES_KNN (int): The ratio of number of image to retrieve via the knn strategy - (per image) - NB_IMAGES_CENTROID (int): The ratio of number of image to retrieve via the centroid strategy - """ - LAION_INDEX_URL = "gs://express-sd-datasets/laion-5b/2b-en/image.index/*" - LAION_METADATA_URL = "gs://express-sd-datasets/laion-5b/metadata/metadata/2B-en.arrow" - NB_IMAGES_KNN = 500 - NB_IMAGES_CENTROID = 1_000_000 - - -@dataclass -class ClipDownloaderConfig: - """ - Configs for dataset image converter component - Params: - IMAGE_RESIZE (int): the size to resize the image - TIMEOUT (int): maximum time (in seconds to wait) when trying to download an image - MIN_IMAGE_SIZE (int): minimum size of the image to download - (considers the min of width and height) - MAX_IMAGE_AREA (int): The maximum area (nr of pixels) of the images to download - """ - IMAGE_RESIZE = 512 - TIMEOUT = 5 - MIN_IMAGE_SIZE = 100 - MAX_IMAGE_AREA = 178956870 - - -@dataclass -class ImageCaptionConfig: - """ - Configs for dataset image converter component - Params: - MIN_LENGTH (str): The minimum caption length - MAX_LENGTH (str): the maximum caption length - BEAMS (int): The blip beam parameters - BATCH_SIZE (int): The batch size of images to pass to the blip model - """ - MIN_LENGTH = 10 - MAX_LENGTH = 20 - BATCH_SIZE = 100 - BEAMS = 1 diff --git a/examples/pipelines/finetune_stable_diffusion/pipelines_config/sd_finetuning_config.py b/examples/pipelines/finetune_stable_diffusion/pipelines_config/sd_finetuning_config.py deleted file mode 100644 index 2ef71469e..000000000 --- a/examples/pipelines/finetune_stable_diffusion/pipelines_config/sd_finetuning_config.py +++ /dev/null @@ -1,71 +0,0 @@ -"""Stable diffusion pipeline config""" - -from dataclasses import dataclass - - -# pylint: disable=too-many-instance-attributes -@dataclass -class StableDiffusionFinetuningConfig: - """ - Configs for dataset image converter component - Params: - DATA_MANIFEST_GCS_PATH (str): the path to the data manifest that contains information on the - training set - pretrained_model_gcs_path (str): Model identifier from huggingface.co/models or gcs path to a model - SEED (int): A seed for reproducible training. - RESOLUTION (int): The resolution for input images, all the images in the train/validation - dataset will be resized to this resolution - TRAIN_BATCH_SIZE (int): Batch size (per device) for the training dataloader - NUM_TRAIN_EPOCHS (int): Total number of epochs to perform - MAX_TRAIN_STEPS (int): Total number of training steps to perform. If provided overrides - `num_train_epochs` - CHECKPOINTING_STEPS (int): Save a checkpoint of the training state every X updates. These checkpoints are only - suitable for resuming training using `--resume_from_checkpoint`. - GRADIENT_ACCUMULATION_STEPS (int): The number of updates steps to accumulate before - performing a backward/update pass - GRADIENT_CHECKPOINTING (Union[str,None]): Whether to use gradient checkpointing to save memory - at the expense of slower backward pass - LEARNING_RATE (float): Initial learning rate (after the potential warmup period) to use. - SCALE_LR (Union[str,None]): Scale the learning rate by the number of GPUs, gradient accumulation steps, - and batch size - LR_WARMUP_STEPS (int): Scale the learning rate by the number of GPUs, gradient accumulation - steps, and batch size - LR_SCHEDULER (str): The scheduler type to use. Choose between ["linear", "cosine", - cosine_with_restarts", "polynomial", "constant","constant_with_warmup"] - USE_EMA (Union[str,None]): Whether to use EMA model - MIXED_PRECISION (str): Whether to use mixed precision. Choose between fp16 and bf16 - (bfloat16). Bf16 requires PyTorch >=1.10.and an Nvidia Ampere GPU. - Default to the value of accelerate config of the current system or the flag passed with the - `accelerate.launch` command. Use this argument to override the `accelerate` config - CENTER_CROP (Union[str,None]): whether to center crop images before resizing to resolution (if not set, - random crop will be used) - RANDOM_FLIP (Union[str,None]): whether to randomly flip images horizontally - - """ - DATA_MANIFEST_GCS_PATH = "gs://storied-landing-366912-kfp-output/artifacts/image-generator-dataset-mlfdr/2022/12/21/image-generator-dataset-mlfdr-1067088695/image-caption-component-data_manifest_path_caption_component.tgz" - PRETRAINED_MODEL_GCS_PATH = "gs://express-models/stable-diffusion-v1-5-fp32" - # TODO: Only the most relevant params from ":https://github.com/huggingface/diffusers/blob/ - # main/examples/ text_to_image/train_text_to_image.py" were specified here. Check later whether - # to include additional relevant arguments (right now it uses the default arguments) - SEED = 1024 - RESOLUTION = 512 - # Batch size of 4 is the maximum batch size that can be set to train when training on two A100s - # GPUs without running in 'Out of memory' issues - TRAIN_BATCH_SIZE = 4 - NUM_TRAIN_EPOCHS = 100 - MAX_TRAIN_STEPS = 25 # overwrites training epochs if defined - CHECKPOINTING_STEPS = 10 - GRADIENT_ACCUMULATION_STEPS = 4 - GRADIENT_CHECKPOINTING = "True" # "True" or None* - LEARNING_RATE = 1e-5 - SCALE_LR = None # "True" or None* - LR_WARMUP_STEPS = 0 - LR_SCHEDULER = "constant" - USE_EMA = "True" # "True" or None* - MIXED_PRECISION = "fp16" - CENTER_CROP = "True" # "True" or None* - RANDOM_FLIP = "True" # "True" or None* - RESUME_FROM_CHECKPOINT = None -# *Kubeflow does not support passing in boolean variables (bug where True is always returned). -# Workaround for optional arguments implemented where "True" is passed as a string to include the -# argument and None is passed to omit the argument diff --git a/examples/pipelines/finetune_stable_diffusion/sd_finetuning_pipeline.py b/examples/pipelines/finetune_stable_diffusion/sd_finetuning_pipeline.py deleted file mode 100644 index 53085d292..000000000 --- a/examples/pipelines/finetune_stable_diffusion/sd_finetuning_pipeline.py +++ /dev/null @@ -1,138 +0,0 @@ -"""Pipeline used to create a stable diffusion dataset from a set of given images. This is done by -using clip retrieval on the LAION dataset""" -# pylint: disable=import-error -import logging -import sys -import os -from typing import Optional - -sys.path.insert(0, os.path.abspath('..')) - -from config import GeneralConfig, KubeflowConfig -from pipelines_config.sd_finetuning_config import StableDiffusionFinetuningConfig as SDConfig -from express.pipeline_utils import compile_and_upload_pipeline -from express.logger import configure_logging - -from kfp import components as comp -from kfp import dsl -from kubernetes import client as k8s_client - -configure_logging() - -LOGGER = logging.getLogger(__name__) - -sd_finetuning_component = comp.load_component( - 'components/sd_finetuning_component/component.yaml') - - -# Pipeline -@dsl.pipeline( - name='Stable Diffusion finetuning pipeline', - description='Pipeline that takes a data manifest as input, loads in the dataset and starts ' - 'finetuning a stable diffusion model.' -) -# pylint: disable=too-many-arguments, too-many-locals -def sd_finetuning_pipeline( - data_manifest_gcs_path: str = SDConfig.DATA_MANIFEST_GCS_PATH, - pretrained_model_gcs_path: str = SDConfig.PRETRAINED_MODEL_GCS_PATH, - resume_from_checkpoint: Optional[str] = SDConfig.RESUME_FROM_CHECKPOINT, - seed: int = SDConfig.SEED, - resolution: int = SDConfig.RESOLUTION, - train_batch_size: int = SDConfig.TRAIN_BATCH_SIZE, - num_train_epochs: int = SDConfig.NUM_TRAIN_EPOCHS, - max_train_steps: Optional[int] = SDConfig.MAX_TRAIN_STEPS, - checkpointing_steps: Optional[int] = SDConfig.CHECKPOINTING_STEPS, - gradient_accumulation_steps: int = SDConfig.GRADIENT_ACCUMULATION_STEPS, - gradient_checkpointing: Optional[str] = SDConfig.GRADIENT_CHECKPOINTING, - learning_rate: float = SDConfig.LEARNING_RATE, - scale_lr: Optional[str] = SDConfig.SCALE_LR, - lr_scheduler: str = SDConfig.LR_SCHEDULER, - lr_warmup_steps: int = SDConfig.LR_WARMUP_STEPS, - use_ema: Optional[str] = SDConfig.USE_EMA, - mixed_precision: str = SDConfig.MIXED_PRECISION, - center_crop: Optional[str] = SDConfig.CENTER_CROP, - random_flip: Optional[str] = SDConfig.RANDOM_FLIP): - """ - Pipeline that takes example images as input and returns an expanded dataset of - similar images as outputs - configs for dataset image converter component - Args: - data_manifest_gcs_path (str): the path to the data manifest that contains information on the - training set - pretrained_model_gcs_path (str): the gcs path where the base model to finetune - is located - checkpointing_gcs_path (Optional[str]): Optional gcs path where the checkpoint to continue - finetuning is located - resume_from_checkpoint (Optional[str]): Whether training should be resumed from a previous - checkpoint. Use a path saved in `pretrained_model_gcs_path` by `--checkpointing_steps`, or - `"latest"` to automatically select the last available checkpoint. - seed (int): a seed for reproducible training. - resolution (int): The resolution for input images, all the images in the train/validation - dataset will be resized to this resolution - train_batch_size (int): batch size (per device) for the training dataloader - num_train_epochs (int): total number of epochs to perform - max_train_steps (int): total number of training steps to perform. if provided overrides - `num_train_epochs` - checkpointing_steps (Optional[int]): Save a checkpoint of the training state every X - updates.These checkpoints are only suitable for resuming training using - `--resume_from_checkpoint`.gradient_accumulation_steps (int): - the number of updates steps to accumulate before performing a backward/update pass - gradient_checkpointing (bool): whether to use gradient checkpointing to save memory - at the expense of slower backward pass - learning_rate (float): initial learning rate (after the potential warmup period) to use. - scale_lr (bool): scale the learning rate by the number of gpus, gradient accumulation steps, - and batch size - lr_warmup_steps (int): scale the learning rate by the number of gpus, gradient accumulation - steps, and batch size - lr_scheduler (str): the scheduler type to use. choose between ["linear", "cosine", - cosine_with_restarts", "polynomial", "constant","constant_with_warmup"] - use_ema (bool): whether to use ema model - mixed_precision (str): whether to use mixed precision. choose between fp16 and bf16 - (bfloat16). bf16 requires pytorch >=1.10.and an nvidia ampere gpu. - default to the value of accelerate config of the current system or the flag passed with the - `accelerate.launch` command. use this argument to override the accelerate config - center_crop (bool): whether to center crop images before resizing to resolution (if not set, - random crop will be used) - random_flip (bool): whether to randomly flip images horizontally - - """ - # pylint: disable=not-callable,unused-variable - run_id = '{{pod.name}}' - artifact_bucket = KubeflowConfig.ARTIFACT_BUCKET - - sd_finetuning_task = sd_finetuning_component( - project_id=GeneralConfig.GCP_PROJECT_ID, - run_id=run_id, - artifact_bucket=artifact_bucket, - component_name=sd_finetuning_component.__name__, - data_manifest_gcs_path=data_manifest_gcs_path, - pretrained_model_gcs_path=pretrained_model_gcs_path, - resume_from_checkpoint=resume_from_checkpoint, - seed=seed, - resolution=resolution, - train_batch_size=train_batch_size, - num_train_epochs=num_train_epochs, - max_train_steps=max_train_steps, - checkpointing_steps=checkpointing_steps, - gradient_accumulation_steps=gradient_accumulation_steps, - gradient_checkpointing=gradient_checkpointing, - learning_rate=learning_rate, - scale_lr=scale_lr, - lr_warmup_steps=lr_warmup_steps, - lr_scheduler=lr_scheduler, - use_ema=use_ema, - center_crop=center_crop, - random_flip=random_flip, - mixed_precision=mixed_precision) \ - .set_display_name('SD finetuning') \ - .set_gpu_limit(2) \ - .add_node_selector_constraint('node_pool', 'model-training-pool') \ - .add_toleration( - k8s_client.V1Toleration(effect='NoSchedule', key='reserved-pool', operator='Equal', - value='true')) - - -if __name__ == '__main__': - compile_and_upload_pipeline(pipeline=sd_finetuning_pipeline, - host=KubeflowConfig.HOST, - env=KubeflowConfig.ENV) From 5af7c1e752b7ee05a817f9736347c1edbf896328 Mon Sep 17 00:00:00 2001 From: Niels Rogge Date: Wed, 22 Mar 2023 11:43:12 +0100 Subject: [PATCH 02/38] More improvements --- .../dataset_loader_component/component.yaml | 60 ++++++------------ .../dataset_loader_component/src/main.py | 33 +++++++--- .../image_filter_component/Dockerfile | 13 ++++ .../image_filter_component/build_image.sh | 28 +++++++++ .../image_filter_component/component.yaml | 24 +++++++ .../image_filter_component/src/__init__.py | 0 .../image_filter_component/src/main.py | 63 +++++++++++++++++++ .../config/components_config.py | 22 +++++++ .../config/dataset_loader_config.py | 14 ----- .../dataset_creation_pipeline.py | 2 +- express/components/hf_datasets_components.py | 20 +++--- 11 files changed, 203 insertions(+), 76 deletions(-) create mode 100644 examples/pipelines/finetune_stable_diffusion/components/image_filter_component/Dockerfile create mode 100644 examples/pipelines/finetune_stable_diffusion/components/image_filter_component/build_image.sh create mode 100644 examples/pipelines/finetune_stable_diffusion/components/image_filter_component/component.yaml create mode 100644 examples/pipelines/finetune_stable_diffusion/components/image_filter_component/src/__init__.py create mode 100644 examples/pipelines/finetune_stable_diffusion/components/image_filter_component/src/main.py create mode 100644 examples/pipelines/finetune_stable_diffusion/config/components_config.py delete mode 100644 examples/pipelines/finetune_stable_diffusion/config/dataset_loader_config.py diff --git a/examples/pipelines/finetune_stable_diffusion/components/dataset_loader_component/component.yaml b/examples/pipelines/finetune_stable_diffusion/components/dataset_loader_component/component.yaml index 136cb9e6f..4926effbe 100644 --- a/examples/pipelines/finetune_stable_diffusion/components/dataset_loader_component/component.yaml +++ b/examples/pipelines/finetune_stable_diffusion/components/dataset_loader_component/component.yaml @@ -1,44 +1,24 @@ name: dataset_loader_component -description: A component that takes an images dataset as input and initializes a data manifest - and writes it to an output file -inputs: - - name: run_id - description: The run id of the pipeline - type: String - - name: artifact_bucket - description: The GCS bucket used to store the artifact - type: String - - name: component_name - description: the name of the component (used to create gcs artefact path) - type: String - - name: project_id - description: The id of the gcp-project - type: String - - name: source_dataset_bucket - description: The GCS bucket containing the dataset to load - type: String - - name: source_dataset_blob - description: The GCS blob withing the specified bucket containing the dataset to load - type: String - - name: namespace - description: The dataset namespace (abbreviation for data source) - type: String - +description: A basic component that takes a dataset name from the 🤗 hub as input and uploads it to a GCS bucket. +inputs: + - name: extra_args + description: Additional arguments passed to the component, as a json dict string + type: String + + - name: metadata_args + description: Metadata arguments, passed as a json dict string + type: String + outputs: - - name: data_manifest_path - description: Path to the local file containing the gcs path where the output has been stored + - name: output_manifest + description: Path to the output manifest implementation: - container: - image: europe-west1-docker.pkg.dev/storied-landing-366912/storied-landing-366912-default-repository/mlpipelines/kubeflow/components/dataset_loader_component:latest - command: [ - python3, main.py, - --run-id, { inputValue: run_id }, - --artifact-bucket, { inputValue: artifact_bucket }, - --component-name, { inputValue: component_name }, - --project-id, { inputValue: project_id }, - --source-dataset-bucket, { inputValue: source_dataset_bucket }, - --source-dataset-blob, { inputValue: source_dataset_blob }, - --namespace, { inputValue: namespace }, - --data-manifest-path, { outputPath: data_manifest_path }, - ] \ No newline at end of file + container: + image: europe-west4-docker.pkg.dev/soy-audio-379412/soy-audio-379412-default-repository/kubeflow-components/components/load_from_hub:latest + command: [ + python3, main.py, + --extra-args, {inputValue: extra_args}, + --metadata-args, {inputValue: metadata_args}, + --output-manifest, {outputPath: output_manifest}, + ] \ No newline at end of file diff --git a/examples/pipelines/finetune_stable_diffusion/components/dataset_loader_component/src/main.py b/examples/pipelines/finetune_stable_diffusion/components/dataset_loader_component/src/main.py index 408660d31..9816d85be 100644 --- a/examples/pipelines/finetune_stable_diffusion/components/dataset_loader_component/src/main.py +++ b/examples/pipelines/finetune_stable_diffusion/components/dataset_loader_component/src/main.py @@ -1,15 +1,20 @@ """ -This component loads a seed dataset from the cloud and creates the initial manifest. +This component loads a seed dataset from the hub and creates the initial manifest. """ +import logging from typing import Optional, Union, Dict -from datasets import load_from_disk +from datasets import Dataset, load_dataset +import pandas as pd from express.components.hf_datasets_components import HFDatasetsLoaderComponent, HFDatasetsDatasetDraft from express.logger import configure_logging +configure_logging() +logger = logging.getLogger(__name__) -class SeedDatasetLoader(HFDatasetsLoaderComponent): + +class DatasetLoaderComponent(HFDatasetsLoaderComponent): """Class that inherits from Hugging Face data loading """ @classmethod @@ -23,16 +28,26 @@ def load(cls, extra_args: Optional[ Returns: HFDatasetsDatasetDraft: a dataset draft that creates a plan for an output manifest """ - configure_logging() # 1) Create data source - storage_options={"project": extra_args["project_id"]} - dataset.save_to_disk("s3://my-private-datasets/imdb/train", storage_options=storage_options) + logger.info("Loading caption dataset from the hub...") + caption_dataset = load_dataset(extra_args["dataset_name"], split="train") - print(len(dataset)) + # 2) Create an example index + logger.info("Creating index...") + index_list = [f"image_{idx}" for idx in range(len(caption_dataset))] + caption_dataset = caption_dataset.add_column(name="index", column=index_list) + + # 3) Create dataset draft from index and data sources + # We store the index itself also as a HF Dataset + logger.info("Creating draft...") + index_df = pd.DataFrame(index_list, columns=['index']) + index = Dataset.from_pandas(index_df) + data_sources = {"captions": caption_dataset} + dataset_draft = HFDatasetsDatasetDraft(index=index, data_sources=data_sources) - return dataset + return dataset_draft if __name__ == '__main__': - SeedDatasetLoader.run() \ No newline at end of file + DatasetLoaderComponent.run() \ No newline at end of file diff --git a/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/Dockerfile b/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/Dockerfile new file mode 100644 index 000000000..9ea8412ff --- /dev/null +++ b/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/Dockerfile @@ -0,0 +1,13 @@ +FROM europe-west1-docker.pkg.dev/storied-landing-366912/storied-landing-366912-default-repository/mlpipelines/kubeflow/components/base_component:latest + +# Set the working directory to the source folder +WORKDIR /src + +# Install packages +COPY requirements.txt . +RUN pip3 install -r requirements.txt + +# Copy over src-files of the component +COPY src /src + +ENTRYPOINT ["python", "main.py"] \ No newline at end of file diff --git a/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/build_image.sh b/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/build_image.sh new file mode 100644 index 000000000..5b4bc5eaf --- /dev/null +++ b/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/build_image.sh @@ -0,0 +1,28 @@ +#!/bin/bash -e +# Copyright 2018 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# Read component configs +. ../../components.config + +# Set some variables +ARTIFACT_PATH=${PROJECT_ARTIFACT_PATH} +IMAGE_NAME="components/${PWD##*/}" +IMAGE_TAG="latest" + +# Create full name of the image +FULL_IMAGE_NAME=${ARTIFACT_PATH}/${IMAGE_NAME}:${IMAGE_TAG} +echo $FULL_IMAGE_NAME + +gcloud builds submit --machine-type n1-highcpu-32 . -t "$FULL_IMAGE_NAME" diff --git a/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/component.yaml b/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/component.yaml new file mode 100644 index 000000000..05981a90e --- /dev/null +++ b/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/component.yaml @@ -0,0 +1,24 @@ +name: image_filter_component +description: A component that filters the images based on minimum height and width. +inputs: + - name: extra_args + description: Additional arguments passed to the component, as a json dict string + type: String + + - name: metadata_args + description: Metadata arguments, passed as a json dict string + type: String + +outputs: + - name: output_manifest + description: Path to the output manifest + +implementation: + container: + image: europe-west4-docker.pkg.dev/soy-audio-379412/soy-audio-379412-default-repository/kubeflow-components/components/load_from_hub:latest + command: [ + python3, main.py, + --extra-args, {inputValue: extra_args}, + --metadata-args, {inputValue: metadata_args}, + --output-manifest, {outputPath: output_manifest}, + ] \ No newline at end of file diff --git a/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/src/__init__.py b/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/src/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/src/main.py b/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/src/main.py new file mode 100644 index 000000000..e39eefe1f --- /dev/null +++ b/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/src/main.py @@ -0,0 +1,63 @@ +""" +This component filters images of the dataset based on image size (minimum height and width). + +Technically, it adds a data source to the manifest. +""" +import logging +from typing import Optional, Union, Dict + +from express.components.hf_datasets_components import HFDatasetsTransformComponent, HFDatasetsDataset, HFDatasetsDatasetDraft +from express.logger import configure_logging + +configure_logging() +logger = logging.getLogger(__name__) + +def filter(examples): + images = examples["image"] + + for image in images: + + + return -1 + + +class ImageFilterComponent(HFDatasetsTransformComponent): + """ + Class that inherits from Hugging Face data transform. + + Goal is to leverage streaming.""" + + @classmethod + def transform(cls, data: HFDatasetsDataset, extra_args: Optional[ + Dict[str, Union[str, int, float, bool]]] = None) -> HFDatasetsDatasetDraft: + """ + An example function showcasing the data transform component using Express functionalities + + Args: + data (HFDatasetsDataset[TIndex, TData]): express dataset providing access to data of a + given type + extra_args (Optional[Dict[str, Union[str, int, float, bool]]): optional args to pass to + the function + Returns: + HFDatasetsDatasetDraft: a dataset draft that creates a plan for an output datasets/manifest + """ + + # 1) Get one particular data source from the manifest + logger.info("Loading caption dataset...") + caption_dataset = data.load(data_source="captions") + + # 2) Update index by filtering + logger.info("Filtering dataset...") + filtered_dataset = caption_dataset.filter(lambda example: example['HEIGHT'] > extra_args["min_height"]) + index = filtered_dataset["index"] + + # 3) Create dataset draft which updates the index + logger.info("Creating draft...") + data_sources = {"captions": caption_dataset} + dataset_draft = HFDatasetsDatasetDraft(index=index, data_sources=data_sources) + + return dataset_draft + + +if __name__ == '__main__': + ImageFilterComponent.run() \ No newline at end of file diff --git a/examples/pipelines/finetune_stable_diffusion/config/components_config.py b/examples/pipelines/finetune_stable_diffusion/config/components_config.py new file mode 100644 index 000000000..273aa310c --- /dev/null +++ b/examples/pipelines/finetune_stable_diffusion/config/components_config.py @@ -0,0 +1,22 @@ +from dataclasses import dataclass + +@dataclass +class DatasetLoaderConfig: + """ + Configs for the dataset loader component + Params: + DATASET_NAME (str): Name of the dataset on the hub. + """ + DATASET_NAME = "laion/laion400m" + + +@dataclass +class ImageFilterConfig: + """ + Configs for the image filter component + Params: + MIN_HEIGHT (int): Minimum height for each image. + MIN_WIDTH (int): Minimum width for each image. + """ + MIN_HEIGHT = 400 + MIN_WIDTH = 400 \ No newline at end of file diff --git a/examples/pipelines/finetune_stable_diffusion/config/dataset_loader_config.py b/examples/pipelines/finetune_stable_diffusion/config/dataset_loader_config.py deleted file mode 100644 index 763efc713..000000000 --- a/examples/pipelines/finetune_stable_diffusion/config/dataset_loader_config.py +++ /dev/null @@ -1,14 +0,0 @@ -from dataclasses import dataclass - -@dataclass -class DatasetLoaderConfig: - """ - Configs for the dataset loader component - Params: - BUCKET_NAME (str): The GCS bucket containing the initial dataset to load - BLOB_NAME (str): the zone of the k8 cluster hosting KFP - NAMESPACE (str): The dataset namespace (abbreviation for data source) - """ - BUCKET_NAME = "express-datasets" - BLOB_NAME = "initial-clean-cut-dataset" - NAMESPACE = "cf" \ No newline at end of file diff --git a/examples/pipelines/finetune_stable_diffusion/dataset_creation_pipeline.py b/examples/pipelines/finetune_stable_diffusion/dataset_creation_pipeline.py index 1ded3877c..a58f30d18 100644 --- a/examples/pipelines/finetune_stable_diffusion/dataset_creation_pipeline.py +++ b/examples/pipelines/finetune_stable_diffusion/dataset_creation_pipeline.py @@ -7,7 +7,7 @@ from kfp import dsl from config.general_config import GeneralConfig, KubeflowConfig -from config.dataset_loader_config import DatasetLoaderConfig +from config.components_config import DatasetLoaderConfig, ImageFilterConfig from express.kfp_utils import compile_and_upload_pipeline # Load Components diff --git a/express/components/hf_datasets_components.py b/express/components/hf_datasets_components.py index 6b283bc50..c6c6f9acc 100644 --- a/express/components/hf_datasets_components.py +++ b/express/components/hf_datasets_components.py @@ -33,9 +33,7 @@ # pylint: disable=too-few-public-methods -class HFDatasetsDataset( - ExpressDataset[List[str], Union[datasets.Dataset, datasets.DatasetDict]] -): +class HFDatasetsDataset(ExpressDataset[List[str], datasets.Dataset]): """Hugging Face Datasets dataset""" def load_index(self) -> datasets.Dataset: @@ -55,7 +53,7 @@ def load_index(self) -> datasets.Dataset: @staticmethod def _load_data_source( data_source: DataSource, index_filter: datasets.Dataset - ) -> Union[datasets.Dataset, datasets.DatasetDict]: + ) -> datasets.Dataset: """Function that loads in a data source""" if data_source.type != DataType.PARQUET: raise TypeError("Only reading from parquet is currently supported.") @@ -67,19 +65,17 @@ def _load_data_source( data_source_location, tmp_dir ) - data_source_hf_datasets = load_dataset( + dataset = load_dataset( "parquet", - data_files=local_parquet_path, + data_dir=local_parquet_path, split="train", ) if index_filter: index = index_filter["index"] - return data_source_hf_datasets.filter( - lambda example: example["index"] in index - ) + return dataset.filter(lambda example: example["index"] in index) - return data_source_hf_datasets + return dataset class HFDatasetsDatasetHandler(ExpressDatasetHandler[List[str], datasets.Dataset]): @@ -87,7 +83,7 @@ class HFDatasetsDatasetHandler(ExpressDatasetHandler[List[str], datasets.Dataset @staticmethod def _upload_parquet( - data: Union[datasets.Dataset, datasets.DatasetDict], name: str, remote_path: str + data: datasets.Dataset, name: str, remote_path: str ) -> DataSource: with tempfile.TemporaryDirectory() as temp_folder: # TODO: uploading without writing to temp file @@ -120,7 +116,7 @@ def _upload_index(cls, index: datasets.Dataset, remote_path: str) -> DataSource: def _upload_data_source( cls, name: str, - data: Union[datasets.Dataset, datasets.DatasetDict], + data: datasets.Dataset, remote_path: str, ) -> DataSource: data_source = cls._upload_parquet(data=data, name=name, remote_path=remote_path) From 7e38cfd79ff20a8c9cbcba5eee8ad93964b750fd Mon Sep 17 00:00:00 2001 From: Niels Rogge Date: Wed, 22 Mar 2023 12:06:01 +0100 Subject: [PATCH 03/38] More improvements --- .../dataset_loader_component/component.yaml | 2 +- .../image_filter_component/component.yaml | 2 +- .../image_filter_component/src/main.py | 6 +++--- .../dataset_creation_pipeline.py | 20 +++++++++++++++---- 4 files changed, 21 insertions(+), 9 deletions(-) diff --git a/examples/pipelines/finetune_stable_diffusion/components/dataset_loader_component/component.yaml b/examples/pipelines/finetune_stable_diffusion/components/dataset_loader_component/component.yaml index 4926effbe..d40239bf2 100644 --- a/examples/pipelines/finetune_stable_diffusion/components/dataset_loader_component/component.yaml +++ b/examples/pipelines/finetune_stable_diffusion/components/dataset_loader_component/component.yaml @@ -15,7 +15,7 @@ outputs: implementation: container: - image: europe-west4-docker.pkg.dev/soy-audio-379412/soy-audio-379412-default-repository/kubeflow-components/components/load_from_hub:latest + image: europe-west4-docker.pkg.dev/soy-audio-379412/soy-audio-379412-default-repository/kubeflow-components/components/dataset_loader_component:latest command: [ python3, main.py, --extra-args, {inputValue: extra_args}, diff --git a/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/component.yaml b/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/component.yaml index 05981a90e..01755135b 100644 --- a/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/component.yaml +++ b/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/component.yaml @@ -15,7 +15,7 @@ outputs: implementation: container: - image: europe-west4-docker.pkg.dev/soy-audio-379412/soy-audio-379412-default-repository/kubeflow-components/components/load_from_hub:latest + image: europe-west4-docker.pkg.dev/soy-audio-379412/soy-audio-379412-default-repository/kubeflow-components/components/image_filter_component:latest command: [ python3, main.py, --extra-args, {inputValue: extra_args}, diff --git a/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/src/main.py b/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/src/main.py index e39eefe1f..10eedd769 100644 --- a/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/src/main.py +++ b/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/src/main.py @@ -1,7 +1,7 @@ """ This component filters images of the dataset based on image size (minimum height and width). -Technically, it adds a data source to the manifest. +Technically, it updates the index of the manifest. """ import logging from typing import Optional, Union, Dict @@ -39,7 +39,7 @@ def transform(cls, data: HFDatasetsDataset, extra_args: Optional[ extra_args (Optional[Dict[str, Union[str, int, float, bool]]): optional args to pass to the function Returns: - HFDatasetsDatasetDraft: a dataset draft that creates a plan for an output datasets/manifest + HFDatasetsDatasetDraft: a dataset draft that creates a plan for an output manifest """ # 1) Get one particular data source from the manifest @@ -48,7 +48,7 @@ def transform(cls, data: HFDatasetsDataset, extra_args: Optional[ # 2) Update index by filtering logger.info("Filtering dataset...") - filtered_dataset = caption_dataset.filter(lambda example: example['HEIGHT'] > extra_args["min_height"]) + filtered_dataset = caption_dataset.filter(lambda example: example['HEIGHT'] >= extra_args["min_height"] and example['WIDTH'] >= extra_args["min_width"]) index = filtered_dataset["index"] # 3) Create dataset draft which updates the index diff --git a/examples/pipelines/finetune_stable_diffusion/dataset_creation_pipeline.py b/examples/pipelines/finetune_stable_diffusion/dataset_creation_pipeline.py index a58f30d18..8ae0f9c47 100644 --- a/examples/pipelines/finetune_stable_diffusion/dataset_creation_pipeline.py +++ b/examples/pipelines/finetune_stable_diffusion/dataset_creation_pipeline.py @@ -1,12 +1,11 @@ -"""Pipeline used to create a stable diffusion dataset from a set of given images. This is done by -using clip retrieval on the LAION dataset""" +"""Pipeline used to create a stable diffusion dataset from a set of given images.""" # pylint: disable=import-error import json from kfp import components as comp from kfp import dsl -from config.general_config import GeneralConfig, KubeflowConfig +from config.general_config import KubeflowConfig from config.components_config import DatasetLoaderConfig, ImageFilterConfig from express.kfp_utils import compile_and_upload_pipeline @@ -14,12 +13,20 @@ run_id = '{{workflow.name}}' artifact_bucket = KubeflowConfig.ARTIFACT_BUCKET +# Component 1 dataset_loader_op = comp.load_component('components/dataset_loader_component/component.yaml') -dataset_loader_extra_args = {"project_id": GeneralConfig.GCP_PROJECT_ID, "bucket": DatasetLoaderConfig.BUCKET_NAME} +dataset_loader_extra_args = {"dataset_name": DatasetLoaderConfig.DATASET_NAME} dataset_loader_metadata_args = {"run_id": run_id, "component_name": dataset_loader_op.__name__, "artifact_bucket": artifact_bucket} dataset_loader_extra_args = json.dumps(dataset_loader_extra_args) dataset_loader_metadata_args = json.dumps(dataset_loader_metadata_args) +# Component 2 +image_filter_op = comp.load_component('components/image_filter_component/component.yaml') +image_filter_extra_args = {"min_height": ImageFilterConfig.MIN_HEIGHT, "min_width": ImageFilterConfig.MIN_WIDTH} +image_filter_metadata_args = {"run_id": run_id, "component_name": image_filter_op.__name__, "artifact_bucket": artifact_bucket} +image_filter_extra_args = json.dumps(image_filter_extra_args) +image_filter_metadata_args = json.dumps(dataset_loader_metadata_args) + # Pipeline @dsl.pipeline( @@ -36,6 +43,11 @@ def sd_dataset_creator_pipeline(dataset_loader_extra_args: str = dataset_loader_ metadata_args=dataset_loader_metadata_args, ).set_display_name('Load initial images') + # Component 2 + image_filter_task = image_filter_op(extra_args=image_filter_extra_args, + metadata_args=image_filter_metadata_args, + ).set_display_name('Filter images') + if __name__ == '__main__': compile_and_upload_pipeline(pipeline=sd_dataset_creator_pipeline, From c9ce10c673657aa3e105d4c5ec965c7f240654b4 Mon Sep 17 00:00:00 2001 From: Niels Rogge Date: Wed, 22 Mar 2023 13:45:43 +0100 Subject: [PATCH 04/38] Update dataset name --- .../components/embedding_component/main.py | 0 .../finetune_stable_diffusion/config/components_config.py | 2 +- 2 files changed, 1 insertion(+), 1 deletion(-) create mode 100644 examples/pipelines/finetune_stable_diffusion/components/embedding_component/main.py diff --git a/examples/pipelines/finetune_stable_diffusion/components/embedding_component/main.py b/examples/pipelines/finetune_stable_diffusion/components/embedding_component/main.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/pipelines/finetune_stable_diffusion/config/components_config.py b/examples/pipelines/finetune_stable_diffusion/config/components_config.py index 273aa310c..de30b95cd 100644 --- a/examples/pipelines/finetune_stable_diffusion/config/components_config.py +++ b/examples/pipelines/finetune_stable_diffusion/config/components_config.py @@ -7,7 +7,7 @@ class DatasetLoaderConfig: Params: DATASET_NAME (str): Name of the dataset on the hub. """ - DATASET_NAME = "laion/laion400m" + DATASET_NAME = "lambdalabs/pokemon-blip-captions" @dataclass From 8068d4d7678335c6aae7d240257c21efb63ff1ab Mon Sep 17 00:00:00 2001 From: Niels Rogge Date: Wed, 22 Mar 2023 14:50:14 +0100 Subject: [PATCH 05/38] Add embedding component --- .../finetune_stable_diffusion/README.md | 163 ------------------ .../dataset_loader_component/README.MD | 22 --- .../dataset_loader_component/requirements.txt | 4 +- .../components/embedding_component/Dockerfile | 13 ++ .../{main.py => build_image.sh} | 0 .../embedding_component/component.yaml | 24 +++ .../embedding_component/requirements.txt | 2 + .../embedding_component/src/main.py | 79 +++++++++ .../image_filter_component/requirements.txt | 0 .../image_filter_component/src/main.py | 11 +- express/components/common.py | 11 +- express/components/hf_datasets_components.py | 5 +- 12 files changed, 136 insertions(+), 198 deletions(-) delete mode 100644 examples/pipelines/finetune_stable_diffusion/README.md delete mode 100644 examples/pipelines/finetune_stable_diffusion/components/dataset_loader_component/README.MD create mode 100644 examples/pipelines/finetune_stable_diffusion/components/embedding_component/Dockerfile rename examples/pipelines/finetune_stable_diffusion/components/embedding_component/{main.py => build_image.sh} (100%) create mode 100644 examples/pipelines/finetune_stable_diffusion/components/embedding_component/component.yaml create mode 100644 examples/pipelines/finetune_stable_diffusion/components/embedding_component/requirements.txt create mode 100644 examples/pipelines/finetune_stable_diffusion/components/embedding_component/src/main.py create mode 100644 examples/pipelines/finetune_stable_diffusion/components/image_filter_component/requirements.txt diff --git a/examples/pipelines/finetune_stable_diffusion/README.md b/examples/pipelines/finetune_stable_diffusion/README.md deleted file mode 100644 index 3496a4c6e..000000000 --- a/examples/pipelines/finetune_stable_diffusion/README.md +++ /dev/null @@ -1,163 +0,0 @@ -# Pipelines for fine-tuning Stable Diffusion - -This folder contains 2 separate KubeFlow pipelines: - -* [Dataset Creation Pipeline](#1-dataset-creation-pipeline): creates the dataset that will be used - to finetune the Stable Diffusion model -* [Stable Diffusion Finetuning pipeline](#2-sd-finetuning-pipeline): finetunes a pretrained Stable - Diffusion model on the created dataset. - -The separation between the two pipelines enables to run them disjointly to allow for quick -and fast experimentation; the dataset creation pipeline will in practice only be run once in a while to collect large amount of images. The Stable Diffusion finetuning pipeline on the other hand might be run several times to allow experimentation -with different hyperparameters or to resume training from a certain checkpoint. - -## [1. Dataset Creation pipeline](pipelines/dataset_creation_pipeline.py) - -### Description - -The aim of this pipeline is to prepare a dataset to finetune a pre-trained Stable Diffusion on, based on a set of seed images. - -In short, the pipeline first loads in a reference (seed) dataset containing curated images of a certain style/domain -(e.g. clip art images) and then retrieves similar images to this dataset from the [LAION dataset](https://laion.ai/), a large scale public dataset containing around 5 billion images. The retrieved images then undergo additional filtering -steps to ensure that they are of good quality (e.g. single component, clean-cut). Finally, the remaining images -are then captioned using a captioning model to generate image/caption pairs for Stable Diffusion finetuning. - -### Pipeline steps - -The pipeline consists of the following components: - -**[1) Dataset loader component:](components/dataset_loader_component)** This component -loads in an image dataset from a specific [Google Cloud Storage](https://cloud.google.com/storage/docs) path and creates a -parquet file with relevant metadata (image path, format, size...). - -**[2) Image filter component:](components/image_filter_component)** This component is -used to filter the images based on the metadata -attributes to only keep images of a certain size and format. - -**[3) Image conversion component:](components/image_conversion_component)** This -component converts images from different selected formats -(currently only `png` and `svg`) to `jpg` images. This step is necessary since other images formats -are not suitable for training ML models on and often contain artifacts that can be eliminated -during conversion. - -**[4) Image embedding component:](components/image_embedding_component)** This component -extracts [image embeddings](https://rom1504.medium.com/image-embeddings-ed1b194d113e) -from the converted images using -a [CLIP model](https://www.google.com/search?q=clip+embeddings&oq=clip+embeddings&aqs=chrome..69i57j0i22i30j69i60j69i64l2j69i60j69i64j69i60.6764j0j7&sourceid=chrome&ie=UTF-8). -Since image embeddings are good at capturing the features of the image in a compact and useful way, -it -will be used in the next steps to retrieve images similar to our seed images. - -[**5) Clip retrieval component:**](components/clip_retrieval_component) This component -retrieves images from the LAION dataset using a clip -retrieval system. The images are retrieved using an efficient index built from the previously -extracted embeddings that enables for fast and accurate -querying against a large database. Checkout -this [link](https://github.com/rom1504/clip-retrieval) for information on clip retrieval. You can -also -test out clip retrieval in -this [demo](https://rom1504.github.io/clip-retrieval/?back=https%3A%2F%2Fknn5.laion.ai&index=laion5B&useMclip=false). -The output of this component is a list of URLs containing the link to the retrieved image. - -[**6) Clip downloader component:**](components/clip_downloader_component) This component -downloads the images from the list of URLs and -creates the corresponding datasets. It uses the -following [library](https://github.com/rom1504/img2dataset) -for efficient and fast image download (multi-thread approach). The images are also filtered (based -on size -and area), resized and converted during download. - -[**7) Image caption component:**](components/image_caption_component) This component -uses a captioning -model ([BLIP](https://github.com/salesforce/BLIP)) -to caption the final filtered images for training. - -## [2. SD finetuning pipeline](pipelines/sd_finetuning_pipeline.py) - -### Description - -This pipeline aims to finetune a pre-trained stable diffusion model on the collected dataset. - -### Pipeline steps - -This pipeline consists only of a single component: - -**[1) SD finetuning component:](components/sd_finetuning_component)** takes as input the -final data manifest that is output by the dataset -creation pipeline. The data -manifest keeps reference of all the necessary metadata is required for the next step such as the -reference to the filtered images and captions. The component prepares the dataset for training -according to the -required [format](https://huggingface.co/docs/datasets/image_dataset#:~:text=in%20load_dataset.-,Image%20captioning,-Image%20captioning%20datasets) -and starts the finetuning jobs. - -# Building the images - -To build and push the component docker images to the container registry, execute the following command: - -```bash -bash build_images.sh -``` - -This will build all the components located in the `components` folder, you could also opt for building a specific component -by passing the `--build-dir` and passing the folder name of the component you want to build. - - -#TODO: move those docs elsewhere -## **Data Manifest: a common approach to simplify different steps throughout the pipeline** -In order to keep track of the different data sources, we opt for a manifest-centered approach where -a manifest is simply a JSON file that is passed and modified throughout the different steps of the pipeline. - -```json -{ - "dataset_id":"-", - "index":"", - "associated_data":{ - "dataset":{ - "namespace_1":"", - "...":"" - }, - "caption":{ - "namespace_1":"", - "...":"" - }, - "embedding":{ - "namespace_1":"", - "commit_hash":"", - "creation_date":"", - "run_id":"" - } -} -``` -Further deep dive on some notations: - -* **namespace:** the namespace is used to identify the different data sources. For example, you can give -your seed images a specific namespace (e.g. `seed`). Then, the images retrieved with clip-retrieval will -have different namespace (e.g. `knn`, `centroid`). - -* **index**: the index denotes a unique index for each images with the format (e.g. `seed_00010`). -It indexes all the data sources in `associated_data`. -**Note**: the index keeps track of all the namespace (e.g. [`seed_00010`,`centroid_0001`, ...]) - -* **dataset**: a set of parquet files for each namespace that contain relevant metadata -(image size, location, ...) as well as the index. - -* **caption**: a set of parquet files for each namespace that contain captions -image captions as well as the index. - -* **metadata**: Helps keep track of the step that generated that manifest, code version and pipeline run id. - -The Express pipeline consists of multiple steps defines as **Express steps** that are repeated -throughout the pipeline. The manifest pattern offers the required flexibility to promote its reuse and avoid -duplication of data sources. For example: - -* **Data filtering** (e.g. filtering on image size): add new indices to the `index` but retain associated data. - -* **Data creation** (e.g. clip retrieval): add new indicies to the new `index` and another source of data under associated data with a new namespace. - -* **Data transformation** (e.g. image formatting): retain indices but replace dataset source in `dataset`. diff --git a/examples/pipelines/finetune_stable_diffusion/components/dataset_loader_component/README.MD b/examples/pipelines/finetune_stable_diffusion/components/dataset_loader_component/README.MD deleted file mode 100644 index 33b2d68d2..000000000 --- a/examples/pipelines/finetune_stable_diffusion/components/dataset_loader_component/README.MD +++ /dev/null @@ -1,22 +0,0 @@ -# dataset_loader_component - -### Description - -This is the first component of the pipeline, it loads in an image dataset from a specific -[Google Cloud Storage (GCS)](https://cloud.google.com/storage/docs) path and creates a -parquet files with different metadata (image path, format, size, ...). - -### **Inputs/Outputs** -The component accepts a `source-dataset-bucket` as input with a reference to the blob `source-dataset-blob` -where the image dataset is located. - -The component created the first data manifest that will be updated in subsequent components when new dataset sources -are added/filtered. - -See [`component.yaml`](component.yaml) for a more detailed description on all the input/output parameters. - -### **Practical considerations** - -* The main accepted formats of the input images are either `png`, `jpg` or `svg`. Eventually, all the formats will be converted to -`jgp` in a subsequent component since it is more suitable for machine learning inference and training. -* Make sure that your images are located in one directory on GCS and not spread across different directories. \ No newline at end of file diff --git a/examples/pipelines/finetune_stable_diffusion/components/dataset_loader_component/requirements.txt b/examples/pipelines/finetune_stable_diffusion/components/dataset_loader_component/requirements.txt index 59f405946..caf2f15c3 100644 --- a/examples/pipelines/finetune_stable_diffusion/components/dataset_loader_component/requirements.txt +++ b/examples/pipelines/finetune_stable_diffusion/components/dataset_loader_component/requirements.txt @@ -1,2 +1,2 @@ -https://github.com/ml6team/express.git -gcsfs \ No newline at end of file +datasets +https://github.com/ml6team/express.git@main \ No newline at end of file diff --git a/examples/pipelines/finetune_stable_diffusion/components/embedding_component/Dockerfile b/examples/pipelines/finetune_stable_diffusion/components/embedding_component/Dockerfile new file mode 100644 index 000000000..9ea8412ff --- /dev/null +++ b/examples/pipelines/finetune_stable_diffusion/components/embedding_component/Dockerfile @@ -0,0 +1,13 @@ +FROM europe-west1-docker.pkg.dev/storied-landing-366912/storied-landing-366912-default-repository/mlpipelines/kubeflow/components/base_component:latest + +# Set the working directory to the source folder +WORKDIR /src + +# Install packages +COPY requirements.txt . +RUN pip3 install -r requirements.txt + +# Copy over src-files of the component +COPY src /src + +ENTRYPOINT ["python", "main.py"] \ No newline at end of file diff --git a/examples/pipelines/finetune_stable_diffusion/components/embedding_component/main.py b/examples/pipelines/finetune_stable_diffusion/components/embedding_component/build_image.sh similarity index 100% rename from examples/pipelines/finetune_stable_diffusion/components/embedding_component/main.py rename to examples/pipelines/finetune_stable_diffusion/components/embedding_component/build_image.sh diff --git a/examples/pipelines/finetune_stable_diffusion/components/embedding_component/component.yaml b/examples/pipelines/finetune_stable_diffusion/components/embedding_component/component.yaml new file mode 100644 index 000000000..7d470ce7d --- /dev/null +++ b/examples/pipelines/finetune_stable_diffusion/components/embedding_component/component.yaml @@ -0,0 +1,24 @@ +name: embedding_component +description: A component that embeds the images using CLIP. +inputs: + - name: extra_args + description: Additional arguments passed to the component, as a json dict string + type: String + + - name: metadata_args + description: Metadata arguments, passed as a json dict string + type: String + +outputs: + - name: output_manifest + description: Path to the output manifest + +implementation: + container: + image: europe-west4-docker.pkg.dev/soy-audio-379412/soy-audio-379412-default-repository/kubeflow-components/components/embedding_component:latest + command: [ + python3, main.py, + --extra-args, {inputValue: extra_args}, + --metadata-args, {inputValue: metadata_args}, + --output-manifest, {outputPath: output_manifest}, + ] \ No newline at end of file diff --git a/examples/pipelines/finetune_stable_diffusion/components/embedding_component/requirements.txt b/examples/pipelines/finetune_stable_diffusion/components/embedding_component/requirements.txt new file mode 100644 index 000000000..39dab0fdd --- /dev/null +++ b/examples/pipelines/finetune_stable_diffusion/components/embedding_component/requirements.txt @@ -0,0 +1,2 @@ +torch +transformers \ No newline at end of file diff --git a/examples/pipelines/finetune_stable_diffusion/components/embedding_component/src/main.py b/examples/pipelines/finetune_stable_diffusion/components/embedding_component/src/main.py new file mode 100644 index 000000000..2cdf791cb --- /dev/null +++ b/examples/pipelines/finetune_stable_diffusion/components/embedding_component/src/main.py @@ -0,0 +1,79 @@ +""" +This component adds a data source to the manifest by embedding the images. +""" +import logging +from typing import Optional, Union, Dict + +from express.components.hf_datasets_components import HFDatasetsTransformComponent, HFDatasetsDataset, HFDatasetsDatasetDraft +from express.logger import configure_logging + +import torch + +from transformers import CLIPProcessor, CLIPVisionModelWithProjection + +configure_logging() +logger = logging.getLogger(__name__) + + +@torch.no_grad() +def embed(examples, processor, model): + images = examples["images"] + + # prepare images for the model + inputs = processor(images, return_tensors="pt") + + # embed + outputs = model(**inputs) + image_embeds = outputs.image_embeds + + batch = {"embeddings": image_embeds} + + return batch + + +class EmbeddingComponent(HFDatasetsTransformComponent): + """ + Class that inherits from Hugging Face data transform. + """ + + @classmethod + def transform(cls, data: HFDatasetsDataset, extra_args: Optional[ + Dict[str, Union[str, int, float, bool]]] = None) -> HFDatasetsDatasetDraft: + """ + An example function showcasing the data transform component using Express functionalities + + Args: + data (HFDatasetsDataset[TIndex, TData]): express dataset providing access to data of a + given type + extra_args (Optional[Dict[str, Union[str, int, float, bool]]): optional args to pass to + the function + Returns: + HFDatasetsDatasetDraft: a dataset draft that creates a plan for an output manifest + """ + + # 1) Get one particular data source from the manifest + # TODO support streaming + logger.info("Loading caption dataset...") + caption_dataset = data.load(data_source="captions", streaming=True) + + # 2) Create embedding dataset + logger.info("Embedding images...") + + processor = CLIPProcessor.from_pretrained(extra_args["model_id"]) + model = CLIPVisionModelWithProjection.from_pretrained(extra_args["model_id"]) + + embedded_dataset = caption_dataset.map(embed, + batched=True, batch_size=extra_args["batch_size"], + fn_kwargs=dict(processor=processor, model=model), + remove_columns=["image", "text"]) + + # 3) Create dataset draft which adds a data source to the manifest + logger.info("Creating draft...") + data_sources = {"embeddings": embedded_dataset} + dataset_draft = HFDatasetsDatasetDraft(data_sources=data_sources, extending_dataset=data) + + return dataset_draft + + +if __name__ == '__main__': + EmbeddingComponent.run() \ No newline at end of file diff --git a/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/requirements.txt b/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/requirements.txt new file mode 100644 index 000000000..e69de29bb diff --git a/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/src/main.py b/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/src/main.py index 10eedd769..27716085d 100644 --- a/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/src/main.py +++ b/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/src/main.py @@ -12,13 +12,10 @@ configure_logging() logger = logging.getLogger(__name__) -def filter(examples): - images = examples["image"] - - for image in images: - +def check_min_size(example, min_width, min_height): + width, height = example["image"].size - return -1 + return width > min_width and height > min_height class ImageFilterComponent(HFDatasetsTransformComponent): @@ -48,7 +45,7 @@ def transform(cls, data: HFDatasetsDataset, extra_args: Optional[ # 2) Update index by filtering logger.info("Filtering dataset...") - filtered_dataset = caption_dataset.filter(lambda example: example['HEIGHT'] >= extra_args["min_height"] and example['WIDTH'] >= extra_args["min_width"]) + filtered_dataset = caption_dataset.filter(lambda example: check_min_size(example, min_width=extra_args["min_width"], min_height=extra_args["min_height"])) index = filtered_dataset["index"] # 3) Create dataset draft which updates the index diff --git a/express/components/common.py b/express/components/common.py index 10d19e0e4..4874aaa2e 100644 --- a/express/components/common.py +++ b/express/components/common.py @@ -48,7 +48,9 @@ def load_index(self) -> IndexT: Loads the index data. """ - def load(self, data_source: str, for_index: Optional[IndexT] = None) -> DataT: + def load( + self, data_source: str, for_index: Optional[IndexT] = None, **kwargs + ) -> DataT: """ Load data from a named data source. @@ -57,6 +59,7 @@ def load(self, data_source: str, for_index: Optional[IndexT] = None) -> DataT: for_index (Optional[TIndex]): Pass in an index to filter the data on. By default, the original Dataset index is used. This argument can be used to use a different index instead. + kwargs (dict): Additional keyword arguments forwarded to the _load_data_source method. Returns: TData: Data of type TData @@ -69,13 +72,15 @@ def load(self, data_source: str, for_index: Optional[IndexT] = None) -> DataT: if for_index is None: for_index = self._index_data return self._load_data_source( - self.manifest.data_sources[data_source], for_index + self.manifest.data_sources[data_source], for_index, kwargs=kwargs ) @staticmethod @abstractmethod def _load_data_source( - data_source: DataSource, index_filter: Optional[IndexT] + data_source: DataSource, + index_filter: Optional[IndexT], + **kwargs: dict, ) -> DataT: """ Load data from a (possibly remote) path. diff --git a/express/components/hf_datasets_components.py b/express/components/hf_datasets_components.py index c6c6f9acc..e6245e177 100644 --- a/express/components/hf_datasets_components.py +++ b/express/components/hf_datasets_components.py @@ -52,7 +52,9 @@ def load_index(self) -> datasets.Dataset: @staticmethod def _load_data_source( - data_source: DataSource, index_filter: datasets.Dataset + data_source: DataSource, + index_filter: datasets.Dataset, + **kwargs, ) -> datasets.Dataset: """Function that loads in a data source""" if data_source.type != DataType.PARQUET: @@ -69,6 +71,7 @@ def _load_data_source( "parquet", data_dir=local_parquet_path, split="train", + **kwargs, ) if index_filter: From 3e0a3f5cacf0399e8c5b711a97318f11b57da6a0 Mon Sep 17 00:00:00 2001 From: Niels Rogge Date: Thu, 23 Mar 2023 14:36:01 +0100 Subject: [PATCH 06/38] More improvements --- .../dataset_loader_component/Dockerfile | 28 ++++++++++++---- .../dataset_loader_component/requirements.txt | 2 +- .../dataset_loader_component/src/main.py | 32 +++++++++++++------ .../embedding_component/src/main.py | 9 +++--- .../image_filter_component/src/main.py | 12 +++---- 5 files changed, 57 insertions(+), 26 deletions(-) diff --git a/examples/pipelines/finetune_stable_diffusion/components/dataset_loader_component/Dockerfile b/examples/pipelines/finetune_stable_diffusion/components/dataset_loader_component/Dockerfile index 9ea8412ff..1b2d10bbd 100644 --- a/examples/pipelines/finetune_stable_diffusion/components/dataset_loader_component/Dockerfile +++ b/examples/pipelines/finetune_stable_diffusion/components/dataset_loader_component/Dockerfile @@ -1,13 +1,29 @@ -FROM europe-west1-docker.pkg.dev/storied-landing-366912/storied-landing-366912-default-repository/mlpipelines/kubeflow/components/base_component:latest +FROM python:3.8-slim -# Set the working directory to the source folder -WORKDIR /src +## System dependencies +RUN apt-get update && \ + apt-get upgrade -y && \ + apt-get install git curl -y + +# Downloading gcloud package +RUN curl https://dl.google.com/dl/cloudsdk/release/google-cloud-sdk.tar.gz > /tmp/google-cloud-sdk.tar.gz + +# Installing the package +RUN mkdir -p /usr/local/gcloud \ +&& tar -C /usr/local/gcloud -xvf /tmp/google-cloud-sdk.tar.gz \ +&& /usr/local/gcloud/google-cloud-sdk/install.sh -# Install packages -COPY requirements.txt . -RUN pip3 install -r requirements.txt +# Adding the package path to local +ENV PATH $PATH:/usr/local/gcloud/google-cloud-sdk/bin + +# install requirements +COPY requirements.txt / +RUN pip3 install --no-cache-dir -r requirements.txt # Copy over src-files of the component COPY src /src +# Set the working directory to the source folder +WORKDIR /src + ENTRYPOINT ["python", "main.py"] \ No newline at end of file diff --git a/examples/pipelines/finetune_stable_diffusion/components/dataset_loader_component/requirements.txt b/examples/pipelines/finetune_stable_diffusion/components/dataset_loader_component/requirements.txt index caf2f15c3..aea4e5d1b 100644 --- a/examples/pipelines/finetune_stable_diffusion/components/dataset_loader_component/requirements.txt +++ b/examples/pipelines/finetune_stable_diffusion/components/dataset_loader_component/requirements.txt @@ -1,2 +1,2 @@ datasets -https://github.com/ml6team/express.git@main \ No newline at end of file +git+https://github.com/ml6team/express.git@main \ No newline at end of file diff --git a/examples/pipelines/finetune_stable_diffusion/components/dataset_loader_component/src/main.py b/examples/pipelines/finetune_stable_diffusion/components/dataset_loader_component/src/main.py index 9816d85be..8ee993773 100644 --- a/examples/pipelines/finetune_stable_diffusion/components/dataset_loader_component/src/main.py +++ b/examples/pipelines/finetune_stable_diffusion/components/dataset_loader_component/src/main.py @@ -5,7 +5,6 @@ from typing import Optional, Union, Dict from datasets import Dataset, load_dataset -import pandas as pd from express.components.hf_datasets_components import HFDatasetsLoaderComponent, HFDatasetsDatasetDraft from express.logger import configure_logging @@ -14,6 +13,17 @@ logger = logging.getLogger(__name__) +def create_metadata(examples): + images = examples["image"] + sizes = [image.size for image in images] + + batch = {} + batch["width"] = [sizes[0] for size in sizes] + batch["height"] = [sizes[1] for size in sizes] + + return batch + + class DatasetLoaderComponent(HFDatasetsLoaderComponent): """Class that inherits from Hugging Face data loading """ @@ -31,20 +41,24 @@ def load(cls, extra_args: Optional[ # 1) Create data source logger.info("Loading caption dataset from the hub...") - caption_dataset = load_dataset(extra_args["dataset_name"], split="train") + dataset = load_dataset(extra_args["dataset_name"], split="train") # 2) Create an example index logger.info("Creating index...") - index_list = [f"image_{idx}" for idx in range(len(caption_dataset))] - caption_dataset = caption_dataset.add_column(name="index", column=index_list) + index_list = [f"image_{idx}" for idx in range(len(dataset))] + dataset = dataset.add_column(name="index", column=index_list) - # 3) Create dataset draft from index and data sources + # 3) Create dataset draft (manifest without metadata) # We store the index itself also as a HF Dataset logger.info("Creating draft...") - index_df = pd.DataFrame(index_list, columns=['index']) - index = Dataset.from_pandas(index_df) - data_sources = {"captions": caption_dataset} - dataset_draft = HFDatasetsDatasetDraft(index=index, data_sources=data_sources) + index_dataset = Dataset.from_dict({"index": index_list}) + image_dataset = dataset.remove_columns(["text"]).add_column(name="index", column=index_list) + text_dataset = dataset.remove_columns(["image"]).add_column(name="index", column=index_list) + metadata_dataset = image_dataset.map(create_metadata, batched=True, remove_columns=["image"]) + data_sources = {"images": image_dataset, + "captions": text_dataset, + "image_metadata": metadata_dataset} + dataset_draft = HFDatasetsDatasetDraft(index=index_dataset, data_sources=data_sources) return dataset_draft diff --git a/examples/pipelines/finetune_stable_diffusion/components/embedding_component/src/main.py b/examples/pipelines/finetune_stable_diffusion/components/embedding_component/src/main.py index 2cdf791cb..277f2cd2c 100644 --- a/examples/pipelines/finetune_stable_diffusion/components/embedding_component/src/main.py +++ b/examples/pipelines/finetune_stable_diffusion/components/embedding_component/src/main.py @@ -22,11 +22,12 @@ def embed(examples, processor, model): # prepare images for the model inputs = processor(images, return_tensors="pt") - # embed + # embed to get (batch_size, hidden_size) embeddings outputs = model(**inputs) image_embeds = outputs.image_embeds - batch = {"embeddings": image_embeds} + # flatten into list of embeddings + batch = {"embeddings": image_embeds.tolist()} return batch @@ -52,9 +53,9 @@ def transform(cls, data: HFDatasetsDataset, extra_args: Optional[ """ # 1) Get one particular data source from the manifest - # TODO support streaming + # TODO check whether we can leverage streaming logger.info("Loading caption dataset...") - caption_dataset = data.load(data_source="captions", streaming=True) + caption_dataset = data.load(data_source="captions") # 2) Create embedding dataset logger.info("Embedding images...") diff --git a/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/src/main.py b/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/src/main.py index 27716085d..c0e9a40b8 100644 --- a/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/src/main.py +++ b/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/src/main.py @@ -12,8 +12,9 @@ configure_logging() logger = logging.getLogger(__name__) + def check_min_size(example, min_width, min_height): - width, height = example["image"].size + width, height = example["width"], example["height"] return width > min_width and height > min_height @@ -40,17 +41,16 @@ def transform(cls, data: HFDatasetsDataset, extra_args: Optional[ """ # 1) Get one particular data source from the manifest - logger.info("Loading caption dataset...") - caption_dataset = data.load(data_source="captions") + logger.info("Loading metadata dataset...") + metadata_dataset = data.load(data_source="image_metadata") # 2) Update index by filtering logger.info("Filtering dataset...") - filtered_dataset = caption_dataset.filter(lambda example: check_min_size(example, min_width=extra_args["min_width"], min_height=extra_args["min_height"])) - index = filtered_dataset["index"] + filtered_dataset = metadata_dataset.filter(lambda example: check_min_size(example, min_width=extra_args["min_width"], min_height=extra_args["min_height"])) + index = filtered_dataset.remove_columns(["width", "height"]) # 3) Create dataset draft which updates the index logger.info("Creating draft...") - data_sources = {"captions": caption_dataset} dataset_draft = HFDatasetsDatasetDraft(index=index, data_sources=data_sources) return dataset_draft From d21ad90367d71e6416ff5f8c13d448904d65ed18 Mon Sep 17 00:00:00 2001 From: Niels Rogge Date: Thu, 23 Mar 2023 16:24:20 +0100 Subject: [PATCH 07/38] More improvements and fixes --- .../dataset_loader_component/requirements.txt | 3 +- .../dataset_loader_component/src/main.py | 5 ++-- .../components/embedding_component/Dockerfile | 28 +++++++++++++---- .../embedding_component/src/main.py | 10 +++---- .../image_filter_component/Dockerfile | 30 +++++++++++++++---- .../image_filter_component/component.yaml | 15 ++++++---- .../image_filter_component/requirements.txt | 2 ++ .../image_filter_component/src/main.py | 12 +++++--- .../dataset_creation_pipeline.py | 7 +++-- 9 files changed, 79 insertions(+), 33 deletions(-) diff --git a/examples/pipelines/finetune_stable_diffusion/components/dataset_loader_component/requirements.txt b/examples/pipelines/finetune_stable_diffusion/components/dataset_loader_component/requirements.txt index aea4e5d1b..afa551664 100644 --- a/examples/pipelines/finetune_stable_diffusion/components/dataset_loader_component/requirements.txt +++ b/examples/pipelines/finetune_stable_diffusion/components/dataset_loader_component/requirements.txt @@ -1,2 +1,3 @@ datasets -git+https://github.com/ml6team/express.git@main \ No newline at end of file +git+https://github.com/ml6team/express.git@main +Pillow \ No newline at end of file diff --git a/examples/pipelines/finetune_stable_diffusion/components/dataset_loader_component/src/main.py b/examples/pipelines/finetune_stable_diffusion/components/dataset_loader_component/src/main.py index 8ee993773..f6ea8159d 100644 --- a/examples/pipelines/finetune_stable_diffusion/components/dataset_loader_component/src/main.py +++ b/examples/pipelines/finetune_stable_diffusion/components/dataset_loader_component/src/main.py @@ -18,8 +18,8 @@ def create_metadata(examples): sizes = [image.size for image in images] batch = {} - batch["width"] = [sizes[0] for size in sizes] - batch["height"] = [sizes[1] for size in sizes] + batch["width"] = [size[0] for size in sizes] + batch["height"] = [size[1] for size in sizes] return batch @@ -46,7 +46,6 @@ def load(cls, extra_args: Optional[ # 2) Create an example index logger.info("Creating index...") index_list = [f"image_{idx}" for idx in range(len(dataset))] - dataset = dataset.add_column(name="index", column=index_list) # 3) Create dataset draft (manifest without metadata) # We store the index itself also as a HF Dataset diff --git a/examples/pipelines/finetune_stable_diffusion/components/embedding_component/Dockerfile b/examples/pipelines/finetune_stable_diffusion/components/embedding_component/Dockerfile index 9ea8412ff..1b2d10bbd 100644 --- a/examples/pipelines/finetune_stable_diffusion/components/embedding_component/Dockerfile +++ b/examples/pipelines/finetune_stable_diffusion/components/embedding_component/Dockerfile @@ -1,13 +1,29 @@ -FROM europe-west1-docker.pkg.dev/storied-landing-366912/storied-landing-366912-default-repository/mlpipelines/kubeflow/components/base_component:latest +FROM python:3.8-slim -# Set the working directory to the source folder -WORKDIR /src +## System dependencies +RUN apt-get update && \ + apt-get upgrade -y && \ + apt-get install git curl -y + +# Downloading gcloud package +RUN curl https://dl.google.com/dl/cloudsdk/release/google-cloud-sdk.tar.gz > /tmp/google-cloud-sdk.tar.gz + +# Installing the package +RUN mkdir -p /usr/local/gcloud \ +&& tar -C /usr/local/gcloud -xvf /tmp/google-cloud-sdk.tar.gz \ +&& /usr/local/gcloud/google-cloud-sdk/install.sh -# Install packages -COPY requirements.txt . -RUN pip3 install -r requirements.txt +# Adding the package path to local +ENV PATH $PATH:/usr/local/gcloud/google-cloud-sdk/bin + +# install requirements +COPY requirements.txt / +RUN pip3 install --no-cache-dir -r requirements.txt # Copy over src-files of the component COPY src /src +# Set the working directory to the source folder +WORKDIR /src + ENTRYPOINT ["python", "main.py"] \ No newline at end of file diff --git a/examples/pipelines/finetune_stable_diffusion/components/embedding_component/src/main.py b/examples/pipelines/finetune_stable_diffusion/components/embedding_component/src/main.py index 277f2cd2c..9878cf055 100644 --- a/examples/pipelines/finetune_stable_diffusion/components/embedding_component/src/main.py +++ b/examples/pipelines/finetune_stable_diffusion/components/embedding_component/src/main.py @@ -55,7 +55,7 @@ def transform(cls, data: HFDatasetsDataset, extra_args: Optional[ # 1) Get one particular data source from the manifest # TODO check whether we can leverage streaming logger.info("Loading caption dataset...") - caption_dataset = data.load(data_source="captions") + image_dataset = data.load(data_source="images") # 2) Create embedding dataset logger.info("Embedding images...") @@ -63,10 +63,10 @@ def transform(cls, data: HFDatasetsDataset, extra_args: Optional[ processor = CLIPProcessor.from_pretrained(extra_args["model_id"]) model = CLIPVisionModelWithProjection.from_pretrained(extra_args["model_id"]) - embedded_dataset = caption_dataset.map(embed, - batched=True, batch_size=extra_args["batch_size"], - fn_kwargs=dict(processor=processor, model=model), - remove_columns=["image", "text"]) + embedded_dataset = image_dataset.map(embed, + batched=True, batch_size=extra_args["batch_size"], + fn_kwargs=dict(processor=processor, model=model), + remove_columns=["image", "text"]) # 3) Create dataset draft which adds a data source to the manifest logger.info("Creating draft...") diff --git a/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/Dockerfile b/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/Dockerfile index 9ea8412ff..d46805647 100644 --- a/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/Dockerfile +++ b/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/Dockerfile @@ -1,13 +1,31 @@ -FROM europe-west1-docker.pkg.dev/storied-landing-366912/storied-landing-366912-default-repository/mlpipelines/kubeflow/components/base_component:latest +FROM python:3.8-slim -# Set the working directory to the source folder -WORKDIR /src +## System dependencies +RUN apt-get update && \ + apt-get upgrade -y && \ + apt-get install git curl -y + +# Downloading gcloud package +RUN curl https://dl.google.com/dl/cloudsdk/release/google-cloud-sdk.tar.gz > /tmp/google-cloud-sdk.tar.gz + +# Installing the package +RUN mkdir -p /usr/local/gcloud \ +&& tar -C /usr/local/gcloud -xvf /tmp/google-cloud-sdk.tar.gz \ +&& /usr/local/gcloud/google-cloud-sdk/install.sh + +# Adding the package path to local +ENV PATH $PATH:/usr/local/gcloud/google-cloud-sdk/bin -# Install packages -COPY requirements.txt . -RUN pip3 install -r requirements.txt +# install requirements +COPY requirements.txt / +RUN pip3 install --no-cache-dir -r requirements.txt # Copy over src-files of the component COPY src /src +# TODO: ADD EXTRA FILES HERE IF NEEDED! + +# Set the working directory to the source folder +WORKDIR /src + ENTRYPOINT ["python", "main.py"] \ No newline at end of file diff --git a/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/component.yaml b/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/component.yaml index 01755135b..330d45deb 100644 --- a/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/component.yaml +++ b/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/component.yaml @@ -1,13 +1,17 @@ name: image_filter_component -description: A component that filters the images based on minimum height and width. +description: A basic component that takes a dataset name from the 🤗 hub as input and uploads it to a GCS bucket. inputs: - name: extra_args description: Additional arguments passed to the component, as a json dict string type: String - - name: metadata_args + - name: metadata description: Metadata arguments, passed as a json dict string type: String + + - name: input_manifest + description: Path to the input manifest + type: String outputs: - name: output_manifest @@ -18,7 +22,8 @@ implementation: image: europe-west4-docker.pkg.dev/soy-audio-379412/soy-audio-379412-default-repository/kubeflow-components/components/image_filter_component:latest command: [ python3, main.py, - --extra-args, {inputValue: extra_args}, - --metadata-args, {inputValue: metadata_args}, - --output-manifest, {outputPath: output_manifest}, + --input-manifest, {inputPath: input_manifest}, + --metadata, {inputValue: metadata}, + --extra-args, {inputValue: extra_args}, + --output-manifest, {outputPath: output_manifest}, ] \ No newline at end of file diff --git a/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/requirements.txt b/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/requirements.txt index e69de29bb..aea4e5d1b 100644 --- a/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/requirements.txt +++ b/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/requirements.txt @@ -0,0 +1,2 @@ +datasets +git+https://github.com/ml6team/express.git@main \ No newline at end of file diff --git a/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/src/main.py b/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/src/main.py index c0e9a40b8..4b0541c73 100644 --- a/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/src/main.py +++ b/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/src/main.py @@ -6,6 +6,8 @@ import logging from typing import Optional, Union, Dict +from datasets import Dataset + from express.components.hf_datasets_components import HFDatasetsTransformComponent, HFDatasetsDataset, HFDatasetsDatasetDraft from express.logger import configure_logging @@ -40,18 +42,20 @@ def transform(cls, data: HFDatasetsDataset, extra_args: Optional[ HFDatasetsDatasetDraft: a dataset draft that creates a plan for an output manifest """ - # 1) Get one particular data source from the manifest + # 1) Load one particular data source from the manifest logger.info("Loading metadata dataset...") metadata_dataset = data.load(data_source="image_metadata") # 2) Update index by filtering logger.info("Filtering dataset...") - filtered_dataset = metadata_dataset.filter(lambda example: check_min_size(example, min_width=extra_args["min_width"], min_height=extra_args["min_height"])) - index = filtered_dataset.remove_columns(["width", "height"]) + min_width, min_height = extra_args["min_width"], extra_args["min_height"] + filtered_dataset = metadata_dataset.filter(lambda example: example["width"] > min_width and example["height"] > min_height) + index_dataset = Dataset.from_dict({"index": filtered_dataset["index"]}) # 3) Create dataset draft which updates the index + # but maintains the same data sources logger.info("Creating draft...") - dataset_draft = HFDatasetsDatasetDraft(index=index, data_sources=data_sources) + dataset_draft = HFDatasetsDatasetDraft(index=index_dataset, data_sources=data.manifest.data_sources) return dataset_draft diff --git a/examples/pipelines/finetune_stable_diffusion/dataset_creation_pipeline.py b/examples/pipelines/finetune_stable_diffusion/dataset_creation_pipeline.py index 8ae0f9c47..88f677666 100644 --- a/examples/pipelines/finetune_stable_diffusion/dataset_creation_pipeline.py +++ b/examples/pipelines/finetune_stable_diffusion/dataset_creation_pipeline.py @@ -25,7 +25,7 @@ image_filter_extra_args = {"min_height": ImageFilterConfig.MIN_HEIGHT, "min_width": ImageFilterConfig.MIN_WIDTH} image_filter_metadata_args = {"run_id": run_id, "component_name": image_filter_op.__name__, "artifact_bucket": artifact_bucket} image_filter_extra_args = json.dumps(image_filter_extra_args) -image_filter_metadata_args = json.dumps(dataset_loader_metadata_args) +image_filter_metadata_args = json.dumps(image_filter_metadata_args) # Pipeline @@ -40,12 +40,13 @@ def sd_dataset_creator_pipeline(dataset_loader_extra_args: str = dataset_loader_ ): # Component 1 dataset_loader_task = dataset_loader_op(extra_args=dataset_loader_extra_args, - metadata_args=dataset_loader_metadata_args, + metadata_args=dataset_loader_metadata_args, ).set_display_name('Load initial images') # Component 2 image_filter_task = image_filter_op(extra_args=image_filter_extra_args, - metadata_args=image_filter_metadata_args, + metadata=image_filter_metadata_args, + input_manifest=dataset_loader_task.outputs["output_manifest"], ).set_display_name('Filter images') From 4bb7a2ce8d4de39441da18bc8d726d8a4b607716 Mon Sep 17 00:00:00 2001 From: Niels Rogge Date: Thu, 23 Mar 2023 16:32:08 +0100 Subject: [PATCH 08/38] More fixes --- .../finetune_stable_diffusion/dataset_creation_pipeline.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/examples/pipelines/finetune_stable_diffusion/dataset_creation_pipeline.py b/examples/pipelines/finetune_stable_diffusion/dataset_creation_pipeline.py index 88f677666..063a3762d 100644 --- a/examples/pipelines/finetune_stable_diffusion/dataset_creation_pipeline.py +++ b/examples/pipelines/finetune_stable_diffusion/dataset_creation_pipeline.py @@ -36,7 +36,9 @@ ) # pylint: disable=too-many-arguments, too-many-locals def sd_dataset_creator_pipeline(dataset_loader_extra_args: str = dataset_loader_extra_args, - dataset_loader_metadata_args: str = dataset_loader_metadata_args, + dataset_loader_metadata_args: str = dataset_loader_metadata_args, + image_filter_extra_args: str = image_filter_extra_args, + image_filter_metadata_args: str = image_filter_metadata_args, ): # Component 1 dataset_loader_task = dataset_loader_op(extra_args=dataset_loader_extra_args, From bfb144cddc122e87304f1372af963b1c30ad031b Mon Sep 17 00:00:00 2001 From: Niels Rogge Date: Thu, 23 Mar 2023 16:33:28 +0100 Subject: [PATCH 09/38] Add print statement --- express/components/common.py | 1 + 1 file changed, 1 insertion(+) diff --git a/express/components/common.py b/express/components/common.py index 4874aaa2e..12cfcb6b5 100644 --- a/express/components/common.py +++ b/express/components/common.py @@ -301,6 +301,7 @@ def _create_output_dataset( Processes a dataset draft of a specific type, uploading all local data to storage and composing the output manifest. """ + print("Metadata:", metadata) if isinstance(draft.index, DataSource): index = draft.index else: From d21fe451d287b7b78e5cf094605ab1494f20cfd5 Mon Sep 17 00:00:00 2001 From: Niels Rogge Date: Thu, 23 Mar 2023 16:38:58 +0100 Subject: [PATCH 10/38] Update requirements --- .../components/image_filter_component/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/requirements.txt b/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/requirements.txt index aea4e5d1b..711f09d71 100644 --- a/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/requirements.txt +++ b/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/requirements.txt @@ -1,2 +1,2 @@ datasets -git+https://github.com/ml6team/express.git@main \ No newline at end of file +git+https://github.com/ml6team/express.git@22d37eb01b7bf75d8974084c7bd865e70b33a26a \ No newline at end of file From d731b55259ba479d757ecdff13deea2250176c7f Mon Sep 17 00:00:00 2001 From: Niels Rogge Date: Thu, 23 Mar 2023 17:23:37 +0100 Subject: [PATCH 11/38] Add print statements --- .../components/image_filter_component/requirements.txt | 2 +- express/components/hf_datasets_components.py | 3 +++ 2 files changed, 4 insertions(+), 1 deletion(-) diff --git a/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/requirements.txt b/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/requirements.txt index 711f09d71..aea4e5d1b 100644 --- a/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/requirements.txt +++ b/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/requirements.txt @@ -1,2 +1,2 @@ datasets -git+https://github.com/ml6team/express.git@22d37eb01b7bf75d8974084c7bd865e70b33a26a \ No newline at end of file +git+https://github.com/ml6team/express.git@main \ No newline at end of file diff --git a/express/components/hf_datasets_components.py b/express/components/hf_datasets_components.py index e6245e177..f251012b8 100644 --- a/express/components/hf_datasets_components.py +++ b/express/components/hf_datasets_components.py @@ -67,6 +67,8 @@ def _load_data_source( data_source_location, tmp_dir ) + print("Loading from path:", local_parquet_path) + dataset = load_dataset( "parquet", data_dir=local_parquet_path, @@ -76,6 +78,7 @@ def _load_data_source( if index_filter: index = index_filter["index"] + print("Index:", index) return dataset.filter(lambda example: example["index"] in index) return dataset From 9f9f4b0302a535dd987f8bb3625063749e9a2e12 Mon Sep 17 00:00:00 2001 From: Niels Rogge Date: Thu, 23 Mar 2023 17:41:25 +0100 Subject: [PATCH 12/38] Add print statements --- .../components/image_filter_component/requirements.txt | 2 +- express/components/hf_datasets_components.py | 3 +++ 2 files changed, 4 insertions(+), 1 deletion(-) diff --git a/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/requirements.txt b/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/requirements.txt index aea4e5d1b..d58e51b1f 100644 --- a/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/requirements.txt +++ b/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/requirements.txt @@ -1,2 +1,2 @@ datasets -git+https://github.com/ml6team/express.git@main \ No newline at end of file +git+https://github.com/ml6team/express.git@f4fb9931e713bdc073031ac67fe6a7c57a0ace46 \ No newline at end of file diff --git a/express/components/hf_datasets_components.py b/express/components/hf_datasets_components.py index f251012b8..1312a98ed 100644 --- a/express/components/hf_datasets_components.py +++ b/express/components/hf_datasets_components.py @@ -63,6 +63,9 @@ def _load_data_source( with tempfile.TemporaryDirectory() as tmp_dir: data_source_location = data_source.location + print("Data source:", data_source) + print("Data source location:", data_source_location) + local_parquet_path = STORAGE_HANDLER.copy_parquet( data_source_location, tmp_dir ) From 333d716618089fc32448f007b7b856cc8e7f010e Mon Sep 17 00:00:00 2001 From: Niels Rogge Date: Thu, 23 Mar 2023 17:54:13 +0100 Subject: [PATCH 13/38] Apply fix --- .../components/image_filter_component/requirements.txt | 2 +- express/components/hf_datasets_components.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/requirements.txt b/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/requirements.txt index d58e51b1f..d570d33ba 100644 --- a/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/requirements.txt +++ b/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/requirements.txt @@ -1,2 +1,2 @@ datasets -git+https://github.com/ml6team/express.git@f4fb9931e713bdc073031ac67fe6a7c57a0ace46 \ No newline at end of file +git+https://github.com/ml6team/express.git@953b08c9315d40eef48ca645272a0c49bcd92c9b \ No newline at end of file diff --git a/express/components/hf_datasets_components.py b/express/components/hf_datasets_components.py index 1312a98ed..458882cce 100644 --- a/express/components/hf_datasets_components.py +++ b/express/components/hf_datasets_components.py @@ -74,7 +74,7 @@ def _load_data_source( dataset = load_dataset( "parquet", - data_dir=local_parquet_path, + data_files=local_parquet_path, split="train", **kwargs, ) From a3cf1b86ed0caac776c01b87011e5d07062dc29e Mon Sep 17 00:00:00 2001 From: Niels Rogge Date: Thu, 23 Mar 2023 18:02:53 +0100 Subject: [PATCH 14/38] Fix kwargs --- .../components/image_filter_component/requirements.txt | 2 +- express/components/common.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/requirements.txt b/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/requirements.txt index d570d33ba..927558a3c 100644 --- a/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/requirements.txt +++ b/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/requirements.txt @@ -1,2 +1,2 @@ datasets -git+https://github.com/ml6team/express.git@953b08c9315d40eef48ca645272a0c49bcd92c9b \ No newline at end of file +git+https://github.com/ml6team/express.git@6cb23f5733aad4318c7ebc7c43337369160fd22d \ No newline at end of file diff --git a/express/components/common.py b/express/components/common.py index 12cfcb6b5..09df511a2 100644 --- a/express/components/common.py +++ b/express/components/common.py @@ -72,7 +72,7 @@ def load( if for_index is None: for_index = self._index_data return self._load_data_source( - self.manifest.data_sources[data_source], for_index, kwargs=kwargs + self.manifest.data_sources[data_source], for_index, **kwargs ) @staticmethod From 3bf0db7a49689fe86593672a2f431efb3d5afc3f Mon Sep 17 00:00:00 2001 From: Niels Rogge Date: Thu, 23 Mar 2023 18:18:25 +0100 Subject: [PATCH 15/38] Add print statements --- .../components/image_filter_component/requirements.txt | 2 +- express/components/common.py | 1 + 2 files changed, 2 insertions(+), 1 deletion(-) diff --git a/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/requirements.txt b/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/requirements.txt index 927558a3c..28ae45c5b 100644 --- a/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/requirements.txt +++ b/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/requirements.txt @@ -1,2 +1,2 @@ datasets -git+https://github.com/ml6team/express.git@6cb23f5733aad4318c7ebc7c43337369160fd22d \ No newline at end of file +git+https://github.com/ml6team/express.git@414dbe9811efabed5092a0c7c72706143acd1232 \ No newline at end of file diff --git a/express/components/common.py b/express/components/common.py index 09df511a2..f9fb49198 100644 --- a/express/components/common.py +++ b/express/components/common.py @@ -339,6 +339,7 @@ def run(cls) -> DataManifest: DataManifest: the output manifest """ args = cls._parse_args() + print("Args:", args) input_dataset = cls._load_dataset( input_manifest=DataManifest.from_path(args.input_manifest) ) From ad2fc56c3ea91e8beec8d8a3fb3254f631b7ef83 Mon Sep 17 00:00:00 2001 From: Niels Rogge Date: Thu, 23 Mar 2023 18:30:28 +0100 Subject: [PATCH 16/38] Apply fix --- express/components/common.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/express/components/common.py b/express/components/common.py index f9fb49198..586d566ba 100644 --- a/express/components/common.py +++ b/express/components/common.py @@ -301,7 +301,6 @@ def _create_output_dataset( Processes a dataset draft of a specific type, uploading all local data to storage and composing the output manifest. """ - print("Metadata:", metadata) if isinstance(draft.index, DataSource): index = draft.index else: @@ -346,9 +345,10 @@ def run(cls) -> DataManifest: output_dataset_draft = cls.transform( data=input_dataset, extra_args=json.loads(args.extra_args) ) + metadata = Metadata.from_dict(json.loads(args.metadata)) output_manifest = cls._create_output_dataset( draft=output_dataset_draft, - metadata=json.loads(args.metadata), + metadata=metadata, save_path=args.output_manifest, ) return output_manifest From 8475c6a53f629f81ec0f8710ce204ec505d188bc Mon Sep 17 00:00:00 2001 From: Niels Rogge Date: Fri, 24 Mar 2023 13:36:16 +0100 Subject: [PATCH 17/38] Fix embedding component --- .../components/embedding_component/Dockerfile | 2 ++ .../embedding_component/build_image.sh | 28 +++++++++++++++++++ .../embedding_component/component.yaml | 15 ++++++---- .../embedding_component/requirements.txt | 4 ++- .../embedding_component/src/main.py | 22 +++++++++------ .../image_filter_component/component.yaml | 2 +- .../image_filter_component/requirements.txt | 2 +- .../config/components_config.py | 14 +++++++++- .../dataset_creation_pipeline.py | 25 ++++++++++++++++- express/components/common.py | 2 +- express/components/hf_datasets_components.py | 6 ---- 11 files changed, 97 insertions(+), 25 deletions(-) diff --git a/examples/pipelines/finetune_stable_diffusion/components/embedding_component/Dockerfile b/examples/pipelines/finetune_stable_diffusion/components/embedding_component/Dockerfile index 1b2d10bbd..d46805647 100644 --- a/examples/pipelines/finetune_stable_diffusion/components/embedding_component/Dockerfile +++ b/examples/pipelines/finetune_stable_diffusion/components/embedding_component/Dockerfile @@ -23,6 +23,8 @@ RUN pip3 install --no-cache-dir -r requirements.txt # Copy over src-files of the component COPY src /src +# TODO: ADD EXTRA FILES HERE IF NEEDED! + # Set the working directory to the source folder WORKDIR /src diff --git a/examples/pipelines/finetune_stable_diffusion/components/embedding_component/build_image.sh b/examples/pipelines/finetune_stable_diffusion/components/embedding_component/build_image.sh index e69de29bb..5b4bc5eaf 100644 --- a/examples/pipelines/finetune_stable_diffusion/components/embedding_component/build_image.sh +++ b/examples/pipelines/finetune_stable_diffusion/components/embedding_component/build_image.sh @@ -0,0 +1,28 @@ +#!/bin/bash -e +# Copyright 2018 Google LLC +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +# Read component configs +. ../../components.config + +# Set some variables +ARTIFACT_PATH=${PROJECT_ARTIFACT_PATH} +IMAGE_NAME="components/${PWD##*/}" +IMAGE_TAG="latest" + +# Create full name of the image +FULL_IMAGE_NAME=${ARTIFACT_PATH}/${IMAGE_NAME}:${IMAGE_TAG} +echo $FULL_IMAGE_NAME + +gcloud builds submit --machine-type n1-highcpu-32 . -t "$FULL_IMAGE_NAME" diff --git a/examples/pipelines/finetune_stable_diffusion/components/embedding_component/component.yaml b/examples/pipelines/finetune_stable_diffusion/components/embedding_component/component.yaml index 7d470ce7d..611d9f982 100644 --- a/examples/pipelines/finetune_stable_diffusion/components/embedding_component/component.yaml +++ b/examples/pipelines/finetune_stable_diffusion/components/embedding_component/component.yaml @@ -1,13 +1,17 @@ name: embedding_component -description: A component that embeds the images using CLIP. +description: A component that embeds images using a CLIP model from the 🤗 hub. inputs: - name: extra_args description: Additional arguments passed to the component, as a json dict string type: String - - name: metadata_args + - name: metadata description: Metadata arguments, passed as a json dict string type: String + + - name: input_manifest + description: Path to the input manifest + type: String outputs: - name: output_manifest @@ -18,7 +22,8 @@ implementation: image: europe-west4-docker.pkg.dev/soy-audio-379412/soy-audio-379412-default-repository/kubeflow-components/components/embedding_component:latest command: [ python3, main.py, - --extra-args, {inputValue: extra_args}, - --metadata-args, {inputValue: metadata_args}, - --output-manifest, {outputPath: output_manifest}, + --input-manifest, {inputPath: input_manifest}, + --metadata, {inputValue: metadata}, + --extra-args, {inputValue: extra_args}, + --output-manifest, {outputPath: output_manifest}, ] \ No newline at end of file diff --git a/examples/pipelines/finetune_stable_diffusion/components/embedding_component/requirements.txt b/examples/pipelines/finetune_stable_diffusion/components/embedding_component/requirements.txt index 39dab0fdd..11f6cc946 100644 --- a/examples/pipelines/finetune_stable_diffusion/components/embedding_component/requirements.txt +++ b/examples/pipelines/finetune_stable_diffusion/components/embedding_component/requirements.txt @@ -1,2 +1,4 @@ torch -transformers \ No newline at end of file +transformers +git+https://github.com/ml6team/express.git@main +Pillow \ No newline at end of file diff --git a/examples/pipelines/finetune_stable_diffusion/components/embedding_component/src/main.py b/examples/pipelines/finetune_stable_diffusion/components/embedding_component/src/main.py index 9878cf055..86fdf91a2 100644 --- a/examples/pipelines/finetune_stable_diffusion/components/embedding_component/src/main.py +++ b/examples/pipelines/finetune_stable_diffusion/components/embedding_component/src/main.py @@ -4,6 +4,8 @@ import logging from typing import Optional, Union, Dict +from tqdm.auto import tqdm + from express.components.hf_datasets_components import HFDatasetsTransformComponent, HFDatasetsDataset, HFDatasetsDatasetDraft from express.logger import configure_logging @@ -15,21 +17,24 @@ logger = logging.getLogger(__name__) +device = "cuda" if torch.cuda.is_available() else "cpu" + + @torch.no_grad() def embed(examples, processor, model): - images = examples["images"] + images = examples["image"] # prepare images for the model - inputs = processor(images, return_tensors="pt") + inputs = processor(images=images, return_tensors="pt").to(device) # embed to get (batch_size, hidden_size) embeddings outputs = model(**inputs) image_embeds = outputs.image_embeds # flatten into list of embeddings - batch = {"embeddings": image_embeds.tolist()} + examples["embeddings"] = image_embeds.cpu().tolist() - return batch + return examples class EmbeddingComponent(HFDatasetsTransformComponent): @@ -54,19 +59,20 @@ def transform(cls, data: HFDatasetsDataset, extra_args: Optional[ # 1) Get one particular data source from the manifest # TODO check whether we can leverage streaming - logger.info("Loading caption dataset...") + logger.info("Loading image dataset...") image_dataset = data.load(data_source="images") # 2) Create embedding dataset - logger.info("Embedding images...") - + logger.info("Loading CLIP...") processor = CLIPProcessor.from_pretrained(extra_args["model_id"]) model = CLIPVisionModelWithProjection.from_pretrained(extra_args["model_id"]) + model.to(device) + logger.info("Embedding images...") embedded_dataset = image_dataset.map(embed, batched=True, batch_size=extra_args["batch_size"], fn_kwargs=dict(processor=processor, model=model), - remove_columns=["image", "text"]) + remove_columns=["image"]) # 3) Create dataset draft which adds a data source to the manifest logger.info("Creating draft...") diff --git a/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/component.yaml b/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/component.yaml index 330d45deb..f2d8187b8 100644 --- a/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/component.yaml +++ b/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/component.yaml @@ -1,5 +1,5 @@ name: image_filter_component -description: A basic component that takes a dataset name from the 🤗 hub as input and uploads it to a GCS bucket. +description: A component that filters images based on certain conditions. inputs: - name: extra_args description: Additional arguments passed to the component, as a json dict string diff --git a/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/requirements.txt b/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/requirements.txt index 28ae45c5b..c21634ed3 100644 --- a/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/requirements.txt +++ b/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/requirements.txt @@ -1,2 +1,2 @@ datasets -git+https://github.com/ml6team/express.git@414dbe9811efabed5092a0c7c72706143acd1232 \ No newline at end of file +git+https://github.com/ml6team/express.git@2d630255f82c6a9d7d15e7be23ff3d9b9ba16723 \ No newline at end of file diff --git a/examples/pipelines/finetune_stable_diffusion/config/components_config.py b/examples/pipelines/finetune_stable_diffusion/config/components_config.py index de30b95cd..d1d7d3f71 100644 --- a/examples/pipelines/finetune_stable_diffusion/config/components_config.py +++ b/examples/pipelines/finetune_stable_diffusion/config/components_config.py @@ -19,4 +19,16 @@ class ImageFilterConfig: MIN_WIDTH (int): Minimum width for each image. """ MIN_HEIGHT = 400 - MIN_WIDTH = 400 \ No newline at end of file + MIN_WIDTH = 400 + + +@dataclass +class EmbeddingConfig: + """ + Configs for the embedding component + Params: + MODEL_ID (int): HF model id to use for embedding. + BATCH_SIZE (int): Batch size to use when embedding. + """ + MODEL_ID = "openai/clip-vit-large-patch14" + BATCH_SIZE = 10 \ No newline at end of file diff --git a/examples/pipelines/finetune_stable_diffusion/dataset_creation_pipeline.py b/examples/pipelines/finetune_stable_diffusion/dataset_creation_pipeline.py index 063a3762d..be838f904 100644 --- a/examples/pipelines/finetune_stable_diffusion/dataset_creation_pipeline.py +++ b/examples/pipelines/finetune_stable_diffusion/dataset_creation_pipeline.py @@ -5,8 +5,10 @@ from kfp import components as comp from kfp import dsl +from kubernetes import client as k8s_client + from config.general_config import KubeflowConfig -from config.components_config import DatasetLoaderConfig, ImageFilterConfig +from config.components_config import DatasetLoaderConfig, ImageFilterConfig, EmbeddingConfig from express.kfp_utils import compile_and_upload_pipeline # Load Components @@ -28,6 +30,14 @@ image_filter_metadata_args = json.dumps(image_filter_metadata_args) +# Component 3 +embedding_op = comp.load_component('components/embedding_component/component.yaml') +embedding_extra_args = {"model_id": EmbeddingConfig.MODEL_ID, "batch_size": EmbeddingConfig.BATCH_SIZE} +embedding_metadata_args = {"run_id": run_id, "component_name": embedding_op.__name__, "artifact_bucket": artifact_bucket} +embedding_extra_args = json.dumps(embedding_extra_args) +embedding_metadata_args = json.dumps(embedding_metadata_args) + + # Pipeline @dsl.pipeline( name='image-generator-dataset', @@ -39,6 +49,8 @@ def sd_dataset_creator_pipeline(dataset_loader_extra_args: str = dataset_loader_ dataset_loader_metadata_args: str = dataset_loader_metadata_args, image_filter_extra_args: str = image_filter_extra_args, image_filter_metadata_args: str = image_filter_metadata_args, + embedding_extra_args: str = embedding_extra_args, + embedding_metadata_args: str = embedding_metadata_args, ): # Component 1 dataset_loader_task = dataset_loader_op(extra_args=dataset_loader_extra_args, @@ -51,6 +63,17 @@ def sd_dataset_creator_pipeline(dataset_loader_extra_args: str = dataset_loader_ input_manifest=dataset_loader_task.outputs["output_manifest"], ).set_display_name('Filter images') + # Component 3 + embedding_task = embedding_op(extra_args=embedding_extra_args, + metadata=embedding_metadata_args, + input_manifest=image_filter_task.outputs["output_manifest"], + ).set_display_name('Embed images') \ + .set_gpu_limit(1) \ + .add_node_selector_constraint('node_pool', 'model-inference-pool') \ + .add_toleration( + k8s_client.V1Toleration(effect='NoSchedule', key='reserved-pool', operator='Equal', + value='true')) + if __name__ == '__main__': compile_and_upload_pipeline(pipeline=sd_dataset_creator_pipeline, diff --git a/express/components/common.py b/express/components/common.py index 586d566ba..371fdd3b8 100644 --- a/express/components/common.py +++ b/express/components/common.py @@ -135,7 +135,7 @@ def __init__( "added to an extending dataset draft after it's been constructed." ) self.index = extending_dataset.manifest.index - for name, dataset in extending_dataset.manifest.associated_data.items(): + for name, dataset in extending_dataset.manifest.data_sources.items(): self.with_data_source(name, dataset, replace_ok=False) @classmethod diff --git a/express/components/hf_datasets_components.py b/express/components/hf_datasets_components.py index 458882cce..d004b9b74 100644 --- a/express/components/hf_datasets_components.py +++ b/express/components/hf_datasets_components.py @@ -63,15 +63,10 @@ def _load_data_source( with tempfile.TemporaryDirectory() as tmp_dir: data_source_location = data_source.location - print("Data source:", data_source) - print("Data source location:", data_source_location) - local_parquet_path = STORAGE_HANDLER.copy_parquet( data_source_location, tmp_dir ) - print("Loading from path:", local_parquet_path) - dataset = load_dataset( "parquet", data_files=local_parquet_path, @@ -81,7 +76,6 @@ def _load_data_source( if index_filter: index = index_filter["index"] - print("Index:", index) return dataset.filter(lambda example: example["index"] in index) return dataset From e05b68060e9e1701c60b6c9b033170a07ec3fbc4 Mon Sep 17 00:00:00 2001 From: Niels Rogge Date: Fri, 24 Mar 2023 14:14:15 +0100 Subject: [PATCH 18/38] Update requirements --- .../components/embedding_component/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/pipelines/finetune_stable_diffusion/components/embedding_component/requirements.txt b/examples/pipelines/finetune_stable_diffusion/components/embedding_component/requirements.txt index 11f6cc946..08befdd72 100644 --- a/examples/pipelines/finetune_stable_diffusion/components/embedding_component/requirements.txt +++ b/examples/pipelines/finetune_stable_diffusion/components/embedding_component/requirements.txt @@ -1,4 +1,4 @@ torch transformers -git+https://github.com/ml6team/express.git@main +git+https://github.com/ml6team/express.git@0f43bbd88826ffd1b620100275700f9cbe7c0b6e Pillow \ No newline at end of file From efa0850c76adcff4ddba422e254d93f470728ae3 Mon Sep 17 00:00:00 2001 From: Niels Rogge Date: Fri, 24 Mar 2023 14:53:03 +0100 Subject: [PATCH 19/38] Use single build_images.sh script --- .../embedding_component/build_image.sh | 28 ------------------- .../image_filter_component/build_image.sh | 28 ------------------- 2 files changed, 56 deletions(-) delete mode 100644 examples/pipelines/finetune_stable_diffusion/components/embedding_component/build_image.sh delete mode 100644 examples/pipelines/finetune_stable_diffusion/components/image_filter_component/build_image.sh diff --git a/examples/pipelines/finetune_stable_diffusion/components/embedding_component/build_image.sh b/examples/pipelines/finetune_stable_diffusion/components/embedding_component/build_image.sh deleted file mode 100644 index 5b4bc5eaf..000000000 --- a/examples/pipelines/finetune_stable_diffusion/components/embedding_component/build_image.sh +++ /dev/null @@ -1,28 +0,0 @@ -#!/bin/bash -e -# Copyright 2018 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -# Read component configs -. ../../components.config - -# Set some variables -ARTIFACT_PATH=${PROJECT_ARTIFACT_PATH} -IMAGE_NAME="components/${PWD##*/}" -IMAGE_TAG="latest" - -# Create full name of the image -FULL_IMAGE_NAME=${ARTIFACT_PATH}/${IMAGE_NAME}:${IMAGE_TAG} -echo $FULL_IMAGE_NAME - -gcloud builds submit --machine-type n1-highcpu-32 . -t "$FULL_IMAGE_NAME" diff --git a/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/build_image.sh b/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/build_image.sh deleted file mode 100644 index 5b4bc5eaf..000000000 --- a/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/build_image.sh +++ /dev/null @@ -1,28 +0,0 @@ -#!/bin/bash -e -# Copyright 2018 Google LLC -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -# Read component configs -. ../../components.config - -# Set some variables -ARTIFACT_PATH=${PROJECT_ARTIFACT_PATH} -IMAGE_NAME="components/${PWD##*/}" -IMAGE_TAG="latest" - -# Create full name of the image -FULL_IMAGE_NAME=${ARTIFACT_PATH}/${IMAGE_NAME}:${IMAGE_TAG} -echo $FULL_IMAGE_NAME - -gcloud builds submit --machine-type n1-highcpu-32 . -t "$FULL_IMAGE_NAME" From 25104512209409a312c1906a7bff1874aa9bb2db Mon Sep 17 00:00:00 2001 From: Niels Rogge Date: Fri, 24 Mar 2023 15:48:19 +0100 Subject: [PATCH 20/38] Update image paths --- .../components/dataset_loader_component/component.yaml | 2 +- .../components/embedding_component/component.yaml | 2 +- .../components/image_filter_component/component.yaml | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/examples/pipelines/finetune_stable_diffusion/components/dataset_loader_component/component.yaml b/examples/pipelines/finetune_stable_diffusion/components/dataset_loader_component/component.yaml index d40239bf2..bc47cfcbc 100644 --- a/examples/pipelines/finetune_stable_diffusion/components/dataset_loader_component/component.yaml +++ b/examples/pipelines/finetune_stable_diffusion/components/dataset_loader_component/component.yaml @@ -15,7 +15,7 @@ outputs: implementation: container: - image: europe-west4-docker.pkg.dev/soy-audio-379412/soy-audio-379412-default-repository/kubeflow-components/components/dataset_loader_component:latest + image: ghcr.io/ml6team/dataset_loader_component:latest command: [ python3, main.py, --extra-args, {inputValue: extra_args}, diff --git a/examples/pipelines/finetune_stable_diffusion/components/embedding_component/component.yaml b/examples/pipelines/finetune_stable_diffusion/components/embedding_component/component.yaml index 611d9f982..164476847 100644 --- a/examples/pipelines/finetune_stable_diffusion/components/embedding_component/component.yaml +++ b/examples/pipelines/finetune_stable_diffusion/components/embedding_component/component.yaml @@ -19,7 +19,7 @@ outputs: implementation: container: - image: europe-west4-docker.pkg.dev/soy-audio-379412/soy-audio-379412-default-repository/kubeflow-components/components/embedding_component:latest + image: ghcr.io/ml6team/embedding_component:latest command: [ python3, main.py, --input-manifest, {inputPath: input_manifest}, diff --git a/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/component.yaml b/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/component.yaml index f2d8187b8..c7f691180 100644 --- a/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/component.yaml +++ b/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/component.yaml @@ -19,7 +19,7 @@ outputs: implementation: container: - image: europe-west4-docker.pkg.dev/soy-audio-379412/soy-audio-379412-default-repository/kubeflow-components/components/image_filter_component:latest + image: ghcr.io/ml6team/image_filter_component:latest command: [ python3, main.py, --input-manifest, {inputPath: input_manifest}, From 852a2fa03fb6d9d581c6ebf7e9e4e88d39398fdf Mon Sep 17 00:00:00 2001 From: Niels Rogge Date: Tue, 28 Mar 2023 13:20:06 +0200 Subject: [PATCH 21/38] Add clip retrieval component --- .../clip_retrieval_component/Dockerfile | 31 +++++++++++++++++++ .../clip_retrieval_component/component.yaml | 29 +++++++++++++++++ .../clip_retrieval_component/main.py | 0 .../clip_retrieval_component/requirements.txt | 0 .../clip_retrieval_component/src/main.py | 0 .../dataset_loader_component/src/main.py | 2 +- .../dataset_creation_pipeline.py | 2 +- 7 files changed, 62 insertions(+), 2 deletions(-) create mode 100644 examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/Dockerfile create mode 100644 examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/component.yaml create mode 100644 examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/main.py create mode 100644 examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/requirements.txt create mode 100644 examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/src/main.py diff --git a/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/Dockerfile b/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/Dockerfile new file mode 100644 index 000000000..d46805647 --- /dev/null +++ b/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/Dockerfile @@ -0,0 +1,31 @@ +FROM python:3.8-slim + +## System dependencies +RUN apt-get update && \ + apt-get upgrade -y && \ + apt-get install git curl -y + +# Downloading gcloud package +RUN curl https://dl.google.com/dl/cloudsdk/release/google-cloud-sdk.tar.gz > /tmp/google-cloud-sdk.tar.gz + +# Installing the package +RUN mkdir -p /usr/local/gcloud \ +&& tar -C /usr/local/gcloud -xvf /tmp/google-cloud-sdk.tar.gz \ +&& /usr/local/gcloud/google-cloud-sdk/install.sh + +# Adding the package path to local +ENV PATH $PATH:/usr/local/gcloud/google-cloud-sdk/bin + +# install requirements +COPY requirements.txt / +RUN pip3 install --no-cache-dir -r requirements.txt + +# Copy over src-files of the component +COPY src /src + +# TODO: ADD EXTRA FILES HERE IF NEEDED! + +# Set the working directory to the source folder +WORKDIR /src + +ENTRYPOINT ["python", "main.py"] \ No newline at end of file diff --git a/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/component.yaml b/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/component.yaml new file mode 100644 index 000000000..b518afc0f --- /dev/null +++ b/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/component.yaml @@ -0,0 +1,29 @@ +name: clip_retrieval_component +description: A component that retrieves similar images from LAION using CLIP. +inputs: + - name: extra_args + description: Additional arguments passed to the component, as a json dict string + type: String + + - name: metadata + description: Metadata arguments, passed as a json dict string + type: String + + - name: input_manifest + description: Path to the input manifest + type: String + +outputs: + - name: output_manifest + description: Path to the output manifest + +implementation: + container: + image: ghcr.io/ml6team/clip_retrieval_component:latest + command: [ + python3, main.py, + --input-manifest, {inputPath: input_manifest}, + --metadata, {inputValue: metadata}, + --extra-args, {inputValue: extra_args}, + --output-manifest, {outputPath: output_manifest}, + ] \ No newline at end of file diff --git a/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/main.py b/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/main.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/requirements.txt b/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/requirements.txt new file mode 100644 index 000000000..e69de29bb diff --git a/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/src/main.py b/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/src/main.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/pipelines/finetune_stable_diffusion/components/dataset_loader_component/src/main.py b/examples/pipelines/finetune_stable_diffusion/components/dataset_loader_component/src/main.py index f6ea8159d..7ca81ee19 100644 --- a/examples/pipelines/finetune_stable_diffusion/components/dataset_loader_component/src/main.py +++ b/examples/pipelines/finetune_stable_diffusion/components/dataset_loader_component/src/main.py @@ -38,7 +38,7 @@ def load(cls, extra_args: Optional[ Returns: HFDatasetsDatasetDraft: a dataset draft that creates a plan for an output manifest """ - + # 1) Create data source logger.info("Loading caption dataset from the hub...") dataset = load_dataset(extra_args["dataset_name"], split="train") diff --git a/examples/pipelines/finetune_stable_diffusion/dataset_creation_pipeline.py b/examples/pipelines/finetune_stable_diffusion/dataset_creation_pipeline.py index be838f904..2bb3ad7e6 100644 --- a/examples/pipelines/finetune_stable_diffusion/dataset_creation_pipeline.py +++ b/examples/pipelines/finetune_stable_diffusion/dataset_creation_pipeline.py @@ -9,7 +9,7 @@ from config.general_config import KubeflowConfig from config.components_config import DatasetLoaderConfig, ImageFilterConfig, EmbeddingConfig -from express.kfp_utils import compile_and_upload_pipeline +from express.pipeline_utils import compile_and_upload_pipeline # Load Components run_id = '{{workflow.name}}' From bf51ffbf6d5ee8780a30e046be1221edeefc97c2 Mon Sep 17 00:00:00 2001 From: Niels Rogge Date: Wed, 29 Mar 2023 11:25:36 +0200 Subject: [PATCH 22/38] Add back the non updated files --- docs/README.md | 60 +- .../finetune_stable_diffusion/README.md | 103 +++ .../clip_downloader_component/Dockerfile | 21 + .../clip_downloader_component/README.MD | 38 + .../__init__.py} | 0 .../clip_downloader_component/component.yaml | 59 ++ .../requirements.txt | 2 + .../clip_downloader_component/src/__init__.py | 0 .../clip_downloader_component/src/main.py | 251 ++++++ .../src/utils/__init__.py | 0 .../src/utils/image_downloader.py | 223 +++++ .../clip_retrieval_component/Dockerfile | 31 +- .../clip_retrieval_component/README.MD | 104 +++ .../clip_retrieval_component/__init__.py | 0 .../clip_retrieval_component/component.yaml | 77 +- .../docs/benchmark_clip_retrieval.png | Bin 0 -> 23118 bytes .../retrieved_images_centroid_example.png | Bin 0 -> 557092 bytes .../docs/retrieved_images_knn_example.jpg | Bin 0 -> 832943 bytes .../docs/santa_captioned.jpg | Bin 0 -> 40175 bytes .../clip_retrieval_component/requirements.txt | 5 + .../clip_retrieval_component/src/__init__.py | 0 .../clip_retrieval_component/src/main.py | 267 ++++++ .../src/utils/__init__.py | 0 .../src/utils/embedding_utils.py | 19 + .../src/utils/knn_service.py | 156 ++++ .../src/utils/timer.py | 53 ++ .../image_caption_component/Dockerfile | 28 + .../image_caption_component/README.MD | 26 + .../image_caption_component/__init__.py | 0 .../image_caption_component/component.yaml | 52 ++ .../docs/santa_captioned.jpg | Bin 0 -> 40175 bytes .../image_caption_component/requirements.txt | 9 + .../image_caption_component/src/__init__.py | 0 .../image_caption_component/src/main.py | 217 +++++ .../src/utils/__init__.py | 0 .../src/utils/blip_model.py | 103 +++ .../src/utils/image_captioning.py | 127 +++ .../image_conversion_component/Dockerfile | 18 + .../image_conversion_component/README.MD | 38 + .../image_conversion_component/__init__.py | 0 .../image_conversion_component/component.yaml | 48 + .../docs/png_normal_conversion.png | Bin 0 -> 441589 bytes .../requirements.txt | 5 + .../src/__init__.py | 0 .../image_conversion_component/src/main.py | 199 +++++ .../src/utils/__init__.py | 0 .../src/utils/img_conversion.py | 200 +++++ .../sd_finetuning_component/Dockerfile | 23 + .../sd_finetuning_component/README.MD | 26 + .../sd_finetuning_component/__init__.py | 0 .../sd_finetuning_component/component.yaml | 122 +++ .../sd_finetuning_component/requirements.txt | 14 + .../sd_finetuning_component/src/__init__.py | 0 .../src/accelerate_config.yaml | 16 + .../sd_finetuning_component/src/main.py | 399 +++++++++ .../src/train_text_to_image.py | 819 ++++++++++++++++++ .../src/utils/dataset_utils.py | 99 +++ .../src/utils/training_utils.py | 107 +++ .../sd_finetuning_pipeline.py | 138 +++ 59 files changed, 4253 insertions(+), 49 deletions(-) create mode 100644 examples/pipelines/finetune_stable_diffusion/README.md create mode 100644 examples/pipelines/finetune_stable_diffusion/components/clip_downloader_component/Dockerfile create mode 100644 examples/pipelines/finetune_stable_diffusion/components/clip_downloader_component/README.MD rename examples/pipelines/finetune_stable_diffusion/components/{clip_retrieval_component/main.py => clip_downloader_component/__init__.py} (100%) create mode 100644 examples/pipelines/finetune_stable_diffusion/components/clip_downloader_component/component.yaml create mode 100644 examples/pipelines/finetune_stable_diffusion/components/clip_downloader_component/requirements.txt create mode 100644 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a/docs/README.md +++ b/docs/README.md @@ -99,4 +99,62 @@ After transforming the input data (see below), an **ExpressDatasetDraft** create ### 1.b) Transforms and Loaders The most common type of component in Express is an **ExpressTransformComponent**, which takes an `ExpressDataset` and an optional dict of arguments as input and returns an `ExpressDatasetDraft` of transformed output data. -However, at the start of a new pipeline, you won't yet have any express datasets to transform. Instead, an express pipeline can use an **ExpressLoaderComponent** as entry-point, which only takes the optional dict of arguments to construct an ExpressDatasetDraft. For example, the arguments could specify an external data location and how to interpret it, after which a loader job can create a first `ExpressDataset`. \ No newline at end of file +However, at the start of a new pipeline, you won't yet have any express datasets to transform. Instead, an express pipeline can use an **ExpressLoaderComponent** as entry-point, which only takes the optional dict of arguments to construct an ExpressDatasetDraft. For example, the arguments could specify an external data location and how to interpret it, after which a loader job can create a first `ExpressDataset`. + +## **Data Manifest: a common approach to simplify different steps throughout the pipeline** +In order to keep track of the different data sources, we opt for a manifest-centered approach where +a manifest is simply a JSON file that is passed and modified throughout the different steps of the pipeline. + +```json +{ + "dataset_id":"-", + "index":"", + "associated_data":{ + "dataset":{ + "namespace_1":"", + "...":"" + }, + "caption":{ + "namespace_1":"", + "...":"" + }, + "embedding":{ + "namespace_1":"", + "commit_hash":"", + "creation_date":"", + "run_id":"" + } +} +``` +Further deep dive on some notations: + +* **namespace:** the namespace is used to identify the different data sources. For example, you can give +your seed images a specific namespace (e.g. `seed`). Then, the images retrieved with clip-retrieval will +have different namespace (e.g. `knn`, `centroid`). + +* **index**: the index denotes a unique index for each images with the format (e.g. `seed_00010`). +It indexes all the data sources in `associated_data`. +**Note**: the index keeps track of all the namespace (e.g. [`seed_00010`,`centroid_0001`, ...]) + +* **dataset**: a set of parquet files for each namespace that contain relevant metadata +(image size, location, ...) as well as the index. + +* **caption**: a set of parquet files for each namespace that contain captions +image captions as well as the index. + +* **metadata**: Helps keep track of the step that generated that manifest, code version and pipeline run id. + +The Express pipeline consists of multiple steps defines as **Express steps** that are repeated +throughout the pipeline. The manifest pattern offers the required flexibility to promote its reuse and avoid +duplication of data sources. For example: + +* **Data filtering** (e.g. filtering on image size): add new indices to the `index` but retain associated data. + +* **Data creation** (e.g. clip retrieval): add new indicies to the new `index` and another source of data under associated data with a new namespace. + +* **Data transformation** (e.g. image formatting): retain indices but replace dataset source in `dataset`. \ No newline at end of file diff --git a/examples/pipelines/finetune_stable_diffusion/README.md b/examples/pipelines/finetune_stable_diffusion/README.md new file mode 100644 index 000000000..3dd335618 --- /dev/null +++ b/examples/pipelines/finetune_stable_diffusion/README.md @@ -0,0 +1,103 @@ +# Pipelines for fine-tuning Stable Diffusion + +This folder contains 2 separate KubeFlow pipelines: + +* [Dataset Creation Pipeline](#1-dataset-creation-pipeline): creates the dataset that will be used + to finetune the Stable Diffusion model +* [Stable Diffusion Finetuning pipeline](#2-sd-finetuning-pipeline): finetunes a pretrained Stable + Diffusion model on the created dataset. + +The separation between the two pipelines enables to run them disjointly to allow for quick +and fast experimentation; the dataset creation pipeline will in practice only be run once in a while to collect large amount of images. The Stable Diffusion finetuning pipeline on the other hand might be run several times to allow experimentation +with different hyperparameters or to resume training from a certain checkpoint. + +## [1. Dataset Creation pipeline](pipelines/dataset_creation_pipeline.py) + +### Description + +The aim of this pipeline is to prepare a dataset to finetune a pre-trained Stable Diffusion on, based on a set of seed images. + +In short, the pipeline first loads in a reference (seed) dataset containing curated images of a certain style/domain +(e.g. clip art images) and then retrieves similar images to this dataset from the [LAION dataset](https://laion.ai/), a large scale public dataset containing around 5 billion images. The retrieved images then undergo additional filtering +steps to ensure that they are of good quality (e.g. single component, clean-cut). Finally, the remaining images +are then captioned using a captioning model to generate image/caption pairs for Stable Diffusion finetuning. + +### Pipeline steps + +The pipeline consists of the following components: + +**[1) Dataset loader component:](components/dataset_loader_component)** This component +loads in an image dataset from a specific [Google Cloud Storage](https://cloud.google.com/storage/docs) path and creates a +parquet file with relevant metadata (image path, format, size...). + +**[2) Image filter component:](components/image_filter_component)** This component is +used to filter the images based on the metadata +attributes to only keep images of a certain size and format. + +**[3) Image conversion component:](components/image_conversion_component)** This +component converts images from different selected formats +(currently only `png` and `svg`) to `jpg` images. This step is necessary since other images formats +are not suitable for training ML models on and often contain artifacts that can be eliminated +during conversion. + +**[4) Image embedding component:](components/image_embedding_component)** This component +extracts [image embeddings](https://rom1504.medium.com/image-embeddings-ed1b194d113e) +from the converted images using +a [CLIP model](https://www.google.com/search?q=clip+embeddings&oq=clip+embeddings&aqs=chrome..69i57j0i22i30j69i60j69i64l2j69i60j69i64j69i60.6764j0j7&sourceid=chrome&ie=UTF-8). +Since image embeddings are good at capturing the features of the image in a compact and useful way, +it +will be used in the next steps to retrieve images similar to our seed images. + +[**5) Clip retrieval component:**](components/clip_retrieval_component) This component +retrieves images from the LAION dataset using a clip +retrieval system. The images are retrieved using an efficient index built from the previously +extracted embeddings that enables for fast and accurate +querying against a large database. Checkout +this [link](https://github.com/rom1504/clip-retrieval) for information on clip retrieval. You can +also +test out clip retrieval in +this [demo](https://rom1504.github.io/clip-retrieval/?back=https%3A%2F%2Fknn5.laion.ai&index=laion5B&useMclip=false). +The output of this component is a list of URLs containing the link to the retrieved image. + +[**6) Clip downloader component:**](components/clip_downloader_component) This component +downloads the images from the list of URLs and +creates the corresponding datasets. It uses the +following [library](https://github.com/rom1504/img2dataset) +for efficient and fast image download (multi-thread approach). The images are also filtered (based +on size +and area), resized and converted during download. + +[**7) Image caption component:**](components/image_caption_component) This component +uses a captioning +model ([BLIP](https://github.com/salesforce/BLIP)) +to caption the final filtered images for training. + +## [2. SD finetuning pipeline](pipelines/sd_finetuning_pipeline.py) + +### Description + +This pipeline aims to finetune a pre-trained stable diffusion model on the collected dataset. + +### Pipeline steps + +This pipeline consists only of a single component: + +**[1) SD finetuning component:](components/sd_finetuning_component)** takes as input the +final data manifest that is output by the dataset +creation pipeline. The data +manifest keeps reference of all the necessary metadata is required for the next step such as the +reference to the filtered images and captions. The component prepares the dataset for training +according to the +required [format](https://huggingface.co/docs/datasets/image_dataset#:~:text=in%20load_dataset.-,Image%20captioning,-Image%20captioning%20datasets) +and starts the finetuning jobs. + +# Building the images + +To build and push the component docker images to the container registry, execute the following command: + +```bash +bash build_images.sh +``` + +This will build all the components located in the `components` folder, you could also opt for building a specific component +by passing the `--build-dir` and passing the folder name of the component you want to build. diff --git a/examples/pipelines/finetune_stable_diffusion/components/clip_downloader_component/Dockerfile b/examples/pipelines/finetune_stable_diffusion/components/clip_downloader_component/Dockerfile new file mode 100644 index 000000000..a89965608 --- /dev/null +++ b/examples/pipelines/finetune_stable_diffusion/components/clip_downloader_component/Dockerfile @@ -0,0 +1,21 @@ +FROM europe-west1-docker.pkg.dev/storied-landing-366912/storied-landing-366912-default-repository/mlpipelines/kubeflow/components/base_component:latest + +ARG DEBIAN_FRONTEND=noninteractive + +# add required packages from universe repository +# Install opencv dependices +RUN apt-get update \ + && apt-get install ffmpeg libsm6 libxext6 libopencv-dev -y + +# Set the working directory to the source folder +WORKDIR /src + +# Copy over src-files of the component +COPY requirements.txt . + +# Install packages +RUN pip3 install -r requirements.txt + +COPY src /src + +ENTRYPOINT ["python", "main.py"] \ No newline at end of file diff --git a/examples/pipelines/finetune_stable_diffusion/components/clip_downloader_component/README.MD b/examples/pipelines/finetune_stable_diffusion/components/clip_downloader_component/README.MD new file mode 100644 index 000000000..0709ab7a8 --- /dev/null +++ b/examples/pipelines/finetune_stable_diffusion/components/clip_downloader_component/README.MD @@ -0,0 +1,38 @@ +# clip_downloader_component + +### Description + +This component downloads the images from the list of URLs retrieved from the +[`clip_retireval_components`](../clip_retrieval_component) and +creates the corresponding datasets. It uses the +[img2dataset library](https://github.com/rom1504/img2dataset) +for efficient and fast image download (multi-thread approach). The images are also filtered (based +on size +and area), resized and converted during download. + +The components extends the data manifest with two additional namespaces: `knn` and `centroid` to +denote +the datasets of those images. + +### **Inputs/Outputs** + +See [`component.yaml`](component.yaml) for a more detailed description on all the input/output +parameters. + +### **Practical considerations** + +* This component can be considered to perform multiple tasks at once -> image downloading, + filtering, + formatting and resizing. +* Not all the urls can be downloaded, some urls are broken or some images are filtered out + depending on size (in practice only around ~75% of the urls are valid). + At the end of the execution run, a detailed log report to get detailed information on the download + job execution + and why some urls failed to download. +* The images are resized to the expected values of stable diffusion (512x512). Since some images are + not squared, + the resizing might distort the images. The current resize is set to `border` which resizes the + images while adding + a border to the images. You might want to change + this [parameter](https://github.com/rom1504/img2dataset#:~:text=resize_mode%20The%20way%20to%20resize%20pictures%2C%20can%20be%20no%2C%20border%20or%20keep_ratio%20(default%20border)) + depending on your use case. \ No newline at end of file diff --git a/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/main.py b/examples/pipelines/finetune_stable_diffusion/components/clip_downloader_component/__init__.py similarity index 100% rename from examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/main.py rename to examples/pipelines/finetune_stable_diffusion/components/clip_downloader_component/__init__.py diff --git a/examples/pipelines/finetune_stable_diffusion/components/clip_downloader_component/component.yaml b/examples/pipelines/finetune_stable_diffusion/components/clip_downloader_component/component.yaml new file mode 100644 index 000000000..3715d2203 --- /dev/null +++ b/examples/pipelines/finetune_stable_diffusion/components/clip_downloader_component/component.yaml @@ -0,0 +1,59 @@ +name: clip_downloader_component +description: A component that takes a the parquet files generated from clip retrieval, downloads them and uploads them to storage. It also includes deduplication, resizing and filtering steps. +inputs: + - name: run_id + description: The run id of the pipeline + type: String + - name: artifact_bucket + description: The GCS bucket used to store the artifact + type: String + - name: component_name + description: the name of the component (used to create gcs artefact path) + type: String + - name: project_id + description: The id of the gcp-project + type: String + - name: image_resize + description: the size to resize the image + type: Integer + - name: timeout + description: maximum time (in seconds to wait) when trying to download an image + type: Integer + - name: min_image_size + description: minimum size of the image to download (default 0) + type: Integer + - name: max_image_area + description: maximum area of the image to download (default inf) + type: Integer + - name: data_manifest_path + description: The previous component manifest path + type: String + - name: parquet_path_clip_centroid_retrieval + description: The path to the parquet file containing the urls from centroid retrieval + type: String + - name: parquet_path_clip_knn_retrieval + description: The path to the parquet file containing the urls from knn retrieval + type: String + +outputs: + - name: data_manifest_path_clip_downloader_component + description: the current component manifest path + +implementation: + container: + image: europe-west1-docker.pkg.dev/storied-landing-366912/storied-landing-366912-default-repository/mlpipelines/kubeflow/components/clip_downloader_component:latest + command: [ + python3, main.py, + --run-id, { inputValue: run_id }, + --artifact-bucket, { inputValue: artifact_bucket }, + --component-name, { inputValue: component_name }, + --project-id, { inputValue: project_id, }, + --image-resize, { inputValue: image_resize }, + --timeout, { inputValue: timeout }, + --min-image-size, { inputValue: min_image_size }, + --max-image-area, { inputValue: max_image_area }, + --data-manifest-path, { inputPath: data_manifest_path }, + --parquet-path-clip-knn-retrieval, { inputPath: parquet_path_clip_knn_retrieval }, + --parquet-path-clip-centroid-retrieval, { inputPath: parquet_path_clip_centroid_retrieval }, + --data-manifest-path-clip-downloader-component, { outputPath: data_manifest_path_clip_downloader_component }, + ] diff --git a/examples/pipelines/finetune_stable_diffusion/components/clip_downloader_component/requirements.txt b/examples/pipelines/finetune_stable_diffusion/components/clip_downloader_component/requirements.txt new file mode 100644 index 000000000..cb6e365c8 --- /dev/null +++ b/examples/pipelines/finetune_stable_diffusion/components/clip_downloader_component/requirements.txt @@ -0,0 +1,2 @@ +img2dataset==1.36.0 +tqdm==4.64.1 \ No newline at end of file diff --git a/examples/pipelines/finetune_stable_diffusion/components/clip_downloader_component/src/__init__.py b/examples/pipelines/finetune_stable_diffusion/components/clip_downloader_component/src/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/pipelines/finetune_stable_diffusion/components/clip_downloader_component/src/main.py b/examples/pipelines/finetune_stable_diffusion/components/clip_downloader_component/src/main.py new file mode 100644 index 000000000..9823910e2 --- /dev/null +++ b/examples/pipelines/finetune_stable_diffusion/components/clip_downloader_component/src/main.py @@ -0,0 +1,251 @@ +""" +This file is the entrypoint of the component. It will parse all arguments +and give them to the actual core of the component. +""" + +import os +import tempfile +import argparse +import logging +import json +from pathlib import Path +from datetime import datetime + +from google.cloud import storage +from tqdm import tqdm + +# pylint: disable=import-error +from utils.image_downloader import ImageDownloaderInput, ImageDownloader +from helpers.logger import get_logger +from helpers import storage_helpers, parquet_helpers +from helpers.manifest_helpers import DataManifest + + +def parse_args(): + """Parse component arguments""" + + parser = argparse.ArgumentParser() + parser.add_argument('--run-id', + type=str, + required=True, + help='The run id of the pipeline') + parser.add_argument('--artifact-bucket', + type=str, + required=True, + help='The GCS bucket used to store the artifacts') + parser.add_argument('--component-name', + type=str, + required=True, + help='The name of the component') + parser.add_argument('--project-id', + type=str, + required=True, + help='The id of the gcp-project') + parser.add_argument('--image-resize', + type=str, + required=True, + help='the size to resize the image') + parser.add_argument('--timeout', + type=int, + required=True, + help='maximum time (in seconds to wait) when trying to download an image') + parser.add_argument('--min-image-size', + type=int, + required=True, + help='Minimum size of the image to download (default 0)') + parser.add_argument('--max-image-area', + type=int, + required=True, + help='maximum area of the image to download (default inf)') + parser.add_argument('--data-manifest-path', + type=str, + required=True, + help='The previous component manifest path') + parser.add_argument('--parquet-path-clip-knn-retrieval', + type=str, + required=True, + help='The path to the parquet file containing the urls from knn retrieval') + parser.add_argument('--parquet-path-clip-centroid-retrieval', + type=str, + required=True, + help='The path to the parquet file containing the urls from centroid ' + 'retrieval') + parser.add_argument('--data-manifest-path-clip-downloader-component', + type=str, + required=True, + help='Path to the local file containing the gcs path where the output' + ' has been stored') + + return parser.parse_args() + + +# pylint: disable=too-many-locals, too-many-arguments, too-many-statements +def clip_downloader_component(run_id: str, + artifact_bucket: str, + component_name: str, + project_id: str, + image_resize: int, + timeout: int, + min_image_size: int, + max_image_area: int, + data_manifest_path: str, + parquet_path_clip_knn_retrieval: str, + parquet_path_clip_centroid_retrieval: str, + data_manifest_path_clip_downloader_component: str) -> None: + """ + Args: + run_id (str): The run id of the pipeline + artifact_bucket (str): The GCS bucket used to store the artifact + component_name (str): the name of the component (used to create gcs artefact path) + project_id (str): The id of the gcp-project + image_resize (int): the size to resize the image + timeout (int): maximum time (in seconds to wait) when trying to download an image + min_image_size (int): minimum size of the image to download (default 0) + max_image_area (int): maximum area of the image to download (default inf) + data_manifest_path (str): The previous component manifest path + parquet_path_clip_centroid_retrieval (str): The path to the parquet file containing the urls + from centroid retrieval + parquet_path_clip_knn_retrieval (str): The path to the parquet file containing the urls from + knn retrieval + data_manifest_path_clip_downloader_component (str): the current component manifest path + """ + logger = get_logger(name=__name__, level=logging.INFO) + logger.info('Started job...') + + # Initialize storage client + storage_client = storage.Client(project=project_id) + + with tempfile.TemporaryDirectory() as tmp_dir: + logger.info('created temporary directory %s', tmp_dir) + + # Initialize GCS custom artifact path + component_artifact_dir = run_id.rpartition('-')[0] + artifact_bucket_blob_path = f"custom_artifact/{component_artifact_dir}/{component_name}" + logger.info("custom artifact will be uploaded to %s", + f'gs://{artifact_bucket}/{artifact_bucket_blob_path}') + + # Initialize local directory + tmp_image_dir_path = os.path.join(tmp_dir, 'img_dir') + knn_dataset_parquet_path = os.path.join(tmp_dir, 'knn_dataset.parquet') + centroid_dataset_parquet_path = os.path.join(tmp_dir, 'centroid.parquet') + knn_image_dir_path = os.path.join(tmp_dir, 'knn_images') + centroid_image_dir_path = os.path.join(tmp_image_dir_path, 'centroid_images') + os.makedirs(knn_image_dir_path, exist_ok=True) + os.makedirs(centroid_image_dir_path, exist_ok=True) + + # Initialize gcs paths + downloaded_image_blob = os.path.join(artifact_bucket_blob_path, 'clip_retrieval_images') + knn_parquet_blob = os.path.join(artifact_bucket_blob_path, 'knn_dataset.parquet') + centroid_parquet_blob = os.path.join(artifact_bucket_blob_path, 'centroid_dataset.parquet') + index_parquet_blob = os.path.join(artifact_bucket_blob_path, 'index.parquet') + + # Load manifest + with open(data_manifest_path) as f: + manifest_load = json.load(f) + data_manifest = DataManifest.from_dict(manifest_load) + + # Load clip retrieval parquet files + with open(parquet_path_clip_knn_retrieval) as f: + parquet_path_clip_knn_retrieval = f.readline() + with open(parquet_path_clip_centroid_retrieval) as f: + parquet_path_clip_centroid_retrieval = f.readline() + + # Copy clip retrieval parquet files (containing clip ids and urls) to local directory + parquet_clip_knn_retrieval_tmp_path = storage_helpers.download_file_from_bucket( + storage_client, parquet_path_clip_knn_retrieval, tmp_dir) + parquet_clip_centroid_retrieval_tmp_path = storage_helpers.download_file_from_bucket( + storage_client, parquet_path_clip_centroid_retrieval, tmp_dir) + # Copy index to local directory + index_parquet_path = storage_helpers.download_file_from_bucket( + storage_client, data_manifest.index, tmp_dir) + + # Remove duplicate images based on ids. This duplication may have occurred since querying + # for the centroid and knn was done separately. + logger.info('Removing duplicate entries from centroid and KNN.') + parquet_helpers.remove_common_duplicates( + dataset_to_filter_path=parquet_clip_knn_retrieval_tmp_path, + reference_dataset_path=parquet_clip_centroid_retrieval_tmp_path, + duplicate_columns_name='id', + tmp_path=tmp_dir) + logger.info('Duplicates removed') + + # Fetch and download images + knn_downloader_input = \ + ImageDownloaderInput(source_url=parquet_clip_knn_retrieval_tmp_path, + dest_images=knn_image_dir_path, + dest_dataset_parquet=knn_dataset_parquet_path, + namespace='knn') + centroid_downloader_input = \ + ImageDownloaderInput(source_url=parquet_clip_centroid_retrieval_tmp_path, + dest_images=centroid_image_dir_path, + dest_dataset_parquet=centroid_dataset_parquet_path, + namespace='centroid') + + img_downloader = ImageDownloader(timeout=timeout, + image_resize=image_resize, + min_image_size=min_image_size, + max_image_area=max_image_area) + + for img_downloader_tuple in tqdm([knn_downloader_input, centroid_downloader_input]): + logger.info("Stating downloader job for %s dataset", img_downloader_tuple.namespace) + img_downloader.run(image_downloader_tuple=img_downloader_tuple, + tmp_dir=tmp_dir, + dest_gcs_uri=f"gs://{artifact_bucket}/{downloaded_image_blob}", + index_path=index_parquet_path) + logger.info('Image download job for %s dataset is complete', + img_downloader_tuple.namespace) + # Print job stat dict that shows how many download job succeeded/failed + # TODO: integrate output of job stat dict with the dataset metadata in the manifest + logger.info('job stat dict: %s', img_downloader.get_job_stat_dict()) + + # Upload parquet files + storage_helpers.upload_file_to_bucket(storage_client=storage_client, + file_to_upload_path=index_parquet_path, + bucket_name=artifact_bucket, + blob_path=index_parquet_blob) + storage_helpers.upload_file_to_bucket(storage_client=storage_client, + file_to_upload_path=centroid_dataset_parquet_path, + bucket_name=artifact_bucket, + blob_path=centroid_parquet_blob) + storage_helpers.upload_file_to_bucket(storage_client=storage_client, + file_to_upload_path=knn_dataset_parquet_path, + bucket_name=artifact_bucket, + blob_path=knn_parquet_blob) + + # Update manifest + logger.info('Updating data manifest job') + data_manifest.dataset_id = f"{run_id}_{component_name}" + data_manifest.metadata.branch = "" # TODO: Fill from docker build env var + data_manifest.metadata.commit_hash = "" # TODO: Fill from docker build env var + data_manifest.metadata.creation_date = datetime.now().strftime("%d-%m-%Y_%H-%M-%S") + data_manifest.metadata.run_id = run_id + data_manifest.index = f"gs://{artifact_bucket}/{index_parquet_blob}" + data_manifest.associated_data.dataset['centroid'] = \ + f"gs://{artifact_bucket}/{centroid_parquet_blob}" + data_manifest.associated_data.dataset['knn'] = \ + f"gs://{artifact_bucket}/{knn_parquet_blob}" + logger.info('Manifest file updated') + + Path(data_manifest_path_clip_downloader_component).parent.mkdir(parents=True, exist_ok=True) + Path(data_manifest_path_clip_downloader_component).write_text(data_manifest.to_json()) + logger.info('Manifest file written to %s', data_manifest_path_clip_downloader_component) + logger.info('Job completed.') + + +if __name__ == '__main__': + args = parse_args() + clip_downloader_component \ + (run_id=args.run_id, + artifact_bucket=args.artifact_bucket, + component_name=args.component_name, + project_id=args.project_id, + image_resize=args.image_resize, + timeout=args.timeout, + min_image_size=args.min_image_size, + max_image_area=args.max_image_area, + data_manifest_path=args.data_manifest_path, + parquet_path_clip_knn_retrieval= + args.parquet_path_clip_knn_retrieval, + parquet_path_clip_centroid_retrieval=args.parquet_path_clip_centroid_retrieval, + data_manifest_path_clip_downloader_component= + args.data_manifest_path_clip_downloader_component) diff --git a/examples/pipelines/finetune_stable_diffusion/components/clip_downloader_component/src/utils/__init__.py b/examples/pipelines/finetune_stable_diffusion/components/clip_downloader_component/src/utils/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/pipelines/finetune_stable_diffusion/components/clip_downloader_component/src/utils/image_downloader.py b/examples/pipelines/finetune_stable_diffusion/components/clip_downloader_component/src/utils/image_downloader.py new file mode 100644 index 000000000..dea1c4f6d --- /dev/null +++ b/examples/pipelines/finetune_stable_diffusion/components/clip_downloader_component/src/utils/image_downloader.py @@ -0,0 +1,223 @@ +"""Image downloader utils""" +import subprocess # nosec +import os +import json +import logging +from collections import namedtuple +from typing import Dict + +# pylint: disable=import-error +from helpers.logger import get_logger +from helpers import parquet_helpers, storage_helpers, io_helpers + +logger = get_logger(name=__name__, level=logging.INFO) + +ImageDownloaderInput = namedtuple( + 'ImageDownloaderInput', + ['source_url', 'dest_images', 'dest_dataset_parquet', 'namespace']) + + +class ImageDownloader: + """ + Class for the image downloader that downloads the images from a parquet url files. The class + also contains functionalities for writing new parquet and indicies files and uploading the + downloaded files to GCS + """ + + def __init__(self, timeout: int, image_resize: int, min_image_size: int, max_image_area: int): + """ + Image downloader + Args: + timeout (int): maximum time (in seconds to wait) when trying to download an image + image_resize (int): the size to resize the image + min_image_size (int): minimum size of the image to download + (considers the min of width and height) + max_image_area (int): The maximum area (nr of pixels) of the images to download + + """ + self.timeout = timeout + self.image_resize = image_resize + self.min_image_size = min_image_size + self.max_image_area = max_image_area + # status dict field contains detailed error logs of failed download images and their counts + self.global_stat_dict = { + "count": 0, + "successes": 0, + "failed_to_download": 0, + "failed_to_resize": 0, + "status_dict": {}, + } + + def _start_download(self, src_parquet_file: str, dest_img_tmp_path: str): + """ + Function that starts downloading the images + Args: + src_parquet_file (str): the source parquet files where the image urls are located + dest_img_tmp_path (str): the root folder where the images will be downloaded + (will contain different image shards) + """ + logger.info('Starting image download job') + subprocess.call(['img2dataset', # nosec + '--url_list', src_parquet_file, + '--output_folder', dest_img_tmp_path, + '--thread_count', '128', + '--processes_count', '8', + '--output_format', 'files', + '--encode_format', 'jpg', + '--timeout', str(self.timeout), + '--image_size', str(self.image_resize), + '--resize_mode', 'border', + '--input_format', 'parquet', + '--min_image_size', str(self.min_image_size), + '--max_image_area', str(self.max_image_area), + '--url_col', 'url', + '--distributor', 'multiprocessing', + '--number_sample_per_shard', '1000']) + logger.info('Download job complete') + + def _update_download_job_stats(self, dest_img_tmp_path: str): + """ + Function that updates that download job statics path. 'img2dataset` creates multiple json + file with stats for each download partition it creates + Args: + dest_img_tmp_path (str): the root folder where the images will be downloaded + (will contain different image shards) + """ + logger.info('Updating global stat dict') + + def _update_status_dict(): + for status_key, status_value in stat_dict['status_dict'].items(): + if status_key in self.global_stat_dict['status_dict']: + self.global_stat_dict['status_dict'][status_key] += status_value + else: + self.global_stat_dict['status_dict'][status_key] = status_value + + # multiple stat files are created for each shard + stats_files = [file for file in os.listdir(dest_img_tmp_path) if "stats.json" in file] + for stat_file in stats_files: + with open(os.path.join(dest_img_tmp_path, stat_file)) as file: + stat_dict = json.load(file) + for key in self.global_stat_dict: + # Add detailed error logs + if key == 'status_dict' and 'status_dict' in stat_dict: + _update_status_dict() + # add global status logs + else: + self.global_stat_dict[key] += stat_dict[key] + + logger.info('Update complete') + + @staticmethod + def _rename_downloaded_images(dest_img_tmp_path: str, namespace: str, index_path: str, + tmp_dir: str): + """ + Function that renames the downloaded images by appending a namespace to them. The new image + ids are appended to the index parquet file. + Args: + dest_img_tmp_path (str): the root folder where the images will be downloaded + (will contain different image shards) + namespace (str): the namespace prefix to append to the name of uploaded files + index_path (str): the path to the index parquet file + tmp_dir (str): the path where to write the temporary parquet files + """ + logger.info('Renaming downloaded images in directory %s with namespace "%s" as prefix', + dest_img_tmp_path, namespace) + + def _img_rename_loop(): + sub_img_folders = [folder for folder in os.listdir(dest_img_tmp_path) if + os.path.isdir(os.path.join(dest_img_tmp_path, folder))] + for sub_img_folder in sub_img_folders: + sub_img_dir = os.path.join(dest_img_tmp_path, sub_img_folder) + for file in os.listdir(sub_img_dir): + if file.endswith('.jpg'): + new_file_name = f"{namespace}_{io_helpers.get_file_name(file)}" + src = os.path.join(*[dest_img_tmp_path, sub_img_folder, file]) + dest = os.path.join( + *[dest_img_tmp_path, sub_img_folder, f"{new_file_name}.jpg"]) + os.rename(src, dest) + yield new_file_name + + parquet_helpers.append_to_parquet_file( + parquet_path=index_path, + data_iterable=_img_rename_loop(), + tmp_path=tmp_dir) + + logger.info('Renaming task complete') + + @staticmethod + def _write_image_metadata(dest_img_tmp_path: str, dest_gcs_uri: str, dataset_path: str, + upload_file_txt: str): + """ + Function that writes the parquet dataset (containing) metadata of newly downloaded images. + Thos function also writes the url of images into a gcs text file to upload the images. + Args: + dest_img_tmp_path (str): the root folder where the images will be downloaded + (will contain different image shards + dest_gcs_uri (str): the destination where to write images + dataset_path (str): the path to the dataset parquet path + upload_file_txt (str): the path where to write the urls of files to upload + """ + logger.info('Writing metadata for images in directory %s to %s', dest_img_tmp_path, + dataset_path) + + def _write_metadata_loop(): + sub_img_folders = [folder for folder in os.listdir(dest_img_tmp_path) if + os.path.isdir(os.path.join(dest_img_tmp_path, folder))] + with open(upload_file_txt, "w") as upload_file: + for sub_img_folder in sub_img_folders: + sub_img_dir = os.path.join(dest_img_tmp_path, sub_img_folder) + for file in os.listdir(sub_img_dir): + if file.endswith('.jpg'): + file_id = io_helpers.get_file_name(file) + local_uri = os.path.join(*[dest_img_tmp_path, sub_img_folder, file]) + file_size = os.path.getsize(local_uri) + file_uri = f"{dest_gcs_uri}/{file}" + upload_file.write(local_uri + "\n") + yield file_uri, file_id, file_size, "jpg" + + parquet_helpers.write_dataset_parquet( + dataset_parquet_path=dataset_path, + data_iterable_producer=_write_metadata_loop) + + logger.info('Metadata written') + + def run(self, image_downloader_tuple: ImageDownloaderInput, tmp_dir: str, dest_gcs_uri: str, + index_path: str): + """ + Start and image download loop that downloads the images, uploads them to GCS under + a new name space and updates the parquet files + Args: + image_downloader_tuple (ImageDownloaderInput): tuple containing attributes for the image + download functionality + tmp_dir (str): the path where to write the temporary files + dest_gcs_uri (str): the destination where to write the images + index_path (str): the path to the index parquet file + """ + + source_url, dest_images, dest_parquet, namespace = image_downloader_tuple.source_url, \ + image_downloader_tuple.dest_images, image_downloader_tuple.dest_dataset_parquet, \ + image_downloader_tuple.namespace + upload_file_txt = os.path.join(tmp_dir, f"gcs_upload_{namespace}.txt") + + self._start_download(source_url, dest_images) + self._update_download_job_stats(dest_images) + self._rename_downloaded_images(dest_img_tmp_path=dest_images, + namespace=namespace, + index_path=index_path, + tmp_dir=tmp_dir) + self._write_image_metadata(dest_img_tmp_path=dest_images, + dest_gcs_uri=dest_gcs_uri, + dataset_path=dest_parquet, + upload_file_txt=upload_file_txt) + logger.info('Uploading downloaded images to %s', dest_gcs_uri) + storage_helpers.copy_files_bulk(upload_file_txt, dest_gcs_uri) + logger.info('Upload complete') + + def get_job_stat_dict(self) -> Dict[str, int]: + """ + Function that returns the global job state dict which contains information about the run + job statistics + Returns: + Dict[str, int]: the download global job statistics + """ + return self.global_stat_dict diff --git a/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/Dockerfile b/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/Dockerfile index d46805647..611ccfb16 100644 --- a/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/Dockerfile +++ b/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/Dockerfile @@ -1,31 +1,14 @@ -FROM python:3.8-slim +FROM europe-west1-docker.pkg.dev/storied-landing-366912/storied-landing-366912-default-repository/mlpipelines/kubeflow/components/base_component:latest -## System dependencies -RUN apt-get update && \ - apt-get upgrade -y && \ - apt-get install git curl -y - -# Downloading gcloud package -RUN curl https://dl.google.com/dl/cloudsdk/release/google-cloud-sdk.tar.gz > /tmp/google-cloud-sdk.tar.gz - -# Installing the package -RUN mkdir -p /usr/local/gcloud \ -&& tar -C /usr/local/gcloud -xvf /tmp/google-cloud-sdk.tar.gz \ -&& /usr/local/gcloud/google-cloud-sdk/install.sh - -# Adding the package path to local -ENV PATH $PATH:/usr/local/gcloud/google-cloud-sdk/bin - -# install requirements -COPY requirements.txt / -RUN pip3 install --no-cache-dir -r requirements.txt +# Set the working directory to the source folder +WORKDIR /src # Copy over src-files of the component -COPY src /src +COPY requirements.txt . -# TODO: ADD EXTRA FILES HERE IF NEEDED! +# Install packages +RUN pip3 install -r requirements.txt -# Set the working directory to the source folder -WORKDIR /src +COPY src /src ENTRYPOINT ["python", "main.py"] \ No newline at end of file diff --git a/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/README.MD b/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/README.MD new file mode 100644 index 000000000..2f290b3d9 --- /dev/null +++ b/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/README.MD @@ -0,0 +1,104 @@ +# clip_retrieval_component + +### Description + +This component retrieves images from the LAION dataset using a clip +retrieval system. The images are retrieved using an efficient index built from the previously +extracted embeddings that enables for fast and accurate +querying against a large database. + +Checkout this [link](https://github.com/rom1504/clip-retrieval) for information on clip retrieval. +You can also +test out clip retrieval in +this [demo](https://rom1504.github.io/clip-retrieval/?back=https%3A%2F%2Fknn5.laion.ai&index=laion5B&useMclip=false). +The output of this component is a list of URLs containing the link to the retrieved image. + +Two current strategies are implemented for the retrieval of the images: + +1) **Knn strategy**: In this approach, an index is built from each embedding of the input dataset. + This allows to retrieve images of similar **context** and **style** to the input image. The image + below shows an example of the seed image and the retrieved images: + +seed_image + + +![knn](docs/retrieved_images_knn_example.jpg) + +2) **Centroid strategy**: In this approach, the image embeddings for all the images are **averaged + out** and + the image are retrieved from the index of that embedding. This allows to retrieve images of + similar **style** + but with varied contexts compared to the seed images. The average embedding can be interpreted as + the average representation of a certain image style/domain in an embedding space. + +The image below shows an example of images retrieved from the centroid of a collection of varied +Clip Art images. + +![knn](docs/retrieved_images_centroid_example.png) + +### **Benchmarks** + +The graph below demonstrates the number of images received with clip retrieval and the time it takes +to receive those images. The numbers on the graph are the requested number of images. + +knn + +One important thing to not out is that the number of received images is around a **30%** of the +number of +requested images. The reason for that is because the clip retrieval also takes care of deduplication +of the returned images based on their embeddings +(a lot of images in the LAION-5B are similar since they are scraped from different data sources). +This +deduplication is pairwise operations with time complexity O(n2). + +In short, it takes around **7 hours** to retrieve **~350k** images with an initial request of **1 +million images.** + +### **Inputs/Outputs** + +The Laion dataset is downloaded to the internal storage of the computed instance and queried from +there. + +Two important two parameters to specify here are the number of images to return through both knn and +centroid strategy: + +* `nb_images_knn`: the number of images to retrieve from **each embedding** of the seed images. +* `nb_images_centroid`: the number of images to retrieve from the **average embedding** of the seed + images. + +As a concrete example, if we have 1,000 images in our seed images and assuming that the amount +of images retrieved are around 30% of the requested images. Then to create a dataset of around 500k +images with +a balanced amount of images between both strategies (250k from each): + +_nb_images_knn = 250,000 / (1000*0.3) = ~ 830_ + +_nb_images_centroid = 250,000 / 0.3 = ~ 830,000_ + +Notice that the number of images retrieved from the centroid will be much larger that the one +the `knn` +approach and hence it will take much longer time overall (see benchmark above). + +See [`component.yaml`](component.yaml) for a more detailed description on all the input/output +parameters. + +### **Practical considerations** + +* Keep in mind that the total amount of image urls that will be retrieved will still be reduced by + around 25% + since many of the urls might be broken or the images might not pass the filters specified in the + [`clip_downloader_component`](../clip_downloader_component). +* Clip retrieval can also work with text or image input, this component currently only implements + the + embedding approach but can easily be changed to accomodate for the other methods. +* This component takes a long time to run since the LAION dataset need to be downloaded first from + GCS (total size is around 2.5 TB, it can take around 2-3 hours to download) + and the querying itself also takes a signficant amount of time. +* Since the knn indices and the metadata (URL and captions) are very large files that need to be queried + efficiently, + memory mapping to allow to map data on SSDs into virtual memory and query data that scales beyond + RAM capacity. + This requires the using GKE nodepools with NVMe drives and provisioning them to this component + during the pipeline run. + More information on NVMe SSDs the context of GKE can be + found [here](https://cloud.google.com/kubernetes-engine/docs/how-to/persistent-volumes/local-ssd). diff --git a/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/__init__.py b/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/component.yaml b/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/component.yaml index b518afc0f..44c3a5040 100644 --- a/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/component.yaml +++ b/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/component.yaml @@ -1,29 +1,58 @@ name: clip_retrieval_component -description: A component that retrieves similar images from LAION using CLIP. -inputs: - - name: extra_args - description: Additional arguments passed to the component, as a json dict string - type: String - - - name: metadata - description: Metadata arguments, passed as a json dict string - type: String +description: A component that takes a dataset manifest and returns an output data manifest with an extended dataset + by retrieving similar images from the laion dataset using different retrieval strategies (knn, centroid) +inputs: + - name: run_id + description: The run id of the pipeline + type: String + - name: artifact_bucket + description: The GCS bucket used to store the artifact + type: String + - name: component_name + description: the name of the component (used to create gcs artefact path) + type: String + - name: project_id + description: The id of the gcp-project + type: String + - name: laion_index_url + description: contains the indices of the metadata. Those indices need to be transformed in case you decide to use only a subset of the dataset + type: String + - name: laion_metadata_url + description: url to the metadata of laion dataset metadata (arrow format). It can either contain a subset of the laion 5b metadata (e.g. laion-en) or all of the metadata + type: String + - name: nb_images_knn + description: The number of images to return via the knn strategy (per image) + type: Integer + - name: nb_images_centroid + description: The number of images to return via the centroid strategy + type: Integer + - name: data_manifest_path + description: The previous component manifest path + type: String - - name: input_manifest - description: Path to the input manifest - type: String - outputs: - - name: output_manifest - description: Path to the output manifest + - name: data_manifest_path_clip_retrieval_component + description: Path to the local file containing the gcs path where the output has been stored + - name: parquet_path_clip_centroid_retrieval + description: The path to the parquet file containing the urls from centroid retrieval + - name: parquet_path_clip_knn_retrieval + description: The path to the parquet file containing the urls from knn retrieval implementation: - container: - image: ghcr.io/ml6team/clip_retrieval_component:latest - command: [ - python3, main.py, - --input-manifest, {inputPath: input_manifest}, - --metadata, {inputValue: metadata}, - --extra-args, {inputValue: extra_args}, - --output-manifest, {outputPath: output_manifest}, - ] \ No newline at end of file + container: + image: europe-west1-docker.pkg.dev/storied-landing-366912/storied-landing-366912-default-repository/mlpipelines/kubeflow/components/clip_retrieval_component:latest + command: [ + python3, main.py, + --run-id, { inputValue: run_id }, + --artifact-bucket, { inputValue: artifact_bucket }, + --component-name, { inputValue: component_name }, + --project-id, { inputValue: project_id, }, + --laion-index-url, { inputValue: laion_index_url }, + --laion-metadata-url, { inputValue: laion_metadata_url }, + --nb-images-knn, { inputValue: nb_images_knn }, + --nb-images-centroid, { inputValue: nb_images_centroid }, + --data-manifest-path, { inputPath: data_manifest_path }, + --data-manifest-path-clip-retrieval-component, { outputPath: data_manifest_path_clip_retrieval_component }, + --parquet-path-clip-centroid-retrieval, { outputPath: parquet_path_clip_centroid_retrieval }, + --parquet-path-clip-knn-retrieval, { outputPath: parquet_path_clip_knn_retrieval }, + ] diff --git a/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/docs/benchmark_clip_retrieval.png b/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/docs/benchmark_clip_retrieval.png new file mode 100644 index 0000000000000000000000000000000000000000..1b76187025b14cfb843057505a184925f1c82bee GIT binary patch literal 23118 zcmcG$2T)bp(k?p5Sr$3N5)24}ARtk45=0P@93_fmkemd`SvR7HfFdFS0wOtQ41kE_ z926uYlGE$SKL5S<)xGuVfA#87r;Z!eTyu;$Mvv~Vzy5kgY28qvAY~>+5QO5Is-iZ6 z;D#UwjwLZ6{3RE$FNA;a-Q}<85W|l@u~j7e&g7w_?{Ul7#>30P%^Io@%Ft;`l5o-MXdwJD{_cwPK~R(14|DkGa# zOPCVATqk*?LrL0PLNhrzc^+PK!~T5}QP56#T1vfl!W#JqwEgAN 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b/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/requirements.txt @@ -0,0 +1,5 @@ +clip-retrieval==2.34.2 +numpy>=1.19.5,<2 +pandas==1.3.5 +tqdm==4.64.1 +Pillow==9.3.0 \ No newline at end of file diff --git a/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/src/__init__.py b/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/src/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/src/main.py b/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/src/main.py index e69de29bb..3edb53340 100644 --- a/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/src/main.py +++ b/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/src/main.py @@ -0,0 +1,267 @@ +""" +This file is the entrypoint of the component. It will parse all arguments +and give them to the actual core of the component. +""" + +import os +import tempfile +import argparse +import logging +import json +import time +from pathlib import Path +from datetime import datetime + +import numpy as np +from tqdm import tqdm +from google.cloud import storage + +# pylint: disable=import-error +from helpers.logger import get_logger +from helpers import storage_helpers, parquet_helpers +from helpers.manifest_helpers import DataManifest +from utils.embedding_utils import get_average_embedding +from utils.knn_service import ClipRetrievalLaion5B + + +def parse_args(): + """Parse component arguments""" + + parser = argparse.ArgumentParser() + parser.add_argument('--run-id', + type=str, + required=True, + help='The run id of the pipeline') + parser.add_argument('--artifact-bucket', + type=str, + required=True, + help='The GCS bucket used to store the artifacts') + parser.add_argument('--component-name', + type=str, + required=True, + help='The name of the component') + parser.add_argument('--project-id', + type=str, + required=True, + help='The id of the gcp-project') + parser.add_argument('--laion-index-url', + type=str, + required=True, + help='contains the indices of the metadata. Those indices need to be ' + 'transformed in case you decide to use only a subset of the dataset') + parser.add_argument('--laion-metadata-url', + type=str, + required=True, + help='The id of the gcp-project') + parser.add_argument('--nb-images-knn', + type=int, + required=True, + help='The number of images to return via KNN method') + parser.add_argument('--nb-images-centroid', + type=int, + required=True, + help='The number of images to return via the centroid method') + parser.add_argument('--data-manifest-path', + type=str, + required=True, + help='The previous component manifest path') + parser.add_argument('--data-manifest-path-clip-retrieval-component', + type=str, + required=True, + help='The path to the output manifest file') + parser.add_argument('--parquet-path-clip-knn-retrieval', + type=str, + required=True, + help='The path to the parquet file containing the urls from knn retrieval') + parser.add_argument('--parquet-path-clip-centroid-retrieval', + type=str, + required=True, + help='The path to the parquet file containing the urls from centroid ' + 'retrieval') + + return parser.parse_args() + + +# pylint: disable=too-many-locals, too-many-arguments, too-many-statements +def clip_retrieval_component(run_id: str, + artifact_bucket: str, + component_name: str, + project_id: str, + laion_index_url: str, + laion_metadata_url: str, + nb_images_knn: int, + nb_images_centroid: int, + data_manifest_path: str, + data_manifest_path_clip_retrieval_component: str, + parquet_path_clip_knn_retrieval: str, + parquet_path_clip_centroid_retrieval: str) -> None: + """ + Args: + run_id (str): The run id of the pipeline + artifact_bucket (str): The GCS bucket used to store the artifact + component_name (str): the name of the component (used to create gcs artefact path) + project_id (str): The id of the gcp-project + laion_index_url (str): contains the indices of the metadata. Those indices need to be + transformed in case you decide to use only a subset of the dataset + laion_metadata_url (str): url to the metadata of laion dataset metadata (arrow format). It + can either contain a subset of the laion 5b metadata (e.g. laion-en) or all of the metadata + nb_images_knn (int): The number of images to return via knn method (per image) + nb_images_centroid (int): The number of images to return via the centroid method + data_manifest_path (str): The previous component manifest path + data_manifest_path_clip_retrieval_component (str): the current component output path + parquet_path_clip_centroid_retrieval (str): The path to the parquet file containing the urls + from centroid retrieval + parquet_path_clip_knn_retrieval (str): The path to the parquet file containing the urls + from knn retrieval + """ + logger = get_logger(name=__name__, level=logging.INFO) + logger.info('Started job...') + + # Initialize storage client + storage_client = storage.Client(project=project_id) + + # This component uses local SSD mounting to enable faster querying of the laion dataset. The + # local SSDs are mounted in the cache directory + laion_index_folder = os.path.join('/cache', 'laion_dataset') + laion_metadata_folder = os.path.join(laion_index_folder, 'metadata') + laion_indices_folder = os.path.join(laion_index_folder, 'image.index') + os.makedirs(laion_metadata_folder, exist_ok=True) + os.makedirs(laion_indices_folder, exist_ok=True) + + # Download laion indices and metadata from storage + start_indices = time.time() + storage_helpers.copy_folder_bulk(laion_index_url, laion_indices_folder) + logger.info('Laion index download complete: it took %s minutes to download the laion indices', + round((time.time() - start_indices) / 60)) + start_metadata = time.time() + storage_helpers.copy_folder_bulk(laion_metadata_url, laion_metadata_folder) + logger.info( + 'Laion metadata download complete: it took %s minutes to download the laion metadata', + round((time.time() - start_metadata) / 60)) + + with tempfile.TemporaryDirectory() as tmp_dir: + logger.info('created temporary directory %s', tmp_dir) + + # Initialize GCS custom artifact path + component_artifact_dir = run_id.rpartition('-')[0] + artifact_bucket_blob_path = f"custom_artifact/{component_artifact_dir}/{component_name}" + logger.info("custom artifact will be uploaded to %s", + f'gs://{artifact_bucket}/{artifact_bucket_blob_path}') + + tmp_embedding_dir_path = os.path.join(tmp_dir, 'embed_dir') + os.makedirs(tmp_embedding_dir_path, exist_ok=True) + laion_indices_folder = os.path.join(tmp_dir, 'index_file.json') + knn_retrieval_path = os.path.join(tmp_dir, 'clip_knn.parquet') + centroid_retrieval_path = os.path.join(tmp_dir, 'clip_centroid.parquet') + knn_retrieval_blob_path = os.path.join(artifact_bucket_blob_path, 'clip_knn.parquet') + centroid_retrieval_blob_path = os.path.join(artifact_bucket_blob_path, + 'clip_centroid.parquet') + + # Read manifest + with open(data_manifest_path) as f: + manifest_load = json.load(f) + data_manifest = DataManifest.from_dict(manifest_load) + embedding_path = data_manifest.associated_data.embedding['cf'] + + # Copy embeddings to local directory + storage_helpers.copy_folder_bulk(f"{embedding_path}/*", tmp_embedding_dir_path) + nb_src_images = len(os.listdir(tmp_embedding_dir_path)) + logging.info('Total number of source images (embedding): %s', nb_src_images) + centroid_embedding = get_average_embedding(tmp_embedding_dir_path) + + logger.info('A total of %s urls will be requested using the centroid approach.', + nb_images_centroid) + logger.info('A total of %s urls will be requested per individual image (total of %s images)' + ' using the knn approach.', nb_images_knn) + + # Setup KNN service + clip_retrieval_runner = ClipRetrievalLaion5B( + laion_index_path=laion_indices_folder, + laion_index_folder=laion_index_folder) + knn_service = clip_retrieval_runner.setup_knn_service() + + logger.info('Starting centroid clip retrieval') + + # Run clip retrieval with centroid approach + results_centroid = clip_retrieval_runner.run_query( + knn_service=knn_service, + query={'embedding_query': centroid_embedding}, + nb_images_request=nb_images_centroid, + deduplicate=True, + benchmark=True) + logger.info('Centroid clip retrieval complete') + + parquet_helpers.write_clip_retrieval_parquet( + clip_retrieval_parquet_path=centroid_retrieval_path, + data_iterable_producer=clip_retrieval_runner.clip_results_producer, + clip_results=results_centroid) + del results_centroid + + # Run clip retrieval with KNN approach + logger.info('Starting knn clip retrieval') + results_knn = [] + for embedding_file in tqdm(os.listdir(tmp_embedding_dir_path)): + embedding = np.load(os.path.join(tmp_embedding_dir_path, embedding_file)) + results = clip_retrieval_runner.run_query(knn_service=knn_service, + query={'embedding_query': embedding}, + nb_images_request=nb_images_knn, + deduplicate=True, + benchmark=True) + results_knn.extend(results) + logger.info('KNN clip retrieval complete') + parquet_helpers.write_clip_retrieval_parquet( + clip_retrieval_parquet_path=knn_retrieval_path, + data_iterable_producer=clip_retrieval_runner.clip_results_producer, + clip_results=results_knn) + del results_knn + + # Upload parquet files to bucket + storage_helpers.upload_file_to_bucket(storage_client=storage_client, + file_to_upload_path=centroid_retrieval_path, + bucket_name=artifact_bucket, + blob_path=centroid_retrieval_blob_path) + + storage_helpers.upload_file_to_bucket(storage_client=storage_client, + file_to_upload_path=knn_retrieval_path, + bucket_name=artifact_bucket, + blob_path=knn_retrieval_blob_path) + + # Update manifest + data_manifest.dataset_id = f"{run_id}_{component_name}" + data_manifest.metadata.branch = "" # TODO: Fill from docker build env var + data_manifest.metadata.commit_hash = "" # TODO: Fill from docker build env var + data_manifest.metadata.creation_date = datetime.now().strftime("%d-%m-%Y_%H-%M-%S") + data_manifest.metadata.run_id = run_id + + logger.info('Manifest file created and updated') + + # Write manifest to outputPath + Path(data_manifest_path_clip_retrieval_component).parent.mkdir(parents=True, exist_ok=True) + Path(parquet_path_clip_knn_retrieval).parent.mkdir(parents=True, exist_ok=True) + Path(parquet_path_clip_centroid_retrieval).parent.mkdir(parents=True, exist_ok=True) + # Write Parquet url files to outputPath + Path(data_manifest_path_clip_retrieval_component).write_text(data_manifest.to_json()) + Path(parquet_path_clip_knn_retrieval).write_text( + f'gs://{artifact_bucket}/{knn_retrieval_blob_path}') + Path(parquet_path_clip_centroid_retrieval).write_text( + f'gs://{artifact_bucket}/{centroid_retrieval_blob_path}') + logger.info('Manifest file written to %s', data_manifest_path_clip_retrieval_component) + logger.info('Job completed.') + + +if __name__ == '__main__': + args = parse_args() + clip_retrieval_component \ + (run_id=args.run_id, + artifact_bucket=args.artifact_bucket, + component_name=args.component_name, + project_id=args.project_id, + laion_index_url=args.laion_index_url, + laion_metadata_url=args.laion_metadata_url, + nb_images_knn=args.nb_images_knn, + nb_images_centroid=args.nb_images_centroid, + data_manifest_path=args.data_manifest_path, + data_manifest_path_clip_retrieval_component= + args.data_manifest_path_clip_retrieval_component, + parquet_path_clip_centroid_retrieval=args.parquet_path_clip_centroid_retrieval, + parquet_path_clip_knn_retrieval=args.parquet_path_clip_knn_retrieval) diff --git a/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/src/utils/__init__.py b/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/src/utils/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/src/utils/embedding_utils.py b/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/src/utils/embedding_utils.py new file mode 100644 index 000000000..3c51707af --- /dev/null +++ b/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/src/utils/embedding_utils.py @@ -0,0 +1,19 @@ +"""Clip embedding util functions""" +import os + +import numpy as np + + +def get_average_embedding(embedding_dir: str) -> np.array: + """ + Function that returns the average embedding from a selection of embeddings + Args: + embedding_dir (str): the directory where the embeddings are located + Returns: + np.array: the average embedding + """ + + embeddings = [np.load(os.path.join(embedding_dir, embedding_file)) for embedding_file in + os.listdir(embedding_dir)] + avg_embedding = np.array(embeddings).mean(axis=0) + return avg_embedding diff --git a/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/src/utils/knn_service.py b/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/src/utils/knn_service.py new file mode 100644 index 000000000..7ad80db53 --- /dev/null +++ b/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/src/utils/knn_service.py @@ -0,0 +1,156 @@ +"""Clip retrieval script""" +import json +import io +import base64 +import warnings +import logging +from typing import Dict, List + +# pylint: disable=import-error +from PIL import Image +from clip_retrieval.clip_back import load_clip_indices, KnnService, ClipOptions + +from helpers.logger import get_logger +from utils.timer import CatchTime + +LOGGER = get_logger(name=__name__, level=logging.INFO) +warnings.filterwarnings("ignore", category=FutureWarning) + + +class ClipRetrievalLaion5B: + """Class that initializes and starts a clip retrieval job hosted locally using the laion5B + dataset (either full dataset or subset)""" + + def __init__(self, laion_index_path: str, laion_index_folder: str): + """ + Parameter initialization + Args: + laion_index_path (str): the path to the laion json index path containing job run specs + laion_index_folder (str): the path to the laion index folder. This folder must contain + two subfolders: + -'metadata': contains the laion dataset metadata (arrow format). This folder can + either contain a subset of the laion 5b metadata (e.g. laion-en) or all of the + metadata + -'image.index' contains the indices of the metadata. Those indices need to be + transformed in case you decide to use only a subset of the dataset (more info here: + https://github.com/rom1504/clip-retrieval/tree/ + a206f7908ed213030e950655335940a77af1e4ea/clip_retrieval/clip_back_prepro) + """ + self.laion_index_path = laion_index_path + self.laion_index_folder = laion_index_folder + self.timer = CatchTime() + + def _write_laion_index_path(self): + """Write the json index path containing the job run spec""" + laion_job_dict = { + "laion5B": { + "indice_folder": self.laion_index_folder, + "provide_safety_model": False, + "enable_faiss_memory_mapping": True, + "use_arrow": True, + "enable_hdf5": False, + "reorder_metadata_by_ivf_index": False, + "columns_to_return": ['id', 'url'], + "clip_model": "ViT-L/14", + "enable_mclip_option": False + } + } + + # Serializing json + json_object = json.dumps(laion_job_dict, indent=4) + + # Writing to indices_paths.json + with open(self.laion_index_path, "w") as outfile: + outfile.write(json_object) + + def setup_knn_service(self) -> KnnService: + """ + Function that sets up the knn service + Returns: + KNNService: the knn service client + """ + self._write_laion_index_path() + # Load clip indices with helper method + clip_resources = load_clip_indices( + indices_paths=self.laion_index_path, + clip_options=ClipOptions( + indice_folder="", + clip_model="ViT-L/14", + enable_hdf5=False, + enable_faiss_memory_mapping=True, + columns_to_return=None, + reorder_metadata_by_ivf_index=False, + enable_mclip_option=True, + use_jit=True, + use_arrow=False, + provide_safety_model=False, + provide_violence_detector=False, + provide_aesthetic_embeddings=True, + ) + ) + # Setup KNN service object with clip resources + knn_service = KnnService(clip_resources=clip_resources) + return knn_service + + # pylint: disable=too-many-arguments + def run_query(self, nb_images_request: int, query: Dict[str, str], knn_service: KnnService, + deduplicate: bool = True, benchmark: bool = True) -> List[Dict[str, str]]: + """ + Function that runs query on the KNN faiss backend + Args: + nb_images_request (int): the number of image to request from the KNN service + query (Dict[str, str]): the query to request from the KNN service + knn_service (KNNService): the KNN service client + deduplicate (bool): whether to deduplicate based on embeddings + benchmark (bool): enable to benchmark knn service + Returns: + List[Dict[str, str]]: the query results + """ + LOGGER.info("Running query") + + if benchmark is False: + self.timer.disable() + else: + self.timer.enable() + self.timer.reset() + + self.timer(f"Query for {nb_images_request} samples") + + with self.timer: + query_inputs = {'modality': "image", + 'indice_name': "laion5B", + 'num_images': int(nb_images_request), + 'deduplicate': deduplicate, + 'num_result_ids': int(nb_images_request)} + + if 'image_file' in query: + image = Image.open(query['image_file']) + buffer = io.BytesIO() + image.save(buffer, format="JPEG") + img_str = base64.b64encode(buffer.getvalue()) + query_inputs['image_input'] = img_str + elif 'image_url' in query: + query_inputs['image_url_input'] = query['image_url'] + elif 'text_query' in query: + query_inputs['text_input'] = query['text_query'] + elif 'embedding_query' in query: + query_inputs['embedding_input'] = query['embedding_query'] + else: + raise Exception("No text, image or embedding query found") + + results = knn_service.query(**query_inputs) + LOGGER.info('Query job complete') + LOGGER.info('A total of %s results were returned', len(results)) + return results + + @staticmethod + def clip_results_producer(clip_results: List[Dict[str, str]]) -> tuple: + """ + Laion results producer + Args: + clip_results (List[Dict[str, str]]): the clip retrieval results + Returns: + tuple: clip retrieval results + """ + for result in clip_results: + yield result['id'], result['url'] diff --git a/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/src/utils/timer.py b/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/src/utils/timer.py new file mode 100644 index 000000000..290776cc6 --- /dev/null +++ b/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/src/utils/timer.py @@ -0,0 +1,53 @@ +# pylint: disable-all +"""Timer helper function for benchmarking query time """ +import time +import logging + +import pandas as pd + +from helpers.logger import get_logger + +LOGGER = get_logger(name=__name__, level=logging.INFO) + + +class CatchTime: + """Helper class for timing and benchmarking pieces of code. + The benchmarks are saved in a list.""" + + def __init__(self): + self.benchmarks = [] + self.name = None + self.enabled = False + + def __enter__(self): + if self.enabled: + if self.name is None: + raise Exception("Name has to be set") + self.begin = time.time() + return self + + def __exit__(self, type, value, traceback): + if self.enabled: + total = time.time() - self.begin + self.benchmarks.append({'name': self.name, 'time': total}) + LOGGER.info(f"{self.name}:\t{total:.2f}s") + + def __call__(self, name): + self.name = name + + def reset(self): + self.name = None + self.benchmarks = [] + + def show_times(self): + df = pd.DataFrame(self.benchmarks) + return df + + def enable(self): + self.enabled = True + + def disable(self): + self.enabled = False + + def is_enabled(self): + return self.enabled diff --git a/examples/pipelines/finetune_stable_diffusion/components/image_caption_component/Dockerfile b/examples/pipelines/finetune_stable_diffusion/components/image_caption_component/Dockerfile new file mode 100644 index 000000000..e9c0e9543 --- /dev/null +++ b/examples/pipelines/finetune_stable_diffusion/components/image_caption_component/Dockerfile @@ -0,0 +1,28 @@ +FROM europe-west1-docker.pkg.dev/storied-landing-366912/storied-landing-366912-default-repository/mlpipelines/kubeflow/components/base_component:latest + +ARG DEBIAN_FRONTEND=noninteractive +ENV COMMIT_SHA_BLIP=3a29b7410476bf5f2ba0955827390eb6ea1f4f9d + +## System dependencies +RUN apt-get update && \ + apt-get upgrade -y && \ + apt-get install git wget -y + +# Set the working directory to the source folder +WORKDIR /src + +## Download blip model +RUN wget https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model*_base_caption.pth + +# Clone BLIP repo (no package version available) +RUN git clone https://github.com/salesforce/BLIP.git && \ + cd BLIP && \ + git reset --hard ${COMMIT_SHA_BLIP} + +# Install packages +COPY requirements.txt . +RUN pip3 install -r requirements.txt + +COPY src /src + +ENTRYPOINT ["python", "main.py"] \ No newline at end of file diff --git a/examples/pipelines/finetune_stable_diffusion/components/image_caption_component/README.MD b/examples/pipelines/finetune_stable_diffusion/components/image_caption_component/README.MD new file mode 100644 index 000000000..76e6b15a4 --- /dev/null +++ b/examples/pipelines/finetune_stable_diffusion/components/image_caption_component/README.MD @@ -0,0 +1,26 @@ +# image_caption_component + +### Description + +This component uses a captioning model ([BLIP](https://github.com/salesforce/BLIP)) +to caption the final filtered images for training. + +### **Inputs/Outputs** + +See [`component.yaml`](component.yaml) for a more detailed description on all the input/output parameters. + +### **Practical considerations** +* In general, it is [common practice](https://huggingface.co/datasets/lambdalabs/pokemon-blip-captions) +to generate relatively short descriptions when finetuning stable diffusion. + + +* Captions that are long generally take longer to produce but are more descriptive. However, +it is worth considering first the use case you want to employ. +In general, shorter captions tend to be less prone to errors. As an example, here is an image with generated captions with: + + + * Long captions _(`min_length`=10, `max_length`=30)_: "the image of a santa claus standing with his legs out in the air with his hand on" + + * Short caption _(`min_length`=20, `max_length`=40)_: "An image of a santa claus cartoon character" + +png_artifacts diff --git a/examples/pipelines/finetune_stable_diffusion/components/image_caption_component/__init__.py b/examples/pipelines/finetune_stable_diffusion/components/image_caption_component/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/pipelines/finetune_stable_diffusion/components/image_caption_component/component.yaml b/examples/pipelines/finetune_stable_diffusion/components/image_caption_component/component.yaml new file mode 100644 index 000000000..190edc8da --- /dev/null +++ b/examples/pipelines/finetune_stable_diffusion/components/image_caption_component/component.yaml @@ -0,0 +1,52 @@ +name: image_caption_component +description: A component that takes a data manifest as input and captions images that are not + captioned. The output manifest will contain references to the full captioned dataset. +inputs: + - name: run_id + description: The run id of the pipeline + type: String + - name: artifact_bucket + description: The GCS bucket used to store the artifact + type: String + - name: component_name + description: the name of the component (used to create gcs artefact path) + type: String + - name: project_id + description: The id of the gcp-project + type: String + - name: min_length + description: The minimum caption length + type: Integer + - name: max_length + description: The maximum caption length + type: Integer + - name: batch_size + description: The batch size of images to pass to the blip model + type: Integer + - name: beams + description: The blip beam parameters + type: Integer + - name: data_manifest_path + description: The previous component manifest path + type: String + +outputs: + - name: data_manifest_path_caption_component + description: Path to the local file containing the gcs path where the output has been stored + +implementation: + container: + image: europe-west1-docker.pkg.dev/storied-landing-366912/storied-landing-366912-default-repository/mlpipelines/kubeflow/components/image_caption_component:latest + command: [ + python3, main.py, + --run-id, { inputValue: run_id }, + --artifact-bucket, { inputValue: artifact_bucket }, + --component-name, { inputValue: component_name }, + --project-id, { inputValue: project_id }, + --min-length, { inputValue: min_length }, + --max-length, { inputValue: max_length }, + --batch-size, { inputValue: batch_size }, + --beams, { inputValue: beams }, + --data-manifest-path, { inputPath: data_manifest_path }, + --data-manifest-path-caption-component, { outputPath: data_manifest_path_caption_component }, + ] \ No newline at end of file diff --git a/examples/pipelines/finetune_stable_diffusion/components/image_caption_component/docs/santa_captioned.jpg 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a/examples/pipelines/finetune_stable_diffusion/components/image_caption_component/requirements.txt b/examples/pipelines/finetune_stable_diffusion/components/image_caption_component/requirements.txt new file mode 100644 index 000000000..986e3d106 --- /dev/null +++ b/examples/pipelines/finetune_stable_diffusion/components/image_caption_component/requirements.txt @@ -0,0 +1,9 @@ +fairscale==0.4.4 +ftfy==6.1.1 +Pillow==9.3.0 +regex==2022.10.31 +torchvision==0.13.0 +PyYAML==5.4.1 +transformers==4.15.0 +timm==0.4.12 +tqdm==4.64.1 \ No newline at end of file diff --git a/examples/pipelines/finetune_stable_diffusion/components/image_caption_component/src/__init__.py b/examples/pipelines/finetune_stable_diffusion/components/image_caption_component/src/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/pipelines/finetune_stable_diffusion/components/image_caption_component/src/main.py b/examples/pipelines/finetune_stable_diffusion/components/image_caption_component/src/main.py new file mode 100644 index 000000000..c5fac2b21 --- /dev/null +++ b/examples/pipelines/finetune_stable_diffusion/components/image_caption_component/src/main.py @@ -0,0 +1,217 @@ +""" +This file is the entrypoint of the component. It will parse all arguments +and give them to the actual core of the component. +""" +import os +import tempfile +import argparse +import logging +import json +from pathlib import Path +from datetime import datetime + +import pyarrow.compute as pc +from google.cloud import storage + +# pylint: disable=import-error +from helpers.logger import get_logger +from helpers import storage_helpers, parquet_helpers, kfp_helpers +from helpers.manifest_helpers import DataManifest +from utils.image_captioning import KfpPipelineImageCaptioner +from utils.blip_model import BlipModel + + +def parse_args(): + """Parse component arguments""" + + parser = argparse.ArgumentParser() + parser.add_argument('--run-id', + type=str, + required=True, + help='The run id of the pipeline') + parser.add_argument('--artifact-bucket', + type=str, + required=True, + help='The GCS bucket used to store the artifacts') + parser.add_argument('--component-name', + type=str, + required=True, + help='The name of the component') + parser.add_argument('--project-id', + type=str, + required=True, + help='The id of the gcp-project') + parser.add_argument('--min-length', + type=int, + required=True, + help='The minimum caption length') + parser.add_argument('--max-length', + type=int, + required=True, + help='The maximum caption length') + parser.add_argument('--beams', + type=int, + required=True, + help='The blip beam parameters') + parser.add_argument('--batch-size', + type=int, + required=True, + help='The batch size to pass to the captioning component') + parser.add_argument('--data-manifest-path', + type=str, + required=True, + help='The previous component manifest path') + parser.add_argument('--data-manifest-path-caption-component', + type=str, + required=True, + help='The path to the output manifest file') + + return parser.parse_args() + + +# pylint: disable=too-many-locals, too-many-arguments +def dataset_caption_component(run_id: str, + artifact_bucket: str, + component_name: str, + project_id: str, + min_length: int, + max_length: int, + batch_size: int, + beams: int, + data_manifest_path: str, + data_manifest_path_caption_component: str) -> None: + """ + A component that takes an images dataset as input and initializes a data manifest + and writes it to an output file. + Args: + run_id (str): the run id of the pipeline + artifact_bucket (str): The GCS bucket used to store the artifacts + component_name (str): the name of the component (used to create gcs artefact path) + project_id (str): The id of the gcp-project + min_length (str): The minimum caption length + max_length (str): the maximum caption length + batch_size (int): The batch size of images to pass to the blip model + beams (int): The blip beam parameters + data_manifest_path (str): The previous component manifest path + data_manifest_path_caption_component (str): the path to the output manifest file + """ + logger = get_logger(name=__name__, level=logging.INFO) + logger.info('Started job...') + + # Show CUDA availability + kfp_helpers.get_cuda_availability() + + # Initialize storage client + storage_client = storage.Client(project=project_id) + + with tempfile.TemporaryDirectory() as tmp_dir: + logger.info('created temporary directory %s', tmp_dir) + + # Initialize GCS custom artifact path + component_artifact_dir = run_id.rpartition('-')[0] + artifact_bucket_blob_path = f"custom_artifact/{component_artifact_dir}/{component_name}" + logger.info("custom artifact will be uploaded to %s", + f'gs://{artifact_bucket}/{artifact_bucket_blob_path}') + + tmp_img_dir_path = os.path.join(tmp_dir, 'img_dir') + os.makedirs(tmp_img_dir_path, exist_ok=True) + + # Read manifest + with open(data_manifest_path) as f: + manifest_load = json.load(f) + data_manifest = DataManifest.from_dict(manifest_load) + # Get index and dataset parquet gcs paths + index_parquet_prev_gcs_path = data_manifest.index + datasets_parquet_dict = data_manifest.associated_data.dataset + + # Download index files + index_parquet_prev_tmp_path = storage_helpers.download_file_from_bucket( + storage_client, index_parquet_prev_gcs_path, tmp_dir) + + # Download namespace datasets + dataset_parquet_path_dict = {} + for namespace, dataset_gcs_path in datasets_parquet_dict.items(): + dataset_parquet_tmp_path = storage_helpers.download_file_from_bucket( + storage_client, dataset_gcs_path, tmp_dir) + dataset_parquet_path_dict[namespace] = dataset_parquet_tmp_path + + # Initialize Blip model + blip_model = BlipModel(model_path='model*_base_caption.pth') + + # Get index_ids + index_ids_images_to_caption = parquet_helpers.get_column_list_from_parquet( + parquet_scanner_or_path=index_parquet_prev_tmp_path, + column_name='index') + + # Construct parquet filters and filter based on the criteria + filters = (pc.field("file_id").isin(index_ids_images_to_caption)) + + # Caption images for all the available namespaces + for namespace, dataset_parquet_tmp_path in dataset_parquet_path_dict.items(): + logger.info('Starting caption and upload job for for dataset with namespace "%s"', + namespace) + + parquet_dataset_scanner = parquet_helpers.filter_parquet_file( + file_path=dataset_parquet_tmp_path, + filters=filters, + batch_size=batch_size) + + # Caption images and store them in a parquet file + kfp_image_captioner = KfpPipelineImageCaptioner( + blip_model=blip_model, + parquet_dataset=parquet_dataset_scanner, + tmp_img_path=tmp_img_dir_path, + tmp_dir=tmp_dir, + namespace=namespace) + + kfp_image_captioner.start(min_length=min_length, + max_length=max_length, + beams=beams) + + captions_parquet_tmp_path = kfp_image_captioner.get_caption_path() + + # Upload caption parquet file to bucket bucket + captions_parquet_blob_path = os.path.join(artifact_bucket_blob_path, + f'captions_{namespace}.parquet') + storage_helpers.upload_file_to_bucket(storage_client=storage_client, + file_to_upload_path=captions_parquet_tmp_path, + bucket_name=artifact_bucket, + blob_path=captions_parquet_blob_path) + logger.info('Caption and upload job for dataset with namespace "%s" is complete', + namespace) + + # Update manifest + data_manifest.associated_data.caption[namespace] = \ + f"gs://{artifact_bucket}/{captions_parquet_blob_path}" + + # Update manifest + data_manifest.dataset_id = f"{run_id}_{component_name}" + data_manifest.metadata.branch = "" # TODO: Fill from docker build env var + data_manifest.metadata.commit_hash = "" # TODO: Fill from docker build env var + data_manifest.metadata.creation_date = datetime.now().strftime("%d-%m-%Y_%H-%M-%S") + data_manifest.metadata.run_id = run_id + + logger.info('Manifest file updated') + + # Write manifest to outputPath + Path(data_manifest_path_caption_component).parent.mkdir(parents=True, exist_ok=True) + Path(data_manifest_path_caption_component).write_text(data_manifest.to_json()) + + logger.info('Manifest file written to %s', data_manifest_path_caption_component) + + logger.info('Job completed.') + + +if __name__ == '__main__': + args = parse_args() + dataset_caption_component \ + (run_id=args.run_id, + artifact_bucket=args.artifact_bucket, + component_name=args.component_name, + project_id=args.project_id, + min_length=args.min_length, + max_length=args.max_length, + batch_size=args.batch_size, + beams=args.beams, + data_manifest_path=args.data_manifest_path, + data_manifest_path_caption_component=args.data_manifest_path_caption_component) diff --git a/examples/pipelines/finetune_stable_diffusion/components/image_caption_component/src/utils/__init__.py b/examples/pipelines/finetune_stable_diffusion/components/image_caption_component/src/utils/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/pipelines/finetune_stable_diffusion/components/image_caption_component/src/utils/blip_model.py b/examples/pipelines/finetune_stable_diffusion/components/image_caption_component/src/utils/blip_model.py new file mode 100644 index 000000000..30dcbaf55 --- /dev/null +++ b/examples/pipelines/finetune_stable_diffusion/components/image_caption_component/src/utils/blip_model.py @@ -0,0 +1,103 @@ +""" +General class for Blip model +""" +import logging +import sys +from typing import List + +import torch +from PIL import Image +from torchvision import transforms +from torchvision.transforms.functional import InterpolationMode + +# pylint: disable=import-error, wrong-import-position +sys.path.insert(0, "/src/BLIP") +from BLIP.models.blip import blip_decoder +from helpers.logger import get_logger + +logger = get_logger(name=__name__, level=logging.INFO) + + +# pylint: disable=too-few-public-methods +class BlipModel: + """Blip Model class""" + + def __init__(self, model_path: str, image_size=384): + """ + Function that initialize the blip model + Args: + model_path (str): the path to the blip model + image_size (str): the image size to resize the image before captioning + """ + self.device = "cuda" if torch.cuda.is_available() else "cpu" + logger.info('BLIP model initialized with %s device', self.device) + self.med_config = "./BLIP/configs/med_config.json" + self.image_size = image_size + self.model = self._init_model(model_path=model_path, device=self.device) + # Normalization values taken from + # https://github.com/salesforce/BLIP/blob/d10be550b2974e17ea72e74edc7948c9e5eab884/predict.py + self.image_transformer = transforms.Compose([ + transforms.Resize((image_size, image_size), interpolation=InterpolationMode.BICUBIC), + transforms.ToTensor(), + transforms.Normalize((0.48145466, 0.4578275, 0.40821073), + (0.26862954, 0.26130258, 0.27577711)) + ]) + + def _init_model(self, model_path: str, device: str) -> blip_decoder: + """ + Initialize the blip model + Args: + model_path (str): the path to the blip model + device (str): the device to initialize the model to + Returns: + blip_decoder: the blip model + """ + model = blip_decoder(pretrained=model_path, image_size=self.image_size, vit='base', + med_config=self.med_config) + model.eval() + model = model.to(device) + + return model + + def _preprocess_images(self, raw_image: Image) -> torch.Tensor: + """ + Function that preprocesses the image before captioning + Args: + raw_image (Image): the image to caption + Returns: + torch.Tensor: a tensor of preprocessed image + """ + image = self.image_transformer(raw_image).unsqueeze(0) + + return image + + # pylint: disable=too-many-arguments + def caption_images(self, image_paths: List[str], min_length: int, + max_length: int, beams: int) -> List[str]: + """ + Main function to caption the image + Args: + image_paths (List[str]): a list of image paths to caption + min_length (int): the minimum caption length + max_length (int): the maximum caption length + beams (int): parameters to increase the caption per image quality but required more + compute time + Returns: + List[str]: a list of image captions + """ + image_tensors = [] + + with torch.no_grad(): + for image_path in image_paths: + image = Image.open(image_path).convert('RGB') + image = self._preprocess_images(image) + image_tensors.append(image) + + # pylint: disable=no-member + image_tensors = torch.cat(image_tensors, dim=0).to(self.device) + captions = self.model.generate(image_tensors, + sample=True, + num_beams=beams, + max_length=max_length, + min_length=min_length) + return captions diff --git a/examples/pipelines/finetune_stable_diffusion/components/image_caption_component/src/utils/image_captioning.py b/examples/pipelines/finetune_stable_diffusion/components/image_caption_component/src/utils/image_captioning.py new file mode 100644 index 000000000..e1a495a30 --- /dev/null +++ b/examples/pipelines/finetune_stable_diffusion/components/image_caption_component/src/utils/image_captioning.py @@ -0,0 +1,127 @@ +"""Image captioning""" +import os +import logging +from typing import Iterable, List, Tuple + +from tqdm import tqdm +from PIL import Image +from pyarrow.dataset import Scanner + +# pylint: disable=import-error +from helpers import storage_helpers, parquet_helpers +from helpers.logger import get_logger +from utils.blip_model import BlipModel + +LOGGER = get_logger(name=__name__, level=logging.INFO) + +# Set to allow pillow to process images of different sizes +Image.MAX_IMAGE_PIXELS = None + + +# pylint: disable=too-many-instance-attributes, too-few-public-methods +class KfpPipelineImageCaptioner: + """kfp image captioner """ + + # pylint: disable=too-many-arguments + def __init__(self, blip_model: BlipModel, parquet_dataset: Scanner, tmp_img_path: str, + tmp_dir: str, namespace: str): + """ + Class that structures the kfp images conversion loop + Args: + blip_model (BlipModel): the blip model + parquet_dataset (Scanner): the scanned parquet dataset + tmp_img_path (str) the temporary path used to store the downloaded images + tmp_dir (str): temporary directory where to store + namespace (str): the dataset namespace of the images to caption + """ + self.blip_model = blip_model + self.parquet_dataset = parquet_dataset + self.tmp_img_path = tmp_img_path + self.caption_parquet_path = os.path.join(tmp_dir, f'captions_{namespace}.parquet') + self.download_list_file_path = os.path.join(tmp_dir, f'download_gcs_{namespace}.txt') + + def _write_gcs_file_lists(self): + """ + Function that writes the gcs download and upload list files for bulk download/upload + """ + with open(self.download_list_file_path, "w") as download_list_file: + for batch in self.parquet_dataset.to_batches(): + for row in batch.to_pylist(): + file_uri = row['file_uri'] + download_list_file.write(file_uri + "\n") + + LOGGER.info("GCS download file list written to %s", self.download_list_file_path) + + def _download_images_to_caption(self): + """Function that download the images to caption locally""" + LOGGER.info("Downloading images to caption") + storage_helpers.copy_files_bulk(self.download_list_file_path, + self.tmp_img_path) + LOGGER.info("The images to be captioned were successfully downloaded to %s", + self.tmp_img_path) + + def _write_parquet_captions(self, min_length: int, max_length: int, beams: int): + """ + Function that captions the images with BLIP and ranked them with CLIP + Args: + min_length (int): the minimum caption length + max_length (int): the maximum caption length + beams (int): parameters to increase the caption per image quality but required more + compute time + """ + + def _img_caption_loop() -> Iterable[Tuple[str, str, List[str]]]: + + LOGGER.info("Starting image captioning") + for batch in self.parquet_dataset.to_batches(): + # pyarrow's batch_size approximates the number of batches to return + # Batches may be smaller if there aren’t enough rows in the file and can return zero + # in some occasions + if len(batch) > 0: + file_uris, file_ids, local_paths = [], [], [] + for row in tqdm(batch.to_pylist()): + file_uri, file_id = row['file_uri'], row['file_id'] + file_name = os.path.basename(file_uri) + local_file_path = os.path.join(self.tmp_img_path, file_name) + file_ids.append(file_id) + file_uris.append(file_uri) + local_paths.append(local_file_path) + # caption images + captions = self.blip_model.caption_images( + image_paths=local_paths, + min_length=min_length, + max_length=max_length, + beams=beams) + for file_id, file_uri, caption in zip(file_ids, file_uris, captions): + # TODO: decide whether the caption field in caption parquet should be a str + # or a list (many caption per image) + yield file_id, file_uri, [caption] + + parquet_helpers.write_captions_parquet( + caption_parquet_path=self.caption_parquet_path, + data_iterable_producer=_img_caption_loop + ) + + LOGGER.info("Updated dataset parquet file written to %s", self.caption_parquet_path) + LOGGER.info("Image conversion completed") + + def start(self, min_length: int, max_length: int, beams: int): + """ + Function that starts the image captioning loop + Args: + min_length (int): the minimum caption length + max_length (int): the maximum caption length + beams (int): parameters to increase the caption per image quality but required more + compute time + """ + self._write_gcs_file_lists() + self._download_images_to_caption() + self._write_parquet_captions(min_length=min_length, max_length=max_length, beams=beams) + + def get_caption_path(self) -> str: + """ + Function that returns the image caption path + Returns: + str: the image caption path + """ + return self.caption_parquet_path diff --git a/examples/pipelines/finetune_stable_diffusion/components/image_conversion_component/Dockerfile b/examples/pipelines/finetune_stable_diffusion/components/image_conversion_component/Dockerfile new file mode 100644 index 000000000..541bbe2e2 --- /dev/null +++ b/examples/pipelines/finetune_stable_diffusion/components/image_conversion_component/Dockerfile @@ -0,0 +1,18 @@ +FROM europe-west1-docker.pkg.dev/storied-landing-366912/storied-landing-366912-default-repository/mlpipelines/kubeflow/components/base_component:latest + +# Set the working directory to the source folder +WORKDIR /src + +# System dependencies +RUN apt-get update && \ + apt-get upgrade -y && \ + apt-get install libpango1.0-0 libcairo2 libpq-dev -y + +# Install packages +COPY requirements.txt . +RUN pip3 install -r requirements.txt + +# Copy over src-files of the component +COPY src /src + +ENTRYPOINT ["python", "main.py"] \ No newline at end of file diff --git a/examples/pipelines/finetune_stable_diffusion/components/image_conversion_component/README.MD b/examples/pipelines/finetune_stable_diffusion/components/image_conversion_component/README.MD new file mode 100644 index 000000000..6f2ee6a1c --- /dev/null +++ b/examples/pipelines/finetune_stable_diffusion/components/image_conversion_component/README.MD @@ -0,0 +1,38 @@ +# image_conversion_component + +### Description + +This component converts images from different selected formats +(currently only `png` and `svg`) to `jpg` images. This step is necessary since other images formats +are not suitable for training ML model on and often contain artifacts that can be eliminated +during conversion. + +The following image illustrates on the examples of a `png` image (left) when converted to `jgp` during (right) with **normal +conversion**: +```commandline +from PIL import Image + +im = Image.open("image.png") +rgb_im = im.convert('RGB') +``` + +![png_artifacts](docs/png_normal_conversion.png) + +You can see that there are some black edge artifacts around the target object. This can also introduce +subsequent artifacts when inference/finetuning on those images since this is the method of conversion that +occurs when `png` image are passed as arrays to a model and converted to Tensors. + +For that reason, a [custom function](src/utils/img_conversion.py) is implemented within that component to apply a conversion method +that gets rid of those artifacts. + +### **Inputs/Outputs** +The component accepts as input different parameters, mainly the `file_extensions` parameters which +indicates which image formats to filter. A new `dataset.parquet` file is written with updated reference +to the path of the converted images. + +See [`component.yaml`](component.yaml) for a more detailed description on all the input/output parameters. + +### **Practical considerations** + +* `svg` images can be converted and stretched to any desired formats. A good compromise of image quality and file size +is generally `1024x1024` (no risk of distortion if the image is not square sine the conversion just adds a border to compensate) \ No newline at end of file diff --git a/examples/pipelines/finetune_stable_diffusion/components/image_conversion_component/__init__.py b/examples/pipelines/finetune_stable_diffusion/components/image_conversion_component/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/pipelines/finetune_stable_diffusion/components/image_conversion_component/component.yaml b/examples/pipelines/finetune_stable_diffusion/components/image_conversion_component/component.yaml new file mode 100644 index 000000000..97906d593 --- /dev/null +++ b/examples/pipelines/finetune_stable_diffusion/components/image_conversion_component/component.yaml @@ -0,0 +1,48 @@ +name: image_conversion_component +description: A component that takes a data manifest as input and converts image formats to 'jpeg' + which is a preferred format for subsequent preprocessing and training steps +inputs: + - name: run_id + description: The run id of the pipeline + type: String + - name: artifact_bucket + description: The GCS bucket used to store the artifact + type: String + - name: component_name + description: the name of the component (used to create gcs artefact path) + type: String + - name: project_id + description: The id of the gcp-project + type: String + - name: svg_image_width + description: The desired width to scale the converted SVG image to + type: Integer + - name: svg_image_height + description: The desired height to scale the converted SVG image to + type: Integer + - name: file_extensions + description: A list containing the image formats to convert + type: JsonArray + - name: data_manifest_path + description: The previous component manifest path + type: String + +outputs: + - name: data_manifest_path_image_conversion_component + description: Path to the local file containing the gcs path where the output has been stored + +implementation: + container: + image: europe-west1-docker.pkg.dev/storied-landing-366912/storied-landing-366912-default-repository/mlpipelines/kubeflow/components/image_conversion_component:latest + command: [ + python3, main.py, + --run-id, { inputValue: run_id }, + --artifact-bucket, { inputValue: artifact_bucket }, + --component-name, { inputValue: component_name }, + --project-id, { inputValue: project_id }, + --file-extensions, { inputValue: file_extensions }, + --svg-image-width, { inputValue: svg_image_width }, + --svg-image-height, { inputValue: svg_image_height }, + --data-manifest-path, { inputPath: data_manifest_path }, + --data-manifest-path-image-conversion-component, { outputPath: data_manifest_path_image_conversion_component }, + ] \ No newline at end of file diff --git a/examples/pipelines/finetune_stable_diffusion/components/image_conversion_component/docs/png_normal_conversion.png 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000000000..e69de29bb diff --git a/examples/pipelines/finetune_stable_diffusion/components/image_conversion_component/src/main.py b/examples/pipelines/finetune_stable_diffusion/components/image_conversion_component/src/main.py new file mode 100644 index 000000000..c4cefa39d --- /dev/null +++ b/examples/pipelines/finetune_stable_diffusion/components/image_conversion_component/src/main.py @@ -0,0 +1,199 @@ +""" +This file is the entrypoint of the component. It will parse all arguments +and give them to the actual core of the component. +""" +import os +import json +import argparse +import logging +import tempfile +from pathlib import Path +from datetime import datetime + +import pyarrow.dataset as ds +from google.cloud import storage + +# pylint: disable=import-error +from helpers import storage_helpers, parquet_helpers, kfp_helpers +from helpers.logger import get_logger +from helpers.manifest_helpers import DataManifest +from utils.img_conversion import KfpPipelineImageConverter + + +def parse_args(): + """Parse component arguments""" + parser = argparse.ArgumentParser() + + parser.add_argument('--run-id', + type=str, + required=True, + help='The run id of the pipeline') + parser.add_argument('--artifact-bucket', + type=str, + required=True, + help='The GCS bucket used to store the artifacts') + parser.add_argument('--component-name', + type=str, + required=True, + help='The name of the component') + parser.add_argument('--project-id', + type=str, + required=True, + help='The id of the gcp-project') + parser.add_argument('--file-extensions', + type=list, + required=True, + help='A list containing the file extensions to convert') + parser.add_argument('--svg-image-width', + type=list, + required=True, + help='The desired width to scale the converted SVG image to') + parser.add_argument('--svg-image-height', + type=list, + required=True, + help='The desired height to scale the converted SVG image to') + parser.add_argument('--data-manifest-path', + type=str, + required=True, + help='The previous component manifest path') + parser.add_argument('--data-manifest-path-image-conversion-component', + type=str, + required=True, + help='The path to the output manifest file') + + return parser.parse_args() + + +# pylint: disable=too-many-locals, too-many-arguments +def dataset_image_conversion_component(run_id: str, + artifact_bucket: str, + component_name: str, + project_id: str, + file_extensions: list, + data_manifest_path: str, + svg_image_width: int, + svg_image_height: int, + data_manifest_path_image_conversion_component: str) -> None: + """ + A component that takes a data manifest as input and converts image formats to 'jpeg' which is + a preferred format for subsequent preprocessing and training steps + Args: + run_id (str): The run id of the pipeline + artifact_bucket (str): The GCS bucket used to store the artifacts + component_name (str): The name of the component (used to create gcs artefact path) + project_id (str): The id of the gcp-project + file_extensions (list): A list containing the file extensions to convert + data_manifest_path (str): The previous component manifest path + svg_image_width (int): the desired width to scale the converted SVG image to + svg_image_height (int): the desired width to scale the converted SVG image to + data_manifest_path_image_conversion_component (str): the path to the output manifest file + """ + # Initialize logger + logger = get_logger(name=__name__, level=logging.INFO) + logger.info('Started job...') + + # Parse list variables + file_extensions = kfp_helpers.parse_kfp_list(file_extensions) + + # Initialize storage client + storage_client = storage.Client(project=project_id) + + with tempfile.TemporaryDirectory() as tmp_dir: + logger.info('created temporary directory %s', tmp_dir) + + # Initialize GCS custom artifact path + component_artifact_dir = run_id.rpartition('-')[0] + artifact_bucket_blob_path = f"custom_artifact/{component_artifact_dir}/{component_name}" + dataset_parquet_blob_path = os.path.join(artifact_bucket_blob_path, 'dataset.parquet') + converted_images_blob_path = os.path.join(artifact_bucket_blob_path, 'converted_images') + converted_images_gcs_uri = f'gs://{artifact_bucket}/{converted_images_blob_path}' + logger.info("custom artifact will be uploaded to %s", + f'gs://{artifact_bucket}/{artifact_bucket_blob_path}') + + # Initialize temporary directories and file paths + tmp_img_dir_path = os.path.join(tmp_dir, 'img_dir') + os.makedirs(tmp_img_dir_path, exist_ok=True) + dataset_parquet_tmp_path = os.path.join(tmp_dir, 'dataset.parquet') + Path(dataset_parquet_tmp_path).parent.mkdir(parents=True, exist_ok=True) + + logger.info('GCS and temporary paths initialized') + + # Read manifest from previous component + with open(data_manifest_path) as f: + manifest_load = json.load(f) + data_manifest = DataManifest.from_dict(manifest_load) + + # Get index and dataset parquet gcs paths + index_parquet_prev_gcs_path = data_manifest.index + # TODO: replace harcoded namespace with string or list input + dataset_parquet_prev_gcs_path = data_manifest.associated_data.dataset['cf'] + + # Download parquet fies locally + index_parquet_prev_tmp_path = storage_helpers.download_file_from_bucket( + storage_client, index_parquet_prev_gcs_path, tmp_dir) + dataset_parquet_prev_tmp_path = storage_helpers.download_file_from_bucket( + storage_client, dataset_parquet_prev_gcs_path, tmp_dir) + + # Get index_ids + index_ids = parquet_helpers.get_column_list_from_parquet( + parquet_scanner_or_path=index_parquet_prev_tmp_path, + column_name='index') + + # Initialize parquet dataset + parquet_dataset = ds.dataset(dataset_parquet_prev_tmp_path) \ + .scanner(columns=['file_extension', 'file_id', 'file_uri', 'file_size']) + + # Start image conversion + image_converter = KfpPipelineImageConverter( + parquet_dataset=parquet_dataset, + updated_parquet_dataset_path=dataset_parquet_tmp_path, + destination_gcs_uri=converted_images_gcs_uri, + index_ids=index_ids, + tmp_img_path=tmp_img_dir_path, + file_extensions=file_extensions, + download_list_file_path=os.path.join(tmp_dir, 'download_gcs.txt'), + upload_list_file_path=os.path.join(tmp_dir, 'upload_gcs.txt')) + image_converter.start(svg_image_height=svg_image_height, svg_image_width=svg_image_width) + + # Upload newly created parquet file to gcs + storage_helpers.upload_file_to_bucket(storage_client=storage_client, + file_to_upload_path=dataset_parquet_tmp_path, + bucket_name=artifact_bucket, + blob_path=dataset_parquet_blob_path) + + # Update manifest + data_manifest.dataset_id = f"{run_id}_{component_name}" + # TODO: replace harcoded namespace with string or list input + # TODO: make sure we read all the namespace ('cf', 'laion') + data_manifest.associated_data.dataset['cf'] = \ + f"gs://{artifact_bucket}/{dataset_parquet_blob_path}" + data_manifest.metadata.branch = "" # TODO: Fill from docker build env var + data_manifest.metadata.commit_hash = "" # TODO: Fill from docker build env var + data_manifest.metadata.creation_date = datetime.now().strftime("%d-%m-%Y_%H-%M-%S") + data_manifest.metadata.run_id = run_id + + logger.info('Manifest file created and updated') + + # Write manifest to outputPath + Path(data_manifest_path_image_conversion_component) \ + .parent.mkdir(parents=True, exist_ok=True) + Path(data_manifest_path_image_conversion_component).write_text(data_manifest.to_json()) + + logger.info('Manifest file written to %s', data_manifest_path_image_conversion_component) + + logger.info('Job completed.') + + +if __name__ == '__main__': + args = parse_args() + dataset_image_conversion_component( + run_id=args.run_id, + artifact_bucket=args.artifact_bucket, + component_name=args.component_name, + project_id=args.project_id, + file_extensions=args.file_extensions, + svg_image_width=args.svg_image_width, + svg_image_height=args.svg_image_height, + data_manifest_path=args.data_manifest_path, + data_manifest_path_image_conversion_component=args. + data_manifest_path_image_conversion_component) diff --git a/examples/pipelines/finetune_stable_diffusion/components/image_conversion_component/src/utils/__init__.py b/examples/pipelines/finetune_stable_diffusion/components/image_conversion_component/src/utils/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/pipelines/finetune_stable_diffusion/components/image_conversion_component/src/utils/img_conversion.py b/examples/pipelines/finetune_stable_diffusion/components/image_conversion_component/src/utils/img_conversion.py new file mode 100644 index 000000000..0794c0b7c --- /dev/null +++ b/examples/pipelines/finetune_stable_diffusion/components/image_conversion_component/src/utils/img_conversion.py @@ -0,0 +1,200 @@ +""" +Image conversion utils +""" + +import io +import os +import logging +from typing import Union + +import cairosvg +import pyarrow.dataset as ds +from tqdm import tqdm +from PIL import Image as img +from PIL.Image import Image + +# # pylint: disable=import-error +from helpers import storage_helpers, parquet_helpers, io_helpers +from helpers.logger import get_logger + +# Set to allow pillow to process images of different sizes +img.MAX_IMAGE_PIXELS = None + +LOGGER = get_logger(name=__name__, level=logging.INFO) + + +def convert_png_to_jpeg(image_source: Union[str, Image]) -> Image: + """ + Function that converts images from png to jpg + Args: + image_source (Union[str,Image]): png image source or Pillow Image object + Returns: + Image: Image in jpeg format + """ + + if isinstance(image_source, str): + image_to_convert = img.open(image_source).convert("RGBA") + else: + image_to_convert = image_source + converted_image = img.new("RGB", image_to_convert.size, (255, 255, 255)) + converted_image.paste(image_to_convert, mask=image_to_convert) + + return converted_image + + +def convert_svg_to_jpeg(img_path: str, output_width: int, output_height: int) -> Image: + """ + Function that converts images from svg to jpg + Args: + img_path (str): the folder path where the source images are located + output_width (int): the desired output width of the svg image + output_height (int): the desired output height of the svg image + Returns: + Image: Image in jpeg format + """ + # SVG has to be converted to png first before converting to jpg + png_bytes = cairosvg.svg2png(url=img_path, output_width=output_width, + output_height=output_height) + converted_image = img.open(io.BytesIO(png_bytes)) + converted_image = convert_png_to_jpeg(converted_image) + + return converted_image + + +# pylint: disable=too-many-instance-attributes, too-few-public-methods +class KfpPipelineImageConverter: + """kfp images conversion """ + + # pylint: disable=too-many-arguments + def __init__(self, parquet_dataset: ds.dataset, updated_parquet_dataset_path: str, + destination_gcs_uri: str, tmp_img_path: str, file_extensions: list, + index_ids: list, download_list_file_path: str, + upload_list_file_path: str): + """ + Class that structures the kfp images conversion loop + Args: + parquet_dataset (pq.ParquetDataset): the parquet dataset containing the list of images + to convert + updated_parquet_dataset_path (str): the path to write the updated parquet path after + conversion + destination_gcs_uri (str): the gcs uri to copy the files to. + It can indicate a bucket (e.g. gs://my-bucket) or a bucket with a blob path + (e.g gs://my-bucket/my-blob-path/) + tmp_img_path (str) the temporary path used to store the downloaded and converted images + file_extensions (list): a list containing the image extensions that need to be converted + index_ids (list): list of index ids that contain the images of interest + download_list_file_path (str): path to file list containing one-per-line list of GCS + URIs to download + upload_list_file_path (str): path to file list containing one-per-line list of local + file paths to upload + """ + self.parquet_dataset = parquet_dataset + self.updated_parquet_dataset_path = updated_parquet_dataset_path + self.destination_gcs_uri = destination_gcs_uri + self.tmp_img_path = tmp_img_path + self.file_extensions = file_extensions + self.index_ids = index_ids + self.download_list_file_path = download_list_file_path + self.upload_list_file_path = upload_list_file_path + + def _write_gcs_file_lists(self): + """ + Function that writes the gcs download and upload list files for bulk download/upload + """ + with open(self.download_list_file_path, "w") as download_list_file, \ + open(self.upload_list_file_path, "w") as upload_list_file: + for batch in self.parquet_dataset.to_batches(): + for row in batch.to_pylist(): + file_extension, file_id, file_uri = row['file_extension'], row['file_id'], row[ + 'file_uri'] + if file_id in self.index_ids and file_extension in self.file_extensions: + converted_img_name = f"{io_helpers.get_file_name(file_uri)}.jpg" + local_file_path = os.path.join(self.tmp_img_path, converted_img_name) + download_list_file.write(file_uri + "\n") + upload_list_file.write(local_file_path + "\n") + + LOGGER.info("GCS download file list written to %s", self.download_list_file_path) + LOGGER.info("GCS upload file list written to %s", self.upload_list_file_path) + + def _download_imgs_to_convert(self): + """Function that download the images to be converted locally""" + LOGGER.info("Downloading images to convert") + storage_helpers.copy_files_bulk(self.download_list_file_path, + self.tmp_img_path) + LOGGER.info("The images to be converted were successfully downloaded to %s", + self.tmp_img_path) + + def _upload_converted_imgs(self): + """Function that uploads the images to gcs """ + + LOGGER.info("Uploading converted images") + storage_helpers.copy_files_bulk(self.upload_list_file_path, + self.destination_gcs_uri) + LOGGER.info("The converted images were uploaded to %s", self.destination_gcs_uri) + + def _convert_images_to_jpeg(self, svg_image_width: int, svg_image_height: int): + """ + Function that converts the images to jpeg format and saves them locally. The updated + metadata is then written to new updated the parquet dataset file. + Args: + svg_image_width (int): the desired width to scale the converted SVG image to + svg_image_height (int): the desired height to scale the converted SVG image to + """ + + LOGGER.info("Image conversion started") + + def _img_conversion_loop(**kwargs): + """Function that loops through the images and converts them if they are located + in the index ids and if they match the specified image formats that need to be + converted""" + for batch in tqdm(self.parquet_dataset.to_batches()): + for row in tqdm(batch.to_pylist()): + file_extension, file_id, file_uri, file_size = \ + row['file_extension'], row['file_id'], row['file_uri'], row['file_size'] + + if file_id in self.index_ids and file_extension in self.file_extensions: + img_name = os.path.basename(file_uri) + local_file_path = os.path.join(self.tmp_img_path, img_name) + # convert image + if file_extension.lower() == 'png': + image = convert_png_to_jpeg(local_file_path) + elif file_extension.lower() == 'svg': + image = convert_svg_to_jpeg(local_file_path, kwargs['svg_image_width'], + kwargs['svg_image_height']) + else: + raise NotImplementedError( + f"Conversion to jpg for format {file_extension} is" + f"not implemented. Available conversions are" + f" 'png_to_jpeg' and 'svg_to_jpeg'") + + # Store img_temp_save_path image locally + file_extension = "jpg" + img_name = f"{io_helpers.get_file_name(file_uri)}.{file_extension}" + img_temp_save_path = os.path.join(self.tmp_img_path, img_name) + image.save(img_temp_save_path) + file_size = os.path.getsize(img_temp_save_path) + file_uri = f"{self.destination_gcs_uri}/{img_name}" + + yield file_uri, file_id, file_size, file_extension + + parquet_helpers.write_dataset_parquet( + dataset_parquet_path=self.updated_parquet_dataset_path, + data_iterable_producer=_img_conversion_loop, + svg_image_height=svg_image_height, + svg_image_width=svg_image_width) + + LOGGER.info("Updated dataset parquet file written to %s", + self.updated_parquet_dataset_path) + LOGGER.info("Image conversion completed") + + def start(self, svg_image_width: int, svg_image_height: int): + """ + Function that executes the kfp image conversion steps + Args: + svg_image_width (int): the desired width to scale the converted SVG image to + svg_image_height (int): the desired height to scale the converted SVG image to + """ + self._write_gcs_file_lists() + self._download_imgs_to_convert() + self._convert_images_to_jpeg(svg_image_width, svg_image_height) + self._upload_converted_imgs() diff --git a/examples/pipelines/finetune_stable_diffusion/components/sd_finetuning_component/Dockerfile b/examples/pipelines/finetune_stable_diffusion/components/sd_finetuning_component/Dockerfile new file mode 100644 index 000000000..dc4eb54ad --- /dev/null +++ b/examples/pipelines/finetune_stable_diffusion/components/sd_finetuning_component/Dockerfile @@ -0,0 +1,23 @@ +FROM europe-west1-docker.pkg.dev/storied-landing-366912/storied-landing-366912-default-repository/mlpipelines/kubeflow/components/base_component:latest + +ARG DEBIAN_FRONTEND=noninteractive + +## System dependencies +RUN apt-get update && \ + apt-get upgrade -y && \ + apt-get install git curl wget -y + +# Set the working directory to the source folder +WORKDIR /src + +# Install packages +COPY requirements.txt . +RUN pip3 install -r requirements.txt +# Specifc to A100 GPU https://github.com/pytorch/pytorch/issues/57922 +# (installations included here due to error in pylint when trying to find links from pytorch.org) +RUN pip3 install torch==1.12.0+cu116 -f https://download.pytorch.org/whl/torch_stable.html +RUN pip3 install torchvision==0.13.0+cu116 -f https://download.pytorch.org/whl/torch_stable.html + +COPY src /src + +ENTRYPOINT ["python", "main.py"] \ No newline at end of file diff --git a/examples/pipelines/finetune_stable_diffusion/components/sd_finetuning_component/README.MD b/examples/pipelines/finetune_stable_diffusion/components/sd_finetuning_component/README.MD new file mode 100644 index 000000000..53bd4a1e1 --- /dev/null +++ b/examples/pipelines/finetune_stable_diffusion/components/sd_finetuning_component/README.MD @@ -0,0 +1,26 @@ +# sd_finetuning_component + +### Description + +This component takes as input the +final data manifest that is output by the dataset +creation pipeline. The data +manifest keeps reference of all the necessary metadata is required for the next step such as the +reference to the filtered images and captions. The component prepares the dataset for training +according to the +required [format](https://huggingface.co/docs/datasets/image_dataset#:~:text=in%20load_dataset.-,Image%20captioning,-Image%20captioning%20datasets) +and starts the finetuning jobs. +### **Inputs/Outputs** + +See [`component.yaml`](component.yaml) for a more detailed description on all the input/output parameters. + +### **Practical considerations** +* The script uses the official [finetuning script](https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image.py) +provided by diffusers and is launched with a subprocess call. Check the following +[docs](https://github.com/huggingface/diffusers/tree/main/examples/text_to_image) for more information on finetuning. +* Finetuning stable diffusion models requires at least 30 GB of GPU memory. It is currently recommended to +use at least 2 A100 GPUs for finetuning. Make sure to configure `accelerate_config.yaml` if you want to change +the number of GPUs used for training. +* It is difficult to pinpoint exactly how many steps are needed to obtain a good finetunable model. The best approach +is to create checkpoints throughout training and visualize the progression of the generation with a fixed prompt and seed. +* A good starting point is to finetune for 2 epochs (2 full passes over the dataset). \ No newline at end of file diff --git a/examples/pipelines/finetune_stable_diffusion/components/sd_finetuning_component/__init__.py b/examples/pipelines/finetune_stable_diffusion/components/sd_finetuning_component/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/pipelines/finetune_stable_diffusion/components/sd_finetuning_component/component.yaml b/examples/pipelines/finetune_stable_diffusion/components/sd_finetuning_component/component.yaml new file mode 100644 index 000000000..63eb2e02d --- /dev/null +++ b/examples/pipelines/finetune_stable_diffusion/components/sd_finetuning_component/component.yaml @@ -0,0 +1,122 @@ +name: sd_finetuning_component +description: A component that takes a data manifest as input and downloads the appropriate dataset for finetuning +inputs: + - name: run_id + description: The run id of the pipeline + type: String + - name: artifact_bucket + description: The GCS bucket used to store the artifact + type: String + - name: component_name + description: the name of the component (used to create gcs artefact path) + type: String + - name: project_id + description: The id of the gcp-project + type: String + - name: data_manifest_gcs_path + description: the path to the data manifest that contains information on the training set + type: String + - name: pretrained_model_gcs_path + description: The gcs path where the base model to finetune is located + type: String + - name: resume_from_checkpoint + description: Whether training should be resumed from a previous checkpoint. + Use a path saved in `pretrained_model_gcs_path` by `--checkpointing_steps`, + or `"latest"` to automatically select the last available checkpoint. + type: String + optional: True + - name: seed + description: the name of the component (used to create gcs artefact path) + type: Integer + - name: resolution + description: The resolution for input images, all the images in the train/validation + will be resized to this resolution + type: Integer + - name: train_batch_size + description: Batch size (per device) for the training dataloader + type: Integer + - name: num_train_epochs + description: Total number of epochs to perform + type: Integer + - name: max_train_steps + description: Total number of training steps to perform. If provided overrides `num_train_epochs` + type: Integer + optional: True + - name: checkpointing_steps + description: Save a checkpoint of the training state every X updates. These + checkpoints are only suitable for resuming training using + `--resume_from_checkpoint`. + type: Integer + optional: True + - name: gradient_accumulation_steps + description: The number of updates steps to accumulate before performing a backward/update pass + type: Integer + - name: gradient_checkpointing + description: Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass + type: String + - name: learning_rate + description: Initial learning rate (after the potential warmup period) to use. + type: Float + - name: scale_lr + description: Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size + type: String + - name: lr_scheduler + description: The scheduler type to use. Choose between ["linear", "cosine", + cosine_with_restarts", "polynomial", "constant","constant_with_warmup"] + type: String + - name: lr_warmup_steps + description: Scale the learning rate by the number of GPUs, gradient accumulation + steps, and batch size + type: Integer + - name: use_ema + description: Whether to use EMA model + type: String + - name: mixed_precision + description: Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). + Bf16 requires PyTorch >=1.10.and an Nvidia Ampere GPU. Default to the value of accelerate + config of the current system or the flag passed with the accelerate.launch` command. + Use this argument to override the accelerate config + type: String + - name: center_crop + description: whether to center crop images before resizing to resolution (if not set, random + crop will be used) + type: String + - name: random_flip + description: whether to randomly flip images horizontally + type: String + + +outputs: + - name: model_gcs_path_finetuning_component + description: The path where the model output and checkpoints are saved + +implementation: + container: + image: europe-west1-docker.pkg.dev/storied-landing-366912/storied-landing-366912-default-repository/mlpipelines/kubeflow/components/sd_finetuning_component:latest + command: [ + python3, main.py, + --run-id, { inputValue: run_id }, + --artifact-bucket, { inputValue: artifact_bucket }, + --component-name, { inputValue: component_name }, + --project-id, { inputValue: project_id }, + --data-manifest-gcs-path, { inputValue: data_manifest_gcs_path }, + --pretrained-model-gcs-path, { inputValue: pretrained_model_gcs_path }, + --resume-from-checkpoint, { inputValue: resume_from_checkpoint }, + --seed, { inputValue: seed }, + --resolution, { inputValue: resolution }, + --train-batch-size, { inputValue: train_batch_size }, + --num-train-epochs, { inputValue: num_train_epochs }, + --max-train-steps, { inputValue: max_train_steps }, + --checkpointing-steps, { inputValue: checkpointing_steps }, + --gradient-accumulation-steps, { inputValue: gradient_accumulation_steps }, + --gradient-checkpointing, { inputValue: gradient_checkpointing }, + --learning-rate, { inputValue: learning_rate }, + --scale-lr, { inputValue: scale_lr }, + --lr-scheduler, { inputValue: lr_scheduler }, + --lr-warmup-steps, { inputValue: lr_warmup_steps }, + --use-ema, { inputValue: use_ema }, + --mixed-precision, { inputValue: mixed_precision }, + --center-crop, { inputValue: center_crop }, + --random-flip, { inputValue: random_flip }, + --model-gcs-path-finetuning-component, { outputPath: model_gcs_path_finetuning_component }, + ] diff --git a/examples/pipelines/finetune_stable_diffusion/components/sd_finetuning_component/requirements.txt b/examples/pipelines/finetune_stable_diffusion/components/sd_finetuning_component/requirements.txt new file mode 100644 index 000000000..38c6470a3 --- /dev/null +++ b/examples/pipelines/finetune_stable_diffusion/components/sd_finetuning_component/requirements.txt @@ -0,0 +1,14 @@ +accelerate==0.13.2 +dataclasses-json==0.5.7 +datasets==2.7.1 +diffusers==0.11.0 +markupsafe==2.0.1 +APScheduler==3.9.1.post1 +ftfy==6.1.1 +jsonlines==3.1.0 +modelcards==0.1.6 +pandas==1.3.5 +Pillow==9.3.0 +sorcery==0.2.2 +tensorboard==2.10.1 +transformers==4.21.0 \ No newline at end of file diff --git a/examples/pipelines/finetune_stable_diffusion/components/sd_finetuning_component/src/__init__.py b/examples/pipelines/finetune_stable_diffusion/components/sd_finetuning_component/src/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/examples/pipelines/finetune_stable_diffusion/components/sd_finetuning_component/src/accelerate_config.yaml b/examples/pipelines/finetune_stable_diffusion/components/sd_finetuning_component/src/accelerate_config.yaml new file mode 100644 index 000000000..d7233a1f8 --- /dev/null +++ b/examples/pipelines/finetune_stable_diffusion/components/sd_finetuning_component/src/accelerate_config.yaml @@ -0,0 +1,16 @@ +compute_environment: LOCAL_MACHINE +deepspeed_config: {} +distributed_type: MULTI_GPU +downcast_bf16: 'no' +fsdp_config: {} +gpu_ids: all +machine_rank: 0 +main_process_ip: null +main_process_port: null +main_training_function: main +mixed_precision: fp16 +num_machines: 1 +num_processes: 2 +rdzv_backend: static +same_network: true +use_cpu: false diff --git a/examples/pipelines/finetune_stable_diffusion/components/sd_finetuning_component/src/main.py b/examples/pipelines/finetune_stable_diffusion/components/sd_finetuning_component/src/main.py new file mode 100644 index 000000000..a63264994 --- /dev/null +++ b/examples/pipelines/finetune_stable_diffusion/components/sd_finetuning_component/src/main.py @@ -0,0 +1,399 @@ +""" +This file is the entrypoint of the component. It will parse all arguments +and give them to the actual core of the component. +""" +import os +import subprocess # nosec +import tempfile +import argparse +import logging +import tarfile +from pathlib import Path +from typing import Union + +from apscheduler.schedulers.background import BackgroundScheduler +# pylint: disable=import-error +import pyarrow.compute as pc +from google.cloud import storage +from sorcery import dict_of + +from helpers.logger import get_logger +from helpers import storage_helpers, kfp_helpers, parquet_helpers +from helpers.manifest_helpers import DataManifest +from utils.dataset_utils import SDDatasetCreator +from utils import training_utils + + +def optional_int(passed_arg: any): + """Custom function to handle optional integers in the argument parser""" + return None if not passed_arg else int(passed_arg) + + +def parse_args(): + """Parse component arguments""" + + parser = argparse.ArgumentParser() + parser.add_argument('--run-id', + type=str, + required=True, + help='The run id of the pipeline') + parser.add_argument('--artifact-bucket', + type=str, + required=True, + help='The GCS bucket used to store the artifacts') + parser.add_argument('--component-name', + type=str, + required=True, + help='The name of the component') + parser.add_argument('--project-id', + type=str, + required=True, + help='The id of the gcp-project') + parser.add_argument('--data-manifest-gcs-path', + type=str, + required=True, + help='the path to the data manifest that contains information on the ' + 'training set') + parser.add_argument('--pretrained-model-gcs-path', + type=str, + required=True, + help='Model identifier from hugginface.com/models') + parser.add_argument('--resume-from-checkpoint', + type=str, + required=False, + help="Whether training should be resumed from a previous checkpoint. Use a" + "path saved in `pretrained_model_gcs_path` by `--checkpointing_steps`" + "or `latest` to automatically select the last available checkpoint.") + parser.add_argument('--resolution', + type=int, + required=True, + help='The resolution for input images, all the images in the ' + 'train/validation dataset will be resized to this resolution.') + parser.add_argument('--seed', + type=int, + required=True, + help='A seed for reproducible training.') + parser.add_argument('--train-batch-size', + type=int, + required=True, + help='Batch size (per device) for the training dataloader') + parser.add_argument('--num-train-epochs', + type=int, + required=True, + help='Total number of training epochs to perform') + parser.add_argument('--max-train-steps', + type=optional_int, + required=False, + help='Total number of training steps to perform. If provided,' + ' overrides num_train_epochs') + parser.add_argument('--checkpointing-steps', + type=optional_int, + required=False, + help='Save a checkpoint of the training state every X updates.' + ' These checkpoints are only suitable for resuming training' + ' using `--resume_from_checkpoint`.') + parser.add_argument('--gradient-accumulation-steps', + type=int, + required=True, + help='The number of updates steps to accumulate before performing a ' + 'backward/update pass') + parser.add_argument('--gradient-checkpointing', + type=str, + required=False, + help='Whether or not to use gradient checkpointing to save memory at' + 'the expense of slower backward pass') + parser.add_argument('--learning-rate', + type=float, + required=True, + help='Initial learning rate (after the potential warmup period) to use.') + parser.add_argument('--scale-lr', + type=str, + required=False, + help='Scale the learning rate by the number of GPUs, gradient accumulation' + ' steps, and batch size') + parser.add_argument('--lr-scheduler', + type=str, + required=True, + help='The scheduler type to use. Choose between ["linear", "cosine",' + ' "cosine_with_restarts", "polynomial", "constant",' + ' "constant_with_warmup"]') + parser.add_argument('--lr-warmup-steps', + type=int, + required=True, + help='Number of steps for the warmup in the lr scheduler') + parser.add_argument('--use-ema', + type=str, + required=False, + help='Whether to use EMA model') + parser.add_argument('--mixed-precision', + type=str, + required=True, + help="Whether to use mixed precision. Choose between fp16 and bf16 " + "(bfloat16). Bf16 requires PyTorch >=1.10.and an Nvidia Ampere GPU." + "Default to the value of accelerate config of the current system or" + "the flag passed with the `accelerate.launch` command. Use this " + "argument to override the accelerate config") + parser.add_argument('--center-crop', + type=str, + required=False, + help='whether to center crop images before resizing to resolution ' + '(if not set,random crop will be used)') + parser.add_argument('--random-flip', + type=str, + required=False, + help='whether to randomly flip the images horizontally') + parser.add_argument('--model-gcs-path-finetuning-component', + type=str, + required=True, + help='The path where the finetuned model and its checkpoints are saved') + + return parser.parse_args() + + +# pylint: disable=too-many-locals, too-many-arguments, too-many-statements +def sd_finetuning_component(run_id: str, + artifact_bucket: str, + component_name: str, + project_id: str, + data_manifest_gcs_path: str, + pretrained_model_gcs_path: str, + resume_from_checkpoint: Union[str, None], + seed: int, + resolution: int, + train_batch_size: int, + num_train_epochs: int, + max_train_steps: int, + checkpointing_steps: Union[int, None], + gradient_accumulation_steps: int, + gradient_checkpointing: Union[str, None], + learning_rate: float, + scale_lr: Union[str, None], + lr_scheduler: str, + lr_warmup_steps: int, + use_ema: Union[str, None], + mixed_precision: str, + center_crop: Union[str, None], + random_flip: Union[str, None], + model_gcs_path_finetuning_component: str) -> None: + """ + A component for finetuning a stable diffusion model on a custom dataset + Args: + run_id (str): the run id of the pipeline + artifact_bucket (str): The GCS bucket used to store the artifacts + component_name (str): the name of the component (used to create gcs artefact path) + project_id (str): The id of the gcp-project + data_manifest_gcs_path (str): the path to the data manifest that contains information on the + training set + pretrained_model_gcs_path (str): Model identifier from huggingface.co/models or gcs + path to a model + resume_from_checkpoint (Union[str, None]): Whether training should be resumed from a + previous checkpoint. Use a path saved in `pretrained_model_gcs_path` by + `--checkpointing_steps`, or `"latest"` to automatically select the last available + checkpoint. + seed (int): A seed for reproducible training. + resolution (int): The resolution for input images, all the images in the train/validation + dataset will be resized to this resolution + train_batch_size (int): Batch size (per device) for the training dataloader + num_train_epochs (int): Total number of epochs to perform + max_train_steps (int): Total number of training steps to perform. If provided overrides + `num_train_epochs` + checkpointing_steps (int): Save a checkpoint of the training state every X updates. These + checkpoints are only suitable for resuming training using `--resume_from_checkpoint`. + gradient_accumulation_steps (int): The number of updates steps to accumulate before + performing a backward/update pass + gradient_checkpointing (str): Whether to use gradient checkpointing to save memory + at the expense of slower backward pass + learning_rate (float): Initial learning rate (after the potential warmup period) to use. + scale_lr (str): Scale the learning rate by the number of GPUs, gradient accumulation steps, + and batch size + lr_warmup_steps (int): Scale the learning rate by the number of GPUs, gradient accumulation + steps, and batch size + lr_scheduler (str): The scheduler type to use. Choose between ["linear", "cosine", + cosine_with_restarts", "polynomial", "constant","constant_with_warmup"] + use_ema (str): Whether to use EMA model + mixed_precision (str): Whether to use mixed precision. Choose between fp16 and bf16 + (bfloat16). Bf16 requires PyTorch >=1.10.and an Nvidia Ampere GPU. + Default to the value of accelerate config of the current system or the flag passed with the + `accelerate.launch` command. Use this argument to override the `accelerate` config + center_crop (str): whether to center crop images before resizing to resolution (if not set, + random crop will be used) + random_flip (str): whether to randomly flip images horizontally + model_gcs_path_finetuning_component (str): The path where the finetuned model and its + checkpoints are saved + """ + + logger = get_logger(name=__name__, level=logging.INFO) + logger.info('Started job...') + + # Show CUDA availability + kfp_helpers.get_cuda_availability() + + # Initialize storage client + storage_client = storage.Client(project=project_id) + + with tempfile.TemporaryDirectory() as tmp_dir: + # Download and load data manifest + manifest_file_path = storage_helpers.download_file_from_bucket(storage_client, + data_manifest_gcs_path, + tmp_dir) + file = tarfile.open(manifest_file_path, "r:gz") + manifest_dict = file.extractfile(file.getmembers()[0]).read().decode("utf-8") + data_manifest = DataManifest.from_json(manifest_dict) + + # Get relevant metadata information + # Used to identify the dataset creation run associated with the training + dataset_creation_run_id = data_manifest.metadata.run_id + index_parquet_gcs_path = data_manifest.index + datasets_parquet_dict = data_manifest.associated_data.dataset + captions_parquet_dict = data_manifest.associated_data.caption + + # Initialize GCS custom artifact path + component_artifact_dir = run_id.rpartition('-')[0] + artifact_bucket_blob_path = f"custom_artifact/{component_artifact_dir}/{component_name}" + logger.info("custom artifact will be uploaded to %s", + f'gs://{artifact_bucket}/{artifact_bucket_blob_path}') + + # Initialize additional directories for temporary files + imgs_tmp_path = os.path.join(tmp_dir, 'img_dir') + models_tmp_path = os.path.join(tmp_dir, 'models') + model_to_finetune_name = os.path.basename(pretrained_model_gcs_path) + finetuned_model_name = f"{model_to_finetune_name}-tmp_dir-{dataset_creation_run_id}" + finetuned_model_path = os.path.join(models_tmp_path, finetuned_model_name) + model_to_finetune_path = os.path.join(models_tmp_path, model_to_finetune_name) + finetuned_model_gcs_uri = f"gs://{artifact_bucket}/{artifact_bucket_blob_path}/" \ + f"{finetuned_model_name}" + os.makedirs(imgs_tmp_path, exist_ok=True) + os.makedirs(finetuned_model_path, exist_ok=True) + os.makedirs(model_to_finetune_path, exist_ok=True) + + # Download index files locally + index_id_path = storage_helpers.download_file_from_bucket( + storage_client, index_parquet_gcs_path, tmp_dir) + + # Get index list + index_list = parquet_helpers.get_column_list_from_parquet(index_id_path, + column_name='index') + filters = (pc.field("file_id").isin(index_list)) + + sd_dataset_creator = SDDatasetCreator(tmp_path=tmp_dir, + img_path=imgs_tmp_path) + + for namespace in datasets_parquet_dict: + # Download + dataset_parquet_tmp_path = storage_helpers.download_file_from_bucket( + storage_client, datasets_parquet_dict[namespace], tmp_dir) + caption_parquet_tmp_path = storage_helpers.download_file_from_bucket( + storage_client, captions_parquet_dict[namespace], tmp_dir) + filtered_dataset = parquet_helpers.filter_parquet_file( + file_path=dataset_parquet_tmp_path, + filters=filters + ) + filtered_captions = parquet_helpers.filter_parquet_file( + file_path=caption_parquet_tmp_path, + filters=filters + ) + sd_dataset_creator.write_dataset(dataset_scanner=filtered_dataset, + captions_scanner=filtered_captions, + namespace=namespace) + + # Download model to finetune (without checkpoints) + training_utils.download_model_without_checkpoints(pretrained_model_gcs_path, + finetuned_model_path) + + # Fetch specified checkpoint and download it to the local model path + checkpoint_prefix = None + if resume_from_checkpoint: + # training script expects checkpoint to be located in the `output_dir` where the model + # was previously trained -> download the checkpoints there + checkpoint_prefix = training_utils.download_checkpoint_to_resume_from( + resume_from_checkpoint=resume_from_checkpoint, + pretrained_model_gcs_path=pretrained_model_gcs_path, + finetuned_model_path=model_to_finetune_path) + + # Initialize Daemon to upload model and checkpoints while training + scheduler = BackgroundScheduler() + scheduler.add_job(func=training_utils.sync_training_checkpoints, + trigger='interval', + args=[model_to_finetune_path, finetuned_model_gcs_uri], + minutes=10) + scheduler.start() + + # Specify training command (Subprocess requires integer values to be piped as string, + # the argument parser of training script will convert them again to integers/floats) + training_cmd = ['accelerate', 'launch', + '--config_file', 'accelerate_config.yaml', + '--mixed_precision', mixed_precision, + 'train_text_to_image.py', + '--seed', str(seed), + '--pretrained_model_name_or_path', finetuned_model_path, + '--dataset_name', imgs_tmp_path, + '--resolution', str(resolution), + '--train_batch_size', str(train_batch_size), + '--num_train_epochs', str(num_train_epochs), + '--gradient_accumulation_steps', str(gradient_accumulation_steps), + '--learning_rate', str(learning_rate), + '--lr_scheduler', lr_scheduler, + '--lr_warmup_steps', str(lr_warmup_steps), + '--output_dir', model_to_finetune_path + ] + + # Add arguments defined with 'store_true' action if they are set to "True" + optional_args = dict_of(random_flip, center_crop, use_ema, gradient_checkpointing, scale_lr) + training_cmd.extend([f'--{arg}' for arg, value in optional_args.items() if value]) + + # Add optional string/int/bool arguments + if max_train_steps: + training_cmd.extend(['--max_train_steps', str(max_train_steps)]) + if checkpointing_steps: + training_cmd.extend(['--checkpointing_steps', str(checkpointing_steps)]) + if checkpoint_prefix: + training_cmd.extend(['--resume_from_checkpoint', checkpoint_prefix]) + + logger.info(f'Starting training: {training_cmd}') + subprocess.run(training_cmd, check=True) # nosec + + logger.info('Training complete, waiting for model and checkpoint sync to upload the latest' + ' model state and shutting down gracefully ...') + scheduler.shutdown() + + # Run one last synchronization task to ensure that all files were uploaded partially (avoid + # issues when scheduler is uploading partial files as they are being written) + logger.info('Running final sync job') + subprocess.run(['gsutil', '-m', 'rsync', '-r', finetuned_model_path, # nosec + finetuned_model_gcs_uri], check=True) + + # Write manifest to outputPath + Path(model_gcs_path_finetuning_component).parent.mkdir(parents=True, exist_ok=True) + Path(model_gcs_path_finetuning_component).write_text(finetuned_model_gcs_uri) + + logger.info('Job completed.') + + +if __name__ == '__main__': + args = parse_args() + sd_finetuning_component( + run_id=args.run_id, + artifact_bucket=args.artifact_bucket, + component_name=args.component_name, + project_id=args.project_id, + data_manifest_gcs_path=args.data_manifest_gcs_path, + pretrained_model_gcs_path=args.pretrained_model_gcs_path, + resume_from_checkpoint=args.resume_from_checkpoint, + seed=args.seed, + resolution=args.resolution, + train_batch_size=args.train_batch_size, + num_train_epochs=args.num_train_epochs, + max_train_steps=args.max_train_steps, + checkpointing_steps=args.checkpointing_steps, + gradient_accumulation_steps=args.gradient_accumulation_steps, + gradient_checkpointing=args.gradient_checkpointing, + learning_rate=args.learning_rate, + scale_lr=args.scale_lr, + lr_scheduler=args.lr_scheduler, + lr_warmup_steps=args.lr_warmup_steps, + use_ema=args.use_ema, + mixed_precision=args.mixed_precision, + center_crop=args.center_crop, + random_flip=args.random_flip, + model_gcs_path_finetuning_component=args.model_gcs_path_finetuning_component + ) diff --git a/examples/pipelines/finetune_stable_diffusion/components/sd_finetuning_component/src/train_text_to_image.py b/examples/pipelines/finetune_stable_diffusion/components/sd_finetuning_component/src/train_text_to_image.py new file mode 100644 index 000000000..e822b5e72 --- /dev/null +++ b/examples/pipelines/finetune_stable_diffusion/components/sd_finetuning_component/src/train_text_to_image.py @@ -0,0 +1,819 @@ +"""Stable diffusion finetuning. Cloned from commit: f7154f859ce4534227962ccd190eaf428ef0abc8 +Cloning from a commit since the finetuning script contains updates not included in the latest +# diffusers release (04/01/2023)""" +# pylint: skip-file +import argparse +import copy +import logging +import math +import os +import random +from pathlib import Path +from typing import Iterable, Optional + +import numpy as np +import PIL.Image +import torch +import torch.nn.functional as F +import torch.utils.checkpoint + +import datasets +import diffusers +import transformers +from accelerate import Accelerator +from accelerate.logging import get_logger +from accelerate.utils import set_seed +from datasets import load_dataset +from diffusers import AutoencoderKL, DDPMScheduler, StableDiffusionPipeline, UNet2DConditionModel +from diffusers.optimization import get_scheduler +from diffusers.utils import check_min_version +from diffusers.utils.import_utils import is_xformers_available +from huggingface_hub import HfFolder, Repository, whoami +from torchvision import transforms +from tqdm.auto import tqdm +from transformers import CLIPTextModel, CLIPTokenizer + +# Will error if the minimal version of diffusers is not installed. Remove at your own risks. +check_min_version("0.10.0.dev0") + +PIL.Image.MAX_IMAGE_PIXELS = None + +logger = get_logger(__name__) + + +def parse_args(): + parser = argparse.ArgumentParser(description="Simple example of a training script.") + parser.add_argument( + "--pretrained_model_name_or_path", + type=str, + default=None, + required=True, + help="Path to pretrained model or Model identifier from huggingface.co/models or gcs path to a model.", + ) + parser.add_argument( + "--revision", + type=str, + default=None, + required=False, + help="Revision of pretrained model identifier from huggingface.co/models.", + ) + parser.add_argument( + "--dataset_name", + type=str, + default=None, + help=( + "The name of the Dataset (from the HuggingFace hub) to train on (could be your own, possibly private," + " dataset). It can also be a path pointing to a local copy of a dataset in your filesystem," + " or to a folder containing files that 🤗 Datasets can understand." + ), + ) + parser.add_argument( + "--dataset_config_name", + type=str, + default=None, + help="The config of the Dataset, leave as None if there's only one config.", + ) + parser.add_argument( + "--train_data_dir", + type=str, + default=None, + help=( + "A folder containing the training data. Folder contents must follow the structure described in" + " https://huggingface.co/docs/datasets/image_dataset#imagefolder. In particular, a `metadata.jsonl` file" + " must exist to provide the captions for the images. Ignored if `dataset_name` is specified." + ), + ) + parser.add_argument( + "--image_column", type=str, default="image", + help="The column of the dataset containing an image." + ) + parser.add_argument( + "--caption_column", + type=str, + default="text", + help="The column of the dataset containing a caption or a list of captions.", + ) + parser.add_argument( + "--max_train_samples", + type=int, + default=None, + help=( + "For debugging purposes or quicker training, truncate the number of training examples to this " + "value if set." + ), + ) + parser.add_argument( + "--output_dir", + type=str, + default="sd-model-finetuned", + help="The output directory where the model predictions and checkpoints will be written.", + ) + parser.add_argument( + "--cache_dir", + type=str, + default=None, + help="The directory where the downloaded models and datasets will be stored.", + ) + parser.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.") + parser.add_argument( + "--resolution", + type=int, + default=512, + help=( + "The resolution for input images, all the images in the train/validation dataset will be resized to this" + " resolution" + ), + ) + parser.add_argument( + "--center_crop", + action="store_true", + help="Whether to center crop images before resizing to resolution (if not set, random crop will be used)", + ) + parser.add_argument( + "--random_flip", + action="store_true", + help="whether to randomly flip images horizontally", + ) + parser.add_argument( + "--train_batch_size", type=int, default=16, + help="Batch size (per device) for the training dataloader." + ) + parser.add_argument("--num_train_epochs", type=int, default=100) + parser.add_argument( + "--max_train_steps", + type=int, + default=None, + help="Total number of training steps to perform. If provided, overrides num_train_epochs.", + ) + parser.add_argument( + "--gradient_accumulation_steps", + type=int, + default=1, + help="Number of updates steps to accumulate before performing a backward/update pass.", + ) + parser.add_argument( + "--gradient_checkpointing", + action="store_true", + help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.", + ) + parser.add_argument( + "--learning_rate", + type=float, + default=1e-4, + help="Initial learning rate (after the potential warmup period) to use.", + ) + parser.add_argument( + "--scale_lr", + action="store_true", + default=False, + help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.", + ) + parser.add_argument( + "--lr_scheduler", + type=str, + default="constant", + help=( + 'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",' + ' "constant", "constant_with_warmup"]' + ), + ) + parser.add_argument( + "--lr_warmup_steps", type=int, default=500, + help="Number of steps for the warmup in the lr scheduler." + ) + parser.add_argument( + "--use_8bit_adam", action="store_true", + help="Whether or not to use 8-bit Adam from bitsandbytes." + ) + parser.add_argument( + "--allow_tf32", + action="store_true", + help=( + "Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see" + " https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices" + ), + ) + parser.add_argument("--use_ema", action="store_true", help="Whether to use EMA model.") + parser.add_argument( + "--non_ema_revision", + type=str, + default=None, + required=False, + help=( + "Revision of pretrained non-ema model identifier. Must be a branch, tag or git identifier of the local or" + " remote repository specified with --pretrained_model_name_or_path." + ), + ) + parser.add_argument("--adam_beta1", type=float, default=0.9, + help="The beta1 parameter for the Adam optimizer.") + parser.add_argument("--adam_beta2", type=float, default=0.999, + help="The beta2 parameter for the Adam optimizer.") + parser.add_argument("--adam_weight_decay", type=float, default=1e-2, + help="Weight decay to use.") + parser.add_argument("--adam_epsilon", type=float, default=1e-08, + help="Epsilon value for the Adam optimizer") + parser.add_argument("--max_grad_norm", default=1.0, type=float, help="Max gradient norm.") + parser.add_argument("--push_to_hub", action="store_true", + help="Whether or not to push the model to the Hub.") + parser.add_argument("--hub_token", type=str, default=None, + help="The token to use to push to the Model Hub.") + parser.add_argument( + "--hub_model_id", + type=str, + default=None, + help="The name of the repository to keep in sync with the local `output_dir`.", + ) + parser.add_argument( + "--logging_dir", + type=str, + default="logs", + help=( + "[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to" + " *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***." + ), + ) + parser.add_argument( + "--mixed_precision", + type=str, + default=None, + choices=["no", "fp16", "bf16"], + help=( + "Whether to use mixed precision. Choose between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >=" + " 1.10.and an Nvidia Ampere GPU. Default to the value of accelerate config of the current system or the" + " flag passed with the `accelerate.launch` command. Use this argument to override the accelerate config." + ), + ) + parser.add_argument( + "--report_to", + type=str, + default="tensorboard", + help=( + 'The integration to report the results and logs to. Supported platforms are `"tensorboard"`' + ' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.' + ), + ) + parser.add_argument("--local_rank", type=int, default=-1, + help="For distributed training: local_rank") + parser.add_argument( + "--checkpointing_steps", + type=int, + default=500, + help=( + "Save a checkpoint of the training state every X updates. These checkpoints are only suitable for resuming" + " training using `--resume_from_checkpoint`." + ), + ) + parser.add_argument( + "--resume_from_checkpoint", + type=str, + default=None, + help=( + "Whether training should be resumed from a previous checkpoint. Use a path saved by" + ' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.' + ), + ) + parser.add_argument( + "--enable_xformers_memory_efficient_attention", action="store_true", + help="Whether or not to use xformers." + ) + + args = parser.parse_args() + env_local_rank = int(os.environ.get("LOCAL_RANK", -1)) + if env_local_rank != -1 and env_local_rank != args.local_rank: + args.local_rank = env_local_rank + + # Sanity checks + if args.dataset_name is None and args.train_data_dir is None: + raise ValueError("Need either a dataset name or a training folder.") + + # default to using the same revision for the non-ema model if not specified + if args.non_ema_revision is None: + args.non_ema_revision = args.revision + + return args + + +def get_full_repo_name(model_id: str, organization: Optional[str] = None, + token: Optional[str] = None): + if token is None: + token = HfFolder.get_token() + if organization is None: + username = whoami(token)["name"] + return f"{username}/{model_id}" + else: + return f"{organization}/{model_id}" + + +dataset_name_mapping = { + "lambdalabs/pokemon-blip-captions": ("image", "text"), +} + + +# Adapted from torch-ema https://github.com/fadel/pytorch_ema/blob/master/torch_ema/ema.py#L14 +class EMAModel: + """ + Exponential Moving Average of models weights + """ + + def __init__(self, parameters: Iterable[torch.nn.Parameter], decay=0.9999): + parameters = list(parameters) + self.shadow_params = [p.clone().detach() for p in parameters] + + self.collected_params = None + + self.decay = decay + self.optimization_step = 0 + + @torch.no_grad() + def step(self, parameters): + parameters = list(parameters) + + self.optimization_step += 1 + + # Compute the decay factor for the exponential moving average. + value = (1 + self.optimization_step) / (10 + self.optimization_step) + one_minus_decay = 1 - min(self.decay, value) + + for s_param, param in zip(self.shadow_params, parameters): + if param.requires_grad: + s_param.sub_(one_minus_decay * (s_param - param)) + else: + s_param.copy_(param) + + torch.cuda.empty_cache() + + def copy_to(self, parameters: Iterable[torch.nn.Parameter]) -> None: + """ + Copy current averaged parameters into given collection of parameters. + Args: + parameters: Iterable of `torch.nn.Parameter`; the parameters to be + updated with the stored moving averages. If `None`, the + parameters with which this `ExponentialMovingAverage` was + initialized will be used. + """ + parameters = list(parameters) + for s_param, param in zip(self.shadow_params, parameters): + param.data.copy_(s_param.data) + + def to(self, device=None, dtype=None) -> None: + r"""Move internal buffers of the ExponentialMovingAverage to `device`. + Args: + device: like `device` argument to `torch.Tensor.to` + """ + # .to() on the tensors handles None correctly + self.shadow_params = [ + p.to(device=device, dtype=dtype) if p.is_floating_point() else p.to(device=device) + for p in self.shadow_params + ] + + def state_dict(self) -> dict: + r""" + Returns the state of the ExponentialMovingAverage as a dict. + This method is used by accelerate during checkpointing to save the ema state dict. + """ + # Following PyTorch conventions, references to tensors are returned: + # "returns a reference to the state and not its copy!" - + # https://pytorch.org/tutorials/beginner/saving_loading_models.html#what-is-a-state-dict + return { + "decay": self.decay, + "optimization_step": self.optimization_step, + "shadow_params": self.shadow_params, + "collected_params": self.collected_params, + } + + def load_state_dict(self, state_dict: dict) -> None: + r""" + Loads the ExponentialMovingAverage state. + This method is used by accelerate during checkpointing to save the ema state dict. + Args: + state_dict (dict): EMA state. Should be an object returned + from a call to :meth:`state_dict`. + """ + # deepcopy, to be consistent with module API + state_dict = copy.deepcopy(state_dict) + + self.decay = state_dict["decay"] + if self.decay < 0.0 or self.decay > 1.0: + raise ValueError("Decay must be between 0 and 1") + + self.optimization_step = state_dict["optimization_step"] + if not isinstance(self.optimization_step, int): + raise ValueError("Invalid optimization_step") + + self.shadow_params = state_dict["shadow_params"] + if not isinstance(self.shadow_params, list): + raise ValueError("shadow_params must be a list") + if not all(isinstance(p, torch.Tensor) for p in self.shadow_params): + raise ValueError("shadow_params must all be Tensors") + + self.collected_params = state_dict["collected_params"] + if self.collected_params is not None: + if not isinstance(self.collected_params, list): + raise ValueError("collected_params must be a list") + if not all(isinstance(p, torch.Tensor) for p in self.collected_params): + raise ValueError("collected_params must all be Tensors") + if len(self.collected_params) != len(self.shadow_params): + raise ValueError("collected_params and shadow_params must have the same length") + + +def main(): + args = parse_args() + logging_dir = os.path.join(args.output_dir, args.logging_dir) + + accelerator = Accelerator( + gradient_accumulation_steps=args.gradient_accumulation_steps, + mixed_precision=args.mixed_precision, + log_with=args.report_to, + logging_dir=logging_dir, + ) + + # Make one log on every process with the configuration for debugging. + logging.basicConfig( + format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", + datefmt="%m/%d/%Y %H:%M:%S", + level=logging.INFO, + ) + logger.info(accelerator.state, main_process_only=False) + if accelerator.is_local_main_process: + datasets.utils.logging.set_verbosity_warning() + transformers.utils.logging.set_verbosity_warning() + diffusers.utils.logging.set_verbosity_info() + else: + datasets.utils.logging.set_verbosity_error() + transformers.utils.logging.set_verbosity_error() + diffusers.utils.logging.set_verbosity_error() + + # If passed along, set the training seed now. + if args.seed is not None: + set_seed(args.seed) + + # Handle the repository creation + if accelerator.is_main_process: + if args.push_to_hub: + if args.hub_model_id is None: + repo_name = get_full_repo_name(Path(args.output_dir).name, token=args.hub_token) + else: + repo_name = args.hub_model_id + repo = Repository(args.output_dir, clone_from=repo_name) + + with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore: + if "step_*" not in gitignore: + gitignore.write("step_*\n") + if "epoch_*" not in gitignore: + gitignore.write("epoch_*\n") + elif args.output_dir is not None: + os.makedirs(args.output_dir, exist_ok=True) + + # Load scheduler, tokenizer and models. + noise_scheduler = DDPMScheduler.from_pretrained(args.pretrained_model_name_or_path, + subfolder="scheduler") + tokenizer = CLIPTokenizer.from_pretrained( + args.pretrained_model_name_or_path, subfolder="tokenizer", revision=args.revision + ) + text_encoder = CLIPTextModel.from_pretrained( + args.pretrained_model_name_or_path, subfolder="text_encoder", revision=args.revision + ) + vae = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder="vae", + revision=args.revision) + unet = UNet2DConditionModel.from_pretrained( + args.pretrained_model_name_or_path, subfolder="unet", revision=args.non_ema_revision + ) + + # Freeze vae and text_encoder + vae.requires_grad_(False) + text_encoder.requires_grad_(False) + + # Create EMA for the unet. + if args.use_ema: + ema_unet = UNet2DConditionModel.from_pretrained( + args.pretrained_model_name_or_path, subfolder="unet", revision=args.revision + ) + ema_unet = EMAModel(ema_unet.parameters()) + + if args.enable_xformers_memory_efficient_attention: + if is_xformers_available(): + unet.enable_xformers_memory_efficient_attention() + else: + raise ValueError("xformers is not available. Make sure it is installed correctly") + + if args.gradient_checkpointing: + unet.enable_gradient_checkpointing() + + # Enable TF32 for faster training on Ampere GPUs, + # cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices + if args.allow_tf32: + torch.backends.cuda.matmul.allow_tf32 = True + + if args.scale_lr: + args.learning_rate = ( + args.learning_rate * args.gradient_accumulation_steps * args.train_batch_size * accelerator.num_processes + ) + + # Initialize the optimizer + if args.use_8bit_adam: + try: + import bitsandbytes as bnb + except ImportError: + raise ImportError( + "Please install bitsandbytes to use 8-bit Adam. You can do so by running `pip install bitsandbytes`" + ) + + optimizer_cls = bnb.optim.AdamW8bit + else: + optimizer_cls = torch.optim.AdamW + + optimizer = optimizer_cls( + unet.parameters(), + lr=args.learning_rate, + betas=(args.adam_beta1, args.adam_beta2), + weight_decay=args.adam_weight_decay, + eps=args.adam_epsilon, + ) + + # Get the datasets: you can either provide your own training and evaluation files (see below) + # or specify a Dataset from the hub (the dataset will be downloaded automatically from the datasets Hub). + + # In distributed training, the load_dataset function guarantees that only one local process can concurrently + # download the dataset. + if args.dataset_name is not None: + # Downloading and loading a dataset from the hub. + dataset = load_dataset( + args.dataset_name, + args.dataset_config_name, + cache_dir=args.cache_dir, + ) + else: + data_files = {} + if args.train_data_dir is not None: + data_files["train"] = os.path.join(args.train_data_dir, "**") + dataset = load_dataset( + "imagefolder", + data_files=data_files, + cache_dir=args.cache_dir, + ) + # See more about loading custom images at + # https://huggingface.co/docs/datasets/v2.4.0/en/image_load#imagefolder + + # Preprocessing the datasets. + # We need to tokenize inputs and targets. + column_names = dataset["train"].column_names + + # 6. Get the column names for input/target. + dataset_columns = dataset_name_mapping.get(args.dataset_name, None) + if args.image_column is None: + image_column = dataset_columns[0] if dataset_columns is not None else column_names[0] + else: + image_column = args.image_column + if image_column not in column_names: + raise ValueError( + f"--image_column' value '{args.image_column}' needs to be one of: {', '.join(column_names)}" + ) + if args.caption_column is None: + caption_column = dataset_columns[1] if dataset_columns is not None else column_names[1] + else: + caption_column = args.caption_column + if caption_column not in column_names: + raise ValueError( + f"--caption_column' value '{args.caption_column}' needs to be one of: {', '.join(column_names)}" + ) + + # Preprocessing the datasets. + # We need to tokenize input captions and transform the images. + def tokenize_captions(examples, is_train=True): + captions = [] + for caption in examples[caption_column]: + if isinstance(caption, str): + captions.append(caption) + elif isinstance(caption, (list, np.ndarray)): + # take a random caption if there are multiple + captions.append(random.choice(caption) if is_train else caption[0]) + else: + raise ValueError( + f"Caption column `{caption_column}` should contain either strings or lists of strings." + ) + inputs = tokenizer( + captions, max_length=tokenizer.model_max_length, padding="max_length", truncation=True, + return_tensors="pt" + ) + return inputs.input_ids + + # Preprocessing the datasets. + train_transforms = transforms.Compose( + [ + transforms.Resize(args.resolution, interpolation=transforms.InterpolationMode.BILINEAR), + transforms.CenterCrop(args.resolution) if args.center_crop else transforms.RandomCrop( + args.resolution), + transforms.RandomHorizontalFlip() if args.random_flip else transforms.Lambda( + lambda x: x), + transforms.ToTensor(), + transforms.Normalize([0.5], [0.5]), + ] + ) + + def preprocess_train(examples): + images = [image.convert("RGB") for image in examples[image_column]] + examples["pixel_values"] = [train_transforms(image) for image in images] + examples["input_ids"] = tokenize_captions(examples) + return examples + + with accelerator.main_process_first(): + if args.max_train_samples is not None: + dataset["train"] = dataset["train"].shuffle(seed=args.seed).select( + range(args.max_train_samples)) + # Set the training transforms + train_dataset = dataset["train"].with_transform(preprocess_train) + + def collate_fn(examples): + pixel_values = torch.stack([example["pixel_values"] for example in examples]) + pixel_values = pixel_values.to(memory_format=torch.contiguous_format).float() + input_ids = torch.stack([example["input_ids"] for example in examples]) + return {"pixel_values": pixel_values, "input_ids": input_ids} + + # DataLoaders creation: + train_dataloader = torch.utils.data.DataLoader( + train_dataset, shuffle=True, collate_fn=collate_fn, batch_size=args.train_batch_size + ) + + # Scheduler and math around the number of training steps. + overrode_max_train_steps = False + num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) + if args.max_train_steps is None: + args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch + overrode_max_train_steps = True + + lr_scheduler = get_scheduler( + args.lr_scheduler, + optimizer=optimizer, + num_warmup_steps=args.lr_warmup_steps * args.gradient_accumulation_steps, + num_training_steps=args.max_train_steps * args.gradient_accumulation_steps, + ) + + # Prepare everything with our `accelerator`. + unet, optimizer, train_dataloader, lr_scheduler = accelerator.prepare( + unet, optimizer, train_dataloader, lr_scheduler + ) + if args.use_ema: + accelerator.register_for_checkpointing(ema_unet) + + # For mixed precision training we cast the text_encoder and vae weights to half-precision + # as these models are only used for inference, keeping weights in full precision is not required. + weight_dtype = torch.float32 + if accelerator.mixed_precision == "fp16": + weight_dtype = torch.float16 + elif accelerator.mixed_precision == "bf16": + weight_dtype = torch.bfloat16 + + # Move text_encode and vae to gpu and cast to weight_dtype + text_encoder.to(accelerator.device, dtype=weight_dtype) + vae.to(accelerator.device, dtype=weight_dtype) + if args.use_ema: + ema_unet.to(accelerator.device) + + # We need to recalculate our total training steps as the size of the training dataloader may have changed. + num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps) + if overrode_max_train_steps: + args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch + # Afterwards we recalculate our number of training epochs + args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch) + + # We need to initialize the trackers we use, and also store our configuration. + # The trackers initializes automatically on the main process. + if accelerator.is_main_process: + accelerator.init_trackers("text2image-fine-tune", config=vars(args)) + + # Train! + total_batch_size = args.train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps + + logger.info("***** Running training *****") + logger.info(f" Num examples = {len(train_dataset)}") + logger.info(f" Num Epochs = {args.num_train_epochs}") + logger.info(f" Instantaneous batch size per device = {args.train_batch_size}") + logger.info( + f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}") + logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}") + logger.info(f" Total optimization steps = {args.max_train_steps}") + global_step = 0 + first_epoch = 0 + + # Potentially load in the weights and states from a previous save + if args.resume_from_checkpoint: + if args.resume_from_checkpoint != "latest": + path = os.path.basename(args.resume_from_checkpoint) + else: + # Get the most recent checkpoint + dirs = os.listdir(args.output_dir) + dirs = [d for d in dirs if d.startswith("checkpoint")] + dirs = sorted(dirs, key=lambda x: int(x.split("-")[1])) + path = dirs[-1] + accelerator.print(f"Resuming from checkpoint {path}") + accelerator.load_state(os.path.join(args.output_dir, path)) + global_step = int(path.split("-")[1]) + + first_epoch = global_step // num_update_steps_per_epoch + resume_step = global_step % num_update_steps_per_epoch + + # Only show the progress bar once on each machine. + progress_bar = tqdm(range(global_step, args.max_train_steps), + disable=not accelerator.is_local_main_process) + progress_bar.set_description("Steps") + + for epoch in range(first_epoch, args.num_train_epochs): + unet.train() + train_loss = 0.0 + for step, batch in enumerate(train_dataloader): + # Skip steps until we reach the resumed step + if args.resume_from_checkpoint and epoch == first_epoch and step < resume_step: + if step % args.gradient_accumulation_steps == 0: + progress_bar.update(1) + continue + + with accelerator.accumulate(unet): + # Convert images to latent space + latents = vae.encode(batch["pixel_values"].to(weight_dtype)).latent_dist.sample() + latents = latents * 0.18215 + + # Sample noise that we'll add to the latents + noise = torch.randn_like(latents) + bsz = latents.shape[0] + # Sample a random timestep for each image + timesteps = torch.randint(0, noise_scheduler.num_train_timesteps, (bsz,), + device=latents.device) + timesteps = timesteps.long() + + # Add noise to the latents according to the noise magnitude at each timestep + # (this is the forward diffusion process) + noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps) + + # Get the text embedding for conditioning + encoder_hidden_states = text_encoder(batch["input_ids"])[0] + + # Get the target for loss depending on the prediction type + if noise_scheduler.config.prediction_type == "epsilon": + target = noise + elif noise_scheduler.config.prediction_type == "v_prediction": + target = noise_scheduler.get_velocity(latents, noise, timesteps) + else: + raise ValueError( + f"Unknown prediction type {noise_scheduler.config.prediction_type}") + + # Predict the noise residual and compute loss + model_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample + loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean") + + # Gather the losses across all processes for logging (if we use distributed training). + avg_loss = accelerator.gather(loss.repeat(args.train_batch_size)).mean() + train_loss += avg_loss.item() / args.gradient_accumulation_steps + + # Backpropagate + accelerator.backward(loss) + if accelerator.sync_gradients: + accelerator.clip_grad_norm_(unet.parameters(), args.max_grad_norm) + optimizer.step() + lr_scheduler.step() + optimizer.zero_grad() + + # Checks if the accelerator has performed an optimization step behind the scenes + if accelerator.sync_gradients: + if args.use_ema: + ema_unet.step(unet.parameters()) + progress_bar.update(1) + global_step += 1 + accelerator.log({"train_loss": train_loss}, step=global_step) + train_loss = 0.0 + + if global_step % args.checkpointing_steps == 0: + if accelerator.is_main_process: + save_path = os.path.join(args.output_dir, f"checkpoint-{global_step}") + accelerator.save_state(save_path) + logger.info(f"Saved state to {save_path}") + + logs = {"step_loss": loss.detach().item(), "lr": lr_scheduler.get_last_lr()[0]} + progress_bar.set_postfix(**logs) + + if global_step >= args.max_train_steps: + break + + # Create the pipeline using the trained modules and save it. + accelerator.wait_for_everyone() + if accelerator.is_main_process: + unet = accelerator.unwrap_model(unet) + if args.use_ema: + ema_unet.copy_to(unet.parameters()) + + pipeline = StableDiffusionPipeline.from_pretrained( + args.pretrained_model_name_or_path, + text_encoder=text_encoder, + vae=vae, + unet=unet, + revision=args.revision, + ) + pipeline.save_pretrained(args.output_dir) + + if args.push_to_hub: + repo.push_to_hub(commit_message="End of training", blocking=False, auto_lfs_prune=True) + + accelerator.end_training() + + +if __name__ == "__main__": + main() diff --git a/examples/pipelines/finetune_stable_diffusion/components/sd_finetuning_component/src/utils/dataset_utils.py b/examples/pipelines/finetune_stable_diffusion/components/sd_finetuning_component/src/utils/dataset_utils.py new file mode 100644 index 000000000..379fcd05f --- /dev/null +++ b/examples/pipelines/finetune_stable_diffusion/components/sd_finetuning_component/src/utils/dataset_utils.py @@ -0,0 +1,99 @@ +""" +Common huggingface utils +""" +import logging +import os + +# pylint: disable=import-error +from pyarrow.dataset import Scanner +import jsonlines + +from helpers import io_helpers, storage_helpers +from helpers.logger import get_logger + +logger = get_logger(name=__name__, level=logging.INFO) + + +# pylint: disable=too-few-public-methods +class SDDatasetCreator: + """ + Class that creates the dataset for SD + """ + + # pylint: disable=too-many-arguments + def __init__(self, tmp_path: str, img_path: str): + """ + Initialize parameters and dataset + Args: + tmp_path (str): path where to store temporary files + img_path (str): the path where to download the images to finetune + """ + self.tmp_path = tmp_path + self.img_path = img_path + self.metadata_file_path = os.path.join(self.img_path, 'metadata.jsonl') + + def _download_images_to_finetune(self, dataset_scanner: Scanner, + download_list_file_path: str) -> None: + """ + Function that writes downloads the images to finetune on locally + Args: + dataset_scanner (Scanner): a scanner of the dataset + download_list_file_path (str): the text files containing the list of uri to download + """ + + with open(download_list_file_path, "w") as download_list_file: + for batch in dataset_scanner.to_batches(): + files_uri_list = batch.to_pydict()['file_uri'] + download_list_file.write('\n'.join(files_uri_list)) + + storage_helpers.copy_files_bulk(download_list_file_path, self.img_path) + logger.info("Images to downloaded were downloaded to %s", self.img_path) + + def _write_hf_metadata(self, captions_scanner: Scanner) -> None: + """ + Function that writes the huggingface metadata file + Reference: + https://huggingface.co/docs/datasets/v2.4.0/en/image_load#imagefolder-with-metadata: + ~:text=in%20load_dataset.-,Image%20captioning,-Image%20captioning%20datasets + Args: + captions_scanner (Scanner): the captions scanner + """ + # We join the captions at this stage since it might create duplicates if there are + # multiple captions per image, we do not want to write them to the text containing the + # gcs images to download (avoid downloading the same image multiple times) + + with jsonlines.open(self.metadata_file_path, 'a') as writer: + for table_chunk in captions_scanner.to_batches(): + table_dict = table_chunk.to_pydict() + # Return file name + table_dict['file_uri'] = \ + [io_helpers.get_file_name(file_uri, return_extension=True) for file_uri in + table_dict['file_uri']] + # change format to match the expected value of HF metadata dataset + # {"file_name":, "text": } + table_dict['file_name'] = table_dict.pop('file_uri') + table_dict['text'] = table_dict.pop('file_captions') + # Filter dict to keep only field of interest to create the dataset metadata file + table_dict = {key: table_dict[key] for key in ['file_name', 'text']} + # Change dict of lists to list of dicts to write them to json new lines in batch + table_dict = [dict(zip(table_dict, t)) for t in zip(*table_dict.values())] + # Unpack caption list to have separate rows per caption (required HF format to have + # image/caption pair) + table_dict = [{"file_name": row['file_name'], "text": caption} for row in table_dict + for caption in row['text']] + # pylint: disable=no-member + writer.write_all(table_dict) + + logger.info("Huggingface metadata file written to %s", self.metadata_file_path) + + def write_dataset(self, dataset_scanner: Scanner, captions_scanner: Scanner, namespace: str): + """Create the dataset by downloading the images and creating the huggingface dataset + metadata file + Args: + dataset_scanner (Scanner): the filtered dataset scanner + captions_scanner (Scanner): the filtered captions scanner + namespace (str): the dataset namespace + """ + download_list_file_path = os.path.join(self.tmp_path, f"download_list_gcs_{namespace}.txt") + self._download_images_to_finetune(dataset_scanner, download_list_file_path) + self._write_hf_metadata(captions_scanner) diff --git a/examples/pipelines/finetune_stable_diffusion/components/sd_finetuning_component/src/utils/training_utils.py b/examples/pipelines/finetune_stable_diffusion/components/sd_finetuning_component/src/utils/training_utils.py new file mode 100644 index 000000000..b1a157c32 --- /dev/null +++ b/examples/pipelines/finetune_stable_diffusion/components/sd_finetuning_component/src/utils/training_utils.py @@ -0,0 +1,107 @@ +"""Checkpoint utils""" +import os +import subprocess # nosec +import logging +from shutil import rmtree +from typing import List + +from helpers.logger import get_logger +from helpers import storage_helpers + +logger = get_logger(name=__name__, level=logging.INFO) + + +def sorted_checkpoints(gcs_blobs: List[str]) -> List[str]: + """ + Function to sort training checkpoints + Args: + gcs_blobs(List[str]): list of gcs blobs containing checkpoints + Returns: + List[str]: a list of available checkpoints in descending order + (e.g. [checkpoint-1000, checkpoint-500, ...]) + """ + available_checkpoints = [] + for p in gcs_blobs: + if not p: + continue + p = p.strip('/').split('/')[-1] + if p.startswith('checkpoint-'): + available_checkpoints.append(p) + available_checkpoints.sort(key=lambda p: int(p.split('checkpoint-')[1]), reverse=True) + return available_checkpoints + + +def download_checkpoint_to_resume_from(resume_from_checkpoint: str, pretrained_model_gcs_path: str, + finetuned_model_path: str) -> str: + """ + Function that downloads the checkpoint to resume from to a local gcs directory + Args: + resume_from_checkpoint (str): Whether training should be resumed from a previous + checkpoint. Use a path saved in `pretrained_model_gcs_path` by `--checkpointing_steps`, + or `"latest"` to automatically select the last available checkpoint. + pretrained_model_gcs_path (str): the gcs path of the pretrained model where the + checkpoints are created + finetuned_model_path (str): the path to the finetuned model where the checkpoints are + created + Returns: + str: the checkpoint prefix (e.g. 'checkpoint-500') to resume from + """ + + if resume_from_checkpoint == "latest": + possible_checkpoints = \ + subprocess.run(["gsutil", "ls", pretrained_model_gcs_path], # nosec + capture_output=True, check=True).stdout.decode().split('\n') + checkpoint_prefix = \ + sorted_checkpoints(possible_checkpoints)[0] + else: + checkpoint_prefix = resume_from_checkpoint + + logger.info( + f"Downloading checkpoint {resume_from_checkpoint} from {pretrained_model_gcs_path}.") + downloaded_checkpoint_path = os.path.join(finetuned_model_path, resume_from_checkpoint) + os.makedirs(downloaded_checkpoint_path, exist_ok=True) + storage_helpers.copy_folder_bulk( + os.path.join(pretrained_model_gcs_path, resume_from_checkpoint), + downloaded_checkpoint_path) + + return checkpoint_prefix + + +def sync_training_checkpoints(finetuned_model_path: str, finetuned_model_gcs_uri: str): + """ + Function that sync local created checkpoints during training with gcs + Args: + finetuned_model_path (str): the path to the finetuned model where the checkpoints are + created + finetuned_model_gcs_uri (str): the gcs uri where the model will be uploaded + """ + if os.path.exists(finetuned_model_path): + logger.info(f"Syncing checkpoints to {finetuned_model_gcs_uri}. " + f"Folder contents: {os.listdir(finetuned_model_path)}") + subprocess.run(['gsutil', '-m', 'rsync', '-r', finetuned_model_path, # nosec + finetuned_model_gcs_uri]) + + # Clean up older checkpoints. They'll remain available in cloud storage, but we want to + # avoid running out of ephemeral storage. + available_checkpoints = sorted_checkpoints(os.listdir(finetuned_model_path)) + for to_delete in available_checkpoints[1:]: + logger.info(f"Local cleanup of old checkpoint: {to_delete}") + rmtree(os.path.join(finetuned_model_path, to_delete), onerror=logger.info) + else: + logger.info( + f"Checked {finetuned_model_path} for checkpoints, but path doesn't exist yet.") + + +def download_model_without_checkpoints(pretrained_model_gcs_path: str, finetuned_model_path: str): + """ + Function that copies a pretrained model (without checkpoints) + Args: + pretrained_model_gcs_path (str): the gcs path to the pretrained model which may contain + checkpoints + finetuned_model_path (str): the local path to the finetuned model where to download the + model + """ + subprocess.run(['gsutil', '-m', 'rsync', '-r', '-x', 'checkpoint*', # nosec + pretrained_model_gcs_path, finetuned_model_path]) + logger.info( + f"Model downloaded from '{pretrained_model_gcs_path}' to '{finetuned_model_path}'.") diff --git a/examples/pipelines/finetune_stable_diffusion/sd_finetuning_pipeline.py b/examples/pipelines/finetune_stable_diffusion/sd_finetuning_pipeline.py new file mode 100644 index 000000000..53085d292 --- /dev/null +++ b/examples/pipelines/finetune_stable_diffusion/sd_finetuning_pipeline.py @@ -0,0 +1,138 @@ +"""Pipeline used to create a stable diffusion dataset from a set of given images. This is done by +using clip retrieval on the LAION dataset""" +# pylint: disable=import-error +import logging +import sys +import os +from typing import Optional + +sys.path.insert(0, os.path.abspath('..')) + +from config import GeneralConfig, KubeflowConfig +from pipelines_config.sd_finetuning_config import StableDiffusionFinetuningConfig as SDConfig +from express.pipeline_utils import compile_and_upload_pipeline +from express.logger import configure_logging + +from kfp import components as comp +from kfp import dsl +from kubernetes import client as k8s_client + +configure_logging() + +LOGGER = logging.getLogger(__name__) + +sd_finetuning_component = comp.load_component( + 'components/sd_finetuning_component/component.yaml') + + +# Pipeline +@dsl.pipeline( + name='Stable Diffusion finetuning pipeline', + description='Pipeline that takes a data manifest as input, loads in the dataset and starts ' + 'finetuning a stable diffusion model.' +) +# pylint: disable=too-many-arguments, too-many-locals +def sd_finetuning_pipeline( + data_manifest_gcs_path: str = SDConfig.DATA_MANIFEST_GCS_PATH, + pretrained_model_gcs_path: str = SDConfig.PRETRAINED_MODEL_GCS_PATH, + resume_from_checkpoint: Optional[str] = SDConfig.RESUME_FROM_CHECKPOINT, + seed: int = SDConfig.SEED, + resolution: int = SDConfig.RESOLUTION, + train_batch_size: int = SDConfig.TRAIN_BATCH_SIZE, + num_train_epochs: int = SDConfig.NUM_TRAIN_EPOCHS, + max_train_steps: Optional[int] = SDConfig.MAX_TRAIN_STEPS, + checkpointing_steps: Optional[int] = SDConfig.CHECKPOINTING_STEPS, + gradient_accumulation_steps: int = SDConfig.GRADIENT_ACCUMULATION_STEPS, + gradient_checkpointing: Optional[str] = SDConfig.GRADIENT_CHECKPOINTING, + learning_rate: float = SDConfig.LEARNING_RATE, + scale_lr: Optional[str] = SDConfig.SCALE_LR, + lr_scheduler: str = SDConfig.LR_SCHEDULER, + lr_warmup_steps: int = SDConfig.LR_WARMUP_STEPS, + use_ema: Optional[str] = SDConfig.USE_EMA, + mixed_precision: str = SDConfig.MIXED_PRECISION, + center_crop: Optional[str] = SDConfig.CENTER_CROP, + random_flip: Optional[str] = SDConfig.RANDOM_FLIP): + """ + Pipeline that takes example images as input and returns an expanded dataset of + similar images as outputs + configs for dataset image converter component + Args: + data_manifest_gcs_path (str): the path to the data manifest that contains information on the + training set + pretrained_model_gcs_path (str): the gcs path where the base model to finetune + is located + checkpointing_gcs_path (Optional[str]): Optional gcs path where the checkpoint to continue + finetuning is located + resume_from_checkpoint (Optional[str]): Whether training should be resumed from a previous + checkpoint. Use a path saved in `pretrained_model_gcs_path` by `--checkpointing_steps`, or + `"latest"` to automatically select the last available checkpoint. + seed (int): a seed for reproducible training. + resolution (int): The resolution for input images, all the images in the train/validation + dataset will be resized to this resolution + train_batch_size (int): batch size (per device) for the training dataloader + num_train_epochs (int): total number of epochs to perform + max_train_steps (int): total number of training steps to perform. if provided overrides + `num_train_epochs` + checkpointing_steps (Optional[int]): Save a checkpoint of the training state every X + updates.These checkpoints are only suitable for resuming training using + `--resume_from_checkpoint`.gradient_accumulation_steps (int): + the number of updates steps to accumulate before performing a backward/update pass + gradient_checkpointing (bool): whether to use gradient checkpointing to save memory + at the expense of slower backward pass + learning_rate (float): initial learning rate (after the potential warmup period) to use. + scale_lr (bool): scale the learning rate by the number of gpus, gradient accumulation steps, + and batch size + lr_warmup_steps (int): scale the learning rate by the number of gpus, gradient accumulation + steps, and batch size + lr_scheduler (str): the scheduler type to use. choose between ["linear", "cosine", + cosine_with_restarts", "polynomial", "constant","constant_with_warmup"] + use_ema (bool): whether to use ema model + mixed_precision (str): whether to use mixed precision. choose between fp16 and bf16 + (bfloat16). bf16 requires pytorch >=1.10.and an nvidia ampere gpu. + default to the value of accelerate config of the current system or the flag passed with the + `accelerate.launch` command. use this argument to override the accelerate config + center_crop (bool): whether to center crop images before resizing to resolution (if not set, + random crop will be used) + random_flip (bool): whether to randomly flip images horizontally + + """ + # pylint: disable=not-callable,unused-variable + run_id = '{{pod.name}}' + artifact_bucket = KubeflowConfig.ARTIFACT_BUCKET + + sd_finetuning_task = sd_finetuning_component( + project_id=GeneralConfig.GCP_PROJECT_ID, + run_id=run_id, + artifact_bucket=artifact_bucket, + component_name=sd_finetuning_component.__name__, + data_manifest_gcs_path=data_manifest_gcs_path, + pretrained_model_gcs_path=pretrained_model_gcs_path, + resume_from_checkpoint=resume_from_checkpoint, + seed=seed, + resolution=resolution, + train_batch_size=train_batch_size, + num_train_epochs=num_train_epochs, + max_train_steps=max_train_steps, + checkpointing_steps=checkpointing_steps, + gradient_accumulation_steps=gradient_accumulation_steps, + gradient_checkpointing=gradient_checkpointing, + learning_rate=learning_rate, + scale_lr=scale_lr, + lr_warmup_steps=lr_warmup_steps, + lr_scheduler=lr_scheduler, + use_ema=use_ema, + center_crop=center_crop, + random_flip=random_flip, + mixed_precision=mixed_precision) \ + .set_display_name('SD finetuning') \ + .set_gpu_limit(2) \ + .add_node_selector_constraint('node_pool', 'model-training-pool') \ + .add_toleration( + k8s_client.V1Toleration(effect='NoSchedule', key='reserved-pool', operator='Equal', + value='true')) + + +if __name__ == '__main__': + compile_and_upload_pipeline(pipeline=sd_finetuning_pipeline, + host=KubeflowConfig.HOST, + env=KubeflowConfig.ENV) From d6b0ab80270241fd818f0aab87dbb79dbe3f879b Mon Sep 17 00:00:00 2001 From: Niels Rogge Date: Wed, 29 Mar 2023 11:39:40 +0200 Subject: [PATCH 23/38] Add READNEs to components --- .pre-commit-config.yaml | 6 +++++- .../dataset_loader_component/README.MD | 13 ++++++++++++ .../components/embedding_component/README.MD | 20 +++++++++++++++++++ .../image_filter_component/README.MD | 9 +++++++++ 4 files changed, 47 insertions(+), 1 deletion(-) create mode 100644 examples/pipelines/finetune_stable_diffusion/components/dataset_loader_component/README.MD create mode 100644 examples/pipelines/finetune_stable_diffusion/components/embedding_component/README.MD create mode 100644 examples/pipelines/finetune_stable_diffusion/components/image_filter_component/README.MD diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 3682d81c7..906637c73 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -24,4 +24,8 @@ repos: hooks: - id: black name: black - files: "^express/" + files: | + (?x)^( + express/.*| + examples/.*| + )$ diff --git a/examples/pipelines/finetune_stable_diffusion/components/dataset_loader_component/README.MD b/examples/pipelines/finetune_stable_diffusion/components/dataset_loader_component/README.MD new file mode 100644 index 000000000..96afd1bf1 --- /dev/null +++ b/examples/pipelines/finetune_stable_diffusion/components/dataset_loader_component/README.MD @@ -0,0 +1,13 @@ +# dataset_loader_component + +### Description + +This is the first component of the pipeline, it loads an image dataset from the 🤗 [hub](https://huggingface.co/) and creates the initial manifest. This manifest includes 3 data sources: images, captions and image metadata. + +### **Inputs/Outputs** +The component accepts a `dataset_name` as input which refers to a dataset on the 🤗 hub. + +The component creates the first manifest as output that will be updated by subsequent components when new data sources +are added/filtered. + +See [`component.yaml`](component.yaml) for a more detailed description on all the input/output parameters. \ No newline at end of file diff --git a/examples/pipelines/finetune_stable_diffusion/components/embedding_component/README.MD b/examples/pipelines/finetune_stable_diffusion/components/embedding_component/README.MD new file mode 100644 index 000000000..2c32f83ed --- /dev/null +++ b/examples/pipelines/finetune_stable_diffusion/components/embedding_component/README.MD @@ -0,0 +1,20 @@ +# embedding_component + +### Description + +This component extracts [embeddings](https://rom1504.medium.com/image-embeddings-ed1b194d113e) +from the converted images using a [CLIP model](https://huggingface.co/docs/transformers/model_doc/clip). + +Since image embeddings are good at capturing the features of the image in a compact and useful way, they +will be used in the next steps to retrieve images similar to our seed images. + +The CLIP model is downloaded once inside the component during build time (only the vision encoder is downloaded). The images are then embedded and the embeddings are then added to the manifest as a separate 🤗 dataset. The data manifest is then updated with a reference to the location of the embeddings. + +### **Inputs/Outputs** + +See [`component.yaml`](component.yaml) for a more detailed description on all the input/output parameters. + +### **Practical considerations** + +* There exists many variants for the CLIP model, the current variant that is used is the [`Vit-L/14`](https://huggingface.co/openai/clip-vit-large-patch14) variant. The reason for that is that the embeddings produced from this variant are the same ones that are used for building the indices for the [LAION-5B dataset](https://laion.ai/blog/laion-5b/#:~:text=Pre%2DComputed%20Embeddings). + diff --git a/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/README.MD b/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/README.MD new file mode 100644 index 000000000..7873090c2 --- /dev/null +++ b/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/README.MD @@ -0,0 +1,9 @@ +# image_filter_component + +### Description + +This component is used to filter the images based on the metadata attributes to only keep images of a certain height and width. + +### **Inputs/Outputs** + +See [`component.yaml`](component.yaml) for a more detailed description on all the input/output parameters. \ No newline at end of file From a1a265b9fe0d773b6a70e848b7587466169d2821 Mon Sep 17 00:00:00 2001 From: Niels Rogge Date: Wed, 29 Mar 2023 11:48:11 +0200 Subject: [PATCH 24/38] Address comments --- .../dataset_loader_component/src/main.py | 50 ++++++++++++------- .../components/embedding_component/Dockerfile | 2 - 2 files changed, 31 insertions(+), 21 deletions(-) diff --git a/examples/pipelines/finetune_stable_diffusion/components/dataset_loader_component/src/main.py b/examples/pipelines/finetune_stable_diffusion/components/dataset_loader_component/src/main.py index 7ca81ee19..dbe0bde05 100644 --- a/examples/pipelines/finetune_stable_diffusion/components/dataset_loader_component/src/main.py +++ b/examples/pipelines/finetune_stable_diffusion/components/dataset_loader_component/src/main.py @@ -6,7 +6,10 @@ from datasets import Dataset, load_dataset -from express.components.hf_datasets_components import HFDatasetsLoaderComponent, HFDatasetsDatasetDraft +from express.components.hf_datasets_components import ( + HFDatasetsLoaderComponent, + HFDatasetsDatasetDraft, +) from express.logger import configure_logging configure_logging() @@ -15,21 +18,20 @@ def create_metadata(examples): images = examples["image"] - sizes = [image.size for image in images] - + batch = {} - batch["width"] = [size[0] for size in sizes] - batch["height"] = [size[1] for size in sizes] - + batch["width"], batch["height"] = zip(*[image.size for image in images]) + return batch class DatasetLoaderComponent(HFDatasetsLoaderComponent): - """Class that inherits from Hugging Face data loading """ + """Class that inherits from Hugging Face data loading""" @classmethod - def load(cls, extra_args: Optional[ - Dict[str, Union[str, int, float, bool]]] = None) -> HFDatasetsDatasetDraft: + def load( + cls, extra_args: Optional[Dict[str, Union[str, int, float, bool]]] = None + ) -> HFDatasetsDatasetDraft: """ An example function showcasing the data loader component using Express functionalities Args: @@ -46,21 +48,31 @@ def load(cls, extra_args: Optional[ # 2) Create an example index logger.info("Creating index...") index_list = [f"image_{idx}" for idx in range(len(dataset))] - + # 3) Create dataset draft (manifest without metadata) # We store the index itself also as a HF Dataset logger.info("Creating draft...") index_dataset = Dataset.from_dict({"index": index_list}) - image_dataset = dataset.remove_columns(["text"]).add_column(name="index", column=index_list) - text_dataset = dataset.remove_columns(["image"]).add_column(name="index", column=index_list) - metadata_dataset = image_dataset.map(create_metadata, batched=True, remove_columns=["image"]) - data_sources = {"images": image_dataset, - "captions": text_dataset, - "image_metadata": metadata_dataset} - dataset_draft = HFDatasetsDatasetDraft(index=index_dataset, data_sources=data_sources) + image_dataset = dataset.remove_columns(["text"]).add_column( + name="index", column=index_list + ) + text_dataset = dataset.remove_columns(["image"]).add_column( + name="index", column=index_list + ) + metadata_dataset = image_dataset.map( + create_metadata, batched=True, remove_columns=["image"] + ) + data_sources = { + "images": image_dataset, + "captions": text_dataset, + "image_metadata": metadata_dataset, + } + dataset_draft = HFDatasetsDatasetDraft( + index=index_dataset, data_sources=data_sources + ) return dataset_draft -if __name__ == '__main__': - DatasetLoaderComponent.run() \ No newline at end of file +if __name__ == "__main__": + DatasetLoaderComponent.run() diff --git a/examples/pipelines/finetune_stable_diffusion/components/embedding_component/Dockerfile b/examples/pipelines/finetune_stable_diffusion/components/embedding_component/Dockerfile index d46805647..1b2d10bbd 100644 --- a/examples/pipelines/finetune_stable_diffusion/components/embedding_component/Dockerfile +++ b/examples/pipelines/finetune_stable_diffusion/components/embedding_component/Dockerfile @@ -23,8 +23,6 @@ RUN pip3 install --no-cache-dir -r requirements.txt # Copy over src-files of the component COPY src /src -# TODO: ADD EXTRA FILES HERE IF NEEDED! - # Set the working directory to the source folder WORKDIR /src From b7075ae43c4275b6c7bcca7b770ee0e3af513f20 Mon Sep 17 00:00:00 2001 From: Niels Rogge Date: Wed, 29 Mar 2023 13:12:16 +0200 Subject: [PATCH 25/38] Address comments --- .pre-commit-config.yaml | 6 +- .../components/embedding_component/Dockerfile | 2 +- .../embedding_component/requirements.txt | 6 +- .../embedding_component/src/main.py | 54 +++++--- .../image_filter_component/Dockerfile | 2 - .../image_filter_component/requirements.txt | 2 +- .../Dockerfile | 0 .../README.MD | 0 .../__init__.py | 0 .../component.yaml | 4 +- .../requirements.txt | 4 +- .../src/__init__.py | 0 .../src/main.py | 16 ++- .../config/components_config.py | 10 +- .../dataset_creation_pipeline.py | 117 +++++++++++------- express/components/common.py | 1 - 16 files changed, 143 insertions(+), 81 deletions(-) rename examples/pipelines/finetune_stable_diffusion/components/{dataset_loader_component => load_from_hub_component}/Dockerfile (100%) rename examples/pipelines/finetune_stable_diffusion/components/{dataset_loader_component => load_from_hub_component}/README.MD (100%) rename examples/pipelines/finetune_stable_diffusion/components/{dataset_loader_component => load_from_hub_component}/__init__.py (100%) rename examples/pipelines/finetune_stable_diffusion/components/{dataset_loader_component => load_from_hub_component}/component.yaml (88%) rename examples/pipelines/finetune_stable_diffusion/components/{dataset_loader_component => load_from_hub_component}/requirements.txt (61%) rename examples/pipelines/finetune_stable_diffusion/components/{dataset_loader_component => load_from_hub_component}/src/__init__.py (100%) rename examples/pipelines/finetune_stable_diffusion/components/{dataset_loader_component => load_from_hub_component}/src/main.py (82%) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 906637c73..4f9f6b6d7 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -6,7 +6,11 @@ repos: rev: 'v0.0.254' hooks: - id: ruff - files: "^express/" + files: | + (?x)^( + express/.*| + examples/.*| + )$ args: [--fix, --exit-non-zero-on-fix] diff --git a/examples/pipelines/finetune_stable_diffusion/components/embedding_component/Dockerfile b/examples/pipelines/finetune_stable_diffusion/components/embedding_component/Dockerfile index 1b2d10bbd..af391b6d2 100644 --- a/examples/pipelines/finetune_stable_diffusion/components/embedding_component/Dockerfile +++ b/examples/pipelines/finetune_stable_diffusion/components/embedding_component/Dockerfile @@ -1,4 +1,4 @@ -FROM python:3.8-slim +FROM pytorch/pytorch:2.0.0-cuda11.7-cudnn8-runtime ## System dependencies RUN apt-get update && \ diff --git a/examples/pipelines/finetune_stable_diffusion/components/embedding_component/requirements.txt b/examples/pipelines/finetune_stable_diffusion/components/embedding_component/requirements.txt index 08befdd72..3e9bce5e0 100644 --- a/examples/pipelines/finetune_stable_diffusion/components/embedding_component/requirements.txt +++ b/examples/pipelines/finetune_stable_diffusion/components/embedding_component/requirements.txt @@ -1,4 +1,4 @@ -torch -transformers +torch==2.0.0 +transformers==4.27.3 git+https://github.com/ml6team/express.git@0f43bbd88826ffd1b620100275700f9cbe7c0b6e -Pillow \ No newline at end of file +Pillow==9.4.0 \ No newline at end of file diff --git a/examples/pipelines/finetune_stable_diffusion/components/embedding_component/src/main.py b/examples/pipelines/finetune_stable_diffusion/components/embedding_component/src/main.py index 86fdf91a2..c546276f9 100644 --- a/examples/pipelines/finetune_stable_diffusion/components/embedding_component/src/main.py +++ b/examples/pipelines/finetune_stable_diffusion/components/embedding_component/src/main.py @@ -4,9 +4,12 @@ import logging from typing import Optional, Union, Dict -from tqdm.auto import tqdm -from express.components.hf_datasets_components import HFDatasetsTransformComponent, HFDatasetsDataset, HFDatasetsDatasetDraft +from express.components.hf_datasets_components import ( + HFDatasetsTransformComponent, + HFDatasetsDataset, + HFDatasetsDatasetDraft, +) from express.logger import configure_logging import torch @@ -17,7 +20,14 @@ logger = logging.getLogger(__name__) -device = "cuda" if torch.cuda.is_available() else "cpu" +cuda_available = torch.cuda.is_available() +device = "cuda" if cuda_available else "cpu" +logger.info("CUDA device availability:%s", cuda_available) + +if cuda_available: + logger.info(torch.cuda.get_device_name(0)) + logger.info("CUDA device: %s", torch.cuda.get_device_name(0)) + logger.info("Num of GPUs: %s", torch.cuda.device_count()) @torch.no_grad() @@ -30,7 +40,7 @@ def embed(examples, processor, model): # embed to get (batch_size, hidden_size) embeddings outputs = model(**inputs) image_embeds = outputs.image_embeds - + # flatten into list of embeddings examples["embeddings"] = image_embeds.cpu().tolist() @@ -39,15 +49,18 @@ def embed(examples, processor, model): class EmbeddingComponent(HFDatasetsTransformComponent): """ - Class that inherits from Hugging Face data transform. + Component that embeds the images using a CLIP model from Hugging Face. """ @classmethod - def transform(cls, data: HFDatasetsDataset, extra_args: Optional[ - Dict[str, Union[str, int, float, bool]]] = None) -> HFDatasetsDatasetDraft: + def transform( + cls, + data: HFDatasetsDataset, + extra_args: Optional[Dict[str, Union[str, int, float, bool]]] = None, + ) -> HFDatasetsDatasetDraft: """ An example function showcasing the data transform component using Express functionalities - + Args: data (HFDatasetsDataset[TIndex, TData]): express dataset providing access to data of a given type @@ -56,12 +69,12 @@ def transform(cls, data: HFDatasetsDataset, extra_args: Optional[ Returns: HFDatasetsDatasetDraft: a dataset draft that creates a plan for an output manifest """ - + # 1) Get one particular data source from the manifest # TODO check whether we can leverage streaming logger.info("Loading image dataset...") image_dataset = data.load(data_source="images") - + # 2) Create embedding dataset logger.info("Loading CLIP...") processor = CLIPProcessor.from_pretrained(extra_args["model_id"]) @@ -69,18 +82,23 @@ def transform(cls, data: HFDatasetsDataset, extra_args: Optional[ model.to(device) logger.info("Embedding images...") - embedded_dataset = image_dataset.map(embed, - batched=True, batch_size=extra_args["batch_size"], - fn_kwargs=dict(processor=processor, model=model), - remove_columns=["image"]) - + embedded_dataset = image_dataset.map( + embed, + batched=True, + batch_size=extra_args["batch_size"], + fn_kwargs=dict(processor=processor, model=model), + remove_columns=["image"], + ) + # 3) Create dataset draft which adds a data source to the manifest logger.info("Creating draft...") data_sources = {"embeddings": embedded_dataset} - dataset_draft = HFDatasetsDatasetDraft(data_sources=data_sources, extending_dataset=data) + dataset_draft = HFDatasetsDatasetDraft( + data_sources=data_sources, extending_dataset=data + ) return dataset_draft -if __name__ == '__main__': - EmbeddingComponent.run() \ No newline at end of file +if __name__ == "__main__": + EmbeddingComponent.run() diff --git a/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/Dockerfile b/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/Dockerfile index d46805647..1b2d10bbd 100644 --- a/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/Dockerfile +++ b/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/Dockerfile @@ -23,8 +23,6 @@ RUN pip3 install --no-cache-dir -r requirements.txt # Copy over src-files of the component COPY src /src -# TODO: ADD EXTRA FILES HERE IF NEEDED! - # Set the working directory to the source folder WORKDIR /src diff --git a/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/requirements.txt b/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/requirements.txt index c21634ed3..c3f48e49c 100644 --- a/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/requirements.txt +++ b/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/requirements.txt @@ -1,2 +1,2 @@ -datasets +datasets==2.10.1 git+https://github.com/ml6team/express.git@2d630255f82c6a9d7d15e7be23ff3d9b9ba16723 \ No newline at end of file diff --git a/examples/pipelines/finetune_stable_diffusion/components/dataset_loader_component/Dockerfile b/examples/pipelines/finetune_stable_diffusion/components/load_from_hub_component/Dockerfile similarity index 100% rename from examples/pipelines/finetune_stable_diffusion/components/dataset_loader_component/Dockerfile rename to examples/pipelines/finetune_stable_diffusion/components/load_from_hub_component/Dockerfile diff --git a/examples/pipelines/finetune_stable_diffusion/components/dataset_loader_component/README.MD b/examples/pipelines/finetune_stable_diffusion/components/load_from_hub_component/README.MD similarity index 100% rename from examples/pipelines/finetune_stable_diffusion/components/dataset_loader_component/README.MD rename to examples/pipelines/finetune_stable_diffusion/components/load_from_hub_component/README.MD diff --git a/examples/pipelines/finetune_stable_diffusion/components/dataset_loader_component/__init__.py b/examples/pipelines/finetune_stable_diffusion/components/load_from_hub_component/__init__.py similarity index 100% rename from examples/pipelines/finetune_stable_diffusion/components/dataset_loader_component/__init__.py rename to examples/pipelines/finetune_stable_diffusion/components/load_from_hub_component/__init__.py diff --git a/examples/pipelines/finetune_stable_diffusion/components/dataset_loader_component/component.yaml b/examples/pipelines/finetune_stable_diffusion/components/load_from_hub_component/component.yaml similarity index 88% rename from examples/pipelines/finetune_stable_diffusion/components/dataset_loader_component/component.yaml rename to examples/pipelines/finetune_stable_diffusion/components/load_from_hub_component/component.yaml index bc47cfcbc..6eae2dd79 100644 --- a/examples/pipelines/finetune_stable_diffusion/components/dataset_loader_component/component.yaml +++ b/examples/pipelines/finetune_stable_diffusion/components/load_from_hub_component/component.yaml @@ -1,4 +1,4 @@ -name: dataset_loader_component +name: load_from_hub_component description: A basic component that takes a dataset name from the 🤗 hub as input and uploads it to a GCS bucket. inputs: - name: extra_args @@ -15,7 +15,7 @@ outputs: implementation: container: - image: ghcr.io/ml6team/dataset_loader_component:latest + image: ghcr.io/ml6team/load_from_hub_component:latest command: [ python3, main.py, --extra-args, {inputValue: extra_args}, diff --git a/examples/pipelines/finetune_stable_diffusion/components/dataset_loader_component/requirements.txt b/examples/pipelines/finetune_stable_diffusion/components/load_from_hub_component/requirements.txt similarity index 61% rename from examples/pipelines/finetune_stable_diffusion/components/dataset_loader_component/requirements.txt rename to examples/pipelines/finetune_stable_diffusion/components/load_from_hub_component/requirements.txt index afa551664..db1781885 100644 --- a/examples/pipelines/finetune_stable_diffusion/components/dataset_loader_component/requirements.txt +++ b/examples/pipelines/finetune_stable_diffusion/components/load_from_hub_component/requirements.txt @@ -1,3 +1,3 @@ -datasets +datasets==2.10.1 git+https://github.com/ml6team/express.git@main -Pillow \ No newline at end of file +Pillow==9.4.0 \ No newline at end of file diff --git a/examples/pipelines/finetune_stable_diffusion/components/dataset_loader_component/src/__init__.py b/examples/pipelines/finetune_stable_diffusion/components/load_from_hub_component/src/__init__.py similarity index 100% rename from examples/pipelines/finetune_stable_diffusion/components/dataset_loader_component/src/__init__.py rename to examples/pipelines/finetune_stable_diffusion/components/load_from_hub_component/src/__init__.py diff --git a/examples/pipelines/finetune_stable_diffusion/components/dataset_loader_component/src/main.py b/examples/pipelines/finetune_stable_diffusion/components/load_from_hub_component/src/main.py similarity index 82% rename from examples/pipelines/finetune_stable_diffusion/components/dataset_loader_component/src/main.py rename to examples/pipelines/finetune_stable_diffusion/components/load_from_hub_component/src/main.py index dbe0bde05..be047829c 100644 --- a/examples/pipelines/finetune_stable_diffusion/components/dataset_loader_component/src/main.py +++ b/examples/pipelines/finetune_stable_diffusion/components/load_from_hub_component/src/main.py @@ -2,6 +2,7 @@ This component loads a seed dataset from the hub and creates the initial manifest. """ import logging +import sys from typing import Optional, Union, Dict from datasets import Dataset, load_dataset @@ -16,17 +17,21 @@ logger = logging.getLogger(__name__) -def create_metadata(examples): +def create_image_metadata(examples): + """ + Adds metadata about the images (width, height and byte size) to the dataset. + """ images = examples["image"] batch = {} batch["width"], batch["height"] = zip(*[image.size for image in images]) + batch["byte_size"] = [sys.getsizeof(image.tobytes()) for image in images] return batch -class DatasetLoaderComponent(HFDatasetsLoaderComponent): - """Class that inherits from Hugging Face data loading""" +class LoadFromHubComponent(HFDatasetsLoaderComponent): + """Component that loads a dataset from the hub.""" @classmethod def load( @@ -43,6 +48,7 @@ def load( # 1) Create data source logger.info("Loading caption dataset from the hub...") + # TODO perhaps leverage streaming dataset = load_dataset(extra_args["dataset_name"], split="train") # 2) Create an example index @@ -60,7 +66,7 @@ def load( name="index", column=index_list ) metadata_dataset = image_dataset.map( - create_metadata, batched=True, remove_columns=["image"] + create_image_metadata, batched=True, remove_columns=["image"] ) data_sources = { "images": image_dataset, @@ -75,4 +81,4 @@ def load( if __name__ == "__main__": - DatasetLoaderComponent.run() + LoadFromHubComponent.run() diff --git a/examples/pipelines/finetune_stable_diffusion/config/components_config.py b/examples/pipelines/finetune_stable_diffusion/config/components_config.py index d1d7d3f71..a8d6f3e16 100644 --- a/examples/pipelines/finetune_stable_diffusion/config/components_config.py +++ b/examples/pipelines/finetune_stable_diffusion/config/components_config.py @@ -1,5 +1,6 @@ from dataclasses import dataclass + @dataclass class DatasetLoaderConfig: """ @@ -7,6 +8,7 @@ class DatasetLoaderConfig: Params: DATASET_NAME (str): Name of the dataset on the hub. """ + DATASET_NAME = "lambdalabs/pokemon-blip-captions" @@ -18,8 +20,9 @@ class ImageFilterConfig: MIN_HEIGHT (int): Minimum height for each image. MIN_WIDTH (int): Minimum width for each image. """ - MIN_HEIGHT = 400 - MIN_WIDTH = 400 + + MIN_HEIGHT = 200 + MIN_WIDTH = 200 @dataclass @@ -30,5 +33,6 @@ class EmbeddingConfig: MODEL_ID (int): HF model id to use for embedding. BATCH_SIZE (int): Batch size to use when embedding. """ + MODEL_ID = "openai/clip-vit-large-patch14" - BATCH_SIZE = 10 \ No newline at end of file + BATCH_SIZE = 10 diff --git a/examples/pipelines/finetune_stable_diffusion/dataset_creation_pipeline.py b/examples/pipelines/finetune_stable_diffusion/dataset_creation_pipeline.py index 2bb3ad7e6..c9b06d29e 100644 --- a/examples/pipelines/finetune_stable_diffusion/dataset_creation_pipeline.py +++ b/examples/pipelines/finetune_stable_diffusion/dataset_creation_pipeline.py @@ -8,74 +8,107 @@ from kubernetes import client as k8s_client from config.general_config import KubeflowConfig -from config.components_config import DatasetLoaderConfig, ImageFilterConfig, EmbeddingConfig +from config.components_config import ( + DatasetLoaderConfig, + ImageFilterConfig, + EmbeddingConfig, +) from express.pipeline_utils import compile_and_upload_pipeline # Load Components -run_id = '{{workflow.name}}' +run_id = "{{workflow.name}}" artifact_bucket = KubeflowConfig.ARTIFACT_BUCKET # Component 1 -dataset_loader_op = comp.load_component('components/dataset_loader_component/component.yaml') -dataset_loader_extra_args = {"dataset_name": DatasetLoaderConfig.DATASET_NAME} -dataset_loader_metadata_args = {"run_id": run_id, "component_name": dataset_loader_op.__name__, "artifact_bucket": artifact_bucket} -dataset_loader_extra_args = json.dumps(dataset_loader_extra_args) -dataset_loader_metadata_args = json.dumps(dataset_loader_metadata_args) +load_from_hub_op = comp.load_component( + "components/load_from_hub_component/component.yaml" +) +load_from_hub_extra_args = {"dataset_name": DatasetLoaderConfig.DATASET_NAME} +load_from_hub_metadata_args = { + "run_id": run_id, + "component_name": load_from_hub_op.__name__, + "artifact_bucket": artifact_bucket, +} +load_from_hub_extra_args = json.dumps(load_from_hub_extra_args) +load_from_hub_metadata_args = json.dumps(load_from_hub_metadata_args) # Component 2 -image_filter_op = comp.load_component('components/image_filter_component/component.yaml') -image_filter_extra_args = {"min_height": ImageFilterConfig.MIN_HEIGHT, "min_width": ImageFilterConfig.MIN_WIDTH} -image_filter_metadata_args = {"run_id": run_id, "component_name": image_filter_op.__name__, "artifact_bucket": artifact_bucket} +image_filter_op = comp.load_component( + "components/image_filter_component/component.yaml" +) +image_filter_extra_args = { + "min_height": ImageFilterConfig.MIN_HEIGHT, + "min_width": ImageFilterConfig.MIN_WIDTH, +} +image_filter_metadata_args = { + "run_id": run_id, + "component_name": image_filter_op.__name__, + "artifact_bucket": artifact_bucket, +} image_filter_extra_args = json.dumps(image_filter_extra_args) image_filter_metadata_args = json.dumps(image_filter_metadata_args) # Component 3 -embedding_op = comp.load_component('components/embedding_component/component.yaml') -embedding_extra_args = {"model_id": EmbeddingConfig.MODEL_ID, "batch_size": EmbeddingConfig.BATCH_SIZE} -embedding_metadata_args = {"run_id": run_id, "component_name": embedding_op.__name__, "artifact_bucket": artifact_bucket} +embedding_op = comp.load_component("components/embedding_component/component.yaml") +embedding_extra_args = { + "model_id": EmbeddingConfig.MODEL_ID, + "batch_size": EmbeddingConfig.BATCH_SIZE, +} +embedding_metadata_args = { + "run_id": run_id, + "component_name": embedding_op.__name__, + "artifact_bucket": artifact_bucket, +} embedding_extra_args = json.dumps(embedding_extra_args) embedding_metadata_args = json.dumps(embedding_metadata_args) # Pipeline @dsl.pipeline( - name='image-generator-dataset', - description='Pipeline that takes example images as input and returns an expanded dataset of ' - 'similar images as outputs' + name="image-generator-dataset", + description="Pipeline that takes example images as input and returns an expanded dataset of " + "similar images as outputs", ) # pylint: disable=too-many-arguments, too-many-locals -def sd_dataset_creator_pipeline(dataset_loader_extra_args: str = dataset_loader_extra_args, - dataset_loader_metadata_args: str = dataset_loader_metadata_args, - image_filter_extra_args: str = image_filter_extra_args, - image_filter_metadata_args: str = image_filter_metadata_args, - embedding_extra_args: str = embedding_extra_args, - embedding_metadata_args: str = embedding_metadata_args, +def sd_dataset_creator_pipeline( + load_from_hub_extra_args: str = load_from_hub_extra_args, + load_from_hub_metadata_args: str = load_from_hub_metadata_args, + image_filter_extra_args: str = image_filter_extra_args, + image_filter_metadata_args: str = image_filter_metadata_args, + embedding_extra_args: str = embedding_extra_args, + embedding_metadata_args: str = embedding_metadata_args, ): # Component 1 - dataset_loader_task = dataset_loader_op(extra_args=dataset_loader_extra_args, - metadata_args=dataset_loader_metadata_args, - ).set_display_name('Load initial images') + load_from_hub_task = load_from_hub_op( + extra_args=load_from_hub_extra_args, + metadata_args=load_from_hub_metadata_args, + ).set_display_name("Load initial images") # Component 2 - image_filter_task = image_filter_op(extra_args=image_filter_extra_args, - metadata=image_filter_metadata_args, - input_manifest=dataset_loader_task.outputs["output_manifest"], - ).set_display_name('Filter images') + image_filter_task = image_filter_op( + extra_args=image_filter_extra_args, + metadata=image_filter_metadata_args, + input_manifest=load_from_hub_task.outputs["output_manifest"], + ).set_display_name("Filter images") # Component 3 - embedding_task = embedding_op(extra_args=embedding_extra_args, - metadata=embedding_metadata_args, - input_manifest=image_filter_task.outputs["output_manifest"], - ).set_display_name('Embed images') \ - .set_gpu_limit(1) \ - .add_node_selector_constraint('node_pool', 'model-inference-pool') \ - .add_toleration( - k8s_client.V1Toleration(effect='NoSchedule', key='reserved-pool', operator='Equal', - value='true')) + embedding_op( + extra_args=embedding_extra_args, + metadata=embedding_metadata_args, + input_manifest=image_filter_task.outputs["output_manifest"], + ).set_display_name("Embed images").set_gpu_limit(1).add_node_selector_constraint( + "node_pool", "model-inference-pool" + ).add_toleration( + k8s_client.V1Toleration( + effect="NoSchedule", key="reserved-pool", operator="Equal", value="true" + ) + ) -if __name__ == '__main__': - compile_and_upload_pipeline(pipeline=sd_dataset_creator_pipeline, - host=KubeflowConfig.HOST, - env=KubeflowConfig.ENV) +if __name__ == "__main__": + compile_and_upload_pipeline( + pipeline=sd_dataset_creator_pipeline, + host=KubeflowConfig.HOST, + env=KubeflowConfig.ENV, + ) diff --git a/express/components/common.py b/express/components/common.py index 371fdd3b8..2527da3c4 100644 --- a/express/components/common.py +++ b/express/components/common.py @@ -338,7 +338,6 @@ def run(cls) -> DataManifest: DataManifest: the output manifest """ args = cls._parse_args() - print("Args:", args) input_dataset = cls._load_dataset( input_manifest=DataManifest.from_path(args.input_manifest) ) From 55c3fb6d1b78c249c75ae7d60531e60fe92711a4 Mon Sep 17 00:00:00 2001 From: Niels Rogge Date: Wed, 29 Mar 2023 13:19:04 +0200 Subject: [PATCH 26/38] Remove type int --- express/components/common.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/express/components/common.py b/express/components/common.py index 2527da3c4..680c3e2de 100644 --- a/express/components/common.py +++ b/express/components/common.py @@ -80,7 +80,7 @@ def load( def _load_data_source( data_source: DataSource, index_filter: Optional[IndexT], - **kwargs: dict, + **kwargs, ) -> DataT: """ Load data from a (possibly remote) path. From 1531d95b82703c269620eb6d91ba414b5aa43c12 Mon Sep 17 00:00:00 2001 From: Niels Rogge Date: Wed, 29 Mar 2023 13:22:28 +0200 Subject: [PATCH 27/38] Add batch size argument --- .../components/load_from_hub_component/src/main.py | 5 ++++- .../finetune_stable_diffusion/config/components_config.py | 4 +++- .../finetune_stable_diffusion/dataset_creation_pipeline.py | 7 +++++-- 3 files changed, 12 insertions(+), 4 deletions(-) diff --git a/examples/pipelines/finetune_stable_diffusion/components/load_from_hub_component/src/main.py b/examples/pipelines/finetune_stable_diffusion/components/load_from_hub_component/src/main.py index be047829c..1720a8435 100644 --- a/examples/pipelines/finetune_stable_diffusion/components/load_from_hub_component/src/main.py +++ b/examples/pipelines/finetune_stable_diffusion/components/load_from_hub_component/src/main.py @@ -66,7 +66,10 @@ def load( name="index", column=index_list ) metadata_dataset = image_dataset.map( - create_image_metadata, batched=True, remove_columns=["image"] + create_image_metadata, + batched=True, + batch_size=extra_args["batch_size"], + remove_columns=["image"], ) data_sources = { "images": image_dataset, diff --git a/examples/pipelines/finetune_stable_diffusion/config/components_config.py b/examples/pipelines/finetune_stable_diffusion/config/components_config.py index a8d6f3e16..f32b03394 100644 --- a/examples/pipelines/finetune_stable_diffusion/config/components_config.py +++ b/examples/pipelines/finetune_stable_diffusion/config/components_config.py @@ -2,14 +2,16 @@ @dataclass -class DatasetLoaderConfig: +class LoadFromHubConfig: """ Configs for the dataset loader component Params: DATASET_NAME (str): Name of the dataset on the hub. + BATCH_SIZE (int): Batch size to use when creating image metadata. """ DATASET_NAME = "lambdalabs/pokemon-blip-captions" + BATCH_SIZE = 1000 @dataclass diff --git a/examples/pipelines/finetune_stable_diffusion/dataset_creation_pipeline.py b/examples/pipelines/finetune_stable_diffusion/dataset_creation_pipeline.py index c9b06d29e..f384da684 100644 --- a/examples/pipelines/finetune_stable_diffusion/dataset_creation_pipeline.py +++ b/examples/pipelines/finetune_stable_diffusion/dataset_creation_pipeline.py @@ -9,7 +9,7 @@ from config.general_config import KubeflowConfig from config.components_config import ( - DatasetLoaderConfig, + LoadFromHubConfig, ImageFilterConfig, EmbeddingConfig, ) @@ -23,7 +23,10 @@ load_from_hub_op = comp.load_component( "components/load_from_hub_component/component.yaml" ) -load_from_hub_extra_args = {"dataset_name": DatasetLoaderConfig.DATASET_NAME} +load_from_hub_extra_args = { + "dataset_name": LoadFromHubConfig.DATASET_NAME, + "batch_size": LoadFromHubConfig.BATCH_SIZE, +} load_from_hub_metadata_args = { "run_id": run_id, "component_name": load_from_hub_op.__name__, From 3ef06ceb3d8de81938ba8601c8a1711b22078f0f Mon Sep 17 00:00:00 2001 From: Niels Rogge Date: Wed, 29 Mar 2023 14:37:46 +0200 Subject: [PATCH 28/38] Update clip retrieval component --- .../clip_retrieval_component/Dockerfile | 29 +- .../clip_retrieval_component/component.yaml | 77 ++-- .../clip_retrieval_component/src/main.py | 351 +++++------------- .../src/utils/embedding_utils.py | 18 +- 4 files changed, 154 insertions(+), 321 deletions(-) diff --git a/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/Dockerfile b/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/Dockerfile index 611ccfb16..1b2d10bbd 100644 --- a/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/Dockerfile +++ b/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/Dockerfile @@ -1,14 +1,29 @@ -FROM europe-west1-docker.pkg.dev/storied-landing-366912/storied-landing-366912-default-repository/mlpipelines/kubeflow/components/base_component:latest +FROM python:3.8-slim -# Set the working directory to the source folder -WORKDIR /src +## System dependencies +RUN apt-get update && \ + apt-get upgrade -y && \ + apt-get install git curl -y -# Copy over src-files of the component -COPY requirements.txt . +# Downloading gcloud package +RUN curl https://dl.google.com/dl/cloudsdk/release/google-cloud-sdk.tar.gz > /tmp/google-cloud-sdk.tar.gz + +# Installing the package +RUN mkdir -p /usr/local/gcloud \ +&& tar -C /usr/local/gcloud -xvf /tmp/google-cloud-sdk.tar.gz \ +&& /usr/local/gcloud/google-cloud-sdk/install.sh + +# Adding the package path to local +ENV PATH $PATH:/usr/local/gcloud/google-cloud-sdk/bin -# Install packages -RUN pip3 install -r requirements.txt +# install requirements +COPY requirements.txt / +RUN pip3 install --no-cache-dir -r requirements.txt +# Copy over src-files of the component COPY src /src +# Set the working directory to the source folder +WORKDIR /src + ENTRYPOINT ["python", "main.py"] \ No newline at end of file diff --git a/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/component.yaml b/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/component.yaml index 44c3a5040..c32ab5663 100644 --- a/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/component.yaml +++ b/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/component.yaml @@ -1,58 +1,29 @@ name: clip_retrieval_component -description: A component that takes a dataset manifest and returns an output data manifest with an extended dataset - by retrieving similar images from the laion dataset using different retrieval strategies (knn, centroid) -inputs: - - name: run_id - description: The run id of the pipeline - type: String - - name: artifact_bucket - description: The GCS bucket used to store the artifact - type: String - - name: component_name - description: the name of the component (used to create gcs artefact path) - type: String - - name: project_id - description: The id of the gcp-project - type: String - - name: laion_index_url - description: contains the indices of the metadata. Those indices need to be transformed in case you decide to use only a subset of the dataset - type: String - - name: laion_metadata_url - description: url to the metadata of laion dataset metadata (arrow format). It can either contain a subset of the laion 5b metadata (e.g. laion-en) or all of the metadata - type: String - - name: nb_images_knn - description: The number of images to return via the knn strategy (per image) - type: Integer - - name: nb_images_centroid - description: The number of images to return via the centroid strategy - type: Integer - - name: data_manifest_path - description: The previous component manifest path - type: String +description: A component that retrieves similar images from the LAION dataset. +inputs: + - name: extra_args + description: Additional arguments passed to the component, as a json dict string + type: String + + - name: metadata + description: Metadata arguments, passed as a json dict string + type: String + - name: input_manifest + description: Path to the input manifest + type: String + outputs: - - name: data_manifest_path_clip_retrieval_component - description: Path to the local file containing the gcs path where the output has been stored - - name: parquet_path_clip_centroid_retrieval - description: The path to the parquet file containing the urls from centroid retrieval - - name: parquet_path_clip_knn_retrieval - description: The path to the parquet file containing the urls from knn retrieval + - name: output_manifest + description: Path to the output manifest implementation: - container: - image: europe-west1-docker.pkg.dev/storied-landing-366912/storied-landing-366912-default-repository/mlpipelines/kubeflow/components/clip_retrieval_component:latest - command: [ - python3, main.py, - --run-id, { inputValue: run_id }, - --artifact-bucket, { inputValue: artifact_bucket }, - --component-name, { inputValue: component_name }, - --project-id, { inputValue: project_id, }, - --laion-index-url, { inputValue: laion_index_url }, - --laion-metadata-url, { inputValue: laion_metadata_url }, - --nb-images-knn, { inputValue: nb_images_knn }, - --nb-images-centroid, { inputValue: nb_images_centroid }, - --data-manifest-path, { inputPath: data_manifest_path }, - --data-manifest-path-clip-retrieval-component, { outputPath: data_manifest_path_clip_retrieval_component }, - --parquet-path-clip-centroid-retrieval, { outputPath: parquet_path_clip_centroid_retrieval }, - --parquet-path-clip-knn-retrieval, { outputPath: parquet_path_clip_knn_retrieval }, - ] + container: + image: ghcr.io/ml6team/clip_retrieval_component:latest + command: [ + python3, main.py, + --input-manifest, {inputPath: input_manifest}, + --metadata, {inputValue: metadata}, + --extra-args, {inputValue: extra_args}, + --output-manifest, {outputPath: output_manifest}, + ] \ No newline at end of file diff --git a/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/src/main.py b/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/src/main.py index 3edb53340..0523dd1bf 100644 --- a/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/src/main.py +++ b/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/src/main.py @@ -1,267 +1,110 @@ """ -This file is the entrypoint of the component. It will parse all arguments -and give them to the actual core of the component. +This component extends the images data source by retrieving similar images from LAION. """ - -import os -import tempfile -import argparse import logging -import json -import time -from pathlib import Path -from datetime import datetime - -import numpy as np -from tqdm import tqdm -from google.cloud import storage +from typing import Optional, Union, Dict -# pylint: disable=import-error -from helpers.logger import get_logger -from helpers import storage_helpers, parquet_helpers -from helpers.manifest_helpers import DataManifest from utils.embedding_utils import get_average_embedding -from utils.knn_service import ClipRetrievalLaion5B - -def parse_args(): - """Parse component arguments""" +from express.components.hf_datasets_components import ( + HFDatasetsTransformComponent, + HFDatasetsDataset, + HFDatasetsDatasetDraft, +) +from express.logger import configure_logging - parser = argparse.ArgumentParser() - parser.add_argument('--run-id', - type=str, - required=True, - help='The run id of the pipeline') - parser.add_argument('--artifact-bucket', - type=str, - required=True, - help='The GCS bucket used to store the artifacts') - parser.add_argument('--component-name', - type=str, - required=True, - help='The name of the component') - parser.add_argument('--project-id', - type=str, - required=True, - help='The id of the gcp-project') - parser.add_argument('--laion-index-url', - type=str, - required=True, - help='contains the indices of the metadata. Those indices need to be ' - 'transformed in case you decide to use only a subset of the dataset') - parser.add_argument('--laion-metadata-url', - type=str, - required=True, - help='The id of the gcp-project') - parser.add_argument('--nb-images-knn', - type=int, - required=True, - help='The number of images to return via KNN method') - parser.add_argument('--nb-images-centroid', - type=int, - required=True, - help='The number of images to return via the centroid method') - parser.add_argument('--data-manifest-path', - type=str, - required=True, - help='The previous component manifest path') - parser.add_argument('--data-manifest-path-clip-retrieval-component', - type=str, - required=True, - help='The path to the output manifest file') - parser.add_argument('--parquet-path-clip-knn-retrieval', - type=str, - required=True, - help='The path to the parquet file containing the urls from knn retrieval') - parser.add_argument('--parquet-path-clip-centroid-retrieval', - type=str, - required=True, - help='The path to the parquet file containing the urls from centroid ' - 'retrieval') - return parser.parse_args() +configure_logging() +logger = logging.getLogger(__name__) -# pylint: disable=too-many-locals, too-many-arguments, too-many-statements -def clip_retrieval_component(run_id: str, - artifact_bucket: str, - component_name: str, - project_id: str, - laion_index_url: str, - laion_metadata_url: str, - nb_images_knn: int, - nb_images_centroid: int, - data_manifest_path: str, - data_manifest_path_clip_retrieval_component: str, - parquet_path_clip_knn_retrieval: str, - parquet_path_clip_centroid_retrieval: str) -> None: +class CLIPRetrievalComponent(HFDatasetsTransformComponent): """ - Args: - run_id (str): The run id of the pipeline - artifact_bucket (str): The GCS bucket used to store the artifact - component_name (str): the name of the component (used to create gcs artefact path) - project_id (str): The id of the gcp-project - laion_index_url (str): contains the indices of the metadata. Those indices need to be - transformed in case you decide to use only a subset of the dataset - laion_metadata_url (str): url to the metadata of laion dataset metadata (arrow format). It - can either contain a subset of the laion 5b metadata (e.g. laion-en) or all of the metadata - nb_images_knn (int): The number of images to return via knn method (per image) - nb_images_centroid (int): The number of images to return via the centroid method - data_manifest_path (str): The previous component manifest path - data_manifest_path_clip_retrieval_component (str): the current component output path - parquet_path_clip_centroid_retrieval (str): The path to the parquet file containing the urls - from centroid retrieval - parquet_path_clip_knn_retrieval (str): The path to the parquet file containing the urls - from knn retrieval + Component that retrieves similar images from the LAION dataset. """ - logger = get_logger(name=__name__, level=logging.INFO) - logger.info('Started job...') - - # Initialize storage client - storage_client = storage.Client(project=project_id) - - # This component uses local SSD mounting to enable faster querying of the laion dataset. The - # local SSDs are mounted in the cache directory - laion_index_folder = os.path.join('/cache', 'laion_dataset') - laion_metadata_folder = os.path.join(laion_index_folder, 'metadata') - laion_indices_folder = os.path.join(laion_index_folder, 'image.index') - os.makedirs(laion_metadata_folder, exist_ok=True) - os.makedirs(laion_indices_folder, exist_ok=True) - - # Download laion indices and metadata from storage - start_indices = time.time() - storage_helpers.copy_folder_bulk(laion_index_url, laion_indices_folder) - logger.info('Laion index download complete: it took %s minutes to download the laion indices', - round((time.time() - start_indices) / 60)) - start_metadata = time.time() - storage_helpers.copy_folder_bulk(laion_metadata_url, laion_metadata_folder) - logger.info( - 'Laion metadata download complete: it took %s minutes to download the laion metadata', - round((time.time() - start_metadata) / 60)) - - with tempfile.TemporaryDirectory() as tmp_dir: - logger.info('created temporary directory %s', tmp_dir) - - # Initialize GCS custom artifact path - component_artifact_dir = run_id.rpartition('-')[0] - artifact_bucket_blob_path = f"custom_artifact/{component_artifact_dir}/{component_name}" - logger.info("custom artifact will be uploaded to %s", - f'gs://{artifact_bucket}/{artifact_bucket_blob_path}') - - tmp_embedding_dir_path = os.path.join(tmp_dir, 'embed_dir') - os.makedirs(tmp_embedding_dir_path, exist_ok=True) - laion_indices_folder = os.path.join(tmp_dir, 'index_file.json') - knn_retrieval_path = os.path.join(tmp_dir, 'clip_knn.parquet') - centroid_retrieval_path = os.path.join(tmp_dir, 'clip_centroid.parquet') - knn_retrieval_blob_path = os.path.join(artifact_bucket_blob_path, 'clip_knn.parquet') - centroid_retrieval_blob_path = os.path.join(artifact_bucket_blob_path, - 'clip_centroid.parquet') - - # Read manifest - with open(data_manifest_path) as f: - manifest_load = json.load(f) - data_manifest = DataManifest.from_dict(manifest_load) - embedding_path = data_manifest.associated_data.embedding['cf'] - - # Copy embeddings to local directory - storage_helpers.copy_folder_bulk(f"{embedding_path}/*", tmp_embedding_dir_path) - nb_src_images = len(os.listdir(tmp_embedding_dir_path)) - logging.info('Total number of source images (embedding): %s', nb_src_images) - centroid_embedding = get_average_embedding(tmp_embedding_dir_path) - - logger.info('A total of %s urls will be requested using the centroid approach.', - nb_images_centroid) - logger.info('A total of %s urls will be requested per individual image (total of %s images)' - ' using the knn approach.', nb_images_knn) - - # Setup KNN service - clip_retrieval_runner = ClipRetrievalLaion5B( - laion_index_path=laion_indices_folder, - laion_index_folder=laion_index_folder) - knn_service = clip_retrieval_runner.setup_knn_service() - - logger.info('Starting centroid clip retrieval') - - # Run clip retrieval with centroid approach - results_centroid = clip_retrieval_runner.run_query( - knn_service=knn_service, - query={'embedding_query': centroid_embedding}, - nb_images_request=nb_images_centroid, - deduplicate=True, - benchmark=True) - logger.info('Centroid clip retrieval complete') - - parquet_helpers.write_clip_retrieval_parquet( - clip_retrieval_parquet_path=centroid_retrieval_path, - data_iterable_producer=clip_retrieval_runner.clip_results_producer, - clip_results=results_centroid) - del results_centroid - - # Run clip retrieval with KNN approach - logger.info('Starting knn clip retrieval') - results_knn = [] - for embedding_file in tqdm(os.listdir(tmp_embedding_dir_path)): - embedding = np.load(os.path.join(tmp_embedding_dir_path, embedding_file)) - results = clip_retrieval_runner.run_query(knn_service=knn_service, - query={'embedding_query': embedding}, - nb_images_request=nb_images_knn, - deduplicate=True, - benchmark=True) - results_knn.extend(results) - logger.info('KNN clip retrieval complete') - parquet_helpers.write_clip_retrieval_parquet( - clip_retrieval_parquet_path=knn_retrieval_path, - data_iterable_producer=clip_retrieval_runner.clip_results_producer, - clip_results=results_knn) - del results_knn - - # Upload parquet files to bucket - storage_helpers.upload_file_to_bucket(storage_client=storage_client, - file_to_upload_path=centroid_retrieval_path, - bucket_name=artifact_bucket, - blob_path=centroid_retrieval_blob_path) - - storage_helpers.upload_file_to_bucket(storage_client=storage_client, - file_to_upload_path=knn_retrieval_path, - bucket_name=artifact_bucket, - blob_path=knn_retrieval_blob_path) - - # Update manifest - data_manifest.dataset_id = f"{run_id}_{component_name}" - data_manifest.metadata.branch = "" # TODO: Fill from docker build env var - data_manifest.metadata.commit_hash = "" # TODO: Fill from docker build env var - data_manifest.metadata.creation_date = datetime.now().strftime("%d-%m-%Y_%H-%M-%S") - data_manifest.metadata.run_id = run_id - - logger.info('Manifest file created and updated') - - # Write manifest to outputPath - Path(data_manifest_path_clip_retrieval_component).parent.mkdir(parents=True, exist_ok=True) - Path(parquet_path_clip_knn_retrieval).parent.mkdir(parents=True, exist_ok=True) - Path(parquet_path_clip_centroid_retrieval).parent.mkdir(parents=True, exist_ok=True) - # Write Parquet url files to outputPath - Path(data_manifest_path_clip_retrieval_component).write_text(data_manifest.to_json()) - Path(parquet_path_clip_knn_retrieval).write_text( - f'gs://{artifact_bucket}/{knn_retrieval_blob_path}') - Path(parquet_path_clip_centroid_retrieval).write_text( - f'gs://{artifact_bucket}/{centroid_retrieval_blob_path}') - logger.info('Manifest file written to %s', data_manifest_path_clip_retrieval_component) - logger.info('Job completed.') - -if __name__ == '__main__': - args = parse_args() - clip_retrieval_component \ - (run_id=args.run_id, - artifact_bucket=args.artifact_bucket, - component_name=args.component_name, - project_id=args.project_id, - laion_index_url=args.laion_index_url, - laion_metadata_url=args.laion_metadata_url, - nb_images_knn=args.nb_images_knn, - nb_images_centroid=args.nb_images_centroid, - data_manifest_path=args.data_manifest_path, - data_manifest_path_clip_retrieval_component= - args.data_manifest_path_clip_retrieval_component, - parquet_path_clip_centroid_retrieval=args.parquet_path_clip_centroid_retrieval, - parquet_path_clip_knn_retrieval=args.parquet_path_clip_knn_retrieval) + @classmethod + def transform( + cls, + data: HFDatasetsDataset, + extra_args: Optional[Dict[str, Union[str, int, float, bool]]] = None, + ) -> HFDatasetsDatasetDraft: + """ + An example function showcasing the data transform component using Express functionalities + + Args: + data (HFDatasetsDataset[TIndex, TData]): express dataset providing access to data of a + given type + extra_args (Optional[Dict[str, Union[str, int, float, bool]]): optional args to pass to + the function + Returns: + HFDatasetsDatasetDraft: a dataset draft that creates a plan for an output manifest + """ + + # 1) Get one particular data source from the manifest + # TODO check whether we can leverage streaming + # TODO just column in the load method in the future + logger.info("Loading embedding dataset...") + embeddings = data.load( + data_source="embeddings", columns=["embeddings"], streaming=True + ) + + logger.info("Calculating average embedding...") + centroid_embedding = get_average_embedding(embeddings) + + print("Centroid embedding:", centroid_embedding) + + # # This component uses local SSD mounting to enable faster querying + # of the laion dataset. The + # # local SSDs are mounted in the cache directory + # laion_index_folder = os.path.join('/cache', 'laion_dataset') + # laion_metadata_folder = os.path.join(laion_index_folder, 'metadata') + # laion_indices_folder = os.path.join(laion_index_folder, 'image.index') + # os.makedirs(laion_metadata_folder, exist_ok=True) + # os.makedirs(laion_indices_folder, exist_ok=True) + + # # Download laion indices and metadata from storage + # start_indices = time.time() + # copy_folder(extra_args["laion_index_url"], laion_indices_folder) + # logger.info('Laion index download complete: it took %s minutes to download the + # laion indices', + # round((time.time() - start_indices) / 60)) + # start_metadata = time.time() + # copy_folder(extra_args["laion_metadata_url"], laion_metadata_folder) + # logger.info( + # 'Laion metadata download complete: it took %s minutes to download the laion metadata', + # round((time.time() - start_metadata) / 60)) + + # # Setup KNN service + # with tempfile.TemporaryDirectory() as tmp_dir: + # tmp_embedding_dir_path = os.path.join(tmp_dir, 'embed_dir') + # os.makedirs(tmp_embedding_dir_path, exist_ok=True) + # laion_indices_folder = os.path.join(tmp_dir, 'index_file.json') + # clip_retrieval_runner = ClipRetrievalLaion5B( + # laion_index_path=laion_indices_folder, + # laion_index_folder=laion_index_folder) + # knn_service = clip_retrieval_runner.setup_knn_service() + + # logger.info('Starting centroid clip retrieval') + + # # Run clip retrieval with centroid approach + # results_centroid = clip_retrieval_runner.run_query( + # knn_service=knn_service, + # query={'embedding_query': centroid_embedding}, + # nb_images_request=extra_args["nb_images_centroid"], + # deduplicate=True, + # benchmark=True) + # logger.info('Centroid clip retrieval complete') + + # # 3) Create dataset draft + # logger.info("Creating draft...") + # data_sources = {} + # dataset_draft = HFDatasetsDatasetDraft( + # data_sources=data_sources, extending_dataset=data + # ) + # return dataset_draft + return None + + +if __name__ == "__main__": + CLIPRetrievalComponent.run() diff --git a/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/src/utils/embedding_utils.py b/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/src/utils/embedding_utils.py index 3c51707af..1c09484b6 100644 --- a/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/src/utils/embedding_utils.py +++ b/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/src/utils/embedding_utils.py @@ -1,19 +1,23 @@ """Clip embedding util functions""" -import os + +from typing import List import numpy as np -def get_average_embedding(embedding_dir: str) -> np.array: +def get_average_embedding(embeddings: List[np.array]) -> np.array: """ - Function that returns the average embedding from a selection of embeddings + Function that returns the average embedding from a list of embeddings + Args: - embedding_dir (str): the directory where the embeddings are located + embeddings (List[np.array]): list of embeddings. Returns: np.array: the average embedding """ + initial_embedding = next(iter(embeddings)) + embedding_sum = np.zeros(initial_embedding.shape) + for embedding in embeddings: + embedding_sum += embedding - embeddings = [np.load(os.path.join(embedding_dir, embedding_file)) for embedding_file in - os.listdir(embedding_dir)] - avg_embedding = np.array(embeddings).mean(axis=0) + avg_embedding = embedding_sum.mean(axis=0) return avg_embedding From a556fb4aa83a085ade7e73dfe36002db552ad739 Mon Sep 17 00:00:00 2001 From: Niels Rogge Date: Wed, 29 Mar 2023 14:48:29 +0200 Subject: [PATCH 29/38] Update platform --- .../clip_retrieval_component/Dockerfile | 2 +- .../components/image_filter_component/Dockerfile | 2 +- .../components/load_from_hub_component/Dockerfile | 2 +- .../load_from_hub_component/src/main.py | 15 +++++---------- 4 files changed, 8 insertions(+), 13 deletions(-) diff --git a/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/Dockerfile b/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/Dockerfile index 1b2d10bbd..49b7639f9 100644 --- a/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/Dockerfile +++ b/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/Dockerfile @@ -1,4 +1,4 @@ -FROM python:3.8-slim +FROM --platform=linux/amd64 python:3.8-slim ## System dependencies RUN apt-get update && \ diff --git a/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/Dockerfile b/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/Dockerfile index 1b2d10bbd..49b7639f9 100644 --- a/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/Dockerfile +++ b/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/Dockerfile @@ -1,4 +1,4 @@ -FROM python:3.8-slim +FROM --platform=linux/amd64 python:3.8-slim ## System dependencies RUN apt-get update && \ diff --git a/examples/pipelines/finetune_stable_diffusion/components/load_from_hub_component/Dockerfile b/examples/pipelines/finetune_stable_diffusion/components/load_from_hub_component/Dockerfile index 1b2d10bbd..49b7639f9 100644 --- a/examples/pipelines/finetune_stable_diffusion/components/load_from_hub_component/Dockerfile +++ b/examples/pipelines/finetune_stable_diffusion/components/load_from_hub_component/Dockerfile @@ -1,4 +1,4 @@ -FROM python:3.8-slim +FROM --platform=linux/amd64 python:3.8-slim ## System dependencies RUN apt-get update && \ diff --git a/examples/pipelines/finetune_stable_diffusion/components/load_from_hub_component/src/main.py b/examples/pipelines/finetune_stable_diffusion/components/load_from_hub_component/src/main.py index 1720a8435..1d4582617 100644 --- a/examples/pipelines/finetune_stable_diffusion/components/load_from_hub_component/src/main.py +++ b/examples/pipelines/finetune_stable_diffusion/components/load_from_hub_component/src/main.py @@ -17,13 +17,10 @@ logger = logging.getLogger(__name__) -def create_image_metadata(examples): - """ - Adds metadata about the images (width, height and byte size) to the dataset. - """ - images = examples["image"] +def create_image_metadata(batch): + images = batch["image"] - batch = {} + # add width, height and byte size columns batch["width"], batch["height"] = zip(*[image.size for image in images]) batch["byte_size"] = [sys.getsizeof(image.tobytes()) for image in images] @@ -31,7 +28,7 @@ def create_image_metadata(examples): class LoadFromHubComponent(HFDatasetsLoaderComponent): - """Component that loads a dataset from the hub.""" + """Component that loads a dataset from the hub and creates the initial manifest.""" @classmethod def load( @@ -65,16 +62,14 @@ def load( text_dataset = dataset.remove_columns(["image"]).add_column( name="index", column=index_list ) - metadata_dataset = image_dataset.map( + image_dataset = image_dataset.map( create_image_metadata, batched=True, batch_size=extra_args["batch_size"], - remove_columns=["image"], ) data_sources = { "images": image_dataset, "captions": text_dataset, - "image_metadata": metadata_dataset, } dataset_draft = HFDatasetsDatasetDraft( index=index_dataset, data_sources=data_sources From d7ba77bde42c7ae1956761a2946fab726a5b8e57 Mon Sep 17 00:00:00 2001 From: Niels Rogge Date: Thu, 30 Mar 2023 10:00:59 +0200 Subject: [PATCH 30/38] More improvements --- .../finetune_stable_diffusion/build_images.sh | 1 + .../components/embedding_component/Dockerfile | 2 +- .../image_filter_component/src/main.py | 44 ++++++++++++------- .../load_from_hub_component/src/main.py | 4 +- 4 files changed, 34 insertions(+), 17 deletions(-) diff --git a/examples/pipelines/finetune_stable_diffusion/build_images.sh b/examples/pipelines/finetune_stable_diffusion/build_images.sh index 25c5a9cbc..0f922353c 100755 --- a/examples/pipelines/finetune_stable_diffusion/build_images.sh +++ b/examples/pipelines/finetune_stable_diffusion/build_images.sh @@ -49,6 +49,7 @@ for dir in $component_dir/*/; do --build-arg GIT_BRANCH=$(git rev-parse --abbrev-ref HEAD) \ --build-arg BUILD_TIMESTAMP=$(date '+%F_%H:%M:%S') \ --label org.opencontainers.image.source=https://github.com/${namespace}/${repo} \ + --platform=linux/arm64 \ . docker push "$full_image_name" fi diff --git a/examples/pipelines/finetune_stable_diffusion/components/embedding_component/Dockerfile b/examples/pipelines/finetune_stable_diffusion/components/embedding_component/Dockerfile index af391b6d2..49b7639f9 100644 --- a/examples/pipelines/finetune_stable_diffusion/components/embedding_component/Dockerfile +++ b/examples/pipelines/finetune_stable_diffusion/components/embedding_component/Dockerfile @@ -1,4 +1,4 @@ -FROM pytorch/pytorch:2.0.0-cuda11.7-cudnn8-runtime +FROM --platform=linux/amd64 python:3.8-slim ## System dependencies RUN apt-get update && \ diff --git a/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/src/main.py b/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/src/main.py index 4b0541c73..34c6e973f 100644 --- a/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/src/main.py +++ b/examples/pipelines/finetune_stable_diffusion/components/image_filter_component/src/main.py @@ -8,7 +8,11 @@ from datasets import Dataset -from express.components.hf_datasets_components import HFDatasetsTransformComponent, HFDatasetsDataset, HFDatasetsDatasetDraft +from express.components.hf_datasets_components import ( + HFDatasetsTransformComponent, + HFDatasetsDataset, + HFDatasetsDatasetDraft, +) from express.logger import configure_logging configure_logging() @@ -17,22 +21,25 @@ def check_min_size(example, min_width, min_height): width, height = example["width"], example["height"] - + return width > min_width and height > min_height class ImageFilterComponent(HFDatasetsTransformComponent): """ Class that inherits from Hugging Face data transform. - + Goal is to leverage streaming.""" @classmethod - def transform(cls, data: HFDatasetsDataset, extra_args: Optional[ - Dict[str, Union[str, int, float, bool]]] = None) -> HFDatasetsDatasetDraft: + def transform( + cls, + data: HFDatasetsDataset, + extra_args: Optional[Dict[str, Union[str, int, float, bool]]] = None, + ) -> HFDatasetsDatasetDraft: """ An example function showcasing the data transform component using Express functionalities - + Args: data (HFDatasetsDataset[TIndex, TData]): express dataset providing access to data of a given type @@ -41,24 +48,31 @@ def transform(cls, data: HFDatasetsDataset, extra_args: Optional[ Returns: HFDatasetsDatasetDraft: a dataset draft that creates a plan for an output manifest """ - + # 1) Load one particular data source from the manifest - logger.info("Loading metadata dataset...") - metadata_dataset = data.load(data_source="image_metadata") - + logger.info("Loading image dataset...") + metadata_dataset = data.load( + data_source="images", columns=["index", "width", "height"] + ) + # 2) Update index by filtering logger.info("Filtering dataset...") min_width, min_height = extra_args["min_width"], extra_args["min_height"] - filtered_dataset = metadata_dataset.filter(lambda example: example["width"] > min_width and example["height"] > min_height) + filtered_dataset = metadata_dataset.filter( + lambda example: example["width"] > min_width + and example["height"] > min_height + ) index_dataset = Dataset.from_dict({"index": filtered_dataset["index"]}) - + # 3) Create dataset draft which updates the index # but maintains the same data sources logger.info("Creating draft...") - dataset_draft = HFDatasetsDatasetDraft(index=index_dataset, data_sources=data.manifest.data_sources) + dataset_draft = HFDatasetsDatasetDraft( + index=index_dataset, data_sources=data.manifest.data_sources + ) return dataset_draft -if __name__ == '__main__': - ImageFilterComponent.run() \ No newline at end of file +if __name__ == "__main__": + ImageFilterComponent.run() diff --git a/examples/pipelines/finetune_stable_diffusion/components/load_from_hub_component/src/main.py b/examples/pipelines/finetune_stable_diffusion/components/load_from_hub_component/src/main.py index 1d4582617..08a782ee5 100644 --- a/examples/pipelines/finetune_stable_diffusion/components/load_from_hub_component/src/main.py +++ b/examples/pipelines/finetune_stable_diffusion/components/load_from_hub_component/src/main.py @@ -21,7 +21,9 @@ def create_image_metadata(batch): images = batch["image"] # add width, height and byte size columns - batch["width"], batch["height"] = zip(*[image.size for image in images]) + widths, heights = zip(*[image.size for image in images]) + batch["width"] = list(widths) + batch["height"] = list(heights) batch["byte_size"] = [sys.getsizeof(image.tobytes()) for image in images] return batch From e1e3e5957a5067b66689e9809363059045181950 Mon Sep 17 00:00:00 2001 From: Niels Rogge Date: Thu, 30 Mar 2023 14:26:30 +0200 Subject: [PATCH 31/38] Update dockerfile --- .../components/embedding_component/Dockerfile | 6 ++--- .../config/components_config.py | 22 +++++++++++++++++++ 2 files changed, 25 insertions(+), 3 deletions(-) diff --git a/examples/pipelines/finetune_stable_diffusion/components/embedding_component/Dockerfile b/examples/pipelines/finetune_stable_diffusion/components/embedding_component/Dockerfile index 49b7639f9..2d626921d 100644 --- a/examples/pipelines/finetune_stable_diffusion/components/embedding_component/Dockerfile +++ b/examples/pipelines/finetune_stable_diffusion/components/embedding_component/Dockerfile @@ -1,4 +1,4 @@ -FROM --platform=linux/amd64 python:3.8-slim +FROM --platform=linux/amd64 pytorch/pytorch:2.0.0-cuda11.7-cudnn8-runtime ## System dependencies RUN apt-get update && \ @@ -17,8 +17,8 @@ RUN mkdir -p /usr/local/gcloud \ ENV PATH $PATH:/usr/local/gcloud/google-cloud-sdk/bin # install requirements -COPY requirements.txt / -RUN pip3 install --no-cache-dir -r requirements.txt +COPY requirements.txt /tmp/requirements.txt +RUN python3 -m pip install -r /tmp/requirements.txt # Copy over src-files of the component COPY src /src diff --git a/examples/pipelines/finetune_stable_diffusion/config/components_config.py b/examples/pipelines/finetune_stable_diffusion/config/components_config.py index f32b03394..4d4550ec8 100644 --- a/examples/pipelines/finetune_stable_diffusion/config/components_config.py +++ b/examples/pipelines/finetune_stable_diffusion/config/components_config.py @@ -38,3 +38,25 @@ class EmbeddingConfig: MODEL_ID = "openai/clip-vit-large-patch14" BATCH_SIZE = 10 + + +@dataclass +class ClipRetrievalConfig: + """ + Configs for CLIP image retrieval component + Params: + LAION_INDEX_URL(str): url of the indices of the metadata. Those indices need to be + transformed in case you decide to use only a subset of the dataset + LAION_METADATA_URL (str): url to the metadata of laion dataset metadata (arrow format). It + can either contain a subset of the laion 5b metadata (e.g. laion-en) or all of the metadata + NB_IMAGES_KNN (int): The ratio of number of image to retrieve via the knn strategy + (per image) + NB_IMAGES_CENTROID (int): The ratio of number of image to retrieve via the centroid strategy + """ + + LAION_INDEX_URL = "gs://express-sd-datasets/laion-5b/2b-en/image.index/*" + LAION_METADATA_URL = ( + "gs://express-sd-datasets/laion-5b/metadata/metadata/2B-en.arrow" + ) + NB_IMAGES_KNN = 500 + NB_IMAGES_CENTROID = 1_000_000 From 89f75d3773a932de95cbf01e0900f8e230e2b2c2 Mon Sep 17 00:00:00 2001 From: Niels Rogge Date: Thu, 30 Mar 2023 14:49:14 +0200 Subject: [PATCH 32/38] Run pre-commit run --all-files --- .pre-commit-config.yaml | 12 ++++++++++-- .../sd_finetuning_pipeline.py | 2 +- .../components/load_from_hub/src/main.py | 2 +- .../hf_dataset_pipeline/hf_dataset_pipeline.py | 2 +- 4 files changed, 13 insertions(+), 5 deletions(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 4f9f6b6d7..ca192ac7d 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -9,7 +9,11 @@ repos: files: | (?x)^( express/.*| - examples/.*| + examples/pipelines/hf_dataset_pipeline| + examples/pipelines/finetune_stable_diffusion/components/load_from_hub_component| + examples/pipelines/finetune_stable_diffusion/components/image_filter_component| + examples/pipelines/finetune_stable_diffusion/components/embedding_component| + examples/pipelines/finetune_stable_diffusion/components/dataset_creation_pipeline.py| )$ args: [--fix, --exit-non-zero-on-fix] @@ -31,5 +35,9 @@ repos: files: | (?x)^( express/.*| - examples/.*| + examples/pipelines/hf_dataset_pipeline| + examples/pipelines/finetune_stable_diffusion/components/load_from_hub_component| + examples/pipelines/finetune_stable_diffusion/components/image_filter_component| + examples/pipelines/finetune_stable_diffusion/components/embedding_component| + examples/pipelines/finetune_stable_diffusion/components/dataset_creation_pipeline.py| )$ diff --git a/examples/pipelines/finetune_stable_diffusion/sd_finetuning_pipeline.py b/examples/pipelines/finetune_stable_diffusion/sd_finetuning_pipeline.py index 53085d292..a6a02ce91 100644 --- a/examples/pipelines/finetune_stable_diffusion/sd_finetuning_pipeline.py +++ b/examples/pipelines/finetune_stable_diffusion/sd_finetuning_pipeline.py @@ -100,7 +100,7 @@ def sd_finetuning_pipeline( run_id = '{{pod.name}}' artifact_bucket = KubeflowConfig.ARTIFACT_BUCKET - sd_finetuning_task = sd_finetuning_component( + sd_finetuning_component( project_id=GeneralConfig.GCP_PROJECT_ID, run_id=run_id, artifact_bucket=artifact_bucket, diff --git a/examples/pipelines/hf_dataset_pipeline/components/load_from_hub/src/main.py b/examples/pipelines/hf_dataset_pipeline/components/load_from_hub/src/main.py index e1313abea..c1491916a 100644 --- a/examples/pipelines/hf_dataset_pipeline/components/load_from_hub/src/main.py +++ b/examples/pipelines/hf_dataset_pipeline/components/load_from_hub/src/main.py @@ -5,7 +5,7 @@ from typing import Optional, Union, Dict import pandas as pd -from datasets import Dataset, load_dataset, concatenate_datasets +from datasets import Dataset, load_dataset from express.components.hf_datasets_components import HFDatasetsLoaderComponent, HFDatasetsDatasetDraft from express.logger import configure_logging diff --git a/examples/pipelines/hf_dataset_pipeline/hf_dataset_pipeline.py b/examples/pipelines/hf_dataset_pipeline/hf_dataset_pipeline.py index 7e6599bd6..b6110fdcd 100644 --- a/examples/pipelines/hf_dataset_pipeline/hf_dataset_pipeline.py +++ b/examples/pipelines/hf_dataset_pipeline/hf_dataset_pipeline.py @@ -45,7 +45,7 @@ def hf_dataset_pipeline(load_from_hub_extra_args: str = load_from_hub_extra_args ).set_display_name('Load from hub component') # Component 2 - add_captions_task = add_captions_op(extra_args=add_captions_extra_args, + add_captions_op(extra_args=add_captions_extra_args, metadata=add_captions_metadata_args, input_manifest=load_from_hub_task.outputs["output_manifest"], ).set_display_name('Add captions component') From 83718c343557549c7cf6dffa6cc94e10de6ad4a7 Mon Sep 17 00:00:00 2001 From: Niels Rogge Date: Thu, 30 Mar 2023 14:58:40 +0200 Subject: [PATCH 33/38] Add retrieval mini component --- .../dataset_creation_pipeline.py | 29 ++++++++++++++++++- 1 file changed, 28 insertions(+), 1 deletion(-) diff --git a/examples/pipelines/finetune_stable_diffusion/dataset_creation_pipeline.py b/examples/pipelines/finetune_stable_diffusion/dataset_creation_pipeline.py index f384da684..7e20c140b 100644 --- a/examples/pipelines/finetune_stable_diffusion/dataset_creation_pipeline.py +++ b/examples/pipelines/finetune_stable_diffusion/dataset_creation_pipeline.py @@ -12,6 +12,7 @@ LoadFromHubConfig, ImageFilterConfig, EmbeddingConfig, + ClipRetrievalConfig, ) from express.pipeline_utils import compile_and_upload_pipeline @@ -67,6 +68,23 @@ embedding_metadata_args = json.dumps(embedding_metadata_args) +# Component 4 +clip_retrieval_op = comp.load_component("components/clip_retrieval_component/component.yaml") +clip_retrieval_extra_args = { + "model_id": ClipRetrievalConfig.LAION_INDEX_URL, + "batch_size": ClipRetrievalConfig.LAION_METADATA_URL, + "num_images_knn":ClipRetrievalConfig.NUM_IMAGES_KNN, + "num_images_centroid": ClipRetrievalConfig.NUM_IMAGES_CENTROID, +} +clip_retrieval_metadata_args = { + "run_id": run_id, + "component_name": clip_retrieval_op.__name__, + "artifact_bucket": artifact_bucket, +} +clip_retrieval_extra_args = json.dumps(clip_retrieval_extra_args) +clip_retrieval_metadata_args = json.dumps(clip_retrieval_metadata_args) + + # Pipeline @dsl.pipeline( name="image-generator-dataset", @@ -81,6 +99,8 @@ def sd_dataset_creator_pipeline( image_filter_metadata_args: str = image_filter_metadata_args, embedding_extra_args: str = embedding_extra_args, embedding_metadata_args: str = embedding_metadata_args, + clip_retrieval_extra_args: str = clip_retrieval_extra_args, + clip_retrieval_metadata_args: str = clip_retrieval_metadata_args, ): # Component 1 load_from_hub_task = load_from_hub_op( @@ -96,7 +116,7 @@ def sd_dataset_creator_pipeline( ).set_display_name("Filter images") # Component 3 - embedding_op( + embedding_task = embedding_op( extra_args=embedding_extra_args, metadata=embedding_metadata_args, input_manifest=image_filter_task.outputs["output_manifest"], @@ -108,6 +128,13 @@ def sd_dataset_creator_pipeline( ) ) + # Component 4 + clip_retrieval_op( + extra_args=clip_retrieval_extra_args, + metadata=clip_retrieval_metadata_args, + input_manifest=embedding_task.outputs["output_manifest"], + ).set_display_name("Retrieve images") + if __name__ == "__main__": compile_and_upload_pipeline( From 7d28a250a07741a7d72bc2ec701a3b76f0866da5 Mon Sep 17 00:00:00 2001 From: Niels Rogge Date: Thu, 30 Mar 2023 14:59:45 +0200 Subject: [PATCH 34/38] Fix path --- .pre-commit-config.yaml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index ca192ac7d..423d94f4a 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -13,7 +13,7 @@ repos: examples/pipelines/finetune_stable_diffusion/components/load_from_hub_component| examples/pipelines/finetune_stable_diffusion/components/image_filter_component| examples/pipelines/finetune_stable_diffusion/components/embedding_component| - examples/pipelines/finetune_stable_diffusion/components/dataset_creation_pipeline.py| + examples/pipelines/finetune_stable_diffusion/dataset_creation_pipeline.py| )$ args: [--fix, --exit-non-zero-on-fix] @@ -39,5 +39,5 @@ repos: examples/pipelines/finetune_stable_diffusion/components/load_from_hub_component| examples/pipelines/finetune_stable_diffusion/components/image_filter_component| examples/pipelines/finetune_stable_diffusion/components/embedding_component| - examples/pipelines/finetune_stable_diffusion/components/dataset_creation_pipeline.py| + examples/pipelines/finetune_stable_diffusion/dataset_creation_pipeline.py| )$ From a458a7174802215d14f6cc0c37994089c72b6b76 Mon Sep 17 00:00:00 2001 From: Niels Rogge Date: Thu, 30 Mar 2023 15:06:17 +0200 Subject: [PATCH 35/38] Fix pipeline --- .../config/components_config.py | 4 +-- .../dataset_creation_pipeline.py | 28 +++++++++++-------- 2 files changed, 19 insertions(+), 13 deletions(-) diff --git a/examples/pipelines/finetune_stable_diffusion/config/components_config.py b/examples/pipelines/finetune_stable_diffusion/config/components_config.py index 4d4550ec8..df19ccc14 100644 --- a/examples/pipelines/finetune_stable_diffusion/config/components_config.py +++ b/examples/pipelines/finetune_stable_diffusion/config/components_config.py @@ -58,5 +58,5 @@ class ClipRetrievalConfig: LAION_METADATA_URL = ( "gs://express-sd-datasets/laion-5b/metadata/metadata/2B-en.arrow" ) - NB_IMAGES_KNN = 500 - NB_IMAGES_CENTROID = 1_000_000 + NUM_IMAGES_KNN = 500 + NUM_IMAGES_CENTROID = 1_000_000 diff --git a/examples/pipelines/finetune_stable_diffusion/dataset_creation_pipeline.py b/examples/pipelines/finetune_stable_diffusion/dataset_creation_pipeline.py index 7e20c140b..ddcd5493c 100644 --- a/examples/pipelines/finetune_stable_diffusion/dataset_creation_pipeline.py +++ b/examples/pipelines/finetune_stable_diffusion/dataset_creation_pipeline.py @@ -69,11 +69,13 @@ # Component 4 -clip_retrieval_op = comp.load_component("components/clip_retrieval_component/component.yaml") +clip_retrieval_op = comp.load_component( + "components/clip_retrieval_component/component.yaml" +) clip_retrieval_extra_args = { "model_id": ClipRetrievalConfig.LAION_INDEX_URL, "batch_size": ClipRetrievalConfig.LAION_METADATA_URL, - "num_images_knn":ClipRetrievalConfig.NUM_IMAGES_KNN, + "num_images_knn": ClipRetrievalConfig.NUM_IMAGES_KNN, "num_images_centroid": ClipRetrievalConfig.NUM_IMAGES_CENTROID, } clip_retrieval_metadata_args = { @@ -116,15 +118,19 @@ def sd_dataset_creator_pipeline( ).set_display_name("Filter images") # Component 3 - embedding_task = embedding_op( - extra_args=embedding_extra_args, - metadata=embedding_metadata_args, - input_manifest=image_filter_task.outputs["output_manifest"], - ).set_display_name("Embed images").set_gpu_limit(1).add_node_selector_constraint( - "node_pool", "model-inference-pool" - ).add_toleration( - k8s_client.V1Toleration( - effect="NoSchedule", key="reserved-pool", operator="Equal", value="true" + embedding_task = ( + embedding_op( + extra_args=embedding_extra_args, + metadata=embedding_metadata_args, + input_manifest=image_filter_task.outputs["output_manifest"], + ) + .set_display_name("Embed images") + .set_gpu_limit(1) + .add_node_selector_constraint("node_pool", "model-inference-pool") + .add_toleration( + k8s_client.V1Toleration( + effect="NoSchedule", key="reserved-pool", operator="Equal", value="true" + ) ) ) From fe4501f0c1d2c1634b72a7e05e84606e75ce65b6 Mon Sep 17 00:00:00 2001 From: Niels Rogge Date: Thu, 30 Mar 2023 16:44:03 +0200 Subject: [PATCH 36/38] More improvements --- .../clip_retrieval_component/requirements.txt | 11 +++++++---- .../clip_retrieval_component/src/main.py | 6 ++---- .../src/utils/embedding_utils.py | 16 ++++++++-------- .../components/embedding_component/src/main.py | 2 +- express/components/hf_datasets_components.py | 6 ++++++ 5 files changed, 24 insertions(+), 17 deletions(-) diff --git a/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/requirements.txt b/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/requirements.txt index 219501322..a0463d58f 100644 --- a/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/requirements.txt +++ b/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/requirements.txt @@ -1,5 +1,8 @@ -clip-retrieval==2.34.2 -numpy>=1.19.5,<2 -pandas==1.3.5 -tqdm==4.64.1 +git+https://github.com/ml6team/express.git@3cc6ed0c2c1d21777ab32d21f3d96f0c58e36090 +datasets==2.11.0 +numpy==1.24.2 +# clip-retrieval==2.34.2 +# numpy>=1.19.5,<2 +# pandas==1.3.5 +# tqdm==4.64.1 Pillow==9.3.0 \ No newline at end of file diff --git a/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/src/main.py b/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/src/main.py index 0523dd1bf..b2d5ed2ae 100644 --- a/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/src/main.py +++ b/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/src/main.py @@ -45,12 +45,10 @@ def transform( # TODO check whether we can leverage streaming # TODO just column in the load method in the future logger.info("Loading embedding dataset...") - embeddings = data.load( - data_source="embeddings", columns=["embeddings"], streaming=True - ) + dataset = data.load(data_source="embeddings", columns=["index", "embeddings"]) logger.info("Calculating average embedding...") - centroid_embedding = get_average_embedding(embeddings) + centroid_embedding = get_average_embedding(dataset) print("Centroid embedding:", centroid_embedding) diff --git a/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/src/utils/embedding_utils.py b/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/src/utils/embedding_utils.py index 1c09484b6..97f76d67d 100644 --- a/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/src/utils/embedding_utils.py +++ b/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/src/utils/embedding_utils.py @@ -2,22 +2,22 @@ from typing import List +from datasets import Dataset + import numpy as np -def get_average_embedding(embeddings: List[np.array]) -> np.array: +def get_average_embedding(embeddings_dataset: Dataset) -> np.array: """ Function that returns the average embedding from a list of embeddings Args: - embeddings (List[np.array]): list of embeddings. + embeddings_dataset (datasets.Dataset): list of embeddings. Returns: np.array: the average embedding """ - initial_embedding = next(iter(embeddings)) - embedding_sum = np.zeros(initial_embedding.shape) - for embedding in embeddings: - embedding_sum += embedding - - avg_embedding = embedding_sum.mean(axis=0) + embeddings = [np.array(embedding) for embedding in embeddings_dataset["embeddings"]] + + avg_embedding = np.mean(embeddings, axis=0) + return avg_embedding diff --git a/examples/pipelines/finetune_stable_diffusion/components/embedding_component/src/main.py b/examples/pipelines/finetune_stable_diffusion/components/embedding_component/src/main.py index c546276f9..e91d24e20 100644 --- a/examples/pipelines/finetune_stable_diffusion/components/embedding_component/src/main.py +++ b/examples/pipelines/finetune_stable_diffusion/components/embedding_component/src/main.py @@ -87,7 +87,7 @@ def transform( batched=True, batch_size=extra_args["batch_size"], fn_kwargs=dict(processor=processor, model=model), - remove_columns=["image"], + remove_columns=["image", "width", "height", "byte_size"], ) # 3) Create dataset draft which adds a data source to the manifest diff --git a/express/components/hf_datasets_components.py b/express/components/hf_datasets_components.py index d004b9b74..d2502aaf7 100644 --- a/express/components/hf_datasets_components.py +++ b/express/components/hf_datasets_components.py @@ -67,6 +67,12 @@ def _load_data_source( data_source_location, tmp_dir ) + if "columns" in kwargs: + if "index" not in kwargs["columns"]: + raise ValueError( + "Please also include the index when specifying columns" + ) + dataset = load_dataset( "parquet", data_files=local_parquet_path, From fb0ea88cfdc5a605a73dbd75b96756bc6a2d3321 Mon Sep 17 00:00:00 2001 From: Niels Rogge Date: Fri, 31 Mar 2023 15:24:14 +0200 Subject: [PATCH 37/38] Update CLIP retrieval component --- .../clip_retrieval_component/Dockerfile | 8 +- .../clip_retrieval_component/requirements.txt | 7 +- .../clip_retrieval_component/src/main.py | 121 ++++++++++-------- .../dataset_creation_pipeline.py | 15 ++- 4 files changed, 88 insertions(+), 63 deletions(-) diff --git a/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/Dockerfile b/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/Dockerfile index 49b7639f9..89ca3132a 100644 --- a/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/Dockerfile +++ b/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/Dockerfile @@ -16,9 +16,11 @@ RUN mkdir -p /usr/local/gcloud \ # Adding the package path to local ENV PATH $PATH:/usr/local/gcloud/google-cloud-sdk/bin -# install requirements -COPY requirements.txt / -RUN pip3 install --no-cache-dir -r requirements.txt +# Copy over src-files of the component +COPY requirements.txt . + +# Install packages +RUN pip3 install -r requirements.txt # Copy over src-files of the component COPY src /src diff --git a/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/requirements.txt b/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/requirements.txt index a0463d58f..266dc5e07 100644 --- a/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/requirements.txt +++ b/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/requirements.txt @@ -1,8 +1,7 @@ git+https://github.com/ml6team/express.git@3cc6ed0c2c1d21777ab32d21f3d96f0c58e36090 datasets==2.11.0 numpy==1.24.2 -# clip-retrieval==2.34.2 -# numpy>=1.19.5,<2 -# pandas==1.3.5 -# tqdm==4.64.1 +clip-retrieval==2.36.1 +numpy==1.24.2 +tqdm==4.65.0 Pillow==9.3.0 \ No newline at end of file diff --git a/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/src/main.py b/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/src/main.py index b2d5ed2ae..f1560c25d 100644 --- a/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/src/main.py +++ b/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/src/main.py @@ -2,10 +2,18 @@ This component extends the images data source by retrieving similar images from LAION. """ import logging +import os +import tempfile +import time +from tqdm import tqdm from typing import Optional, Union, Dict +import numpy as np + from utils.embedding_utils import get_average_embedding +from utils.knn_service import ClipRetrievalLaion5B +from express.gcp_storage import copy_folder from express.components.hf_datasets_components import ( HFDatasetsTransformComponent, HFDatasetsDataset, @@ -43,65 +51,68 @@ def transform( # 1) Get one particular data source from the manifest # TODO check whether we can leverage streaming - # TODO just column in the load method in the future + # TODO make it possible to not having to also load the index column logger.info("Loading embedding dataset...") dataset = data.load(data_source="embeddings", columns=["index", "embeddings"]) - logger.info("Calculating average embedding...") + logger.info("Calculating centroid embedding...") centroid_embedding = get_average_embedding(dataset) - - print("Centroid embedding:", centroid_embedding) - - # # This component uses local SSD mounting to enable faster querying - # of the laion dataset. The - # # local SSDs are mounted in the cache directory - # laion_index_folder = os.path.join('/cache', 'laion_dataset') - # laion_metadata_folder = os.path.join(laion_index_folder, 'metadata') - # laion_indices_folder = os.path.join(laion_index_folder, 'image.index') - # os.makedirs(laion_metadata_folder, exist_ok=True) - # os.makedirs(laion_indices_folder, exist_ok=True) - - # # Download laion indices and metadata from storage - # start_indices = time.time() - # copy_folder(extra_args["laion_index_url"], laion_indices_folder) - # logger.info('Laion index download complete: it took %s minutes to download the - # laion indices', - # round((time.time() - start_indices) / 60)) - # start_metadata = time.time() - # copy_folder(extra_args["laion_metadata_url"], laion_metadata_folder) - # logger.info( - # 'Laion metadata download complete: it took %s minutes to download the laion metadata', - # round((time.time() - start_metadata) / 60)) - - # # Setup KNN service - # with tempfile.TemporaryDirectory() as tmp_dir: - # tmp_embedding_dir_path = os.path.join(tmp_dir, 'embed_dir') - # os.makedirs(tmp_embedding_dir_path, exist_ok=True) - # laion_indices_folder = os.path.join(tmp_dir, 'index_file.json') - # clip_retrieval_runner = ClipRetrievalLaion5B( - # laion_index_path=laion_indices_folder, - # laion_index_folder=laion_index_folder) - # knn_service = clip_retrieval_runner.setup_knn_service() - - # logger.info('Starting centroid clip retrieval') - - # # Run clip retrieval with centroid approach - # results_centroid = clip_retrieval_runner.run_query( - # knn_service=knn_service, - # query={'embedding_query': centroid_embedding}, - # nb_images_request=extra_args["nb_images_centroid"], - # deduplicate=True, - # benchmark=True) - # logger.info('Centroid clip retrieval complete') - - # # 3) Create dataset draft - # logger.info("Creating draft...") - # data_sources = {} - # dataset_draft = HFDatasetsDatasetDraft( - # data_sources=data_sources, extending_dataset=data - # ) - # return dataset_draft - return None + logger.info("Done calculating centroid embedding.") + + # This component uses local SSD mounting to enable faster querying + # of the LAION dataset. The local SSDs are mounted in the cache directory + laion_index_folder = os.path.join('/cache', 'laion_dataset') + laion_metadata_folder = os.path.join(laion_index_folder, 'metadata') + laion_indices_folder = os.path.join(laion_index_folder, 'image.index') + os.makedirs(laion_metadata_folder, exist_ok=True) + os.makedirs(laion_indices_folder, exist_ok=True) + + # Download LAION indices and metadata from storage + logger.info("Downloading LAION index...") + start_indices = time.time() + copy_folder(extra_args["laion_index_url"], laion_indices_folder) + logger.info("LAION index download complete: it took %s minutes", + round((time.time() - start_indices) / 60)) + + logger.info("Downloading LAION metadata...") + start_metadata = time.time() + copy_folder(extra_args["laion_metadata_url"], laion_metadata_folder) + logger.info("LAION metadata download complete: it took %s minutes", + round((time.time() - start_metadata) / 60)) + + # Setup KNN service + with tempfile.TemporaryDirectory() as tmp_dir: + tmp_embedding_dir_path = os.path.join(tmp_dir, 'embed_dir') + os.makedirs(tmp_embedding_dir_path, exist_ok=True) + laion_indices_folder = os.path.join(tmp_dir, 'index_file.json') + clip_retrieval_runner = ClipRetrievalLaion5B( + laion_index_path=laion_indices_folder, + laion_index_folder=laion_index_folder) + knn_service = clip_retrieval_runner.setup_knn_service() + + # Run CLIP retrieval with KNN approach + logger.info('Starting KNN CLIP retrieval') + results_knn = [] + for embedding in tqdm(dataset["embeddings"]): + embedding = np.array(embedding) + results = clip_retrieval_runner.run_query(knn_service=knn_service, + query={'embedding_query': embedding}, + nb_images_request=extra_args["num_images_knn"], + deduplicate=True, + benchmark=True) + results_knn.extend(results) + logger.info('KNN clip retrieval complete') + + # TODO add CLIP retrieval with centroid approach + + # Create dataset draft + # This component doesn't change the index nor the data sources + logger.info("Creating draft...") + dataset_draft = HFDatasetsDatasetDraft( + index=data.manifest.index, + data_sources=data.manifest.data_sources, + ) + return dataset_draft if __name__ == "__main__": diff --git a/examples/pipelines/finetune_stable_diffusion/dataset_creation_pipeline.py b/examples/pipelines/finetune_stable_diffusion/dataset_creation_pipeline.py index ddcd5493c..815ee8c91 100644 --- a/examples/pipelines/finetune_stable_diffusion/dataset_creation_pipeline.py +++ b/examples/pipelines/finetune_stable_diffusion/dataset_creation_pipeline.py @@ -104,6 +104,13 @@ def sd_dataset_creator_pipeline( clip_retrieval_extra_args: str = clip_retrieval_extra_args, clip_retrieval_metadata_args: str = clip_retrieval_metadata_args, ): + # Define necessary volume mounts (local ssd) + local_ssd_volume = dsl.PipelineVolume( + volume=k8s_client.V1Volume( + name="scratch-volume", empty_dir=k8s_client.V1EmptyDirVolumeSource() + ) + ) + # Component 1 load_from_hub_task = load_from_hub_op( extra_args=load_from_hub_extra_args, @@ -139,7 +146,13 @@ def sd_dataset_creator_pipeline( extra_args=clip_retrieval_extra_args, metadata=clip_retrieval_metadata_args, input_manifest=embedding_task.outputs["output_manifest"], - ).set_display_name("Retrieve images") + ).set_display_name("Retrieve images").set_ephemeral_storage_request( + "2T" + ).add_pvolumes( + {"/cache": local_ssd_volume} + ).add_node_selector_constraint( + "node_pool", "nvme-pool" + ) if __name__ == "__main__": From 0b3b85116414102ddaed0c729203184e9c42f1f2 Mon Sep 17 00:00:00 2001 From: Niels Rogge Date: Tue, 4 Apr 2023 11:25:08 +0200 Subject: [PATCH 38/38] Use old implementation of clip retrieval --- .../clip_retrieval_component/Dockerfile | 23 +- .../clip_retrieval_component/component.yaml | 77 +++-- .../clip_retrieval_component/requirements.txt | 10 +- .../clip_retrieval_component/src/main.py | 324 +++++++++++++----- .../src/utils/embedding_utils.py | 20 +- 5 files changed, 304 insertions(+), 150 deletions(-) diff --git a/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/Dockerfile b/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/Dockerfile index 89ca3132a..611ccfb16 100644 --- a/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/Dockerfile +++ b/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/Dockerfile @@ -1,20 +1,7 @@ -FROM --platform=linux/amd64 python:3.8-slim +FROM europe-west1-docker.pkg.dev/storied-landing-366912/storied-landing-366912-default-repository/mlpipelines/kubeflow/components/base_component:latest -## System dependencies -RUN apt-get update && \ - apt-get upgrade -y && \ - apt-get install git curl -y - -# Downloading gcloud package -RUN curl https://dl.google.com/dl/cloudsdk/release/google-cloud-sdk.tar.gz > /tmp/google-cloud-sdk.tar.gz - -# Installing the package -RUN mkdir -p /usr/local/gcloud \ -&& tar -C /usr/local/gcloud -xvf /tmp/google-cloud-sdk.tar.gz \ -&& /usr/local/gcloud/google-cloud-sdk/install.sh - -# Adding the package path to local -ENV PATH $PATH:/usr/local/gcloud/google-cloud-sdk/bin +# Set the working directory to the source folder +WORKDIR /src # Copy over src-files of the component COPY requirements.txt . @@ -22,10 +9,6 @@ COPY requirements.txt . # Install packages RUN pip3 install -r requirements.txt -# Copy over src-files of the component COPY src /src -# Set the working directory to the source folder -WORKDIR /src - ENTRYPOINT ["python", "main.py"] \ No newline at end of file diff --git a/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/component.yaml b/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/component.yaml index c32ab5663..44c3a5040 100644 --- a/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/component.yaml +++ b/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/component.yaml @@ -1,29 +1,58 @@ name: clip_retrieval_component -description: A component that retrieves similar images from the LAION dataset. -inputs: - - name: extra_args - description: Additional arguments passed to the component, as a json dict string - type: String - - - name: metadata - description: Metadata arguments, passed as a json dict string - type: String +description: A component that takes a dataset manifest and returns an output data manifest with an extended dataset + by retrieving similar images from the laion dataset using different retrieval strategies (knn, centroid) +inputs: + - name: run_id + description: The run id of the pipeline + type: String + - name: artifact_bucket + description: The GCS bucket used to store the artifact + type: String + - name: component_name + description: the name of the component (used to create gcs artefact path) + type: String + - name: project_id + description: The id of the gcp-project + type: String + - name: laion_index_url + description: contains the indices of the metadata. Those indices need to be transformed in case you decide to use only a subset of the dataset + type: String + - name: laion_metadata_url + description: url to the metadata of laion dataset metadata (arrow format). It can either contain a subset of the laion 5b metadata (e.g. laion-en) or all of the metadata + type: String + - name: nb_images_knn + description: The number of images to return via the knn strategy (per image) + type: Integer + - name: nb_images_centroid + description: The number of images to return via the centroid strategy + type: Integer + - name: data_manifest_path + description: The previous component manifest path + type: String - - name: input_manifest - description: Path to the input manifest - type: String - outputs: - - name: output_manifest - description: Path to the output manifest + - name: data_manifest_path_clip_retrieval_component + description: Path to the local file containing the gcs path where the output has been stored + - name: parquet_path_clip_centroid_retrieval + description: The path to the parquet file containing the urls from centroid retrieval + - name: parquet_path_clip_knn_retrieval + description: The path to the parquet file containing the urls from knn retrieval implementation: - container: - image: ghcr.io/ml6team/clip_retrieval_component:latest - command: [ - python3, main.py, - --input-manifest, {inputPath: input_manifest}, - --metadata, {inputValue: metadata}, - --extra-args, {inputValue: extra_args}, - --output-manifest, {outputPath: output_manifest}, - ] \ No newline at end of file + container: + image: europe-west1-docker.pkg.dev/storied-landing-366912/storied-landing-366912-default-repository/mlpipelines/kubeflow/components/clip_retrieval_component:latest + command: [ + python3, main.py, + --run-id, { inputValue: run_id }, + --artifact-bucket, { inputValue: artifact_bucket }, + --component-name, { inputValue: component_name }, + --project-id, { inputValue: project_id, }, + --laion-index-url, { inputValue: laion_index_url }, + --laion-metadata-url, { inputValue: laion_metadata_url }, + --nb-images-knn, { inputValue: nb_images_knn }, + --nb-images-centroid, { inputValue: nb_images_centroid }, + --data-manifest-path, { inputPath: data_manifest_path }, + --data-manifest-path-clip-retrieval-component, { outputPath: data_manifest_path_clip_retrieval_component }, + --parquet-path-clip-centroid-retrieval, { outputPath: parquet_path_clip_centroid_retrieval }, + --parquet-path-clip-knn-retrieval, { outputPath: parquet_path_clip_knn_retrieval }, + ] diff --git a/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/requirements.txt b/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/requirements.txt index 266dc5e07..219501322 100644 --- a/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/requirements.txt +++ b/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/requirements.txt @@ -1,7 +1,5 @@ -git+https://github.com/ml6team/express.git@3cc6ed0c2c1d21777ab32d21f3d96f0c58e36090 -datasets==2.11.0 -numpy==1.24.2 -clip-retrieval==2.36.1 -numpy==1.24.2 -tqdm==4.65.0 +clip-retrieval==2.34.2 +numpy>=1.19.5,<2 +pandas==1.3.5 +tqdm==4.64.1 Pillow==9.3.0 \ No newline at end of file diff --git a/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/src/main.py b/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/src/main.py index f1560c25d..3edb53340 100644 --- a/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/src/main.py +++ b/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/src/main.py @@ -1,119 +1,267 @@ """ -This component extends the images data source by retrieving similar images from LAION. +This file is the entrypoint of the component. It will parse all arguments +and give them to the actual core of the component. """ -import logging + import os import tempfile +import argparse +import logging +import json import time -from tqdm import tqdm -from typing import Optional, Union, Dict +from pathlib import Path +from datetime import datetime import numpy as np +from tqdm import tqdm +from google.cloud import storage +# pylint: disable=import-error +from helpers.logger import get_logger +from helpers import storage_helpers, parquet_helpers +from helpers.manifest_helpers import DataManifest from utils.embedding_utils import get_average_embedding from utils.knn_service import ClipRetrievalLaion5B -from express.gcp_storage import copy_folder -from express.components.hf_datasets_components import ( - HFDatasetsTransformComponent, - HFDatasetsDataset, - HFDatasetsDatasetDraft, -) -from express.logger import configure_logging +def parse_args(): + """Parse component arguments""" + + parser = argparse.ArgumentParser() + parser.add_argument('--run-id', + type=str, + required=True, + help='The run id of the pipeline') + parser.add_argument('--artifact-bucket', + type=str, + required=True, + help='The GCS bucket used to store the artifacts') + parser.add_argument('--component-name', + type=str, + required=True, + help='The name of the component') + parser.add_argument('--project-id', + type=str, + required=True, + help='The id of the gcp-project') + parser.add_argument('--laion-index-url', + type=str, + required=True, + help='contains the indices of the metadata. Those indices need to be ' + 'transformed in case you decide to use only a subset of the dataset') + parser.add_argument('--laion-metadata-url', + type=str, + required=True, + help='The id of the gcp-project') + parser.add_argument('--nb-images-knn', + type=int, + required=True, + help='The number of images to return via KNN method') + parser.add_argument('--nb-images-centroid', + type=int, + required=True, + help='The number of images to return via the centroid method') + parser.add_argument('--data-manifest-path', + type=str, + required=True, + help='The previous component manifest path') + parser.add_argument('--data-manifest-path-clip-retrieval-component', + type=str, + required=True, + help='The path to the output manifest file') + parser.add_argument('--parquet-path-clip-knn-retrieval', + type=str, + required=True, + help='The path to the parquet file containing the urls from knn retrieval') + parser.add_argument('--parquet-path-clip-centroid-retrieval', + type=str, + required=True, + help='The path to the parquet file containing the urls from centroid ' + 'retrieval') -configure_logging() -logger = logging.getLogger(__name__) + return parser.parse_args() -class CLIPRetrievalComponent(HFDatasetsTransformComponent): +# pylint: disable=too-many-locals, too-many-arguments, too-many-statements +def clip_retrieval_component(run_id: str, + artifact_bucket: str, + component_name: str, + project_id: str, + laion_index_url: str, + laion_metadata_url: str, + nb_images_knn: int, + nb_images_centroid: int, + data_manifest_path: str, + data_manifest_path_clip_retrieval_component: str, + parquet_path_clip_knn_retrieval: str, + parquet_path_clip_centroid_retrieval: str) -> None: """ - Component that retrieves similar images from the LAION dataset. + Args: + run_id (str): The run id of the pipeline + artifact_bucket (str): The GCS bucket used to store the artifact + component_name (str): the name of the component (used to create gcs artefact path) + project_id (str): The id of the gcp-project + laion_index_url (str): contains the indices of the metadata. Those indices need to be + transformed in case you decide to use only a subset of the dataset + laion_metadata_url (str): url to the metadata of laion dataset metadata (arrow format). It + can either contain a subset of the laion 5b metadata (e.g. laion-en) or all of the metadata + nb_images_knn (int): The number of images to return via knn method (per image) + nb_images_centroid (int): The number of images to return via the centroid method + data_manifest_path (str): The previous component manifest path + data_manifest_path_clip_retrieval_component (str): the current component output path + parquet_path_clip_centroid_retrieval (str): The path to the parquet file containing the urls + from centroid retrieval + parquet_path_clip_knn_retrieval (str): The path to the parquet file containing the urls + from knn retrieval """ + logger = get_logger(name=__name__, level=logging.INFO) + logger.info('Started job...') + + # Initialize storage client + storage_client = storage.Client(project=project_id) + + # This component uses local SSD mounting to enable faster querying of the laion dataset. The + # local SSDs are mounted in the cache directory + laion_index_folder = os.path.join('/cache', 'laion_dataset') + laion_metadata_folder = os.path.join(laion_index_folder, 'metadata') + laion_indices_folder = os.path.join(laion_index_folder, 'image.index') + os.makedirs(laion_metadata_folder, exist_ok=True) + os.makedirs(laion_indices_folder, exist_ok=True) + + # Download laion indices and metadata from storage + start_indices = time.time() + storage_helpers.copy_folder_bulk(laion_index_url, laion_indices_folder) + logger.info('Laion index download complete: it took %s minutes to download the laion indices', + round((time.time() - start_indices) / 60)) + start_metadata = time.time() + storage_helpers.copy_folder_bulk(laion_metadata_url, laion_metadata_folder) + logger.info( + 'Laion metadata download complete: it took %s minutes to download the laion metadata', + round((time.time() - start_metadata) / 60)) + + with tempfile.TemporaryDirectory() as tmp_dir: + logger.info('created temporary directory %s', tmp_dir) + + # Initialize GCS custom artifact path + component_artifact_dir = run_id.rpartition('-')[0] + artifact_bucket_blob_path = f"custom_artifact/{component_artifact_dir}/{component_name}" + logger.info("custom artifact will be uploaded to %s", + f'gs://{artifact_bucket}/{artifact_bucket_blob_path}') - @classmethod - def transform( - cls, - data: HFDatasetsDataset, - extra_args: Optional[Dict[str, Union[str, int, float, bool]]] = None, - ) -> HFDatasetsDatasetDraft: - """ - An example function showcasing the data transform component using Express functionalities - - Args: - data (HFDatasetsDataset[TIndex, TData]): express dataset providing access to data of a - given type - extra_args (Optional[Dict[str, Union[str, int, float, bool]]): optional args to pass to - the function - Returns: - HFDatasetsDatasetDraft: a dataset draft that creates a plan for an output manifest - """ - - # 1) Get one particular data source from the manifest - # TODO check whether we can leverage streaming - # TODO make it possible to not having to also load the index column - logger.info("Loading embedding dataset...") - dataset = data.load(data_source="embeddings", columns=["index", "embeddings"]) - - logger.info("Calculating centroid embedding...") - centroid_embedding = get_average_embedding(dataset) - logger.info("Done calculating centroid embedding.") - - # This component uses local SSD mounting to enable faster querying - # of the LAION dataset. The local SSDs are mounted in the cache directory - laion_index_folder = os.path.join('/cache', 'laion_dataset') - laion_metadata_folder = os.path.join(laion_index_folder, 'metadata') - laion_indices_folder = os.path.join(laion_index_folder, 'image.index') - os.makedirs(laion_metadata_folder, exist_ok=True) - os.makedirs(laion_indices_folder, exist_ok=True) - - # Download LAION indices and metadata from storage - logger.info("Downloading LAION index...") - start_indices = time.time() - copy_folder(extra_args["laion_index_url"], laion_indices_folder) - logger.info("LAION index download complete: it took %s minutes", - round((time.time() - start_indices) / 60)) - - logger.info("Downloading LAION metadata...") - start_metadata = time.time() - copy_folder(extra_args["laion_metadata_url"], laion_metadata_folder) - logger.info("LAION metadata download complete: it took %s minutes", - round((time.time() - start_metadata) / 60)) + tmp_embedding_dir_path = os.path.join(tmp_dir, 'embed_dir') + os.makedirs(tmp_embedding_dir_path, exist_ok=True) + laion_indices_folder = os.path.join(tmp_dir, 'index_file.json') + knn_retrieval_path = os.path.join(tmp_dir, 'clip_knn.parquet') + centroid_retrieval_path = os.path.join(tmp_dir, 'clip_centroid.parquet') + knn_retrieval_blob_path = os.path.join(artifact_bucket_blob_path, 'clip_knn.parquet') + centroid_retrieval_blob_path = os.path.join(artifact_bucket_blob_path, + 'clip_centroid.parquet') + + # Read manifest + with open(data_manifest_path) as f: + manifest_load = json.load(f) + data_manifest = DataManifest.from_dict(manifest_load) + embedding_path = data_manifest.associated_data.embedding['cf'] + + # Copy embeddings to local directory + storage_helpers.copy_folder_bulk(f"{embedding_path}/*", tmp_embedding_dir_path) + nb_src_images = len(os.listdir(tmp_embedding_dir_path)) + logging.info('Total number of source images (embedding): %s', nb_src_images) + centroid_embedding = get_average_embedding(tmp_embedding_dir_path) + + logger.info('A total of %s urls will be requested using the centroid approach.', + nb_images_centroid) + logger.info('A total of %s urls will be requested per individual image (total of %s images)' + ' using the knn approach.', nb_images_knn) # Setup KNN service - with tempfile.TemporaryDirectory() as tmp_dir: - tmp_embedding_dir_path = os.path.join(tmp_dir, 'embed_dir') - os.makedirs(tmp_embedding_dir_path, exist_ok=True) - laion_indices_folder = os.path.join(tmp_dir, 'index_file.json') - clip_retrieval_runner = ClipRetrievalLaion5B( - laion_index_path=laion_indices_folder, - laion_index_folder=laion_index_folder) - knn_service = clip_retrieval_runner.setup_knn_service() - - # Run CLIP retrieval with KNN approach - logger.info('Starting KNN CLIP retrieval') + clip_retrieval_runner = ClipRetrievalLaion5B( + laion_index_path=laion_indices_folder, + laion_index_folder=laion_index_folder) + knn_service = clip_retrieval_runner.setup_knn_service() + + logger.info('Starting centroid clip retrieval') + + # Run clip retrieval with centroid approach + results_centroid = clip_retrieval_runner.run_query( + knn_service=knn_service, + query={'embedding_query': centroid_embedding}, + nb_images_request=nb_images_centroid, + deduplicate=True, + benchmark=True) + logger.info('Centroid clip retrieval complete') + + parquet_helpers.write_clip_retrieval_parquet( + clip_retrieval_parquet_path=centroid_retrieval_path, + data_iterable_producer=clip_retrieval_runner.clip_results_producer, + clip_results=results_centroid) + del results_centroid + + # Run clip retrieval with KNN approach + logger.info('Starting knn clip retrieval') results_knn = [] - for embedding in tqdm(dataset["embeddings"]): - embedding = np.array(embedding) + for embedding_file in tqdm(os.listdir(tmp_embedding_dir_path)): + embedding = np.load(os.path.join(tmp_embedding_dir_path, embedding_file)) results = clip_retrieval_runner.run_query(knn_service=knn_service, query={'embedding_query': embedding}, - nb_images_request=extra_args["num_images_knn"], + nb_images_request=nb_images_knn, deduplicate=True, benchmark=True) results_knn.extend(results) logger.info('KNN clip retrieval complete') + parquet_helpers.write_clip_retrieval_parquet( + clip_retrieval_parquet_path=knn_retrieval_path, + data_iterable_producer=clip_retrieval_runner.clip_results_producer, + clip_results=results_knn) + del results_knn + + # Upload parquet files to bucket + storage_helpers.upload_file_to_bucket(storage_client=storage_client, + file_to_upload_path=centroid_retrieval_path, + bucket_name=artifact_bucket, + blob_path=centroid_retrieval_blob_path) + + storage_helpers.upload_file_to_bucket(storage_client=storage_client, + file_to_upload_path=knn_retrieval_path, + bucket_name=artifact_bucket, + blob_path=knn_retrieval_blob_path) + + # Update manifest + data_manifest.dataset_id = f"{run_id}_{component_name}" + data_manifest.metadata.branch = "" # TODO: Fill from docker build env var + data_manifest.metadata.commit_hash = "" # TODO: Fill from docker build env var + data_manifest.metadata.creation_date = datetime.now().strftime("%d-%m-%Y_%H-%M-%S") + data_manifest.metadata.run_id = run_id - # TODO add CLIP retrieval with centroid approach + logger.info('Manifest file created and updated') - # Create dataset draft - # This component doesn't change the index nor the data sources - logger.info("Creating draft...") - dataset_draft = HFDatasetsDatasetDraft( - index=data.manifest.index, - data_sources=data.manifest.data_sources, - ) - return dataset_draft + # Write manifest to outputPath + Path(data_manifest_path_clip_retrieval_component).parent.mkdir(parents=True, exist_ok=True) + Path(parquet_path_clip_knn_retrieval).parent.mkdir(parents=True, exist_ok=True) + Path(parquet_path_clip_centroid_retrieval).parent.mkdir(parents=True, exist_ok=True) + # Write Parquet url files to outputPath + Path(data_manifest_path_clip_retrieval_component).write_text(data_manifest.to_json()) + Path(parquet_path_clip_knn_retrieval).write_text( + f'gs://{artifact_bucket}/{knn_retrieval_blob_path}') + Path(parquet_path_clip_centroid_retrieval).write_text( + f'gs://{artifact_bucket}/{centroid_retrieval_blob_path}') + logger.info('Manifest file written to %s', data_manifest_path_clip_retrieval_component) + logger.info('Job completed.') -if __name__ == "__main__": - CLIPRetrievalComponent.run() +if __name__ == '__main__': + args = parse_args() + clip_retrieval_component \ + (run_id=args.run_id, + artifact_bucket=args.artifact_bucket, + component_name=args.component_name, + project_id=args.project_id, + laion_index_url=args.laion_index_url, + laion_metadata_url=args.laion_metadata_url, + nb_images_knn=args.nb_images_knn, + nb_images_centroid=args.nb_images_centroid, + data_manifest_path=args.data_manifest_path, + data_manifest_path_clip_retrieval_component= + args.data_manifest_path_clip_retrieval_component, + parquet_path_clip_centroid_retrieval=args.parquet_path_clip_centroid_retrieval, + parquet_path_clip_knn_retrieval=args.parquet_path_clip_knn_retrieval) diff --git a/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/src/utils/embedding_utils.py b/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/src/utils/embedding_utils.py index 97f76d67d..3c51707af 100644 --- a/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/src/utils/embedding_utils.py +++ b/examples/pipelines/finetune_stable_diffusion/components/clip_retrieval_component/src/utils/embedding_utils.py @@ -1,23 +1,19 @@ """Clip embedding util functions""" - -from typing import List - -from datasets import Dataset +import os import numpy as np -def get_average_embedding(embeddings_dataset: Dataset) -> np.array: +def get_average_embedding(embedding_dir: str) -> np.array: """ - Function that returns the average embedding from a list of embeddings - + Function that returns the average embedding from a selection of embeddings Args: - embeddings_dataset (datasets.Dataset): list of embeddings. + embedding_dir (str): the directory where the embeddings are located Returns: np.array: the average embedding """ - embeddings = [np.array(embedding) for embedding in embeddings_dataset["embeddings"]] - - avg_embedding = np.mean(embeddings, axis=0) - + + embeddings = [np.load(os.path.join(embedding_dir, embedding_file)) for embedding_file in + os.listdir(embedding_dir)] + avg_embedding = np.array(embeddings).mean(axis=0) return avg_embedding

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