Billy D.
321eca5943
- qlora_pdf_pipeline.py: 6-step QLoRA fine-tuning pipeline (S3 PDFs → prepare data → train → evaluate → push to Gitea → MLflow) - .gitea/workflows/compile-upload.yaml: auto-compile and upload all pipelines to Kubeflow on push, with ntfy notifications
905 lines
44 KiB
YAML
905 lines
44 KiB
YAML
# PIPELINE DEFINITION
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# Name: qlora-pdf-fine-tuning
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# Description: Fine-tune Llama 3.1 70B via QLoRA on PDFs from the Quobjects training-data bucket. Pushes the adapter to Gitea and logs metrics to MLflow.
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# Inputs:
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# aws_access_key_id: str [Default: '']
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# aws_secret_access_key: str [Default: '']
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# base_model: str [Default: 'meta-llama/Llama-3.1-70B-Instruct']
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# batch_size: int [Default: 2.0]
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# chunk_overlap: int [Default: 64.0]
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# chunk_size: int [Default: 512.0]
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# gitea_owner: str [Default: 'daviestechlabs']
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# gitea_password: str [Default: '']
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# gitea_repo: str [Default: 'qlora-adapters']
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# gitea_url: str [Default: 'http://gitea-http.gitea.svc.cluster.local:3000']
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# gitea_username: str [Default: '']
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# gradient_accumulation_steps: int [Default: 8.0]
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# learning_rate: float [Default: 0.0002]
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# lora_alpha: int [Default: 16.0]
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# lora_dropout: float [Default: 0.05]
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# lora_r: int [Default: 64.0]
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# max_seq_length: int [Default: 2048.0]
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# mlflow_tracking_uri: str [Default: 'http://mlflow.mlflow.svc.cluster.local:80']
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# num_epochs: int [Default: 3.0]
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# s3_bucket: str [Default: 'training-data']
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# s3_endpoint: str [Default: 'candlekeep.lab.daviestechlabs.io']
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# s3_prefix: str [Default: '']
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components:
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comp-evaluate-adapter:
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executorLabel: exec-evaluate-adapter
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inputDefinitions:
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parameters:
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adapter_path:
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parameterType: STRING
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base_model:
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parameterType: STRING
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outputDefinitions:
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parameters:
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passed:
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parameterType: BOOLEAN
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report:
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parameterType: STRING
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comp-fetch-pdfs-from-s3:
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executorLabel: exec-fetch-pdfs-from-s3
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inputDefinitions:
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parameters:
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aws_access_key_id:
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parameterType: STRING
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aws_secret_access_key:
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parameterType: STRING
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s3_bucket:
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parameterType: STRING
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s3_endpoint:
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parameterType: STRING
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s3_prefix:
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parameterType: STRING
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outputDefinitions:
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parameters:
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num_files:
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parameterType: NUMBER_INTEGER
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pdf_dir:
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parameterType: STRING
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comp-log-training-metrics:
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executorLabel: exec-log-training-metrics
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inputDefinitions:
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parameters:
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base_model:
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parameterType: STRING
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eval_loss:
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parameterType: NUMBER_DOUBLE
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experiment_name:
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defaultValue: qlora-pdf-training
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isOptional: true
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parameterType: STRING
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learning_rate:
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parameterType: NUMBER_DOUBLE
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lora_alpha:
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parameterType: NUMBER_INTEGER
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lora_r:
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parameterType: NUMBER_INTEGER
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mlflow_tracking_uri:
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defaultValue: http://mlflow.mlflow.svc.cluster.local:80
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isOptional: true
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parameterType: STRING
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num_epochs:
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parameterType: NUMBER_INTEGER
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num_pdfs:
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parameterType: NUMBER_INTEGER
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num_train:
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parameterType: NUMBER_INTEGER
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num_val:
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parameterType: NUMBER_INTEGER
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repo_url:
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parameterType: STRING
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train_loss:
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parameterType: NUMBER_DOUBLE
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comp-prepare-training-data:
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executorLabel: exec-prepare-training-data
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inputDefinitions:
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parameters:
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chunk_overlap:
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defaultValue: 64.0
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isOptional: true
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parameterType: NUMBER_INTEGER
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chunk_size:
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defaultValue: 512.0
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isOptional: true
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parameterType: NUMBER_INTEGER
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max_seq_length:
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defaultValue: 2048.0
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isOptional: true
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parameterType: NUMBER_INTEGER
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pdf_dir:
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parameterType: STRING
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outputDefinitions:
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parameters:
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dataset_path:
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parameterType: STRING
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num_train:
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parameterType: NUMBER_INTEGER
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num_val:
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parameterType: NUMBER_INTEGER
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comp-push-adapter-to-gitea:
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executorLabel: exec-push-adapter-to-gitea
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inputDefinitions:
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parameters:
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adapter_path:
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parameterType: STRING
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branch:
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defaultValue: main
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isOptional: true
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parameterType: STRING
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commit_message:
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defaultValue: 'feat: add QLoRA adapter from PDF training pipeline'
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isOptional: true
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parameterType: STRING
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gitea_owner:
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parameterType: STRING
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gitea_password:
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parameterType: STRING
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gitea_repo:
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parameterType: STRING
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gitea_url:
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parameterType: STRING
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gitea_username:
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parameterType: STRING
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outputDefinitions:
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parameters:
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files_pushed:
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parameterType: NUMBER_INTEGER
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repo_url:
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parameterType: STRING
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comp-train-qlora:
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executorLabel: exec-train-qlora
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inputDefinitions:
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parameters:
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base_model:
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parameterType: STRING
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batch_size:
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defaultValue: 2.0
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isOptional: true
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parameterType: NUMBER_INTEGER
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dataset_path:
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parameterType: STRING
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gradient_accumulation_steps:
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defaultValue: 8.0
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isOptional: true
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parameterType: NUMBER_INTEGER
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learning_rate:
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defaultValue: 0.0002
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isOptional: true
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parameterType: NUMBER_DOUBLE
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lora_alpha:
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defaultValue: 16.0
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isOptional: true
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parameterType: NUMBER_INTEGER
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lora_dropout:
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defaultValue: 0.05
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isOptional: true
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parameterType: NUMBER_DOUBLE
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lora_r:
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defaultValue: 64.0
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isOptional: true
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parameterType: NUMBER_INTEGER
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max_seq_length:
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defaultValue: 2048.0
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isOptional: true
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parameterType: NUMBER_INTEGER
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num_epochs:
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defaultValue: 3.0
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isOptional: true
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parameterType: NUMBER_INTEGER
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outputDefinitions:
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parameters:
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adapter_path:
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parameterType: STRING
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eval_loss:
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parameterType: NUMBER_DOUBLE
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train_loss:
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parameterType: NUMBER_DOUBLE
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deploymentSpec:
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executors:
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exec-evaluate-adapter:
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container:
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args:
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- --executor_input
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- '{{$}}'
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- --function_to_execute
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- evaluate_adapter
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command:
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- sh
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- -c
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- "\nif ! [ -x \"$(command -v pip)\" ]; then\n python3 -m ensurepip ||\
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\ python3 -m ensurepip --user || apt-get install python3-pip\nfi\n\nPIP_DISABLE_PIP_VERSION_CHECK=1\
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\ python3 -m pip install --quiet --no-warn-script-location 'kfp==2.12.1'\
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\ '--no-deps' 'typing-extensions>=3.7.4,<5; python_version<\"3.9\"' &&\
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\ python3 -m pip install --quiet --no-warn-script-location 'torch' 'transformers'\
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\ 'peft' 'bitsandbytes' 'accelerate' 'scipy' && \"$0\" \"$@\"\n"
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- sh
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- -ec
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- 'program_path=$(mktemp -d)
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printf "%s" "$0" > "$program_path/ephemeral_component.py"
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_KFP_RUNTIME=true python3 -m kfp.dsl.executor_main --component_module_path "$program_path/ephemeral_component.py" "$@"
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'
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- "\nimport kfp\nfrom kfp import dsl\nfrom kfp.dsl import *\nfrom typing import\
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\ *\n\ndef evaluate_adapter(\n adapter_path: str,\n base_model: str,\n\
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) -> NamedTuple(\"EvalOutput\", [(\"report\", str), (\"passed\", bool)]):\n\
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\ \"\"\"Load the QLoRA adapter and run a few sanity-check prompts.\"\"\
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\"\n import torch\n from transformers import AutoModelForCausalLM,\
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\ AutoTokenizer, BitsAndBytesConfig\n from peft import PeftModel\n\n\
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\ bnb_config = BitsAndBytesConfig(\n load_in_4bit=True,\n \
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\ bnb_4bit_quant_type=\"nf4\",\n bnb_4bit_compute_dtype=torch.bfloat16,\n\
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\ bnb_4bit_use_double_quant=True,\n )\n\n print(f\"Loading\
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\ base model {base_model} \u2026\")\n model = AutoModelForCausalLM.from_pretrained(\n\
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\ base_model,\n quantization_config=bnb_config,\n device_map=\"\
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auto\",\n trust_remote_code=True,\n torch_dtype=torch.bfloat16,\n\
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\ )\n tokenizer = AutoTokenizer.from_pretrained(base_model, trust_remote_code=True)\n\
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\n print(f\"Loading adapter from {adapter_path} \u2026\")\n model\
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\ = PeftModel.from_pretrained(model, adapter_path)\n model.eval()\n\n\
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\ test_prompts = [\n \"Summarise the key points from the training\
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\ material.\",\n \"What are the main topics covered in the source\
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\ documents?\",\n \"Explain the most important concept from the training\
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\ data.\",\n ]\n\n lines = []\n for prompt in test_prompts:\n \
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\ messages = [\n {\"role\": \"system\", \"content\": \"\
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You are a helpful assistant.\"},\n {\"role\": \"user\", \"content\"\
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: prompt},\n ]\n input_text = tokenizer.apply_chat_template(\n\
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\ messages, tokenize=False, add_generation_prompt=True\n \
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\ )\n inputs = tokenizer(input_text, return_tensors=\"pt\").to(model.device)\n\
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\ with torch.no_grad():\n out = model.generate(**inputs,\
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\ max_new_tokens=128, temperature=0.7, do_sample=True)\n response\
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\ = tokenizer.decode(out[0][inputs[\"input_ids\"].shape[1]:], skip_special_tokens=True)\n\
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\ lines.append(f\"Q: {prompt}\\nA: {response}\\n\")\n print(lines[-1])\n\
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\n report = \"\\n\".join(lines)\n # Simple heuristic: did the model\
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\ produce non-empty responses?\n passed = all(len(l.split(\"A:\")[1].strip())\
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\ > 10 for l in lines)\n print(f\"Evaluation passed: {passed}\")\n\n\
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\ from collections import namedtuple\n\n return namedtuple(\"EvalOutput\"\
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, [\"report\", \"passed\"])(\n report=report, passed=passed\n \
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\ )\n\n"
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image: python:3.13-slim
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resources:
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accelerator:
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resourceCount: '1'
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resourceType: gpu
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exec-fetch-pdfs-from-s3:
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container:
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args:
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- --executor_input
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- '{{$}}'
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- --function_to_execute
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- fetch_pdfs_from_s3
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command:
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- sh
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- -c
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- "\nif ! [ -x \"$(command -v pip)\" ]; then\n python3 -m ensurepip ||\
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\ python3 -m ensurepip --user || apt-get install python3-pip\nfi\n\nPIP_DISABLE_PIP_VERSION_CHECK=1\
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\ python3 -m pip install --quiet --no-warn-script-location 'kfp==2.12.1'\
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\ '--no-deps' 'typing-extensions>=3.7.4,<5; python_version<\"3.9\"' &&\
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\ python3 -m pip install --quiet --no-warn-script-location 'boto3' && \"\
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$0\" \"$@\"\n"
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- sh
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- -ec
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- 'program_path=$(mktemp -d)
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printf "%s" "$0" > "$program_path/ephemeral_component.py"
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_KFP_RUNTIME=true python3 -m kfp.dsl.executor_main --component_module_path "$program_path/ephemeral_component.py" "$@"
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'
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- "\nimport kfp\nfrom kfp import dsl\nfrom kfp.dsl import *\nfrom typing import\
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\ *\n\ndef fetch_pdfs_from_s3(\n s3_endpoint: str,\n s3_bucket: str,\n\
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\ s3_prefix: str,\n aws_access_key_id: str,\n aws_secret_access_key:\
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\ str,\n) -> NamedTuple(\"PDFOutput\", [(\"pdf_dir\", str), (\"num_files\"\
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, int)]):\n \"\"\"Download all PDFs from a Quobjects S3 bucket.\"\"\"\
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\n import os\n import boto3\n from botocore.client import Config\n\
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\n out_dir = \"/tmp/pdfs\"\n os.makedirs(out_dir, exist_ok=True)\n\
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\n client = boto3.client(\n \"s3\",\n endpoint_url=f\"\
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http://{s3_endpoint}\",\n aws_access_key_id=aws_access_key_id,\n\
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\ aws_secret_access_key=aws_secret_access_key,\n region_name=\"\
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us-east-1\",\n config=Config(signature_version=\"s3v4\"),\n )\n\
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\n paginator = client.get_paginator(\"list_objects_v2\")\n count =\
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\ 0\n for page in paginator.paginate(Bucket=s3_bucket, Prefix=s3_prefix):\n\
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\ for obj in page.get(\"Contents\", []):\n key = obj[\"\
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Key\"]\n if key.lower().endswith(\".pdf\"):\n \
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\ local_path = os.path.join(out_dir, os.path.basename(key))\n \
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\ print(f\"Downloading: {key} \u2192 {local_path}\")\n \
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\ client.download_file(s3_bucket, key, local_path)\n count\
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\ += 1\n\n print(f\"Downloaded {count} PDFs to {out_dir}\")\n from\
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\ collections import namedtuple\n\n return namedtuple(\"PDFOutput\",\
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\ [\"pdf_dir\", \"num_files\"])(\n pdf_dir=out_dir, num_files=count\n\
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\ )\n\n"
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image: python:3.13-slim
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exec-log-training-metrics:
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container:
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args:
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- --executor_input
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- '{{$}}'
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- --function_to_execute
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- log_training_metrics
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command:
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- sh
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- -c
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- "\nif ! [ -x \"$(command -v pip)\" ]; then\n python3 -m ensurepip ||\
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\ python3 -m ensurepip --user || apt-get install python3-pip\nfi\n\nPIP_DISABLE_PIP_VERSION_CHECK=1\
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\ python3 -m pip install --quiet --no-warn-script-location 'kfp==2.12.1'\
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\ '--no-deps' 'typing-extensions>=3.7.4,<5; python_version<\"3.9\"' &&\
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\ python3 -m pip install --quiet --no-warn-script-location 'mlflow==2.22.0'\
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\ && \"$0\" \"$@\"\n"
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- sh
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- -ec
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- 'program_path=$(mktemp -d)
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printf "%s" "$0" > "$program_path/ephemeral_component.py"
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_KFP_RUNTIME=true python3 -m kfp.dsl.executor_main --component_module_path "$program_path/ephemeral_component.py" "$@"
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'
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- "\nimport kfp\nfrom kfp import dsl\nfrom kfp.dsl import *\nfrom typing import\
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\ *\n\ndef log_training_metrics(\n base_model: str,\n train_loss:\
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\ float,\n eval_loss: float,\n num_train: int,\n num_val: int,\n\
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\ num_pdfs: int,\n lora_r: int,\n lora_alpha: int,\n learning_rate:\
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\ float,\n num_epochs: int,\n repo_url: str,\n mlflow_tracking_uri:\
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\ str = \"http://mlflow.mlflow.svc.cluster.local:80\",\n experiment_name:\
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\ str = \"qlora-pdf-training\",\n):\n \"\"\"Log the full training run\
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\ to MLflow.\"\"\"\n import mlflow\n\n mlflow.set_tracking_uri(mlflow_tracking_uri)\n\
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\ mlflow.set_experiment(experiment_name)\n\n with mlflow.start_run(run_name=f\"\
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qlora-{base_model.split('/')[-1]}\"):\n mlflow.log_params(\n \
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\ {\n \"base_model\": base_model,\n \
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\ \"lora_r\": lora_r,\n \"lora_alpha\": lora_alpha,\n \
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\ \"learning_rate\": learning_rate,\n \"num_epochs\"\
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: num_epochs,\n \"num_pdfs\": num_pdfs,\n \
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\ \"data_source\": \"quobjects/training-data\",\n }\n \
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\ )\n mlflow.log_metrics(\n {\n \"train_loss\"\
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: train_loss,\n \"eval_loss\": eval_loss,\n \
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\ \"train_samples\": float(num_train),\n \"val_samples\"\
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: float(num_val),\n }\n )\n mlflow.set_tag(\"adapter_repo\"\
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, repo_url)\n\n"
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image: python:3.13-slim
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exec-prepare-training-data:
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container:
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args:
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- --executor_input
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- '{{$}}'
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- --function_to_execute
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- prepare_training_data
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command:
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- sh
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- -c
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- "\nif ! [ -x \"$(command -v pip)\" ]; then\n python3 -m ensurepip ||\
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\ python3 -m ensurepip --user || apt-get install python3-pip\nfi\n\nPIP_DISABLE_PIP_VERSION_CHECK=1\
|
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\ python3 -m pip install --quiet --no-warn-script-location 'kfp==2.12.1'\
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\ '--no-deps' 'typing-extensions>=3.7.4,<5; python_version<\"3.9\"' &&\
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\ python3 -m pip install --quiet --no-warn-script-location 'pymupdf' &&\
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\ \"$0\" \"$@\"\n"
|
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- sh
|
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- -ec
|
|
- 'program_path=$(mktemp -d)
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|
|
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printf "%s" "$0" > "$program_path/ephemeral_component.py"
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_KFP_RUNTIME=true python3 -m kfp.dsl.executor_main --component_module_path "$program_path/ephemeral_component.py" "$@"
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|
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'
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- "\nimport kfp\nfrom kfp import dsl\nfrom kfp.dsl import *\nfrom typing import\
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\ *\n\ndef prepare_training_data(\n pdf_dir: str,\n max_seq_length:\
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\ int = 2048,\n chunk_size: int = 512,\n chunk_overlap: int = 64,\n\
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) -> NamedTuple(\"DataOutput\", [(\"dataset_path\", str), (\"num_train\"\
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, int), (\"num_val\", int)]):\n \"\"\"Extract text from PDFs, chunk it,\
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\ and format as instruction-tuning pairs.\"\"\"\n import json\n import\
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\ os\n import fitz # PyMuPDF\n\n out_dir = \"/tmp/training_data\"\
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\n os.makedirs(out_dir, exist_ok=True)\n\n # 1. Extract text from\
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\ all PDFs\n all_chunks: list[dict] = []\n for fname in sorted(os.listdir(pdf_dir)):\n\
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\ if not fname.lower().endswith(\".pdf\"):\n continue\n\
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\ path = os.path.join(pdf_dir, fname)\n print(f\"Extracting:\
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\ {fname}\")\n try:\n doc = fitz.open(path)\n \
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\ full_text = \"\"\n for page in doc:\n full_text\
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\ += page.get_text() + \"\\n\"\n doc.close()\n except\
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\ Exception as e:\n print(f\" SKIP ({e})\")\n continue\n\
|
|
\n # 2. Chunk text with overlap\n words = full_text.split()\n\
|
|
\ for i in range(0, len(words), chunk_size - chunk_overlap):\n \
|
|
\ chunk_words = words[i : i + chunk_size]\n if len(chunk_words)\
|
|
\ < 50:\n continue # skip tiny trailing chunks\n \
|
|
\ chunk_text = \" \".join(chunk_words)\n all_chunks.append({\"\
|
|
text\": chunk_text, \"source\": fname})\n\n print(f\"Total chunks: {len(all_chunks)}\"\
|
|
)\n if not all_chunks:\n raise ValueError(\"No text extracted\
|
|
\ from PDFs \u2014 check your bucket\")\n\n # 3. Format as Llama 3 chat\
|
|
\ training pairs\n # We create self-supervised pairs: model learns\
|
|
\ to continue/explain the content\n samples = []\n for chunk in all_chunks:\n\
|
|
\ text = chunk[\"text\"]\n source = chunk[\"source\"]\n \
|
|
\ # Split chunk roughly in half for input/output\n words = text.split()\n\
|
|
\ mid = len(words) // 2\n context = \" \".join(words[:mid])\n\
|
|
\ continuation = \" \".join(words[mid:])\n\n samples.append(\n\
|
|
\ {\n \"messages\": [\n {\n\
|
|
\ \"role\": \"system\",\n \
|
|
\ \"content\": (\n \"You are a knowledgeable\
|
|
\ assistant. \"\n \"Continue the information\
|
|
\ accurately and coherently.\"\n ),\n \
|
|
\ },\n {\n \"role\": \"\
|
|
user\",\n \"content\": f\"Continue the following\
|
|
\ passage from {source}:\\n\\n{context}\",\n },\n \
|
|
\ {\"role\": \"assistant\", \"content\": continuation},\n\
|
|
\ ]\n }\n )\n\n # 4. Train/val split\
|
|
\ (90/10)\n import random\n\n random.seed(42)\n random.shuffle(samples)\n\
|
|
\ split = int(len(samples) * 0.9)\n train = samples[:split]\n val\
|
|
\ = samples[split:]\n\n train_path = os.path.join(out_dir, \"train.json\"\
|
|
)\n val_path = os.path.join(out_dir, \"val.json\")\n with open(train_path,\
|
|
\ \"w\") as f:\n json.dump(train, f)\n with open(val_path, \"\
|
|
w\") as f:\n json.dump(val, f)\n\n print(f\"Train: {len(train)}\
|
|
\ samples, Val: {len(val)} samples\")\n from collections import namedtuple\n\
|
|
\n return namedtuple(\"DataOutput\", [\"dataset_path\", \"num_train\"\
|
|
, \"num_val\"])(\n dataset_path=out_dir, num_train=len(train), num_val=len(val)\n\
|
|
\ )\n\n"
|
|
image: python:3.13-slim
|
|
exec-push-adapter-to-gitea:
|
|
container:
|
|
args:
|
|
- --executor_input
|
|
- '{{$}}'
|
|
- --function_to_execute
|
|
- push_adapter_to_gitea
|
|
command:
|
|
- sh
|
|
- -c
|
|
- "\nif ! [ -x \"$(command -v pip)\" ]; then\n python3 -m ensurepip ||\
|
|
\ python3 -m ensurepip --user || apt-get install python3-pip\nfi\n\nPIP_DISABLE_PIP_VERSION_CHECK=1\
|
|
\ python3 -m pip install --quiet --no-warn-script-location 'kfp==2.12.1'\
|
|
\ '--no-deps' 'typing-extensions>=3.7.4,<5; python_version<\"3.9\"' &&\
|
|
\ python3 -m pip install --quiet --no-warn-script-location 'requests' &&\
|
|
\ \"$0\" \"$@\"\n"
|
|
- sh
|
|
- -ec
|
|
- 'program_path=$(mktemp -d)
|
|
|
|
|
|
printf "%s" "$0" > "$program_path/ephemeral_component.py"
|
|
|
|
_KFP_RUNTIME=true python3 -m kfp.dsl.executor_main --component_module_path "$program_path/ephemeral_component.py" "$@"
|
|
|
|
'
|
|
- "\nimport kfp\nfrom kfp import dsl\nfrom kfp.dsl import *\nfrom typing import\
|
|
\ *\n\ndef push_adapter_to_gitea(\n adapter_path: str,\n gitea_url:\
|
|
\ str,\n gitea_owner: str,\n gitea_repo: str,\n gitea_username:\
|
|
\ str,\n gitea_password: str,\n branch: str = \"main\",\n commit_message:\
|
|
\ str = \"feat: add QLoRA adapter from PDF training pipeline\",\n) -> NamedTuple(\"\
|
|
PushOutput\", [(\"repo_url\", str), (\"files_pushed\", int)]):\n \"\"\
|
|
\"Push the QLoRA adapter files to a Gitea repository via the API.\"\"\"\n\
|
|
\ import base64\n import json\n import os\n import requests\n\
|
|
\n api_base = f\"{gitea_url}/api/v1\"\n auth = (gitea_username, gitea_password)\n\
|
|
\ repo_api = f\"{api_base}/repos/{gitea_owner}/{gitea_repo}\"\n\n \
|
|
\ # Check if repo exists, create if not\n resp = requests.get(repo_api,\
|
|
\ auth=auth, timeout=30)\n if resp.status_code == 404:\n print(f\"\
|
|
Creating repo {gitea_owner}/{gitea_repo} \u2026\")\n create_resp\
|
|
\ = requests.post(\n f\"{api_base}/orgs/{gitea_owner}/repos\"\
|
|
\n if gitea_owner != gitea_username\n else f\"{api_base}/user/repos\"\
|
|
,\n auth=auth,\n json={\n \"name\"\
|
|
: gitea_repo,\n \"description\": \"QLoRA adapters trained\
|
|
\ from PDF documents\",\n \"private\": False,\n \
|
|
\ \"auto_init\": True,\n },\n timeout=30,\n\
|
|
\ )\n create_resp.raise_for_status()\n print(f\"Created:\
|
|
\ {create_resp.json().get('html_url')}\")\n\n # Collect all adapter files\n\
|
|
\ files_to_push = []\n for root, dirs, files in os.walk(adapter_path):\n\
|
|
\ for fname in files:\n fpath = os.path.join(root, fname)\n\
|
|
\ rel_path = os.path.relpath(fpath, adapter_path)\n \
|
|
\ with open(fpath, \"rb\") as f:\n content = base64.b64encode(f.read()).decode(\"\
|
|
utf-8\")\n files_to_push.append({\"path\": rel_path, \"content\"\
|
|
: content})\n\n print(f\"Pushing {len(files_to_push)} files to {gitea_owner}/{gitea_repo}\"\
|
|
)\n\n # Push each file via Gitea contents API\n pushed = 0\n for\
|
|
\ item in files_to_push:\n file_api = f\"{repo_api}/contents/{item['path']}\"\
|
|
\n\n # Check if file already exists (need SHA for update)\n \
|
|
\ existing = requests.get(file_api, auth=auth, params={\"ref\": branch},\
|
|
\ timeout=30)\n payload = {\n \"message\": commit_message,\n\
|
|
\ \"content\": item[\"content\"],\n \"branch\": branch,\n\
|
|
\ }\n if existing.status_code == 200:\n payload[\"\
|
|
sha\"] = existing.json()[\"sha\"]\n resp = requests.put(file_api,\
|
|
\ auth=auth, json=payload, timeout=60)\n else:\n resp\
|
|
\ = requests.post(file_api, auth=auth, json=payload, timeout=60)\n\n \
|
|
\ if resp.status_code in (200, 201):\n pushed += 1\n \
|
|
\ print(f\" \u2713 {item['path']}\")\n else:\n \
|
|
\ print(f\" \u2717 {item['path']}: {resp.status_code} {resp.text[:200]}\"\
|
|
)\n\n repo_url = f\"{gitea_url}/{gitea_owner}/{gitea_repo}\"\n print(f\"\
|
|
Pushed {pushed}/{len(files_to_push)} files to {repo_url}\")\n\n from\
|
|
\ collections import namedtuple\n\n return namedtuple(\"PushOutput\"\
|
|
, [\"repo_url\", \"files_pushed\"])(\n repo_url=repo_url, files_pushed=pushed\n\
|
|
\ )\n\n"
|
|
image: python:3.13-slim
|
|
exec-train-qlora:
|
|
container:
|
|
args:
|
|
- --executor_input
|
|
- '{{$}}'
|
|
- --function_to_execute
|
|
- train_qlora
|
|
command:
|
|
- sh
|
|
- -c
|
|
- "\nif ! [ -x \"$(command -v pip)\" ]; then\n python3 -m ensurepip ||\
|
|
\ python3 -m ensurepip --user || apt-get install python3-pip\nfi\n\nPIP_DISABLE_PIP_VERSION_CHECK=1\
|
|
\ python3 -m pip install --quiet --no-warn-script-location 'kfp==2.12.1'\
|
|
\ '--no-deps' 'typing-extensions>=3.7.4,<5; python_version<\"3.9\"' &&\
|
|
\ python3 -m pip install --quiet --no-warn-script-location 'torch' 'transformers'\
|
|
\ 'peft' 'datasets' 'accelerate' 'bitsandbytes' 'scipy' 'trl' && \"$0\"\
|
|
\ \"$@\"\n"
|
|
- sh
|
|
- -ec
|
|
- 'program_path=$(mktemp -d)
|
|
|
|
|
|
printf "%s" "$0" > "$program_path/ephemeral_component.py"
|
|
|
|
_KFP_RUNTIME=true python3 -m kfp.dsl.executor_main --component_module_path "$program_path/ephemeral_component.py" "$@"
|
|
|
|
'
|
|
- "\nimport kfp\nfrom kfp import dsl\nfrom kfp.dsl import *\nfrom typing import\
|
|
\ *\n\ndef train_qlora(\n dataset_path: str,\n base_model: str,\n\
|
|
\ learning_rate: float = 2e-4,\n num_epochs: int = 3,\n batch_size:\
|
|
\ int = 2,\n gradient_accumulation_steps: int = 8,\n max_seq_length:\
|
|
\ int = 2048,\n lora_r: int = 64,\n lora_alpha: int = 16,\n lora_dropout:\
|
|
\ float = 0.05,\n) -> NamedTuple(\n \"TrainOutput\",\n [(\"adapter_path\"\
|
|
, str), (\"train_loss\", float), (\"eval_loss\", float)],\n):\n \"\"\"\
|
|
QLoRA fine-tune Llama 3.1 70B with 4-bit NF4 quantization.\"\"\"\n import\
|
|
\ json\n import os\n\n import torch\n from datasets import Dataset\n\
|
|
\ from peft import LoraConfig, get_peft_model, prepare_model_for_kbit_training\n\
|
|
\ from transformers import (\n AutoModelForCausalLM,\n \
|
|
\ AutoTokenizer,\n BitsAndBytesConfig,\n TrainingArguments,\n\
|
|
\ )\n from trl import SFTTrainer\n\n output_dir = \"/tmp/qlora_output\"\
|
|
\n os.makedirs(output_dir, exist_ok=True)\n\n # \u2500\u2500 Load\
|
|
\ data \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\
|
|
\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\
|
|
\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\
|
|
\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n with open(os.path.join(dataset_path,\
|
|
\ \"train.json\")) as f:\n train_data = json.load(f)\n with open(os.path.join(dataset_path,\
|
|
\ \"val.json\")) as f:\n val_data = json.load(f)\n\n print(f\"\
|
|
Loaded {len(train_data)} train / {len(val_data)} val samples\")\n\n #\
|
|
\ \u2500\u2500 Tokenizer \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\
|
|
\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\
|
|
\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\
|
|
\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n \
|
|
\ print(f\"Loading tokenizer: {base_model}\")\n tokenizer = AutoTokenizer.from_pretrained(base_model,\
|
|
\ trust_remote_code=True)\n if tokenizer.pad_token is None:\n \
|
|
\ tokenizer.pad_token = tokenizer.eos_token\n tokenizer.padding_side\
|
|
\ = \"right\"\n\n # \u2500\u2500 Format with chat template \u2500\u2500\
|
|
\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\
|
|
\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\
|
|
\u2500\n def format_chat(sample):\n return {\"text\": tokenizer.apply_chat_template(\n\
|
|
\ sample[\"messages\"], tokenize=False, add_generation_prompt=False\n\
|
|
\ )}\n\n train_ds = Dataset.from_list(train_data).map(format_chat)\n\
|
|
\ val_ds = Dataset.from_list(val_data).map(format_chat)\n\n # \u2500\
|
|
\u2500 4-bit quantisation \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\
|
|
\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\
|
|
\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\
|
|
\u2500\u2500\n bnb_config = BitsAndBytesConfig(\n load_in_4bit=True,\n\
|
|
\ bnb_4bit_quant_type=\"nf4\",\n bnb_4bit_compute_dtype=torch.bfloat16,\n\
|
|
\ bnb_4bit_use_double_quant=True,\n )\n\n print(f\"Loading\
|
|
\ model: {base_model} (4-bit NF4)\")\n model = AutoModelForCausalLM.from_pretrained(\n\
|
|
\ base_model,\n quantization_config=bnb_config,\n device_map=\"\
|
|
auto\",\n trust_remote_code=True,\n torch_dtype=torch.bfloat16,\n\
|
|
\ )\n model = prepare_model_for_kbit_training(model)\n\n # \u2500\
|
|
\u2500 LoRA config \u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\
|
|
\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\
|
|
\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\
|
|
\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n lora_config = LoraConfig(\n\
|
|
\ r=lora_r,\n lora_alpha=lora_alpha,\n target_modules=[\n\
|
|
\ \"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\",\n \
|
|
\ \"gate_proj\", \"up_proj\", \"down_proj\",\n ],\n lora_dropout=lora_dropout,\n\
|
|
\ bias=\"none\",\n task_type=\"CAUSAL_LM\",\n )\n\n \
|
|
\ model = get_peft_model(model, lora_config)\n model.print_trainable_parameters()\n\
|
|
\n # \u2500\u2500 Training args \u2500\u2500\u2500\u2500\u2500\u2500\u2500\
|
|
\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\
|
|
\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\
|
|
\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\n training_args = TrainingArguments(\n\
|
|
\ output_dir=os.path.join(output_dir, \"checkpoints\"),\n \
|
|
\ num_train_epochs=num_epochs,\n per_device_train_batch_size=batch_size,\n\
|
|
\ per_device_eval_batch_size=batch_size,\n gradient_accumulation_steps=gradient_accumulation_steps,\n\
|
|
\ learning_rate=learning_rate,\n bf16=True,\n logging_steps=5,\n\
|
|
\ eval_strategy=\"steps\",\n eval_steps=50,\n save_strategy=\"\
|
|
steps\",\n save_steps=100,\n save_total_limit=2,\n \
|
|
\ load_best_model_at_end=True,\n metric_for_best_model=\"eval_loss\"\
|
|
,\n report_to=\"none\",\n warmup_ratio=0.03,\n lr_scheduler_type=\"\
|
|
cosine\",\n optim=\"paged_adamw_8bit\",\n max_grad_norm=0.3,\n\
|
|
\ group_by_length=True,\n )\n\n # \u2500\u2500 SFTTrainer \u2500\
|
|
\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\
|
|
\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\
|
|
\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\
|
|
\u2500\u2500\u2500\u2500\u2500\n trainer = SFTTrainer(\n model=model,\n\
|
|
\ args=training_args,\n train_dataset=train_ds,\n eval_dataset=val_ds,\n\
|
|
\ tokenizer=tokenizer,\n max_seq_length=max_seq_length,\n\
|
|
\ dataset_text_field=\"text\",\n packing=True, # pack short\
|
|
\ samples for efficiency\n )\n\n print(\"Starting QLoRA training \u2026\
|
|
\")\n result = trainer.train()\n train_loss = result.training_loss\n\
|
|
\n eval_result = trainer.evaluate()\n eval_loss = eval_result.get(\"\
|
|
eval_loss\", 0.0)\n\n print(f\"Train loss: {train_loss:.4f}, Eval loss:\
|
|
\ {eval_loss:.4f}\")\n\n # \u2500\u2500 Save adapter \u2500\u2500\u2500\
|
|
\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\
|
|
\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\
|
|
\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\
|
|
\u2500\n adapter_path = os.path.join(output_dir, \"adapter\")\n model.save_pretrained(adapter_path)\n\
|
|
\ tokenizer.save_pretrained(adapter_path)\n\n metadata = {\n \
|
|
\ \"base_model\": base_model,\n \"lora_r\": lora_r,\n \"\
|
|
lora_alpha\": lora_alpha,\n \"lora_dropout\": lora_dropout,\n \
|
|
\ \"learning_rate\": learning_rate,\n \"num_epochs\": num_epochs,\n\
|
|
\ \"batch_size\": batch_size,\n \"gradient_accumulation_steps\"\
|
|
: gradient_accumulation_steps,\n \"max_seq_length\": max_seq_length,\n\
|
|
\ \"train_samples\": len(train_data),\n \"val_samples\": len(val_data),\n\
|
|
\ \"train_loss\": train_loss,\n \"eval_loss\": eval_loss,\n\
|
|
\ }\n with open(os.path.join(adapter_path, \"training_metadata.json\"\
|
|
), \"w\") as f:\n json.dump(metadata, f, indent=2)\n\n print(f\"\
|
|
Adapter saved to {adapter_path}\")\n\n from collections import namedtuple\n\
|
|
\n return namedtuple(\"TrainOutput\", [\"adapter_path\", \"train_loss\"\
|
|
, \"eval_loss\"])(\n adapter_path=adapter_path,\n train_loss=train_loss,\n\
|
|
\ eval_loss=eval_loss,\n )\n\n"
|
|
image: python:3.13-slim
|
|
resources:
|
|
accelerator:
|
|
resourceCount: '1'
|
|
resourceType: gpu
|
|
pipelineInfo:
|
|
description: Fine-tune Llama 3.1 70B via QLoRA on PDFs from the Quobjects training-data
|
|
bucket. Pushes the adapter to Gitea and logs metrics to MLflow.
|
|
name: qlora-pdf-fine-tuning
|
|
root:
|
|
dag:
|
|
tasks:
|
|
evaluate-adapter:
|
|
cachingOptions:
|
|
enableCache: true
|
|
componentRef:
|
|
name: comp-evaluate-adapter
|
|
dependentTasks:
|
|
- train-qlora
|
|
inputs:
|
|
parameters:
|
|
adapter_path:
|
|
taskOutputParameter:
|
|
outputParameterKey: adapter_path
|
|
producerTask: train-qlora
|
|
base_model:
|
|
componentInputParameter: base_model
|
|
taskInfo:
|
|
name: evaluate-adapter
|
|
fetch-pdfs-from-s3:
|
|
cachingOptions:
|
|
enableCache: true
|
|
componentRef:
|
|
name: comp-fetch-pdfs-from-s3
|
|
inputs:
|
|
parameters:
|
|
aws_access_key_id:
|
|
componentInputParameter: aws_access_key_id
|
|
aws_secret_access_key:
|
|
componentInputParameter: aws_secret_access_key
|
|
s3_bucket:
|
|
componentInputParameter: s3_bucket
|
|
s3_endpoint:
|
|
componentInputParameter: s3_endpoint
|
|
s3_prefix:
|
|
componentInputParameter: s3_prefix
|
|
taskInfo:
|
|
name: fetch-pdfs-from-s3
|
|
log-training-metrics:
|
|
cachingOptions:
|
|
enableCache: true
|
|
componentRef:
|
|
name: comp-log-training-metrics
|
|
dependentTasks:
|
|
- fetch-pdfs-from-s3
|
|
- prepare-training-data
|
|
- push-adapter-to-gitea
|
|
- train-qlora
|
|
inputs:
|
|
parameters:
|
|
base_model:
|
|
componentInputParameter: base_model
|
|
eval_loss:
|
|
taskOutputParameter:
|
|
outputParameterKey: eval_loss
|
|
producerTask: train-qlora
|
|
learning_rate:
|
|
componentInputParameter: learning_rate
|
|
lora_alpha:
|
|
componentInputParameter: lora_alpha
|
|
lora_r:
|
|
componentInputParameter: lora_r
|
|
mlflow_tracking_uri:
|
|
componentInputParameter: mlflow_tracking_uri
|
|
num_epochs:
|
|
componentInputParameter: num_epochs
|
|
num_pdfs:
|
|
taskOutputParameter:
|
|
outputParameterKey: num_files
|
|
producerTask: fetch-pdfs-from-s3
|
|
num_train:
|
|
taskOutputParameter:
|
|
outputParameterKey: num_train
|
|
producerTask: prepare-training-data
|
|
num_val:
|
|
taskOutputParameter:
|
|
outputParameterKey: num_val
|
|
producerTask: prepare-training-data
|
|
repo_url:
|
|
taskOutputParameter:
|
|
outputParameterKey: repo_url
|
|
producerTask: push-adapter-to-gitea
|
|
train_loss:
|
|
taskOutputParameter:
|
|
outputParameterKey: train_loss
|
|
producerTask: train-qlora
|
|
taskInfo:
|
|
name: log-training-metrics
|
|
prepare-training-data:
|
|
cachingOptions:
|
|
enableCache: true
|
|
componentRef:
|
|
name: comp-prepare-training-data
|
|
dependentTasks:
|
|
- fetch-pdfs-from-s3
|
|
inputs:
|
|
parameters:
|
|
chunk_overlap:
|
|
componentInputParameter: chunk_overlap
|
|
chunk_size:
|
|
componentInputParameter: chunk_size
|
|
max_seq_length:
|
|
componentInputParameter: max_seq_length
|
|
pdf_dir:
|
|
taskOutputParameter:
|
|
outputParameterKey: pdf_dir
|
|
producerTask: fetch-pdfs-from-s3
|
|
taskInfo:
|
|
name: prepare-training-data
|
|
push-adapter-to-gitea:
|
|
cachingOptions:
|
|
enableCache: true
|
|
componentRef:
|
|
name: comp-push-adapter-to-gitea
|
|
dependentTasks:
|
|
- train-qlora
|
|
inputs:
|
|
parameters:
|
|
adapter_path:
|
|
taskOutputParameter:
|
|
outputParameterKey: adapter_path
|
|
producerTask: train-qlora
|
|
gitea_owner:
|
|
componentInputParameter: gitea_owner
|
|
gitea_password:
|
|
componentInputParameter: gitea_password
|
|
gitea_repo:
|
|
componentInputParameter: gitea_repo
|
|
gitea_url:
|
|
componentInputParameter: gitea_url
|
|
gitea_username:
|
|
componentInputParameter: gitea_username
|
|
taskInfo:
|
|
name: push-adapter-to-gitea
|
|
train-qlora:
|
|
cachingOptions:
|
|
enableCache: true
|
|
componentRef:
|
|
name: comp-train-qlora
|
|
dependentTasks:
|
|
- prepare-training-data
|
|
inputs:
|
|
parameters:
|
|
base_model:
|
|
componentInputParameter: base_model
|
|
batch_size:
|
|
componentInputParameter: batch_size
|
|
dataset_path:
|
|
taskOutputParameter:
|
|
outputParameterKey: dataset_path
|
|
producerTask: prepare-training-data
|
|
gradient_accumulation_steps:
|
|
componentInputParameter: gradient_accumulation_steps
|
|
learning_rate:
|
|
componentInputParameter: learning_rate
|
|
lora_alpha:
|
|
componentInputParameter: lora_alpha
|
|
lora_dropout:
|
|
componentInputParameter: lora_dropout
|
|
lora_r:
|
|
componentInputParameter: lora_r
|
|
max_seq_length:
|
|
componentInputParameter: max_seq_length
|
|
num_epochs:
|
|
componentInputParameter: num_epochs
|
|
taskInfo:
|
|
name: train-qlora
|
|
inputDefinitions:
|
|
parameters:
|
|
aws_access_key_id:
|
|
defaultValue: ''
|
|
isOptional: true
|
|
parameterType: STRING
|
|
aws_secret_access_key:
|
|
defaultValue: ''
|
|
isOptional: true
|
|
parameterType: STRING
|
|
base_model:
|
|
defaultValue: meta-llama/Llama-3.1-70B-Instruct
|
|
isOptional: true
|
|
parameterType: STRING
|
|
batch_size:
|
|
defaultValue: 2.0
|
|
isOptional: true
|
|
parameterType: NUMBER_INTEGER
|
|
chunk_overlap:
|
|
defaultValue: 64.0
|
|
isOptional: true
|
|
parameterType: NUMBER_INTEGER
|
|
chunk_size:
|
|
defaultValue: 512.0
|
|
isOptional: true
|
|
parameterType: NUMBER_INTEGER
|
|
gitea_owner:
|
|
defaultValue: daviestechlabs
|
|
isOptional: true
|
|
parameterType: STRING
|
|
gitea_password:
|
|
defaultValue: ''
|
|
isOptional: true
|
|
parameterType: STRING
|
|
gitea_repo:
|
|
defaultValue: qlora-adapters
|
|
isOptional: true
|
|
parameterType: STRING
|
|
gitea_url:
|
|
defaultValue: http://gitea-http.gitea.svc.cluster.local:3000
|
|
isOptional: true
|
|
parameterType: STRING
|
|
gitea_username:
|
|
defaultValue: ''
|
|
isOptional: true
|
|
parameterType: STRING
|
|
gradient_accumulation_steps:
|
|
defaultValue: 8.0
|
|
isOptional: true
|
|
parameterType: NUMBER_INTEGER
|
|
learning_rate:
|
|
defaultValue: 0.0002
|
|
isOptional: true
|
|
parameterType: NUMBER_DOUBLE
|
|
lora_alpha:
|
|
defaultValue: 16.0
|
|
isOptional: true
|
|
parameterType: NUMBER_INTEGER
|
|
lora_dropout:
|
|
defaultValue: 0.05
|
|
isOptional: true
|
|
parameterType: NUMBER_DOUBLE
|
|
lora_r:
|
|
defaultValue: 64.0
|
|
isOptional: true
|
|
parameterType: NUMBER_INTEGER
|
|
max_seq_length:
|
|
defaultValue: 2048.0
|
|
isOptional: true
|
|
parameterType: NUMBER_INTEGER
|
|
mlflow_tracking_uri:
|
|
defaultValue: http://mlflow.mlflow.svc.cluster.local:80
|
|
isOptional: true
|
|
parameterType: STRING
|
|
num_epochs:
|
|
defaultValue: 3.0
|
|
isOptional: true
|
|
parameterType: NUMBER_INTEGER
|
|
s3_bucket:
|
|
defaultValue: training-data
|
|
isOptional: true
|
|
parameterType: STRING
|
|
s3_endpoint:
|
|
defaultValue: candlekeep.lab.daviestechlabs.io
|
|
isOptional: true
|
|
parameterType: STRING
|
|
s3_prefix:
|
|
defaultValue: ''
|
|
isOptional: true
|
|
parameterType: STRING
|
|
schemaVersion: 2.1.0
|
|
sdkVersion: kfp-2.12.1
|