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add new input parameters #271

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Feb 25, 2025
Merged

add new input parameters #271

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62 changes: 50 additions & 12 deletions pipeline.py
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Original file line number Diff line number Diff line change
Expand Up @@ -70,20 +70,36 @@
description="InstructLab pipeline",
)
def ilab_pipeline(
# Model I/O
input_model_uri: str,
output_oci_model_uri: str = "",
output_oci_registry_secret: str = None,
output_model_name: str = None,
output_model_version_name: str = None,
output_model_registry_name: str = None,
output_model_registry_namespace: str = "rhoai-model-registries",
output_modelcar_base_image: str = "registry.access.redhat.com/ubi9-micro:latest",
# SDG phase
sdg_repo_url: str = "https://github.com/instructlab/taxonomy.git",
sdg_repo_url: str = None,
sdg_repo_secret: str = "taxonomy-repo-secret",
sdg_repo_branch: Optional[str] = None,
sdg_repo_pr: Optional[
int
] = None, # FIXME: https://issues.redhat.com/browse/RHOAIRFE-467
sdg_base_model: str = "s3://<BUCKET>/<PATH_TO_MODEL>",
sdg_teacher_secret: str = "teacher-secret",
sdg_base_model: str = None,
sdg_scale_factor: int = 30, # https://github.com/instructlab/instructlab/blob/v0.21.2/tests/testdata/default_config.yaml#L125
sdg_pipeline: str = "/usr/share/instructlab/sdg/pipelines/agentic", # https://github.com/instructlab/instructlab/blob/v0.21.2/tests/testdata/default_config.yaml#L122
sdg_max_batch_len: int = 5000, # https://github.com/instructlab/instructlab/blob/v0.21.2/tests/testdata/default_config.yaml#L334
sdg_sample_size: float = 1.0, # FIXME: Not present in default config. Not configurable upstream at this point, capability added via https://github.com/instructlab/sdg/pull/432
# Training phase
train_nproc_per_node: int = 2, # FIXME: Not present in default config. Arbitrary value chosen to demonstrate multi-node multi-gpu capabilities. Needs proper reference architecture justification.
train_nnodes: int = 2, # FIXME: Not present in default config. Arbitrary value chosen to demonstrate multi-node multi-gpu capabilities. Needs proper reference architecture justification.
train_tolerations: Optional[list] = None,
train_node_selectors: Optional[dict] = None,
train_gpu_identifier: str = "nvidia.com/gpu",
train_gpu_per_worker: int = 2, # FIXME: Not present in default config. Arbitrary value chosen to demonstrate multi-node multi-gpu capabilities. Needs proper reference architecture justification.
train_cpu_per_worker: str = "2", # FIXME: Not present in default config. Arbitrary value chosen to demonstrate multi-node multi-gpu capabilities. Needs proper reference architecture justification.
train_memory_per_worker: str = "2Gi", # FIXME: Not present in default config. Arbitrary value chosen to demonstrate multi-node multi-gpu capabilities. Needs proper reference architecture justification.
train_num_workers: int = 2, # FIXME: Not present in default config. Arbitrary value chosen to demonstrate multi-node multi-gpu capabilities. Needs proper reference architecture justification.
train_num_epochs_phase_1: int = 7, # https://github.com/instructlab/instructlab/blob/v0.21.2/tests/testdata/default_config.yaml#L364
train_num_epochs_phase_2: int = 10, # https://github.com/instructlab/instructlab/blob/v0.21.2/tests/testdata/default_config.yaml#L377
train_effective_batch_size_phase_1: int = 128, # https://github.com/instructlab/instructlab/blob/v0.21.2/tests/testdata/default_config.yaml#L357
Expand All @@ -103,23 +119,42 @@ def ilab_pipeline(
final_eval_few_shots: int = 5, # https://github.com/instructlab/instructlab/blob/v0.21.2/tests/testdata/default_config.yaml#L56
final_eval_batch_size: str = "auto", # https://github.com/instructlab/instructlab/blob/v0.21.2/tests/testdata/default_config.yaml#L52
final_eval_merge_system_user_message: bool = False, # https://github.com/instructlab/instructlab/blob/v0.21.2/src/instructlab/model/evaluate.py#L474
# General Evaluation Inputs
eval_gpu_identifier: str = "nvidia.com/gpu",
eval_judge_secret: str = "judge-secret",
# Other options
k8s_storage_class_name: str = "standard", # FIXME: https://github.com/kubeflow/pipelines/issues/11396, https://issues.redhat.com/browse/RHOAIRFE-470
):
"""InstructLab pipeline

Args:
sdg_repo_url: SDG parameter. Points to a taxonomy git repository
input_model_uri: URI pointing to a model in an OCI or S3 registry.
output_oci_model_uri: The URI path to the OCI registry where the output model is pushed to.
output_oci_registry_secret: The secret key to use for OCI output registry.
output_model_name: Model Registration parameter. The name of the model used during model registration.
output_model_version_name: Model Registration parameter. The version of the model used during model registration.
output_model_registry_name: Model Registration parameter. The name of the model registry used for model registration.
output_model_registry_namespace: Model Registration parameter. The namespace of the model used during model registration.
output_modelcar_base_image: The base image used for output model.

sdg_repo_url: SDG parameter. Points to a taxonomy git repository. E.g. "https://github.com/instructlab/taxonomy.git"
sdg_repo_secret: SDG parameter. The name of the k8s secret holding access credentials to the sdg_repo_url.
sdg_repo_branch: SDG parameter. Points to a branch within the taxonomy git repository. If set, has priority over sdg_repo_pr
sdg_repo_pr: SDG parameter. Points to a pull request against the taxonomy git repository
sdg_base_model: SDG parameter. LLM model used to generate the synthetic dataset
sdg_teacher_secret: SDG parameter. The name of the k8s secret key holding access credentials to the teacher server.
sdg_base_model: SDG parameter. LLM model used to generate the synthetic dataset. E.g. "s3://<BUCKET>/<PATH_TO_MODEL>"
sdg_scale_factor: SDG parameter. The total number of instructions to be generated.
sdg_pipeline: SDG parameter. Data generation pipeline to use. Available: 'simple', 'full', or a valid path to a directory of pipeline workflow YAML files. Note that 'full' requires a larger teacher model, Mixtral-8x7b.
sdg_max_batch_len: SDG parameter. Maximum tokens per gpu for each batch that will be handled in a single step.
sdg_sample_size: SDG parameter. Represents the sdg skills recipe sampling size as percentage in decimal form.

train_nproc_per_node: Training parameter. Number of GPUs per each node/worker to use for training.
train_nnodes: Training parameter. Number of nodes/workers to train on.
train_tolerations: Training parameter. List of tolerations applied to training pods.
train_node_selectors: Training parameter. A JSON containing node selectors applied to training pods.
train_gpu_identifier: Training parameter. The GPU type used for training pods, e.g. nvidia.com/gpu
train_gpu_per_worker: Training parameter. Number of GPUs per each node/worker to use for training.
train_cpu_per_worker: Training parameter. Number of CPUs per each node/worker to use for training.
train_memory_per_worker: Training parameter. Memory per GPU per each node/worker to use for training.
train_num_workers: Training parameter. Number of nodes/workers to train on.
train_num_epochs_phase_1: Training parameter for in Phase 1. Number of epochs to run training.
train_num_epochs_phase_2: Training parameter for in Phase 2. Number of epochs to run training.
train_effective_batch_size_phase_1: Training parameter for in Phase 1. The number of samples in a batch that the model should see before its parameters are updated.
Expand All @@ -140,6 +175,9 @@ def ilab_pipeline(
final_eval_batch_size: Final model evaluation parameter for MMLU. Batch size for evaluation. Valid values are a positive integer or 'auto' to select the largest batch size that will fit in memory.
final_eval_merge_system_user_message: Final model evaluation parameter for MT Bench Branch. Boolean indicating whether to merge system and user messages (required for Mistral based judges)

eval_gpu_identifier: General evaluation parameter. The GPU type used for training pods, e.g. nvidia.com/gpu
eval_judge_secret: General evaluation parameter: The name of the k8s secret key holding access credentials to the judge server.

k8s_storage_class_name: A Kubernetes StorageClass name for persistent volumes. Selected StorageClass must support RWX PersistentVolumes.
"""

Expand Down Expand Up @@ -285,8 +323,8 @@ def ilab_pipeline(
output_pvc_name=output_pvc_task.output,
phase_num=1,
base_image=RHELAI_IMAGE,
nproc_per_node=train_nproc_per_node,
nnodes=train_nnodes,
nproc_per_node=train_gpu_per_worker,
nnodes=train_num_workers,
num_epochs=train_num_epochs_phase_1,
effective_batch_size=train_effective_batch_size_phase_1,
learning_rate=train_learning_rate_phase_1,
Expand All @@ -306,8 +344,8 @@ def ilab_pipeline(
output_pvc_name=output_pvc_task.output,
phase_num=2,
base_image=RHELAI_IMAGE,
nproc_per_node=train_nproc_per_node,
nnodes=train_nnodes,
nproc_per_node=train_gpu_per_worker,
nnodes=train_num_workers,
num_epochs=train_num_epochs_phase_2,
effective_batch_size=train_effective_batch_size_phase_2,
learning_rate=train_learning_rate_phase_2,
Expand Down
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