verl_utils

Build verl-format parquet datasets for reinforcement fine-tuning, including the per-task formatting functions and an optional held-out test split.

Dataset builders that turn MedVision tasks into verl-ready RFT datasets.

This module bridges the MedVision benchmark data pipeline and the verl GRPO trainer. It provides per-task formatting functions that reshape a raw example into the record layout expected by verl’s RLHFDataset (prompt, ground_truth, data_source, ability, reward_model, extra_info, and an embedded images field), plus the two top-level entry points that load, split, format, and clean a full dataset ready to be written to Parquet for training.

The formatters cover the three MedVision task families (TumorLesion, AngleDistance, and Detection), each in a CoT variant (using the full <think>/<answer> reasoning SYSTEM_PROMPT and populating extra_info with ground-truth landmark coordinates for process-level rewards) and a non-CoT “lite” variant (using SYSTEM_PROMPT_LITE with a minimal extra_info).

medvision_bm.rft.verl.verl_utils.prepare_dataset_for_verl(*, tasks_list_json_path, limit_train_sample, limit_val_sample, mapping_func, model_family_name, model_hf, num_workers_concat_datasets=4, num_workers_format_dataset=32, tag_ds=None, new_shape_hw=None, download_mode='reuse_dataset_if_exists')

Build a verl-ready RFT dataset (train/validation) from a task list.

Loads the datasets named in the task list, splits and limits them, maps each example through a per-task formatting function to produce the record fields verl expects, and drops all columns except the ones verl consumes. The result is ready to be written to Parquet for GRPO training.

Parameters:

• tasks_list_json_path – Path to the JSON task list describing which MedVision datasets/tasks to load.

• limit_train_sample – Maximum number of training samples to keep (total).

• limit_val_sample – Maximum number of validation samples to keep (total).

• mapping_func – Per-task formatting function (one of the _format_data_*_verl functions in this module) applied to each example to build the verl record fields.

• model_family_name – Model family name, passed to the formatter as model_name (used for model-specific image/prompt handling).

• model_hf – Hugging Face model identifier, passed to the formatter as model_hf.

• num_workers_concat_datasets – Worker count for concatenating the loaded per-task datasets.

• num_workers_format_dataset – Worker count for the formatting map step.

• tag_ds – Optional dataset tag used during loading.

• new_shape_hw – Optional target image shape (height, width) for resizing during image processing.

• download_mode – Hugging Face datasets download mode (default reuses an existing local copy).

Returns:

A dataset containing only the verl-required columns (prompt, ground_truth, data_source, ability, reward_model, extra_info) plus the embedded images column.

medvision_bm.rft.verl.verl_utils.prepare_dataset_for_verl_with_testset(*, tasks_list_json_path, limit_train_sample, limit_val_sample, mapping_func, model_family_name, model_hf, limit_test_sample=None, limit_train_sample_per_subset=None, limit_test_sample_per_subset=None, num_workers_concat_datasets=4, num_workers_format_dataset=32, tag_ds=None, new_shape_hw=None, download_mode='reuse_dataset_if_exists')

Build a verl-ready RFT dataset with an extra held-out test split.

Same as prepare_dataset_for_verl(), but uses the train/validation/test splitter so a test split is produced as well. The test set is not consumed by RFT via verl; it is prepared for debugging and future flexibility. Each example is mapped through the given formatting function and the dataset is cleaned down to only the verl-required columns.

Parameters:

• tasks_list_json_path – Path to the JSON task list describing which MedVision datasets/tasks to load.

• limit_train_sample – Maximum number of training samples to keep (total).

• limit_val_sample – Maximum number of validation samples to keep (total).

• mapping_func – Per-task formatting function (one of the _format_data_*_verl functions in this module) applied to each example to build the verl record fields.

• model_family_name – Model family name, passed to the formatter as model_name (used for model-specific image/prompt handling).

• model_hf – Hugging Face model identifier, passed to the formatter as model_hf.

• limit_test_sample – Maximum number of test samples to keep (total).

• limit_train_sample_per_subset – Optional per-subset cap on training samples.

• limit_test_sample_per_subset – Optional per-subset cap on test samples.

• num_workers_concat_datasets – Worker count for concatenating the loaded per-task datasets.

• num_workers_format_dataset – Worker count for the formatting map step.

• tag_ds – Optional dataset tag used during loading.

• new_shape_hw – Optional target image shape (height, width) for resizing during image processing.

• download_mode – Hugging Face datasets download mode (default reuses an existing local copy).

Returns:

A dataset containing only the verl-required columns (prompt, ground_truth, data_source, ability, reward_model, extra_info) plus the embedded images column.