version: "1.0.0" name: data-conventions description: Data format conventions for datasets and plmodules

Data Conventions

Pytree as Data Structure

Data is passed around as pytrees — nested Python containers (lists, tuples, dicts). Every leaf must be a torch.Tensor or np.ndarray with batch size as the leading dimension.

• Use torch.Tensor for data that will be sent to devices (model inputs, labels).
• Use np.ndarray for data that won't be sent to devices (e.g., string descriptions use NumPy string arrays).

Dataset Item Format

The outermost pytree is typically a dict with these top-level keys:

• description — Human-readable explanation of the sample. Highly encouraged for debugging.
• data — Model input. Never include labels here.
• label (optional) — Ground truth.

Operator Learning Example

{
    "description": np.array(['...'] * batch_size, dtype=np.dtypes.StringDType()),
    "data": {
        "fx": torch.randn(batch_size, f_len, fx_dim),  # operator input function values
        "fy": torch.randn(batch_size, f_len, fy_dim),  # operator input function inputs
        "fm": torch.ones(batch_size, f_len, dtype=torch.bool),  # mask
        "gx": torch.randn(batch_size, g_len, gx_dim),  # operator output query points
        "gm": torch.ones(batch_size, g_len, dtype=torch.bool),
    },
    "label": torch.randn(batch_size, g_len, gy_dim),
}

f = operator input function, g = operator output function, x = function input, y = function output, m = mask. gy is excluded from data since it is the label.

In-Context Operator Learning Example

{
    "description": np.array(['...'] * batch_size, dtype=np.dtypes.StringDType()),
    "data": {
        "ex_fx": torch.randn(batch_size, ex_num, f_len, fx_dim),  # example input functions
        "ex_fy": torch.randn(batch_size, ex_num, f_len, fy_dim),
        "ex_fm": torch.ones(batch_size, ex_num, f_len, dtype=torch.bool),
        "ex_gx": torch.randn(batch_size, ex_num, g_len, gx_dim),
        "ex_gy": torch.randn(batch_size, ex_num, g_len, gy_dim),
        "ex_gm": torch.ones(batch_size, ex_num, g_len, dtype=torch.bool),
        "qn_fx": torch.randn(batch_size, qn_num, f_len, fx_dim),  # query input functions
        "qn_fy": torch.randn(batch_size, qn_num, f_len, fy_dim),
        "qn_fm": torch.ones(batch_size, qn_num, f_len, dtype=torch.bool),
        "qn_gx": torch.randn(batch_size, qn_num, g_len, gx_dim),
        "qn_gm": torch.ones(batch_size, qn_num, g_len, dtype=torch.bool),
    },
    "label": torch.randn(batch_size, qn_num, g_len, gy_dim),
}

ex = example, qn = question. qn_gy is excluded from data since it is the label.

validation_step Return Format

validation_step in plmodules should return a pytree. Each leaf should be a torch.Tensor or np.ndarray, keeping the batch dimension (do not pool over batch).

Suggested top-level keys:

• preds — Model predictions (e.g., images or tensors).
• errors — Detailed prediction errors (e.g., per-sample error maps).
• metrics — Scalar-ish evaluation metrics with shape (batch_size, ...). The flattened size of ... should remain small.

The return value is passed directly as outputs to on_validation_batch_end in all callbacks.