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reformat docstrings for sphinx

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smgr
2026-03-05 01:36:40 -08:00
parent 805262dfc4
commit faeef9c72a
3 changed files with 117 additions and 102 deletions
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2
pyproject.toml
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@@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
[project]
name = "trainlib"
version = "0.1.0"
version = "0.1.1"
description = "Minimal framework for ML modeling. Supports advanced dataset operations and streamlined training."
requires-python = ">=3.13"
authors = [

90
trainlib/dataset.py
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@@ -1,5 +1,5 @@
"""
Marginalizing out the modality layer:
.. admonition:: Marginalizing out the modality layer
With ``domain`` being an instance variable, one possible interpretation of
the object structures here is that one could completely abstract away
@@ -58,7 +58,10 @@ Marginalizing out the modality layer:
particular case of ``_process_batch_data()``, it feels much better when
it's on the inside.)
Holding:
.. admonition:: Holding area
.. code-block:: python
@abstractmethod
def _get_uri_groups(self) -> Iterable[tuple[U, ...]]:
Get URI groups for each batch.
@@ -67,19 +70,19 @@ Holding:
metadata file), zip the URIs such that we have a tuple of URIs per
batch.
Note that this effectively defines the index style over batches in the
attached domain. We get an ``int -> tuple[U, ...]`` map that turns
batch indices into URIs that can be read under the domain.
Note that this effectively defines the index style over batches in
the attached domain. We get an ``int -> tuple[U, ...]`` map that
turns batch indices into URIs that can be read under the domain.
``get_batch()`` turns an integer index into its corresponding
``tuple[U, ...]``, reading the resources with ``_read_resources()`` in
the tuple, treating them as providers of batched data.
``tuple[U, ...]``, reading the resources with ``_read_resources()``
in the tuple, treating them as providers of batched data.
``_read_resources()`` passes through to the attached domain logic,
which, although common, need not supply an explicit iterable of batch
items: we just access items with ``__getitem__()`` and may ask for
``__len__``. So the returned URI group collection (this method) does
need to be iterable to measure the number of batches, but the batch
objects that are ultimately produced by these URI groups need not be
iterables themselves.
which, although common, need not supply an explicit iterable of
batch items: we just access items with ``__getitem__()`` and may
ask for ``__len__``. So the returned URI group collection (this
method) does need to be iterable to measure the number of batches,
but the batch objects that are ultimately produced by these URI
groups need not be iterables themselves.
raise NotImplementedError
@@ -91,20 +94,24 @@ Holding:
Read batch files at the provided paths.
This method should operate on a single tuple from the list of batch
tuples returned by the ``_get_uri_groups()`` method. That is, it reads
all of the resources for a single batch and returns a tuple of the same
size with their contents.
tuples returned by the ``_get_uri_groups()`` method. That is, it
reads all of the resources for a single batch and returns a tuple
of the same size with their contents.
Note: the dependence on a batch index is mostly here to make
multi-dataset composition easier later. In-dataset, you don't need to
know the batch index to to simply process URIs, but across datasets you
need it to find out the origin of the batch (and process those URIs
accordingly).
multi-dataset composition easier later. In-dataset, you don't need
to know the batch index to to simply process URIs, but across
datasets you need it to find out the origin of the batch (and
process those URIs accordingly).
return tuple(self.domain.read(uri) for uri in uri_group)
# pulling the type variable out of the inline generic b/c `ty` has trouble
# understanding bound type variables in subclasses (specifically with Self@)
.. code-block:: python
# pulling the type variable out of the inline generic b/c `ty` has
# trouble understanding bound type variables in subclasses
# (specifically with Self@)
T = TypeVar("T", bound=NamedTuple)
class NamedTupleDataset[I](Dataset):
@@ -156,38 +163,41 @@ class BatchedDataset[U, R, I](Dataset):
which are used to concretize a domain ``Domain[U, R]``), and an item type
``T`` (which has a ``tuple`` upper bound).
Pipeline overview:
```
.. admonition:: Pipeline overview
.. code-block:: python
Domain -> [U] (get _batch_uris)
U -> R (domain access ; Rs provide batches)
R -> [I] (cache here ; _process_batch_data to use load_transform)
[I] -> I (human item obj ; _get_item)
I -> **P (final packed item ; __getitem__ to use transform)
```
Note^1: as far as positioning, this class is meant to play nice with
PyTorch DataLoaders, hence the inheritance from ``torch.Dataset``. The
value add for this over the ``torch.Dataset`` base is almost entirely in
the logic it implements to map out of *batched resources* that are holding
data, and flattening it out into typical dataset items. There are also some
QoL items when it comes to splitting and balancing samples.
value add for this over the ``torch.Dataset`` base is almost entirely
in the logic it implements to map out of *batched resources* that are
holding data, and flattening it out into typical dataset items. There
are also some QoL items when it comes to splitting and balancing
samples.
Note^2: even though ``Domains`` implement iterators over their URIs, this
doesn't imply a ``BatchedDataset`` is iterable. This just means we can walk
over the resources that provide data, but we don't necessarily presuppose
an ordered walk over samples within batches. Point being:
Note^2: even though ``Domains`` implement iterators over their URIs,
this doesn't imply a ``BatchedDataset`` is iterable. This just means we
can walk over the resources that provide data, but we don't necessarily
presuppose an ordered walk over samples within batches. Point being:
``torch.Dataset``, not ``torch.IterableDataset``, is the appropriate
superclass, even when we're working around iterable ``Domains``.
Note^3: transforms are expected to operate on ``I``-items and produce
``I``-items. They shouldn't be the "introducers" of ``I`` types from some
other intermediate representation, nor should they map from ``I`` to
something else. Point being: the dataset definition should be able to map
resources ``R`` to ``I`` without a transform: that much should be baked
into the class definition. If you find you're expecting the transform to do
that for you, you should consider pulling in some common structure across
the allowed transforms and make it a fixed part of the class.
``I``-items. They shouldn't be the "introducers" of ``I`` types from
some other intermediate representation, nor should they map from ``I``
to something else. Point being: the dataset definition should be able
to map resources ``R`` to ``I`` without a transform: that much should
be baked into the class definition. If you find you're expecting the
transform to do that for you, you should consider pulling in some
common structure across the allowed transforms and make it a fixed part
of the class.
"""
def __init__(

29
trainlib/trainer.py
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@@ -268,19 +268,21 @@ class Trainer[I, K: EstimatorKwargs]:
"""
Note: this method attempts to implement a general scheme for passing
needed items to the estimator's loss function from the dataloader. The
abstract `Estimator` base only requires the model output be provided
abstract ``Estimator`` base only requires the model output be provided
for any given loss calculation, but concrete estimators will often
require additional arguments (e.g., labels or length masks, as
is the case with sequential models). In any case, this method defers
any further logic to the `loss` method of the underlying estimator, so
require additional arguments (e.g., labels or length masks, as is the
case with sequential models). In any case, this method defers any
further logic to the ``loss`` method of the underlying estimator, so
one should take care to synchronize the sample structure with `dataset`
to match that expected by `self.estimator.loss(...)`.
to match that expected by ``self.estimator.loss(...)``.
On batch_estimator_map:
.. admonition:: On batch_estimator_map
Dataloader collate functions are responsible for mapping a collection
of items into an item of collections, roughly speaking. If items are
tuples of tensors,
Dataloader collate functions are responsible for mapping a
collection of items into an item of collections, roughly speaking.
If items are tuples of tensors,
.. code-block::
[
( [1, 1], [1, 1] ),
@@ -291,6 +293,8 @@ class Trainer[I, K: EstimatorKwargs]:
the collate function maps back into the item skeleton, producing a
single tuple of (stacked) tensors
.. code-block::
( [[1, 1],
[2, 2],
[3, 3]],
@@ -299,9 +303,10 @@ class Trainer[I, K: EstimatorKwargs]:
[2, 2],
[3, 3]] )
This function should map from batches (which should be *item shaped*,
i.e., have an `I` skeleton, even if stacked items may be different on
the inside) into estimator keyword arguments (type `K`).
This function should map from batches (which should be *item
shaped*, i.e., have an ``I`` skeleton, even if stacked items may be
different on the inside) into estimator keyword arguments (type
``K``).
Parameters:
lr: learning rate (default: 1e-3)