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fix(torch): make MinMaxRandomDataset iterable so inspect() can use it - #156
ait.inspect() crashes with ZeroDivisionError when given a MinMaxRandomDataset, either directly or wrapped in DataLoaderFactory, which is the dynamic shape setup the aitune/torch/dataloader.py module docstring shows.
The cause is that MinMaxRandomDataset is a list subclass (through DynamicShapeDataset) whose __init__ never calls list.__init__. It keeps the samples in self.samples and overrides __len__ (which returns num_samples) and __getitem__ (which reads self.samples), but the list iterator in C does not go through those overrides. So len(ds) is num_samples while list(ds) and iter(ds) are empty. inspect() calls ensure_enough_samples(), which does list(islice(cycle(ds), n)) and gets [], and then SingleBatchDatasetWrapper([], 1) divides by zero on the first fetch. tune() never noticed because DataLoaderFactory reaches the samples by index through _DynamicShapeDatasetWrapper.
A short script on main shows it. For MinMaxRandomDataset(4, [InputConfig((3,), (6,), kwarg_name="x")]) it printed len 4, list(ds) 0, repr [], and ensure_enough_samples(ds, 10) returned 0 items. inspect() on a small module failed with ZeroDivisionError: integer modulo by zero in SingleBatchDatasetWrapper.__getitem__, both for the bare dataset and for DataLoaderFactory(ds).
The fix passes the generated samples to list.__init__ and drops the __len__ and __getitem__ overrides, so the list storage is the only copy and indexing, iteration and assignment all see the same samples. samples stays available as a property that returns the dataset itself, in case anything outside the repo reads it. Generation is unchanged and still happens once in the constructor, so seeded datasets produce the same tensors as before. DynamicShapeDataset itself was fine, since its overrides just delegate to list, and it is the only list subclass in the module.
With only that change the factory case got through inspect() but broke something else. ensure_enough_samples() wrote the trimmed plain list back into the DataLoaderFactory it was passed, so after inspect(model, factory) the user's factory held 10 mixed shape samples instead of the MinMaxRandomDataset. A later samples_generator(factory, [2]), which is what tune() does with the same factory, then failed with RuntimeError: stack expects each tensor to be equal size. The second change returns a copy.copy() of the factory with the trimmed dataset, so the caller's object keeps its dataset, and a shallow copy also keeps collate_fn, num_workers and any create_dataloader override from a subclass. inspect() is the only caller and already uses the return value. If you would rather keep the in place behaviour, I can drop this part and leave only the dataset fix.
Tests added. test_iterating_yields_all_samples in tests/unit/torch/test_random_dataset.py checks that list(ds) has len(ds) items in index order, with and without min/max shapes, and test_assigned_sample_is_seen_by_indexing_and_iteration checks that ds[0] = x shows up through indexing, iteration and samples. test_ensure_enough_samples_min_max_random_dataset and test_ensure_enough_samples_keeps_dataloader_factory_dataset in tests/unit/torch/test_dataloader.py check that it returns n samples, and that the original factory still has its dataset and still batches the three shapes at batch size 2. test_inspect_min_max_random_dataset in tests/unit/torch/inspecting/test_inspecting.py runs inspect() on CPU with the dataset passed directly and through a factory. The six original test cases fail on main with the source change reverted and pass with it. Without the copy fix, the factory test and the factory variant of the inspect test still fail.
I tested on macOS with CPU only torch. TensorRT, Torch-TensorRT, ModelOpt and Triton are stubbed so the modules import. tests/unit/torch and tests/unit/utils give the same 48 failures as on main (GPU and torch.compile dependent tests that also fail on main here). The dataloader.py doctests pass. ruff 0.16.5 check and format pass on the changed files, and pyrefly 1.4.0 reports the same errors as on main. I have not run the GPU functional tests.
A CHANGELOG entry is added under 0.7.0.
Summary by CodeRabbit
Bug Fixes
Inspection now accepts MinMaxRandomDataset directly or through a data loader factory.
Preparing a factory’s dataset with additional samples leaves the original factory and its dataset unchanged.
Random datasets provide consistent samples through iteration, indexing, and the samples property. Iteration preserves sample order, and replacing a sample is reflected across these access methods.
MinMaxRandomDataset subclasses DynamicShapeDataset, which is a list, but
its __init__ never called list.__init__. The samples lived only in
self.samples, and __len__ and __getitem__ were overridden to read them.
The C level list iterator ignores those overrides, so iter(ds) and
list(ds) were empty while len(ds) was num_samples.
inspect() calls ensure_enough_samples(), which cycles over the dataset
with itertools and so got an empty list back. samples_generator() then
wrapped it in SingleBatchDatasetWrapper and the first fetch failed with
ZeroDivisionError on index % len(self.dataset). Passing the dataset
through DataLoaderFactory ended the same way.
Populate the list storage with the generated samples in __init__. The
samples are generated the same way as before, so seeded runs give the
same data.
ensure_enough_samples() also assigned the trimmed list back to the
DataLoaderFactory it was given. A factory shared between inspect() and
tune() therefore lost its MinMaxRandomDataset, and batch sizes above
one failed in torch.stack because samples of different shapes were
batched together. Return a shallow copy of the factory instead, so the
caller's factory keeps its dataset, collate_fn and any subclass
behaviour.
Signed-off-by: Dmitry Voropaev <dy.voropaev@gmail.com>
MinMaxRandomDataset now uses inherited list storage for its generated samples. ensure_enough_samples returns a shallow copy of a DataLoaderFactory when expanding its dataset. Tests cover sample identity, factory preservation, and inspection.
MinMaxRandomDataset initializes inherited list storage from its generated samples. Tests check iteration, indexing, and sample identity.
Expand samples without replacing the supplied factory aitune/torch/dataloader.py, tests/unit/torch/test_dataloader.py, tests/unit/torch/inspecting/test_inspecting.py, CHANGELOG.md
ensure_enough_samples shallow-copies a DataLoaderFactory before replacing its dataset. Tests cover expansion, preservation of the original factory, and inspection directly or through a factory. The changelog records the inspection support and factory behavior.
This change makes MinMaxRandomDataset iterable so inspect() works, and it no longer replaces the caller's factory dataset. The only remaining concern is that external code which assigns dataset.samples or passes dataset.samples to a factory would behave differently. No such usage exists in the repository, so the risk is small and merging is reasonable with awareness of this compatibility change.
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The title clearly and concisely describes the main change: making MinMaxRandomDataset iterable so inspect() can use it directly or through a DataLoaderFactory.
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Actionable comments posted: 1
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Inline comments:
Review comments at @aitune/torch/dataloader.py:
- Line 295: Use one mutable backing store for the dataset’s iteration, indexed
access, and mutation: update the initialization around
super().__init__(self.samples) and the dataset accessors so assigning dataset[0]
changes the sample returned by both iteration and indexing.
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Reviewing files that changed from the base of the PR and between 1880f38 and 4abe7e4.
Files selected for processing: 5
CHANGELOG.md
aitune/torch/dataloader.py
tests/unit/torch/inspecting/test_inspecting.py
tests/unit/torch/test_dataloader.py
tests/unit/torch/test_random_dataset.py
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The previous commit filled the list storage but kept self.samples and
the __len__/__getitem__ overrides, so after dataset[i] = x iteration
saw the new sample and indexing the old one. Drop the overrides and
expose samples as a property returning the dataset itself.
Signed-off-by: Dmitry Voropaev <dy.voropaev@gmail.com>
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Additional comments: 2
aitune/torch/dataloader.py-296-297: 1
296-297: Minor · Functional Correctness · Quick win
Unverified finding
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Keep samples assignable.
Existing callers can replace the public samples list, but this read-only property makes dataset.samples = new_samples raise AttributeError. Add a setter that updates the list storage. (github.com)
aitune/torch/dataloader.py-299-299: 1
299-299: Major · Data Integrity & Integration · Heavy lift
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Preserve batching behavior for samples.
Previously, dataset.samples was a plain list. It is now a DynamicShapeDataset, so DataLoaderFactory(dataset.samples) selects dynamic-shape batching. For fixed-shape samples and a batch size of two, it repeats each sample instead of batching distinct samples. Preserve the previous batching behavior for callers that pass samples to a factory. (github.com)
Reviewing files that changed from the base of the PR and between 5ff18bc and 8eed7c7.
Files selected for processing: 3
CHANGELOG.md
aitune/torch/dataloader.py
tests/unit/torch/inspecting/test_inspecting.py
Files skipped from review as they are similar to previous changes: 1
CHANGELOG.md
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ait.inspect()crashes withZeroDivisionErrorwhen given aMinMaxRandomDataset, either directly or wrapped inDataLoaderFactory, which is the dynamic shape setup theaitune/torch/dataloader.pymodule docstring shows.The cause is that
MinMaxRandomDatasetis alistsubclass (throughDynamicShapeDataset) whose__init__never callslist.__init__. It keeps the samples inself.samplesand overrides__len__(which returnsnum_samples) and__getitem__(which readsself.samples), but the list iterator in C does not go through those overrides. Solen(ds)isnum_sampleswhilelist(ds)anditer(ds)are empty.inspect()callsensure_enough_samples(), which doeslist(islice(cycle(ds), n))and gets[], and thenSingleBatchDatasetWrapper([], 1)divides by zero on the first fetch.tune()never noticed becauseDataLoaderFactoryreaches the samples by index through_DynamicShapeDatasetWrapper.A short script on main shows it. For
MinMaxRandomDataset(4, [InputConfig((3,), (6,), kwarg_name="x")])it printedlen 4,list(ds) 0,repr [], andensure_enough_samples(ds, 10)returned 0 items.inspect()on a small module failed withZeroDivisionError: integer modulo by zeroinSingleBatchDatasetWrapper.__getitem__, both for the bare dataset and forDataLoaderFactory(ds).The fix passes the generated samples to
list.__init__and drops the__len__and__getitem__overrides, so the list storage is the only copy and indexing, iteration and assignment all see the same samples.samplesstays available as a property that returns the dataset itself, in case anything outside the repo reads it. Generation is unchanged and still happens once in the constructor, so seeded datasets produce the same tensors as before.DynamicShapeDatasetitself was fine, since its overrides just delegate tolist, and it is the onlylistsubclass in the module.With only that change the factory case got through
inspect()but broke something else.ensure_enough_samples()wrote the trimmed plain list back into theDataLoaderFactoryit was passed, so afterinspect(model, factory)the user's factory held 10 mixed shape samples instead of theMinMaxRandomDataset. A latersamples_generator(factory, [2]), which is whattune()does with the same factory, then failed withRuntimeError: stack expects each tensor to be equal size. The second change returns acopy.copy()of the factory with the trimmed dataset, so the caller's object keeps its dataset, and a shallow copy also keepscollate_fn,num_workersand anycreate_dataloaderoverride from a subclass.inspect()is the only caller and already uses the return value. If you would rather keep the in place behaviour, I can drop this part and leave only the dataset fix.Tests added.
test_iterating_yields_all_samplesintests/unit/torch/test_random_dataset.pychecks thatlist(ds)haslen(ds)items in index order, with and without min/max shapes, andtest_assigned_sample_is_seen_by_indexing_and_iterationchecks thatds[0] = xshows up through indexing, iteration andsamples.test_ensure_enough_samples_min_max_random_datasetandtest_ensure_enough_samples_keeps_dataloader_factory_datasetintests/unit/torch/test_dataloader.pycheck that it returns n samples, and that the original factory still has its dataset and still batches the three shapes at batch size 2.test_inspect_min_max_random_datasetintests/unit/torch/inspecting/test_inspecting.pyrunsinspect()on CPU with the dataset passed directly and through a factory. The six original test cases fail on main with the source change reverted and pass with it. Without the copy fix, the factory test and the factory variant of the inspect test still fail.I tested on macOS with CPU only torch. TensorRT, Torch-TensorRT, ModelOpt and Triton are stubbed so the modules import.
tests/unit/torchandtests/unit/utilsgive the same 48 failures as on main (GPU and torch.compile dependent tests that also fail on main here). Thedataloader.pydoctests pass. ruff 0.16.5 check and format pass on the changed files, and pyrefly 1.4.0 reports the same errors as on main. I have not run the GPU functional tests.A CHANGELOG entry is added under 0.7.0.
Summary by CodeRabbit
MinMaxRandomDatasetdirectly or through a data loader factory.samplesproperty. Iteration preserves sample order, and replacing a sample is reflected across these access methods.