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Move string vocabularies to CUDA as separate tensors - #1304
Copy each string vocabulary into its own CUDA memory in TableTensor.from_pandas(..., device="cuda"), instead of splitting one shared copy into views. This fixes recompiles on almost every batch in compiled predict and compiled preprocessing, and a compiled crash in the native category lookup of #1275.
Description of the change
What this fixes.
Recompiles: compiled model.predict and compiled processors such as AlignCategories.transform recompile whenever the vocabulary sizes of a batch differ from the previous one, which is almost every batch.
Why this is needed even though from_pandas runs before the model.from_pandas itself is never compiled, but the tables it builds are the inputs of compiled model.predict and processors such as AlignCategories.transform. torch.compile guards on the layout of every input. For a StringTensor, that layout includes its storage offset, which __tensor_flatten__ returns as metadata. Dynamo compares that metadata by equality on every call (check_subclass_metadata), so a new storage offset means a new graph.
On main, from_pandas concatenates all string vocabularies, copies them to the device once and splits the copy into views (torch.cat(tensors).to(device).split(sizes)). A view's storage offset is the total size of the vocabularies before it, so it changes from batch to batch:
The shared-buffer views also break the native category lookup in #1275, in Inductor-generated while_loop code.
The change. Plain tensor vocabularies of one dtype still move together. String vocabularies now move one by one with .to(device), which copies only each vocabulary's own bytes, offsets and validity, so each starts at storage offset 0, like a CPU vocabulary, and batches no longer differ in layout.
Only from_pandas with a non-CPU device is affected. from_arrow, from_cudf and .to(device) of a CPU table move or build each vocabulary on its own and do not pack them into one buffer.
Validation
NVIDIA L4, PyTorch 2.14.1+cu130: test/tensor passed on CUDA. The script above compiles a second graph on main and reuses the first graph with this change.
TableTensor.from_pandas(..., device="cuda") on the context table and 35 prediction batches per dataset, three passes, two runs each. Peak is device memory allocated above the starting point.
Dataset
String columns
Main p50 ms
This p50 ms
Main peak KiB
This peak KiB
credit-g
13
3.49
4.04
36.0
47.5
Diabetes130US
39
8.13
10.45
1544.0
1578.0
kddcup09_appetency
38
7.89
10.06
1866.5
1892.5
Amazon_employee_access
9
2.79
3.04
479.5
482.0
Food_Delivery_Time
3
1.66
1.54
146.0
148.0
synthetic-text
3
1.50
1.38
459.5
461.5
Building a table is up to 2.3 ms slower with many string columns, since each vocabulary is a separate copy, and peak memory is up to 34 KiB higher. Staging all vocabularies in one pinned buffer and copying them in one batch was about 1.4 ms faster on Diabetes130US, but needed about 50 lines of offset bookkeeping.
✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 82.39%. Comparing base (699bdda) to head (ed94b92).
✅ All tests successful. No failed tests found.
Device transfers now batch standard tensors that share a data type. Tensor subclasses, including string tensors, are transferred individually. This makes transfer behavior consistent for standard tensors and their subclasses, while preserving batched transfers for standard tensors.
_move_categories groups exact plain Tensor instances by dtype for concatenated device transfers. Other tensor categories use individual .to(device) calls. The categorical conversion test checks that the second category tensor has a storage offset of zero.
Changes: Category device transfers
Layer / File(s)
Summary
Category transfer routing sdm/tensor/table.py, test/tensor/test_table.py
_move_categories groups exact plain Tensor instances by dtype and moves other categories individually. The categorical conversion test asserts that the second category tensor has a storage offset of zero.
The change is likely mergeable, but a focused CUDA regression test should cover grouped categorical transfers.
Pre-merge checks: 4 · 1
Failed checks: 1 warning
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Passed checks: 4 passed
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Title check
The title clearly and concisely describes the main change: moving string vocabularies to CUDA as separate tensors.
Description check
The description directly explains the CUDA vocabulary-copy change, its effects on compiled graph recompilation and category lookup, scope, validation, and performance trade-offs.
Linked Issues check
Check skipped because no linked issues were found for this pull request.
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Check skipped because no linked issues were found for this pull request.
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Review comment at @sdm/tensor/table.py at line 149:
Update the multi-line split call in the `stage` flow to pass its split-size
argument by the `split_size` keyword, preserving the existing value and
behavior.
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Review comment at @test/tensor/test_table.py around lines 1368 - 1369:
Update the multi-line TableTensor.from_pandas call to pass the DataFrame using
the named df argument, keeping the existing stypes and device arguments
unchanged.
If both calls run without a compiled graph, both frame counts are zero and this assertion passes. Add assert frames[0] > 0 so the test detects that case. CompileCounterWithBackend increments frame_count when it receives a graph. (raw.githubusercontent.com)
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Review comment at @test/tensor/test_table.py at line 1375:
Update the frame-count assertions using frames to verify frames[0] is greater
than zero before checking that frames[0] equals frames[1], so the test fails if
neither call compiles a graph.
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Assert zero storage offsets in the CUDA pandas test.
The test already creates string vocabularies with different sizes, but it checks only values and devices.
Suggested test assertion
assert all(
category.device == device for category in tensor.categorical.categories
)
+ assert all(+ category.storage_offset() == 0+ for category in tensor.categorical.categories+ if isinstance(category, StringTensor)+ )
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Review comment at @sdm/tensor/table.py around lines 97 - 98:
In the CUDA pandas test, extend the checks on tensor.categorical.categories to
assert that each StringTensor category has a storage offset of zero; leave the
existing value and device checks unchanged.
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Add CUDA coverage for string-vocabulary storage offsets.
The changed routing avoids the nonzero offset produced by the old shared-copy-and-split path. The existing CUDA test already has string vocabularies of different sizes, but it checks only values and devices. Add two cases with different first-vocabulary sizes and assert that the second vocabulary has storage offset zero in each case.
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Review comment at @sdm/tensor/table.py at line 95:
Add CUDA assertions in the existing test for string vocabularies to verify the
second vocabulary’s storage offset is zero for two cases with different
first-vocabulary sizes. Keep the existing value and device checks.
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The device-parameterized test has two StringTensor categories and only one plain Tensor category. It therefore does not execute _move_categories' same-dtype concatenation and split path. Add a non-CPU test with same-dtype numeric categories of different lengths and distinct values.
This detects incorrect split boundaries, category association, values, and device placement. The inspected code does not establish a zero-offset contract for grouped plain categories, so that layout should not be asserted unless the contract is defined.
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Review comment at @test/tensor/test_table.py around lines 1533 - 1539:
Add a device-parameterized test alongside the existing from-pandas categorical
tests that exercises `_move_categories` with multiple plain-Tensor categories
sharing a dtype but having different lengths and distinct values. Assert the
expected category codes, category values and order, and device placement; do not
assert storage offsets.
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Nitpick comments:
Review comments at @test/tensor/test_table.py:
- Around line 1533-1539: Add a device-parameterized test alongside the existing
from-pandas categorical tests that exercises `_move_categories` with multiple
plain-Tensor categories sharing a dtype but having different lengths and
distinct values. Assert the expected category codes, category values and order,
and device placement; do not assert storage offsets.
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Summary
Copy each string vocabulary into its own CUDA memory in
TableTensor.from_pandas(..., device="cuda"), instead of splitting one shared copy into views. This fixes recompiles on almost every batch in compiledpredictand compiled preprocessing, and a compiled crash in the native category lookup of #1275.Description of the change
What this fixes.
model.predictand compiled processors such asAlignCategories.transformrecompile whenever the vocabulary sizes of a batch differ from the previous one, which is almost every batch.AlignCategories.transformfails on Food_Delivery_Time and credit-g (NameError: name 's55' is not defined).Why this is needed even though
from_pandasruns before the model.from_pandasitself is never compiled, but the tables it builds are the inputs of compiledmodel.predictand processors such asAlignCategories.transform.torch.compileguards on the layout of every input. For aStringTensor, that layout includes its storage offset, which__tensor_flatten__returns as metadata. Dynamo compares that metadata by equality on every call (check_subclass_metadata), so a new storage offset means a new graph.On main,
from_pandasconcatenates all string vocabularies, copies them to the device once and splits the copy into views (torch.cat(tensors).to(device).split(sizes)). A view's storage offset is the total size of the vocabularies before it, so it changes from batch to batch:The shared-buffer views also break the native category lookup in #1275, in Inductor-generated
while_loopcode.The change. Plain tensor vocabularies of one dtype still move together. String vocabularies now move one by one with
.to(device), which copies only each vocabulary's own bytes, offsets and validity, so each starts at storage offset 0, like a CPU vocabulary, and batches no longer differ in layout.Only
from_pandaswith a non-CPU device is affected.from_arrow,from_cudfand.to(device)of a CPU table move or build each vocabulary on its own and do not pack them into one buffer.Validation
NVIDIA L4, PyTorch 2.14.1+cu130:
test/tensorpassed on CUDA. The script above compiles a second graph on main and reuses the first graph with this change.TableTensor.from_pandas(..., device="cuda")on the context table and 35 prediction batches per dataset, three passes, two runs each. Peak is device memory allocated above the starting point.Building a table is up to 2.3 ms slower with many string columns, since each vocabulary is a separate copy, and peak memory is up to 34 KiB higher. Staging all vocabularies in one pinned buffer and copying them in one batch was about 1.4 ms faster on Diabetes130US, but needed about 50 lines of offset bookkeeping.