[None][test] Pin the float32 precision of the attention-plugin rotary table - #18774
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WalkthroughChangesSinusoidal position dtype enforcement
Priority: ⬇️ Low Estimated code review effort: 2 (Simple) | ~10 minutes Change: Refactor Merge Risk: 🔵 Low · up to The implementation enforces fp32, but regression tests do not fully protect the precision contract and long-context boundaries. The change is low risk with these test follow-ups. 🚥 Pre-merge checks | ✅ 4 | ❌ 1❌ Failed checks (1 warning)
✅ Passed checks (4 passed)
Full details: Description checkExplanation The description includes the required sections and provides detailed technical context and test information. However, it conflicts with the changeset by describing a characterization test as newly added and stating that behavior does not change, while the PR removes the public dtype argument and the test. It also does not clearly document the API-breaking change. Resolution Update the description to state that the public dtype argument and characterization test were removed. Explain that removing the argument is an API-breaking change, add the required api-breaking label, and include BREAKING in the title if required by repository policy. ✨ Finishing Touches🧪 Generate unit tests (beta)
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In `@tests/unittest/_torch/attention/test_rope_frequency_precision.py`:
- Line 80: Update the position-zero assertion in the RoPE frequency precision
test to use an exact array comparison instead of assert_allclose, preserving the
existing reference row, HEAD_DIM, and THETA inputs.
- Around line 77-80: Update test_the_table_is_exact_at_position_zero to use an
exact comparison for the position-zero row, either via
np.testing.assert_array_equal or zero relative and absolute tolerances, while
retaining the existing row generation and reference comparison.
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… table ``RopeEmbeddingUtils.create_sinusoidal_positions_for_attention_plugin`` rounds ``inv_freq`` to float32 before multiplying it by the position, so the absolute angle error in the stored table grows linearly with position: negligible in short contexts, about a milliradian at 32k. Add a CPU-only characterization test that pins that behaviour with measured numbers -- exact at position 0, below 1e-4 under 8k, 1.5e-5 / 1.3e-4 / 5.6e-4 at 1024 / 8127 / 32768 within 4x bands, and linear in position -- plus the bound the alternative construction reaches (inv_freq built in float64 and rounded once, flat at ~3e-8) and the returned dtypes and shapes. Building the table in double precision was evaluated and deliberately not adopted: reference implementations round their own frequency table to single precision too, so making one side exact removes half of a two-sided rounding difference and changes which near-ties flip rather than improving agreement. The test documents the accepted drift so that any future change to the table construction is made deliberately and re-measured. No behaviour change. Signed-off-by: Brian Nguyen <brnguyen@nvidia.com>
Signed-off-by: Brian Nguyen <brnguyen@nvidia.com>
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Actionable comments posted: 3
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Inline comments:
In `@tests/unittest/_torch/attention/test_rope_frequency_precision.py`:
- Line 85: Update the test loop in the RoPE frequency precision test to include
a near-8k position below the boundary, such as a value in the 1025–7999 range,
with its separately measured expected bound; alternatively rename the test to
accurately describe that it only samples short-context positions.
- Line 104: The assertion around the measured error at position 32768 uses an
overly broad fourfold tolerance. Replace the measured / 4.0 and measured * 4.0
bounds with a narrower tolerance justified by observed float32 and math-library
variation, while preserving the intended validation of RoPE frequency precision.
- Line 123: tighten the linearity assertion near the far/near ratio check by
replacing the broad rel=0.5 tolerance with a measured tolerance appropriate to
the test’s dtype, quantization mode, backend, and expected variance; add an
intermediate position assertion if needed to verify linear behavior across the
range.
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The fp32 pin is documented at the variable itself in create_sinusoidal_positions_for_attention_plugin, which is no longer a caller argument, so the test-side guard is redundant (review feedback). Signed-off-by: Brian Nguyen <brnguyen@nvidia.com>
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/bot skip --comment "Latest full run on this commit (pipeline 60411) passed every stage except the GB300-12_GPUs-3_Nodes disagg perf-sanity cache_transceiver_precheck, which fails creating the NIXL/UCX transfer backend (transferAgent.cpp:484) before any test code runs. That environment failure has been killing runs on unrelated PRs (e.g. #18393) since Sep 12 and cannot be caused by this PR, which only pins a numpy dtype inside a test-support function and deletes assertions from one unit test. Pipeline 60236 was fully green on the previous commit 6995aa1; the only delta to this head is removing those test assertions." |
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/bot skip --comment "Re-issue: the previous skip completed while the killed run was still finalizing and its status landed last. Evidence unchanged: pipeline 60411 passed every stage except the GB300 disagg cache_transceiver_precheck (NIXL/UCX backend creation, fails before any test code, hitting unrelated PRs since Sep 12), and pipeline 60236 was fully green on the previous commit whose only delta is removing test assertions." |
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4 participants
Description
RopeEmbeddingUtils.create_sinusoidal_positions_for_attention_pluginroundsinv_freqto float32 before multiplying it by the position. The stored angle isposition * inv_freq, so a relative error of ~6e-8 ininv_freqbecomes anabsolute angle error that grows linearly with position: negligible in short
contexts, about a milliradian at 32k.
This PR adds a characterization test for that. It changes no behaviour; it pins
the current numbers so that a future change to the table construction is made
deliberately and re-measured, rather than silently shifting a table that is
built for every model.
Building
inv_freqin float64 and rounding once at the end was evaluated anddeliberately not adopted. It does make this side exact (flat ~3e-8 at every
position, including 128k), but reference implementations round their own
frequency table to single precision too, so removing one side of a two-sided
rounding difference lands on a different set of near-ties rather than on better
agreement in long-context logits comparisons. Below 8k nothing moves either way.
That alternative is kept in the test as an executable record of the bound it
reaches, so the question does not have to be re-derived from scratch.
The tests live next to
tests/unittest/_torch/attention/test_rotary_embedding.py,the existing home for rotary/positional tests.
Test Coverage
New file
tests/unittest/_torch/attention/test_rope_frequency_precision.py(CPU-only, numpy, no GPU and no model weights). Six tests, 10 cases:
5.6e-4) held inside 4x bands, so both a regression and an unannounced
improvement fail;
than a per-entry one);
Local run:
pytest tests/unittest/_torch/attention/test_rope_frequency_precision.py-> 10 passed.
PR Checklist
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PR description clearly explains what and why. If using CodeRabbit's summary, please make sure it makes sense.
PR Follows TRT-LLM CODING GUIDELINES to the best of your knowledge.
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If PR introduces API changes, an appropriate PR label is added - either
api-compatibleorapi-breaking. Forapi-breaking, includeBREAKINGin the PR title.Any new dependencies have been scanned for license and vulnerabilities
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Documentation updated as needed
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Please check this after reviewing the above items as appropriate for this PR.
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/bot help.Dev Engineer Review
create_sinusoidal_positions_for_attention_pluginnow always usesnp.float32and no longer acceptsdtype. Callers that passdtyperequire updates. The removed characterization test reduces direct precision coverage.QA Engineer Review
No test changes.
Per-File QA Perspective
tensorrt_llm/functional.py: Verify fixed-np.float32output, expected position-dependent precision drift, and compatibility with callers that no longer passdtype.