[None][fix] Compute the Cosmos3 rotary table with a broadcast multiply, not a K=1 matmul - #19532
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…y, not a K=1 matmul The 3D mRoPE angle table was built as inv_freq @ position_ids, an fp32 batched matmul with inner dimension 1. That is an outer product, but as a GEMM it runs in TF32 wherever fp32 GEMMs are allowed to (NGC PyTorch images set TORCH_ALLOW_TF32_CUBLAS_OVERRIDE=1), and TF32 cannot represent positions above 2048. Cosmos3 positions reach ~10k, so on Blackwell the rotary phase of late tokens was off by up to 5 rad and late video frames smeared. Use an elementwise multiply instead. Same arithmetic, no GEMM, immune to matmul precision settings; output is bit-identical on stacks that were already correct. Signed-off-by: Igor Shovkun <ishovkun@nvidia.com>
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Included review availability: Your plan provides up to 12 included reviews per hour; 11 remain after this review. WalkthroughThe Cosmos3 rotary embedding now computes frequencies with elementwise multiplication. CUDA tests check cosine and sine outputs for long text and vision sequences with TF32 enabled and disabled. ChangesCosmos3 RoPE precision
Priority: ➖ Normal Estimated code review effort: 2 (Simple) | ~10 minutes Change: Bug fix Merge Risk: ⚪ Minimal · up to The RoPE precision change has no newly established merge-blocking issue in this review; it is mergeable after normal checks. 🚥 Pre-merge checks | ✅ 4 | ❌ 1❌ Failed checks (1 warning)
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Keeps the check next to the other Cosmos3 transformer tests instead of a new file, so no test-list change is needed. Signed-off-by: Igor Shovkun <ishovkun@nvidia.com>
Signed-off-by: Igor Shovkun <ishovkun@nvidia.com>
Signed-off-by: Igor Shovkun <ishovkun@nvidia.com>
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In `@tests/unittest/_torch/visual_gen/test_cosmos3_transformer.py`:
- Line 878: Update the _require_cuda helper to also skip when
torch.cuda.get_device_capability() is below (8, 0), while preserving the
existing CUDA-availability skip and allowing TF32 coverage only on
Ampere-or-newer GPUs.
- Line 910: Update the position_ids construction in
TestRotaryTablePrecision.test_rotary_table_matches_fp64_under_tf32 so both
spatial axes use the integer base values, replacing the fractional third-axis
expression while preserving the temporal-axis calculation.
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tensorrt_llm/_torch/visual_gen/models/cosmos3/transformer_cosmos3.pytests/unittest/_torch/visual_gen/test_cosmos3_transformer.py
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- tensorrt_llm/_torch/visual_gen/models/cosmos3/transformer_cosmos3.py
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…atial ids TF32 only exists from Ampere on, so allow_tf32=True passed trivially on older GPUs and looked like coverage it did not provide. The third mRoPE axis also used half-integral positions, which no Cosmos3 pathway produces: text ids share one integer ramp across all three axes and only the vision temporal axis is fps-scaled. Signed-off-by: Igor Shovkun <ishovkun@nvidia.com>
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…anity case This case fails on every PR that runs the GB300 4-GPU perf-sanity stage, at a consistent -18.2% to -19.2% against a 7.6871 baseline. Six failures across three unrelated PRs (NVIDIA#19499, NVIDIA#19500, NVIDIA#19532) in builds 885, 886, 888, 889, 890 and 892 of L0_Test-SBSA-Multi-GPU. Nothing in those PRs touches the DeepSeek or collectives path. NVBug 6778910 tracks this exact parametrization but is currently closed as fixed. The code fix it cites (NVIDIA#19443) was closed without merging, and the change that did merge (NVIDIA#19424) edits the disaggregated config while the bug and this failure are the aggregated ctx_only case, so the aggregated regression appears to have been closed prematurely. Signed-off-by: Igor Shovkun <ishovkun@nvidia.com>
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The waiver commit doesn't belong here, and it's likely unnecessary on a current base — details inline. The Description section also never mentions it; only the auto-generated block does.
Title tag: [None] is thin for this one. A video-quality regression in shipped images, confirmed by two reporters on GB200, should carry an NVBug or JIRA so the fix is traceable from the bug side — the NVBUG currently in the body belongs to the unrelated waiver.
Follow-up, not for this PR: the same inv_freq @ position_ids K=1 GEMM is still live in
tensorrt_llm/_torch/models/modeling_gemma4_vision.py:202tensorrt_llm/_torch/auto_deploy/models/patches/phi.py:75,116,151tensorrt_llm/_torch/auto_deploy/models/custom/modeling_eagle.py:245tensorrt_llm/_torch/auto_deploy/models/custom/modeling_nemotron_flash.py:504tensorrt_llm/_torch/auto_deploy/models/custom/modeling_qwen3_5_moe.py:293
Kernel selection is shape-dependent, so I'm not claiming these are broken — but they're the same construct under the same container flag and worth an audit, Gemma4 vision especially since it's on the live PyTorch backend path. If more than one is affected, a shared helper beats six copies of your comment.
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Every Cosmos3 request calls the rotary forward with two dtypes: the text tower passes an int64 ramp, and vision, audio and action pass fp32 when fps modulation is on and int64 when it is off. The test only built fp32 ids, so the int64 path was unexercised. Add it as a separate parametrization, since torch.stack needs one common dtype, and assert the returned dtype follows the probe rather than the position ids. Signed-off-by: Igor Shovkun <ishovkun@nvidia.com>
Signed-off-by: Igor Shovkun <ishovkun@nvidia.com>
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Dev Engineer Review
QA Engineer Review
tests/unittest/_torch/visual_gen/test_cosmos3_transformer.py. The CUDA tests compare cosine and sine tables with an fp64 reference for text and fps-scaled vision positions at lengths 4096–16384, with TF32 enabled and disabled. They check output dtype, an absolute-error limit of5e-3, and restoration of the original TF32 setting; they skip without CUDA or below SM80.tests/integration/test_lists/test-db/l0_b200.yml. No corresponding manual-QA list entry was found.Per-File QA Perspective
tensorrt_llm/_torch/visual_gen/models/cosmos3/transformer_cosmos3.py: Verify the rotary cosine and sine tables remain accurate for large positions when TF32 is enabled and disabled. The observable change replaces matrix multiplication with elementwise multiplication; it does not change the public API.tests/unittest/_torch/visual_gen/test_cosmos3_transformer.py: Covers text and vision rotary tables against an fp64 reference under both TF32 settings. The file is listed for CI intests/integration/test_lists/test-db/l0_b200.yml.Description
Cosmos3 video quality degrades on Blackwell inside the NGC release images: frames past t≈40 smear and never recover (Laplacian sharpness retention 0.63 vs 0.94 on a healthy stack). Reported and confirmed by two people on GB200 during the cookbook review in NVIDIA/cosmos#310 (see NVIDIA/cosmos#310 (review)); the same image on B200 is degraded as well (0.72), H100/H200 are fine.
Root cause: NGC PyTorch images set
TORCH_ALLOW_TF32_CUBLAS_OVERRIDE=1, so fp32 GEMMs run in TF32.Qwen3VLTextRotaryEmbedding.forwardbuilt the 3D mRoPE angle table asinv_freq @ position_ids, an fp32 batched matmul with inner dimension 1. On sm100 cuBLAS serves that shape with a TF32 kernel for the sequence lengths Cosmos3 produces, and TF32's 10-bit mantissa cannot represent positions above 2048. Cosmos3 positions reach ~10k (4096 text tokens, modality margin, fps-scaled vision positions), so the rotary phase of late tokens is off by up to 5 rad. Late frames carry the largest positions, hence the time-dependent smearing. On sm90 cuBLAS picks a non-TF32 kernel for that shape, and pip torch 2.13 is exact on both.Fix: the table is an outer product, so compute it with an elementwise multiply. Same arithmetic, no GEMM, unaffected by any matmul precision setting. Output is bit-identical on stacks that were already correct (native aarch64 build and the rc27 wheel: same output sha256 before and after) and healthy inside the unchanged NGC image on GB200 (retention 0.939). No change to container environment or global torch flags.
Evidence, same GB200, same cookbook forward-dynamics request, seed 0:
TORCH_ALLOW_TF32_CUBLAS_OVERRIDE=0Micro-check of the K=1 fp32 matmul against fp64 inside the image: H200 0.0005, B200 5.06 rad, GB200 5.06 rad; the elementwise multiply is 4.85e-4 (the fp32 floor) everywhere.
The diff is two files: the one-line change in
transformer_cosmos3.pyand its regression test. An earlier revision of this branch also carried awaives.txtline for an unrelated GB300 DeepSeek perf-sanity case; that has been dropped and the branch merged with current main. The case was already fixed on main by #19424, which this branch's base predated by about eight hours, so no waiver is needed.Follow-up, not in this PR
The same
inv_freq @ position_idsK=1 GEMM is still present at:tensorrt_llm/_torch/models/modeling_gemma4_vision.py:202tensorrt_llm/_torch/auto_deploy/models/patches/phi.py:75,116,151tensorrt_llm/_torch/auto_deploy/models/custom/modeling_eagle.py:245tensorrt_llm/_torch/auto_deploy/models/custom/modeling_nemotron_flash.py:504tensorrt_llm/_torch/auto_deploy/models/custom/modeling_qwen3_5_moe.py:293Kernel selection is shape-dependent, so these are not being claimed as broken. They are the same construct under the same container flag and deserve an audit,
modeling_gemma4_vision.pyfirst since it sits on the live PyTorch backend path. Note that each of the AutoDeploy sites wraps the matmul intorch.autocast(enabled=False)under a comment about forcing float32 for long contexts; autocast and TF32 are separate switches, so that guard does not prevent this. If more than one turns out to be affected, a shared helper would beat repeating the comment at each site.Test Coverage
TestRotaryTablePrecisionintests/unittest/_torch/visual_gen/test_cosmos3_transformer.py(already inl0_b200.yml): builds the rotary table at sequence lengths 4096/6240/8192/10336/16384, fractional temporal axis included, withallow_tf32False and True, and checks cos/sin against an fp64 reference within the fp32 floor. Skipped below SM80, where TF32 does not exist and the case would pass without covering anything. No checkpoint needed. Verified on GB200 inside the rc26 image: the old matmul fails theallow_tf32=Truecases at 4096..10336 (cuBLAS picks a TF32 kernel only at some sizes; 16384 happens to be exact), the fix passes 10/10; pip torch 2.13 passes 10/10 with and without the override.nvcr.io/nvidia/tensorrt-llm/release:1.3.0rc26.dev202609060001with and without the fix (table above).PR Checklist
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