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[model, ci] fix: scatter Flux joint attention output per stream under Ulysses SP - #1248
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… Ulysses SP FluxJointAttention ran one reverse all-to-all over the concatenated [text_full, image_full] attention output, so each rank got a contiguous slice across the text/image boundary and then split it at the local text length. Every rank ended up with text and image tokens from the wrong positions, even with divisible lengths. Scatter the text and image parts separately.
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| Check name | Status | Explanation | Resolution |
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Docstring coverage is 0.00% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 5 functions across 2 files. (1 skipped: 1 … | Write docstrings for the functions missing them to satisfy the coverage threshold. |
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Check name Status Explanation Description Check Check skipped - CodeRabbit’s high-level summary is enabled. Title check The title clearly and concisely describes the main fix: scattering Flux joint-attention output per stream under Ulysses sequence parallelism. It also identifies the affected model area and CI scope. Linked Issues check Issue #1247requires separate redistribution of the text and image attention outputs.FluxJointAttention.forwardnow callsgather_heads_scatter_seqseparately for the text and image segments. The …Out of Scope Changes check The model change, regression test, and GPU workflow entry directly support issue #1247. The reviewed changes contain no demonstrated unrelated work.
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Docstring coverage is 0.00% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 5 functions across 2 files. (1 skipped: 1 unsupported.)
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In `@tests/parallel/ulysses/test_flux_joint_attn_ulysses.py`:
- Line 20: Update the module-level skip condition in
test_flux_joint_attn_ulysses to check get_device_type() before calling
get_dist_comm_backend(). This ensures CPU-only hosts skip cleanly without
selecting an unsupported distributed backend; preserve the existing c10d
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The per-stream reverse all-to-all fixes the text/image shard routing. I found two issues in the new regression test: it uses FP32 with the installed FlashAttention backend, and it is not enumerated in the unit-test workflows. Details are attached to the corresponding lines. Static checks passed; I could not run the two-GPU test in the local environment.
The FP32 no-mask comparison hits flash_attn whenever it is importable, and those kernels reject FP32. Pin the test to SDPA, and enumerate it in the GPU Ulysses step so the fix is actually covered.
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Fixed both in 599a101. FP32 vs FA: patched FLASH_ATTN_3/2_AVAILABLE to False on the test so the no-mask case runs on SDPA, same as the padding test. CI: added the file to the Ulysses step in gpu_unit_tests.yml. Left npu_unit_tests.yml alone since Flux isn't in the NPU supported-models list (https://github.com/ByteDance-Seed/VeOmni/blob/main/docs/hardware_support/get_started_npu.md#supported-models). |
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Inline comments:
Review comments at @.github/workflows/gpu_unit_tests.yml:
- Line 130: Add `tests/parallel/ulysses/test_flux_joint_attn_ulysses.py` to the
NPU workflow alongside the existing Ulysses tests, using the workflow’s
established pytest flags. If NPU execution is unsupported, document the specific
backend limitation instead of silently omitting the test.
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Inline comments:
Review comments at @veomni/models/transformers/flux/modeling_flux.py:
- Around line 258-267: Update the separate stream gathers in the Ulysses output
path to track each stream’s valid per-rank length and remove padding immediately
after `gather_heads_scatter_seq`. Preserve the text/image shard boundary when
concatenating, and do not split the result using only
`hidden_states_b.shape[1]`.
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…ision Merge main 62beaef, which landed the CFG-calibrated Ref2VA training (ByteDance-Seed#1258), the Ulysses gradient scaling (ByteDance-Seed#1281) and the Flux SP fix (ByteDance-Seed#1248). Only the DiT wrapper import conflicted: keep the official FP32 projection import alongside main's CFG objective import. Every other file auto-merged, and both sides' changes are preserved: - the six FP32 projections, the FP32 time MLP, the AdaLN cast order and the rejection of FP32-rounded timesteps/positions - the CFG negative branch, the SP gradient compensation and the async all-to-all loop reset Validation: tests/models/test_minimax_h3_packing.py and tests/parallel/ulysses/test_all_gather.py pass 118 and skip 7 on CPU/Gloo; make quality passes. A BF16 CFG probe on the merged tree matches serial and packed loss and gradients to 6.2e-4 relative error with the FP32 islands preserved. CFG under FSDP2/BF16 and the accelerator-only kernel paths are still unverified here.
What does this PR do?
Fixes #1247.
FluxJointAttentionran one reverse all-to-all over the concatenated[text_full, image_full]attention output, so each rank got a slice across the text/image boundary. This scatters the text and image parts separately, so each rank gets back its own shard of each stream.Independent of #1246 (non-divisible padding). They touch different parts of the file and merge cleanly.
Test
New
tests/parallel/ulysses/test_flux_joint_attn_ulysses.py(2 GPUs): runsFluxJointAttentionon local shards with sp=2, with and without an entity-style mask, and compares against the same module on the full sequence. Fails on main (no_mask: image), passes here.Full model, SP=2 vs SP off, 2x L4, fp32 (output / grad rel err):
With a larger init (std 0.1) the #1246-only errors rise to ~1e-3 to 2e-2, while this PR stays ~1e-6.
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