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RFC #420 stack 10c/10 (base test-h3, merge bottom-up): … → #494 → 10a (#586) → 10b (#587) → this PR (10c). This PR merges after 10b. This PR's own diff (1.5k lines). The H3 PRs #567 and #568 → #575 are stacked on the top of this layer.
Replaces #539 (closed, superseded by this stack). The split is described in 10a. The top of this stack has the same tree as #539's head 17d0e9e9 apart from nine docs/operators/h3-*.md files whose commit-hash text was updated in #483–#494 after #539 was cut (17 lines).
Evidence. Measurement outputs (logs, JSON, figures) are not part of this diff. They are on the fork's pr_assets branch and every link is fixed to commit 967000e9. Runs on the heads of this stack: B200 pending/b200-split-539, H100 pending/h100-split-539 (each with tests.json, redaction.json and SHA256SUMS). Evidence of the earlier #539 runs: pr539/.
This stack
layer
PR
own diff
content
10a
10a
3.6k
block 0 replay and diagnostics
10b
10b
8.8k
full-model qualification
10c
10c
1.5k
generation gate
What
RFC #420 closeout row ws1_dual_trajectory, generation part: three-seed generation with the candidate and its comparison against the provider, built to survive preemption. Not claimed as passed (see Status).
Generation follows examples/models/minimax_h3/generation-contract.json: the pinned public reference image and prompt, seeds 42/43/44, 124 frames, 1344×768, 24 fps with joint 32 kHz audio. generation-compare.sbatch and h3_generation_compare.py check identical initial noise and shapes and report PSNR, SSIM, temporal RMSE and audio differences against the provider.
Resumable generation (h3_full_generation.py, rl_engine/validation/models/h3_generation_resume.py). Candidate generation takes about 20 hours per seed, so it runs as one resumable task per seed. Each saved step has a sidecar with the digest of the transformer's inputs and of the saved file, written atomically. On resume the saved transformer calls are replayed from disk and the pipeline's own scheduler turns them into the next latents; every replayed call's input digest is checked. Recomputing the last saved step and requiring a bitwise match is --verify-last-step (opt-in).
A run is tied to the git trees of the model paths, not to a commit, so a commit that changes only orchestration code can continue it; h3_generation_adopt.py moves an older commit-based run over, only if those paths are byte-identical.
Job scripts: generation.sbatch runs one seed per array task and refuses to start unless the numerical gates of 10b passed; generation-resume-check.sbatch checks resume; generation-compare.sbatch compares afterwards.
--provider-variant on h3_full_generation generates the provider's videos with one SDPA backend forced on the transformer's forward only, for the quality-bounds comparison below.
Prior art & reuse decision
No operator is written here. The reference is the diffusers pipeline; its own generation is the baseline, including variants with one SDPA backend forced.
Results
B200
Generation from clean checkouts at 4792c8a9 / 2451ae5c (same model code), 124 frames 1344×768, 49 steps. Evidence on pr_assets: h3-full-model-b200/.
Generation quality vs the provider, bounds fixed before generation
not passed: seed 44 passes, seeds 42 and 43 do not
Generation quality per seed (bounds: PSNR ≥ 30 dB, SSIM ≥ 0.99, temporal RMSE ≤ 0.01, audio relative L2 ≤ 0.05; a failing value is bold):
seed
PSNR (dB)
SSIM
temporal RMSE
audio relative L2
passed
42
37.58
0.9917
0.00991
0.0807
no
43
32.64
0.9757
0.01056
0.0762
no
44
38.75
0.9945
0.00734
0.0456
yes
The same bounds applied to the provider with one SDPA backend forced on the transformer (same seeds, inputs and conditioning):
provider variant
seeds passing
PSNR (dB), seeds 42 / 43 / 44
audio relative L2, seeds 42 / 43 / 44
flash attention
0 of 3
39.08 / 23.98 / 39.33
0.054 / 0.264 / 0.056
memory-efficient attention
0 of 3
39.43 / 30.22 / 42.59
0.094 / 0.135 / 0.116
candidate
1 of 3
37.58 / 32.64 / 38.75
0.081 / 0.076 / 0.046
First denoising step replayed from its saved packed inputs (nothing has fed back yet), relative L2 of the video output against the provider's saved step (replaying the provider is bitwise equal to it):
configuration (seed 43 unless noted)
video relative L2
candidate (seeds 42 / 43 / 44)
0.0285 / 0.0212 / 0.0274
provider, flash / memory-efficient attention
0.0219 / 0.0218
provider, cuBLASLt
0 (bitwise equal at this step)
candidate with attention / the six GEMMs / SwiGLU / the AdaLN projection run as the provider
0.0225 / 0.0217 / 0.0198 / 0.0193
candidate with Q/K norm, the two norms or the residuals run as the provider
0.0212 (unchanged by the swap)
candidate with all interim operators / every node run as the provider
0.0192 / 0
H100 (Hopper)
A one-step generation preflight (128×128) completed on 4 × NVIDIA H100; it checks the flow only and is not quality evidence. The three-seed generation (about 20 hours per seed) was not run on H100.
Status
Generation quality: not passed. Against the provider, with the bounds fixed before candidate generation, seed 44 passes and seeds 42 and 43 do not: audio relative L2 is 0.081 and 0.076 (bound 0.05), and seed 43 also misses SSIM (0.9757) and temporal RMSE (0.01056). The bounds are unchanged.
What the comparison says about those bounds.
The bounds had only been checked against a bitwise-identical repeat of the provider (PSNR 300 dB, zero error). The provider with one SDPA backend forced on the transformer, run through the same 49 steps with the same seeds, inputs and conditioning, fails them too: flash attention 0 of 3 seeds, memory-efficient attention 0 of 3, and audio relative L2 exceeds 0.05 on every seed of both (0.054 to 0.264).
The first denoising step shows the same: the candidate differs from the provider's step by 0.021 to 0.029 relative L2 on video, and the provider with flash or memory-efficient attention differs from its own default by 0.022. Running any one group of block nodes as the provider's leaves 0.019 to 0.023; replacing the norms or the residuals changes nothing at this step.
This is not a claim that the candidate is better than the provider variants: on seeds 42 and 44 its PSNR and temporal RMSE are slightly worse than both variants', on seed 43 better, and it passes one seed where they pass none.
The generation bounds were not changed and the candidate is not accepted on generation quality. A rule that tolerates a change of kernel, like the one for the numerical gates in 10b, would be a second rule designed after seeing a failure; it is left to the reviewers.
Resume checks. On the real pipeline at preflight size (128×128, 6 sampling steps), one uninterrupted run and runs preempted and resumed give byte-identical step files, initial inputs, video and audio (8 files); a resume with nothing left to compute and the opt-in verification both reproduce the uninterrupted run.
Cost. Candidate generation is more than 50× slower than the provider's (about 20 hours per seed, against about 1 h for the provider's).
571 passed, 4 skipped (sglang and matplotlib on the path)
573 passed, 2 skipped
full tests/ (minus tests/distributed and a ROCm import-error file)
73 failed, 3541 passed; the same 73 fail on upstream/test-h3 (measured on the heads before the re-cut; same layer code)
pre-commit, mypy
clean
Skips as in 10a. tests/models/minimax_h3/test_h3_generation_resume.py (22 CPU tests, included in the counts) drives a fake denoising loop through the same resume code: a run preempted once or repeatedly equals an uninterrupted one, nothing is recomputed by default, the opt-in verification catches a drifting recompute, other identities, corrupted files and signature changes are rejected, and adopting a commit-based run is tested.
Scope / Known limitations
Generation quality is not passed (1 of 3 seeds), against bounds that the provider with another attention kernel does not pass either.
The three-seed generation was run on B200 only.
CUDA only; not run on AMD hardware.
Summary by CodeRabbit
New Features
Added MiniMax-H3 block and full-model validation, generation comparison, and resumable generation workflows, alongside diagnostic and evidence-reporting tools.
Added optional extraction and loading of block-0 weights.
Added documentation for the H3 one-block forward and backward workflow.
Bug Fixes
Improved validation of H3 dimensions, layouts, tensor strides, and kernel input bounds.
Optimized distributed row gathering for smaller outputs.
Tests
Expanded H3 coverage for CUDA kernels, distributed operations, validation, and generation resume behavior. CI now runs additional H3 tests on pushes and pull requests targeting test-h3.
Documentation
Clarified performance measurement scope and distributed gathering behavior.
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tests/models/minimax_h3/test_h3_full_model.py
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Walkthrough
The pull request adds MiniMax-H3 block and full-model validation, numerical acceptance and evidence tools, resumable generation workflows, and distributed operator updates. It also adds CI coverage, qualification scripts, tests, reports, and documentation.
Changes: H3 validation and qualification
Layer / File(s)
Summary
Block replay and diagnostics rl_engine/validation/models/*, rl_engine/reference/minimax_h3/*, tools/validation/models/*, tests/models/minimax_h3/*, docs/operators/*
Adds packed layouts, golden and provider operations, block replay, diagnostics, ULP sweeps, plots, pinned weights, and validation tests.
Full-model evidence and acceptance rl_engine/validation/models/h3_full_model.py, tools/validation/models/h3_full_*, tests/models/minimax_h3/test_h3_full_model.py
Adds zero-width and grid-bound checks, overlapping-table handling, small-output gather behavior, and corresponding tests.
CI and supporting documentation .github/workflows/ci.yml, docs/operators/*, examples/models/minimax_h3/README.md
Adds the H3 test branch and full-model test step. Documentation records block validation, qualification criteria, timing scope, and current generation results.
sequenceDiagram
participant CandidateJob as candidate.sbatch
participant Prepare as h3_candidate_prepare.py
participant Runner as h3_full_model.py
participant Compare as h3_full_compare.py
participant Ensemble as h3_full_ensemble_rule.py
participant Generate as h3_full_generation.py
CandidateJob->>Prepare: verify checkpoint and candidate backend
CandidateJob->>Runner: run candidate full-model validation
CandidateJob->>Compare: compare candidate evidence
CandidateJob->>Ensemble: check ensemble acceptance
CandidateJob->>Generate: run generation preflight
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Possibly related issues
RL-Align/RL-Kernel#420: The issue describes deterministic H3 trajectory validation. This pull request adds generation comparison and resumable trajectory checks.
Merge Risk: Low · previous review bb709
This change adds H3 validation and resumable generation tooling. The earlier concerns about resume identity coverage and mixed-device tensors are addressed in the current head. One documentation inaccuracy remains: the 8 KiB gather cutoff rationale is overstated for 8 ranks. It does not affect behavior and can be fixed before or after merge. Note that the generation quality gate itself did not pass for seeds 42 and 43, as the author reports.
Pre-merge checks: 4 · 1
Failed checks: 1 warning
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Passed checks: 4 passed
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The title clearly identifies the MiniMax-H3 generation gate, 50-block generation comparison, and resume validation. These are central objectives of the pull request.
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time_proj is a hooked boundary but FullModelTrace.validate did not require it, so a run that
stopped calling it still passed the acceptance gate. It is required in the forward set; it has no
parameters and its input carries no gradient, so it is left out of the backward requirement.
Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
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Please include QueryChunkAttention and provider_variant_context in the numerical identity, or move them into tracked modules, so resume cannot mix outputs from different implementations.
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Done in 0f21456, with the second option: QueryChunkAttention, provider_variant_context and PROVIDER_VARIANTS moved to rl_engine/validation/models/h3_full_model.py, which is already in MODEL_PATHS, and both runners import them from there (behaviour unchanged). test_the_numerics_the_generation_runner_adds_are_inside_the_identified_model_paths checks that the files defining them are covered by MODEL_PATHS and that the runners use the same objects. Consequence: the tree of that path changed, so a numerical gate that passed at an earlier commit does not authorize generation at this one; the gate has to be run again on the new tree.
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Please validate the saved run identity before skipping a completed seed; otherwise --resume can silently reuse results from different model code or settings.
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Done in 1e2a51e: StepStore.verify_identity raises ResumeError unless run.json holds exactly the current identity (model-path trees, contract, image, prompt, flags, settings), and --resume calls it through finished_seed_is_reusable before it skips a seed that has a report.json; a finished seed without run.json is refused too. Tests cover the same identity (skipped), a different identity (raises) and a missing run.json (raises). Finished seeds from before the change of the model paths are refused on resume for the same reason.
reference.sbatch and candidate.sbatch called h3_full_generation and h3_generation_compare, which
do not exist before the generation gate. Both scripts now stop after the full-model steps and no
longer take the reference image; the README sections that describe generation move with the
commands.
Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
The gradient bounds are above 1, so a gradient that is exactly zero (relative error exactly 1)
passed them. A candidate comparison now records each tensor's reference and candidate L2 norms
(tensor_l2), and the ensemble check fails any gradient tensor that is exactly zero where the
reference is not; a comparison without the norms is refused. The provider calibration output is
unchanged.
Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
…jobs
The generation steps of reference.sbatch and candidate.sbatch (preflight, reference and repeat
generation, the repeat comparison, the candidate preflight) and the README sections that describe
generation, moved here from the full-model PR, where the modules they call do not exist.
Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
…xt into a tracked module
Both decide the generated numbers (the candidate attention wrapper and the forced SDPA backend /
BLAS library) but lived under tools/, outside h3_generation_resume.MODEL_PATHS, so changing them
did not change a run's identity or invalidate the numerical gate. They now live in
rl_engine/validation/models/h3_full_model.py, which is a model path; the runners import them from
there. A test checks that the defining files are covered by MODEL_PATHS and that the runners use
the same objects. Behaviour is unchanged.
Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
…d seed
A seed with a report.json was skipped on --resume without looking at its run.json, so results of
different model code, inputs or settings could be reused silently. StepStore.verify_identity
raises ResumeError unless run.json holds exactly the current identity; a finished seed without
run.json is refused too. The skip decision is a function with tests.
Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
Re: automated review, examples/models/minimax_h3/full-model-ensemble-rule.json L4 (floors are not serialized)
Valid as documentation, not changed: the artifact is frozen and its digest is cited. The floors (5e-3 for gradient-like keys, 1e-3 otherwise) are defined in h3_full_ensemble_rule._floor, and the module docstring says they are as in the single-configuration rule. Recording them in the file (for example as floor_by_kind) belongs to the next rule version.
Re: automated review, examples/models/minimax_h3/full-model-ensemble-rule.json L17-L20 (duplicates_dropped and sources)
providers drives the bound; sources records every comparison offered to calibrate. A configuration is dropped only when its error table equals an earlier kept one exactly, so the maxima are the same with or without it. Anything that recomputes the bounds has to iterate providers, not sources. Writing this into the file is a change of the frozen artifact for a later version.
Re: automated review, examples/models/minimax_h3/full-model-ensemble-rule.json L814 (ensemble_max_abs is not enforced)
Informational by design, and stated as such: the rule string says "max_abs reported" and the module docstring says max_abs, set by one element, is reported but not gated because held-out configurations exceed the others' maximum by several times on it. The large values are measured absolute differences of tensors that are themselves large; an absolute threshold would have to be calibrated separately and is not part of this rule.
…ation
- h3_full_compare: --reference-manifest and --reference-root must be given together, instead of
failing later with an opaque error.
- h3_candidate_prepare: create a nested --output (still refusing one that exists).
- FullModelTrace records the nodes a diagnostic hybrid replayed with the provider and validate()
reports them as diagnostic_provider_nodes (empty for a candidate); a hybrid still completes.
- Tests: the ensemble check's process exit code is asserted (1 on failure, 0 on pass) and its
output must have been written; an empty rope tuple no longer lets the device test pass.
- h3-tp-adaln-3mod.md: the gather padding adds whole rows, so the buffer exceeds 256 KiB by up to
one row of every rank.
Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
Re: automated review, .github/workflows/ci.yml L79 (the CPU step runs only test_h3_full_model.py)
Valid in principle, not changed in this push. test_h3_full_ensemble_rule.py and test_h3_generation_resume.py run on CPU (they need torch and numpy), but the CPU workflow installs only CPU torch and .[dev], I cannot reproduce that environment locally, and an import that is missing there would turn the default CI red, so I did not add them blindly. test_h3_plot_block.py would be skipped there, since matplotlib is not installed in that job. If the maintainers want them in the default job, the step is the place and CI will show whether the environment suffices.
Re: automated review, examples/models/minimax_h3/full-model-thresholds.json L487-L488 (nodes.time_proj has no .grad entry)
Intentional: time_proj has no parameters and its input carries no gradient, so a run never records a gradient for it. It is the only non-block node without a .grad twin in bounds. Nothing looks a bound up by a constructed <node>.grad name: the comparison requires the tensor inventory of the run to equal the keys of bounds exactly (threshold tensor inventory differs), so a missing key cannot surface as a lookup failure, and #587 requires time_proj in the forward trace only.
Not hand-adjusted. h3_full_compare freezes 1.5 x measured provider error + floor with separate floors for forward and gradient tensors (1e-3 and 5e-3; the comment above the generator says so), so two tensors with the same measured error differ by exactly the gap between the floors: here nodes.audio_proj_out.grad is 0.034129260422720985 and audio is 0.030129260422720988, a difference of 0.004 = 5e-3 - 1e-3. The identical max_abs of audio and nodes.audio_proj_out is the same tensor seen at two taps.
…en two tests, gate the ensemble test in CI
- h3_full_compare: with --reference-root the compared run must be <reference-root>/fp32-reference.
The manifest verifies the files under the root, but the tensors (and gradients.json, which the
run's report does not pin) were read from whatever --reference named.
- The candidate_prepare test expects the FileNotFoundError of the missing reference evidence
instead of any exception.
- ci.yml: the CPU job also runs test_h3_full_ensemble_rule.py (standard library, torch and
numpy only; checked with no GPU visible).
Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
Re: automated review, examples/models/minimax_h3/full-model-ensemble-rule.json L3 (rule documents one floor, two are used)
Right that the rule text says "floor" in the singular while the bounds use two: 1e-3 for forward tensors and 5e-3 for .grad / parameter.* / input_gradients.* (h3_full_ensemble_rule._floor). The artifact is frozen and its digest is cited, so I have not edited it; the classes are defined in code and in the module docstring ("floors as in the single-configuration rule"). Writing them into the file (for example as floor_by_kind) belongs to the next rule version. Same answer as my earlier comments on this point.
Same finding as before, and my earlier answer stands: it is a measured value, not a default. In the ensemble rule built from the same comparisons nodes.block.49.grad has ensemble_relative_l2 0.0203 and ensemble_max_abs 4.6e-9, norm_out.grad has 0.0206 and audio_proj_out.grad 0.0211, while nodes.block.48.grad has 1.17 and 4.0e-5. The max_abs bound of block.49.grad looks like its neighbours' only because the bound is 1.5 x measured + 0.005, and a measured value far below the floor (4.6e-9, or 4.0e-5) disappears under it; the measured values themselves differ by four orders of magnitude. What I cannot explain is why the error grows about 50x inside the backward of block 49. That is an open question about the model, not a sign that this entry is mis-keyed, and I am not going to re-derive the bound on a guess; it stays as frozen.
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Actionable comments posted: 1
🪄 Fix comments: Fix CodeRabbit comments on this PR
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Inline comments:
Review comments at @rl_engine/validation/models/h3_full_model.py:
- Around line 267-279: Update provider_variant_context so the blas-cublaslt
setting is scoped to the context body: save the current preferred BLAS library,
set cublaslt, and restore the saved library in a finally block when the context
exits. Preserve the existing SDPA backend behavior and no-op behavior for other
variants, and update the context-manager implementation and imports as needed.
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test_h3_generation_resume.py is the only coverage of h3_generation_resume.py and the resume
decisions of the generation runner; it needs only torch and numpy and passes with no GPU visible
(54 passed together with the two other H3 contract test files of this step).
Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
Done in 64bd640: the step now also runs tests/models/minimax_h3/test_h3_generation_resume.py. I ran the three files of the step together with no GPU visible: 54 passed. I did not add test_h3_plot_block.py: the CPU job does not install matplotlib, so its importorskip would skip it on every run and it would add no coverage.
Re: automated review, examples/models/minimax_h3/full-model-ensemble-rule.json L911-L912 (nodes.block.49.grad looks like a zero/detached placeholder)
It is not a placeholder, and the comparison itself shows it. A zero or detached candidate gradient measures a relative error of exactly 1.0 against the reference (||ref - 0|| / ||ref||), and the new check rejects an exactly zero gradient outright. The recorded ensemble_relative_l2 is 0.0203 for every provider configuration, which can only come from reference and provider gradients that are both nonzero and agree to about 2 %; its neighbours norm_out.grad (0.0206) and audio_proj_out.grad (0.0211) have the same size. The small max_abs (4.6e-9) is the true magnitude of the gradient at that tap, and the bound is the tightest of all gradient keys (0.0577), not an uninformative one. As in my earlier comments, the part I cannot explain is why the error grows about 50x inside the backward of block 49; the entry stays as frozen.
… exits
provider_variant_context('blas-cublaslt') set torch's process-global preferred BLAS library and
never restored it, so in the generation runner, where only the transformer forward runs inside
the context, the choice leaked into the VAE decode and the next seed. It is now a context manager
that restores the previous library on exit, also when the body raises. The full-model runner
keeps the variant for its whole with-block, so its runs are unchanged. With tests.
Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
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Multiple frozen relative_l2 bounds for gradient entries exceed 1.0 (e.g. nodes.block.0.grad = 1.028, nodes.block.1.grad = 1.031, nodes.block.10.grad = 1.179, parameter.transformer_blocks.29.attn.norm_q.weight = 1.640), while the file declares the rule "1.5 * independent BF16 provider error + fixed floor". A relative-L2 tolerance >= 1.0 means a candidate gradient may differ from the frozen reference by more than the reference's own L2 norm and still be accepted — such entries cannot distinguish a correct kernel from a broken/zeroed/sign-flipped one, so they are not a usable parity contract for those tensors.
This pattern is characteristic of a generator that divides the provider error by a near-zero reference norm without an epsilon floor. Before freezing these values, confirm the generator clamps relative_l2 to a meaningful ceiling (or guards the reference norm) rather than emitting noise-dominated ratios, and that the comparator does not treat these entries as sufficient on their own.
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Confirmed, and the same finding was raised earlier on full-model-thresholds.json (line 38 of this file), where the reasoning is spelled out. The short version:
It is a property of the measured data, not of the generator. relative_l2 is ||reference - other|| / ||reference|| with the FP32 reference in the denominator (h3_full_compare.compare), and the bound is 1.5 x the error of an independent BF16 provider plus a floor. The BF16 provider's own interior block gradients are more than 100 % off the FP32 reference in relative L2, so no bound derived from a real BF16 provider can be tighter than 1 on those keys.
The suggested 1.0 would make things worse. A gradient that is exactly zero has a relative error of exactly 1.0, and check rejects only relative_l2 > bound, so a bound of 1.0 accepts a zeroed gradient. The bounds are also frozen and do not move after the numbers were seen.
What does protect these keys: the ensemble rule's check records the candidate's tensor_l2 and fails any gradient that is exactly zero where the reference is not (zeroed_gradients), so the zeroed case is rejected whatever the bound is. The outputs, the three input gradients and the forward taps of every block have bounds far below 1.
So these entries still cannot tell a mildly wrong interior gradient from a right one on their own. Whether the saturated gradient keys stay gating or become reported-only is a decision for the maintainers.
The CPU torch wheel used in CI is built without cuBLASLt and refuses to select it, so the real-library restoration test failed there. It now skips in that case; the fake-library tests still cover the restoration logic on every build.
Signed-off-by: Wenbo Ji <36562829+fusheng-ji@users.noreply.github.com>
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Flink-ddd
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This stack
What
RFC #420 closeout row
ws1_dual_trajectory, generation part: three-seed generation with the candidate and its comparison against the provider, built to survive preemption. Not claimed as passed (see Status).examples/models/minimax_h3/generation-contract.json: the pinned public reference image and prompt, seeds 42/43/44, 124 frames, 1344×768, 24 fps with joint 32 kHz audio.generation-compare.sbatchandh3_generation_compare.pycheck identical initial noise and shapes and report PSNR, SSIM, temporal RMSE and audio differences against the provider.h3_full_generation.py,rl_engine/validation/models/h3_generation_resume.py). Candidate generation takes about 20 hours per seed, so it runs as one resumable task per seed. Each saved step has a sidecar with the digest of the transformer's inputs and of the saved file, written atomically. On resume the saved transformer calls are replayed from disk and the pipeline's own scheduler turns them into the next latents; every replayed call's input digest is checked. Recomputing the last saved step and requiring a bitwise match is--verify-last-step(opt-in).h3_generation_adopt.pymoves an older commit-based run over, only if those paths are byte-identical.generation.sbatchruns one seed per array task and refuses to start unless the numerical gates of 10b passed;generation-resume-check.sbatchchecks resume;generation-compare.sbatchcompares afterwards.--provider-variantonh3_full_generationgenerates the provider's videos with one SDPA backend forced on the transformer's forward only, for the quality-bounds comparison below.Prior art & reuse decision
No operator is written here. The reference is the diffusers pipeline; its own generation is the baseline, including variants with one SDPA backend forced.
Results
B200
Generation from clean checkouts at
4792c8a9/2451ae5c(same model code), 124 frames 1344×768, 49 steps. Evidence onpr_assets:h3-full-model-b200/.Generation quality per seed (bounds: PSNR ≥ 30 dB, SSIM ≥ 0.99, temporal RMSE ≤ 0.01, audio relative L2 ≤ 0.05; a failing value is bold):
The same bounds applied to the provider with one SDPA backend forced on the transformer (same seeds, inputs and conditioning):
First denoising step replayed from its saved packed inputs (nothing has fed back yet), relative L2 of the video output against the provider's saved step (replaying the provider is bitwise equal to it):
H100 (Hopper)
A one-step generation preflight (128×128) completed on 4 × NVIDIA H100; it checks the flow only and is not quality evidence. The three-seed generation (about 20 hours per seed) was not run on H100.
Status
Tests
Layer head
6fe346df; same commands as 10a.tests/models/minimax_h3+test_h3_full_attention.py(2 GPUs)tests/(minustests/distributedand a ROCm import-error file)upstream/test-h3(measured on the heads before the re-cut; same layer code)Skips as in 10a.
tests/models/minimax_h3/test_h3_generation_resume.py(22 CPU tests, included in the counts) drives a fake denoising loop through the same resume code: a run preempted once or repeatedly equals an uninterrupted one, nothing is recomputed by default, the opt-in verification catches a drifting recompute, other identities, corrupted files and signature changes are rejected, and adopting a commit-based run is tested.Scope / Known limitations
Summary by CodeRabbit
test-h3.