[DSv4.1] Add FROST saved-state Engram gate forward and backward - #1113
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Navigate logical layers of code changes, visualize relationships, and explore their blast radius. No actionable comments were generated in the recent review. 🎉 ℹ️ Recent review info⚙️ Run configurationConfiguration used: Repository: NVIDIA/cudnn-frontend/.coderabbit.yaml Review profile: CHILL Plan: Enterprise Run ID: 📒 Files selected for processing (1)
Included review availability: Your plan provides up to 12 included reviews per hour; 11 remain after this review. 📝 WalkthroughWalkthroughAdds SM100 Engram saved-state forward and backward kernels, public APIs, validation tests, documentation, and benchmarks. The implementation supports caller-owned saved state, packed KV gradients, validated execution plans, CUDA graphs, and reference comparisons. ChangesEngram saved-state gate
Priority: ➖ Normal Estimated code review effort: 5 (Critical) | ~90 minutes Change: Feature Sequence Diagram(s)sequenceDiagram
participant Caller
participant EngramAPI
participant ForwardKernel
participant BackwardAPI
participant ReductionKernels
Caller->>EngramAPI: execute saved forward
EngramAPI->>ForwardKernel: validate and launch
ForwardKernel-->>Caller: return BF16 output and saved state
Caller->>BackwardAPI: execute saved backward
BackwardAPI->>ReductionKernels: compute moments and weight reductions
BackwardAPI-->>Caller: return dX, dKV, and dWeight
Suggested reviewers: Merge Risk: ⚪ Minimal · up to No actionable merge-blocking risk is identified; normal runtime validation remains appropriate. 🚥 Pre-merge checks | ✅ 4 | ❌ 1❌ Failed checks (1 warning)
✅ Passed checks (4 passed)
✨ Finishing Touches🧪 Generate unit tests (beta)
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YangXu1990uiuc
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Actionable comments posted: 2
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Inline comments:
In `@benchmark/engram/bench_saved_gate.py`:
- Line 7: Update the argparse description docstring by inserting a space between
“width” and “6144” so both help output and the JSON result’s scope field use the
corrected text.
In `@python/cudnn/engram/api.py`:
- Line 217: Update the affected docstring and ValueError messages to insert
spaces between words and numeric values: use “of 64,” “by 64,” and “at most
8192.” Preserve the existing validation behavior and message wording otherwise,
including the checks in the saved-backward validation flow.
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📒 Files selected for processing (15)
benchmark/engram/README.mdbenchmark/engram/bench_saved_gate.pybenchmark/engram/references.pydocs/fe-oss-apis/engram_saved_gate.mddocs/fe-oss-apis/overview.mddocs/fern/versions/latest.ymlpython/cudnn/__init__.pypython/cudnn/engram/__init__.pypython/cudnn/engram/_backward.pypython/cudnn/engram/_forward.pypython/cudnn/engram/_math.pypython/cudnn/engram/_reductions.pypython/cudnn/engram/api.pytest/python/fe_api/test_engram_saved_gate.pytest/python/test_frost_kernel_name_prefix.py
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Codex bot review · model: gpt-6-astra
Reviewed 9eb91504cf94 against base 9e9050c4013e.
Reviewed the complete API/kernel/test/benchmark change and its current-base integration. No actionable finding. Saved-state ownership, packed KV gradient layout, deterministic weight reduction, device binding, alias checks, explicit-stream execution and precompiled launch paths are consistent with the documented contract. Independent numerical and repeated consumer checks passed within the scope below.
Validation: Exact head on SM100 with PyTorch 2.13/cu130, cuDNN 9.26, CUTLASS DSL 4.7.0 and Triton 3.7.1: all 22 focused API tests passed, explicitly selecting L1. Clean local integration into develop 9e9050c: the same 22 passed. This covers all gate gradients, saved-state rebinding, poisoned/changed-input graph replay, no-JIT/allocation/sync detectors, explicit streams, metadata-only plans, aliases and workspace checks. Eight additional independent FP64-reference cases passed: signed-root scores below and above the positive/negative clamp, zero score, mixed token masks, and odd forward lengths 1/3/65 with epsilon 0.03. Checked output and all gradients for the clamp cases; masked residuals remain exact. Base import and all four lazy exports also behaved correctly with optional dependencies deliberately blocked. Both new CuTe kernels pass the repository naming detector. Read the pinned upstream Engram implementation and checked the per-stream normalization, combined q/k weight, signed-root clamp and residual semantics against it: https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash/blob/dba1be0a40aa45a94ad051997016db3960a90277/inference/model.py . Ran the unmodified full consumer benchmark twice on SM100 with 148 SMs, N=4096/H=5120/embedding width=6144. Both runs passed FROST checks across seven input generations, all five gradients, eager and changed-input graph execution, poisoning/negative controls, captured-kernel traces and post-timing validation. The ordinary and autotuned compiled-native backward controls failed gradient checks and were recorded/excluded, not counted as valid timing baselines. Against each run's fastest valid native control, complete forward+backward paired latency fell 15.50% and 16.66% for CUDA Graph, and 15.18% and 14.94% for eager wall time; FROST won all eight paired blocks in both runs/modes. Forward-only graph latency improved about 3.4-5.1% in this environment, unlike the author's reported forward-only regression; these results remain hardware/toolchain-specific and do not establish whole-model throughput. Separate CPU enqueue-only gate forward+backward samples (excluding projection and synchronization) measured medians about 147 us versus 855 us for eager PyTorch at N=64, and 170 us versus 1898 us at N=4096. These compare the explicit gate API with an eager reference, not with the compiled full-consumer timing controls. Live head/base and mergeability rechecked. Current-head Style and Docs checks passed; merge-requirements was still failing. No pending fork workflow or current-head test-pipeline acknowledgment observed. No broad CI run was triggered because live lane selection/compiler configuration could not be verified with the available access.
Limitations: Validation used compatible existing native bindings for these Python-only additions, not a fresh C++ binding build. It does not replace release/CI validation across supported environments. Benchmarks use synthetic BF16 inputs and unlocked clocks on one SM100 device. They cover the floating projection/gate/gradient consumer, not lookup, FP8/QAT, distributed collectives, optimizer or full-model training. Only the N=4096 complete consumer was independently timed; API correctness additionally covers N up to 8192.
Approved: no P0 or high-risk P1 found. Remaining findings stay with the owner; merge timing stays with the owner.
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@cudnn-ci-bot run oss python_tests |
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🏁 Pipeline finished SHA: 28 passed, 1 failed, 2 canceled, 5 manual
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@cudnn-ci-bot run oss python_tests |
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🏁 Pipeline finished SHA: 29 passed, 1 canceled, 5 manual
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Codex bot review · model: gpt-6-astra
Reviewed dcd2f5ac6e7b against base bd3c2b6144fd.
No actionable finding in this incremental update. The only change since the independently approved 9eb9150 is the private dV-stride identifier rename in the backward kernel and its host launcher. The argument stays in the same position, and both call sites pass it positionally. The existing approval remains in place.
Validation: Verified that _backward.py is the only file changed since 9eb9150. Inverse-renaming dv_stride to dvs produces an identical Python AST; syntax compilation and git diff --check pass. The public API and its compile/execute call sites are unchanged. Confirmed pipeline 69293056's mirror commit identifies the exact dcd2f5a head and the owner's existing OSS/python-tests request. CI is pending; no duplicate run requested.
Limitations: Source-only follow-up for an identifier rename. I did not repeat the previous independent GPU/numerical review or benchmark runs and make no new performance claim.
Comment only: GitHub does not allow reviewing one's own PR with approval/request-changes.
3 participants
Before submitting
pre-commit runand committed any formatting changes.AGENTS.mdfor each directory this PR touches (see root AGENTS.md § Reviewing a PR) and my changes comply, or I explain the exception below.cat-*, one or morearea:*/op:*, and oneorig-*(see label list).Affected area
FE OSS kernels or CuTeDSL
Summary
Add prepared FROST forward/backward APIs for the DSv4.1 four-stream Engram gate, with caller-owned saved state and packed KV gradients.
Why
Saved scalar state and packed gradient output reduce work in the floating training consumer. Two B200 runs show 10.20–10.48% lower CUDA Graph latency for the connected projection/gate/backward subgraph. Forward-only regresses; this is not an inference or full-model speedup.
Related issues
No linked issue.
Release tracking: Frontend 1.30.0, cudnn-frontend project.
API and compatibility impact
Add explicit SM100 forward/backward APIs for the four-stream Engram gate.
Forward returns the BF16 residual and caller-owned FP32 saved state. Backward
writes BF16 input and packed KV gradients, plus deterministic FP32 weight
gradients. The packed KV gradient feeds existing projection-gradient GEMMs
without separate key/value packing kernels.
The prepared APIs use
backend="frost", support preallocated outputs, explicitworkspace and streams, and compile before execution or CUDA Graph capture.
They include convenience wrappers, lazy exports, documentation and tests.
Requirements are CuTe DSL >=4.7.0 and, for backward, Triton >=3.7.0. The gate
has four streams and hidden dimension 5120; backward supports positive token
counts divisible by 64, up to 8192.
Testing
Focused validation
22 focused API tests passed, including the device-binding regression. Current-head independent review also ran the 22 tests and checked the current-base integration. The device-binding fix changes host validation; device kernels and benchmark code are unchanged from the frozen B200 performance runs.
Focused reproduction command, from a CUDA-enabled environment importing this PR's built package:
GitHub
pre-commitandfern-checkpassed on head9eb91504cf9455ed5d77b29963b0fa29830929b2before this description-only update. Existing local/B200 validation is separate from PR CI. Performance scope and original source attribution remain in the benchmark record below.PR CI
Primary CI pipeline 69290013 completed on
9eb91504cf9455ed5d77b29963b0fa29830929b2: 28 jobs passed, including all SM80/SM90/SM100 Python, OSS and sanitizer lanes plus builds. Its only failure was a scan false positive on the private dV-stride identifier, fixed in current headdcd2f5ac6e7b7c7ff2a5ac8ac3767c707fd640d6. Syntax, inverse-rename AST equivalence and file-scoped pre-commit checks pass; the independent incremental review confirmed that this is the only change. The additional CI run was already launched for the rename and is tracked separately.The SM107 OSS lane and AI triage were canceled. SM107 is outside this API's explicitly enforced
(10, 0)support contract; cancellation is not a test pass and does not establish SM107 coverage. The ordinary OSS lane uses CuTe DSL 4.6.2 and default L0 selection, so the separate explicit L0/L1 numerical validation above remains necessary evidence for cases excluded by those defaults.Frozen B200 benchmark, numerical coverage and source attribution
Two independent B200 runs measured the complete floating training consumer:
projection, gate, both gradient GEMMs and all five input/parameter gradients.
At N4096/H5120 with synthetic BF16 activations and embedding width 6144,
paired-block latency fell 10.20–10.48% with CUDA Graph and
12.34–12.49% in eager wall time, against each run's fastest valid measured
native control. Frost won all eight paired blocks in each run and mode.
The benchmark retains eight native controls and checks seven input generations.
Compiled-native backward controls that failed gradient checks remain recorded
and are excluded from those timings.
Forward-only regresses: inference forward was 2.94–6.50% slower and training
forward 4.97–6.20% slower with CUDA Graph. The benefit is scoped to the complete
floating training consumer. The public API covers the gate; quantized lookup,
FP8/QAT, collectives, autograd registration and full-model training are outside
this change.
Validation:
saved-state lifetime, all gate gradients, changed-input Graph replay,
explicit streams, alias rejection and no allocation/JIT/sync on execute.
TensorDesc(device="cuda")plan binding. All 22 focusedAPI tests passed on B200 with no skips. Both new device-binding tests
failed against the original API with the expected errors and passed
against the fix. Six CPU metadata cases also passed.
fix. Performance numbers remain the existing two runs.
Fern validation; the full naming guard reproduces 17 existing upstream
SDPA failures, while both new Engram CuTe kernels pass.
post-rebase host build validation is described below.
Normalization and signed-square-root semantics follow
DeepSeek-V4.1-Flash.
The new kernels and benchmark controls are authored for this work using
CUTLASS CuTe DSL, Triton and PyTorch. Projection GEMMs use existing providers.