Add experimental SM100-optimized bulk causal conv1d forward - #798
Add experimental SM100-optimized bulk causal conv1d forward#798YangXu1990uiuc wants to merge 9 commits into
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Included review availability: Your plan provides up to 12 included reviews per hour; 10 remain after this review. 📝 WalkthroughWalkthroughExpanded the experimental SM100-suffixed BF16 width-four bulk causal-convolution forward API to supported SM80–SM121 architectures. The change adds scalar and Vec8 kernels, schedule selection, dense and packed stateful execution, public exports, documentation, validation tests, and a B200 benchmark. ChangesPortable bulk causal-convolution forward
Estimated code review effort: 4 (Complex) | ~60 minutes Merge Risk: ⚪ Minimal · up to This adds an experimental, architecture-gated bulk causal-convolution API with documented validation and no actionable merge-blocking risk remaining beyond normal checks and review. Sequence Diagram(s)sequenceDiagram
participant Caller
participant causal_conv1d_bulk_fwd_wrapper_sm100
participant CausalConv1dBulkFwdSm100
participant CausalConv1dBulkForwardKernel
Caller->>causal_conv1d_bulk_fwd_wrapper_sm100: Submit dense or packed BF16 tensors and optional state
causal_conv1d_bulk_fwd_wrapper_sm100->>CausalConv1dBulkFwdSm100: Build or retrieve schedule-specific API
CausalConv1dBulkFwdSm100->>CausalConv1dBulkForwardKernel: Launch scalar or Vec8 kernel on the selected stream
CausalConv1dBulkForwardKernel-->>CausalConv1dBulkFwdSm100: Write output and optional final state
CausalConv1dBulkFwdSm100-->>Caller: Return wrapper output
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✅ Passed checks (4 passed)
Full details: Description checkExplanation The description includes all required sections, explains the API and compatibility impact, lists related issues, and provides detailed testing results. The unchecked Projects item is clearly explained as requiring maintainer action.
✨ Finishing Touches 💡 1🛠️ Fix failing CI checks 💡
🧪 Generate unit tests (beta)
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In `@python/cudnn/causal_conv1d_bulk_sm100/api.py`:
- Around line 130-140: Update the alignment capture in APIBase.__init__ to
record data_ptr() remainders only for framework tensor inputs, skipping
metadata-only TensorDesc values across X, Weight, Output, cu_seqlens, Initial
state, and Final state while preserving the existing alignment widths.
In `@test/python/fe_api/causal_conv1d_bulk/test_causal_conv1d_bulk_sm100.py`:
- Line 162: Update the pytest.raises assertion’s match pattern to use a raw
string while preserving the existing regex and expected error text.
- Around line 546-563: Call _load_class() at the beginning of
test_invalid_cu_seqlens_fail_closed_in_fresh_process so the test inherits the
existing CuTe DSL availability skip gate before launching the subprocess; leave
the worker invocation and assertions unchanged.
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docs/fe-oss-apis/causal_conv1d_bulk_contract.mddocs/fe-oss-apis/overview.mdpython/cudnn/README.mdpython/cudnn/__init__.pypython/cudnn/causal_conv1d_bulk_sm100/__init__.pypython/cudnn/causal_conv1d_bulk_sm100/api.pypython/cudnn/causal_conv1d_bulk_sm100/kernel.pytest/python/fe_api/causal_conv1d_bulk/benchmark_causal_conv1d_bulk_sm100.pytest/python/fe_api/causal_conv1d_bulk/conftest.pytest/python/fe_api/causal_conv1d_bulk/reference.pytest/python/fe_api/causal_conv1d_bulk/test_causal_conv1d_bulk_contract_unit.pytest/python/fe_api/causal_conv1d_bulk/test_causal_conv1d_bulk_sm100.py
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Inline comments:
In `@python/cudnn/causal_conv1d_bulk_sm100/api.py`:
- Around line 128-129: Update the sample validation around _make_tensor_desc so
required samples sample_x, sample_weight, and sample_output reject None while
preserving None for optional samples. Ensure invalid required inputs raise the
intended TypeError before constructor data_ptr() access and check_support().
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YangXu1990uiuc
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Superseded by #799, which now contains this full-sequence forward implementation together with backward, the semantic API, decode update, and their shared state/architecture contracts. |
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Before submitting
pre-commit runand committed any formatting changes.cat-*, one or moremod-*, and oneorig-*(see label list).Frontend 1.29.0; the current token does not have Projects scope, so Project assignment remains for a maintainer.Affected area
FE OSS kernels or CuTeDSL.
Summary
Downstream model impact (combined attribution)
On B200, BF16 batch-one fprop+bprop all-on proxies containing this causal-convolution forward feature measured, at 8K/16K/32K respectively: Qwen3.5-9B 32-layer decoder backbone 1.1530x / 1.1722x / 1.1931x; Qwen3.8-27B 64-layer decoder backbone 1.1354x / 1.1967x / 1.0898x; GLM-5.3-Flash 34-layer KDA substack 1.0732x / 1.0950x / 1.0966x; and the official-count Kimi-K3 feature substack 1.3567x / 1.3238x / 1.2189x. These are combined results: Qwen also changes GDN, SwiGLU, and SDPA; GLM also changes KDA; Kimi also changes KDA and SiTU. They are not marginal speedups attributable to #798 or assembled-model results. Qwen and GLM contain this exact current head; Kimi uses a patch-equivalent rebased implementation.
Add an experimental, independent, SM100-optimized bulk causal-conv1d forward API and CuTeDSL kernel for the BF16, width-4, fused-SiLU operation used by short-convolution model blocks.
The implementation supports:
[B, T, D]and packed[1, total_T, D]inputs;[N, D, 4]initial and final states;Treuse for a compiled shape signature;No external causal-conv kernel implementation was copied. FLA is used only as an independently installed interoperability/performance comparison in the manual benchmark; permanent correctness tests use the in-tree PyTorch reference.
Why
The frontend has a decode-oriented causal-conv companion in #797, but no matching bulk/prefill primitive. This fills that forward feature gap with a standalone frontend implementation while keeping the contract narrow enough to validate and optimize independently.
Related issues
Related to #797.
API and compatibility impact
This adds the experimental
CausalConv1dBulkFwdSm100class andcausal_conv1d_bulk_fwd_wrapper_sm100convenience wrapper; the original suffix is retained while the API evolves. The exact functional targets are SM80/86/87/89/90/100/103/110/120/121. SM100/103/110/120/121 use the vec8 path when D is divisible by eight; SM80-SM90 use a compile-time scalar schedule. Unsupported architectures and inputs fail closed; there is no framework fallback, conversion, host synchronization, or backward implementation.The API checks for
nvidia-cutlass-dsl >= 4.7before lazily importing the kernel. The repository-wide dependency floor is unchanged. The optional state layout is intended to be compatible with the pending decode companion in #797; cross-API state tests remain conditional on that PR landing.Architecture policy and validation
Architecture admission and schedule selection are separate. The vec8 path contains
mul.f32x2/fma.rn.f32x2, whose PTX target floor is SM100. Pre-Blackwell targets therefore compile only the ordinary-FP32 scalar kernel, even whenD % 8 == 0. The gate is an explicit target allowlist rather than an open-ended>=comparison because the current DSL does not accept every numerical capability between listed targets.The benchmark harness itself runs on every functional target, requires neither ComputeLab nor Slurm, and records ordinary hardware/software metadata plus optional Slurm fields.
No performance numbers were collected outside B200. Functional validation:
ComputeLab B200 performance
Equal-layout cuDNN backend NWH baseline (current head)
The exact clean current head
678e3570272ced7009eadb9603b4eb911b76cb8awas rechecked on one NVIDIA B200 (SM100), PyTorch 2.13.0+cu130, cuDNN 9.26, andnvidia-cutlass-dsl4.7.0. Both direct arms consume the same contiguous[1, T, D]BF16 input, implement width-four causal depthwise convolution plus SiLU, and write caller-preallocated output. Each number is the median of 15 alternating-order rounds with 10 calls per CUDA-event interval in one process. Ratios arebackend NWH / FE, so values above 1 mean FE is faster.bias=Nonebias=Nonebias=Nonebias=NoneA separate same-process
torch.profiler/CUPTI pass found exactly one kernel per arm per call and reproduced the CUDA-event ratios closely. This corroborates a kernel-throughput advantage rather than wrapper allocation or host-enqueue overhead. The profiler route was the FE vec8 kernel versuscudnn_causal_conv1d_nwh_fwd_k4_silu_cutlass__bfloat16_t. All shapes passed an independent FP32 reference; maximum absolute error divided by the reference range was below0.00343.For
bias=None, FE compiles the bias load/add out; the backend's public no-bias path supplies the all-zero bias buffer required by its ABI. A second equal-layout current-head run with a real nonzero BF16 bias gives the following signature-level result:bias=NoneThe original 1.8-2.1x statement therefore applies specifically to
bias=None; it is not used as a blanket claim for the nonzero-bias specialization.FLA interoperability baseline
Measured on one NVIDIA B200 (SM100), PyTorch 2.13.0+cu130, cuDNN 9.26,
nvidia-cutlass-dsl4.7.0, and FLA 0.5.2. Each number is the median of 21 alternating-order rounds with 5 calls per CUDA-event interval in one process. These rows usebias=None. Ratios areFLA / FE, so values above 1 mean FE is faster.These are direct-API CUDA-event elapsed measurements, not profiler-isolated kernel-active cycles or model end-to-end speedups. The FE direct arm uses caller-preallocated outputs while the FLA direct API allocates its outputs internally; the wrapper/public pair is the more symmetric allocation-inclusive comparison. CUDA events may also include device idle gaps while the host enqueues work.
A separate same-process
torch.profiler/CUPTI capture isolates active kernel duration after the final stream-lifetime fix. Each implementation ran 20 direct-API plus 20 public-wrapper calls, yielding 40 launches of each kernel:These profiler ratios are also primitive-only and are intentionally reported separately from API elapsed and any future model-proxy result.
Testing
pre-commit run --files <all changed files>: passed (black,black-jupyter; clang-format not applicable).36 passed.SLURM_JOB_IDandSLURMD_NODENAMEunset: completed successfully and omitted the optional Slurm object.36 passed, 1 warning in 35.94s(job4004734).GitHub CI does not exercise this SM100 GPU runtime path, so the ComputeLab result above is the relevant runtime validation.
Follow-ups are backward support, FLA/Transformers adapters with route proof, conditional cross-API state tests with #797, nonzero-bias specialization tuning, per-architecture tuning beyond the SM100 schedule, and model-proxy measurements after integration.