[TRTLLM-15585][feat] Wire SkipSoftmax sparse attention into the CuTeDSL backend - #17781
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Included review availability: Your plan includes up to 12 reviews per rolling hour; 11 remain after this review. WalkthroughCuTeDSL FMHA now supports timestep-aware SkipSoftmax thresholds. Configuration lowering, backend validation, CUDA graph registration, documentation, and integration tests cover SkipSoftmax with quantized attention. ChangesCuTeDSL SkipSoftmax integration
Estimated code review effort: 3 (Moderate) | ~20 minutes Merge Risk: 🟡 Moderate · up to This PR extends timestep-aware sparse attention to the CuTeDSL backend and changes the nested configuration contract. Merge readiness remains blocked until the required LLM-args golden manifest is regenerated or explicitly accepted, because stale generated artifacts could leave configuration behavior inconsistent; the documentation header also needs follow-up. Sequence Diagram(s)sequenceDiagram
participant AttentionModule
participant CuTeDSLAttention
participant cute_dsl_fmha_fwd
AttentionModule->>CuTeDSLAttention: provide sparse_params
CuTeDSLAttention->>cute_dsl_fmha_fwd: pass scheduler context and timestep
cute_dsl_fmha_fwd-->>CuTeDSLAttention: execute FMHA with resolved threshold
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In `@tensorrt_llm/visual_gen/args.py`:
- Around line 180-207: Regenerate the LLM-args golden manifest using the
repository’s generator script to reflect the updated AttentionConfig validation,
including the valid CUTEDSL SkipSoftmax and quantized-attention combination, and
commit the resulting llm_args_golden_manifest.json.
In `@tests/unittest/_torch/visual_gen/sparse_attention/test_skip_softmax.py`:
- Around line 112-124: Update the test registry or CI/QA test list to include
tests from test_skip_softmax.py, including the four changed tests such as
test_cutedsl_api_accepts_skip_softmax_with_quantized_attention. Use the existing
naming and grouping conventions, without changing the test implementations.
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tensorrt_llm/_torch/visual_gen/attention_backend/cute_dsl/fmha.pytensorrt_llm/_torch/visual_gen/attention_backend/utils.pytensorrt_llm/_torch/visual_gen/models/modeling.pytensorrt_llm/_torch/visual_gen/modules/attention.pytensorrt_llm/visual_gen/args.pytensorrt_llm/visual_gen/sparse_attention.pytests/unittest/_torch/visual_gen/sparse_attention/test_skip_softmax.py
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Addresses CodeRabbit review comment on NVIDIA#17781: the file was never registered in test-db (pre-existing gap, not introduced by the prior commit), so its new CuTeDSL SkipSoftmax tests were not selected by CI. Registered as a whole-file entry alongside its visual_gen siblings, matching the existing convention in this list. The other review comment (regenerate the LLM-args golden manifest) is a false positive: `python3 scripts/generate_llm_args_golden_manifest.py --check` exits 0 against this branch. The prior commit changed only AttentionConfig validator *logic* (which backends skip_softmax may compose with), not the schema shape the manifest captures, so regeneration produces no diff. Signed-off-by: Kanghwan Jang <kanghwanj@nvidia.com> Signed-off-by: Kanghwan Jang <861393+karljang@users.noreply.github.com>
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A few things to settle before this merges.
Docs contradict the new behavior. docs/source/visual-gen/features/sparse-attention.md:93 still says "Skip Softmax Attention only works with the TRTLLM attention backend in VisualGen. Set attention_config.backend to TRTLLM when enabling it," and every example below it uses backend: TRTLLM. This PR makes that statement false and is exactly the kind of user-facing capability change the doc exists to convey. Update the sentence and add a CUTEDSL example (including whether quant_attention_config may now be combined).
Relaxing the CuTeDSL quant × sparse mutex is not validated. [args.py:204](https://github.com/NVIDIA/TensorRT-LLM/pull/17781/files#diff-4c385f4bd5d1cdc01fcc984d21ede4817330ac74e33d284d819eaed98254aa66R204) now admits quant_attention_config + skip_softmax on CUTEDSL, which routes a nonzero skip_threshold_log2 into the block-scaled (MXFP8/NVFP4) kernel path. The only new test for it asserts the Pydantic object parses; per the test plan the GPU runs that would have exercised the quantized FMHA path were unavailable on the test node. Either land numerical evidence for skip_softmax + quantized QK/V on CuTeDSL, or keep the mutex for the quantized case and relax it in a follow-up once it's measured. The old error message asserted the dispatcher can't do both — worth being sure that claim was only about VSA.
Ticket tag. This is a feature, not a chore; [None] should be a JIRA ID per the repo convention.
Everything else reads well — factoring resolve_disabled_until_timestep() out so the CUDA-graph phase key and the per-layer lowering can no longer disagree is the right fix, and it's a bug the previous code could actually hit with a checkpoint-provided cutoff.
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SkipSoftmax was previously TRTLLM-only. This lowers the shared SkipSoftmaxAttentionConfig/SkipSoftmaxParams path into CuTeDSLAttention too, so the CuTeDSL dense FMHA kernel gets the same timestep-aware sparsity scheduling. - AttentionConfig: skip_softmax now composes with backend=CUTEDSL (and quant_attention_config), not just TRTLLM. VSA remains CUTEDSL-only and still mutually exclusive with quant_attention_config. - cute_dsl/fmha.py: cute_dsl_fmha_fwd and CuTeDSLAttention accept sparse_params (SkipSoftmaxParams); the runtime threshold is resolved per-timestep via the shared scheduler instead of a static value. - attention.py: sparse_params is now lowered for backend in (TRTLLM, CUTEDSL), not just TRTLLM. - sparse_attention.py: factor disabled_until_timestep resolution into SkipSoftmaxAttentionConfig.resolve_disabled_until_timestep(), reused by both to_sparse_params() and BaseDiffusionModel's CUDA-graph phase tracking (modeling.py), which previously only read the raw field. - test_skip_softmax.py: new coverage for CuTeDSL param lowering, timestep-tracked threshold resolution, and CUDA-graph phase cutoff under the checkpoint-provided disabled_until_timestep. Signed-off-by: Kanghwan Jang <kanghwanj@nvidia.com> Signed-off-by: Kanghwan Jang <861393+karljang@users.noreply.github.com>
Addresses CodeRabbit review comment on NVIDIA#17781: the file was never registered in test-db (pre-existing gap, not introduced by the prior commit), so its new CuTeDSL SkipSoftmax tests were not selected by CI. Registered as a whole-file entry alongside its visual_gen siblings, matching the existing convention in this list. The other review comment (regenerate the LLM-args golden manifest) is a false positive: `python3 scripts/generate_llm_args_golden_manifest.py --check` exits 0 against this branch. The prior commit changed only AttentionConfig validator *logic* (which backends skip_softmax may compose with), not the schema shape the manifest captures, so regeneration produces no diff. Signed-off-by: Kanghwan Jang <kanghwanj@nvidia.com> Signed-off-by: Kanghwan Jang <861393+karljang@users.noreply.github.com>
…step test - sparse_attention.py: SkipSoftmaxAttentionConfig.resolve_disabled_until_timestep takes explicit keyword-only checkpoint_config/pretrained_config instead of bare **kwargs, so a mistyped keyword at a call site raises TypeError instead of silently resolving to None (both existing call sites -- to_sparse_params's checkpoint_config= and BaseDiffusionModel's pretrained_config= -- already used the right names, so behavior is unchanged; this only tightens the contract). - cute_dsl/fmha.py: document CuTeDSLAttention's skip_softmax_threshold_scale as a legacy/debug-only knob superseded by sparse_params for every in-tree construction path (create_attention never forwards it). - test_skip_softmax.py: add test_forward_threads_timestep_and_sparse_params_to_kernel_call, which monkeypatches cute_dsl_fmha_fwd and asserts CuTeDSLAttention.forward's timestep kwarg reaches the kernel call unchanged -- the one link in the timestep-gating chain the existing CPU tests didn't cover. Signed-off-by: Kanghwan Jang <kanghwanj@nvidia.com> Signed-off-by: Kanghwan Jang <861393+karljang@users.noreply.github.com>
… block-scaled numerical coverage - sparse-attention.md previously said SkipSoftmax only works with TRTLLM; update it to cover the CUTEDSL backend added in this PR, including that it composes with quant_attention_config block-scaled Q/K recipes. - Add test_cute_dsl_fmha_blockscaled_forward_skip_softmax: numerical coverage for SkipSoftmax combined with the CUTEDSL MXFP8/NVFP4 block-scaled Q/K path, checked against dense SDPA via cosine similarity (mirrors test_attention_trtllm_sage.py's skip_softmax check). Signed-off-by: Kanghwan Jang <861393+karljang@users.noreply.github.com>
…max test, fail-open warning - test_skip_softmax.py: test_forward_threads_timestep_and_sparse_params_to_kernel_call called CuTeDSLAttention.forward(), which raises ImportError in _prepare_inputs before ever reaching the monkeypatched kernel launcher. This file is registered in l0_cpu.yml, so it errored (not skipped) on a CPU-only image without the CuTe DSL runtime. Add a skipif guard. - test_attention_cute_dsl.py: the new block-scaled skip_softmax test used skip_softmax_threshold_scale_factor=0.3, which at seq_len_kv=512 is far below the kernel skip threshold and never actually skips a block, so the test exercised the dense path under a different name. Calibrated (via a threshold sweep on this seed/shape/dtype) to 700.0, past the first block-skip transition with margin, and added an assertion that the skip-softmax output measurably diverges from a same-inputs dense run to confirm skipping actually happened. Loosened the SDPA-reference cosine bound from 0.99 to 0.95 to match: SkipSoftmax discards real information once a block is skipped, so it cannot both truly skip and hit the same bound as a threshold picked specifically not to skip. Verified on a B200 (sm_100a). - fmha.py: _resolve_skip_softmax_threshold_scale_factor fails open when sparse_params has disabled_until_timestep configured but timestep is None (runs unthrottled instead of respecting the cutoff). Add a logger.warning_once so a caller that forgets to thread timestep through gets a log line instead of a silent quality regression. Signed-off-by: Kanghwan Jang <861393+karljang@users.noreply.github.com>
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Params wiring & test coverage both LGTM.
Kernels called by cute_dsl_fmha_fwd already exposes a skip_threshold_log2 parameter. This PR carefully maps TRTLLM's timestep-aware skipSm mechanism to this parameter interface.
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Adds Sol-Attn (arXiv:2607.24027) as a third sparse-attention algorithm for VisualGen, alongside `skip_softmax` and VSA. It folds dynamic block routing, sparse computation, and an approximation-correction term into a single online-softmax pass. Config surface: `SolAttnAttentionConfig` in `visual_gen/args.py` / `sparse_attention.py` -- `tau` (routing threshold), `thresh_type` (`diag`/`exact`), `kv_splits`, `disabled_until_timestep` (dense-prefix cutoff), and `dense_layers` (comma/range layer-skip spec). Dispatch goes through `create_attention` the same way `skip_softmax` and `vsa` do. Cross-attention (`SEPARATE_QKV`) falls back to VANILLA, and context-parallel (`cp_size > 1`) and quantized attention are both rejected, mirroring VSA's existing guards. Dense prefix ------------ `disabled_until_timestep` follows skip-softmax's field of the same name and the same sense: the layer runs dense while the normalized denoising timestep is at or above the cutoff, and switches to the sparse kernel below it. The value arrives as a forward kwarg, which `modules/attention.py` already threads to every backend and every VisualGen pipeline normalizes by `num_train_timesteps`, so no per-pipeline wiring is needed and there is no process-wide state. `models/wan/pipeline_wan.py` is untouched. Because the prefix swaps kernels without changing tensor shapes, the two phases must not share a captured CUDA graph; `register_cuda_graph_extra_key_fns` registers `sol_attn_phase` from the same `kwargs["timestep"]` source as `skip_softmax_phase`. `dense_layers` needs no key, being fixed per layer at construction. Kernel scope ------------ The kernel is vendored from its reference implementation (see `cute_dsl_kernels/blackwell/sol_attn/THIRD_PARTY_NOTICES.md` for the upstream pin and its currency check). Only the two architectures with hardware evidence are carried: sm100 (B200/GB200) and sm120 (RTX Blackwell). Upstream's sm89 and sm90 kernels and its Triton reference path are not included; sm90 covers H100/H200/GH200 and should return in a follow-up with measurements behind it rather than ship unvalidated. Upstream also vendors a copy of FlashAttention's CuTe DSL helpers. That copy is not carried: TensorRT-LLM already depends on flash-attn-4, which provides the same `flash_attn.cute` modules, verified on B200 to give bit-identical output. `preprocess.py` implements the routing/threshold stage in Triton, so Triton is a required runtime dependency on every Sol-Attn path. Failure behaviour ----------------- Inputs the kernel cannot serve -- unsupported architecture, `head_dim` other than 128, non-bf16 dtype, or mismatched k/v -- fall back to dense SDPA with a `warning_once` naming the specific reason, and increment `dense_fallback_calls` alongside `kernel_calls`. Kernel exceptions take the same path. `SOL_ATTN_STRICT=1` raises instead, for both arms. Without this the feature degrades to a silent no-op for a whole run and surfaces only as absent speedup. Tests ----- New `tests/unittest/_torch/visual_gen/test_attention_cute_dsl_sol_attn.py`, registered in `l0_b200.yml`: backend-factory dispatch, cross-attention VANILLA fallback, context-parallel and quantized-attention rejection, GQA/MQA rejection, the `dense_layers` guard, dense-prefix phase semantics at and either side of the cutoff (including tensor-valued timesteps), fail-open on a missing timestep, both CUDA-graph key cases, kernel-eligibility reasons, `SOL_ATTN_STRICT` on the eligibility path, dense-fallback numerics and counters, and arch-list drift between `SUPPORTED_ARCHS` and `_CUTE_BACKENDS`. 31 tests plus one documented skip for GPU kernel-vs-dense equivalence at full routing. Validation ---------- * B200 (sm100): 31/31 pass, and 68 passed alongside `test_attention_cute_dsl.py`, which NVIDIA#17781 extended. Kernel output bit-identical across a 12-point (shape, tau) sweep; `kernel_calls=12`, `dense_fallback_calls=0` under `SOL_ATTN_STRICT=1`. * RTX 5090 (sm120): resolves to `cute_sm120`; 9/9 sweep points ran with no dense fallback. End-to-end generation was not possible on that GPU because 32 GB is insufficient for the models used here, so sm120 has kernel-level evidence only. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Signed-off-by: Kanghwan Jang <861393+karljang@users.noreply.github.com>
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Adds Sol-Attn (arXiv:2607.24027) as a third sparse-attention algorithm for VisualGen, alongside `skip_softmax` and VSA. It folds dynamic block routing, sparse computation, and an approximation-correction term into a single online-softmax pass. Config surface: `SolAttnAttentionConfig` in `visual_gen/args.py` / `sparse_attention.py` -- `tau` (routing threshold), `thresh_type` (`diag`/`exact`), `kv_splits`, `disabled_until_timestep` (dense-prefix cutoff), and `dense_layers` (comma/range layer-skip spec). Dispatch goes through `create_attention` the same way `skip_softmax` and `vsa` do. Cross-attention (`SEPARATE_QKV`) falls back to VANILLA, and context-parallel (`cp_size > 1`) and quantized attention are both rejected, mirroring VSA's existing guards. Dense prefix ------------ `disabled_until_timestep` follows skip-softmax's field of the same name and the same sense: the layer runs dense while the normalized denoising timestep is at or above the cutoff, and switches to the sparse kernel below it. The value arrives as a forward kwarg, which `modules/attention.py` already threads to every backend and every VisualGen pipeline normalizes by `num_train_timesteps`, so no per-pipeline wiring is needed and there is no process-wide state. `models/wan/pipeline_wan.py` is untouched. Because the prefix swaps kernels without changing tensor shapes, the two phases must not share a captured CUDA graph; `register_cuda_graph_extra_key_fns` registers `sol_attn_phase` from the same `kwargs["timestep"]` source as `skip_softmax_phase`. `dense_layers` needs no key, being fixed per layer at construction. Kernel scope ------------ The kernel is vendored from its reference implementation (see `cute_dsl_kernels/blackwell/sol_attn/THIRD_PARTY_NOTICES.md` for the upstream pin and its currency check). Only the two architectures with hardware evidence are carried: sm100 (B200/GB200) and sm120 (RTX Blackwell). Upstream's sm89 and sm90 kernels and its Triton reference path are not included; sm90 covers H100/H200/GH200 and should return in a follow-up with measurements behind it rather than ship unvalidated. Every vendored file carries an SPDX Apache-2.0 header naming its NVlabs/Sana origin; the two files that derive from FlashAttention additionally cite BSD-3-Clause and point at `sm100/LICENSE.flash-attention`, and the cuDNN Frontend license the SM120 kernel adapts is vendored at `sm120/LICENSE.cudnn-frontend` at the commit the notices cite. Upstream also vendors a copy of FlashAttention's CuTe DSL helpers. That copy is not carried: TensorRT-LLM already depends on flash-attn-4, which provides the same `flash_attn.cute` modules, verified on B200 to give bit-identical output. `preprocess.py` implements the routing/threshold stage in Triton, so Triton is a required runtime dependency on every Sol-Attn path. Failure behaviour ----------------- Inputs the kernel cannot serve -- unsupported architecture, `head_dim` other than 128, non-bf16 dtype, or mismatched k/v -- fall back to dense SDPA with a `warning_once` naming the specific reason, and increment `dense_fallback_calls` alongside `kernel_calls`. Kernel exceptions take the same path. `SOL_ATTN_STRICT=1` raises instead, for both arms. Without this the feature degrades to a silent no-op for a whole run and surfaces only as absent speedup. Docs ---- `docs/source/visual-gen/features/sparse-attention.md` gains a `sol_attn` row and a section covering the YAML surface, the sm100/sm120 + head_dim=128 + bf16 + MHA constraints, the cutoff semantics, and the fallback/`SOL_ATTN_STRICT` behaviour. Its claim that VSA is the only CUTEDSL algorithm mutually exclusive with quantized attention is corrected, since Sol-Attn now is too. Tests ----- New `tests/unittest/_torch/visual_gen/test_attention_cute_dsl_sol_attn.py`, registered in `l0_b200.yml` (sm100) and `l0_gb202.yml` (sm120): backend-factory dispatch, cross-attention VANILLA fallback, context-parallel and quantized-attention rejection, GQA/MQA rejection, the `dense_layers` guard, dense-prefix phase semantics at and either side of the cutoff (including tensor-valued timesteps), fail-open on a missing timestep, both CUDA-graph key cases, kernel-eligibility reasons, `SOL_ATTN_STRICT` on the eligibility path, dense-fallback numerics and counters, arch-list drift between `SUPPORTED_ARCHS` and `_CUTE_BACKENDS`, and `kv_splits` rejection. 32 tests plus one documented skip for GPU kernel-vs-dense equivalence at full routing. Validation ---------- * B200 (sm100): 31/31 pass, and 68 passed alongside `test_attention_cute_dsl.py`, which NVIDIA#17781 extended. Kernel output bit-identical across a 12-point (shape, tau) sweep; `kernel_calls=12`, `dense_fallback_calls=0` under `SOL_ATTN_STRICT=1`. Denoise time on B200, 50 steps, mean of 2 reps after 1 warmup, against a dense CuTeDSL baseline: Wan2.2-TI2V-5B 1.127x without CUDA graphs and 1.200x with them; Wan2.2-T2V-A14B 1.451x without and 1.406x with. Enabling graphs helps the 5B and slightly hurts A14B; the cause is not established, so the best A14B configuration remains graphs-off. Run-to-run spread was under 0.06% throughout. * RTX 5090 (sm120): resolves to `cute_sm120`; 9/9 sweep points ran with no dense fallback. End-to-end generation was not possible on that GPU because 32 GB is insufficient for the models used here, so sm120 has kernel-level evidence only. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Signed-off-by: Kanghwan Jang <861393+karljang@users.noreply.github.com>
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Adds Sol-Attn (arXiv:2607.24027) as a third sparse-attention algorithm for VisualGen, alongside `skip_softmax` and VSA. It folds dynamic block routing, sparse computation, and an approximation-correction term into a single online-softmax pass. Config surface: `SolAttnAttentionConfig` in `visual_gen/args.py` / `sparse_attention.py` -- `tau` (routing threshold), `thresh_type` (`diag`/`exact`), `kv_splits`, `disabled_until_timestep` (dense-prefix cutoff), and `dense_layers` (comma/range layer-skip spec). Dispatch goes through `create_attention` the same way `skip_softmax` and `vsa` do. Cross-attention (`SEPARATE_QKV`) falls back to VANILLA, and context-parallel (`cp_size > 1`) and quantized attention are both rejected, mirroring VSA's existing guards. Dense prefix ------------ `disabled_until_timestep` follows skip-softmax's field of the same name and the same sense: the layer runs dense while the normalized denoising timestep is at or above the cutoff, and switches to the sparse kernel below it. The value arrives as a forward kwarg, which `modules/attention.py` already threads to every backend and every VisualGen pipeline normalizes by `num_train_timesteps`, so no per-pipeline wiring is needed and there is no process-wide state. `models/wan/pipeline_wan.py` is untouched. Because the prefix swaps kernels without changing tensor shapes, the two phases must not share a captured CUDA graph; `register_cuda_graph_extra_key_fns` registers `sol_attn_phase` from the same `kwargs["timestep"]` source as `skip_softmax_phase`. `dense_layers` needs no key, being fixed per layer at construction. Kernel scope ------------ The kernel is vendored from its reference implementation (see `cute_dsl_kernels/blackwell/sol_attn/THIRD_PARTY_NOTICES.md` for the upstream pin and its currency check). Only the two architectures with hardware evidence are carried: sm100 (B200/GB200) and sm120 (RTX Blackwell). Upstream's sm89 and sm90 kernels and its Triton reference path are not included; sm90 covers H100/H200/GH200 and should return in a follow-up with measurements behind it rather than ship unvalidated. Every vendored file carries an SPDX Apache-2.0 header naming its NVlabs/Sana origin; the two files that derive from FlashAttention additionally cite BSD-3-Clause and point at `sm100/LICENSE.flash-attention`, and the cuDNN Frontend license the SM120 kernel adapts is vendored at `sm120/LICENSE.cudnn-frontend` at the commit the notices cite. Upstream also vendors a copy of FlashAttention's CuTe DSL helpers. That copy is not carried: TensorRT-LLM already depends on flash-attn-4, which provides the same `flash_attn.cute` modules, verified on B200 to give bit-identical output. `preprocess.py` implements the routing/threshold stage in Triton, so Triton is a required runtime dependency on every Sol-Attn path. Failure behaviour ----------------- Inputs the kernel cannot serve -- unsupported architecture, `head_dim` other than 128, non-bf16 dtype, or mismatched k/v -- fall back to dense SDPA with a `warning_once` naming the specific reason, and increment `dense_fallback_calls` alongside `kernel_calls`. Kernel exceptions take the same path. `SOL_ATTN_STRICT=1` raises instead, for both arms. Without this the feature degrades to a silent no-op for a whole run and surfaces only as absent speedup. Docs ---- `docs/source/visual-gen/features/sparse-attention.md` gains a `sol_attn` row and a section covering the YAML surface, the sm100/sm120 + head_dim=128 + bf16 + MHA constraints, the cutoff semantics, and the fallback/`SOL_ATTN_STRICT` behaviour. Its claim that VSA is the only CUTEDSL algorithm mutually exclusive with quantized attention is corrected, since Sol-Attn now is too. Tests ----- New `tests/unittest/_torch/visual_gen/test_attention_cute_dsl_sol_attn.py`, registered in `l0_b200.yml` (sm100) and `l0_gb202.yml` (sm120): backend-factory dispatch, cross-attention VANILLA fallback, context-parallel and quantized-attention rejection, GQA/MQA rejection, the `dense_layers` guard, dense-prefix phase semantics at and either side of the cutoff (including tensor-valued timesteps), fail-open on a missing timestep, both CUDA-graph key cases, kernel-eligibility reasons, `SOL_ATTN_STRICT` on the eligibility path, dense-fallback numerics and counters, arch-list drift between `SUPPORTED_ARCHS` and `_CUTE_BACKENDS`, and `kv_splits` rejection. 32 tests plus one documented skip for GPU kernel-vs-dense equivalence at full routing. Validation ---------- * B200 (sm100): 31/31 pass, and 68 passed alongside `test_attention_cute_dsl.py`, which NVIDIA#17781 extended. Kernel output bit-identical across a 12-point (shape, tau) sweep; `kernel_calls=12`, `dense_fallback_calls=0` under `SOL_ATTN_STRICT=1`. Denoise time on B200, 50 steps, mean of 2 reps after 1 warmup, against a dense CuTeDSL baseline: Wan2.2-TI2V-5B 1.127x without CUDA graphs and 1.200x with them; Wan2.2-T2V-A14B 1.451x without and 1.406x with. Enabling graphs helps the 5B and slightly hurts A14B; the cause is not established, so the best A14B configuration remains graphs-off. Run-to-run spread was under 0.06% throughout. * RTX 5090 (sm120): resolves to `cute_sm120`; 9/9 sweep points ran with no dense fallback. End-to-end generation was not possible on that GPU because 32 GB is insufficient for the models used here, so sm120 has kernel-level evidence only. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Signed-off-by: Kanghwan Jang <861393+karljang@users.noreply.github.com>
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Summary
SkipSoftmax sparse attention was previously TRTLLM-only. This lowers the shared
SkipSoftmaxAttentionConfig/SkipSoftmaxParamspath intoCuTeDSLAttentiontoo, so theCuTeDSL dense FMHA kernel gets the same timestep-aware sparsity scheduling.
AttentionConfig:skip_softmaxnow composes withbackend=CUTEDSL(andquant_attention_config), not justTRTLLM. VSA remains CUTEDSL-only and still mutuallyexclusive with
quant_attention_config.cute_dsl/fmha.py:cute_dsl_fmha_fwdandCuTeDSLAttentionacceptsparse_params(
SkipSoftmaxParams); the runtime threshold is resolved per-timestep via the shared schedulerinstead of a static value.
attention.py:sparse_paramsis now lowered forbackend in (TRTLLM, CUTEDSL), not justTRTLLM.sparse_attention.py: factoreddisabled_until_timestepresolution intoSkipSoftmaxAttentionConfig.resolve_disabled_until_timestep(), reused by bothto_sparse_params()andBaseDiffusionModel's CUDA-graph phase tracking (modeling.py), whichpreviously only read the raw field.
test_skip_softmax.py: new coverage for CuTeDSL param lowering, timestep-tracked thresholdresolution, and CUDA-graph phase cutoff under the checkpoint-provided
disabled_until_timestep.Test plan
tests/unittest/_torch/visual_gen/sparse_attention/test_skip_softmax.py— 36/36 passedtest_attention_cute_dsl.py,test_attention_cute_dsl_vsa.py,test_attention_integration.py— 65 passed, 67 skipped, 14 failed (pre-existing/environment: SAGE fp8 FMHA kernel not
available on the H200 test node; confirmed identical failures on the unmodified baseline via
git stash)pre-commit runon all changed files — clean🤖 Generated with Claude Code
Dev Engineer Review
disabled_until_timestepresolution inresolve_disabled_until_timestep().skip_softmaxwith CuTeDSL quantized attention.tests/integration/test_lists/test-db/l0_cpu.yml.QA Engineer Review
tests/unittest/_torch/visual_gen/sparse_attention/test_skip_softmax.pyintests/integration/test_lists/test-db/l0_cpu.yml.