[None][refactor] Modularize sparse Top-K selection

[lfr-0531]

Dev Engineer Review

• Added the reusable TopK module for Torch, CUDA Radix, CuTe DSL Radix, CUDA GVR, and CuTe DSL GVR.
• Centralized GVR state and reusable Radix/GVR workspace management.
• Updated DSA Indexer and TriAttention to use shared Top-K dispatch.
• Preserved the CUDA Radix fallback for compressed MTP decode.
• Removed the obsolete use_custom_topk API and heuristic warmup export.
• Updated DSA metadata, warmup handling, and prior-index state preservation.
• Review focus: backend fallback conditions, buffer ownership, row-bound handling, and compatibility with callers of the removed use_custom_topk argument.
• CI later completed successfully for commit 6edec12 in pipeline #54896.

QA Engineer Review

• Added TopK unit tests for Torch prefill/decode behavior, CuTe DSL Radix fallback, GVR state handling, CUDA Radix dispatch, CUDA GVR workspace management, unsupported configurations, and error handling.
• Updated DSA Indexer tests for Top-K selection, warmup dispatch, shared state, prefill/decode paths, cached requests, MTP paths, draft replay, and row ordering.
• Test-list coverage in tests/integration/test_lists/ is not confirmed.
• Verdict: needs follow-up.

Description

Sparse attention currently selects Top-K through backend-specific branches in the DSA Indexer, while GVR prior indices and scratch buffers live in shared attention metadata. TriAttention also calls the CuTe DSL Top-K operation directly. This makes implementation selection and state ownership fragmented across callers.

This PR introduces a reusable internal TopK module with Torch, CUDA Radix, CuTe DSL Radix, CUDA GVR, and CuTe DSL GVR implementations. The module provides one prefill/decode entry point, owns GVR runtime state, and preserves the CUDA Radix fallback for compressed MTP decode. DSA Indexer and TriAttention now instantiate and call this module instead of implementing their own dispatch. The existing model-engine warmup flow from main remains unchanged.

This builds on the sparse-attention backend refactor merged in #12733. The DSA, TriAttention, and test changes are included together because they are the production consumers of the shared module.

Test Coverage

• tests/unittest/_torch/modules/test_top_k.py: 8 passed
• DSA metadata warmup dispatch and Indexer/TopK ownership tests: 6 passed
• test_triattention_selection_compaction.py::test_union_eager_cuda_resolves_heavy_ties_and_ragged_lengths[4-64]: 1 passed
• Changed-file formatting, lint, Python syntax, DCO, and test-list AST validation passed

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Walkthrough

The PR adds a shared TopK module with Torch, radix, and GVR implementations. DSA and TriAttention use it for prefill and decode selection. DSA removes heuristic and manual TopK paths, updates metadata handling, and expands test coverage.

Changes

Shared TopK selection

Layer / File(s)	Summary
TopK abstraction and backend dispatch tensorrt_llm/_torch/modules/top_k.py, tests/unittest/_torch/modules/test_top_k.py	Adds configurable Torch, CUDA radix, CuTe DSL radix, CUDA GVR, and CuTe DSL GVR implementations. Adds GVR state management, workspace handling, and Torch fallback behavior.
DSA TopK integration tensorrt_llm/_torch/attention_backend/sparse/dsa/*, tests/unittest/_torch/attention/sparse/dsa/test_dsa_indexer.py	Routes DSA prefill and decode through self.top_k. Removes heuristic warmup, manual fallback paths, and the use_custom_topk parameter. Updates scheduler lengths, GVR state updates, draft-forward lifecycle handling, and related tests.
TriAttention selection integration tensorrt_llm/_torch/kv_cache_compression/triattention/triattention.py, cpp/tensorrt_llm/thop/IndexerTopKOp.cpp	Configures CuTe DSL radix TopK and uses it for decode selection. Updates the radix scratch-buffer documentation.

Estimated code review effort: 4 (Complex) | ~45 minutes

Merge Risk: 🟡 Moderate · up to bdb98

The refactor centralizes Top-K selection and GVR state, but the current code can allocate too few GVR prior-index rows when request capacity is smaller than the active decode batch, risking decode-time failures. Merge should wait for this capacity handling to be corrected or explicitly accepted; the remaining concerns are bounded test and maintenance follow-ups.

Sequence Diagram(s)

sequenceDiagram
  participant DSAIndexer
  participant TopK
  participant GVRState
  DSAIndexer->>TopK: Submit prefill or decode scores and selection metadata
  TopK->>GVRState: Update prior indices after prefill
  GVRState-->>DSAIndexer: Provide prior state for decode selection

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Suggested labels: ci: full pre-merge approved

Suggested reviewers: bowenfu, yunruis

🚥 Pre-merge checks | ✅ 4 | ❌ 1

❌ Failed checks (1 warning)

Check name	Status	Explanation	Resolution
Docstring Coverage	⚠️ Warning	Docstring coverage is 45.10% which is insufficient. The required threshold is 80.00%.	Write docstrings for the functions missing them to satisfy the coverage threshold.

✅ Passed checks (4 passed)

Check name	Status	Explanation
Title check	✅ Passed	The title clearly and concisely describes the refactor of sparse Top-K selection and follows the required ticket and type format.
Description check	✅ Passed	The description explains the motivation, implementation, affected consumers, test coverage, and checklist completion.
Linked Issues check	✅ Passed	Check skipped because no linked issues were found for this pull request.
Out of Scope Changes check	✅ Passed	Check skipped because no linked issues were found for this pull request.

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Caution

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⚠️ Outside diff range comments (1)

tensorrt_llm/_torch/attention_backend/sparse/dsa/metadata.py (1)

222-246: 🩺 Stability & Availability | 🟠 Major | ⚡ Quick win

Precompile GVR variants and use the indexer Top-K

• Graph warmup precompiles GVR only for captured shapes. This guard skips heuristic mode, and the explicit helper invokes only cute_dsl_indexer_topk_decode. Cover eager, mixed-batch, and CUDA-graph-disabled paths before serving.
• Pass the indexer’s index_topk to the radix warmup. DeepSeek V4 sets metadata.num_sparse_topk to window_size + max_compressed_indices[compress_ratio], which is 128 + index_topk for ratio 4. TopK uses index_topk, so the current warmup compiles an unused variant.

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In `@tensorrt_llm/_torch/attention_backend/sparse/dsa/metadata.py` around lines
222 - 246, Update warmup_cute_dsl_radix_topk to precompile the GVR variants
needed for eager, mixed-batch, and CUDA-graph-disabled execution, including
heuristic mode where applicable. Pass the indexer’s index_topk rather than
sparse_metadata_params.num_sparse_topk when invoking the radix warmup, while
preserving existing eligibility guards.

🧹 Nitpick comments (2)

tests/unittest/_torch/attention/sparse/dsa/test_dsa_indexer.py (1)

293-307: 📐 Maintainability & Code Quality | 🔵 Trivial | ⚡ Quick win

Test coverage summary for tests/unittest/_torch/attention/sparse/dsa/test_dsa_indexer.py.

Added test functions: test_metadata_warmup_cute_dsl_radix_topk_dispatch, test_indexer_configures_one_top_k_module. Added helper: _set_torch_top_k.

Modified test functions: test_indexer_decode_custom_vs_fallback, test_indexer_decode_mtp_topk_reuse, test_indexer_prefill_chunked_custom_vs_fallback, test_indexer_prefill_single_pass_custom_vs_fallback, test_indexer_topk_multi_request_with_different_cache. Each dropped the removed use_custom_topk argument and now switches the fallback path through _set_torch_top_k. No test functions were removed.

Test-list status: this module already exists, so the modified tests keep their current entries. The two new test functions run as part of the same module, so no new entry under tests/integration/test_lists/test-db/ or tests/integration/test_lists/qa/ is required for them.

Coverage verdict: needs follow-up. test_indexer_configures_one_top_k_module asserts only the CUDA_RADIX default pair. The new dispatch branches in Indexer.__init__ that select CUTE_DSL_GVR, CUTE_DSL_RADIX, and CUDA_GVR (tensorrt_llm/_torch/attention_backend/sparse/dsa/indexer.py lines 634-643) are not covered. Add parameterized cases over use_cute_dsl_topk and enable_heuristic_topk so the mapping is pinned.

As per path instructions, "Always produce a test coverage summary, even if no issues are found."

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In `@tests/unittest/_torch/attention/sparse/dsa/test_dsa_indexer.py` around lines
293 - 307, Extend test_indexer_configures_one_top_k_module with parameterized
cases covering use_cute_dsl_topk and enable_heuristic_topk combinations, and
assert each combination selects the expected prefill and decode
TopKImplementation, including CUTE_DSL_GVR, CUTE_DSL_RADIX, CUDA_GVR, and the
existing CUDA_RADIX default.

Source: Path instructions

tests/unittest/_torch/modules/test_top_k.py (1)

64-250: 📐 Maintainability & Code Quality | 🔵 Trivial | ⚡ Quick win

Add tests for the remaining branches.

• Added: all eight test_top_k.py test functions. No tests were modified or removed.
• CI coverage: all eight tests are included by unittest/_torch/modules in tests/integration/test_lists/test-db/l0_cpu.yml.
• The CUDA GVR test uses CPU tensors. Add a CUDA case for buffer device placement.
• Add a test for _forward_prefill() raising NotImplementedError.
• Coverage verdict: insufficient.

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only still-valid issues, skip the rest with a brief reason, keep changes
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In `@tests/unittest/_torch/modules/test_top_k.py` around lines 64 - 250, Add
coverage for the remaining branches in TopK: add a CUDA GVR test that verifies
_gvr_prior_indices and _cuda_gvr_scratch are allocated on the CUDA device, and
add a test asserting _forward_prefill() raises NotImplementedError. Keep the
tests focused on these behaviors and use the existing
TopKImplementation.CUDA_GVR setup and test conventions.

Source: Path instructions

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Inline comments:
In `@tensorrt_llm/_torch/attention_backend/sparse/dsa/indexer.py`:
- Around line 1508-1520: Update the has_prefill and
metadata.skip_indexer_for_ctx_reqs path to seed the GVR prior from the copied
dense top-k indices in topk_indices_buffer. Call
self.top_k.update_gvr_prior_from_prefill with the relevant context indices,
sequence lengths, and request_offset=num_generations, while preserving the
existing non-skipped-indexer update behavior without duplicating updates.

In `@tensorrt_llm/_torch/modules/top_k.py`:
- Around line 261-288: Update _ensure_gvr_buffers to allocate _gvr_prior_indices
and _gvr_row_order on scores.device, using scores-based allocation while
preserving existing capacity-growth and contents-copy behavior. Ensure both
buffers match the device of scores regardless of whether the owning module was
moved with .to().

---

Outside diff comments:
In `@tensorrt_llm/_torch/attention_backend/sparse/dsa/metadata.py`:
- Around line 222-246: Update warmup_cute_dsl_radix_topk to precompile the GVR
variants needed for eager, mixed-batch, and CUDA-graph-disabled execution,
including heuristic mode where applicable. Pass the indexer’s index_topk rather
than sparse_metadata_params.num_sparse_topk when invoking the radix warmup,
while preserving existing eligibility guards.

---

Nitpick comments:
In `@tests/unittest/_torch/attention/sparse/dsa/test_dsa_indexer.py`:
- Around line 293-307: Extend test_indexer_configures_one_top_k_module with
parameterized cases covering use_cute_dsl_topk and enable_heuristic_topk
combinations, and assert each combination selects the expected prefill and
decode TopKImplementation, including CUTE_DSL_GVR, CUTE_DSL_RADIX, CUDA_GVR, and
the existing CUDA_RADIX default.

In `@tests/unittest/_torch/modules/test_top_k.py`:
- Around line 64-250: Add coverage for the remaining branches in TopK: add a
CUDA GVR test that verifies _gvr_prior_indices and _cuda_gvr_scratch are
allocated on the CUDA device, and add a test asserting _forward_prefill() raises
NotImplementedError. Keep the tests focused on these behaviors and use the
existing TopKImplementation.CUDA_GVR setup and test conventions.

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• tensorrt_llm/_torch/attention_backend/sparse/dsa/__init__.py
• tensorrt_llm/_torch/attention_backend/sparse/dsa/indexer.py
• tensorrt_llm/_torch/attention_backend/sparse/dsa/metadata.py
• tensorrt_llm/_torch/kv_cache_compression/triattention/triattention.py
• tensorrt_llm/_torch/modules/top_k.py
• tests/unittest/_torch/attention/sparse/dsa/test_dsa_indexer.py
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• tensorrt_llm/_torch/attention_backend/sparse/dsa/init.py

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Inline comments:
In `@tensorrt_llm/_torch/modules/top_k.py`:
- Around line 207-210: Update the request_capacity calculation near row_capacity
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• tensorrt_llm/_torch/attention_backend/sparse/dsa/indexer.py
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• tests/unittest/_torch/attention/sparse/dsa/test_dsa_indexer.py
• tests/unittest/_torch/modules/test_top_k.py

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tests/unittest/_torch/attention/sparse/dsa/test_dsa_indexer.py (1)

3398-3417: 📐 Maintainability & Code Quality | 🟡 Minor | ⚡ Quick win

Add QA registration for test_indexer_prefill_single_pass_custom_vs_fallback. CI covers it through the unittest/_torch/attention entries in tests/integration/test_lists/test-db/l0_h100.yml and related lists. No QA list registers this test. Coverage verdict: needs follow-up because no cbts_touchmap.sqlite or CBTS coverage report is available.

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In `@tests/unittest/_torch/attention/sparse/dsa/test_dsa_indexer.py` around lines
3398 - 3417, Add QA-list registration for
test_indexer_prefill_single_pass_custom_vs_fallback in the appropriate
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related lists used for CI coverage. Preserve the test’s existing discovery and
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Inline comments:
In `@tensorrt_llm/_torch/modules/top_k.py`:
- Around line 32-33: Update the TopK documentation to define the public
gvr_prior_indices tensor contract: state when callers must provide it, its
expected shape, dtype, device, and how it is updated across calls. Apply the
same documentation wherever the related argument is exposed, including the
additional referenced declaration.

---

Outside diff comments:
In `@tests/unittest/_torch/attention/sparse/dsa/test_dsa_indexer.py`:
- Around line 3398-3417: Add QA-list registration for
test_indexer_prefill_single_pass_custom_vs_fallback in the appropriate
unittest/_torch/attention test-list YAML files, including l0_h100.yml and any
related lists used for CI coverage. Preserve the test’s existing discovery and
execution behavior.

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• cpp/tensorrt_llm/thop/IndexerTopKOp.cpp
• tensorrt_llm/_torch/attention_backend/sparse/dsa/indexer.py
• tensorrt_llm/_torch/attention_backend/sparse/dsa/metadata.py
• tensorrt_llm/_torch/modules/top_k.py
• tests/unittest/_torch/attention/sparse/dsa/test_dsa_indexer.py
• tests/unittest/_torch/modules/test_top_k.py

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• tensorrt_llm/_torch/attention_backend/sparse/dsa/metadata.py
• tensorrt_llm/_torch/attention_backend/sparse/dsa/indexer.py

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Inline comments:
In `@tests/unittest/_torch/attention/sparse/dsa/test_dsa_indexer.py`:
- Around line 3438-3443: Remove the broad try/except around sparse_attn_indexer
in the indexer skip test so failures propagate with their original exception and
traceback; only catch a specific expected availability exception if the test
requires that fallback, chaining it from the original error.
- Around line 3426-3431: Update the test setup around TopK to skip unless the
GPU is Blackwell-or-newer and IS_CUTLASS_DSL_AVAILABLE is true, matching the
production conditions for CUTE_DSL_GVR; replace the insufficient skip_pre_hopper
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• tensorrt_llm/_torch/kv_cache_compression/triattention/triattention.py
• tensorrt_llm/_torch/modules/top_k.py
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• tests/unittest/_torch/modules/test_top_k.py

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• tensorrt_llm/_torch/attention_backend/sparse/dsa/init.py

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• cpp/tensorrt_llm/thop/IndexerTopKOp.cpp
• tensorrt_llm/_torch/kv_cache_compression/triattention/triattention.py
• tests/unittest/_torch/modules/test_top_k.py
• tensorrt_llm/_torch/attention_backend/sparse/dsa/metadata.py
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