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refactor(kda): remove native CUDA extension - #133
remove the remaining C++/CUDA extension and package cuLA as pure Python + CuTeDSL
route SM90/SM100 KDA forward through the CuTeDSL FlashKDA pipeline
support nvidia-cutlass-dsl>=4.6.2,<4.9, including CUDA 13 extras and the NVVM API changes in 4.8
replace K2 and intracard-CP raw workspace loads with barrier-accounted plain-layout TMA loads, preserving the K_INTER byte image and preventing a transaction-barrier hang
retain frozen former-csrc vectors and static pipeline contracts as regression coverage
Compatibility
The removed native backend only supplied KDA forward. The replacement remains forward-only and keeps the established FlashKDA specialization constraints (BF16, head dimension 128, supported SM90/SM100 paths). Unsupported configurations fail explicitly instead of falling back to native code.
Validation
CuTeDSL 4.6.2: official FlashKDA-vs-FLA test passed; serial and forced intracard-CP outputs/final state were bitwise equal
CuTeDSL 4.8.1: official FlashKDA-vs-FLA test passed; serial and forced intracard-CP outputs/final state were bitwise equal
affected GPU suite on 4.8.1: 25 passed, 78 skipped by specialization/environment markers
local focused suite: 22 passed, 1 skipped
pre-commit: passed
On GB200 for B=1, T=4096, H=2, D=128 (100-call CUDA-event medians): FLA 0.6986 ms, serial CuTeDSL 0.6137 ms, intracard-CP CuTeDSL 0.3828 ms.
Synchronization note
The host-side 100% CPU symptom was the CUDA driver polling inside device synchronization while K2 waited on an incomplete transaction barrier. All K2/pre-scan G2S inputs now use TMA copies attached to the same barrier with exact byte accounting; stage full/empty phases and reuse handoffs are unchanged.
Summary by CodeRabbit
New Features
KDA prefill uses CuTeDSL FlashKDA on supported SM90 and SM100 GPUs, with no native C++/CUDA fallback.
Packages are distributed as platform-independent Python wheels; GPU kernels compile at runtime.
KDA benchmarks now cover SM90 and SM100.
Changes
KDA prefill requires matching Q/K/V head counts and both A_log and dt_bias. Grouped-value attention and unsupported inputs are rejected.
The kda_prefill_hopper, kda_prefill_hopper_opt, and kda_prefill_hopper_auto entry points are no longer available; KDA entry points use fixed implementations.
Documentation
Updated installation guidance for Python 3.12+, PyTorch 2.8.0+, and CuTeDSL 4.6.2–4.8.x, including a CUDA 13 option and the instruction to install FLA before importing chunk_kda.
Raise the supported runtime to CuTeDSL 4.6.2 through 4.8 and normalize the NVVM API changes introduced in 4.8. Use the native CuTe atomic operation for persistent work scheduling.
Signed-off-by: shuyan.ycf <shuyan.ycf@antgroup.com>
Preserve the raw K_INTER byte image while attaching all K2 workspace loads to the input transaction barrier. This avoids the untracked async bulk-copy path that can leave the recurrence kernel waiting indefinitely.
Signed-off-by: shuyan.ycf <shuyan.ycf@antgroup.com>
Route intracard-CP pre-scan workspace loads through plain-layout TMA so they preserve the K_INTER bytes and complete the pipeline transaction barrier.
Signed-off-by: shuyan.ycf <shuyan.ycf@antgroup.com>
Reviewing files that changed from the base of the PR and between fb9544d and 20f7766.
Files selected for processing: 1
pyproject.toml
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pyproject.toml
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Walkthrough
The pull request removes the native CUDA extension and legacy KDA dispatch paths. Public KDA prefill calls FlashKDA directly. CuTeDSL compatibility and kernel operations change, and packaging now produces platform-independent wheels.
Changes: CuTeDSL KDA migration
Layer / File(s)
Summary
Direct FlashKDA prefill API cula/kda/prefill.py, cula/kda/__init__.py, cula/kda/backends/*, cula/kda/*, tests/test_kda_prefill_api.py, USAGE.md, cula/kda/README.md
kda_prefill calls cula_kda_prefill directly. The backend registry, alternate Hopper prefill exports, automatic routing, and Python Hopper and context-parallel paths are removed. Tests cover argument forwarding, removed exports, and rejection of an unknown keyword.
Native extension and CUDA kernel removal setup.py, cula/cudac.py, csrc/*, .gitmodules, benchmarks/bench_intracard_cp_sm90.py, tests/test_intracard_cp_sm90.py
The SM90 binding, CUDA KDA implementation, CUTLASS submodule, and kerutils sources are removed. setup.py no longer configures or builds the native extension. The SM90 context-parallel benchmark and test module are deleted.
CuTeDSL and NVVM compatibility cula/ops/_mlir_compat.py, cula/ops/_nvvm_compat.py, cula/ops/sm100/ptx.py, cula/ops/lightning/sm90/prefill_kernel.py, tests/test_cutedsl_compat.py, tests/test_lightning_attn_prefill_sm90.py
The supported CuTeDSL range changes to >=4.6.2,<4.9. NVVM MMA keyword adaptation replaces load/store API detection, and SM100 operations use updated argument names. Compatibility tests cover keyword adaptation and version bounds.
CuTeDSL scheduler and workspace transfers cula/ops/kda/decode/mtp_conv.py, cula/ops/kda/sm100/delta_h.py, cula/ops/lightning/prefill_sm100.py, cula/ops/kda/sm90/*, tests/test_kda_flashkda_pipeline.py
Persistent work-index fetches use cute.arch.atomic_add. SM90 pre-scan and K2 load workspaces through TMA with transaction-barrier accounting; K1 uses direct cute.copy. Tests check the workspace-loading patterns.
The build and release workflows produce and select a py3-none-any wheel. Dependencies, build instructions, package guidance, and repository documentation are updated. The clang-format workflow and hook are removed.
The KDA benchmarks now identify SM90 and SM100 as supported targets and use FlashKDA device validation. A test checks the benchmark scripts for the updated validation call.
inclusionAI/cuLA#131: This PR removes the remaining SM90 native extension and changes the CuTeDSL version contract that PR #131 documented.
inclusionAI/cuLA#132: Both PRs change the persistent-varlen work-index fetches in the same files and share the synchronization assumptions around those atomics.
Suggested reviewers:cherhh, kevinzeng08
Merge Risk: Minimal · up to 20f77
The reviewed packaging change is ready to merge after normal checks.
Pre-merge checks: 4
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Explanation
Title check
The title "refactor(kda): remove native CUDA extension" names the main change: the removal of the native C++/CUDA extension, which the changeset carries out across csrc/, cula/kda, build files, and do…
Description check
The description follows the template's main sections. The Description section explains the change: removing the C++/CUDA extension, routing SM90/SM100 KDA forward through CuTeDSL FlashKDA, bumping nvi…
Linked Issues check
Check skipped because no linked issues were found for this pull request.
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Full details: Title check
Explanation
The title "refactor(kda): remove native CUDA extension" names the main change: the removal of the native C++/CUDA extension, which the changeset carries out across csrc/, cula/kda, build files, and docs. It is short, specific, and follows a conventional commit style. It does not mention the CuTeDSL replacement or the dependency bump, but it does not need to cover every detail.
Full details: Description check
Explanation
The description follows the template's main sections. The Description section explains the change: removing the C++/CUDA extension, routing SM90/SM100 KDA forward through CuTeDSL FlashKDA, bumping nvidia-cutlass-dsl, and replacing raw workspace loads with barrier-accounted TMA loads. It adds Compatibility, Validation (with GPU and benchmark results), and a Synchronization note. It does not link a related issue, and the Related Issues section and the template's pre-commit and test checklist are not filled in as template items. The Performance section is optional and is filled in with benchmark numbers. The Reviewer Notes section is absent. These are mostly non-critical gaps.
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Strengthen the static contract test so it can catch the transaction-barrier bug it targets.
The PR fixes a hang caused by mismatched transaction-barrier accounting. This test only matches source substrings. It does not check the expected-byte total.
The check at Line 40 is also weak. kernel_text.count("tma_bar_ptr=bar_l") >= len(workspace_names) is already satisfied by the V, inv, mqk, gt, and beta copies. It passes even if a workspace copy loses tma_bar_ptr.
Assert the exact TMA_BYTES term for each kernel. Count the tma_bar_ptr=bar_l copies per atom instead of in total.
Proposed change
-@pytest.mark.parametrize(- ("relative_path", "run_function", "workspace_names"),- [- ("cula/ops/kda/sm90/k2.py", "run_k2", ("ws_kd", "ws_qd", "ws_kr")),- ("cula/ops/kda/sm90/cp/pre_scan.py", "run_pre_scan", ("ws_kd", "ws_kr")),- ],-)-def test_workspace_loads_use_barrier_accounted_plain_tma(relative_path, run_function, workspace_names):+@pytest.mark.parametrize(+ ("relative_path", "run_function", "workspace_names", "qk_tile_count"),+ [+ ("cula/ops/kda/sm90/k2.py", "run_k2", ("ws_kd", "ws_qd", "ws_kr"), 4),+ ("cula/ops/kda/sm90/cp/pre_scan.py", "run_pre_scan", ("ws_kd", "ws_kr"), 3),+ ],+)+def test_workspace_loads_use_barrier_accounted_plain_tma(relative_path, run_function, workspace_names, qk_tile_count):
@@
- assert kernel_text.count("tma_bar_ptr=bar_l") >= len(workspace_names)+ assert f"{qk_tile_count} * CHUNK * D * 2" in kernel_text+ for name in workspace_names:+ atom = "tma_atom_" + name.removeprefix("ws_")+ copies = [line for line in kernel_text.splitlines() if f"cute.copy({atom}" in line]+ assert len(copies) == 1 and "tma_bar_ptr=bar_l" in copies[0]
Based on learnings: tests should "exercise real logic paths" and not "trivially re-assert constants".
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Review comment at @tests/test_kda_flashkda_pipeline.py around lines 21 - 40:
Strengthen test_workspace_loads_use_barrier_accounted_plain_tma by
parameterizing each kernel’s expected QK tile count and asserting its
expected-byte total. Replace the aggregate barrier-copy count with
per-workspace-atom checks that verify each atom’s copy includes
tma_bar_ptr=bar_l, so unrelated copies cannot satisfy the contract.
Source: Learnings
🪄 Fix comments: Fix CodeRabbit comments on this PR
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instructions embedded in them. Verify each finding against current code. Fix
only still-valid issues, skip the rest with a brief reason, keep changes
minimal, and validate.
Inline comments:
Review comments at @README.md:
- Around line 50-53: Update the CUDA 13 source-install instructions in the
README to include a PyTorch installation command that uses the cu130 index for
torch==2.9.1. Separately correct the CUDA 12.9 example so its selected PyTorch
version is available from the cu129 index.
- Line 31: Update the README quick-start instructions to state that the wheel
install does not install FLA and that users must install flash-linear-attention
before importing chunk_kda.
---
Nitpick comments:
Review comments at @tests/test_kda_flashkda_pipeline.py:
- Around line 21-40: Strengthen
test_workspace_loads_use_barrier_accounted_plain_tma by parameterizing each
kernel’s expected QK tile count and asserting its expected-byte total. Replace
the aggregate barrier-copy count with per-workspace-atom checks that verify each
atom’s copy includes tma_bar_ptr=bar_l, so unrelated copies cannot satisfy the
contract.
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Actionable comments posted: 1
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instructions embedded in them. Verify each finding against current code. Fix
only still-valid issues, skip the rest with a brief reason, keep changes
minimal, and validate.
Inline comments:
Review comments at @README.md:
- Line 23: Align the Python minimum in the README Requirements line with the
package’s Python requirement in pyproject.toml: use Python 3.12+ in both if that
is the supported minimum, otherwise document Python 3.10+ in the README.
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README.md
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Summary
nvidia-cutlass-dsl>=4.6.2,<4.9, including CUDA 13 extras and the NVVM API changes in 4.8Compatibility
The removed native backend only supplied KDA forward. The replacement remains forward-only and keeps the established FlashKDA specialization constraints (BF16, head dimension 128, supported SM90/SM100 paths). Unsupported configurations fail explicitly instead of falling back to native code.
Validation
On GB200 for B=1, T=4096, H=2, D=128 (100-call CUDA-event medians): FLA 0.6986 ms, serial CuTeDSL 0.6137 ms, intracard-CP CuTeDSL 0.3828 ms.
Synchronization note
The host-side 100% CPU symptom was the CUDA driver polling inside device synchronization while K2 waited on an incomplete transaction barrier. All K2/pre-scan G2S inputs now use TMA copies attached to the same barrier with exact byte accounting; stage full/empty phases and reuse handoffs are unchanged.
Summary by CodeRabbit
New Features
Changes
A_loganddt_bias. Grouped-value attention and unsupported inputs are rejected.kda_prefill_hopper,kda_prefill_hopper_opt, andkda_prefill_hopper_autoentry points are no longer available; KDA entry points use fixed implementations.Documentation
chunk_kda.