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[TRTLLM-6898][feat] Add Cute DSL nvfp4 linear op #7632
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[TRTLLM-6898][feat] Add Cute DSL nvfp4 linear op #7632
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Signed-off-by: Mindy Li <[email protected]>
Signed-off-by: Mindy Li <[email protected]>
Signed-off-by: Mindy Li <[email protected]>
Signed-off-by: Mindy Li <[email protected]>
📝 WalkthroughWalkthroughAdds a new Blackwell SM100 CuTe-DSL persistent block-scaled GEMM kernel and utilities, integrates a Python 3.12+ gated FP4/FP8 GEMM path into Torch custom ops, wires it into Linear via a new flag and scalar alpha handling, updates dependencies, and introduces unit/perf tests for the new path. Changes
Sequence Diagram(s)sequenceDiagram
autonumber
actor User
participant Linear as Linear (NVFP4)
participant Ops as torch_custom_ops
participant Tuner as AutoTuner/TunableRunner
participant Kernel as KernelWrapper
participant GPU as Device Kernel
User->>Linear: forward(input)
alt use_cute_dsl_nvfp4_blockscaling_mm
Linear->>Ops: cute_dsl_nvfp4_gemm_blackwell(input, weight, scales, alpha_s)
Ops->>Ops: Check Python>=3.12 & HAS_CUTLASS_DSL
alt Available
Ops->>Tuner: get_valid_tactics()
Tuner->>Kernel: compile/select tactic
Kernel->>GPU: launch(m, n, k, pointers, epilogue)
GPU-->>Kernel: result
Kernel-->>Ops: BF16 tensor
Ops-->>Linear: BF16 tensor
else Unavailable
Ops-->>Linear: raise RuntimeError
end
else legacy path
Linear->>Ops: nvfp4_gemm(..., alpha)
Ops-->>Linear: result
end
Linear-->>User: output
sequenceDiagram
autonumber
participant Ops as torch_custom_ops (init)
participant Env as Runtime
Ops->>Env: import cutlass DSL (Python>=3.12)
alt Success
Env-->>Ops: loaded
Ops-->>Ops: HAS_CUTLASS_DSL=True
else Failure
Env-->>Ops: not available
Ops-->>Ops: HAS_CUTLASS_DSL=False
end
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Signed-off-by: Mindy Li <[email protected]>
Signed-off-by: Mindy Li <[email protected]>
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Summary by CodeRabbit
Description
[TRTLLM-6898][feat]Add Cute DSL nvfp4 linear op-step1
(1) install nvidia-cutlass-dsl 4.1.0 pkg. (require python >= 3.12)
(2) add nvfp4_bs_gemm into trtllm linear op.
(3) I copy the nvfp4 cute_dsl kernel from cutlass dsl examples, and add scalar parameter support to adapt to trtllm's linear op's interface.
Test Coverage
op UT:
pytest -s -o log_cli=true tests/unittest/_torch/thop/parallel/test_fp4_linear.py -k "test_fp4_linear"
model UT:

pytest -s -o log_cli=true "tests/integration/defs/accuracy/test_llm_api_pytorch.py::TestDeepSeekV3Lite::test_nvfp4"
Perf
to add perf data here.
TODO;
add pdl, and other optimizations for this kernel.
PR Checklist
Please review the following before submitting your PR:
PR description clearly explains what and why. If using CodeRabbit's summary, please make sure it makes sense.
PR Follows TRT-LLM CODING GUIDELINES to the best of your knowledge.
Test cases are provided for new code paths (see test instructions)
Any new dependencies have been scanned for license and vulnerabilities
CODEOWNERS updated if ownership changes
Documentation updated as needed
The reviewers assigned automatically/manually are appropriate for the PR.
Please check this after reviewing the above items as appropriate for this PR.
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