Add dem_sampling CPU/GPU across C++ and Python#479
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kvmto wants to merge 17 commits intoNVIDIA:mainfrom
Open
Add dem_sampling CPU/GPU across C++ and Python#479kvmto wants to merge 17 commits intoNVIDIA:mainfrom
kvmto wants to merge 17 commits intoNVIDIA:mainfrom
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Introduce dem_sampling implementations for CPU and cuStabilizer-backed GPU paths in C++, and expose them through pybind/Python with torch tensor and device-pointer support. Add C++/Python coverage for backend paths and wire build/packaging checks so cuStabilizer requirements are enforced for shipping. Signed-off-by: kvmto <kmato@nvidia.com>
Signed-off-by: kvmto <kmato@nvidia.com>
Signed-off-by: kvmto <kmato@nvidia.com>
bmhowe23
reviewed
Apr 1, 2026
libs/qec/pyproject.toml.cu12
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| "tensorrt-cu12" | ||
| ] | ||
| dem_sampling = [ | ||
| "torch", |
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Is there a way to do DEM sampling without Torch?
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yes with numpy, which is the most likely case for the usage of the cpu implementation
Signed-off-by: kvmto <kmato@nvidia.com>
Signed-off-by: kvmto <kmato@nvidia.com>
Signed-off-by: kvmto <kmato@nvidia.com>
Signed-off-by: kvmto <kmato@nvidia.com>
Signed-off-by: kvmto <kmato@nvidia.com>
Signed-off-by: kvmto <kmato@nvidia.com>
Signed-off-by: kvmto <kmato@nvidia.com>
Signed-off-by: kvmto <kmato@nvidia.com>
- Use cudaMallocAsync/cudaFreeAsync for all GPU temporaries to avoid
implicit device synchronization that breaks multi-stream concurrency
(critical for PyTorch CUDA stream integration)
- Replace synchronous cudaMemcpy with cudaMemcpyAsync on the caller's
stream for the probability D->H copy
- Add grid dimension overflow guards before every CUDA kernel launch
- Handle numShots=0 gracefully in both C++ CPU path and Python binding
- Binarize check_matrix with & 1u in CPU path to match GPU kernel
behavior and prevent uint8 dot-product overflow
- Clear sticky CUDA errors (cudaGetLastError) on all failure paths in
the Python binding's GPU allocation/copy helpers
- Fix pre-existing test_non_default_cuda_stream assertion that compared
torch.device("cuda") against torch.device("cuda", index=0)
- Add 12 new tests covering zero-shot edge case, non-binary H matrix
CPU/GPU parity, and seedless code path (5 C++, 7 Python)
Signed-off-by: kvmto <kmato@nvidia.com>
Signed-off-by: kvmto <kmato@nvidia.com>
Signed-off-by: kvmto <kmato@nvidia.com>
Signed-off-by: kvmto <kmato@nvidia.com>
Signed-off-by: kvmto <kmato@nvidia.com>
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Summary
dem_samplingin C++ with both CPU and cuStabilizer-backed GPU paths.cudaq_qec.dem_sampling) with backend selection (auto/cpu/gpu), plus PyTorch tensor support including CUDA device-pointer flow for GPU execution.Build/Packaging updates
FindcuStabilizerCMake module and QEC CMake integration.CUDAQ_QEC_REQUIRE_CUSTABILIZERenforcement path for builds that must ship GPU support.libs/qec/pyproject.toml.cu12andlibs/qec/pyproject.toml.cu13fordem_samplingoptional dependencies (including torch + cuquantum extras).Test plan
dem_samplingunit tests (DemSamplingCPU,DemSamplingGPU, and related QEC integration tests) in CI.libs/qec/python/tests/test_dem_sampling.pyin CI with CUDA-enabled torch.