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Add an optional Numba-CUDA-MLIR Gaussian xFit backend - #85
Gaussian fits repeatedly evaluate the model and analytic derivatives through CuPy expressions. Add an optional Numba-CUDA-MLIR backend with separate residual and weighted-Jacobian CUDA kernels, per-thread kernel reuse, and explicit stream and buffer lifetimes. The shared LM solver and final diagnostics are preserved, including widened FP32 normal-equation accumulation.
Install cuphoton[numba-cuda-mlir] on Linux with CUDA 13 and select --backend numba-cuda-mlir. Device-pipeline configuration and bulk execution carry the selection through. Built-in Gaussian difference/split modes support float32/float64, masks and variance weights; sampled models and finite differences fail explicitly.
CI covers scientific parity, tiny weights, independent and delayed producer streams, CLI selection and actual xScan-to-xFit inference without GPU skips. Integration checks also require the existing CuPy and cuTile fused callbacks to remain active.
Earlier free-threaded Python 3.14 testing with Numba-CUDA-MLIR 0.5.4 exposed an intermittent concurrent-startup failure in dependency type dispatch. Python 3.12 qualification does not establish free-threaded concurrent-startup reliability.
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Files selected for processing: 29
.github/workflows/ci.yml
README.md
THIRD_PARTY_NOTICES.md
docs/components/xfit.md
examples/xfit/benchmark_gaussian.py
pyproject.toml
src/cuphoton/core/bulk.py
src/cuphoton/core/dragon.py
src/cuphoton/core/execution.py
src/cuphoton/core/runtime.py
src/cuphoton/xfit/_numba_mlir.py
src/cuphoton/xfit/_types.py
src/cuphoton/xfit/api.py
src/cuphoton/xfit/backend.py
src/cuphoton/xfit/commands.py
src/cuphoton/xfit/executor.py
src/cuphoton/xscan/device_pipeline.py
src/cuphoton/xscan/pipeline_benchmark/runner.py
src/cuphoton/xscan/xfit_features.py
tests/core/test_bulk.py
tests/core/test_dragon.py
tests/core/test_runtime.py
tests/test_package_layout.py
tests/xfit/test_executor.py
tests/xfit/test_models.py
tests/xfit/test_numba_mlir.py
tests/xfit/test_numba_mlir_commands.py
tests/xscan/test_numba_mlir_pipeline.py
tests/xscan/test_pipeline_benchmark.py
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Walkthrough
Adds an optional Numba-CUDA-MLIR backend for Gaussian xFit. Connects it to device fitting, distributed execution, and xscan pipelines. Adds GPU identity and runtime metadata support, package documentation, and GPU CI tests.
Changes: Numba-CUDA-MLIR xFit
Layer / File(s)
Summary
Declare and resolve the backend pyproject.toml, README.md, THIRD_PARTY_NOTICES.md, src/cuphoton/xfit/backend.py, src/cuphoton/xfit/_types.py, src/cuphoton/xfit/commands.py, src/cuphoton/xfit/executor.py, docs/components/xfit.md, examples/xfit/benchmark_gaussian.py, tests/test_package_layout.py, tests/xfit/test_executor.py, tests/xfit/test_models.py
Adds the optional dependencies and backend selection. Exposes the backend in distributed execution, CLI help, documentation, benchmark choices, and package metadata.
Adds cached CUDA kernels and a stream-bound GaussianWorkspace. Device fitting uses its residual and normal-equation methods. Tests compare numerical results, CLI outputs, and edge cases against CuPy. GPU CI runs the new tests and uploads their report.
Route backend through xscan src/cuphoton/xscan/device_pipeline.py, src/cuphoton/xscan/xfit_features.py, src/cuphoton/xscan/pipeline_benchmark/runner.py, tests/xscan/test_numba_mlir_pipeline.py, tests/xscan/test_pipeline_benchmark.py
Adds backend configuration, evidence checks, and device-feature validation. Pipeline provenance records the backend source and installed package version. Tests cover pipeline stages, evidence, and backend comparisons.
Uses CuPy identity collection and metadata for the new backend. Adds compiler-version metadata and updates workload identity validation and tests.
Priority: Normal
Merge Risk: Minimal · previous review 2bdf6
The optional backend has no established issue requiring a change before merge. GPU and free-threaded startup behavior still need the planned environment-specific validation.
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LGTM for the built-in Gaussian backend. Reviewed kernel parity, stream ownership, dispatch, and pipeline integration. All 35 selected tests passed with GPU execution available and no skips.
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Actionable comments posted: 1
Prompt to fix review comments
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only still-valid issues, skip the rest with a brief reason, keep changes
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Inline comments:
Review comments at @src/cuphoton/xfit/api.py:
- Line 1070: Update _fit_dipoles_backend to reject custom GaussianDipoleModel
subclasses for both native and numba-cuda-mlir backends, including subclasses
retained or returned by to_backend. Keep the exact base-class model supported
and include the selected backend in the ValueError message.
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Reviewing files that changed from the base of the PR and between 7cce7a5 and 8488c3a.
Files ignored due to path filters: 1
uv.lock is excluded by !**/*.lock
Files selected for processing: 29
.github/workflows/ci.yml
README.md
THIRD_PARTY_NOTICES.md
docs/components/xfit.md
examples/xfit/benchmark_gaussian.py
pyproject.toml
src/cuphoton/core/bulk.py
src/cuphoton/core/dragon.py
src/cuphoton/core/execution.py
src/cuphoton/core/runtime.py
src/cuphoton/xfit/_numba_mlir.py
src/cuphoton/xfit/_types.py
src/cuphoton/xfit/api.py
src/cuphoton/xfit/backend.py
src/cuphoton/xfit/commands.py
src/cuphoton/xfit/executor.py
src/cuphoton/xscan/device_pipeline.py
src/cuphoton/xscan/pipeline_benchmark/runner.py
src/cuphoton/xscan/xfit_features.py
tests/core/test_bulk.py
tests/core/test_dragon.py
tests/core/test_runtime.py
tests/test_package_layout.py
tests/xfit/test_executor.py
tests/xfit/test_models.py
tests/xfit/test_numba_mlir.py
tests/xfit/test_numba_mlir_commands.py
tests/xscan/test_numba_mlir_pipeline.py
tests/xscan/test_pipeline_benchmark.py
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Fuse Gaussian model and analytic Jacobian evaluation while preserving
the reference weighted normal contractions and solver behavior.
Keep CuPy as the default and expose the optional backend through xFit
and the device pipeline, with real CLI and scientific coverage.
Signed-off-by: Trent Nelson <trentn@nvidia.com>
Include the Numba-CUDA-MLIR installation extra and device-fit backend
argument in the existing public contract assertions.
Signed-off-by: Trent Nelson <trentn@nvidia.com>
Keep the device signature and shared pipeline diagnostics consistent
when native and Numba-CUDA-MLIR execution are both available.
Signed-off-by: Trent Nelson <trentn@nvidia.com>
Apply the existing fixed-model guard before backend loading and model
conversion so fused kernels cannot silently ignore custom operations.
Signed-off-by: Trent Nelson <trentn@nvidia.com>
Use the shared widened accumulation for the Numba Gaussian callback.
Retain the caller inputs and both outputs through queued stream work.
Cover tiny weighted derivatives whose products underflow before their
representable sum is formed.
Signed-off-by: Trent Nelson <trentn@nvidia.com>
Record delegated callback use in the existing full-fit comparisons.
Require CuPy and cuTile fusion to execute their selected kernels while
retaining the numerical comparisons and existing test cases.
Signed-off-by: Trent Nelson <trentn@nvidia.com>
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Gaussian fits repeatedly evaluate the model and analytic derivatives through CuPy expressions. Add an optional Numba-CUDA-MLIR backend with separate residual and weighted-Jacobian CUDA kernels, per-thread kernel reuse, and explicit stream and buffer lifetimes. The shared LM solver and final diagnostics are preserved, including widened FP32 normal-equation accumulation.
Install
cuphoton[numba-cuda-mlir]on Linux with CUDA 13 and select--backend numba-cuda-mlir. Device-pipeline configuration and bulk execution carry the selection through. Built-in Gaussian difference/split modes support float32/float64, masks and variance weights; sampled models and finite differences fail explicitly.CI covers scientific parity, tiny weights, independent and delayed producer streams, CLI selection and actual xScan-to-xFit inference without GPU skips. Integration checks also require the existing CuPy and cuTile fused callbacks to remain active.
Earlier free-threaded Python 3.14 testing with Numba-CUDA-MLIR 0.5.4 exposed an intermittent concurrent-startup failure in dependency type dispatch. Python 3.12 qualification does not establish free-threaded concurrent-startup reliability.