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Jungfraujoch/image_analysis/structure_refinement/ModelScaleGPU.h
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leonarski_f 46ad2c10ed Refinement GPU: the robust scale fit on the card, the pool back in every trial slot
The score's robust fine pass (pseudo-Huber IRLS of ModelScaleBox::robust) on the device, so that refinement on
a GPU no longer fits its scale on the host every cycle - with the trials side by side that was a robust grid
search on one thread per slot (13-133 s per refinement on the seven timing sets, against 1.7-8 s before the
score commit).

- ModelScaleGPU: the weights multiply the WSSR and J^T J / J^T r in scale_sums (a weight row per fit,
  reserved at the first robust fit); robust_residuals / robust_weights kernels; the median of each fit's
  first residuals taken on the host as FitModelScale takes it (nth_element at n/2, on the fits' threads);
  LevMarBatch split out of FitBatch so that the rounds restart from where a fit stands. FitSolvent(...,
  robust) refits the coarse winner in the fine pass, as FitModelScale's robust fit does; the plain fit is
  unchanged.
- ModelScaleGPUPool::Fit(..., robust); its streams at the highest priority: a fit is a chain of small
  launches the host waits on, and on a card shared with the trials' long kernels each wait queued behind
  them (refit.scale 58-87 ms -> 14-45 ms per cycle, the result the same to the bit).
- Refinement: the main context and every trial slot fit on their pool (robust); the CPU engine keeps
  FitModelScale's robust fit.

The card's fit is FitModelScale's to rounding (float residuals and weights, the steps' anisotropic factor in
float), deterministic run to run and whichever engine a fit takes: R-work/R-free of the 14 parity sets on
the GPU equal the CPU engine's to 4 digits (largest difference 1.4e-6 on the 3.6 A set).
Test: ModelScaleGPUPool_RobustMatchesFitModelScale (one reflection in fifty a 4x outlier, five cells).

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_015CeEGgpKdfkCTJb3YNMJcu
(cherry picked from commit d15f048cbd7ed2d6b430f9f9cb5018ca284da8f0)
2026-10-26 01:38:24 +01:00

108 lines
5.2 KiB
C++

// SPDX-FileCopyrightText: 2026 Filip Leonarski, Paul Scherrer Institute <filip.leonarski@psi.ch>
// SPDX-License-Identifier: GPL-3.0-only
#pragma once
#include <array>
#include <cstddef>
#include <vector>
#include <cuda_runtime.h>
#include "../indexing/CUDAMemHelpers.h"
// The rigid body's scale fit on the GPU: what RigidBodyTarget::Residuals does with a gemmi
// Scaling<float> (use_solvent, k_sol and b_sol fixed) - fit_isotropic_b_approximately() followed by
// gemmi's Levenberg-Marquardt, and FitModelScale's k_sol/b_sol grid. The sums over the reflections run
// on the device, accumulated in double in a fixed order; the Levenberg-Marquardt control (the damping,
// the 7x7 solve, the stop rules) is gemmi's own, ported line for line and run on the host.
struct ModelScaleParams {
double k_overall = 1.0;
double b_star[6] = {0, 0, 0, 0, 0, 0}; // gemmi SMat33 order u11 u22 u33 u12 u13 u23
};
struct ModelSolventFit {
double k_sol = 0.35, b_sol = 46.0; // gemmi's Scaling defaults when the fit does not run
double r = 1.0; // ModelScaleReport::r_work_fit
int n_grid = 0;
ModelScaleParams scale; // the winner's k_overall and b_star
};
// One fit's parameters for one launch, and which sums to take over the points.
struct ModelScaleFitState {
double k_overall;
double b_star[6];
double k_sol, b_sol;
int mode;
int column; // the fit's row of |Fcalc + solvent| on the device
int weighted; // the robust fit's weights (row `column`) multiply the WSSR and J^T J, J^T r; 0: weight 1
double c; // the robust fit's scale of the residuals (ModelScaling.cpp, RobustReweight)
};
class ModelScaleGPU {
public:
static size_t DeviceBytes(size_t max_points);
ModelScaleGPU(cudaStream_t stream, size_t max_points);
// Per zone: the Scaling points in gemmi prepare_points() order, adp_symmetry_constraints(sg) rows
// and the cell's fractionalization matrix (UnitCell::frac.mat, row-major). n <= max_points.
void SetPoints(const std::vector<std::array<int, 3>> &hkl,
const std::vector<double> &stol2,
const std::vector<float> &fobs,
const std::vector<float> &sigma,
const std::vector<std::array<double, 6>> &constraints,
const double frac[9]);
// fit_isotropic_b_approximately() + fit_parameters() at a fixed solvent pair, from k_overall = 1 and
// b_star = 0. d_fcmol, d_fmask: one float2 per point on the device.
ModelScaleParams Fit(const float2 *d_fcmol, const float2 *d_fmask, double k_sol, double b_sol);
// FitModelScale (ModelScaling.cpp) with the default box, b_sol up to b_hi: the same grid, the same
// winner. Throws ModelScaleGPUTooFewReflections where FitModelScale would chain the grid points
// (five or fewer reflections for the isotropic fit).
// robust: the fine pass by robust least squares, as FitModelScale with ModelScaleBox::robust - the
// reweighting on the device, the median of each fit's first residuals on the host on `nthreads` threads.
ModelSolventFit FitSolvent(const float2 *d_fcmol, const float2 *d_fmask, double b_hi = 80.0,
bool robust = false, size_t nthreads = 1);
// What a robust FitSolvent takes on the card beyond DeviceBytes(), reserved at its first call.
static size_t RobustBytes(size_t max_points);
private:
struct LevMarRun;
// Queues the copy of `fits` to the device. host_fits_ is written only after the stream has been
// synchronized, which every Reduce() ends with.
void UploadFits(const std::vector<ModelScaleFitState> &fits);
// The sums of the first `nfits` uploaded fits over all points, one row of SLOTS doubles per fit, on
// the host.
const double *Reduce(int nfits);
const double *Sums(const std::vector<ModelScaleFitState> &fits);
void FitBatch(const float2 *d_fcmol, const float2 *d_fmask, std::vector<ModelScaleFitState> &fits);
// gemmi's Levenberg-Marquardt for every fit in `fits` from the parameters it holds.
void LevMarBatch(std::vector<ModelScaleFitState> &fits);
// The robust rounds of a batch FitBatch() has fitted: reweight, fit again, ROBUST_ROUNDS times.
void RobustBatch(std::vector<ModelScaleFitState> &fits, size_t nthreads);
cudaStream_t stream_;
size_t max_points_;
int n_ = 0;
int n_strong_ = 0; // points fit_isotropic_b_approximately() fits on
int n_params_ = 1; // k_overall + one per constraint row
double constraints_[6][6] = {};
double frac_[9] = {};
CudaDevicePtr<int> hkl_; // 3 per point
CudaDevicePtr<double> stol2_;
CudaDevicePtr<float> fobs_;
CudaDevicePtr<unsigned char> strong_;
CudaDevicePtr<float> f_abs_; // per fit of a batch, per point
CudaDevicePtr<ModelScaleFitState> fits_;
CudaDevicePtr<double> partial_; // per fit, per block, per slot
CudaDevicePtr<double> sums_; // per fit, per slot
CudaHostPtr<ModelScaleFitState> host_fits_;
CudaHostPtr<double> host_sums_;
CudaDevicePtr<float> weight_, residual_; // the robust fit's, per fit of a batch, per point
CudaHostPtr<float> host_residual_;
};