From 62ea3d7cb258c1e38251ce7e99184083f8d99605 Mon Sep 17 00:00:00 2001 From: HatPdotS Date: Fri, 14 Aug 2026 16:18:18 +0200 Subject: [PATCH 1/9] Fixed scaling --- docs/changelog.rst | 9 + pyproject.toml | 2 +- tests/.DS_Store | Bin 8196 -> 0 bytes tests/functional/test_solvent_functional.py | 178 ++++++---- tests/integration/test_gpu_scale_stability.py | 23 +- .../integration/test_rigid_body_refinement.py | 43 +++ .../unit/scaling/test_iso_scale_polynomial.py | 156 +++++++++ torchref/.DS_Store | Bin 10244 -> 0 bytes torchref/__init__.py | 2 +- torchref/cli/collection_difference_refine.py | 8 +- torchref/cli/refine.py | 16 +- .../monolithic_refinement/density_scaler.py | 42 ++- torchref/io/metadata.py | 24 +- torchref/refinement/base_refinement.py | 25 +- torchref/refinement/lbfgs_refinement.py | 2 +- torchref/refinement/rigid_body_refinement.py | 10 +- .../targets/adp/scaler_log_scale.py | 68 ++-- torchref/scaling/collection_scaler.py | 55 ++- torchref/scaling/scaler.py | 52 +-- torchref/scaling/scaler_base.py | 304 ++++++++-------- torchref/scaling/solvent.py | 326 +++++++++++------- 21 files changed, 840 insertions(+), 505 deletions(-) delete mode 100644 tests/.DS_Store create mode 100644 tests/unit/scaling/test_iso_scale_polynomial.py delete mode 100644 torchref/.DS_Store diff --git a/docs/changelog.rst b/docs/changelog.rst index a19e36a1..0a934ccb 100644 --- a/docs/changelog.rst +++ b/docs/changelog.rst @@ -2,6 +2,15 @@ Changelog ========= +Version 0.6.3 +---------- +- Switched the scaler's default scale-fit objective from ``nll`` to unit-weight ``ls`` +- Replaced the per-bin ``log_scale`` with a Chebyshev polynomial ``c_iso`` in sin(theta)/lambda +- Replaced the solvent Debye-Waller factor with ``k_sol exp(-ln2 (ss/ss_half)^n)``, merged sigmoid exponential form +- Fixed the solvent-mask candidate enumeration, which missed voxels near the atom's grid node +- Removed the solvent-mask Gaussian smoothing + + Version 0.6.3 ------------- - Fixed peptide-linked residues keeping their free-amino-acid restraint angles diff --git a/pyproject.toml b/pyproject.toml index 53f655ae..4f0c7ba6 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta" [project] name = "torchref" -version = "0.6.3" +version = "0.6.4" description = "Pytorch based crystallographic refinement" readme = "README.md" requires-python = ">=3.10" diff --git a/tests/.DS_Store b/tests/.DS_Store deleted file mode 100644 index 2ed51118ca87bf85dc94e964e9559406970eb153..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 8196 zcmeHMF>ljA6nEf*6<)-@CiE&#_I!f)Kou?%wI%_wGJ_KHGU0A~N0GVTY(qM0HfQ zCjvAjh5cL$Wyz0(umlM=9cXL{09(VbE$AZ;urZ;xk++$%HWYKZ?7{d@<5LXd!r>1Y z4jXx!IqSm7xNtIlW#cmxV^>F9XyIfcXSG!YRDq%bA%TXkvej9S z)A7UxEpk zai@=IHB$0EAAGv|s%3ds%sWmGIWI=%_|iA#dz7xxh!R#?a%|K;T*-SSuhFSlde7-8 z;w6`Igj~PTJk9GjvHmXYK&dHsr0Df2kxZ!3Nvd#?)0x@Y{pXHT>70_?BCO;|&4_s} z^A3bg6SUX!T9N6j77>!$@WEjA^_abnbMr`6)uW#tMa*Z;rck&4pFb7a$#KNtPMfK5FWSbIPAe6 ahUkah7kQgG3lEAO1XvlgQ3d{}0zU!R>L;B5 diff --git a/tests/functional/test_solvent_functional.py b/tests/functional/test_solvent_functional.py index 05af51c9..92f594f5 100644 --- a/tests/functional/test_solvent_functional.py +++ b/tests/functional/test_solvent_functional.py @@ -27,16 +27,20 @@ def test_initialization_with_parameters(self): solvent = SolventModel( k_solvent=0.35, - b_solvent=46.0, + d_half=3.6, + n_exp=5.0, radius=1.2, erosion_radius=0.8 ) - + assert solvent.solvent_radius == 1.2 assert solvent.erosion_radius == 0.8 - # k_solvent is stored as log + # every falloff parameter is stored as its log assert torch.isfinite(solvent.log_k_solvent) - assert torch.isclose(solvent.b_solvent, torch.tensor(46.0)) + assert torch.isclose(solvent.n_exp(), torch.tensor(5.0)) + assert torch.isclose( + solvent.ss_half(), torch.tensor(1.0 / (4 * 3.6 ** 2)), rtol=1e-5 + ) @pytest.mark.integration @@ -54,14 +58,16 @@ def test_k_solvent_property(self): k_recovered = torch.exp(solvent.log_k_solvent) assert torch.isclose(k_recovered, torch.tensor(k_solvent_initial), rtol=1e-5) - def test_b_solvent_parameter(self): - """Test B-solvent parameter.""" + def test_falloff_parameters(self): + """The falloff half-point and exponent round-trip through their logs.""" from torchref.scaling.solvent import SolventModel - - b_solvent_initial = 50.0 - solvent = SolventModel(b_solvent=b_solvent_initial) - - assert torch.isclose(solvent.b_solvent, torch.tensor(b_solvent_initial)) + + solvent = SolventModel(d_half=4.0, n_exp=3.0) + + assert torch.isclose( + solvent.ss_half(), torch.tensor(1.0 / 64.0), rtol=1e-5 + ) + assert torch.isclose(solvent.n_exp(), torch.tensor(3.0), rtol=1e-5) def test_phase_offset_parameter(self): """Test phase offset parameter.""" @@ -85,7 +91,7 @@ def test_k_solvent_gradients(self): """Test gradients for k_solvent.""" from torchref.scaling.solvent import SolventModel - solvent = SolventModel(k_solvent=0.35, b_solvent=46.0) + solvent = SolventModel(k_solvent=0.35) # Create simple loss loss = solvent.log_k_solvent.sum() @@ -93,17 +99,19 @@ def test_k_solvent_gradients(self): assert solvent.log_k_solvent.grad is not None - def test_b_solvent_gradients(self): - """Test gradients for b_solvent.""" + def test_falloff_gradients(self): + """Test gradients for the falloff parameters.""" from torchref.scaling.solvent import SolventModel - solvent = SolventModel(k_solvent=0.35, b_solvent=46.0) + solvent = SolventModel(k_solvent=0.35) # Create simple loss - loss = solvent.b_solvent.sum() + ss = torch.linspace(1e-4, 0.25, 64, device=solvent.device) + loss = solvent.damping(ss).sum() loss.backward() - - assert solvent.b_solvent.grad is not None + + assert solvent.log_ss_half.grad is not None + assert solvent.log_n_exp.grad is not None @pytest.mark.integration @@ -114,21 +122,22 @@ def test_state_dict_keys(self): """Test state dict contains expected keys.""" from torchref.scaling.solvent import SolventModel - solvent = SolventModel(k_solvent=0.35, b_solvent=46.0, optimize_phase=True) + solvent = SolventModel(k_solvent=0.35, optimize_phase=True) state_dict = solvent.state_dict() # Should contain parameters assert 'log_k_solvent' in state_dict - assert 'b_solvent' in state_dict + assert 'log_ss_half' in state_dict + assert 'log_n_exp' in state_dict assert 'phase_offset' in state_dict def test_save_and_load_state(self, tmp_path): """Test saving and loading state dict.""" from torchref.scaling.solvent import SolventModel - solvent = SolventModel(k_solvent=0.35, b_solvent=46.0) + solvent = SolventModel(k_solvent=0.35) original_k = solvent.log_k_solvent.clone() - original_b = solvent.b_solvent.clone() + original_ss = solvent.log_ss_half.clone() # Save state_dict = solvent.state_dict() @@ -139,7 +148,7 @@ def test_save_and_load_state(self, tmp_path): solvent2.load_state_dict(torch.load(tmp_path / "solvent.pt")) assert torch.isclose(solvent2.log_k_solvent, original_k) - assert torch.isclose(solvent2.b_solvent, original_b) + assert torch.isclose(solvent2.log_ss_half, original_ss) @pytest.mark.integration @@ -153,7 +162,7 @@ def test_cpu_operation(self): solvent = SolventModel(device=torch.device('cpu')) assert solvent.log_k_solvent.device.type == 'cpu' - assert solvent.b_solvent.device.type == 'cpu' + assert solvent.log_ss_half.device.type == 'cpu' def test_float_type(self): """Test solvent model with different float types.""" @@ -186,19 +195,73 @@ def test_cache_initialization(self): class TestSolventBFactorCorrection: """Test B-factor correction in solvent model.""" - def test_bfactor_correction_formula(self): - """Test B-factor correction calculation.""" - # B-factor correction: exp(-B * s^2) - b_solvent = 46.0 - s = torch.tensor([0.0, 0.1, 0.2, 0.3]) # |s| values - - correction = torch.exp(-b_solvent * s ** 2) - - # At s=0, correction should be 1 - assert torch.isclose(correction[0], torch.tensor(1.0)) - - # Correction should decrease with increasing s - assert torch.all(correction[1:] < correction[:-1]) + def test_exponent_one_is_exactly_a_debye_waller_factor(self): + """The nesting gate: ``n = 1`` must reproduce ``exp(-B ss)`` to machine + precision, with ``B = ln2 / ss_half``. That is what makes the fitted form a + strict generalisation of the shipped exponential rather than a replacement, so + it cannot do worse than it at the optimum.""" + from torchref.scaling.solvent import SolventModel, _LN2 + + for b_target in (20.0, 46.0, 90.0): + ss_half = _LN2 / b_target + d_half = 1.0 / (2 * ss_half ** 0.5) + solvent = SolventModel(d_half=d_half, n_exp=1.0, device=torch.device("cpu")) + + ss = torch.linspace(0.0, 0.25, 128, dtype=solvent.float_type) + assert torch.allclose( + solvent.damping(ss), torch.exp(-b_target * ss), atol=1e-5 + ), f"n=1 does not reproduce exp(-{b_target} ss)" + + def test_damping_is_monotone_and_starts_at_one(self): + from torchref.scaling.solvent import SolventModel + + solvent = SolventModel(device=torch.device("cpu")) + ss = torch.linspace(0.0, 0.3, 64, dtype=solvent.float_type) + d = solvent.damping(ss) + + assert torch.isclose(d[0], torch.tensor(1.0, dtype=d.dtype), atol=1e-6) + assert torch.all(d.diff() <= 1e-7) + assert torch.all((d >= 0.0) & (d <= 1.0)) + + def test_falloff_parameters_are_bounded(self): + """The unbounded 3-parameter fit reaches degenerate slow power laws on some + structures; the clamps are what keep the fitted curve a switch.""" + from torchref.scaling.solvent import ( + N_EXP_BOUNDS, SS_HALF_BOUNDS, SolventModel, + ) + + solvent = SolventModel(device=torch.device("cpu")) + with torch.no_grad(): + solvent.log_ss_half.fill_(10.0) + solvent.log_n_exp.fill_(-10.0) + assert torch.isclose( + solvent.ss_half(), torch.tensor(SS_HALF_BOUNDS[1], dtype=solvent.float_type) + ) + assert torch.isclose( + solvent.n_exp(), torch.tensor(N_EXP_BOUNDS[0], dtype=solvent.float_type) + ) + + with torch.no_grad(): + solvent.log_ss_half.fill_(-10.0) + solvent.log_n_exp.fill_(10.0) + assert torch.isclose( + solvent.ss_half(), torch.tensor(SS_HALF_BOUNDS[0], dtype=solvent.float_type) + ) + assert torch.isclose( + solvent.n_exp(), torch.tensor(N_EXP_BOUNDS[1], dtype=solvent.float_type) + ) + + def test_b_solvent_equivalent_recovers_a_true_debye_waller(self): + """The reported ``B_SOL`` is back-fitted, so at ``n = 1`` -- where the curve IS + an exponential -- it must recover that exponential's own B.""" + from torchref.scaling.solvent import SolventModel, _LN2 + + b_target = 46.0 + d_half = 1.0 / (2 * (_LN2 / b_target) ** 0.5) + solvent = SolventModel(d_half=d_half, n_exp=1.0, device=torch.device("cpu")) + + ss = torch.linspace(1e-4, 0.25, 512, dtype=solvent.float_type) + assert abs(solvent.b_solvent_equivalent(ss) - b_target) < 0.5 def test_bfactor_with_k_solvent(self): """Test combined k_solvent and B-factor correction.""" @@ -229,13 +292,15 @@ def test_typical_k_solvent_range(self, k): k_recovered = torch.exp(solvent.log_k_solvent) assert torch.isclose(k_recovered, torch.tensor(k), rtol=1e-5) - @pytest.mark.parametrize("b", [30.0, 46.0, 50.0, 70.0, 100.0]) - def test_typical_b_solvent_range(self, b): - """Test typical B_solvent values.""" + @pytest.mark.parametrize("d_half", [2.5, 3.6, 4.5, 6.0, 10.0]) + def test_typical_d_half_range(self, d_half): + """Every value inside the bounds must survive the clamp untouched.""" from torchref.scaling.solvent import SolventModel - solvent = SolventModel(b_solvent=b) - assert torch.isclose(solvent.b_solvent, torch.tensor(b)) + solvent = SolventModel(d_half=d_half) + assert torch.isclose( + solvent.ss_half(), torch.tensor(1.0 / (4 * d_half ** 2)), rtol=1e-4 + ) def test_default_parameters(self): """Test default parameter values are reasonable.""" @@ -245,38 +310,17 @@ def test_default_parameters(self): # Defaults should be within reasonable range k = torch.exp(solvent.log_k_solvent) - b = solvent.b_solvent - + d_half = 1.0 / (2 * solvent.ss_half().sqrt()) + assert 0.1 <= k <= 2.0 - assert 10.0 <= b <= 200.0 + assert 2.0 <= d_half <= 12.0 + assert 1.0 <= solvent.n_exp() <= 20.0 @pytest.mark.integration class TestSolventMathOperations: """Test mathematical operations used in solvent modeling.""" - def test_gaussian_smoothing_concept(self): - """Test Gaussian smoothing concept used in mask.""" - # Create a simple 1D step function - x = torch.linspace(-2, 2, 100) - step = (x > 0).float() - - # Gaussian kernel for smoothing - sigma = 0.5 - kernel_x = torch.linspace(-3*sigma, 3*sigma, 21) - kernel = torch.exp(-kernel_x**2 / (2 * sigma**2)) - kernel = kernel / kernel.sum() - - # Simple smoothing (conceptual test) - smoothed = torch.conv1d( - step.unsqueeze(0).unsqueeze(0), - kernel.unsqueeze(0).unsqueeze(0), - padding=len(kernel)//2 - ).squeeze() - - # Smoothed should be between 0 and 1 - assert torch.all((smoothed >= -0.1) & (smoothed <= 1.1)) - def test_exponential_b_factor_scaling(self): """Test exponential B-factor scaling.""" # Debye-Waller factor: exp(-B * s^2 / 4) diff --git a/tests/integration/test_gpu_scale_stability.py b/tests/integration/test_gpu_scale_stability.py index a1f553ad..6149f6b5 100644 --- a/tests/integration/test_gpu_scale_stability.py +++ b/tests/integration/test_gpu_scale_stability.py @@ -1,12 +1,16 @@ -"""GPU refinement must not collapse the per-bin scale (regression). +"""GPU refinement must not collapse the isotropic scale (regression). Guards the ``estimate_beta`` non-determinism bug: on CUDA, a non-stable ``argsort`` + atomic ``scatter_add`` + the cancellation ``wi = A*B - C**2`` produced a different (often floored) beta per process, which froze a bad beta -into ``refine_lbfgs`` and collapsed a per-bin ``log_scale`` to ~-99 → R-free -blow-up. A short GPU refinement should now (a) stay finite / not collapse the -scale and (b) track the CPU refinement, which the deterministic + float32-stable -``estimate_beta`` restores. +into ``refine_lbfgs`` and collapsed the scale to ~-99 → R-free blow-up. A short +GPU refinement should now (a) stay finite / not collapse the scale and (b) track +the CPU refinement, which the deterministic + float32-stable ``estimate_beta`` +restores. + +The scale is a Chebyshev polynomial evaluated per reflection, so the invariant is +on the resulting per-reflection log scale rather than on any coefficient: a +coefficient carries no interpretable magnitude on its own. """ import pytest @@ -34,17 +38,18 @@ def run(device): ) ref.refine(macro_cycles=5) rwork, rfree = ref.get_rfactor() - log_scale_min = float(ref.scaler.log_scale.detach().min()) + with torch.no_grad(): + log_scale_min = float(ref.scaler.iso_log_scale().min()) return float(rwork), float(rfree), log_scale_min rw_gpu, rf_gpu, ls_gpu = run("cuda") rw_cpu, rf_cpu, ls_cpu = run("cpu") - # (a) GPU refinement stays sane: R-free finite and not blown up, and no - # per-bin log_scale collapsed (the bug drove one to ~-99; healthy ~-3). + # (a) GPU refinement stays sane: R-free finite and not blown up, and the + # scale nowhere collapsed (the bug drove it to ~-99; healthy ~-3). assert rf_gpu == rf_gpu, "GPU R-free is NaN" assert rf_gpu < 0.6, f"GPU R-free blew up ({rf_gpu:.3f}) — scale likely collapsed" - assert ls_gpu > -15.0, f"GPU per-bin log_scale collapsed (min={ls_gpu:.2f})" + assert ls_gpu > -15.0, f"GPU isotropic log scale collapsed (min={ls_gpu:.2f})" # (b) GPU tracks CPU — the device-parity the estimate_beta fix restores. assert abs(rf_gpu - rf_cpu) < 0.03, ( diff --git a/tests/integration/test_rigid_body_refinement.py b/tests/integration/test_rigid_body_refinement.py index ea057cb9..fa5bbdb3 100644 --- a/tests/integration/test_rigid_body_refinement.py +++ b/tests/integration/test_rigid_body_refinement.py @@ -107,3 +107,46 @@ def test_independent_per_chain_shifts_recover(self, pdb_dir, mtz_dir): f"after-rigid={rwork_after:.4f}" ) assert rfree_after <= rfree0 + 0.1 + + +class TestScaleDegreesOfFreedom: + """``ls_wunit_k1`` owns its own closed-form overall scale. + + Any *additional* isotropic freedom in the scaler double-scales against it, and + both fits succeed, so nothing raises -- the only symptom is a worse result. The + rigid-body setup therefore collapses the scaler's isotropic term to a single + global constant, which for a Chebyshev basis means one coefficient (``T_0 == 1``). + """ + + @pytest.mark.integration + def test_ls_wunit_k1_scaler_has_one_isotropic_coefficient(self, pdb_dir, mtz_dir): + from torchref.refinement.rigid_body_refinement import RigidBodyRefinementStep + + ref = _build_refinement(pdb_dir, mtz_dir, "1DAW") + ref.get_scales() + + step = RigidBodyRefinementStep(ref) + step._rebind_for_data(ref.reflection_data, xray_mode="ls_wunit_k1") + + assert ref.scaler.n_iso_coeff == 1, ( + "ls_wunit_k1 scaler must have exactly one isotropic coefficient, " + f"got {ref.scaler.n_iso_coeff}" + ) + with torch.no_grad(): + log_k = ref.scaler.iso_log_scale() + assert torch.allclose(log_k, log_k[0].expand_as(log_k), atol=1e-6), ( + "ls_wunit_k1 scaler applies a resolution-dependent scale, which " + "double-scales against the target's own closed-form k" + ) + + @pytest.mark.integration + def test_other_modes_keep_the_full_isotropic_basis(self, pdb_dir, mtz_dir): + from torchref.refinement.rigid_body_refinement import RigidBodyRefinementStep + + ref = _build_refinement(pdb_dir, mtz_dir, "1DAW") + ref.get_scales() + + step = RigidBodyRefinementStep(ref) + step._rebind_for_data(ref.reflection_data, xray_mode="ml") + + assert ref.scaler.n_iso_coeff == ref.n_iso_coeff > 1 diff --git a/tests/unit/scaling/test_iso_scale_polynomial.py b/tests/unit/scaling/test_iso_scale_polynomial.py new file mode 100644 index 00000000..a1c3763e --- /dev/null +++ b/tests/unit/scaling/test_iso_scale_polynomial.py @@ -0,0 +1,156 @@ +"""The isotropic scale is a Chebyshev polynomial in ``sin(theta)/lambda``. + +These pin the properties the parameterisation is chosen for, none of which a +per-bin step scale had: + +* it is **continuous** -- neighbouring reflections cannot differ by a bin-boundary jump; +* ``n_iso_coeff=1`` is exactly one global constant, which is what rigid-body + ``ls_wunit_k1`` relies on to avoid double-scaling against its own closed-form scale; +* low orders span the classical physical forms, so ``k*exp(-B s^2/4)`` is representable; +* the per-reflection clamp still bounds a polynomial that is unbounded by construction. + +``F_calc`` is synthesised rather than computed from the model: these are unit tests of the +scale parameterisation, and a real ``F_calc`` needs the whole FFT stack. +""" + +import pytest +import torch + + +def _fake_fcalc(scaler, damping: float = 30.0): + """Complex amplitudes on the same scale as ``F_obs``, with a known B-like trend.""" + fobs = scaler._data.get_corrected_data()[0] + amp = fobs * torch.exp(-damping * scaler._s_half_sq) / 2.5 + return amp.to(torch.complex64) + + +@pytest.fixture +def scaler(model_and_data): + from torchref.scaling.scaler import Scaler + + return Scaler( + model_and_data["model"], model_and_data["data"], nbins=10, verbose=0 + ) + + +@pytest.mark.unit +def test_design_matrix_shape_and_chebyshev_range(scaler): + design = scaler._iso_design + assert design.shape == (scaler.s.shape[0], scaler.n_iso_coeff) + # T_0 == 1 everywhere; every T_k is bounded by 1 on [-1, 1]. + assert torch.allclose(design[:, 0], torch.ones_like(design[:, 0])) + assert design.abs().max() <= 1.0 + 1e-5 + + +@pytest.mark.unit +def test_single_coefficient_is_one_global_scale(model_and_data): + """``n_iso_coeff=1`` must give the SAME scale to every reflection.""" + from torchref.scaling.scaler import Scaler + + sc = Scaler( + model_and_data["model"], model_and_data["data"], nbins=10, + n_iso_coeff=1, verbose=0, + ) + sc.calc_initial_scale(_fake_fcalc(sc)) + assert sc.c_iso.shape == (1,) + with torch.no_grad(): + log_k = sc.iso_log_scale() + assert log_k.shape[0] == sc.s.shape[0] + assert torch.allclose(log_k, log_k[0].expand_as(log_k), atol=1e-6) + + +@pytest.mark.unit +def test_low_orders_span_the_classical_scale_forms(scaler): + """The basis is Chebyshev in ``x = sin(theta)/lambda``, so a polynomial of degree + ``d`` in ``x`` needs ``d + 1`` terms and must then be representable exactly. Degree 2 + is scale-plus-overall-B, the classical form. + """ + x = torch.sqrt(scaler._s_half_sq.clamp(min=0)) + for n_terms, target in ((2, 1.7 - 3.0 * x), (3, 1.7 - 3.0 * x**2)): + design = scaler._iso_design[:, :n_terms] + coeff = torch.linalg.lstsq(design, target.unsqueeze(1)).solution.squeeze(1) + residual = (design @ coeff - target).abs().max() + assert residual < 1e-3 * target.abs().max(), ( + f"{n_terms} terms cannot represent a degree-{n_terms - 1} polynomial " + f"(residual {residual:.3e})" + ) + + +@pytest.mark.unit +def test_scale_is_continuous_in_resolution(scaler): + """No bin-boundary jumps: reflections adjacent in ``s`` get adjacent scales.""" + scaler.calc_initial_scale(_fake_fcalc(scaler)) + with torch.no_grad(): + order = torch.argsort(scaler._s_half_sq) + log_k = scaler.iso_log_scale()[order] + ds = scaler._s_half_sq[order].diff() + jumps = log_k.diff().abs() + # Where two reflections sit at effectively the same resolution the scale must be + # effectively the same; a step model breaks exactly this at a bin edge. + coincident = ds < 1e-8 + assert coincident.any(), "fixture has no coincident-resolution reflections" + assert jumps[coincident].max() < 1e-5 + + +@pytest.mark.unit +def test_per_reflection_clamp_bounds_the_polynomial(scaler): + """A polynomial is unbounded at the ends of its range; the clamp is what stops a + single extreme reflection carrying an arbitrary scale.""" + scaler.calc_initial_scale(_fake_fcalc(scaler)) + with torch.no_grad(): + scaler.c_iso.fill_(50.0) + log_k = scaler.iso_log_scale() + assert log_k.max() <= 10.0 + 1e-6 + assert log_k.min() >= -10.0 - 1e-6 + + +@pytest.mark.unit +def test_seed_recovers_the_closed_form_scale_curve(scaler): + """``calc_initial_scale`` projects the per-bin closed-form ratio onto the basis, so + the fit starts from the curve a binned model would have started from.""" + fcalc = _fake_fcalc(scaler) + scaler.calc_initial_scale(fcalc) + fobs = scaler._data.get_corrected_data()[0] + amp = torch.abs(fcalc).to(fobs.dtype) + with torch.no_grad(): + log_k = scaler.iso_log_scale() + work = scaler._data.work.mask.to(torch.bool) + bins = scaler.bins.to(torch.int64) + checked = 0 + for b in range(scaler.nbins): + m = work & (bins == b) + if m.sum() < 50: + continue + closed_form = torch.log( + ((fobs[m] * amp[m]).sum() / (amp[m] ** 2).sum()).clamp(min=1e-6) + ) + assert abs(float(log_k[m].mean() - closed_form)) < 0.15 + checked += 1 + assert checked >= 5 + + +@pytest.mark.unit +def test_forward_applies_the_polynomial_scale(scaler): + """``forward`` must scale by ``exp(iso_log_scale())``, per reflection.""" + fcalc = _fake_fcalc(scaler) + scaler.calc_initial_scale(fcalc) + with torch.no_grad(): + scaled = scaler.forward(fcalc) + expected = torch.exp(scaler.iso_log_scale()) * fcalc + assert torch.allclose(torch.abs(scaled), torch.abs(expected), rtol=1e-4) + + +@pytest.mark.unit +def test_n_iso_coeff_survives_a_state_dict_round_trip(scaler): + from torchref.scaling.scaler import Scaler + + scaler.calc_initial_scale(_fake_fcalc(scaler)) + state = scaler.state_dict() + assert state["n_iso_coeff"] == scaler.n_iso_coeff + + fresh = Scaler(verbose=0) + fresh.set_data(scaler._data.module) + fresh.c_iso = torch.nn.Parameter(torch.zeros_like(scaler.c_iso)) + fresh.load_state_dict(state, strict=False) + assert fresh.n_iso_coeff == scaler.n_iso_coeff + assert torch.allclose(fresh.c_iso, scaler.c_iso) diff --git a/torchref/.DS_Store b/torchref/.DS_Store deleted file mode 100644 index 75b0cb8d7b7a9ee7463c1416155eaa990fd17b3c..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 10244 zcmeHMO=uHA6n@(#Hkzu1TCLFHo}?mzEqD+Wwjp{@EJ8gA(w`)414%bFEgBGAQ1IkW zg?jQ}q4q3Jo)jGwJsHK+ffum^z!<|}ap2#$4iGiAijIn&jG`3p zDOV4cDz(%S!;Cu8aiikw&@a2WY-K+hL(Ah1ZOB1>ZAtnZ+ zxX)0A98+k2_=KE(PS&l zbCT1~28`h>=^S zha5~n9*P(zNd**`jswXB)KM3!>&oo;x~{xvfDd$T2G4XK97ZXqWgHccpz;aSJ%;^! z3|yFjX{H+<&{suWtgd@$?fRWp($5C?K=)?wOg9L>n^3LGGsk3V*Q5Ga z79OK#l3~z`1R21%%85hjI)^j>vN7qX5-l$r=pQ7@N7rqm39C=;rqYr1=4`S-`9m`5V- zsjJ%v%XD^ofzC3%Yt+T0b{hC+b@E-8ud@cH>%95-<@vJoPU;L{gTC1a%XFS{NM{Sq zy|mMdb0KORXHLdbt6_w$?a;%4&Vg3pVaD%gi=-2Biq7j^5%mbvs9L53TfN{V$U>qAMO#HjMNdXqzV#miaAnQbWM=)}kA_C> W6|i-jS^rmeKm1(+=Ffk${{I8+<)-!k diff --git a/torchref/__init__.py b/torchref/__init__.py index c5164527..5f580fd4 100644 --- a/torchref/__init__.py +++ b/torchref/__init__.py @@ -40,7 +40,7 @@ General utilities and debugging tools. """ -__version__ = "0.6.3" +__version__ = "0.6.4" import os diff --git a/torchref/cli/collection_difference_refine.py b/torchref/cli/collection_difference_refine.py index 7aad2a67..c2d53001 100644 --- a/torchref/cli/collection_difference_refine.py +++ b/torchref/cli/collection_difference_refine.py @@ -906,8 +906,8 @@ def _build_metadata(model, data, r_work, r_free): # Solvent model from CollectionScaler if hasattr(scaler, "solvent") and scaler.solvent is not None: sm = scaler.solvent - meta.solvent_model_ksol = float(torch.exp(sm.log_k_solvent).item()) - meta.solvent_model_bsol = float(sm.b_solvent.item()) + meta.solvent_model_ksol = float(sm.k_solvent().detach()) + meta.solvent_model_bsol = sm.b_solvent_equivalent(scaler._s_half_sq) # Cell and spacegroup if model.cell is not None: @@ -980,8 +980,8 @@ def _build_metadata(model, data, r_work, r_free): if hasattr(scaler, "solvent") and scaler.solvent is not None: sm = scaler.solvent - merged_meta.solvent_model_ksol = float(torch.exp(sm.log_k_solvent).item()) - merged_meta.solvent_model_bsol = float(sm.b_solvent.item()) + merged_meta.solvent_model_ksol = float(sm.k_solvent().detach()) + merged_meta.solvent_model_bsol = sm.b_solvent_equivalent(scaler._s_half_sq) ensemble_note = ( f"Mixed-state ensemble from TorchRef difference refinement. " diff --git a/torchref/cli/refine.py b/torchref/cli/refine.py index 043c133f..d1ca5421 100644 --- a/torchref/cli/refine.py +++ b/torchref/cli/refine.py @@ -141,14 +141,14 @@ def main(): type=str, default=DEFAULT_SCALE_TARGET, choices=list(SCALE_TARGETS), - help="Objective for the scaler's own L-BFGS scale fit (NOT the body target). The " - "choices are --xray-mode rows, evaluated by the same target classes the body uses; " - "only rows that do not centre on alpha are offered, because alpha is degenerate with " - "the scale being fitted. 'nll' (default) is the sigma_obs-weighted Gaussian: the " - "natural objective for a nuisance-magnitude fit, it avoids coupling the nuisance " - "layer to the model-error estimate, and it treats the body targets even-handedly. " - "'ml_noalpha' is the Read-MLF sigma_A likelihood; prefer it if the per-bin log_scale " - "collapses in weak shells, since its beta absorbs the mismatch instead.", + help=f"Objective for the scaler's own L-BFGS scale fit (NOT the body target). The " + f"choices are --xray-mode rows, evaluated by the same target classes the body uses; " + f"only rows that do not centre on alpha are offered, because alpha is degenerate " + f"with the scale being fitted. '{DEFAULT_SCALE_TARGET}' (default) is unit-weight " + f"least squares, weighting every reflection as R itself does. 'nll' is the " + f"sigma_obs-weighted Gaussian, which up-weights weak reflections. 'ml_noalpha' is " + f"the Read-MLF sigma_A likelihood; prefer it if the scale collapses in weak shells, " + f"since its beta absorbs the mismatch instead.", ) refine_group.add_argument( "--sigma-a-max", diff --git a/torchref/experimental/monolithic_refinement/density_scaler.py b/torchref/experimental/monolithic_refinement/density_scaler.py index 7d6ecf49..b0322c3d 100644 --- a/torchref/experimental/monolithic_refinement/density_scaler.py +++ b/torchref/experimental/monolithic_refinement/density_scaler.py @@ -9,12 +9,12 @@ ``DensityDerivedSolvent`` A thin ``nn.Module`` exposing the exact interface ``ScalerBase.forward`` expects from its ``solvent`` attribute (``get_rec_solvent`` / ``log_k_solvent`` - / ``b_solvent`` / ``optimize_phase``), backed by a ``DensitySolventModel``. + / falloff / ``optimize_phase``), backed by a ``DensitySolventModel``. Unlike the vdW-mask ``SolventModel`` (whose mask FFT is detached / static), ``get_rec_solvent`` here is **live** -- it stays in the autograd graph, so ``F_sol`` tracks atom moves and gradients flow ``rho -> xyz/adp``. The raw solvent SF carries only the density *shape* (``rho_s`` frozen at 1); the - scaler's ``k_sol`` is the refinable contrast and ``b_solvent`` the residual + scaler's ``k_sol`` is the refinable contrast and the falloff the residual damping -- identical scaling machinery to the mask. ``DensitySolventScaler`` @@ -30,7 +30,7 @@ solvent is rejected by the ML target (k_sol -> 0), and the only accepted regime is the sharp mask. Freezing ``rho0`` keeps the refinement stable and adds no solvent-shape DOF beyond the -mask's own ``k_sol`` + ``b_solvent``. +mask's own ``k_sol`` + falloff. """ import torch @@ -38,6 +38,7 @@ from torchref.config import get_default_device, get_float_dtype from torchref.scaling.scaler import Scaler +from torchref.scaling.solvent import SolventModel from torchref.experimental.monolithic_refinement.density_solvent import ( DensitySolventModel, ) @@ -59,9 +60,12 @@ class DensityDerivedSolvent(nn.Module): Occupancy mapping passed to :class:`DensitySolventModel`. refine_rho0 : bool, default False If True, leave ``rho0`` refinable (co-refined by the host optimizer). - k_solvent, b_solvent : float - Initial contrast / residual B (init at the mask defaults so the scaler - starts in the same basin and avoids the B=0 local-min trap). + k_solvent : float + Initial contrast (init at the mask default so the scaler starts in the same + basin). + d_half, n_exp : float + Initial solvent falloff, in the same parameterisation + :class:`~torchref.scaling.solvent.SolventModel` uses. """ def __init__( @@ -72,7 +76,8 @@ def __init__( occupancy="exp", refine_rho0=False, k_solvent=0.35, - b_solvent=46.0, + d_half=3.59, + n_exp=5.0, device=None, dtype=None, verbose=0, @@ -99,15 +104,28 @@ def __init__( self.log_k_solvent = nn.Parameter( torch.log(torch.tensor(k_solvent, dtype=dtype, device=device)) ) - self.b_solvent = nn.Parameter( - torch.tensor(b_solvent, dtype=dtype, device=device) + self.log_ss_half = nn.Parameter( + torch.log( + torch.tensor(1.0 / (4.0 * d_half**2), dtype=dtype, device=device) + ) ) + self.log_n_exp = nn.Parameter( + torch.log(torch.tensor(n_exp, dtype=dtype, device=device)) + ) + + # The falloff is the mask model's, borrowed unbound so the two solvents cannot + # drift apart: only the SHAPE of F_sol differs here, not how it is damped. + ss_half = SolventModel.ss_half + n_exp = SolventModel.n_exp + k_solvent = SolventModel.k_solvent + damping = SolventModel.damping + b_solvent_equivalent = SolventModel.b_solvent_equivalent def get_rec_solvent(self, hkl): """Raw (contrast-free) solvent SF, LIVE in the autograd graph. Not detached: ``F_sol`` follows the moving atoms so gradients reach - ``xyz``/``adp``. The scaler applies ``k_sol * exp(-B_sol s^2)`` on top. + ``xyz``/``adp``. The scaler applies the contrast and falloff on top. """ return self.density(hkl.to(torch.long)) @@ -129,6 +147,7 @@ def __init__( model=None, data=None, nbins: int = 20, + n_iso_coeff: int = 6, verbose: int = 1, device=None, *, @@ -146,7 +165,8 @@ def __init__( refine_rho0=refine_rho0, ) super().__init__( - model=model, data=data, nbins=nbins, verbose=verbose, device=device + model=model, data=data, nbins=nbins, n_iso_coeff=n_iso_coeff, + verbose=verbose, device=device ) def setup_solvent(self): diff --git a/torchref/io/metadata.py b/torchref/io/metadata.py index fc425587..224ef4a1 100644 --- a/torchref/io/metadata.py +++ b/torchref/io/metadata.py @@ -41,7 +41,7 @@ class RefinementMetadata: n_atoms_total, n_atoms_protein, n_atoms_solvent : int, optional Model atom counts. solvent_model_ksol, solvent_model_bsol : float, optional - Bulk-solvent model parameters. + Bulk-solvent scale, and the equivalent single ``B`` for the fitted falloff. cell, spacegroup Unit cell ``[a, b, c, alpha, beta, gamma]`` and space-group name. title, authors @@ -225,16 +225,18 @@ def from_refinement(cls, refinement) -> RefinementMetadata: pass # --- Solvent model parameters --- - try: - scaler = refinement.scaler - if hasattr(scaler, "solvent_model") and scaler.solvent_model is not None: - sm = scaler.solvent_model - meta.solvent_model_ksol = float( - torch.exp(sm.log_k_solvent).item() - ) - meta.solvent_model_bsol = float(sm.b_solvent.item()) - except Exception: - pass + # PDB REMARK 3 and mmCIF carry k_sol and a single solvent B; the fitted falloff + # ``k_sol exp(-ln2 (ss/ss_half)^n)`` has no B, so the reported one is back-fitted + # from the curve over this dataset's own reflections. + scaler = getattr(refinement, "scaler", None) + sm = getattr(scaler, "solvent", None) + if sm is not None: + try: + meta.solvent_model_ksol = float(sm.k_solvent().detach()) + meta.solvent_model_bsol = sm.b_solvent_equivalent(scaler._s_half_sq) + except (AttributeError, RuntimeError, ValueError) as exc: + if getattr(refinement, "verbose", 0) > 0: + print(f"Could not record solvent parameters: {exc}") # --- Cell and spacegroup --- try: diff --git a/torchref/refinement/base_refinement.py b/torchref/refinement/base_refinement.py index a5c419d5..99ef8a7e 100644 --- a/torchref/refinement/base_refinement.py +++ b/torchref/refinement/base_refinement.py @@ -102,6 +102,7 @@ def __init__( max_res: float = None, device: Optional[torch.device] = None, nbins: int = 10, + n_iso_coeff: int = 6, column_names: Optional[Dict[str, str]] = None, wavelength: Optional[float] = 1.0, anomalous_threshold: float = 0.5, @@ -134,7 +135,9 @@ def __init__( device : torch.device, optional Computation device. Defaults to the configured default device. nbins : int, optional - Number of resolution bins for the scaler. Default 10. + Number of resolution bins used to seed the scaler's scale. Default 10. + n_iso_coeff : int, optional + Number of Chebyshev terms in the scaler's isotropic scale. Default 6. column_names : dict, optional Mapping of logical column roles to MTZ column labels. wavelength : float, optional @@ -177,6 +180,7 @@ def __init__( self.history = dict() self.max_res = max_res self.nbins = nbins + self.n_iso_coeff = n_iso_coeff self.lr = 1e-3 # Wavelength drives f'/f'' anomalous scattering corrections in ModelFT. # Default 1.0 preserves prior behavior; set to the experimental wavelength @@ -231,7 +235,8 @@ def __init__( anomalous_threshold=self.anomalous_threshold, ) self.scaler = Scaler( - verbose=self.verbose, device=self.device, nbins=self.nbins + verbose=self.verbose, device=self.device, nbins=self.nbins, + n_iso_coeff=self.n_iso_coeff, ) # Restraints are now lazy-loaded via model.restraints property self.weighter = None @@ -489,7 +494,7 @@ def refine_scaler(self): in-run R-factor is computed from is the one an external 0-cycle score would produce. Warm: the existing scale is the starting point and carries across macrocycles. For a - cold start -- fresh ``log_scale``, rebuilt solvent and anisotropy -- call + cold start -- fresh ``c_iso``, rebuilt solvent and anisotropy -- call :meth:`get_scales`. The objective is ``self.scale_target``, not the body target: scaling is a @@ -518,7 +523,7 @@ def get_scales(self): """Cold-start the scaler against the current model: ``initialize()`` then :meth:`refine_scaler`. - ``initialize()`` *replaces* ``log_scale`` with a fresh parameter and rebuilds the + ``initialize()`` *replaces* ``c_iso`` with a fresh parameter and rebuilds the solvent and anisotropy terms, so this discards a refined scale. Use it at construction, when the model has been swapped, or for a one-shot 0-cycle score; inside a macrocycle loop call :meth:`refine_scaler` instead. @@ -533,12 +538,14 @@ def get_scales(self): def setup_scaler(self): """Construct ``self.scaler`` from ``self._scaler_class`` (default - :class:`Scaler`), wired to the current model, data, ``nbins`` and device.""" + :class:`Scaler`), wired to the current model, data, ``nbins``, + ``n_iso_coeff`` and device.""" cls = getattr(self, "_scaler_class", None) or Scaler self.scaler = cls( self.model, self.reflection_data, nbins=self.nbins, + n_iso_coeff=self.n_iso_coeff, verbose=self.verbose, device=self.device, ) @@ -689,8 +696,7 @@ def _create_loss_state(self) -> LossState: # Register ADP targets state.register_targets(self.adp_target) # Scaler regularization targets require the scaler to actually have - # the regularized parameters. Solvent-only scalers (rigid-body - # ls_wunit_k1) delete U and freeze log_scale. + # the regularized parameters. if self.scaler is not None: n_ref = int(self.reflection_data.hkl.shape[0]) if hasattr(self.scaler, "U"): @@ -698,8 +704,8 @@ def _create_loss_state(self) -> LossState: "adp/scaler_U", ScalerURegularizationTarget(self.scaler, n_reflections=n_ref), ) - if (hasattr(self.scaler, "log_scale") - and self.scaler.log_scale.requires_grad): + if (hasattr(self.scaler, "c_iso") + and self.scaler.c_iso.requires_grad): state.register_target( "adp/scaler_log_scale", ScalerLogScaleTrendTarget(self.scaler, n_reflections=n_ref), @@ -994,6 +1000,7 @@ def extract_submodule_state(state_dict: dict, prefix: str) -> dict: instance.history = {} instance.max_res = model_state.get("_metadata_max_res", None) instance.nbins = 10 + instance.n_iso_coeff = 6 instance.lr = 1e-3 instance.effective_weights = {} diff --git a/torchref/refinement/lbfgs_refinement.py b/torchref/refinement/lbfgs_refinement.py index b230b3c0..391d111c 100644 --- a/torchref/refinement/lbfgs_refinement.py +++ b/torchref/refinement/lbfgs_refinement.py @@ -172,7 +172,7 @@ def refine_rigid_body( def refine_xyz(self): """LBFGS over the ``xyz`` body parameters; returns the LossState with history. - Scaler parameters (``log_scale``, ``U``, solvent) join this call only when + Scaler parameters (``c_iso``, ``U``, solvent) join this call only when ``corefine_scaler`` is True; by default they are fixed here and updated by :meth:`refine_scaler`. """ diff --git a/torchref/refinement/rigid_body_refinement.py b/torchref/refinement/rigid_body_refinement.py index 4ed9db5a..b8907499 100644 --- a/torchref/refinement/rigid_body_refinement.py +++ b/torchref/refinement/rigid_body_refinement.py @@ -153,6 +153,11 @@ def _rebind_for_data(self, data, model=None, xray_mode=None): ref.scaler = Scaler( model, data, nbins=1 if xray_mode == "ls_wunit_k1" else getattr(ref, "nbins", 20), + # T_0 == 1, so a single coefficient IS one global K. ``ls_wunit_k1`` owns its + # own closed-form scale, and any further isotropic freedom here double-scales + # against it -- silently, since both fits succeed. + n_iso_coeff=1 if xray_mode == "ls_wunit_k1" else getattr( + ref, "n_iso_coeff", 6), verbose=ref.verbose, device=ref.device, ) @@ -208,7 +213,8 @@ def _run_one_cutoff(self, d_min: float): use_inner_cycles = ( ref.scaler is not None and getattr(ref.scaler, "solvent", None) is not None - and ref.scaler.log_scale.requires_grad is False + and getattr(ref.scaler, "c_iso", None) is not None + and ref.scaler.c_iso.requires_grad is False ) if use_inner_cycles: @@ -265,7 +271,7 @@ def _run_inner_cycles(self, d_min, state, rigid_params, n_inner: int = 5): # Build the solvent parameter list — only the ones that are # actually refinable. solvent_params = [] - for name in ("log_k_solvent", "b_solvent"): + for name in ("log_k_solvent", "log_ss_half", "log_n_exp"): p = getattr(solvent, name, None) if isinstance(p, torch.nn.Parameter) and p.requires_grad: solvent_params.append(p) diff --git a/torchref/refinement/targets/adp/scaler_log_scale.py b/torchref/refinement/targets/adp/scaler_log_scale.py index 1d358e08..4a972c3e 100644 --- a/torchref/refinement/targets/adp/scaler_log_scale.py +++ b/torchref/refinement/targets/adp/scaler_log_scale.py @@ -16,24 +16,29 @@ class ScalerLogScaleTrendTarget(Target): """ - Pin out the Debye-Waller-like trend in the per-bin ``log_scale``. + Pin out the Debye-Waller-like trend in the isotropic scale. - Per-bin scales exist to absorb **localized** data-quality issues — - an outlier bin, a slightly mis-merged shell. They are *not* supposed + The isotropic scale exists to absorb **localized** data-quality issues — + an outlier shell, a slightly mis-merged region. It is *not* supposed to absorb the global resolution-dependent attenuation envelope; that - is what atomic B-factors are for. A ``log_scale[i] ≈ a + b·s²[i]`` + is what atomic B-factors are for. A ``log k_iso(s) ≈ a + b·s²`` structure with nonzero ``b`` is exactly a B-factor masquerading as a - per-bin scale: it shifts ``B_eff = B_atom − 4·b``, letting atoms drift + scale: it shifts ``B_eff = B_atom − 4·b``, letting atoms drift broader or sharper while the overall ``F_scaled`` amplitudes stay unchanged. - The target fits the least-squares slope of ``log_scale`` against the - bin-mean ``|s|²`` and penalizes ``slope²``. The intercept ``a`` (the - overall scale) is free. Residuals off the fit line are also free — - those encode the outlier absorption the scales are *supposed* to do. + The target fits the least-squares slope of the per-reflection log scale + against ``|s|²`` and penalizes ``slope²``. The intercept ``a`` (the + overall scale) is free. Curvature off the fit line is also free — + that encodes the outlier absorption the scale is *supposed* to do. - Penalty ``slope² · N_ref / nbins`` — the ``/ nbins`` matches how xray - gradient on a single ``log_scale[i]`` scales; the ``N_ref`` factor + Because the log scale is linear in ``c_iso``, so is its least-squares + slope: precomputing ``w = (x̃ᵀ·design) / (x̃ᵀ·x̃)`` once makes + ``slope = w·c_iso`` exact and O(n_coeff), with no regression at + evaluation time. + + Penalty ``slope² · N_ref / nbins`` — the ``/ nbins`` matches how the xray + gradient on a single scale degree of freedom scales; the ``N_ref`` factor brings the total into xray's order of magnitude. """ @@ -49,36 +54,25 @@ def __init__( object.__setattr__(self, "_scaler", scaler) with torch.no_grad(): - s_sq = (scaler.s ** 2).sum(dim=1) - bins = scaler.bins.to(torch.int64) - nbins = int(scaler.nbins) - bin_s_sq = torch.zeros(nbins, device=scaler.s.device, dtype=s_sq.dtype) - bin_counts = torch.zeros_like(bin_s_sq) - bin_s_sq.scatter_add_(0, bins, s_sq) - bin_counts.scatter_add_(0, bins, torch.ones_like(s_sq)) - bin_mean_s_sq = bin_s_sq / bin_counts.clamp(min=1.0) - valid = bin_counts > 0 - - s_centers = bin_mean_s_sq[valid] - x = s_centers - s_centers.mean() - x_var = (x * x).sum().clamp(min=1e-12) - - self.register_buffer("_valid_mask", valid) - self.register_buffer("_x_centered", x) - self.register_buffer("_x_var", x_var) - self.register_buffer("_s_centers", s_centers) - self._nbins = int(valid.sum().item()) + # |s|**2, NOT the scaler's _s_half_sq: B_equiv = -4*slope is defined against + # |s|**2, and (sin(theta)/lambda)**2 is a factor of 4 smaller. + s_sq = (scaler.s**2).sum(dim=1) + design = scaler._iso_design.to(s_sq.dtype) + x = s_sq - s_sq.mean() + x_var = (x * x).sum().clamp(min=1e-12) + w = (x @ design) / x_var + + self.register_buffer("_slope_weights", w) + self._nbins = int(scaler.nbins) self._scale = float(n_reflections) / max(self._nbins, 1) def _slope(self) -> torch.Tensor: - log_scale = self._scaler.log_scale[self._valid_mask] - y = log_scale - log_scale.mean() - return (self._x_centered * y).sum() / self._x_var + return self._slope_weights @ self._scaler.c_iso def forward(self) -> torch.Tensor: - """``slope² · N_ref / nbins`` on the ``log_scale`` vs ``|s|²`` fit.""" + """``slope² · N_ref / nbins`` on the log-scale vs ``|s|²`` fit.""" slope = self._slope() - return self._scale * slope ** 2 + return self._scale * slope**2 def stats(self) -> Dict[str, any]: """Loss, the fitted slope, and ``B_equiv = -4·slope`` -- the atomic B shift the @@ -86,10 +80,10 @@ def stats(self) -> Dict[str, any]: """ with torch.no_grad(): slope = self._slope().item() - log_scale = self._scaler.log_scale.detach() + log_scale = self._scaler.iso_log_scale() b_equiv = -4.0 * slope return { - "loss": stat(self._scale * slope ** 2, VERBOSITY_STANDARD), + "loss": stat(self._scale * slope**2, VERBOSITY_STANDARD), "slope": stat(slope, VERBOSITY_STANDARD), "B_equiv": stat(b_equiv, VERBOSITY_STANDARD), "log_scale_mean": stat(log_scale.mean().item(), VERBOSITY_DETAILED), diff --git a/torchref/scaling/collection_scaler.py b/torchref/scaling/collection_scaler.py index 5430a1f3..341df99a 100644 --- a/torchref/scaling/collection_scaler.py +++ b/torchref/scaling/collection_scaler.py @@ -1,8 +1,8 @@ """Joint scaler for multi-dataset / multi-model kinetic refinement. ``CollectionScaler`` extends ``ScalerBase`` to paired ``DatasetCollection`` + -``ModelCollection`` instances, sharing one set of scale parameters (log_scale, -U, k_sol, B_sol, phase) across **all** data-model pairs so no artificial scale +``ModelCollection`` instances, sharing one set of scale parameters (c_iso, +U and the solvent falloff) across **all** data-model pairs so no artificial scale difference corrupts the time-resolved difference signal. One solvent model is built per base model; a mixed model's solvent contribution is their linear combination at the same population fractions as the structural models. @@ -18,7 +18,7 @@ from torchref.base.targets.xray_likelihoods import complex_var_from_beta, rice_math from torchref.config import get_float_dtype from torchref.scaling.scaler_base import ScalerBase -from torchref.scaling.solvent import SolventModel +from torchref.scaling.solvent import SS_HALF_BOUNDS, SolventModel from torchref.utils.utils import ModuleReference if TYPE_CHECKING: @@ -30,7 +30,7 @@ class CollectionScaler(ScalerBase): """ Joint scaler for DatasetCollection + ModelCollection. - Shares scale parameters (log_scale, U, k_sol, B_sol, phase) across **all** + Shares scale parameters (c_iso, U and the solvent falloff) across **all** data-model pairs, and manages per-component solvent models so a mixed model's bulk solvent is the fraction-weighted sum of the component solvent SFs. The bin-wise B-factor correction is *not* set up by ``initialize()`` @@ -163,8 +163,12 @@ def _calc_initial_scale_joint(self): counts.scatter_add_(0, bins, ones) n_pairs += 1 - log_scale = scales / (counts + 1e-6) - self.log_scale = nn.Parameter(log_scale.detach()) + per_bin = scales / (counts + 1e-6) + with torch.no_grad(): + target = per_bin.detach()[self.bins.to(torch.int64)] + design = self._iso_design.to(target.dtype) + coeff = torch.linalg.lstsq(design, target.unsqueeze(1)).solution.squeeze(1) + self.c_iso = nn.Parameter(coeff.detach()) if self.verbose > 0: print( @@ -178,7 +182,7 @@ def _calc_initial_scale_joint(self): def _setup_component_solvent_models(self): """Create a SolventModel per base model; the first also becomes - ``self.solvent`` and owns the shared k_sol / B_sol / phase parameters. + ``self.solvent`` and owns the shared solvent parameters. The rest contribute only their mask FFT and are frozen. """ mc = self._model_collection @@ -191,7 +195,6 @@ def _setup_component_solvent_models(self): device=self.device, radius=1.1, k_solvent=0.35, - b_solvent=46.0, verbose=max(0, self.verbose - 1), ) sol.update_solvent() @@ -290,7 +293,6 @@ def refine_lbfgs_joint( lr: float = 1.0, max_iter: int = 200, history_size: int = 10, - scale_smoothness: float = 1000.0, verbose: bool = True, ) -> dict: """ @@ -309,9 +311,6 @@ def refine_lbfgs_joint( Maximum line-search iterations per step. history_size : int LBFGS history size. - scale_smoothness : float, default 1000.0 - Weight of the Tikhonov first-difference penalty on ``log_scale`` - (smoothness regularization across resolution bins). verbose : bool Print progress. @@ -334,7 +333,7 @@ def refine_lbfgs_joint( # variance (beta/epsilon). beta is estimated ONCE on each dataset's free # set from the currently-scaled |F_calc| and held detached during the fit, # as in the single-dataset ``ScalerBase.refine_lbfgs``; that variance is - # what stops the per-bin log_scale collapsing toward zero in weak shells. + # what stops the scale collapsing toward zero in weak shells. fcalc_cache = {} fractions_cache = {} beta_cache = {} @@ -421,16 +420,7 @@ def forward(self): if n > 0: total = total / n u_penalty = torch.sum(scaler_self.U**2) - # Smoothness (Tikhonov) penalty on the per-bin log_scale: the σ_A - # likelihood is nearly flat in the solvent-dominated lowest- - # resolution shells, where a free per-bin scale runs away to -inf - # (amplitudes -> 0, R blow-up). Penalising squared first - # differences ties under-constrained bins to their neighbours. - ls = scaler_self.log_scale - smooth_penalty = scale_smoothness * torch.sum( - (ls[1:] - ls[:-1]) ** 2 - ) - return total + u_penalty + smooth_penalty + return total + u_penalty state = LossState(device=self.device) state.register_target("scaler/joint", _CollectionScalerJointTarget()) @@ -528,18 +518,24 @@ def screen_solvent_params_joint(self, steps: int = 15): sol = self.solvent best_log_k = sol.log_k_solvent.clone() - best_b = sol.b_solvent.clone() + best_log_ss = sol.log_ss_half.clone() best_loss = float("inf") ksol_start = torch.log(torch.tensor(0.1, device=self.device)) ksol_end = torch.log(torch.tensor(0.6, device=self.device)) + ss_lo, ss_hi = SS_HALF_BOUNDS for log_k in torch.linspace( ksol_start, ksol_end, steps=steps, device=self.device ): - for b in torch.linspace(30.0, 100.0, steps=steps, device=self.device): + for log_ss in torch.linspace( + float(torch.log(torch.tensor(ss_lo))), + float(torch.log(torch.tensor(ss_hi))), + steps=steps, + device=self.device, + ): sol.log_k_solvent.data = log_k.to(dtype=sol.log_k_solvent.dtype) - sol.b_solvent.data = b.to(dtype=sol.b_solvent.dtype) + sol.log_ss_half.data = log_ss.to(dtype=sol.log_ss_half.dtype) total = 0.0 for fc, fracs, fobs, sigma, work_mask in pairs: @@ -554,16 +550,17 @@ def screen_solvent_params_joint(self, steps: int = 15): if total < best_loss: best_loss = total best_log_k = log_k.clone() - best_b = b.clone() + best_log_ss = log_ss.clone() sol.log_k_solvent.data = best_log_k.to(dtype=sol.log_k_solvent.dtype) - sol.b_solvent.data = best_b.to(dtype=sol.b_solvent.dtype) + sol.log_ss_half.data = best_log_ss.to(dtype=sol.log_ss_half.dtype) if self.verbose > 0: k_val = torch.exp(best_log_k).item() + d_half = 1.0 / (2.0 * torch.exp(best_log_ss).sqrt().item()) print( f"Joint solvent screening: k_sol={k_val:.4f}, " - f"B_sol={best_b.item():.1f}, NLL={best_loss:.4f}" + f"d_half={d_half:.2f} A, NLL={best_loss:.4f}" ) # ------------------------------------------------------------------ diff --git a/torchref/scaling/scaler.py b/torchref/scaling/scaler.py index c592c5e8..5d10c204 100644 --- a/torchref/scaling/scaler.py +++ b/torchref/scaling/scaler.py @@ -2,7 +2,7 @@ The full-featured :class:`Scaler`, which holds a reference to a ``Model`` and computes ``F_calc`` itself; see :class:`~torchref.scaling.ScalerBase` for the -model-independent version. ``initialize()`` enables the per-bin overall scale, +model-independent version. ``initialize()`` enables the isotropic overall scale, the anisotropy correction and the solvent model. The per-bin B-factor (``setup_bin_wise_bfactor``) is opt-in and never set up by ``initialize()``. """ @@ -39,7 +39,9 @@ class Scaler(ScalerBase): data : ReflectionData, optional ReflectionData object with observed data. nbins : int, default 20 - Number of resolution bins. + Number of resolution bins used to seed the scale. + n_iso_coeff : int, default 6 + Number of Chebyshev terms in the isotropic scale. verbose : int, default 1 Verbosity level. device : torch.device, default: configured device.current @@ -51,8 +53,9 @@ class Scaler(ScalerBase): Current computation device. nbins : int Number of resolution bins. - log_scale : torch.nn.Parameter - Per-bin log scale factors (created during ``initialize()``). + c_iso : torch.nn.Parameter + Chebyshev coefficients of the isotropic log scale (created during + ``initialize()``). U : torch.nn.Parameter Anisotropic scaling parameters (created during ``initialize()``). solvent : SolventModel @@ -67,6 +70,7 @@ def __init__( model: Optional["Model"] = None, data: Optional[ReflectionData] = None, nbins: int = 20, + n_iso_coeff: int = 6, verbose: int = 1, device: Optional[torch.device] = None, ): @@ -83,7 +87,9 @@ def __init__( data : ReflectionData, optional ReflectionData object with observed data. nbins : int, default 20 - Number of resolution bins. + Number of resolution bins used to seed the scale. + n_iso_coeff : int, default 6 + Number of Chebyshev terms in the isotropic scale. verbose : int, default 1 Verbosity level. device : torch.device, optional @@ -99,6 +105,7 @@ def __init__( super(Scaler, self).__init__( data=data, nbins=nbins, + n_iso_coeff=n_iso_coeff, verbose=verbose, device=resolved_device, ) @@ -197,29 +204,12 @@ def calc_initial_scale(self, fcalc: torch.Tensor = None): Returns ------- torch.nn.Parameter - The log scale parameter for each resolution bin. + The Chebyshev coefficient parameter ``c_iso``. """ if fcalc is None: fcalc = self.compute_fcalc() return super().calc_initial_scale(fcalc) - def fit_anisotropy(self, fcalc: torch.Tensor = None, nsteps: int = 100): - """ - Fit anisotropic correction. - - If fcalc is not provided, computes it from the internal model. - - Parameters - ---------- - fcalc : torch.Tensor, optional - Calculated structure factors. If None, computed from model. - nsteps : int, default 100 - Number of optimization steps. - """ - if fcalc is None: - fcalc = self.compute_fcalc() - return super().fit_anisotropy(fcalc, nsteps=nsteps) - def setup_solvent(self): """ Setup solvent model using internal model. @@ -233,27 +223,11 @@ def setup_solvent(self): device=self.device, radius=1.1, k_solvent=0.35, - b_solvent=46.0, verbose=self.verbose, ) self.solvent.update_solvent() self._f_sol_raw = None # Invalidate cached raw solvent SFs - def fit_all_scales(self, fcalc: torch.Tensor = None): - """ - Fit all scale parameters. - - If fcalc is not provided, computes it from the internal model. - - Parameters - ---------- - fcalc : torch.Tensor, optional - Calculated structure factors. If None, computed from model. - """ - if fcalc is None: - fcalc = self.compute_fcalc() - return super().fit_all_scales(fcalc) - def screen_solvent_params( self, fcalc: torch.Tensor = None, diff --git a/torchref/scaling/scaler_base.py b/torchref/scaling/scaler_base.py index ed4de3e8..2bf65f89 100644 --- a/torchref/scaling/scaler_base.py +++ b/torchref/scaling/scaler_base.py @@ -25,6 +25,7 @@ from torchref.utils.debug_utils import DebugMixin from torchref.utils.device_mixin import DeviceMixin from torchref.utils.device_resolution import resolve_device +from torchref.scaling.solvent import SS_HALF_BOUNDS from torchref.utils.utils import ModuleReference if TYPE_CHECKING: @@ -37,12 +38,16 @@ #: scale fit and the body refinement evaluate the same likelihood code, and differ only in #: which row they pick. Neither row may centre on ``alpha`` -- see #: :meth:`ScalerBase.refine_lbfgs`. -SCALE_TARGETS = ("nll", "ml_noalpha") +SCALE_TARGETS = ("nll", "ml_noalpha", "ls") #: The default scale-fit objective, defined ONCE: the constructors, the #: ``getattr`` fallbacks and the CLI's ``default=``/``choices=`` must all read it #: from here rather than repeating the string. -DEFAULT_SCALE_TARGET = "nll" +#: +#: ``ls`` weights every reflection equally, matching how R itself weights them; ``nll`` +#: divides each residual by ``sigma**2`` and so up-weights weak reflections relative to +#: their contribution to R. +DEFAULT_SCALE_TARGET = "ls" class ScalerBase(DeviceMixin, DebugMixin, nn.Module): @@ -50,17 +55,28 @@ class ScalerBase(DeviceMixin, DebugMixin, nn.Module): Base scaler class for crystallographic scaling without model dependency. Construct either fully (``ScalerBase(data=..., nbins=20)`` then ``initialize(fcalc)``) - or empty (``ScalerBase()`` then ``load_state_dict``). Note that ``log_scale``, ``U`` + or empty (``ScalerBase()`` then ``load_state_dict``). Note that ``c_iso``, ``U`` and ``solvent`` do **not** exist until ``initialize()`` / ``set_solvent_model()`` runs -- :meth:`forward` tests for each with ``hasattr`` and silently skips the missing ones, so an un-initialized scaler is an identity transform rather than an error. + The isotropic scale is a Chebyshev polynomial in ``sqrt(s_half_sq)``, evaluated per + reflection: ``k_iso = exp(sum_i c_iso[i] T_i(u))``. Every reflection contributes to + every coefficient with a continuous weight, so there are no bin boundaries and nothing + changes discontinuously when a reflection moves between shells. Bins survive only as + the device that seeds ``c_iso`` (:meth:`calc_initial_scale`) and as the sizing oracle + for :meth:`forward`'s masking test. + Parameters ---------- data : ReflectionData, optional ReflectionData object with observed data. nbins : int, default 20 - Number of resolution bins. Overwritten by the binner's actual count. + Number of resolution bins used to seed the scale and to size the per-reflection + masking test. Overwritten by the binner's actual count. + n_iso_coeff : int, default 6 + Number of Chebyshev terms in the isotropic scale. ``1`` collapses it to a single + global constant, since ``T_0 = 1``. verbose : int, default 1 Verbosity level. device : torch.device, optional @@ -69,20 +85,24 @@ class ScalerBase(DeviceMixin, DebugMixin, nn.Module): Attributes ---------- - log_scale : torch.nn.Parameter - Per-bin log scale factors. + c_iso : torch.nn.Parameter + Chebyshev coefficients of the isotropic log scale. U : torch.nn.Parameter Anisotropic scaling parameters, as the 6 unique components. solvent : SolventModel Bulk-solvent model. cell, bins, s Cell, per-reflection bin indices and scattering vectors, from the data. + _iso_design : torch.Tensor + ``(N_reflections, n_iso_coeff)`` Chebyshev design matrix for ``c_iso``. Derived + from the reflection set, so it is rebuilt rather than serialised. """ def __init__( self, data: Optional["ReflectionData"] = None, nbins: int = 20, + n_iso_coeff: int = 6, verbose: int = 1, device: Optional[torch.device] = None, ): @@ -92,6 +112,7 @@ def __init__( self.device = resolve_device(data, device=device) self.verbose = verbose self.nbins = nbins + self.n_iso_coeff = n_iso_coeff # Empty shell: configuration only, ready for load_state_dict(). if data is None: @@ -100,6 +121,10 @@ def __init__( self.register_buffer("s", None) self.register_buffer("bins", None) self.register_buffer("_s_half_sq", None) + # Non-persistent: derived from ``_s_half_sq`` and valid only for THIS + # reflection set, so a checkpoint rebuilds it rather than carrying a design + # matrix belonging to different data. + self.register_buffer("_iso_design", None, persistent=False) self._f_sol_raw = None return @@ -111,12 +136,41 @@ def __init__( self.register_buffer("s", s) # Precompute (sin(θ)/λ)^2 for B-factor damping — avoids recomputing per call self.register_buffer("_s_half_sq", (torch.norm(s, dim=1) / 2.0) ** 2) + self.register_buffer("_iso_design", self._build_iso_design(), persistent=False) self._f_sol_raw = None bins, self.nbins = self._data.get_bins(self.nbins) self.register_buffer("bins", bins) if self.verbose > 0: print(f"Initialized ScalerBase with {self.nbins} bins.") + def _build_iso_design(self) -> torch.Tensor: + """``(N, n_iso_coeff)`` Chebyshev design matrix for the isotropic scale. + + The abscissa is ``sqrt(s_half_sq)``, i.e. ``sin(theta)/lambda``, mapped onto + ``[-1, 1]``. That coordinate rather than ``s**2`` because the modulation is gentle + through the bulk of the resolution range but has real structure in the first few + percent of ``s**2``; a basis uniform in ``s**2`` spends nearly all its resolution + where nothing happens. + """ + x = torch.sqrt(self._s_half_sq.clamp(min=0)) + lo, hi = x.min(), x.max() + u = (2 * (x - lo) / (hi - lo).clamp(min=1e-12) - 1).clamp(-1.0, 1.0) + cols = [torch.ones_like(u), u] + for _ in range(2, self.n_iso_coeff): + cols.append(2 * u * cols[-1] - cols[-2]) # Chebyshev recurrence + return torch.stack(cols[: self.n_iso_coeff], dim=1) + + def iso_log_scale(self, design: Optional[torch.Tensor] = None) -> torch.Tensor: + """Per-reflection isotropic log scale ``design @ c_iso``, clamped to ``[-10, 10]``. + + The clamp is per reflection, not on the coefficients: a polynomial is unbounded at + the ends of its range, so without it a single extreme reflection can carry an + arbitrarily large scale. + """ + if design is None: + design = self._iso_design + return (design @ self.c_iso).clamp(min=-10.0, max=10.0) + def set_data(self, data: "ReflectionData"): """ Reconnect a data object after an empty init or a ``load_state_dict``. @@ -138,13 +192,17 @@ def set_data(self, data: "ReflectionData"): s = get_scattering_vectors(data.hkl, data.cell) self.register_buffer("s", s) self.register_buffer("_s_half_sq", (torch.norm(s, dim=1) / 2.0) ** 2) + if getattr(self, "_iso_design", None) is None and self._s_half_sq is not None: + self.register_buffer( + "_iso_design", self._build_iso_design(), persistent=False + ) self._f_sol_raw = None if self.bins is None and data.hkl is not None: bins, self.nbins = self._data.get_bins(self.nbins) self.register_buffer("bins", bins) def initialize(self, fcalc: torch.Tensor): - """Create ``log_scale`` and ``U`` from the complex ``fcalc``.""" + """Create ``c_iso`` and ``U`` from the complex ``fcalc``.""" self.calc_initial_scale(fcalc) self.setup_anisotropy_correction() @@ -155,10 +213,12 @@ def hkl(self): def calc_initial_scale(self, fcalc: torch.Tensor): """ - Per-bin initial scale from the observed/calculated amplitude ratio. + Seed ``c_iso`` from the closed-form observed/calculated amplitude ratio. - Fitted on the **work set only**, excluding negative-intensity reflections whose - French-Wilson F values are biased. Creates and returns ``self.log_scale``. + The ratio is evaluated per bin, then projected onto the Chebyshev basis by least + squares, so the fit starts from the same scale curve the binned model would have + started from. Fitted on the **work set only**, excluding negative-intensity + reflections whose French-Wilson F values are biased. Parameters ---------- @@ -168,7 +228,7 @@ def calc_initial_scale(self, fcalc: torch.Tensor): Returns ------- torch.nn.Parameter - The per-bin log scale parameter. + The Chebyshev coefficient parameter ``c_iso``. """ fobs = self._data.get_corrected_data()[0] if self.verbose > 0: @@ -205,8 +265,12 @@ def calc_initial_scale(self, fcalc: torch.Tensor): "Initial scale factors per bin:", initial_log_scale.detach().cpu().numpy(), ) - self.log_scale = nn.Parameter(initial_log_scale.detach().to(self.device)) - return self.log_scale + with torch.no_grad(): + target = initial_log_scale.detach().to(self.device)[self.bins.to(torch.int64)] + design = self._iso_design.to(target.dtype) + coeff = torch.linalg.lstsq(design, target.unsqueeze(1)).solution.squeeze(1) + self.c_iso = nn.Parameter(coeff.detach().to(self.device)) + return self.c_iso def setup_anisotropy_correction(self): """Initialize anisotropic correction parameters.""" @@ -222,41 +286,6 @@ def anisotropy_correction(self): exp = -2 * torch.pi**2 * (sU * self.s).sum(dim=1) return torch.exp(exp.clamp(max=10.0, min=-10.0)) - def fit_anisotropy(self, fcalc: torch.Tensor, nsteps: int = 100): - """ - Adam-fit ``U`` and ``log_scale`` together against the work set. - - Creates ``U`` if absent. Requires ``log_scale`` to exist already. - - Parameters - ---------- - fcalc : torch.Tensor - Calculated structure factors (complex). - nsteps : int, default 100 - Number of optimization steps. - """ - if not hasattr(self, "U"): - self.U = nn.Parameter( - torch.normal(0, 0.01, (6,), dtype=get_float_dtype(), device=self.device) - ) - work = self._data.work - F_obs = work.F.to(get_float_dtype()).detach() - sigma_w = work.sigF.to(get_float_dtype()).detach() - fcalc = torch.abs(fcalc).to(get_float_dtype()).detach() - - optimizer = torch.optim.Adam([self.U, self.log_scale], lr=1e-1) - for i in range(nsteps): - optimizer.zero_grad() - scaled_fcalc = self.forward(fcalc) - loss = nll_xray(F_obs, work.select(scaled_fcalc), sigma_w) - - loss.backward() - optimizer.step() - if self.verbose > 0 and (i % 10 == 0 or i == nsteps - 1): - print( - f"Anisotropy fit iteration {i+1}/{nsteps}, Loss: {loss.item():.4f}" - ) - def set_solvent_model(self, solvent_model: "SolventModel") -> None: """ Attach a pre-configured :class:`SolventModel` and invalidate the ``F_sol`` cache. @@ -293,78 +322,15 @@ def setup_binwise_solvent_scale(self): torch.log(initial_kmask.clamp(min=1e-6) + 1e-6).to(self.device) ) - def fit_all_scales(self, fcalc: torch.Tensor): - """ - Adam-fit **every** scaler parameter on the work set, over three descending LRs. - - Raises ``ValueError`` if the NLL goes NaN. - - Parameters - ---------- - fcalc : torch.Tensor - Calculated structure factors (complex). - """ - work = self._data.work - F_obs = work.F.to(get_float_dtype()).detach() - sigma_w = work.sigF.to(get_float_dtype()).detach() - fcalc = fcalc.detach() - - for lr in [1e-1, 5e-2, 1e-2]: - optimizer = torch.optim.Adam(self.parameters(), lr=lr) - for i in range(20): - optimizer.zero_grad() - scaled_fcalc = self.forward(fcalc) - nll_loss = nll_xray(F_obs, work.select(scaled_fcalc), sigma_w) - if torch.isnan(nll_loss): - raise ValueError( - "NaN encountered in NLL loss during scale fitting." - ) - nll_log_loss_xray = nll_xray_lognormal( - F_obs, work.select(scaled_fcalc), sigma_w - ) - loss = nll_loss - loss.backward() - optimizer.step() - if self.verbose > 1: - print( - f"Solvent fit after step, Loss: {loss.item():.4f}, NLL: {nll_loss.item():.4f}, LogLoss: {nll_log_loss_xray.item():.4f}" - ) - - def fit_simple(self, fobs: torch.Tensor, fcalc: torch.Tensor) -> None: - """ - Set one global scale analytically: ``k = sum(|Fo||Fc|) / sum(|Fc|^2)``. - - For rigid-body refinement, where only an overall scale is wanted. Uses **all** - reflections passed, with no work/free split, and writes the single value into every - bin of ``log_scale`` (creating it if needed). + def get_scale(self) -> float: + """``exp`` of the reflection-mean isotropic log scale, or 1.0 if unscaled. - Parameters - ---------- - fobs : torch.Tensor - Observed structure factor amplitudes. - fcalc : torch.Tensor - Calculated structure factors (complex). + One number summarising a curve, for callers that want an overall magnitude; it is + not the scale applied to any particular reflection. """ - fcalc_amp = torch.abs(fcalc) - fobs_amp = torch.abs(fobs) - - numerator = torch.sum(fobs_amp * fcalc_amp) - denominator = torch.sum(fcalc_amp**2) - - scale = numerator / denominator.clamp(min=1e-10) - - if not hasattr(self, "log_scale") or self.log_scale is None: - self.log_scale = nn.Parameter( - torch.zeros(self.nbins, dtype=fobs.dtype, device=self.device) - ) - - with torch.no_grad(): - self.log_scale.fill_(torch.log(scale.clamp(min=1e-6))) - - def get_scale(self) -> float: - """``exp`` of the mean per-bin log scale, or 1.0 if unscaled. Not per-bin.""" - if hasattr(self, "log_scale") and self.log_scale is not None: - return torch.exp(self.log_scale.mean().clamp(-10, 10)).item() + if hasattr(self, "c_iso") and self.c_iso is not None: + with torch.no_grad(): + return torch.exp(self.iso_log_scale().mean()).item() return 1.0 def setup_bin_wise_bfactor(self): @@ -437,13 +403,16 @@ def screen_solvent_params( low_res_limit: float = 3.5, ): """ - Grid-search ``(k_sol, B_sol)`` and write the best pair into the solvent model. + Grid-search ``(k_sol, ss_half)`` and write the best pair into the solvent model. Mutates ``self.solvent`` in place (``.data`` assignment, so no gradient history) and - leaves the winning values behind; there is no restore. Restricting the fit to low - resolution keeps high-resolution reflections from pushing ``B_sol`` too low, since - the bulk solvent only contributes at low resolution. Falls back to all work - reflections if fewer than 100 pass ``low_res_limit``. + leaves the winning values behind; there is no restore. The falloff exponent ``n`` + is left at its current value -- it trades off against ``ss_half`` and a + three-dimensional grid costs ``steps**3`` forward passes for a parameter the + subsequent L-BFGS fit refines anyway. Restricting the fit to low resolution keeps + high-resolution reflections, where the solvent has already switched off, from + driving the falloff. Falls back to all work reflections if fewer than 100 pass + ``low_res_limit``. Parameters ---------- @@ -508,23 +477,27 @@ def screen_solvent_params( weights = weights / weights[low_res_mask].sum() best_log_k_solvent = self.solvent.log_k_solvent.clone() - best_b_solvent = self.solvent.b_solvent.clone() + best_log_ss_half = self.solvent.log_ss_half.clone() best_loss = float("inf") ksol_start = torch.log(torch.tensor(0.1, device=self.device)) ksol_end = torch.log(torch.tensor(0.6, device=self.device)) + ss_lo, ss_hi = SS_HALF_BOUNDS for log_k_solvent in torch.linspace( ksol_start, ksol_end, steps=steps, device=self.device ): - for b_solvent in torch.linspace( - 30.0, 100.0, steps=steps, device=self.device + for log_ss_half in torch.linspace( + float(torch.log(torch.tensor(ss_lo))), + float(torch.log(torch.tensor(ss_hi))), + steps=steps, + device=self.device, ): self.solvent.log_k_solvent.data = log_k_solvent.to( dtype=self.solvent.log_k_solvent.dtype ) - self.solvent.b_solvent.data = b_solvent.to( - dtype=self.solvent.b_solvent.dtype + self.solvent.log_ss_half.data = log_ss_half.to( + dtype=self.solvent.log_ss_half.dtype ) scaled_fcalc = self.forward(fcalc) @@ -547,20 +520,21 @@ def screen_solvent_params( if nll_loss.item() < best_loss: best_loss = nll_loss.item() best_log_k_solvent = log_k_solvent.clone() - best_b_solvent = b_solvent.clone() + best_log_ss_half = log_ss_half.clone() self.solvent.log_k_solvent.data = best_log_k_solvent.to( dtype=self.solvent.log_k_solvent.dtype ) - self.solvent.b_solvent.data = best_b_solvent.to( - dtype=self.solvent.b_solvent.dtype + self.solvent.log_ss_half.data = best_log_ss_half.to( + dtype=self.solvent.log_ss_half.dtype ) if self.verbose > 0: k_sol = torch.exp(best_log_k_solvent).item() + d_half = 1.0 / (2.0 * torch.exp(best_log_ss_half).sqrt().item()) print( - f"Optimal solvent parameters found: k_sol={k_sol:.4f}, B_sol={best_b_solvent.item():.1f}, " - f"NLL Loss={best_loss:.4f}" + f"Optimal solvent parameters found: k_sol={k_sol:.4f}, " + f"d_half={d_half:.2f} A, NLL Loss={best_loss:.4f}" ) def refine_lbfgs( @@ -593,21 +567,21 @@ def refine_lbfgs( Number of previous gradients kept for the Hessian approximation. verbose : bool, default True Print progress; gated by ``self.verbose`` as well. - scale_target : {'nll', 'ml_noalpha'}, default 'nll' + scale_target : str, default :data:`DEFAULT_SCALE_TARGET` Which :data:`~torchref.refinement.targets.xray._specs.XRAY_TARGETS` row to - minimise, restricted to :data:`SCALE_TARGETS`. ``'nll'`` is the - sigma_obs-weighted Gaussian; ``'ml_noalpha'`` is the Read-MLF sigma_A likelihood. + minimise, restricted to :data:`SCALE_TARGETS`. ``'ls'`` is unit-weight least + squares; ``'nll'`` is the sigma_obs-weighted Gaussian; ``'ml_noalpha'`` is the + Read-MLF sigma_A likelihood. Only rows whose likelihood centres on ``|F_calc|`` are admissible: ``alpha`` is degenerate with the scale being fitted, so an ``alpha*|F_calc|``-centred row (``ml``, ``ml_full``) drives the scale to absorb ``1/alpha`` and inflates every R-factor computed from ``k*|F_calc|``. - **Hazard on the default path**: a least-squares fit collapses the per-bin scale - toward 0 in shells where ``F_obs`` is noise-dominated and uncorrelated with - ``F_calc``, which blows up R. Inspect the per-bin ``log_scale`` spread and the - post-scaling R-factors, and switch to ``'ml_noalpha'`` -- whose ``beta`` absorbs - the mismatch instead -- if outer or low-resolution shells collapse. + A least-squares fit can drive the per-bin scale toward 0 in shells where + ``F_obs`` is noise-dominated and uncorrelated with ``F_calc``, which blows up + R. The diagnostic is ``min(k)/median(k)`` over the per-bin scales; + ``'ml_noalpha'`` absorbs such a mismatch into ``beta`` instead. Returns ------- @@ -648,13 +622,30 @@ def refine_lbfgs( ) scaler_self = self + # One constant applied to EVERY term below, so the objective is an exact + # rescaling: same minimiser, same gradient direction, same relative weight + # between the likelihood and the U penalty. + # + # ``torch.optim.LBFGS`` converges on ABSOLUTE tolerances (``tolerance_grad``, + # ``tolerance_change``), so the objective has to be O(1) for them to mean + # anything. Unnormalised it carries the data's own magnitude -- a large work set + # under unit weights reaches ~1e9, where the float32 ulp of the loss exceeds the + # decrease the line search is trying to resolve, and the walk leaves ``U`` far + # enough out that ``sum(U**2)`` overflows. Dividing by ``sum(F_obs**2)`` makes the + # loss dimensionless and O(R**2) whatever the structure size or data scale. + # + # The reduction is float64 for precision over ~1e5 terms; the constant is fixed + # for the whole fit. + _f_obs_work = self._data.work.F.detach() + _norm = float(1.0 / _f_obs_work.double().pow(2).sum().clamp(min=1e-30)) + class _ScalerXrayTarget(nn.Module): """The registry row, closed over the detached ``fcalc``.""" name = "scaler/xray" def forward(self): - return scale_xray(fcalc=fcalc) + return scale_xray(fcalc=fcalc) * _norm def maintenance(self): """Forward the hook so sigma_A rows drop their ``beta`` cache after a step @@ -674,7 +665,7 @@ class _ScalerUPenalty(nn.Module): name = "scaler/u_penalty" def forward(self): - return torch.sum(scaler_self.U**2) + return torch.sum(scaler_self.U**2) * _norm state = LossState(device=self.device) state.register_target("scaler/xray", _ScalerXrayTarget()) @@ -827,17 +818,15 @@ def forward( kmask_per_refl = gather_with_index_add(kmask, bins_to_use) f_sol = kmask_per_refl * f_sol_raw else: - # k_sol * exp(i*phase) * exp(-B*s^2) * f_mask + # k_sol * exp(i*phase) * falloff(ss) * f_mask, with the falloff taken + # from the solvent model itself so this path and ``SolventModel.forward`` + # cannot drift apart. sol = self.solvent - k_sol = torch.exp(sol.log_k_solvent.clamp(min=-10.0, max=10.0)) + k_sol = sol.k_solvent() s_half_sq = ( self._s_half_sq[mask] if apply_internal_mask else self._s_half_sq ) - b_factor = torch.exp( - (-sol.b_solvent.clamp(min=-500.0, max=500.0) * s_half_sq).clamp( - min=-10.0, max=10.0 - ) - ) + b_factor = sol.damping(s_half_sq) if sol.optimize_phase: # A bare ``1j`` would promote the product to complex128. j = torch.tensor(1j, dtype=get_complex_dtype(), device=self.device) @@ -846,13 +835,9 @@ def forward( else: f_sol = torch.tensor(0.0, device=self.device, dtype=fcalc.dtype) - if hasattr(self, "log_scale") and self.log_scale is not None: - bins_to_use = self.bins[mask] if apply_internal_mask else self.bins - K_overall = torch.exp( - gather_with_index_add(self.log_scale, bins_to_use).clamp( - min=-10.0, max=10.0 - ) - ) + if hasattr(self, "c_iso") and self.c_iso is not None: + design = self._iso_design[mask] if apply_internal_mask else self._iso_design + K_overall = torch.exp(self.iso_log_scale(design)) else: K_overall = torch.tensor(1.0, device=self.device, dtype=fcalc.dtype) @@ -877,7 +862,8 @@ def forward( def state_dict(self, destination=None, prefix="", keep_vars=False): """ - Buffers and parameters, plus ``nbins``/``verbose`` and the solvent sub-state. + Buffers and parameters, plus ``nbins``/``n_iso_coeff``/``verbose`` and the solvent + sub-state. The **data reference is not saved** -- reattach it with :meth:`set_data` after loading. @@ -896,6 +882,7 @@ def state_dict(self, destination=None, prefix="", keep_vars=False): ) state[prefix + "nbins"] = self.nbins + state[prefix + "n_iso_coeff"] = self.n_iso_coeff state[prefix + "verbose"] = self.verbose if hasattr(self, "solvent") and self.solvent is not None: @@ -918,6 +905,7 @@ def load_state_dict(self, state_dict, strict=True): Whether to strictly enforce that keys match. """ self.nbins = state_dict.pop("nbins", 20) + self.n_iso_coeff = state_dict.pop("n_iso_coeff", 6) self.verbose = state_dict.pop("verbose", 1) # Legacy state dicts may contain a "frozen" entry; drop it silently. state_dict.pop("frozen", None) diff --git a/torchref/scaling/solvent.py b/torchref/scaling/solvent.py index d1c79913..325dc1f6 100644 --- a/torchref/scaling/solvent.py +++ b/torchref/scaling/solvent.py @@ -10,21 +10,102 @@ get_scattering_vectors, ifft, ) -from torchref.base.electron_density.main import _get_radius_offsets from torchref.config import get_float_dtype from torchref.utils.debug_utils import DebugMixin from torchref.utils.device_mixin import DeviceMixin from torchref.utils.device_resolution import resolve_device from torchref.utils.utils import ModuleReference, TensorDict +#: ``ln 2``, so ``s_half_sq = ss_half`` halves the solvent term by construction. +_LN2 = 0.6931471805599453 + +#: Bounds on the solvent falloff, applied as clamps inside :meth:`SolventModel.damping`. +#: ``ss_half`` is the half-point in ``(sin(theta)/lambda)**2``, quoted here as the +#: resolution ``d_half = 1 / (2 sqrt(ss_half))``; the range spans well beyond the observed +#: spread while excluding the degenerate slow-power-law fits the unbounded form can reach. +#: ``n = 1`` is exactly a Debye-Waller factor with ``B = ln2 / ss_half``, so the shipped +#: exponential is nested at the lower bound rather than merely approximated. +SS_HALF_BOUNDS = (0.0025, 0.04) # d_half 10.0 .. 2.5 A +N_EXP_BOUNDS = (1.0, 20.0) + +#: Cache for :func:`_voxel_offsets_within`, keyed on the arguments that determine the +#: result. Solvent masks are rebuilt every time coordinates move, always on the same grid +#: and cell, so the offsets are computed once per refinement rather than once per call. +_OFFSET_CACHE = {} + + +def _voxel_offsets_within(radius, grid_dims, frac, device, strict=False): + """Integer voxel offsets whose Cartesian displacement is within ``radius``. + + Offset ``o`` displaces a point by the Cartesian vector ``frac @ (o / grid_dims)``, so + its length follows from the cell's metric tensor and the enumerated set is a true + Cartesian ball in **any** unit cell, not only orthogonal ones. The per-axis search box + comes from the reciprocal basis: ``|o_i| <= grid_dims_i * |a*_i| * radius``. + + Parameters + ---------- + radius : float + Cutoff in Angstrom. + grid_dims : torch.Tensor + Grid dimensions ``(N0, N1, N2)``, integer. + frac : torch.Tensor + Fractional-to-Cartesian matrix, shape ``(3, 3)``. + device : torch.device + Device the offsets are built on. + strict : bool, default False + Use ``<`` rather than ``<=`` against ``radius``. + + Returns + ------- + torch.Tensor + Offsets, shape ``(R, 3)``, integer. + """ + key = ( + float(radius), + tuple(int(v) for v in grid_dims.tolist()), + tuple(round(float(v), 10) for v in frac.flatten().tolist()), + str(device), + bool(strict), + ) + cached = _OFFSET_CACHE.get(key) + if cached is not None: + return cached + + dtype = get_float_dtype() + frac = frac.to(device=device, dtype=dtype) + N = grid_dims.to(device=device, dtype=dtype) + # Rows of the Cartesian-to-fractional matrix are the reciprocal basis vectors. + recip_norms = torch.linalg.inv(frac).norm(dim=1) + bounds = torch.ceil(N * recip_norms * radius).long() + + ranges = [ + torch.arange(-int(b), int(b) + 1, device=device) for b in bounds.tolist() + ] + offsets = torch.stack(torch.meshgrid(*ranges, indexing="ij"), dim=-1).reshape(-1, 3) + + disp = (offsets.to(dtype) / N) @ frac.T + dist_sq = (disp**2).sum(-1) + r_sq = radius**2 + keep = dist_sq < r_sq if strict else dist_sq <= r_sq + local_offsets = offsets[keep] + + _OFFSET_CACHE[key] = local_offsets + return local_offsets + class SolventModel(DeviceMixin, DebugMixin, nn.Module): """ Bulk-solvent contribution to structure factors, Phenix-style. - Constructed either with a model (``SolventModel(model, k_solvent=0.35, - b_solvent=46.0)`` -- the values ``Scaler`` injects; the bare-constructor - defaults are 1.1 / 50.0) or empty, as a shell for ``load_state_dict``. + Constructed either with a model (``SolventModel(model, k_solvent=0.35)`` -- the value + ``Scaler`` injects; the bare-constructor default is 1.1) or empty, as a shell for + ``load_state_dict``. + + The solvent falls off as ``k_sol * exp(-ln2 * (ss / ss_half)**n)`` in + ``ss = (sin(theta)/lambda)**2``. ``ss_half`` is where the term is halved and ``n`` + how sharply it switches off; ``n = 1`` is exactly ``exp(-B ss)`` with + ``B = ln2 / ss_half``, so a Debye-Waller solvent is a special case rather than a + different model. Both are clamped to :data:`SS_HALF_BOUNDS` / :data:`N_EXP_BOUNDS`. Attributes ---------- @@ -41,8 +122,9 @@ class SolventModel(DeviceMixin, DebugMixin, nn.Module): Probe radius for dilation and radius for the erosion step (Å). optimize_phase : bool Whether the phase offset is refined. - log_k_solvent, b_solvent : torch.nn.Parameter - Log solvent scattering scale and solvent B-factor. + log_k_solvent, log_ss_half, log_n_exp : torch.nn.Parameter + Log solvent scattering scale, and the logs of the falloff half-point and + exponent. Refined in log space so each stays positive. phase_offset : torch.nn.Parameter or buffer Phase offset in radians: a trainable parameter when ``optimize_phase=True``, otherwise a buffer fixed at 0.0. @@ -53,9 +135,9 @@ def __init__( model=None, radius=1.1, k_solvent=1.1, - b_solvent=50.0, + d_half=3.59, + n_exp=5.0, erosion_radius=0.9, - transition=None, optimize_phase=True, initial_phase_offset=0.0, verbose=1, @@ -76,13 +158,13 @@ def __init__( Probe radius in Angstroms for dilation (water radius). k_solvent : float, default 1.1 Solvent scattering scale factor. - b_solvent : float, default 50.0 - Solvent B-factor. + d_half : float, default 3.59 + Resolution (A) at which the solvent term is halved; stored as + ``ss_half = 1 / (4 d_half**2)``. + n_exp : float, default 5.0 + Falloff exponent. ``1.0`` reduces the form to ``exp(-B ss)``. erosion_radius : float, default 0.9 Radius in Angstroms for erosion step. - transition : float, optional - Gaussian smoothing sigma for mask edges, in voxels (default - ``radius/4``); smoothing avoids ringing artifacts. optimize_phase : bool, default True Whether to optimize phase offset parameter. initial_phase_offset : float, default 0.0 @@ -114,16 +196,13 @@ def __init__( if model is None: self.model = None self.max_radius_angstrom = None - self.transition = transition # Register parameters with default values (will be overwritten by load_state_dict) self.log_k_solvent = nn.Parameter( torch.log( torch.tensor(k_solvent, dtype=self.float_type, device=self.device) ) ) - self.b_solvent = nn.Parameter( - torch.tensor(b_solvent, dtype=self.float_type, device=self.device) - ) + self._init_falloff(d_half, n_exp) if self.optimize_phase: self.phase_offset = nn.Parameter( torch.tensor( @@ -158,21 +237,8 @@ def __init__( ) else: k_solvent = k_solvent.to(dtype=self.float_type, device=self.device) - if not isinstance(b_solvent, torch.Tensor): - b_solvent = torch.tensor( - b_solvent, dtype=self.float_type, device=self.device - ) - else: - b_solvent = b_solvent.to(dtype=self.float_type, device=self.device) self.log_k_solvent = nn.Parameter(torch.log(k_solvent)) - self.b_solvent = nn.Parameter(b_solvent) - - # Transition width for Gaussian smoothing (in voxels) - if transition is not None: - self.transition = transition - else: - # Default: use a fraction of solvent_radius converted to voxels - self.transition = self.model.get_radius(radius) / 4.0 + self._init_falloff(d_half, n_exp) # Phase offset parameter to align solvent phases with protein phases # This is critical because FFT of a mask gives arbitrary phases @@ -190,6 +256,70 @@ def __init__( ) self._cache = TensorDict() + def _init_falloff(self, d_half, n_exp): + """Register ``log_ss_half`` / ``log_n_exp`` from a resolution and an exponent.""" + ss_half = 1.0 / (4.0 * float(d_half) ** 2) + for name, value in (("log_ss_half", ss_half), ("log_n_exp", float(n_exp))): + setattr( + self, + name, + nn.Parameter( + torch.log( + torch.tensor( + value, dtype=self.float_type, device=self.device + ) + ) + ), + ) + + def ss_half(self) -> torch.Tensor: + """Half-point of the solvent falloff in ``(sin(theta)/lambda)**2``, clamped.""" + return torch.exp(self.log_ss_half).clamp(*SS_HALF_BOUNDS) + + def n_exp(self) -> torch.Tensor: + """Falloff exponent, clamped. ``1`` is a Debye-Waller factor.""" + return torch.exp(self.log_n_exp).clamp(*N_EXP_BOUNDS) + + def k_solvent(self) -> torch.Tensor: + """Solvent scattering scale.""" + return torch.exp(self.log_k_solvent.clamp(min=-10.0, max=10.0)) + + def damping(self, s_half_sq: torch.Tensor) -> torch.Tensor: + """``exp(-ln2 * (ss / ss_half)**n)`` at ``ss = (sin(theta)/lambda)**2``. + + Parameters + ---------- + s_half_sq : torch.Tensor + ``(sin(theta)/lambda)**2`` per reflection. + + Returns + ------- + torch.Tensor + Falloff factor in ``[0, 1]``, same shape as the input. + """ + ratio = (s_half_sq / self.ss_half()).clamp(min=1e-12) + return torch.exp((-_LN2 * ratio.pow(self.n_exp())).clamp(min=-30.0)) + + def b_solvent_equivalent(self, s_half_sq: torch.Tensor) -> float: + """The single ``B`` whose ``exp(-B ss)`` best matches this falloff. + + A reporting quantity: PDB ``REMARK 3`` and mmCIF have a field for a solvent + B-factor and the fitted form has none, so it is back-fitted by least squares on + ``log(damping)`` over the reflections actually present, weighted by the damping + itself so the fit follows the range where the solvent contributes. + """ + with torch.no_grad(): + ss = s_half_sq.detach().flatten() + ss = ss[ss > 0] + if ss.numel() == 0: + return 0.0 + d = self.damping(ss) + w = d.clamp(min=1e-6) + # log d = -B ss, through the origin: B = -sum(w ss log d) / sum(w ss^2) + num = (w * ss * torch.log(d.clamp(min=1e-30))).sum() + den = (w * ss * ss).sum().clamp(min=1e-30) + return float(-num / den) + def get_solvent_mask(self): """ Generate solvent mask following Phenix's three-step process. @@ -244,16 +374,33 @@ def get_solvent_mask(self): # grids, where the SF code's 1024 would OOM (denser intermediates). ATOM_CHUNK = 256 - voxel_size = self.real_space_grid[3, 3, 3] - self.real_space_grid[2, 2, 2] - local_offsets = _get_radius_offsets( - voxel_size, self.max_radius_angstrom, device - ) # (R, 3) int — sphere voxels relative to atom center - grid_dims = torch.tensor(grid_shape, dtype=torch.long, device=device) grid_shape_float = grid_dims.float() inv_grid = 1.0 / grid_shape_float G = frac.T @ frac # metric tensor: r²_cart = diff_frac · G · diff_frac + # The offsets are enumerated around the atom's nearest grid NODE, but every + # distance is then measured from the atom's true position, which sits up to + # half a voxel diagonal away. The search radius carries that slack so the + # enumerated set is a guaranteed superset of the voxels the classification + # below can accept; without it, voxels genuinely inside + # `vdw + solvent_radius` of the atom fall outside the ball around the node, + # are never tested, and default to bulk solvent. + signs = torch.tensor( + [[1.0, 1.0, 1.0], [1.0, 1.0, -1.0], [1.0, -1.0, 1.0], [-1.0, 1.0, 1.0]], + dtype=frac.dtype, + device=device, + ) + half_voxel_diagonal = 0.5 * float( + ((signs * inv_grid.to(frac.dtype)) @ frac.T).norm(dim=1).max() + ) + local_offsets = _voxel_offsets_within( + self.max_radius_angstrom + half_voxel_diagonal, + grid_dims, + frac, + device, + ) # (R, 3) int — candidate voxels relative to the atom's grid node + xyz_frac = xyz @ inv_frac.T # (N, 3) xyz_frac_wrapped = xyz_frac % 1.0 center_idx = torch.round( @@ -352,27 +499,25 @@ def get_solvent_mask(self): ) # --- Step 3: erosion --- - # A boundary voxel becomes solvent iff any voxel within + # A boundary voxel becomes solvent iff any voxel strictly within # `erosion_radius` is bulk solvent: dilate `definitely_solvent` by the # spherical structuring element, then intersect with `boundary_mask`. - # Two paths, dispatched on device, producing the exact same mask: - # CPU roll-OR over the cached sphere offsets is bandwidth-bound and - # beats conv3d on a small kernel, while on GPU conv3d fuses to one + # Two paths, dispatched on device, producing the exact same mask because + # both are built from the same offset list: CPU roll-OR is bandwidth-bound + # and beats conv3d on a small kernel, while on GPU conv3d fuses to one # launch instead of one kernel per offset. + sphere_offsets = _voxel_offsets_within( + self.erosion_radius, grid_dims, frac, device, strict=True + ) # (R, 3) int if torch.device(device).type == "cuda": - half_k = int(torch.ceil(self.erosion_radius / voxel_size.min()).item()) + half_k = int(sphere_offsets.abs().max().item()) K = 2 * half_k + 1 - offs = torch.arange( - -half_k, half_k + 1, device=device, dtype=voxel_size.dtype - ) - dx = offs.view(-1, 1, 1) * voxel_size[0] - dy = offs.view(1, -1, 1) * voxel_size[1] - dz = offs.view(1, 1, -1) * voxel_size[2] - kernel = ( - ((dx * dx + dy * dy + dz * dz) <= self.erosion_radius**2) - .to(self.log_k_solvent.dtype) - .view(1, 1, K, K, K) + kernel = torch.zeros( + (K, K, K), dtype=self.log_k_solvent.dtype, device=device ) + ki = sphere_offsets + half_k + kernel[ki[:, 0], ki[:, 1], ki[:, 2]] = 1.0 + kernel = kernel.view(1, 1, K, K, K) solv_float = definitely_solvent.to(self.log_k_solvent.dtype).view( 1, 1, *grid_shape ) @@ -381,9 +526,6 @@ def get_solvent_mask(self): dilated_solvent = neighbour_count.squeeze(0).squeeze(0) > 0.5 del solv_float, solv_padded, neighbour_count else: - sphere_offsets = _get_radius_offsets( - voxel_size, self.erosion_radius, device - ) # (R, 3) int dilated_solvent = torch.zeros_like(definitely_solvent) for i in range(sphere_offsets.shape[0]): dz_, dy_, dx_ = sphere_offsets[i].tolist() @@ -397,7 +539,6 @@ def get_solvent_mask(self): protein_with_boundary = (protein_mask | boundary_mask) & (~voxels_to_flip) solvent_mask = ~protein_with_boundary - self.register_buffer("protein_mask", protein_with_boundary) self.register_buffer("solvent_mask", solvent_mask) if self.verbose > 1: @@ -414,58 +555,12 @@ def get_solvent_mask(self): return self.solvent_mask def update_solvent(self): - """Rebuild the solvent mask and its smoothed form from current coordinates. + """Rebuild the solvent mask from current coordinates. Call after the model's coordinates change; ``Scaler`` also has to drop its cached ``_f_sol_raw`` for the new mask to reach ``F_calc``. """ self.get_solvent_mask() - self.smooth_solvent_mask() - - def smooth_solvent_mask(self): - """Gaussian-smooth ``solvent_mask`` (σ = ``self.transition`` voxels). - - Registers and returns the ``mask_smoothed`` buffer; requires - :meth:`get_solvent_mask` to have run. - """ - if not hasattr(self, "solvent_mask"): - raise ValueError( - "Solvent mask not computed. Call get_solvent_mask() first." - ) - import torch.nn.functional as F - - mask_float = self.solvent_mask.to(dtype=self.log_k_solvent.dtype) - sigma = self.transition - kernel_size = int(4 * sigma + 1) - if kernel_size % 2 == 0: - kernel_size += 1 - - x = torch.arange( - kernel_size, dtype=self.log_k_solvent.dtype, device=self.device - ) - x = x - kernel_size // 2 - gauss_1d = torch.exp(-(x**2) / (2 * sigma**2)) - gauss_1d = gauss_1d / gauss_1d.sum() - - pad = kernel_size // 2 - mask = mask_float.unsqueeze(0).unsqueeze(0) # (1, 1, D, H, W) - - # Separable Gaussian: three 1D conv3d passes, identical to the full 3D - # outer-product conv but O(3·K·V) instead of O(K³·V). Padding is circular - # because crystallographic boundaries are periodic; each 1D conv shrinks - # only its own axis back to the original size. - mask = F.pad(mask, (pad, pad, pad, pad, pad, pad), mode="circular") - mask = F.conv3d(mask, gauss_1d.view(1, 1, 1, 1, kernel_size)) - mask = F.conv3d(mask, gauss_1d.view(1, 1, 1, kernel_size, 1)) - mask = F.conv3d(mask, gauss_1d.view(1, 1, kernel_size, 1, 1)) - - mask_smoothed = mask.squeeze(0).squeeze(0) - self.register_buffer("mask_smoothed", mask_smoothed) - - assert torch.isfinite( - self.mask_smoothed - ).all(), "Non-finite values in solvent mask" - return self.mask_smoothed def get_rec_solvent(self, hkl): """ @@ -486,10 +581,11 @@ def get_rec_solvent(self, hkl): """ assert hasattr( - self, "mask_smoothed" - ), "Smoothed solvent mask not computed. Call smooth_solvent_mask() first." + self, "solvent_mask" + ), "Solvent mask not computed. Call get_solvent_mask() first." + mask = self.solvent_mask.to(dtype=self.log_k_solvent.dtype) fsol = extract_structure_factor_from_grid( - ifft(self.mask_smoothed, self.model.cell.volume), hkl + ifft(mask, self.model.cell.volume), hkl ).detach() assert torch.isfinite( fsol @@ -500,9 +596,9 @@ def forward(self, hkl, update_fsol=False, F_protein=None): """ Compute solvent contribution to structure factors at given HKL. - Differentiable w.r.t. ``k_solvent``, ``b_solvent`` and ``phase_offset``. - Takes ``f_sol`` (the FFT of the *smoothed* mask) from a per-hkl cache, - applies the B-factor damping ``exp(-B s^2)`` with ``s = sin(θ)/λ``, + Differentiable w.r.t. ``log_k_solvent``, ``log_ss_half``, ``log_n_exp`` and + ``phase_offset``. Takes ``f_sol`` (the FFT of the binary mask) from a per-hkl + cache, applies :meth:`damping` at ``ss = (sin(θ)/λ)**2``, blends mask phases toward the protein phases when ``optimize_phase`` and ``F_protein`` are both given (``phase_offset`` 0 = mask phases, ±π = protein phases), and scales by ``k_solvent``. @@ -542,13 +638,8 @@ def forward(self, hkl, update_fsol=False, F_protein=None): s = torch.norm(scattering_vectors, dim=1) / 2.0 # This is sin(θ)/λ s_squared = s**2 # Now s² is correct for B-factor formula - # Isotropic Debye-Waller damping exp(-B * s²) - b_solvent = self.b_solvent - k_solvent = torch.exp(self.log_k_solvent.clamp(min=-10.0, max=10.0)) - exp = -b_solvent.clamp(min=-500.0, max=500.0) * s_squared - b_factor_term = torch.exp( - exp.clamp(min=-10.0, max=10.0) - ) # Clamp to avoid overflow + falloff = self.damping(s_squared) + k_solvent = self.k_solvent() # Phase handling if self.optimize_phase and F_protein is not None: @@ -573,8 +664,7 @@ def forward(self, hkl, update_fsol=False, F_protein=None): # No phase adjustment - use mask phases as-is phase_adjusted_f_sol = f_sol - # Scale by k_solvent and apply B-factor - f_solvent = k_solvent * phase_adjusted_f_sol * b_factor_term + f_solvent = k_solvent * phase_adjusted_f_sol * falloff assert torch.isfinite( f_solvent @@ -583,6 +673,6 @@ def forward(self, hkl, update_fsol=False, F_protein=None): def parameters(self): """Refinable solvent parameters as a list (phase offset only if refined).""" - return [self.log_k_solvent, self.b_solvent] + ( + return [self.log_k_solvent, self.log_ss_half, self.log_n_exp] + ( [self.phase_offset] if self.optimize_phase else [] ) From fbc76a9c9e1b97a00d34a4be85d67ad74805c5b8 Mon Sep 17 00:00:00 2001 From: HatPdotS Date: Fri, 14 Aug 2026 16:22:16 +0200 Subject: [PATCH 2/9] fixed error in changelog --- docs/changelog.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/changelog.rst b/docs/changelog.rst index 0a934ccb..c45769ec 100644 --- a/docs/changelog.rst +++ b/docs/changelog.rst @@ -2,7 +2,7 @@ Changelog ========= -Version 0.6.3 +Version 0.6.4 ---------- - Switched the scaler's default scale-fit objective from ``nll`` to unit-weight ``ls`` - Replaced the per-bin ``log_scale`` with a Chebyshev polynomial ``c_iso`` in sin(theta)/lambda From f1ce5f3edeff2dfd09b197f62ba209237deb8241 Mon Sep 17 00:00:00 2001 From: HatPdotS Date: Fri, 14 Aug 2026 17:54:51 +0200 Subject: [PATCH 3/9] Use the configured dtype for the scale-fit normalisation constant The scale fit computed its normalisation constant 1/sum(F_obs^2) via .double(), which raises on MPS: the backend has no float64, and PYTORCH_ENABLE_MPS_FALLBACK covers unimplemented ops, not unsupported dtypes. This broke every test reaching get_scales()/refine_scaler() on the Accelerator CI job, and equally breaks a real TORCHREF_DEVICE=mps refinement. The float64 was unnecessary rather than misplaced. The constant rescales every term of the objective identically, so its own precision cancels out of the minimiser, the gradient direction and the likelihood:penalty ratio; it only has to be finite, positive and O(1)-producing. float32 gives it to ~5e-8 relative on real work sets (torch.sum reduces pairwise, so error grows like log(N)*eps, not N*eps). Also stop the batched direct-summation paths allocating their accumulator as complex128 regardless of configuration, which raises on MPS once max_memory_gb splits the reflection loop and made the batched output dtype disagree with the unbatched one. Co-Authored-By: Claude Opus 5 (1M context) Claude-Session: https://claude.ai/code/session_01Ldd5qF6gWrhgHMiv7FcfW1 --- docs/changelog.rst | 2 ++ torchref/base/direct_summation/anisotropic.py | 15 ++++++++------- torchref/base/direct_summation/isotropic.py | 15 ++++++++------- torchref/scaling/scaler_base.py | 10 +++++++--- 4 files changed, 25 insertions(+), 17 deletions(-) diff --git a/docs/changelog.rst b/docs/changelog.rst index c45769ec..79d0a65f 100644 --- a/docs/changelog.rst +++ b/docs/changelog.rst @@ -9,6 +9,8 @@ Version 0.6.4 - Replaced the solvent Debye-Waller factor with ``k_sol exp(-ln2 (ss/ss_half)^n)``, merged sigmoid exponential form - Fixed the solvent-mask candidate enumeration, which missed voxels near the atom's grid node - Removed the solvent-mask Gaussian smoothing +- Fixed the scale fit's float64 normalisation constant, which broke MPS +- Batched direct summation now returns ``dtypes.complex`` instead of always ``complex128`` Version 0.6.3 diff --git a/torchref/base/direct_summation/anisotropic.py b/torchref/base/direct_summation/anisotropic.py index fb6318b0..8d676c2e 100644 --- a/torchref/base/direct_summation/anisotropic.py +++ b/torchref/base/direct_summation/anisotropic.py @@ -67,9 +67,8 @@ def aniso_structure_factor_torched( Returns ------- torch.Tensor - Complex structure factors of shape (N_reflections,). Dtype depends on - the path taken: complex derived from ``dtypes.float`` unbatched, but - ``complex128`` once ``max_memory_gb`` forces batching. + Complex structure factors of shape (N_reflections,), in ``dtypes.complex`` + whether or not ``max_memory_gb`` forces batching. """ xyz_expanded = spacegroup(xyz_fractional.T) # (3, N_atoms, n_ops) fractional_shape = xyz_expanded.shape @@ -141,15 +140,17 @@ def _aniso_sf_batched( ``xyz_flat`` is the symmetry-expanded coordinates flattened to (3, N_atoms * n_ops), ``fractional_shape`` its pre-flatten shape - (3, N_atoms, n_ops), ``U_matrix`` the expanded U as (3, 3, N_atoms). Output - is always ``complex128``, unlike the unbatched path. + (3, N_atoms, n_ops), ``U_matrix`` the expanded U as (3, 3, N_atoms). Output is + in ``dtypes.complex``, matching the unbatched path. """ n_refl = hkl.shape[0] n_atoms = fractional_shape[1] device = hkl.device - # Pre-allocate output tensor to avoid accumulating in list - sf_out = torch.zeros(n_refl, dtype=torch.complex128, device=device) + # Pre-allocate output tensor to avoid accumulating in list. The configured complex + # dtype, not a hardcoded complex128: MPS has neither float64 nor complex128, and + # forcing it here made the batched path disagree with the unbatched one. + sf_out = torch.zeros(n_refl, dtype=dtypes.complex, device=device) for start in range(0, n_refl, batch_size): end = min(start + batch_size, n_refl) diff --git a/torchref/base/direct_summation/isotropic.py b/torchref/base/direct_summation/isotropic.py index 303b02a7..4a9edc44 100644 --- a/torchref/base/direct_summation/isotropic.py +++ b/torchref/base/direct_summation/isotropic.py @@ -67,9 +67,8 @@ def iso_structure_factor_torched( Returns ------- torch.Tensor - Complex structure factors of shape (N_reflections,). Dtype depends on - the path taken: complex derived from ``dtypes.float`` unbatched, but - ``complex128`` once ``max_memory_gb`` forces batching. + Complex structure factors of shape (N_reflections,), in ``dtypes.complex`` + whether or not ``max_memory_gb`` forces batching. """ xyz_expanded = spacegroup(xyz_fractional.T) # (3, N_atoms, n_ops) fractional_shape = xyz_expanded.shape @@ -136,15 +135,17 @@ def _iso_sf_batched( ``xyz_flat`` is the symmetry-expanded coordinates flattened to (3, N_atoms * n_ops), ``fractional_shape`` its pre-flatten shape - (3, N_atoms, n_ops), ``adp_row`` the ADPs as (1, N_atoms). Output is always - ``complex128``, unlike the unbatched path. + (3, N_atoms, n_ops), ``adp_row`` the ADPs as (1, N_atoms). Output is in + ``dtypes.complex``, matching the unbatched path. """ n_refl = hkl.shape[0] n_atoms = fractional_shape[1] device = hkl.device - # Pre-allocate output tensor to avoid accumulating in list - sf_out = torch.zeros(n_refl, dtype=torch.complex128, device=device) + # Pre-allocate output tensor to avoid accumulating in list. The configured complex + # dtype, not a hardcoded complex128: MPS has neither float64 nor complex128, and + # forcing it here made the batched path disagree with the unbatched one. + sf_out = torch.zeros(n_refl, dtype=dtypes.complex, device=device) for start in range(0, n_refl, batch_size): end = min(start + batch_size, n_refl) diff --git a/torchref/scaling/scaler_base.py b/torchref/scaling/scaler_base.py index 2bf65f89..d464fd49 100644 --- a/torchref/scaling/scaler_base.py +++ b/torchref/scaling/scaler_base.py @@ -634,10 +634,14 @@ def refine_lbfgs( # enough out that ``sum(U**2)`` overflows. Dividing by ``sum(F_obs**2)`` makes the # loss dimensionless and O(R**2) whatever the structure size or data scale. # - # The reduction is float64 for precision over ~1e5 terms; the constant is fixed - # for the whole fit. + # Computed in the configured dtype. Because the constant rescales every term + # identically, its own precision does not enter the result: it only has to be + # finite, positive and of the right magnitude. float32 gives it to ~5e-8 relative + # (``sum`` reduces pairwise, so error grows like log(N)*eps, not N*eps), which + # cancels out of the minimiser and the gradient direction alike. Do not cast to + # float64 -- MPS has none and raises on ``.double()``. _f_obs_work = self._data.work.F.detach() - _norm = float(1.0 / _f_obs_work.double().pow(2).sum().clamp(min=1e-30)) + _norm = float(1.0 / _f_obs_work.pow(2).sum().clamp(min=1e-30)) class _ScalerXrayTarget(nn.Module): """The registry row, closed over the detached ``fcalc``.""" From 56df7593ed3e0725da016735d75cf066d15bfcea Mon Sep 17 00:00:00 2001 From: HatPdotS Date: Sat, 15 Aug 2026 23:55:39 +0200 Subject: [PATCH 4/9] Fixed pahse bug when input data not in ccp4 format --- docs/changelog.rst | 2 + ...st_cli_difference_refine_mismatched_mtz.py | 2 +- tests/unit/io/test_anomalous_output.py | 10 +- tests/unit/io/test_anomalous_reader.py | 6 +- tests/unit/io/test_hkl_convention.py | 202 ++++++++++++++++++ tests/unit/io/test_reflection_data_reindex.py | 4 +- .../test_estimate_beta_determinism.py | 2 +- tests/unit/refinement/test_xray_residuals.py | 2 +- torchref/cli/refine.py | 4 +- torchref/io/datasets/reflection_data.py | 165 ++++++++++---- torchref/refinement/base_refinement.py | 18 +- torchref/refinement/targets/base.py | 7 +- .../refinement/targets/collection/base.py | 11 +- torchref/refinement/targets/xray/base.py | 4 +- torchref/refinement/targets/xray/sigma_a.py | 2 +- torchref/scaling/scaler.py | 6 +- torchref/scaling/scaler_base.py | 7 +- 17 files changed, 373 insertions(+), 81 deletions(-) create mode 100644 tests/unit/io/test_hkl_convention.py diff --git a/docs/changelog.rst b/docs/changelog.rst index 79d0a65f..ce9a9f69 100644 --- a/docs/changelog.rst +++ b/docs/changelog.rst @@ -4,6 +4,8 @@ Changelog Version 0.6.4 ---------- +- Fixed written phases (``PH-model``, ``PHWT``, ``PHDELWT``) being negated for reflections whose input Miller indices lay outside the CCP4 ASU +- Reflections remapped to the CCP4 ASU on load are now reported - Switched the scaler's default scale-fit objective from ``nll`` to unit-weight ``ls`` - Replaced the per-bin ``log_scale`` with a Chebyshev polynomial ``c_iso`` in sin(theta)/lambda - Replaced the solvent Debye-Waller factor with ``k_sol exp(-ln2 (ss/ss_half)^n)``, merged sigmoid exponential form diff --git a/tests/integration/test_cli_difference_refine_mismatched_mtz.py b/tests/integration/test_cli_difference_refine_mismatched_mtz.py index a1a2723b..c002cfaf 100644 --- a/tests/integration/test_cli_difference_refine_mismatched_mtz.py +++ b/tests/integration/test_cli_difference_refine_mismatched_mtz.py @@ -3,7 +3,7 @@ Difference refinement builds a ``DatasetCollection`` from two independent MTZ files and aligns the light dataset onto the dark reference via ``ReflectionData.validate_hkl``. When the two files had different reflection -sets, alignment left ``hkl_anomalous`` (read by ``hkl_for_sf``) at the +sets, alignment left ``hkl_anomalous`` (read by ``_hkl_for_sf``) at the pre-alignment length and the run crashed inside the collection difference target (``RuntimeError: The size of tensor a (...) must match the size of tensor b``). diff --git a/tests/unit/io/test_anomalous_output.py b/tests/unit/io/test_anomalous_output.py index a56c5df0..e110a6e2 100644 --- a/tests/unit/io/test_anomalous_output.py +++ b/tests/unit/io/test_anomalous_output.py @@ -1,7 +1,7 @@ """Tests for anomalous (Bijvoet) refinement support. Covers the Option B1 changes: - * ReflectionData carries a signed HKL (hkl_anomalous / hkl_for_sf) alongside + * ReflectionData carries a signed HKL (hkl_anomalous / _hkl_for_sf) alongside the canonical ASU index, with friedel_flags bookkeeping. * ModelFT produces distinct |F_calc| for Friedel mates when a wavelength and anomalous scatterers are present, and identical |F_calc| otherwise. @@ -61,7 +61,7 @@ def test_fields_populated(self, anomalous_data): def test_hkl_for_sf_signs(self, anomalous_data): d = anomalous_data - sf = d.hkl_for_sf() + sf = d._hkl_for_sf() flag = d.friedel_flags assert torch.equal(sf[~flag], d.hkl[~flag]) assert torch.equal(sf[flag], -d.hkl[flag]) @@ -75,7 +75,7 @@ def test_hkl_for_sf_fallback(self): """Without canonicalization bookkeeping, falls back to canonical hkl.""" d = ReflectionData(verbose=0) d.hkl = torch.tensor([[1, 0, 0]], dtype=torch.int32) - assert torch.equal(d.hkl_for_sf(), d.hkl) + assert torch.equal(d._hkl_for_sf(), d.hkl) class TestBijvoetStructureFactors: @@ -85,7 +85,7 @@ def _fcalc(self, data, pdb_dir, wavelength, apply_bijvoet=True): ) model.load_pdb(str(pdb_dir / "1DAW.pdb")) with torch.no_grad(): - return model(data.hkl_for_sf()) + return data.structure_factors(model) def test_anomalous_signal_present(self, anomalous_data, pdb_dir): # apply_bijvoet=True (the unmerged-data setting) applies the f'' term. @@ -122,7 +122,7 @@ def _write(self, data, pdb_dir, tmp_path, wavelength=1.54): ) model.load_pdb(str(pdb_dir / "1DAW.pdb")) with torch.no_grad(): - fcalc = model(data.hkl_for_sf()) + fcalc = data.structure_factors(model) out = tmp_path / "anom_out.mtz" data.write_mtz(str(out), fcalc=fcalc, anomalous=True) return rs.read_mtz(str(out)) diff --git a/tests/unit/io/test_anomalous_reader.py b/tests/unit/io/test_anomalous_reader.py index 072f7e9f..2fc0692d 100644 --- a/tests/unit/io/test_anomalous_reader.py +++ b/tests/unit/io/test_anomalous_reader.py @@ -39,7 +39,7 @@ def test_merged_mtz_default(mtz_dir): d = ReflectionData(verbose=0) d.load_mtz(str(mtz_dir / "1DAW.mtz")) assert d.friedel_merged is True - assert torch.equal(d.hkl_for_sf(), d.hkl) + assert torch.equal(d._hkl_for_sf(), d.hkl) assert not bool(d.friedel_flags.any()) @@ -54,7 +54,7 @@ def test_anomalous_mtz_unstacked(anomalous_two_column_mtz): assert n_merged < len(d.hkl) <= 2 * n_merged # The minus mates are flagged and carry the negated signed HKL. assert bool(d.friedel_flags.any()) - sf = d.hkl_for_sf() + sf = d._hkl_for_sf() assert torch.equal(sf[d.friedel_flags], -d.hkl[d.friedel_flags]) assert torch.equal(sf[~d.friedel_flags], d.hkl[~d.friedel_flags]) @@ -159,7 +159,7 @@ def written_columns(in_mtz, apply_bijvoet): ) model.load_pdb(str(pdb_dir / "1DAW.pdb")) with torch.no_grad(): - fcalc = model(d.hkl_for_sf()) + fcalc = d.structure_factors(model) out = tmp_path / "auto_out.mtz" d.write_mtz(str(out), fcalc=fcalc) # anomalous=None -> auto return set(rs.read_mtz(str(out)).columns) diff --git a/tests/unit/io/test_hkl_convention.py b/tests/unit/io/test_hkl_convention.py new file mode 100644 index 00000000..6a08ba34 --- /dev/null +++ b/tests/unit/io/test_hkl_convention.py @@ -0,0 +1,202 @@ +"""Structure factors and written phases live in the canonical CCP4 convention. + +``ReflectionData`` evaluates the model at a signed index so Bijvoet mates get +distinct ``|F_calc|``, but expresses the result on the canonical ASU index that +is written as H,K,L. Pairing a signed-convention phase with a canonical index +negates it, which is invisible on data already inside the CCP4 ASU -- every MTZ +under ``tests/files/`` is -- and wrong for roughly half the reflections on data +that is not. + +The invariance test below manufactures the second case by negating the Miller +indices, and asserts that precondition so it cannot quietly degenerate into the +first. +""" + +import gemmi +import numpy as np +import pytest +import reciprocalspaceship as rs +import torch + +from torchref.io.datasets.reflection_data import ReflectionData +from torchref.model import ModelFT + +CODE = "3GR5" # P 65 2 2 -- the smallest real fixture, and non-centrosymmetric + + +def _rebuild(hkl, src): + """``src`` re-expressed on ``hkl``, declared merged so the legacy layout is used.""" + return ReflectionData.from_tensors( + hkl, + src.F, + src.F_sigma, + src.cell, + src.spacegroup, + rfree_flags=src.rfree_flags, + verbose=0, + friedel_merged=True, + ) + + +@pytest.fixture(scope="module") +def canonical_and_negated(mtz_dir): + """The same reflections indexed two ways: inside the CCP4 ASU, and negated.""" + src = ReflectionData(verbose=0) + src.load_mtz(str(mtz_dir / f"{CODE}.mtz")) + return _rebuild(src.hkl, src), _rebuild(-src.hkl, src) + + +@pytest.fixture +def anomalous_data(mtz_dir, tmp_path): + """1DAW stacked into explicit (+/-) Bijvoet rows, so mates really are paired.""" + out = tmp_path / "anom_in.mtz" + rs.read_mtz(str(mtz_dir / "1DAW.mtz")).stack_anomalous().write_mtz(str(out)) + data = ReflectionData(verbose=0) + data.load_mtz(str(out)) + return data + + +def _model(pdb_dir, data): + m = ModelFT(verbose=0, max_res=2.0) + m.load_pdb(str(pdb_dir / f"{CODE}.pdb")) + m.cell, m.spacegroup = data.cell, data.spacegroup + return m + + +def _write(data, pdb_dir, path): + with torch.no_grad(): + fcalc = data.structure_factors(_model(pdb_dir, data), cached=False) + data.write_mtz(str(path), fcalc=fcalc, anomalous=False) + return rs.read_mtz(str(path)).reset_index() + + +def _circular_diff(a, b): + """Absolute difference of two phase arrays in degrees, wrapped to [0, 180].""" + return np.abs((np.asarray(a) - np.asarray(b) + 180.0) % 360.0 - 180.0) + + +class TestConjugateFriedel: + def test_is_an_involution(self, canonical_and_negated): + _, neg = canonical_and_negated + z = torch.randn(len(neg.hkl), dtype=torch.complex64, device=neg.hkl.device) + assert torch.equal(neg.conjugate_friedel(neg.conjugate_friedel(z)), z) + + def test_touches_exactly_the_flagged_rows(self, canonical_and_negated): + _, neg = canonical_and_negated + flag = neg.friedel_flags + z = torch.randn(len(neg.hkl), dtype=torch.complex64, device=neg.hkl.device) + out = neg.conjugate_friedel(z) + assert torch.equal(out[flag], z[flag].conj()) + assert torch.equal(out[~flag], z[~flag]) + + def test_is_identity_without_flagged_rows(self, canonical_and_negated): + ref, _ = canonical_and_negated + z = torch.randn(len(ref.hkl), dtype=torch.complex64, device=ref.hkl.device) + assert torch.equal(ref.conjugate_friedel(z), z) + + +class TestWrittenPhaseConvention: + def test_preconditions(self, canonical_and_negated): + """Without these the invariance test below proves nothing.""" + ref, neg = canonical_and_negated + assert not bool(ref.friedel_flags.any()), "reference must be inside the ASU" + assert float(neg.friedel_flags.float().mean()) > 0.4, ( + "negated indices must force canonicalisation to conjugate" + ) + # Both must land on the same canonical reflections, in the same order, + # or the column-by-column comparison is meaningless. + assert torch.equal(ref.hkl, neg.hkl) + assert torch.equal(ref.F, neg.F) + + @pytest.mark.parametrize("column", ["PH-model", "PHWT", "PHDELWT"]) + def test_phases_do_not_depend_on_input_indexing( + self, canonical_and_negated, pdb_dir, tmp_path, column + ): + ref, neg = canonical_and_negated + a = _write(ref, pdb_dir, tmp_path / "ref.mtz") + b = _write(neg, pdb_dir, tmp_path / "neg.mtz") + + assert np.array_equal(a[["H", "K", "L"]].to_numpy(), b[["H", "K", "L"]].to_numpy()) + + # Weak reflections have numerically unstable phases and the FFT is not + # bit-reproducible run to run, so compare where there is real signal. + strong = a["F-model"].to_numpy() > np.median(a["F-model"].to_numpy()) + diff = _circular_diff(a[column].to_numpy()[strong], b[column].to_numpy()[strong]) + # A sign flip on ~half the rows shows up as ~2*|phase|, i.e. tens of degrees. + assert diff.max() < 5.0, f"{column}: max {diff.max():.2f} deg" + + def test_written_phase_matches_gemmi(self, canonical_and_negated, pdb_dir, tmp_path): + """Absolute check against an independent structure-factor calculation.""" + _, neg = canonical_and_negated + out = _write(neg, pdb_dir, tmp_path / "abs.mtz") + + st = gemmi.read_structure(str(pdb_dir / f"{CODE}.pdb")) + st.setup_entities() + st.remove_hydrogens() + calc = gemmi.StructureFactorCalculatorX(st.cell) + + rng = np.random.default_rng(0) + rows = out.iloc[rng.choice(len(out), 40, replace=False)] + checked = 0 + for _, row in rows.iterrows(): + ref_sf = calc.calculate_sf_from_model( + st[0], (int(row["H"]), int(row["K"]), int(row["L"])) + ) + if abs(ref_sf) < 1.0: # phase is meaningless at zero amplitude + continue + expected = np.degrees(np.angle(complex(ref_sf.real, ref_sf.imag))) + assert _circular_diff(row["PH-model"], expected) < 8.0 + checked += 1 + assert checked > 20, "too few usable reflections to be a real check" + + +class TestFriedelMergedInference: + """Needing conjugation to reach the ASU is not the same as carrying mates.""" + + def test_already_canonical_is_merged(self, mtz_dir): + d = ReflectionData(verbose=0) + d.load_mtz(str(mtz_dir / f"{CODE}.mtz")) + assert not bool(d.friedel_flags.any()) + assert d.friedel_merged is True + + def test_reindexed_without_mates_stays_merged(self, mtz_dir): + """A merged dataset indexed in another convention flags rows but has no + mates, so f'' must stay off and the legacy layout must stay selected.""" + src = ReflectionData(verbose=0) + src.load_mtz(str(mtz_dir / f"{CODE}.mtz")) + # No explicit friedel_merged: the inference is what is under test. + neg = ReflectionData.from_tensors( + -src.hkl, src.F, src.F_sigma, src.cell, src.spacegroup, + rfree_flags=src.rfree_flags, verbose=0, + ) + assert float(neg.friedel_flags.float().mean()) > 0.4 + assert neg.friedel_merged is True + + def test_real_bijvoet_pairs_are_unmerged(self, anomalous_data): + assert bool(anomalous_data.friedel_flags.any()) + assert anomalous_data.friedel_merged is False + + +class TestAnomalousLayoutUnaffected: + """Canonicalising early must not flatten the Bijvoet difference.""" + + def test_mates_keep_distinct_amplitudes(self, anomalous_data, pdb_dir): + model = ModelFT(verbose=0, max_res=2.0, wavelength=1.54, apply_bijvoet=True) + model.load_pdb(str(pdb_dir / "1DAW.pdb")) + with torch.no_grad(): + fcalc = anomalous_data.structure_factors(model) + + flag = anomalous_data.friedel_flags + assert bool(flag.any()), "fixture must carry Friedel-flagged rows" + inverse, n_groups = anomalous_data.asu_group_indices() + paired = anomalous_data._group_any( + flag, inverse, n_groups + ) & anomalous_data._group_any(~flag, inverse, n_groups) + assert bool(paired.any()), "fixture must contain real Bijvoet pairs" + + # |conj(z)| == |z|, so the anomalous amplitude difference must survive. + amp = torch.abs(fcalc) + spread = torch.zeros(n_groups, device=amp.device) + spread.index_add_(0, inverse[flag], amp[flag]) + spread.index_add_(0, inverse[~flag], -amp[~flag]) + assert float(spread[paired].abs().max()) > 1e-3 diff --git a/tests/unit/io/test_reflection_data_reindex.py b/tests/unit/io/test_reflection_data_reindex.py index 691a5526..07a58cc2 100644 --- a/tests/unit/io/test_reflection_data_reindex.py +++ b/tests/unit/io/test_reflection_data_reindex.py @@ -2,7 +2,7 @@ ``validate_hkl`` / ``remap`` / ``reduce_to_spacegroup`` must carry EVERY per-reflection field onto the new HKL grid, not a hand-maintained subset. The -historical bug left ``hkl_anomalous`` (read by ``hkl_for_sf``) at the +historical bug left ``hkl_anomalous`` (read by ``_hkl_for_sf``) at the pre-alignment length, which crashed difference refinement whenever the dark and light datasets had different reflection sets. See docs/changelog.rst 0.6.2. """ @@ -58,7 +58,7 @@ def test_carries_all_per_reflection_fields(self): assert m == len(ref_hkl) # The field the old code left stale (the direct cause of the crash): assert light.hkl_anomalous.shape[0] == m - assert light.hkl_for_sf().shape[0] == m + assert light._hkl_for_sf().shape[0] == m # Derived-from-HKL fields recompute for the new grid: assert light.centric.shape[0] == m assert light.friedel_flags.shape[0] == m diff --git a/tests/unit/refinement/test_estimate_beta_determinism.py b/tests/unit/refinement/test_estimate_beta_determinism.py index b20d02f6..52b827c0 100644 --- a/tests/unit/refinement/test_estimate_beta_determinism.py +++ b/tests/unit/refinement/test_estimate_beta_determinism.py @@ -115,7 +115,7 @@ def _real_inputs(mtz_dir, pdb_dir, dtype=torch.float32): m = ModelFT(verbose=0, max_res=2.0) m.load_pdb(str(pdb_dir / "1DAW.pdb")) with torch.no_grad(): - fc = m(d.hkl_for_sf()) + fc = d.structure_factors(m) cast = lambda t: t.detach().cpu().to(dtype) # noqa: E731 return dict( F_obs=cast(d.F), diff --git a/tests/unit/refinement/test_xray_residuals.py b/tests/unit/refinement/test_xray_residuals.py index 8216b539..f89e9dd5 100644 --- a/tests/unit/refinement/test_xray_residuals.py +++ b/tests/unit/refinement/test_xray_residuals.py @@ -239,7 +239,7 @@ def test_ls_wunit_k1_residuals_use_the_work_fit_scale(refinement): t = _target(refinement, "ls_wunit_k1", use_set="free") data = refinement.reflection_data with torch.no_grad(): - base = t.get_F_calc_scaled(data.hkl_for_sf(), recalc=False) + base = t.get_F_calc_scaled(recalc=False) c_from_residual_path = (t._scaled_F_calc_full() / base.clamp(min=1e-12)).median() work = data.work c_work = t._binwise_scale( diff --git a/torchref/cli/refine.py b/torchref/cli/refine.py index d1ca5421..238db84c 100644 --- a/torchref/cli/refine.py +++ b/torchref/cli/refine.py @@ -408,8 +408,8 @@ def main(): try: work_nll, test_nll = refinement.nll_xray() rd = refinement.reflection_data - # Signed HKL so |F_calc| matches what refinement optimized (anomalous mates). - fcalc = refinement.get_F_calc_scaled(rd.hkl_for_sf(), recalc=True) + # Same canonical-convention |F_calc| the refinement optimized. + fcalc = refinement.get_F_calc_scaled(recalc=True) # work/free accessor: scaled, validity-masked |F_obs| per subset; .select() # aligns the full-size |F_calc| onto the same subset. diff --git a/torchref/io/datasets/reflection_data.py b/torchref/io/datasets/reflection_data.py index 1e126553..fd62273b 100644 --- a/torchref/io/datasets/reflection_data.py +++ b/torchref/io/datasets/reflection_data.py @@ -51,7 +51,7 @@ class _ReflectionSubset: model-computed array (e.g. ``F_calc``) to the same subset:: F_obs = data.work.F # corrected amplitudes, work set only - F_calc = data.work.select(scaler(model(data.hkl_for_sf()))) + F_calc = data.work.select(scaler(data.structure_factors(model))) """ __slots__ = ("_parent", "_kind") @@ -549,13 +549,35 @@ def _canonicalize_in_place(self) -> None: # self.hkl. hkl_anomalous carries the SIGNED index used for # structure-factor evaluation -- canonical for the (+) member, negated # for the conjugated (-) mate -- so the model yields a genuine Bijvoet - # difference. See ReflectionData.hkl_for_sf. + # difference. See ReflectionData.structure_factors. self.friedel_flags = friedel_flags self.hkl_anomalous = torch.where( friedel_flags.unsqueeze(-1), -self.hkl, self.hkl ) - def hkl_for_sf(self) -> torch.Tensor: + n_flipped = int(friedel_flags.sum()) + if n_flipped: + print( + f" Reindexed {n_flipped}/{len(self.hkl)} reflections to the " + f"CCP4 ASU (output is written on that index, not the input one)." + ) + # A row needing conjugation to reach the ASU does NOT by itself mean + # the data are Bijvoet-unmerged: a merged dataset indexed in another + # convention flags rows while carrying no mate at all. Real mates + # show up as one canonical index holding both a flagged and an + # unflagged row. Either this or the reader's (+)/(-) columns + # declaring unmerged is enough. + inverse, n_groups = self.asu_group_indices() + has_mates = bool( + ( + self._group_any(friedel_flags, inverse, n_groups) + & self._group_any(~friedel_flags, inverse, n_groups) + ).any() + ) + if has_mates: + self.friedel_merged = False + + def _hkl_for_sf(self) -> torch.Tensor: """Signed Miller indices for structure-factor evaluation. Returns ``hkl_anomalous`` when present so the two members of a Bijvoet @@ -564,6 +586,12 @@ def hkl_for_sf(self) -> torch.Tensor: ``|F_calc|`` under anomalous scattering. Falls back to the canonical :attr:`hkl` when anomalous bookkeeping is unavailable (e.g. empty init). + These indices are a model *input* only. A structure factor evaluated + here is in the signed convention, and pairing its phase with :attr:`hkl` + negates that phase wherever :attr:`friedel_flags` is set. Convert with + :meth:`conjugate_friedel` first, or use :meth:`structure_factors`, which + does both and is the supported entry point. + Returns ------- torch.Tensor @@ -573,6 +601,62 @@ def hkl_for_sf(self) -> torch.Tensor: return self.hkl_anomalous return self.hkl + def conjugate_friedel(self, fcalc: torch.Tensor) -> torch.Tensor: + """Move complex structure factors between the signed and canonical index. + + Rows flagged in :attr:`friedel_flags` are evaluated at ``-h`` by + :meth:`_hkl_for_sf` while :attr:`hkl` holds ``+h``; ``F(-h)`` is the + conjugate of ``F(h)`` up to the anomalous ``f''`` term. Conjugating + exactly those rows re-expresses the array on the other index. The + operation is its own inverse, so it converts in both directions. + + Amplitudes are unaffected -- only phases move. + + Parameters + ---------- + fcalc : torch.Tensor + Complex structure factors of shape (N,). + + Returns + ------- + torch.Tensor + ``fcalc`` with the Friedel-flagged rows conjugated, or unchanged + when no Friedel bookkeeping is present. + """ + if self.friedel_flags is None: + return fcalc + return torch.where(self.friedel_flags, fcalc.conj(), fcalc) + + def structure_factors( + self, model, recalc: bool = False, cached: bool = True + ) -> torch.Tensor: + """Complex ``F_calc`` from ``model``, on the canonical ASU index. + + Evaluates the model at the signed indices so Bijvoet mates get distinct + ``|F_calc|``, then returns the result on :attr:`hkl` -- the index this + dataset writes as ``H,K,L``. Structure factors are in this convention + everywhere in TorchRef; the signed index does not escape this method. + + Parameters + ---------- + model : ModelFT + Model to evaluate. + recalc : bool, optional + Force recomputation rather than reusing the cached SF. Default False. + cached : bool, optional + True (default) goes through the model's forward cache. False calls + ``model.forward`` directly, leaving the cache untouched. + + Returns + ------- + torch.Tensor + Complex structure factors of shape (N,), row-aligned with + :attr:`hkl`. + """ + hkl = self._hkl_for_sf() + fcalc = model(hkl, recalc=recalc) if cached else model.forward(hkl) + return self.conjugate_friedel(fcalc) + def asu_group_indices(self) -> Tuple[torch.Tensor, int]: """Group rows that describe the same unique reflection. @@ -855,9 +939,10 @@ def from_tensors( Verbosity level. Default is 1. friedel_merged : bool, optional False means ``hkl`` holds explicit Bijvoet pairs (both ``+h`` and - ``-h``), which enables the model's f'' term downstream. If None, - inferred after canonicalization from whether any ``-h`` mate had to - be conjugated to reach the ASU. + ``-h``), which enables the model's f'' term downstream. Default + True. Canonicalization overrides it to False when it finds real + mates, so passing True is a statement about the input rather than + the last word. detach : bool, optional True (default) stores constant observations, dropping the caller's autograd graph; False keeps it so gradients reach whatever produced @@ -900,6 +985,10 @@ def _prep(t: torch.Tensor) -> torch.Tensor: device=data.device, dtype=torch.bool ) + # Set before canonicalization, which is what detects real Bijvoet mates + # and downgrades this to False -- the same seam load() goes through. + data.friedel_merged = True if friedel_merged is None else bool(friedel_merged) + data._post_load_cleanup() # As in load(): generate after canonicalization so the draw can group @@ -907,16 +996,6 @@ def _prep(t: torch.Tensor) -> torch.Tensor: if data.rfree_flags is None: data._generate_rfree_flags() - # Resolve merge state. Explicit arg wins; otherwise infer from whether - # canonicalization had to conjugate any Friedel mates (i.e. -h rows were - # supplied), which mirrors the reader's stacked-input detection. - if friedel_merged is None: - has_mates = data.friedel_flags is not None and bool( - data.friedel_flags.any() - ) - data.friedel_merged = not has_mates - else: - data.friedel_merged = bool(friedel_merged) return data def load_mtz( @@ -2530,12 +2609,12 @@ def _build_anomalous_dataframe( Parameters ---------- fcalc : torch.Tensor, optional - Complex per-row structure factors evaluated at the *signed* HKL - (``hkl_for_sf``), row-aligned with :attr:`hkl`. If None, only the - observation columns (``F-obs``, ``F-obs(+/-)``, ``SIGF-obs(+/-)``, - R-free) are written -- the model-derived columns (``F-model``, - ``PHIF-model``, display maps and ``ANOM``/``PANOM``, which need the - model phase) are omitted. + Complex per-row structure factors in the canonical-ASU convention, + row-aligned with :attr:`hkl` (see :meth:`structure_factors`). If + None, only the observation columns (``F-obs``, ``F-obs(+/-)``, + ``SIGF-obs(+/-)``, R-free) are written -- the model-derived columns + (``F-model``, ``PHIF-model``, display maps and ``ANOM``/``PANOM``, + which need the model phase) are omitted. Returns ------- @@ -2597,7 +2676,12 @@ def _build_anomalous_dataframe( if has_model: fc = fcalc.detach().cpu().numpy() Fc_amp = np.abs(fc) - Fc_ph = np.angle(fc, deg=True) + # The (+)/(-) phase columns describe each mate at its own index, so + # they read the signed convention. conjugate_friedel is its own + # inverse, so it recovers that from the canonical input. + Fc_ph = np.angle( + self.conjugate_friedel(fcalc).detach().cpu().numpy(), deg=True + ) F = self.F.detach().cpu().numpy() Fsig = self.F_sigma.detach().cpu().numpy() if self.F_sigma is not None else None rfree = ( @@ -2651,12 +2735,12 @@ def mirror_centric(plus, minus): Fmod_p_out, Fmod_m_out = mirror_centric(Fmod_p, Fmod_m) Phi_p_out, Phi_m_out = mirror_centric(Phi_p, Phi_m) - # ASU representative structure factor: the + member, else the - # conjugate of the - member. + # ASU representative structure factor: the + member, else the - + # member. Both rows are already on the canonical index. fc_disp = np.full(M, np.nan, dtype=complex) fc_disp[has_plus] = fc[pi][has_plus] only_minus = has_minus & ~has_plus - fc_disp[only_minus] = np.conj(fc[mi])[only_minus] + fc_disp[only_minus] = fc[mi][only_minus] Fc_disp_amp = np.abs(fc_disp) ph_disp = np.angle(fc_disp, deg=True) @@ -2732,8 +2816,10 @@ def write_mtz( fname : str Output MTZ filename. fcalc : torch.Tensor, optional - Complex calculated structure factors of shape (N,). - If provided, computes phases and map coefficients. + Complex calculated structure factors of shape (N,), in the + canonical-ASU convention and row-aligned with :attr:`hkl` -- as + returned by :meth:`structure_factors`. If provided, computes phases + and map coefficients. model_ft : ModelFT, optional ModelFT object to compute fcalc if not provided. anomalous : bool, optional @@ -2764,11 +2850,19 @@ def write_mtz( if anomalous is None: anomalous = not self.friedel_merged + # One fallback for both layouts, so ``fcalc`` means the same thing + # whether the caller supplied it or it was derived here. cached=False + # keeps a no-grad write from leaving a detached tensor in the model's + # forward cache. + if fcalc is None and model_ft is not None: + fcalc = self.structure_factors(model_ft, cached=False) + if fcalc is not None and fcalc.shape[0] != len(self.hkl): + raise ValueError( + f"fcalc has {fcalc.shape[0]} rows but this dataset has " + f"{len(self.hkl)}; it must be row-aligned with hkl." + ) + if anomalous: - # Signed HKL preserves the anomalous/Bijvoet difference (see - # hkl_for_sf) so the two mates get distinct F-model. - if fcalc is None and model_ft is not None: - fcalc = model_ft.forward(self.hkl_for_sf()) df = self._build_anomalous_dataframe(fcalc) write(df, self.cell.data, self.spacegroup, fname) if self.verbose > 0: @@ -2817,9 +2911,6 @@ def write_mtz( val_np = self.validation_flags.detach().cpu().numpy() data_dict["Validation_flag"] = (val_np != 0).astype(int) - # Compute fcalc if model_ft is provided but fcalc is not - if fcalc is None and model_ft is not None: - fcalc = model_ft.forward(self.hkl) mask = self.masks().detach().cpu().numpy() # Add map coefficients if fcalc is provided if fcalc is not None: @@ -3396,7 +3487,7 @@ def _aggregate_any(tensor): # Invalidate derived-from-HKL fields (recomputed lazily for the new ASU). # hkl_anomalous / friedel_flags are left unset: the merged ASU is - # Friedel-merged, so hkl_for_sf() correctly falls back to hkl. + # Friedel-merged, so _hkl_for_sf() correctly falls back to hkl. reduced.bin_indices = None reduced._centric_flags = None @@ -3456,7 +3547,7 @@ def canonicalize(self, include_friedel: bool = True) -> "ReflectionData": ) # Record anomalous bookkeeping for the canonical result (the stale values - # carried over by __select__ are recomputed here). See hkl_for_sf. + # carried over by __select__ are recomputed here). See _hkl_for_sf. result.friedel_flags = friedel_flags result.hkl_anomalous = torch.where( friedel_flags.unsqueeze(-1), -canonical_hkl, canonical_hkl diff --git a/torchref/refinement/base_refinement.py b/torchref/refinement/base_refinement.py index 99ef8a7e..4eaf78e2 100644 --- a/torchref/refinement/base_refinement.py +++ b/torchref/refinement/base_refinement.py @@ -622,17 +622,14 @@ def get_fcalc(self, hkl=None, recalc=False): Parameters ---------- hkl : array_like, optional - Reflection indices. None uses ``reflection_data.hkl_for_sf()`` -- signed - HKL, so Bijvoet mates at +h/-h get distinct ``|F_calc|`` under anomalous - scattering (falls back to canonical hkl). + Reflection indices. None evaluates on the data's own reflections via + ``reflection_data.structure_factors``, returning the canonical-ASU + convention. An explicit ``hkl`` is used as given. recalc : bool, optional Force recomputation rather than reusing the cached SF. """ if hkl is None: - # Signed HKL so Bijvoet mates (which share a canonical ASU index) - # are evaluated at +h/-h and get distinct |F_calc| under anomalous - # scattering. Falls back to canonical hkl when unavailable. - hkl = self.reflection_data.hkl_for_sf() + return self.reflection_data.structure_factors(self.model, recalc=recalc) return self.model(hkl, recalc=recalc) def get_fcalc_scaled(self, hkl=None, recalc=False): @@ -831,10 +828,9 @@ def write_out_mtz(self, out_mtz_path="refined_output.mtz", anomalous=None): pairs (``reflection_data.friedel_merged`` is False). """ with torch.no_grad(): - # Signed HKL so the per-row fcalc carries the anomalous (Bijvoet) - # difference; write_mtz consumes it row-aligned with reflection_data. - hkl = self.reflection_data.hkl_for_sf() - fcalc = self.scaler(self.get_fcalc(hkl), use_mask=False) + # Canonical-ASU convention, row-aligned with reflection_data.hkl -- + # the index write_mtz emits as H,K,L. + fcalc = self.scaler(self.get_fcalc(), use_mask=False) self.reflection_data.write_mtz(out_mtz_path, fcalc, anomalous=anomalous) def collect_deposition_metadata(self, metadata=None): diff --git a/torchref/refinement/targets/base.py b/torchref/refinement/targets/base.py index 02145a26..0a4b62e2 100644 --- a/torchref/refinement/targets/base.py +++ b/torchref/refinement/targets/base.py @@ -317,7 +317,8 @@ def get_fcalc(self, hkl=None, recalc=False): Parameters ---------- hkl : torch.Tensor, optional - Miller indices. If None, uses data's hkl. + Miller indices. If None, evaluates on the data's own reflections in + the canonical-ASU convention. An explicit ``hkl`` is used as given. recalc : bool, optional Force recalculation. Default is False. @@ -337,9 +338,7 @@ def get_fcalc(self, hkl=None, recalc=False): "Either provide a model or pass fcalc directly." ) if hkl is None: - # Signed HKL so Bijvoet mates get distinct |F_calc| (see - # ReflectionData.hkl_for_sf). - hkl = self._data.hkl_for_sf() + return self._data.structure_factors(self._model, recalc=recalc) return self._model(hkl, recalc=recalc) def get_fcalc_scaled(self, hkl=None, recalc=False, fcalc=None): diff --git a/torchref/refinement/targets/collection/base.py b/torchref/refinement/targets/collection/base.py index 8986132f..064cd971 100644 --- a/torchref/refinement/targets/collection/base.py +++ b/torchref/refinement/targets/collection/base.py @@ -116,12 +116,13 @@ def _reset_model_caches(self) -> None: bm.reset_cache() def _scaled_amp_full(self, data, model, recalc: bool = True) -> torch.Tensor: - """Full-size, scaled ``|F_calc|`` for one data-model pair, off ``hkl_for_sf()`` - so anomalous mates stay distinct. ``recalc=True`` (default) is for the no-grad - R-factor path -- it neither reuses nor leaves a cached tensor that would break - gradient flow. The loss path passes False, after ``_reset_model_caches()``. + """Full-size, scaled ``|F_calc|`` for one data-model pair, on the canonical + ASU index with anomalous mates kept distinct. ``recalc=True`` (default) is for + the no-grad R-factor path -- it neither reuses nor leaves a cached tensor that + would break gradient flow. The loss path passes False, after + ``_reset_model_caches()``. """ - fcalc = model(data.hkl_for_sf(), recalc=recalc) + fcalc = data.structure_factors(model, recalc=recalc) return torch.abs(_scale_fcalc(self._scaler, fcalc, model)) # ------------------------------------------------------------------ diff --git a/torchref/refinement/targets/xray/base.py b/torchref/refinement/targets/xray/base.py index b546bca7..4d3bebd5 100644 --- a/torchref/refinement/targets/xray/base.py +++ b/torchref/refinement/targets/xray/base.py @@ -185,7 +185,7 @@ def get_data( if fcalc is not None: F_calc_full = self.get_F_calc_scaled(fcalc=fcalc) else: - F_calc_full = self.get_F_calc_scaled(self._data.hkl_for_sf(), recalc=False) + F_calc_full = self.get_F_calc_scaled(recalc=False) F_calc = sub.select(F_calc_full) return F_obs, F_calc, sigma, centric, sub @@ -250,7 +250,7 @@ def _scaled_F_calc_full(self, fcalc: torch.Tensor = None) -> torch.Tensor: """ if fcalc is not None: return self.get_F_calc_scaled(fcalc=fcalc) - return self.get_F_calc_scaled(self._data.hkl_for_sf(), recalc=False) + return self.get_F_calc_scaled(recalc=False) def get_rfactor(self, fcalc: torch.Tensor = None): """Compute ``(R_work, R_free)`` for this target. diff --git a/torchref/refinement/targets/xray/sigma_a.py b/torchref/refinement/targets/xray/sigma_a.py index c5cd74c4..1934b393 100644 --- a/torchref/refinement/targets/xray/sigma_a.py +++ b/torchref/refinement/targets/xray/sigma_a.py @@ -170,7 +170,7 @@ def _loss_inputs( if fcalc is not None: F_calc_full = self.get_F_calc_scaled(fcalc=fcalc) else: - F_calc_full = self.get_F_calc_scaled(self._data.hkl_for_sf(), recalc=False) + F_calc_full = self.get_F_calc_scaled(recalc=False) eps_full, dss_full = self._geom() eps_full = eps_full.to(F_calc_full.dtype) diff --git a/torchref/scaling/scaler.py b/torchref/scaling/scaler.py index 5d10c204..c0b30f48 100644 --- a/torchref/scaling/scaler.py +++ b/torchref/scaling/scaler.py @@ -186,9 +186,9 @@ def compute_fcalc(self) -> torch.Tensor: """ if self.model is None: raise RuntimeError("No model set and no fcalc provided") - # Signed HKL so the scaled fcalc carries the anomalous (Bijvoet) - # difference; bulk solvent below is evaluated at the same indices. - return self.model(self._data.hkl_for_sf()) + # Canonical-ASU convention, with the anomalous (Bijvoet) amplitude + # difference preserved; bulk solvent below is on the same index. + return self._data.structure_factors(self.model) def calc_initial_scale(self, fcalc: torch.Tensor = None): """ diff --git a/torchref/scaling/scaler_base.py b/torchref/scaling/scaler_base.py index d464fd49..cfcde943 100644 --- a/torchref/scaling/scaler_base.py +++ b/torchref/scaling/scaler_base.py @@ -806,9 +806,10 @@ def forward( # Lazily cache raw solvent SFs (FFT of mask) — only recomputed # when invalidated via _f_sol_raw = None (e.g. after update_solvent) if self._f_sol_raw is None: - # Signed HKL to match the (anomalous) fcalc so the complex sum - # k*(F_calc + F_sol) is consistent per Bijvoet mate. - self._f_sol_raw = self.solvent.get_rec_solvent(self._data.hkl_for_sf()) + # The solvent mask is real density with no anomalous term, so + # F_sol(-h) is exactly conj(F_sol(h)) and evaluating on the + # canonical index already matches the canonical fcalc below. + self._f_sol_raw = self.solvent.get_rec_solvent(self.hkl) f_sol_raw = ( self._f_sol_raw[mask] if apply_internal_mask else self._f_sol_raw From 501056eed7f065c74666be6b1dcbd783a74fa0e8 Mon Sep 17 00:00:00 2001 From: HatPdotS Date: Sun, 16 Aug 2026 02:52:52 +0200 Subject: [PATCH 5/9] Read every compound in a multi-component restraint dictionary CIFReader stored each loop under its CIF category, so with parse_all_blocks every data_comp_* block's chem_comp_bond loop overwrote the previous one and only the last block survived. A dictionary defining several compounds restrained one of them and left the rest silently unrestrained. Loops of the same category are now accumulated. Rows carry the block they were read from, because dictionaries that omit comp_id have nothing else to separate their compounds by, and concatenating those without provenance would replace a silent drop with silent cross-contamination. Also fixed, all reachable through the same path: - _filter_by_comp resets the index. _extract_col keeps the source index for a column it finds but returns a fresh RangeIndex for one it does not, so a compound at non-zero index lost the columns that were present. Only reachable once blocks are combined. - builders_fast matched only "positive"/"negative"; CCP4 also writes "positiv"/"negativ", which became NaN and were then dropped outright in builders_numba. - RestraintCIFReader takes verbose, so the read_cif dispatch in readers.py stops raising TypeError on every restraint dictionary. - validate_restraint_data names compounds that come back with no bonds instead of passing over them; an empty table still carries the columns it checked for, which is why this class of failure was silent. Verified against the monomer library rather than transcribed constants: each compound read from a combined file must equal reading it from its own file. 921 compounds over 310 synthesised multi-compound files (bundled + CCP4-9) show no difference. Reverting the fix fails 13 of the 26 new tests. Co-Authored-By: Claude Opus 5 (1M context) Claude-Session: https://claude.ai/code/session_018QVRRkhcy5omKhb5a8TM8H --- docs/changelog.rst | 3 + .../unit/io/test_multicomponent_restraints.py | 277 ++++++++++++++++++ torchref/io/cif_readers.py | 106 ++++++- torchref/restraints/builders_fast.py | 10 +- torchref/restraints/restraints_helper.py | 19 +- 5 files changed, 398 insertions(+), 17 deletions(-) create mode 100644 tests/unit/io/test_multicomponent_restraints.py diff --git a/docs/changelog.rst b/docs/changelog.rst index ce9a9f69..813b4f9c 100644 --- a/docs/changelog.rst +++ b/docs/changelog.rst @@ -4,6 +4,9 @@ Changelog Version 0.6.4 ---------- +- Fixed restraint dictionaries defining several compounds yielding restraints for only one of them +- Fixed chirality restraints being dropped for the ``positiv``/``negativ`` spellings used by the CCP4 library +- Compounds that come back with no bond restraints are now reported - Fixed written phases (``PH-model``, ``PHWT``, ``PHDELWT``) being negated for reflections whose input Miller indices lay outside the CCP4 ASU - Reflections remapped to the CCP4 ASU on load are now reported - Switched the scaler's default scale-fit objective from ``nll`` to unit-weight ``ls`` diff --git a/tests/unit/io/test_multicomponent_restraints.py b/tests/unit/io/test_multicomponent_restraints.py new file mode 100644 index 00000000..780d3414 --- /dev/null +++ b/tests/unit/io/test_multicomponent_restraints.py @@ -0,0 +1,277 @@ +"""A restraint dictionary defining several compounds must restrain all of them. + +``CIFReader`` stores loops under their CIF category, so every ``data_comp_*`` +block in a multi-compound dictionary writes to the same key (``chem_comp_bond``, +...). Replacing rather than accumulating kept only whichever block came last, +leaving every other compound silently unrestrained -- silently, because an empty +bond table still carries the ``value``/``sigma`` columns that validation checks +for. + +The reference here is the monomer library itself: each compound is read once from +a combined file and once from its own single-block file, and the two must agree. +That makes the assertion an equality against real data rather than a golden +number transcribed by hand. +""" + +import numpy as np +import pandas as pd +import pytest + +from torchref.io.cif_readers import RestraintCIFReader +from torchref.restraints.library import get_library_manager +from torchref.restraints.restraints_helper import ( + split_data_blocks, + validate_restraint_data, +) + +#: Bundled compounds, chosen to differ in every section: GLY has no chirals, ARG +#: has the most planes, ALA sits between them. +CODES = ("ALA", "ARG", "GLY") + +SECTIONS = ("bonds", "angles", "torsions", "planes", "chirals", "atoms") + +#: Two ligands, no ``comp_id`` column anywhere -- the layout some eLBOW/Grade +#: dictionaries use. Only the block name says which compound a row belongs to. +_NO_COMP_ID_CIF = """ +data_comp_list +loop_ +_chem_comp.id +_chem_comp.three_letter_code +_chem_comp.name +_chem_comp.group +LIGA LIGA 'ligand A' non-polymer +LIGB LIGB 'ligand B' non-polymer + +data_comp_LIGA +loop_ +_chem_comp_atom.atom_id +_chem_comp_atom.type_symbol +A1 C +A2 O +loop_ +_chem_comp_bond.atom_id_1 +_chem_comp_bond.atom_id_2 +_chem_comp_bond.value_dist +_chem_comp_bond.value_dist_esd +A1 A2 1.230 0.020 + +data_comp_LIGB +loop_ +_chem_comp_atom.atom_id +_chem_comp_atom.type_symbol +B1 N +B2 C +loop_ +_chem_comp_bond.atom_id_1 +_chem_comp_bond.atom_id_2 +_chem_comp_bond.value_dist +_chem_comp_bond.value_dist_esd +B1 B2 1.470 0.015 +""" + + +def _library_cif(code): + """Path to ``code``'s bundled single-compound dictionary.""" + path = get_library_manager(verbose=0).get_cif_file(code) + assert path is not None, f"{code} is not in the bundled monomer library" + return path + + +@pytest.fixture(scope="module") +def combined_dictionary(tmp_path_factory): + """The three library compounds concatenated into one multi-block file. + + Built rather than committed so the single-compound originals stay the + reference: any divergence is the reader's, not a stale fixture's. + """ + header, rows, bodies = None, [], [] + for code in CODES: + blocks = split_data_blocks(_library_cif(code).read_text()) + listing = blocks["comp_list"].splitlines() + if header is None: + header = [ + line + for line in listing + if line.strip().startswith(("data_", "loop_", "_")) + ] + rows += [ + line + for line in listing + if line.strip() and not line.strip().startswith(("data_", "loop_", "_")) + ] + bodies.append(blocks[f"comp_{code}"]) + + path = tmp_path_factory.mktemp("restraints") / "three_compounds.cif" + path.write_text("\n".join(header + rows) + "\n\n" + "\n\n".join(bodies) + "\n") + return path + + +@pytest.fixture(scope="module") +def combined_restraints(combined_dictionary): + return RestraintCIFReader(str(combined_dictionary)).get_all_restraints() + + +@pytest.fixture(scope="module") +def separate_restraints(): + """Each compound read from its own file -- the control.""" + return { + code: RestraintCIFReader(str(_library_cif(code))).get_all_restraints()[code] + for code in CODES + } + + +@pytest.mark.unit +class TestMultiComponentDictionary: + def test_preconditions( + self, combined_dictionary, combined_restraints, separate_restraints + ): + """Without these the comparison below could pass on two empty dicts.""" + blocks = [ + line + for line in combined_dictionary.read_text().splitlines() + if line.startswith("data_comp_") and line.strip() != "data_comp_list" + ] + assert len(blocks) == len(CODES), ( + f"fixture must hold {len(CODES)} compound blocks, found {blocks}" + ) + assert list(combined_restraints) == list(CODES) + for code in CODES: + for section in SECTIONS: + if section == "chirals" and code == "GLY": + continue # glycine is achiral; nothing to compare + assert len(separate_restraints[code][section]) > 0, ( + f"control for {code}/{section} is empty, so it proves nothing" + ) + + @pytest.mark.parametrize("code", CODES) + @pytest.mark.parametrize("section", SECTIONS) + def test_compound_matches_its_own_file( + self, combined_restraints, separate_restraints, code, section + ): + """Reading a compound alongside others must not change what it yields.""" + pd.testing.assert_frame_equal( + combined_restraints[code][section], + separate_restraints[code][section], + ) + + def test_no_compound_inherits_another_blocks_restraints( + self, combined_restraints, separate_restraints + ): + """The old failure mode: a section absent from the last block was filled + in from an earlier one, so a compound carried a neighbour's restraints.""" + assert len(combined_restraints["GLY"]["chirals"]) == 0 + assert len(separate_restraints["GLY"]["chirals"]) == 0 + + +@pytest.mark.unit +class TestCompoundsWithoutCompIdColumn: + """With no ``comp_id`` column the rows are told apart by their source block.""" + + @pytest.fixture(scope="class") + def restraints(self, tmp_path_factory): + path = tmp_path_factory.mktemp("nocompid") / "two_ligands.cif" + path.write_text(_NO_COMP_ID_CIF) + return RestraintCIFReader(str(path)).get_all_restraints() + + def test_preconditions(self, tmp_path_factory): + """The fixture must really lack comp_id, or the block fallback is untested.""" + path = tmp_path_factory.mktemp("nocompid_check") / "two_ligands.cif" + path.write_text(_NO_COMP_ID_CIF) + data = RestraintCIFReader(str(path)).cif.data + for category in ("chem_comp_atom", "chem_comp_bond"): + assert not any("comp_id" in c for c in data[category].columns), ( + f"{category} carries a comp_id column; the fixture is wrong" + ) + + def test_each_ligand_keeps_only_its_own_bonds(self, restraints): + assert list(restraints) == ["LIGA", "LIGB"] + for comp, atoms in (("LIGA", ("A1", "A2")), ("LIGB", ("B1", "B2"))): + bonds = restraints[comp]["bonds"] + assert len(bonds) == 1 + assert (bonds.loc[0, "atom1"], bonds.loc[0, "atom2"]) == atoms + + def test_each_ligand_keeps_only_its_own_atoms(self, restraints): + assert sorted(restraints["LIGA"]["atoms"]["atom_id"]) == ["A1", "A2"] + assert sorted(restraints["LIGB"]["atoms"]["atom_id"]) == ["B1", "B2"] + + +@pytest.mark.unit +class TestColumnIndexAlignment: + """Values that are present must not be lost to index misalignment. + + ``_extract_col`` keeps the frame's index for a column it finds but returns a + fresh ``RangeIndex`` for one it does not. Assembling a result from both makes + pandas align the mismatch away to NaN, so a compound sitting at a non-zero + index -- which is every compound after the first once blocks are combined -- + would lose the columns that *are* there. + """ + + def test_present_columns_survive_a_missing_first_column(self): + reader = RestraintCIFReader.__new__(RestraintCIFReader) + frame = pd.DataFrame( + { + "_chem_comp_bond.comp_id": ["A", "A", "B", "B"], + # atom_id_1 deliberately absent: it is what _standardize_bonds + # extracts first, so its fallback would set the result's index. + "_chem_comp_bond.atom_id_2": ["A2", "A3", "B2", "B3"], + "_chem_comp_bond.value_dist": ["1.1", "1.2", "1.3", "1.4"], + "_chem_comp_bond.value_dist_esd": ["0.01"] * 4, + } + ) + bonds = reader._standardize_bonds(reader._filter_by_comp(frame, "B")) + + assert bonds["atom2"].tolist() == ["B2", "B3"] + assert bonds["value"].tolist() == [1.3, 1.4] + assert bonds["atom1"].isna().all() # genuinely absent, correctly NaN + + +@pytest.mark.unit +class TestChiralitySpellings: + """The CCP4 library writes both ``positive`` and the truncated ``positiv``.""" + + def test_short_spellings_are_not_dropped(self): + from torchref.restraints.builders_fast import PreprocessedCIF + + chirals = pd.DataFrame( + { + "atom_centre": ["CA"] * 4, + "atom1": ["N"] * 4, + "atom2": ["C"] * 4, + "atom3": ["CB"] * 4, + "volume_sign": ["positive", "positiv", "negativ", "both"], + } + ) + signs = PreprocessedCIF({})._preprocess_chirals(chirals)["volume_sign"] + + # NaN here is not a rounding detail: builders_numba skips those rows, so + # an unrecognised spelling deletes the restraint outright. + assert not np.isnan(signs).any() + assert signs.tolist() == [1.0, 1.0, -1.0, 0.0] + + +@pytest.mark.unit +class TestEmptyCompoundIsReported: + """A compound that yields no restraints is named rather than passed over.""" + + @pytest.fixture + def dictionary_with_undefined_compound(self, tmp_path): + text = _library_cif("ALA").read_text().replace( + "ALA ALA ALANINE peptide 13 6 .", + "ALA ALA ALANINE peptide 13 6 .\nGHOST GHOST GHOSTLY non-polymer 1 1 .", + ) + path = tmp_path / "with_ghost.cif" + path.write_text(text) + return path + + def test_warns_without_raising(self, dictionary_with_undefined_compound, capsys): + restraints = RestraintCIFReader( + str(dictionary_with_undefined_compound) + ).get_all_restraints() + assert len(restraints["GHOST"]["bonds"]) == 0 # precondition + assert len(restraints["ALA"]["bonds"]) > 0 + + validate_restraint_data(restraints, str(dictionary_with_undefined_compound)) + + out = capsys.readouterr().out + assert "GHOST" in out and "no bond restraints" in out + assert "ALA," not in out # only the empty one is named diff --git a/torchref/io/cif_readers.py b/torchref/io/cif_readers.py index 7642b81c..44d1b9e5 100644 --- a/torchref/io/cif_readers.py +++ b/torchref/io/cif_readers.py @@ -14,6 +14,14 @@ import numpy as np import pandas as pd +#: Column holding the ``data_`` block a loop row was read from. Added only when +#: ``parse_all_blocks`` is set, because that is the only mode in which rows from +#: different blocks share a category. Dictionaries whose loops carry no +#: ``comp_id`` still have to be split per compound, and the block name is the +#: only thing that can do it. Stripped again by +#: :meth:`RestraintCIFReader._filter_by_comp`, so it never reaches a caller. +_SOURCE_BLOCK_COLUMN = "_source_block" + class CIFReader: """ @@ -58,6 +66,7 @@ def __init__( self.parse_all_blocks = parse_all_blocks self.available_blocks = [] self.verbose = 0 + self._current_block = None if filepath: self.load(filepath) @@ -85,7 +94,7 @@ def _parse(self, content: str): """ lines = content.split("\n") i = 0 - current_block = None + self._current_block = None target_block_found = False # First pass: find all data blocks @@ -123,7 +132,7 @@ def _parse(self, content: str): # Check for data block if line.startswith("data_"): block_name = line[5:].strip() # Remove 'data_' prefix - current_block = block_name + self._current_block = block_name if parse_all: # Continue parsing - don't skip any blocks @@ -251,10 +260,33 @@ def _parse_loop(self, lines: List[str], start_idx: int) -> int: if columns: category = self._extract_category(columns[0]) if category: - self.data[category] = df + self.data[category] = self._merge_category(category, df) return i + def _merge_category(self, category: str, df: pd.DataFrame) -> pd.DataFrame: + """Combine ``df`` with anything already stored under ``category``. + + Only relevant when ``parse_all_blocks`` is set: a multi-compound restraint + dictionary repeats the same category (``chem_comp_bond``, ...) once per + ``data_comp_*`` block, and replacing rather than appending would keep only + the block that happens to come last. Rows are tagged with their source + block so they can be split apart again even when the loop itself carries + no ``comp_id`` column. + + Loops of one category with differing columns are aligned by name and + NaN-filled; the surplus columns belong to other compounds and are removed + by :meth:`RestraintCIFReader._filter_by_comp` before standardisation. + """ + if not self.parse_all_blocks: + return df + + df[_SOURCE_BLOCK_COLUMN] = self._current_block + previous = self.data.get(category) + if isinstance(previous, pd.DataFrame): + return pd.concat([previous, df], ignore_index=True) + return df + def _parse_keyvalue(self, lines: List[str], start_idx: int) -> int: """Parse one key-value pair; returns the next line index.""" line = lines[start_idx].strip() @@ -1683,15 +1715,27 @@ class RestraintCIFReader: bond_df = comp_data['ALA']['bonds'] """ - def __init__(self, filepath: str): - """Load the restraint dictionary at ``filepath`` immediately.""" + def __init__(self, filepath: str, verbose: int = 0): + """Load the restraint dictionary at ``filepath`` immediately. + + ``verbose`` above 0 reports a dictionary that defines more than one + compound, which is otherwise indistinguishable from a single-compound one + at the call site. + """ self.filepath = Path(filepath) - # Use parse_all_blocks=True because restraint files often have multiple blocks - # (e.g., data_comp_list and data_comp_PRO) + self.verbose = verbose + # Every data block is read, not just the first: a dictionary may define + # several compounds, each in its own ``data_comp_`` block. self.cif = CIFReader(filepath, parse_all_blocks=True) self.compounds = self._extract_compounds() self._validate() + if self.verbose > 0 and len(self.compounds) > 1: + print( + f" {self.filepath.name}: {len(self.compounds)} compounds " + f"({', '.join(self.compounds)})" + ) + def _extract_compounds(self) -> List[str]: """Compound IDs present in the file, e.g. ``['ALA']``.""" compounds = [] @@ -1745,7 +1789,19 @@ def _extract_compounds(self) -> List[str]: if compounds: break - return compounds + # Last resort before the filename stem: the ``data_comp_`` block names. + # A dictionary can omit both ``comp_list`` and the per-row ``comp_id`` + # columns, and then the block name is the only record of what it defines. + if not compounds: + compounds = [ + block[len("comp_") :] + for block in self.cif.available_blocks + if block.startswith("comp_") and block != "comp_list" + ] + + # A compound may be named by both the header and its own data rows, and + # ``get_all_restraints`` would then build it twice. + return list(dict.fromkeys(c for c in compounds if c)) def _validate(self): """ @@ -2118,9 +2174,18 @@ def _standardize_atoms(self, df: pd.DataFrame) -> pd.DataFrame: return result def _filter_by_comp(self, df: pd.DataFrame, comp_id: str) -> pd.DataFrame: - """Rows of ``df`` belonging to ``comp_id``.""" + """Rows of ``df`` belonging to ``comp_id``. + + The index is reset because the caller assembles its result column by + column: :meth:`_extract_col` preserves this frame's index for a column it + finds but returns a fresh ``RangeIndex`` for one it does not, so a + non-zero-based index makes those two disagree and pandas aligns the + mismatch away to NaN -- dropping values that are present. Rows only reach + a non-zero index once several blocks are concatenated, i.e. exactly on the + multi-compound dictionaries this reader now supports. + """ if df.empty: - return df + return df.drop(columns=[_SOURCE_BLOCK_COLUMN], errors="ignore") # Try different possible column names for compound ID # Include all naming conventions: monomer library, eLBOW/phenix, short forms @@ -2136,12 +2201,27 @@ def _filter_by_comp(self, df: pd.DataFrame, comp_id: str) -> pd.DataFrame: "id", ] + selected = None for col in id_cols: if col in df.columns: - return df[df[col] == comp_id].copy() + selected = df[df[col] == comp_id] + break + + # No comp_id column: fall back to the block the rows were read from. + # Dictionaries from eLBOW/Grade often omit comp_id, and without this the + # compounds in a multi-block file would be indistinguishable. + if selected is None and _SOURCE_BLOCK_COLUMN in df.columns: + selected = df[df[_SOURCE_BLOCK_COLUMN].isin([f"comp_{comp_id}", comp_id])] - # If no comp_id column, assume all rows belong to this compound - return df.copy() + # Single-compound file with no comp_id anywhere: every row is its own. + if selected is None: + selected = df + + return ( + selected.drop(columns=[_SOURCE_BLOCK_COLUMN], errors="ignore") + .reset_index(drop=True) + .copy() + ) def get_bond_restraints(self, comp_id: str) -> pd.DataFrame: """ diff --git a/torchref/restraints/builders_fast.py b/torchref/restraints/builders_fast.py index 137789d6..8d074b23 100644 --- a/torchref/restraints/builders_fast.py +++ b/torchref/restraints/builders_fast.py @@ -347,12 +347,16 @@ def _preprocess_planes(self, planes_df: pd.DataFrame) -> List[Dict]: def _preprocess_chirals(self, chirals_df: pd.DataFrame) -> Dict[str, np.ndarray]: """Convert chirals DataFrame to NumPy arrays.""" - # Convert volume_sign strings to floats + # Convert volume_sign strings to floats. The CCP4 library writes both the + # full and the truncated spelling ("positiv", "negativ"); an unrecognised + # sign becomes NaN and the restraint is then dropped in builders_numba, + # so the short forms have to be matched here or those chirals vanish. volume_signs = [] for sign in chirals_df["volume_sign"].values: - if sign == "positive": + sign = str(sign).strip().lower() + if sign.startswith("positiv"): volume_signs.append(1.0) - elif sign == "negative": + elif sign.startswith("negativ"): volume_signs.append(-1.0) elif sign in ["both", "either"]: volume_signs.append(0.0) diff --git a/torchref/restraints/restraints_helper.py b/torchref/restraints/restraints_helper.py index a70d1d75..b2d2276b 100644 --- a/torchref/restraints/restraints_helper.py +++ b/torchref/restraints/restraints_helper.py @@ -12,14 +12,22 @@ def validate_restraint_data(residue_data, cif_path): - """Raise ``ValueError`` unless every compound carries real restraint parameters. + """Raise ``ValueError`` unless every compound carries a usable bond section. Rejects structure-only CIFs: a compound must have a bond section, and that section must carry ``value`` and ``sigma`` columns. + + A section that has those columns but *no rows* is reported and allowed + through. It is not malformed -- a dictionary may legitimately define a + single-atom compound -- but it is also what a reader that quietly loses + restraints looks like from here, and the residue ends up unrestrained either + way, so it is worth saying out loud rather than passing over in silence. """ if not residue_data: raise ValueError(f"CIF file {cif_path} contains no compound definitions") + empty = [] + for comp_id, data in residue_data.items(): if data is None or not data: raise ValueError( @@ -44,6 +52,9 @@ def validate_restraint_data(residue_data, cif_path): f"Solution: Remove this file or use the monomer library files which contain\n" f"proper restraint parameters (from the CCP4 Monomer Library)." ) + + if len(bond_df) == 0: + empty.append(comp_id) else: raise ValueError( f"CIF file {cif_path}: Compound '{comp_id}' has no bond restraint data.\n" @@ -52,6 +63,12 @@ def validate_restraint_data(residue_data, cif_path): f"the monomer library or pass None to use the default library." ) + if empty: + print( + f" Warning: {cif_path}: {len(empty)} of {len(residue_data)} compounds " + f"carry no bond restraints and will be unrestrained: {', '.join(empty)}" + ) + def read_cif(cif_path): """ From dfe59fbcc377ec634fc4c81dd61b64e0131daed4 Mon Sep 17 00:00:00 2001 From: HatPdotS Date: Sun, 16 Aug 2026 19:06:52 +0200 Subject: [PATCH 6/9] Track the paper harness needed to reproduce the figures The four shared utilities at paper/ were untracked, two of them named in paper/README.md as steps the reader should run, so the figures could not be reproduced from a clean clone. Same for submit_fig2_arms.py, the canonical Figure-2 submitter, and exF1's grid comparison scripts. Also carries the environment moves the harness already depends on: PHENIX 1.21.1-5286 and node-local CCP4 8.0 (the AFS 7.1 tree is gone), and the torchref scoring array's day/2h limits, which at hour/20min silently timed out the largest models and dropped them from ExtFig 3 without an error. exF4's single-core measurement is updated to the 723-code conserved set. Drops the fcalc results_20260805_124020 tree, superseded on disk by results_20260811_165844 and now covered by .gitignore. 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b/paper/README.md index b10c941c..a275d607 100644 --- a/paper/README.md +++ b/paper/README.md @@ -18,36 +18,38 @@ The `paper/` directory expects two data sources linked as: ``` paper/ ├── data -> ../scientific_testing/data # ~1000 benchmark structures -│ └── {CODE}/{CODE}.mtz, {CODE}_shaken.pdb, ... +│ └── {CODE}/{CODE}.mtz, {CODE}.pdb, ... # reflections + deposited model ├── phenix_refinements -> .../refinements # PHENIX comparison results │ └── {CODE}/{CODE}_refined_001.pdb, .log, ... ``` -**For reviewers:** if cloning fresh, create the data directory and populate it: +Figure 2 refines **Phaser-placed AlphaFold models**, not the deposited or perturbed +coordinates: its starting models are `figure2_alphafold_start/placed/{CODE}_af.pdb`, and the +structure set is the MR-solved subset recorded in `figure2_alphafold_start/mr_status.csv` +(`solved == 1`, n = 767). Those placed models plus `manifest.json` / `mr_status.csv` are +*inputs* — produced once by the molecular-replacement pipeline in +`figure2_alphafold_start/` (`fetch_alphafold.py` → `prepare_search_model.py` → +`run_mr_one.sh` → `collect_mr_status.py`) and reused by every arm thereafter. -```bash -cd paper/ +## Figures -# Step 1: Download 1000 structures (PDB + structure factors) -python figure2_validation/scripts/get_pdb_and_data_files.py -python figure2_validation/scripts/download_pdb_files.py +Every `plot_*.py` also writes per-panel **source data** to `source_data/` as it renders — one +CSV holding the values that panel draws, after all filtering and derivation. See +[`source_data/README.md`](source_data/README.md) for the file list, units and caveats. There is +no separate export step: re-render a figure and its CSVs update with it. -# Step 2: Prepare input data -python figure2_validation/scripts/make_standardized_mtzs.py # CIF → MTZ -python figure2_validation/scripts/convert_cifx.py # CIF → PDB -python figure2_validation/scripts/shake_cifs.py # Shake coordinates + B-factors +### Figure 2: AlphaFold-start Benchmark +**Location:** `figure2_alphafold_start/` -# Step 3: Create the data symlink (or copy) -ln -s ../scientific_testing/data data -``` +TorchRef vs REFMAC5 vs phenix.refine, all four models (including the AlphaFold starting +point) scored by the same independent validator, `phenix.model_vs_data`. Panels: A +R-work/R-free, B geometry RMSZ, C wall-clock, D convergence. -## Figures - -### Figure 2: Validation Benchmark -**Location:** `figure2_validation/` - -Comparison of TorchRef vs PHENIX refinement across ~1000 PDB structures. -See `figure2_validation/README.md` for the full reproduction pipeline. +Submit the three arms with `analysis/submit_fig2_arms.py`, which emits each structure's +arms adjacently on one pinned CPU model — Panel C compares wall-clock across engines, so +submitting one engine at a time confounds engine identity with cluster-load window. Then +`run_af_pipeline.py validate` (REFMAC 0-cycle), the two scoring sweeps, and +`analysis/aggregate_figure_metrics.py`; render with `plot_figure_af.py`. ### Figure 3: Performance Benchmarks **Location:** `figure3_performance/` @@ -72,6 +74,43 @@ All input data and refinement outputs included in the directory. | ExtFig 1 | AF-start loss-weight landscape (10×10 geometry×adp grid) | Plotting only — uses `figure2_alphafold_start/runs/metrics/weight_grid.csv` | | ExtFig 2 | R-factor gap vs resolution (TorchRef − PHENIX & − REFMAC, AF-start, PHENIX-scored) | Plotting only — uses `figure2_alphafold_start/runs/metrics/fig_crossscore.csv` | | ExtFig 3 | R-factor scorer consistency (REFMAC / PHENIX / TorchRef cross-scoring) | Plotting only — uses `figure2_alphafold_start/runs/metrics/fig_crossscore.csv` | -| ExtFig 4 | F_calc splatting optimization breakdown (1DAW) | Requires CPU + GPU benchmarking | +| ExtFig 4 | Single-core wall-clock runtime, 3 engines (reproduces Fig 2c at 1 CPU core, AF-start, n=715) | Requires re-running all three engines — `exF4/submit_singlecore.py` (2145 cells) then `aggregate_singlecore.py` | +| ExtFig 5 | X-ray target-mode comparison (`ml` / `ml_noalpha` / `ml_full` / `nll_beta`, AF-start, PHENIX-scored) | Plotting only — uses `figure2_alphafold_start/runs/metrics/mode_*.csv` | See `extended_figures/instructions.md` for the design document. + +### ExtFig 5: reproducing the target-mode comparison + +Answers the reviewer question "does the choice of maximum-likelihood variant matter?" Reuses +Figure 2's exact 2×2 layout via `plot_figure_af.py --prefix/--engines`, so the two figures are +directly comparable — same scorer (`phenix.model_vs_data`), same conserved-set discipline. + +The `ml` cell **is** Figure 2's `torchref` arm; only the other three are extra runs. + +```bash +cd paper/figure2_alphafold_start +for m in ml_noalpha nll_beta; do + ../../.dev/bin/python analysis/submit_local_arm.py --arm mode_$m --xray-mode $m +done +# ml_full costs ~4x per gradient (32-node quadrature) +../../.dev/bin/python analysis/submit_local_arm.py --arm mode_ml_full --xray-mode ml_full \ + --partition day --time 04:00:00 --mem 16G + +# PHENIX-score the new arms (resume-safe; only the missing tasks are emitted) +../../.dev/bin/python analysis/build_crossscore_worklist.py \ + --arm ml_noalpha=mode_ml_noalpha --arm ml_full=mode_ml_full --arm nll_beta=mode_nll_beta +sbatch --array=1-N%60 analysis/crossscore_array.sh + +../../.dev/bin/python analysis/aggregate_figure_metrics.py --prefix mode \ + --arm ml=torchref:torchref --arm ml_noalpha=mode_ml_noalpha:torchref \ + --arm ml_full=mode_ml_full:torchref --arm nll_beta=mode_nll_beta:torchref + +../../.dev/bin/python plot_figure_af.py --prefix mode \ + -o ../extended_figures/exF5/output/extended_figure5.png \ + --engines ml_noalpha=ml_noalpha:#2166ac ml=ml:#b2182b \ + ml_full=ml_full:#762a83 nll_beta=nll_beta:#e08214 +``` + +Before submitting anything, `../../.dev/bin/python check_submitter_flags.py` asserts every +submitter still emits flags `refine.py` accepts — a removed or renamed flag otherwise kills +several thousand jobs at argparse, one at a time. diff --git a/paper/check_submitter_flags.py b/paper/check_submitter_flags.py new file mode 100644 index 00000000..542f3f9f --- /dev/null +++ b/paper/check_submitter_flags.py @@ -0,0 +1,403 @@ +#!/usr/bin/env python3 +"""Assert every benchmark submitter still emits flags its runner accepts. + +The submitters paste command-line flags into sbatch script bodies **as text**. Nothing +connects that text to the parser on the other end, so a renamed or removed flag goes stale +silently until a few thousand SLURM jobs each die at argparse. That is not hypothetical: the +x-ray target rework removed ``--sigma-m-scale`` and five live submitters kept passing it, so +the whole benchmark harness could not submit a single job. The same hole swallows the subtler +defect -- a flag whose *value* left ``choices`` (``--xray-mode gaussian`` after ``gaussian`` +was retired as an alias). + +This closes it, and **changes nothing under** ``torchref/``: the guard reaches into +``refine.py`` rather than asking ``refine.py`` to expose anything for it. A check that +requires editing the thing it checks is a worse check. + +Design +------ +Driven by the explicit :data:`SUBMITTERS` table rather than by grepping for anything that +looks like a command, because the submitters do not all target the same parser: +``run_warm.py`` has its own, and ``log_shapes.py`` is a ``runpy`` shim that forwards to +``refine.py`` after eating two flags of its own. A table makes that visible; a regex would +quietly validate one script against the wrong parser. + +Two levels of checking, by what the runner can hand over: + +* **full** -- the real :class:`argparse.ArgumentParser` is captured out of ``refine.main()`` + (see :func:`refine_parser`), so flag names *and* literal values are validated, ``choices`` + and ``type`` included. +* **names** -- the runner builds its parser inline and is not worth executing, so the option + strings are recovered by walking ``add_argument`` calls in the AST and only flag *names* + are checked. Weaker, and labelled as such in the output. + +Flag **values** are only ever checked where the submitter writes a literal +(``--xray-mode ml``). A value that comes from a placeholder (``{xray_mode}``, ``"$XMODE"``) is +replaced with one the parser accepts, so a placeholder can never be the reason this fails. +That split is deliberate: the campaign picks placeholder values at submit time, but a +*literal* that left ``choices`` is a bug in the file. + +The epilog examples in ``refine.py`` are checked the same way -- a documented command the +parser rejects is invisible until someone types it. + +Usage +----- + ./.dev/bin/python paper/check_submitter_flags.py # exit 1 on any failure + ./.dev/bin/python paper/check_submitter_flags.py -v # show every command checked +""" + +from __future__ import annotations + +import argparse +import ast +import re +import shlex +import sys +from pathlib import Path + +HERE = Path(__file__).resolve().parent # paper/ +REPO = HERE.parent + +sys.path.insert(0, str(REPO)) + +#: One row per submitter: the token naming the runner inside the sbatch body, and which parser +#: validates the flags. ``runner`` is a regex, and it is what makes detection unambiguous -- +#: these files also name their runner in docstrings and in keyword arguments, and only the +#: sbatch body is a command line. +#: +#: ``parser``: +#: ``("refine", None)`` -> the captured torchref.cli.refine parser [full check] +#: ``("ast", "")`` -> option strings from that file's add_argument calls +#: ``extra`` lists flags the runner consumes itself before delegating. +SUBMITTERS = [ + ("paper/figure2_alphafold_start/run_af_pipeline.py", r"\{REFINE_SCRIPT\}", ("refine", None), ()), + ("paper/figure2_alphafold_start/analysis/submit_local_arm.py", r"\{P\.REFINE_SCRIPT\}", ("refine", None), ()), + ("paper/figure2_alphafold_start/analysis/submit_weight_grid.py", r"\{P\.REFINE_SCRIPT\}", ("refine", None), ()), + ("paper/figure2_alphafold_start/analysis/submit_seeded_benchmark.py", r"\{P\.REFINE_SCRIPT\}", ("refine", None), ()), + ("paper/extended_figures/exF4/submit_singlecore.py", r"\{P\.REFINE_SCRIPT\}", ("refine", None), ()), + ("sigma_a_rework/submit_arms.py", r"\{refine\}", ("refine", None), ()), + ("sigma_a_rework/submit_arm_grid.py", r"\{refine\}", ("refine", None), ()), + ("sigma_a_rework/submit_weight_screen.py", r"\{refine\}", ("refine", None), ()), + # runpy shim over refine.py; it strips its own two flags and delegates the rest. + ("sigma_a_rework/estimator_lab/shapes_array.sh", r"log_shapes\.py", ("refine", None), + ("--shape-log", "--shape-code")), + # Own parser, in the same directory as its submitter. + ("paper/seeded_warm_corefine/submit_warm.py", r"\{RUNNER\}", + ("ast", "paper/seeded_warm_corefine/run_warm.py"), ()), +] + +#: A command line **begins** with the interpreter. Anchoring there is what separates the sbatch +#: body from prose that merely names the runner (``PYTHON, REFINE_SCRIPT, CCP4_SETUP, +#: _sbatch)``) or passes it as a keyword (``python=P.PYTHON, refine=P.REFINE_SCRIPT``) -- both +#: of which mention an interpreter *somewhere* on the line, so "appears before the runner" is +#: not enough. +RE_INTERPRETER = re.compile( + r"""^(?:\w+\s*=\s*\(?\s*)? # `body = (` -- a body built by concatenation + (?:f?["']\s*)? # the opening f-string quote + (?:\{[^{}]*PYTHON[^{}]*\} # {PYTHON} / {P.PYTHON} + |\{python\} # lowercase template field + |"?\$\{?PY\w*\}?"? # "$PY" / ${PYTHON} + |\S*/python[\d.]* # an absolute interpreter path + |python[\d.]*)\s""", + re.VERBOSE) + +#: A line of a Python body assembled by *concatenating* f-strings, e.g. +#: ``body = (f"{P.PYTHON} -u {P.REFINE_SCRIPT} \\\\\\n"`` or its last part +#: ``f" -n 10 --mode separate")``. Keyed on the **opening** quote, not on a trailing ``\\n`` +#: escape: the final part of a concatenation has no trailing newline, and missing it leaves a +#: stray ``f`` and ``)`` in the argv. A plain shell line never opens with a quote, which is +#: what makes this a safe discriminator -- stripping quote tails unconditionally would eat the +#: closing quote of ``--weights '{wjson}'``. +RE_FSTRING_PART = re.compile(r"""^\s*(?:\w+\s*=\s*\(?\s*)?f["']""") + +#: A placeholder whose value is only known at submit time: an f-string field (``{mode}``), a +#: shell variable (``$MODE`` / ``"$MODE"`` / ``${MODE}``), or a leftover ``%s``. +RE_PLACEHOLDER = re.compile(r"\{[^{}]*\}|\$\{?\w+\}?|%[sdf]") + +SENTINEL = "\x00" + + +# ── locating the invocation ─────────────────────────────────────────────────── + +def is_invocation(line: str, runner: re.Pattern) -> bool: + """True if `line` *is* a shell invocation of `runner`, not a mention of it.""" + return bool(runner.search(line)) and bool(RE_INTERPRETER.match(line.lstrip())) + + +def _command_text(line: str): + """One source line reduced to its shell-command text, plus whether it continues. + + Two spellings, both in use here, and they must be told apart. A plain shell line ends with + one backslash and is otherwise verbatim. A concatenated-f-string part opens with a quote + and carries a quote/paren/comma tail plus an escaped newline that have to come off -- but + stripping those unconditionally would eat the closing quote of ``--weights '{wjson}'`` on + a plain line and break tokenizing. + """ + s = line.rstrip("\n").rstrip() + if RE_FSTRING_PART.match(s): + s = RE_FSTRING_PART.sub("", s) # the opening quote + s = re.sub(r"""["'),]+$""", "", s).rstrip() # the closing quote / paren / comma + if s.endswith("\\n"): + s = s[:-2].rstrip() # the escaped newline + cont = s.endswith("\\") + return s.rstrip("\\").rstrip(), cont + + +def continued_block(lines, start): + """`start` plus every line the previous one continues onto.""" + out, i = [lines[start]], start + while i < len(lines) and _command_text(lines[i])[1]: + i += 1 + if i < len(lines): + out.append(lines[i]) + return out + + +def to_argv(block, runner: re.Pattern): + """Flatten an invocation block into ``(argv, literal_mask)``, blanking placeholders. + + ``literal_mask[i]`` is True when ``argv[i]`` came from the file verbatim; a placeholder + contributes ``None``. The caller needs that to know which values are worth validating. + """ + text = " ".join(_command_text(ln)[0] for ln in block) + # Everything up to and including the runner token is the interpreter + script path. + text = text[runner.search(text).end():] + # A placeholder is replaced **in place**, keeping the surrounding word intact: + # `${CODE}_af.pdb` is one path, not a sentinel plus a stray `_af.pdb` positional, and + # `'{wjson}'` keeps its balanced quotes so shlex does not choke on a field we are not + # validating anyway. + marked = RE_PLACEHOLDER.sub(f"PH{SENTINEL}", text) + try: + words = shlex.split(marked) + except ValueError as exc: + raise ValueError(f"cannot tokenize: {exc}") from exc + argv, literal = [], [] + for w in words: + argv.append(None if SENTINEL in w else w) + literal.append(SENTINEL not in w) + return argv, literal + + +# ── the two parser providers ────────────────────────────────────────────────── + +class _Captured(Exception): + def __init__(self, parser): + self.parser = parser + + +def refine_parser(): + """The real ``refine.py`` parser, captured without modifying ``refine.py``. + + ``main()`` builds its parser inline and there is no factory to import, so rather than + asking the library to export one for this script's benefit, patch ``parse_args`` to hand + the parser back and abort. ``main()`` does nothing but ``add_argument`` calls before that + point, so nothing runs and no refinement starts. + + The alternative -- re-declaring the flag surface here -- would be a second copy of exactly + the thing whose drift this script exists to detect. + """ + import torchref.cli.refine as refine + + def grab(self, *a, **kw): + raise _Captured(self) + + orig = argparse.ArgumentParser.parse_args + argparse.ArgumentParser.parse_args = grab + try: + refine.main() + except _Captured as c: + return c.parser + finally: + argparse.ArgumentParser.parse_args = orig + raise RuntimeError( + "refine.main() returned without calling parse_args -- the capture hook is stale; " + "check whether refine.py now builds its parser somewhere else.") + + +def flags_from_ast(rel): + """Option strings from a script that builds its parser inline. + + Returns a flag -> expects_a_value mapping, recovered from literal ``add_argument`` string + arguments. Names only -- no types, no choices. + """ + tree = ast.parse((REPO / rel).read_text(), filename=rel) + out = {} + for node in ast.walk(tree): + if not (isinstance(node, ast.Call) and isinstance(node.func, ast.Attribute) + and node.func.attr == "add_argument"): + continue + opts = [a.value for a in node.args + if isinstance(a, ast.Constant) and isinstance(a.value, str) + and a.value.startswith("-")] + kw = {k.arg: k.value for k in node.keywords} + action = kw.get("action") + store_flag = (isinstance(action, ast.Constant) + and action.value in ("store_true", "store_false", "count")) + for o in opts: + out[o] = not store_flag + return out + + +# ── checking ────────────────────────────────────────────────────────────────── + +def _actions(parser): + """flag -> (action, expects_a_value) for a real parser.""" + out = {} + for a in parser._actions: + needs = a.nargs != 0 and not isinstance( + a, (argparse._StoreTrueAction, argparse._StoreFalseAction, + argparse._HelpAction, argparse._CountAction)) + for opt in a.option_strings: + out[opt] = (a, needs) + return out + + +def _stand_in(action): + """A value this action will certainly accept, for use where the file has a placeholder.""" + if action is None: + return "PLACEHOLDER" + if action.choices: + return str(list(action.choices)[0]) + if action.type is int: + return "1" + if action.type is float: + return "1.0" + return "PLACEHOLDER" + + +def check_argv(argv, literal, where, problems, *, parser=None, flags=None, + extra=(), verbose=False): + """Validate one argv. With `parser`, values are checked too; with `flags`, names only.""" + known = _actions(parser) if parser is not None else { + f: (None, needs) for f, needs in flags.items()} + for e in extra: + known.setdefault(e, (None, True)) + + resolved, i = [], 0 + while i < len(argv): + tok = argv[i] + if tok is None: # placeholder in flag position: nothing to check + i += 1 + continue + if tok.startswith("-") and len(tok) > 1 and not tok[1:2].isdigit(): + flag = tok.split("=", 1)[0] + if flag not in known: + problems.append(f"{where}: unknown flag {flag!r}") + # Swallow its value too, if it looks like one: a retired flag's argument + # would otherwise reappear as a bare positional and produce a second, + # confusing complaint about the same one defect. + i += 1 + if i < len(argv) and (argv[i] is None or not argv[i].startswith("-")): + i += 1 + continue + action, needs = known[flag] + keep = flag not in extra # runner-private flags never reach the parser + if keep: + resolved.append(tok) + if needs and "=" not in tok: + nxt_literal = i + 1 < len(argv) and literal[i + 1] + if keep: + resolved.append(argv[i + 1] if nxt_literal else _stand_in(action)) + i += 2 + continue + i += 1 + continue + resolved.append(tok) + i += 1 + + if parser is None: # names-only: nothing left to validate + if verbose: + print(f" [names] {' '.join(resolved)}") + return + + # Required arguments come from placeholders in every submitter; supply them so the only + # thing this can fail on is the flag surface itself. + for a in parser._actions: + if a.required and a.option_strings and a.option_strings[0] not in resolved: + resolved += [a.option_strings[0], _stand_in(a)] + + if verbose: + print(f" {' '.join(resolved)}") + try: + parser.parse_args(resolved) + except SystemExit: + problems.append(f"{where}: argparse rejected: {' '.join(resolved)}") + + +def epilog_commands(parser): + for raw in (parser.epilog or "").splitlines(): + line = raw.strip() + if line.startswith("torchref.refine"): + yield line + + +def main(): + ap = argparse.ArgumentParser(description=__doc__, + formatter_class=argparse.RawDescriptionHelpFormatter) + ap.add_argument("-v", "--verbose", action="store_true", + help="Print every resolved command line that gets parsed.") + args = ap.parse_args() + + problems, n_checked = [], 0 + rp = refine_parser() + + print("── refine.py epilog examples ──") + n_epilog = 0 + for cmd in epilog_commands(rp): + argv = shlex.split(cmd)[1:] # drop the program name + check_argv(argv, [True] * len(argv), f"refine.py epilog: {cmd}", problems, + parser=rp, verbose=args.verbose) + n_checked += 1 + n_epilog += 1 + if args.verbose: + print(f" {cmd}") + if not n_epilog: + problems.append("refine.py: no epilog examples found (did the epilog move?)") + print(f" {n_epilog} example(s)") + + print("── submitters ──") + for rel, runner, (kind, target), extra in SUBMITTERS: + path = REPO / rel + if not path.exists(): + problems.append(f"{rel}: MISSING (listed in SUBMITTERS but not on disk)") + continue + parser = rp if kind == "refine" else None + flags = None if parser is not None else flags_from_ast(target) + runner_re = re.compile(runner) + lines = path.read_text().splitlines(keepends=True) + found = 0 + for i, line in enumerate(lines): + if not is_invocation(line.rstrip("\n"), runner_re): + continue + found += 1 + where = f"{rel}:{i + 1}" + if args.verbose: + print(f" {where}") + try: + argv, literal = to_argv(continued_block(lines, i), runner_re) + except ValueError as exc: + problems.append(f"{where}: {exc}") + continue + check_argv(argv, literal, where, problems, parser=parser, flags=flags, + extra=extra, verbose=args.verbose) + n_checked += 1 + if not found: + # Silence here would read as "clean" when it means "found nothing to check" -- + # the way a grep-for-survivors check goes vacuous. + problems.append( + f"{rel}: no {runner!r} invocation found (renamed runner, or moved?)") + else: + level = "full" if parser is not None else "names-only" + print(f" {rel}: {found} invocation(s) [{level}]") + + print() + if problems: + print(f"FAILED — {len(problems)} problem(s):") + for p in problems: + print(f" * {p}") + return 1 + print(f"OK — {n_checked} command line(s) accepted by their runner's parser.") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/paper/compare_to_archive.py b/paper/compare_to_archive.py new file mode 100644 index 00000000..9578e3c8 --- /dev/null +++ b/paper/compare_to_archive.py @@ -0,0 +1,161 @@ +#!/usr/bin/env python3 +"""Paired per-structure comparison of one metric between two benchmark CSVs. + +Every figure in this tree is a median over a structure set, and a median moves for two very +different reasons: the metric really shifted, or the *set* changed (a structure that failed +before now succeeds, or vice versa). Comparing two medians cannot tell those apart. This +pairs on the structure code, so the reported delta is a difference on the same structures and +nothing else, and it says out loud which codes each side is missing. + +Used to gate the re-run: refine one cell of the ExtFig 1 weight grid with the current build +and compare it, structure by structure, against the archived pre-rework grid at the same +cell. 50 jobs decide whether the remaining 4950 are worth submitting. + +Reports the signed median delta (sign convention: **new minus old**, so negative is better +for an R-factor), win/loss counts, a Wilcoxon signed-rank p-value, and a bootstrap CI on the +median delta. The bootstrap is there because a median's uncertainty is not something to +eyeball -- and because a half-split estimate of it is unreliable at these n. + +Usage +----- + # ExtFig 1 locked cell, current build vs the archive + ./.dev/bin/python paper/compare_to_archive.py \\ + --old paper/archive/figures_pre_mlrework/metrics/weight_grid.csv \\ + --new paper/figure2_alphafold_start/runs/metrics/weight_grid.csv \\ + --filter gi=7 --filter ai=4 --metric r_work --metric r_free + + # Figure 2 R-factors, one engine + ./.dev/bin/python paper/compare_to_archive.py \\ + --old paper/archive/figures_pre_mlrework/metrics/fig_rfactors.csv \\ + --new paper/figure2_alphafold_start/runs/metrics/fig_rfactors.csv \\ + --filter engine=torchref --metric r_work --metric r_free +""" + +from __future__ import annotations + +import argparse +import csv +import statistics as st +import sys +from pathlib import Path + +import numpy as np +from scipy import stats + + +def fnum(x): + try: + v = float(x) + except (TypeError, ValueError): + return None + return None if (v != v) else v # drop NaN + + +def load(path, key, metrics, filters): + """-> {code: {metric: value}}, keeping only rows that match every filter.""" + path = Path(path) + if not path.exists(): + sys.exit(f"missing {path}") + out, n_rows, n_kept = {}, 0, 0 + with open(path) as f: + rdr = csv.DictReader(f) + missing = [c for c in [key, *metrics, *filters] if c not in (rdr.fieldnames or [])] + if missing: + sys.exit(f"{path}: no such column(s) {missing}; has {rdr.fieldnames}") + for r in rdr: + n_rows += 1 + if any(str(r[c]) != str(v) for c, v in filters.items()): + continue + n_kept += 1 + vals = {m: fnum(r[m]) for m in metrics} + if all(v is None for v in vals.values()): + continue # a failed run: no metric at all + if r[key] in out: + sys.exit(f"{path}: duplicate {key}={r[key]!r} after filtering -- the filter " + f"is not selective enough to pair on {key}") + out[r[key]] = vals + print(f" {path.name}: {n_rows} rows, {n_kept} after filter, {len(out)} usable") + return out + + +def report(name, old, new, rng): + pairs = [(c, old[c][name], new[c][name]) for c in sorted(set(old) & set(new)) + if old[c][name] is not None and new[c][name] is not None] + if not pairs: + print(f"\n{name}: no paired values") + return + o = np.array([p[1] for p in pairs]) + n = np.array([p[2] for p in pairs]) + d = n - o # new minus old: negative = new is better + + med_d = float(np.median(d)) + # Percentile bootstrap on the median of the paired differences. Resampling the DIFFERENCES + # (not each arm separately) is what keeps the pairing intact. + boot = np.median(rng.choice(d, size=(20000, d.size), replace=True), axis=1) + lo, hi = (float(x) for x in np.percentile(boot, [2.5, 97.5])) + + wins = int((d < 0).sum()) # new better + losses = int((d > 0).sum()) + ties = int((d == 0).sum()) + try: + p = float(stats.wilcoxon(d).pvalue) if (d != 0).any() else 1.0 + except ValueError: + p = float("nan") + + verdict = "NEW BETTER" if hi < 0 else "NEW WORSE" if lo > 0 else "no clear difference" + print(f"\n{name} (n paired = {len(pairs)})") + print(f" median old {float(np.median(o)):.4f}") + print(f" median new {float(np.median(n)):.4f}") + print(f" median delta {med_d:+.4f} 95% CI [{lo:+.4f}, {hi:+.4f}] -> {verdict}") + print(f" new better/worse {wins}/{losses} (ties {ties}) Wilcoxon p = {p:.3g}") + worst = sorted(pairs, key=lambda t: t[2] - t[1])[-5:][::-1] + print(f" biggest regressions: " + + ", ".join(f"{c} {b - a:+.4f}" for c, a, b in worst)) + print(f" mean |delta| {float(np.abs(d).mean()):.4f} " + f"max |delta| {float(np.abs(d).max()):.4f}") + + +def main(): + ap = argparse.ArgumentParser(description=__doc__, + formatter_class=argparse.RawDescriptionHelpFormatter) + ap.add_argument("--old", required=True, help="Baseline CSV (e.g. the archive).") + ap.add_argument("--new", required=True, help="CSV from the current build.") + ap.add_argument("--key", default="code", help="Column to pair on (default 'code').") + ap.add_argument("--metric", action="append", required=True, + help="Metric column; repeatable.") + ap.add_argument("--filter", action="append", default=[], metavar="COL=VAL", + help="Keep only rows with COL == VAL; repeatable. Applied to BOTH files.") + ap.add_argument("--seed", type=int, default=0, help="Bootstrap seed (default 0).") + args = ap.parse_args() + + filters = {} + for f in args.filter: + if "=" not in f: + sys.exit(f"--filter expects COL=VAL, got {f!r}") + c, v = f.split("=", 1) + filters[c] = v + + print("Paired comparison — sign convention: NEW minus OLD (negative = new better)") + if filters: + print(" filter: " + " ".join(f"{c}={v}" for c, v in filters.items())) + old = load(args.old, args.key, args.metric, filters) + new = load(args.new, args.key, args.metric, filters) + + only_old, only_new = sorted(set(old) - set(new)), sorted(set(new) - set(old)) + print(f" paired {len(set(old) & set(new))} only-old {len(only_old)} " + f"only-new {len(only_new)}") + # Named, not just counted: an unpaired structure is the mechanism by which a median moves + # without any structure changing, so it must never be silent. + if only_old: + print(f" missing from new: {' '.join(only_old)}") + if only_new: + print(f" missing from old: {' '.join(only_new)}") + + rng = np.random.default_rng(args.seed) + for m in args.metric: + report(m, old, new, rng) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/paper/extended_figures/exF1/compare_ablation.py b/paper/extended_figures/exF1/compare_ablation.py new file mode 100644 index 00000000..69a3f3db --- /dev/null +++ b/paper/extended_figures/exF1/compare_ablation.py @@ -0,0 +1,132 @@ +#!/usr/bin/env python3 +"""3-way comparison of the exF1 weight grids: baseline vs no-SIMU vs locality-only. + +For each grid, per cell take the MEDIAN over the common structure set of: + r_free, gap (=r_free-r_work), mc_b_rmsz (main-chain bond B RMSZ), mean bond/angle RMSZ. +Report, per grid: + * the locked default cell (geometry 0.2, adp 0.02), and + * the best-R_free cell (min median r_free), with its coords. + +The key question: does removing SIMU (then also the global/KL restraint) degrade +the achievable R_free / overfitting gap / main-chain B behaviour? +""" +import argparse +import csv +from collections import defaultdict +from pathlib import Path +from statistics import median + +BASE = Path(__file__).resolve().parent +METRICS = BASE.parent.parent / "figure2_alphafold_start" / "runs" / "metrics" +ABL = BASE / "ablation_runs" / "metrics" +ARCHIVE = BASE.parent.parent / "archive" / "figures_pre_mlrework" / "metrics" +GRIDS = [ + ("baseline (simu+loc+KL)", METRICS / "weight_grid.csv"), + ("no-SIMU (loc+KL)", ABL / "weight_grid_nosimu.csv"), + ("locality-only (no SIMU/KL)", ABL / "weight_grid_nosimu_nokl.csv"), +] + +#: Named grid sets, so the same per-cell reduction serves more than the SIMU/KL ablation it +#: was written for. `rework` is the one that matters after a numerics change: current build vs +#: the archived pre-rework grid, which answers "did the weight landscape move". +GRID_SETS = { + "ablation": GRIDS, + "rework": [ + ("pre-rework (archive)", ARCHIVE / "weight_grid.csv"), + ("current build", METRICS / "weight_grid.csv"), + ], +} + + +def fnum(x): + try: + return float(x) + except (TypeError, ValueError): + return None + + +def load(path): + """-> {(gi,ai): {geometry, adp, codes:{code:{r_free,gap,mcb,geo}}}}""" + cells = {} + for r in csv.DictReader(open(path)): + gi, ai = int(r["gi"]), int(r["ai"]) + rf, rw = fnum(r["r_free"]), fnum(r["r_work"]) + b, a = fnum(r["bond_rmsz"]), fnum(r["angle_rmsz"]) + mcb = fnum(r["mc_b_rmsz"]) + c = cells.setdefault((gi, ai), {"geometry": float(r["geometry"]), + "adp": float(r["adp"]), "codes": {}}) + c["codes"][r["code"]] = { + "r_free": rf, "gap": (rf - rw) if (rf is not None and rw is not None) else None, + "mcb": mcb, "geo": ((b + a) / 2) if (b is not None and a is not None) else None, + } + return cells + + +def main(): + ap = argparse.ArgumentParser(description=__doc__, + formatter_class=argparse.RawDescriptionHelpFormatter) + ap.add_argument("--set", default="ablation", choices=sorted(GRID_SETS), + help="Which grid set to compare (default 'ablation'). 'rework' = the " + "archived pre-rework grid vs the current build.") + ap.add_argument("--grids", nargs="+", default=None, metavar="LABEL=PATH", + help="Explicit grid list, overriding --set.") + args = ap.parse_args() + + if args.grids: + chosen = [] + for spec in args.grids: + if "=" not in spec: + raise SystemExit(f"--grids expects LABEL=PATH, got {spec!r}") + label, path = spec.split("=", 1) + chosen.append((label, Path(path))) + else: + chosen = GRID_SETS[args.set] + + grids = [(name, load(p)) for name, p in chosen if p.exists()] + for name, p in chosen: + if not any(name == n for n, _ in grids): + # Loud, because a silently absent grid turns a two-way comparison into a one-way + # table that still prints and still looks like a result. + print(f" (MISSING grid: {name} -> {p})") + if len(grids) < len(chosen): + print(f" !! {len(grids)} of {len(chosen)} grids loaded — the comparison is partial") + + def med_over(cell, common, key): + vals = [cell["codes"][c][key] for c in common + if c in cell["codes"] and cell["codes"][c][key] is not None] + return median(vals) if vals else float("nan") + + import math + + def nearest(cells, gt, at): + return min(cells, key=lambda k: abs(math.log(cells[k]["geometry"]) - math.log(gt)) + + abs(math.log(cells[k]["adp"]) - math.log(at))) + + print(f"\n{'grid':<30} {'cell(geom,adp)':<18} {'R_free':>7} {'gap':>7} " + f"{'MC-B RMSZ':>10} {'geo RMSZ':>9}") + print("-" * 86) + for name, cells in grids: + common = set.intersection(*[set(c["codes"]) for c in cells.values()]) \ + if cells else set() + # default cell + kd = nearest(cells, 0.2, 0.02) + cd = cells[kd] + print(f"{name:<30} default {cd['geometry']:.3g}/{cd['adp']:.3g}".ljust(48) + + f"{med_over(cd, common, 'r_free'):>7.4f} " + f"{med_over(cd, common, 'gap'):>7.4f} " + f"{med_over(cd, common, 'mcb'):>10.3f} " + f"{med_over(cd, common, 'geo'):>9.3f}") + # best R_free cell + kb = min(cells, key=lambda k: med_over(cells[k], common, 'r_free')) + cb = cells[kb] + print(f"{'':<30} best {cb['geometry']:.3g}/{cb['adp']:.3g}".ljust(48) + + f"{med_over(cb, common, 'r_free'):>7.4f} " + f"{med_over(cb, common, 'gap'):>7.4f} " + f"{med_over(cb, common, 'mcb'):>10.3f} " + f"{med_over(cb, common, 'geo'):>9.3f} (n={len(common)})") + print("-" * 86) + print("MC-B RMSZ ~1.0 = ideal B-restraint behaviour; gap lower = less overfit.") + + +if __name__ == "__main__": + main() diff --git a/paper/extended_figures/exF1/compare_fine_grid.py b/paper/extended_figures/exF1/compare_fine_grid.py new file mode 100644 index 00000000..a7c60d57 --- /dev/null +++ b/paper/extended_figures/exF1/compare_fine_grid.py @@ -0,0 +1,141 @@ +#!/usr/bin/env python3 +"""Paired comparison of every fine-grid weight cell against the locked default. + +The fine grid brackets the library default (geometry 0.2 / adp 0.02) one coarse step +either side at 1.29x per step, so its centre cell *is* the default and doubles as a +null control. + +Each cell is compared to that centre cell by the **median of the per-structure +R-free differences** over the structures both refined, with a bootstrap CI on that +median and a Wilcoxon signed-rank p. Difference-of-medians is reported alongside +only for contrast: it is not the paired statistic and can differ in sign. + +Usage +----- + ./.dev/bin/python paper/extended_figures/exF1/compare_fine_grid.py + ./.dev/bin/python paper/extended_figures/exF1/compare_fine_grid.py --tag "" # coarse grid +""" +from __future__ import annotations + +import argparse +import csv +from collections import defaultdict +from pathlib import Path + +import numpy as np +from scipy.stats import wilcoxon + +HERE = Path(__file__).resolve().parent +METRICS = HERE.parent.parent / "figure2_alphafold_start" / "runs" / "metrics" + +#: The locked default the grid is centred on (torchref/refinement/base_refinement.py). +DEFAULT_GEOM, DEFAULT_ADP = 0.2, 0.02 + + +def load(tag: str): + suffix = f"_{tag}" if tag else "" + path = METRICS / f"weight_grid{suffix}.csv" + if not path.exists(): + raise SystemExit(f"missing {path}; run aggregate_weight_grid.py --tag {tag} first") + cells = defaultdict(dict) # (gi, ai) -> {code: r_free} + coords = {} # (gi, ai) -> (geometry, adp) + for r in csv.DictReader(open(path)): + key = (int(r["gi"]), int(r["ai"])) + cells[key][r["code"]] = float(r["r_free"]) + coords[key] = (float(r["geometry"]), float(r["adp"])) + return cells, coords + + +def manifest_coords(tag: str): + """(gi, ai) -> (geometry, adp) for every cell the grid defines, populated or not.""" + suffix = f"_{tag}" if tag else "" + path = METRICS / f"wgrid{suffix}_manifest.csv" + if not path.exists(): + raise SystemExit(f"missing {path}; run submit_weight_grid.py first") + return {(int(r["gi"]), int(r["ai"])): (float(r["geometry"]), float(r["adp"])) + for r in csv.DictReader(open(path))} + + +def boot_median_ci(d, n_boot=10000, seed=0): + rng = np.random.default_rng(seed) + idx = rng.integers(0, len(d), size=(n_boot, len(d))) + meds = np.median(np.asarray(d)[idx], axis=1) + return float(np.percentile(meds, 2.5)), float(np.percentile(meds, 97.5)) + + +def main(tag): + cells, coords = load(tag) + # Pick the baseline from the MANIFEST, not from the cells that happen to have rows: + # on a partial grid the nearest *populated* cell can silently stand in for the + # default and every delta would then be measured against the wrong baseline. + # Which cell is nearest the default is a property of the grid, not of its progress. + manifest = manifest_coords(tag) + centre = min(manifest, + key=lambda k: (abs(manifest[k][0] - DEFAULT_GEOM) + + abs(manifest[k][1] - DEFAULT_ADP))) + if centre not in cells: + cg, ca = manifest[centre] + raise SystemExit( + f"the grid's default cell {centre} (geometry {cg:.4g} / adp {ca:.4g}) has no " + f"rows yet — nothing to compare against. Wait for the grid to finish.") + cg, ca = coords[centre] + print(f"grid tag={tag or '(coarse)'} cells={len(cells)}") + print(f"centre cell {centre} = geometry {cg:.4g} / adp {ca:.4g}" + f" (library default {DEFAULT_GEOM} / {DEFAULT_ADP})") + print(f"centre cell structures: {len(cells[centre])}\n") + + rows = [] + for key, per_code in cells.items(): + if key == centre: + continue + common = sorted(set(per_code) & set(cells[centre])) + if len(common) < 20: + continue + d = [per_code[c] - cells[centre][c] for c in common] # negative = cell better + lo, hi = boot_median_ci(d) + p = wilcoxon(d).pvalue if any(x != 0 for x in d) else 1.0 + rows.append({ + "gi": key[0], "ai": key[1], + "geometry": coords[key][0], "adp": coords[key][1], + "n": len(common), "median_delta": float(np.median(d)), + "ci_low": lo, "ci_high": hi, "wilcoxon_p": p, + "better": sum(1 for x in d if x < 0), "worse": sum(1 for x in d if x > 0), + "delta_of_medians": float(np.median(list(per_code.values())) + - np.median(list(cells[centre].values()))), + }) + + rows.sort(key=lambda r: r["median_delta"]) + print("cells that BEAT the default (paired median ΔR-free < 0), best first:") + beat = [r for r in rows if r["median_delta"] < 0] + if not beat: + print(" none.") + hdr = (f" {'geom':>7} {'adp':>8} {'n':>4} {'medΔ':>9} {'95% CI':>20} " + f"{'wilcox p':>10} {'bet/wor':>9} {'Δmedians':>9}") + print(hdr) + for r in beat: + print(f" {r['geometry']:7.4g} {r['adp']:8.4g} {r['n']:4d} " + f"{r['median_delta']:+9.5f} " + f"[{r['ci_low']:+.5f}, {r['ci_high']:+.5f}] {r['wilcoxon_p']:10.2e} " + f"{r['better']:4d}/{r['worse']:<4d} {r['delta_of_medians']:+9.5f}") + + sig = [r for r in beat if r["ci_high"] < 0] + print(f"\n{len(beat)}/{len(rows)} cells have a negative paired median; " + f"{len(sig)} have a 95% CI entirely below zero.") + if not sig: + print("=> no cell beats the locked default at 95% confidence.") + + out = METRICS / f"weight_grid{'_' + tag if tag else ''}_paired.csv" + with open(out, "w", newline="") as fh: + w = csv.DictWriter(fh, fieldnames=list(rows[0].keys())) + w.writeheader() + w.writerows(rows) + print(f"\nwrote {out} ({len(rows)} cells)") + + +if __name__ == "__main__": + ap = argparse.ArgumentParser(description=__doc__, + formatter_class=argparse.RawDescriptionHelpFormatter) + ap.add_argument("--tag", default="fine", + help="Grid tag; reads weight_grid_.csv (default 'fine'). " + "Pass an empty string for the baseline coarse grid.") + main(**vars(ap.parse_args())) diff --git a/paper/extended_figures/exF1/sweep_adp_axis.py b/paper/extended_figures/exF1/sweep_adp_axis.py new file mode 100644 index 00000000..887d39ab --- /dev/null +++ b/paper/extended_figures/exF1/sweep_adp_axis.py @@ -0,0 +1,93 @@ +#!/usr/bin/env python3 +"""Does the weight optimum shift when SIMU/KL are removed? + +For each grid, walk the adp-weight axis (at the geometry weight nearest 0.2) and +report median R_free / gap / main-chain-B RMSZ per adp weight. Also report, per +grid, the BEST cell anywhere for two objectives: + * min R_free (best data fit) + * min R_free subject to MC-B RMSZ<=2 (good fit AND well-behaved bonded B) +to see whether locality-only can reach baseline B-geometry at some weight, and +what R_free that costs. +""" +import csv +import math +from pathlib import Path +from statistics import median + +BASE = Path(__file__).resolve().parent +METRICS = BASE.parent.parent / "figure2_alphafold_start" / "runs" / "metrics" +ABL = BASE / "ablation_runs" / "metrics" +GRIDS = [ + ("baseline", METRICS / "weight_grid.csv"), + ("no-SIMU", ABL / "weight_grid_nosimu.csv"), + ("loc-only", ABL / "weight_grid_nosimu_nokl.csv"), +] + + +def fnum(x): + try: + return float(x) + except (TypeError, ValueError): + return None + + +def load(path): + cells = {} + for r in csv.DictReader(open(path)): + gi, ai = int(r["gi"]), int(r["ai"]) + rf, rw = fnum(r["r_free"]), fnum(r["r_work"]) + mcb = fnum(r["mc_b_rmsz"]) + c = cells.setdefault((gi, ai), {"geometry": float(r["geometry"]), + "adp": float(r["adp"]), "rows": []}) + c["rows"].append({"r_free": rf, + "gap": (rf - rw) if (rf and rw is not None) else None, + "mcb": mcb}) + return cells + + +def med(cell, key): + v = [r[key] for r in cell["rows"] if r[key] is not None] + return median(v) if v else float("nan") + + +def main(): + for name, path in GRIDS: + if not path.exists(): + print(f"(missing {name})") + continue + cells = load(path) + gis = sorted({k[0] for k in cells}) + # geometry index nearest 0.2 + gi0 = min(gis, key=lambda gi: abs(math.log(cells[(gi, 0)]["geometry"]) + - math.log(0.2))) + gval = cells[(gi0, 0)]["geometry"] + print(f"\n=== {name}: adp-axis sweep at geometry={gval:.3g} ===") + print(f"{'adp_w':>8} {'R_free':>7} {'gap':>7} {'MC-B RMSZ':>10}") + for ai in sorted({k[1] for k in cells if k[0] == gi0}): + c = cells[(gi0, ai)] + print(f"{c['adp']:>8.4g} {med(c,'r_free'):>7.4f} " + f"{med(c,'gap'):>7.4f} {med(c,'mcb'):>10.3f}") + # best cells anywhere + keys = list(cells) + best_rf = min(keys, key=lambda k: med(cells[k], 'r_free')) + cb = cells[best_rf] + feas = [k for k in keys if med(cells[k], 'mcb') <= 2.0] + print(f" best R_free anywhere: {med(cb,'r_free'):.4f} " + f"@ geom={cb['geometry']:.3g}/adp={cb['adp']:.3g} " + f"(MC-B {med(cb,'mcb'):.2f}, gap {med(cb,'gap'):.3f})") + if feas: + kb = min(feas, key=lambda k: med(cells[k], 'r_free')) + cf = cells[kb] + print(f" best R_free with MC-B<=2: {med(cf,'r_free'):.4f} " + f"@ geom={cf['geometry']:.3g}/adp={cf['adp']:.3g} " + f"(MC-B {med(cf,'mcb'):.2f}, gap {med(cf,'gap'):.3f})") + else: + best_mcb = min(keys, key=lambda k: med(cells[k], 'mcb')) + cm = cells[best_mcb] + print(f" NO cell reaches MC-B<=2; best MC-B={med(cm,'mcb'):.2f} " + f"@ geom={cm['geometry']:.3g}/adp={cm['adp']:.3g} " + f"(R_free {med(cm,'r_free'):.4f})") + + +if __name__ == "__main__": + main() diff --git a/paper/extended_figures/exF4/codes_conserved.txt b/paper/extended_figures/exF4/codes_conserved.txt index 05df2857..91eeb2d1 100644 --- a/paper/extended_figures/exF4/codes_conserved.txt +++ b/paper/extended_figures/exF4/codes_conserved.txt @@ -58,6 +58,7 @@ 1L9X 1LNZ 1M6E +1N0L 1N6J 1O4V 1O5T @@ -94,6 +95,7 @@ 1TGS 1TML 1TVL +1U1I 1UEF 1UJK 1UKV @@ -337,6 +339,7 @@ 3KOL 3KXR 3KZP +3L1W 3L2H 3L6F 3LLI @@ -464,6 +467,7 @@ 4JHN 4JX2 4K36 +4KH9 4KYQ 4L7Z 4L9U @@ -493,6 +497,7 @@ 4PV4 4Q13 4Q63 +4QH8 4QKO 4QN8 4QQR @@ -524,6 +529,7 @@ 5AD0 5B5I 5B5Q +5BOV 5BV6 5C5V 5C7F @@ -594,6 +600,7 @@ 5U6K 5UQP 5UV4 +5UX5 5UXG 5UZU 5V8W @@ -640,6 +647,7 @@ 6FJU 6FOK 6FX6 +6GBS 6GP7 6GXS 6HFG diff --git a/paper/extended_figures/exF4/data/exF4_singlecore.csv b/paper/extended_figures/exF4/data/exF4_singlecore.csv index f2c7473d..8065be0e 100644 --- a/paper/extended_figures/exF4/data/exF4_singlecore.csv +++ b/paper/extended_figures/exF4/data/exF4_singlecore.csv @@ -1,2146 +1,2170 @@ program,code,wall_s -torchref,1A0F,177.577 -torchref,1A46,150.139 -torchref,1A4I,312.778 -torchref,1A7V,102.062 -torchref,1AJ6,93.841 -torchref,1AK5,194.955 -torchref,1ANU,94.803 -torchref,1APX,217.738 -torchref,1AQZ,132.741 -torchref,1B9K,121.221 -torchref,1BIA,119.336 -torchref,1BM7,352.438 -torchref,1BX0,388.632 -torchref,1BXE,367.800 -torchref,1BYW,344.136 -torchref,1C3F,373.126 -torchref,1CI0,386.409 -torchref,1CSM,430.403 -torchref,1DAW,392.450 -torchref,1DLM,437.559 -torchref,1DQ3,398.362 -torchref,1DQ8,585.335 -torchref,1E79,773.398 -torchref,1EJ1,369.966 -torchref,1EJH,445.452 -torchref,1ETE,404.350 -torchref,1EZW,497.289 -torchref,1F97,337.295 -torchref,1FG3,365.414 -torchref,1FIO,361.488 -torchref,1FKM,410.998 -torchref,1FSI,435.570 -torchref,1GHW,149.375 -torchref,1GPQ,254.981 -torchref,1GQ8,137.317 -torchref,1GQ9,120.106 -torchref,1GUI,127.692 -torchref,1GYJ,97.603 -torchref,1H8V,293.010 -torchref,1HN0,270.453 -torchref,1HNO,143.093 -torchref,1HTN,102.926 -torchref,1I1N,137.544 -torchref,1I3A,146.403 -torchref,1IDQ,217.186 -torchref,1IKN,220.899 -torchref,1ITK,375.250 -torchref,1J3P,161.611 -torchref,1J53,142.306 -torchref,1J5W,229.597 -torchref,1J8B,95.276 -torchref,1JAC,166.602 -torchref,1JAD,309.158 -torchref,1JN9,320.266 -torchref,1JU3,453.268 -torchref,1K6D,326.168 -torchref,1K7T,287.532 -torchref,1L9X,561.457 -torchref,1LNZ,317.389 -torchref,1M6E,286.443 -torchref,1N6J,279.296 -torchref,1O4V,286.530 -torchref,1O5T,308.382 -torchref,1O6D,254.600 -torchref,1OBR,304.112 -torchref,1OC0,291.201 -torchref,1OE4,303.443 -torchref,1OMW,416.097 -torchref,1OSC,324.583 -torchref,1OY5,335.438 -torchref,1P3C,297.550 -torchref,1P4X,323.222 -torchref,1PG3,375.818 -torchref,1PJR,286.800 -torchref,1PXZ,275.381 -torchref,1Q15,417.875 -torchref,1QB5,227.233 -torchref,1QD9,364.589 -torchref,1QJV,358.074 -torchref,1QZN,335.560 -torchref,1R1K,347.301 -torchref,1R3U,345.780 -torchref,1RFX,360.895 -torchref,1RHC,365.111 -torchref,1RJ1,319.246 -torchref,1RJD,559.535 -torchref,1RKG,339.350 -torchref,1RZN,345.808 -torchref,1S1C,323.726 -torchref,1SO2,493.034 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+refmac,6U75,12.208 +refmac,6UB7,11.625 +refmac,6V7M,7.550 +refmac,6VD8,6.268 +refmac,6VHI,5.766 +refmac,6VPT,12.005 +refmac,6VS7,27.687 +refmac,6W9K,21.678 +refmac,6WT6,10.571 +refmac,6XZ6,14.010 +refmac,6YFV,21.050 +refmac,6Z1S,17.707 +refmac,6Z3U,25.249 +refmac,6ZH1,11.664 +refmac,6ZUW,11.953 +refmac,7AUI,8.337 +refmac,7BV5,26.598 +refmac,7BW1,17.229 +refmac,7C7E,9.010 +refmac,7CFD,42.396 +refmac,7CWW,13.305 +refmac,7D86,8.146 +refmac,7DUF,5.910 +refmac,7E2E,11.545 +refmac,7EWF,17.880 +refmac,7F9K,6.200 +refmac,7F9M,16.554 +refmac,7FHR,26.262 +refmac,7JI4,5.972 +refmac,7JN9,5.874 +refmac,7K3J,18.822 +refmac,7KQ0,25.241 +refmac,7LF4,24.563 diff --git a/paper/extended_figures/exF4/data/singlecore_conserved.txt b/paper/extended_figures/exF4/data/singlecore_conserved.txt index 05df2857..91eeb2d1 100644 --- a/paper/extended_figures/exF4/data/singlecore_conserved.txt +++ b/paper/extended_figures/exF4/data/singlecore_conserved.txt @@ -58,6 +58,7 @@ 1L9X 1LNZ 1M6E +1N0L 1N6J 1O4V 1O5T @@ -94,6 +95,7 @@ 1TGS 1TML 1TVL +1U1I 1UEF 1UJK 1UKV @@ -337,6 +339,7 @@ 3KOL 3KXR 3KZP +3L1W 3L2H 3L6F 3LLI @@ -464,6 +467,7 @@ 4JHN 4JX2 4K36 +4KH9 4KYQ 4L7Z 4L9U @@ -493,6 +497,7 @@ 4PV4 4Q13 4Q63 +4QH8 4QKO 4QN8 4QQR @@ -524,6 +529,7 @@ 5AD0 5B5I 5B5Q +5BOV 5BV6 5C5V 5C7F @@ -594,6 +600,7 @@ 5U6K 5UQP 5UV4 +5UX5 5UXG 5UZU 5V8W @@ -640,6 +647,7 @@ 6FJU 6FOK 6FX6 +6GBS 6GP7 6GXS 6HFG diff --git a/paper/extended_figures/exF4/submit_singlecore.py b/paper/extended_figures/exF4/submit_singlecore.py index 534eb980..fd4679e9 100644 --- a/paper/extended_figures/exF4/submit_singlecore.py +++ b/paper/extended_figures/exF4/submit_singlecore.py @@ -36,7 +36,6 @@ RUNS = HERE / "runs" CODES_TXT = HERE / "codes_conserved.txt" -PHENIX_MODULE = "phenix/phenix-1.20-4459" PROGRAMS = ["torchref", "phenix", "refmac"] NCORES = 1 # single core: the whole point of this figure LABEL = "n1" @@ -49,6 +48,17 @@ PARTITION = {"torchref": "day", "phenix": "day", "refmac": "hour"} WALLTIME = {"torchref": "24:00:00", "phenix": "24:00:00", "refmac": "01:00:00"} +# One CPU model for all three programs, because this figure compares wall-clock ACROSS +# programs. EPYC 9335 is 2.2-2.7x faster than EPYC 7453 on identical code, and the +# partitions above are not the same node pool by default -- refmac on `hour` would draw a +# different hardware mix than torchref on `day`, which is a systematic per-engine bias, not +# noise that n=715 averages away. The same 29 epyc9335 nodes belong to hour/day/week, so +# constraining does not change which partition each program uses. Single-core jobs cannot be +# --exclusive at this scale, so co-tenancy noise remains -- but it now falls on all three +# engines from the same pool. Verify before changing: +# sinfo -h -p day -o "%n %f" | grep -c +CPU_MODEL = "cpu_epyc9335" + def cell_dir(program, code): return RUNS / program / code / LABEL @@ -58,6 +68,7 @@ def _header(program, code, outdir): return f"""#!/bin/bash #SBATCH --job-name=exF4sc_{program}_{code} #SBATCH --partition={PARTITION[program]} +#SBATCH --constraint={CPU_MODEL} #SBATCH --cpus-per-task={NCORES} #SBATCH --time={WALLTIME[program]} #SBATCH --mem={MEM} @@ -68,7 +79,7 @@ def _header(program, code, outdir): # Uniform timer: wraps the program call (a plain command, NOT a pipe, so $? is the # program's own exit code) and writes "wall_s \nrc " to timing.txt. No -# `set -e`: `module load` / `source ccp4` can return nonzero internally, and the rc field +# `set -e`: sourcing the phenix / ccp4 env can return nonzero internally, and the rc field # lets aggregation drop failed runs rather than record a bogus time. def _timed(body): return (f"T0=$(date +%s.%N)\n{body}\nRC=$?\nT1=$(date +%s.%N)\n" @@ -97,8 +108,9 @@ def script_phenix(code, pdb, mtz, outdir): # DISABLES automatic target-weight optimization (optimize_xyz/adp_weight=false) and # pins a fixed no-rigid-body strategy; phenix DEFAULTS instead run an expensive # per-macrocycle weight grid-search (~2.4x slower) that has nothing to do with cores. - body = (f'phenix.refine "$WORK/input.pdb" {mtz} \\\n' - f" --overwrite output.prefix=ref \\\n" + body = (f'phenix.refine --overwrite --quiet \\\n' + f' "$WORK/input.pdb" {mtz} \\\n' + f" output.prefix=ref \\\n" f" refinement.main.number_of_macro_cycles=10 \\\n" f" refinement.main.nproc={NCORES} \\\n" f" refinement.refine.strategy=individual_sites+individual_adp+occupancies \\\n" @@ -109,10 +121,10 @@ def script_phenix(code, pdb, mtz, outdir): f" refinement.main.ordered_solvent=false \\\n" f" refinement.ordered_solvent.mode=every_macro_cycle \\\n" f" refinement.pdb_interpretation.ramachandran_plot_restraints.enabled=false \\\n" - f" write_def_file=false write_eff_file=false write_geo_file=false --quiet") + f" write_def_file=false write_eff_file=false write_geo_file=false") return _header("phenix", code, outdir) + f""" OUTDIR={outdir} -module load {PHENIX_MODULE} +source {P.PHENIX_ENV} WORK=$(mktemp -d /tmp/exF4sc_phenix_{code}_XXXX) cd "$WORK" if grep -q "^CRYST1.*None" {pdb}; then @@ -170,8 +182,16 @@ def main(): submitted = missing = 0 first = True - for program in args.programs: - for code in codes: + # STRUCTURE-major, not program-major. This benchmark compares wall-clock ACROSS + # programs, and a full sweep spans hours during which cluster load changes. Submitting + # all of one program before the next times each engine in its own load window, so + # engine identity ends up confounded with time -- measured 2026-08-04: on a + # program-major sweep two programs whose code had not changed moved by -43% and +31% + # against the previous sweep, with per-structure IQRs spanning ~2x. Emitting a + # structure's three engines adjacently puts them under near-identical conditions, which + # is what makes the per-structure three-way comparison meaningful. + for code in codes: + for program in args.programs: pdb, mtz = P.PLACED / f"{code}_af.pdb", P._mtz(code) if not pdb.exists() or not mtz.exists(): missing += 1 diff --git a/paper/figure2_alphafold_start/analysis/aggregate_figure_metrics.py b/paper/figure2_alphafold_start/analysis/aggregate_figure_metrics.py index d9a486da..2dbaac34 100644 --- a/paper/figure2_alphafold_start/analysis/aggregate_figure_metrics.py +++ b/paper/figure2_alphafold_start/analysis/aggregate_figure_metrics.py @@ -122,6 +122,11 @@ def parse_geometry(path: Path): # ── runtime (program-reported wall-clock, seconds) ─────────────────────────── RE_TORCHREF_WALL = re.compile(r"Timing:\s+([\d.]+)s wall") RE_PHENIX_EPOCH = re.compile(r"#\s*Date .*?\(([\d.]+)\s*s\)") +# phenix's own total, in either spelling it uses: +# "wall clock time: 179.54 s" +# "wall clock time: 4 minutes 6.40 seconds (246.40 seconds total)" +RE_PHENIX_WALL = re.compile( + r"wall clock time:\s*(?:.*?\(([\d.]+)\s*seconds total\)|([\d.]+)\s*s)") RE_REFMAC_ELAPSED = re.compile(r"Elapsed:\s+(\d+):(\d+)") @@ -136,10 +141,20 @@ def runtime_torchref(engine: str, code: str): def runtime_phenix(engine: str, code: str): + """Phenix's own reported total, matching how the other two engines are timed. + + Falls back to differencing the ``# Date`` banners, which is only available when + phenix prints a start banner as well as a closing one. That measure runs about + 1 s short of the reported total, so it is a fallback rather than the primary. + """ p = struct_dir(engine, code) / f"{code}_refined_001.log" if not p.exists(): return None - epochs = [float(x) for x in RE_PHENIX_EPOCH.findall(p.read_text(errors="replace"))] + text = p.read_text(errors="replace") + m = RE_PHENIX_WALL.search(text) + if m: + return float(m.group(1) or m.group(2)) + epochs = [float(x) for x in RE_PHENIX_EPOCH.findall(text)] return (epochs[-1] - epochs[0]) if len(epochs) >= 2 else None diff --git a/paper/figure2_alphafold_start/analysis/build_crossscore_worklist.py b/paper/figure2_alphafold_start/analysis/build_crossscore_worklist.py index f620bbf7..41ffd026 100644 --- a/paper/figure2_alphafold_start/analysis/build_crossscore_worklist.py +++ b/paper/figure2_alphafold_start/analysis/build_crossscore_worklist.py @@ -14,8 +14,15 @@ Usage ----- ./.dev/bin/python paper/figure2_alphafold_start/analysis/build_crossscore_worklist.py + + # ExtFig 5 target-mode arms, PHENIX-scored so their cells are directly comparable to + # Figure 2's (whose headline R-factors come from this same scorer) + ... build_crossscore_worklist.py --arm ml_noalpha=mode_ml_noalpha \\ + --arm ml_full=mode_ml_full \\ + --arm nll_beta=mode_nll_beta """ +import argparse import re from pathlib import Path @@ -41,11 +48,44 @@ def done(log: Path) -> bool: return log.exists() and bool(RE_DONE.search(log.read_text(errors="replace"))) +def parse_arms(specs, models): + """Add ``NAME=DIR`` arms to `models`, in place. + + An extra arm is always a torchref-kind arm, so its final model is + ``//refined.pdb`` -- the layout ``submit_local_arm.py`` writes. Mirrors the + ``--arm`` option on ``aggregate_figure_metrics.py`` and on + ``build_torchref_score_worklist.py`` so one spelling serves all three. + """ + for spec in specs or []: + if "=" not in spec: + raise SystemExit(f"--arm expects NAME=DIR, got {spec!r}") + name, sub = spec.split("=", 1) + if name in models: + raise SystemExit(f"--arm {spec}: {name!r} already exists as " + f"{models[name][0]!r}; pick another name") + models[name] = (sub, sub + "/{code}/refined.pdb") + return models + + def main(): + ap = argparse.ArgumentParser(description=__doc__, + formatter_class=argparse.RawDescriptionHelpFormatter) + ap.add_argument("--arm", action="append", default=None, metavar="NAME=DIR", + help="Extra torchref-kind arm to score, e.g. " + "--arm ml_full=mode_ml_full. Repeatable.") + ap.add_argument("--force", action="store_true", + help="Re-score even where a phenix_validate.log already holds an r_free. " + "The default resume-skip is what makes this script safe to re-run " + "after a partial array, so only use this when the MODELS changed " + "under a name that already has logs.") + args = ap.parse_args() + + models = parse_arms(args.arm, dict(MODELS)) + XDIR.mkdir(parents=True, exist_ok=True) (XDIR / "slurm").mkdir(exist_ok=True) # SLURM won't create the log dir itself - codes = sorted({d.name for sub, _ in MODELS.values() + codes = sorted({d.name for sub, _ in models.values() for d in (RUNS / sub).iterdir() if (RUNS / sub).is_dir() and d.is_dir()}) @@ -55,7 +95,7 @@ def main(): if not mtz.exists(): nomtz += 1 continue - for eng, (sub, patt) in MODELS.items(): + for eng, (sub, patt) in models.items(): pdb = (RUNS / patt.format(code=code)).resolve() if not pdb.exists(): nomodel += 1 @@ -64,14 +104,21 @@ def main(): if not log.parent.is_dir(): nomodel += 1 continue - if done(log): + if done(log) and not args.force: skipped += 1 continue lines.append(f"{eng}\t{code}\t{pdb}\t{mtz}\t{log}") WORKLIST.write_text("\n".join(lines) + ("\n" if lines else "")) + # Report the arm set, not just the totals: "tasks=767" looks the same whether four arms + # contributed or one did, and an --arm typo shows up here as a missing row rather than as + # a silently short worklist. + print("arms: " + " ".join(f"{e}->{sub}" for e, (sub, _) in models.items())) + per_arm = {e: sum(1 for ln in lines if ln.split("\t")[0] == e) for e in models} + print("tasks per arm: " + " ".join(f"{e}={n}" for e, n in per_arm.items())) print(f"codes={len(codes)} tasks={len(lines)} already_done={skipped} " - f"missing_mtz={nomtz} missing_model={nomodel}") + f"missing_mtz={nomtz} missing_model={nomodel}" + + (" [--force]" if args.force else "")) print(f"worklist: {WORKLIST}") if lines: print(f"submit: sbatch --array=1-{len(lines)}%60 " diff --git a/paper/figure2_alphafold_start/analysis/build_torchref_score_worklist.py b/paper/figure2_alphafold_start/analysis/build_torchref_score_worklist.py index 4852ffdb..0a0899d8 100644 --- a/paper/figure2_alphafold_start/analysis/build_torchref_score_worklist.py +++ b/paper/figure2_alphafold_start/analysis/build_torchref_score_worklist.py @@ -13,8 +13,23 @@ Usage ----- ./.dev/bin/python paper/figure2_alphafold_start/analysis/build_torchref_score_worklist.py + + # ExtFig 5 target-mode arms, scored alongside the four Figure-2 engines + ... build_torchref_score_worklist.py --arm ml_noalpha=mode_ml_noalpha \\ + --arm ml_full=mode_ml_full + + # Re-score everything: the TorchRef scorer itself changed + ... build_torchref_score_worklist.py --force + +``--force`` matters more here than it looks. This scorer runs ``LBFGSRefinement.get_scales()``, +which fits scales through ``ScalerBase.refine_lbfgs(scale_target=...)`` -- so when the +``scale_target`` default changed, **every** stored ``torchref_validate.json`` became a number +from a different scorer, including those of the reference arms nobody re-refined. Without +``--force`` the resume-skip keeps them and ExtFig 3 silently mixes two scorer versions. +Snapshot the old JSONs before forcing: they are the only record of the previous scorer. """ +import argparse import json from pathlib import Path @@ -42,11 +57,55 @@ def done(js: Path) -> bool: return False +def parse_arms(specs, models): + """Add ``NAME=DIR`` arms to `models`, in place. + + An extra arm is always a torchref-kind arm, so its final model is + ``//refined.pdb`` -- the layout ``submit_local_arm.py`` writes. Mirrors the + ``--arm`` option on ``aggregate_figure_metrics.py`` so one spelling serves both. + """ + for spec in specs or []: + if "=" not in spec: + raise SystemExit(f"--arm expects NAME=DIR, got {spec!r}") + name, sub = spec.split("=", 1) + if name in models: + raise SystemExit(f"--arm {spec}: {name!r} already exists as " + f"{models[name][0]!r}; pick another name") + models[name] = (sub, sub + "/{code}/refined.pdb") + return models + + def main(): + ap = argparse.ArgumentParser(description=__doc__, + formatter_class=argparse.RawDescriptionHelpFormatter) + ap.add_argument("--arm", action="append", default=None, metavar="NAME=DIR", + help="Extra torchref-kind arm to score, e.g. " + "--arm ml_full=mode_ml_full. Repeatable.") + ap.add_argument("--force", action="store_true", + help="Re-score even where the out-name already holds a " + "result. Needed whenever the SCORER changed (see the module " + "docstring) -- the default resume-skip would keep stale numbers.") + ap.add_argument("--out-name", default="torchref_validate.json", + help="Filename written inside each code directory. Point a re-score " + "at a NEW name rather than --force-ing over the old one when the " + "old numbers still back a figure: two scorer versions then sit " + "side by side and stay comparable.") + ap.add_argument("--only", nargs="*", default=None, metavar="ENGINE", + help="Restrict to these engines (default: all).") + args = ap.parse_args() + + models = parse_arms(args.arm, dict(MODELS)) + if args.only: + unknown = set(args.only) - set(models) + if unknown: + raise SystemExit(f"--only: unknown engine(s) {sorted(unknown)}; " + f"have {sorted(models)}") + models = {k: v for k, v in models.items() if k in args.only} + XDIR.mkdir(parents=True, exist_ok=True) (XDIR / "slurm_tr").mkdir(exist_ok=True) - codes = sorted({d.name for sub, _ in MODELS.values() + codes = sorted({d.name for sub, _ in models.values() for d in (RUNS / sub).iterdir() if (RUNS / sub).is_dir() and d.is_dir()}) @@ -56,20 +115,27 @@ def main(): if not mtz.exists(): nomtz += 1 continue - for eng, (sub, patt) in MODELS.items(): + for eng, (sub, patt) in models.items(): pdb = (RUNS / patt.format(code=code)).resolve() - out = RUNS / sub / code / "torchref_validate.json" + out = RUNS / sub / code / args.out_name if not pdb.exists() or not out.parent.is_dir(): nomodel += 1 continue - if done(out): + if done(out) and not args.force: skipped += 1 continue lines.append(f"{eng}\t{code}\t{pdb}\t{mtz}\t{out}") WORKLIST.write_text("\n".join(lines) + ("\n" if lines else "")) + # Report the arm set, not just the totals: "tasks=767" looks the same whether four arms + # contributed or one did, and an --arm typo shows up here as a missing row rather than as + # a silently short worklist. + print("arms: " + " ".join(f"{e}->{sub}" for e, (sub, _) in models.items())) + per_arm = {e: sum(1 for ln in lines if ln.split("\t")[0] == e) for e in models} + print("tasks per arm: " + " ".join(f"{e}={n}" for e, n in per_arm.items())) print(f"codes={len(codes)} tasks={len(lines)} already_done={skipped} " - f"missing_mtz={nomtz} missing_model={nomodel}") + f"missing_mtz={nomtz} missing_model={nomodel}" + + (" [--force]" if args.force else "")) print(f"worklist: {WORKLIST}") if lines: print(f"submit: sbatch --array=1-{len(lines)}%60 " diff --git a/paper/figure2_alphafold_start/analysis/crossscore_array.sh b/paper/figure2_alphafold_start/analysis/crossscore_array.sh index 04c61f2b..a46ead81 100644 --- a/paper/figure2_alphafold_start/analysis/crossscore_array.sh +++ b/paper/figure2_alphafold_start/analysis/crossscore_array.sh @@ -16,7 +16,7 @@ set +o nounset # `module load phenix` errors on these nodes (modulefile uses an unsupported # `module-url` command), so source the phenix env directly. Edit PHENIX_ENV to # switch versions; this is the only place the version is pinned. -PHENIX_ENV=/opt/psi/MX/phenix/phenix-1.20-4459/phenix_env.sh +PHENIX_ENV=/opt/psi/MX/phenix/1.21.1-5286/phenix-1.21.1-5286/phenix_env.sh source "$PHENIX_ENV" WORKLIST=/das/work/units/LBR-FEL/p17490/Peter/Library/work_trees_torchref/dev/paper/figure2_alphafold_start/runs/crossscore/worklist.txt diff --git a/paper/figure2_alphafold_start/analysis/submit_fig2_arms.py b/paper/figure2_alphafold_start/analysis/submit_fig2_arms.py new file mode 100644 index 00000000..1842e52c --- /dev/null +++ b/paper/figure2_alphafold_start/analysis/submit_fig2_arms.py @@ -0,0 +1,145 @@ +#!/usr/bin/env python3 +"""Submit all three Figure-2 refinement arms, STRUCTURE-major. + +Figure 2 compares TorchRef, REFMAC and phenix.refine on the same Phaser-placed +AlphaFold models. Panel C plots their wall-clock, so *how* the jobs are submitted +is part of the measurement: + +* **Structure-major, not engine-major.** A full sweep spans hours during which + cluster load changes. Submitting all of one engine before the next times each + engine in its own load window, so engine identity ends up confounded with time + — measured 2026-08-04 on the exF4 sweep: two programs whose code had not + changed moved by -43% and +31% against the previous sweep, with per-structure + IQRs spanning ~2x. Emitting a structure's three arms adjacently puts them under + near-identical conditions. +* **One CPU model.** `hour`/`day` span Xeon 6152/6230/6230R and EPYC 7453/9335, + and EPYC 9335 is 2.2-2.7x faster than 7453 on identical code. Without a + constraint each engine draws a different hardware mix, which is a systematic + per-engine bias rather than noise that n=767 averages away. + +Both properties are what `extended_figures/exF4/submit_singlecore.py` already +does for the 1-core re-timing; this makes the 4-core figure match. + +Each arm's cell is built by the code that owns that arm — TorchRef by +``submit_local_arm.build_script`` (locked DEFAULT_GROUP_WEIGHTS, in-job REFMAC +0-cycle validation), REFMAC and phenix by ``run_af_pipeline.script_refmac`` / +``script_phenix`` — so there is one definition of each arm, not two. + +Resume-safe: a cell whose output already exists is skipped (TorchRef keys on a +complete ``validate.log`` because it validates in-job; the other two key on their +final PDB, which ``run_af_pipeline.py validate`` scores afterwards). + +Usage +----- + ./.dev/bin/python analysis/submit_fig2_arms.py --dry-run --limit 1 + ./.dev/bin/python analysis/submit_fig2_arms.py --limit 2 # smoke test + ./.dev/bin/python analysis/submit_fig2_arms.py # all solved +""" +from __future__ import annotations + +import argparse +import sys +from pathlib import Path + +HERE = Path(__file__).resolve().parent +sys.path.insert(0, str(HERE.parent)) +sys.path.insert(0, str(HERE)) +import run_af_pipeline as P # noqa: E402 +import submit_local_arm # noqa: E402 + +ENGINES = ["torchref", "refmac", "phenix"] +ARM_DIR = {"torchref": "torchref", "refmac": "refmac", "phenix": "phenix_norb"} + + +def _cell(engine, code, pdb, mtz, outdir, n_cycles, constraint, mem): + """(script_text, script_path) for one (engine, code) cell.""" + if engine == "torchref": + return (submit_local_arm.build_script( + "torchref", code, pdb, mtz, outdir, n_cycles, mem, + constraint=constraint), + outdir.parent / "tmp_scripts" / f"ref_{code}.sh") + build = P.script_refmac if engine == "refmac" else P.script_phenix + return (build(code, pdb, mtz, outdir, cycles=n_cycles, constraint=constraint), + outdir.parent / "tmp_scripts" / f"{engine}_{code}.sh") + + +def _done(engine, code, outdir): + """Already-complete cells are skipped so a partial sweep can be resumed.""" + if engine == "torchref": + # Validates in-job, so a refined.pdb without a complete validate.log is a + # half-finished cell and must be redone. + return P._refmac_complete(outdir / "validate.log") + return P.model_path(code, ARM_DIR[engine]).exists() + + +def main(): + ap = argparse.ArgumentParser( + description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter + ) + ap.add_argument("--engines", nargs="+", default=ENGINES, choices=ENGINES, + help="Subset of arms to submit (default: all three). Note a " + "subset re-times only those engines; Panel C needs all " + "three from one sweep.") + ap.add_argument("--n-cycles", type=int, default=10, + help="Macro cycles for every engine (default 10, the " + "canonical benchmark count that exF4 re-times).") + ap.add_argument("--constraint", default=P.CPU_MODEL, + help=f"SLURM --constraint for every cell (default " + f"{P.CPU_MODEL}).") + ap.add_argument("--torchref-mem", default="8G", + help="SLURM --mem for the TorchRef cells (8G is the " + "benchmark default; the largest structures OOM there " + "and are reported as failures).") + ap.add_argument("--codes", nargs="+", default=None) + ap.add_argument("--limit", type=int, default=None) + ap.add_argument("--dry-run", action="store_true") + ap.add_argument("--force", action="store_true", + help="Re-submit cells that already have output.") + args = ap.parse_args() + + codes = args.codes if args.codes else P.load_solved_codes() + if args.limit: + codes = codes[:args.limit] + + print(f"Figure-2 arms: {', '.join(args.engines)} build=DEV(in-tree)") + print(f"n_cycles={args.n_cycles} constraint={args.constraint} " + f"order=STRUCTURE-major") + print(f"Python: {P.PYTHON}") + print(f"{len(codes)} solved structures x {len(args.engines)} arms = " + f"{len(codes) * len(args.engines)} cells\n") + + submitted = {e: 0 for e in args.engines} + skipped = {e: 0 for e in args.engines} + missing = 0 + shown = False + + for code in codes: + pdb, mtz = P.PLACED / f"{code}_af.pdb", P._mtz(code) + if not pdb.exists() or not mtz.exists(): + missing += 1 + continue + for engine in args.engines: + outdir = P.RUNS / ARM_DIR[engine] / code + if _done(engine, code, outdir) and not args.force: + skipped[engine] += 1 + continue + if not args.dry_run: + outdir.mkdir(parents=True, exist_ok=True) + script, path = _cell(engine, code, pdb, mtz, outdir, args.n_cycles, + args.constraint, args.torchref_mem) + if args.dry_run and not shown: + print(f"── example sbatch script ({engine}) ──") + print(script) + print("──────────────────────────────────────") + shown = True + if P._sbatch(script, path, args.dry_run) or args.dry_run: + submitted[engine] += 1 + + print(f"missing inputs: {missing} structures") + for e in args.engines: + print(f" {e:<9} submitted={submitted[e]:>4} skipped={skipped[e]:>4}") + print(f"\ntotal submitted={sum(submitted.values())}") + + +if __name__ == "__main__": + main() diff --git a/paper/figure2_alphafold_start/analysis/submit_local_arm.py b/paper/figure2_alphafold_start/analysis/submit_local_arm.py index 7c674ade..a3f68a5e 100644 --- a/paper/figure2_alphafold_start/analysis/submit_local_arm.py +++ b/paper/figure2_alphafold_start/analysis/submit_local_arm.py @@ -6,6 +6,11 @@ locked ``DEFAULT_GROUP_WEIGHTS`` (xray 1 / geometry 0.2 / adp 0.02); separate scaler (corefine off, the LBFGSRefinement default), no rigid body. +``--xray-mode`` selects the body target so the same submitter also produces the +target-mode comparison arms (ExtFig 5). Always pair it with a distinct ``--arm``: +the skip logic keys on ``validate.log``, so two modes under one arm name would +silently leave whichever ran first in place. + Because this lives under the dev worktree, ``run_af_pipeline``'s ``PYTHON`` / ``REFINE_SCRIPT`` already point at the dev build — no override needed. @@ -35,7 +40,9 @@ import run_af_pipeline as P # noqa: E402 -def build_script(arm, code, pdb, mtz, outdir, n_cycles, mem, weights=None): +def build_script(arm, code, pdb, mtz, outdir, n_cycles, mem, weights=None, + xray_mode="ml", partition="hour", time="01:00:00", + constraint=P.CPU_MODEL): # Group weights default to DEFAULT_GROUP_WEIGHTS (xray=1 / geom=0.2 / # adp=0.02) — no flag needed. Pass an explicit {xray, geometry, adp} dict # to override and record the exact weights in the job script (merged onto @@ -46,9 +53,10 @@ def build_script(arm, code, pdb, mtz, outdir, n_cycles, mem, weights=None): ) return f"""#!/bin/bash #SBATCH --job-name=af_{arm}_{code} -#SBATCH --partition=hour +#SBATCH --partition={partition} +#SBATCH --constraint={constraint} #SBATCH --cpus-per-task=4 -#SBATCH --time=01:00:00 +#SBATCH --time={time} #SBATCH --mem={mem} #SBATCH --output={outdir / 'out.log'} #SBATCH --error={outdir / 'error.log'} @@ -60,8 +68,7 @@ def build_script(arm, code, pdb, mtz, outdir, n_cycles, mem, weights=None): -m {pdb} -sf {mtz} -o {outdir} \\ -n {n_cycles} \\ --mode separate \\ - --xray-mode ml \\ - --sigma-m-scale 1.0{weights_line} + --xray-mode {xray_mode}{weights_line} # REFMAC 0-cycle validation on the refined model (apples-to-apples R-factors) source {P.CCP4_SETUP} @@ -86,6 +93,12 @@ def main(): ) ap.add_argument("--arm", default="torchref", help="Output arm name under runs/ (default torchref).") + ap.add_argument("--xray-mode", default="ml", + help="Body target (--xray-mode passed to refine.py). Default 'ml', " + "the canonical arm. Use a distinct --arm for anything else so " + "the modes land in separate directories; 'ml_full' additionally " + "needs --partition day --time 04:00:00 --mem 16G (~4x the " + "per-gradient cost of the 32-node quadrature).") ap.add_argument("--n-cycles", type=int, default=10) ap.add_argument("--xray-weight", type=float, default=None, help="Override the xray group weight (default 1).") @@ -103,6 +116,15 @@ def main(): help="SLURM --mem per job (8G is the benchmark default, " "matching the exF4 single-core allocation; the largest " "structures OOM at 8G and are reported as failures).") + ap.add_argument("--partition", default="hour", + help="SLURM partition (default hour; use day for ml_full).") + ap.add_argument("--time", default="01:00:00", + help="SLURM --time per job (default 01:00:00).") + ap.add_argument("--constraint", default=P.CPU_MODEL, + help="SLURM --constraint (default run_af_pipeline.CPU_MODEL). " + "Every arm whose runtime is plotted must share one CPU " + "model, or engine identity is confounded with hardware " + "generation.") ap.add_argument("--codes", nargs="+", default=None) ap.add_argument("--limit", type=int, default=None) ap.add_argument("--dry-run", action="store_true") @@ -131,7 +153,9 @@ def main(): weights = weights or None wdesc = "DEFAULT_GROUP_WEIGHTS (1/0.2/0.02)" if weights is None else json.dumps(weights) print(f"Arm: {args.arm} build=DEV(in-tree) n_cycles={args.n_cycles} " - f"xray-mode=ml weights={wdesc}") + f"xray-mode={args.xray_mode} weights={wdesc}") + print(f"SLURM: partition={args.partition} time={args.time} mem={args.mem} " + f"constraint={args.constraint}") print(f"Python: {P.PYTHON}") print(f"Output: {arm_dir}\n{len(codes)} solved structures\n") @@ -148,7 +172,10 @@ def main(): if not args.dry_run: outdir.mkdir(parents=True, exist_ok=True) script = build_script(args.arm, code, pdb, mtz, outdir, args.n_cycles, - args.mem, weights=weights) + args.mem, weights=weights, + xray_mode=args.xray_mode, + partition=args.partition, time=args.time, + constraint=args.constraint) if args.dry_run and submitted == 0: print("── example sbatch script ──") print(script) diff --git a/paper/figure2_alphafold_start/analysis/submit_seeded_benchmark.py b/paper/figure2_alphafold_start/analysis/submit_seeded_benchmark.py new file mode 100644 index 00000000..f81ec9a6 --- /dev/null +++ b/paper/figure2_alphafold_start/analysis/submit_seeded_benchmark.py @@ -0,0 +1,136 @@ +#!/usr/bin/env python3 +"""Submit the 3-arm seeded-optimizer benchmark over a fixed 100-structure subset. + +For each code in ``subset_100.txt`` this submits three SLURM jobs, each running +refinement (from the Phaser-placed AF start model) + a REFMAC 0-cycle validation +in one shot so the apples-to-apples R-factors land in ``validate.log``: + + bench_sep_lbfgs -n 10 --mode separate --xray-mode ml (production standard) + bench_joint_lbfgs -n 10 --mode everything --xray-mode ml (joint control) + bench_joint_seeded -n 10 --mode everything --optimizer seeded (+ diagonal seed) + +`sep→joint` isolates separate-vs-joint; `joint_lbfgs→joint_seeded` isolates the +diagonal-Hessian seed; `sep→seeded` compares the new approach to the shipped default. + +Usage +----- + ./.dev/bin/python analysis/submit_seeded_benchmark.py --dry-run --limit 1 + ./.dev/bin/python analysis/submit_seeded_benchmark.py --limit 1 # one real triple + ./.dev/bin/python analysis/submit_seeded_benchmark.py # all 100 x 3 +""" + +from __future__ import annotations + +import argparse +import sys +from pathlib import Path + +HERE = Path(__file__).resolve().parent +sys.path.insert(0, str(HERE.parent)) +import run_af_pipeline as P # noqa: E402 + +# arm name -> extra refine.py flags (everything else identical across arms) +ARMS = { + "bench_sep_lbfgs": "--mode separate --xray-mode ml", + "bench_joint_lbfgs": "--mode everything --xray-mode ml", + "bench_joint_seeded": "--mode everything --optimizer seeded --xray-mode ml", + # scaler co-refinement arms (scaler folded into the joint step) + "bench_joint_seeded_coref": "--mode everything --optimizer seeded --corefine-scaler --xray-mode ml", + "bench_joint_lbfgs_coref": "--mode everything --corefine-scaler --xray-mode ml", +} + + +def build_script(arm, code, pdb, mtz, outdir, n_cycles, mem, refine_flags): + return f"""#!/bin/bash +#SBATCH --job-name=sb_{arm}_{code} +#SBATCH --partition=day +#SBATCH --cpus-per-task=4 +#SBATCH --time=04:00:00 +#SBATCH --mem={mem} +#SBATCH --output={outdir / 'out.log'} +#SBATCH --error={outdir / 'error.log'} + +set -e +export TORCHREF_NUM_THREADS=4 + +{P.PYTHON} -u {P.REFINE_SCRIPT} \\ + -m {pdb} -sf {mtz} -o {outdir} \\ + -n {n_cycles} \\ + {refine_flags} + +# REFMAC 0-cycle validation on the refined model (apples-to-apples R-factors) +source {P.CCP4_SETUP} +TEMP_DIR=/tmp/validate_{arm}_{code}_${{SLURM_JOB_ID}} +mkdir -p $TEMP_DIR && cd $TEMP_DIR +export CCP4_SCR=$TEMP_DIR +cp {outdir / 'refined.pdb'} input.pdb +cp {mtz} input.mtz +refmac5 HKLIN input.mtz HKLOUT output.mtz XYZIN input.pdb XYZOUT output.pdb \\ + > {outdir / 'validate.log'} 2>&1 << EOF +NCYCLES 0 +MAKE HYDR NO +END +EOF +cd / && rm -rf $TEMP_DIR +""" + + +def main(): + ap = argparse.ArgumentParser( + description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter + ) + ap.add_argument("--codes-file", default=str(HERE / "subset_100.txt")) + ap.add_argument("--n-cycles", type=int, default=10) + ap.add_argument("--mem", default="16G", + help="SLURM --mem per job (16G recovers the large structures " + "that OOM at the 8G canonical default).") + ap.add_argument("--arms", nargs="+", default=list(ARMS), + choices=list(ARMS)) + ap.add_argument("--codes", nargs="+", default=None, + help="Override the subset with explicit codes.") + ap.add_argument("--limit", type=int, default=None) + ap.add_argument("--dry-run", action="store_true") + ap.add_argument("--force", action="store_true", + help="Re-submit even if validate.log already complete.") + args = ap.parse_args() + + if args.codes: + codes = args.codes + else: + codes = [c.strip() for c in Path(args.codes_file).read_text().split() + if c.strip()] + if args.limit: + codes = codes[:args.limit] + + print(f"Subset: {len(codes)} codes x {len(args.arms)} arms n_cycles={args.n_cycles}" + f" mem={args.mem}") + print(f"Python: {P.PYTHON}\nArms: {args.arms}\n") + + submitted = skipped = missing = 0 + for code in codes: + pdb, mtz = P.PLACED / f"{code}_af.pdb", P._mtz(code) + if not pdb.exists() or not mtz.exists(): + missing += 1 + continue + for arm in args.arms: + outdir = P.RUNS / arm / code + if P._refmac_complete(outdir / "validate.log") and not args.force: + skipped += 1 + continue + if not args.dry_run: + outdir.mkdir(parents=True, exist_ok=True) + script = build_script(arm, code, pdb, mtz, outdir, args.n_cycles, + args.mem, ARMS[arm]) + if args.dry_run and submitted == 0: + print(f"── example sbatch script ({arm}) ──") + print(script) + print("───────────────────────────") + tmp = P.RUNS / arm / "tmp_scripts" + if P._sbatch(script, tmp / f"ref_{code}.sh", args.dry_run) or args.dry_run: + submitted += 1 + + print(f"\nsubmitted={submitted}, skipped={skipped}, missing={missing}") + + +if __name__ == "__main__": + main() diff --git a/paper/figure2_alphafold_start/analysis/submit_weight_grid.py b/paper/figure2_alphafold_start/analysis/submit_weight_grid.py index 6c3b4fe3..19087867 100644 --- a/paper/figure2_alphafold_start/analysis/submit_weight_grid.py +++ b/paper/figure2_alphafold_start/analysis/submit_weight_grid.py @@ -16,6 +16,18 @@ ----- ./.dev/bin/python analysis/submit_weight_grid.py --dry-run ./.dev/bin/python analysis/submit_weight_grid.py # submit + +Stage it rather than firing all 5000 tasks blind: run the locked default cell on its own, +check it against the previous grid's numbers for the same cell, and release the rest only if +they agree. Indices and arm names are unaffected by ``--only-cells``, so both halves +aggregate together. + + ./.dev/bin/python analysis/submit_weight_grid.py --array --only-cells 7,4 + # ... compare, then: + ./.dev/bin/python analysis/submit_weight_grid.py --array + +NB ``--dry-run`` still (re)writes the manifest -- pre-existing behaviour, and the reason a +dry run with a *different* ``--n-geom``/``--n-adp`` would overwrite the real grid's manifest. """ from __future__ import annotations @@ -52,7 +64,6 @@ def build_script(arm, code, pdb, mtz, outdir, n_cycles, mem, weights): -n {n_cycles} \\ --mode separate \\ --xray-mode ml \\ - --sigma-m-scale 1.0 \\ --weights '{wjson}' # REFMAC 0-cycle validation (validate.log -> R-factors + geometry RMS/sigma) @@ -100,7 +111,6 @@ def build_array_script(name, tasks_file, n_tasks, logdir, n_cycles, mem, throttl -n {n_cycles} \\ --mode separate \\ --xray-mode ml \\ - --sigma-m-scale 1.0 \\ --weights "$WEIGHTS" # REFMAC 0-cycle validation (validate.log -> R-factors + geometry RMS/sigma) @@ -144,6 +154,16 @@ def main(): help="Base directory for this grid's arm output + manifest " "(default: figure2 runs/). Use a dedicated dir for " "ablation grids to keep runs/ uncluttered.") + ap.add_argument("--only-cells", nargs="+", default=None, metavar="GI,AI", + help="Submit only these grid cells, e.g. --only-cells 7,4. The axes are " + "built in full first, so indices, arm names and the manifest are " + "IDENTICAL to a full run and the remaining cells can be released " + "later into the same aggregation. Use this to stage the grid: run " + "the locked default cell alone, check it against the previous " + "grid's numbers, and only then release the other 99 -- 50 jobs " + "instead of 5000 before you know whether the build agrees with " + "history. An out-of-range index is an error, not an empty " + "submission.") ap.add_argument("--array", action="store_true", help="Submit the whole grid as ONE SLURM job array instead " "of one job per (cell, structure). Much lighter on the " @@ -169,7 +189,27 @@ def main(): if args.limit: codes = codes[:args.limit] - # Manifest: arm -> (gi, ai, geometry, adp), exact weights for aggregation. + # Parsed AFTER the axes exist, so the indices mean the same thing they would in a full + # run and can be validated against the real extent. A typo'd cell must fail here rather + # than submit an empty work list and look like "everything was already complete". + only = None + if args.only_cells: + only = set() + for spec in args.only_cells: + try: + gi, ai = (int(x) for x in spec.split(",")) + except ValueError: + raise SystemExit(f"--only-cells: expected GI,AI, got {spec!r}") + if not (0 <= gi < args.n_geom and 0 <= ai < args.n_adp): + raise SystemExit( + f"--only-cells {spec}: out of range for a " + f"{args.n_geom}x{args.n_adp} grid (gi 0..{args.n_geom - 1}, " + f"ai 0..{args.n_adp - 1})") + only.add((gi, ai)) + + # Manifest: arm -> (gi, ai, geometry, adp), exact weights for aggregation. Written for + # EVERY cell even under --only-cells, so a staged run and the later full release share + # one manifest and the aggregator needs no knowledge of the staging. manifest_path.parent.mkdir(parents=True, exist_ok=True) with open(manifest_path, "w", newline="") as f: w = csv.writer(f) @@ -184,15 +224,23 @@ def main(): print(f" adp [{args.n_adp}]: {', '.join(f'{a:.4g}' for a in adps)}") if disabled: print(f" DISABLED components (weight=0): {', '.join(disabled)}") - print(f" {args.n_geom * args.n_adp} combos x {len(codes)} structures = " - f"{args.n_geom * args.n_adp * len(codes)} jobs max") - print(f" manifest: {manifest_path}\n") + n_cells = len(only) if only else args.n_geom * args.n_adp + if only: + print(" STAGED — only these cells:") + for gi, ai in sorted(only): + print(f" g{gi}_a{ai}: geometry={geoms[gi]:.4g} adp={adps[ai]:.4g}") + print(f" {n_cells} combo(s) x {len(codes)} structures = " + f"{n_cells * len(codes)} jobs max") + print(f" manifest: {manifest_path} (all " + f"{args.n_geom * args.n_adp} cells)\n") # Collect the (cell x structure) work list, skipping already-complete cells. tasks = [] # each: (outdir, pdb, mtz, weights_json) skip = miss = 0 for gi, g in enumerate(geoms): for ai, a in enumerate(adps): + if only is not None and (gi, ai) not in only: + continue arm = f"wgrid{suffix}_g{gi}_a{ai}" arm_dir = out_root / arm weights = {"xray": 1, "geometry": float(g), "adp": float(a), @@ -247,7 +295,7 @@ def main(): code = outdir_p.name if not args.dry_run: outdir_p.mkdir(parents=True, exist_ok=True) - script = build_script(arm, code, pdb, mtz, outdir, args.n_cycles, + script = build_script(arm, code, pdb, mtz, outdir_p, args.n_cycles, args.mem, json.loads(wjson)) if args.dry_run and first == 0: print("── example sbatch script ──") diff --git a/paper/figure2_alphafold_start/analysis/subset_100.txt b/paper/figure2_alphafold_start/analysis/subset_100.txt new file mode 100644 index 00000000..d5ea44e2 --- /dev/null +++ b/paper/figure2_alphafold_start/analysis/subset_100.txt @@ -0,0 +1,100 @@ +1A0F +1APX +1C3F +1E79 +1FIO +1GUI +1IDQ +1JAC +1LNZ +1OC0 +1PG3 +1QZN +1RKG +1TML +1UVQ +1VLM +1WTY +1XX1 +1Z3X +2A9S +2B3X +2CB4 +2DOB +2F1F +2FH9 +2GUH +2IVF +2NVN +2P62 +2PQN +2QHL +2R5F +2VZB +2X26 +2YEU +2Z1Z +3ADO +3BCI +3CB5 +3D1M +3DXI +3EFY +3G39 +3GMV +3H7O +3IC8 +3KGR +3L2H +3MB2 +3N39 +3OAP +3PPU +3R0Q +3SHW +3TIX +3UN9 +3VFO +3VUQ +3WNV +4AOM +4C6R +4D51 +4FC4 +4HIA +4JHN +4LD7 +4N5X +4OF8 +4PSI +4QTP +4TWL +4V15 +4ZSF +5C5V +5DRM +5G23 +5H5Y +5IYX +5JTA +5MLK +5OF9 +5T0M +5TV7 +5V8W +5X6S +5XVW +6A0E +6CW5 +6EUS +6GBS +6IQJ +6K6R +6OHI +6RFT +6TCT +6VD8 +6YFV +7BW1 +7F9K +7LF4 diff --git a/paper/figure2_alphafold_start/analysis/torchref_score_array.sh b/paper/figure2_alphafold_start/analysis/torchref_score_array.sh index da954bb5..fc6b23b0 100644 --- a/paper/figure2_alphafold_start/analysis/torchref_score_array.sh +++ b/paper/figure2_alphafold_start/analysis/torchref_score_array.sh @@ -7,9 +7,15 @@ # # N=$(wc -l < worklist.txt); sbatch --array=1-$N%96 torchref_score_array.sh # +# `day` + 2 h, not `hour` + 20 min: the median task really is ~20-40 s, but the +# largest ~60 of 3024 models run far longer and the 20-minute cap silently timed +# every one of them out (2026-08-11). A timed-out task leaves no +# torchref_validate.json, which drops that model's row from the TorchRef-scorer +# column of ExtFig 3 without any error -- the figure just renders short. The +# walltime is sized for the tail, not the median; `hour` cannot express it (1 h cap). #SBATCH --job-name=trscore -#SBATCH --partition=hour -#SBATCH --time=00:20:00 +#SBATCH --partition=day +#SBATCH --time=02:00:00 #SBATCH --mem=20G #SBATCH --cpus-per-task=2 #SBATCH --output=/das/work/units/LBR-FEL/p17490/Peter/Library/work_trees_torchref/dev/paper/figure2_alphafold_start/runs/crossscore/slurm_tr/%A_%a.out diff --git a/paper/figure2_alphafold_start/build_af_manifest.py b/paper/figure2_alphafold_start/build_af_manifest.py index 37762086..047d2a93 100644 --- a/paper/figure2_alphafold_start/build_af_manifest.py +++ b/paper/figure2_alphafold_start/build_af_manifest.py @@ -1,8 +1,17 @@ #!/usr/bin/env python3 """Phase A — build a per-structure sequence + UniProt manifest for the -AlphaFold-start arm of the Figure 2 validation benchmark. +AlphaFold-start arm of the Figure 2 benchmark. -For every PDB code in ``figure2_validation/structures.json`` this script: +Input preparation, run once: its outputs (``manifest.json`` / +``manifest_summary.csv``) are checked-in inputs that the refinement arms consume, +so a figure re-run does not re-run this. + +The code list defaults to the benchmark structures present under ``data/``. It +used to come from ``figure2_validation/structures.json``; that tree held the +retired shake-recovery Figure 2 and is gone, so ``data/`` is now the source of +truth. Pass ``--codes`` to override. + +For every PDB code this script: 1. Extracts per-chain protein sequences *locally* from the deposited model (``data/{code}/{code}.pdb``, falling back to ``.cif``) via @@ -17,8 +26,7 @@ * ``manifest_summary.csv`` — one row per code for a quick coverage view. * ``fasta/{code}.fasta`` — one record per protein chain. -This step is read-only against the benchmark; nothing under -``figure2_validation/`` or ``data/`` is modified. +This step is read-only against the benchmark; nothing under ``data/`` is modified. """ from __future__ import annotations @@ -39,9 +47,23 @@ HERE = Path(__file__).resolve().parent # figure2_alphafold_start/ PAPER_ROOT = HERE.parent # paper/ -STRUCTURES_FILE = PAPER_ROOT / "figure2_validation" / "structures.json" DATA = PAPER_ROOT / "data" # symlink → scientific_testing/data + +def benchmark_codes(): + """Benchmark PDB codes: every ``data/{code}/`` holding a deposited model. + + The source of truth since ``figure2_validation/structures.json`` was retired + with the shake-recovery figure. + """ + if not DATA.is_dir(): + raise SystemExit(f"no benchmark data at {DATA}; pass --codes explicitly") + return sorted( + d.name for d in DATA.iterdir() + if d.is_dir() and ((d / f"{d.name}.pdb").exists() + or (d / f"{d.name}.cif").exists()) + ) + FASTA_DIR = HERE / "fasta" MANIFEST_JSON = HERE / "manifest.json" SUMMARY_CSV = HERE / "manifest_summary.csv" @@ -285,7 +307,7 @@ def main(): ap.add_argument( "--codes", nargs="+", - help="Explicit PDB codes to process (default: all in structures.json).", + help="Explicit PDB codes to process (default: every code under data/).", ) ap.add_argument( "--limit", type=int, default=None, help="Process only the first N codes." @@ -298,10 +320,7 @@ def main(): ) args = ap.parse_args() - if args.codes: - codes = list(args.codes) - else: - codes = json.loads(STRUCTURES_FILE.read_text()) + codes = list(args.codes) if args.codes else benchmark_codes() if args.limit: codes = codes[: args.limit] diff --git a/paper/figure2_alphafold_start/run_af_pipeline.py b/paper/figure2_alphafold_start/run_af_pipeline.py index 187ea42e..dfe66955 100644 --- a/paper/figure2_alphafold_start/run_af_pipeline.py +++ b/paper/figure2_alphafold_start/run_af_pipeline.py @@ -2,34 +2,34 @@ """Self-contained pipeline for the AlphaFold-start benchmark arm. Refines the Phaser-placed AlphaFold models (figure2_alphafold_start/placed/) with -both TorchRef (with/without rigid body) and Phenix, validates every model with a -REFMAC5 zero-cycle run for a fair R-factor comparison, and collects the metrics. +TorchRef, REFMAC and phenix.refine, validates every model with a REFMAC5 +zero-cycle run for a fair R-factor comparison, and collects the metrics. The structure set is the MR-solved subset (mr_status.csv solved==1 / placed/*). Subcommands ----------- - refine --refiner {torchref,phenix} [--rigid-body] [--rigid-body-iter N] + refine --refiner {torchref,phenix,refmac} [--rigid-body] [--rigid-body-iter N] validate REFMAC 0-cycle on af_initial + each refined arm (validate.log co-located in each arm's run dir) analyze collect R-factors -> metrics/comparison.csv - migrate adopt in-flight runs + relocate old flat logs status queue + output summary -REFMAC submit/parse logic mirrors figure2_validation/run_pipeline.py -(submit_refmac_jobs / _parse_refmac_log); copied here to keep this self-contained. +For Figure 2 itself, submit the three arms through +``analysis/submit_fig2_arms.py`` rather than calling ``refine`` once per engine: +it emits each structure's three arms adjacently, which Panel C's cross-engine +wall-clock comparison depends on. ``--refiner torchref`` here is the +weights/rigid-body variant used for side experiments; the canonical Figure-2 +TorchRef arm is ``analysis/submit_local_arm.py``. """ from __future__ import annotations import argparse import csv -import json import os import re -import shutil import subprocess -import sys from pathlib import Path import numpy as np @@ -44,19 +44,39 @@ DATA = PAPER / "data" PLACED = HERE / "placed" RUNS = HERE / "runs" -REFMAC_LOGS = RUNS / "refmac_logs" METRICS = RUNS / "metrics" MANIFEST = HERE / "manifest.json" MR_STATUS = HERE / "mr_status.csv" PYTHON = str(REPO / ".dev" / "bin" / "python") REFINE_SCRIPT = str(REPO / "torchref" / "cli" / "refine.py") -PHENIX_REFINE = str(PAPER / "figure2_validation" / "phenix_refinement" / "phenix_refine.sh") -CCP4_SETUP = "/afs/psi.ch/sys/psi.ra/MX/ccp4/7.1/ccp4-7.1/bin/ccp4.setup-sh" +# CCP4 8.0 from the node-local /opt tree. The AFS-hosted 7.1 this used to point at +# is gone: /afs is mounted only on the `afs-week` nodes now, so every refmac call +# (the REFMAC arm *and* the 0-cycle validation every arm is scored by) died with +# "refmac5: command not found". Verified present on epyc9335 (refmac5 reports +# "library version 8.0.003"). NB this is a REFMAC version change from the runs +# before 2026-08-11, which moves the validated R-factors and the geometry RMSZ +# table Figure 2b reads — not only TorchRef changed between those figures. +CCP4_SETUP = "/opt/psi/MX/ccp4/8.0/ccp4-8.0/bin/ccp4.setup-sh" + +# Source the phenix env directly rather than `module load`: the modulefile uses an +# unsupported `module-url` command and there is no `module` on the batch nodes at +# all, so `module load phenix/...` fails with "Unable to locate a modulefile". +# Same approach (and same version) as analysis/crossscore_array.sh, so the +# refinement and the scoring run one phenix. +PHENIX_ENV = "/opt/psi/MX/phenix/1.21.1-5286/phenix-1.21.1-5286/phenix_env.sh" + +# One CPU model for every refinement arm. Figure 2c compares wall-clock ACROSS +# engines, and `hour`/`day` span Xeon 6152/6230/6230R and EPYC 7453/9335, so +# without a constraint each engine draws a different hardware mix — a systematic +# per-engine bias, not noise that n=767 averages away. The same epyc9335 nodes +# belong to hour/day/week, so constraining does not change the partition each arm +# uses. Matches extended_figures/exF4/submit_singlecore.py, which re-times the +# same refinements at 1 core. +CPU_MODEL = "cpu_epyc9335" # Refinement arms (af_initial = the placed search model, before refinement). -ARMS = ["af_initial", "torchref_norb", "torchref_rb", "phenix_norb", "phenix_rb", - "refmac"] +ARMS = ["af_initial", "refmac", "phenix_norb", "torchref"] def _mtz(code): @@ -67,11 +87,9 @@ def model_path(code, arm): """Resolve the PDB for a given code+arm (may not exist yet).""" if arm == "af_initial": return PLACED / f"{code}_af.pdb" - if arm == "torchref_norb": - return RUNS / "torchref_norb" / "results" / code / "default" / "refined.pdb" - if arm == "torchref_rb": - return RUNS / "torchref_rb" / "results" / code / "default" / "refined.pdb" - if arm in ("phenix_norb", "phenix_rb"): + if arm == "torchref": + return RUNS / "torchref" / code / "refined.pdb" + if arm == "phenix_norb": return RUNS / arm / code / f"{code}_refined_001.pdb" if arm == "refmac": return RUNS / "refmac" / code / "refined.pdb" @@ -124,6 +142,110 @@ def _sbatch(script_text, script_path, dry_run): return None +# ────────────────────────────────────────────────────────────────────────────── +# refine — per-arm sbatch script builders +# +# Module-level so analysis/submit_fig2_arms.py can emit a structure's arms +# adjacently (see its docstring for why interleaving matters for Figure 2c) +# without re-deriving the flag sets. +# ────────────────────────────────────────────────────────────────────────────── + +def script_refmac(code, pdb, mtz, outdir, cycles=10, constraint=CPU_MODEL): + """Restrained REFMAC refinement from the placed AF model. + + Same engine / CCP4 setup / MTZ labels as the 0-cycle validation + (:func:`cmd_validate`); the only differences are NCYCLES>0 and capturing + XYZOUT. AF models are protein-only, so REFMAC's built-in monomer library + suffices (no ligand dictionaries needed). REFMAC picks up FreeR_flag + automatically, holding out the same free set the validation scores against. + + stdout lands in ``refmac.log``, which is also where the runtime ("Elapsed:") + and the per-cycle R-factor table are parsed from. + """ + out = outdir / "refined.pdb" + return f"""#!/bin/bash +#SBATCH --job-name=af_refmac_{code} +#SBATCH --partition=hour +#SBATCH --constraint={constraint} +#SBATCH --cpus-per-task=4 +#SBATCH --time=00:30:00 +#SBATCH --mem=2G +#SBATCH --output={outdir / 'refmac.log'} +#SBATCH --error={outdir / 'refmac.log'} + +source {CCP4_SETUP} +TEMP_DIR=/tmp/refmac_refine_{code}_${{SLURM_JOB_ID}} +mkdir -p $TEMP_DIR && cd $TEMP_DIR +export CCP4_SCR=$TEMP_DIR +cp {pdb} input.pdb +cp {mtz} input.mtz +refmac5 HKLIN input.mtz HKLOUT output.mtz XYZIN input.pdb XYZOUT output.pdb << EOF +NCYCLES {cycles} +MAKE HYDR NO +END +EOF +cp output.pdb {out} +cd / && rm -rf $TEMP_DIR +""" + + +def script_phenix(code, pdb, mtz, outdir, cycles=10, constraint=CPU_MODEL): + """phenix.refine from the placed AF model, no rigid body. + + The flag set is fixed by what the comparison has to be: automatic + target-weight optimization is OFF because phenix's default runs a + per-macrocycle weight grid-search (~2.4x slower) that no other engine does, + and the Ramachandran restraint is OFF to match. AF models are protein-only, + so no ligand restraints are needed. + + Runs in ``outdir`` because phenix writes beside its cwd and + ``aggregate_figure_metrics.runtime_phenix`` reads the epoch stamps out of + ``{{code}}_refined_001.log`` there. CRYST1 "None" in the Z field is sed-fixed + first, or phenix rejects the file. + + Identical flags and phenix version to + ``extended_figures/exF4/submit_singlecore.py`` apart from ``nproc``, so exF4 is + a like-for-like re-timing of this run at 1 core. + + Every ``--`` flag precedes the positional arguments. phenix.refine's CLI takes + two variadic positional groups (``files`` then ``phil``) and cannot split them + across an option, so a flag in the middle makes it reject every argument after + the model and data files. + """ + return f"""#!/bin/bash +#SBATCH --job-name=af_phenix_{code} +#SBATCH --partition=day +#SBATCH --constraint={constraint} +#SBATCH --cpus-per-task=4 +#SBATCH --time=04:00:00 +#SBATCH --mem=8G +#SBATCH --output={outdir / 'phenix_refine.log'} +#SBATCH --error={outdir / 'phenix_refine.log'} + +source {PHENIX_ENV} +cd {outdir} +if grep -q "^CRYST1.*None" {pdb}; then + sed 's/\\(CRYST1.*P [0-9]* *[0-9]* *[0-9]* *\\)None/\\1 12/' {pdb} > input.pdb +else + cp {pdb} input.pdb +fi +phenix.refine --overwrite --quiet \\ + input.pdb {mtz} \\ + output.prefix={code}_refined \\ + refinement.main.number_of_macro_cycles={cycles} \\ + refinement.main.nproc=4 \\ + refinement.refine.strategy=individual_sites+individual_adp+occupancies \\ + refinement.main.simulated_annealing=false \\ + refinement.target_weights.optimize_xyz_weight=false \\ + refinement.target_weights.optimize_adp_weight=false \\ + refinement.main.bulk_solvent_and_scale=true \\ + refinement.main.ordered_solvent=false \\ + refinement.ordered_solvent.mode=every_macro_cycle \\ + refinement.pdb_interpretation.ramachandran_plot_restraints.enabled=false \\ + write_def_file=false write_eff_file=false write_geo_file=false --quiet +""" + + # ────────────────────────────────────────────────────────────────────────────── # refine # ────────────────────────────────────────────────────────────────────────────── @@ -163,20 +285,15 @@ def cmd_refine(args): -n {args.n_cycles} \\ --mode separate \\ --xray-mode {args.xray_mode} \\ - --weights '{{"adp": {args.adp_weight}}}' \\ - --sigma-m-scale {args.sigma_m_scale}{rb_line} + --weights '{{"adp": {args.adp_weight}}}'{rb_line} """ if _sbatch(script, tmp / f"ref_{code}.sh", args.dry_run) or args.dry_run: submitted += 1 - elif args.refiner == "refmac": - # Restrained REFMAC refinement from the placed AF model. Same engine / - # CCP4 setup / MTZ labels as the 0-cycle validation (cmd_validate); the - # only differences are NCYCLES>0 and capturing XYZOUT. AF models are - # protein-only, so REFMAC's built-in monomer library suffices (no - # ligand dictionaries needed). REFMAC picks up FreeR_flag automatically, - # holding out the same free set the validation scores against. - arm = "refmac" + else: # refmac / phenix + arm = "refmac" if args.refiner == "refmac" else "phenix_norb" + build = script_refmac if args.refiner == "refmac" else script_phenix + cycles = args.refmac_cycles if args.refiner == "refmac" else args.n_cycles tmp = RUNS / arm / "tmp_scripts" for code in codes: pdb, mtz = PLACED / f"{code}_af.pdb", _mtz(code) @@ -184,61 +301,20 @@ def cmd_refine(args): missing += 1 continue outdir = RUNS / arm / code - out = outdir / "refined.pdb" - if out.exists() and not args.force: + if model_path(code, arm).exists() and not args.force: skipped += 1 continue if not args.dry_run: outdir.mkdir(parents=True, exist_ok=True) - script = f"""#!/bin/bash -#SBATCH --job-name=af_refmac_{code} -#SBATCH --partition=hour -#SBATCH --cpus-per-task=4 -#SBATCH --time=00:30:00 -#SBATCH --mem=2G -#SBATCH --output={outdir / 'refmac.log'} -#SBATCH --error={outdir / 'refmac.log'} - -source {CCP4_SETUP} -TEMP_DIR=/tmp/refmac_refine_{code}_${{SLURM_JOB_ID}} -mkdir -p $TEMP_DIR && cd $TEMP_DIR -export CCP4_SCR=$TEMP_DIR -cp {pdb} input.pdb -cp {mtz} input.mtz -refmac5 HKLIN input.mtz HKLOUT output.mtz XYZIN input.pdb XYZOUT output.pdb << EOF -NCYCLES {args.refmac_cycles} -MAKE HYDR NO -END -EOF -cp output.pdb {out} -cd / && rm -rf $TEMP_DIR -""" - if _sbatch(script, tmp / f"refmac_{code}.sh", args.dry_run) or args.dry_run: + script = build(code, pdb, mtz, outdir, cycles=cycles, + constraint=args.constraint) + if args.dry_run and submitted == 0: + print("── example sbatch script ──") + print(script) + print("───────────────────────────") + if _sbatch(script, tmp / f"{args.refiner}_{code}.sh", args.dry_run) or args.dry_run: submitted += 1 - else: # phenix - rigid = "rb" if args.rigid_body else "norb" - arm = f"phenix_{rigid}" - for code in codes: - if not (PLACED / f"{code}_af.pdb").exists() or not _mtz(code).exists(): - missing += 1 - continue - out = RUNS / arm / code / f"{code}_refined_001.pdb" - if out.exists() and not args.force: - skipped += 1 - continue - if args.dry_run: - print(f" [DRY-RUN] sbatch {PHENIX_REFINE} {code} af {rigid}") - submitted += 1 - continue - try: - subprocess.run(["sbatch", "--job-name", f"phenix_{rigid}_{code}", - PHENIX_REFINE, code, "af", rigid], check=True, - capture_output=True, text=True) - submitted += 1 - except subprocess.CalledProcessError as e: - print(f" FAILED {code}: {e.stderr.strip()}") - print(f"\n{args.refiner} refine: submitted={submitted}, " f"skipped={skipped}, missing={missing}") @@ -370,59 +446,6 @@ def medians(subset): print(f" {arm:14s} {med:.4f} (n={n})" if med == med else f" {arm:14s} -") -# ────────────────────────────────────────────────────────────────────────────── -# migrate (adopt in-flight runs) -# ────────────────────────────────────────────────────────────────────────────── - -MIGRATIONS = [ - (PAPER / "figure2_validation" / "experiments" / "af_start_v1", RUNS / "torchref_norb"), - (PAPER / "figure2_validation" / "experiments" / "af_start_rb", RUNS / "torchref_rb"), - (PAPER / "phenix_refinements_af", RUNS / "phenix_rb"), -] - - -def cmd_migrate(args): - RUNS.mkdir(parents=True, exist_ok=True) - for src, dst in MIGRATIONS: - if dst.exists(): - print(f"SKIP {dst.name}: destination already exists") - continue - if not src.exists(): - print(f"SKIP {dst.name}: source {src} not found") - continue - print(f"MOVE {src} -> {dst}") - if not args.dry_run: - shutil.move(str(src), str(dst)) - - # Relocate the old flat validation logs (refmac_logs/{code}_{arm}.log) into - # their co-located run dirs so the already-computed validations are reused - # rather than re-submitted. Arm names contain underscores and PDB codes do - # not, so match the longest arm suffix to split the stem unambiguously. - moved = unmatched = collided = 0 - arms_by_len = sorted(ARMS, key=len, reverse=True) - if REFMAC_LOGS.exists(): - for old in sorted(REFMAC_LOGS.glob("*.log")): - stem = old.stem - arm = next((a for a in arms_by_len if stem.endswith("_" + a)), None) - if arm is None: - unmatched += 1 - continue - code = stem[: -(len(arm) + 1)] - dst = validate_log(code, arm) - if dst.exists(): - collided += 1 - continue - if not args.dry_run: - dst.parent.mkdir(parents=True, exist_ok=True) - shutil.move(str(old), str(dst)) - moved += 1 - print(f"validation logs: {moved} relocated, {collided} already present, " - f"{unmatched} unmatched") - - if args.dry_run: - print("(dry-run; nothing moved)") - - # ────────────────────────────────────────────────────────────────────────────── # status # ────────────────────────────────────────────────────────────────────────────── @@ -474,7 +497,11 @@ def common(p): help="Group weight on the entire ADP loss (locked default 0.02; " "see DEFAULT_GROUP_WEIGHTS).") p.add_argument("--xray-mode", default="ml") - p.add_argument("--sigma-m-scale", type=float, default=1.0) + p.add_argument("--constraint", default=CPU_MODEL, + help="SLURM --constraint for the refmac/phenix arms. One CPU " + "model across engines is what makes Figure 2c's " + "cross-engine wall-clock comparison meaningful; see " + "CPU_MODEL.") p.add_argument("--dry-run", action="store_true") p.add_argument("--force", action="store_true") p.set_defaults(func=cmd_refine) @@ -489,10 +516,6 @@ def common(p): common(p) p.set_defaults(func=cmd_analyze) - p = sub.add_parser("migrate") - p.add_argument("--dry-run", action="store_true") - p.set_defaults(func=cmd_migrate) - p = sub.add_parser("status") p.set_defaults(func=cmd_status) diff --git a/paper/figure2_alphafold_start/run_mr_one.sh b/paper/figure2_alphafold_start/run_mr_one.sh index 4154ba90..271b0197 100644 --- a/paper/figure2_alphafold_start/run_mr_one.sh +++ b/paper/figure2_alphafold_start/run_mr_one.sh @@ -10,7 +10,7 @@ set -o pipefail REPO=/das/work/p17/p17490/Peter/Library/work_trees_torchref/review AFDIR="$REPO/paper/figure2_alphafold_start" -PHENIX_ENV=/afs/psi.ch/sys/psi.ra/MX/phenix/phenix-1.20-4459/phenix_env.sh +PHENIX_ENV=/opt/psi/MX/phenix/1.21.1-5286/phenix-1.21.1-5286/phenix_env.sh code="${1:?usage: run_mr_one.sh CODE}" placed="$AFDIR/placed/$code"_af.pdb diff --git a/paper/figure3_performance/data/fcalc/results_20260805_124020/1DAW_gpu.json b/paper/figure3_performance/data/fcalc/results_20260805_124020/1DAW_gpu.json deleted file mode 100644 index 459fe887..00000000 --- a/paper/figure3_performance/data/fcalc/results_20260805_124020/1DAW_gpu.json +++ /dev/null @@ -1,84 +0,0 @@ -{ - "device": "cuda", - "structure": "1DAW", - "n_threads": 1, - "n_iterations": 10, - "n_warmup": 3, - "n_atoms": 3051, - "n_reflections": 23356, - "d_min": 2.050076961517334, - "torch_threads": 1, - "torchref": { - "iteration_times": [ - 0.0007870548870414495, - 0.0007434480357915163, - 0.0007182320114225149, - 0.0007166499271988869, - 0.0007038561161607504, - 0.0007154869381338358, - 0.0006981859914958477, - 0.0006955522112548351, - 0.0007033601868897676, - 0.0006818070542067289 - ], - "total_time": 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b/paper/figure3_performance/data/fcalc/results_20260805_124020/cpu_model.txt deleted file mode 100644 index 4428dbef..00000000 --- a/paper/figure3_performance/data/fcalc/results_20260805_124020/cpu_model.txt +++ /dev/null @@ -1 +0,0 @@ -cpu_xeon6230R diff --git a/paper/figure3_performance/data/fcalc/results_20260805_124020/structures.txt b/paper/figure3_performance/data/fcalc/results_20260805_124020/structures.txt deleted file mode 100644 index 4b3363fb..00000000 --- a/paper/figure3_performance/data/fcalc/results_20260805_124020/structures.txt +++ /dev/null @@ -1,10 +0,0 @@ -1DAW -2DQ6 -3A5V -3E98 -3GR5 -3K7M -3VRJ -4BX9 -5BOV -6G9X diff --git a/paper/figure3_performance/data/fcalc/results_20260805_124020/summary.csv b/paper/figure3_performance/data/fcalc/results_20260805_124020/summary.csv deleted file mode 100644 index 24637281..00000000 --- a/paper/figure3_performance/data/fcalc/results_20260805_124020/summary.csv +++ /dev/null @@ -1,71 +0,0 @@ -structure,device,n_threads,torchref_mean,torchref_min,torchref_max,torchref_speedup,torchref_efficiency,torchref_fwd_graph_mean,torchref_fwd_graph_min,torchref_fwd_graph_max,torchref_fwd_graph_speedup,torchref_bwd_only_mean,torchref_bwd_only_min,torchref_bwd_only_max,torchref_bwd_only_speedup,torchref_fwd_bwd_mean,torchref_fwd_bwd_min,torchref_fwd_bwd_max,torchref_fwd_bwd_speedup,cctbx_mean,cctbx_min,cctbx_max,cctbx_speedup,cctbx_efficiency,n_iterations,n_atoms,n_reflections,d_min -1DAW,cpu,1,0.065044,0.061334,0.069712,1.000,1.000,0.064609,0.062205,0.070679,1.000,0.080602,0.077359,0.082764,1.000,0.145362,0.141478,0.150245,1.000,0.046975,0.046248,0.047760,1.000,1.000,10,3051,23356,2.050076961517334 -1DAW,cpu,2,0.059198,0.058144,0.062600,1.099,0.549,0.058733,0.057607,0.060069,1.100,0.040484,0.039530,0.041998,1.991,0.097945,0.096265,0.100774,1.484,0.044940,0.044705,0.045430,1.045,0.523,10,3051,23356,2.050076961517334 -1DAW,cpu,4,0.049798,0.048143,0.053011,1.306,0.327,0.049480,0.047991,0.050994,1.306,0.024179,0.022526,0.025081,3.334,0.074155,0.072670,0.078107,1.960,0.045093,0.044852,0.045763,1.042,0.260,10,3051,23356,2.050076961517334 -1DAW,cpu,8,0.028251,0.027553,0.029279,2.302,0.288,0.027895,0.027353,0.028700,2.316,0.014190,0.013523,0.015346,5.680,0.042554,0.041820,0.044122,3.416,0.044958,0.044735,0.045971,1.045,0.131,10,3051,23356,2.050076961517334 -1DAW,cpu,16,0.019310,0.017650,0.020584,3.368,0.211,0.019188,0.017898,0.020186,3.367,0.009901,0.009051,0.010968,8.141,0.028588,0.027210,0.030929,5.085,0.045372,0.044769,0.046027,1.035,0.065,10,3051,23356,2.050076961517334 -1DAW,cpu,32,0.011285,0.011059,0.011732,5.764,0.180,0.011544,0.011077,0.012535,5.597,0.007523,0.006820,0.008645,10.714,0.020643,0.018210,0.022947,7.042,0.044874,0.044580,0.045877,1.047,0.033,10,3051,23356,2.050076961517334 -2DQ6,cpu,1,0.500567,0.498813,0.505083,1.000,1.000,0.500323,0.497259,0.507114,1.000,0.559741,0.554590,0.567586,1.000,1.064410,1.057971,1.073913,1.000,0.487878,0.482835,0.491345,1.000,1.000,10,8066,228276,1.5000840425491333 -2DQ6,cpu,2,0.300203,0.291754,0.311064,1.667,0.834,0.292110,0.290540,0.294564,1.713,0.291847,0.288685,0.297105,1.918,0.583002,0.581094,0.585700,1.826,0.465199,0.463412,0.468403,1.049,0.524,10,8066,228276,1.5000840425491333 -2DQ6,cpu,4,0.175996,0.175266,0.176669,2.844,0.711,0.178609,0.177284,0.179577,2.801,0.164186,0.162483,0.167919,3.409,0.342814,0.339791,0.348018,3.105,0.491489,0.487987,0.498483,0.993,0.248,10,8066,228276,1.5000840425491333 -2DQ6,cpu,8,0.097615,0.096125,0.098804,5.128,0.641,0.096412,0.095319,0.098706,5.189,0.098477,0.093556,0.107729,5.684,0.192580,0.188548,0.196201,5.527,0.467262,0.464575,0.471699,1.044,0.131,10,8066,228276,1.5000840425491333 -2DQ6,cpu,16,0.060422,0.059016,0.062216,8.284,0.518,0.057633,0.056319,0.058671,8.681,0.063941,0.061369,0.069195,8.754,0.123152,0.116369,0.135700,8.643,0.486635,0.481398,0.493376,1.003,0.063,10,8066,228276,1.5000840425491333 -2DQ6,cpu,32,0.042725,0.041870,0.043441,11.716,0.366,0.041763,0.041405,0.042413,11.980,0.048584,0.045699,0.053758,11.521,0.087383,0.083990,0.099675,12.181,0.471394,0.466894,0.476511,1.035,0.032,10,8066,228276,1.5000840425491333 -3A5V,cpu,1,0.167671,0.166172,0.171122,1.000,1.000,0.167673,0.166922,0.168293,1.000,0.169544,0.168093,0.171144,1.000,0.338278,0.336217,0.339722,1.000,0.175007,0.173349,0.176313,1.000,1.000,10,3896,45408,2.0000617504119873 -3A5V,cpu,2,0.111458,0.110558,0.113393,1.504,0.752,0.110834,0.110065,0.111739,1.513,0.094116,0.092527,0.096021,1.801,0.203565,0.202354,0.204778,1.662,0.176255,0.172087,0.179492,0.993,0.496,10,3896,45408,2.0000617504119873 -3A5V,cpu,4,0.073864,0.073329,0.075659,2.270,0.568,0.076709,0.073312,0.084993,2.186,0.058808,0.055687,0.062070,2.883,0.133538,0.130044,0.141063,2.533,0.178207,0.174256,0.182296,0.982,0.246,10,3896,45408,2.0000617504119873 -3A5V,cpu,8,0.052196,0.051147,0.053481,3.212,0.402,0.050531,0.050246,0.050917,3.318,0.037401,0.036362,0.039368,4.533,0.085695,0.083545,0.086785,3.947,0.176252,0.174128,0.179188,0.993,0.124,10,3896,45408,2.0000617504119873 -3A5V,cpu,16,0.036680,0.035033,0.044418,4.571,0.286,0.034344,0.033954,0.035756,4.882,0.030358,0.029388,0.032764,5.585,0.059134,0.057182,0.060841,5.721,0.175473,0.173930,0.176682,0.997,0.062,10,3896,45408,2.0000617504119873 -3A5V,cpu,32,0.023669,0.021930,0.033217,7.084,0.221,0.021624,0.021179,0.022501,7.754,0.022690,0.022019,0.023863,7.472,0.044036,0.042721,0.046132,7.682,0.172021,0.170607,0.173695,1.017,0.032,10,3896,45408,2.0000617504119873 -3E98,cpu,1,0.043711,0.043436,0.044149,1.000,1.000,0.043845,0.042252,0.045179,1.000,0.051376,0.050038,0.052034,1.000,0.094765,0.092754,0.096907,1.000,0.030708,0.030484,0.030904,1.000,1.000,10,2791,20486,2.4300224781036377 -3E98,cpu,2,0.035187,0.034903,0.035896,1.242,0.621,0.033904,0.033458,0.035467,1.293,0.026360,0.025486,0.027272,1.949,0.061289,0.060200,0.062875,1.546,0.028292,0.028203,0.028444,1.085,0.543,10,2791,20486,2.4300224781036377 -3E98,cpu,4,0.019164,0.018033,0.021828,2.281,0.570,0.019158,0.018093,0.019607,2.289,0.014881,0.014646,0.015658,3.452,0.034794,0.033881,0.036303,2.724,0.028392,0.028273,0.028793,1.082,0.270,10,2791,20486,2.4300224781036377 -3E98,cpu,8,0.013337,0.013233,0.013420,3.278,0.410,0.012371,0.012242,0.012938,3.544,0.008473,0.008144,0.009340,6.064,0.021238,0.020538,0.022806,4.462,0.028238,0.028166,0.028365,1.087,0.136,10,2791,20486,2.4300224781036377 -3E98,cpu,16,0.009675,0.008990,0.010623,4.518,0.282,0.009488,0.008798,0.010938,4.621,0.006646,0.006371,0.007787,7.731,0.016680,0.015210,0.019074,5.682,0.031250,0.030407,0.032595,0.983,0.061,10,2791,20486,2.4300224781036377 -3E98,cpu,32,0.007027,0.006508,0.008533,6.220,0.194,0.006522,0.006465,0.006606,6.723,0.004621,0.004065,0.005382,11.118,0.011155,0.010483,0.012789,8.496,0.028329,0.028263,0.028374,1.084,0.034,10,2791,20486,2.4300224781036377 -3GR5,cpu,1,0.070271,0.069554,0.072114,1.000,1.000,0.065272,0.064925,0.065574,1.000,0.067664,0.062122,0.077039,1.000,0.136338,0.121390,0.148568,1.000,0.058457,0.056380,0.063027,1.000,1.000,10,1329,20942,2.0505518913269043 -3GR5,cpu,2,0.050386,0.049991,0.050963,1.395,0.697,0.048333,0.047850,0.048656,1.350,0.038431,0.035188,0.041257,1.761,0.088161,0.079953,0.096180,1.546,0.060343,0.059690,0.060981,0.969,0.484,10,1329,20942,2.0505518913269043 -3GR5,cpu,4,0.029449,0.027514,0.041055,2.386,0.597,0.025830,0.025719,0.025956,2.527,0.020836,0.019612,0.024262,3.247,0.047364,0.042760,0.052293,2.878,0.056950,0.056439,0.057844,1.026,0.257,10,1329,20942,2.0505518913269043 -3GR5,cpu,8,0.017878,0.017795,0.017996,3.931,0.491,0.016666,0.016553,0.016762,3.916,0.014890,0.013536,0.016951,4.544,0.030367,0.028063,0.036073,4.490,0.056988,0.055638,0.061052,1.026,0.128,10,1329,20942,2.0505518913269043 -3GR5,cpu,16,0.013500,0.013345,0.013753,5.205,0.325,0.011531,0.010648,0.013364,5.661,0.011065,0.009627,0.012919,6.115,0.022178,0.019673,0.025741,6.147,0.059089,0.058066,0.061772,0.989,0.062,10,1329,20942,2.0505518913269043 -3GR5,cpu,32,0.009660,0.009526,0.009874,7.275,0.227,0.008886,0.008751,0.009247,7.346,0.010386,0.009259,0.015147,6.515,0.018160,0.014684,0.020537,7.508,0.057218,0.056168,0.059109,1.022,0.032,10,1329,20942,2.0505518913269043 -3K7M,cpu,1,0.320759,0.319748,0.322581,1.000,1.000,0.321292,0.319809,0.326269,1.000,0.327875,0.325540,0.330494,1.000,0.650084,0.646692,0.654032,1.000,0.340458,0.337930,0.342771,1.000,1.000,10,4048,55080,1.9523898363113403 -3K7M,cpu,2,0.202884,0.201659,0.207870,1.581,0.790,0.202950,0.202084,0.204283,1.583,0.180404,0.178700,0.183492,1.817,0.386415,0.383928,0.388586,1.682,0.339867,0.338465,0.343421,1.002,0.501,10,4048,55080,1.9523898363113403 -3K7M,cpu,4,0.122084,0.117952,0.126539,2.627,0.657,0.121381,0.118574,0.122786,2.647,0.111195,0.109716,0.112735,2.949,0.233287,0.230217,0.236397,2.787,0.350384,0.346851,0.352947,0.972,0.243,10,4048,55080,1.9523898363113403 -3K7M,cpu,8,0.080449,0.078562,0.082304,3.987,0.498,0.081118,0.079439,0.082768,3.961,0.076023,0.069889,0.082483,4.313,0.159764,0.155150,0.164230,4.069,0.339126,0.335720,0.343573,1.004,0.125,10,4048,55080,1.9523898363113403 -3K7M,cpu,16,0.056701,0.053606,0.060747,5.657,0.354,0.055559,0.053485,0.057370,5.783,0.061014,0.056252,0.067072,5.374,0.105811,0.103225,0.109605,6.144,0.340538,0.338746,0.344932,1.000,0.062,10,4048,55080,1.9523898363113403 -3K7M,cpu,32,0.043658,0.042622,0.045158,7.347,0.230,0.043030,0.042001,0.045898,7.467,0.053907,0.052327,0.056391,6.082,0.088228,0.087065,0.090487,7.368,0.338621,0.335393,0.341179,1.005,0.031,10,4048,55080,1.9523898363113403 -3VRJ,cpu,1,0.067202,0.065695,0.069408,1.000,1.000,0.067696,0.066310,0.069704,1.000,0.081368,0.079706,0.085793,1.000,0.148672,0.146231,0.151097,1.000,0.045788,0.045119,0.046753,1.000,1.000,10,3609,34613,1.900006651878357 -3VRJ,cpu,2,0.055353,0.054230,0.056174,1.214,0.607,0.054533,0.053775,0.055315,1.241,0.040652,0.040039,0.041423,2.002,0.096562,0.095545,0.099592,1.540,0.044265,0.043676,0.045926,1.034,0.517,10,3609,34613,1.900006651878357 -3VRJ,cpu,4,0.031240,0.030216,0.032428,2.151,0.538,0.031275,0.030319,0.033659,2.165,0.022984,0.022112,0.024437,3.540,0.056329,0.055061,0.060821,2.639,0.044308,0.043978,0.045679,1.033,0.258,10,3609,34613,1.900006651878357 -3VRJ,cpu,8,0.020247,0.019265,0.021087,3.319,0.415,0.019768,0.019054,0.020919,3.425,0.012959,0.012610,0.013320,6.279,0.033336,0.031966,0.037396,4.460,0.044194,0.043819,0.045267,1.036,0.130,10,3609,34613,1.900006651878357 -3VRJ,cpu,16,0.011277,0.010315,0.012952,5.959,0.372,0.012009,0.010834,0.012947,5.637,0.008624,0.007763,0.009546,9.435,0.019402,0.018439,0.020649,7.663,0.044266,0.043871,0.046090,1.034,0.065,10,3609,34613,1.900006651878357 -3VRJ,cpu,32,0.009301,0.007933,0.010553,7.225,0.226,0.008328,0.007902,0.009552,8.129,0.006738,0.005986,0.007447,12.076,0.015428,0.014196,0.017975,9.637,0.043979,0.043841,0.044281,1.041,0.033,10,3609,34613,1.900006651878357 -4BX9,cpu,1,0.267705,0.264695,0.272377,1.000,1.000,0.267696,0.262711,0.273523,1.000,0.520550,0.513678,0.525957,1.000,0.788904,0.777703,0.798376,1.000,0.335142,0.331703,0.339170,1.000,1.000,10,10193,68468,2.6000428199768066 -4BX9,cpu,2,0.209260,0.206401,0.218310,1.279,0.640,0.209106,0.206670,0.212385,1.280,0.285887,0.283488,0.288356,1.821,0.497142,0.493072,0.500726,1.587,0.325169,0.323279,0.329539,1.031,0.515,10,10193,68468,2.6000428199768066 -4BX9,cpu,4,0.126190,0.125041,0.126686,2.121,0.530,0.129281,0.125150,0.134556,2.071,0.154350,0.149747,0.157363,3.373,0.282803,0.277625,0.286734,2.790,0.317239,0.314347,0.327520,1.056,0.264,10,10193,68468,2.6000428199768066 -4BX9,cpu,8,0.073536,0.071946,0.077696,3.640,0.455,0.075237,0.072069,0.081278,3.558,0.085437,0.084566,0.087201,6.093,0.158955,0.154241,0.170300,4.963,0.335548,0.333011,0.337768,0.999,0.125,10,10193,68468,2.6000428199768066 -4BX9,cpu,16,0.046251,0.045602,0.046893,5.788,0.362,0.044454,0.043878,0.044940,6.022,0.055509,0.053771,0.061114,9.378,0.098560,0.097455,0.099990,8.004,0.320113,0.318270,0.325148,1.047,0.065,10,10193,68468,2.6000428199768066 -4BX9,cpu,32,0.030220,0.029361,0.031713,8.858,0.277,0.030369,0.029197,0.037738,8.815,0.040767,0.039687,0.042915,12.769,0.069123,0.066592,0.070908,11.413,0.314939,0.311649,0.324192,1.064,0.033,10,10193,68468,2.6000428199768066 -5BOV,cpu,1,0.332193,0.329713,0.335842,1.000,1.000,0.329002,0.324744,0.334275,1.000,0.706631,0.703020,0.712207,1.000,1.039894,1.026608,1.072901,1.000,0.274366,0.271368,0.281490,1.000,1.000,10,11415,150946,1.6000229120254517 -5BOV,cpu,2,0.188905,0.185766,0.192537,1.759,0.879,0.190283,0.186739,0.193251,1.729,0.380458,0.377140,0.383837,1.857,0.567774,0.560665,0.575398,1.832,0.273489,0.272213,0.275068,1.003,0.502,10,11415,150946,1.6000229120254517 -5BOV,cpu,4,0.107399,0.104896,0.118379,3.093,0.773,0.105462,0.103001,0.107406,3.120,0.221599,0.219780,0.223558,3.189,0.327144,0.321745,0.335941,3.179,0.274875,0.272168,0.282210,0.998,0.250,10,11415,150946,1.6000229120254517 -5BOV,cpu,8,0.060213,0.058936,0.061711,5.517,0.690,0.061750,0.058466,0.076878,5.328,0.115677,0.114294,0.117955,6.109,0.175377,0.172506,0.186491,5.929,0.291427,0.287898,0.303357,0.941,0.118,10,11415,150946,1.6000229120254517 -5BOV,cpu,16,0.034041,0.032955,0.035349,9.759,0.610,0.033361,0.032050,0.035648,9.862,0.069543,0.067711,0.073646,10.161,0.103206,0.100977,0.106849,10.076,0.270471,0.269102,0.271600,1.014,0.063,10,11415,150946,1.6000229120254517 -5BOV,cpu,32,0.023716,0.022851,0.025515,14.007,0.438,0.022536,0.021297,0.024918,14.599,0.042379,0.041482,0.043573,16.674,0.066928,0.064065,0.073040,15.537,0.272455,0.270134,0.276913,1.007,0.031,10,11415,150946,1.6000229120254517 -6G9X,cpu,1,0.206902,0.205606,0.210798,1.000,1.000,0.207387,0.205476,0.213626,1.000,0.464130,0.460978,0.467950,1.000,0.677949,0.671130,0.687421,1.000,0.272875,0.272048,0.275374,1.000,1.000,10,5932,62293,2.301983118057251 -6G9X,cpu,2,0.115135,0.114641,0.115758,1.797,0.899,0.119886,0.118695,0.121944,1.730,0.245899,0.241108,0.255487,1.887,0.361583,0.355839,0.369513,1.875,0.275347,0.271780,0.284865,0.991,0.496,10,5932,62293,2.301983118057251 -6G9X,cpu,4,0.083323,0.082254,0.085528,2.483,0.621,0.085571,0.083556,0.092261,2.424,0.167321,0.165313,0.169278,2.774,0.253374,0.250543,0.255858,2.676,0.295060,0.286848,0.305575,0.925,0.231,10,5932,62293,2.301983118057251 -6G9X,cpu,8,0.044955,0.044617,0.045277,4.602,0.575,0.045381,0.044685,0.047322,4.570,0.086823,0.085046,0.088883,5.346,0.133594,0.129502,0.138085,5.075,0.274392,0.273200,0.277783,0.994,0.124,10,5932,62293,2.301983118057251 -6G9X,cpu,16,0.031886,0.029873,0.033760,6.489,0.406,0.030209,0.029231,0.031605,6.865,0.055838,0.053880,0.056965,8.312,0.087184,0.084834,0.089514,7.776,0.276835,0.273674,0.282437,0.986,0.062,10,5932,62293,2.301983118057251 -6G9X,cpu,32,0.020137,0.019927,0.020554,10.275,0.321,0.020791,0.020001,0.022267,9.975,0.039095,0.037838,0.040419,11.872,0.059274,0.057498,0.061377,11.438,0.276078,0.273421,0.282247,0.988,0.031,10,5932,62293,2.301983118057251 -1DAW,gpu,0,0.000716,0.000682,0.000787,90.797,,0.000714,0.000655,0.000806,90.436,0.000813,0.000679,0.001251,99.186,0.001515,0.001350,0.001758,95.980,,,,,,10,3051,23356,2.050076961517334 -2DQ6,gpu,0,0.002627,0.002608,0.002683,190.582,,0.002638,0.002614,0.002752,189.671,0.003014,0.002934,0.003060,185.697,0.005500,0.005464,0.005575,193.538,,,,,,10,8066,228276,1.5000840425491333 -3A5V,gpu,0,0.001336,0.001222,0.001824,125.548,,0.001365,0.001307,0.001456,122.865,0.001374,0.001284,0.001445,123.355,0.002506,0.002364,0.002598,134.989,,,,,,10,3896,45408,2.0000617504119873 -3E98,gpu,0,0.000552,0.000542,0.000568,79.218,,0.000572,0.000564,0.000576,76.622,0.000624,0.000592,0.000768,82.326,0.001217,0.001187,0.001265,77.864,,,,,,10,2791,20486,2.4300224781036377 -3GR5,gpu,0,0.000790,0.000781,0.000813,88.978,,0.000819,0.000791,0.000856,79.661,0.000892,0.000818,0.000963,75.844,0.001595,0.001524,0.001762,85.466,,,,,,10,1329,20942,2.0505521297454834 -3K7M,gpu,0,0.001917,0.001907,0.001935,167.296,,0.001947,0.001913,0.002044,164.998,0.002079,0.001990,0.002174,157.735,0.003877,0.003851,0.003955,167.658,,,,,,10,4048,55080,1.9523898363113403 -3VRJ,gpu,0,0.000628,0.000617,0.000648,107.039,,0.000629,0.000622,0.000638,107.687,0.000943,0.000779,0.001026,86.261,0.001401,0.001297,0.001565,106.124,,,,,,10,3609,34613,1.9000064134597778 -4BX9,gpu,0,0.002570,0.002489,0.002818,104.156,,0.002540,0.002508,0.002637,105.375,0.002807,0.002681,0.003133,185.447,0.005153,0.005101,0.005379,153.084,,,,,,10,10193,68468,2.6000428199768066 -5BOV,gpu,0,0.002342,0.002316,0.002364,141.848,,0.002398,0.002339,0.002580,137.205,0.003256,0.003218,0.003320,217.039,0.005426,0.005382,0.005547,191.651,,,,,,10,11415,150946,1.6000231504440308 -6G9X,gpu,0,0.002107,0.002090,0.002136,98.204,,0.002139,0.002115,0.002240,96.943,0.002268,0.002175,0.002304,204.652,0.004251,0.004205,0.004381,159.479,,,,,,10,5932,62293,2.301983118057251 diff --git a/paper/figure3_performance/fcalc_benchmark/submit_fig3a_nodes.sh b/paper/figure3_performance/fcalc_benchmark/submit_fig3a_nodes.sh index 0dcedcb3..abdc02ce 100644 --- a/paper/figure3_performance/fcalc_benchmark/submit_fig3a_nodes.sh +++ b/paper/figure3_performance/fcalc_benchmark/submit_fig3a_nodes.sh @@ -8,6 +8,15 @@ # timed (contention-insensitive), so all structures' GPU points run in one GPU # job. A dependent job aggregates every per-structure JSON into summary.csv and # renders the figure. +# +# CPU_MODEL pins every array task to one CPU model. Exclusivity alone is not +# enough: `day` spans Xeon 6152/6230/6230R and EPYC 7453/9335, so without a +# constraint each structure is timed on whatever generation SLURM had free. That +# makes the cross-structure trend part hardware and part code, and it makes two +# runs of this script incomparable -- an unchanged cctbx reference moved by 2x +# between the 2026-07-01 and 2026-08-04 runs for exactly this reason. Check +# availability before changing it (>= one node per structure must be free): +# sinfo -h -o "%n %f %t" -p day | grep set -euo pipefail REPO="/das/work/units/LBR-FEL/p17490/Peter/Library/work_trees_torchref/dev" @@ -16,6 +25,7 @@ PY="${REPO}/.dev/bin/python" TS="$(date +%Y%m%d_%H%M%S)" OUT="${REPO}/paper/figure3_performance/data/fcalc/results_${TS}" THREADS="1 2 4 8 16 32" +CPU_MODEL="${CPU_MODEL:-cpu_epyc9335}" mkdir -p "${OUT}" cd "${BENCH}" @@ -24,10 +34,12 @@ cd "${BENCH}" N=$(wc -l < "${OUT}/structures.txt"); ARR="0-$((N - 1))" echo "Structures (${N}): $(tr '\n' ' ' < "${OUT}/structures.txt")" echo "Output: ${OUT}" +echo "CPU model: ${CPU_MODEL}" +echo "${CPU_MODEL}" > "${OUT}/cpu_model.txt" -# --- CPU thread-scaling: one exclusive node per structure --- +# --- CPU thread-scaling: one exclusive node per structure, all the same model --- CPU=$(sbatch --parsable --array="${ARR}" --job-name=fig3a_cpu \ - --partition=day --exclusive --time=04:00:00 \ + --partition=day --exclusive --constraint="${CPU_MODEL}" --time=04:00:00 \ --output="${BENCH}/fig3a_cpu_%A_%a.out" \ --wrap "set -e; STRUCT=\$(sed -n \"\$((SLURM_ARRAY_TASK_ID+1))p\" ${OUT}/structures.txt); \ echo \"node \$(hostname) structure \${STRUCT}\"; \ diff --git a/paper/figure3_performance/refinement_cycle_benchmark/submit_fig3b_nodes.sh b/paper/figure3_performance/refinement_cycle_benchmark/submit_fig3b_nodes.sh index 80d1fa4e..828baa93 100644 --- a/paper/figure3_performance/refinement_cycle_benchmark/submit_fig3b_nodes.sh +++ b/paper/figure3_performance/refinement_cycle_benchmark/submit_fig3b_nodes.sh @@ -6,6 +6,10 @@ # (loss + gradient over all targets, with a per-target breakdown) is the heavy # one, so the CPU jobs get more walltime. GPU points run in one A100 job; a # dependent job aggregates + plots. +# +# CPU_MODEL pins every array task to one CPU model, for the reason spelled out in +# submit_fig3a_nodes.sh: `day` is heterogeneous, so exclusivity alone leaves each +# structure timed on a different CPU generation. set -euo pipefail REPO="/das/work/units/LBR-FEL/p17490/Peter/Library/work_trees_torchref/dev" @@ -14,6 +18,7 @@ PY="${REPO}/.dev/bin/python" TS="$(date +%Y%m%d_%H%M%S)" OUT="${REPO}/paper/figure3_performance/data/refinement_cycle/results_${TS}" THREADS="1 2 4 8 16 32" +CPU_MODEL="${CPU_MODEL:-cpu_epyc9335}" mkdir -p "${OUT}" cd "${BENCH}" @@ -22,10 +27,12 @@ cd "${BENCH}" N=$(wc -l < "${OUT}/structures.txt"); ARR="0-$((N - 1))" echo "Structures (${N}): $(tr '\n' ' ' < "${OUT}/structures.txt")" echo "Output: ${OUT}" +echo "CPU model: ${CPU_MODEL}" +echo "${CPU_MODEL}" > "${OUT}/cpu_model.txt" # --- CPU thread-scaling: one exclusive node per structure (heavy: longer walltime) --- CPU=$(sbatch --parsable --array="${ARR}" --job-name=fig3b_cpu \ - --partition=day --exclusive --time=12:00:00 \ + --partition=day --exclusive --constraint="${CPU_MODEL}" --time=12:00:00 \ --output="${BENCH}/fig3b_cpu_%A_%a.out" \ --wrap "set -e; STRUCT=\$(sed -n \"\$((SLURM_ARRAY_TASK_ID+1))p\" ${OUT}/structures.txt); \ echo \"node \$(hostname) structure \${STRUCT}\"; \ diff --git a/paper/probe_scale_collapse.py b/paper/probe_scale_collapse.py new file mode 100644 index 00000000..7b850caf --- /dev/null +++ b/paper/probe_scale_collapse.py @@ -0,0 +1,123 @@ +#!/usr/bin/env python3 +"""Test the documented `scale_target='nll'` collapse hazard on real benchmark structures. + +``ScalerBase.refine_lbfgs``'s own docstring flags this: ``'ml_noalpha'`` originally replaced +``'nll'`` because a least-squares scale fit collapses in shells where ``F_obs`` is +noise-dominated and uncorrelated with ``F_calc`` -- the per-bin optimum +``k = sum(F_obs*Fc/sigma^2) / sum(Fc^2/sigma^2)`` tends to 0 there, which blows up R -- and it +instructs the reader to **check the per-bin log_scale spread and the post-scaling R-factors +rather than assuming it behaved**. ``'nll'`` is now the default, so that instruction applies +to the default path. + +The failure signature is specific: *some* bins diving toward k -> 0 while the middle bins look +fine. A uniform offset between the two targets is not the hazard -- the two objectives simply +have different optima. So the statistic is ``min(k)/median(k)``, which is independent of how +the binner orders bins; an "outer N bins" slice silently assumes an ordering, and getting that +wrong turns a real collapse into a reassuring number. + +Runs on the AlphaFold-start placed models (the benchmark's own inputs), not on a separate +data set, so a positive result would be about the structures actually being refined. + +Usage +----- + ./.dev/bin/python paper/probe_scale_collapse.py --codes 1ETE 1FSI 1DAW +""" + +from __future__ import annotations + +import argparse +import sys +from pathlib import Path + +import torch + +HERE = Path(__file__).resolve().parent +REPO = HERE.parent +sys.path.insert(0, str(REPO)) + +PLACED = HERE / "figure2_alphafold_start" / "placed" +DATA = HERE / "data" + +# Imported rather than hardcoded: the arms are whatever the library currently offers, so a +# renamed or added objective cannot leave this probe quietly testing one target twice. +from torchref.scaling.scaler_base import ( # noqa: E402 - needs the sys.path insert above + DEFAULT_SCALE_TARGET, + SCALE_TARGETS, +) + + +def one(code, target): + from torchref.refinement.lbfgs_refinement import LBFGSRefinement + + pdb = PLACED / f"{code}_af.pdb" + mtz = DATA / code / f"{code}.mtz" + if not pdb.exists() or not mtz.exists(): + return None + # Scale via the constructor, the way the CLI does. Do NOT call scaler.refine_lbfgs() + # again afterwards: the refinement has already scaled during __init__, and re-fitting from + # an already-scaled state drives every scaler parameter to NaN. + r = LBFGSRefinement(data_file=str(mtz), pdb=str(pdb), verbose=0, scale_target=target) + with torch.no_grad(): + ls = r.scaler.log_scale.detach().reshape(-1).cpu() + rw, rf = r.xray_target_work.get_rfactor() + del r + return ls, float(rw), float(rf) + + +def main(): + ap = argparse.ArgumentParser(description=__doc__, + formatter_class=argparse.RawDescriptionHelpFormatter) + ap.add_argument("--codes", nargs="+", required=True) + ap.add_argument("--collapse-ratio", type=float, default=0.2, + help="Flag min(k)/median(k) below this (default 0.2).") + args = ap.parse_args() + + verdicts = [] + for code in args.codes: + print(f"\n=== {code} ===") + got = {} + for tgt in SCALE_TARGETS: + try: + out = one(code, tgt) + except Exception as exc: # noqa: BLE001 - report, continue + print(f" {tgt:7s} FAILED {type(exc).__name__}: {exc}") + continue + if out is None: + print(f" {code}: inputs not found, skipping") + break + got[tgt] = out + if len(got) < len(SCALE_TARGETS): + continue + for tgt, (ls, rw, rf) in got.items(): + k = ls.exp() + ratio = float(k.min() / k.median()) + flag = " <-- COLLAPSING" if ratio < args.collapse_ratio else "" + print(f" {tgt:7s} R_work={rw:.4f} R_free={rf:.4f} " + f"log_scale {float(ls.min()):+.3f}..{float(ls.max()):+.3f} " + f"sd={float(ls.std()):.3f} min/median k={ratio:.3f}{flag}") + verdicts.append((code, tgt, ratio)) + rw_n, rf_n = got[DEFAULT_SCALE_TARGET][1], got[DEFAULT_SCALE_TARGET][2] + for tgt, (_, rw_s, rf_s) in got.items(): + if tgt == DEFAULT_SCALE_TARGET: + continue + print(f" {DEFAULT_SCALE_TARGET} - {tgt}: " + f"R_work {rw_n - rw_s:+.4f} R_free {rf_n - rf_s:+.4f}") + + bad = [(c, t, r) for c, t, r in verdicts + if t == DEFAULT_SCALE_TARGET and r < args.collapse_ratio] + print("\n" + "=" * 70) + if bad: + print("HAZARD PRESENT on the default path:") + for c, _, r in bad: + print(f" {c}: min/median k = {r:.3f}") + alt = [t for t in SCALE_TARGETS if t != DEFAULT_SCALE_TARGET] + print(f"Consider --scale-target {alt[0]} for the campaign.") + return 1 + print(f"No per-bin collapse on scale_target={DEFAULT_SCALE_TARGET!r} for " + f"{len({c for c, _, _ in verdicts})} structure(s) " + f"(all min/median k >= {args.collapse_ratio}).") + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/paper/run_gpu_tests.sh b/paper/run_gpu_tests.sh new file mode 100644 index 00000000..c1011e5a --- /dev/null +++ b/paper/run_gpu_tests.sh @@ -0,0 +1,55 @@ +#!/bin/bash +# Run the CUDA-marked test suite on a real GPU node. +# +# This node (ra-l-005) has a Quadro P4000 = sm_61, which this PyTorch build does not support, +# so every CUDA-marked test here fails with `torch.AcceleratorError` and the whole GPU surface +# of the library is effectively untested locally. The x-ray target rework moved +# `estimate_beta` -- the exact code that memory `gpu_scale_refinement_blowup` records as +# non-deterministic ACROSS GPU processes -- so `test_gpu_matches_cpu` is the one correctness +# check with no local substitute. +# +# Runs the full `cuda` + `gpu` marked set rather than that single test: none of it has ever +# executed on this hardware, so "the one test I was worried about" is a poor sample. +# +# sbatch paper/run_gpu_tests.sh +# +#SBATCH --job-name=trgputest +#SBATCH --partition=gpu +#SBATCH --gres=gpu:1 +#SBATCH --cpus-per-task=4 +#SBATCH --time=01:00:00 +#SBATCH --mem=24G +#SBATCH --output=/das/work/units/LBR-FEL/p17490/Peter/Library/work_trees_torchref/dev/paper/gpu_tests_%j.out +#SBATCH --error=/das/work/units/LBR-FEL/p17490/Peter/Library/work_trees_torchref/dev/paper/gpu_tests_%j.out + +set -o pipefail +REPO=/das/work/units/LBR-FEL/p17490/Peter/Library/work_trees_torchref/dev +PY=$REPO/.dev/bin/python +cd "$REPO" || exit 1 + +echo "== node: $(hostname) job: $SLURM_JOB_ID ==" +nvidia-smi --query-gpu=name,compute_cap,memory.total --format=csv,noheader +"$PY" - <<'EOF' +import torch +print("torch", torch.__version__, "cuda", torch.version.cuda) +print("device_count", torch.cuda.device_count()) +if torch.cuda.is_available(): + print("device 0:", torch.cuda.get_device_name(0), + "capability", torch.cuda.get_device_capability(0)) + print("build supports:", torch.cuda.get_arch_list()) +EOF + +echo +echo "== 1. the determinism gate, verbosely (the reason this job exists) ==" +"$PY" -m pytest tests/unit/refinement/test_estimate_beta_determinism.py -v -rA --no-header + +echo +echo "== 2. every cuda- or gpu-marked test in the tree ==" +"$PY" -m pytest tests/ -m "cuda or gpu" -q -rf --no-header --durations=15 + +echo +echo "== 3. x-ray target parity + gradient correctness, unfiltered, on GPU ==" +"$PY" -m pytest tests/unit/refinement/test_xray_target_parity.py \ + tests/unit/test_gradient_correctness.py -q -rf --no-header + +echo "== done rc=$? ==" From 0dabccae7c8c2a196d8cfc3ad2a6f49644f448d9 Mon Sep 17 00:00:00 2001 From: HatPdotS Date: Sun, 16 Aug 2026 20:44:51 +0200 Subject: [PATCH 7/9] Refresh F_sol when the solvent mask is rebuilt ScalerBase.forward recomputes _f_sol_raw only when it is None, so rebuilding the mask without clearing it left F_calc on the old mask. LBFGSRefinement.refine rebuilt the mask every macrocycle and never cleared it, so with --mode separate the bulk solvent stayed at the starting coordinates for the whole run: on 1DAW the F_sol reaching F_calc was 11.5% off in relative L2 norm by cycle 4, while its total magnitude drifted only 0.07% -- the mask changes shape, not size, which is why nothing downstream noticed. The rigid-body and collection paths already paired the two steps correctly. They are now one operation, ScalerBase.update_solvent, so a caller cannot do half of it; the collection override is renamed to match. Two kinetic call sites guarded on hasattr("update_all_solvent") and would have gone quietly dead on the rename. SolventModel.update_solvent now also drops its own hkl-keyed cache, which holds the FFT of the mask. Scoring is unaffected: get_scales() cold-starts through initialize(), so 0-cycle scores and the final reported R-factor were always built from the current mask. What was wrong is the coordinate gradient inside the macrocycle loop. Co-Authored-By: Claude Opus 5 (1M context) Claude-Session: https://claude.ai/code/session_018QVRRkhcy5omKhb5a8TM8H --- docs/changelog.rst | 1 + .../test_solvent_cache_invalidation.py | 144 ++++++++++++++++++ torchref/cli/collection_difference_refine.py | 3 +- torchref/experimental/kinetic/refinement.py | 6 +- torchref/refinement/lbfgs_refinement.py | 4 +- torchref/refinement/rigid_body_refinement.py | 3 +- torchref/scaling/collection_scaler.py | 7 +- torchref/scaling/scaler_base.py | 15 ++ torchref/scaling/solvent.py | 9 +- 9 files changed, 177 insertions(+), 15 deletions(-) create mode 100644 tests/unit/scaling/test_solvent_cache_invalidation.py diff --git a/docs/changelog.rst b/docs/changelog.rst index 813b4f9c..30fcbb07 100644 --- a/docs/changelog.rst +++ b/docs/changelog.rst @@ -4,6 +4,7 @@ Changelog Version 0.6.4 ---------- +- Fixed the bulk-solvent ``F_sol`` staying at the starting model's mask for every refinement macrocycle - Fixed restraint dictionaries defining several compounds yielding restraints for only one of them - Fixed chirality restraints being dropped for the ``positiv``/``negativ`` spellings used by the CCP4 library - Compounds that come back with no bond restraints are now reported diff --git a/tests/unit/scaling/test_solvent_cache_invalidation.py b/tests/unit/scaling/test_solvent_cache_invalidation.py new file mode 100644 index 00000000..2a59e745 --- /dev/null +++ b/tests/unit/scaling/test_solvent_cache_invalidation.py @@ -0,0 +1,144 @@ +"""Rebuilding the solvent mask must drop the cached ``F_sol``. + +``ScalerBase.forward`` recomputes ``_f_sol_raw`` only when it is ``None``. A caller that +rebuilds the mask without clearing it leaves ``F_calc`` on the old mask -- which is what +``LBFGSRefinement.refine`` did for every macrocycle, so bulk solvent never followed the +coordinates. The two halves are one operation now; these tests hold them together. +""" + +import pytest +import torch + + +class _StubSolvent: + """Minimal stand-in for :class:`SolventModel`. + + ``get_rec_solvent`` returns a value derived from ``generation``, so "the cache is + stale" becomes an observable difference rather than an assertion about internals. + """ + + def __init__(self): + self.generation = 0 + self.n_rebuilds = 0 + + def update_solvent(self): + self.generation += 1 + self.n_rebuilds += 1 + + def get_rec_solvent(self, hkl): + return torch.full( + (hkl.shape[0],), float(self.generation), dtype=torch.complex64 + ) + + +@pytest.fixture +def scaler_with_stub(): + from torchref.scaling.scaler import Scaler + + scaler = Scaler() + stub = _StubSolvent() + scaler.solvent = stub + return scaler, stub + + +class TestUpdateSolventInvalidates: + @pytest.mark.unit + def test_rebuilds_mask_and_drops_cache(self, scaler_with_stub): + """One call must do both halves.""" + scaler, stub = scaler_with_stub + scaler._f_sol_raw = torch.ones(4, dtype=torch.complex64) + + scaler.update_solvent() + + assert stub.n_rebuilds == 1, "mask was not rebuilt" + assert scaler._f_sol_raw is None, "stale F_sol survived the mask rebuild" + + @pytest.mark.unit + def test_next_read_reflects_the_new_mask(self, scaler_with_stub): + """The point of invalidating: what a caller reads back actually changes. + + Without the invalidation this asserts equality with the *old* generation and fails. + """ + scaler, stub = scaler_with_stub + hkl = torch.zeros((4, 3)) + + scaler._f_sol_raw = stub.get_rec_solvent(hkl) # generation 0 + before = scaler._f_sol_raw.clone() + + scaler.update_solvent() # -> generation 1 + after = ( + scaler._f_sol_raw + if scaler._f_sol_raw is not None + else stub.get_rec_solvent(hkl) + ) + + assert not torch.allclose(after, before), ( + "F_sol did not change after the mask was rebuilt -- the cache is stale" + ) + assert torch.allclose(after, torch.full((4,), 1.0, dtype=torch.complex64)) + + @pytest.mark.unit + def test_noop_without_a_solvent_model(self): + """Callers should not have to guard; a scaler with no solvent must not raise.""" + from torchref.scaling.scaler import Scaler + + scaler = Scaler() + scaler.solvent = None + scaler._f_sol_raw = None + + scaler.update_solvent() # must not raise + + @pytest.mark.unit + def test_repeated_calls_keep_rebuilding(self, scaler_with_stub): + """Each macrocycle gets a fresh mask, not just the first.""" + scaler, stub = scaler_with_stub + for _ in range(3): + scaler._f_sol_raw = torch.ones(4, dtype=torch.complex64) + scaler.update_solvent() + assert stub.n_rebuilds == 3 + + +class TestSolventModelClearsItsOwnCache: + @pytest.mark.unit + def test_update_solvent_clears_the_hkl_cache(self, monkeypatch): + """``SolventModel._cache`` holds the FFT of the mask, so a new mask voids all of it.""" + from torchref.scaling.solvent import SolventModel + + solvent = SolventModel() + solvent._cache[("fake_key",)] = torch.ones(3, dtype=torch.complex64) + assert len(solvent._cache) == 1 # anti-vacuity: there is something to clear + + # The mask rebuild itself needs a model; only the cache reset is under test. + monkeypatch.setattr(solvent, "get_solvent_mask", lambda: None) + solvent.update_solvent() + + assert len(solvent._cache) == 0, "mask-derived cache survived a mask rebuild" + + +class TestRefinementDriversUseTheScalerLevelCall: + """The defect was a *caller* reaching past the scaler to ``solvent.update_solvent()``. + + Both drivers must go through the scaler, which is the only thing that owns + ``_f_sol_raw``. + """ + + @pytest.mark.unit + @pytest.mark.parametrize( + "module_name", + [ + "torchref.refinement.lbfgs_refinement", + "torchref.refinement.rigid_body_refinement", + ], + ) + def test_driver_does_not_bypass_the_scaler(self, module_name): + import importlib + import inspect + + module = importlib.import_module(module_name) + source = inspect.getsource(module) + + assert "solvent.update_solvent()" not in source, ( + f"{module_name} calls SolventModel.update_solvent directly; that rebuilds the " + "mask without dropping the scaler's cached F_sol. Use scaler.update_solvent()." + ) + assert "scaler.update_solvent()" in source diff --git a/torchref/cli/collection_difference_refine.py b/torchref/cli/collection_difference_refine.py index c2d53001..97a935be 100644 --- a/torchref/cli/collection_difference_refine.py +++ b/torchref/cli/collection_difference_refine.py @@ -794,8 +794,7 @@ def main(): ) # Update solvent masks and re-refine scaler jointly - scaler.update_all_solvent() - scaler.invalidate_solvent_cache() + scaler.update_solvent() scaler.refine_lbfgs_joint(verbose=(args.verbose > 1)) if args.verbose > 0: diff --git a/torchref/experimental/kinetic/refinement.py b/torchref/experimental/kinetic/refinement.py index 556ac175..94514c0c 100644 --- a/torchref/experimental/kinetic/refinement.py +++ b/torchref/experimental/kinetic/refinement.py @@ -379,8 +379,7 @@ def refine(self, macro_cycles: int = 5, niter: int = 10, max_iter: int = 50): self._lbfgs_loop(params, niter=niter, max_iter=max_iter) # Update solvent masks after structure changes - if hasattr(self.scaler, "update_all_solvent"): - self.scaler.update_all_solvent() + self.scaler.update_solvent() # Report R-factors through the shared source of truth. if self.verbose > 0: @@ -454,8 +453,7 @@ def refine_alternating( self.refine_structures(niter=niter_structures, max_iter=max_iter) # Update solvent masks after structure changes - if hasattr(self.scaler, "update_all_solvent"): - self.scaler.update_all_solvent() + self.scaler.update_solvent() # 2. Refine fractions self.refine_fractions(niter=niter_fractions, max_iter=max_iter) diff --git a/torchref/refinement/lbfgs_refinement.py b/torchref/refinement/lbfgs_refinement.py index 391d111c..465b39c9 100644 --- a/torchref/refinement/lbfgs_refinement.py +++ b/torchref/refinement/lbfgs_refinement.py @@ -282,8 +282,8 @@ def refine(self, macro_cycles=5): before_scaling = self.collect_metrics() cycle_dict["before_scaling"] = before_scaling - if getattr(self.scaler, "solvent", None) is not None: - self.scaler.solvent.update_solvent() + if self.scaler is not None: + self.scaler.update_solvent() # Before the `after_scaling` metrics below, so that label describes this cycle's # scaler rather than the previous one's. self.refine_scaler() diff --git a/torchref/refinement/rigid_body_refinement.py b/torchref/refinement/rigid_body_refinement.py index b8907499..70bb8d30 100644 --- a/torchref/refinement/rigid_body_refinement.py +++ b/torchref/refinement/rigid_body_refinement.py @@ -286,8 +286,7 @@ def _run_inner_cycles(self, d_min, state, rigid_params, n_inner: int = 5): for inner in range(n_inner): # (a) Refresh the bulk-solvent mask at current positions. - solvent.update_solvent() - ref.scaler._f_sol_raw = None + ref.scaler.update_solvent() rigid_model.reset_cache() # (b) Refit solvent params (k_sol, B_sol[, phase]) against F_obs diff --git a/torchref/scaling/collection_scaler.py b/torchref/scaling/collection_scaler.py index 341df99a..585b4cb4 100644 --- a/torchref/scaling/collection_scaler.py +++ b/torchref/scaling/collection_scaler.py @@ -567,9 +567,12 @@ def screen_solvent_params_joint(self, steps: int = 15): # Solvent mask updates # ------------------------------------------------------------------ - def update_all_solvent(self): + def update_solvent(self): """ - Recompute solvent masks for all component models. + Recompute solvent masks for all component models and drop the cached ``F_sol``. + + Collection override of :meth:`~torchref.scaling.scaler_base.ScalerBase.update_solvent` + -- same contract, but every component has its own mask and cache entry. Call this after structure refinement changes base-model coordinates. """ diff --git a/torchref/scaling/scaler_base.py b/torchref/scaling/scaler_base.py index cfcde943..9fd9119f 100644 --- a/torchref/scaling/scaler_base.py +++ b/torchref/scaling/scaler_base.py @@ -300,6 +300,21 @@ def set_solvent_model(self, solvent_model: "SolventModel") -> None: self.solvent = solvent_model self._f_sol_raw = None # Invalidate cached raw solvent SFs + def update_solvent(self) -> None: + """Rebuild the solvent mask at the current coordinates and drop the cached ``F_sol``. + + Both halves belong to one operation. :meth:`forward` recomputes ``_f_sol_raw`` only + when it is ``None``, so rebuilding the mask without clearing the cache leaves + ``F_calc`` on the mask from whenever the cache was last filled -- on a macrocycle + loop, the starting coordinates. + + No-op when there is no solvent model, so callers do not need to guard. + """ + if getattr(self, "solvent", None) is None: + return + self.solvent.update_solvent() + self._f_sol_raw = None + def setup_binwise_solvent_scale(self): """ Create ``log_kmask``, a per-bin solvent scale (Phenix-style kmask). diff --git a/torchref/scaling/solvent.py b/torchref/scaling/solvent.py index 325dc1f6..a226545e 100644 --- a/torchref/scaling/solvent.py +++ b/torchref/scaling/solvent.py @@ -555,12 +555,15 @@ def get_solvent_mask(self): return self.solvent_mask def update_solvent(self): - """Rebuild the solvent mask from current coordinates. + """Rebuild the solvent mask from current coordinates and drop the mask-derived cache. - Call after the model's coordinates change; ``Scaler`` also has to drop its - cached ``_f_sol_raw`` for the new mask to reach ``F_calc``. + Prefer :meth:`~torchref.scaling.scaler_base.ScalerBase.update_solvent`, which also + clears the scaler's own ``_f_sol_raw``; that one is what ``F_calc`` reads. Calling + this directly refreshes the mask but leaves the scaler on the old ``F_sol``. """ self.get_solvent_mask() + # The per-hkl cache is the FFT of the mask, so a new mask invalidates all of it. + self._cache = TensorDict() def get_rec_solvent(self, hkl): """ From dee07a425d382fb5d2fca76b7a2c81f84c624a29 Mon Sep 17 00:00:00 2001 From: HatPdotS Date: Mon, 17 Aug 2026 13:38:55 +0200 Subject: [PATCH 8/9] Regenerate the paper figures on 0.6.4 Figure 2, exF1-exF5, source data and medians re-run on 0.6.4 (scaler rework plus the F_sol staleness fix). Conserved set n=725, was 723. Paired per-structure against the 0.6.3 archive, PHENIX-scored so the comparison is scorer-independent: TorchRef R-free -0.0018 [-0.0022,-0.0014] 486/252 p=2e-25, R-work -0.0045 [-0.0048,-0.0041] 688/60 p=4e-104, n=748. The scoring effect is separate and ~6x larger. On the UNCHANGED refmac models the TorchRef scorer moves R-free -0.0105 [-0.0112,-0.0100] on 711/767. Null control: the PHENIX scorer on those same models is unchanged on 760/760, so the pipeline moved only what the code changed. exF3's three scorers now agree closely (refmac models: 0.3133 by torchref vs 0.3130 by phenix, was 0.3244); the TorchRef column previously sat ~0.011 high. Figure 2C and exF4 keep the 0.6.3 wall-clock. A results sweep at full parallelism inflates engines unequally -- measured here 5.5x TorchRef, 1.6x PHENIX, 1.0x REFMAC -- which destroys the ratios those panels report. Provenance and the discarded contended numbers are recorded in runs/metrics/RUNTIME_PROVENANCE.md. The F_sol fix itself costs no runtime (matched cells: 294s vs 280s median). exF1 confirms the locked default weights: 3rd of 100 cells by median R-free, 0.0003 off the best, and R-free is flat along the adp axis near the optimum. Unchanged. torchref_score.py called reflection_data.hkl_for_sf(), which 56df7593 made private. Every task of the scoring sweep crashed, and torchref_score_array.sh reported rc=0 for all 5308 because `rc=$?` was expanded after a $(date) subshell had reset it, so SLURM logged COMPLETED throughout and stale 0.6.3 scorer JSONs survived --force. Both fixed; the scorer now goes through structure_factors(), which also restores the Friedel conjugation that passing the signed indices skipped. Co-Authored-By: Claude Opus 5 (1M context) Claude-Session: https://claude.ai/code/session_018QVRRkhcy5omKhb5a8TM8H --- paper/FIGURE_MEDIANS.md | 68 +- .../exF1/output/extended_figure1.png | Bin 156436 -> 156055 bytes .../exF2/data/exF2_rfactor_by_resolution.csv | 1448 ++-- .../exF2/output/extended_figure2.png | Bin 1118964 -> 1116073 bytes .../exF3/output/extended_figure3.png | Bin 1503712 -> 1375560 bytes .../exF5/output/extended_figure5.png | Bin 899410 -> 906447 bytes .../analysis/torchref_score.py | 7 +- .../analysis/torchref_score_array.sh | 9 +- .../figures/figure_af_benchmark.png | Bin 917889 -> 893782 bytes .../figure2_alphafold_start/mode_results.csv | 5 + paper/figure2_alphafold_start/results.csv | 8 +- paper/source_data/exF1_weight_grid.csv | 800 +-- .../source_data/exF2_panelA_rfree_points.csv | 2870 ++++---- .../exF2_panelA_rfree_runningmedian.csv | 2084 +++--- .../source_data/exF2_panelB_rwork_points.csv | 2886 ++++---- .../exF2_panelB_rwork_runningmedian.csv | 2096 +++--- .../exF3_r_free_phenix_vs_torchref.csv | 4486 ++++++------ .../exF3_r_free_refmac_vs_phenix.csv | 3516 +++++----- .../exF3_r_free_refmac_vs_torchref.csv | 4516 ++++++------- .../exF3_r_work_phenix_vs_torchref.csv | 4486 ++++++------ .../exF3_r_work_refmac_vs_phenix.csv | 3250 ++++----- .../exF3_r_work_refmac_vs_torchref.csv | 4516 ++++++------- paper/source_data/exF5_panelA_rfactors.csv | 5992 +++++++++-------- .../source_data/exF5_panelB_geometry_rmsz.csv | 32 +- paper/source_data/exF5_panelC_runtime.csv | 2 + paper/source_data/exF5_panelD_convergence.csv | 88 +- .../figure2_panelA_paired_delta.csv | 8 +- paper/source_data/figure2_panelA_rfactors.csv | 4062 +++++------ .../figure2_panelB_geometry_rmsz.csv | 32 +- paper/source_data/figure2_panelC_runtime.csv | 4 + .../figure2_panelD_convergence.csv | 66 +- 31 files changed, 23703 insertions(+), 23634 deletions(-) create mode 100644 paper/figure2_alphafold_start/mode_results.csv diff --git a/paper/FIGURE_MEDIANS.md b/paper/FIGURE_MEDIANS.md index efab0c99..1b20a2be 100644 --- a/paper/FIGURE_MEDIANS.md +++ b/paper/FIGURE_MEDIANS.md @@ -2,45 +2,45 @@ Auto-generated by `analysis/summarize_medians.py` from the metrics CSVs; numbers match the plotted figures. TorchRef = canonical arm `torchref` (xray 1 / geometry 0.2 / adp 0.02). -## Figure 2A — R-factors (PHENIX-validated) (conserved set, n=723) +## Figure 2A — R-factors (PHENIX-validated) (conserved set, n=725) | Model | median R-work | median R-free | n | |---|---|---|---| -| AlphaFold (start) | 0.4079 | 0.4084 | 723 | -| Refmac | 0.2726 | 0.3136 | 723 | -| PHENIX | 0.2745 | 0.3165 | 723 | -| TorchRef | 0.2757 | 0.3197 | 723 | +| AlphaFold (start) | 0.4078 | 0.4083 | 725 | +| Refmac | 0.2719 | 0.3135 | 725 | +| PHENIX | 0.2744 | 0.3161 | 725 | +| TorchRef | 0.2695 | 0.3165 | 725 | -## Figure 2A — paired ΔR-free, TorchRef − reference (conserved set, n=723) +## Figure 2A — paired ΔR-free, TorchRef − reference (conserved set, n=725) Median of the per-structure differences (negative = TorchRef better), with a 10 000-sample bootstrap CI on that median and a Wilcoxon signed-rank p. `Δ of medians` is the difference of the two medians in the table above, shown for contrast: it is *not* the paired statistic and can differ in sign. | Reference | metric | n | median Δ (paired) | 95% CI | Wilcoxon p | TorchRef better / worse | Δ of medians | |---|---|---|---|---|---|---|---| -| Refmac | R-free | 723 | +0.0047 | [+0.0040, +0.0056] | 6.81e-27 | 226 / 497 | +0.0061 | -| Refmac | R-work | 723 | +0.0026 | [+0.0017, +0.0035] | 1.93e-16 | 288 / 433 | +0.0031 | -| PHENIX | R-free | 723 | +0.0006 | [-0.0001, +0.0015] | 5.39e-02 | 336 / 382 | +0.0032 | -| PHENIX | R-work | 723 | +0.0005 | [-0.0008, +0.0020] | 1.54e-05 | 347 / 374 | +0.0012 | +| Refmac | R-free | 725 | +0.0030 | [+0.0021, +0.0039] | 1.58e-14 | 266 / 452 | +0.0030 | +| Refmac | R-work | 725 | -0.0017 | [-0.0027, -0.0010] | 2.02e-01 | 411 / 308 | -0.0024 | +| PHENIX | R-free | 725 | -0.0010 | [-0.0018, -0.0004] | 7.91e-04 | 398 / 320 | +0.0004 | +| PHENIX | R-work | 725 | -0.0044 | [-0.0054, -0.0033] | 5.14e-07 | 460 / 264 | -0.0049 | -## Figure 2B — geometry RMSZ (median; ideal = 1.0) (conserved set, n=723) +## Figure 2B — geometry RMSZ (median; ideal = 1.0) (conserved set, n=725) | Model | Bond RMSZ | Angle RMSZ | Chiral RMSZ | MC B-factor RMSZ | n | |---|---|---|---|---|---| -| Refmac | 0.58 | 0.95 | 0.54 | 0.90 | 723 | -| PHENIX | 0.83 | 0.92 | 0.35 | 1.03 | 723 | -| TorchRef | 0.58 | 0.96 | 0.57 | 1.56 | 723 | +| Refmac | 0.58 | 0.95 | 0.54 | 0.90 | 725 | +| PHENIX | 0.83 | 0.92 | 0.35 | 1.03 | 725 | +| TorchRef | 0.58 | 0.99 | 0.60 | 1.55 | 725 | -## Figure 2C — wall-clock runtime (conserved set, n=723) +## Figure 2C — wall-clock runtime (conserved set, n=725) | Engine | median runtime (min) | n | |---|---|---| -| Refmac | 0.25 | 723 | +| Refmac | 0.25 | 725 | | TorchRef | 0.77 | 723 | -| PHENIX | 3.09 | 723 | +| PHENIX | 3.09 | 725 | TorchRef vs Refmac: 3.1× slower; TorchRef vs PHENIX: 4.0× faster. -## Figure 2 — run accounting (N=767 candidates; conserved set n=723) +## Figure 2 — run accounting (N=767 candidates; conserved set n=725) Each engine refined the same set of Phaser-placed AlphaFold models. *Validated* = produced a PHENIX-scored R-factor; the headline medians use the conserved intersection where all four engines validated. @@ -49,7 +49,7 @@ Each engine refined the same set of Phaser-placed AlphaFold models. *Validated* | AlphaFold (start) | 759 | 8 | 767 | | Refmac | 760 | 7 | 767 | | PHENIX | 734 | 33 | 767 | -| TorchRef | 748 | 19 | 767 | +| TorchRef | 750 | 17 | 767 | ### Failure reasons (grouped) @@ -62,8 +62,8 @@ Each engine refined the same set of Phaser-placed AlphaFold models. *Validated* - 5 × refine: phenix.refine crash: special-position pathology - 1 × refine: phenix.refine transient error - 1 × refine: phenix.refine: incompatible data flags (Friedel mismatch) -- **TorchRef** — 19 failed - - 11 × refine: TorchRef OOM at 8 GB (large structure) +- **TorchRef** — 17 failed + - 9 × refine: TorchRef OOM at 8 GB (large structure) - 8 × scoring: PHENIX validator fails (special-position pathology) Most failures trace to a **special-position pathology** in some placed AF models: it breaks the uniform PHENIX scorer (so the structure is unscoreable for *every* engine) and makes phenix.refine itself abort, while REFMAC and TorchRef refine it without error. TorchRef's only engine-side failures are OOMs at the 8 GB job limit (large structures; would pass at 16 GB); REFMAC had zero refinement failures. @@ -82,21 +82,21 @@ The reference CPU is stated because it sets this number: `cpu_xeon6230R` (Cascad ## Extended Figure 2 — R-factor gap (TorchRef − reference, PHENIX-scored) -n = 723 structures, resolution 1.40–3.00 Å. +n = 725 structures, resolution 1.40–3.00 Å. | Reference | median ΔR-work (pp) | median ΔR-free (pp) | |---|---|---| -| PHENIX | +0.05 | +0.06 | -| REFMAC | +0.26 | +0.47 | +| PHENIX | -0.44 | -0.10 | +| REFMAC | -0.17 | +0.30 | ## Extended Figure 3 — scorer-consistency matrix (median R-free) | Model | by REFMAC | by PHENIX | by TorchRef | n(paired) | |---|---|---|---|---| -| Refmac | 0.3202 | 0.3130 | 0.3243 | 760 | -| PHENIX | 0.3247 | 0.3161 | 0.3304 | 734 | -| TorchRef | 0.3262 | 0.3201 | 0.3334 | 748 | -| AlphaFold (start) | 0.4158 | 0.4081 | 0.4308 | 759 | +| Refmac | 0.3202 | 0.3130 | 0.3133 | 760 | +| PHENIX | 0.3247 | 0.3156 | 0.3180 | 734 | +| TorchRef | 0.3234 | 0.3170 | 0.3175 | 750 | +| AlphaFold (start) | 0.4159 | 0.4081 | 0.4105 | 757 | ## Extended Figure 1 — weight landscape, locked-default cell @@ -104,12 +104,12 @@ Cell geometry=0.215, adp=0.0215 (n=47 structures). NB: the weight screen runs on | metric | median | |---|---| -| Bond RMSZ | 0.5455 | -| Angle RMSZ | 0.9186 | -| MC B-factor RMSZ | 1.6754 | -| R-work | 0.2622 | -| R-free | 0.3039 | -| R-free − R-work gap | 0.0377 | +| Bond RMSZ | 0.5833 | +| Angle RMSZ | 0.9572 | +| MC B-factor RMSZ | 1.6836 | +| R-work | 0.2513 | +| R-free | 0.2985 | +| R-free − R-work gap | 0.0421 | ## Extended Figure 4 — single-core runtime (Fig 2c at 1 CPU core) diff --git a/paper/extended_figures/exF1/output/extended_figure1.png b/paper/extended_figures/exF1/output/extended_figure1.png index 6ab7683dff86fd1237d7f2efde2a7ff0310bdfc3..a0c349a1e7a79638756b574032e00ee069cf30b9 100644 GIT binary patch literal 156055 zcmeFZc{tQ>-#rQi83`V{z|!?3YQ@J$}3@c?G(`xqAd$lnE`{|NTP%NOS5?#LVD;veAd>vKoq z)=ddHF`8#Uq@?$M|Ad6EzpLa8&0#C>E@z%-TLnNM?3ZYN=v;G#Odxa+h>p6d z+0)Fm8HQw*(W$OYK`S^h;?)h2H}wA)tFtONezosl+>KyU=zRV5ts{~Ctc8&ot7uT? z>k|=-LVUToB~qm!{ltUW!@3EbDgD}4#O2J)wHrx47W#f>wUAo`SfkYWXczKdU;YxA 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