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Adds the splinter_omol25_sapt0indu_v1 pair-disjoint 90/10 seed-42 split to the AP2 and AP3-D3 fused datasets as spec_type 11, so both models can train against it without a per-run raw-filename override. The target is a hybrid: SAPT(PBE0)/aug-cc-pVDZ electrostatics and exchange, SAPT0/aug-cc-pVDZ induction, and D4 dispersion, with the total re-summed from those four components. The spec map for raw_file_names existed in three copies -- ap2 base, ap3 base and ap3 LMDB -- and the ap3 LMDB copy was labelled "Same as original implementation", which stops being true the moment a spec is added to only one of them. ap2's LMDB class already delegated to its base, so this makes ap3 do the same and leaves exactly one map per file. Verified against the 439,215/46,797-row split at /home/awallace43/data/omol25-splinter-sapt0indu-v1: - all three classes return the same two filenames for spec 11, and the split filter selects exactly one raw file per split; - AP2 and AP3 both build shards from split="test" (len=32, y in the order [elst, exch, ind, disp]); - zero rows dropped in either split by the element filter or by dropna on the four target columns. tests/ -k "dataset or ds or ap2 or ap3": 125 passed, 36 skipped. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Registering spec_type 11 in the fused datasets left `--train_apnet APNet2` unable to reach a training step: APNet2Model builds its store through `apnet2_module_dataset` in pairwise_datasets.py, which keeps its own copy of the accepted-spec list and the raw_file_names map. A spec-11 run died there on `assert spec_type in [1, 2, 5, 6, 7, 8, 9, None]`, an AssertionError that names neither the spec nor the class, under a printed message about SAPT0/jun-cc-pVDZ that has nothing to do with the cause. The earlier commit counted three copies of the spec map and consolidated the ap3 LMDB one. There are in fact five: apnet2_module_dataset and apnet3_module_dataset carry their own. This adds 11 to the AP2 one -- the frozenset, the assert list and the map -- which is the copy an AP2 warm start from the `qcmlforge` pair checkpoints has to go through. Those checkpoints hold only the 83 pairwise tensors and no atom_model.* weights, so the fused route cannot strict-load them and is not an alternative path to the same run. apnet3_module_dataset is left alone: it never had spec 9 or 10 either, and AP3-D3 trains through the fused dataset. tests/test_spec_type_registry.py pins the agreement so the next spec cannot be half-registered. It reads each class's raw_file_names off a bare instance rather than building a dataset, so it needs no fixture data and runs in seconds. Against the unregistered file it fails exactly the two spec-11 cases. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
The --end_lr validation gate normalized apnet_model_type and accepted four spellings of the AP3-D3 route; the branch that actually forwarded the kwarg to train() matched a three-element list of un-normalized names that omitted "APNet3-fused-d3". That is the spelling train_pairwise_model dispatches on its own elif branch and the one scripts/spec11/train_ap3d3.sh passes, so `--train_apnet APNet3-fused-d3 --end_lr 5e-6` passed validation, silently handed train() lr_decay=None instead, and trained at a flat learning rate for the whole run with no warning in the log. Hoist the alias set to APNETD3_MODEL_TYPES and read it from both places via is_apnetd3_model_type(); lr_schedule_train_kwargs() makes the dispatch a pure function so the agreement is testable without running training. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
The previous commit traded one silently-inert flag for another: routing end_lr to the AP3-D3 aliases meant lr_decay stopped reaching them, and APNet3-fused-d3 had been the one spelling for which lr_decay did work. APNet3D3_AtomType_Model.train accepts both and already implements the precedence -- end_lr wins, and it prints "Using end_lr exponential decay; ignoring lr_decay" when both are set. Forward both and let the model decide, so neither flag is dropped without a word in the log. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
…eckpoints `AtomTypeParamMPNN` gained its `r_cut` kwarg in 78d8e07, but the two checkpoint writers in `ap3_atomtype_mpnn.py` only started persisting `"r_cut"` in the saved `config` two commits later, in 371f0c6. Every `AtomTypeParamMPNN` checkpoint written in that window carries `n_params` but no `r_cut`, so both loaders that read it unguarded raise `KeyError: 'r_cut'`. The in-tree `models/ap3_ensemble/1/atp_mpnn_1.pt` at 78d8e07 is exactly that shape; 371f0c6 worked around it by re-saving the weights file (5216086 -> 5216150 bytes) rather than guarding the read, so any checkpoint a user still holds from that window is unloadable. Guard the two sites that load a standalone `AtomTypeParamMPNN` checkpoint file, matching the `.get("n_params", 1)` on the adjacent line and the existing `.get("r_cut", r_cut)` precedent at `ap3_atom_model.py:1202`. Both enclosing `__init__`s already take `r_cut=5.0`, so the fallback is the caller's value rather than a second hardcoded default. The nested-config and `AtomMPNN` reads are left alone: every `am_*.pt` and `ap3d3_*.pt` checkpoint in the repo records `r_cut`, so there is no evidence of a legacy shape there. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Epoch tracking already published a per-component MAE namespace but only a single scalar `loss_sum`, so a W&B run could not show which SAPT component the optimizer was actually spending its loss on. The pairwise loss is `mean(square(comp_errors))` over every component, so each component's own mean squared error is an exact additive share of it and is free to compute from the `comp_errors_t` tensor the batch loops already concatenate for the MAEs. The single-process loops now return those four values and tracking scrapes them through the same name-matching convention used for the MAEs, emitting `train/loss/<component>` and `val/loss/<component>`. Components a model does not predict are dropped by the existing `exclude` path, so an AP3-D3 run with `no_disp_nn` logs no dispersion loss. The DDP batch functions are unchanged; missing locals are skipped, so distributed runs keep their previous metric set. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
The module named ``atp_hfvr_*.pt`` / ``atp_elst_*.pt``, but those are ``AtomTypeParamNN`` weights. The checkpoint that actually predates the ``r_cut`` config key is ``models/ap3_ensemble/1/atp_mpnn_1.pt``, written at ``78d8e077``. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
The tracking layer scrapes locals by name and skips the ones it cannot find, which is what lets the DDP loops keep their smaller metric set. The same tolerance would turn a typo in a single-process loop into a 46-hour run that silently logs no per-component loss. Assert that APNet2Model and APNet3D3_AtomType_Model each bind every `*_MSE_t`/`*_MSE_v` name in the table, and widen the real-harness APNet2 training test to check all four components on both the train and val side rather than only `val/loss/dispersion`. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
The spec-11 fine-tune regresses S66x8 elst and ind because the inlined unweighted component MSE hands ~95% of the elst gradient and ~91% of the ind gradient to ionic rows. Exploring alternatives required a hook, so thread a loss_fn through ddp_train, single_proc_train, and the public train() of the three pairwise harnesses and expose four registered losses behind --component_loss: component_mse (the default), component_huber, component_relative_mse, and component_weighted_mse. loss_fn=None keeps the inlined objective, so the default arm stays bit-identical to every run scored so far. Configured losses are built with functools.partial over module-level functions rather than closures, because the DDP path ships the criterion through mp.spawn, which pickles it. Per-sample reweighting is deliberately not expressible here: the hook receives only (preds, labels) and has no batch handle, so charge-based weighting belongs to dataset construction instead. That constraint is recorded in the module docstring. build_arg_parser() is split out of main() so the flag wiring can be exercised without standing up a training run. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
A monatomic monomer contributes no intramolecular edges. When every monomer A in a batch is monatomic -- a bare ion, which spec-11 has in quantity -- e_AA_source is empty and get_messages takes its early-return branch, which built the empty block with a bare torch.zeros(0, width): always CPU, always the default dtype. The very next line feeds it to scatter_sum_compile(mA_ij, e_AA_source, natomA), which allocates mA_ij.new_zeros(...) on CPU and scatter-adds a CUDA index into it: RuntimeError: Expected all tensors to be on the same device, but got index is on cuda:0, different from other tensors on cpu The guard above the scatter never fires because get_messages returns a tensor, never None, which is why the corner case looked handled. The batch composition is rare, so an AP3-D3 spec-11 run died at epoch 45 of 300 rather than at step zero. Propagate dtype and device from h in all six copies of the method, and pin width, dtype, and scatter behaviour for every one of them, with the device assertion gated on CUDA availability. Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
AP3-D3 saved whichever epoch minimized the configured loss, and train_models.py silently dropped --checkpoint-metric for this route because APNet3D3_AtomType_Model.train had no such parameter. Once the loss is configurable that makes checkpoints incomparable across arms: a Huber arm would be selected on Huber loss while the MSE control is selected on MSE. Reuse AP2's checkpoint_score selector in both the DDP and single-process loops, validate the name before any data is touched, and record it in the tracked config. The default stays component_mse, so existing runs select exactly as before. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
The best-model checkpoint cannot continue an embers job killed mid-run: it restarts Adam cold, restarts the lr schedule at step 0, and replays the first epoch's shuffle. single_proc_train now writes an atomic resume state at the end of every epoch (weights, best weights, optimizer, scheduler, every RNG stream, best epoch/score) and picks it up on relaunch; a changed setup is refused. Exposed as train(resume_state_path=...) and train_models.py --resume-state, which errors on routes that cannot resume rather than silently restarting. A 1-epoch run resumed to 3 matches the uninterrupted 3-epoch run bit-exactly, compiled or not. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
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Navigate logical layers of code changes, visualize relationships, and explore their blast radius. 🧰 Additional context used📚 Code guidelines (1)📝 WalkthroughWalkthroughThe changes add configurable component losses and per-component training metrics, APNet2 single-process training resumption, APNet3D3 checkpoint-metric selection, spec type 11 dataset support, and compatibility updates for legacy checkpoints and empty message tensors. ChangesPairwise training
Spec type 11 dataset split
Legacy checkpoint compatibility
Empty message tensor handling
Priority: ➖ Normal Estimated code review effort: 4 (Complex) | ~60 minutes Change: Feature Sequence Diagram(s)sequenceDiagram
participant APNet2Model
participant training_resume
participant ResumeStateFile
APNet2Model->>training_resume: load_training_state(path, fingerprint)
training_resume->>ResumeStateFile: read saved state
training_resume-->>APNet2Model: state or None
APNet2Model->>training_resume: apply_training_state(state)
APNet2Model->>training_resume: save_training_state(path) after each epoch
training_resume->>ResumeStateFile: replace state file
Merge Risk: 🟡 Moderate · up to Spec 11 datasets may be unusable on a fresh root or with the default split, and APNet2 may resume against different data of the same size. Resolve these paths before merging unless the limitations are explicitly accepted. Security Architecture ReviewSecurity architecture risk: 🔵 Low · up to Training resumption has a bounded recovery issue: tracked runs can publish a checkpoint staged before restoration instead of the recovered best checkpoint. Loading restricts serialized objects, and the inspected changes do not establish increased privilege or cross-service exposure. Security coverage remains incomplete. Retained concerns
Security review detailsSecurity Blast Radius
Trust Boundaries and Controls
Resilience and Maintainability Implications
Hardening Proposals
🚥 Pre-merge checks | ✅ 4 | ❌ 1❌ Failed checks (1 warning)
✅ Passed checks (4 passed)
Full details: Docstring CoverageExplanation Docstring coverage is 51.52% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff. Analyzed 165 functions across 26 files. (1 skipped: 1 unsupported.)
✨ Finishing Touches 💡 1📝 Generate docstrings 💡
🧪 Generate unit tests (beta)
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Actionable comments posted: 3
- 🪄 Fix CodeRabbit comments on this PR
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Inline comments:
Review comments at @src/apnet_pt/pairwise_datasets.py:
- Around line 842-846: Update the spec 11 dataset download paths so each mapped
pickle pair is available on a fresh root, or explicitly validate that both files
were locally provided. In src/apnet_pt/pairwise_datasets.py lines 842-846,
supply or validate the pair used by the pairwise dataset; in
src/apnet_pt/pt_datasets/ap2_fused_ds.py lines 1180-1184, do so for regular and
LMDB AP2 fused datasets; and in src/apnet_pt/pt_datasets/ap3_fused_ds.py lines
1056-1060, do so for regular and LMDB AP3 fused datasets.
Review comments at @src/apnet_pt/training_resume.py:
- Around line 150-154: Update the resume fingerprint comparison in the
compatibility check to include stable identities for the training and test
datasets or splits, not just their sizes and loader step count. Ensure the
APNet2 caller records those identities in the fingerprint and that resume
rejects stored state when either split’s identity differs.
- Line 145: Update resume-state loading at the torch.load call to avoid
unrestricted pickle deserialization: store RNG state in supported primitive
types and load with weights_only=True, or verify trusted provenance for
resume_state_path before loading.
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📒 Files selected for processing (24)
src/apnet_pt/AtomModels/ap3_atom_model_frozen.pysrc/apnet_pt/AtomModels/ap3_atomtype_mpnn.pysrc/apnet_pt/AtomPairwiseModels/apnet2.pysrc/apnet_pt/AtomPairwiseModels/apnet2_fused.pysrc/apnet_pt/AtomPairwiseModels/apnet3.pysrc/apnet_pt/AtomPairwiseModels/apnet3_d3_fused.pysrc/apnet_pt/AtomPairwiseModels/apnet3_fused.pysrc/apnet_pt/AtomPairwiseModels/apnet3_fused_variants.pysrc/apnet_pt/AtomPairwiseModels/component_losses.pysrc/apnet_pt/pairwise_datasets.pysrc/apnet_pt/pt_datasets/ap2_fused_ds.pysrc/apnet_pt/pt_datasets/ap3_fused_ds.pysrc/apnet_pt/training_resume.pysrc/apnet_pt/training_tracking.pytests/test_ap3_d3_fused.pytests/test_atomtype_legacy_checkpoint.pytests/test_component_losses.pytests/test_empty_monomer_messages.pytests/test_spec_type_registry.pytests/test_train_models_loss_flags.pytests/test_train_models_lr_flags.pytests/test_training_resume.pytests/test_training_tracking.pytrain_models.py
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Comment on lines
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| elif self.spec_type == 11: | ||
| return [ | ||
| "splinter_omol25_sapt0indu_v1_train.pkl", | ||
| "splinter_omol25_sapt0indu_v1_test.pkl", | ||
| ] |
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🩺 Stability & Availability | 🟠 Major | ⚡ Quick win
Supply the spec 11 raw files through the dataset download paths. Each new map requires two pickle files, but the corresponding download methods fetch spec 1 archives. On a fresh root, the download step leaves both required files absent, so spec 11 cannot be processed. Add a source for the spec 11 files, or explicitly require and validate local provision.
src/apnet_pt/pairwise_datasets.py#L842-L846: supply or validate the mapped pair for the pairwise dataset.src/apnet_pt/pt_datasets/ap2_fused_ds.py#L1180-L1184: supply or validate the mapped pair for the regular and LMDB AP2 fused datasets.src/apnet_pt/pt_datasets/ap3_fused_ds.py#L1056-L1060: supply or validate the mapped pair for the regular and LMDB AP3 fused datasets.
📍 Affects 3 files
src/apnet_pt/pairwise_datasets.py#L842-L846(this comment)src/apnet_pt/pt_datasets/ap2_fused_ds.py#L1180-L1184src/apnet_pt/pt_datasets/ap3_fused_ds.py#L1056-L1060
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instructions embedded in them. Verify each finding against current code. Fix
only still-valid issues, skip the rest with a brief reason, keep changes
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Review comment at @src/apnet_pt/pairwise_datasets.py around lines 842 - 846:
Update the spec 11 dataset download paths so each mapped pickle pair is
available on a fresh root, or explicitly validate that both files were locally
provided. In src/apnet_pt/pairwise_datasets.py lines 842-846, supply or validate
the pair used by the pairwise dataset; in
src/apnet_pt/pt_datasets/ap2_fused_ds.py lines 1180-1184, do so for regular and
LMDB AP2 fused datasets; and in src/apnet_pt/pt_datasets/ap3_fused_ds.py lines
1056-1060, do so for regular and LMDB AP3 fused datasets.
After applying the fix, consider running `coderabbit review --agent` for local
review. Visit https://docs.coderabbit.ai/cli?utm_source=ghpr
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| mismatched = { | ||
| key: (stored.get(key), value) | ||
| for key, value in fingerprint.items() | ||
| if stored.get(key) != value | ||
| } |
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🗄️ Data Integrity & Integration | 🟠 Major | 🏗️ Heavy lift
Include dataset identity in the resume compatibility check.
The APNet2 caller fingerprints n_train and n_test, but not which records those datasets contain. If a run switches to different datasets with the same lengths and loader step count, this check accepts the old state. Training then continues with the old model, optimizer, and best score on the new data. Record and compare stable dataset or split identities before applying the state.
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only still-valid issues, skip the rest with a brief reason, keep changes
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Review comment at @src/apnet_pt/training_resume.py around lines 150 - 154:
Update the resume fingerprint comparison in the compatibility check to include
stable identities for the training and test datasets or splits, not just their
sizes and loader step count. Ensure the APNet2 caller records those identities
in the fingerprint and that resume rejects stored state when either split’s
identity differs.
After applying the fix, consider running `coderabbit review --agent` for local
review. Visit https://docs.coderabbit.ai/cli?utm_source=ghpr
The five accepted-spec checks this branch edited printed a stale "must be 1 or 2" line and raised an empty ValueError from a caught assert. They now raise a ValueError naming the requested spec and the supported set. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Review fixes for the component-loss and resume controls: - The single-process APNet2, APNet2-fused and AP3-D3 loops build torch.nn.MSELoss() again when no loss is selected. The transfer branch hands the criterion unsqueezed batch.y, so substituting the inlined MSE was not guaranteed bit-identical there. - A selected loss with transfer_learning=True raises: those losses expect (n_dimer, n_component) rows, not summed totals. - --component_loss raises on routes whose train() has no loss_fn instead of being dropped by the unsupported-kwarg filter, matching --resume-state. - component_weighted_mse takes one weight per predicted component, so three under no_disp_nn, with a message saying so. - per_component_mse replaces six copies of the component-MSE block; its zero dispersion follows the errors' device and dtype. - The resume state is format v2 and loads with weights_only=True: NumPy RNG keys travel as a tensor and NumPy-scalar learning rates as floats. A v1 file is refused with an explanation. - validate_checkpoint_metric replaces AP3-D3's import of a private name. - The tracking comment and the W&B spec no longer call train/loss/<component> a share of the optimised loss, and the spec documents the new keys, their route coverage, and the resume state. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
The transfer-learning loops of APNet2, APNet2-fused and AP3-D3 handed the criterion batch.y as stored. A (n_dimer, 1) label column against (n_dimer,) predictions broadcast to an n_dimer x n_dimer grid inside MSELoss. Labels are now reshaped to the predictions' shape, so column labels train exactly like flat ones and a label-count mismatch raises. Also shortens the docstrings, comments and tests added on this branch: one helper for the refuse-instead-of-drop CLI checks, one converter for the weights_only resume state, and merged overlapping tests. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
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Actionable comments posted: 1
- 🪄 Fix CodeRabbit comments on this PR
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Inline comments:
Review comments at @src/apnet_pt/pairwise_datasets.py:
- Line 709: Update the split filtering in the pairwise AP2 path and the fused
AP2/AP3 file-backed and LMDB paths that use supported_spec_types so split="all"
processes both mapped spec_type=11 files instead of skipping them. Add a focused
construction test using small spec_type=11 raw-file fixtures and assert
processing creates usable output.
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📒 Files selected for processing (21)
docs/specs/wandb-training.mdsrc/apnet_pt/AtomPairwiseModels/apnet2.pysrc/apnet_pt/AtomPairwiseModels/apnet2_fused.pysrc/apnet_pt/AtomPairwiseModels/apnet2_parity.pysrc/apnet_pt/AtomPairwiseModels/apnet3.pysrc/apnet_pt/AtomPairwiseModels/apnet3_d3_fused.pysrc/apnet_pt/AtomPairwiseModels/apnet3_fused.pysrc/apnet_pt/AtomPairwiseModels/apnet3_fused_variants.pysrc/apnet_pt/AtomPairwiseModels/component_losses.pysrc/apnet_pt/pairwise_datasets.pysrc/apnet_pt/pt_datasets/ap2_fused_ds.pysrc/apnet_pt/pt_datasets/ap3_fused_ds.pysrc/apnet_pt/training_resume.pysrc/apnet_pt/training_tracking.pytests/test_component_losses.pytests/test_empty_monomer_messages.pytests/test_spec_type_registry.pytests/test_train_models_loss_flags.pytests/test_training_resume.pytests/test_transfer_label_shape.pytrain_models.py
🚧 Files skipped from review as they are similar to previous changes (4)
- src/apnet_pt/AtomPairwiseModels/apnet3_fused_variants.py
- src/apnet_pt/AtomPairwiseModels/apnet3.py
- src/apnet_pt/AtomPairwiseModels/apnet3_fused.py
- src/apnet_pt/training_tracking.py
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| except AssertionError: | ||
| print("Currently spec_type must be 1 or 2 for SAPT0/jun-cc-pVDZ") | ||
| raise ValueError | ||
| supported_spec_types = (1, 2, 5, 6, 7, 8, 9, 11, None) |
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🩺 Stability & Availability | 🟠 Major | ⚡ Quick win
🔎 Supported by static analysis
🏁 Script executed:
rg -n 'split ==|split in|self.split|def process|raw_file_names|def __init__' src/apnet_pt/pairwise_datasets.py src/apnet_pt/pt_datasets/ap2_fused_ds.py src/apnet_pt/pt_datasets/ap3_fused_ds.py | tail -180
sed -n '680,745p;835,865p;870,940p' src/apnet_pt/pairwise_datasets.py
sed -n '1010,1060p;1170,1205p' src/apnet_pt/pt_datasets/ap2_fused_ds.py
sed -n '865,915p;1045,1075p;1600,1645p;1800,1825p' src/apnet_pt/pt_datasets/ap3_fused_ds.pyRepository: Awallace3/QCMLForge
Length of output: 25188
🏁 Script executed:
#!/bin/bash
set -e
printf '%s\n' '--- helper definitions and class anchors ---'
rg -n -C 6 'def is_split_db_config|def spec_type_uses_split_files|class .*Dataset|def raw_file_names|def process|if self.split_db|if self.split not in|for i in|i =' src/apnet_pt/pairwise_datasets.py src/apnet_pt/pt_datasets/ap2_fused_ds.py src/apnet_pt/pt_datasets/ap3_fused_ds.py
printf '%s\n' '--- pairwise module dataset ---'
sed -n '560,590p;660,760p;800,890p;950,1065p;1180,1270p' src/apnet_pt/pairwise_datasets.py
printf '%s\n' '--- AP2 file-backed dataset ---'
sed -n '780,830p;980,1070p;1125,1225p;1290,1415p' src/apnet_pt/pt_datasets/ap2_fused_ds.py
printf '%s\n' '--- AP2 LMDB dataset ---'
sed -n '1610,1710p;1750,1840p;1870,1965p' src/apnet_pt/pt_datasets/ap2_fused_ds.py
printf '%s\n' '--- AP3 file-backed dataset ---'
sed -n '720,760p;830,945p;1000,1090p;1225,1325p' src/apnet_pt/pt_datasets/ap3_fused_ds.py
printf '%s\n' '--- AP3 LMDB dataset ---'
sed -n '1550,1670p;1730,1830p;1985,2070p' src/apnet_pt/pt_datasets/ap3_fused_ds.py
printf '%s\n' '--- split-11 download/raw-file references ---'
rg -n -C 8 'splinter_omol25_sapt0indu_v1|39449167|fileserver_url|def download' src/apnet_pt/pairwise_datasets.py src/apnet_pt/pt_datasets/ap2_fused_ds.py src/apnet_pt/pt_datasets/ap3_fused_ds.pyRepository: Awallace3/QCMLForge
Length of output: 43059
🏁 Script executed:
#!/bin/bash
set -e
rg -n -C 8 'def process|if self.split not in|for i in|i =' src/apnet_pt/pairwise_datasets.py src/apnet_pt/pt_datasets/ap2_fused_ds.py src/apnet_pt/pt_datasets/ap3_fused_ds.pyRepository: Awallace3/QCMLForge
Length of output: 41742
🏁 Script executed:
#!/bin/bash
set -e
printf '%s\n' '--- pairwise AP2 process after raw-file loop ---'
sed -n '1018,1145p' src/apnet_pt/pairwise_datasets.py
printf '%s\n' '--- pairwise AP3 process after raw-file loop ---'
sed -n '1535,1665p' src/apnet_pt/pairwise_datasets.py
printf '%s\n' '--- fused AP2 file-backed process ---'
sed -n '1290,1455p' src/apnet_pt/pt_datasets/ap2_fused_ds.py
printf '%s\n' '--- fused AP2 LMDB process ---'
sed -n '1884,1985p' src/apnet_pt/pt_datasets/ap2_fused_ds.py
printf '%s\n' '--- fused AP3 file-backed process ---'
sed -n '1238,1385p' src/apnet_pt/pt_datasets/ap3_fused_ds.py
printf '%s\n' '--- fused AP3 LMDB process ---'
sed -n '1998,2135p' src/apnet_pt/pt_datasets/ap3_fused_ds.pyRepository: Awallace3/QCMLForge
Length of output: 36050
Process both spec type 11 files when split="all".
For spec_type=11, split_db is enabled and raw_file_names returns separate train and test files. The processors then check whether "all" occurs in each filename stem, so both files are skipped. split_name becoming empty does not change this filter, and no normalization or exception handles "all".
Apply the correction to the pairwise AP2 path and to the AP2 and AP3 fused file-backed and LMDB paths. When split="all", process both mapped files. Alternatively, reject "all" for split-file spec types before processing.
The fused file-backed loops do not need an i-after-empty-loop fix in the shown code. The material failure is that they receive no dimers and create no data objects. The LMDB paths likewise leave the input arrays empty and do not store processed data.
Add a focused construction test for spec_type=11 with the default split and small raw-file fixtures. Assert that processing creates usable output. Supplying or validating the raw files alone does not fix this behavior because present files are still skipped.
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Review comment at @src/apnet_pt/pairwise_datasets.py at line 709:
Update the split filtering in the pairwise AP2 path and the fused AP2/AP3
file-backed and LMDB paths that use supported_spec_types so split="all"
processes both mapped spec_type=11 files instead of skipping them. Add a focused
construction test using small spec_type=11 raw-file fixtures and assert
processing creates usable output.
After applying the fix, consider running `coderabbit review --agent` for local
review. Visit https://docs.coderabbit.ai/cli?utm_source=ghpr
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Summary
Registers the OMol25 + SPLINTER hybrid SAPT split as
spec_type=11and adds the training controls the fine-tunes on it needed. The new options default off. With no new flags, a component-target run callstrain()with the same arguments, uses the sametorch.nn.MSELoss()criterion, and selects its checkpoint as before. Behaviour changes only in the two fixes below.Fixes:
get_messagesreturnedtorch.zeros(0, width)on CPU, and the next scatter raised a device mismatch on CUDA. The block now takesh's device and dtype, in all six copies of the method.batch.yas stored. A(n_dimer, 1)label column against(n_dimer,)predictions broadcast to ann_dimer x n_dimergrid. Labels are now reshaped to the predictions' shape, so column labels train exactly like flat ones and a label-count mismatch raises. Flat labels train as before.AtomTypeParamMPNNcheckpoints: checkpoints written between 78d8e07 and 371f0c6 have nor_cutkey and now fall back to the caller'sr_cut. The same fallback is applied at one load site inInducedDipoleModel.Also included: spec-10/11 dataset docstrings, a
training/checkpoint_metrickey in the AP3-D3 W&B config, and updates todocs/specs/wandb-training.mdcovering the new loss keys (§9.4) and the resume state (§4, §5.3, §17).Evidence
spec_type=11fails the accepted-spec checks in the fused datasets. An all-monatomic-A batch raises a CUDA device mismatch, and an AP3-D3 spec-11 run died at epoch 45 of 300. Transfer training on column labels fails (test_column_labels_train_exactly_like_flat_labelsfails on the previous commit).413 passed, 57 skipped.loss_fnand one with an explicittorch.nn.MSELoss()end with identical weights.Notes
train/loss/<component>is the raw component MSE whatever objective is optimised. It is not a share ofloss_sumunder Huber, relative, or weighted losses.component_weighted_msetakes one weight per predicted component, so three underno_disp_nn.(preds, batch.y)transfer pattern remains inapnet3,apnet3_fused,apnet3_fused_variants, anddapnet2, which this PR does not touch.Merge Danger
Door: two-way
Model checkpoints and processed stores keep their format.
--resume-stateadds a separateqcmlforge-training-resume-v2file that loads withtorch.load(weights_only=True). v1 files from earlier commits on this branch are refused with an explanation.Blast Radius: training-entrypoints
train_models.pygains flags, andbuild_arg_parser()is split out ofmain(). Existing arguments are unaffected. The pairwise loops return per-component MSEs alongside the MAEs, which matters only to code that unpacks those tuples directly.🤖 Generated with Claude Code
Summary by CodeRabbit
New Features
Bug Fixes