feat(rewards): add a prepare hook to the reward protocol and scalers - #373
feat(rewards): add a prepare hook to the reward protocol and scalers#373manzuoni-astera wants to merge 3 commits into
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Guidance backprops the value and FK steering picks with argmin, so every reward here is really a loss. Nothing said so. Now the protocol docstring does, and points at the contract test that catches a term with the wrong sign.
The structure-factor reward from diff-use#324 is built in two phases, but nothing in src/ ever called the second one, so it could not run from the pipeline at all. Adds PreparableRewardFunctionProtocol and a prepare_reward_if_needed helper, called from both trajectory scalers once the model atom array exists. prepare() mutates the reward and returns None. The tmol reward in diff-use#319 and the torchref one in diff-use#372 both need this hook. Also replaces an `or` fallback on an AtomArray with a reward_atom_array property. Whether an empty AtomArray is falsy is biotite's call, not ours.
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📝 WalkthroughWalkthroughThe PR adds an optional reward preparation protocol, centralizes reward atom selection, and prepares compatible rewards before FK steering or pure guidance sampling. Tests verify topology binding, device forwarding, preparation order, repeated preparation, and minimized-reward sign conventions. ChangesReward preparation flow
Estimated code review effort: 3 (Moderate) | ~20 minutes Mergeability Score: 🟡 Moderate · up to The PR adds reward preparation before denoising, but the integration coverage does not yet prove that the model’s atom topology is passed or that the prepared reward is actually evaluated. This leaves a bounded correctness risk in the new integration path, so the assertions should be strengthened or explicitly accepted before merge. Sequence Diagram(s)sequenceDiagram
participant ProcessedStructure
participant SamplingScaler
participant RewardFunction
ProcessedStructure->>SamplingScaler: provide reward_atom_array and coordinate device
SamplingScaler->>RewardFunction: prepare(atom_array, device)
SamplingScaler->>RewardFunction: evaluate during sampling
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Pull request overview
This PR makes two-phase reward functions (configured in __init__, finalized once the model atom topology is known) reachable from real guidance runs by introducing a standard prepare() hook in the reward protocol and invoking it from the trajectory scalers before sampling begins.
Changes:
- Added
PreparableRewardFunctionProtocolplus aprepare_reward_if_needed()helper to standardize and safely no-op preparation for non-preparable rewards. - Wired reward preparation into both
PureGuidanceandFKSteeringimmediately afterSampleworksProcessedStructureis created. - Centralized “which atom array rewards should follow” via
SampleworksProcessedStructure.reward_atom_array, and added unit/integration tests to ensure preparation happens exactly once before first reward evaluation.
Reviewed changes
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Show a summary per file
| File | Description |
|---|---|
| tests/rewards/test_reward_function_contract.py | Clarifies/enforces the “reward is minimized” sign convention in the contract test docs. |
| tests/rewards/test_prepare_hook.py | Adds focused unit tests for the new prepare hook helper and protocol behavior. |
| tests/integration/test_pipeline_integration.py | Adds integration coverage ensuring trajectory scalers prepare rewards once, before first __call__. |
| src/sampleworks/eval/structure_utils.py | Introduces reward_atom_array property and uses it in to_reward_inputs() to avoid falsy-AtomArray behavior. |
| src/sampleworks/core/scalers/pure_guidance.py | Calls prepare_reward_if_needed() before the denoising loop. |
| src/sampleworks/core/scalers/fk_steering.py | Calls prepare_reward_if_needed() before the denoising loop. |
| src/sampleworks/core/rewards/protocol.py | Adds PreparableRewardFunctionProtocol, prepare_reward_if_needed(), and documents the minimized-loss sign convention. |
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Inline comments:
In `@tests/integration/test_pipeline_integration.py`:
- Line 636: Strengthen the assertion around reward.prepared_atom_counts by
configuring mock_wrapper and mock_structure with reconciled but distinguishable
atom counts or ordering, then assert the prepared topology matches the model
topology rather than only its atom count. Ensure the test would fail if the
structure atom array were passed instead.
- Around line 612-637: Update RecordingPreparableReward and the assertions after
trajectory_scaler.sample() to track total __call__ invocations and assert at
least one reward evaluation occurred, while retaining the existing
prepared_atom_counts and calls_before_prepare checks.
In `@tests/rewards/test_prepare_hook.py`:
- Around line 15-85: Add NumPy-style docstrings to the helper methods and test
functions in tests/rewards/test_prepare_hook.py lines 15-85, including
PlainReward.__call__, PreparableReward.__init__, prepare, __call__,
make_atom_array, and each test; also document
RecordingPreparableReward.__init__, prepare, and __call__ in
tests/integration/test_pipeline_integration.py lines 604-615. No other behavior
changes are needed.
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📒 Files selected for processing (7)
src/sampleworks/core/rewards/protocol.pysrc/sampleworks/core/scalers/fk_steering.pysrc/sampleworks/core/scalers/pure_guidance.pysrc/sampleworks/eval/structure_utils.pytests/integration/test_pipeline_integration.pytests/rewards/test_prepare_hook.pytests/rewards/test_reward_function_contract.py
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The reward was never actually called, so 'no calls before prepare' held trivially. Uses a real DPS step scaler, counts the calls, and drives a mismatch case where the model has four atoms and the structure five, so preparing against the wrong array fails the test. Both from CodeRabbit on diff-use#373.
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@manzuoni-astera The main issue is that I suggest bringing in something like |
Summary
The structure-factor reward merged in #324 cannot run. It is built in two phases, and nothing
in
src/has ever called the second one, so the reward has tests but no way to reach a guidancejob. This adds a prepare hook to the reward protocol and calls it from both trajectory scalers
once the model atom array exists. It makes #324's reward reachable and gives #319 and #372 one
hook to share instead of three private variants. Independent of the registry work in #374 and
#375.
Changes
PreparableRewardFunctionProtocoljoinscore/rewards/protocol.py, alongside aprepare_reward_if_needed(reward, atom_array, *, device)helper that no-ops on rewards whichdon't implement it. Callers apply it unconditionally, so the density path is untouched.
The signature is the one #324 already shipped:
prepare(atom_array, *, device) -> None, mutatingthe reward rather than returning a new object. Worth settling now, because the two rewards queued
behind this disagree with it. #319's prepare returns a value, and #372's takes a structure rather
than an atom array.
Both call sites sit in
sample(), right after the processed structure is built and before thedenoising loop:
core/scalers/pure_guidance.pyandcore/scalers/fk_steering.py. They passprocessed_structure.reward_atom_array, a new property that returns the model atom array whenthe model exposes one and the input structure's otherwise. That is deliberately the same choice
to_reward_inputsmakes. If the two ever diverge, a reward's atom ordering stops matching thecoordinate tensor it is scoring, and the failure is silent: wrong numbers, no error.
The property also replaces
model_atom_array or atom_array. Whether an emptyAtomArrayisfalsy is biotite's decision, not something we should build on.
Also in here: the protocol docstring now states that rewards are minimized, since guidance
backprops the value and FK steering selects with
argmin. Nothing said so before, and weightedcombinations in #375 only mean something if every term agrees on the sign.
Testing
tests/rewards/test_prepare_hook.pycovers the helper directly: it prepares a two-phase rewardwith the atom array and device it was given, leaves a one-phase reward alone, and can be re-run
to rebind a reward to a different topology.
tests/integration/test_pipeline_integration.pydrives both trajectory scalers end to end with arecording reward, asserting that
prepareran exactly once, before the first evaluation, withthe model's atom array. The model there has four atoms and the structure five, so preparing
against the input structure fails the test rather than passing by coincidence. A real
DataSpaceDPSScaleris used so the reward is genuinely evaluated. Both of those came out ofCodeRabbit review on this PR, and the first version of the test passed vacuously without them.
CI is green: lint, four typecheck environments, four test environments. The GPU workflow has not
run.
Rollout
Nothing. No new dependencies, no CLI or configuration changes, and no behavior change for any
reward that doesn't implement
prepare. Merge this before #374 and #375, which build on it.