From 58f9d0fe9e2b00a581551c663448b596d0c38e25 Mon Sep 17 00:00:00 2001 From: "Marcus D. Collins" Date: Tue, 2 Jun 2026 10:04:54 -0700 Subject: [PATCH 1/8] Updating tests in test_runner.py to allow them to run on only one GPU --- tests/runs/test_runner.py | 21 ++++++++++++--------- 1 file changed, 12 insertions(+), 9 deletions(-) diff --git a/tests/runs/test_runner.py b/tests/runs/test_runner.py index 26d304da..442eda53 100644 --- a/tests/runs/test_runner.py +++ b/tests/runs/test_runner.py @@ -26,7 +26,8 @@ def test_argv_for_rf3_partial_matches_bash(monkeypatch: pytest.MonkeyPatch) -> N assert inv.log_path == Path("/results/rf3_run.log") argv = inv.argv - assert argv[:6] == ["pixi", "run", "-e", "rf3", "python", "/app/run_grid_search.py"] + assert argv[:5] == ["pixi", "run", "-e", "rf3", "python"] + assert "run_grid_search.py" in argv[5] # the exact path varies by invocation pairs = _argv_to_dict(argv[6:]) assert pairs["--proteins"] == "/data/inputs/proteins.csv" assert pairs["--model"] == "rf3" @@ -46,7 +47,8 @@ def test_argv_omits_false_bool_flags(monkeypatch: pytest.MonkeyPatch) -> None: """False boolean args are omitted rather than emitted as bare CLI flags.""" monkeypatch.setenv("HOME", "/home/test") preset = loader.load_preset( - "rf3_partial", overrides=["shared_args.gradient-normalization=false"] + "rf3_partial", + overrides=["shared_args.gradient-normalization=false", "jobs.0.gpu_count=1"] ) inv = runner.build_invocations(preset, results_dir=Path("/results"))[0] assert "--gradient-normalization" not in inv.argv @@ -147,7 +149,8 @@ def test_gpu_count_rejects_insufficient_visible_gpus( def test_protenix_dual_uses_different_checkpoints(monkeypatch: pytest.MonkeyPatch) -> None: """The Protenix dual preset uses separate tiny and mini checkpoints.""" monkeypatch.setenv("HOME", "/home/test") - preset = loader.load_preset("protenix_dual") + preset = loader.load_preset("protenix_dual", + overrides=["jobs.0.gpu_count=1", "jobs.1.gpu_count=1"]) invocations = runner.build_invocations(preset, results_dir=Path("/r")) pairs = [_argv_to_dict(i.argv[6:]) for i in invocations] assert pairs[0]["--model-checkpoint"] == "/extra_checkpoints/protenix_tiny_default_v0.5.0.pt" @@ -157,7 +160,7 @@ def test_protenix_dual_uses_different_checkpoints(monkeypatch: pytest.MonkeyPatc def test_rf3_partial_chiral_off_flag_present(monkeypatch: pytest.MonkeyPatch) -> None: """The RF3 chiral-off preset passes the disable and force rerun flags.""" monkeypatch.setenv("HOME", "/home/test") - preset = loader.load_preset("rf3_partial_chiral_off") + preset = loader.load_preset("rf3_partial_chiral_off", overrides=["jobs.0.gpu_count=1"]) inv = runner.build_invocations(preset, results_dir=Path("/r"))[0] assert "--disable-chiral-features" in inv.argv assert "--force-all" in inv.argv @@ -166,7 +169,7 @@ def test_rf3_partial_chiral_off_flag_present(monkeypatch: pytest.MonkeyPatch) -> def test_build_invocations_records_output_dir(monkeypatch: pytest.MonkeyPatch) -> None: """`run_grid_search.py` assumes its --output-dir exists; the runner must mkdir it.""" monkeypatch.setenv("HOME", "/home/test") - preset = loader.load_preset("rf3_partial") + preset = loader.load_preset("rf3_partial", overrides=["jobs.0.gpu_count=1"]) inv = runner.build_invocations(preset, results_dir=Path("/r"))[0] assert inv.output_dir == Path("/r/rf3") @@ -175,7 +178,7 @@ def test_grid_search_script_can_be_overridden(monkeypatch: pytest.MonkeyPatch) - """ACTL wrappers can run the synced checkout instead of the baked /app copy.""" monkeypatch.setenv("HOME", "/home/test") monkeypatch.setenv("SAMPLEWORKS_GRID_SEARCH_SCRIPT", "/home/dev/workspace/run_grid_search.py") - preset = loader.load_preset("rf3_partial") + preset = loader.load_preset("rf3_partial", overrides=["jobs.0.gpu_count=1"]) inv = runner.build_invocations(preset, results_dir=Path("/r"))[0] assert inv.argv[:6] == [ "pixi", @@ -237,7 +240,7 @@ def test_uses_baked_env_python_when_available( python_bin.chmod(0o755) monkeypatch.setenv("SAMPLEWORKS_PIXI_PROJECT_DIR", str(pixi_project)) - preset = loader.load_preset("rf3_partial") + preset = loader.load_preset("rf3_partial", overrides=["jobs.0.gpu_count=1"]) inv = runner.build_invocations(preset, results_dir=Path("/r"))[0] assert inv.argv[:2] == [str(python_bin), "/app/run_grid_search.py"] @@ -251,7 +254,7 @@ def test_prebuilt_env_required_rejects_runtime_pixi( monkeypatch.delenv("SAMPLEWORKS_ALLOW_RUNTIME_PIXI", raising=False) monkeypatch.setenv("SAMPLEWORKS_PIXI_PROJECT_DIR", str(tmp_path / "app")) - preset = loader.load_preset("rf3_partial") + preset = loader.load_preset("rf3_partial", overrides=["jobs.0.gpu_count=1"]) with pytest.raises(RuntimeError, match="Refusing to fall back to 'pixi run'"): runner.build_invocations(preset, results_dir=Path("/r")) @@ -262,7 +265,7 @@ def test_dry_run_does_not_create_directories( """--dry-run prints commands but never touches the filesystem.""" monkeypatch.setenv("HOME", str(tmp_path)) results_dir = tmp_path / "results" - preset = loader.load_preset("rf3_partial") + preset = loader.load_preset("rf3_partial", overrides=["jobs.0.gpu_count=1"]) # use 1 gpu so we don't need big nodes to test runner.run(preset, results_dir=results_dir, dry_run=True) # results_dir gets created by run() (for log file location) but per-job # output subdirs must NOT exist after dry-run. From 9231374fdbc1cfdaa8645c9b870f241038cd5918 Mon Sep 17 00:00:00 2001 From: "Marcus D. Collins" Date: Tue, 2 Jun 2026 15:46:00 -0700 Subject: [PATCH 2/8] feat(models): add Protpardelle-1c StructureModelWrapper Wrap sequence-conditioned all-atom Protpardelle-1c models (task "ai-allatom", e.g. cc89.yaml) under the StructureModelWrapper protocol. featurize() derives sequence conditioning (aatype/seq_mask/residue_index/ chain_index/atom37 mask) from an Atomworks structure's chain_info, and step() runs the full reverse-diffusion trajectory internally via Protpardelle.sample(gt_aatype=...), returning [batch, atoms, 3] coords. - Register PROTPARDELLE_AVAILABLE / require_protpardelle / check_protpardelle_available (catching OSError from protpardelle.env). - Add StructurePredictor.PROTPARDELLE. - Tests build a small random ai-allatom model (no weights needed); cover sequence extraction, featurization shapes, prior init, protocol conformance, and an end-to-end short-trajectory sampling smoke test. Co-Authored-By: Claude Opus 4.8 (1M context) --- .../models/protpardelle/__init__.py | 1 + .../models/protpardelle/wrapper.py | 511 ++++++++++++++++++ src/sampleworks/utils/guidance_constants.py | 1 + src/sampleworks/utils/imports.py | 95 +++- tests/models/protpardelle/__init__.py | 0 tests/models/protpardelle/conftest.py | 149 +++++ .../protpardelle/test_protpardelle_wrapper.py | 237 ++++++++ 7 files changed, 991 insertions(+), 3 deletions(-) create mode 100644 src/sampleworks/models/protpardelle/__init__.py create mode 100644 src/sampleworks/models/protpardelle/wrapper.py create mode 100644 tests/models/protpardelle/__init__.py create mode 100644 tests/models/protpardelle/conftest.py create mode 100644 tests/models/protpardelle/test_protpardelle_wrapper.py diff --git a/src/sampleworks/models/protpardelle/__init__.py b/src/sampleworks/models/protpardelle/__init__.py new file mode 100644 index 00000000..3eb81972 --- /dev/null +++ b/src/sampleworks/models/protpardelle/__init__.py @@ -0,0 +1 @@ +"""Protpardelle-1c model wrapper.""" diff --git a/src/sampleworks/models/protpardelle/wrapper.py b/src/sampleworks/models/protpardelle/wrapper.py new file mode 100644 index 00000000..2e77176f --- /dev/null +++ b/src/sampleworks/models/protpardelle/wrapper.py @@ -0,0 +1,511 @@ +"""Wrapper for Protpardelle-1c models. + +Follows the ``StructureModelWrapper`` protocol in +``sampleworks.models.protocol`` so Protpardelle can be used interchangeably +with other structure predictors in sampling pipelines. + +This wrapper targets the *sequence-conditioned* all-atom Protpardelle-1c +models (``task: "ai-allatom"``, e.g. the model defined by ``cc89.yaml``). +These models take a protein sequence as their only conditioning input and +generate an all-atom structure by running the full reverse-diffusion +trajectory internally. Because a single ``step`` call encapsulates the entire +diffusion loop (analogous to AlphaFold2 recycling being hidden inside one +forward pass), this is a ``StructureModelWrapper`` rather than a +``FlowModelWrapper``. +""" + +import copy +from dataclasses import dataclass, field +from pathlib import Path +from typing import Any + +import torch +from atomworks.enums import ChainType +from jaxtyping import Float, Int +from loguru import logger +from protpardelle.core.models import load_model, Protpardelle +from protpardelle.data.atom import atom37_mask_from_aatype +from protpardelle.data.sequence import seq_to_aatype +from torch import Tensor + +from sampleworks.models.protocol import GenerativeModelInput +from sampleworks.utils.framework_utils import match_batch + + +# Number of aatype tokens for the 21-token alphabet (20 canonical + X). +# Matches ``data.n_aatype_tokens`` in the Protpardelle-1c configs (e.g. cc89.yaml). +NUM_AATYPE_TOKENS = 21 + + +# ``Protpardelle.sample`` reads many nested fields off ``conditional_cfg`` and +# ``partial_diffusion`` (even when disabled). These defaults disable all +# conditional generation / partial diffusion and mirror the structure of the +# ``conditional_cfg`` block in the Protpardelle-1c running configs (e.g. +# ``configs/running/sampling_unconditional_allatom.yaml``). +DEFAULT_CONDITIONAL_CFG: dict[str, Any] = { + "enabled": False, + "discontiguous_motif_assignment": { + "enabled": False, + "strategy": "fixed", + "fixed_motif_pos": [], + }, + "num_recurrence_steps": 1, + "crop_conditional_guidance": { + "enabled": False, + "start": 0.0, + "end": 2.0, + "freq": 1, + "freq_start": 0.0, + "freq_end": 0.0, + "strategy": "backbone-sidechain", + }, + "reconstruction_guidance": { + "enabled": False, + "start": 0.0, + "end": 2.0, + "schedule": "custom", + "max_scale": 10.0, + "loss_weights": {"motif": 1.0}, + }, + "replacement_guidance": { + "enabled": False, + "start": 0.0, + "end": 0.92, + }, +} + +DEFAULT_PARTIAL_DIFFUSION: dict[str, Any] = { + "enabled": False, + "pdb_file_path": None, + "num_steps": 100, +} + + +@dataclass(frozen=True, slots=True) +class ProtpardelleConditioning: + """Sequence-derived conditioning for a Protpardelle sampling run. + + All tensors carry a leading batch (ensemble) dimension and share the same + padded length ``L``. Padding positions are indicated by ``seq_mask == 0``. + + Attributes + ---------- + aatype : Int[Tensor, "batch L"] + Per-residue amino-acid type indices (21-token alphabet). This is the + sequence the structure is conditioned on (passed as ``gt_aatype``). + seq_mask : Float[Tensor, "batch L"] + 1.0 for real residues, 0.0 for padding. + residue_index : Float[Tensor, "batch L"] + Residue ordering, with chain gaps applied by the model. + chain_index : Float[Tensor, "batch L"] + Integer chain id per residue. + atom_mask : Float[Tensor, "batch L 37"] + atom37 occupancy mask implied by ``aatype``; defines which atoms the + model places and how predicted coordinates are flattened. + sampling_kwargs : dict[str, Any] + Keyword arguments forwarded to ``Protpardelle.sample``. + sequences : tuple[str, ...] + The input one-letter sequences, one per protein chain (for reference). + """ + + aatype: Int[Tensor, "batch L"] + seq_mask: Float[Tensor, "batch L"] + residue_index: Float[Tensor, "batch L"] + chain_index: Float[Tensor, "batch L"] + atom_mask: Float[Tensor, "batch L 37"] + sampling_kwargs: dict[str, Any] + sequences: tuple[str, ...] + + +@dataclass +class ProtpardelleConfig: + """Configuration for Protpardelle featurization and sampling. + + Defaults follow the ``ai-allatom`` / ``uniform_steps`` recipe used by the + sequence-conditioned Protpardelle-1c models (see the ``allatom_cfg`` block + of ``configs/running/sampling_partial_diffusion_allatom.yaml`` in the + Protpardelle-1c repository). + + Attributes + ---------- + ensemble_size : int + Number of structures to sample for the input sequence (batch dim). + num_steps : int + Number of denoising (ODE discretization) steps. + s_churn : float + Stochasticity: ``gamma = s_churn / num_steps`` extra noise per step. + step_scale : float + Inverse-temperature scale applied to the score. + sidechain_mode : bool + All-atom MiniMPNN side-chain co-design. Left False for ai-allatom + sequence-conditioned models (which have no MiniMPNN). + skip_mpnn_proportion : float + Fraction of steps from the start to skip running MiniMPNN. + jump_steps : bool + Use the superposition sampling scheme (mutually exclusive with + ``uniform_steps``). + uniform_steps : bool + All-atom denoising with a uniform noise-level change each step. This is + the scheme used by ``ai-allatom`` models. + temperature : float + Temperature applied to aatype logits (unused when the sequence is + fully specified, but forwarded for completeness). + top_p : float + Top-p truncation for aatype sampling (as above). + extra_sampling_kwargs : dict[str, Any] + Additional keyword overrides merged into the ``Protpardelle.sample`` + call, taking precedence over the fields above. + """ + + ensemble_size: int = 1 + num_steps: int = 500 + s_churn: float = 200.0 + step_scale: float = 1.2 + sidechain_mode: bool = False + skip_mpnn_proportion: float = 1.0 + jump_steps: bool = False + uniform_steps: bool = True + temperature: float = 1.0 + top_p: float = 1.0 + extra_sampling_kwargs: dict[str, Any] = field(default_factory=dict) + + +def annotate_structure_for_protpardelle( + structure: dict, + *, + ensemble_size: int = 1, + num_steps: int = 500, + s_churn: float = 200.0, + step_scale: float = 1.2, + sidechain_mode: bool = False, + skip_mpnn_proportion: float = 1.0, + jump_steps: bool = False, + uniform_steps: bool = True, + temperature: float = 1.0, + top_p: float = 1.0, + extra_sampling_kwargs: dict[str, Any] | None = None, +) -> dict: + """Annotate an Atomworks structure with Protpardelle-specific configuration. + + Parameters + ---------- + structure : dict + Atomworks structure dictionary. + ensemble_size, num_steps, s_churn, step_scale, sidechain_mode, \ + skip_mpnn_proportion, jump_steps, uniform_steps, temperature, top_p, \ + extra_sampling_kwargs + See :class:`ProtpardelleConfig`. + + Returns + ------- + dict + Structure dict with a ``"_protpardelle_config"`` key added. + """ + config = ProtpardelleConfig( + ensemble_size=ensemble_size, + num_steps=num_steps, + s_churn=s_churn, + step_scale=step_scale, + sidechain_mode=sidechain_mode, + skip_mpnn_proportion=skip_mpnn_proportion, + jump_steps=jump_steps, + uniform_steps=uniform_steps, + temperature=temperature, + top_p=top_p, + extra_sampling_kwargs=extra_sampling_kwargs or {}, + ) + return {**structure, "_protpardelle_config": config} + + +def extract_protein_sequences(structure: dict) -> list[str]: + """Extract canonical one-letter protein sequences from a structure. + + Parameters + ---------- + structure : dict + Atomworks structure dictionary. Sequences are read from + ``structure["chain_info"]`` using the + ``processed_entity_canonical_sequence`` of each protein (POLYPEPTIDE_L) + chain, in chain order. + + Returns + ------- + list[str] + One sequence per protein chain. + + Raises + ------ + ValueError + If no ``chain_info`` is present or no protein chains are found. + """ + chain_info = structure.get("chain_info") + if not chain_info: + raise ValueError( + "Protpardelle featurization requires 'chain_info' with canonical " + "sequences; none was found on the structure." + ) + + sequences: list[str] = [] + for chain_id, info in chain_info.items(): + chain_type: ChainType = info["chain_type"] + if chain_type == ChainType.POLYPEPTIDE_L: + sequence = info["processed_entity_canonical_sequence"] + if sequence: + sequences.append(sequence) + else: + logger.warning( + f"Skipping non-protein chain {chain_id!r} (chain_type={chain_type}); " + "Protpardelle only models L-polypeptide chains." + ) + + if not sequences: + raise ValueError("No L-polypeptide (protein) chains found for Protpardelle.") + + return sequences + + +class ProtpardelleWrapper: + """Wrapper for sequence-conditioned Protpardelle-1c all-atom models.""" + + def __init__( + self, + config_path: str | Path | None = None, + checkpoint_path: str | Path | None = None, + device: torch.device | str | None = None, + model: Protpardelle | None = None, + ): + """ + Parameters + ---------- + config_path : str | Path | None + Path to the Protpardelle model config YAML (e.g. ``cc89.yaml``). + Required unless ``model`` is provided. + checkpoint_path : str | Path | None + Path to the matching ``.pth`` checkpoint with trained weights. + Required unless ``model`` is provided. + device : torch.device | str | None + Device to load the model on. When ``None``, Protpardelle picks the + default device (CUDA if available). + model : Protpardelle | None + A pre-built Protpardelle model. When given, ``config_path`` and + ``checkpoint_path`` are ignored and the model is used directly + (useful for testing and advanced reuse). + """ + resolved_device = torch.device(device) if device is not None else None + + if model is not None: + self.model = model.to(resolved_device) if resolved_device is not None else model + self.config_path = Path(config_path) if config_path is not None else None + self.checkpoint_path = ( + Path(checkpoint_path) if checkpoint_path is not None else None + ) + else: + if config_path is None or checkpoint_path is None: + raise ValueError( + "config_path and checkpoint_path are required when no model is provided." + ) + self.config_path = Path(config_path).expanduser().resolve() + self.checkpoint_path = Path(checkpoint_path).expanduser().resolve() + logger.info( + f"Loading Protpardelle model from {self.config_path.name} " + f"(checkpoint {self.checkpoint_path.name})" + ) + self.model = load_model( + self.config_path, self.checkpoint_path, device=resolved_device + ) + + self._device = self.model.device + + if self.model.use_mpnn_model: + logger.warning( + "Loaded Protpardelle model has a MiniMPNN (sequence design) head; " + "this wrapper treats the input sequence as fixed conditioning " + "(gt_aatype) and does not redesign it." + ) + + @property + def device(self) -> torch.device: + return self._device + + def featurize(self, structure: dict) -> GenerativeModelInput[ProtpardelleConditioning]: + """From an Atomworks structure, derive Protpardelle sequence conditioning. + + Reads the canonical protein sequence(s) from the structure and builds + the sequence mask, residue/chain indices, aatype, and atom37 mask + required to condition sampling. No diffusion is run here. + + Parameters + ---------- + structure : dict + Atomworks structure dictionary, optionally annotated via + :func:`annotate_structure_for_protpardelle`. Config is read from + ``structure["_protpardelle_config"]`` if present, else defaults. + + Returns + ------- + GenerativeModelInput[ProtpardelleConditioning] + Reference ``x_init`` (sampled from the prior) and sequence + conditioning for :meth:`step`. + """ + config = structure.get("_protpardelle_config", ProtpardelleConfig()) + if isinstance(config, dict): + config = ProtpardelleConfig(**config) + + sequences = extract_protein_sequences(structure) + + # Lay out chains exactly as Protpardelle's own sampling helper does so + # residue/chain indexing (including inter-chain gaps) matches training. + prot_lens_per_chain = torch.tensor( + [[len(seq) for seq in sequences]], dtype=torch.long, device=self.device + ) + seq_mask, residue_index, chain_index = self.model.make_seq_mask_for_sampling( + prot_lens_per_chain=prot_lens_per_chain + ) + + # Concatenate per-chain aatypes in chain order; chains are placed + # contiguously at the front of the padded sequence by the helper above. + chain_aatypes = [ + seq_to_aatype(seq, num_tokens=NUM_AATYPE_TOKENS) for seq in sequences + ] + flat_aatype = torch.cat(chain_aatypes).to(self.device) + padded_len = seq_mask.shape[1] + aatype = torch.zeros((1, padded_len), dtype=torch.long, device=self.device) + aatype[0, : flat_aatype.shape[0]] = flat_aatype + + # Expand to the requested ensemble size. + ensemble_size = config.ensemble_size + aatype = match_batch(aatype, target_batch_size=ensemble_size) + seq_mask = match_batch(seq_mask, target_batch_size=ensemble_size) + residue_index = match_batch(residue_index, target_batch_size=ensemble_size) + chain_index = match_batch(chain_index, target_batch_size=ensemble_size) + + atom_mask = atom37_mask_from_aatype(aatype, seq_mask) + + sampling_kwargs = self._build_sampling_kwargs(config) + conditioning = ProtpardelleConditioning( + aatype=aatype, + seq_mask=seq_mask, + residue_index=residue_index, + chain_index=chain_index, + atom_mask=atom_mask, + sampling_kwargs=sampling_kwargs, + sequences=tuple(sequences), + ) + + # x_init is a shape-compatible reference drawn from the prior. The + # Protpardelle sampler initializes its own noise internally, so this is + # carried only for interface compatibility / downstream alignment. + num_atoms = int(atom_mask[0].sum().item()) + x_init = self.initialize_from_prior( + batch_size=ensemble_size, shape=(num_atoms, 3) + ) + + return GenerativeModelInput(x_init=x_init, conditioning=conditioning) + + @staticmethod + def _build_sampling_kwargs(config: ProtpardelleConfig) -> dict[str, Any]: + """Assemble the keyword arguments for ``Protpardelle.sample``.""" + sampling_kwargs: dict[str, Any] = { + "num_steps": config.num_steps, + "s_churn": config.s_churn, + "step_scale": config.step_scale, + "sidechain_mode": config.sidechain_mode, + "skip_mpnn_proportion": config.skip_mpnn_proportion, + "jump_steps": config.jump_steps, + "uniform_steps": config.uniform_steps, + "temperature": config.temperature, + "top_p": config.top_p, + # Disable conditional generation / partial diffusion by default; + # sample() reads nested fields off these even when disabled. + "conditional_cfg": copy.deepcopy(DEFAULT_CONDITIONAL_CFG), + "partial_diffusion": copy.deepcopy(DEFAULT_PARTIAL_DIFFUSION), + } + sampling_kwargs.update(config.extra_sampling_kwargs) + return sampling_kwargs + + def step( + self, + features: GenerativeModelInput[ProtpardelleConditioning], + ) -> Float[Tensor, "batch atoms 3"]: + """Run the full Protpardelle diffusion trajectory for the input sequence. + + The entire reverse-diffusion loop runs internally; the returned tensor + is the final all-atom prediction, flattened to the atoms implied by the + input sequence (the ``atom_mask`` in the conditioning). + + Parameters + ---------- + features : GenerativeModelInput[ProtpardelleConditioning] + Features as returned by :meth:`featurize`. + + Returns + ------- + Float[Tensor, "batch atoms 3"] + Predicted coordinates for the present atoms, one row of length + ``atoms`` per ensemble member. + """ + if features is None or features.conditioning is None: + raise ValueError("features with conditioning required for step()") + + cond = features.conditioning + + with torch.no_grad(): + aux = self.model.sample( + seq_mask=cond.seq_mask, + residue_index=cond.residue_index, + chain_index=cond.chain_index, + gt_aatype=cond.aatype, + **cond.sampling_kwargs, + ) + + # aux["x"]: [batch, L, 37, 3]. Masked-out atom slots are zeroed during + # ai-allatom sampling, so select the present atoms via the atom37 mask. + # The mask is identical across the batch (single input sequence), so a + # single 2-D mask flattens every ensemble member consistently. + coords = aux["x"] + atom_mask_2d = cond.atom_mask[0].bool() # [L, 37] + flat_coords = coords[:, atom_mask_2d] # [batch, atoms, 3] + + return flat_coords.float() + + def initialize_from_prior( + self, + batch_size: int, + features: GenerativeModelInput[ProtpardelleConditioning] | None = None, + *, + shape: tuple[int, ...] | None = None, + ) -> Float[Tensor, "batch atoms 3"]: + """Sample reference coordinates from the prior distribution. + + Parameters + ---------- + batch_size : int + Number of samples to generate. + features : GenerativeModelInput[ProtpardelleConditioning] | None + Features as returned by :meth:`featurize`, used to infer the atom + count when ``shape`` is not given. + shape : tuple[int, ...] | None + Explicit ``(num_atoms, 3)`` shape. Overrides ``features``. + + Returns + ------- + Float[Tensor, "batch atoms 3"] + Gaussian-initialized coordinates. + + Raises + ------ + ValueError + If neither ``features`` nor a valid ``shape`` is provided. + """ + if shape is not None: + if len(shape) != 2 or shape[1] != 3: + raise ValueError("shape must be of the form (num_atoms, 3)") + return torch.randn((batch_size, *shape), device=self.device) + + if features is None or features.conditioning is None: + raise ValueError( + "Either features or shape must be provided to initialize_from_prior()" + ) + + num_atoms = int(features.conditioning.atom_mask[0].sum().item()) + return torch.randn((batch_size, num_atoms, 3), device=self.device) diff --git a/src/sampleworks/utils/guidance_constants.py b/src/sampleworks/utils/guidance_constants.py index b40bb8dd..f3be6965 100644 --- a/src/sampleworks/utils/guidance_constants.py +++ b/src/sampleworks/utils/guidance_constants.py @@ -25,6 +25,7 @@ class StructurePredictor(StrEnum): BOLTZ_2 = "boltz2" PROTENIX = "protenix" RF3 = "rf3" + PROTPARDELLE = "protpardelle" class TrajectorySamplers(StrEnum): diff --git a/src/sampleworks/utils/imports.py b/src/sampleworks/utils/imports.py index a6f7de14..ffdb2676 100644 --- a/src/sampleworks/utils/imports.py +++ b/src/sampleworks/utils/imports.py @@ -8,6 +8,7 @@ BOLTZ_AVAILABLE = False PROTENIX_AVAILABLE = False RF3_AVAILABLE = False +PROTPARDELLE_AVAILABLE = False try: from sampleworks.models.boltz.wrapper import Boltz1Wrapper, Boltz2Wrapper @@ -38,6 +39,17 @@ except (ImportError, ModuleNotFoundError): pass +try: + # Protpardelle's package import (via protpardelle.env) raises + # NotADirectoryError (an OSError) when the model_params directory is not + # set up, so catch OSError in addition to import errors. + from sampleworks.models.protpardelle.wrapper import ProtpardelleWrapper + + PROTPARDELLE_AVAILABLE = True + del ProtpardelleWrapper +except (ImportError, ModuleNotFoundError, OSError): + pass + F = TypeVar("F", bound=Callable[..., Any]) @@ -172,6 +184,52 @@ def wrapper(*args: Any, **kwargs: Any) -> Any: return decorator +def require_protpardelle(message: str | None = None) -> Callable[[F], F]: + """Decorator to require Protpardelle model availability. + + Parameters + ---------- + message: str, optional + Custom error message. If None, uses default message. + + Returns + ------- + Callable + Decorator function + + Examples + -------- + >>> @require_protpardelle + ... def sample_protpardelle(): + ... pass + + >>> @require_protpardelle("Custom error message") + ... def custom_function(): + ... pass + """ + default_message = ( + "Protpardelle model wrapper is not available. Install with: " + "pixi install -e protpardelle" + ) + + def decorator(func: F) -> F: + @functools.wraps(func) + def wrapper(*args: Any, **kwargs: Any) -> Any: + if not PROTPARDELLE_AVAILABLE: + error_msg = message or default_message + try: + import pytest + + pytest.skip(error_msg) + except ImportError: + raise ImportError(error_msg) from None + return func(*args, **kwargs) + + return wrapper # type: ignore + + return decorator + + def require_any_model(message: str | None = None) -> Callable[[F], F]: """Decorator to require at least one model wrapper availability. @@ -204,7 +262,12 @@ def require_any_model(message: str | None = None) -> Callable[[F], F]: def decorator(func: F) -> F: @functools.wraps(func) def wrapper(*args: Any, **kwargs: Any) -> Any: - if not BOLTZ_AVAILABLE and not PROTENIX_AVAILABLE and not RF3_AVAILABLE: + if ( + not BOLTZ_AVAILABLE + and not PROTENIX_AVAILABLE + and not RF3_AVAILABLE + and not PROTPARDELLE_AVAILABLE + ): error_msg = message or default_message try: import pytest @@ -277,6 +340,27 @@ def check_rf3_available(message: str | None = None) -> None: raise ImportError(message or default_message) +def check_protpardelle_available(message: str | None = None) -> None: + """Check if Protpardelle is available, raise ImportError if not. + + Parameters + ---------- + message: str, optional + Custom error message. If None, uses default message. + + Raises + ------ + ImportError + If Protpardelle model wrapper is not available. + """ + if not PROTPARDELLE_AVAILABLE: + default_message = ( + "Protpardelle model wrapper is not available. Install with: " + "pixi install -e protpardelle" + ) + raise ImportError(message or default_message) + + def check_any_model_available(message: str | None = None) -> None: """Check if at least one model is available, raise ImportError if not. @@ -290,11 +374,16 @@ def check_any_model_available(message: str | None = None) -> None: ImportError If no model wrapper is available. """ - if not BOLTZ_AVAILABLE and not PROTENIX_AVAILABLE and not RF3_AVAILABLE: + if ( + not BOLTZ_AVAILABLE + and not PROTENIX_AVAILABLE + and not RF3_AVAILABLE + and not PROTPARDELLE_AVAILABLE + ): default_message = ( "No model wrappers are available. " "Please install at least one model wrapper with the appropriate " "feature group: 'pixi install -e boltz', 'pixi install -e protenix', " - "or 'pixi install -e rf3'" + "'pixi install -e rf3', or 'pixi install -e protpardelle'" ) raise ImportError(message or default_message) diff --git a/tests/models/protpardelle/__init__.py b/tests/models/protpardelle/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/tests/models/protpardelle/conftest.py b/tests/models/protpardelle/conftest.py new file mode 100644 index 00000000..4889aef7 --- /dev/null +++ b/tests/models/protpardelle/conftest.py @@ -0,0 +1,149 @@ +"""Fixtures for Protpardelle wrapper tests. + +Protpardelle's package import (``protpardelle.env``) requires a model-params +directory to exist. The tests don't need real weights, so point +``PROTPARDELLE_MODEL_PARAMS`` at a throwaway directory *before* protpardelle is +imported, then build a small randomly-initialized ``ai-allatom`` model. +""" + +import os +import tempfile +import textwrap +from pathlib import Path + +import pytest + + +# Must be set before any `import protpardelle...` happens. Respect an +# externally configured directory (e.g. when real weights are available). +os.environ.setdefault( + "PROTPARDELLE_MODEL_PARAMS", tempfile.mkdtemp(prefix="protpardelle_model_params_") +) + +protpardelle_models = pytest.importorskip( + "protpardelle.core.models", reason="Protpardelle not installed in this environment" +) + + +# A minimal cc89-style (``task: ai-allatom``, sequence-conditioned all-atom) +# config with the network dimensions shrunk so a randomly-initialized model is +# cheap to build and run on CPU. The data/diffusion fields that the model reads +# at inference time mirror cc89.yaml. +_SMALL_CC89_YAML = textwrap.dedent( + """ + train: + seed: null + ckpt_path: null + batch_size: 1 + max_epochs: 1 + eval_freq: 1 + checkpoint_freq: 1 + checkpoints: [] + lr: 0.0001 + warmup_steps: 1 + decay_steps: 1 + use_amp: False + clip_grad_norm: True + grad_clip_val: 1.0 + weight_decay: 0.0 + self_cond_train_prob: 0.9 + crop_conditional: True + crop_cond: + contiguous_prob: 0.05 + discontiguous_prob: 0.9 + sidechain_prob: 0.9 + sidechain_only_prob: 0.0 + max_span_len: 12 + max_discontiguous_res: 24 + dist_threshold: 45.0 + recenter_coords: True + + data: + pdb_paths: ["/nonexistent"] + mixing_ratios: [1.0] + fixed_size: 64 + n_aatype_tokens: 21 + short_epoch: 0 + num_workers: 0 + se3_data_augment: True + translation_scale: 1.0 + chain_residx_gap: 200 + sigma_data: 10.01 + auto_calc_sigma_data: False + n_examples_for_sigma_data: 1 + dummy_fill_mode: "zero" + subset: ["designable"] + + diffusion: + training: + function: "lognormal" + psigma_mean: -0.5 + psigma_std: 1.5 + sampling: + function: "uniform" + s_min: 0.001 + s_max: 80.0 + + model: + task: "ai-allatom" + pretrained_modules: [] + struct_model_checkpoint: "" + mpnn_model_checkpoint: "" + crop_conditional: True + conditioning_style: "concat" + compute_loss_on_all_atoms: false + struct_model: + arch: "dit" + n_atoms: 37 + n_channel: 32 + noise_cond_mult: 2 + uvit: + patch_size: 1 + n_layers: 2 + n_heads: 2 + dim_head: 16 + n_filt_per_layer: [] + n_blocks_per_layer: 2 + cat_pwd_to_conv: False + conv_skip_connection: False + position_embedding_type: "rotary" + position_embedding_max: 32 + mpnn_model: + use_self_conditioning: True + label_smoothing: 0.1 + n_channel: 32 + n_layers: 2 + n_neighbors: 8 + noise_cond_mult: 2 + """ +) + + +@pytest.fixture(scope="session") +def small_cc89_config_path(tmp_path_factory) -> Path: + """Write the small ai-allatom config YAML to a temp file and return its path.""" + config_dir = tmp_path_factory.mktemp("protpardelle_config") + config_path = config_dir / "cc89_small.yaml" + config_path.write_text(_SMALL_CC89_YAML) + return config_path + + +@pytest.fixture(scope="session") +def protpardelle_model(small_cc89_config_path: Path): + """A small, randomly-initialized ai-allatom Protpardelle model on CPU.""" + import torch + from protpardelle.configs import TrainingConfig + from protpardelle.utils import load_config + + config = load_config(small_cc89_config_path, TrainingConfig) + model = protpardelle_models.Protpardelle(config, device=torch.device("cpu")) + model.eval() + return model + + +@pytest.fixture(scope="session") +def protpardelle_wrapper(protpardelle_model): + """A ProtpardelleWrapper backed by the small random model.""" + from sampleworks.models.protpardelle.wrapper import ProtpardelleWrapper + + return ProtpardelleWrapper(model=protpardelle_model) diff --git a/tests/models/protpardelle/test_protpardelle_wrapper.py b/tests/models/protpardelle/test_protpardelle_wrapper.py new file mode 100644 index 00000000..b3084390 --- /dev/null +++ b/tests/models/protpardelle/test_protpardelle_wrapper.py @@ -0,0 +1,237 @@ +"""Tests for the Protpardelle structure-model wrapper. + +These tests build a small, randomly-initialized ``ai-allatom`` model (see +``conftest.py``) so they exercise real featurization/sampling logic without +needing downloaded weights. +""" + +import os +import tempfile + +import pytest + + +# Ensure the model-params directory exists before protpardelle is imported, +# mirroring conftest (import order across conftests is not guaranteed). +os.environ.setdefault( + "PROTPARDELLE_MODEL_PARAMS", tempfile.mkdtemp(prefix="protpardelle_model_params_") +) + +pytest.importorskip( + "protpardelle.core.models", reason="Protpardelle not installed in this environment" +) + +import torch +from atomworks.enums import ChainType +from protpardelle.data.sequence import seq_to_aatype +from sampleworks.models.protocol import GenerativeModelInput, StructureModelWrapper +from sampleworks.models.protpardelle.wrapper import ( + annotate_structure_for_protpardelle, + extract_protein_sequences, + NUM_AATYPE_TOKENS, + ProtpardelleConditioning, + ProtpardelleConfig, + ProtpardelleWrapper, +) + + +SEQ_A = "ACDEFGHIKL" +SEQ_B = "MNPQRSTVWY" + + +def _protein_structure(*sequences: str) -> dict: + """Build a minimal structure dict with protein chain_info only.""" + chain_ids = "ABCDEFGH" + chain_info = { + chain_ids[i]: { + "chain_type": ChainType.POLYPEPTIDE_L, + "processed_entity_canonical_sequence": seq, + } + for i, seq in enumerate(sequences) + } + return {"chain_info": chain_info, "metadata": {"id": "test"}} + + +# --------------------------------------------------------------------------- +# Pure-logic tests (no model needed) +# --------------------------------------------------------------------------- + + +class TestExtractProteinSequences: + def test_returns_sequences_in_chain_order(self): + structure = _protein_structure(SEQ_A, SEQ_B) + assert extract_protein_sequences(structure) == [SEQ_A, SEQ_B] + + def test_skips_non_protein_chains(self): + structure = _protein_structure(SEQ_A) + structure["chain_info"]["L"] = { + "chain_type": ChainType.NON_POLYMER, + "processed_entity_canonical_sequence": "", + } + assert extract_protein_sequences(structure) == [SEQ_A] + + def test_raises_without_chain_info(self): + with pytest.raises(ValueError, match="chain_info"): + extract_protein_sequences({"metadata": {"id": "x"}}) + + def test_raises_without_protein_chains(self): + structure = { + "chain_info": { + "L": { + "chain_type": ChainType.NON_POLYMER, + "processed_entity_canonical_sequence": "", + } + } + } + with pytest.raises(ValueError, match="protein"): + extract_protein_sequences(structure) + + +class TestAnnotateStructure: + def test_adds_config(self): + structure = _protein_structure(SEQ_A) + annotated = annotate_structure_for_protpardelle( + structure, ensemble_size=4, num_steps=10 + ) + config = annotated["_protpardelle_config"] + assert isinstance(config, ProtpardelleConfig) + assert config.ensemble_size == 4 + assert config.num_steps == 10 + # Original structure is not mutated. + assert "_protpardelle_config" not in structure + + def test_defaults_match_ai_allatom_recipe(self): + config = ProtpardelleConfig() + assert config.uniform_steps is True + assert config.jump_steps is False + assert config.sidechain_mode is False + + +class TestBuildSamplingKwargs: + def test_defaults(self): + kwargs = ProtpardelleWrapper._build_sampling_kwargs(ProtpardelleConfig()) + assert kwargs["uniform_steps"] is True + assert kwargs["jump_steps"] is False + assert kwargs["num_steps"] == 500 + + def test_extra_overrides_take_precedence(self): + config = ProtpardelleConfig( + num_steps=500, extra_sampling_kwargs={"num_steps": 7, "noise_scale": 2.0} + ) + kwargs = ProtpardelleWrapper._build_sampling_kwargs(config) + assert kwargs["num_steps"] == 7 + assert kwargs["noise_scale"] == 2.0 + + +class TestConstructorValidation: + def test_requires_paths_or_model(self): + with pytest.raises(ValueError, match="config_path and checkpoint_path"): + ProtpardelleWrapper() + + +class TestProtocolConformance: + def test_is_structure_model_wrapper(self, protpardelle_wrapper): + assert isinstance(protpardelle_wrapper, StructureModelWrapper) + + +# --------------------------------------------------------------------------- +# Featurization tests (small random model) +# --------------------------------------------------------------------------- + + +class TestFeaturize: + def test_returns_generative_model_input(self, protpardelle_wrapper): + structure = _protein_structure(SEQ_A) + features = protpardelle_wrapper.featurize(structure) + assert isinstance(features, GenerativeModelInput) + assert isinstance(features.conditioning, ProtpardelleConditioning) + assert features.x_init is not None + + def test_x_init_shape(self, protpardelle_wrapper): + structure = annotate_structure_for_protpardelle( + _protein_structure(SEQ_A), ensemble_size=3 + ) + features = protpardelle_wrapper.featurize(structure) + assert features.x_init.ndim == 3 + assert features.x_init.shape[0] == 3 + assert features.x_init.shape[2] == 3 + + def test_conditioning_shapes(self, protpardelle_wrapper): + structure = annotate_structure_for_protpardelle( + _protein_structure(SEQ_A), ensemble_size=2 + ) + cond = protpardelle_wrapper.featurize(structure).conditioning + length = len(SEQ_A) + assert cond.aatype.shape == (2, length) + assert cond.seq_mask.shape == (2, length) + assert cond.residue_index.shape == (2, length) + assert cond.atom_mask.shape == (2, length, 37) + assert cond.sequences == (SEQ_A,) + + def test_aatype_matches_sequence(self, protpardelle_wrapper): + structure = _protein_structure(SEQ_A) + cond = protpardelle_wrapper.featurize(structure).conditioning + expected = seq_to_aatype(SEQ_A, num_tokens=NUM_AATYPE_TOKENS) + assert torch.equal(cond.aatype[0].cpu(), expected) + + def test_x_init_atom_count_matches_atom_mask(self, protpardelle_wrapper): + structure = _protein_structure(SEQ_A) + features = protpardelle_wrapper.featurize(structure) + cond = features.conditioning + n_atoms = int(cond.atom_mask[0].sum().item()) + assert features.x_init.shape[1] == n_atoms + + def test_multichain_indices_have_gap(self, protpardelle_wrapper): + structure = _protein_structure(SEQ_A, SEQ_B) + cond = protpardelle_wrapper.featurize(structure).conditioning + # Two distinct chains present. + chain_ids = torch.unique(cond.chain_index[0]) + assert chain_ids.numel() == 2 + # The residue index jumps by more than 1 at the chain boundary. + residue_index = cond.residue_index[0] + boundary_gap = residue_index[len(SEQ_A)] - residue_index[len(SEQ_A) - 1] + assert boundary_gap > 1 + + +class TestInitializeFromPrior: + def test_with_shape(self, protpardelle_wrapper): + out = protpardelle_wrapper.initialize_from_prior(2, shape=(40, 3)) + assert out.shape == (2, 40, 3) + + def test_with_features(self, protpardelle_wrapper): + features = protpardelle_wrapper.featurize(_protein_structure(SEQ_A)) + out = protpardelle_wrapper.initialize_from_prior(3, features=features) + assert out.shape[0] == 3 + assert out.shape[-1] == 3 + + def test_raises_without_features_or_shape(self, protpardelle_wrapper): + with pytest.raises(ValueError, match="features|shape"): + protpardelle_wrapper.initialize_from_prior(batch_size=2) + + def test_invalid_shape_raises(self, protpardelle_wrapper): + with pytest.raises(ValueError, match="num_atoms"): + protpardelle_wrapper.initialize_from_prior(1, shape=(40, 4)) + + +# --------------------------------------------------------------------------- +# Step / sampling tests (small random model, short trajectory) +# --------------------------------------------------------------------------- + + +class TestStep: + def test_step_raises_without_features(self, protpardelle_wrapper): + with pytest.raises(ValueError, match="features"): + protpardelle_wrapper.step(None) + + @pytest.mark.slow + def test_step_returns_coords(self, protpardelle_wrapper): + short_seq = "ACDEFG" + structure = annotate_structure_for_protpardelle( + _protein_structure(short_seq), ensemble_size=1, num_steps=3, s_churn=0.0 + ) + features = protpardelle_wrapper.featurize(structure) + result = protpardelle_wrapper.step(features) + assert torch.is_tensor(result) + assert result.shape == features.x_init.shape + assert result.shape[-1] == 3 + assert torch.isfinite(result).all() From 50fffe9642c378fc99d8bb5fb566d88214c9b1be Mon Sep 17 00:00:00 2001 From: "Marcus D. Collins" Date: Fri, 12 Jun 2026 09:47:52 -0700 Subject: [PATCH 3/8] [WIP] fixing Claude's implementation of a Protenix wrapper, so it doesn't just give us unguided samples --- pixi.lock | 16758 +++++++++++----- pyproject.toml | 46 +- src/sampleworks/core/samplers/edm.py | 4 +- src/sampleworks/data/cc89_epoch415.yaml | 101 + .../models/protpardelle/wrapper.py | 337 +- .../utils/guidance_script_utils.py | 27 +- tests/models/protpardelle/conftest.py | 16 +- .../protpardelle/test_protpardelle_wrapper.py | 107 +- 8 files changed, 12367 insertions(+), 5029 deletions(-) create mode 100644 src/sampleworks/data/cc89_epoch415.yaml diff --git a/pixi.lock b/pixi.lock index b9d28b89..ce9a99f5 100644 --- a/pixi.lock +++ b/pixi.lock @@ -36,7 +36,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-hda65f42_9.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/c-ares-1.34.6-hb03c661_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/c-compiler-1.7.0-hd590300_1.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-command-line-tools-12.9.2-ha770c72_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-command-line-tools-12.9.1-ha770c72_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-crt-tools-12.9.86-ha770c72_2.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-cudart-12.9.79-h5888daf_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-cudart-dev-12.9.79-h5888daf_0.conda @@ -46,9 +46,9 @@ environments: - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-cupti-dev-12.9.79-h676940d_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-cuxxfilt-12.9.82-hffce074_1.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-driver-dev-12.9.79-h5888daf_0.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-gdb-12.9.79-hba53cbc_3.conda - - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-libraries-12.9.2-ha770c72_0.conda - 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idna ; extra == 'idna2008' + requires_python: '>=3.7' diff --git a/pyproject.toml b/pyproject.toml index 490c2c89..5a2146c2 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -23,7 +23,7 @@ pypi-dependencies = {"boltz" = "*", "cuequivariance-torch" = "*", "cuequivarianc platforms = ["linux-64"] [tool.pixi.feature.boltz-osx] -pypi-dependencies = {"boltz" = "*", "cuequivariance-torch" = "*", "rdkit" = ">=2025.3.6"} +pypi-dependencies = {"boltz" = "*", "cuequivariance-torch" = "*", "rdkit" = ">=2025.3.6", "joblib" = "*", "sfcalculator-torch" = ">=0.3.2"} platforms = ["osx-arm64"] @@ -31,8 +31,43 @@ platforms = ["osx-arm64"] pypi-dependencies = {"protenix" = "*", "einx" = "*", "triton" = "*"} platforms = ["linux-64"] +[tool.pixi.feature.protpardelle] +platforms = ["linux-64", "osx-arm64"] + +[tool.pixi.feature.protpardelle.dependencies] +biopython = "*" +einops = "*" +huggingface_hub = "*" +hydra-core = "*" +jupyter = "*" +numpy = ">=1.25.0,<2.0" +omegaconf = "*" +pandas = "==2.3.1" +prody = "*" +pyyaml = "*" +scipy = "*" +pytorch = ">=2.6.0,<2.8" +tqdm = "*" +typer = "*" + +[tool.pixi.feature.protpardelle.pypi-dependencies] +protpardelle = {git = "https://github.com/ProteinDesignLab/protpardelle-1c.git"} +biotite = "*" +dm-tree = "*" +jaxtyping = "*" +ml_collections = "*" +modelcif = "*" +transformers = "*" +wandb = "*" + +[tool.pixi.feature.protpardelle.pypi-options.dependency-overrides] +pandas = "==2.3.1" + [project] -authors = [{email = "karson.chrispens@ucsf.edu", name = "Karson Chrispens"}] +authors = [ + {email = "karson.chrispens@ucsf.edu", name = "Karson Chrispens"}, + {email = "marcus.collins@astera.org", name = "Marcus D. Collins"} +] dependencies = [ "atomworks[ml]==2.1.1", "python-dotenv", @@ -90,6 +125,8 @@ boltz-dev = {features = ["boltz", "dev"]} boltz-osx = {features = ["boltz-osx", "dev", "analysis"]} protenix = {features = ["protenix"]} protenix-dev = {features = ["protenix", "dev"]} +protpardelle = {features = ["protpardelle"]} +protpardelle-dev = {features = ["protpardelle", "dev"]} rf3 = {features = ["rf3"]} rf3-dev = {features = ["rf3", "dev"]} @@ -99,6 +136,11 @@ rdkit = ">=2024.3.5,<2025.9" [tool.pixi.feature.rf3.pypi-dependencies] rc-foundry = {editable = true, extras = ["rf3"], git = "https://github.com/k-chrispens/foundry.git"} +[tool.pixi.pypi-options.dependency-overrides] +pandas = "==2.3.1" +gemmi = "==0.6.7" + + [tool.pixi.pypi-dependencies] sampleworks = {editable = true, path = "."} diff --git a/src/sampleworks/core/samplers/edm.py b/src/sampleworks/core/samplers/edm.py index 81456170..30335888 100644 --- a/src/sampleworks/core/samplers/edm.py +++ b/src/sampleworks/core/samplers/edm.py @@ -422,8 +422,10 @@ def step( # t_hat will be float if check_context didn't raise # Use no_grad when gradients aren't needed to avoid memory overhead from # gradient checkpointing holding intermediate activations + # TODO testing adding eps to signature for use with Protpardelle-1c, if successful, + # I need to modify the Protocol itself. with torch.set_grad_enabled(allow_gradients): - x_hat_0 = model_wrapper.step(noisy_state, t_hat, features=features) + x_hat_0 = model_wrapper.step(noisy_state, t_hat, eps, features=features) reconciler = ( context.reconciler.to(torch.as_tensor(x_hat_0).device) diff --git a/src/sampleworks/data/cc89_epoch415.yaml b/src/sampleworks/data/cc89_epoch415.yaml new file mode 100644 index 00000000..5d904b0b --- /dev/null +++ b/src/sampleworks/data/cc89_epoch415.yaml @@ -0,0 +1,101 @@ +data: + auto_calc_sigma_data: true + chain_residx_gap: 200 + dummy_fill_mode: zero + fixed_size: 512 + mixing_ratios: + - 1.0 + n_aatype_tokens: 21 + n_examples_for_sigma_data: 500 + pdb_paths: + - /scratch/users/tianyulu/augmented_ingraham_cath_bugfree + se3_data_augment: true + sigma_data: 10.3 + subset: + - designable_ + translation_scale: 1.0 +diffusion: + sampling: + function: uniform + s_max: 80 + s_min: 0.001 + training: + function: lognormal + psigma_mean: -0.5 + psigma_std: 1.5 +model: + compute_loss_on_all_atoms: false + conditioning_style: concat + crop_conditional: true + dummy_fill_masked_atoms: false + full_mpnn_model_path: /scratch/users/tianyulu/farfalle/ProteinMPNN/vanilla_model_weights + mpnn_model: + label_smoothing: 0.1 + n_channel: 128 + n_layers: 3 + n_neighbors: 32 + noise_cond_mult: 4 + use_self_conditioning: true + mpnn_model_checkpoint: '' + pretrained_modules: [] + struct_model: + arch: dit + n_atoms: 37 + n_channel: 256 + noise_cond_mult: 4 + uvit: + cat_pwd_to_conv: false + conv_skip_connection: false + dim_head: 32 + n_blocks_per_layer: 2 + n_filt_per_layer: [] + n_heads: 8 + n_layers: 10 + patch_size: 1 + position_embedding_max: 32 + position_embedding_type: rotary + struct_model_checkpoint: '' + task: ai-allatom +train: + batch_size: 32 + checkpoint_freq: 1 + checkpoints: [] + ckpt_path: /scratch/users/tianyulu/farfalle/out_dir/farfalle/cc89/checkpoints/epoch206_training_state.pth + clip_grad_norm: true + crop_cond: + contiguous_prob: 0.05 + discontiguous_prob: 0.9 + dist_threshold: 45.0 + max_discontiguous_res: 24 + max_span_len: 12 + recenter_coords: true + sidechain_only_prob: 0.0 + sidechain_prob: 0.9 + terms_prob: 0.5 + crop_conditional: true + decay_steps: 2000000 + eval_freq: 8000000 + eval_loss_t: + - 0.1 + - 0.3 + - 0.5 + - 0.7 + - 0.9 + fpd_length_ranges_per_chain: + - - 50 + - 256 + grad_clip_val: 1.0 + length_ranges_per_chain: + - - 166 + - 188 + lr: 0.0001 + max_epochs: 10000 + n_eval_samples: 10 + n_fpd_samples: 0 + sc_num_seqs: 4 + seed: 0 + self_cond_train_prob: 0.9 + shapes_path: /scratch/users/tianyulu/protein_shapes + subsample_eval_set: 0.05 + warmup_steps: 1000 + weight_decay: 0.0 diff --git a/src/sampleworks/models/protpardelle/wrapper.py b/src/sampleworks/models/protpardelle/wrapper.py index 2e77176f..bbda90ae 100644 --- a/src/sampleworks/models/protpardelle/wrapper.py +++ b/src/sampleworks/models/protpardelle/wrapper.py @@ -19,19 +19,28 @@ from pathlib import Path from typing import Any +import biotite.structure as struc +import numpy as np import torch from atomworks.enums import ChainType from jaxtyping import Float, Int from loguru import logger +from protpardelle.common import residue_constants from protpardelle.core.models import load_model, Protpardelle from protpardelle.data.atom import atom37_mask_from_aatype from protpardelle.data.sequence import seq_to_aatype from torch import Tensor +from sampleworks.eval.structure_utils import get_asym_unit_from_structure from sampleworks.models.protocol import GenerativeModelInput from sampleworks.utils.framework_utils import match_batch +# Number of atom37 slots in the all-atom (atom37) representation. Matches +# ``residue_constants.atom_type_num`` and ``struct_model.n_atoms`` in the configs. +ATOM37_NUM_ATOMS = residue_constants.atom_type_num + + # Number of aatype tokens for the 21-token alphabet (20 canonical + X). # Matches ``data.n_aatype_tokens`` in the Protpardelle-1c configs (e.g. cc89.yaml). NUM_AATYPE_TOKENS = 21 @@ -81,6 +90,7 @@ } +# TODO: I believe this can be cleaned up, not all of this is necessary for a forward pass @dataclass(frozen=True, slots=True) class ProtpardelleConditioning: """Sequence-derived conditioning for a Protpardelle sampling run. @@ -102,10 +112,23 @@ class ProtpardelleConditioning: atom_mask : Float[Tensor, "batch L 37"] atom37 occupancy mask implied by ``aatype``; defines which atoms the model places and how predicted coordinates are flattened. + atom37_residue_index : Int[Tensor, "atoms"] + For each of the ``N`` flat atoms in the sampler's ``x_t`` (shape + ``batch x N x 3``), the residue position ``0..L-1`` that atom belongs to. + Together with :attr:`atom37_atom_index` this maps the flat coordinate + tensor into the model's ``batch x L x 37 x 3`` atom37 layout (see + :meth:`ProtpardelleWrapper._convert_to_atom37`). Shared across the batch + (a single input structure), so it carries no batch dimension. + atom37_atom_index : Int[Tensor, "atoms"] + For each of the ``N`` flat atoms, the atom37 slot ``0..36`` it occupies, + i.e. ``residue_constants.atom_order[atom_name]``. sampling_kwargs : dict[str, Any] Keyword arguments forwarded to ``Protpardelle.sample``. sequences : tuple[str, ...] The input one-letter sequences, one per protein chain (for reference). + x_self_conditioning: Float[Tensor, "batch L 37"] + Self-conditioning for the input structure, + essentially the previously denoised coordinates (optional). """ aatype: Int[Tensor, "batch L"] @@ -113,8 +136,11 @@ class ProtpardelleConditioning: residue_index: Float[Tensor, "batch L"] chain_index: Float[Tensor, "batch L"] atom_mask: Float[Tensor, "batch L 37"] + atom37_residue_index: Int[Tensor, "atoms"] + atom37_atom_index: Int[Tensor, "atoms"] sampling_kwargs: dict[str, Any] sequences: tuple[str, ...] + x_self_conditioning: Float[Tensor, "batch L 37"] | None = None @dataclass @@ -129,38 +155,40 @@ class ProtpardelleConfig: Attributes ---------- ensemble_size : int - Number of structures to sample for the input sequence (batch dim). + Number of structures to sample for the input sequence (batch dim). Default 8. num_steps : int - Number of denoising (ODE discretization) steps. + Number of denoising (ODE discretization) steps. Defaults to 200. s_churn : float Stochasticity: ``gamma = s_churn / num_steps`` extra noise per step. + Protpardelle recommends 0.2 * num_steps. 0 is deterministic sampling + Defaults to 40.0 step_scale : float - Inverse-temperature scale applied to the score. + Inverse-temperature scale applied to the score. Default 1.0 sidechain_mode : bool All-atom MiniMPNN side-chain co-design. Left False for ai-allatom - sequence-conditioned models (which have no MiniMPNN). + sequence-conditioned models (which do not use MiniMPNN). skip_mpnn_proportion : float - Fraction of steps from the start to skip running MiniMPNN. + Fraction of steps from the start to skip running MiniMPNN. Default 1.0. (no MPNN) jump_steps : bool Use the superposition sampling scheme (mutually exclusive with - ``uniform_steps``). + ``uniform_steps``). Default False. uniform_steps : bool All-atom denoising with a uniform noise-level change each step. This is - the scheme used by ``ai-allatom`` models. + the scheme used by ``ai-allatom`` models. Default True. temperature : float Temperature applied to aatype logits (unused when the sequence is fully specified, but forwarded for completeness). top_p : float - Top-p truncation for aatype sampling (as above). + Top-p truncation for aatype sampling (also kept only for completeness, this is for MPNN). extra_sampling_kwargs : dict[str, Any] Additional keyword overrides merged into the ``Protpardelle.sample`` call, taking precedence over the fields above. """ - ensemble_size: int = 1 - num_steps: int = 500 - s_churn: float = 200.0 - step_scale: float = 1.2 + ensemble_size: int = 8 + num_steps: int = 200 + s_churn: float = 40.0 # Protpardelle recommends 0.2 * num_steps. 0 is deterministic sampling + step_scale: float = 1.0 sidechain_mode: bool = False skip_mpnn_proportion: float = 1.0 jump_steps: bool = False @@ -170,13 +198,16 @@ class ProtpardelleConfig: extra_sampling_kwargs: dict[str, Any] = field(default_factory=dict) +# TODO: I need to check whether these defaults make sense. In fact I don't think +# we will use most of these (e.g. skip_mpnn_proportion, since we don't do seq design. +# Check the Protpardelle configs. def annotate_structure_for_protpardelle( structure: dict, *, - ensemble_size: int = 1, - num_steps: int = 500, - s_churn: float = 200.0, - step_scale: float = 1.2, + ensemble_size: int = 8, + num_steps: int = 200, + s_churn: float = 40.0, + step_scale: float = 1.0, sidechain_mode: bool = False, skip_mpnn_proportion: float = 1.0, jump_steps: bool = False, @@ -218,7 +249,8 @@ def annotate_structure_for_protpardelle( def extract_protein_sequences(structure: dict) -> list[str]: - """Extract canonical one-letter protein sequences from a structure. + """Extract canonical one-letter protein sequences from an Atomworks structure dictionary, + created by the Atomworks ``parse`` method. Parameters ---------- @@ -269,52 +301,40 @@ class ProtpardelleWrapper: def __init__( self, - config_path: str | Path | None = None, - checkpoint_path: str | Path | None = None, - device: torch.device | str | None = None, + checkpoint_path: str | Path, + config_path: str | Path, + device: torch.device | str, model: Protpardelle | None = None, ): """ Parameters ---------- - config_path : str | Path | None + config_path : str | Path Path to the Protpardelle model config YAML (e.g. ``cc89.yaml``). Required unless ``model`` is provided. - checkpoint_path : str | Path | None + checkpoint_path : str | Path Path to the matching ``.pth`` checkpoint with trained weights. Required unless ``model`` is provided. - device : torch.device | str | None + device : torch.device | str Device to load the model on. When ``None``, Protpardelle picks the default device (CUDA if available). model : Protpardelle | None A pre-built Protpardelle model. When given, ``config_path`` and - ``checkpoint_path`` are ignored and the model is used directly - (useful for testing and advanced reuse). + ``checkpoint_path`` are still required (but only used for record keeping); + the model is used directly (useful for testing and advanced reuse). """ - resolved_device = torch.device(device) if device is not None else None + self._device = torch.device(device) + self.config_path = Path(config_path).expanduser().resolve() + self.checkpoint_path = Path(checkpoint_path).expanduser().resolve() if model is not None: - self.model = model.to(resolved_device) if resolved_device is not None else model - self.config_path = Path(config_path) if config_path is not None else None - self.checkpoint_path = ( - Path(checkpoint_path) if checkpoint_path is not None else None - ) + self.model = model.to(self._device).eval() else: - if config_path is None or checkpoint_path is None: - raise ValueError( - "config_path and checkpoint_path are required when no model is provided." - ) - self.config_path = Path(config_path).expanduser().resolve() - self.checkpoint_path = Path(checkpoint_path).expanduser().resolve() logger.info( f"Loading Protpardelle model from {self.config_path.name} " f"(checkpoint {self.checkpoint_path.name})" ) - self.model = load_model( - self.config_path, self.checkpoint_path, device=resolved_device - ) - - self._device = self.model.device + self.model = load_model(self.config_path, self.checkpoint_path, device=self._device) if self.model.use_mpnn_model: logger.warning( @@ -351,6 +371,13 @@ def featurize(self, structure: dict) -> GenerativeModelInput[ProtpardelleConditi if isinstance(config, dict): config = ProtpardelleConfig(**config) + if "asym_unit" not in structure: + raise ValueError( + "Protpardelle featurization requires an 'asym_unit' atom array to " + "map flat coordinates into the atom37 layout; none was found on " + "the structure." + ) + sequences = extract_protein_sequences(structure) # Lay out chains exactly as Protpardelle's own sampling helper does so @@ -379,7 +406,21 @@ def featurize(self, structure: dict) -> GenerativeModelInput[ProtpardelleConditi residue_index = match_batch(residue_index, target_batch_size=ensemble_size) chain_index = match_batch(chain_index, target_batch_size=ensemble_size) - atom_mask = atom37_mask_from_aatype(aatype, seq_mask) + # Map the flat per-atom coordinates the sampler works with (``x_t``, + # shape ``batch x N x 3``) onto the model's ``batch x L x 37 x 3`` atom37 + # layout. The mapping is derived from the input structure's atom names. + atom_array = get_asym_unit_from_structure(structure, atom_array_index=0) + atom37_residue_index, atom37_atom_index = self._atom37_indices_from_atom_array( + atom_array + ) + + # This should be the way to make the atom mask: + # atom_mask = atom37_mask_from_aatype(aatype, seq_mask) but it doesn't handle OXT, so: + atom_mask = torch.zeros( + (padded_len, ATOM37_NUM_ATOMS), dtype=torch.float, device=self.device + ) + atom_mask[atom37_residue_index, atom37_atom_index] = 1 + atom_mask = match_batch(atom_mask[None, :, :], target_batch_size=ensemble_size) sampling_kwargs = self._build_sampling_kwargs(config) conditioning = ProtpardelleConditioning( @@ -388,20 +429,148 @@ def featurize(self, structure: dict) -> GenerativeModelInput[ProtpardelleConditi residue_index=residue_index, chain_index=chain_index, atom_mask=atom_mask, + atom37_residue_index=atom37_residue_index, + atom37_atom_index=atom37_atom_index, sampling_kwargs=sampling_kwargs, sequences=tuple(sequences), + x_self_cond=None ) - # x_init is a shape-compatible reference drawn from the prior. The - # Protpardelle sampler initializes its own noise internally, so this is - # carried only for interface compatibility / downstream alignment. + # x_init is a shape-compatible reference drawn from a Gaussian prior. + # TODO: Claude put this in. I don't think we really need it. Our sampler + # will call the initalize_from_prior() method, see, e.g., + # sampleworks.core.scalers.pure_guidance.PureGuidance.sample num_atoms = int(atom_mask[0].sum().item()) - x_init = self.initialize_from_prior( - batch_size=ensemble_size, shape=(num_atoms, 3) - ) + x_init = self.initialize_from_prior(batch_size=ensemble_size, shape=(num_atoms, 3)) return GenerativeModelInput(x_init=x_init, conditioning=conditioning) + def _atom37_indices_from_atom_array( + self, atom_array + ) -> tuple[Int[Tensor, "atoms"], Int[Tensor, "atoms"]]: + """Derive per-atom atom37 destination indices from an Atomworks atom array. + + For each atom in ``atom_array`` (the order the sampler's flat ``x_t`` + follows), computes the residue position ``0..L-1`` and the atom37 slot + ``0..36`` (``residue_constants.atom_order[atom_name]``). This mirrors how + :func:`protpardelle.data.pdb_io.read_pdb` scatters atoms into the per-residue + ``pos`` array of shape ``(37, 3)``. + + Parameters + ---------- + atom_array : biotite.structure.AtomArray + Single-model atom array with ``atom_name`` / ``res_id`` / ``chain_id`` + annotations (a single frame; stacks are reduced to frame 0 upstream). + + Returns + ------- + tuple[Int[Tensor, "atoms"], Int[Tensor, "atoms"]] + ``(atom37_residue_index, atom37_atom_index)``, both ``long`` tensors of + length ``N`` on :attr:`device`. + + Raises + ------ + ValueError + If any atom name is not a recognized atom37 atom type, which would + break the one-to-one correspondence with ``x_t``. + """ + atom_names = np.asarray(atom_array.atom_name) + + unknown = sorted(set(atom_names) - set(residue_constants.atom_order)) + if unknown: + raise ValueError( + "asym_unit contains atom names not present in the atom37 " + f"representation: {unknown}. Protpardelle's atom37 conversion " + "requires every atom to map to a canonical atom type." + ) + + atom_slots = np.array( + [residue_constants.atom_order[name] for name in atom_names], dtype=np.int64 + ) + + # Contiguous residue ordinal (0..L-1) per atom, in atom order across chains. + residue_starts = struc.get_residue_starts(atom_array) + is_start = np.zeros(len(atom_names), dtype=np.int64) + is_start[residue_starts] = 1 + residue_ordinals = np.cumsum(is_start) - 1 + + atom37_residue_index = torch.as_tensor( + residue_ordinals, dtype=torch.long, device=self.device + ) + atom37_atom_index = torch.as_tensor( + atom_slots, dtype=torch.long, device=self.device + ) + return atom37_residue_index, atom37_atom_index + + def _convert_to_atom37( + self, + conditioning: ProtpardelleConditioning, + x_t: Float[Tensor, "batch atoms 3"], + ) -> Float[Tensor, "batch L 37 3"]: + """Scatter flat coordinates ``x_t`` into Protpardelle's atom37 layout. + + Our sampler represents structures as ``x_t`` of shape ``batch x N x 3`` + (``N`` = number of atoms), whereas Protpardelle expects + ``batch x L x 37 x 3`` (``L`` = residues, 37 = atom37 slots). Using the + per-atom residue/slot maps computed in :meth:`featurize`, this places each + atom's coordinate at ``[batch, residue, slot]`` exactly like the ``pos`` + tensor built in :func:`protpardelle.data.pdb_io.read_pdb`. Slots with no + corresponding atom stay zero. + + The conversion is a differentiable scatter (``index_put``), so gradients + flow from the returned tensor back to ``x_t``. + + Parameters + ---------- + conditioning : ProtpardelleConditioning + Conditioning from :meth:`featurize`; supplies ``L`` (via ``seq_mask``) + and the ``atom37_residue_index`` / ``atom37_atom_index`` maps. + x_t : Float[Tensor, "batch atoms 3"] + Flat per-atom coordinates. + + Returns + ------- + Float[Tensor, "batch L 37 3"] + Coordinates in atom37 layout, on the same device/dtype as ``x_t``. + + Raises + ------ + ValueError + If ``x_t``'s atom count disagrees with the atom37 index maps. + """ + batch_size, num_atoms, _ = x_t.shape + num_residues = conditioning.seq_mask.shape[1] + + residue_index = conditioning.atom37_residue_index.to(x_t.device) + atom_index = conditioning.atom37_atom_index.to(x_t.device) + + if residue_index.shape[0] != num_atoms: + raise ValueError( + f"x_t has {num_atoms} atoms but the atom37 index maps describe " + f"{residue_index.shape[0]} atoms; these must match." + ) + + x_t_atom37 = torch.zeros( + (batch_size, num_residues, ATOM37_NUM_ATOMS, 3), + dtype=x_t.dtype, + device=x_t.device, + ) + + # Flatten (batch, atom) so a single index_put scatters every coordinate. + batch_index = torch.arange(batch_size, device=x_t.device).repeat_interleave( + num_atoms + ) + flat_residue_index = residue_index.repeat(batch_size) + flat_atom_index = atom_index.repeat(batch_size) + values = x_t.reshape(batch_size * num_atoms, 3) + + # Out-of-place index_put keeps the op differentiable w.r.t. ``values`` + # (hence ``x_t``); the freshly-zeroed destination needs no gradient. + x_t_atom37 = x_t_atom37.index_put( + (batch_index, flat_residue_index, flat_atom_index), values + ) + return x_t_atom37 + @staticmethod def _build_sampling_kwargs(config: ProtpardelleConfig) -> dict[str, Any]: """Assemble the keyword arguments for ``Protpardelle.sample``.""" @@ -425,9 +594,16 @@ def _build_sampling_kwargs(config: ProtpardelleConfig) -> dict[str, Any]: def step( self, - features: GenerativeModelInput[ProtpardelleConditioning], + x_t: Float[Tensor, "batch atoms 3"], + t: Float[Tensor, "*batch"] | float, + sigma_float: float, + *, + features: GenerativeModelInput[ProtpardelleConditioning] | None = None, ) -> Float[Tensor, "batch atoms 3"]: - """Run the full Protpardelle diffusion trajectory for the input sequence. + """ + This runs just the forward pass of the Protpardelle model. See + protpardelle-1c/src/protpardelle/core/models.py:L1760 + (commit ee378400f25b801fa481028000f9060183d7fb4c on branch main) The entire reverse-diffusion loop runs internally; the returned tensor is the final all-atom prediction, flattened to the atoms implied by the @@ -435,8 +611,12 @@ def step( Parameters ---------- - features : GenerativeModelInput[ProtpardelleConditioning] - Features as returned by :meth:`featurize`. + x_t : Float[Tensor, "batch atoms 3"] + Noisy structure at timestep :math:`t`. + t : Float[Tensor, "*batch"] | float + Current timestep/noise level (:math:`\hat{t}` from EDM schedule). + features : GenerativeModelInput[BoltzConditioning] | None + Model features as returned by ``featurize``. Returns ------- @@ -449,25 +629,52 @@ def step( cond = features.conditioning - with torch.no_grad(): - aux = self.model.sample( - seq_mask=cond.seq_mask, - residue_index=cond.residue_index, - chain_index=cond.chain_index, - gt_aatype=cond.aatype, - **cond.sampling_kwargs, - ) + # Our x_t is B x N x 3 (N = number of atoms); Protpardelle expects + # B x L x 37 x 3. Scatter into the atom37 layout (gradient-preserving). + x_t_atom37 = self._convert_to_atom37(cond, x_t) + + noise_level = torch.full((cond.seq_mask.shape[0],), sigma_float, device=self.device) + + # To understand all these arguments better, you can study the (complicated) + # Protpardelle.sample method. Hints: partial diffusion enabled. cc.enabled = False! + # https://github.com/ProteinDesignLab/protpardelle-1c/src/protpardelle/core/models.py + # The call to .forward() is at line 1766 + x0, s_logprobs, x_self_cond, s_self_cond = self.model.forward( + noisy_coords=x_t_atom37, + noise_level=noise_level, + seq_mask=cond.seq_mask, + residue_index=cond.residue_index, + chain_index=cond.chain_index, + hotspot_mask=None, # we don't support this yet + struct_self_cond=( + features.conditioning.x_self_cond + if self.model.config.train.self_cond_train_prob > 0.5 # true for cc89 + else None + ), + struct_crop_cond=None, + sse_cond=None, # secondary structure conditioning, TODO maybe use? + adj_cond=None, # adjacency conditioning, # TODO not sure what this is for exactly + seq_self_cond=None, # we don't do sequence design. + seq_crop_cond=None, + run_mpnn_model=False, # we don't want to do sequence design + ) + + # TODO: our features are immutable... but we need to update the self-cond + # this might be a way to do it: + # https://stackoverflow.com/questions/63803794/how-to-freeze-individual-field-of-non-frozen-dataclass + features.conditioning.x_self_cond = x_self_cond - # aux["x"]: [batch, L, 37, 3]. Masked-out atom slots are zeroed during - # ai-allatom sampling, so select the present atoms via the atom37 mask. + # x0: [batch, L, 37, 3]. Masked-out atom slots are zeroed during + # ai-allatom sampling, so select the correct atoms via the atom37 mask. # The mask is identical across the batch (single input sequence), so a # single 2-D mask flattens every ensemble member consistently. - coords = aux["x"] atom_mask_2d = cond.atom_mask[0].bool() # [L, 37] - flat_coords = coords[:, atom_mask_2d] # [batch, atoms, 3] + flat_coords = x0[:, atom_mask_2d] # [batch, atoms, 3] return flat_coords.float() + # This just generates the input noise. When doing partial diffusion, the + # noise is added to the input coordinates with some weight.. def initialize_from_prior( self, batch_size: int, diff --git a/src/sampleworks/utils/guidance_script_utils.py b/src/sampleworks/utils/guidance_script_utils.py index f477ab73..99be8fe7 100644 --- a/src/sampleworks/utils/guidance_script_utils.py +++ b/src/sampleworks/utils/guidance_script_utils.py @@ -60,6 +60,12 @@ except ImportError: RF3Wrapper = None # ty:ignore[invalid-assignment] logger.warning("Failed to import RF3, hopefully you're running a different model") +try: + from sampleworks.models.protpardelle.wrapper import ProtpardelleWrapper +except ImportError: + ProtpardelleWrapper = None + logger.warning("Failed to import Protpardelle, hopefully you're running a different model") + from sampleworks.utils.torch_utils import try_gpu @@ -214,6 +220,15 @@ def get_model_and_device( msa_manager=MSAManager(), device=device, ) + elif model_type == StructurePredictor.PROTPARDELLE: + if ProtpardelleWrapper is None: + raise ImportError("Protpardelle dependencies not installed") + logger.debug(f"Loading Protpardelle model from {validated_checkpoint_path}") + model_wrapper = ProtpardelleWrapper( + config_path=str(Path("src/sampleworks/data/cc89_epoch415.yaml").expanduser().resolve()), + checkpoint_path=validated_checkpoint_path, + device=device, + ) else: raise ValueError(f"Unknown model type: {model_type}") @@ -482,6 +497,16 @@ def _run_guidance(args: GuidanceConfig, guidance_type: str, model_wrapper, devic ensemble_size=args.ensemble_size, recycling_steps=recycling_steps, ) + elif "Protpardelle" in wrapper_class_name: + from sampleworks.models.protpardelle.wrapper import annotate_structure_for_protpardelle + + # TODO: this is where we need to pass in things like step scale, s_churn, etc... + # I'm not entirely sure what all the args are yet though. + structure = annotate_structure_for_protpardelle( + structure, + ensemble_size=args.ensemble_size, + recycling_steps=recycling_steps, + ) else: raise ValueError(f"Unknown model wrapper class: {wrapper_class_name}") @@ -692,7 +717,7 @@ def run_guidance_job_queue(job_queue_path: str) -> list[JobResult]: device, model_wrapper = get_model_and_device( str(template_job.device), template_job.model_checkpoint, - template_job.model, + template_job.model, # this is not actually the model, it's the model name, e.g. boltz2 method=template_job.method if hasattr(template_job, "method") else None, ) job_results = [] diff --git a/tests/models/protpardelle/conftest.py b/tests/models/protpardelle/conftest.py index 4889aef7..5b0ba8e8 100644 --- a/tests/models/protpardelle/conftest.py +++ b/tests/models/protpardelle/conftest.py @@ -142,8 +142,18 @@ def protpardelle_model(small_cc89_config_path: Path): @pytest.fixture(scope="session") -def protpardelle_wrapper(protpardelle_model): - """A ProtpardelleWrapper backed by the small random model.""" +def protpardelle_wrapper(protpardelle_model, small_cc89_config_path): + """A ProtpardelleWrapper backed by the small random model. + + A pre-built ``model`` is supplied, so ``checkpoint_path`` is only kept for + record-keeping and need not point at a real file. + """ + import torch from sampleworks.models.protpardelle.wrapper import ProtpardelleWrapper - return ProtpardelleWrapper(model=protpardelle_model) + return ProtpardelleWrapper( + checkpoint_path=small_cc89_config_path.parent / "unused.pth", + config_path=small_cc89_config_path, + device=torch.device("cpu"), + model=protpardelle_model, + ) diff --git a/tests/models/protpardelle/test_protpardelle_wrapper.py b/tests/models/protpardelle/test_protpardelle_wrapper.py index b3084390..b8e62240 100644 --- a/tests/models/protpardelle/test_protpardelle_wrapper.py +++ b/tests/models/protpardelle/test_protpardelle_wrapper.py @@ -21,8 +21,11 @@ "protpardelle.core.models", reason="Protpardelle not installed in this environment" ) +import biotite.structure as struc +import numpy as np import torch from atomworks.enums import ChainType +from protpardelle.common import residue_constants from protpardelle.data.sequence import seq_to_aatype from sampleworks.models.protocol import GenerativeModelInput, StructureModelWrapper from sampleworks.models.protpardelle.wrapper import ( @@ -39,8 +42,38 @@ SEQ_B = "MNPQRSTVWY" +def _build_asym_unit(sequences) -> struc.AtomArray: + """Build a biotite AtomArray for the given chains. + + Each residue carries exactly the atom37 heavy atoms implied by its type + (matching ``atom37_mask_from_aatype``), so the atom count agrees with the + conditioning's ``atom_mask``. Coordinates are arbitrary but distinct. + """ + chain_ids = "ABCDEFGH" + atom_mask = np.asarray(residue_constants.restype_atom37_mask) + atom_names, res_ids, chains = [], [], [] + for chain_idx, seq in enumerate(sequences): + for res_pos, aa in enumerate(seq): + restype = residue_constants.restype_order[aa] + present = [ + residue_constants.atom_types[slot] + for slot in range(residue_constants.atom_type_num) + if atom_mask[restype][slot] + ] + atom_names.extend(present) + res_ids.extend([res_pos + 1] * len(present)) + chains.extend([chain_ids[chain_idx]] * len(present)) + + arr = struc.AtomArray(len(atom_names)) + arr.atom_name = np.array(atom_names) + arr.res_id = np.array(res_ids) + arr.chain_id = np.array(chains) + arr.coord = np.arange(len(atom_names) * 3, dtype=np.float32).reshape(-1, 3) + return arr + + def _protein_structure(*sequences: str) -> dict: - """Build a minimal structure dict with protein chain_info only.""" + """Build a minimal structure dict with protein chain_info and an asym_unit.""" chain_ids = "ABCDEFGH" chain_info = { chain_ids[i]: { @@ -49,7 +82,11 @@ def _protein_structure(*sequences: str) -> dict: } for i, seq in enumerate(sequences) } - return {"chain_info": chain_info, "metadata": {"id": "test"}} + return { + "chain_info": chain_info, + "asym_unit": _build_asym_unit(sequences), + "metadata": {"id": "test"}, + } # --------------------------------------------------------------------------- @@ -112,7 +149,7 @@ def test_defaults(self): kwargs = ProtpardelleWrapper._build_sampling_kwargs(ProtpardelleConfig()) assert kwargs["uniform_steps"] is True assert kwargs["jump_steps"] is False - assert kwargs["num_steps"] == 500 + assert kwargs["num_steps"] == ProtpardelleConfig().num_steps def test_extra_overrides_take_precedence(self): config = ProtpardelleConfig( @@ -124,8 +161,9 @@ def test_extra_overrides_take_precedence(self): class TestConstructorValidation: - def test_requires_paths_or_model(self): - with pytest.raises(ValueError, match="config_path and checkpoint_path"): + def test_requires_checkpoint_and_config_paths(self): + # checkpoint_path, config_path and device are required positional args. + with pytest.raises(TypeError, match="required positional argument"): ProtpardelleWrapper() @@ -181,6 +219,23 @@ def test_x_init_atom_count_matches_atom_mask(self, protpardelle_wrapper): n_atoms = int(cond.atom_mask[0].sum().item()) assert features.x_init.shape[1] == n_atoms + def test_atom37_index_maps_present(self, protpardelle_wrapper): + structure = _protein_structure(SEQ_A) + cond = protpardelle_wrapper.featurize(structure).conditioning + n_atoms = int(cond.atom_mask[0].sum().item()) + # One entry per flat atom, matching the atom37 occupancy count. + assert cond.atom37_residue_index.shape == (n_atoms,) + assert cond.atom37_atom_index.shape == (n_atoms,) + # Slots are valid atom37 indices; residues stay within L. + assert int(cond.atom37_atom_index.max()) < 37 + assert int(cond.atom37_residue_index.max()) == len(SEQ_A) - 1 + + def test_featurize_requires_asym_unit(self, protpardelle_wrapper): + structure = _protein_structure(SEQ_A) + del structure["asym_unit"] + with pytest.raises(ValueError, match="asym_unit"): + protpardelle_wrapper.featurize(structure) + def test_multichain_indices_have_gap(self, protpardelle_wrapper): structure = _protein_structure(SEQ_A, SEQ_B) cond = protpardelle_wrapper.featurize(structure).conditioning @@ -218,11 +273,51 @@ def test_invalid_shape_raises(self, protpardelle_wrapper): # --------------------------------------------------------------------------- +class TestConvertToAtom37: + def test_shape_and_placement(self, protpardelle_wrapper): + features = protpardelle_wrapper.featurize(_protein_structure(SEQ_A)) + cond = features.conditioning + n_atoms = int(cond.atom_mask[0].sum().item()) + + x_t = torch.randn(2, n_atoms, 3) + out = protpardelle_wrapper._convert_to_atom37(cond, x_t) + + assert out.shape == (2, len(SEQ_A), 37, 3) + # Every flat atom lands at its (residue, slot) destination. + res_idx = cond.atom37_residue_index + atom_idx = cond.atom37_atom_index + for n in range(n_atoms): + assert torch.allclose(out[:, res_idx[n], atom_idx[n]], x_t[:, n]) + # Total non-zero atom slots equals the number of placed atoms. + occupied = (out.abs().sum(-1) > 0).sum().item() + assert occupied == 2 * n_atoms + + def test_gradients_flow_back_to_x_t(self, protpardelle_wrapper): + features = protpardelle_wrapper.featurize(_protein_structure(SEQ_A)) + cond = features.conditioning + n_atoms = int(cond.atom_mask[0].sum().item()) + + x_t = torch.randn(1, n_atoms, 3, requires_grad=True) + out = protpardelle_wrapper._convert_to_atom37(cond, x_t) + out.sum().backward() + # Each input coordinate is placed exactly once -> unit gradient. + assert torch.allclose(x_t.grad, torch.ones_like(x_t)) + + def test_atom_count_mismatch_raises(self, protpardelle_wrapper): + cond = protpardelle_wrapper.featurize(_protein_structure(SEQ_A)).conditioning + with pytest.raises(ValueError, match="atoms"): + protpardelle_wrapper._convert_to_atom37(cond, torch.randn(1, 3, 3)) + + class TestStep: def test_step_raises_without_features(self, protpardelle_wrapper): with pytest.raises(ValueError, match="features"): - protpardelle_wrapper.step(None) + protpardelle_wrapper.step(torch.zeros(1, 1, 3), 0.0) + @pytest.mark.skip( + reason="step() is intentionally incomplete (noise level / conditioning " + "wiring still TODO); only _convert_to_atom37 is implemented so far." + ) @pytest.mark.slow def test_step_returns_coords(self, protpardelle_wrapper): short_seq = "ACDEFG" From 6774d82e2fb5f1c2f22544450ef0eb1f287b0fad Mon Sep 17 00:00:00 2001 From: "Marcus D. Collins" Date: Fri, 12 Jun 2026 15:52:12 -0700 Subject: [PATCH 4/8] get default model config from protpardelle module rather than keeping our own copy. --- .../models/protpardelle/wrapper.py | 82 +++++++------------ 1 file changed, 30 insertions(+), 52 deletions(-) diff --git a/src/sampleworks/models/protpardelle/wrapper.py b/src/sampleworks/models/protpardelle/wrapper.py index bbda90ae..a92527f5 100644 --- a/src/sampleworks/models/protpardelle/wrapper.py +++ b/src/sampleworks/models/protpardelle/wrapper.py @@ -17,17 +17,18 @@ import copy from dataclasses import dataclass, field from pathlib import Path +from importlib.resources import files from typing import Any import biotite.structure as struc import numpy as np import torch +import yaml from atomworks.enums import ChainType from jaxtyping import Float, Int from loguru import logger from protpardelle.common import residue_constants from protpardelle.core.models import load_model, Protpardelle -from protpardelle.data.atom import atom37_mask_from_aatype from protpardelle.data.sequence import seq_to_aatype from torch import Tensor @@ -45,53 +46,19 @@ # Matches ``data.n_aatype_tokens`` in the Protpardelle-1c configs (e.g. cc89.yaml). NUM_AATYPE_TOKENS = 21 +# get default model configurations from protpardelle-1c/cc89 +sampling_partial_diffusion_all_atom = files("protpardelle").joinpath( + "configs/running/sampling_partial_diffusion_allatom.yaml" +) +with open(str(sampling_partial_diffusion_all_atom), "r") as f: + DEFAULT_CONFIG = yaml.safe_load(f) -# ``Protpardelle.sample`` reads many nested fields off ``conditional_cfg`` and -# ``partial_diffusion`` (even when disabled). These defaults disable all -# conditional generation / partial diffusion and mirror the structure of the -# ``conditional_cfg`` block in the Protpardelle-1c running configs (e.g. -# ``configs/running/sampling_unconditional_allatom.yaml``). -DEFAULT_CONDITIONAL_CFG: dict[str, Any] = { - "enabled": False, - "discontiguous_motif_assignment": { - "enabled": False, - "strategy": "fixed", - "fixed_motif_pos": [], - }, - "num_recurrence_steps": 1, - "crop_conditional_guidance": { - "enabled": False, - "start": 0.0, - "end": 2.0, - "freq": 1, - "freq_start": 0.0, - "freq_end": 0.0, - "strategy": "backbone-sidechain", - }, - "reconstruction_guidance": { - "enabled": False, - "start": 0.0, - "end": 2.0, - "schedule": "custom", - "max_scale": 10.0, - "loss_weights": {"motif": 1.0}, - }, - "replacement_guidance": { - "enabled": False, - "start": 0.0, - "end": 0.92, - }, -} - -DEFAULT_PARTIAL_DIFFUSION: dict[str, Any] = { - "enabled": False, - "pdb_file_path": None, - "num_steps": 100, -} +DEFAULT_CONDITIONAL_CFG: dict[str, Any] = DEFAULT_CONFIG["sampling"]["conditional_cfg"] +DEFAULT_PARTIAL_DIFFUSION: dict[str, Any] = DEFAULT_CONFIG["sampling"]["partial_diffusion"] # TODO: I believe this can be cleaned up, not all of this is necessary for a forward pass -@dataclass(frozen=True, slots=True) +@dataclass(slots=True) class ProtpardelleConditioning: """Sequence-derived conditioning for a Protpardelle sampling run. @@ -142,6 +109,19 @@ class ProtpardelleConditioning: sequences: tuple[str, ...] x_self_conditioning: Float[Tensor, "batch L 37"] | None = None + _FROZEN = frozenset( + {"aatype", "seq_mask", "residue_index", "chain_index", + "atom_mask", "sampling_kwargs", "sequences"} + ) + + def __setattr__(self, key, value): + if key in self._FROZEN: + raise AttributeError( + f"Cannot set attribute {key!r} on {self.__class__.__name__}, it is frozen!" + ) + super().__setattr__(key, value) + + @dataclass class ProtpardelleConfig: @@ -392,7 +372,7 @@ def featurize(self, structure: dict) -> GenerativeModelInput[ProtpardelleConditi # Concatenate per-chain aatypes in chain order; chains are placed # contiguously at the front of the padded sequence by the helper above. chain_aatypes = [ - seq_to_aatype(seq, num_tokens=NUM_AATYPE_TOKENS) for seq in sequences + seq_to_aatype(seq, num_tokens=NUM_AATYPE_TOKENS) for seq in sequences # ty: ignore ] flat_aatype = torch.cat(chain_aatypes).to(self.device) padded_len = seq_mask.shape[1] @@ -433,7 +413,7 @@ def featurize(self, structure: dict) -> GenerativeModelInput[ProtpardelleConditi atom37_atom_index=atom37_atom_index, sampling_kwargs=sampling_kwargs, sequences=tuple(sequences), - x_self_cond=None + x_self_conditioning=None ) # x_init is a shape-compatible reference drawn from a Gaussian prior. @@ -636,7 +616,7 @@ def step( noise_level = torch.full((cond.seq_mask.shape[0],), sigma_float, device=self.device) # To understand all these arguments better, you can study the (complicated) - # Protpardelle.sample method. Hints: partial diffusion enabled. cc.enabled = False! + # Protpardelle.sample method. Hints: partial diffusion is enabled and cc.enabled = False! # https://github.com/ProteinDesignLab/protpardelle-1c/src/protpardelle/core/models.py # The call to .forward() is at line 1766 x0, s_logprobs, x_self_cond, s_self_cond = self.model.forward( @@ -647,7 +627,7 @@ def step( chain_index=cond.chain_index, hotspot_mask=None, # we don't support this yet struct_self_cond=( - features.conditioning.x_self_cond + features.conditioning.x_self_conditioning if self.model.config.train.self_cond_train_prob > 0.5 # true for cc89 else None ), @@ -659,10 +639,8 @@ def step( run_mpnn_model=False, # we don't want to do sequence design ) - # TODO: our features are immutable... but we need to update the self-cond - # this might be a way to do it: - # https://stackoverflow.com/questions/63803794/how-to-freeze-individual-field-of-non-frozen-dataclass - features.conditioning.x_self_cond = x_self_cond + # pass the self-conditioning to the next step by updating the features. + features.conditioning.x_self_conditioning = x_self_cond # x0: [batch, L, 37, 3]. Masked-out atom slots are zeroed during # ai-allatom sampling, so select the correct atoms via the atom37 mask. From 40a7f3038c7bfe0b82f0851ab91c0d762950d680 Mon Sep 17 00:00:00 2001 From: "Marcus D. Collins" Date: Fri, 12 Jun 2026 18:57:35 -0700 Subject: [PATCH 5/8] Starting to debug Protpardelle on actl --- src/sampleworks/cli/guidance.py | 2 ++ src/sampleworks/utils/guidance_script_arguments.py | 11 +++++++++++ src/sampleworks/utils/guidance_script_utils.py | 3 +-- 3 files changed, 14 insertions(+), 2 deletions(-) diff --git a/src/sampleworks/cli/guidance.py b/src/sampleworks/cli/guidance.py index 5f71fe22..1066c20f 100644 --- a/src/sampleworks/cli/guidance.py +++ b/src/sampleworks/cli/guidance.py @@ -3,6 +3,7 @@ from __future__ import annotations import sys +from loguru import logger from sampleworks.utils.guidance_script_arguments import GuidanceConfig from sampleworks.utils.guidance_script_utils import get_model_and_device, run_guidance @@ -10,6 +11,7 @@ def main(argv: list[str] | None = None) -> int: config = GuidanceConfig.from_cli(argv) + logger.info(f"Running guidance with config: {config}") device, model_wrapper = get_model_and_device( config.device, getattr(config, "model_checkpoint", None), diff --git a/src/sampleworks/utils/guidance_script_arguments.py b/src/sampleworks/utils/guidance_script_arguments.py index 45f06862..f18c542f 100644 --- a/src/sampleworks/utils/guidance_script_arguments.py +++ b/src/sampleworks/utils/guidance_script_arguments.py @@ -606,11 +606,22 @@ def add_rf3_specific_args(parser: argparse.ArgumentParser | GuidanceConfig): ) +def add_protpardelle_specific_args(parser: argparse.ArgumentParser | GuidanceConfig): + """Add CLI arguments specific to Protpardelle guidance runs.""" + parser.add_argument( + "--model-checkpoint", + type=str, + default=None, + help="Path to Protpardelle checkpoint (default: auto-resolved from /checkpoints/ or pixi env)", + ) + + _MODEL_ARG_ADDERS: dict[str, Any] = { "boltz1": add_boltz1_specific_args, "boltz2": add_boltz2_specific_args, "protenix": add_protenix_specific_args, "rf3": add_rf3_specific_args, + "protpardelle": add_protpardelle_specific_args, } _GUIDANCE_ARG_ADDERS: dict[str, Any] = { diff --git a/src/sampleworks/utils/guidance_script_utils.py b/src/sampleworks/utils/guidance_script_utils.py index 99be8fe7..1e95b539 100644 --- a/src/sampleworks/utils/guidance_script_utils.py +++ b/src/sampleworks/utils/guidance_script_utils.py @@ -504,8 +504,7 @@ def _run_guidance(args: GuidanceConfig, guidance_type: str, model_wrapper, devic # I'm not entirely sure what all the args are yet though. structure = annotate_structure_for_protpardelle( structure, - ensemble_size=args.ensemble_size, - recycling_steps=recycling_steps, + ensemble_size=args.ensemble_size ) else: raise ValueError(f"Unknown model wrapper class: {wrapper_class_name}") From c53f5ada7d73c559f0ae91a5c6e020187a1f87e9 Mon Sep 17 00:00:00 2001 From: manzuoni-astera Date: Tue, 23 Jun 2026 17:08:34 -0400 Subject: [PATCH 6/8] fix(protpardelle): complete denoising step wiring (#267) ## Summary Completes the Protpardelle debugging work from ENG-75 / #257 on top of `mdc/add-protpardelle`. This PR intentionally targets `mdc/add-protpardelle` so the review contains only the final Protpardelle fixes, not the whole in-progress integration branch. Key changes: - Restores the EDM sampler/model-wrapper call shape to the protocol-compatible `step(noisy_state, t_hat, features=features)` after removing the temporary extra `eps` argument. - Implements Protpardelle step-time noise handling via `_expand_noise_level()`, broadcasting scalar or per-batch EDM timesteps to Protpardelle's expected `B x L` tensor. - Moves Protpardelle step inputs/conditioning tensors onto the wrapper device before the model forward pass. - Keeps `prot_lens_per_chain` on CPU when calling Protpardelle's sampling helper to avoid CPU/GPU device mismatch in helper-created residue indices. - Maps Atomworks selenomethionine atom name `SE` into Protpardelle atom37's methionine sulfur slot `SD`. - Fixes `ProtpardelleConditioning` immutability so dataclass construction can complete before selected conditioning fields become frozen. - Allows packaged Protpardelle config/data under `src/sampleworks/data/**` through `.actlignore` so ACTL sync includes the runtime YAML config. - Re-enables the slow `step()` behavior test and adds coverage for the MSE selenium atom mapping. ## Validation Local static checks: - `uvx ty check src/sampleworks/models/protpardelle/wrapper.py src/sampleworks/core/samplers/edm.py tests/models/protpardelle/test_protpardelle_wrapper.py` - `uvx ruff check src/sampleworks/models/protpardelle/wrapper.py src/sampleworks/core/samplers/edm.py tests/models/protpardelle/test_protpardelle_wrapper.py` Remote ACTL / Protpardelle environment checks: - `pixi run -e protpardelle-dev pytest tests/models/protpardelle -m "not slow"` - `27 passed, 1 deselected` - `pixi run -e protpardelle-dev pytest tests/models/protpardelle/test_protpardelle_wrapper.py::TestStep::test_step_returns_coords` - `1 passed` - Exact reduced Protpardelle guidance smoke command from #257 using the shared checkpoint and 1VME inputs - completed successfully with `Guidance run successfully!` - wrote results to `output/protpardelle-smoke-guided/` Non-blocking warnings observed during the smoke run were expected environment warnings for unavailable optional model/tool paths and missing mirror environment variables; they did not prevent the Protpardelle run from completing. Refs #257. Co-authored-by: xraymemory --- .actlignore | 4 +- src/sampleworks/core/samplers/edm.py | 6 +- .../models/protpardelle/wrapper.py | 111 ++++++++++++++---- .../protpardelle/test_protpardelle_wrapper.py | 19 ++- 4 files changed, 106 insertions(+), 34 deletions(-) diff --git a/.actlignore b/.actlignore index 247ebc3c..167a601d 100644 --- a/.actlignore +++ b/.actlignore @@ -3,7 +3,9 @@ .pixi/ grid_search_results/ outputs/ -/data/ +data/ +!/src/sampleworks/data/ +!/src/sampleworks/data/** initial_dataset_40*/ checkpoints/ release_data/ diff --git a/src/sampleworks/core/samplers/edm.py b/src/sampleworks/core/samplers/edm.py index 30335888..1f81c551 100644 --- a/src/sampleworks/core/samplers/edm.py +++ b/src/sampleworks/core/samplers/edm.py @@ -415,17 +415,15 @@ def step( # Store eps separately for proper frame transformation # eps_scale will be float if check_context didn't raise - eps = torch.randn_like(maybe_augmented_state) * eps_scale # ty: ignore[unsupported-operator] + eps = torch.randn_like(maybe_augmented_state) * eps_scale noisy_state = maybe_augmented_state + eps noisy_state = torch.as_tensor(noisy_state).detach().requires_grad_(allow_gradients) # t_hat will be float if check_context didn't raise # Use no_grad when gradients aren't needed to avoid memory overhead from # gradient checkpointing holding intermediate activations - # TODO testing adding eps to signature for use with Protpardelle-1c, if successful, - # I need to modify the Protocol itself. with torch.set_grad_enabled(allow_gradients): - x_hat_0 = model_wrapper.step(noisy_state, t_hat, eps, features=features) + x_hat_0 = model_wrapper.step(noisy_state, t_hat, features=features) reconciler = ( context.reconciler.to(torch.as_tensor(x_hat_0).device) diff --git a/src/sampleworks/models/protpardelle/wrapper.py b/src/sampleworks/models/protpardelle/wrapper.py index a92527f5..b1e0844e 100644 --- a/src/sampleworks/models/protpardelle/wrapper.py +++ b/src/sampleworks/models/protpardelle/wrapper.py @@ -14,10 +14,12 @@ ``FlowModelWrapper``. """ +from __future__ import annotations + import copy from dataclasses import dataclass, field -from pathlib import Path from importlib.resources import files +from pathlib import Path from typing import Any import biotite.structure as struc @@ -46,6 +48,10 @@ # Matches ``data.n_aatype_tokens`` in the Protpardelle-1c configs (e.g. cc89.yaml). NUM_AATYPE_TOKENS = 21 +# Atomworks keeps selenomethionine atoms as ``SE`` while Protpardelle's atom37 +# representation uses the canonical methionine sulfur slot, ``SD``. +ATOM37_ATOM_NAME_ALIASES = {"SE": "SD"} + # get default model configurations from protpardelle-1c/cc89 sampling_partial_diffusion_all_atom = files("protpardelle").joinpath( "configs/running/sampling_partial_diffusion_allatom.yaml" @@ -103,23 +109,28 @@ class ProtpardelleConditioning: residue_index: Float[Tensor, "batch L"] chain_index: Float[Tensor, "batch L"] atom_mask: Float[Tensor, "batch L 37"] - atom37_residue_index: Int[Tensor, "atoms"] - atom37_atom_index: Int[Tensor, "atoms"] + atom37_residue_index: Tensor + atom37_atom_index: Tensor sampling_kwargs: dict[str, Any] sequences: tuple[str, ...] x_self_conditioning: Float[Tensor, "batch L 37"] | None = None + _initialized: bool = field(default=False, init=False, repr=False) _FROZEN = frozenset( {"aatype", "seq_mask", "residue_index", "chain_index", "atom_mask", "sampling_kwargs", "sequences"} ) + def __post_init__(self) -> None: + """Mark construction complete so selected conditioning fields become immutable.""" + object.__setattr__(self, "_initialized", True) + def __setattr__(self, key, value): - if key in self._FROZEN: + if key in self._FROZEN and getattr(self, "_initialized", False): raise AttributeError( f"Cannot set attribute {key!r} on {self.__class__.__name__}, it is frozen!" ) - super().__setattr__(key, value) + object.__setattr__(self, key, value) @@ -362,18 +373,17 @@ def featurize(self, structure: dict) -> GenerativeModelInput[ProtpardelleConditi # Lay out chains exactly as Protpardelle's own sampling helper does so # residue/chain indexing (including inter-chain gaps) matches training. - prot_lens_per_chain = torch.tensor( - [[len(seq) for seq in sequences]], dtype=torch.long, device=self.device - ) + # Keep the length tensor on CPU because Protpardelle's helper constructs + # intermediate residue indices on CPU before moving its outputs to the + # model device. + prot_lens_per_chain = torch.tensor([[len(seq) for seq in sequences]], dtype=torch.long) seq_mask, residue_index, chain_index = self.model.make_seq_mask_for_sampling( prot_lens_per_chain=prot_lens_per_chain ) # Concatenate per-chain aatypes in chain order; chains are placed # contiguously at the front of the padded sequence by the helper above. - chain_aatypes = [ - seq_to_aatype(seq, num_tokens=NUM_AATYPE_TOKENS) for seq in sequences # ty: ignore - ] + chain_aatypes = [seq_to_aatype(seq, num_tokens=NUM_AATYPE_TOKENS) for seq in sequences] flat_aatype = torch.cat(chain_aatypes).to(self.device) padded_len = seq_mask.shape[1] aatype = torch.zeros((1, padded_len), dtype=torch.long, device=self.device) @@ -413,7 +423,7 @@ def featurize(self, structure: dict) -> GenerativeModelInput[ProtpardelleConditi atom37_atom_index=atom37_atom_index, sampling_kwargs=sampling_kwargs, sequences=tuple(sequences), - x_self_conditioning=None + x_self_conditioning=None, ) # x_init is a shape-compatible reference drawn from a Gaussian prior. @@ -427,7 +437,7 @@ def featurize(self, structure: dict) -> GenerativeModelInput[ProtpardelleConditi def _atom37_indices_from_atom_array( self, atom_array - ) -> tuple[Int[Tensor, "atoms"], Int[Tensor, "atoms"]]: + ) -> tuple[Tensor, Tensor]: """Derive per-atom atom37 destination indices from an Atomworks atom array. For each atom in ``atom_array`` (the order the sampler's flat ``x_t`` @@ -455,8 +465,11 @@ def _atom37_indices_from_atom_array( break the one-to-one correspondence with ``x_t``. """ atom_names = np.asarray(atom_array.atom_name) + atom37_names = np.asarray( + [ATOM37_ATOM_NAME_ALIASES.get(str(name), str(name)) for name in atom_names] + ) - unknown = sorted(set(atom_names) - set(residue_constants.atom_order)) + unknown = sorted(set(atom37_names) - set(residue_constants.atom_order)) if unknown: raise ValueError( "asym_unit contains atom names not present in the atom37 " @@ -465,7 +478,7 @@ def _atom37_indices_from_atom_array( ) atom_slots = np.array( - [residue_constants.atom_order[name] for name in atom_names], dtype=np.int64 + [residue_constants.atom_order[name] for name in atom37_names], dtype=np.int64 ) # Contiguous residue ordinal (0..L-1) per atom, in atom order across chains. @@ -572,11 +585,55 @@ def _build_sampling_kwargs(config: ProtpardelleConfig) -> dict[str, Any]: sampling_kwargs.update(config.extra_sampling_kwargs) return sampling_kwargs + def _expand_noise_level( + self, + t: Float[Tensor, "*batch"] | float, + conditioning: ProtpardelleConditioning, + dtype: torch.dtype, + ) -> Float[Tensor, "batch L"]: + """Convert a sampler timestep/noise scalar to Protpardelle's ``B x L`` tensor. + + Parameters + ---------- + t : Float[Tensor, "*batch"] | float + Sampler noise level for the current EDM step. May be a scalar or one + value per ensemble member. + conditioning : ProtpardelleConditioning + Conditioning whose sequence mask defines the batch and padded length. + dtype : torch.dtype + Floating dtype to use for the model call. + + Returns + ------- + Float[Tensor, "batch L"] + Noise level broadcast across valid/padded sequence positions on the + wrapper device. + """ + seq_mask = conditioning.seq_mask.to(device=self.device, dtype=dtype) + noise_level = torch.as_tensor(t, device=self.device, dtype=dtype) + + if noise_level.ndim == 0: + return noise_level.expand_as(seq_mask).clone() + + if noise_level.ndim == 1: + if noise_level.shape[0] != seq_mask.shape[0]: + raise ValueError( + f"Noise level batch size {noise_level.shape[0]} does not match " + f"conditioning batch size {seq_mask.shape[0]}." + ) + return noise_level[:, None].expand_as(seq_mask).clone() + + if noise_level.shape != seq_mask.shape: + raise ValueError( + f"Noise level shape {tuple(noise_level.shape)} must be scalar, " + f"batch-sized, or match seq_mask shape {tuple(seq_mask.shape)}." + ) + return noise_level + def step( self, x_t: Float[Tensor, "batch atoms 3"], t: Float[Tensor, "*batch"] | float, - sigma_float: float, *, features: GenerativeModelInput[ProtpardelleConditioning] | None = None, ) -> Float[Tensor, "batch atoms 3"]: @@ -594,7 +651,7 @@ def step( x_t : Float[Tensor, "batch atoms 3"] Noisy structure at timestep :math:`t`. t : Float[Tensor, "*batch"] | float - Current timestep/noise level (:math:`\hat{t}` from EDM schedule). + Current timestep/noise level (:math:`\\hat{t}` from EDM schedule). features : GenerativeModelInput[BoltzConditioning] | None Model features as returned by ``featurize``. @@ -608,12 +665,20 @@ def step( raise ValueError("features with conditioning required for step()") cond = features.conditioning + x_t = x_t.to(device=self.device) # Our x_t is B x N x 3 (N = number of atoms); Protpardelle expects # B x L x 37 x 3. Scatter into the atom37 layout (gradient-preserving). x_t_atom37 = self._convert_to_atom37(cond, x_t) - noise_level = torch.full((cond.seq_mask.shape[0],), sigma_float, device=self.device) + noise_level = self._expand_noise_level(t, cond, x_t_atom37.dtype) + + seq_mask = cond.seq_mask.to(device=self.device, dtype=x_t_atom37.dtype) + residue_index = cond.residue_index.to(device=self.device) + chain_index = cond.chain_index.to(device=self.device) + struct_self_cond = cond.x_self_conditioning + if struct_self_cond is not None: + struct_self_cond = struct_self_cond.to(device=self.device, dtype=x_t_atom37.dtype) # To understand all these arguments better, you can study the (complicated) # Protpardelle.sample method. Hints: partial diffusion is enabled and cc.enabled = False! @@ -622,12 +687,12 @@ def step( x0, s_logprobs, x_self_cond, s_self_cond = self.model.forward( noisy_coords=x_t_atom37, noise_level=noise_level, - seq_mask=cond.seq_mask, - residue_index=cond.residue_index, - chain_index=cond.chain_index, + seq_mask=seq_mask, + residue_index=residue_index, + chain_index=chain_index, hotspot_mask=None, # we don't support this yet struct_self_cond=( - features.conditioning.x_self_conditioning + struct_self_cond if self.model.config.train.self_cond_train_prob > 0.5 # true for cc89 else None ), @@ -646,7 +711,7 @@ def step( # ai-allatom sampling, so select the correct atoms via the atom37 mask. # The mask is identical across the batch (single input sequence), so a # single 2-D mask flattens every ensemble member consistently. - atom_mask_2d = cond.atom_mask[0].bool() # [L, 37] + atom_mask_2d = cond.atom_mask[0].to(device=x0.device).bool() # [L, 37] flat_coords = x0[:, atom_mask_2d] # [batch, atoms, 3] return flat_coords.float() diff --git a/tests/models/protpardelle/test_protpardelle_wrapper.py b/tests/models/protpardelle/test_protpardelle_wrapper.py index b8e62240..f3c9999c 100644 --- a/tests/models/protpardelle/test_protpardelle_wrapper.py +++ b/tests/models/protpardelle/test_protpardelle_wrapper.py @@ -164,7 +164,7 @@ class TestConstructorValidation: def test_requires_checkpoint_and_config_paths(self): # checkpoint_path, config_path and device are required positional args. with pytest.raises(TypeError, match="required positional argument"): - ProtpardelleWrapper() + ProtpardelleWrapper() # ty: ignore[missing-argument] class TestProtocolConformance: @@ -230,6 +230,17 @@ def test_atom37_index_maps_present(self, protpardelle_wrapper): assert int(cond.atom37_atom_index.max()) < 37 assert int(cond.atom37_residue_index.max()) == len(SEQ_A) - 1 + def test_mse_selenium_maps_to_methionine_sd(self, protpardelle_wrapper): + structure = _protein_structure("M") + atom_array = structure["asym_unit"] + selenium_mask = atom_array.atom_name == "SD" + atom_array.atom_name[selenium_mask] = "SE" + + cond = protpardelle_wrapper.featurize(structure).conditioning + selenium_index = int(np.flatnonzero(selenium_mask)[0]) + + assert cond.atom37_atom_index[selenium_index].item() == residue_constants.atom_order["SD"] + def test_featurize_requires_asym_unit(self, protpardelle_wrapper): structure = _protein_structure(SEQ_A) del structure["asym_unit"] @@ -314,10 +325,6 @@ def test_step_raises_without_features(self, protpardelle_wrapper): with pytest.raises(ValueError, match="features"): protpardelle_wrapper.step(torch.zeros(1, 1, 3), 0.0) - @pytest.mark.skip( - reason="step() is intentionally incomplete (noise level / conditioning " - "wiring still TODO); only _convert_to_atom37 is implemented so far." - ) @pytest.mark.slow def test_step_returns_coords(self, protpardelle_wrapper): short_seq = "ACDEFG" @@ -325,7 +332,7 @@ def test_step_returns_coords(self, protpardelle_wrapper): _protein_structure(short_seq), ensemble_size=1, num_steps=3, s_churn=0.0 ) features = protpardelle_wrapper.featurize(structure) - result = protpardelle_wrapper.step(features) + result = protpardelle_wrapper.step(features.x_init, 1.0, features=features) assert torch.is_tensor(result) assert result.shape == features.x_init.shape assert result.shape[-1] == 3 From 6713208cb033773f9d839303253e81111584c644 Mon Sep 17 00:00:00 2001 From: "Marcus D. Collins" Date: Mon, 29 Jun 2026 09:30:53 -0700 Subject: [PATCH 7/8] Modify EDMSamplerConfig options for Protpardelle-1c --- src/sampleworks/models/protpardelle/wrapper.py | 3 ++- src/sampleworks/utils/guidance_script_utils.py | 7 +++++++ 2 files changed, 9 insertions(+), 1 deletion(-) diff --git a/src/sampleworks/models/protpardelle/wrapper.py b/src/sampleworks/models/protpardelle/wrapper.py index b1e0844e..0417ad6a 100644 --- a/src/sampleworks/models/protpardelle/wrapper.py +++ b/src/sampleworks/models/protpardelle/wrapper.py @@ -705,6 +705,7 @@ def step( ) # pass the self-conditioning to the next step by updating the features. + # TODO: I wonder if we need to adjust this since we will apply additional guidance. features.conditioning.x_self_conditioning = x_self_cond # x0: [batch, L, 37, 3]. Masked-out atom slots are zeroed during @@ -714,7 +715,7 @@ def step( atom_mask_2d = cond.atom_mask[0].to(device=x0.device).bool() # [L, 37] flat_coords = x0[:, atom_mask_2d] # [batch, atoms, 3] - return flat_coords.float() + return flat_coords # This just generates the input noise. When doing partial diffusion, the # noise is added to the input coordinates with some weight.. diff --git a/src/sampleworks/utils/guidance_script_utils.py b/src/sampleworks/utils/guidance_script_utils.py index 1e95b539..2a45448d 100644 --- a/src/sampleworks/utils/guidance_script_utils.py +++ b/src/sampleworks/utils/guidance_script_utils.py @@ -468,6 +468,7 @@ def _run_guidance(args: GuidanceConfig, guidance_type: str, model_wrapper, devic if args.num_diffusion_steps is not None and args.num_diffusion_steps <= 0: raise ValueError("num_diffusion_steps must be > 0") + edm_sampler_kwargs = {} # i.e. use defaults. if "Protenix" in wrapper_class_name: from sampleworks.models.protenix.wrapper import annotate_structure_for_protenix @@ -506,6 +507,10 @@ def _run_guidance(args: GuidanceConfig, guidance_type: str, model_wrapper, devic structure, ensemble_size=args.ensemble_size ) + edm_sampler_kwargs = { + "s_max": 80, "s_min": 0.001, "gamma_0": 0.08, "gamma_min": 0.00, + "sigma_data": 10.3, "step_scale": 1.0 + } else: raise ValueError(f"Unknown model wrapper class: {wrapper_class_name}") @@ -514,11 +519,13 @@ def _run_guidance(args: GuidanceConfig, guidance_type: str, model_wrapper, devic ) # Create sampler with model-appropriate settings + sampler_config = EDMSamplerConfig( device=str(device), augmentation=args.augmentation, align_to_input=args.align_to_input, alignment_reverse_diffusion=use_alignment_for_reverse_diffusion, + **edm_sampler_kwargs ) sampler = AF3EDMSampler( config=sampler_config, From 4ac3e5be0d2761a6b09d0c60644d63757f32ac83 Mon Sep 17 00:00:00 2001 From: M E A Date: Tue, 30 Jun 2026 15:55:15 -0400 Subject: [PATCH 8/8] fix(protpardelle): pipe sampling options through guidance CLI (#270) ## Summary - add Protpardelle-specific CLI flags for config path and sampling controls - forward config path into ProtpardelleWrapper construction - forward sampling options into annotate_structure_for_protpardelle - keep guidance --help lightweight by lazy-loading runtime dependencies Fixes #258 ## Validation - uvx ty check src/sampleworks/utils/guidance_script_arguments.py src/sampleworks/utils/guidance_script_utils.py src/sampleworks/cli/guidance.py tests/cli/test_guidance_cli.py tests/models/protpardelle/test_protpardelle_wrapper.py - uvx ruff check src/sampleworks/utils/guidance_script_arguments.py src/sampleworks/utils/guidance_script_utils.py src/sampleworks/cli/guidance.py tests/cli/test_guidance_cli.py tests/models/protpardelle/test_protpardelle_wrapper.py - PYTHONPATH=src uvx --python 3.12 --with pytest pytest --noconftest tests/cli/test_guidance_cli.py -q - PYTHONPATH=src uvx --python 3.12 --with pytest pytest --noconftest tests/models/protpardelle/test_protpardelle_wrapper.py -q (skipped locally: Protpardelle not installed) --------- Co-authored-by: Marcus D. Collins Co-authored-by: Claude Opus 4.8 (1M context) --- src/sampleworks/cli/guidance.py | 7 +- src/sampleworks/core/samplers/edm.py | 4 +- .../models/protpardelle/wrapper.py | 17 +++-- .../utils/guidance_script_arguments.py | 66 ++++++++++++++++++- .../utils/guidance_script_utils.py | 20 ++++-- tests/cli/test_guidance_cli.py | 41 +++++++++++- .../protpardelle/test_protpardelle_wrapper.py | 28 ++++++++ 7 files changed, 167 insertions(+), 16 deletions(-) diff --git a/src/sampleworks/cli/guidance.py b/src/sampleworks/cli/guidance.py index 1066c20f..3e95885e 100644 --- a/src/sampleworks/cli/guidance.py +++ b/src/sampleworks/cli/guidance.py @@ -6,17 +6,22 @@ from loguru import logger from sampleworks.utils.guidance_script_arguments import GuidanceConfig -from sampleworks.utils.guidance_script_utils import get_model_and_device, run_guidance def main(argv: list[str] | None = None) -> int: config = GuidanceConfig.from_cli(argv) + + from loguru import logger + + from sampleworks.utils.guidance_script_utils import get_model_and_device, run_guidance + logger.info(f"Running guidance with config: {config}") device, model_wrapper = get_model_and_device( config.device, getattr(config, "model_checkpoint", None), config.model, method=getattr(config, "method", None), + protpardelle_config_path=getattr(config, "protpardelle_config_path", None), ) result = run_guidance(config, config.guidance_type, model_wrapper, device) return result.exit_code diff --git a/src/sampleworks/core/samplers/edm.py b/src/sampleworks/core/samplers/edm.py index 1f81c551..b0be24b6 100644 --- a/src/sampleworks/core/samplers/edm.py +++ b/src/sampleworks/core/samplers/edm.py @@ -422,8 +422,10 @@ def step( # t_hat will be float if check_context didn't raise # Use no_grad when gradients aren't needed to avoid memory overhead from # gradient checkpointing holding intermediate activations + # TODO testing adding eps to signature for use with Protpardelle-1c, if successful, + # I need to modify the Protocol itself. with torch.set_grad_enabled(allow_gradients): - x_hat_0 = model_wrapper.step(noisy_state, t_hat, features=features) + x_hat_0 = model_wrapper.step(noisy_state, t_hat, eps, features=features) reconciler = ( context.reconciler.to(torch.as_tensor(x_hat_0).device) diff --git a/src/sampleworks/models/protpardelle/wrapper.py b/src/sampleworks/models/protpardelle/wrapper.py index 0417ad6a..c6468438 100644 --- a/src/sampleworks/models/protpardelle/wrapper.py +++ b/src/sampleworks/models/protpardelle/wrapper.py @@ -383,7 +383,9 @@ def featurize(self, structure: dict) -> GenerativeModelInput[ProtpardelleConditi # Concatenate per-chain aatypes in chain order; chains are placed # contiguously at the front of the padded sequence by the helper above. - chain_aatypes = [seq_to_aatype(seq, num_tokens=NUM_AATYPE_TOKENS) for seq in sequences] + chain_aatypes = [ + seq_to_aatype(seq, num_tokens=NUM_AATYPE_TOKENS) for seq in sequences # ty: ignore + ] flat_aatype = torch.cat(chain_aatypes).to(self.device) padded_len = seq_mask.shape[1] aatype = torch.zeros((1, padded_len), dtype=torch.long, device=self.device) @@ -423,7 +425,7 @@ def featurize(self, structure: dict) -> GenerativeModelInput[ProtpardelleConditi atom37_atom_index=atom37_atom_index, sampling_kwargs=sampling_kwargs, sequences=tuple(sequences), - x_self_conditioning=None, + x_self_conditioning=None ) # x_init is a shape-compatible reference drawn from a Gaussian prior. @@ -437,7 +439,7 @@ def featurize(self, structure: dict) -> GenerativeModelInput[ProtpardelleConditi def _atom37_indices_from_atom_array( self, atom_array - ) -> tuple[Tensor, Tensor]: + ) -> tuple[Int[Tensor, "atoms"], Int[Tensor, "atoms"]]: """Derive per-atom atom37 destination indices from an Atomworks atom array. For each atom in ``atom_array`` (the order the sampler's flat ``x_t`` @@ -634,6 +636,7 @@ def step( self, x_t: Float[Tensor, "batch atoms 3"], t: Float[Tensor, "*batch"] | float, + sigma_float: float, *, features: GenerativeModelInput[ProtpardelleConditioning] | None = None, ) -> Float[Tensor, "batch atoms 3"]: @@ -642,16 +645,16 @@ def step( protpardelle-1c/src/protpardelle/core/models.py:L1760 (commit ee378400f25b801fa481028000f9060183d7fb4c on branch main) - The entire reverse-diffusion loop runs internally; the returned tensor - is the final all-atom prediction, flattened to the atoms implied by the - input sequence (the ``atom_mask`` in the conditioning). + The returned tensor is the final all-atom prediction, flattened to the + atoms implied by the input sequence (the ``atom_mask`` in the + conditioning). Parameters ---------- x_t : Float[Tensor, "batch atoms 3"] Noisy structure at timestep :math:`t`. t : Float[Tensor, "*batch"] | float - Current timestep/noise level (:math:`\\hat{t}` from EDM schedule). + Current timestep/noise level (:math:`\hat{t}` from EDM schedule). features : GenerativeModelInput[BoltzConditioning] | None Model features as returned by ``featurize``. diff --git a/src/sampleworks/utils/guidance_script_arguments.py b/src/sampleworks/utils/guidance_script_arguments.py index f18c542f..0364b3e5 100644 --- a/src/sampleworks/utils/guidance_script_arguments.py +++ b/src/sampleworks/utils/guidance_script_arguments.py @@ -197,6 +197,15 @@ def validate_model_checkpoint( "msa_path", "disable_chiral_features", "track_chiral_features", + "protpardelle_config_path", + "protpardelle_s_churn", + "protpardelle_step_scale", + "protpardelle_sidechain_mode", + "protpardelle_skip_mpnn_proportion", + "protpardelle_jump_steps", + "protpardelle_uniform_steps", + "protpardelle_temperature", + "protpardelle_top_p", # generic (overridable) "ensemble_size", "recycling_steps", @@ -612,7 +621,62 @@ def add_protpardelle_specific_args(parser: argparse.ArgumentParser | GuidanceCon "--model-checkpoint", type=str, default=None, - help="Path to Protpardelle checkpoint (default: auto-resolved from /checkpoints/ or pixi env)", + help=( + "Path to Protpardelle checkpoint " + "(default: auto-resolved from /checkpoints/ or pixi env)" + ), + ) + parser.add_argument( + "--protpardelle-config-path", + type=str, + default=None, + help="Path to the Protpardelle model config YAML (default: bundled cc89 config)", + ) + parser.add_argument( + "--protpardelle-s-churn", + type=float, + default=40.0, + help="Protpardelle stochasticity parameter forwarded as s_churn", + ) + parser.add_argument( + "--protpardelle-step-scale", + type=float, + default=1.0, + help="Protpardelle score inverse-temperature scale forwarded as step_scale", + ) + parser.add_argument( + "--protpardelle-sidechain-mode", + action="store_true", + help="Enable Protpardelle all-atom MiniMPNN side-chain co-design", + ) + parser.add_argument( + "--protpardelle-skip-mpnn-proportion", + type=float, + default=1.0, + help="Fraction of denoising steps to skip MiniMPNN at the start", + ) + parser.add_argument( + "--protpardelle-jump-steps", + action="store_true", + help="Use Protpardelle superposition jump-step sampling", + ) + parser.add_argument( + "--protpardelle-uniform-steps", + action=argparse.BooleanOptionalAction, + default=True, + help="Use Protpardelle uniform-step sampling (default: enabled)", + ) + parser.add_argument( + "--protpardelle-temperature", + type=float, + default=1.0, + help="Temperature applied to Protpardelle aatype logits", + ) + parser.add_argument( + "--protpardelle-top-p", + type=float, + default=1.0, + help="Top-p truncation for Protpardelle aatype sampling", ) diff --git a/src/sampleworks/utils/guidance_script_utils.py b/src/sampleworks/utils/guidance_script_utils.py index 2a45448d..1a6905b6 100644 --- a/src/sampleworks/utils/guidance_script_utils.py +++ b/src/sampleworks/utils/guidance_script_utils.py @@ -63,7 +63,7 @@ try: from sampleworks.models.protpardelle.wrapper import ProtpardelleWrapper except ImportError: - ProtpardelleWrapper = None + ProtpardelleWrapper = None # ty:ignore[invalid-assignment] logger.warning("Failed to import Protpardelle, hopefully you're running a different model") from sampleworks.utils.torch_utils import try_gpu @@ -174,6 +174,7 @@ def get_model_and_device( model_type: str, method: str | None = None, model: Any = None, + protpardelle_config_path: str | None = None, ) -> tuple[torch.device, Any]: """Validate a checkpoint, choose a device, and construct the model wrapper.""" validated_checkpoint_path = validate_model_checkpoint(model_type, model_checkpoint_path) @@ -224,8 +225,9 @@ def get_model_and_device( if ProtpardelleWrapper is None: raise ImportError("Protpardelle dependencies not installed") logger.debug(f"Loading Protpardelle model from {validated_checkpoint_path}") + config_path = protpardelle_config_path or "src/sampleworks/data/cc89_epoch415.yaml" model_wrapper = ProtpardelleWrapper( - config_path=str(Path("src/sampleworks/data/cc89_epoch415.yaml").expanduser().resolve()), + config_path=str(Path(config_path).expanduser().resolve()), checkpoint_path=validated_checkpoint_path, device=device, ) @@ -501,11 +503,18 @@ def _run_guidance(args: GuidanceConfig, guidance_type: str, model_wrapper, devic elif "Protpardelle" in wrapper_class_name: from sampleworks.models.protpardelle.wrapper import annotate_structure_for_protpardelle - # TODO: this is where we need to pass in things like step scale, s_churn, etc... - # I'm not entirely sure what all the args are yet though. structure = annotate_structure_for_protpardelle( structure, - ensemble_size=args.ensemble_size + ensemble_size=args.ensemble_size, + num_steps=args.num_diffusion_steps, + s_churn=getattr(args, "protpardelle_s_churn", 40.0), + step_scale=getattr(args, "protpardelle_step_scale", 1.0), + sidechain_mode=getattr(args, "protpardelle_sidechain_mode", False), + skip_mpnn_proportion=getattr(args, "protpardelle_skip_mpnn_proportion", 1.0), + jump_steps=getattr(args, "protpardelle_jump_steps", False), + uniform_steps=getattr(args, "protpardelle_uniform_steps", True), + temperature=getattr(args, "protpardelle_temperature", 1.0), + top_p=getattr(args, "protpardelle_top_p", 1.0), ) edm_sampler_kwargs = { "s_max": 80, "s_min": 0.001, "gamma_0": 0.08, "gamma_min": 0.00, @@ -725,6 +734,7 @@ def run_guidance_job_queue(job_queue_path: str) -> list[JobResult]: template_job.model_checkpoint, template_job.model, # this is not actually the model, it's the model name, e.g. boltz2 method=template_job.method if hasattr(template_job, "method") else None, + protpardelle_config_path=getattr(template_job, "protpardelle_config_path", None), ) job_results = [] for i, job in enumerate(job_queue): diff --git a/tests/cli/test_guidance_cli.py b/tests/cli/test_guidance_cli.py index 0272009e..d905beb3 100644 --- a/tests/cli/test_guidance_cli.py +++ b/tests/cli/test_guidance_cli.py @@ -66,7 +66,46 @@ def test_model_specific_args_rf3_msa(self): "/data/msa.a3m", ] + COMMON_ARGS config = GuidanceConfig.from_cli(argv) - assert config.msa_path == "/data/msa.a3m" # ty: ignore[unresolved-attribute] + assert getattr(config, "msa_path") == "/data/msa.a3m" + + def test_model_specific_args_protpardelle_sampling(self): + argv = [ + "--model", + "protpardelle", + "--guidance-type", + "pure_guidance", + "--model-checkpoint", + "/data/cc89.pth", + "--protpardelle-config-path", + "/data/cc89.yaml", + "--protpardelle-s-churn", + "2.5", + "--protpardelle-step-scale", + "0.75", + "--protpardelle-sidechain-mode", + "--protpardelle-skip-mpnn-proportion", + "0.25", + "--protpardelle-jump-steps", + "--no-protpardelle-uniform-steps", + "--protpardelle-temperature", + "0.9", + "--protpardelle-top-p", + "0.8", + "--num-diffusion-steps", + "64", + ] + COMMON_ARGS + config = GuidanceConfig.from_cli(argv) + assert getattr(config, "model_checkpoint") == "/data/cc89.pth" + assert getattr(config, "protpardelle_config_path") == "/data/cc89.yaml" + assert getattr(config, "protpardelle_s_churn") == 2.5 + assert getattr(config, "protpardelle_step_scale") == 0.75 + assert getattr(config, "protpardelle_sidechain_mode") is True + assert getattr(config, "protpardelle_skip_mpnn_proportion") == 0.25 + assert getattr(config, "protpardelle_jump_steps") is True + assert getattr(config, "protpardelle_uniform_steps") is False + assert getattr(config, "protpardelle_temperature") == 0.9 + assert getattr(config, "protpardelle_top_p") == 0.8 + assert config.num_diffusion_steps == 64 def test_guidance_specific_args_fk(self): argv = [ diff --git a/tests/models/protpardelle/test_protpardelle_wrapper.py b/tests/models/protpardelle/test_protpardelle_wrapper.py index f3c9999c..c31c9b84 100644 --- a/tests/models/protpardelle/test_protpardelle_wrapper.py +++ b/tests/models/protpardelle/test_protpardelle_wrapper.py @@ -143,6 +143,34 @@ def test_defaults_match_ai_allatom_recipe(self): assert config.jump_steps is False assert config.sidechain_mode is False + def test_adds_sampling_options(self): + structure = _protein_structure(SEQ_A) + annotated = annotate_structure_for_protpardelle( + structure, + ensemble_size=2, + num_steps=64, + s_churn=2.5, + step_scale=0.75, + sidechain_mode=True, + skip_mpnn_proportion=0.25, + jump_steps=True, + uniform_steps=False, + temperature=0.9, + top_p=0.8, + ) + + config = annotated["_protpardelle_config"] + assert config.ensemble_size == 2 + assert config.num_steps == 64 + assert config.s_churn == 2.5 + assert config.step_scale == 0.75 + assert config.sidechain_mode is True + assert config.skip_mpnn_proportion == 0.25 + assert config.jump_steps is True + assert config.uniform_steps is False + assert config.temperature == 0.9 + assert config.top_p == 0.8 + class TestBuildSamplingKwargs: def test_defaults(self):