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Ports the Uni-Mol v1 transformer backbone to the array-API dpmodel layer: self-attention that returns its pre-softmax logits, the pre-LN encoder layer, the pair-carrying encoder stack with both norm regularisers, the Gaussian distance basis and the two-layer head. Sources are Uni-Mol 90f52c4 and Uni-Core ace6fae, both MIT licensed; the file header records the provenance per class. Adds "gelu_erf", the exact error-function GELU that Uni-Mol uses, together with an xp_erf backend dispatch. The existing "gelu" and "gelu_tf" keep their current meaning, the tanh approximation, which differs from the exact form by up to 4.7e-4 per element. Verified against tensors dumped from upstream running on the same inputs: with upstream's own attention bias the encoder agrees to 7e-16 relative in fp64. Including the Gaussian basis the agreement is 1e-7 relative, which is one fp32 unit in the last place: upstream evaluates the basis in fp32 because it pretrains an fp16 model, and NumPy and Torch round that last place differently. That behaviour is reproduced by default and can be switched off. No existing code path changes: the new modules are not imported anywhere yet.
Ports the masking and coordinate-noise pipeline of Uni-Mol molecular pretraining from Uni-Mol 90f52c4 (MIT): conformer sampling, the hydrogen policy, cropping, centring, the 90/5/5 corruption, BOS/EOS insertion, and the distance and edge-type construction. Upstream expresses each step as a lazy dataset wrapper; these are plain functions over one frame, which is what a deepmd data loader can call. Corruption belongs on the data side rather than inside a loss because the PyTorch-Exportable backend runs the model before the loss sees a frame, which is also how upstream does it. The legacy numpy.random interface is used deliberately and every call carries a noqa with the reason: upstream seeds the global legacy PRNG, and a Generator would draw a different stream, giving different masks and different noise for the same seed. Verified against tensors dumped from upstream at seed 1, epoch 1, molecules 0-3 of the bundled example data: tokens, loss targets, edge types and both coordinate arrays are bitwise identical, which means the whole random stream is reproduced, down to which atoms are masked and what noise each one gets. The distance matrices differ by 1.9e-6 absolute, the float32 rounding between scipy's distance_matrix and a sqrt of summed squares.
"gelu_erf" was added to the dpmodel table in the previous commit. The name also has to reach the whitelist in deepmd/common.py, because that is what argcheck validates a configuration against, and every backend table has to answer to it: TensorFlow asserts at import that the whitelist is a subset of its own table, so registering the name without a TF entry would break importing deepmd.tf.common. PyTorch, PyTorch-Exportable and Paddle would each raise at runtime instead. All four array backends resolve "gelu_erf" to the exact error-function GELU and agree with torch's own to rounding: 0 for pt and pt_expt, 2.2e-16 for dpmodel. "gelu" and "gelu_tf" keep their current meaning everywhere.
Ports the three pretraining heads (element prediction, coordinate denoising through the pair channel, pairwise distance prediction) and the five-term objective from Uni-Mol 90f52c4 (MIT), with upstream's README weights of 1, 5, 10, 0.01 and 0.01 and its hard-coded distance normalisation. The coordinate update takes the post-deepmodeling#211 form: the normaliser counts every non-padding token, BOS and EOS included, and pairs touching padding are zeroed before the sum. The distance term covers the corrupted rows against every non-padding column, diagonal included. Verified against tensors dumped from upstream, on both a small random model and the released mol_pre_all_h_220816 weights. Heads agree to fp64 rounding: 5e-16 relative on the logits, 2.5e-16 on the distances, 1.8e-20 on the coordinates. All five loss terms agree to 1e-7 relative or better; that floor is upstream's own, since it evaluates log_softmax and both norm regularisers in fp32 regardless of model precision, and those casts are reproduced.
Wraps the ported Uni-Mol v1 backbone in the descriptor interface: it turns a padded deepmd frame into Uni-Mol's token sequence, runs the encoder, and returns the per-atom representation with the two virtual tokens dropped. A second entry point returns everything at token resolution, because the five-tuple cannot carry the virtual tokens or the norm regularisers that the pretraining heads need. Real atoms are identified from the neighbour list rather than from atype: by the time a descriptor is called, virtual atoms have been clamped to type 0 and cannot be told apart from a real first element, while the neighbour list still shows them as empty rows. Frames with fewer than two real atoms are rejected, since that inference is ambiguous for them. Uni-Mol's own 31-token vocabulary is kept because the released weights are indexed by it, and a deepmd type_map is mapped onto it, with unknown elements becoming [UNK]. The descriptor declares itself non-periodic, non-extensive, stat-free and unavailable for edge-parallel or communication paths, and it rejects frames that carry periodic images. The virtual tokens sit at the centroid of the real atoms by default, which keeps the sequence translation invariant; "origin" reproduces upstream exactly for data that its own pipeline has already centred. Checked end to end against the upstream dump, driven through deepmd-shaped inputs: the token sequence is identical, the node representation agrees to 8.2e-9 relative and the pair-delta norm to 6.2e-8, both inherited from the fp32 Gaussian basis. Padding length does not affect the result, as intended.
Adds the converter for the released mol_pre_all_h_220816 and mol_pre_no_h_220816 files (MIT). Parameter names line up one to one with the ported modules, but the arrays do not: deepmd stores a linear weight as (num_in, num_out) and applies it as x @ w, the transpose of torch.nn.Linear.weight, and names layer-norm parameters w/b. Every weight is renamed and transposed rather than loaded directly, so there is no "just add a prefix" path on this backend. The released files carry only their weights and no training state, so they read with weights_only=True. Torch is imported lazily and only to read the file, which keeps the converter off every other code path. Also adds an option to round the pairwise distances to fp32 before the Gaussian basis. Upstream precomputes its distance matrix in fp32 in the data pipeline, while the descriptor computes distances inside the model, which is more accurate and is what gradients flow through. The Gaussian basis is narrow enough that the difference matters: on the released 15-layer weights the node representation lands 5.1e-6 from upstream with fp64 distances and 3.7e-7 with upstream's own fp32 rounding. The default stays on the accurate path. Measured on the released weights driven through deepmd-shaped inputs: the encoder fed upstream's own attention bias agrees to 1.2e-15 relative at full depth, so the remaining gap is entirely the two precision choices upstream makes in front of it.
Wraps the three pretraining heads as a fitting: the element head reads the node representation, the coordinate head reads the pair delta, the distance head reads the pair representation. None is reducible to a frame total and none is differentiated with respect to coordinates, because the task denoises structures rather than modelling a potential energy surface. Upstream's distance objective counts the two virtual tokens among the columns, so the distance output keeps them and is padded to max_atoms + 2 columns, which the loss masks back down. That keeps the output shape static, as the output definition requires, without dropping columns the objective needs. The heads read token-resolution backbone output, which the descriptor's five-tuple cannot carry, so they are driven through call_tokens; the standard call raises with that explanation rather than silently returning something else. The loss now gathers the corrupted positions itself, since the model emits one row per local atom. End-to-end on the released mol_pre_all_h_220816 weights, driven through deepmd-shaped inputs: all five terms of the objective agree with upstream, the worst at 5.6e-7 relative and the total at 3.4e-7.
Three entries, all labelled PyTorch-Exportable: the unimol descriptor, the unimol_pretrain fitting and the unimol loss, with upstream's defaults, which are 15 layers of width 512 with 64 heads for the backbone and weights of 1, 5, 10, 0.01 and 0.01 for the objective. The two precision switches are exposed as arguments, since they decide whether a run reproduces upstream's published numbers or takes the more accurate path, and the docs say which is which. A complete Uni-Mol configuration now normalizes, so the components are reachable from a training input file.
Adds the atomic model and the model class. The atomic model overrides one method to route the backbone's token-resolution output into the heads, because the standard descriptor five-tuple cannot carry the virtual tokens, the pair channel or the norm regularisers. It also returns the head outputs untouched by out-stat: self-supervised targets have no per-element bias to add back. The two norm regularisers are frame scalars, but only per-atom variables survive the atomic-output machinery, so each is broadcast over the local atoms and the loss averages it back with the real-atom mask, which returns the original value exactly. A configuration now goes all the way through: argcheck normalizes it, the model factory picks UniMolPretrainModel by fitting type, and the model returns the three head outputs plus the two regularisers. Driven that way on the released weights, the five-term objective still matches upstream, total at 3.4e-7 relative.
Registers the descriptor, the fitting, the loss and the model. The wrappers are thin, as elsewhere in this backend: the descriptor adds parameter sharing for multi-task training, where level 0 shares the whole backbone and level 1 only the token embedding, and the loss is a straight re-export because the dpmodel one is a pure function of predictions and labels. Two bugs that only the real backend could show, both fixed here: - The element-to-token lookup table and the token embedding were read as plain arrays, so on a CUDA model they stayed on the host and indexing failed. They are now placed on the device of the incoming data, as are the four Gaussian basis tables. - The two regularisers were broadcast with a fill value that torch refuses when it is a tensor rather than a number; they are broadcast by addition now. Checked on GPU through the registered path: a configuration normalizes, the factory builds the model, and the five-term objective on the released weights matches upstream with the total at 8.1e-8 relative.
Adds the golden archive and two test files. Every expected value was produced by running upstream Uni-Mol 90f52c4 unmodified on CPU over four molecules of its own example data at a fixed seed and epoch; the header of the dpmodel test says how to regenerate it. The dpmodel tests cover the data-side transforms, the encoder, the Gaussian basis, the three heads, the descriptor and the five-term objective, plus serialization round trips and the two guards the descriptor raises. The transform test asserts bitwise equality on tokens, targets, edge types and both coordinate arrays, which is what shows the random stream itself is reproduced rather than merely its statistics. The PyTorch-Exportable tests cover the registered path end to end, agreement with the array-API implementation on identical weights, the objective against upstream, and that gradients reach the parameters. Tolerances have stated causes rather than being tuned until they pass. Where upstream's fp32 Gaussian basis is in play, agreement is one fp32 unit in the last place; a dedicated test measures that gap so the looser bound elsewhere is justified, and with the basis in full precision the two backends agree to fp64 rounding.
Uni-Mol regularises with dropout at three sites, 0.1 each on the embedding, on the attention probabilities and on both residual branches, while deepmd has no dropout anywhere. The rates were already carried in the configuration; this makes them act. The array API has no random numbers, so the helper dispatches to torch when a training step needs it and is the identity during inference, which is what the array-API backends are for. Training on a non-torch backend raises rather than quietly dropping the regularisation, which would be a silent parity bug. The flag travels down the call chain rather than relying on nested module state, since the encoder's sub-objects are plain data on the array-API path. A test pins the behaviour: eval-mode forwards are bit-identical to each other, train-mode forwards under different seeds are not.
Uni-Mol ships its pretraining set as one LMDB file of pickled dicts with about ten conformers per molecule; deepmd reads a different layout. The conversion runs once, offline, and streams, so the 115 GB set does not have to fit in memory. One conformer becomes one frame, so ordinary frame sampling stands in for upstream's per-epoch conformer draw, and frames of the same molecule share a system id. The two-dimensional RDKit conformer that upstream appends while loading is added here instead, behind a flag, so the training data path never needs RDKit. Records that cannot be used are skipped rather than written misleadingly: a single-atom molecule, which the descriptor cannot tell from padding, and any molecule with an element outside the Uni-Mol vocabulary, which would silently become [UNK]. Tested against deepmd's own reader: coordinates, elements and the zero cell come back matching the source.
Adds the last pieces between the model and a configuration file. The LMDB reader gains a per-frame transform hook, carried on the decoder configuration so it reaches every decoding path, worker processes included, and defaulting to none so decoding is unchanged without it. Self-supervised objectives have to corrupt their inputs and derive their labels there, because the PyTorch-Exportable backend runs the model before the loss sees a frame. The transform builder turns a converted frame into a corrupted one plus its labels. Masked atoms are carried as a [MASK] pseudo-element, which the model's type_map must declare, and a randomly drawn replacement maps back onto a type the model knows. The loss now derives the distance target and the token column mask when they are not supplied. Storing the distance target would cost O(natoms^2) per frame, which is impractical at 209 million conformers; deriving it from the clean coordinates and the real-atom mask gives the same number, to the fp32 rounding of the stored alternative. Also adds the documentation page, its toctree entry and a pretraining example whose configuration is checked against argcheck in the test suite.
A short training run on GPU turned up the last of these: the two virtual tokens, the position index and the zero centroid were built without a device, so they landed on the host while the rest of the batch was on the accelerator, and concatenating them failed. The same omission was present in the loss, when it derives the token mask and the clean distances, and in the fitting, when it broadcasts the regularisers and pads the distance output. Array-API code has to say where an array lives; only operations derived from an existing array inherit it. Every construction now takes the device of the data it will be combined with. With this, training runs: converting the bundled example molecules, installing the transform on the reader and stepping Adam for 60 steps takes the objective from 8.48 to 3.02, with all five terms falling.
Calling the model with a cell used to die on an allocation of several million gigabytes rather than on a readable error: the descriptor has no cut-off, so the neighbour-list builder went looking for an astronomical number of periodic images, and the descriptor's own check on extended atoms never got the chance to fire. Both model classes now reject a non-zero cell up front, with an explanation. The upper entry point, which builds its own neighbour list from coordinates and types, is covered by a test as well; it was previously exercised only through the lower one.
Until now the Uni-Mol corruption had to be installed by hand, so a training run started from a configuration file would have found no labels. The loss base class gains an optional frame_transform, defaulting to none, and the PyTorch-Exportable trainer installs whatever the task's objective returns on that task's datasets, right where it already registers the label requirements. Supervised losses return nothing and their data path is untouched. The corruption settings move onto the loss, which is where they belong: the labels are whatever the corruption produced. They are exposed through argcheck, so the masking rate, the 90/5/5 split, the noise and the seed are all configurable, with upstream's values as defaults. A dataset type that cannot take a transform now fails with an explanation rather than with missing labels much later.
The documentation now says how training is launched, that the dataset has to be an LMDB one because the corruption happens as frames are read, that the objective carries the corruption settings, and that the type_map needs the [MASK] pseudo-element. The example configuration gains that pseudo-element and the corruption settings with upstream's values, and a test validates it against argcheck. It is checked there rather than in the shared example test, because that one also requires the referenced dataset to exist in the repository, while this example points at data the user converts from upstream.
Running the command line end to end turned up five gaps that no unit test would have shown, because each sits in the path between a configuration file and the first training step. - The trainer's loss factory did not know the objective, so a configuration naming it was rejected outright. - The fitting was missing the accessors the atomic model calls on any fitting: frame and atomic parameter dimensions, the default frame parameter, selected types, exclusion re-initialisation, case embeddings and input statistics. The ones that do not apply now say so instead of raising AttributeError. - The per-frame transform ran after the reader checked that the mandatory fields were present, so a self-supervised run failed on the very labels the transform was about to produce. It now runs before that check. - The converter wrote a zero cell to mark a molecule, and the neighbour-list builder took it for a real cell and tried to invert it. Molecular frames now carry no cell at all. - The example pointed at its dataset with a list, while LMDB datasets are addressed with a plain string. The example and the documentation say so now. With these, a run from the shipped example trains: both the training and validation curves report all five terms and a checkpoint is written.
Covers everything between a configuration file and the first training step: the loss factory, the accessors the atomic model calls on any fitting, the reader hook that produces the labels, and the absence of a cell on molecular frames. Each of those was broken at some point, and none of the component tests would have shown it.
Freezing a Uni-Mol model failed with "does not support periodic images", which is not what a user doing that was attempting: the export machinery feeds the ghost-atom layout with symbolic dimensions, not a periodic cell. The guard now names both cases, since the underlying requirement is the same one, that every atom be local, and the documentation says so too.
Recipes carried over from other frameworks often assume a different epsilon than PyTorch's, and Uni-Mol is one of them: it pretrains with 1e-6 where the default here is 1e-8. The option defaults to the current value, so existing configurations are unaffected, and the example now carries upstream's optimizer values. This matches the value, not the placement: upstream's own Adam puts epsilon outside the bias correction, so the update differs slightly early in training whatever epsilon is configured. The documentation says so.
An adversarial review of this branch found that the token embedding and all four Gaussian basis tables never received a gradient. They were assigned as bare numpy arrays, and the PyTorch-Exportable wrapper turns a bare array into a buffer, not a parameter: 2,701 values in a small model, and the whole element-pair affine table in a real one, sat frozen at their initial values while the rest of the network trained. Inference and checkpoint parity were unaffected, which is why the parity tests did not catch it. They are layers now, which is how deepmd expresses a trained array. The same review found that every module was handed the same seed. Since each layer seeds its own generator, two layers of the same shape drew identical numbers: all fifteen encoder blocks started bitwise identical, and so did several head pairs. Seeds are split with child_seed, as everywhere else in deepmd. With a seed set, the layers now differ and a from-scratch run no longer starts from a degenerate state. Parity with the released weights is unchanged: the backbone still lands at 3.7e-7 relative and the five-term objective at 3.4e-7.
Three defects the review found in the data path. The corruption was frozen: the objective built its transform once with the default epoch, so every frame was masked identically on every pass. Upstream draws afresh each epoch. The transform now counts how often it has seen each frame and uses that count where upstream uses the epoch, so a molecule is corrupted differently each time it comes round. Passing an epoch explicitly is refused, since it is no longer a build-time constant. Cropping moved out of the transform. A frame's atom count and the batch layout are settled before any per-frame transform runs, so shortening a frame there would leave it inconsistent with the batch it belongs to. The converter applies the size cap instead, which is also where upstream's other preprocessing lives. An element the model's type_map cannot express is no longer drawn as a random replacement. It used to be mapped onto [MASK], which quietly turned a random-element atom into a masked one and skewed the 90/5/5 split. With the full element set nothing is excluded and the distribution is upstream's. Also: the descriptor now honours its configured precision instead of silently working in the input dtype; the distance head refuses a frame wider than the width it declares rather than returning a wider array than its output definition; TensorFlow's exact GELU computes its square root in the tensor dtype rather than rounding it through fp32; and every array construction states its dtype, which the repository's pylint gate requires. The golden archive is regenerated with two molecules instead of four, which brings it under the repository's file-size limit while keeping frames of different lengths. pre-commit now passes on every changed file.
Making them parameters was not enough: reading them through the array API's asarray, which the device fix had introduced, copied them out of the autograd graph, so they still received no gradient. They are indexed directly now. Parameters already live on the model's device, so the wrapper was never needed for them; it stays only for the plain lookup table, which is not a parameter. The tests that should have caught both of these are the ones the review found could not fail, so they are strengthened here: - the gradient test names the backbone parameters it expects to reach, rather than accepting any parameter with a gradient, which the three heads alone satisfied; - the dropout test also builds a model with every rate at zero and asserts that training mode is then deterministic, which a single hard-coded dropout call would not survive; - the descriptor's five-tuple entry point is compared against the token-resolution one by value, not only by shape; - the masking statistics are measured by running the ported corruption over two dozen molecules rather than by reading the fixture back; - the norm regularisers get a direct test of the hinge and of the masked mean, including an all-padding row, since the golden values for them are zero and constrain nothing; - the released-checkpoint importer gets a test, driven with the golden's upstream-named weights, covering both the transposed projections and the untransposed lookup tables; - the data fixture is large enough that the 15% selection selects something, and a new test pins that revisiting a frame corrupts it differently.
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Thanks, this is a careful port: the encoder matches upstream to 1e-11, the loss terms and weights line up, and the transform hook is cleanly isolated from the existing data path. Three blocking points inline (distance target under the default virtual_token_position, non-reproducible corruption seed, non-atomic dataset replacement), and three non-blocking notes below.
Non-blocking:
deepmd/dpmodel/descriptor/unimol.pyL90-91 documentsmax_seq_lenas "kept for configuration compatibility", butget_rcut()(L223-225) derives the reported cutoff from it, so it is not inert. Either the docstring or the dependency should change.deepmd/dpmodel/loss/unimol.pyL76-77_frame_scalardivides byxp.sum(weights)with no guard; a frame with zero real atoms gives NaN._smooth_l1and_masked_nllin the same file already guard the empty case, so this is just for consistency.- The trainer builds a fresh transform per dataset, so the validation set is re-corrupted on every pass and the validation loss is not comparable across epochs. Worth one sentence in
doc/model/unimol.md.
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The port is in good shape overall, and the current CI is green, but I still see three correctness/data-integrity blockers on this head.
-
The default
virtual_token_position="centroid"is inconsistent with the distance target. The descriptor places CLS/SEP at the centroid of the corrupted coordinates, while_clean_distances()always places the target virtual tokens at the origin (the clean centroid). As soon as coordinate noise moves the corrupted centroid, the distance head is trained against labels for different virtual-token positions. Please either make the target use the same virtual-token rule, or force/useoriginconsistently on the pretraining path, and add coverage for the default configuration rather than onlyorigin. -
data_seedis documented as reproducible in the single-process case, butUniMolFrameTransformcreatesself.streamfrom an unseededSeedSequence, and_next_epoch()additionally mixes inos.getpid(). Two fresh single-process runs with the samedata_seedtherefore do not generate the same corruption. The stream identity should be derived deterministically from the configured seed plus an explicit dataset/stream discriminator; worker scheduling may still limit multiprocess reproducibility, but the stated single-process guarantee should hold. -
The converter still deletes an existing
dstbefore renaming the completed staging directory. A failed rename or interruption in that gap loses the previous valid dataset. Please publish with a backup/restore transaction (or equivalent stable indirection) so failure leaves the old dataset recoverable.
I checked the earlier concern about the loss mask as well: the generic atomic-model finalization adds the mask output, so I am not treating that older comment as a blocker here.
Reviewed by ChatGPT (GPT-5.6 Sol).
Three blocking, three not. The default virtual_token_position placed the virtual tokens at the centroid of the coordinates the descriptor was handed, which during pretraining are the corrupted ones, while the distance target places them at the origin. Every corrupted row's two virtual columns therefore trained against a label for a different position, off by about the size of the noise: on a six-atom frame with one noised atom the descriptor put them 0.17 A from where the target assumed. Pretraining now requires 'origin', which is where upstream puts them and, since the corruption centres every frame, where the clean centroid is -- so nothing is given up. The only end-to-end training test in the suite had been running with the wrong labels and now sets it, and a new test covers the default being refused rather than silently mistrained. The number standing in for the epoch was drawn from OS entropy, so two runs of one configuration disagreed and the seed's documented guarantee was false. That was a regression from fixing the worker-pickling bug: replacing the counter with a random stream id fixed the freeze but broke reproducibility. The stream is derived from the seed and a caller's label now, and the process id keys only the cache, never the seed. The converter deleted the old dataset before renaming the new one into place, leaving a window with neither -- the loss the staging directory exists to prevent. It moves the old one aside, renames, then removes it, and puts it back if the rename fails. Non-blocking: max_seq_len is documented as feeding get_rcut rather than as inert; _frame_scalar guards its divisor like the two reductions beside it; and the docs say the validation set is re-corrupted every pass, so its loss reads as a trend rather than a comparable number.
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In `@source/tests/pt_expt/model/test_unimol.py`:
- Line 254: Update the test setup around normalize() to remove
virtual_token_position from config["model"]["descriptor"] before normalization,
so the test exercises the omitted-key/default path rather than an explicitly
supplied "centroid" value.
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deepmd/dpmodel/atomic_model/unimol_atomic_model.pydeepmd/dpmodel/descriptor/unimol.pydeepmd/dpmodel/loss/unimol.pydeepmd/dpmodel/utils/unimol_transform.pydeepmd/utils/unimol_data.pydoc/model/unimol.mdexamples/unimol/pretrain/input.jsonsource/tests/common/dpmodel/test_unimol_data.pysource/tests/pt_expt/model/test_unimol.py
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- deepmd/utils/unimol_data.py
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Re-reviewed the changed head. The first two blockers from my previous review are fixed: Uni-Mol pretraining now rejects any virtual-token placement other than origin, and the corruption stream is derived deterministically from the configured seed plus an explicit stream label, with coverage for reproducibility and independent train/validation streams. The non-blocking zero-mask reduction and documentation points were also cleaned up. Exact-head Python, CUDA, C++, C-library, package-build, and CodeQL workflows are green.
The dataset-publication blocker is only partially addressed. Moving dst to dst.replaced and then moving dst.partial to dst is still two separate renames; after the first succeeds and before the second succeeds, dst does not exist. A process crash or interruption in that window leaves the previous valid dataset only under the recovery name, so consumers of dst still see an outage and no automatic rollback occurs. The code comment's claim that "every instant has either the old dataset or the new one in place" is therefore not true. This is the same issue I raised on the prior head, so I am not adding a duplicate inline comment. Please use a publication scheme with a stable indirection/versioned target, or another mechanism where the externally visible dataset path remains valid across interruption; at minimum, startup recovery should restore a stranded .replaced dataset before doing any new conversion work.
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The three blocking points from the previous round are addressed (virtual-token position, seed-derived stream, rename-aside in the converter), and the new tests pass at this head. Two problems remain, one of them introduced by the stream change; details inline.
Non-blocking, from the same change to _next_epoch (
deepmd-kit/deepmd/dpmodel/utils/unimol_transform.py
Lines 318 to 343 in 3b48dd1
- The generator entropy is now
[seed, stream]only, so every decoder worker (DP_LMDB_NUM_WORKERS>1) seeds an identical generator and draws the same epoch sequence at each call; frames still differ throughindex, but the per-worker decorrelation the oldpidentropy gave is gone. The docstring should state that the reproducibility guarantee holds for a single decoding process and that with workers the draws are shared across them. _EPOCH_STREAMSis never reset, so "one process decoding a run twice draws the same sequence" holds only for the first run in an interpreter; the new test has to clear the table by hand to simulate a fresh process. Either key the table by a per-transform token, or expose a reset the trainer calls when it installs the transforms.
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Re-reviewed this unchanged head because new substantive discussion exposed additional production-path problems. Exact-head CI remains fully green, but the current implementation still has blocking reproducibility/data-recovery issues.
I verified the new findings against the current code and am not duplicating their existing inline threads:
-
Training and validation still do not receive independent corruption streams in production.
Training.__init__creates a fresh transform object for each dataset, but both calls go throughUniMolLoss.frame_transform(type_map), which does not pass astream=label. Consequently both transforms use the default stream, derive the sameself.stream, and_EPOCH_STREAMSkeys them to the same process-local generator. A validation draw therefore advances the sequence later used by training (and separate Uni-Mol tasks can collide similarly). Please make the caller supply a stable, distinct stream identity such as<model_key>/trainingand<model_key>/validation, and add an end-to-end regression through the loss/trainer wiring rather than only constructing transforms with distinct labels manually. -
The converter recovery path can destroy the only surviving previous dataset after an interrupted publication. If a prior run died after
dst -> dst.replacedbut beforestaging -> dst, the next run currently deletesdst.replacedat startup even thoughdstis absent. If that new conversion then fails, neither old nor new data remains. When.replacedexists anddstis missing, restore it first; then test the stranded-backup, replacement, and rollback paths. The earlier point also remains: the two-renames sequence cannot truthfully guarantee thatdstitself is valid at every instant, so the comment should describe recoverability rather than atomic visibility unless a stable indirection is used.
The previously reviewed Uni-Mol fidelity fixes remain intact, and current Test Python, Test CUDA, Test C++, Build C++, Build C library, CodeQL, and PyPI workflows all pass. These failures are integration/recovery semantics that the present tests do not exercise.
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Two problems, one of them introduced by the previous round's fix. Training and validation shared a corruption stream in production. Making the stream deterministic fixed reproducibility but removed what had been keeping the two datasets apart: the entropy that used to seed them separately. Both calls into `frame_transform` then took the same default label, hashed to the same stream, and drew from one generator, so a validation pass advanced the corruption training was about to see. The label now comes from the caller, and the trainer builds it from the task and the split, which also keeps two Uni-Mol tasks in one multi-task run apart. The test that was supposed to cover this passed two labels by hand, which no caller does, so it stayed green throughout. The replacement goes through the trainer and asks what the trainer actually did; it fails with the previous wiring restored. The converter could destroy the last copy of a dataset. A run interrupted between the two renames leaves the only complete copy beside the destination; the next run deleted it on the way past, and if that run then failed to publish, neither copy remained. It is restored instead, and the comment no longer claims the destination is readable at every instant, which two renames cannot promise -- what they promise is that a complete dataset is always somewhere. Also from the review: the epoch stream is reset when the trainer installs its transforms, so a second run in one interpreter repeats the first rather than continuing it; and the docstring now says what more than one decoder process does to the guarantee, which is that every worker draws the same sequence.
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Re-reviewed the new head. The two blockers from my previous review are addressed:
- The trainer now supplies distinct deterministic corruption stream labels per task and split (
<model_key>/trainingand<model_key>/validation), and the new trainer-path regression verifies the transforms actually installed on production training and validation datasets have different stream identities.reset_epoch_streams()also restores same-seed repeatability for a second run in the same interpreter. - The converter now restores a stranded
dst.replacedwhen it is the only surviving previous dataset, then keeps the old dataset recoverable if publishing the completed staging directory fails. The new fault-injection regression covers exactly the interrupted-between-renames recovery path.
I also checked the latest delta-focused CodeRabbit pass; it reports no new actionable findings on these seven changed files. I found no new high-confidence blocker in this head. I am not approving yet only because exact-head CI is still incomplete: Build C++, Test CUDA, Build C library, CodeQL, and PyPI are green, while Test Python and Test C++ are still running. Once those relevant checks pass on this same head, this is ready for approval from my review perspective.
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Previous blocking findings have been addressed, the reviewed head has not moved, and the exact-head CI matrix is now fully green (Build C++, Test C++, Test Python, Test CUDA, Build C library, CodeQL, and package/PyPI). I found no remaining high-confidence blocking issue in the reviewed change set.
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The guard on `virtual_token_position` was only ever reached by a test that wrote `"centroid"` by hand. That exercises the guard but not the path a user takes, which is to leave the key out and get whatever argcheck fills in -- so the test would have kept passing if the default moved out from under it. The key is now deleted, the filled-in default asserted, and the hand-written value kept as a second test. The norm regularisers are scalars broadcast over the atoms, and the atomic model zeroes its outputs at the padded rows, so averaging one over every row divides it by the fraction of the frame that is real. `mask` is what prevents that, and nothing checked it. A padded batch now asserts that the padded rows are zero, that the masked mean is the scalar, and that the unmasked mean is that scalar diluted by exactly the padding fraction. Two things that writing the second test turned up are worth recording, because both would have made it pass while proving nothing. The fixture pads with `atype = 0`, a real element, while a virtual atom is marked by a negative type -- so the first version found no padding at all. And `x_norm` is the wrong quantity to probe: upstream penalises only the part of `|norm - sqrt(d)|` past a tolerance of 1.0, so behind a LayerNorm it is exactly zero and compares equal either way. The test pads with `-1` and probes `delta_pair_norm`.
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Re-reviewed the new head relative to the previously approved 92ef3ef1a164035ac7b041041e3c6d9f982497f4. This delta is test-only and closes the two remaining coverage gaps I was looking for: the virtual-token guard is now exercised through the actual omitted-key/argcheck-default path, and the norm-regularizer test now uses real padding (atype = -1) and demonstrates both the masked result and the exact dilution that would occur without the mask. I found no new high-confidence blocker in this head.
I am not re-approving yet because the exact-head CI is still incomplete. Test CUDA is green, while Build C library, Test Python, Build C++, CodeQL, and package/PyPI are still running and Test C++ is queued. Once those relevant checks pass on this same head, this is ready for approval from my review perspective.
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Third pass, on 92ef3ef.
Both blockers from the previous round are fixed, and the fixes are tested for real: I restored the pre-fix tree (3b48dd1) and ran the new tests against it. test_the_trainer_gives_each_dataset_its_own_corruption fails there (both datasets draw the same stream) and passes at HEAD; test_a_stranded_backup_survives_a_failed_publish fails there ("the only copy of the dataset was destroyed") and passes at HEAD. Thanks for routing stream through the trainer and for the restore-instead-of-delete publish.
Two new items are inline. One of them (gelu_erf on the tf2 backend) is a wrong-gradient bug in code this PR adds to every backend, so I am keeping the request-changes state for that alone.
Non-blocking, take or leave:
- A negative
data_seedcrashes at the first decoded frame:_next_epochfeeds[seed, stream]tonp.random.default_rng, which rejects negative entropy, and only the stream label is laundered through_stream_entropy.argchecksets no lower bound.deepmd-kit/deepmd/dpmodel/utils/unimol_transform.py
Lines 332 to 346 in 92ef3ef
- The
Noneside ofmask_token_head/coord_head/dist_headis exercised by no test (sixis not Nonebranches).deepmd-kit/deepmd/dpmodel/fitting/unimol_pretrain.py
Lines 108 to 133 in 92ef3ef
- The new
adam_epsis not plumbed into the HybridMuon path: the Adam/AdamW branch passeseps=, the HybridMuon branch forwardsadam_betasonly, and the optimizer's Adam-style update hard-codesADAM_EPS = 1e-20.deepmd-kit/deepmd/pt_expt/train/training.py
Lines 2300 to 2314 in 92ef3ef
CI: 54 of 58 checks passed, the 4 others are label-gated skips; the Python and C++ suites ran.
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Re-reviewed this unchanged head because new substantive discussion exposed a backend-correctness problem that was not covered by my previous pass. I independently verified the existing inline finding against the current head, so I am not duplicating that thread.
Blocking: xp_erf() has native branches for JAX, Torch, and NumPy, but no branch for the TensorFlow-backed deepmd._vendors.ndtensorflow namespace. That namespace therefore falls into the generic SciPy/NumPy conversion. Its Array.__array__ explicitly rejects graph-mode conversion, so gelu_erf fails under tf.function; in eager mode the NumPy round-trip detaches the erf term from GradientTape, producing a wrong derivative. Because this PR registers gelu_erf as a generally valid activation, this is a correctness issue beyond the Uni-Mol-only path. Please dispatch ndtensorflow to tf.math.erf on the wrapped tensor (returning the ndtensorflow array type) and add a TensorFlow gradient/graph-mode regression, not only a forward-value consistency check.
The exact-head checks are now completed without a failing check, but that does not cover this gradient/tracing behavior. The current head remained unchanged immediately before this review.
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`xp_erf` is introduced by this pull request, along with the exact-erf GELU it serves, so this is a defect this pull request introduced rather than a pre-existing one being tidied up. Every namespace other than JAX and torch fell through to a NumPy round-trip. For an `ndtensorflow` array that is wrong twice over: under `tf.function` the conversion is refused outright, because `__array__` raises on a graph tensor; and in eager mode it succeeds while detaching the `erf` factor from the tape, so the exact GELU differentiates as though it were `Phi(x)` alone. The second failure is silent and reaches every backend user of `gelu_erf`, not only Uni-Mol. Removing the branch again makes the point: the gradient test fails with the derivative collapsed onto `Phi(x)`, the graph-mode test fails, and the forward-value test still passes. A test that compares values could not have found this. The docstring for the dropout rates is corrected in the same commit: they are applied by the shared encoder for torch arrays in training mode, not inert and deferred to the PyTorch-Exportable wrapper. Two notes on the tests. The gradient probe uses an even number of points so the grid straddles zero without landing on it, because `relu` and `relu6` have no derivative there -- autodiff reports the subgradient 0 while a central difference reports 0.5, and neither is wrong. And the namespace anchor is deliberately not gated on `INSTALLED_TF2`: `find_spec` does not import the module, so it runs everywhere, including the ordinary runs where the tf2 cases skip. A guard that skips alongside the thing it guards would protect nothing.
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Re-reviewed the new head relative to the previously blocked 4f43a33279745ca550991c2c8de08aee14f1548f. The TensorFlow exact-GELU blocker is addressed correctly: xp_erf() now dispatches the ndtensorflow namespace to tf.math.erf on the wrapped tensor instead of round-tripping through NumPy, and the new tests cover both GradientTape correctness and tf.function traceability. The accompanying dropout documentation correction also matches the implementation. I found no new high-confidence blocker in this delta.
I am not approving yet because exact-head CI is still incomplete. Build C++, Build C library, Test CUDA, CodeQL, and package/PyPI are green; Test Python and Test C++ are still running. Once those relevant checks pass on this same head, this is ready for approval from my review perspective.
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The previously identified blocker is fixed on this unchanged head, and the only remaining reason for my last COMMENT was incomplete exact-head CI. That gate has now cleared: Test Python, Test C++, Test CUDA, Build C++, Build C library, CodeQL, and Build/upload to PyPI all completed successfully for this exact SHA. I found no remaining high-confidence blocking issue in the reviewed change set.
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This branch carries the objective; the backbone and the loss it is built on live on 0912_unimol_core (deepmodeling#6019), which has had three rounds of review since this branch was cut. Merging rather than rebasing keeps the published history intact. The review round this picks up that matters here is the corruption stream. njzjz-bot noted on this branch that training and validation still share one: `UniMolLoss.frame_transform()` took no stream label, so both datasets derived the same one and a validation pass advanced the training corruption. That is the same defect wanghan-iapcm raised on deepmodeling#6019, fixed there by threading the label from the trainer, and this merge brings the fix here rather than duplicating it.
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Approving at a309c88. The three threads from my earlier reviews are addressed at this head, checked against the head tree rather than against the replies.
xp_erfnow has a native TensorFlow branch (deepmd/dpmodel/array_api.py), returningtf.math.erfon the underlying tensor re-wrapped in the same namespace, ahead of the SciPy round-trip. Two new cases insource/tests/consistent/test_activation.pycover the gradient throughtf.GradientTapeand traceability undertf.function. I restoredarray_api.pyfrom 92ef3ef with the tests at head: exactly those two fail forgelu_erfand the forward-value case still passes, so the regression test does expose the defect.- The dropout docstring on
DescrptUniMolnow matchesunimol_nn/encoder.py::dropout. - The
virtual_token_positionguard, the removal ofSeedSequence()entropy, the staged.partial/.replacedconversion and the per-splitstream=all stand.
I also fetched upstream at the pinned commit 90f52c4 to check the distance-loss column mask: cal_dist_loss selects columns with src_tokens.ne(padding_idx), a position mask with the diagonal included, which is what this port does. The comment in loss/unimol.py is accurate.
Three notes that do not block, all about sync points or documentation rather than the code itself:
virtual_token_positiondefaults to"centroid"in argcheck (deepmd/utils/argcheck.py:2879), butUniMolPretrainAtomicModelrefuses anything other than"origin"(deepmd/dpmodel/atomic_model/unimol_atomic_model.py:52-61), and neither the argcheck doc nordoc/model/unimol.mdsays so. A user who omits the key, as the docs allow, gets theValueErrorfrom the guard. Either make"origin"the default or state the requirement in the doc string; the test added in 4f43a33 pins the current mismatch, so it would need updating with the change.examples/unimol/pretrain/input.jsonis not listed in theinput_filestuple ofsource/tests/common/test_examples.py, so the only example exercising the ~300 new argcheck lines is never run throughnormalize(). It does pass when run by hand.doc/credits.rsthas one citation block per descriptor family and gains none for Uni-Mol, andCITATIONS.bibis untouched, although the code is a port of an externally published, MIT-licensed project.
One more, smaller: gelu_erf is not in ACTIVATION_TO_FUNCTYPE in deepmd/utils/tabulate_math.py or the parallel table in deepmd/tf/utils/tabulate.py, so dp compress on a net using it fails with a generic unknown-activation error. It fails loudly, so this is only about the message — but it is worth a comment recording that compression is deliberately unsupported, because mapping it onto functype 2 would silently pick up the tanh derivatives.
CI note: both CUDA jobs report skipping at this head, so nothing here ran on GPU. The tf2 shard does select the new activation cases (-k tf2 with DP_TEST_TF2_ONLY=1), so those two did execute.
PR title
feat: add the Uni-Mol v1 backbone and its self-supervised pretraining
PR description
Uni-Mol is a molecular representation model: a transformer over all atom pairs
in which geometry enters only through pairwise distances. It was pretrained on
about 209 million RDKit conformers with three self-supervised objectives and no
energies or forces at all. This adds a port of Uni-Mol v1 that is faithful
enough to load the released weights and reproduce the published objective.
Two things motivate it. Uni-Mol's data and objectives become available to
multi-task training alongside DFT-labelled data, which is what makes a
controlled comparison between the two kinds of supervision possible at all.
And molecular property work gains a pretrained backbone with a large user base
behind it.
Scope
Uni-Mol is not a potential energy surface model. It attends over every atom
pair with no cut-off and no smooth envelope, so it is not extensive, it does not
support periodic boundaries, and its forces are neither smooth nor conserved.
The descriptor rejects frames carrying periodic images, declares itself
unavailable for edge-parallel and communication paths, and is not offered for
molecular dynamics or frozen deployment.
Nothing existing changes behaviour. Every new component is reachable only by
name from a configuration,
geluandgelu_tfkeep their current meaning, thenew data hook defaults to off, and no new dependency is added: PyTorch is
imported lazily and only to read a checkpoint file, and RDKit only when the
offline converter is asked for two-dimensional conformers.
What is here
deepmd/dpmodel/descriptor/unimol.py,unimol_nn/): the15-layer pre-layer-norm encoder, self-attention that returns its pre-softmax
logits so the pair representation accumulates across layers, the Gaussian
distance basis with per-element-pair affine parameters, and both norm
regularisers.
fitting/unimol_pretrain.py,loss/unimol.py):element prediction, coordinate denoising through the pair channel, pairwise
distance prediction, with upstream's weights of 1, 5, 10, 0.01 and 0.01.
dpmodel/utils/unimol_transform.py,utils/unimol_data.py): themasking and noise pipeline as plain per-frame functions, a per-frame
transform hook on the LMDB reader, and a streaming converter for the
upstream dataset.
utils/unimol_checkpoint.py): imports the releasedmol_pre_all_h_220816andmol_pre_no_h_220816checkpoints.gelu_erfregistered in every backend activation table.Uni-Mol uses the error-function form; deepmd's
geluis the tanhapproximation, which differs by up to 4.7e-4 per element.
installs it on that task's datasets, next to where it already registers the
label requirements. Supervised losses declare nothing and their data path is
untouched.
documentation and an example configuration.
A run from the shipped example trains:
dp --pt-expt trainreports all fiveterms on both the training and validation curves and writes checkpoints.
How closely it matches upstream
Every component is checked against tensors dumped from upstream Uni-Mol
(commit
90f52c4) running unmodified on the same molecules. The golden archiveships with the tests and the header of
source/tests/common/dpmodel/test_unimol.pysays how to regenerate it.
Bitwise agreement on the transforms is the part worth pausing on: it means the
random stream itself is reproduced, down to which atoms are masked and what
noise each one receives, not merely that the statistics match.
The remaining 3.4e-7 is upstream's own use of fp32 in three places, not an
implementation difference:
switchable with
single_precision_basis;while the descriptor computes distances inside the model, which is more
accurate and is what gradients flow through;
single_precision_distancereproduces upstream's numbers instead, which is what the released-weight check
uses to reach 3.7e-7;
log_softmaxand both norm regularisers are evaluated in fp32, reproduced.Training trajectories cannot be matched exactly in any case: upstream
pretrained a pure fp16 model with fused kernels and its own Adam variant.
Scope of the checks
Beyond the parity tests, three whole-path checks were run, and each found real
defects that component tests had not:
without a device, which land on the host while the batch is on the
accelerator.
fitting, and confirmed the objective actually falls: 8.48 to 3.02 over 60
steps, with every term decreasing.
dp --pt-expt trainfrom a configuration file found five gapsbetween a file and the first step: the trainer's loss factory did not know
the objective; the fitting lacked the accessors the atomic model calls on
any fitting; the transform ran after the reader had already checked for the
labels it was about to produce; the converter wrote a zero cell, which the
neighbour-list builder inverted; and the example addressed its LMDB dataset
with a list rather than a string.
What an adversarial review of this branch found
The branch was reviewed before submission by independent passes over upstream
fidelity, interface compliance, edge cases, test quality and reviewability,
with every finding put to a separate attempt at refutation. Thirty-two survived
and are fixed here. The ones worth knowing about:
gradient. They were bare arrays, which this backend turns into buffers, and
then, once they were parameters, the array-API wrapper that placed them on
the device copied them out of the autograd graph. Inference and checkpoint
parity were unaffected, which is why the parity tests stayed green
throughout.
bitwise identical whenever a seed was set.
molecule identically.
batch layout, which is settled before the transform runs.
The tests that should have caught the first two could not fail: the gradient
test accepted any parameter with a gradient, which the three heads alone
satisfied. Those tests are strengthened rather than merely repaired.
Decisions a reviewer may want to question
atype. Bythe time a descriptor is called, virtual atoms have been clamped to type 0
and are indistinguishable from a real first element. Frames with fewer than
two real atoms are rejected, since that inference is ambiguous for them, and
the converter drops such molecules.
five-tuple cannot carry the two virtual tokens, the pair channel or the norm
regularisers that the heads read, so the atomic model overrides one method
rather than any component being forked.
scalars, but only per-atom variables survive the atomic-output machinery; the
loss averages them back with the real-atom mask, which returns the original
value exactly.
max_atoms + 2, because upstream's objective counts them, and a static shapeis what the output definition needs.
would cost O(natoms^2) per frame, which is impractical at 209 million
conformers.
numpy.randominterface is used deliberately in thetransforms, with a noqa and a reason on every call: upstream seeds the global
legacy generator, and a
Generatorwould draw a different stream.frame_transform. Aself-supervised objective has to corrupt its input as the data is read, and
this is the smallest way to say so without the trainer special-casing a
particular loss. Supervised losses inherit the default and are unaffected.
Third-party code
The ported code follows Uni-Mol (commit
90f52c4) and the Uni-Core modules itbuilds on (commit
ace6fae), both MIT licensed, Copyright (c) DP Technology.Parts of Uni-Core derive in turn from fairseq, Copyright (c) Facebook, Inc. and
its affiliates, also MIT licensed. Each ported file carries its provenance in
the header, naming the upstream file and commit for every class.
Tests
All of it runs under the repository's own gate:
pre-commitpasses on everychanged file.
source/tests/common/dpmodel/test_unimol.py,source/tests/common/dpmodel/test_unimol_data.pyandsource/tests/pt_expt/model/test_unimol.py: 28 tests covering the transforms,the encoder, the basis, the heads, the descriptor, the objective, the
registered model path, a training run driven from a configuration, agreement
between the array-API and PyTorch-Exportable implementations, gradient flow,
dropout behaviour, serialization round trips, the guards, the data conversion
and the reader hook.
Tolerances have stated causes rather than being tuned until they pass. One test
exists only to measure the fp32 basis gap between NumPy and Torch, so that the
looser bounds elsewhere have a number behind them.
Not in this PR
Training the objective on DPA descriptors in multi-task, which needs a
coordinate head over the equivariant features and a new pair readout, and the
removal of the unrelated dead
denoisecode, which is a separate cleanup.Review round three
Three files changed since the last review, in response to @wanghan-iapcm's
second pass.
deepmd/dpmodel/array_api.py—xp_erfnow has a TensorFlow branch.xp_erfis introduced by this pull request, along with the exact-erf GELU itserves, so this is a defect this pull request introduced rather than a
pre-existing one being tidied up. Every namespace other than JAX and torch fell
through to a NumPy round-trip; for an
ndtensorflowarray that is wrong twiceover. Under
tf.functionthe conversion is refused outright, because__array__raises on a graph tensor. In eager mode it succeeds while detachingthe
erffactor from the tape, so the exact GELU differentiates as though itwere
Phi(x)alone — silently, and for every backend user ofgelu_erfratherthan only Uni-Mol.
Removing the branch again makes the point: the gradient test fails with the
derivative collapsed onto
Phi(x), the graph-mode test fails, and theforward-value test still passes. A test that compares values could not have
found this.
deepmd/dpmodel/descriptor/unimol.py— the docstring for the dropout ratessaid they are inert here and applied by the PyTorch-Exportable wrapper. They are
applied by the shared encoder, for torch arrays in training mode; inference is
the identity, and training on another array namespace raises
NotImplementedError.source/tests/consistent/test_activation.py— three tf2 cases: the valuecomparison's gradient counterpart, a
tf.functiontrace, and an anchor on thenamespace name.
Two notes on those tests. The gradient probe uses an even number of points so
the grid straddles zero without landing on it, because
reluandrelu6haveno derivative there: autodiff reports the subgradient 0 while a central
difference reports 0.5, and neither is wrong. And the namespace anchor is
deliberately not gated on
INSTALLED_TF2—find_specdoes not import themodule, so it runs everywhere, including the ordinary runs where the tf2 cases
skip. A guard that skips alongside the thing it guards would protect nothing.
When the tf2 cases run
They are gated on
INSTALLED_TF2, which requiresDEEPMD_TEST_TF2=1. As far asI can see nothing in the repository sets that variable —
test_python.ymlsetsDP_TEST_TF2_ONLYfor the dedicatedsource/tests/tf2job — so on a normal runthey skip rather than fail. I ran them locally with
DEEPMD_TEST_TF2=1andTensorFlow pinned to the CPU (TF 2.21's bundled kernels do not match this
machine's driver): 36 passed. Flagging it because a test that always skips
protects nothing, and I would rather say so than leave the impression that this
path is covered. Whether to wire these into CI is the maintainers' call.
The TF1 backend is not reachable from this change:
xp_erfhas one caller inthe tree,
deepmd/dpmodel/utils/network.py:361, and TF1's owngelu_erfindeepmd/tf/common.pycallstf.math.erfdirectly without going through it.Summary by CodeRabbit
New Features
adam_epsoptimizer option for PyTorch Exportable training.Bug Fixes
Documentation