perf(pt_expt): batch the Hessian-vector products on the graph route by iProzd · Pull Request #6035 · deepmodeling/deepmd-kit

iProzd · 2026-09-21T14:50:55Z

_cal_hessian_ext_graph built the Hessian through torch.autograd.functional.hessian, which with
vectorize=False evaluates one Hessian row per second-order backward pass: 3 * nloc sequential
passes, each far too small to keep the GPU busy.

This replicates the structure along the frame axis the carry-all graph already has, so one forward and
one first-order backward build a graph that every chunk of seed vectors then reuses, and each
second-order backward returns a whole batch of rows. Frames are independent, so the replicated system's
energy is a sum of independent terms and its Hessian is block diagonal — the result is exact, not an
approximation. No vmap is involved, so custom autograd Functions that lack a batching rule keep
working.

Only the graph route changes. The dense route (_cal_hessian_ext) is untouched, and
DP_HESSIAN_HVP_BATCH=1 restores the previous behaviour exactly.

Exactness

In float64 the two routes agree to 1e-14 relative or better — at machine precision on the small
example model, and within about two orders of magnitude of it on the real checkpoint, whose own
unbatched answer is not exactly symmetric either:

model	comparison	result
`examples/water/dpa4`, 16 / 32 atoms	`max\|H_B − H_1\|`, B=24	8.3e-17 / 1.2e-16
DPA-4.0.1-Pro-MPtrj, 4 atoms	`‖H_8 − H_1‖_RMS / ‖H_1‖_RMS`	2.8e-14
DPA-4.0.1-Pro-MPtrj, 4 atoms	unbatched path's own `‖H − Hᵀ‖_RMS / ‖H‖_RMS`	6.4e-16

In float32 the two differ by 1.0e-6 – 3.6e-6 relative RMS, against the unbatched path's own asymmetry of
3.2e-7 – 9.5e-7. That is the longer summation chain, not a different computation — the float64 agreement
above is what settles equivalence.

Speedup

Measured on one H20, model.eval(), float32, TF32 off, DPA-4.0.1-Pro-MPtrj, rcut 6.0 Å. All batch
sizes timed inside one process (load once, switch batch size, warm up then median of 3), so the
ratios carry no process-to-process term.

system	atoms	edges	B=1	B=2	B=4	B=8	B=16
fcc sites in a fixed 8.1 Å cell	8	72	2.92 s	1.87×	3.24×	5.06×	6.40×
dilute 5 Å lattice	216	1,080	74.55 s	1.86×	3.40×	3.89×	4.04×
fcc Al 2×2×2	32	1,728	10.91 s	1.95×	2.83×	3.04×	OOM
fcc Al 3×3×3	108	5,832	44.96 s	1.18×	1.24×	OOM	—

The speedup tracks edge count, not atom count. A 216-atom dilute system with 1,080 edges still gets
3.40×; a 108-atom solid with 5,832 edges gets 1.24×. Batching recovers kernel-launch overhead, and that
overhead stops mattering once a single Hessian-vector product already saturates the GPU. As a rule of
thumb on this hardware, batching pays below ~2,000 edges and buys little above ~4,000.

Memory

Peak memory is linear in the edge count, in the atom count, and in the batch size. Over 18 points
spanning 54 – 19,008 edges and 8 – 512 atoms, one expression fits every measurement to 0.2%:

peak(MiB) = 782 + [4.497 + 3.268*(B-1)] * edges + [10.88 + 7.96*(B-1)] * natoms

("edges" counts directed neighbour pairs including periodic images.) Fitting each batch size separately
gives per-edge coefficients 4.497 / 7.765 / 14.302 / 27.370 / 53.406 for B = 1 / 2 / 4 / 8 / 16 —
increments of 3.268, 3.268, 3.268, 3.260, with no drift. The fit over the smaller systems predicted
65,776 MiB for 256-atom fcc Al before that point was run; it measured 65,733.

So the batch size divides the system size that fits. On a 96 GiB card:

B	fcc-solid density (54 nbr/atom)	30 nbr/atom	dilute (4.5 nbr/atom)
1	381 atoms	663	3,108
2	221	384	1,798
4	120	209	976
8	63 atoms	109	510
16	32	56	261

Measured boundary: 256-atom fcc Al fits at B=1 (65.7 GiB); 500 atoms runs out of memory.

For reference, the first-order (energy/force/virial) pass obeys
peak(MiB) = 508 + 1.293 * edges + 2.93 * natoms (11 points, 0.1%), so the Hessian's peak converges to
3.4× the first-order peak — that ratio bounds what any block-wise or rematerialising scheme could
recover.

Choosing `DP_HESSIAN_HVP_BATCH`

Left unset, the batch is chosen per frame, mirroring what DP_INFER_BATCH_SIZE already does for
inference batches: one Hessian-vector product per frame measures what a replica costs there —
neighbour counts differ between frames, so one measurement does not price them all — and the batch
becomes what the free memory affords, capped at 8. That needs no per-model constants, which a fitted
memory model would, and it costs one row out of 3 * nloc.

The measurement covers a whole product, while each batch step past the first adds only its marginal
share, so the estimate reads high and the batch comes out conservative. That is the direction to err: a
batch that turns out too large is recovered by halving and retrying, and nothing recovers the time lost
to one that was too small.

Setting the variable disables the automatic choice and uses the value given, including above the cap.
The out-of-memory fallback still applies to it — halving the batch, warning which one was used, and
keeping the surviving batch for the rest of the call — because halving changes how the Hessian is
computed, not what it is, and the alternative is ending a run that could have finished. The fallback
recognises wrapped out-of-memory errors via the repository's is_oom_error, since the allocator
failure does not always arrive as torch.OutOfMemoryError (AOTInductor rewraps it in a plain
RuntimeError). DP_INFER_BATCH_SIZE behaves the same way: an explicit value disables
growth, while out-of-memory errors can still reduce the batch.

On a device without CUDA there is no allocator to size against and no recoverable out-of-memory error
to catch, so the automatic choice is 1; an explicit value is still honoured.

Tests

source/tests/pt_expt/model/test_hessian_hvp_batch.py makes the batch size the object under test: a
float64 DPA-1 model meeting the graph-route gate, checked at batch sizes 2, 3, 4, 8 and 16 against the
batch-size-1 result. 3 * nloc is 15, so 2, 4, 8 and 16 leave a partial final chunk and cover the
zero-padding branch while 3 divides evenly, and 16 exceeds 3 * nloc and is clamped. A counter over the
three Hessian implementations asserts that batch sizes 0 and 1 take the unbatched path, that the larger
sizes take the batched helper, and that the dense route neither sees the setting nor changes with it.
The dense Hessian doubles as an independent cross-check, sharing no code with the graph wrapper.

The automatic choice is covered too: that it stays within [1, cap] whatever the free memory, that it
never shrinks as memory grows, that it stays at 1 without CUDA without running the probe, and that the
probe prices one Hessian-vector product rather than the whole Hessian. The fallback is covered by making
the helper refuse batches above 2 and asserting the retry walks 8, 4, 2 and still returns the unbatched
answer, and by making every batch fail and asserting it lands on the original path. Wrapped
out-of-memory errors take the fallback too, in all three forms is_oom_error knows (message marker,
cause chain, AOTInductor signature), while an unrelated RuntimeError still propagates; the surviving
batch is kept for later frames ([8, 4, 2, 2], not [8, 4, 2, 8, 4, 2]); and the automatic choice is
asserted to price once per frame.

test_dpa2_graph_lower.py already reached the batched helper, but only because the batch it happens to
get exceeds 1 -- that coverage would have vanished silently had the batch become 1, which is exactly
what these tests now prevent.

Scope and compatibility

Eager only. The Hessian route does not export or compile (autograd.functional.hessian cannot),
which the surrounding code already notes; nothing here changes what is exportable.
DP_HESSIAN_HVP_BATCH=1 (or 0) takes the previous one-row-at-a-time path unchanged.
Batch sizes are clamped to 3 * nloc, and a final partial chunk is zero-padded so the retained graph
keeps its shape; the padded rows are discarded.
The dense Hessian route and every other backend are untouched.

Usage note

A model whose descriptor carries use_amp=True gates its bfloat16 autocast on self.training, and a
module returned by deserialize() starts in training mode. Calling such a model for inference
without .eval() therefore runs the descriptor under bfloat16 silently — no warning, and
next(model.parameters()).dtype still reports torch.float32. The symptom is ~1e-3 relative noise
between repeated identical calls and much higher peak memory. Every number above was taken with
.eval() asserted. This is pre-existing behaviour, not introduced here, but it is easy to hit when
benchmarking a Hessian.

How the numbers were taken

One process per measurement point, torch.cuda.max_memory_allocated() reset per point, warm-up before
timing, TF32 disabled, model.eval() asserted at entry. Peak memory is reproducible to the byte across
processes (the same 32-atom structure measured 8,900 MiB at B=1 in two independent runs); wall-clock is
not, which is why every speedup above is an in-process ratio.

Summary by CodeRabbit

New Features
- Added configurable batching for Hessian computations, with automatic batch sizing performed per frame.
- Automatic batches are capped at 8 by default and target half of available memory.
- If memory is insufficient, computation retries with smaller batches; the batch that succeeds is reused for subsequent outputs.
- Hessian results remain unchanged, including zero blocks for graphless outputs and constant first derivatives.
Documentation
- Documented the Hessian batching settings and per-frame sizing behavior.

_cal_hessian_ext_graph called torch.autograd.functional.hessian, which with vectorize=False evaluates one Hessian row per second-order backward pass: for DPA-4 that is 3*nloc sequential passes, each far too small to keep the GPU busy. Replicate the structure along the frame axis the carry-all graph already has, so one forward and one first-order backward build a graph that every chunk of seed vectors reuses, and each second-order backward returns `batch` rows at once. Frames are independent, so the replicated energy is a sum of independent terms and its Hessian is block diagonal -- the result is exact, not an approximation, and no vmap is involved, so custom autograd Functions without a batching rule keep working. Measured on one H20, float64, TF32 off, DPA-4 from examples/water/dpa4: natoms B=1 (old) B=24 speedup max|H_B - H_1| 16 2.876 s 0.264 s 10.9x 8.3e-17 32 6.837 s 0.613 s 11.2x 1.2e-16 Peak memory grows with the batch: at 32 atoms 28.3 GiB (B=1) -> 34.6 GiB (B=24). DP_HESSIAN_HVP_BATCH tunes the trade-off; 1 restores the previous behaviour exactly.

Batching the Hessian-vector products needs a batch size, and there is no good fixed one. Peak memory is linear in it while the speedup is not: batching recovers kernel-launch overhead, which stops mattering once a single Hessian-vector product already saturates the device. Measured on one H20 with DPA-4.0.1-Pro-MPtrj in eval mode, float32, TF32 off, over 18 points spanning 54 to 19008 neighbour pairs and 8 to 512 atoms, peak(MiB) = 782 + [4.50 + 3.27*(B-1)]*edges + [10.9 + 7.96*(B-1)]*natoms fits every measurement to 0.2%, while the speedup falls from 5.06x at 72 edges to 1.24x at 5832. A fixed batch of 8 would cut what fits on a 96 GiB card at fcc-solid density from ~381 atoms to ~63, and buy 1.2x on the systems that large: it would turn systems that ran into systems that do not. So size it per call, as DP_INFER_BATCH_SIZE already does for inference batches. One Hessian-vector product is run to measure what a replica costs, the batch becomes what the free memory affords, and it is capped at 8, where the speedup has flattened on every system measured. The measurement covers a whole product while each step past the first adds only its marginal share, so the estimate reads high and the batch comes out conservative. That is the direction to err: a batch that does not fit is recovered by halving and retrying, and nothing recovers the time lost to one that was too small. Reaching 1 hands over to the original one-row-at-a-time path, so the fallback bottoms out in exactly the code a user who asked for 1 would take. An explicit DP_HESSIAN_HVP_BATCH is used as given, including above the cap. The out-of-memory fallback still applies to it, because halving changes how the Hessian is computed and not what it is, and the alternative is ending a run that could have finished. Without CUDA there is no allocator to size against and no recoverable out-of-memory error to catch, so the automatic choice is 1. This also corrects the measurements the comments quoted. They were taken with the model left in training mode, where this checkpoint's use_amp silently enables a bfloat16 autocast, so they described the bf16 path and overstated peak memory by roughly 18x. Likewise the equivalence claim: against the DPA-4.0.1-Pro-MPtrj checkpoint in float64 the two routes agree to 2.8e-14 relative RMS, not the 1e-16 measured on the smaller example model.

The batched Hessian-vector product path was reached only incidentally. test_dpa2_graph_lower exercises it, but only because the batch it happens to get exceeds 1: a batch of 1 would turn that into coverage of the unbatched path alone, without any test failing. Nothing treated the batch as the object under test, so no test compared one batch against another, and none asserted which branch had run. Add a float64 DPA-1 model that meets the graph-route gate (mixed types plus graph lower) and check, for batches 2, 3, 4, 8 and 16, that the Hessian matches the one produced with a batch of 1. 3*nloc is 15, so 2, 4, 8 and 16 leave a partial final chunk and cover the zero-padding branch, while 3 divides evenly and 16 exceeds 3*nloc and is clamped. A counter over the three implementations asserts that batches of 0 and 1 take the unbatched path, that the larger ones take the batched helper, and that the dense route neither sees the setting nor changes with it -- the dense Hessian doubles as an independent cross-check, since it shares no code with the graph wrapper. Cover the automatic choice too: that it stays within [1, cap] whatever the free memory, that it never shrinks as memory grows (with both ends pinned, so monotonicity cannot pass vacuously), that it stays at 1 without CUDA without running the probe, and that the probe prices one Hessian-vector product rather than the whole Hessian. Cover the fallback by making the helper refuse batches above 2 and asserting the retry walks 8, 4, 2 and still returns the unbatched answer, and by making every batch fail and asserting it lands on the original path. Verified non-vacuous by mutation. Of the 23 tests, these many fail when the implementation is broken in each way: dropping the trim that discards padded rows, 10; shifting the seed vectors by one row, 8; letting a batch of 1 enter the batched helper, 6; removing the batch cap, 5; ignoring free memory and always taking the cap, 1; retrying at 1 instead of halving, 1; running the probe without CUDA, 1; ignoring max_rows so the probe prices the whole Hessian, 1.

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📝 Walkthrough

Walkthrough

Changes

The graph Hessian path now computes Hessian-vector products in batches. It supports explicit batch settings and per-frame automatic sizing. Recognized out-of-memory errors trigger batch reduction or fallback, and the fitting batch is reused for later output components.

Graph Hessian batching

Layer / File(s)	Summary
Batch settings and documentation `deepmd/pt_expt/utils/env.py`, `doc/env.md`	Adds environment settings for explicit batch size, automatic batch cap, and memory fraction. Documents per-frame sizing and reuse of the surviving batch after an out-of-memory reduction.
Batched Hessian computation `deepmd/pt_expt/model/make_model.py`, `source/tests/pt_expt/model/test_hessian_hvp_batch.py`	Computes Hessian rows in chunks, returns zero blocks for graphless outputs and constant first derivatives, and tests batching, result agreement, and fallback behavior.
Per-frame batch sizing `deepmd/pt_expt/model/make_model.py`, `source/tests/pt_expt/model/test_hessian_hvp_batch.py`	Measures a Hessian row per frame for automatic sizing, adjusts the batch after recognized out-of-memory errors, and tests batch policy and reuse.

Priority: ➖ Normal

Estimated code review effort: 4 (Complex) | ~45 minutes

Change: Feature

Merge Risk: ⚪ Minimal · up to 923be

The batching change is mergeable after normal checks; no unresolved behavior issue is established.

Security Architecture Review

Security architecture risk: 🔵 Low · up to 923be

No security issue was verified. Batching can increase peak GPU memory use, but automatic sizing is capped and can fall back to the previous one-row computation. The operator override is documented. Security coverage is incomplete, so this is not a minimal-risk assessment.

Retained concerns
No architecture-level concerns identified.

Security review details

Security Blast Radius

inferred — Inputs that increase graph size can increase GPU work and memory demand during a Hessian call. The evidence establishes this resource effect, but not an externally attackable caller or a tenant-wide exposure.

Trust Boundaries and Controls

observed — The batch override comes from process environment configuration. The identified test entrypoints do not provide evidence of attacker control over that setting or of a bypassed identity boundary.

Resilience and Maintainability Implications

observed — Retry batch size is local to the Hessian invocation. Failed batched attempts do not append a partial Hessian result; automatic sizing is repeated for each frame, while a batch reduced by OOM can be reused within the call.

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Inline comments:
In `@deepmd/pt_expt/model/make_model.py`:
- Line 434: Update the batching logic around coord_flat so it preserves the
coordinate graph when create_graph is true and detaches only when create_graph
is false; use the resulting source for the reshape, expand, and contiguous
operations.
- Around line 458-473: Update the Hessian computation around the initial total
gradient and HVP loop to support constant and linear outputs: guard
total.requires_grad, use allow_unused=True, and replace missing gradients with
zeros_like(x); before each second derivative call, guard grad.requires_grad,
emit zero HVP rows when false, and materialize unused second gradients as zero
tensors. Add coverage for constant-output and linear-output cases in the
existing Hessian model tests.
- Line 460: Update the HVP seed construction around the helper containing eye
and the rows loop to avoid allocating the full ndof × ndof identity. Build each
current nb × ndof seed block directly, populate only the count of requested
diagonal entries while preserving row order, and retain the existing batch-shape
padding and final-chunk behavior.
- Around line 642-651: Wrap the automatic batch-one probe in the hvp_batch
initialization path around _auto_hvp_batch with a torch.OutOfMemoryError
handler; on failure, clear the CUDA cache and set hvp_batch to 1 so the
subsequent computation uses the unbatched functional Hessian fallback.

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njzjz-bot

I reviewed the full current 4-file diff and the existing review threads. The main batching idea is sound, but the current head still has blocking behavioral regressions already captured by the unresolved inline comments, so I am not duplicating those comments.

The two correctness issues are independently reproducible from the PyTorch autograd contract:

_hessian_graph_batched_hvp() unconditionally detaches coord_flat. When create_graph=True (the training path), the returned Hessian is therefore disconnected from the caller coordinates, unlike torch.autograd.functional.hessian(..., create_graph=True). This drops higher-order coordinate derivatives.
The new direct torch.autograd.grad sequence does not preserve the old functional.hessian(strict=False) behavior for coordinate-independent or linear outputs. The old path returns an all-zero Hessian; the new path can raise because total or the first derivative has no differentiable graph.

The automatic batching path also needs the existing probe fixes before it is robust: the probe allocates a full ndof x ndof identity even for max_rows=1, and a probe OOM currently escapes before _hessian_graph_row_block() can fall back to the unbatched implementation.

Please address those unresolved inline findings and add focused regressions for higher-order graph preservation plus constant/linear outputs. Exact-head Test Python and Test C++ are also still running at the time of review; the completed CUDA/build/CodeQL/package workflows are green.

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codecov · 2026-09-21T16:25:48Z

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❌ Patch coverage is 77.77778% with 16 lines in your changes missing coverage. Please review.
✅ Project coverage is 77.50%. Comparing base (1313650) to head (923bed5).
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The automatic batch choice returns 1 immediately on a non-CUDA device and never runs the probe -- there is no allocator to size against and no recoverable out-of-memory error to catch, which is what test_auto_batch_is_one_without_cuda asserts. The test that checks the probe prices a single Hessian-vector product therefore cannot pass there, and it was missing the skip its two sibling tests already carry. On CPU: 1 failed, 14 passed, 8 skipped -> 14 passed, 9 skipped. On CUDA: 23 passed, unchanged.

njzjz-bot

Re-reviewed the new head because the SHA changed. The only delta from the previously reviewed 973ad0cef5de08ca82f0627bedacf4398dc1742b is the CUDA-only skip for test_probe_prices_one_product_not_the_whole_hessian; that test-only correction is reasonable, but it does not address the four existing merge-blocking findings in the implementation. The current head still unconditionally detaches coord_flat in the batched HVP path when create_graph=True, still does not materialize zero Hessians for constant/linear coordinate dependence like the previous functional.hessian(strict=False) path, still allocates a full ndof × ndof identity even for the one-row memory probe, and still lets an OOM from the automatic probe escape before the batch-halving/unbatched fallback can run. Those issues remain attached as exact-line unresolved inline threads, so I am not duplicating them.

Exact-head Test CUDA, Build C++, Build C library, CodeQL, and package/PyPI are green; Test Python and Test C++ are still in progress. Please address the existing blocking threads and add the corresponding regressions before requesting another review.

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Review found four problems with the batched Hessian-vector products, all of them in how the batched path differs from the `torch.autograd.functional.hessian` call it replaces. 1. `create_graph` no longer detaches the coordinates. The batched path built its replicas from `coord_flat.detach()` whatever the caller asked for, so with `create_graph=True` the Hessian lost its dependence on the input coordinates. The parameter path was unaffected -- training on Hessian labels still trained -- which is what made the loss silent. `functional.hessian` keeps the input in the graph in this case (`_grad_preprocess`), and now so does this. 2. A constant or linear coordinate dependence yields zeros instead of raising. `functional.hessian` materialises the zero block under its default `strict=False`; differentiating a constant first derivative again raises instead, so a legitimate model became a crash. 3. The memory probe no longer builds an `ndof x ndof` identity. Seeds are now cut one block at a time. The identity is the allocation batching exists to avoid, and building it to decide the batch both risked the out-of-memory error being measured for and inflated the measurement, biasing the chosen batch too small. 4. An out-of-memory error from the probe falls back to one row at a time. Pricing a product is itself a product, so it can be the allocation that does not fit; that escaped before the halving path could run, ending a job that the unbatched route might have completed. Each fix has a test that fails without it: reverting any one of the four fails exactly its own test and leaves the other three green.

njzjz-bot

Re-reviewed the new head. Three of the four previous blockers are fixed correctly: create_graph=True now preserves the coordinate graph, seed blocks no longer allocate the full ndof² identity, and probe OOM now routes to the unbatched fallback. The linear-output zero-Hessian case is also handled.

One part of the original constant/linear-output blocker remains: a truly coordinate-independent output still reaches the first torch.autograd.grad(total, x, ...) with total.requires_grad == False, which raises before the later grad.requires_grad zero-Hessian guard can run. I attached this exact issue inline; the new regression only covers a linear output, not a constant one. The old torch.autograd.functional.hessian(..., strict=False) path returns an all-zero Hessian for both cases, so this is still a behavior regression.

Exact-head Test CUDA, Build C++, Build C library, and PyPI/package are green; Test Python, Test C++, and CodeQL are still running.

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The previous commit handled only half of this. It guarded on `grad.requires_grad`, which catches an output that is linear in the coordinates -- the first derivative exists but is constant. An output that does not depend on the coordinates at all never gets that far: `total.requires_grad` is False, and autograd refuses to differentiate an output carrying no graph, so it raised at the *first* `autograd.grad`, before the guard could run. `torch.autograd.functional.hessian` returns an all-zero Hessian for both shapes under its default `strict=False`, so both have to be materialised here. The check now sits before the first derivative. The regression is parametrised over both shapes rather than only the linear one, which is what let the constant case through: the guard was never exercised with the input it was supposed to catch. Removing this guard alone fails the `constant` case and leaves `linear` green.

njzjz-bot

Re-reviewed the new head. The remaining constant-output Hessian regression from the previous review is now fixed correctly: _hessian_graph_batched_hvp() checks total.requires_grad before the first autograd.grad and materializes the zero Hessian block, while the existing grad.requires_grad guard continues to cover the linear-output case. The regression test now exercises both truly constant and linear coordinate dependence. I also rechecked the complete 4-file diff, existing review threads, and the new two-file delta; I do not see another high-confidence correctness, numerical, API, memory-management, or test-coverage blocker.

All exact-head workflows are green: Test Python, Test C++, Test CUDA, Build C++, Build C library, CodeQL, and package/PyPI.

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wanghan-iapcm

The mathematics here is sound and the write-up is unusually careful, so let me start with what I checked rather than what I want changed.

I reproduced the equivalence claim independently on the head commit. Beyond the shapes the new test file covers, I ran three cases it does not: three frames in one call, two frames with different numbers of real atoms (some atype < 0), and create_graph=True. In float64 the batched and unbatched Hessians agree to 1e-17 – 1e-19 against a scale of 1e-3 – 6e-2 in every one of them, and with a non-degenerate probe scalar the third derivative agrees to 5e-20. Symmetry holds, and the cross-check against the dense route — which shares no code with the graph wrapper — is real independent evidence rather than self-consistency. No tolerance was loosened and no existing test was weakened. The benchmark section states hardware, dtype, TF32, eval mode, warm-up, repeats and timing boundaries, and includes the unfavourable end of the range rather than only the favourable one. I have no correctness objection to the batching itself.

Three things I would like addressed before merge, all about the out-of-memory fallback rather than the numerics:

1. The out-of-memory fallback will not fire for wrapped OOMs. _hessian_graph_row_block catches torch.OutOfMemoryError directly. This repo already has is_oom_error (deepmd/pt/utils/auto_batch_size.py:165) precisely because OOM sometimes arrives as a RuntimeError carrying the real cause in __cause__/__context__ — its own comment names AOTInductor. On those the advertised fallback does not run and the job dies, which matters more here than usual because the new default raises peak memory several-fold.

2. The halved batch is never written back, so the ladder restarts for every component. With three frames and a helper that refuses batches above 2, the attempted sequence is [8, 4, 2, 8, 4, 2, 8, 4, 2], with six warnings. Each failed attempt is a full forward plus a first-order backward. In the regime the fallback exists for, this spends the speedup the PR is about. Keeping the surviving batch in hvp_batch fixes it and also stops the log spam.

3. The batch is priced on the first frame and used for all of them. The comment justifies reuse by "the neighbour count ... is the same for every component of one call", which is true across ci but not across ii: each frame has its own n_real from the atype >= 0 mask and its own neighbour count. A batch priced on a sparse frame is then applied to a dense one, and only the ladder in point 3 recovers it.

Smaller points, take or leave:

doc/env.md says the result does not depend on the batch. Mathematically true, and the float32 differences the description reports (1e-6 relative RMS) are ordinary rounding from a longer summation chain, not a concern. But since the CUDA default now sizes the batch from free memory, a float32 Hessian is no longer bit-reproducible run-to-run on the same machine; a sentence saying "float64 exact, float32 at rounding level, automatic sizing not bit-reproducible" would make the doc precise.
_hvp_replica_cost calls torch.cuda.reset_peak_memory_stats unconditionally, and on CUDA the probe is the default path, so every Hessian call silently clears a process-wide counter that deepmd/pt/utils/auto_batch_size.py reads against a baseline.
The 0.5 free-memory fraction is not in doc/env.md (only the cap of 8 is), and the adjacent DP_INFER_BATCH_SIZE entry documents 0.9.
_hessian_graph_batched_hvp re-implements _WrapperForwardEnergyGraph.__call__'s forward, so a change to forward_common_atomic_graph or to the per-ci reduction has to land in two places. Incidentally, hvp_kwargs and the wrapper's constructor keywords are the same thirteen names with the same values, so wrapper = _WrapperForwardEnergyGraph(**hvp_kwargs) would remove the duplicated argument list outright.
Every test uses a single frame with no virtual atoms. The frame axis is the axis being replicated and n_real != nloc is a real branch. I verified both are correct, so this is coverage rather than a defect, but it is worth pinning.
test_create_graph_keeps_the_path_to_the_coordinates never compares a value, and its probe scalar hessian.sum() is identically zero for a translation-invariant energy — I measured the resulting gradient as exactly zero at every batch size, so torch.isfinite(back).all() carries no signal. (hessian**2).sum() makes it non-degenerate.
The four new parametrize lines lack the trailing comments this repo asks for.
DP_HESSIAN_HVP_BATCH_CAP and DP_HESSIAN_HVP_MEMORY_FRACTION are hard-coded constants with environment-variable-style names.
int(os.environ.get("DP_HESSIAN_HVP_BATCH")) is unguarded, so a malformed value raises at import of the whole backend. Existing code does the same elsewhere, so this is only a suggestion.
The docstring's "Exactly equivalent" is followed immediately by the non-zero differences it measures.

One process note, not an objection to the change: the default path introduced here — automatic sizing, the memory probe, the free-memory budget and the OOM ladder — is CUDA-only, and it is guarded by exactly the tests that skip without CUDA. The CUDA jobs are label-gated and skipped on this PR, so the new default behaviour has not been executed in CI. Everything that did run, passed.

The batched-Hessian ladder and the pricing probe caught only torch.OutOfMemoryError, but the allocator failure does not always arrive with that type: AOTInductor rewraps it in a plain RuntimeError, keeping the original text in the message, only in the __cause__ chain, or stripping both behind its run_func_ signature. A catch keyed on the exception type lets every one of those forms end the run, which the fallback exists to prevent. Both sites now delegate to the repository's is_oom_error (deepmd/pt/utils/auto_batch_size.py), which walks the exception chain and knows the wrapper signatures; it also releases the allocator cache itself on a positive match. The probe fallback and the ladder behave exactly as before for the unwrapped form. The tests feed the ladder all three wrapped forms and assert it still halves 8, 4, 2 and returns the unbatched answer, and that an unrelated RuntimeError propagates instead of being retried smaller; the probe fallback test is parametrised over the plain and wrapped forms.

The halved batch stayed local to _hessian_graph_row_block and was never written back to hvp_batch, so every frame and every output component restarted the ladder from the top: three frames with only batches up to 2 fitting attempted [8, 4, 2, 8, 4, 2, 8, 4, 2] and printed six warnings. Each failed attempt pays a full forward plus a first-order backward, so in exactly the regime the fallback exists for it spent the speedup batching was bought for. The helper now returns the batch that fit alongside the Hessian, and the caller keeps it in hvp_batch, so later calls start where the last one survived. The two-frame test asserts the attempts are [8, 4, 2, 2] and that both frames still return the unbatched answer.

The automatic batch was priced once, on the first frame with real atoms, and reused for every later frame, but each frame has its own neighbour count and its own count of real atoms: a batch priced on a sparse frame is then spent on a dense one, which only the OOM ladder recovers, and a batch priced on a dense frame idles on sparse ones, which nothing recovers. The Hessians accumulated for earlier frames also shrink the free memory a later frame has, which a price taken up front cannot know. Pricing costs a single Hessian-vector product (one row out of 3 * nloc), so run it once per frame; within a frame every output component still shares one price. The CUDA test asserts a two-frame call prices twice and still returns the unbatched answer.

wanghan-iapcm

Re-reviewed at 923bed52. The three inline points of my 09-22 review are addressed at head: both OOM catch sites delegate to is_oom_error, _hessian_graph_row_block returns the batch that fit and the caller keeps it, and the pricing probe now runs inside the frame loop. I restored make_model.py from each fix commit's parent and ran the head test file: the wrapped-OOM tests (three variants) and the surviving-batch test fail pre-fix and pass at head, on CPU. The per-frame pricing test cannot run anywhere as shipped; details inline.

I also re-ran the equivalence check at head in float64 on CPU: three frames in one call, two frames with different numbers of real atoms (one atype < 0), and three frames with a different batch forced per frame all agree with the DP_HESSIAN_HVP_BATCH=1 reference to 1e-19 against a scale of 1e-3, with the virtual-atom rows exactly zero. The exactness claim holds.

Blocking: the one regression test for the newest fix is skipped in every job (inline, test file line 231). One design note on the interaction between the last two fixes (inline, make_model.py line 678).

Suggestions, none of the earlier ones were taken up, so I repeat only those I still think are worth doing:

make_model.py:412 says "Exactly equivalent" and "no approximation is made", then reports a 3e-6 float32 relative RMS difference a few sentences later. "Exact in float64; float32 differs at rounding level" is what the measurements support.

deepmd-kit/deepmd/pt_expt/model/make_model.py

Line 412 in 923bed5

Exactly equivalent to ``torch.autograd.functional.hessian`` on
test_create_graph_keeps_the_path_to_the_coordinates probes with hessian.sum(), which is identically zero for a translation-invariant energy (measured 5e-20, gradient exactly 0.0), so isfinite carries no signal. (hessian**2).sum() is non-degenerate.

deepmd-kit/source/tests/pt_expt/model/test_hessian_hvp_batch.py

Line 288 in 923bed5

hessian.sum(), upstream, retain_graph=True, allow_unused=True
The six parametrize lines have no trailing comment, which the pt_expt test guidance asks for.
Both except blocks build a fresh AutoBatchSize(silent=True) only to call is_oom_error, whose body never touches self. The constructor parses DP_INFER_BATCH_SIZE, so a malformed value there raises from inside the handler and masks the real error. A module-level helper, or AutoBatchSize.is_oom_error(None, e)-style staticmethod use, avoids it.

deepmd-kit/deepmd/pt_expt/model/make_model.py

Line 590 in 923bed5

if not AutoBatchSize(silent=True).is_oom_error(e):
is_oom_error treats any AOTInductor runtime failure as OOM. Upstream that is justified by a ladder that shrinks to 1 and then raises; here a real AOTI bug becomes a "did not fit" warning plus a slow silent rerun on the unbatched path.

doc/env.md:93-97 says out-of-memory errors still reduce the batch, but is_oom_error matches only CUDA markers, so an explicit batch above 1 that does not fit on CPU ends the run.

deepmd-kit/doc/env.md

Lines 93 to 97 in 923bed5

    
           fits and buys little on the systems that need it. 
        
           Setting this variable disables automatic sizing and uses the value given; 
        
           out-of-memory errors can still reduce the batch, halving it, warning which 
        
           batch was used, and keeping the surviving batch for the rest of the call. The

int(os.environ.get("DP_HESSIAN_HVP_BATCH")) at env.py:57-60 is unguarded; an empty or non-numeric value fails the import of the whole backend, and 0 or negatives are accepted.

CI: 58 checks, 4 skipping (both CUDA jobs and two release jobs), everything else passes. 12 of the 35 new test cases are CUDA-gated, so the default automatic-sizing path has still not executed in CI.

wanghan-iapcm · 2026-09-26T07:41:04Z

+        assert probe_rows == 1, "the probe computed more than one row"
+
+    @pytest.mark.skipif(
+        not torch.cuda.is_available(), reason="the automatic choice needs CUDA"


test_the_batch_is_repriced_for_each_frame is the regression test for the per-frame pricing fix (923bed5), but it is gated on torch.cuda.is_available(), and this repository's CUDA jobs report skipping, so it has not run in any CI job. As shipped the file is 23 passed, 12 skipped both with make_model.py from 923bed525^ and at head, so the fix currently has no effective regression coverage.

The gate is not needed. The test only counts calls to _auto_hvp_batch, and _cal_hessian_ext_graph calls that once per frame on every device; on CPU it returns 1 immediately without touching the allocator. Removing only this skipif and running on CPU with CUDA_VISIBLE_DEVICES="":

make_model.py from 923bed525^: FAILED ... test_the_batch_is_repriced_for_each_frame, AssertionError: priced 1 times for 2 frames

make_model.py at head: 1 passed

So the test is right; it just cannot run. Please drop the skipif here. The gates on line 202 and on TestHvpBatchPolicy are legitimate, since _auto_hvp_batch short-circuits before the probe on CPU.

wanghan-iapcm · 2026-09-26T07:41:04Z

+            "spin": spin_frame,
+            "charge_spin": charge_spin_frame,
+        }
+        if auto_batch and n_real:


Not blocking, but worth a decision in the code. auto_batch is fixed at line 649, so in the default configuration every frame overwrites hvp_batch with a fresh probe result, discarding whatever the previous frame's OOM ladder found. That is the batch commit 3049b57 was added to preserve: on a multi-frame call whose true ceiling is below the probe's estimate, each frame re-climbs 8 -> 4 -> 2 and pays a forward plus a first-order backward per failed attempt. test_the_surviving_batch_is_kept_for_the_next_frame does not see this because it pins DP_HESSIAN_HVP_BATCH=8, so it exercises only the explicit-value path.

I asked for per-frame pricing, so this is a trade-off between my two earlier points rather than a defect, and repricing per frame is defensible since frames genuinely differ. Either seed the probe with the previous frame's surviving batch (for instance min(probe, hvp_batch) once a ladder has reduced it) or state in the comment that a per-frame re-climb is accepted, and add a test in auto mode either way.

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	fits and buys little on the systems that need it.

	Setting this variable disables automatic sizing and uses the value given;
	out-of-memory errors can still reduce the batch, halving it, warning which
	batch was used, and keeping the surviving batch for the rest of the call. The

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