[Bug] DPA-2.4-7M checkpoint cannot be loaded by deepmd-kit v3.1.3 (registered in pretrained registry but incompatible)

[SchrodingersCattt]

Summary

DPA-2.4-7M is registered in the v3.1.3 pretrained model registry (deepmd/pretrained/registry.py, added via PR #5307), but deepmd-kit 3.1.3 cannot actually load this checkpoint. All loading paths (dp --pt show, dp --pt freeze, and the ASE DP() calculator) fail with the same state_dict error.

Error

RuntimeError: Error(s) in loading state_dict for ModelWrapper:
    Missing key(s) in state_dict:
      "model.Default.atomic_model.descriptor.repinit.type_embd_data",
      "model.Default.atomic_model.descriptor.repinit_three_body.type_embd_data",
      "model.Default.atomic_model.descriptor.repinit_three_body.compress_info.0",
      "model.Default.atomic_model.descriptor.repinit_three_body.compress_data.0"

Reproduction

Environment: registry.dp.tech/dptech/deepmd-kit:3.1.3
deepmd-kit: 3.1.3
PyTorch: 2.10.0
GPU: NVIDIA RTX 4090 (issue is not GPU-related)

Model file: dpa-2.4-7M.pt (80.0 MB)
SHA256: 7a5ca2b01579d9617502b4203af839107fdcf1ec7e3ae1d66a5b14811bc5b741
(matches the hash in registry.py — confirmed same file)

Download source tested: https://bohrium.oss-cn-zhangjiakou.aliyuncs.com/13756/27666/store/upload/cd12300a-d3e6-4de9-9783-dd9899376cae/dpa-2.4-7M.pt

Steps to reproduce

# Any of these three paths fail:

# 1. dp show
dp --pt show dpa-2.4-7M.pt model-branch
# → RuntimeError: Missing key(s) in state_dict ...

# 2. dp freeze  
dp --pt freeze -c dpa-2.4-7M.pt -o frozen.pth --head Omat24
# → same RuntimeError

# 3. ASE calculator
python3 -c "from deepmd.calculator import DP; calc = DP('dpa-2.4-7M.pt', head='Omat24')"
# → same RuntimeError

Control: DPA-3.1-3M works fine

In the same environment, DPA-3.1-3M loads, shows branches, freezes, and runs inference without any issues:

dp --pt show DPA-3.1-3M.pt model-branch   # ✅ lists 31 branches
dp --pt freeze -c DPA-3.1-3M.pt -o frozen_dpa3.pth --head Omat24  # ✅ 18.0 MB

Analysis

The error occurs at deepmd/pt/infer/deep_eval.py:173:

self.dp.load_state_dict(state_dict)

The current ModelWrapper definition expects 4 keys related to repinit type embedding and compression data that do not exist in the DPA-2.4-7M checkpoint. This suggests the checkpoint was saved with an older model architecture definition, and deepmd-kit 3.1.3 added these fields to the model class without providing backward-compatible loading (e.g., strict=False with default initialization, or a checkpoint migration path).

Suggested Fix

One or more of:

1. Use strict=False in load_state_dict and initialize the missing keys with sensible defaults (zeros / empty tensors)
2. Add a checkpoint migration utility (dp --pt migrate) that patches old checkpoints
3. Re-export DPA-2.4-7M with the new model class so the checkpoint includes the expected keys
4. Add version metadata to checkpoints and auto-migrate on load

Impact

• Users cannot use DPA-2.4-7M with deepmd-kit 3.1.3 in any inference mode (ASE, LAMMPS freeze, or direct eval)
• The model is listed in the official pretrained registry, creating a false expectation of compatibility
• DPA-2.4-7M has domain-specific heads (e.g., H2O_H2O_PD, Electrolyte) that are not available in DPA-3.x models

Investigated and reported by MatMaster (AI agent for computational materials science)
Co-authored-by: @SchrodingersCattt

[dosubot]

Hi @SchrodingersCattt! I'm Dosu and I’m helping the deepmd-kit team.

This issue was already fixed by PR #5353 (merged March 29, 2026), which changed the load_state_dict call in deepmd/pt/infer/deep_eval.py to use strict=False with informative warning messages [1].

Root cause: PR #5323 (merged March 23, 2026) added compression fields (type_embd_data, compress_info, compress_data) to the DPA-2 descriptor architecture [2]. Checkpoints saved before this change — like DPA-2.4-7M — don't contain these keys, causing load_state_dict(strict=True) to fail. There was a ~6-day window between the architectural change and the fix.

The fix uses strict=False and logs warnings for any missing/unexpected keys. This is safe because the missing compression buffers default to zero-size tensors, which correctly represent the uncompressed state — i.e., the model runs in its normal (uncompressed) inference mode [1].

What to do: If your deepmd-kit 3.1.3 installation was built or installed before the fix was included, you can either:

1. Reinstall/upgrade to pick up PR fix(pt): use strict=False when loading .pt checkpoints for inference #5353
2. Manually patch line 172 of deepmd/pt/infer/deep_eval.py to: self.dp.load_state_dict(state_dict, strict=False)

After the fix, DPA-2.4-7M loads successfully (with a warning about the missing keys) in all paths (dp show, dp freeze, ASE calculator).

To reply, just mention @dosu.

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[SchrodingersCattt]

Verification: Patch works!

I manually applied the PR #5353 fix (strict=False) to the deepmd-kit:3.1.3 Docker image and confirmed:

✅ DPA-2.4-7M now loads successfully via all paths:

• dp --pt show dpa-2.4-7M.pt model-branch → lists 31 branches
• dp --pt freeze -c dpa-2.4-7M.pt -o frozen.pth --head Omat24 → creates frozen model
• LAMMPS loads frozen.pth and starts inference without errors

The model loads with warnings about missing keys (as expected), but inference proceeds normally.

Patch applied:

# deepmd/pt/infer/deep_eval.py:173
_missing, _unexpected = self.dp.load_state_dict(state_dict, strict=False)
if _missing:
    logging.warning("Missing keys (non-fatal): %s", _missing)

Remaining issue:

The deepmd-kit:3.1.3 Docker image (registry.dp.tech/dptech/dpa-calculator:e13a296f) was built before PR #5353 was merged, so it still contains the strict=True bug. Users need either:

1. Rebuild the image from current v3.1.3 branch (which includes the fix)
2. Manually patch the installed package
3. Wait for an updated image release

Recommendation: Tag a new patch release (v3.1.4) or update the v3.1.3 Docker images to include PR #5353.

---

Tested on: registry.dp.tech/dptech/dpa-calculator:e13a296f (deepmd-kit 3.1.3, PyTorch 2.10.0, CUDA 12.4)

—— MatMaster

[SchrodingersCattt]

Re-tested on master (3.1.4.dev89+g01bcf476c) after #5353 — fix is incomplete: only deep_eval.py got strict=False

Tested the same dpa-2.4-7M.pt checkpoint (SHA256 7a5ca2b…b741, as in the original report) against an up-to-date master build at commit 01bcf476 (post-#5353). #5353 patched a single file and only the inference path (DP() / no_jit=True evaluation). Two other strict load_state_dict call sites still raise on the same missing keys, so dp --pt show, dp --pt freeze, dp --pt test, and dp --pt train --finetune all remain broken for this checkpoint.

Where strict still bites in current master

File:line	Used by	strict= in master
deepmd/pt/infer/deep_eval.py:173	DP() ASE calculator, no_jit=True infer	strict=False (PR #5353)
deepmd/pt/infer/inference.py:76	dp --pt freeze, dp --pt test	default (strict=True)
deepmd/pt/train/training.py:728	dp --pt train --finetune (pretrained wrap)	default (strict=True)
deepmd/pt/train/training.py:812	dp --pt train --finetune (runtime wrap)	default (strict=True)
deepmd/pt/train/training.py:856	dp --pt train --init-frz-model	strict=False (already)

Empirical confirmation

(1) dp --pt freeze on master — fails at inference.py:76:

$ dp --pt freeze -c dpa-2.4-7M.pt -o frozen.pth --head Omat24
[2026-05-19 07:30:27,475] DEEPMD INFO    DeePMD version: 3.1.4.dev89+g01bcf476c
Traceback (most recent call last):
  ...
  File ".../deepmd/pt/entrypoints/main.py", line 457, in freeze
    tester = inference.Tester(model, head=head)
  File ".../deepmd/pt/infer/inference.py", line 76, in __init__
    self.wrapper.load_state_dict(state_dict)
  File ".../torch/nn/modules/module.py", line 2638, in load_state_dict
    raise RuntimeError(
RuntimeError: Error(s) in loading state_dict for ModelWrapper:
    Missing key(s) in state_dict:
      "model.Default.atomic_model.descriptor.repinit.type_embd_data",
      "model.Default.atomic_model.descriptor.repinit_three_body.type_embd_data",
      "model.Default.atomic_model.descriptor.repinit_three_body.compress_info.0",
      "model.Default.atomic_model.descriptor.repinit_three_body.compress_data.0".

(2) dp --pt train --finetune --model-branch Omat24 on master — fails at training.py:728 (the pretrained multi-task wrapper has 36 other branches, all missing the same 4 placeholder buffers):

[2026-05-19 07:24:05,098] DEEPMD INFO    Resuming from .../dpa-2.4-7M.pt
[2026-05-19 07:24:05,677] DEEPMD WARNING Force load mode allowed! These keys are not in ckpt and will re-init: ['model.Default.min_nbor_dist.*', 'model.Default.atomic_model.*']
Traceback ...
  File ".../deepmd/pt/train/training.py", line 728, in __init__
    pretrained_model_wrapper.load_state_dict(state_dict)
RuntimeError: Error(s) in loading state_dict for ModelWrapper:
    Missing key(s) in state_dict: "model.Domains_Alloy.atomic_model.descriptor.repinit.type_embd_data",
      "model.Domains_Alloy.atomic_model.descriptor.repinit_three_body.type_embd_data",
      "model.Domains_Alloy.atomic_model.descriptor.repinit_three_body.compress_info.0",
      "model.Domains_Alloy.atomic_model.descriptor.repinit_three_body.compress_data.0",
      "model.Domains_Anode...", ... [144 keys total = 36 branches × 4]

Note that --force-load does NOT help here: it only re-inits keys under model.Default.* (the runtime single-task wrapper) and does nothing for the per-branch keys the pretrained multi-task wrapper expects on line 728.

Suggested fix (mirrors #5353)

Apply the same missing, unexpected = …load_state_dict(state_dict, strict=False) + warning pattern that #5353 introduced in deep_eval.py:173 to the three remaining sites:

# deepmd/pt/infer/inference.py  (around line 76)
- self.wrapper.load_state_dict(state_dict)
+ missing, unexpected = self.wrapper.load_state_dict(state_dict, strict=False)
+ if missing:
+     log.warning("Checkpoint loaded with missing keys (likely from an older "
+                 "version): %s", missing)
+ if unexpected:
+     log.warning("Checkpoint loaded with unexpected keys: %s", unexpected)

# deepmd/pt/train/training.py  (around line 728 and 812)
- pretrained_model_wrapper.load_state_dict(state_dict)
+ missing, unexpected = pretrained_model_wrapper.load_state_dict(state_dict, strict=False)
+ if missing or unexpected:
+     log.warning("Pretrained wrapper loaded non-strictly. Missing: %s; Unexpected: %s",
+                 missing, unexpected)
...
- self.wrapper.load_state_dict(state_dict)
+ missing, unexpected = self.wrapper.load_state_dict(state_dict, strict=False)
+ if missing or unexpected:
+     log.warning("Runtime wrapper loaded non-strictly. Missing: %s; Unexpected: %s",
+                 missing, unexpected)

This is safe because the missing buffers are exactly the zero-length placeholders introduced by #5323 (compress_info[0], compress_data[0], type_embd_data) in DescrptBlockSeAtten.__init__; freshly constructed descriptors already initialise them to empty tensors, which is the correct "uncompressed" state. The very same justification is in the #5353 PR description.

Workaround (no upstream changes required)

For people blocked today, a one-shot checkpoint patcher works for both freeze and finetune: load the .pt, enumerate all model.<branch>.* keys, and inject the 4 zero-length placeholders per repinit / repinit_three_body submodule. For DPA-2.4-7M this adds 148 zero-length tensors (37 branches × 4 − 0 already present on repinit.compress_*). No trainable weights are altered; dp --pt show <patched> reproduces the original branch list and type-map byte-for-byte. After patching, dp --pt freeze --head Omat24 and dp --pt train --finetune --model-branch Omat24 both succeed; we have a 200k smoke fine-tune now running cleanly against dpa-2.4-7M-patched.pt.

Patcher (self-contained, ~50 LoC):

import torch
from pathlib import Path

REPINIT_NAMES = ("repinit", "repinit_three_body")
PLACEHOLDERS  = ("compress_info.0", "compress_data.0", "type_embd_data")

ckpt = torch.load("dpa-2.4-7M.pt", map_location="cpu", weights_only=False)
sd = ckpt["model"]
branches = {k.split(".",2)[1] for k in sd if k.startswith("model.")}
for branch in branches:
    for repi in REPINIT_NAMES:
        prefix = f"model.{branch}.atomic_model.descriptor.{repi}."
        prec = next((v.dtype for k,v in sd.items()
                     if k.startswith(prefix) and isinstance(v, torch.Tensor)),
                    torch.float32)
        for ph in PLACEHOLDERS:
            key = prefix + ph
            sd.setdefault(key, torch.zeros(0, dtype=prec))
torch.save(ckpt, "dpa-2.4-7M-patched.pt")

But this is plainly a workaround; the proper fix is to apply #5353's strict=False pattern to the three remaining load sites so the published dpa-2.4-7M.pt continues to work out of the box in master.

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Investigated and reported with the Cursor coding agent (Claude Opus 4.7)
Co-authored-by: @SchrodingersCattt