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【Hackathon 10th Spring No.15】DM2模型复现 - #277

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What changed

  • Add a Paddle implementation of the DM2 structure denoising model, including the NequIP-style denoiser, noise/rattle utilities, edge down-selection, training forward pass, and iterative sampling path.
  • Add an ASE-backed DM2StructureDataset and DM2 SiO2 example configuration under structure_generation/configs/dm2.
  • Register DM2 model and dataset exports, document the new structure generation entry, and add a focused DM2 smoke test.
  • Fix Irreps sorting in the local e3nn Gate helper so DM2's gate construction works at runtime.

Related

Validation

  • python3 -m py_compile ppmat/models/common/e3nn/nn/_gate.py ppmat/models/dm2/dm2.py ppmat/datasets/dm2_dataset.py test/test_dm2_model.py
  • Parsed structure_generation/configs/dm2/dm2_sio2.yaml with PyYAML
  • Ran a targeted DM2 runtime smoke test covering model construction, finite forward loss, and iterative sampling output shapes
  • git diff --cached --check

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@yu0809yu0809 changed the title Add DM2 structure denoising model【Hackathon 10th Spring No.15】DM2模型复现May 2, 2026
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Update after commit 629b630:

  • Moved DM2 diffusion/noise scheduling utilities into ppmat/schedulers via DM2DenoisingScheduler.
  • Added DM2 configs for the RFC-covered systems: dm2_sio2_unconditional.yaml, dm2_sio2_conditional.yaml, and dm2_cuzr.yaml.
  • Added RDF Wasserstein / coordination metric hook for amorphous generation evaluation.
  • Updated structure_generation/sample.py so Sample.sample_params is honored by dataloader-based sampling.
  • Extended DM2 tests for scheduler defaults, fixed-sigma noise, sampling output shape, and optional metric smoke.

Validation run locally:

  • python3 -m py_compile ppmat/schedulers/scheduling_dm2.py ppmat/schedulers/__init__.py ppmat/models/dm2/dm2.py ppmat/datasets/dm2_dataset.py ppmat/metrics/dm2_metric.py ppmat/metrics/__init__.py structure_generation/sample.py test/test_dm2_model.py
  • Parsed all DM2 YAML configs with PyYAML.
  • git diff --check
  • Targeted DM2 runtime smoke: model construction, finite forward loss, scheduler defaults, fixed-sigma noise, and iterative sampling output shapes.
  • Metric smoke: RDF Wasserstein and Si-O coordination on a tiny ASE structure.
  • Real-data shape smoke: official DM2 300-atom SiO2 LAMMPS demo file parses and forward loss is finite.

Note: the code/config/metric path is now in place for RFC review. Final Hackathon numeric acceptance still needs the official uploaded data/checkpoint/log artifacts and long-run parity reports for forward diff, 2+ epoch loss alignment, and sampling metrics.

leeleolay
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这个文件的可视化，表格这里多了下划线，辛苦检查下

Comment threadstructure_generation/configs/dm2/README.md

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辛苦参考其他readme修改下该文件的格式

Comment threadppmat/datasets/dm2_dataset.py

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这里为什么新增了dm2的dataset

Comment threadppmat/models/common/e3nn/nn/_gate.py Outdated
instructions += [tuple(range(i, i + len(irreps_out)))]
i += len(irreps_out)
assert len(irreps_in) == i, (len(irreps_in), i)
irreps_in, p, _ = paddle.sort(x=irreps_in), paddle.argsort(x=irreps_in)

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这里为什么修改

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【Hackathon 10th Spring No.15】DM2模型复现 by yu0809 · Pull Request #277 · PaddlePaddle/PaddleMaterials · GitHub
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【Hackathon 10th Spring No.15】DM2模型复现 - #277
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yu0809 wants to merge 2 commits into
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yu0809:codex/dm2-model
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【Hackathon 10th Spring No.15】DM2模型复现#277
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What changed
  • Add a Paddle implementation of the DM2 structure denoising model, including the NequIP-style denoiser, noise/rattle utilities, edge down-selection, training forward pass, and iterative sampling path.
  • Add an ASE-backed DM2StructureDataset and DM2 SiO2 example configuration under structure_generation/configs/dm2.
  • Register DM2 model and dataset exports, document the new structure generation entry, and add a focused DM2 smoke test.
  • Fix Irreps sorting in the local e3nn Gate helper so DM2's gate construction works at runtime.
Related
Validation
  • python3 -m py_compile ppmat/models/common/e3nn/nn/_gate.py ppmat/models/dm2/dm2.py ppmat/datasets/dm2_dataset.py test/test_dm2_model.py
  • Parsed structure_generation/configs/dm2/dm2_sio2.yaml with PyYAML
  • Ran a targeted DM2 runtime smoke test covering model construction, finite forward loss, and iterative sampling output shapes
  • git diff --cached --check
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Update after commit 629b630:
  • Moved DM2 diffusion/noise scheduling utilities into ppmat/schedulers via DM2DenoisingScheduler.
  • Added DM2 configs for the RFC-covered systems: dm2_sio2_unconditional.yaml, dm2_sio2_conditional.yaml, and dm2_cuzr.yaml.
  • Added RDF Wasserstein / coordination metric hook for amorphous generation evaluation.
  • Updated structure_generation/sample.py so Sample.sample_params is honored by dataloader-based sampling.
  • Extended DM2 tests for scheduler defaults, fixed-sigma noise, sampling output shape, and optional metric smoke.
Validation run locally:
  • python3 -m py_compile ppmat/schedulers/scheduling_dm2.py ppmat/schedulers/__init__.py ppmat/models/dm2/dm2.py ppmat/datasets/dm2_dataset.py ppmat/metrics/dm2_metric.py ppmat/metrics/__init__.py structure_generation/sample.py test/test_dm2_model.py
  • Parsed all DM2 YAML configs with PyYAML.
  • git diff --check
  • Targeted DM2 runtime smoke: model construction, finite forward loss, scheduler defaults, fixed-sigma noise, and iterative sampling output shapes.
  • Metric smoke: RDF Wasserstein and Si-O coordination on a tiny ASE structure.
  • Real-data shape smoke: official DM2 300-atom SiO2 LAMMPS demo file parses and forward loss is finite.
Note: the code/config/metric path is now in place for RFC review. Final Hackathon numeric acceptance still needs the official uploaded data/checkpoint/log artifacts and long-run parity reports for forward diff, 2+ epoch loss alignment, and sampling metrics.
leeleolay
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这个文件的可视化，表格这里多了下划线，辛苦检查下
Comment threadstructure_generation/configs/dm2/README.md
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辛苦参考其他readme修改下该文件的格式
Comment threadppmat/datasets/dm2_dataset.py
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这里为什么新增了dm2的dataset
Comment threadppmat/models/common/e3nn/nn/_gate.py
Outdated

 instructions += [tuple(range(i, i + len(irreps_out)))]
 i += len(irreps_out)
 assert len(irreps_in) == i, (len(irreps_in), i)
 irreps_in, p, _ = paddle.sort(x=irreps_in), paddle.argsort(x=irreps_in)
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这里为什么修改
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【Hackathon 10th Spring No.15】DM2模型复现 by yu0809 · Pull Request #277 · PaddlePaddle/PaddleMaterials · GitHub
Skip to content
【Hackathon 10th Spring No.15】DM2模型复现 - #277
Open
yu0809 wants to merge 2 commits into
PaddlePaddle:developfrom 
yu0809:codex/dm2-model
Open
【Hackathon 10th Spring No.15】DM2模型复现#277
yu0809 wants to merge 2 commits into
PaddlePaddle:developfrom 
yu0809:codex/dm2-model
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@yu0809
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May 2, 2026•

edited

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What changed
  • Add a Paddle implementation of the DM2 structure denoising model, including the NequIP-style denoiser, noise/rattle utilities, edge down-selection, training forward pass, and iterative sampling path.
  • Add an ASE-backed DM2StructureDataset and DM2 SiO2 example configuration under structure_generation/configs/dm2.
  • Register DM2 model and dataset exports, document the new structure generation entry, and add a focused DM2 smoke test.
  • Fix Irreps sorting in the local e3nn Gate helper so DM2's gate construction works at runtime.
Related
Validation
  • python3 -m py_compile ppmat/models/common/e3nn/nn/_gate.py ppmat/models/dm2/dm2.py ppmat/datasets/dm2_dataset.py test/test_dm2_model.py
  • Parsed structure_generation/configs/dm2/dm2_sio2.yaml with PyYAML
  • Ran a targeted DM2 runtime smoke test covering model construction, finite forward loss, and iterative sampling output shapes
  • git diff --cached --check
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@paddle-botpaddle-botBot
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Add DM2 structure denoising model【Hackathon 10th Spring No.15】DM2模型复现May 2, 2026
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Update after commit 629b630:
  • Moved DM2 diffusion/noise scheduling utilities into ppmat/schedulers via DM2DenoisingScheduler.
  • Added DM2 configs for the RFC-covered systems: dm2_sio2_unconditional.yaml, dm2_sio2_conditional.yaml, and dm2_cuzr.yaml.
  • Added RDF Wasserstein / coordination metric hook for amorphous generation evaluation.
  • Updated structure_generation/sample.py so Sample.sample_params is honored by dataloader-based sampling.
  • Extended DM2 tests for scheduler defaults, fixed-sigma noise, sampling output shape, and optional metric smoke.
Validation run locally:
  • python3 -m py_compile ppmat/schedulers/scheduling_dm2.py ppmat/schedulers/__init__.py ppmat/models/dm2/dm2.py ppmat/datasets/dm2_dataset.py ppmat/metrics/dm2_metric.py ppmat/metrics/__init__.py structure_generation/sample.py test/test_dm2_model.py
  • Parsed all DM2 YAML configs with PyYAML.
  • git diff --check
  • Targeted DM2 runtime smoke: model construction, finite forward loss, scheduler defaults, fixed-sigma noise, and iterative sampling output shapes.
  • Metric smoke: RDF Wasserstein and Si-O coordination on a tiny ASE structure.
  • Real-data shape smoke: official DM2 300-atom SiO2 LAMMPS demo file parses and forward loss is finite.
Note: the code/config/metric path is now in place for RFC review. Final Hackathon numeric acceptance still needs the official uploaded data/checkpoint/log artifacts and long-run parity reports for forward diff, 2+ epoch loss alignment, and sampling metrics.
leeleolay
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这个文件的可视化，表格这里多了下划线，辛苦检查下
Comment threadstructure_generation/configs/dm2/README.md
@leeleolayleeleolayMay 8, 2026

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辛苦参考其他readme修改下该文件的格式
Comment threadppmat/datasets/dm2_dataset.py
@leeleolayleeleolayMay 8, 2026

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这里为什么新增了dm2的dataset
Comment threadppmat/models/common/e3nn/nn/_gate.py
Outdated

 instructions += [tuple(range(i, i + len(irreps_out)))]
 i += len(irreps_out)
 assert len(irreps_in) == i, (len(irreps_in), i)
 irreps_in, p, _ = paddle.sort(x=irreps_in), paddle.argsort(x=irreps_in)
@leeleolayleeleolayMay 8, 2026

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这里为什么修改
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【Hackathon 10th Spring No.15】DM2模型复现 by yu0809 · Pull Request #277 · PaddlePaddle/PaddleMaterials · GitHub
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【Hackathon 10th Spring No.15】DM2模型复现 - #277
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yu0809:codex/dm2-model
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【Hackathon 10th Spring No.15】DM2模型复现#277
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What changed
  • Add a Paddle implementation of the DM2 structure denoising model, including the NequIP-style denoiser, noise/rattle utilities, edge down-selection, training forward pass, and iterative sampling path.
  • Add an ASE-backed DM2StructureDataset and DM2 SiO2 example configuration under structure_generation/configs/dm2.
  • Register DM2 model and dataset exports, document the new structure generation entry, and add a focused DM2 smoke test.
  • Fix Irreps sorting in the local e3nn Gate helper so DM2's gate construction works at runtime.
Related
Validation
  • python3 -m py_compile ppmat/models/common/e3nn/nn/_gate.py ppmat/models/dm2/dm2.py ppmat/datasets/dm2_dataset.py test/test_dm2_model.py
  • Parsed structure_generation/configs/dm2/dm2_sio2.yaml with PyYAML
  • Ran a targeted DM2 runtime smoke test covering model construction, finite forward loss, and iterative sampling output shapes
  • git diff --cached --check
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Add DM2 structure denoising model【Hackathon 10th Spring No.15】DM2模型复现May 2, 2026
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Update after commit 629b630:
  • Moved DM2 diffusion/noise scheduling utilities into ppmat/schedulers via DM2DenoisingScheduler.
  • Added DM2 configs for the RFC-covered systems: dm2_sio2_unconditional.yaml, dm2_sio2_conditional.yaml, and dm2_cuzr.yaml.
  • Added RDF Wasserstein / coordination metric hook for amorphous generation evaluation.
  • Updated structure_generation/sample.py so Sample.sample_params is honored by dataloader-based sampling.
  • Extended DM2 tests for scheduler defaults, fixed-sigma noise, sampling output shape, and optional metric smoke.
Validation run locally:
  • python3 -m py_compile ppmat/schedulers/scheduling_dm2.py ppmat/schedulers/__init__.py ppmat/models/dm2/dm2.py ppmat/datasets/dm2_dataset.py ppmat/metrics/dm2_metric.py ppmat/metrics/__init__.py structure_generation/sample.py test/test_dm2_model.py
  • Parsed all DM2 YAML configs with PyYAML.
  • git diff --check
  • Targeted DM2 runtime smoke: model construction, finite forward loss, scheduler defaults, fixed-sigma noise, and iterative sampling output shapes.
  • Metric smoke: RDF Wasserstein and Si-O coordination on a tiny ASE structure.
  • Real-data shape smoke: official DM2 300-atom SiO2 LAMMPS demo file parses and forward loss is finite.
Note: the code/config/metric path is now in place for RFC review. Final Hackathon numeric acceptance still needs the official uploaded data/checkpoint/log artifacts and long-run parity reports for forward diff, 2+ epoch loss alignment, and sampling metrics.
leeleolay
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这个文件的可视化，表格这里多了下划线，辛苦检查下
Comment threadstructure_generation/configs/dm2/README.md
@leeleolayleeleolayMay 8, 2026

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辛苦参考其他readme修改下该文件的格式
Comment threadppmat/datasets/dm2_dataset.py
@leeleolayleeleolayMay 8, 2026

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这里为什么新增了dm2的dataset
Comment threadppmat/models/common/e3nn/nn/_gate.py
Outdated

 instructions += [tuple(range(i, i + len(irreps_out)))]
 i += len(irreps_out)
 assert len(irreps_in) == i, (len(irreps_in), i)
 irreps_in, p, _ = paddle.sort(x=irreps_in), paddle.argsort(x=irreps_in)
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这里为什么修改
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【Hackathon 10th Spring No.15】DM2模型复现 by yu0809 · Pull Request #277 · PaddlePaddle/PaddleMaterials · GitHub
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【Hackathon 10th Spring No.15】DM2模型复现 - #277
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What changed
  • Add a Paddle implementation of the DM2 structure denoising model, including the NequIP-style denoiser, noise/rattle utilities, edge down-selection, training forward pass, and iterative sampling path.
  • Add an ASE-backed DM2StructureDataset and DM2 SiO2 example configuration under structure_generation/configs/dm2.
  • Register DM2 model and dataset exports, document the new structure generation entry, and add a focused DM2 smoke test.
  • Fix Irreps sorting in the local e3nn Gate helper so DM2's gate construction works at runtime.
Related
Validation
  • python3 -m py_compile ppmat/models/common/e3nn/nn/_gate.py ppmat/models/dm2/dm2.py ppmat/datasets/dm2_dataset.py test/test_dm2_model.py
  • Parsed structure_generation/configs/dm2/dm2_sio2.yaml with PyYAML
  • Ran a targeted DM2 runtime smoke test covering model construction, finite forward loss, and iterative sampling output shapes
  • git diff --cached --check
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Update after commit 629b630:
  • Moved DM2 diffusion/noise scheduling utilities into ppmat/schedulers via DM2DenoisingScheduler.
  • Added DM2 configs for the RFC-covered systems: dm2_sio2_unconditional.yaml, dm2_sio2_conditional.yaml, and dm2_cuzr.yaml.
  • Added RDF Wasserstein / coordination metric hook for amorphous generation evaluation.
  • Updated structure_generation/sample.py so Sample.sample_params is honored by dataloader-based sampling.
  • Extended DM2 tests for scheduler defaults, fixed-sigma noise, sampling output shape, and optional metric smoke.
Validation run locally:
  • python3 -m py_compile ppmat/schedulers/scheduling_dm2.py ppmat/schedulers/__init__.py ppmat/models/dm2/dm2.py ppmat/datasets/dm2_dataset.py ppmat/metrics/dm2_metric.py ppmat/metrics/__init__.py structure_generation/sample.py test/test_dm2_model.py
  • Parsed all DM2 YAML configs with PyYAML.
  • git diff --check
  • Targeted DM2 runtime smoke: model construction, finite forward loss, scheduler defaults, fixed-sigma noise, and iterative sampling output shapes.
  • Metric smoke: RDF Wasserstein and Si-O coordination on a tiny ASE structure.
  • Real-data shape smoke: official DM2 300-atom SiO2 LAMMPS demo file parses and forward loss is finite.
Note: the code/config/metric path is now in place for RFC review. Final Hackathon numeric acceptance still needs the official uploaded data/checkpoint/log artifacts and long-run parity reports for forward diff, 2+ epoch loss alignment, and sampling metrics.
leeleolay
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这个文件的可视化，表格这里多了下划线，辛苦检查下
Comment threadstructure_generation/configs/dm2/README.md
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辛苦参考其他readme修改下该文件的格式
Comment threadppmat/datasets/dm2_dataset.py
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这里为什么新增了dm2的dataset
Comment threadppmat/models/common/e3nn/nn/_gate.py
Outdated

 instructions += [tuple(range(i, i + len(irreps_out)))]
 i += len(irreps_out)
 assert len(irreps_in) == i, (len(irreps_in), i)
 irreps_in, p, _ = paddle.sort(x=irreps_in), paddle.argsort(x=irreps_in)
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这里为什么修改
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【Hackathon 10th Spring No.15】DM2模型复现 by yu0809 · Pull Request #277 · PaddlePaddle/PaddleMaterials · GitHub
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【Hackathon 10th Spring No.15】DM2模型复现 - #277
Open
yu0809 wants to merge 2 commits into
PaddlePaddle:developfrom 
yu0809:codex/dm2-model
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【Hackathon 10th Spring No.15】DM2模型复现#277
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What changed
  • Add a Paddle implementation of the DM2 structure denoising model, including the NequIP-style denoiser, noise/rattle utilities, edge down-selection, training forward pass, and iterative sampling path.
  • Add an ASE-backed DM2StructureDataset and DM2 SiO2 example configuration under structure_generation/configs/dm2.
  • Register DM2 model and dataset exports, document the new structure generation entry, and add a focused DM2 smoke test.
  • Fix Irreps sorting in the local e3nn Gate helper so DM2's gate construction works at runtime.
Related
Validation
  • python3 -m py_compile ppmat/models/common/e3nn/nn/_gate.py ppmat/models/dm2/dm2.py ppmat/datasets/dm2_dataset.py test/test_dm2_model.py
  • Parsed structure_generation/configs/dm2/dm2_sio2.yaml with PyYAML
  • Ran a targeted DM2 runtime smoke test covering model construction, finite forward loss, and iterative sampling output shapes
  • git diff --cached --check
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Update after commit 629b630:
  • Moved DM2 diffusion/noise scheduling utilities into ppmat/schedulers via DM2DenoisingScheduler.
  • Added DM2 configs for the RFC-covered systems: dm2_sio2_unconditional.yaml, dm2_sio2_conditional.yaml, and dm2_cuzr.yaml.
  • Added RDF Wasserstein / coordination metric hook for amorphous generation evaluation.
  • Updated structure_generation/sample.py so Sample.sample_params is honored by dataloader-based sampling.
  • Extended DM2 tests for scheduler defaults, fixed-sigma noise, sampling output shape, and optional metric smoke.
Validation run locally:
  • python3 -m py_compile ppmat/schedulers/scheduling_dm2.py ppmat/schedulers/__init__.py ppmat/models/dm2/dm2.py ppmat/datasets/dm2_dataset.py ppmat/metrics/dm2_metric.py ppmat/metrics/__init__.py structure_generation/sample.py test/test_dm2_model.py
  • Parsed all DM2 YAML configs with PyYAML.
  • git diff --check
  • Targeted DM2 runtime smoke: model construction, finite forward loss, scheduler defaults, fixed-sigma noise, and iterative sampling output shapes.
  • Metric smoke: RDF Wasserstein and Si-O coordination on a tiny ASE structure.
  • Real-data shape smoke: official DM2 300-atom SiO2 LAMMPS demo file parses and forward loss is finite.
Note: the code/config/metric path is now in place for RFC review. Final Hackathon numeric acceptance still needs the official uploaded data/checkpoint/log artifacts and long-run parity reports for forward diff, 2+ epoch loss alignment, and sampling metrics.
leeleolay
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这个文件的可视化，表格这里多了下划线，辛苦检查下
Comment threadstructure_generation/configs/dm2/README.md
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辛苦参考其他readme修改下该文件的格式
Comment threadppmat/datasets/dm2_dataset.py
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这里为什么新增了dm2的dataset
Comment threadppmat/models/common/e3nn/nn/_gate.py
Outdated

 instructions += [tuple(range(i, i + len(irreps_out)))]
 i += len(irreps_out)
 assert len(irreps_in) == i, (len(irreps_in), i)
 irreps_in, p, _ = paddle.sort(x=irreps_in), paddle.argsort(x=irreps_in)
@leeleolayleeleolayMay 8, 2026

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这里为什么修改
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【Hackathon 10th Spring No.15】DM2模型复现 by yu0809 · Pull Request #277 · PaddlePaddle/PaddleMaterials · GitHub
Skip to content
【Hackathon 10th Spring No.15】DM2模型复现 - #277
Open
yu0809 wants to merge 2 commits into
PaddlePaddle:developfrom 
yu0809:codex/dm2-model
Open
【Hackathon 10th Spring No.15】DM2模型复现#277
yu0809 wants to merge 2 commits into
PaddlePaddle:developfrom 
yu0809:codex/dm2-model
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What changed
  • Add a Paddle implementation of the DM2 structure denoising model, including the NequIP-style denoiser, noise/rattle utilities, edge down-selection, training forward pass, and iterative sampling path.
  • Add an ASE-backed DM2StructureDataset and DM2 SiO2 example configuration under structure_generation/configs/dm2.
  • Register DM2 model and dataset exports, document the new structure generation entry, and add a focused DM2 smoke test.
  • Fix Irreps sorting in the local e3nn Gate helper so DM2's gate construction works at runtime.
Related
Validation
  • python3 -m py_compile ppmat/models/common/e3nn/nn/_gate.py ppmat/models/dm2/dm2.py ppmat/datasets/dm2_dataset.py test/test_dm2_model.py
  • Parsed structure_generation/configs/dm2/dm2_sio2.yaml with PyYAML
  • Ran a targeted DM2 runtime smoke test covering model construction, finite forward loss, and iterative sampling output shapes
  • git diff --cached --check
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Update after commit 629b630:
  • Moved DM2 diffusion/noise scheduling utilities into ppmat/schedulers via DM2DenoisingScheduler.
  • Added DM2 configs for the RFC-covered systems: dm2_sio2_unconditional.yaml, dm2_sio2_conditional.yaml, and dm2_cuzr.yaml.
  • Added RDF Wasserstein / coordination metric hook for amorphous generation evaluation.
  • Updated structure_generation/sample.py so Sample.sample_params is honored by dataloader-based sampling.
  • Extended DM2 tests for scheduler defaults, fixed-sigma noise, sampling output shape, and optional metric smoke.
Validation run locally:
  • python3 -m py_compile ppmat/schedulers/scheduling_dm2.py ppmat/schedulers/__init__.py ppmat/models/dm2/dm2.py ppmat/datasets/dm2_dataset.py ppmat/metrics/dm2_metric.py ppmat/metrics/__init__.py structure_generation/sample.py test/test_dm2_model.py
  • Parsed all DM2 YAML configs with PyYAML.
  • git diff --check
  • Targeted DM2 runtime smoke: model construction, finite forward loss, scheduler defaults, fixed-sigma noise, and iterative sampling output shapes.
  • Metric smoke: RDF Wasserstein and Si-O coordination on a tiny ASE structure.
  • Real-data shape smoke: official DM2 300-atom SiO2 LAMMPS demo file parses and forward loss is finite.
Note: the code/config/metric path is now in place for RFC review. Final Hackathon numeric acceptance still needs the official uploaded data/checkpoint/log artifacts and long-run parity reports for forward diff, 2+ epoch loss alignment, and sampling metrics.
leeleolay
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这个文件的可视化，表格这里多了下划线，辛苦检查下
Comment threadstructure_generation/configs/dm2/README.md
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辛苦参考其他readme修改下该文件的格式
Comment threadppmat/datasets/dm2_dataset.py
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这里为什么新增了dm2的dataset
Comment threadppmat/models/common/e3nn/nn/_gate.py
Outdated

 instructions += [tuple(range(i, i + len(irreps_out)))]
 i += len(irreps_out)
 assert len(irreps_in) == i, (len(irreps_in), i)
 irreps_in, p, _ = paddle.sort(x=irreps_in), paddle.argsort(x=irreps_in)
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这里为什么修改
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【Hackathon 10th Spring No.15】DM2模型复现 by yu0809 · Pull Request #277 · PaddlePaddle/PaddleMaterials · GitHub
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【Hackathon 10th Spring No.15】DM2模型复现 - #277
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PaddlePaddle:developfrom 
yu0809:codex/dm2-model
Open
【Hackathon 10th Spring No.15】DM2模型复现#277
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What changed
  • Add a Paddle implementation of the DM2 structure denoising model, including the NequIP-style denoiser, noise/rattle utilities, edge down-selection, training forward pass, and iterative sampling path.
  • Add an ASE-backed DM2StructureDataset and DM2 SiO2 example configuration under structure_generation/configs/dm2.
  • Register DM2 model and dataset exports, document the new structure generation entry, and add a focused DM2 smoke test.
  • Fix Irreps sorting in the local e3nn Gate helper so DM2's gate construction works at runtime.
Related
Validation
  • python3 -m py_compile ppmat/models/common/e3nn/nn/_gate.py ppmat/models/dm2/dm2.py ppmat/datasets/dm2_dataset.py test/test_dm2_model.py
  • Parsed structure_generation/configs/dm2/dm2_sio2.yaml with PyYAML
  • Ran a targeted DM2 runtime smoke test covering model construction, finite forward loss, and iterative sampling output shapes
  • git diff --cached --check
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Add DM2 structure denoising model【Hackathon 10th Spring No.15】DM2模型复现May 2, 2026
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Update after commit 629b630:
  • Moved DM2 diffusion/noise scheduling utilities into ppmat/schedulers via DM2DenoisingScheduler.
  • Added DM2 configs for the RFC-covered systems: dm2_sio2_unconditional.yaml, dm2_sio2_conditional.yaml, and dm2_cuzr.yaml.
  • Added RDF Wasserstein / coordination metric hook for amorphous generation evaluation.
  • Updated structure_generation/sample.py so Sample.sample_params is honored by dataloader-based sampling.
  • Extended DM2 tests for scheduler defaults, fixed-sigma noise, sampling output shape, and optional metric smoke.
Validation run locally:
  • python3 -m py_compile ppmat/schedulers/scheduling_dm2.py ppmat/schedulers/__init__.py ppmat/models/dm2/dm2.py ppmat/datasets/dm2_dataset.py ppmat/metrics/dm2_metric.py ppmat/metrics/__init__.py structure_generation/sample.py test/test_dm2_model.py
  • Parsed all DM2 YAML configs with PyYAML.
  • git diff --check
  • Targeted DM2 runtime smoke: model construction, finite forward loss, scheduler defaults, fixed-sigma noise, and iterative sampling output shapes.
  • Metric smoke: RDF Wasserstein and Si-O coordination on a tiny ASE structure.
  • Real-data shape smoke: official DM2 300-atom SiO2 LAMMPS demo file parses and forward loss is finite.
Note: the code/config/metric path is now in place for RFC review. Final Hackathon numeric acceptance still needs the official uploaded data/checkpoint/log artifacts and long-run parity reports for forward diff, 2+ epoch loss alignment, and sampling metrics.
leeleolay
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May 8, 2026
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Comment threadstructure_generation/README.md
@leeleolayleeleolayMay 8, 2026

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这个文件的可视化，表格这里多了下划线，辛苦检查下
Comment threadstructure_generation/configs/dm2/README.md
@leeleolayleeleolayMay 8, 2026

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辛苦参考其他readme修改下该文件的格式
Comment threadppmat/datasets/dm2_dataset.py
@leeleolayleeleolayMay 8, 2026

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这里为什么新增了dm2的dataset
Comment threadppmat/models/common/e3nn/nn/_gate.py
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 instructions += [tuple(range(i, i + len(irreps_out)))]
 i += len(irreps_out)
 assert len(irreps_in) == i, (len(irreps_in), i)
 irreps_in, p, _ = paddle.sort(x=irreps_in), paddle.argsort(x=irreps_in)
@leeleolayleeleolayMay 8, 2026

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这里为什么修改
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