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docs(pt): examples for new dpa2 model (deepmodeling#4138)
small: 3 layers; w three-body; wo g2 attn; medium: 6 layers; w three-body; w g2 attn; large: 12 layers; w three-body; w g2 attn; <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit - **New Features** - Introduced comprehensive JSON configuration files for the DPA2 model, enhancing setup for molecular simulations. - Added detailed README documentation outlining model configurations and input files, aiding user selection based on precision and efficiency needs. - Added parameters for three-body interactions to improve model accuracy. - Configured learning rate settings and loss function preferences for better training dynamics. - **Bug Fixes** - Expanded test coverage by including multiple input file variations for the DPA2 example, ensuring more robust testing. - **Documentation** - Updated training example reference for clarity and included links to README for input variations. <!-- end of auto-generated comment: release notes by coderabbit.ai -->
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| ## Inputs for DPA-2 model | ||
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| This directory contains the input files for training the DPA-2 model (currently supporting PyTorch backend only). Depending on your precision/efficiency requirements, we provide three different levels of model complexity: | ||
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| - `input_torch_small.json`: Our smallest DPA-2 model, optimized for speed. | ||
| - `input_torch_medium.json` (Recommended): Our well-performing DPA-2 model, balancing efficiency and precision. This is a good starting point for most users. | ||
| - `input_torch_large.json`: Our most complex model with the highest precision, suitable for very intricate data structures. | ||
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| For detailed differences in their configurations, please refer to the table below: | ||
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| | Input | Repformer layers | Three-body embedding in Repinit | Pair-wise attention in Repformer | Tuned sub-structures in [#4089](https://github.com/deepmodeling/deepmd-kit/pull/4089) | Description | | ||
| | ------------------------- | ---------------- | ------------------------------- | -------------------------------- | ------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------- | | ||
| | `input_torch_small.json` | 3 | ✓ | ✗ | ✓ | Smallest DPA-2 model, optimized for speed. | | ||
| | `input_torch_medium.json` | 6 | ✓ | ✓ | ✓ | Recommended well-performing DPA-2 model, balancing efficiency and precision. | | ||
| | `input_torch_large.json` | 12 | ✓ | ✓ | ✓ | Most complex model with the highest precision. | |
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| { | ||
| "_comment": "that's all", | ||
| "model": { | ||
| "type_map": [ | ||
| "O", | ||
| "H" | ||
| ], | ||
| "descriptor": { | ||
| "type": "dpa2", | ||
| "repinit": { | ||
| "tebd_dim": 8, | ||
| "rcut": 6.0, | ||
| "rcut_smth": 0.5, | ||
| "nsel": 120, | ||
| "neuron": [ | ||
| 25, | ||
| 50, | ||
| 100 | ||
| ], | ||
| "axis_neuron": 12, | ||
| "activation_function": "tanh", | ||
| "three_body_sel": 40, | ||
| "three_body_rcut": 4.0, | ||
| "three_body_rcut_smth": 3.5, | ||
| "use_three_body": true | ||
| }, | ||
| "repformer": { | ||
| "rcut": 4.0, | ||
| "rcut_smth": 3.5, | ||
| "nsel": 40, | ||
| "nlayers": 6, | ||
| "g1_dim": 128, | ||
| "g2_dim": 32, | ||
| "attn2_hidden": 32, | ||
| "attn2_nhead": 4, | ||
| "attn1_hidden": 128, | ||
| "attn1_nhead": 4, | ||
| "axis_neuron": 4, | ||
| "update_h2": false, | ||
| "update_g1_has_conv": true, | ||
| "update_g1_has_grrg": true, | ||
| "update_g1_has_drrd": true, | ||
| "update_g1_has_attn": false, | ||
| "update_g2_has_g1g1": false, | ||
| "update_g2_has_attn": true, | ||
| "update_style": "res_residual", | ||
| "update_residual": 0.01, | ||
| "update_residual_init": "norm", | ||
| "attn2_has_gate": true, | ||
| "use_sqrt_nnei": true, | ||
| "g1_out_conv": true, | ||
| "g1_out_mlp": true | ||
| }, | ||
| "add_tebd_to_repinit_out": false | ||
| }, | ||
| "fitting_net": { | ||
| "neuron": [ | ||
| 240, | ||
| 240, | ||
| 240 | ||
| ], | ||
| "resnet_dt": true, | ||
| "seed": 1, | ||
| "_comment": " that's all" | ||
| }, | ||
| "_comment": " that's all" | ||
| }, | ||
| "learning_rate": { | ||
| "type": "exp", | ||
| "decay_steps": 5000, | ||
| "start_lr": 0.001, | ||
| "stop_lr": 3.51e-08, | ||
| "_comment": "that's all" | ||
| }, | ||
| "loss": { | ||
| "type": "ener", | ||
| "start_pref_e": 0.02, | ||
| "limit_pref_e": 1, | ||
| "start_pref_f": 1000, | ||
| "limit_pref_f": 1, | ||
| "start_pref_v": 0, | ||
| "limit_pref_v": 0, | ||
| "_comment": " that's all" | ||
| }, | ||
| "training": { | ||
| "stat_file": "./dpa2.hdf5", | ||
| "training_data": { | ||
| "systems": [ | ||
| "../data/data_0", | ||
| "../data/data_1", | ||
| "../data/data_2" | ||
| ], | ||
| "batch_size": 1, | ||
| "_comment": "that's all" | ||
| }, | ||
| "validation_data": { | ||
| "systems": [ | ||
| "../data/data_3" | ||
| ], | ||
| "batch_size": 1, | ||
| "_comment": "that's all" | ||
| }, | ||
| "numb_steps": 1000000, | ||
| "warmup_steps": 0, | ||
| "gradient_max_norm": 5.0, | ||
| "seed": 10, | ||
| "disp_file": "lcurve.out", | ||
| "disp_freq": 100, | ||
| "save_freq": 2000, | ||
| "_comment": "that's all" | ||
| } | ||
| } |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,112 @@ | ||
| { | ||
| "_comment": "that's all", | ||
| "model": { | ||
| "type_map": [ | ||
| "O", | ||
| "H" | ||
| ], | ||
| "descriptor": { | ||
| "type": "dpa2", | ||
| "repinit": { | ||
| "tebd_dim": 8, | ||
| "rcut": 6.0, | ||
| "rcut_smth": 0.5, | ||
| "nsel": 120, | ||
| "neuron": [ | ||
| 25, | ||
| 50, | ||
| 100 | ||
| ], | ||
| "axis_neuron": 12, | ||
| "activation_function": "tanh", | ||
| "three_body_sel": 40, | ||
| "three_body_rcut": 4.0, | ||
| "three_body_rcut_smth": 3.5, | ||
| "use_three_body": true | ||
| }, | ||
| "repformer": { | ||
| "rcut": 4.0, | ||
| "rcut_smth": 3.5, | ||
| "nsel": 40, | ||
| "nlayers": 3, | ||
| "g1_dim": 128, | ||
| "g2_dim": 32, | ||
| "attn2_hidden": 32, | ||
| "attn2_nhead": 4, | ||
| "attn1_hidden": 128, | ||
| "attn1_nhead": 4, | ||
| "axis_neuron": 4, | ||
| "update_h2": false, | ||
| "update_g1_has_conv": true, | ||
| "update_g1_has_grrg": true, | ||
| "update_g1_has_drrd": true, | ||
| "update_g1_has_attn": false, | ||
| "update_g2_has_g1g1": false, | ||
| "update_g2_has_attn": false, | ||
| "update_style": "res_residual", | ||
| "update_residual": 0.01, | ||
| "update_residual_init": "norm", | ||
| "attn2_has_gate": true, | ||
| "use_sqrt_nnei": true, | ||
| "g1_out_conv": true, | ||
| "g1_out_mlp": true | ||
| }, | ||
| "add_tebd_to_repinit_out": false | ||
| }, | ||
| "fitting_net": { | ||
| "neuron": [ | ||
| 240, | ||
| 240, | ||
| 240 | ||
| ], | ||
| "resnet_dt": true, | ||
| "seed": 1, | ||
| "_comment": " that's all" | ||
| }, | ||
| "_comment": " that's all" | ||
| }, | ||
| "learning_rate": { | ||
| "type": "exp", | ||
| "decay_steps": 5000, | ||
| "start_lr": 0.001, | ||
| "stop_lr": 3.51e-08, | ||
| "_comment": "that's all" | ||
| }, | ||
| "loss": { | ||
| "type": "ener", | ||
| "start_pref_e": 0.02, | ||
| "limit_pref_e": 1, | ||
| "start_pref_f": 1000, | ||
| "limit_pref_f": 1, | ||
| "start_pref_v": 0, | ||
| "limit_pref_v": 0, | ||
| "_comment": " that's all" | ||
| }, | ||
| "training": { | ||
| "stat_file": "./dpa2.hdf5", | ||
| "training_data": { | ||
| "systems": [ | ||
| "../data/data_0", | ||
| "../data/data_1", | ||
| "../data/data_2" | ||
| ], | ||
| "batch_size": 1, | ||
| "_comment": "that's all" | ||
| }, | ||
| "validation_data": { | ||
| "systems": [ | ||
| "../data/data_3" | ||
| ], | ||
| "batch_size": 1, | ||
| "_comment": "that's all" | ||
| }, | ||
| "numb_steps": 1000000, | ||
| "warmup_steps": 0, | ||
| "gradient_max_norm": 5.0, | ||
| "seed": 10, | ||
| "disp_file": "lcurve.out", | ||
| "disp_freq": 100, | ||
| "save_freq": 2000, | ||
| "_comment": "that's all" | ||
| } | ||
| } |
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