deepmd.pd.model.descriptor.repformers

deepmd.pd.model.descriptor.repformers#

Classes#

DescrptBlockRepformers

The building block of descriptor.

Functions#

border_op(→ paddle.Tensor)

Module Contents#

deepmd.pd.model.descriptor.repformers.border_op(argument0: paddle.Tensor, argument1: paddle.Tensor, argument2: paddle.Tensor, argument3: paddle.Tensor, argument4: paddle.Tensor, argument5: paddle.Tensor, argument6: paddle.Tensor, argument7: paddle.Tensor, argument8: paddle.Tensor) → paddle.Tensor[source]#

class deepmd.pd.model.descriptor.repformers.DescrptBlockRepformers(rcut: float, rcut_smth: float, sel: int, ntypes: int, nlayers: int = 3, g1_dim: int = 128, g2_dim: int = 16, axis_neuron: int = 4, direct_dist: bool = False, update_g1_has_conv: bool = True, update_g1_has_drrd: bool = True, update_g1_has_grrg: bool = True, update_g1_has_attn: bool = True, update_g2_has_g1g1: bool = True, update_g2_has_attn: bool = True, update_h2: bool = False, attn1_hidden: int = 64, attn1_nhead: int = 4, attn2_hidden: int = 16, attn2_nhead: int = 4, attn2_has_gate: bool = False, activation_function: str = 'tanh', update_style: str = 'res_avg', update_residual: float = 0.001, update_residual_init: str = 'norm', set_davg_zero: bool = True, smooth: bool = True, exclude_types: list[tuple[int, int]] = [], env_protection: float = 0.0, precision: str = 'float64', trainable_ln: bool = True, ln_eps: float | None = 1e-05, seed: int | list[int] | None = None, use_sqrt_nnei: bool = True, g1_out_conv: bool = True, g1_out_mlp: bool = True, trainable: bool = True)[source]#

Bases: deepmd.pd.model.descriptor.descriptor.DescriptorBlock

The building block of descriptor. Given the input descriptor, provide with the atomic coordinates, atomic types and neighbor list, calculate the new descriptor.

rcut[source]#

rcut_smth[source]#

ntypes[source]#

nlayers = 3[source]#

nnei[source]#

ndescrpt[source]#

sel[source]#

sec[source]#

split_sel[source]#

axis_neuron = 4[source]#

set_davg_zero = True[source]#

g1_dim = 128[source]#

g2_dim = 16[source]#

update_g1_has_conv = True[source]#

update_g1_has_drrd = True[source]#

update_g1_has_grrg = True[source]#

update_g1_has_attn = True[source]#

update_g2_has_g1g1 = True[source]#

update_g2_has_attn = True[source]#

update_h2 = False[source]#

attn1_hidden = 64[source]#

attn1_nhead = 4[source]#

attn2_has_gate = False[source]#

attn2_hidden = 16[source]#

attn2_nhead = 4[source]#

activation_function = 'tanh'[source]#

update_style = 'res_avg'[source]#

update_residual = 0.001[source]#

update_residual_init = 'norm'[source]#

direct_dist = False[source]#

act[source]#

smooth = True[source]#

use_sqrt_nnei = True[source]#

g1_out_conv = True[source]#

g1_out_mlp = True[source]#

env_protection = 0.0[source]#

precision = 'float64'[source]#

prec[source]#

trainable_ln = True[source]#

ln_eps = 1e-05[source]#

epsilon = 0.0001[source]#

seed = None[source]#

g2_embd[source]#

layers[source]#

stats = None[source]#

get_rcut() → float[source]#: Returns the cut-off radius.

get_rcut_smth() → float[source]#: Returns the radius where the neighbor information starts to smoothly decay to 0.

get_buffer_rcut() → paddle.Tensor[source]#: Returns the cut-off radius as a buffer-style Tensor.

get_buffer_rcut_smth() → paddle.Tensor[source]#: Returns the radius where the neighbor information starts to smoothly decay to 0 as a buffer-style Tensor.

get_nsel() → int[source]#: Returns the number of selected atoms in the cut-off radius.

get_sel() → list[int][source]#: Returns the number of selected atoms for each type.

get_ntypes() → int[source]#: Returns the number of element types.

get_dim_out() → int[source]#: Returns the output dimension.

get_dim_in() → int[source]#: Returns the input dimension.

get_dim_emb() → int[source]#: Returns the embedding dimension g2.

__setitem__(key: str, value: paddle.Tensor) → None[source]#

__getitem__(key: str) → paddle.Tensor[source]#

mixed_types() → bool[source]#

If true, the descriptor 1. assumes total number of atoms aligned across frames; 2. requires a neighbor list that does not distinguish different atomic types.

If false, the descriptor 1. assumes total number of atoms of each atom type aligned across frames; 2. requires a neighbor list that distinguishes different atomic types.

get_env_protection() → float[source]#: Returns the protection of building environment matrix.

property dim_out: int[source]#: Returns the output dimension of this descriptor.

property dim_in: int[source]#: Returns the atomic input dimension of this descriptor.

property dim_emb: int[source]#: Returns the embedding dimension g2.

reinit_exclude(exclude_types: list[tuple[int, int]] = []) → None[source]#

forward(nlist: paddle.Tensor, extended_coord: paddle.Tensor, extended_atype: paddle.Tensor, extended_atype_embd: paddle.Tensor | None = None, mapping: paddle.Tensor | None = None, type_embedding: paddle.Tensor | None = None, comm_dict: list[paddle.Tensor] | None = None) → paddle.Tensor[source]#: Calculate DescriptorBlock.

compute_input_stats(merged: collections.abc.Callable[[], list[dict]] | list[dict], path: deepmd.utils.path.DPPath | None = None) → None[source]#

Compute the input statistics (e.g. mean and stddev) for the descriptors from packed data.

Parameters:

mergedUnion[Callable[[], list[dict]], list[dict]]

list[dict]: A list of data samples from various data systems.
Each element, merged[i], is a data dictionary containing keys: paddle.Tensor originating from the i-th data system.
Callable[[], list[dict]]: A lazy function that returns data samples in the above format
only when needed. Since the sampling process can be slow and memory-intensive, the lazy function helps by only sampling once.

pathOptional[DPPath]

The path to the stat file.

get_stats() → dict[str, deepmd.utils.env_mat_stat.StatItem][source]#: Get the statistics of the descriptor.

has_message_passing() → bool[source]#: Returns whether the descriptor block has message passing.

need_sorted_nlist_for_lower() → bool[source]#: Returns whether the descriptor block needs sorted nlist when using forward_lower.