deepmd.dpmodel.utils.network

Native DP model format for multiple backends.

See issue #2982 for more information.

Module Contents

Classes

NativeLayer

Native representation of a layer.

LayerNorm

Implementation of Layer Normalization layer.

NetworkCollection

A collection of networks for multiple elements.

Functions

get_activation_fn(→ Callable[[numpy.ndarray], ...)

make_multilayer_network(T_NetworkLayer, ModuleBase)

make_embedding_network(T_Network, T_NetworkLayer)

make_fitting_network(T_EmbeddingNet, T_Network, ...)

Attributes

__version__

NativeNet

EmbeddingNet

FittingNet

deepmd.dpmodel.utils.network.__version__ = 'unknown'[source]
class deepmd.dpmodel.utils.network.NativeLayer(num_in, num_out, bias: bool = True, use_timestep: bool = False, activation_function: str | None = None, resnet: bool = False, precision: str = DEFAULT_PRECISION)[source]

Bases: deepmd.dpmodel.NativeOP

Native representation of a layer.

Parameters:
wnp.ndarray, optional

The weights of the layer.

bnp.ndarray, optional

The biases of the layer.

idtnp.ndarray, optional

The identity matrix of the layer.

activation_functionstr, optional

The activation function of the layer.

resnetbool, optional

Whether the layer is a residual layer.

serialize() → dict[source]

Serialize the layer to a dict.

Returns:
dict

The serialized layer.

classmethod deserialize(data: dict) → NativeLayer[source]

Deserialize the layer from a dict.

Parameters:
datadict

The dict to deserialize from.

check_shape_consistency()[source]
check_type_consistency()[source]
__setitem__(key, value)[source]
__getitem__(key)[source]
dim_in() → int[source]
dim_out() → int[source]
call(x: numpy.ndarray) → numpy.ndarray[source]

Forward pass.

Parameters:
xnp.ndarray

The input.

Returns:
np.ndarray

The output.

deepmd.dpmodel.utils.network.get_activation_fn(activation_function: str) → Callable[[numpy.ndarray], numpy.ndarray][source]
class deepmd.dpmodel.utils.network.LayerNorm(num_in: int, eps: float = 1e-05, uni_init: bool = True, trainable: bool = True, precision: str = DEFAULT_PRECISION)[source]

Bases: NativeLayer

Implementation of Layer Normalization layer.

Parameters:
num_inint

The input dimension of the layer.

epsfloat, optional

A small value added to prevent division by zero in calculations.

uni_initbool, optional

If initialize the weights to be zeros and ones.

serialize() → dict[source]

Serialize the layer to a dict.

Returns:
dict

The serialized layer.

classmethod deserialize(data: dict) → LayerNorm[source]

Deserialize the layer from a dict.

Parameters:
datadict

The dict to deserialize from.

_check_shape_consistency()[source]
__setitem__(key, value)[source]
__getitem__(key)[source]
dim_out() → int[source]
call(x: numpy.ndarray) → numpy.ndarray[source]

Forward pass.

Parameters:
xnp.ndarray

The input.

Returns:
np.ndarray

The output.

static layer_norm_numpy(x, shape, weight=None, bias=None, eps=1e-05)[source]
deepmd.dpmodel.utils.network.make_multilayer_network(T_NetworkLayer, ModuleBase)[source]
deepmd.dpmodel.utils.network.NativeNet[source]
deepmd.dpmodel.utils.network.make_embedding_network(T_Network, T_NetworkLayer)[source]
deepmd.dpmodel.utils.network.EmbeddingNet[source]
deepmd.dpmodel.utils.network.make_fitting_network(T_EmbeddingNet, T_Network, T_NetworkLayer)[source]
deepmd.dpmodel.utils.network.FittingNet[source]
class deepmd.dpmodel.utils.network.NetworkCollection(ndim: int, ntypes: int, network_type: str = 'network', networks: List[NativeNet | dict] = [])[source]

A collection of networks for multiple elements.

The number of dimesions for types might be 0, 1, or 2. - 0: embedding or fitting with type embedding, in () - 1: embedding with type_one_side, or fitting, in (type_i) - 2: embedding without type_one_side, in (type_i, type_j)

Parameters:
ndimint

The number of dimensions.

network_typestr, optional

The type of the network.

networksdict, optional

The networks to initialize with.

NETWORK_TYPE_MAP: ClassVar[Dict[str, type]][source]
check_completeness()[source]

Check whether the collection is complete.

Raises:
RuntimeError

If the collection is incomplete.

_convert_key(key)[source]
__getitem__(key)[source]
__setitem__(key, value)[source]
serialize() → dict[source]

Serialize the networks to a dict.

Returns:
dict

The serialized networks.

classmethod deserialize(data: dict) → NetworkCollection[source]

Deserialize the networks from a dict.

Parameters:
datadict

The dict to deserialize from.