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tf: add explict mixed_types argument to fittings #3583

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Mar 22, 2024
Merged

tf: add explict mixed_types argument to fittings #3583

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4b80d99
tf: add explict mixed_types argument to fittings
njzjz Mar 21, 2024
7261aa4
fix codeql warnings
njzjz Mar 21, 2024
89e640c
fix mixed_types argument for se_atten
njzjz Mar 21, 2024
2a61e3c
keep old behaviors
njzjz Mar 21, 2024
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53 changes: 31 additions & 22 deletions deepmd/tf/fit/dipole.py
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Original file line number Diff line number Diff line change
Expand Up @@ -62,6 +62,9 @@ class DipoleFittingSeA(Fitting):
The precision of the embedding net parameters. Supported options are |PRECISION|
uniform_seed
Only for the purpose of backward compatibility, retrieves the old behavior of using the random seed
mixed_types : bool
If true, use a uniform fitting net for all atom types, otherwise use
different fitting nets for different atom types.
"""

def __init__(
Expand All @@ -76,6 +79,7 @@ def __init__(
activation_function: str = "tanh",
precision: str = "default",
uniform_seed: bool = False,
mixed_types: bool = False,
**kwargs,
) -> None:
"""Constructor."""
Expand All @@ -100,6 +104,7 @@ def __init__(
self.useBN = False
self.fitting_net_variables = None
self.mixed_prec = None
self.mixed_types = mixed_types

def get_sel_type(self) -> int:
"""Get selected type."""
Expand All @@ -109,6 +114,7 @@ def get_out_size(self) -> int:
"""Get the output size. Should be 3."""
return 3

@cast_precision
def _build_lower(self, start_index, natoms, inputs, rot_mat, suffix="", reuse=None):
# cut-out inputs
inputs_i = tf.slice(inputs, [0, start_index, 0], [-1, natoms, -1])
Expand Down Expand Up @@ -172,7 +178,6 @@ def _build_lower(self, start_index, natoms, inputs, rot_mat, suffix="", reuse=No
final_layer = tf.reshape(final_layer, [tf.shape(inputs)[0], natoms, 3])
return final_layer

@cast_precision
def build(
self,
input_d: tf.Tensor,
Expand Down Expand Up @@ -215,8 +220,12 @@ def build(
start_index = 0
inputs = tf.reshape(input_d, [-1, natoms[0], self.dim_descrpt])
rot_mat = tf.reshape(rot_mat, [-1, natoms[0], self.dim_rot_mat])
if nframes is None:
nframes = tf.shape(inputs)[0]

if type_embedding is not None:
if self.mixed_types or type_embedding is not None:
# keep old behavior
self.mixed_types = True
nloc_mask = tf.reshape(
tf.tile(tf.repeat(self.sel_mask, natoms[2:]), [nframes]), [nframes, -1]
)
Expand All @@ -228,13 +237,30 @@ def build(
self.nloc_masked = tf.shape(
tf.reshape(self.atype_nloc_masked, [nframes, -1])
)[1]

if type_embedding is not None:
atype_embed = tf.nn.embedding_lookup(type_embedding, self.atype_nloc_masked)
else:
atype_embed = None

self.atype_embed = atype_embed
if atype_embed is not None:
inputs = tf.reshape(
tf.reshape(inputs, [nframes, natoms[0], self.dim_descrpt])[nloc_mask],
[-1, self.dim_descrpt],
)
rot_mat = tf.reshape(
tf.reshape(rot_mat, [nframes, natoms[0], self.dim_rot_mat_1 * 3])[
nloc_mask
],
[-1, self.dim_rot_mat_1, 3],
)
atype_embed = tf.cast(atype_embed, self.fitting_precision)
type_shape = atype_embed.get_shape().as_list()
inputs = tf.concat([inputs, atype_embed], axis=1)
self.dim_descrpt = self.dim_descrpt + type_shape[1]

if atype_embed is None:
if not self.mixed_types:
count = 0
outs_list = []
for type_i in range(self.ntypes):
Expand All @@ -255,20 +281,6 @@ def build(
count += 1
outs = tf.concat(outs_list, axis=1)
else:
inputs = tf.reshape(
tf.reshape(inputs, [nframes, natoms[0], self.dim_descrpt])[nloc_mask],
[-1, self.dim_descrpt],
)
rot_mat = tf.reshape(
tf.reshape(rot_mat, [nframes, natoms[0], self.dim_rot_mat_1 * 3])[
nloc_mask
],
[-1, self.dim_rot_mat_1, 3],
)
atype_embed = tf.cast(atype_embed, self.fitting_precision)
type_shape = atype_embed.get_shape().as_list()
inputs = tf.concat([inputs, atype_embed], axis=1)
self.dim_descrpt = self.dim_descrpt + type_shape[1]
inputs = tf.reshape(inputs, [nframes, self.nloc_masked, self.dim_descrpt])
rot_mat = tf.reshape(
rot_mat, [nframes, self.nloc_masked, self.dim_rot_mat_1 * 3]
Expand Down Expand Up @@ -354,9 +366,7 @@ def serialize(self, suffix: str) -> dict:
"ntypes": self.ntypes,
"dim_descrpt": self.dim_descrpt,
"embedding_width": self.dim_rot_mat_1,
# very bad design: type embedding is not passed to the class
# TODO: refactor the class for type embedding and dipole fitting
"mixed_types": False,
"mixed_types": self.mixed_types,
"dim_out": 3,
"neuron": self.n_neuron,
"resnet_dt": self.resnet_dt,
Expand All @@ -365,8 +375,7 @@ def serialize(self, suffix: str) -> dict:
"exclude_types": [],
"nets": self.serialize_network(
ntypes=self.ntypes,
# TODO: consider type embeddings in dipole fitting
ndim=1,
ndim=0 if self.mixed_types else 1,
in_dim=self.dim_descrpt,
out_dim=self.dim_rot_mat_1,
neuron=self.n_neuron,
Expand Down
30 changes: 17 additions & 13 deletions deepmd/tf/fit/dos.py
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Original file line number Diff line number Diff line change
Expand Up @@ -95,6 +95,9 @@
use_aparam_as_mask: bool, optional
If True, the atomic parameters will be used as a mask that determines the atom is real/virtual.
And the aparam will not be used as the atomic parameters for embedding.
mixed_types : bool
If true, use a uniform fitting net for all atom types, otherwise use
different fitting nets for different atom types.
"""

def __init__(
Expand All @@ -114,6 +117,7 @@
uniform_seed: bool = False,
layer_name: Optional[List[Optional[str]]] = None,
use_aparam_as_mask: bool = False,
mixed_types: bool = False,
**kwargs,
) -> None:
"""Constructor."""
Expand Down Expand Up @@ -171,6 +175,7 @@
assert (
len(self.layer_name) == len(self.n_neuron) + 1
), "length of layer_name should be that of n_neuron + 1"
self.mixed_types = mixed_types

def get_numb_fparam(self) -> int:
"""Get the number of frame parameters."""
Expand Down Expand Up @@ -504,13 +509,22 @@
tf.slice(atype_nall, [0, 0], [-1, natoms[0]]), [-1]
) ## lammps will make error
if type_embedding is not None:
# keep old behavior
self.mixed_types = True

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atype_embed = tf.nn.embedding_lookup(type_embedding, self.atype_nloc)
else:
atype_embed = None

self.atype_embed = atype_embed
if atype_embed is not None:
atype_embed = tf.cast(atype_embed, GLOBAL_TF_FLOAT_PRECISION)
type_shape = atype_embed.get_shape().as_list()
inputs = tf.concat(

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[tf.reshape(inputs, [-1, self.dim_descrpt]), atype_embed], axis=1
)
self.dim_descrpt = self.dim_descrpt + type_shape[1]

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if atype_embed is None:
if not self.mixed_types:
start_index = 0
outs_list = []
for type_i in range(self.ntypes):
Expand Down Expand Up @@ -541,13 +555,6 @@
outs = tf.concat(outs_list, axis=1)
# with type embedding
else:
atype_embed = tf.cast(atype_embed, GLOBAL_TF_FLOAT_PRECISION)
type_shape = atype_embed.get_shape().as_list()
inputs = tf.concat(
[tf.reshape(inputs, [-1, self.dim_descrpt]), atype_embed], axis=1
)
original_dim_descrpt = self.dim_descrpt
self.dim_descrpt = self.dim_descrpt + type_shape[1]
inputs = tf.reshape(inputs, [-1, natoms[0], self.dim_descrpt])
final_layer = self._build_lower(
0,
Expand Down Expand Up @@ -700,9 +707,7 @@
"var_name": "dos",
"ntypes": self.ntypes,
"dim_descrpt": self.dim_descrpt,
# very bad design: type embedding is not passed to the class
# TODO: refactor the class for DOSFitting and type embedding
"mixed_types": False,
"mixed_types": self.mixed_types,
"dim_out": self.numb_dos,
"neuron": self.n_neuron,
"resnet_dt": self.resnet_dt,
Expand All @@ -715,8 +720,7 @@
"exclude_types": [],
"nets": self.serialize_network(
ntypes=self.ntypes,
# TODO: consider type embeddings for DOSFitting
ndim=1,
ndim=0 if self.mixed_types else 1,
in_dim=self.dim_descrpt + self.numb_fparam + self.numb_aparam,
out_dim=self.numb_dos,
neuron=self.n_neuron,
Expand Down
43 changes: 26 additions & 17 deletions deepmd/tf/fit/ener.py
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Original file line number Diff line number Diff line change
Expand Up @@ -141,6 +141,9 @@ class EnerFitting(Fitting):
use_aparam_as_mask: bool, optional
If True, the atomic parameters will be used as a mask that determines the atom is real/virtual.
And the aparam will not be used as the atomic parameters for embedding.
mixed_types : bool
If true, use a uniform fitting net for all atom types, otherwise use
different fitting nets for different atom types.
"""

def __init__(
Expand All @@ -162,6 +165,7 @@ def __init__(
layer_name: Optional[List[Optional[str]]] = None,
use_aparam_as_mask: bool = False,
spin: Optional[Spin] = None,
mixed_types: bool = False,
**kwargs,
) -> None:
"""Constructor."""
Expand Down Expand Up @@ -238,6 +242,7 @@ def __init__(
assert (
len(self.layer_name) == len(self.n_neuron) + 1
), "length of layer_name should be that of n_neuron + 1"
self.mixed_types = mixed_types

def get_numb_fparam(self) -> int:
"""Get the number of frame parameters."""
Expand Down Expand Up @@ -585,6 +590,8 @@ def build(
)
else:
inputs_zero = tf.zeros_like(inputs, dtype=GLOBAL_TF_FLOAT_PRECISION)
else:
inputs_zero = None

if bias_atom_e is not None:
assert len(bias_atom_e) == self.ntypes
Expand Down Expand Up @@ -628,13 +635,29 @@ def build(
):
type_embedding = nvnmd_cfg.map["t_ebd"]
if type_embedding is not None:
# keep old behavior
self.mixed_types = True
atype_embed = tf.nn.embedding_lookup(type_embedding, self.atype_nloc)
else:
atype_embed = None

self.atype_embed = atype_embed
original_dim_descrpt = self.dim_descrpt
if atype_embed is not None:
atype_embed = tf.cast(atype_embed, GLOBAL_TF_FLOAT_PRECISION)
type_shape = atype_embed.get_shape().as_list()
inputs = tf.concat(
[tf.reshape(inputs, [-1, self.dim_descrpt]), atype_embed], axis=1
)
self.dim_descrpt = self.dim_descrpt + type_shape[1]
if len(self.atom_ener):
assert inputs_zero is not None
inputs_zero = tf.concat(
[tf.reshape(inputs_zero, [-1, original_dim_descrpt]), atype_embed],
Fixed Show fixed Hide fixed
axis=1,
)

if atype_embed is None:
if not self.mixed_types:
start_index = 0
outs_list = []
for type_i in range(ntypes_atom):
Expand Down Expand Up @@ -673,13 +696,6 @@ def build(
outs = tf.concat(outs_list, axis=1)
# with type embedding
else:
atype_embed = tf.cast(atype_embed, GLOBAL_TF_FLOAT_PRECISION)
type_shape = atype_embed.get_shape().as_list()
inputs = tf.concat(
[tf.reshape(inputs, [-1, self.dim_descrpt]), atype_embed], axis=1
)
original_dim_descrpt = self.dim_descrpt
self.dim_descrpt = self.dim_descrpt + type_shape[1]
inputs = tf.reshape(inputs, [-1, natoms[0], self.dim_descrpt])
final_layer = self._build_lower(
0,
Expand All @@ -693,10 +709,6 @@ def build(
)
if len(self.atom_ener):
# remove contribution in vacuum
inputs_zero = tf.concat(
[tf.reshape(inputs_zero, [-1, original_dim_descrpt]), atype_embed],
axis=1,
)
inputs_zero = tf.reshape(inputs_zero, [-1, natoms[0], self.dim_descrpt])
zero_layer = self._build_lower(
0,
Expand Down Expand Up @@ -892,9 +904,7 @@ def serialize(self, suffix: str = "") -> dict:
"var_name": "energy",
"ntypes": self.ntypes,
"dim_descrpt": self.dim_descrpt,
# very bad design: type embedding is not passed to the class
# TODO: refactor the class for energy fitting and type embedding
"mixed_types": False,
"mixed_types": self.mixed_types,
"dim_out": 1,
"neuron": self.n_neuron,
"resnet_dt": self.resnet_dt,
Expand All @@ -912,8 +922,7 @@ def serialize(self, suffix: str = "") -> dict:
"exclude_types": [],
"nets": self.serialize_network(
ntypes=self.ntypes,
# TODO: consider type embeddings for type embedding
ndim=1,
ndim=0 if self.mixed_types else 1,
in_dim=self.dim_descrpt + self.numb_fparam + self.numb_aparam,
neuron=self.n_neuron,
activation_function=self.activation_function_name,
Expand Down
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