Skip to content

Add type embedding #581

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
amcadmus merged 8 commits into from
May 1, 2021
Merged

Add type embedding #581

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
8 commits
Select commit Hold shift + click to select a range
7df619c
refact fit/ener.py, add UT for atom_ener, fix zero divider bug in se_a
Apr 28, 2021
0547de2
add support for type embedding in fitting. fix bugs in fit/ener.py
Apr 28, 2021
b82f882
extend the input dim of embedding net
Apr 28, 2021
8e2a897
Merge remote-tracking branch 'upstream/devel' into devel
Apr 29, 2021
6c7272d
add support for type embedding
Apr 29, 2021
d22068f
update doc for input parameters
Apr 29, 2021
4bf3545
add doc for type_embed, fix bug
Apr 29, 2021
73482ac
add UT for type_one_side
Apr 30, 2021
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
208 changes: 147 additions & 61 deletions deepmd/descriptor/se_a.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -11,7 +11,7 @@
from deepmd.env import default_tf_session_config
from deepmd.utils.network import embedding_net
from deepmd.utils.tabulate import DeepTabulate

from deepmd.utils.type_embed import embed_atom_type

class DescrptSeA ():
@docstring_parameter(list_to_doc(ACTIVATION_FN_DICT.keys()), list_to_doc(PRECISION_DICT.keys()))
Expand Down Expand Up @@ -101,6 +101,11 @@ def __init__ (self,
self.davg = None
self.compress = False
self.place_holders = {}
nei_type = np.array([])
for ii in range(self.ntypes):
nei_type = np.append(nei_type, ii * np.ones(self.sel_a[ii])) # like a mask
self.nei_type = tf.constant(nei_type, dtype = tf.int32)

avg_zero = np.zeros([self.ntypes,self.ndescrpt]).astype(GLOBAL_NP_FLOAT_PRECISION)
std_ones = np.ones ([self.ntypes,self.ndescrpt]).astype(GLOBAL_NP_FLOAT_PRECISION)
sub_graph = tf.Graph()
Expand Down Expand Up @@ -214,7 +219,7 @@ def compute_input_stats (self,
sumr2 = np.sum(sumr2, axis = 0)
suma2 = np.sum(suma2, axis = 0)
for type_i in range(self.ntypes) :
davgunit = [sumr[type_i]/sumn[type_i], 0, 0, 0]
davgunit = [sumr[type_i]/(sumn[type_i]+1e-15), 0, 0, 0]
dstdunit = [self._compute_std(sumr2[type_i], sumr[type_i], sumn[type_i]),
self._compute_std(suma2[type_i], suma[type_i], sumn[type_i]),
self._compute_std(suma2[type_i], suma[type_i], sumn[type_i]),
Expand Down Expand Up @@ -440,11 +445,15 @@ def _pass_filter(self,
reuse = None,
suffix = '',
trainable = True) :
if input_dict is not None:
type_embedding = input_dict.get('type_embedding', None)
else:
type_embedding = None
start_index = 0
inputs = tf.reshape(inputs, [-1, self.ndescrpt * natoms[0]])
output = []
output_qmat = []
if not self.type_one_side:
if not self.type_one_side and type_embedding is None:
for type_i in range(self.ntypes):
inputs_i = tf.slice (inputs,
[ 0, start_index* self.ndescrpt],
Expand All @@ -460,7 +469,7 @@ def _pass_filter(self,
inputs_i = inputs
inputs_i = tf.reshape(inputs_i, [-1, self.ndescrpt])
type_i = -1
layer, qmat = self._filter(tf.cast(inputs_i, self.filter_precision), type_i, name='filter_type_all'+suffix, natoms=natoms, reuse=reuse, seed = self.seed, trainable = trainable, activation_fn = self.filter_activation_fn)
layer, qmat = self._filter(tf.cast(inputs_i, self.filter_precision), type_i, name='filter_type_all'+suffix, natoms=natoms, reuse=reuse, seed = self.seed, trainable = trainable, activation_fn = self.filter_activation_fn, type_embedding=type_embedding)
layer = tf.reshape(layer, [tf.shape(inputs)[0], natoms[0] * self.get_dim_out()])
qmat = tf.reshape(qmat, [tf.shape(inputs)[0], natoms[0] * self.get_dim_rot_mat_1() * 3])
output.append(layer)
Expand Down Expand Up @@ -516,75 +525,152 @@ def _compute_dstats_sys_smth (self,


def _compute_std (self,sumv2, sumv, sumn) :
if sumn == 0:
return 1e-2
val = np.sqrt(sumv2/sumn - np.multiply(sumv/sumn, sumv/sumn))
if np.abs(val) < 1e-2:
val = 1e-2
return val


def _filter(self,
inputs,
type_input,
natoms,
activation_fn=tf.nn.tanh,
stddev=1.0,
bavg=0.0,
name='linear',
reuse=None,
seed=None,
trainable = True):
def _concat_type_embedding(
self,
xyz_scatter,
nframes,
natoms,
type_embedding,
):
te_out_dim = type_embedding.get_shape().as_list()[-1]
nei_embed = tf.nn.embedding_lookup(type_embedding,tf.cast(self.nei_type,dtype=tf.int32)) #nnei*nchnl
nei_embed = tf.tile(nei_embed,(nframes*natoms[0],1))
nei_embed = tf.reshape(nei_embed,[-1,te_out_dim])
embedding_input = tf.concat([xyz_scatter,nei_embed],1)
if not self.type_one_side:
atm_embed = embed_atom_type(self.ntypes, natoms, type_embedding)
atm_embed = tf.tile(atm_embed,(1,self.nnei))
atm_embed = tf.reshape(atm_embed,[-1,te_out_dim])
embedding_input = tf.concat([embedding_input,atm_embed],1)
return embedding_input


def _filter_lower(
self,
start_index,
incrs_index,
inputs,
nframes,
natoms,
type_embedding=None,
is_exclude = False,
activation_fn = None,
bavg = 0.0,
stddev = 1.0,
seed = None,
trainable = True,
suffix = '',
):
"""
input env matrix, returns R.G
"""
outputs_size = [1] + self.filter_neuron
# cut-out inputs
# with natom x (nei_type_i x 4)
inputs_i = tf.slice (inputs,
[ 0, start_index* 4],
[-1, incrs_index* 4] )
shape_i = inputs_i.get_shape().as_list()
# with (natom x nei_type_i) x 4
inputs_reshape = tf.reshape(inputs_i, [-1, 4])
# with (natom x nei_type_i) x 1
xyz_scatter = tf.reshape(tf.slice(inputs_reshape, [0,0],[-1,1]),[-1,1])
if type_embedding is not None:
type_embedding = tf.cast(type_embedding, self.filter_precision)
xyz_scatter = self._concat_type_embedding(
xyz_scatter, nframes, natoms, type_embedding)
if self.compress:
raise RuntimeError('compression of type embedded descriptor is not supported at the moment')
# with (natom x nei_type_i) x out_size
if self.compress and (not is_exclude):
info = [self.lower, self.upper, self.upper * self.table_config[0], self.table_config[1], self.table_config[2], self.table_config[3]]
if self.type_one_side:
net = 'filter_-1_net_' + str(type_i)
else:
net = 'filter_' + str(type_input) + '_net_' + str(type_i)
return op_module.tabulate_fusion(self.table.data[net].astype(self.filter_np_precision), info, xyz_scatter, tf.reshape(inputs_i, [-1, shape_i[1]//4, 4]), last_layer_size = outputs_size[-1])
else:
if (not is_exclude):
xyz_scatter = embedding_net(
xyz_scatter,
self.filter_neuron,
self.filter_precision,
activation_fn = activation_fn,
resnet_dt = self.filter_resnet_dt,
name_suffix = suffix,
stddev = stddev,
bavg = bavg,
seed = seed,
trainable = trainable)
else:
w = tf.zeros((outputs_size[0], outputs_size[-1]), dtype=GLOBAL_TF_FLOAT_PRECISION)
xyz_scatter = tf.matmul(xyz_scatter, w)
# natom x nei_type_i x out_size
xyz_scatter = tf.reshape(xyz_scatter, (-1, shape_i[1]//4, outputs_size[-1]))
return tf.matmul(tf.reshape(inputs_i, [-1, shape_i[1]//4, 4]), xyz_scatter, transpose_a = True)


def _filter(
self,
inputs,
type_input,
natoms,
type_embedding = None,
activation_fn=tf.nn.tanh,
stddev=1.0,
bavg=0.0,
name='linear',
reuse=None,
seed=None,
trainable = True):
nframes = tf.shape(tf.reshape(inputs, [-1, natoms[0], self.ndescrpt]))[0]
# natom x (nei x 4)
shape = inputs.get_shape().as_list()
outputs_size = [1] + self.filter_neuron
outputs_size_2 = self.n_axis_neuron
with tf.variable_scope(name, reuse=reuse):
start_index = 0
xyz_scatter_total = []
for type_i in range(self.ntypes):
# cut-out inputs
# with natom x (nei_type_i x 4)
inputs_i = tf.slice (inputs,
[ 0, start_index* 4],
[-1, self.sel_a[type_i]* 4] )
start_index += self.sel_a[type_i]
shape_i = inputs_i.get_shape().as_list()
# with (natom x nei_type_i) x 4
inputs_reshape = tf.reshape(inputs_i, [-1, 4])
# with (natom x nei_type_i) x 1
xyz_scatter = tf.reshape(tf.slice(inputs_reshape, [0,0],[-1,1]),[-1,1])
# with (natom x nei_type_i) x out_size
if self.compress and (type_input, type_i) not in self.exclude_types:
info = [self.lower, self.upper, self.upper * self.table_config[0], self.table_config[1], self.table_config[2], self.table_config[3]]
if self.type_one_side:
net = 'filter_-1_net_' + str(type_i)
else:
net = 'filter_' + str(type_input) + '_net_' + str(type_i)
if type_i == 0:
xyz_scatter_1 = op_module.tabulate_fusion(self.table.data[net].astype(self.filter_np_precision), info, xyz_scatter, tf.reshape(inputs_i, [-1, shape_i[1]//4, 4]), last_layer_size = outputs_size[-1])
else:
xyz_scatter_1 += op_module.tabulate_fusion(self.table.data[net].astype(self.filter_np_precision), info, xyz_scatter, tf.reshape(inputs_i, [-1, shape_i[1]//4, 4]), last_layer_size = outputs_size[-1])
else:
if (type_input, type_i) not in self.exclude_types:
xyz_scatter = embedding_net(xyz_scatter,
self.filter_neuron,
self.filter_precision,
activation_fn = activation_fn,
resnet_dt = self.filter_resnet_dt,
name_suffix = "_"+str(type_i),
stddev = stddev,
bavg = bavg,
seed = seed,
trainable = trainable)
else:
w = tf.zeros((outputs_size[0], outputs_size[-1]), dtype=GLOBAL_TF_FLOAT_PRECISION)
xyz_scatter = tf.matmul(xyz_scatter, w)
# natom x nei_type_i x out_size
xyz_scatter = tf.reshape(xyz_scatter, (-1, shape_i[1]//4, outputs_size[-1]))
# xyz_scatter_total.append(xyz_scatter)
if type_i == 0 :
xyz_scatter_1 = tf.matmul(tf.reshape(inputs_i, [-1, shape_i[1]//4, 4]), xyz_scatter, transpose_a = True)
else :
xyz_scatter_1 += tf.matmul(tf.reshape(inputs_i, [-1, shape_i[1]//4, 4]), xyz_scatter, transpose_a = True)
if type_embedding is None:
for type_i in range(self.ntypes):
ret = self._filter_lower(
start_index, self.sel_a[type_i],
inputs,
nframes,
natoms,
type_embedding = type_embedding,
is_exclude = (type_input, type_i) in self.exclude_types,
activation_fn = activation_fn,
stddev = stddev,
bavg = bavg,
seed = seed,
trainable = trainable,
suffix = "_"+str(type_i))
if type_i == 0:
xyz_scatter_1 = ret
else:
xyz_scatter_1+= ret
start_index += self.sel_a[type_i]
else :
xyz_scatter_1 = self._filter_lower(
start_index, np.cumsum(self.sel_a)[-1],
inputs,
nframes,
natoms,
type_embedding = type_embedding,
is_exclude = False,
activation_fn = activation_fn,
stddev = stddev,
bavg = bavg,
seed = seed,
trainable = trainable)
# natom x nei x outputs_size
# xyz_scatter = tf.concat(xyz_scatter_total, axis=1)
# natom x nei x 4
Expand Down
Loading