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Merged
wanghan-iapcm merged 3 commits into from
Dec 13, 2022
Merged

store energy bias with interface precision #2174

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786a7e9
store energy bias with interface precision
njzjz Dec 9, 2022
721c899
fix tests
njzjz Dec 9, 2022
706448f
revise the comment
njzjz Dec 12, 2022
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47 changes: 21 additions & 26 deletions deepmd/fit/ener.py
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Original file line number Diff line number Diff line change
Expand Up @@ -140,7 +140,7 @@ def __init__ (self,
self.atom_ener_v = atom_ener
for at, ae in enumerate(atom_ener):
if ae is not None:
self.atom_ener.append(tf.constant(ae, self.fitting_precision, name = "atom_%d_ener" % at))
self.atom_ener.append(tf.constant(ae, GLOBAL_TF_FLOAT_PRECISION, name = "atom_%d_ener" % at))
else:
self.atom_ener.append(None)
self.useBN = False
Expand Down Expand Up @@ -286,6 +286,7 @@ def compute_input_stats(self,
def _compute_std (self, sumv2, sumv, sumn) :
return np.sqrt(sumv2/sumn - np.multiply(sumv/sumn, sumv/sumn))

@cast_precision
def _build_lower(
self,
start_index,
Expand Down Expand Up @@ -368,7 +369,6 @@ def _build_lower(
return final_layer


@cast_precision
def build (self,
inputs : tf.Tensor,
natoms : tf.Tensor,
Expand Down Expand Up @@ -425,12 +425,11 @@ def build (self,
t_daparam = tf.constant(self.numb_aparam,
name = 'daparam',
dtype = tf.int32)
if type_embedding is not None:
self.t_bias_atom_e = tf.get_variable('t_bias_atom_e',
self.bias_atom_e.shape,
dtype=self.fitting_precision,
trainable=False,
initializer=tf.constant_initializer(self.bias_atom_e))
self.t_bias_atom_e = tf.get_variable('t_bias_atom_e',
self.bias_atom_e.shape,
dtype=GLOBAL_TF_FLOAT_PRECISION,
trainable=False,
initializer=tf.constant_initializer(self.bias_atom_e))
if self.numb_fparam > 0:
t_fparam_avg = tf.get_variable('t_fparam_avg',
self.numb_fparam,
Expand Down Expand Up @@ -460,9 +459,9 @@ def build (self,
nframes = input_dict.get('nframes')
if nframes is not None:
# like inputs, but we don't want to add a dependency on inputs
inputs_zero = tf.zeros((nframes, natoms[0], self.dim_descrpt), dtype=self.fitting_precision)
inputs_zero = tf.zeros((nframes, natoms[0], self.dim_descrpt), dtype=GLOBAL_TF_FLOAT_PRECISION)
else:
inputs_zero = tf.zeros_like(inputs, dtype=self.fitting_precision)
inputs_zero = tf.zeros_like(inputs, dtype=GLOBAL_TF_FLOAT_PRECISION)


if bias_atom_e is not None :
Expand All @@ -480,9 +479,9 @@ def build (self,
aparam = (aparam - t_aparam_avg) * t_aparam_istd
aparam = tf.reshape(aparam, [-1, self.numb_aparam * natoms[0]])

atype_nall = tf.reshape(atype, [-1, natoms[1]])
self.atype_nloc = tf.reshape(tf.slice(atype_nall, [0, 0], [-1, natoms[0]]), [-1]) ## lammps will make error
if type_embedding is not None:
atype_nall = tf.reshape(atype, [-1, natoms[1]])
self.atype_nloc = tf.reshape(tf.slice(atype_nall, [0, 0], [-1, natoms[0]]), [-1]) ## lammps will make error
atype_embed = tf.nn.embedding_lookup(type_embedding, self.atype_nloc)
else:
atype_embed = None
Expand All @@ -493,23 +492,19 @@ def build (self,
start_index = 0
outs_list = []
for type_i in range(self.ntypes):
if bias_atom_e is None :
type_bias_ae = 0.0
else :
type_bias_ae = bias_atom_e[type_i]
final_layer = self._build_lower(
start_index, natoms[2+type_i],
inputs, fparam, aparam,
bias_atom_e=type_bias_ae, suffix='_type_'+str(type_i)+suffix, reuse=reuse
bias_atom_e=0., suffix='_type_'+str(type_i)+suffix, reuse=reuse
)
# concat the results
if type_i < len(self.atom_ener) and self.atom_ener[type_i] is not None:
zero_layer = self._build_lower(
start_index, natoms[2+type_i],
inputs_zero, fparam, aparam,
bias_atom_e=type_bias_ae, suffix='_type_'+str(type_i)+suffix, reuse=True
bias_atom_e=0., suffix='_type_'+str(type_i)+suffix, reuse=True
)
final_layer += self.atom_ener[type_i] - zero_layer
final_layer -= zero_layer
final_layer = tf.reshape(final_layer, [tf.shape(inputs)[0], natoms[2+type_i]])
outs_list.append(final_layer)
start_index += natoms[2+type_i]
Expand All @@ -518,7 +513,7 @@ def build (self,
outs = tf.concat(outs_list, axis = 1)
# with type embedding
else:
atype_embed = tf.cast(atype_embed, self.fitting_precision)
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],
Expand Down Expand Up @@ -547,11 +542,11 @@ def build (self,
# atomic energy will be stored in `self.t_bias_atom_e` which is not trainable
final_layer -= zero_layer
outs = tf.reshape(final_layer, [tf.shape(inputs)[0], natoms[0]])
# add bias
self.atom_ener_before = outs
self.add_type = tf.reshape(tf.nn.embedding_lookup(self.t_bias_atom_e, self.atype_nloc), [tf.shape(inputs)[0], natoms[0]])
outs = outs + self.add_type
self.atom_ener_after = outs
# add bias
self.atom_ener_before = outs
self.add_type = tf.reshape(tf.nn.embedding_lookup(self.t_bias_atom_e, self.atype_nloc), [tf.shape(inputs)[0], natoms[0]])
outs = outs + self.add_type
self.atom_ener_after = outs

if self.tot_ener_zero:
force_tot_ener = 0.0
Expand Down Expand Up @@ -592,7 +587,7 @@ def init_variables(self,
try:
self.bias_atom_e = get_tensor_by_name_from_graph(graph, 'fitting_attr%s/t_bias_atom_e' % suffix)
except GraphWithoutTensorError:
# model without type_embedding has no t_bias_atom_e
# for compatibility, old models has no t_bias_atom_e
pass

def change_energy_bias(self,
Expand Down
2 changes: 1 addition & 1 deletion deepmd/model/ener.py
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Original file line number Diff line number Diff line change
Expand Up @@ -170,7 +170,7 @@ def build (self,
suffix = suffix,
)
input_dict['type_embedding'] = type_embedding
input_dict['atype'] = atype_
input_dict['atype'] = atype_

if frz_model == None:
dout \
Expand Down
2 changes: 1 addition & 1 deletion deepmd/model/multi.py
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Original file line number Diff line number Diff line change
Expand Up @@ -195,7 +195,7 @@ def build(self,
suffix=suffix,
)
input_dict['type_embedding'] = type_embedding
input_dict['atype'] = atype_
input_dict['atype'] = atype_

dout \
= self.descrpt.build(coord_,
Expand Down
2 changes: 1 addition & 1 deletion source/tests/test_model_se_a.py
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Original file line number Diff line number Diff line change
Expand Up @@ -69,7 +69,7 @@ def test_model_atom_ener(self):
'default_mesh' : [test_data['default_mesh']]
}
model._compute_input_stat(input_data)
model.descrpt.bias_atom_e = data.compute_energy_shift()
model.fitting.bias_atom_e = np.array(set_atom_ener)

t_prop_c = tf.placeholder(tf.float32, [5], name='t_prop_c')
t_energy = tf.placeholder(GLOBAL_ENER_FLOAT_PRECISION, [None], name='t_energy')
Expand Down