Skip to content

optimize the graph of dplr data modifier #1615

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
wanghan-iapcm merged 1 commit into from
Apr 2, 2022
Merged

optimize the graph of dplr data modifier #1615

Changes from all commits
Commits
Show all changes
1 commit
Select commit
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
46 changes: 11 additions & 35 deletions deepmd/infer/data_modifier.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -109,8 +109,7 @@ def _build_fv_graph_inner(self):
nfxna = -1
self.t_box_reshape = tf.reshape(self.t_box, [-1, 9])
t_nframes = tf.shape(self.t_box_reshape)[0]
# (nframes x natoms_sel) x 1 x 3
self.t_ef_reshape = tf.reshape(self.t_ef, [nfxnas, 1, 3])

# (nframes x natoms) x ndescrpt
self.descrpt = self.graph.get_tensor_by_name(os.path.join(self.modifier_prefix, 'o_rmat:0'))
self.descrpt_deriv = self.graph.get_tensor_by_name(os.path.join(self.modifier_prefix, 'o_rmat_deriv:0'))
Expand All @@ -120,43 +119,20 @@ def _build_fv_graph_inner(self):
# self.descrpt_deriv = tf.reshape(self.descrpt_deriv, [nf, 192 * self.ndescrpt * 3])

# nframes x (natoms_sel x 3)
self.t_tensor_reshpe = tf.reshape(self.t_tensor, [t_nframes, -1])
self.t_ef_reshape = tf.reshape(self.t_ef, [t_nframes, -1])
# nframes x (natoms x 3)
self.t_tensor_reshpe = self._enrich(self.t_tensor_reshpe, dof = 3)
self.t_ef_reshape = self._enrich(self.t_ef_reshape, dof = 3)
# (nframes x natoms) x 3
self.t_tensor_reshpe = tf.reshape(self.t_tensor_reshpe, [nfxna, 3])
# (nframes x natoms) x 1
self.t_dipole_x = tf.slice(self.t_tensor_reshpe, [0, 0], [nfxna, 1])
self.t_dipole_y = tf.slice(self.t_tensor_reshpe, [0, 1], [nfxna, 1])
self.t_dipole_z = tf.slice(self.t_tensor_reshpe, [0, 2], [nfxna, 1])
self.t_dipole_z = tf.reshape(self.t_dipole_z, [nfxna, 1])
self.t_ef_reshape = tf.reshape(self.t_ef_reshape, [nfxna, 3])
# nframes x (natoms_sel x 3)
self.t_tensor_reshape = tf.reshape(self.t_tensor, [t_nframes, -1])
# nframes x (natoms x 3)
self.t_tensor_reshape = self._enrich(self.t_tensor_reshape, dof = 3)
# (nframes x natoms) x 3
self.t_tensor_reshape = tf.reshape(self.t_tensor_reshape, [nfxna, 3])
# (nframes x natoms) x ndescrpt
[self.t_dipole_x_d] = tf.gradients(self.t_dipole_x, self.descrpt)
[self.t_dipole_y_d] = tf.gradients(self.t_dipole_y, self.descrpt)
[self.t_dipole_z_d] = tf.gradients(self.t_dipole_z, self.descrpt)
# nframes x (natoms x ndescrpt)
self.t_dipole_x_d = tf.reshape(self.t_dipole_x_d, [-1, self.t_natoms[0] * self.ndescrpt])
self.t_dipole_y_d = tf.reshape(self.t_dipole_y_d, [-1, self.t_natoms[0] * self.ndescrpt])
self.t_dipole_z_d = tf.reshape(self.t_dipole_z_d, [-1, self.t_natoms[0] * self.ndescrpt])
# nframes x (natoms_sel x ndescrpt)
self.t_dipole_x_d = self._slice_descrpt_deriv(self.t_dipole_x_d)
self.t_dipole_y_d = self._slice_descrpt_deriv(self.t_dipole_y_d)
self.t_dipole_z_d = self._slice_descrpt_deriv(self.t_dipole_z_d)
# (nframes x natoms_sel) x ndescrpt
self.t_dipole_x_d = tf.reshape(self.t_dipole_x_d, [nfxnas, self.ndescrpt])
self.t_dipole_y_d = tf.reshape(self.t_dipole_y_d, [nfxnas, self.ndescrpt])
self.t_dipole_z_d = tf.reshape(self.t_dipole_z_d, [nfxnas, self.ndescrpt])
# (nframes x natoms_sel) x 3 x ndescrpt
self.t_dipole_d = tf.concat([self.t_dipole_x_d, self.t_dipole_y_d, self.t_dipole_z_d], axis = 1)
self.t_dipole_d = tf.reshape(self.t_dipole_d, [nfxnas, 3*self.ndescrpt])
# (nframes x natoms_sel) x 3 x ndescrpt
self.t_dipole_d = tf.reshape(self.t_dipole_d, [-1, 3, self.ndescrpt])
# (nframes x natoms_sel) x 1 x ndescrpt
self.t_ef_d = tf.matmul(self.t_ef_reshape, self.t_dipole_d)
# nframes x (natoms_sel x ndescrpt)
self.t_ef_d = tf.reshape(self.t_ef_d, [t_nframes, -1])
[self.t_ef_d] = tf.gradients(self.t_tensor_reshape, self.descrpt, self.t_ef_reshape)
# nframes x (natoms x ndescrpt)
self.t_ef_d = self._enrich(self.t_ef_d, dof = self.ndescrpt)
self.t_ef_d = tf.reshape(self.t_ef_d, [nf, self.t_natoms[0] * self.ndescrpt])
# t_ef_d is force (with -1), prod_forc takes deriv, so we need the opposite
self.t_ef_d_oppo = -self.t_ef_d
Expand Down