[r2] fix seeds in se_a and se_atten (#3880)

deepmd/descriptor/se_a.py

@@ -1031,6 +1031,8 @@ def _filter_lower(
                             mixed_prec=self.mixed_prec,
                         )
                         net_output = tf.nn.embedding_lookup(net_output, idx)
+                    if (not self.uniform_seed) and (self.seed is not None):
+                        self.seed += self.seed_shift
                     net_output = tf.reshape(net_output, [-1, self.filter_neuron[-1]])
             else:
                 xyz_scatter = self._concat_type_embedding(
@@ -1042,7 +1044,7 @@ def _filter_lower(
                     )
         # natom x 4 x outputs_size
         if nvnmd_cfg.enable:
-            return filter_lower_R42GR(
+            oo = filter_lower_R42GR(
                 type_i,
                 type_input,
                 inputs_i,
@@ -1060,6 +1062,9 @@ def _filter_lower(
                 self.filter_resnet_dt,
                 self.embedding_net_variables,
             )
+            if (not self.uniform_seed) and (self.seed is not None):
+                self.seed += self.seed_shift
+            return oo
         if self.compress and (not is_exclude):
             if self.stripped_type_embedding:
                 net_output = tf.nn.embedding_lookup(

deepmd/descriptor/se_atten.py

@@ -959,6 +959,8 @@
                     uniform_seed=self.uniform_seed,
                     initial_variables=self.attention_layer_variables,
                 )
+                if not self.uniform_seed and self.seed is not None:
+                    self.seed += 1
                 K_c = one_layer(
                     input_xyz,
                     self.att_n,
@@ -972,6 +974,8 @@
                     uniform_seed=self.uniform_seed,
                     initial_variables=self.attention_layer_variables,
                 )
+                if not self.uniform_seed and self.seed is not None:
+                    self.seed += 1
                 V_c = one_layer(
                     input_xyz,
                     self.att_n,
@@ -985,6 +989,8 @@
                     uniform_seed=self.uniform_seed,
                     initial_variables=self.attention_layer_variables,
                 )
+                if not self.uniform_seed and self.seed is not None:
+                    self.seed += 1
                 # # natom x nei_type_i x out_size
                 # xyz_scatter = tf.reshape(xyz_scatter, (-1, shape_i[1] // 4, outputs_size[-1]))
                 # natom x nei_type_i x att_n
@@ -1017,6 +1023,8 @@
                     uniform_seed=self.uniform_seed,
                     initial_variables=self.attention_layer_variables,
                 )
+                if not self.uniform_seed and self.seed is not None:
+                    self.seed += 1
                 input_xyz = tf.keras.layers.LayerNormalization(
                     beta_initializer=tf.constant_initializer(self.beta[i]),
                     gamma_initializer=tf.constant_initializer(self.gamma[i]),
@@ -1080,6 +1088,8 @@
                         initial_variables=self.embedding_net_variables,
                         mixed_prec=self.mixed_prec,
                     )
+                    if (not self.uniform_seed) and (self.seed is not None):
+                        self.seed += self.seed_shift
                 else:
                     if self.attn_layer == 0:
                         log.info(
@@ -1119,6 +1129,8 @@
                             initial_variables=self.embedding_net_variables,
                             mixed_prec=self.mixed_prec,
                         )
+                        if (not self.uniform_seed) and (self.seed is not None):
+                            self.seed += self.seed_shift
                     else:
                         net = "filter_net"
                         info = [
@@ -1176,6 +1188,8 @@
                             initial_variables=self.two_side_embeeding_net_variables,
                             mixed_prec=self.mixed_prec,
                         )
+                        if (not self.uniform_seed) and (self.seed is not None):
+                            self.seed += self.seed_shift
                         two_embd = tf.nn.embedding_lookup(
                             embedding_of_two_side_type_embedding, index_of_two_side
                         )
@@ -1194,8 +1208,6 @@
                             is_sorted=len(self.exclude_types) == 0,
                         )
 
-                if (not self.uniform_seed) and (self.seed is not None):
-                    self.seed += self.seed_shift
             input_r = tf.slice(
                 tf.reshape(inputs_i, (-1, shape_i[1] // 4, 4)), [0, 0, 1], [-1, -1, 3]
             )

source/tests/test_model_se_a_ebd_v2.py

@@ -139,37 +139,37 @@ def test_model(self):
         f = f.reshape([-1])
         v = v.reshape([-1])
 
-        refe = [5.435394596262052014e-01]
+        refe = [6.100037044296185e-01]
         reff = [
-            6.583728125594628944e-02,
-            7.228993116083935744e-02,
-            1.971543579114074483e-03,
-            6.567474563776359853e-02,
-            7.809421727465599983e-02,
-            -4.866958849094786890e-03,
-            -8.670511901715304004e-02,
-            3.525374157021862048e-02,
-            1.415748959800727487e-03,
-            6.375813001810648473e-02,
-            -1.139053242798149790e-01,
-            -4.178593754384440744e-03,
-            -1.471737787218250215e-01,
-            4.189712704724830872e-02,
-            7.011731363309440038e-03,
-            3.860874082716164030e-02,
-            -1.136296927731473005e-01,
-            -1.353471298745012206e-03,
+            8.448651008616304e-02,
+            8.613568658155157e-02,
+            4.377711655236228e-03,
+            9.264613309788312e-02,
+            9.351200240060925e-02,
+            -6.743918515275118e-03,
+            -1.268078358219972e-01,
+            4.855965861982662e-02,
+            1.361334787979757e-04,
+            4.193213089916692e-02,
+            -1.324120032345251e-01,
+            -4.507320444374342e-03,
+            -1.314595297986654e-01,
+            4.120567370248839e-02,
+            7.896917575801866e-03,
+            3.920259153744955e-02,
+            -1.370010180699507e-01,
+            -1.159523750186610e-03,
         ]
         refv = [
-            -4.243979601186427253e-01,
-            1.097173849143971286e-01,
-            1.227299373463585502e-02,
-            1.097173849143970314e-01,
-            -2.462891443164323124e-01,
-            -5.711664180530139426e-03,
-            1.227299373463585502e-02,
-            -5.711664180530143763e-03,
-            -6.217348853341628408e-04,
+            -0.277134219204478,
+            0.088897922530779,
+            0.008633318264458,
+            0.088897922530779,
+            -0.292191560546969,
+            -0.005709595520904,
+            0.008633318264458,
+            -0.005709595520904,
+            -0.000682136341924,
         ]
         refe = np.reshape(refe, [-1])
         reff = np.reshape(reff, [-1])

source/tests/test_pairwise_dprc.py

@@ -519,8 +519,8 @@ def test_model_ener(self):
         # the model is pairwise!
         self.assertAllClose(e[1] + e[2] + e[3] - 3 * e[0], e[4] - e[0])
         self.assertAllClose(f[1] + f[2] + f[3] - 3 * f[0], f[4] - f[0])
-        self.assertAllClose(e[0], 0.189075, 1e-6)
-        self.assertAllClose(f[0, 0], 0.060047, 1e-6)
+        self.assertAllClose(e[0], 4.82969, 1e-6)
+        self.assertAllClose(f[0, 0], -0.104339, 1e-6)
 
     def test_nloc(self):
         jfile = tests_path / "pairwise_dprc.json"