merge devel into master (#1942)

.github/workflows/build_wheel.yml

@@ -28,7 +28,7 @@ jobs:
       - name: Build wheels
         env:
           CIBW_BUILD: "cp36-* cp37-* cp38-* cp39-* cp310-*"
-          CIBW_MANYLINUX_X86_64_IMAGE: ghcr.io/deepmodeling/manylinux_2_24_x86_64_tensorflow
+          CIBW_MANYLINUX_X86_64_IMAGE: ghcr.io/deepmodeling/manylinux_2_28_x86_64_tensorflow
           CIBW_BEFORE_BUILD: pip install tensorflow
           CIBW_SKIP: "*-win32 *-manylinux_i686 *-musllinux*"
         run: |

README.md

@@ -94,6 +94,7 @@ A full [document](doc/train/train-input-auto.rst) on options in the training inp
     - [Descriptor `"se_e2_a"`](doc/model/train-se-e2-a.md)
     - [Descriptor `"se_e2_r"`](doc/model/train-se-e2-r.md)
     - [Descriptor `"se_e3"`](doc/model/train-se-e3.md)
+    - [Descriptor `"se_atten"`](doc/model/train-se-atten.md)
     - [Descriptor `"hybrid"`](doc/model/train-hybrid.md)
     - [Descriptor `sel`](doc/model/sel.md)
     - [Fit energy](doc/model/train-energy.md)

deepmd/common.py

@@ -66,7 +66,7 @@ def gelu(x: tf.Tensor) -> tf.Tensor:
     Original paper
     https://arxiv.org/abs/1606.08415
     """
-    return op_module.gelu(x)
+    return op_module.gelu_custom(x)
 
 
 def gelu_tf(x: tf.Tensor) -> tf.Tensor:
@@ -94,7 +94,7 @@ def gelu_wrapper(x):
             return tensorflow.nn.gelu(x, approximate=True)
         except AttributeError:
             warnings.warn("TensorFlow does not provide an implementation of gelu, please upgrade your TensorFlow version. Fallback to the custom gelu operator.")
-            return op_module.gelu(x)
+            return op_module.gelu_custom(x)
     return (lambda x: gelu_wrapper(x))(x)
 
 # TODO this is not a good way to do things. This is some global variable to which
@@ -405,8 +405,8 @@ def j_loader(filename: Union[str, Path]) -> Dict[str, Any]:
 
 
 def get_activation_func(
-    activation_fn: "_ACTIVATION",
-) -> Callable[[tf.Tensor], tf.Tensor]:
+    activation_fn: Union["_ACTIVATION", None],
+) -> Union[Callable[[tf.Tensor], tf.Tensor], None]:
     """Get activation function callable based on string name.
 
     Parameters
@@ -424,6 +424,8 @@ def get_activation_func(
     RuntimeError
         if unknown activation function is specified
     """
+    if activation_fn is None or activation_fn in ['none', 'None']:
+        return None
     if activation_fn not in ACTIVATION_FN_DICT:
         raise RuntimeError(f"{activation_fn} is not a valid activation function")
     return ACTIVATION_FN_DICT[activation_fn]

deepmd/descriptor/__init__.py

@@ -7,3 +7,4 @@
 from .se_a_ef import DescrptSeAEf
 from .se_a_ef import DescrptSeAEfLower
 from .loc_frame import DescrptLocFrame
+from .se_atten import DescrptSeAtten

deepmd/descriptor/se_a.py

@@ -164,10 +164,7 @@ def __init__ (self,
         self.embedding_net_variables = None
         self.mixed_prec = None
         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)
+        self.nei_type = np.repeat(np.arange(self.ntypes), self.sel_a)  # like a mask
 
         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)
@@ -673,8 +670,9 @@ def _concat_type_embedding(
             embedding:
                 environment of each atom represented by 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))  # shape is [self.nnei, 1+te_out_dim]
+        te_out_dim = type_embedding.get_shape().as_list()[-1]
+        self.t_nei_type = tf.constant(self.nei_type, dtype=tf.int32)
+        nei_embed = tf.nn.embedding_lookup(type_embedding,tf.cast(self.t_nei_type,dtype=tf.int32))  # shape is [self.nnei, 1+te_out_dim]
         nei_embed = tf.tile(nei_embed,(nframes*natoms[0],1))  # shape is [nframes*natoms[0]*self.nnei, te_out_dim]
         nei_embed = tf.reshape(nei_embed,[-1,te_out_dim])
         embedding_input = tf.concat([xyz_scatter,nei_embed],1)  # shape is [nframes*natoms[0]*self.nnei, 1+te_out_dim]