Merge devel into master

.github/workflows/build_wheel.yml

@@ -47,7 +47,7 @@ jobs:
         name: Setup QEMU
         if: matrix.platform_id == 'manylinux_aarch64'
       - name: Build wheels
-        uses: pypa/cibuildwheel@v2.14
+        uses: pypa/cibuildwheel@v2.15
         env:
           CIBW_BUILD_VERBOSITY: 1
           CIBW_ARCHS: all

.pre-commit-config.yaml

@@ -9,7 +9,7 @@ repos:
     -   id: end-of-file-fixer
         exclude: "^.+\\.pbtxt$"
     -   id: check-yaml
-    #-   id: check-json
+    -   id: check-json
     -   id: check-added-large-files
         args: ['--maxkb=1024', '--enforce-all']
         # TODO: remove the following after resolved
@@ -33,7 +33,7 @@ repos:
       files: \.py$
 -   repo: https://github.com/astral-sh/ruff-pre-commit
     # Ruff version.
-    rev: v0.0.280
+    rev: v0.0.286
     hooks:
     - id: ruff
       args: ["--fix"]
@@ -45,7 +45,7 @@ repos:
       args: ["--write"]
 # Python inside docs
 -   repo: https://github.com/asottile/blacken-docs
-    rev: 1.15.0
+    rev: 1.16.0
     hooks:
     -   id: blacken-docs
 # C++
@@ -72,7 +72,7 @@ repos:
     #- id: cmake-lint
 # license header
 - repo: https://github.com/Lucas-C/pre-commit-hooks
-  rev: v1.5.1
+  rev: v1.5.4
   hooks:
     # C++, js
     -   id: insert-license

README.md

@@ -102,6 +102,7 @@ A full [document](doc/train/train-input-auto.rst) on options in the training inp
     - [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 `"se_atten_v2"`](doc/model/train-se-atten.md#descriptor-se_atten_v2)
     - [Descriptor `"hybrid"`](doc/model/train-hybrid.md)
     - [Descriptor `sel`](doc/model/sel.md)
     - [Fit energy](doc/model/train-energy.md)

deepmd/descriptor/__init__.py

@@ -24,6 +24,9 @@
 from .se_atten import (
     DescrptSeAtten,
 )
+from .se_atten_v2 import (
+    DescrptSeAttenV2,
+)
 from .se_r import (
     DescrptSeR,
 )
@@ -41,6 +44,7 @@
     "DescrptSeAEfLower",
     "DescrptSeAMask",
     "DescrptSeAtten",
+    "DescrptSeAttenV2",
     "DescrptSeR",
     "DescrptSeT",
 ]

deepmd/descriptor/se_atten.py

@@ -108,6 +108,13 @@ class DescrptSeAtten(DescrptSeA):
             Whether to mask the diagonal in the attention weights.
     multi_task
             If the model has multi fitting nets to train.
+    stripped_type_embedding
+            Whether to strip the type embedding into a separated embedding network.
+            Default value will be True in `se_atten_v2` descriptor.
+    smooth_type_embdding
+            When using stripped type embedding, whether to dot smooth factor on the network output of type embedding
+            to keep the network smooth, instead of setting `set_davg_zero` to be True.
+            Default value will be True in `se_atten_v2` descriptor.
     """
 
     def __init__(
@@ -133,9 +140,10 @@ def __init__(
         attn_mask: bool = False,
         multi_task: bool = False,
         stripped_type_embedding: bool = False,
+        smooth_type_embdding: bool = False,
         **kwargs,
     ) -> None:
-        if not set_davg_zero:
+        if not set_davg_zero and not (stripped_type_embedding and smooth_type_embdding):
             warnings.warn(
                 "Set 'set_davg_zero' False in descriptor 'se_atten' "
                 "may cause unexpected incontinuity during model inference!"
@@ -166,6 +174,7 @@ def __init__(
                 "2"
             ), "se_atten only support tensorflow version 2.0 or higher."
         self.stripped_type_embedding = stripped_type_embedding
+        self.smooth = smooth_type_embdding
         self.ntypes = ntypes
         self.att_n = attn
         self.attn_layer = attn_layer
@@ -607,6 +616,7 @@ def build(
             sel_a=self.sel_all_a,
             sel_r=self.sel_all_r,
         )
+
         self.nei_type_vec = tf.reshape(self.nei_type_vec, [-1])
         self.nmask = tf.cast(
             tf.reshape(self.nmask, [-1, 1, self.sel_all_a[0]]),
@@ -625,6 +635,41 @@ def build(
             tf.slice(atype, [0, 0], [-1, natoms[0]]), [-1]
         )  ## lammps will have error without this
         self._identity_tensors(suffix=suffix)
+        if self.smooth:
+            self.sliced_avg = tf.reshape(
+                tf.slice(
+                    tf.reshape(self.t_avg, [self.ntypes, -1, 4]), [0, 0, 0], [-1, 1, 1]
+                ),
+                [self.ntypes, 1],
+            )
+            self.sliced_std = tf.reshape(
+                tf.slice(
+                    tf.reshape(self.t_std, [self.ntypes, -1, 4]), [0, 0, 0], [-1, 1, 1]
+                ),
+                [self.ntypes, 1],
+            )
+            self.avg_looked_up = tf.reshape(
+                tf.nn.embedding_lookup(self.sliced_avg, self.atype_nloc),
+                [-1, natoms[0], 1],
+            )
+            self.std_looked_up = tf.reshape(
+                tf.nn.embedding_lookup(self.sliced_std, self.atype_nloc),
+                [-1, natoms[0], 1],
+            )
+            self.recovered_r = (
+                tf.reshape(
+                    tf.slice(tf.reshape(self.descrpt, [-1, 4]), [0, 0], [-1, 1]),
+                    [-1, natoms[0], self.sel_all_a[0]],
+                )
+                * self.std_looked_up
+                + self.avg_looked_up
+            )
+            uu = 1 - self.rcut_r_smth * self.recovered_r
+            self.recovered_switch = -uu * uu * uu + 1
+            self.recovered_switch = tf.clip_by_value(self.recovered_switch, 0.0, 1.0)
+            self.recovered_switch = tf.cast(
+                self.recovered_switch, self.filter_precision
+            )
 
         self.dout, self.qmat = self._pass_filter(
             self.descrpt_reshape,
@@ -1146,9 +1191,10 @@ def _filter_lower(
                         two_embd = tf.nn.embedding_lookup(
                             embedding_of_two_side_type_embedding, index_of_two_side
                         )
-
+                    if self.smooth:
+                        two_embd = two_embd * tf.reshape(self.recovered_switch, [-1, 1])
                     if not self.compress:
-                        xyz_scatter = xyz_scatter * two_embd + two_embd
+                        xyz_scatter = xyz_scatter * two_embd + xyz_scatter
                     else:
                         return op_module.tabulate_fusion_se_atten(
                             tf.cast(self.table.data[net], self.filter_precision),

deepmd/descriptor/se_atten_v2.py

@@ -0,0 +1,115 @@
+# SPDX-License-Identifier: LGPL-3.0-or-later
+import logging
+from typing import (
+    List,
+    Optional,
+)
+
+from .descriptor import (
+    Descriptor,
+)
+from .se_atten import (
+    DescrptSeAtten,
+)
+
+log = logging.getLogger(__name__)
+
+
+@Descriptor.register("se_atten_v2")
+class DescrptSeAttenV2(DescrptSeAtten):
+    r"""Smooth version 2.0 descriptor with attention.
+
+    Parameters
+    ----------
+    rcut
+            The cut-off radius :math:`r_c`
+    rcut_smth
+            From where the environment matrix should be smoothed :math:`r_s`
+    sel : list[str]
+            sel[i] specifies the maxmum number of type i atoms in the cut-off radius
+    neuron : list[int]
+            Number of neurons in each hidden layers of the embedding net :math:`\mathcal{N}`
+    axis_neuron
+            Number of the axis neuron :math:`M_2` (number of columns of the sub-matrix of the embedding matrix)
+    resnet_dt
+            Time-step `dt` in the resnet construction:
+            y = x + dt * \phi (Wx + b)
+    trainable
+            If the weights of embedding net are trainable.
+    seed
+            Random seed for initializing the network parameters.
+    type_one_side
+            Try to build N_types embedding nets. Otherwise, building N_types^2 embedding nets
+    exclude_types : List[List[int]]
+            The excluded pairs of types which have no interaction with each other.
+            For example, `[[0, 1]]` means no interaction between type 0 and type 1.
+    set_davg_zero
+            Set the shift of embedding net input to zero.
+    activation_function
+            The activation function in the embedding net. Supported options are |ACTIVATION_FN|
+    precision
+            The precision of the embedding net parameters. Supported options are |PRECISION|
+    uniform_seed
+            Only for the purpose of backward compatibility, retrieves the old behavior of using the random seed
+    attn
+            The length of hidden vector during scale-dot attention computation.
+    attn_layer
+            The number of layers in attention mechanism.
+    attn_dotr
+            Whether to dot the relative coordinates on the attention weights as a gated scheme.
+    attn_mask
+            Whether to mask the diagonal in the attention weights.
+    multi_task
+            If the model has multi fitting nets to train.
+    """
+
+    def __init__(
+        self,
+        rcut: float,
+        rcut_smth: float,
+        sel: int,
+        ntypes: int,
+        neuron: List[int] = [24, 48, 96],
+        axis_neuron: int = 8,
+        resnet_dt: bool = False,
+        trainable: bool = True,
+        seed: Optional[int] = None,
+        type_one_side: bool = True,
+        set_davg_zero: bool = False,
+        exclude_types: List[List[int]] = [],
+        activation_function: str = "tanh",
+        precision: str = "default",
+        uniform_seed: bool = False,
+        attn: int = 128,
+        attn_layer: int = 2,
+        attn_dotr: bool = True,
+        attn_mask: bool = False,
+        multi_task: bool = False,
+        **kwargs,
+    ) -> None:
+        DescrptSeAtten.__init__(
+            self,
+            rcut,
+            rcut_smth,
+            sel,
+            ntypes,
+            neuron=neuron,
+            axis_neuron=axis_neuron,
+            resnet_dt=resnet_dt,
+            trainable=trainable,
+            seed=seed,
+            type_one_side=type_one_side,
+            set_davg_zero=set_davg_zero,
+            exclude_types=exclude_types,
+            activation_function=activation_function,
+            precision=precision,
+            uniform_seed=uniform_seed,
+            attn=attn,
+            attn_layer=attn_layer,
+            attn_dotr=attn_dotr,
+            attn_mask=attn_mask,
+            multi_task=multi_task,
+            stripped_type_embedding=True,
+            smooth_type_embdding=True,
+            **kwargs,
+        )

deepmd/entrypoints/test.py

@@ -71,7 +71,7 @@ def test(
     set_prefix : str
         string prefix of set
     numb_test : int
-        munber of tests to do
+        munber of tests to do. 0 means all data.
     rand_seed : Optional[int]
         seed for random generator
     shuffle_test : bool
@@ -88,6 +88,9 @@ def test(
     RuntimeError
         if no valid system was found
     """
+    if numb_test == 0:
+        # only float has inf, but should work for min
+        numb_test = float("inf")
     if datafile is not None:
         datalist = open(datafile)
         all_sys = datalist.read().splitlines()
@@ -934,18 +937,40 @@ def test_dipole(
 
     if detail_file is not None:
         detail_path = Path(detail_file)
+        if not atomic:
+            pe = np.concatenate(
+                (
+                    np.reshape(test_data["dipole"][:numb_test], [-1, 3]),
+                    np.reshape(dipole, [-1, 3]),
+                ),
+                axis=1,
+            )
+            header_text = "data_x data_y data_z pred_x pred_y pred_z"
+        else:
+            pe = np.concatenate(
+                (
+                    np.reshape(
+                        test_data["atomic_dipole"][:numb_test], [-1, 3 * sel_natoms]
+                    ),
+                    np.reshape(dipole, [-1, 3 * sel_natoms]),
+                ),
+                axis=1,
+            )
+            header_text = [
+                f"{letter}{number}"
+                for number in range(1, sel_natoms + 1)
+                for letter in ["data_x", "data_y", "data_z"]
+            ] + [
+                f"{letter}{number}"
+                for number in range(1, sel_natoms + 1)
+                for letter in ["pred_x", "pred_y", "pred_z"]
+            ]
+            header_text = " ".join(header_text)
 
-        pe = np.concatenate(
-            (
-                np.reshape(test_data["dipole"][:numb_test], [-1, 3]),
-                np.reshape(dipole, [-1, 3]),
-            ),
-            axis=1,
-        )
         np.savetxt(
             detail_path.with_suffix(".out"),
             pe,
-            header="data_x data_y data_z pred_x pred_y pred_z",
+            header=header_text,
         )
     return {"rmse": (rmse_f, dipole.size)}
 

deepmd/entrypoints/train.py

@@ -445,7 +445,7 @@ def get_min_nbor_dist(jdata, rcut):
 
 
 def parse_auto_sel(sel):
-    if type(sel) is not str:
+    if not isinstance(sel, str):
         return False
     words = sel.split(":")
     if words[0] == "auto":
@@ -476,7 +476,15 @@ def update_one_sel(jdata, descriptor):
     if descriptor["type"] == "loc_frame":
         return descriptor
     rcut = descriptor["rcut"]
-    tmp_sel = get_sel(jdata, rcut, one_type=descriptor["type"] in ("se_atten",))
+    tmp_sel = get_sel(
+        jdata,
+        rcut,
+        one_type=descriptor["type"]
+        in (
+            "se_atten",
+            "se_atten_v2",
+        ),
+    )
     sel = descriptor["sel"]
     if isinstance(sel, int):
         # convert to list and finnally convert back to int
@@ -495,7 +503,10 @@ def update_one_sel(jdata, descriptor):
                     "not less than %d, but you set it to %d. The accuracy"
                     " of your model may get worse." % (ii, tt, dd)
                 )
-    if descriptor["type"] in ("se_atten",):
+    if descriptor["type"] in (
+        "se_atten",
+        "se_atten_v2",
+    ):
         descriptor["sel"] = sel = sum(sel)
     return descriptor