Clean TODOs and convert them into issues

.github/workflows/todo.yml

@@ -0,0 +1,20 @@
+name: TODO workflow
+on:
+  push:
+    branches:
+      - devel
+jobs:
+  build:
+    if: github.repository_owner == 'deepmodeling'
+    runs-on: ubuntu-latest
+    steps:
+    - uses: actions/checkout@v4
+    - name: Run tdg-github-action
+      uses: ribtoks/tdg-github-action@master
+      with:
+        TOKEN: ${{ secrets.GITHUB_TOKEN }}
+        REPO: ${{ github.repository }}
+        SHA: ${{ github.sha }}
+        REF: ${{ github.ref }}
+        EXCLUDE_PATTERN: "(source/3rdparty|.git)/.*"
+        COMMENT_ON_ISSUES: 1

.pre-commit-config.yaml

@@ -12,7 +12,6 @@ repos:
     -   id: check-json
     -   id: check-added-large-files
         args: ['--maxkb=1024', '--enforce-all']
-        # TODO: remove the following after resolved
         exclude: |
             (?x)^(
                 source/tests/infer/dipolecharge_e.pbtxt|

deepmd/common.py

@@ -71,8 +71,9 @@
     )
 
 
-# TODO this is not a good way to do things. This is some global variable to which
-# TODO anyone can write and there is no good way to keep track of the changes
+# TODO: refactor data_requirement to make it not a global variable
+# this is not a good way to do things. This is some global variable to which
+# anyone can write and there is no good way to keep track of the changes
 data_requirement = {}
 
 
@@ -180,9 +181,10 @@ def make_default_mesh(pbc: bool, mixed_type: bool) -> np.ndarray:
     return default_mesh
 
 
-# TODO maybe rename this to j_deprecated and only warn about deprecated keys,
-# TODO if the deprecated_key argument is left empty function puppose is only custom
-# TODO error since dict[key] already raises KeyError when the key is missing
+# TODO: rename j_must_have to j_deprecated and only warn about deprecated keys
+# maybe rename this to j_deprecated and only warn about deprecated keys,
+# if the deprecated_key argument is left empty function puppose is only custom
+# error since dict[key] already raises KeyError when the key is missing
 def j_must_have(
     jdata: Dict[str, "_DICT_VAL"], key: str, deprecated_key: List[str] = []
 ) -> "_DICT_VAL":
@@ -238,7 +240,7 @@ def j_loader(filename: Union[str, Path]) -> Dict[str, Any]:
         raise TypeError("config file must be json, or yaml/yml")
 
 
-# TODO port completely to pathlib when all callers are ported
+# TODO port expand_sys_str completely to pathlib when all callers are ported
 def expand_sys_str(root_dir: Union[str, Path]) -> List[str]:
     """Recursively iterate over directories taking those that contain `type.raw` file.
 

deepmd/dpmodel/fitting/general_fitting.py

@@ -313,7 +313,8 @@ def _call_common(
             )
         xx = descriptor
         if self.remove_vaccum_contribution is not None:
-            # TODO: Idealy, the input for vaccum should be computed;
+            # TODO: comput the input for vaccum when setting remove_vaccum_contribution
+            # Idealy, the input for vaccum should be computed;
             # we consider it as always zero for convenience.
             # Needs a compute_input_stats for vaccum passed from the
             # descriptor.

deepmd/dpmodel/utils/network.py

@@ -90,7 +90,8 @@ def __call__(self):
         return self.count
 
 
-# TODO: should be moved to otherwhere...
+# TODO: move save_dp_model and load_dp_model to a seperated module
+# should be moved to otherwhere...
 def save_dp_model(filename: str, model_dict: dict) -> None:
     """Save a DP model to a file in the native format.
 

deepmd/dpmodel/utils/update_sel.py

@@ -17,5 +17,5 @@ def neighbor_stat(self) -> Type[NeighborStat]:
         return NeighborStat
 
     def hook(self, min_nbor_dist, max_nbor_size):
-        # TODO: save to the model
+        # TODO: save to the model in UpdateSel.hook
         pass

deepmd/pt/entrypoints/main.py

@@ -285,9 +285,7 @@ def freeze(FLAGS):
     torch.jit.save(
         model,
         FLAGS.output,
-        {
-            # TODO: _extra_files
-        },
+        {},
     )
 
 

deepmd/pt/loss/ener.py

@@ -77,7 +77,7 @@ def __init__(
         self.has_f = (start_pref_f != 0.0 and limit_pref_f != 0.0) or inference
         self.has_v = (start_pref_v != 0.0 and limit_pref_v != 0.0) or inference
 
-        # TODO need support for atomic energy and atomic pref
+        # TODO EnergyStdLoss need support for atomic energy and atomic pref
         self.has_ae = (start_pref_ae != 0.0 and limit_pref_ae != 0.0) or inference
         self.has_pf = (start_pref_pf != 0.0 and limit_pref_pf != 0.0) or inference
 

deepmd/pt/loss/ener_spin.py

@@ -47,7 +47,7 @@ def __init__(
         self.has_fr = (start_pref_fr != 0.0 and limit_pref_fr != 0.0) or inference
         self.has_fm = (start_pref_fm != 0.0 and limit_pref_fm != 0.0) or inference
 
-        # TODO need support for virial, atomic energy and atomic pref
+        # TODO EnergySpinLoss needs support for virial, atomic energy and atomic pref
         self.has_v = (start_pref_v != 0.0 and limit_pref_v != 0.0) or inference
         self.has_ae = (start_pref_ae != 0.0 and limit_pref_ae != 0.0) or inference
         self.has_pf = (start_pref_pf != 0.0 and limit_pref_pf != 0.0) or inference

deepmd/pt/model/task/fitting.py

@@ -457,7 +457,8 @@ def _forward_common(
     ):
         xx = descriptor
         if self.remove_vaccum_contribution is not None:
-            # TODO: Idealy, the input for vaccum should be computed;
+            # TODO: compute the input for vaccm when remove_vaccum_contribution is set
+            # Idealy, the input for vaccum should be computed;
             # we consider it as always zero for convenience.
             # Needs a compute_input_stats for vaccum passed from the
             # descriptor.

deepmd/pt/train/training.py

@@ -558,14 +558,16 @@ def get_loss(loss_params, start_lr, _ntypes, _model):
                 output_device=LOCAL_RANK,
             )
 
-        # TODO ZD add lr warmups for multitask
+        # TODO add lr warmups for multitask
+        # author: iProzd
         def warm_up_linear(step, warmup_steps):
             if step < warmup_steps:
                 return step / warmup_steps
             else:
                 return self.lr_exp.value(step - warmup_steps) / self.lr_exp.start_lr
 
-        # TODO ZD add optimizers for multitask
+        # TODO add optimizers for multitask
+        # author: iProzd
         if self.opt_type == "Adam":
             self.optimizer = torch.optim.Adam(
                 self.wrapper.parameters(), lr=self.lr_exp.start_lr

deepmd/pt/utils/update_sel.py

@@ -17,5 +17,5 @@ def neighbor_stat(self) -> Type[NeighborStat]:
         return NeighborStat
 
     def hook(self, min_nbor_dist, max_nbor_size):
-        # TODO: save to the model
+        # TODO: save to the model in UpdateSel.hook
         pass

deepmd/tf/descriptor/descriptor.py

@@ -102,9 +102,6 @@ def get_dim_rot_mat_1(self) -> int:
         int
             the first dimension of the rotation matrix
         """
-        # TODO: I think this method should be implemented as it's called by dipole and
-        # polar fitting network. However, currently not all descriptors have this
-        # method.
         raise NotImplementedError
 
     def get_nlist(self) -> Tuple[tf.Tensor, tf.Tensor, List[int], List[int]]:
@@ -121,8 +118,6 @@ def get_nlist(self) -> Tuple[tf.Tensor, tf.Tensor, List[int], List[int]]:
         sel_r : list[int]
             The number of neighbors with only radial information
         """
-        # TODO: I think this method should be implemented as it's called by energy
-        # model. However, se_ar and hybrid doesn't have this method.
         raise NotImplementedError
 
     @abstractmethod

deepmd/tf/descriptor/se_a.py

@@ -1426,7 +1426,8 @@ def serialize(self, suffix: str = "") -> dict:
             raise NotImplementedError("spin is unsupported")
         assert self.davg is not None
         assert self.dstd is not None
-        # TODO: not sure how to handle type embedding - type embedding is not a model parameter,
+        # TODO: tf: handle type embedding in DescrptSeA.serialize
+        # not sure how to handle type embedding - type embedding is not a model parameter,
         # but instead a part of the input data. Maybe the interface should be refactored...
 
         return {

deepmd/tf/descriptor/se_a_mask.py

@@ -249,10 +249,9 @@ def compute_input_stats(
         **kwargs
             Additional keyword arguments.
         """
-        """
-        TODO: Since not all input atoms are real in se_a_mask,
-        statistics should be reimplemented for se_a_mask descriptor.
-        """
+        # TODO: implement compute_input_stats for DescrptSeAMask
+        # Since not all input atoms are real in se_a_mask,
+        # statistics should be reimplemented for se_a_mask descriptor.
 
         self.davg = None
         self.dstd = None

deepmd/tf/descriptor/se_r.py

@@ -766,7 +766,8 @@ def serialize(self, suffix: str = "") -> dict:
             raise NotImplementedError("spin is unsupported")
         assert self.davg is not None
         assert self.dstd is not None
-        # TODO: not sure how to handle type embedding - type embedding is not a model parameter,
+        # TODO: tf: handle type embedding in DescrptSeR.serialize
+        # not sure how to handle type embedding - type embedding is not a model parameter,
         # but instead a part of the input data. Maybe the interface should be refactored...
         return {
             "@class": "Descriptor",

deepmd/tf/env.py

@@ -157,7 +157,8 @@ def dlopen_library(module: str, filename: str):
     r"(final)_layer_type_(\d+)/(matrix)|"
     r"(final)_layer/(bias)|"
     r"(final)_layer_type_(\d+)/(bias)|"
-    # TODO: not sure how to parse for shared layers...
+    # TODO: supporting extracting parameters for shared layers
+    # not sure how to parse for shared layers...
     # layer_name
     r"share_.+_type_\d/matrix|"
     r"share_.+_type_\d/bias|"

deepmd/tf/fit/dipole.py

@@ -355,7 +355,7 @@ def serialize(self, suffix: str) -> dict:
             "dim_descrpt": self.dim_descrpt,
             "embedding_width": self.dim_rot_mat_1,
             # very bad design: type embedding is not passed to the class
-            # TODO: refactor the class
+            # TODO: refactor the class for type embedding and dipole fitting
             "mixed_types": False,
             "dim_out": 3,
             "neuron": self.n_neuron,
@@ -365,7 +365,7 @@ def serialize(self, suffix: str) -> dict:
             "exclude_types": [],
             "nets": self.serialize_network(
                 ntypes=self.ntypes,
-                # TODO: consider type embeddings
+                # TODO: consider type embeddings in dipole fitting
                 ndim=1,
                 in_dim=self.dim_descrpt,
                 out_dim=self.dim_rot_mat_1,

deepmd/tf/fit/dos.py

@@ -701,7 +701,7 @@ def serialize(self, suffix: str = "") -> dict:
             "ntypes": self.ntypes,
             "dim_descrpt": self.dim_descrpt,
             # very bad design: type embedding is not passed to the class
-            # TODO: refactor the class
+            # TODO: refactor the class for DOSFitting and type embedding
             "mixed_types": False,
             "dim_out": self.numb_dos,
             "neuron": self.n_neuron,
@@ -715,7 +715,7 @@ def serialize(self, suffix: str = "") -> dict:
             "exclude_types": [],
             "nets": self.serialize_network(
                 ntypes=self.ntypes,
-                # TODO: consider type embeddings
+                # TODO: consider type embeddings for DOSFitting
                 ndim=1,
                 in_dim=self.dim_descrpt + self.numb_fparam + self.numb_aparam,
                 out_dim=self.numb_dos,

deepmd/tf/fit/ener.py

@@ -893,7 +893,7 @@ def serialize(self, suffix: str = "") -> dict:
             "ntypes": self.ntypes,
             "dim_descrpt": self.dim_descrpt,
             # very bad design: type embedding is not passed to the class
-            # TODO: refactor the class
+            # TODO: refactor the class for energy fitting and type embedding
             "mixed_types": False,
             "dim_out": 1,
             "neuron": self.n_neuron,
@@ -912,7 +912,7 @@ def serialize(self, suffix: str = "") -> dict:
             "exclude_types": [],
             "nets": self.serialize_network(
                 ntypes=self.ntypes,
-                # TODO: consider type embeddings
+                # TODO: consider type embeddings for type embedding
                 ndim=1,
                 in_dim=self.dim_descrpt + self.numb_fparam + self.numb_aparam,
                 neuron=self.n_neuron,

deepmd/tf/fit/polar.py

@@ -545,7 +545,7 @@ def serialize(self, suffix: str) -> dict:
             "dim_descrpt": self.dim_descrpt,
             "embedding_width": self.dim_rot_mat_1,
             # very bad design: type embedding is not passed to the class
-            # TODO: refactor the class
+            # TODO: refactor the class for polar fitting and type embedding
             "mixed_types": False,
             "dim_out": 3,
             "neuron": self.n_neuron,
@@ -558,7 +558,7 @@ def serialize(self, suffix: str) -> dict:
             "shift_diag": self.shift_diag,
             "nets": self.serialize_network(
                 ntypes=self.ntypes,
-                # TODO: consider type embeddings
+                # TODO: consider type embeddings for polar fitting
                 ndim=1,
                 in_dim=self.dim_descrpt,
                 out_dim=self.dim_rot_mat_1,

deepmd/tf/infer/deep_tensor.py

@@ -412,7 +412,6 @@ def eval_full(
         if ghost_map is not None:
             # add the value of ghost atoms to real atoms
             force = np.reshape(force, [nframes * nout, -1, 3])
-            # TODO: is there some way not to use for loop?
             for ii in range(nframes * nout):
                 np.add.at(force[ii], ghost_map, force[ii, nloc:])
             if atomic:

deepmd/tf/loss/ener.py

@@ -120,7 +120,6 @@ def __init__(
             "atom_pref", 1, atomic=True, must=False, high_prec=False, repeat=3
         )
         # drdq: the partial derivative of atomic coordinates w.r.t. generalized coordinates
-        # TODO: could numb_generalized_coord decided from the training data?
         if self.has_gf > 0:
             add_data_requirement(
                 "drdq",

deepmd/tf/model/linear.py

@@ -54,7 +54,6 @@ def __init__(self, models: List[dict], weights: List[float], **kwargs):
             self.weights = [1 / len(models) for _ in range(len(models))]
         elif weights == "sum":
             self.weights = [1 for _ in range(len(models))]
-        # TODO: add more weights, for example, so-called committee models
         else:
             raise ValueError(f"Invalid weights {weights}")
 

deepmd/tf/train/trainer.py

@@ -296,8 +296,6 @@ def build(self, data=None, stop_batch=0, origin_type_map=None, suffix=""):
                 )
 
             # neighbor_stat is moved to train.py as duplicated
-            # TODO: this is a simple fix but we should have a clear
-            #       architecture to call neighbor stat
         else:
             self.model.enable_compression()
 

deepmd/tf/utils/batch_size.py

@@ -35,6 +35,4 @@ def is_oom_error(self, e: Exception) -> bool:
         e : Exception
             Exception
         """
-        # TODO: it's very slow to catch OOM error; I don't know what TF is doing here
-        # but luckily we only need to catch once
         return isinstance(e, (tf.errors.ResourceExhaustedError, OutOfMemoryError))

deepmd/tf/utils/finetune.py

@@ -100,7 +100,7 @@ def replace_model_params_with_pretrained_model(
         ):
             target_para = pretrained_jdata["model"][config_key]
             cur_para = jdata["model"][config_key]
-            # keep some params that are irrelevant to model structures (need to discuss) TODO
+            # TODO: keep some params that are irrelevant to model structures (need to discuss)
             if "trainable" in cur_para.keys():
                 target_para["trainable"] = cur_para["trainable"]
             log.info(f"Change the '{config_key}' from {cur_para!s} to {target_para!s}.")

deepmd/tf/utils/graph.py

@@ -22,7 +22,6 @@
 )
 
 
-# TODO (JZ): I think in this file we can merge some duplicated lines into one method...
 def load_graph_def(model_file: str) -> Tuple[tf.Graph, tf.GraphDef]:
     """Load graph as well as the graph_def from the frozen model(model_file).
 

deepmd/tf/utils/multi_init.py

@@ -164,7 +164,7 @@ def replace_model_params_with_frz_multi_model(
 def _change_sub_config(jdata: Dict[str, Any], src_jdata: Dict[str, Any], sub_key: str):
     target_para = src_jdata[sub_key]
     cur_para = jdata[sub_key]
-    # keep some params that are irrelevant to model structures (need to discuss) TODO
+    # TODO: keep some params that are irrelevant to model structures (need to discuss)
     if "trainable" in cur_para.keys():
         target_para["trainable"] = cur_para["trainable"]
     log.info(f"Change the '{sub_key}' from {cur_para!s} to {target_para!s}.")

deepmd/tf/utils/update_sel.py

@@ -24,8 +24,6 @@ def neighbor_stat(self) -> Type[NeighborStat]:
 
     def hook(self, min_nbor_dist, max_nbor_size):
         # moved from traier.py as duplicated
-        # TODO: this is a simple fix but we should have a clear
-        #       architecture to call neighbor stat
         tf.constant(
             min_nbor_dist,
             name="train_attr/min_nbor_dist",

deepmd/utils/batch_size.py

@@ -51,7 +51,6 @@ class AutoBatchSize(ABC):
 
     def __init__(self, initial_batch_size: int = 1024, factor: float = 2.0) -> None:
         # See also PyTorchLightning/pytorch-lightning#1638
-        # TODO: discuss a proper initial batch size
         self.current_batch_size = initial_batch_size
         DP_INFER_BATCH_SIZE = int(os.environ.get("DP_INFER_BATCH_SIZE", 0))
         if DP_INFER_BATCH_SIZE > 0:

deepmd/utils/data_system.py

@@ -670,7 +670,6 @@ def print_summary(
             % (
                 _format_name_length(system_dirs[ii], sys_width),
                 natoms[ii],
-                # TODO batch size * nbatches = number of structures
                 batch_size[ii],
                 nbatches[ii],
                 sys_probs[ii],

deepmd/utils/path.py

@@ -39,7 +39,6 @@ def __new__(cls, path: str, mode: str = "r"):
                 return super().__new__(DPOSPath)
             elif os.path.isfile(path.split("#")[0]):
                 # assume h5 if it is not dir
-                # TODO: check if it is a real h5? or just check suffix?
                 return super().__new__(DPH5Path)
             raise FileNotFoundError("%s not found" % path)
         return super().__new__(cls)
@@ -217,7 +216,6 @@ def glob(self, pattern: str) -> List["DPPath"]:
             list of paths
         """
         # currently DPOSPath will only derivative DPOSPath
-        # TODO: discuss if we want to mix DPOSPath and DPH5Path?
         return [type(self)(p, mode=self.mode) for p in self.path.glob(pattern)]
 
     def rglob(self, pattern: str) -> List["DPPath"]: