pt: refactor data stat

deepmd/common.py

@@ -5,6 +5,9 @@
 import platform
 import shutil
 import warnings
+from hashlib import (
+    sha1,
+)
 from pathlib import (
     Path,
 )
@@ -299,3 +302,14 @@
             os.symlink(os.path.relpath(ori_ff, os.path.dirname(new_ff)), new_ff)
         else:
             shutil.copyfile(ori_ff, new_ff)
+
+
+def get_hash(obj) -> str:
+    """Get hash of object.
+
+    Parameters
+    ----------
+    obj
+        object to hash
+    """
+    return sha1(json.dumps(obj).encode("utf-8")).hexdigest()

deepmd/dpmodel/descriptor/make_base_descriptor.py

@@ -9,6 +9,10 @@
     Optional,
 )
 
+from deepmd.utils.path import (
+    DPPath,
+)
+
 
 def make_base_descriptor(
     t_tensor,
@@ -69,14 +73,12 @@ def distinguish_types(self) -> bool:
             """
             pass
 
-        def compute_input_stats(self, merged):
+        def compute_input_stats(
+            self, merged: List[dict], path: Optional[DPPath] = None
+        ):
             """Update mean and stddev for descriptor elements."""
             raise NotImplementedError
 
-        def init_desc_stat(self, **kwargs):
-            """Initialize the model bias by the statistics."""
-            raise NotImplementedError
-
         @abstractmethod
         def fwd(
             self,

deepmd/dpmodel/descriptor/se_e2_a.py

@@ -1,6 +1,10 @@
 # SPDX-License-Identifier: LGPL-3.0-or-later
 import numpy as np
 
+from deepmd.utils.path import (
+    DPPath,
+)
+
 try:
     from deepmd._version import version as __version__
 except ImportError:
@@ -229,14 +233,10 @@ def get_ntypes(self) -> int:
         """Returns the number of element types."""
         return self.ntypes
 
-    def compute_input_stats(self, merged):
+    def compute_input_stats(self, merged: List[dict], path: Optional[DPPath] = None):
         """Update mean and stddev for descriptor elements."""
         raise NotImplementedError
 
-    def init_desc_stat(self, sumr, suma, sumn, sumr2, suma2):
-        """Initialize the model bias by the statistics."""
-        raise NotImplementedError
-
     def cal_g(
         self,
         ss,

deepmd/pt/entrypoints/main.py

@@ -12,6 +12,7 @@
     Union,
 )
 
+import h5py
 import torch
 import torch.distributed as dist
 import torch.version
@@ -46,12 +47,6 @@
 from deepmd.pt.infer import (
     inference,
 )
-from deepmd.pt.model.descriptor import (
-    Descriptor,
-)
-from deepmd.pt.model.task import (
-    Fitting,
-)
 from deepmd.pt.train import (
     training,
 )
@@ -69,7 +64,9 @@
 )
 from deepmd.pt.utils.stat import (
     make_stat_input,
-    process_stat_path,
+)
+from deepmd.utils.path import (
+    DPPath,
 )
 from deepmd.utils.summary import SummaryPrinter as BaseSummaryPrinter
 
@@ -134,19 +131,16 @@
         # noise_settings = None
 
         # stat files
-        hybrid_descrpt = model_params_single["descriptor"]["type"] == "hybrid"
-        if not hybrid_descrpt:
-            stat_file_path_single, has_stat_file_path = process_stat_path(
-                data_dict_single.get("stat_file", None),
-                data_dict_single.get("stat_file_dir", f"stat_files{suffix}"),
-                model_params_single,
-                Descriptor,
-                Fitting,
-            )
-        else:  ### TODO hybrid descriptor not implemented
-            raise NotImplementedError(
-                "data stat for hybrid descriptor is not implemented!"
-            )
+        stat_file_path_single = data_dict_single.get("stat_file", None)
+        if stat_file_path_single is not None:
+            if Path(stat_file_path_single).is_dir():
+                raise ValueError(
+                    f"stat_file should be a file, not a directory: {stat_file_path_single}"
+                )
+            if not Path(stat_file_path_single).is_file():
+                with h5py.File(stat_file_path_single, "w") as f:
+                    pass
+            stat_file_path_single = DPPath(stat_file_path_single, "a")
 
         # validation and training data
         validation_data_single = DpLoaderSet(
@@ -156,7 +150,7 @@
             type_split=type_split,
             noise_settings=noise_settings,
         )
-        if ckpt or finetune_model or has_stat_file_path:
+        if ckpt or finetune_model:
             train_data_single = DpLoaderSet(
                 training_systems,
                 training_dataset_params["batch_size"],

deepmd/pt/model/atomic_model/dp_atomic_model.py

@@ -1,13 +1,11 @@
 # SPDX-License-Identifier: LGPL-3.0-or-later
 import copy
 import logging
-import os
 import sys
 from typing import (
     Dict,
     List,
     Optional,
-    Union,
 )
 
 import torch
@@ -24,6 +22,9 @@
 from deepmd.pt.utils.utils import (
     dict_to_device,
 )
+from deepmd.utils.path import (
+    DPPath,
+)
 
 from .base_atomic_model import (
     BaseAtomicModel,
@@ -160,9 +161,8 @@
 
     def compute_or_load_stat(
         self,
-        type_map: Optional[List[str]] = None,
-        sampled=None,
-        stat_file_path_dict: Optional[Dict[str, Union[str, List[str]]]] = None,
+        sampled,
+        stat_file_path: Optional[DPPath] = None,
     ):
         """
         Compute or load the statistics parameters of the model,
@@ -174,31 +174,18 @@
 
         Parameters
         ----------
-        type_map
-            Mapping atom type to the name (str) of the type.
-            For example `type_map[1]` gives the name of the type 1.
         sampled
             The sampled data frames from different data systems.
-        stat_file_path_dict
+        stat_file_path
             The dictionary of paths to the statistics files.
         """
-        if sampled is not None:  # move data to device
-            for data_sys in sampled:
-                dict_to_device(data_sys)
-        if stat_file_path_dict is not None:
-            if not isinstance(stat_file_path_dict["descriptor"], list):
-                stat_file_dir = os.path.dirname(stat_file_path_dict["descriptor"])
-            else:
-                stat_file_dir = os.path.dirname(stat_file_path_dict["descriptor"][0])
-            if not os.path.exists(stat_file_dir):
-                os.mkdir(stat_file_dir)
-        self.descriptor.compute_or_load_stat(
-            type_map, sampled, stat_file_path_dict["descriptor"]
-        )
+        for data_sys in sampled:
+            dict_to_device(data_sys)
+        if sampled is None:
+            sampled = []
+        self.descriptor.compute_input_stats(sampled, stat_file_path)
         if self.fitting_net is not None:
-            self.fitting_net.compute_or_load_stat(
-                type_map, sampled, stat_file_path_dict["fitting_net"]
-            )
+            self.fitting_net.compute_output_stats(sampled, stat_file_path)
 
     @torch.jit.export
     def get_dim_fparam(self) -> int:

deepmd/pt/model/descriptor/__init__.py

@@ -2,7 +2,6 @@
 from .descriptor import (
     Descriptor,
     DescriptorBlock,
-    compute_std,
     make_default_type_embedding,
 )
 from .dpa1 import (
@@ -32,7 +31,6 @@
 __all__ = [
     "Descriptor",
     "DescriptorBlock",
-    "compute_std",
     "make_default_type_embedding",
     "DescrptBlockSeA",
     "DescrptBlockSeAtten",