feat: add new batch size rules for large systems

deepmd/pd/utils/dataloader.py

@@ -126,16 +126,41 @@
         if isinstance(batch_size, str):
             if batch_size == "auto":
                 rule = 32
+                ceiling = True
             elif batch_size.startswith("auto:"):
                 rule = int(batch_size.split(":")[1])
+                ceiling = True
+            elif batch_size.startswith("max:"):
+                rule = int(batch_size.split(":")[1])
+                ceiling = False
+            elif batch_size.startswith("filter:"):
+                # remove system with more than `filter` atoms
+                rule = int(batch_size.split(":")[1])
+                len_before = len(self.systems)
+                self.systems = [
+                    system for system in self.systems if system._natoms <= rule
+                ]
+                len_after = len(self.systems)
+                if len_before != len_after:
+                    log.warning(
+                        f"Remove {len_before - len_after} systems with more than {rule} atoms"
+                    )
+                if len(self.systems) == 0:
+                    raise ValueError(
+                        f"No system left after removing systems with more than {rule} atoms"
+                    )
+                ceiling = False
             else:
-                rule = None
-                log.error("Unsupported batch size type")
+                raise ValueError(f"Unsupported batch size rule: {batch_size}")
             for ii in self.systems:
                 ni = ii._natoms
                 bsi = rule // ni
-                if bsi * ni < rule:
-                    bsi += 1
+                if ceiling:
+                    if bsi * ni < rule:
+                        bsi += 1
+                else:
+                    if bsi == 0:
+                        bsi = 1
                 self.batch_sizes.append(bsi)
         elif isinstance(batch_size, list):
             self.batch_sizes = batch_size

deepmd/pt/utils/dataloader.py

@@ -120,16 +120,41 @@ def construct_dataset(system):
         if isinstance(batch_size, str):
             if batch_size == "auto":
                 rule = 32
+                ceiling = True
             elif batch_size.startswith("auto:"):
                 rule = int(batch_size.split(":")[1])
+                ceiling = True
+            elif batch_size.startswith("max:"):
+                rule = int(batch_size.split(":")[1])
+                ceiling = False
+            elif batch_size.startswith("filter:"):
+                # remove system with more than `filter` atoms
+                rule = int(batch_size.split(":")[1])
+                len_before = len(self.systems)
+                self.systems = [
+                    system for system in self.systems if system._natoms <= rule
+                ]
+                len_after = len(self.systems)
+                if len_before != len_after:
+                    log.warning(
+                        f"Remove {len_before - len_after} systems with more than {rule} atoms"
+                    )
+                if len(self.systems) == 0:
+                    raise ValueError(
+                        f"No system left after removing systems with more than {rule} atoms"
+                    )
+                ceiling = False
             else:
-                rule = None
-                log.error("Unsupported batch size type")
+                raise ValueError(f"Unsupported batch size rule: {batch_size}")
             for ii in self.systems:
                 ni = ii._natoms
                 bsi = rule // ni
-                if bsi * ni < rule:
-                    bsi += 1
+                if ceiling:
+                    if bsi * ni < rule:
+                        bsi += 1
+                else:
+                    if bsi == 0:
+                        bsi = 1
                 self.batch_sizes.append(bsi)
         elif isinstance(batch_size, list):
             self.batch_sizes = batch_size

deepmd/utils/argcheck.py

@@ -2831,6 +2831,8 @@ def training_data_args():  # ! added by Ziyao: new specification style for data
 - string "auto": automatically determines the batch size so that the batch_size times the number of atoms in the system is no less than 32.\n\n\
 - string "auto:N": automatically determines the batch size so that the batch_size times the number of atoms in the system is no less than N.\n\n\
 - string "mixed:N": the batch data will be sampled from all systems and merged into a mixed system with the batch size N. Only support the se_atten descriptor for TensorFlow backend.\n\n\
+- string "max:N": automatically determines the batch size so that the batch_size times the number of atoms in the system is no more than N.\n\n\
+- string "filter:N": the same as `"max:N"` but removes the systems with the number of atoms larger than `N` from the data set.\n\n\
 If MPI is used, the value should be considered as the batch size per task.'
     doc_auto_prob_style = 'Determine the probability of systems automatically. The method is assigned by this key and can be\n\n\
 - "prob_uniform"  : the probability all the systems are equal, namely 1.0/self.get_nsystems()\n\n\

doc/train/training-advanced.md

@@ -106,7 +106,9 @@ The sections {ref}`training_data <training/training_data>` and {ref}`validation_
   - `list`: the length of which is the same as the {ref}`systems`. The batch size of each system is given by the elements of the list.
   - `int`: all systems use the same batch size.
   - `"auto"`: the same as `"auto:32"`, see `"auto:N"`
-  - `"auto:N"`: automatically determines the batch size so that the {ref}`batch_size <training/training_data/batch_size>` times the number of atoms in the system is no less than `N`.
+  - `"auto:N"`: automatically determines the batch size so that the {ref}`batch_size <training/training_data/batch_size>` times the number of atoms in the system is **no less than** `N`.
+  - `"max:N"`: automatically determines the batch size so that the {ref}`batch_size <training/training_data/batch_size>` times the number of atoms in the system is **no more than** `N`. The minimum batch size is 1. **Supported backends**: PyTorch {{ pytorch_icon }}, Paddle {{ paddle_icon }}
+  - `"filter:N"`: the same as `"max:N"` but removes the systems with the number of atoms larger than `N` from the data set. Throws an error if no system is left in a dataset. **Supported backends**: PyTorch {{ pytorch_icon }}, Paddle {{ paddle_icon }}
 - The key {ref}`numb_batch <training/validation_data/numb_btch>` in {ref}`validate_data <training/validation_data>` gives the number of batches of model validation. Note that the batches may not be from the same system
 
 The section {ref}`mixed_precision <training/mixed_precision>` specifies the mixed precision settings, which will enable the mixed precision training workflow for DeePMD-kit. The keys are explained below:

source/tests/pt/test_dploaderset.py

@@ -0,0 +1,86 @@
+# SPDX-License-Identifier: LGPL-3.0-or-later
+import json
+import unittest
+from pathlib import (
+    Path,
+)
+
+from deepmd.common import (
+    expand_sys_str,
+)
+from deepmd.pt.utils.dataloader import (
+    DpLoaderSet,
+)
+
+
+class TestSampler(unittest.TestCase):
+    def setUp(self) -> None:
+        with open(str(Path(__file__).parent / "water/se_e2_a.json")) as fin:
+            content = fin.read()
+        config = json.loads(content)
+        data_file = [
+            str(Path(__file__).parent / "model/water/data/data_0"),
+        ]
+        config["training"]["training_data"]["systems"] = data_file
+        config["training"]["validation_data"]["systems"] = data_file
+        model_config = config["model"]
+        self.rcut = model_config["descriptor"]["rcut"]
+        self.rcut_smth = model_config["descriptor"]["rcut_smth"]
+        self.sel = model_config["descriptor"]["sel"]
+        self.batch_size = config["training"]["training_data"]["batch_size"]
+        self.systems = config["training"]["validation_data"]["systems"]
+        self.type_map = model_config["type_map"]
+        if isinstance(self.systems, str):
+            self.systems = expand_sys_str(self.systems)
+
+    def get_batch_sizes(self, batch_size) -> int:
+        dataset = DpLoaderSet(
+            self.systems,
+            batch_size,
+            self.type_map,
+            seed=10,
+            shuffle=False,
+        )
+        return dataset.batch_sizes[0]
+
+    def test_batchsize(self) -> None:
+        # 192 atoms, 1 system
+        assert len(self.systems) == 1
+
+        # test: batch_size:int
+        self.assertEqual(self.get_batch_sizes(3), 3)
+
+        # test: batch_size:list[int]
+        self.assertEqual(self.get_batch_sizes([3]), 3)
+
+        # test: batch_size:str = "auto"
+        self.assertEqual(self.get_batch_sizes("auto:384"), 2)
+        self.assertEqual(self.get_batch_sizes("auto:383"), 2)
+        self.assertEqual(self.get_batch_sizes("auto:193"), 2)
+        self.assertEqual(self.get_batch_sizes("auto:192"), 1)
+        self.assertEqual(self.get_batch_sizes("auto:191"), 1)
+        self.assertEqual(self.get_batch_sizes("auto:32"), 1)
+        self.assertEqual(self.get_batch_sizes("auto"), 1)
+
+        # test: batch_size:str = "max"
+        self.assertEqual(self.get_batch_sizes("max:384"), 2)
+        self.assertEqual(self.get_batch_sizes("max:383"), 1)
+        self.assertEqual(self.get_batch_sizes("max:193"), 1)
+        self.assertEqual(self.get_batch_sizes("max:192"), 1)
+        self.assertEqual(self.get_batch_sizes("max:191"), 1)
+
+        # test: batch_size:str = "filter"
+        self.assertEqual(self.get_batch_sizes("filter:193"), 1)
+        self.assertEqual(self.get_batch_sizes("filter:192"), 1)
+        with self.assertLogs(logger="deepmd") as cm:
+            self.assertRaises(ValueError, self.get_batch_sizes, "filter:191")
+        self.assertIn("Remove 1 systems with more than 191 atoms", cm.output[-1])
+
+        # test: unknown batch_size: str
+        with self.assertRaises(ValueError) as context:
+            self.get_batch_sizes("unknown")
+        self.assertIn("Unsupported batch size rule: unknown", str(context.exception))
+
+
+if __name__ == "__main__":
+    unittest.main()