feat(pt): add universal test for loss

deepmd/dpmodel/common.py

@@ -29,7 +29,7 @@
     "double": np.float64,
     "int32": np.int32,
     "int64": np.int64,
-    "bool": bool,
+    "bool": np.bool_,
     "default": GLOBAL_NP_FLOAT_PRECISION,
     # NumPy doesn't have bfloat16 (and doesn't plan to add)
     # ml_dtypes is a solution, but it seems not supporting np.save/np.load
@@ -45,7 +45,7 @@
     np.int32: "int32",
     np.int64: "int64",
     ml_dtypes.bfloat16: "bfloat16",
-    bool: "bool",
+    np.bool_: "bool",
 }
 assert set(RESERVED_PRECISON_DICT.keys()) == set(PRECISION_DICT.values())
 DEFAULT_PRECISION = "float64"

deepmd/pt/loss/dos.py

@@ -151,10 +151,10 @@ def forward(self, input_dict, model, label, natoms, learning_rate=0.0, mae=False
         if self.has_acdf and "atom_dos" in model_pred and "atom_dos" in label:
             find_local = label.get("find_atom_dos", 0.0)
             pref_acdf = pref_acdf * find_local
-            local_tensor_pred_cdf = torch.cusum(
+            local_tensor_pred_cdf = torch.cumsum(
                 model_pred["atom_dos"].reshape([-1, natoms, self.numb_dos]), dim=-1
             )
-            local_tensor_label_cdf = torch.cusum(
+            local_tensor_label_cdf = torch.cumsum(
                 label["atom_dos"].reshape([-1, natoms, self.numb_dos]), dim=-1
             )
             diff = (local_tensor_pred_cdf - local_tensor_label_cdf).reshape(
@@ -199,10 +199,10 @@ def forward(self, input_dict, model, label, natoms, learning_rate=0.0, mae=False
         if self.has_cdf and "dos" in model_pred and "dos" in label:
             find_global = label.get("find_dos", 0.0)
             pref_cdf = pref_cdf * find_global
-            global_tensor_pred_cdf = torch.cusum(
+            global_tensor_pred_cdf = torch.cumsum(
                 model_pred["dos"].reshape([-1, self.numb_dos]), dim=-1
             )
-            global_tensor_label_cdf = torch.cusum(
+            global_tensor_label_cdf = torch.cumsum(
                 label["dos"].reshape([-1, self.numb_dos]), dim=-1
             )
             diff = global_tensor_pred_cdf - global_tensor_label_cdf

source/tests/universal/common/cases/loss/__init__.py

@@ -0,0 +1 @@
+# SPDX-License-Identifier: LGPL-3.0-or-later

source/tests/universal/common/cases/loss/loss.py

@@ -0,0 +1,11 @@
+# SPDX-License-Identifier: LGPL-3.0-or-later
+
+
+from .utils import (
+    LossTestCase,
+)
+
+
+class LossTest(LossTestCase):
+    def setUp(self) -> None:
+        LossTestCase.setUp(self)

source/tests/universal/common/cases/loss/utils.py

@@ -0,0 +1,75 @@
+# SPDX-License-Identifier: LGPL-3.0-or-later
+
+import numpy as np
+
+from deepmd.utils.data import (
+    DataRequirementItem,
+)
+
+from .....seed import (
+    GLOBAL_SEED,
+)
+
+
+class LossTestCase:
+    """Common test case for loss function."""
+
+    def setUp(self):
+        pass
+
+    def test_label_keys(self):
+        module = self.forward_wrapper(self.module)
+        label_requirement = self.module.label_requirement
+        label_dict = {item.key: item for item in label_requirement}
+        label_keys = sorted(label_dict.keys())
+        label_keys_expected = sorted(
+            [key for key in self.key_to_pref_map if self.key_to_pref_map[key] > 0]
+        )
+        np.testing.assert_equal(label_keys_expected, label_keys)
+
+    def test_forward(self):
+        module = self.forward_wrapper(self.module)
+        label_requirement = self.module.label_requirement
+        label_dict = {item.key: item for item in label_requirement}
+        label_keys = sorted(label_dict.keys())
+        natoms = 5
+
+        def fake_model():
+            model_predict = {
+                data_key: fake_input_one_frame(label_dict[data_key], natoms)
+                for data_key in label_keys
+            }
+            if "atom_ener" in model_predict:
+                model_predict["atom_energy"] = model_predict.pop("atom_ener")
+            model_predict.update({"mask_mag": np.ones([1, natoms, 1], dtype=np.bool_)})
+            return model_predict
+
+        labels = {
+            data_key: fake_input_one_frame(label_dict[data_key], natoms)
+            for data_key in label_keys
+        }
+        labels.update({"find_" + data_key: 1.0 for data_key in label_keys})
+
+        _, loss, more_loss = module(
+            {},
+            fake_model,
+            labels,
+            natoms,
+            1.0,
+        )
+
+
+def fake_input_one_frame(data_item: DataRequirementItem, natoms=5) -> np.ndarray:
+    ndof = data_item.ndof
+    atomic = data_item.atomic
+    repeat = data_item.repeat
+    rng = np.random.default_rng(seed=GLOBAL_SEED)
+    nframes = 1
+    dtype = data_item.dtype if data_item.dtype is not None else np.float64
+    if atomic:
+        data = rng.random([nframes, natoms, ndof], dtype)
+    else:
+        data = rng.random([nframes, ndof], dtype)
+    if repeat != 1:
+        data = np.repeat(data, repeat).reshape([nframes, -1])
+    return data

source/tests/universal/dpmodel/loss/__init__.py

@@ -0,0 +1 @@
+# SPDX-License-Identifier: LGPL-3.0-or-later

source/tests/universal/dpmodel/loss/test_loss.py

@@ -0,0 +1,203 @@
+# SPDX-License-Identifier: LGPL-3.0-or-later
+from collections import (
+    OrderedDict,
+)
+
+from ....consistent.common import (
+    parameterize_func,
+)
+
+
+def LossParamEnergy(
+    starter_learning_rate=1.0,
+    pref_e=1.0,
+    pref_f=1.0,
+    pref_v=1.0,
+    pref_ae=1.0,
+):
+    key_to_pref_map = {
+        "energy": pref_e,
+        "force": pref_f,
+        "virial": pref_v,
+        "atom_ener": pref_ae,
+    }
+    input_dict = {
+        "key_to_pref_map": key_to_pref_map,
+        "starter_learning_rate": starter_learning_rate,
+        "start_pref_e": pref_e,
+        "limit_pref_e": pref_e / 2,
+        "start_pref_f": pref_f,
+        "limit_pref_f": pref_f / 2,
+        "start_pref_v": pref_v,
+        "limit_pref_v": pref_v / 2,
+        "start_pref_ae": pref_ae,
+        "limit_pref_ae": pref_ae / 2,
+    }
+    return input_dict
+
+
+LossParamEnergyList = parameterize_func(
+    LossParamEnergy,
+    OrderedDict(
+        {
+            "pref_e": (1.0, 0.0),
+            "pref_f": (1.0, 0.0),
+            "pref_v": (1.0, 0.0),
+            "pref_ae": (1.0, 0.0),
+        }
+    ),
+)
+# to get name for the default function
+LossParamEnergy = LossParamEnergyList[0]
+
+
+def LossParamEnergySpin(
+    starter_learning_rate=1.0,
+    pref_e=1.0,
+    pref_fr=1.0,
+    pref_fm=1.0,
+    pref_v=1.0,
+    pref_ae=1.0,
+):
+    key_to_pref_map = {
+        "energy": pref_e,
+        "force": pref_fr,
+        "force_mag": pref_fm,
+        "virial": pref_v,
+        "atom_ener": pref_ae,
+    }
+    input_dict = {
+        "key_to_pref_map": key_to_pref_map,
+        "starter_learning_rate": starter_learning_rate,
+        "start_pref_e": pref_e,
+        "limit_pref_e": pref_e / 2,
+        "start_pref_fr": pref_fr,
+        "limit_pref_fr": pref_fr / 2,
+        "start_pref_fm": pref_fm,
+        "limit_pref_fm": pref_fm / 2,
+        "start_pref_v": pref_v,
+        "limit_pref_v": pref_v / 2,
+        "start_pref_ae": pref_ae,
+        "limit_pref_ae": pref_ae / 2,
+    }
+    return input_dict
+
+
+LossParamEnergySpinList = parameterize_func(
+    LossParamEnergySpin,
+    OrderedDict(
+        {
+            "pref_e": (1.0, 0.0),
+            "pref_fr": (1.0, 0.0),
+            "pref_fm": (1.0, 0.0),
+            "pref_v": (1.0, 0.0),
+            "pref_ae": (1.0, 0.0),
+        }
+    ),
+)
+# to get name for the default function
+LossParamEnergySpin = LossParamEnergySpinList[0]
+
+
+def LossParamDos(
+    starter_learning_rate=1.0,
+    pref_dos=1.0,
+    pref_ados=1.0,
+):
+    key_to_pref_map = {
+        "dos": pref_dos,
+        "atom_dos": pref_ados,
+    }
+    input_dict = {
+        "key_to_pref_map": key_to_pref_map,
+        "starter_learning_rate": starter_learning_rate,
+        "numb_dos": 2,
+        "start_pref_dos": pref_dos,
+        "limit_pref_dos": pref_dos / 2,
+        "start_pref_ados": pref_ados,
+        "limit_pref_ados": pref_ados / 2,
+        "start_pref_cdf": 0.0,
+        "limit_pref_cdf": 0.0,
+        "start_pref_acdf": 0.0,
+        "limit_pref_acdf": 0.0,
+    }
+    return input_dict
+
+
+LossParamDosList = parameterize_func(
+    LossParamDos,
+    OrderedDict(
+        {
+            "pref_dos": (1.0,),
+            "pref_ados": (1.0, 0.0),
+        }
+    ),
+) + parameterize_func(
+    LossParamDos,
+    OrderedDict(
+        {
+            "pref_dos": (0.0,),
+            "pref_ados": (1.0,),
+        }
+    ),
+)
+
+# to get name for the default function
+LossParamDos = LossParamDosList[0]
+
+
+def LossParamTensor(
+    pref=1.0,
+    pref_atomic=1.0,
+):
+    tensor_name = "test_tensor"
+    key_to_pref_map = {
+        tensor_name: pref,
+        f"atomic_{tensor_name}": pref_atomic,
+    }
+    input_dict = {
+        "key_to_pref_map": key_to_pref_map,
+        "tensor_name": tensor_name,
+        "tensor_size": 2,
+        "label_name": tensor_name,
+        "pref": pref,
+        "pref_atomic": pref_atomic,
+    }
+    return input_dict
+
+
+LossParamTensorList = parameterize_func(
+    LossParamTensor,
+    OrderedDict(
+        {
+            "pref": (1.0,),
+            "pref_atomic": (1.0, 0.0),
+        }
+    ),
+) + parameterize_func(
+    LossParamTensor,
+    OrderedDict(
+        {
+            "pref": (0.0,),
+            "pref_atomic": (1.0,),
+        }
+    ),
+)
+# to get name for the default function
+LossParamTensor = LossParamTensorList[0]
+
+
+def LossParamProperty():
+    key_to_pref_map = {
+        "property": 1.0,
+    }
+    input_dict = {
+        "key_to_pref_map": key_to_pref_map,
+        "task_dim": 2,
+    }
+    return input_dict
+
+
+LossParamPropertyList = [LossParamProperty]
+# to get name for the default function
+LossParamProperty = LossParamPropertyList[0]