feat(pt): support multitask argcheck

deepmd/pt/entrypoints/main.py

@@ -241,9 +241,8 @@
         )
 
     # argcheck
-    if not multi_task:
-        config = update_deepmd_input(config, warning=True, dump="input_v2_compat.json")
-        config = normalize(config)
+    config = update_deepmd_input(config, warning=True, dump="input_v2_compat.json")
+    config = normalize(config, multi_task=multi_task)
 
     # do neighbor stat
     min_nbor_dist = None

deepmd/utils/argcheck.py

@@ -2325,7 +2325,9 @@
     )
 
 
-def training_args():  # ! modified by Ziyao: data configuration isolated.
+def training_args(
+    multi_task=False,
+):  # ! modified by Ziyao: data configuration isolated.
     doc_numb_steps = "Number of training batch. Each training uses one batch of data."
     doc_seed = "The random seed for getting frames from the training data set."
     doc_disp_file = "The file for printing learning curve."
@@ -2364,14 +2366,30 @@
     )
     doc_opt_type = "The type of optimizer to use."
     doc_kf_blocksize = "The blocksize for the Kalman filter."
+    doc_model_prob = "The visiting probability of each model for each training step in the multi-task mode."
+    doc_data_dict = "The multiple definition of the data, used in the multi-task mode."
 
     arg_training_data = training_data_args()
     arg_validation_data = validation_data_args()
     mixed_precision_data = mixed_precision_args()
 
-    args = [
+    data_args = [
         arg_training_data,
         arg_validation_data,
+        Argument(
+            "stat_file", str, optional=True, doc=doc_only_pt_supported + doc_stat_file
+        ),
+    ]
+    args = (
+        data_args
+        if not multi_task
+        else [
+            Argument("model_prob", dict, optional=True, default={}, doc=doc_model_prob),
+            Argument("data_dict", dict, data_args, repeat=True, doc=doc_data_dict),
+        ]
+    )
+
+    args += [
         mixed_precision_data,
         Argument(
             "numb_steps", int, optional=False, doc=doc_numb_steps, alias=["stop_batch"]
@@ -2438,9 +2456,6 @@
             optional=True,
             doc=doc_only_pt_supported + doc_gradient_max_norm,
         ),
-        Argument(
-            "stat_file", str, optional=True, doc=doc_only_pt_supported + doc_stat_file
-        ),
     ]
     variants = [
         Variant(
@@ -2472,6 +2487,34 @@
     return Argument("training", dict, args, variants, doc=doc_training)
 
 
+def multi_model_args():
+    model_dict = model_args()
+    model_dict.name = "model_dict"
+    model_dict.repeat = True
+    model_dict.doc = (
+        "The multiple definition of the model, used in the multi-task mode."
+    )
+    doc_shared_dict = "The definition of the shared parameters used in the `model_dict` within multi-task mode."
+    return Argument(
+        "model",
+        dict,
+        [
+            model_dict,
+            Argument(
+                "shared_dict", dict, optional=True, default={}, doc=doc_shared_dict
+            ),
+        ],
+    )
+
+
+def multi_loss_args():
+    loss_dict = loss_args()
+    loss_dict.name = "loss_dict"
+    loss_dict.repeat = True
+    loss_dict.doc = "The multiple definition of the loss, used in the multi-task mode."
+    return loss_dict
+
+
 def make_index(keys):
     ret = []
     for ii in keys:
@@ -2502,14 +2545,23 @@
     )
 
 
-def gen_args(**kwargs) -> List[Argument]:
-    return [
-        model_args(),
-        learning_rate_args(),
-        loss_args(),
-        training_args(),
-        nvnmd_args(),
-    ]
+def gen_args(multi_task=False) -> List[Argument]:
+    if not multi_task:
+        return [
+            model_args(),
+            learning_rate_args(),
+            loss_args(),
+            training_args(multi_task=multi_task),
+            nvnmd_args(),
+        ]
+    else:
+        return [
+            multi_model_args(),
+            learning_rate_args(),
+            multi_loss_args(),
+            training_args(multi_task=multi_task),
+            nvnmd_args(),
+        ]
 
 
 def gen_json_schema() -> str:
@@ -2524,8 +2576,8 @@
     return json.dumps(generate_json_schema(arg))
 
 
-def normalize(data):
-    base = Argument("base", dict, gen_args())
+def normalize(data, multi_task=False):
+    base = Argument("base", dict, gen_args(multi_task=multi_task))
     data = base.normalize_value(data, trim_pattern="_*")
     base.check_value(data, strict=True)
 

examples/water_multi_task/pytorch_example/input_torch.json

@@ -67,7 +67,6 @@
     "_comment": "that's all"
   },
   "loss_dict": {
-    "_comment": " that's all",
     "water_1": {
       "type": "ener",
       "start_pref_e": 0.02,

source/tests/common/test_examples.py

@@ -15,6 +15,10 @@
     normalize,
 )
 
+from ..pt.test_multitask import (
+    preprocess_shared_params,
+)
+
 p_examples = Path(__file__).parent.parent.parent.parent / "examples"
 
 input_files = (
@@ -51,11 +55,18 @@
     p_examples / "water" / "dpa2" / "input_torch.json",
 )
 
+input_files_multi = (
+    p_examples / "water_multi_task" / "pytorch_example" / "input_torch.json",
+)
+
 
 class TestExamples(unittest.TestCase):
     def test_arguments(self):
-        for fn in input_files:
+        for fn in input_files + input_files_multi:
+            multi_task = fn in input_files_multi
             fn = str(fn)
             with self.subTest(fn=fn):
                 jdata = j_loader(fn)
-                normalize(jdata)
+                if multi_task:
+                    jdata["model"], _ = preprocess_shared_params(jdata["model"])
+                normalize(jdata, multi_task=multi_task)