[WIP] pd: add unitest test_deeppot_dpa_pd.cc

.pre-commit-config.yaml

@@ -7,7 +7,7 @@ repos:
       - id: trailing-whitespace
         exclude: "^.+\\.pbtxt$"
       - id: end-of-file-fixer
-        exclude: "^.+\\.pbtxt$|deeppot_sea.*\\.json$"
+        exclude: "^.+\\.pbtxt$|deeppot_sea.*|deeppot_dpa.*\\.json$"
       - id: check-yaml
       - id: check-json
       - id: check-added-large-files

deepmd/pd/entrypoints/main.py

@@ -374,7 +374,7 @@ def freeze(
                 None,  # fparam
                 None,  # aparam
                 # InputSpec([], dtype="bool", name="do_atomic_virial"),  # do_atomic_virial
-                False,  # do_atomic_virial
+                False,  # do_atomic_virial, NOTE: set to True if needed
             ],
             full_graph=True,
         )
@@ -396,7 +396,7 @@ def freeze(
                 None,  # fparam
                 None,  # aparam
                 # InputSpec([], dtype="bool", name="do_atomic_virial"),  # do_atomic_virial
-                False,  # do_atomic_virial
+                False,  # do_atomic_virial, NOTE: set to True if needed
                 (
                     InputSpec([-1], "int64", name="send_list"),
                     InputSpec([-1], "int32", name="send_proc"),

deepmd/pd/model/model/ener_model.py

@@ -33,6 +33,37 @@ def __init__(
     ) -> None:
         DPModelCommon.__init__(self)
         DPEnergyModel_.__init__(self, *args, **kwargs)
+        self._hessian_enabled = False
+
+    def enable_hessian(self):
+        raise NotImplementedError(
+            "Hessian calculation is not implemented yet on PaddlePaddle platform."
+        )
+
+    def get_observed_type_list(self) -> list[str]:
+        """Get observed types (elements) of the model during data statistics.
+
+        Returns
+        -------
+        observed_type_list: a list of the observed types in this model.
+        """
+        type_map = self.get_type_map()
+        out_bias = self.atomic_model.get_out_bias()[0]
+
+        assert out_bias is not None, "No out_bias found in the model."
+        assert out_bias.dim() == 2, "The supported out_bias should be a 2D tensor."
+        assert out_bias.size(0) == len(type_map), (
+            "The out_bias shape does not match the type_map length."
+        )
+        bias_mask = (
+            paddle.greater_than(paddle.abs(out_bias), 1e-6).any(axis=-1).detach().cpu()
+        )  # 1e-6 for stability
+
+        observed_type_list: list[str] = []
+        for i in range(len(type_map)):
+            if bias_mask[i]:
+                observed_type_list.append(type_map[i])
+        return observed_type_list
 
     def translated_output_def(self):
         out_def_data = self.model_output_def().get_data()
@@ -50,6 +81,8 @@ def translated_output_def(self):
             output_def["atom_virial"].squeeze(-3)
         if "mask" in out_def_data:
             output_def["mask"] = out_def_data["mask"]
+        if self._hessian_enabled:
+            output_def["hessian"] = out_def_data["energy_derv_r_derv_r"]
         return output_def
 
     def forward(
@@ -81,14 +114,12 @@ def forward(
                     model_predict["atom_virial"] = model_ret["energy_derv_c"].squeeze(
                         -3
                     )
-                else:
-                    model_predict["atom_virial"] = paddle.zeros(
-                        [model_predict["energy"].shape[0], 1, 9], dtype=paddle.float64
-                    )
             else:
                 model_predict["force"] = model_ret["dforce"]
             if "mask" in model_ret:
                 model_predict["mask"] = model_ret["mask"]
+            if self._hessian_enabled:
+                model_predict["hessian"] = model_ret["energy_derv_r_derv_r"].squeeze(-2)
         else:
             model_predict = model_ret
             model_predict["updated_coord"] += coord
@@ -128,10 +159,6 @@ def forward_lower(
                     model_predict["extended_virial"] = model_ret[
                         "energy_derv_c"
                     ].squeeze(-3)
-                else:
-                    model_predict["extended_virial"] = paddle.zeros(
-                        [model_predict["energy"].shape[0], 1, 9], dtype=paddle.float64
-                    )
             else:
                 assert model_ret["dforce"] is not None
                 model_predict["dforce"] = model_ret["dforce"]

deepmd/pd/model/model/model.py

@@ -1,5 +1,6 @@
 # SPDX-License-Identifier: LGPL-3.0-or-later
 from typing import (
+    NoReturn,
     Optional,
 )
 
@@ -8,6 +9,9 @@
 from deepmd.dpmodel.model.base_model import (
     make_base_model,
 )
+from deepmd.pd.utils import (
+    env,
+)
 from deepmd.utils.path import (
     DPPath,
 )
@@ -18,13 +22,16 @@ def __init__(self, *args, **kwargs):
         """Construct a basic model for different tasks."""
         paddle.nn.Layer.__init__(self)
         self.model_def_script = ""
-        self.min_nbor_dist = None
+        self.register_buffer(
+            "min_nbor_dist",
+            paddle.to_tensor(-1.0, dtype=paddle.float64, place=env.DEVICE),
+        )
 
     def compute_or_load_stat(
         self,
         sampled_func,
         stat_file_path: Optional[DPPath] = None,
-    ):
+    ) -> NoReturn:
         """
         Compute or load the statistics parameters of the model,
         such as mean and standard deviation of descriptors or the energy bias of the fitting net.
@@ -42,13 +49,24 @@ def compute_or_load_stat(
         """
         raise NotImplementedError
 
+    def get_observed_type_list(self) -> list[str]:
+        """Get observed types (elements) of the model during data statistics.
+
+        Returns
+        -------
+        observed_type_list: a list of the observed types in this model.
+        """
+        raise NotImplementedError
+
     def get_model_def_script(self) -> str:
         """Get the model definition script."""
         return self.model_def_script
 
     def get_min_nbor_dist(self) -> Optional[float]:
         """Get the minimum distance between two atoms."""
-        return self.min_nbor_dist
+        if self.min_nbor_dist.item() == -1.0:
+            return None
+        return self.min_nbor_dist.item()
 
     def get_ntypes(self):
         """Returns the number of element types."""

deepmd/pd/utils/serialization.py

@@ -67,15 +67,15 @@ def deserialize_to_file(model_file: str, data: dict) -> None:
     """
     model.forward = paddle.jit.to_static(
         model.forward,
-        full_graph=True,
         input_spec=[
-            InputSpec([-1, -1, 3], dtype="float64", name="coord"),
-            InputSpec([-1, -1], dtype="int64", name="atype"),
-            InputSpec([-1, 9], dtype="float64", name="box"),
-            None,
-            None,
-            True,
+            InputSpec([-1, -1, 3], dtype="float64", name="coord"),  # coord
+            InputSpec([-1, -1], dtype="int64", name="atype"),  # atype
+            InputSpec([-1, 9], dtype="float64", name="box"),  # box
+            None,  # fparam
+            None,  # aparam
+            True,  # do_atomic_virial
         ],
+        full_graph=True,
     )
     """ example output shape and dtype of forward_lower
     fetch_name_0: atom_energy [1, 192, 1] paddle.float64
@@ -86,17 +86,25 @@ def deserialize_to_file(model_file: str, data: dict) -> None:
     """
     model.forward_lower = paddle.jit.to_static(
         model.forward_lower,
-        full_graph=True,
         input_spec=[
-            InputSpec([-1, -1, 3], dtype="float64", name="coord"),
-            InputSpec([-1, -1], dtype="int32", name="atype"),
-            InputSpec([-1, -1, -1], dtype="int32", name="nlist"),
-            None,
-            None,
-            None,
-            True,
-            None,
+            InputSpec([-1, -1, 3], dtype="float64", name="coord"),  # extended_coord
+            InputSpec([-1, -1], dtype="int32", name="atype"),  # extended_atype
+            InputSpec([-1, -1, -1], dtype="int32", name="nlist"),  # nlist
+            InputSpec([-1, -1], dtype="int64", name="mapping"),  # mapping
+            None,  # fparam
+            None,  # aparam
+            True,  # do_atomic_virial
+            (
+                InputSpec([-1], "int64", name="send_list"),
+                InputSpec([-1], "int32", name="send_proc"),
+                InputSpec([-1], "int32", name="recv_proc"),
+                InputSpec([-1], "int32", name="send_num"),
+                InputSpec([-1], "int32", name="recv_num"),
+                InputSpec([-1], "int64", name="communicator"),
+                # InputSpec([1], "int64", name="has_spin"),
+            ),  # comm_dict
         ],
+        full_graph=True,
     )
     paddle.jit.save(
         model,

doc/install/install-from-source.md

@@ -99,22 +99,16 @@ To install Paddle, run
 
 ```sh
 # cu126
-# release version
-pip install paddlepaddle-gpu==3.1.1 -i https://www.paddlepaddle.org.cn/packages/stable/cu126/
-# nightly-build version
-# pip install --pre paddlepaddle-gpu -i https://www.paddlepaddle.org.cn/packages/nightly/cu126/
+pip install paddlepaddle-gpu==3.1.1 -i https://www.paddlepaddle.org.cn/packages/stable/cu126/ # release
+# pip install --pre paddlepaddle-gpu -i https://www.paddlepaddle.org.cn/packages/nightly/cu126/ # nightly-build
 
 # cu118
-# release version
-pip install paddlepaddle-gpu==3.1.1 -i https://www.paddlepaddle.org.cn/packages/stable/cu118/
-# nightly-build version
-# pip install --pre paddlepaddle-gpu -i https://www.paddlepaddle.org.cn/packages/nightly/cu118/
+pip install paddlepaddle-gpu==3.1.1 -i https://www.paddlepaddle.org.cn/packages/stable/cu118/ # release
+# pip install --pre paddlepaddle-gpu -i https://www.paddlepaddle.org.cn/packages/nightly/cu118/ # nightly-build
 
 # cpu
-# release version
-pip install paddlepaddle==3.1.1 -i https://www.paddlepaddle.org.cn/packages/stable/cpu/
-# nightly-build version
-# pip install --pre paddlepaddle -i https://www.paddlepaddle.org.cn/packages/nightly/cpu/
+pip install paddlepaddle==3.1.1 -i https://www.paddlepaddle.org.cn/packages/stable/cpu/ # release
+# pip install --pre paddlepaddle -i https://www.paddlepaddle.org.cn/packages/nightly/cpu/ # nightly-build
 ```
 
 :::

source/CMakeLists.txt

@@ -67,7 +67,7 @@ if(ENABLE_PADDLE)
           "PADDLE_INFERENCE_DIR is not defined, downloading CPU inference lib to: ${CMAKE_BINARY_DIR}/"
       )
       set(DOWNLOAD_URL
-          "https://paddle-qa.bj.bcebos.com/paddle-pipeline/GITHUB_Docker_Compile_Test_Cpu_Mkl_Avx_D1/6ed5dd3833c32c3b21e14b1fb1a71f5a535a0fcc/paddle_inference.tgz"
+          "https://paddle-org.bj.bcebos.com/paddlescience/deepmd/paddle_inference.tgz"
       )
     endif()
     set(TGZ_FILE "${CMAKE_BINARY_DIR}/paddle_inference.tgz")

source/api_cc/src/DeepPotPD.cc

@@ -15,6 +15,7 @@ using namespace deepmd;
 #include <iomanip>
 #include <iostream>
 #include <sstream>
+#include <stdexcept>
 #include <string>
 
 class Logger {
@@ -669,10 +670,18 @@ void DeepPotPD::get_type_map(std::string& type_map) {
 template <typename BUFFERTYPE>
 void DeepPotPD::get_buffer(const std::string& buffer_name,
                            std::vector<BUFFERTYPE>& buffer_array) {
-  auto buffer_tensor = predictor->GetOutputHandle(buffer_name);
-  int buffer_size = numel(*buffer_tensor);
-  buffer_array.resize(buffer_size);
-  buffer_tensor->CopyToCpu(buffer_array.data());
+  try {
+    auto buffer_tensor = predictor->GetOutputHandle(buffer_name);
+    int buffer_size = numel(*buffer_tensor);
+    buffer_array.resize(buffer_size);
+    buffer_tensor->CopyToCpu(buffer_array.data());
+  } catch (const std::out_of_range& e) {
+    std::cerr << "Error: Output handle for buffer_name '" << buffer_name
+              << "' not found. Check if the model output names are correct."
+              << std::endl;
+    std::cerr << "Underlying exception: " << e.what() << std::endl;
+    throw;
+  }
 }
 
 template <typename BUFFERTYPE>