refine code

HydrogenSulfate · Nov 26, 2023 · 4689924 · 4689924
1 parent 0af71a0
commit 4689924
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diff --git a/deepmd/env.py b/deepmd/env.py
@@ -364,20 +364,6 @@ def get_module(module_name: str) -> "ModuleType":
             import paddle_deepmd_lib
             from paddle.utils import cpp_extension
 
-            # module = cpp_extension.load(
-            #     name="paddle_custom_ops",
-            #     sources=[
-            #         "/workspace/hesensen/deepmd-kit/source/op/paddle/neighbor_stat.cc",
-            #         "/workspace/hesensen/deepmd-kit/source/op/paddle/prod_env_mat.cc",
-            #     ],
-            #     extra_include_paths=[
-            #         "/workspace/hesensen/deepmd-kit/source/lib/include/",
-            #         "/usr/local/cuda/lib64/",
-            #         "/workspace/hesensen/deepmd-kit/source/op/paddle/cub/",
-            #         "/usr/local/cuda/include/"
-            #     ],
-            #     verbose=True,
-            # )
             module = paddle_deepmd_lib
 
         except tf.errors.NotFoundError as e:

diff --git a/deepmd/utils/neighbor_stat.py b/deepmd/utils/neighbor_stat.py
@@ -9,15 +9,6 @@
 from deepmd.env import default_tf_session_config
 from deepmd.env import op_module
 from deepmd.env import tf
-
-# from paddle.utils import cpp_extension
-# op_module = cpp_extension.load(
-#     name="custom_op_paddle2",
-#     sources=["/workspace/hesensen/deepmd-kit/source/op/paddle/neighbor_stat.cc"],
-#     extra_include_paths=["/workspace/hesensen/deepmd-kit/source/lib/include/","/usr/local/cuda/targets/x86_64-linux/include/", "/workspace/hesensen/deepmd-kit/source/op"],
-#     # extra_library_paths=["../build/lib/", "/usr/local/cuda/lib64"],
-#     verbose=True,
-# )
 from deepmd.utils.data_system import DeepmdDataSystem
 from deepmd.utils.parallel_op import ParallelOp
 

diff --git a/deepmd/utils/network.py b/deepmd/utils/network.py
@@ -430,8 +430,6 @@ def __init__(
                     ),
                 )
             )
-            # print(outputs_size[ii-1], precision, False, trainable, outputs_size[ii]+outputs_size[ii-1])
-            # exit()
             bias.append(
                 self.create_parameter(
                     shape=[1, outputs_size[ii]],
@@ -460,91 +458,25 @@ def __init__(
         self.idt = paddle.nn.ParameterList(idt)
 
     def forward(self, xx):
-        # outputs_size = self.outputs_size
-        # print(self.outputs_size)
-        # for ii in range(1, len(outputs_size)):
-        #     # if self.activation_fn is not None:
-        #     hidden = paddle.reshape(
-        #         self.activation_fn(paddle.matmul(xx, self.weight[ii-1]) + self.bias[ii-1]),
-        #         [-1, outputs_size[ii]]
-        #     )
-        #     # print(__file__, 1)
-        #     # else:
-        #     #     hidden = paddle.reshape(
-        #     #         paddle.matmul(xx, self.weight[ii-1]) + self.bias[ii-1],
-        #     #         [-1, outputs_size[ii]]
-        #     #     )
-        #         # print(__file__, 2)
-
-        #     if outputs_size[ii] == outputs_size[ii - 1]:
-        #         if self.resnet_dt:
-        #             xx += hidden * self.idt[ii]
-        #             # print(__file__, 3)
-        #         else:
-        #             xx += hidden
-        #             # print(__file__, 4)
-        #     elif outputs_size[ii] == outputs_size[ii-1] * 2:
-        #         if self.resnet_dt:
-        #             xx = paddle.concat([xx,xx], axis=1) + hidden * self.idt[ii]
-        #             # print(__file__, 5)
-        #         else:
-        #             xx = paddle.concat([xx,xx], axis=1) + hidden
-        #             # print(__file__, 6)
-        #     else:
-        #         # print(__file__, 7)
-        #         xx = hidden
-        # # exit()
-
-        # return xx
-        # if not hasattr(self, "xx1"):
-        #     self.xx1 = xx
-        # paddle.save(self.xx1.numpy(), f"/workspace/hesensen/deepmd_backend/debug_emb/{self.name}_xx1.npy")
-        # paddle.save(self.weight[0].numpy(), f"/workspace/hesensen/deepmd_backend/debug_emb/{self.name}_weight_0.npy")
-        # paddle.save(self.bias[0].numpy(), f"/workspace/hesensen/deepmd_backend/debug_emb/{self.name}_bias_0.npy")
-
         hidden = nn.functional.tanh(
             nn.functional.linear(xx, self.weight[0], self.bias[0])
         ).reshape(
             [-1, 25]
         )  # 1
         xx = hidden  # 7
 
-        # if not hasattr(self, "hidden1"):
-        #     self.hidden1 = hidden
-        # paddle.save(self.hidden1.numpy(), f"/workspace/hesensen/deepmd_backend/debug_emb/{self.name}_hidden1.npy")
-
-        # if not hasattr(self, "xx2"):
-        #     self.xx2 = xx
-        # paddle.save(self.xx2.numpy(), f"/workspace/hesensen/deepmd_backend/debug_emb/{self.name}_xx2.npy")
-
         hidden = nn.functional.tanh(
             nn.functional.linear(xx, self.weight[1], self.bias[1])
         ).reshape(
             [-1, 50]
         )  # 1
         xx = paddle.concat([xx, xx], axis=1) + hidden  # 6
 
-        # if not hasattr(self, "hidden2"):
-        #     self.hidden2 = hidden
-        # paddle.save(self.hidden2.numpy(), f"/workspace/hesensen/deepmd_backend/debug_emb/{self.name}_hidden2.npy")
-
-        # if not hasattr(self, "xx3"):
-        #     self.xx3 = xx
-        # paddle.save(self.xx3.numpy(), f"/workspace/hesensen/deepmd_backend/debug_emb/{self.name}_xx3.npy")
-
         hidden = nn.functional.tanh(
             nn.functional.linear(xx, self.weight[2], self.bias[2])
         ).reshape(
             [-1, 100]
         )  # 1
         xx = paddle.concat([xx, xx], axis=1) + hidden  # 6
 
-        # if not hasattr(self, "hidden3"):
-        #     self.hidden3 = hidden
-        # paddle.save(self.hidden3.numpy(), f"/workspace/hesensen/deepmd_backend/debug_emb/{self.name}_hidden3.npy")
-
-        # if not hasattr(self, "xx4"):
-        #     self.xx4 = xx
-        # paddle.save(self.xx4.numpy(), f"/workspace/hesensen/deepmd_backend/debug_emb/{self.name}_xx4.npy")
-
         return xx