API Improvement for paddle.nn.layer.state_dict 易用性提升 (#64358)

* update state_dict

* udpate

* fix test

* fix test

* fix test

* fix test

* update test

* update test

python/paddle/nn/layer/layers.py

@@ -1871,6 +1871,7 @@ def _state_dict_impl(
         structured_name_prefix="",
         include_non_persistable_buffer=False,
         use_hook=True,
+        keep_vars=True,
     ):
         """
         Get all parameters and persistable buffers of current layer and its sub-layers. And set them into a dict
@@ -1880,23 +1881,30 @@ def _state_dict_impl(
             include_sublayers(bool, optional) : If true, also include the parameters and persistable buffers from sublayers. Default: True.
             include_non_persistable_buffer(bool, optional): If true, include non persistable buffers of current layer and its sub-layers, it is used in pure fp16 and jit.save. Default: False.
             use_hook(bool, optional) : If true, the operations contained in _state_dict_hooks will be appended to the destination. Default: True.
+            keep_vars(bool, optional) : If false, the returned tensors in the state dict are detached from autograd. Default: True.
         """
 
         if destination is None:
             destination = collections.OrderedDict()
         for name, data in self._parameters.items():
             if data is not None:
-                destination[structured_name_prefix + name] = data
+                destination[structured_name_prefix + name] = (
+                    data if keep_vars else data.detach()
+                )
         for name, buffer in self._buffers.items():
             if not include_non_persistable_buffer:
                 if (
                     buffer is not None
                     and name not in self._non_persistable_buffer_names_set
                 ):
-                    destination[structured_name_prefix + name] = buffer
+                    destination[structured_name_prefix + name] = (
+                        buffer if keep_vars else buffer.detach()
+                    )
             else:
                 if buffer is not None:
-                    destination[structured_name_prefix + name] = buffer
+                    destination[structured_name_prefix + name] = (
+                        buffer if keep_vars else buffer.detach()
+                    )
 
         if include_sublayers:
             for layer_name, layer_item in self._sub_layers.items():
@@ -1909,6 +1917,7 @@ def _state_dict_impl(
                             structured_name_prefix + layer_name + ".",
                             include_non_persistable_buffer,
                             use_hook,
+                            keep_vars,
                         )
                     )
                     destination = destination_temp
@@ -1926,6 +1935,7 @@ def to_static_state_dict(
         include_sublayers=True,
         structured_name_prefix="",
         use_hook=True,
+        keep_vars=True,
     ):
         '''
 
@@ -1935,6 +1945,7 @@ def to_static_state_dict(
             destination(dict, optional) : If provide, all the parameters and persistable buffers will be set to this dict . Default: None.
             include_sublayers(bool, optional) : If true, also include the parameters and persistable buffers from sublayers. Default: True.
             use_hook(bool, optional) : If true, the operations contained in _state_dict_hooks will be appended to the destination. Default: True.
+            keep_vars(bool, optional) : If false, the returned tensors in the state dict are detached from autograd. Default: True.
 
         Returns:
             dict, a dict contains all the parameters and persistable buffers.
@@ -1956,6 +1967,7 @@ def to_static_state_dict(
             structured_name_prefix=structured_name_prefix,
             include_non_persistable_buffer=True,
             use_hook=use_hook,
+            keep_vars=keep_vars,
         )
 
     def state_dict(
@@ -1964,6 +1976,7 @@ def state_dict(
         include_sublayers=True,
         structured_name_prefix="",
         use_hook=True,
+        keep_vars=True,
     ):
         '''
         Get all parameters and persistable buffers of current layer and its sub-layers. And set them into a dict
@@ -1972,6 +1985,7 @@ def state_dict(
             destination(dict, optional) : If provide, all the parameters and persistable buffers will be set to this dict . Default: None.
             include_sublayers(bool, optional) : If true, also include the parameters and persistable buffers from sublayers. Default: True.
             use_hook(bool, optional) : If true, the operations contained in _state_dict_hooks will be appended to the destination. Default: True.
+            keep_vars(bool, optional) : If false, the returned tensors in the state dict are detached from autograd. Default: True.
 
         Returns:
             dict: a dict contains all the parameters and persistable buffers.
@@ -1993,6 +2007,7 @@ def state_dict(
             structured_name_prefix=structured_name_prefix,
             include_non_persistable_buffer=False,
             use_hook=use_hook,
+            keep_vars=keep_vars,
         )
 
     @framework.deprecate_stat_dict

test/legacy_test/test_state_dict_convert.py

@@ -35,12 +35,14 @@ def state_dict(
         include_sublayers=True,
         structured_name_prefix="",
         use_hook=True,
+        keep_vars=True,
     ):
         st = super().state_dict(
             destination=destination,
             include_sublayers=include_sublayers,
             structured_name_prefix=structured_name_prefix,
             use_hook=use_hook,
+            keep_vars=keep_vars,
         )
         st["linear.new_weight"] = paddle.transpose(
             st.pop("linear.weight"), [1, 0]
@@ -75,6 +77,17 @@ def is_state_dict_equal(model1, model2):
     return True
 
 
+class MyModel3(nn.Layer):
+    def __init__(self):
+        super().__init__()
+        self.linear = nn.Linear(100, 300)
+        buffer = paddle.to_tensor([0.0])
+        self.register_buffer("model_buffer", buffer, persistable=True)
+
+    def forward(self, x):
+        return self.linear(x)
+
+
 class TestStateDictConvert(unittest.TestCase):
     def test_main(self):
         model1 = MyModel()
@@ -97,5 +110,33 @@ def test_missing_keys_and_unexpected_keys(self):
         self.assertEqual(unexpected_keys[0], "unexpected_keys")
 
 
+class TestStateKeepVars(unittest.TestCase):
+    def test_true(self):
+        model = MyModel3()
+        x = paddle.randn([5, 100])
+        y = model(x)
+        y.backward()
+        st = model.state_dict()
+        has_grad = (
+            (st["linear.weight"].grad == model.linear.weight.grad).all()
+            and (st["linear.bias"].grad == model.linear.bias.grad).all()
+            and st["model_buffer"].grad == model.model_buffer.grad
+        )
+        self.assertEqual(has_grad, True)
+
+    def test_false(self):
+        model = MyModel3()
+        x = paddle.randn([5, 100])
+        y = model(x)
+        y.backward()
+        st = model.state_dict(keep_vars=False)
+        has_grad = (
+            (st["linear.weight"].grad is not None)
+            and (st["linear.bias"].grad is not None)
+            and (st["model_buffer"].grad is not None)
+        )
+        self.assertEqual(has_grad, False)
+
+
 if __name__ == "__main__":
     unittest.main()