Revert `no_grad` changes and add new implementation (#26902)

python/paddle/__init__.py

@@ -259,7 +259,7 @@ from .device import get_device
 from .fluid.dygraph.base import enable_dygraph as disable_static  #DEFINE_ALIAS
 from .fluid.dygraph.base import disable_dygraph as enable_static  #DEFINE_ALIAS
 from .fluid.framework import in_dygraph_mode as in_dynamic_mode  #DEFINE_ALIAS
-from .fluid.dygraph.base import no_grad  #DEFINE_ALIAS
+from .fluid.dygraph.base import no_grad_ as no_grad  #DEFINE_ALIAS
 
 from . import jit
 from . import static

python/paddle/fluid/clip.py

@@ -129,7 +129,7 @@ class GradientClipBase(object):
     def __str__(self):
         raise NotImplementedError()
 
-    @imperative_base.no_grad()
+    @imperative_base.no_grad
     def _dygraph_clip(self, params_grads):
         raise NotImplementedError
 
@@ -258,7 +258,7 @@ class GradientClipByValue(GradientClipBase):
     def __str__(self):
         return "Gradient Clip By Value, min = %f, max=%f" % (self.min, self.max)
 
-    @imperative_base.no_grad()
+    @imperative_base.no_grad
     def _dygraph_clip(self, params_grads):
         params_and_grads = []
         for p, g in params_grads:
@@ -413,7 +413,7 @@ class GradientClipByNorm(GradientClipBase):
     def __str__(self):
         return "Gradient Clip By Norm, clip_norm=%f" % self.clip_norm
 
-    @imperative_base.no_grad()
+    @imperative_base.no_grad
     def _dygraph_clip(self, params_grads):
         params_and_grads = []
         for p, g in params_grads:
@@ -565,7 +565,7 @@ class GradientClipByGlobalNorm(GradientClipBase):
     def __str__(self):
         return "Gradient Clip By GlobalNorm, global_norm=%f" % (self.clip_norm)
 
-    @imperative_base.no_grad()
+    @imperative_base.no_grad
     def _dygraph_clip(self, params_grads):
         params_and_grads = []
         sum_square_list = []

python/paddle/fluid/dygraph/base.py

@@ -12,9 +12,10 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 from ..wrapped_decorator import signature_safe_contextmanager, wrap_decorator
-import inspect
 import decorator
 import contextlib
+import functools
+import inspect
 import sys
 import numpy as np
 from paddle.fluid import core
@@ -26,8 +27,8 @@ import objgraph
 from ..data_feeder import convert_dtype
 
 __all__ = [
-    'no_grad', 'grad', 'guard', 'enable_dygraph', 'disable_dygraph', 'enabled',
-    'to_variable'
+    'no_grad', 'no_grad_', 'grad', 'guard', 'enable_dygraph', 'disable_dygraph',
+    'enabled', 'to_variable'
 ]
 
 
@@ -167,7 +168,80 @@ def disable_dygraph():
         _functional_dygraph_context_manager = None
 
 
-class no_grad:
+@signature_safe_contextmanager
+def _switch_tracer_mode_guard_(is_train=True):
+    tracer = framework._dygraph_tracer()
+    if tracer:
+        mode = tracer._train_mode
+        tracer._train_mode = is_train
+        try:
+            yield
+        finally:
+            tracer._train_mode = mode
+    else:
+        yield
+
+
+def no_grad(func=None):
+    """
+    :api_attr: imperative
+
+    Create a context which disables dygraph gradient calculation.
+    In this mode, the result of every computation will have `stop_gradient=True`.
+
+    Also functions as a decorator. (Make sure to instantiate without parenthesis.)
+
+    Examples:
+
+     .. code-block:: python
+
+        import numpy as np
+        import paddle.fluid as fluid
+
+        # use as generator
+
+        data = np.array([[2, 3], [4, 5]]).astype('float32')
+        with fluid.dygraph.guard():
+            l0 = fluid.Linear(2, 2)  # l0.weight.gradient() is None
+            l1 = fluid.Linear(2, 2)
+            with fluid.dygraph.no_grad():
+                # l1.weight.stop_gradient is False
+                tmp = l1.weight * 2  # tmp.stop_gradient is True
+            x = fluid.dygraph.to_variable(data)
+            y = l0(x) + tmp
+            o = l1(y)
+            o.backward()
+            print(tmp.gradient() is None)  # True
+            print(l0.weight.gradient() is None)  # False
+
+        # use as decorator
+
+        @fluid.dygraph.no_grad
+        def test_layer():
+            with fluid.dygraph.guard():
+                inp = np.ones([3, 1024], dtype='float32')
+                t = fluid.dygraph.base.to_variable(inp)
+                linear1 = fluid.Linear(1024, 4, bias_attr=False)
+                linear2 = fluid.Linear(4, 4)
+                ret = linear1(t)
+                dy_ret = linear2(ret)
+
+        test_layer()
+
+    """
+    if func is None:
+        return _switch_tracer_mode_guard_(is_train=False)
+    else:
+
+        @decorator.decorator
+        def __impl__(func, *args, **kwargs):
+            with _switch_tracer_mode_guard_(is_train=False):
+                return func(*args, **kwargs)
+
+        return __impl__(func)
+
+
+class no_grad_:
     """
     :api_attr: imperative
 

python/paddle/fluid/dygraph/math_op_patch.py

@@ -41,7 +41,7 @@ def monkey_patch_math_varbase():
     The difference is, in dygraph mode, use auto-generated op functions for better performance.
     """
 
-    @no_grad()
+    @no_grad
     def create_tensor(value, dtype, shape):
         out = _varbase_creator(dtype=dtype)
         out = core.ops.fill_constant(out, 'dtype', dtype, 'shape', shape,

python/paddle/fluid/dygraph/parallel.py

@@ -445,7 +445,7 @@ class DataParallel(layers.Layer):
                 self._reshape_inplace(x=g_var, shape=g_shape)
                 assert g_var.shape == g_shape
 
-    @no_grad()
+    @no_grad
     def apply_collective_grads(self):
         """
         AllReduce the Parameters' gradient.

python/paddle/fluid/optimizer.py

@@ -61,7 +61,7 @@ class Optimizer(object):
     but need to use one of it's implementation.
     """
 
-    @imperative_base.no_grad()
+    @imperative_base.no_grad
     def __init__(self,
                  learning_rate,
                  parameter_list=None,
@@ -897,7 +897,7 @@ class Optimizer(object):
             if p.trainable:
                 p.clear_gradient()
 
-    @imperative_base.no_grad()
+    @imperative_base.no_grad
     def minimize(self,
                  loss,
                  startup_program=None,
@@ -1015,7 +1015,7 @@ class SGDOptimizer(Optimizer):
             name=name)
         self.type = "sgd"
 
-    @no_grad()
+    @no_grad
     def _append_optimize_op(self, block, param_and_grad):
         lr = self._create_param_lr(param_and_grad)
         if framework.in_dygraph_mode():
@@ -1552,7 +1552,7 @@ class DGCMomentumOptimizer(Optimizer):
         dgc_op._set_attr(op_maker.kOpRoleVarAttrName(),
                          [param_var.name, grad_var.name])
 
-    @imperative_base.no_grad()
+    @imperative_base.no_grad
     def apply_gradients(self, params_grads):
         params_grads = sorted(params_grads, key=lambda x: x[0].name)
         params_grads, table_param_and_grad, table_optimize_op = \

python/paddle/fluid/tests/unittests/test_imperative_decorator.py

@@ -28,7 +28,7 @@ class TestTracerMode(unittest.TestCase):
     def get_tracer_mode(self):
         assert fluid.in_dygraph_mode(), "Dygraph mode must be enabled"
 
-    @paddle.no_grad()
+    @fluid.dygraph.no_grad
     def no_grad_func(self, a):
         self.assertEqual(self.tracer._train_mode, False)
         return a
@@ -56,35 +56,17 @@ class TestTracerMode(unittest.TestCase):
             def need_no_grad_func(a, b=1):
                 return a + b
 
-            decorated_func = paddle.no_grad()(need_no_grad_func)
+            decorated_func = fluid.dygraph.no_grad(need_no_grad_func)
             self.assertTrue(
                 str(inspect.getargspec(decorated_func)) ==
                 str(inspect.getargspec(need_no_grad_func)))
 
             self.assertEqual(self.tracer._train_mode, self.init_mode)
 
-            def test_gen():
-                for i in range(3):
-                    yield i
-
-            a = 0
-            for i in test_gen():
-                a += i
-
-            @paddle.no_grad()
-            def test_wrapped_gen():
-                for i in range(3):
-                    yield i
-
-            b = 0
-            for i in test_wrapped_gen():
-                b += i
-
-            self.assertEqual(a, b)
-
         with fluid.dygraph.guard():
             self.check_not_support_rlt(False)
 
+        paddle.enable_static()
         with new_program_scope():
             self.check_not_support_rlt(True)
 
@@ -94,5 +76,48 @@ class TestTracerMode2(TestTracerMode):
         self.init_mode = False
 
 
+class TestNoGradClass(unittest.TestCase):
+    @paddle.no_grad()
+    def no_grad_func(self, a):
+        self.assertEqual(self.tracer._train_mode, False)
+        return a
+
+    def test_main(self):
+        paddle.disable_static()
+
+        self.tracer = framework._dygraph_tracer()
+        self.tracer._train_mode = True
+
+        self.assertEqual(self.no_grad_func(1), 1)
+        self.assertEqual(self.no_grad_func.__name__, "no_grad_func")
+
+        def need_no_grad_func(a, b=1):
+            return a + b
+
+        decorated_func = paddle.no_grad()(need_no_grad_func)
+        self.assertEqual(
+            str(inspect.getargspec(decorated_func)),
+            str(inspect.getargspec(need_no_grad_func)))
+
+        def test_gen():
+            for i in range(3):
+                yield i
+
+        a = 0
+        for i in test_gen():
+            a += i
+
+        @paddle.no_grad()
+        def test_wrapped_gen():
+            for i in range(3):
+                yield i
+
+        b = 0
+        for i in test_wrapped_gen():
+            b += i
+
+        self.assertEqual(a, b)
+
+
 if __name__ == '__main__':
     unittest.main()

python/paddle/optimizer/optimizer.py

@@ -98,7 +98,7 @@ class Optimizer(object):
 
     """
 
-    @imperative_base.no_grad()
+    @imperative_base.no_grad
     def __init__(self,
                  learning_rate,
                  parameters=None,
@@ -812,7 +812,7 @@ class Optimizer(object):
             if p.trainable:
                 p.clear_gradient()
 
-    @imperative_base.no_grad()
+    @imperative_base.no_grad
     def minimize(self,
                  loss,
                  startup_program=None,

python/paddle/optimizer/sgd.py

@@ -85,7 +85,7 @@ class SGD(Optimizer):
             name=name)
         self.type = "sgd"
 
-    @no_grad()
+    @no_grad
     def _append_optimize_op(self, block, param_and_grad):
         lr = self._create_param_lr(param_and_grad)
         if framework.in_dygraph_mode():