fix unused var with zero gradient bug in fluid.gradient (#27246)

* fix calcu_gradients

* fix code place

* fix embedding interface usage

python/paddle/fluid/backward.py

@@ -1756,6 +1756,12 @@ def calc_gradient(targets, inputs, target_gradients=None, no_grad_set=None):
     op_path_dict = dict()
     op_path = _find_op_path_(block, targets, inputs, block_no_grad_set,
                              op_path_dict)
+
+    # find no grad var by op_path
+    no_grad_vars = _find_no_grad_vars(block, op_path, targets,
+                                      block_no_grad_set)
+    block_no_grad_set.update(no_grad_vars)
+
     no_grad_dict[0].update(list(map(_append_grad_suffix_, block_no_grad_set)))
     grad_to_var = dict()
     grad_info_map = dict()

python/paddle/fluid/tests/unittests/dygraph_to_static/test_partial_program.py

@@ -14,6 +14,7 @@
 
 from __future__ import print_function
 import numpy as np
+import paddle
 import paddle.fluid as fluid
 from paddle.fluid.layers.utils import flatten
 from paddle.fluid.dygraph import declarative, ProgramTranslator
@@ -151,5 +152,33 @@ class TestWithTrainAndEval(unittest.TestCase):
                              partial_layer._train_program)
 
 
+class GPT2LMHeadModel(fluid.dygraph.Layer):
+    def __init__(self):
+        super(GPT2LMHeadModel, self).__init__()
+        self.embedding0 = paddle.nn.Embedding(20, 16)
+        self.embedding1 = paddle.nn.Embedding(20, 32)
+        self.lm_head_weight = paddle.to_tensor(
+            np.random.rand(2, 3).astype('float32'))
+
+    @declarative
+    def forward(self, x):
+        x = fluid.layers.reshape(x, shape=[-1, 6])
+        x1, x2, x3 = fluid.layers.split(input=x, dim=1, num_or_sections=3)
+        return x1
+
+
+class TestPruneUnusedParamInProgram(unittest.TestCase):
+    def test_prune(self):
+        input_ids = np.array([[15, 11, 6, 3, 18, 13]]).astype("float32")
+
+        place = fluid.CPUPlace()
+        with fluid.dygraph.guard(place):
+            model = GPT2LMHeadModel()
+            model.eval()
+            input_ids = paddle.to_tensor(input_ids)
+            out = model(input_ids)
+            self.assertTrue(np.array_equal(out.numpy(), [[15, 11]]))
+
+
 if __name__ == '__main__':
     unittest.main()

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

@@ -15,7 +15,7 @@
 from __future__ import print_function
 
 import unittest
-
+import numpy as np
 import paddle.fluid as fluid
 import paddle.fluid.layers as layers
 from paddle.fluid.backward import calc_gradient
@@ -81,5 +81,22 @@ class TestDoubleGrad(unittest.TestCase):
         self.assertEqual(12, out[0])
 
 
+class TestGradientWithPrune(unittest.TestCase):
+    def test_prune(self):
+        x = fluid.data(name='x', shape=[3], dtype='float32')
+        x.stop_gradient = False
+        x1, x2, x3 = fluid.layers.split(x, dim=0, num_or_sections=3)
+        y = x1 * 2
+        x1_grad = fluid.gradients(y, x)
+
+        exe = fluid.Executor(fluid.CPUPlace())
+        main = fluid.default_main_program()
+        exe.run(fluid.default_startup_program())
+        out = exe.run(main,
+                      feed={'x': np.ones([3]).astype('float32')},
+                      fetch_list=[x1_grad])
+        self.assertTrue(np.array_equal(out[0], [2., 0., 0.]))
+
+
 if __name__ == "__main__":
     unittest.main()