Merge pull request #1 from reyoung/grad_test_for_multi_inputs

Invoke check_grad many times for no_grad_set

python/paddle/v2/framework/tests/gradient_checker.py

@@ -286,7 +286,7 @@ class GradientChecker(unittest.TestCase):
         for no_grad in no_grad_set:
             if no_grad not in in_names:
                 raise ValueError("no_grad should be in in_names")
-            if name in inputs_to_check:
+            if no_grad in inputs_to_check:
                 raise ValueError("no_grad should not be in inputs_to_check")
 
         backward_op = core.Operator.backward(forward_op, no_grad_set)
@@ -304,25 +304,8 @@ class GradientChecker(unittest.TestCase):
 
         check_names = [grad_var_name(name) for name in inputs_to_check]
         for place in places:
-            # analytic_grads = self.__get_gradient(forward_op, backward_op,
-            #   input_vars, check_names, place)
-            # In fact, the above two lines can be used to replace following
-            # codes. But most of the gradient operators need to handle the case
-            # where one of more of the gradient of the input is not needed.
-            # We change the unit test framework to explicitly test whether
-            # the operator correctly handles this through follow codes.
-            # In addtion, if all the inputs have no gradients, the NOP operator
-            # will be returned by core.Operator.backward(). The following codes
-            # do not test this case.
-            analytic_grads = []
-            for name in inputs_to_check:
-                no_grads = [name for name in no_grad_set]
-                no_grads.extend(filter(lambda x: x != name, inputs_to_check))
-                backward_op = core.Operator.backward(forward_op, set(no_grads))
-                # get analytical gradients according to different device
-                analytic_grads.extend(
-                    self.__get_gradient(forward_op, backward_op, input_vars,
-                                        [grad_var_name(name)], place))
+            analytic_grads = self.__get_gradient(forward_op, backward_op,
+                                                 input_vars, check_names, place)
             self.__assert_is_close(numeric_grads, analytic_grads, check_names,
                                    max_relative_error,
                                    "Gradient Check On %s" % str(place))

python/paddle/v2/framework/tests/test_mul_op.py

@@ -17,16 +17,33 @@ class TestMulOp(unittest.TestCase):
 
 
 class TestMulGradOp(GradientChecker):
-    def test_mul(self):
-        op = create_op("mul")
-        inputs = {
+    def setUp(self):
+        self.op = create_op("mul")
+        self.inputs = {
             'X': np.random.random((32, 84)).astype("float32"),
             'Y': np.random.random((84, 100)).astype("float32")
         }
-        self.compare_grad(op, inputs)
+
+    def test_normal(self):
         # mul op will enlarge the relative error
         self.check_grad(
-            op, inputs, set(["X", "Y"]), "Out", max_relative_error=0.5)
+            self.op, self.inputs, ["X", "Y"], "Out", max_relative_error=0.5)
+
+    def test_ignore_x(self):
+        self.check_grad(
+            self.op,
+            self.inputs, ["Y"],
+            "Out",
+            max_relative_error=0.5,
+            no_grad_set={"X"})
+
+    def test_ignore_y(self):
+        self.check_grad(
+            self.op,
+            self.inputs, ["X"],
+            "Out",
+            max_relative_error=0.5,
+            no_grad_set={"Y"})
 
 
 # TODO(dzh,qijun) : mulgrad test case need transpose feature of blas library

python/paddle/v2/framework/tests/test_rowwise_add_op.py

@@ -17,13 +17,21 @@ class TestRowwiseAddOp(unittest.TestCase):
 
 
 class RowwiseAddGradOpTest(GradientChecker):
-    def test_rowwise_add(self):
-        op = create_op("rowwise_add")
-        inputs = {
+    def setUp(self):
+        self.op = create_op("rowwise_add")
+        self.inputs = {
             "X": np.random.uniform(0.1, 1, [5, 10]).astype("float32"),
             "b": np.random.uniform(0.1, 1, [10]).astype("float32")
         }
-        self.check_grad(op, inputs, set(["X", "b"]), "Out")
+
+    def test_normal(self):
+        self.check_grad(self.op, self.inputs, ["X", "b"], "Out")
+
+    def test_ignore_b(self):
+        self.check_grad(self.op, self.inputs, ["X"], "Out", no_grad_set={"b"})
+
+    def test_ignore_x(self):
+        self.check_grad(self.op, self.inputs, ["b"], "Out", no_grad_set={"X"})
 
 
 if __name__ == '__main__':