MKL-DNN: Add UT to check whether primitives already exist in backward

paddle/fluid/operators/mkldnn/activation_mkldnn_op.cc

@@ -52,11 +52,6 @@ class MKLDNNActivationKernel
                    "Wrong layout/format set for Input x tensor");
 
     Functor functor;
-
-    auto attrs = functor.GetAttrs();
-    for (auto &attr : attrs) {
-      *attr.second = ctx.Attr<float>(attr.first);
-    }
     functor(ctx);
   }
 };
@@ -76,11 +71,6 @@ class MKLDNNActivationGradKernel
         "is_test attribute should be set to False in training phase.");
 
     Functor functor;
-
-    auto attrs = functor.GetAttrs();
-    for (auto &attr : attrs) {
-      *attr.second = ctx.Attr<float>(attr.first);
-    }
     functor(ctx);
   }
 };

python/paddle/fluid/tests/unittests/mkldnn/test_activation_mkldnn_op.py

@@ -18,8 +18,8 @@
 import numpy as np
 import paddle.fluid.core as core
 from paddle.fluid.tests.unittests.op_test import OpTest
-from scipy.special import expit
 from paddle.fluid.tests.unittests.test_activation_op import TestRelu, TestTanh, TestSqrt, TestAbs
+import paddle.fluid as fluid
 
 
 class TestMKLDNNReluDim2(TestRelu):
@@ -97,5 +97,64 @@ def setUp(self):
         self.attrs = {"use_mkldnn": True}
 
 
+# Check if primitives already exist in backward
+class TestMKLDNNReluPrimitivesAlreadyExist(unittest.TestCase):
+    def __assert_close(self, tensor, np_array, msg, atol=1e-4):
+        self.assertTrue(np.allclose(np.array(tensor), np_array, atol=atol), msg)
+
+    def test_check_forward_backward(self):
+        place = core.CPUPlace()
+
+        np.random.seed(123)
+        x = np.random.uniform(-1, 1, [2, 2]).astype(np.float32)
+        out = np.abs(x)
+
+        out_grad = np.random.random_sample(x.shape).astype(np.float32)
+        x_grad = out_grad * np.sign(x)  # Abs grad calculation
+
+        var_dict = {'x': x, 'out': out, 'out@GRAD': out_grad, 'x@GRAD': x_grad}
+        var_names = list(var_dict.keys())
+        ground_truth = {name: var_dict[name] for name in var_names}
+
+        program = fluid.Program()
+        with fluid.program_guard(program):
+            block = program.global_block()
+            for name in ground_truth:
+                block.create_var(
+                    name=name, dtype='float32', shape=ground_truth[name].shape)
+
+            relu_op = block.append_op(
+                type="abs",
+                inputs={"X": block.var('x'), },
+                outputs={"Out": block.var('out')},
+                attrs={"use_mkldnn": True})
+
+            # Generate backward op_desc
+            grad_op_desc_list, op_grad_to_var = core.get_grad_op_desc(
+                relu_op.desc, set(), [])
+            grad_op_desc = grad_op_desc_list[0]
+            new_op_desc = block.desc.append_op()
+            new_op_desc.copy_from(grad_op_desc)
+            for var_name in grad_op_desc.output_arg_names():
+                block.desc.var(var_name.encode("ascii"))
+            grad_op_desc.infer_var_type(block.desc)
+            grad_op_desc.infer_shape(block.desc)
+            for arg in grad_op_desc.output_arg_names():
+                grad_var = block.desc.find_var(arg.encode("ascii"))
+                grad_var.set_dtype(core.VarDesc.VarType.FP32)
+
+            exe = fluid.Executor(place)
+
+            # Do at least 2 iterations
+            for i in range(2):
+                out = exe.run(
+                    program,
+                    feed={name: var_dict[name]
+                          for name in ['x', 'out@GRAD']},
+                    fetch_list=['x@GRAD'])
+
+            self.__assert_close(x_grad, out[0], "x@GRAD")
+
+
 if __name__ == '__main__':
     unittest.main()