Merge pull request #15922 from kbinias/kbinias/reuse-primitives-activations-and-softmax-mkldnn-ut

MKL-DNN: Add Activations and Softmax UTs to check if primitives already exist in backward

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

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import print_function
+
+import numpy as np
+import paddle.fluid.core as core
+import paddle.fluid as fluid
+
+
+def check_if_mkldnn_primitives_exist_in_bwd(test_case, op_type, x, out,
+                                            out_grad, x_grad):
+    def __assert_close(tensor, np_array, msg, atol=1e-4):
+        test_case.assertTrue(
+            np.allclose(
+                np.array(tensor), np_array, atol=atol), msg)
+
+    place = core.CPUPlace()
+
+    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=np.float32, shape=ground_truth[name].shape)
+
+        op = block.append_op(
+            type=op_type,
+            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(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', 'out'])
+
+        __assert_close(x_grad, out[0], 'x@GRAD')

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

@@ -19,7 +19,7 @@ import numpy as np
 import paddle.fluid.core as core
 from paddle.fluid.tests.unittests.op_test import OpTest
 from paddle.fluid.tests.unittests.test_activation_op import TestRelu, TestTanh, TestSqrt, TestAbs
-import paddle.fluid as fluid
+from mkldnn_op_test import check_if_mkldnn_primitives_exist_in_bwd
 
 
 class TestMKLDNNReluDim2(TestRelu):
@@ -98,62 +98,24 @@ class TestMKLDNNAbsDim4(TestAbs):
 
 
 # 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()
+class TestMKLDNNAbsPrimitivesAlreadyExist(unittest.TestCase):
+    def setUp(self):
+        super(TestMKLDNNAbsPrimitivesAlreadyExist, self).setUp()
 
         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")
+        self.op_type = 'abs'
+        self.x = np.random.uniform(-1, 1, [2, 2]).astype(np.float32)
+        self.out = np.abs(self.x)
+        self.out_grad = np.random.random_sample(self.x.shape).astype(np.float32)
+        self.x_grad = self.__abs_bwd(self.x, self.out_grad)
+
+    # Abs grad calculation
+    def __abs_bwd(self, x, out_grad):
+        return out_grad * np.sign(x)
+
+    def test_check(self):
+        check_if_mkldnn_primitives_exist_in_bwd(
+            self, self.op_type, self.x, self.out, self.out_grad, self.x_grad)
 
 
 if __name__ == '__main__':

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

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import print_function
+
+import unittest
+import numpy as np
+from paddle.fluid.tests.unittests.op_test import OpTest
+import paddle.fluid.core as core
+from paddle.fluid.tests.unittests.test_softmax_op import TestSoftmaxOp, stable_softmax
+from mkldnn_op_test import check_if_mkldnn_primitives_exist_in_bwd
+
+
+class TestSoftmaxMKLDNNOp(TestSoftmaxOp):
+    def init_kernel_type(self):
+        self.use_mkldnn = True
+
+
+class TestSoftmaxMKLDNNOp2(TestSoftmaxMKLDNNOp):
+    def get_x_shape(self):
+        return [2, 3, 4, 5]
+
+
+# Check if primitives already exist in backward
+class TestSoftmaxMKLDNNPrimitivesAlreadyExist(unittest.TestCase):
+    def setUp(self):
+        super(TestSoftmaxMKLDNNPrimitivesAlreadyExist, self).setUp()
+
+        np.random.seed(123)
+        self.op_type = 'softmax'
+        self.x = np.random.uniform(-1, 1, 2).astype(np.float32)
+        self.out = stable_softmax(self.x)
+        self.out_grad = np.random.random_sample(self.x.shape).astype(np.float32)
+        self.x_grad = self.__softmax_bwd(self.out, self.out_grad)
+
+    # Softmax grad calculation
+    def __softmax_bwd(self, out, out_grad):
+        return out * (out_grad - np.dot(out, out_grad))
+
+    def test_check(self):
+        check_if_mkldnn_primitives_exist_in_bwd(
+            self, self.op_type, self.x, self.out, self.out_grad, self.x_grad)
+
+
+if __name__ == '__main__':
+    unittest.main()

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

@@ -144,15 +144,5 @@ class TestSoftmaxFP16CUDNNOp2(TestSoftmaxFP16CUDNNOp):
         return [2, 3, 4, 5]
 
 
-class TestSoftmaxMKLDNNOp(TestSoftmaxOp):
-    def init_kernel_type(self):
-        self.use_mkldnn = True
-
-
-class TestSoftmaxMKLDNNOp2(TestSoftmaxMKLDNNOp):
-    def get_x_shape(self):
-        return [2, 3, 4, 5]
-
-
 if __name__ == "__main__":
     unittest.main()