[IPU] update ipu unittests p3 (#40072)

* update ipu UTs part3

* rename uts

* sync api changes

* update uts for new api

* update use_ipumodel()

* split pr

python/paddle/fluid/tests/unittests/ipu/test_matmul_v2_op_ipu.py

+#  Copyright (c) 2022 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.
+
+import unittest
+
+import numpy as np
+import paddle
+import paddle.static
+from paddle.fluid.tests.unittests.ipu.op_test_ipu import IPUOpTest, ExecutionMode
+
+
+@unittest.skipIf(not paddle.is_compiled_with_ipu(),
+                 "core is not compiled with IPU")
+class TestBase(IPUOpTest):
+    def setUp(self):
+        self.set_atol()
+        self.set_training()
+        self.set_data_feed()
+        self.set_feed_attr()
+        self.set_op_attrs()
+
+    @property
+    def fp16_enabled(self):
+        return True
+
+    def set_data_feed(self):
+        x = np.random.uniform(size=[2, 3])
+        y = np.random.uniform(size=[3, 2])
+
+        self.feed_fp32 = {"x": x.astype(np.float32), "y": y.astype(np.float32)}
+        self.feed_fp16 = {"x": x.astype(np.float16), "y": y.astype(np.float16)}
+
+    def set_feed_attr(self):
+        self.feed_shape = [x.shape for x in self.feed_fp32.values()]
+        self.feed_list = list(self.feed_fp32.keys())
+
+    def set_op_attrs(self):
+        self.attrs = {"transpose_x": False, "transpose_y": False}
+
+    def _test_base(self, exec_mode):
+        scope = paddle.static.Scope()
+        main_prog = paddle.static.Program()
+        startup_prog = paddle.static.Program()
+        main_prog.random_seed = self.SEED
+        startup_prog.random_seed = self.SEED
+
+        with paddle.static.scope_guard(scope):
+            with paddle.static.program_guard(main_prog, startup_prog):
+                x = paddle.static.data(
+                    name=self.feed_list[0],
+                    shape=self.feed_shape[0],
+                    dtype='float32')
+                y = paddle.static.data(
+                    name=self.feed_list[1],
+                    shape=self.feed_shape[1],
+                    dtype='float32')
+
+                out = paddle.matmul(x, y, **self.attrs)
+
+            fetch_list = [out.name]
+
+            if exec_mode == ExecutionMode.CPU_FP32:
+                place = paddle.CPUPlace()
+            else:
+                place = paddle.IPUPlace()
+
+            exe = paddle.static.Executor(place)
+            exe.run(startup_prog)
+
+            if exec_mode != ExecutionMode.CPU_FP32:
+                feed_list = self.feed_list
+                ipu_strategy = paddle.static.IpuStrategy()
+                ipu_strategy.set_graph_config(is_training=self.is_training)
+                if exec_mode == ExecutionMode.IPU_POPART_FP16:
+                    ipu_strategy.set_precision_config(enable_fp16=True)
+                program = paddle.static.IpuCompiledProgram(
+                    main_prog,
+                    ipu_strategy=ipu_strategy).compile(feed_list, fetch_list)
+            else:
+                program = main_prog
+
+            feed = self.feed_fp32
+            if exec_mode > ExecutionMode.IPU_FP32:
+                feed = self.feed_fp16
+
+            result = exe.run(program, feed=feed, fetch_list=fetch_list)
+            return result[0]
+
+    def test_base(self):
+        output_dict = {}
+        for mode in ExecutionMode:
+            if mode > ExecutionMode.IPU_FP32 and not self.fp16_enabled:
+                break
+            output_dict[mode] = self._test_base(mode).flatten()
+
+        self.check(output_dict)
+
+
+class TestCase1(TestBase):
+    def set_op_attrs(self):
+        self.attrs = {
+            "transpose_x": True,
+            "transpose_y": True,
+        }
+
+
+class TestCase3(TestBase):
+    def set_data_feed(self):
+        x = np.random.uniform(size=[5, 4, 2, 3])
+        y = np.random.uniform(size=[5, 4, 3, 2])
+
+        self.feed_fp32 = {"x": x.astype(np.float32), "y": y.astype(np.float32)}
+        self.feed_fp16 = {"x": x.astype(np.float16), "y": y.astype(np.float16)}
+
+
+class TestCase4(TestBase):
+    def set_data_feed(self):
+        x = np.random.uniform(size=[4, 2, 3])
+        y = np.random.uniform(size=[4, 3, 2])
+
+        self.feed_fp32 = {"x": x.astype(np.float32), "y": y.astype(np.float32)}
+        self.feed_fp16 = {"x": x.astype(np.float16), "y": y.astype(np.float16)}
+
+
+class TestCase5(TestBase):
+    def set_data_feed(self):
+        x = np.random.uniform(size=[4, 2, 3])
+        y = np.random.uniform(size=[3, 2])
+
+        self.feed_fp32 = {"x": x.astype(np.float32), "y": y.astype(np.float32)}
+        self.feed_fp16 = {"x": x.astype(np.float16), "y": y.astype(np.float16)}
+
+
+class TestCase6(TestBase):
+    def set_data_feed(self):
+        x = np.random.uniform(size=[3])
+        y = np.random.uniform(size=[3])
+
+        self.feed_fp32 = {"x": x.astype(np.float32), "y": y.astype(np.float32)}
+        self.feed_fp16 = {"x": x.astype(np.float16), "y": y.astype(np.float16)}
+
+
+@unittest.skip("not supported")
+class TestCase6_2(TestCase6):
+    def set_data_feed(self):
+        x = np.random.uniform(size=[3])
+        y = np.random.uniform(size=[3])
+
+        self.feed_fp32 = {"x": x.astype(np.float32), "y": y.astype(np.float32)}
+        self.feed_fp16 = {"x": x.astype(np.float16), "y": y.astype(np.float16)}
+
+    def set_op_attrs(self):
+        self.attrs = {"transpose_x": True, "transpose_y": True}
+
+
+class TestCase7(TestBase):
+    def set_data_feed(self):
+        x = np.random.uniform(size=[3, 1])
+        y = np.random.uniform(size=[1, 2])
+
+        self.feed_fp32 = {"x": x.astype(np.float32), "y": y.astype(np.float32)}
+        self.feed_fp16 = {"x": x.astype(np.float16), "y": y.astype(np.float16)}
+
+
+@unittest.skip("dim > 4 is not supported")
+class TestCase8(TestBase):
+    def set_data_feed(self):
+        self.feed = {
+            "x": np.random.uniform(size=[6, 5, 4, 2, 3]).astype('float32'),
+            "y": np.random.uniform(size=[6, 5, 4, 3, 2]).astype('float32'),
+        }
+
+
+if __name__ == "__main__":
+    unittest.main()

python/paddle/fluid/tests/unittests/ipu/test_mean_op_ipu.py

@@ -16,13 +16,8 @@ import unittest
 
 import numpy as np
 import paddle
-import paddle.fluid as fluid
-import paddle.fluid.compiler as compiler
-import paddle.optimizer
 import paddle.static
-from paddle.fluid.tests.unittests.ipu.op_test_ipu import IPUOpTest
-
-paddle.enable_static()
+from paddle.fluid.tests.unittests.ipu.op_test_ipu import IPUOpTest, ExecutionMode
 
 
 @unittest.skipIf(not paddle.is_compiled_with_ipu(),
@@ -31,97 +26,79 @@ class TestBase(IPUOpTest):
     def setUp(self):
         self.set_atol()
         self.set_training()
-        self.set_feed()
-        self.set_attrs()
+        self.set_data_feed()
+        self.set_feed_attr()
+        self.set_op_attrs()
 
-    def set_feed(self):
-        self.feed_shape = []
-        self.feed_shape.append([1, 3, 10, 10])
+    @property
+    def fp16_enabled(self):
+        return True
 
-        self.feed = {}
-        self.feed["in_0"] = np.random.uniform(
-            size=self.feed_shape[0]).astype(np.float32)
+    def set_data_feed(self):
+        data = np.random.uniform(size=[1, 3, 10, 10])
+        self.feed_fp32 = {"in_0": data.astype(np.float32)}
+        self.feed_fp16 = {"in_0": data.astype(np.float16)}
 
-        self.feed_list = list(self.feed.keys())
+    def set_feed_attr(self):
+        self.feed_shape = [x.shape for x in self.feed_fp32.values()]
+        self.feed_list = list(self.feed_fp32.keys())
 
-    def set_attrs(self):
+    def set_op_attrs(self):
         self.attrs = {}
-        self.attrs['axis'] = None
-        self.attrs['keepdim'] = False
 
-    def _test_base(self, run_ipu=True):
-        scope = fluid.core.Scope()
+    def _test_base(self, exec_mode):
+        scope = paddle.static.Scope()
         main_prog = paddle.static.Program()
         startup_prog = paddle.static.Program()
-        SEED = self.SEED
-        main_prog.random_seed = SEED
-        startup_prog.random_seed = SEED
+        main_prog.random_seed = self.SEED
+        startup_prog.random_seed = self.SEED
 
-        with fluid.scope_guard(scope):
+        with paddle.static.scope_guard(scope):
             with paddle.static.program_guard(main_prog, startup_prog):
                 x = paddle.static.data(
                     name=self.feed_list[0],
                     shape=self.feed_shape[0],
                     dtype='float32')
-                out = paddle.mean(x, **self.attrs)
 
-                fetch_list = [out.name]
+                out = paddle.fluid.layers.mean(x)
 
-            if run_ipu:
-                place = paddle.IPUPlace()
-            else:
+            fetch_list = [out.name]
+
+            if exec_mode == ExecutionMode.CPU_FP32:
                 place = paddle.CPUPlace()
+            else:
+                place = paddle.IPUPlace()
+
             exe = paddle.static.Executor(place)
             exe.run(startup_prog)
 
-            if run_ipu:
+            if exec_mode != ExecutionMode.CPU_FP32:
                 feed_list = self.feed_list
                 ipu_strategy = paddle.static.IpuStrategy()
-                ipu_strategy.SetGraphConfig(is_training=self.is_training)
-                program = compiler.IPUCompiledProgram(
+                ipu_strategy.set_graph_config(is_training=self.is_training)
+                if exec_mode == ExecutionMode.IPU_POPART_FP16:
+                    ipu_strategy.set_precision_config(enable_fp16=True)
+                program = paddle.static.IpuCompiledProgram(
                     main_prog,
                     ipu_strategy=ipu_strategy).compile(feed_list, fetch_list)
             else:
                 program = main_prog
 
-            result = exe.run(program, feed=self.feed, fetch_list=fetch_list)
+            feed = self.feed_fp32
+            if exec_mode > ExecutionMode.IPU_FP32:
+                feed = self.feed_fp16
+
+            result = exe.run(program, feed=feed, fetch_list=fetch_list)
             return result[0]
 
     def test_base(self):
-        res0 = self._test_base(True)
-        res1 = self._test_base(False)
-
-        self.assertTrue(
-            np.allclose(
-                res0.flatten(), res1.flatten(), atol=self.atol))
-
+        output_dict = {}
+        for mode in ExecutionMode:
+            if mode > ExecutionMode.IPU_FP32 and not self.fp16_enabled:
+                break
+            output_dict[mode] = self._test_base(mode).flatten()
 
-class TestCase1(TestBase):
-    def set_attrs(self):
-        self.attrs = {}
-        self.attrs['axis'] = 1
-        self.attrs['keepdim'] = False
-
-
-class TestCase2(TestBase):
-    def set_attrs(self):
-        self.attrs = {}
-        self.attrs['axis'] = 2
-        self.attrs['keepdim'] = False
-
-
-class TestCase3(TestBase):
-    def set_attrs(self):
-        self.attrs = {}
-        self.attrs['axis'] = 2
-        self.attrs['keepdim'] = True
-
-
-class TestCase4(TestBase):
-    def set_attrs(self):
-        self.attrs = {}
-        self.attrs['axis'] = None
-        self.attrs['keepdim'] = True
+        self.check(output_dict)
 
 
 if __name__ == "__main__":

python/paddle/fluid/tests/unittests/ipu/test_ipu_model_pipeline.py → python/paddle/fluid/tests/unittests/ipu/test_model_pipeline_ipu.py

@@ -17,8 +17,7 @@ from __future__ import print_function
 import numpy as np
 import unittest
 import paddle
-import paddle.fluid as fluid
-import paddle.fluid.compiler as compiler
+import paddle.static
 
 paddle.enable_static()
 SEED = 2021
@@ -28,7 +27,7 @@ SEED = 2021
                  "core is not compiled with IPU")
 class TestCastNet(unittest.TestCase):
     def _test(self, run_ipu=True):
-        scope = fluid.core.Scope()
+        scope = paddle.static.Scope()
         main_prog = paddle.static.Program()
         startup_prog = paddle.static.Program()
         main_prog.random_seed = SEED
@@ -37,14 +36,14 @@ class TestCastNet(unittest.TestCase):
 
         np_image = np.random.rand(1, 3, 10, 10).astype(np.float32)
 
-        with fluid.scope_guard(scope):
+        with paddle.static.scope_guard(scope):
             with paddle.static.program_guard(main_prog, startup_prog):
                 image = paddle.static.data(
                     name='image', shape=[1, 3, 10, 10], dtype='float32')
-                with fluid.ipu_shard(ipu_index=0):
+                with paddle.static.ipu_shard_guard(index=0):
                     conv1 = paddle.static.nn.conv2d(
                         image, num_filters=3, filter_size=3, bias_attr=False)
-                with fluid.ipu_shard(ipu_index=1):
+                with paddle.static.ipu_shard_guard(index=1):
                     conv2 = paddle.static.nn.conv2d(
                         conv1, num_filters=3, filter_size=3, bias_attr=False)
                     loss = paddle.mean(conv2)
@@ -60,9 +59,10 @@ class TestCastNet(unittest.TestCase):
                 feed_list = [image.name]
                 fetch_list = [loss.name]
                 ipu_strategy = paddle.static.IpuStrategy()
-                ipu_strategy.SetGraphConfig(
+                ipu_strategy.set_graph_config(
                     num_ipus=2, is_training=False, enable_manual_shard=True)
-                program = compiler.IPUCompiledProgram(
+                ipu_strategy.set_pipelining_config(enable_pipelining=False)
+                program = paddle.static.IpuCompiledProgram(
                     main_prog,
                     ipu_strategy=ipu_strategy).compile(feed_list, fetch_list)
             else:

python/paddle/fluid/tests/unittests/ipu/test_mul_op_ipu.py

@@ -16,14 +16,8 @@ import unittest
 
 import numpy as np
 import paddle
-import paddle.fluid as fluid
-import paddle.fluid.compiler as compiler
-import paddle.optimizer
 import paddle.static
-from paddle.fluid.tests.unittests.ipu.op_test_ipu import (IPUOpTest,
-                                                          np_dtype_to_fluid_str)
-
-paddle.enable_static()
+from paddle.fluid.tests.unittests.ipu.op_test_ipu import IPUOpTest, ExecutionMode
 
 
 @unittest.skipIf(not paddle.is_compiled_with_ipu(),
@@ -32,90 +26,98 @@ class TestBase(IPUOpTest):
     def setUp(self):
         self.set_atol()
         self.set_training()
-        self.set_feed()
+        self.set_data_feed()
         self.set_feed_attr()
-        self.set_attrs()
+        self.set_op_attrs()
 
-    def set_feed(self):
-        self.feed = {
-            "x": np.random.uniform(size=[2, 5]).astype('float32'),
-            "y": np.random.uniform(size=[5, 3]).astype('float32'),
-        }
+    @property
+    def fp16_enabled(self):
+        return True
+
+    def set_data_feed(self):
+        x = np.random.uniform(size=[2, 5])
+        y = np.random.uniform(size=[5, 3])
+        self.feed_fp32 = {"x": x.astype(np.float32), "y": y.astype(np.float32)}
+        self.feed_fp16 = {"x": x.astype(np.float16), "y": y.astype(np.float16)}
 
     def set_feed_attr(self):
-        self.feed_shape = [x.shape for x in self.feed.values()]
-        self.feed_list = list(self.feed.keys())
-        self.feed_dtype = [
-            np_dtype_to_fluid_str(x.dtype) for x in self.feed.values()
-        ]
+        self.feed_shape = [x.shape for x in self.feed_fp32.values()]
+        self.feed_list = list(self.feed_fp32.keys())
+        self.feed_dtype = [x.dtype for x in self.feed_fp32.values()]
 
-    def set_attrs(self):
+    def set_op_attrs(self):
         self.attrs = {
             "x_num_col_dims": 1,
             "y_num_col_dims": 1,
         }
 
-    def _test_base(self, run_ipu=True):
-        scope = fluid.core.Scope()
+    def _test_base(self, exec_mode):
+        scope = paddle.static.Scope()
         main_prog = paddle.static.Program()
         startup_prog = paddle.static.Program()
-        SEED = self.SEED
-        main_prog.random_seed = SEED
-        startup_prog.random_seed = SEED
+        main_prog.random_seed = self.SEED
+        startup_prog.random_seed = self.SEED
 
-        with fluid.scope_guard(scope):
+        with paddle.static.scope_guard(scope):
             with paddle.static.program_guard(main_prog, startup_prog):
                 x = paddle.static.data(
                     name=self.feed_list[0],
                     shape=self.feed_shape[0],
-                    dtype=self.feed_dtype[0])
+                    dtype='float32')
                 y = paddle.static.data(
                     name=self.feed_list[1],
                     shape=self.feed_shape[1],
-                    dtype=self.feed_dtype[1])
+                    dtype='float32')
+
                 out = paddle.fluid.layers.mul(x, y, **self.attrs)
 
-                fetch_list = [out.name]
+            fetch_list = [out.name]
 
-            if run_ipu:
-                place = paddle.IPUPlace()
-            else:
+            if exec_mode == ExecutionMode.CPU_FP32:
                 place = paddle.CPUPlace()
+            else:
+                place = paddle.IPUPlace()
+
             exe = paddle.static.Executor(place)
             exe.run(startup_prog)
 
-            if run_ipu:
+            if exec_mode != ExecutionMode.CPU_FP32:
                 feed_list = self.feed_list
                 ipu_strategy = paddle.static.IpuStrategy()
-                ipu_strategy.SetGraphConfig(is_training=self.is_training)
-                program = compiler.IPUCompiledProgram(
+                ipu_strategy.set_graph_config(is_training=self.is_training)
+                if exec_mode == ExecutionMode.IPU_POPART_FP16:
+                    ipu_strategy.set_precision_config(enable_fp16=True)
+                program = paddle.static.IpuCompiledProgram(
                     main_prog,
                     ipu_strategy=ipu_strategy).compile(feed_list, fetch_list)
             else:
                 program = main_prog
 
-            result = exe.run(program, feed=self.feed, fetch_list=fetch_list)
+            feed = self.feed_fp32
+            if exec_mode > ExecutionMode.IPU_FP32:
+                feed = self.feed_fp16
+
+            result = exe.run(program, feed=feed, fetch_list=fetch_list)
             return result[0]
 
     def test_base(self):
-        res0 = self._test_base(False)
-        res1 = self._test_base(True)
+        output_dict = {}
+        for mode in ExecutionMode:
+            if mode > ExecutionMode.IPU_FP32 and not self.fp16_enabled:
+                break
+            output_dict[mode] = self._test_base(mode).flatten()
 
-        self.assertTrue(
-            np.allclose(
-                res0.flatten(), res1.flatten(), atol=self.atol))
-
-        self.assertTrue(res0.shape == res1.shape)
+        self.check(output_dict)
 
 
 class TestCase1(TestBase):
-    def set_feed(self):
-        self.feed = {
-            "x": np.random.uniform(size=[1, 2, 5]).astype('float32'),
-            "y": np.random.uniform(size=[5, 3]).astype('float32'),
-        }
+    def set_data_feed(self):
+        x = np.random.uniform(size=[1, 2, 5])
+        y = np.random.uniform(size=[5, 3])
+        self.feed_fp32 = {"x": x.astype(np.float32), "y": y.astype(np.float32)}
+        self.feed_fp16 = {"x": x.astype(np.float16), "y": y.astype(np.float16)}
 
-    def set_attrs(self):
+    def set_op_attrs(self):
         self.attrs = {
             "x_num_col_dims": 2,
             "y_num_col_dims": 1,
@@ -123,13 +125,13 @@ class TestCase1(TestBase):
 
 
 class TestCase2(TestBase):
-    def set_feed(self):
-        self.feed = {
-            "x": np.random.uniform(size=[3, 4, 2, 9]).astype('float32'),
-            "y": np.random.uniform(size=[3, 6, 1, 2, 3]).astype('float32'),
-        }
+    def set_data_feed(self):
+        x = np.random.uniform(size=[3, 4, 2, 9])
+        y = np.random.uniform(size=[3, 6, 1, 2, 3])
+        self.feed_fp32 = {"x": x.astype(np.float32), "y": y.astype(np.float32)}
+        self.feed_fp16 = {"x": x.astype(np.float16), "y": y.astype(np.float16)}
 
-    def set_attrs(self):
+    def set_op_attrs(self):
         self.attrs = {
             'x_num_col_dims': 2,
             'y_num_col_dims': 2,

python/paddle/fluid/tests/unittests/ipu/test_pool_avg_op_ipu.py

@@ -16,14 +16,8 @@ import unittest
 
 import numpy as np
 import paddle
-import paddle.fluid as fluid
-import paddle.fluid.compiler as compiler
-import paddle.optimizer
 import paddle.static
-from paddle.fluid.tests.unittests.ipu.op_test_ipu import (IPUOpTest,
-                                                          np_dtype_to_fluid_str)
-
-paddle.enable_static()
+from paddle.fluid.tests.unittests.ipu.op_test_ipu import IPUOpTest, ExecutionMode
 
 
 @unittest.skipIf(not paddle.is_compiled_with_ipu(),
@@ -32,23 +26,25 @@ class TestBase(IPUOpTest):
     def setUp(self):
         self.set_atol()
         self.set_training()
-        self.set_feed()
+        self.set_data_feed()
         self.set_feed_attr()
-        self.set_attrs()
+        self.set_op_attrs()
 
-    def set_feed(self):
-        self.feed = {
-            "x": np.random.uniform(size=[1, 3, 10, 10]).astype('float32'),
-        }
+    @property
+    def fp16_enabled(self):
+        return True
+
+    def set_data_feed(self):
+        data = np.random.uniform(size=[1, 3, 10, 10])
+        self.feed_fp32 = {'in_0': data.astype(np.float32)}
+        self.feed_fp16 = {'in_0': data.astype(np.float16)}
 
     def set_feed_attr(self):
-        self.feed_shape = [x.shape for x in self.feed.values()]
-        self.feed_list = list(self.feed.keys())
-        self.feed_dtype = [
-            np_dtype_to_fluid_str(x.dtype) for x in self.feed.values()
-        ]
+        self.feed_shape = [x.shape for x in self.feed_fp32.values()]
+        self.feed_list = list(self.feed_fp32.keys())
+        self.feed_dtype = [x.dtype for x in self.feed_fp32.values()]
 
-    def set_attrs(self):
+    def set_op_attrs(self):
         self.attrs = {
             "pool_size": 3,
             "pool_type": 'avg',
@@ -60,53 +56,59 @@ class TestBase(IPUOpTest):
             "data_format": 'NCHW',
         }
 
-    def _test_base(self, run_ipu=True):
-        scope = fluid.core.Scope()
+    def _test_base(self, exec_mode):
+        scope = paddle.static.Scope()
         main_prog = paddle.static.Program()
         startup_prog = paddle.static.Program()
-        SEED = self.SEED
-        main_prog.random_seed = SEED
-        startup_prog.random_seed = SEED
+        main_prog.random_seed = self.SEED
+        startup_prog.random_seed = self.SEED
 
-        with fluid.scope_guard(scope):
+        with paddle.static.scope_guard(scope):
             with paddle.static.program_guard(main_prog, startup_prog):
                 x = paddle.static.data(
                     name=self.feed_list[0],
                     shape=self.feed_shape[0],
-                    dtype=self.feed_dtype[0])
+                    dtype='float32')
+
                 out = paddle.fluid.layers.pool2d(x, **self.attrs)
 
-                fetch_list = [out.name]
+            fetch_list = [out.name]
 
-            if run_ipu:
-                place = paddle.IPUPlace()
-            else:
+            if exec_mode == ExecutionMode.CPU_FP32:
                 place = paddle.CPUPlace()
+            else:
+                place = paddle.IPUPlace()
+
             exe = paddle.static.Executor(place)
             exe.run(startup_prog)
 
-            if run_ipu:
+            if exec_mode != ExecutionMode.CPU_FP32:
                 feed_list = self.feed_list
                 ipu_strategy = paddle.static.IpuStrategy()
-                ipu_strategy.SetGraphConfig(is_training=self.is_training)
-                program = compiler.IPUCompiledProgram(
+                ipu_strategy.set_graph_config(is_training=self.is_training)
+                if exec_mode == ExecutionMode.IPU_POPART_FP16:
+                    ipu_strategy.set_precision_config(enable_fp16=True)
+                program = paddle.static.IpuCompiledProgram(
                     main_prog,
                     ipu_strategy=ipu_strategy).compile(feed_list, fetch_list)
             else:
                 program = main_prog
 
-            result = exe.run(program, feed=self.feed, fetch_list=fetch_list)
+            feed = self.feed_fp32
+            if exec_mode > ExecutionMode.IPU_FP32:
+                feed = self.feed_fp16
+
+            result = exe.run(program, feed=feed, fetch_list=fetch_list)
             return result[0]
 
     def test_base(self):
-        res0 = self._test_base(False)
-        res1 = self._test_base(True)
-
-        self.assertTrue(
-            np.allclose(
-                res0.flatten(), res1.flatten(), atol=self.atol))
+        output_dict = {}
+        for mode in ExecutionMode:
+            if mode > ExecutionMode.IPU_FP32 and not self.fp16_enabled:
+                break
+            output_dict[mode] = self._test_base(mode).flatten()
 
-        self.assertTrue(res0.shape == res1.shape)
+        self.check(output_dict)
 
 
 class TestCase1(TestBase):

python/paddle/fluid/tests/unittests/ipu/test_pool_max_op_ipu.py

@@ -16,14 +16,8 @@ import unittest
 
 import numpy as np
 import paddle
-import paddle.fluid as fluid
-import paddle.fluid.compiler as compiler
-import paddle.optimizer
 import paddle.static
-from paddle.fluid.tests.unittests.ipu.op_test_ipu import (IPUOpTest,
-                                                          np_dtype_to_fluid_str)
-
-paddle.enable_static()
+from paddle.fluid.tests.unittests.ipu.op_test_ipu import IPUOpTest, ExecutionMode
 
 
 @unittest.skipIf(not paddle.is_compiled_with_ipu(),
@@ -32,23 +26,25 @@ class TestBase(IPUOpTest):
     def setUp(self):
         self.set_atol()
         self.set_training()
-        self.set_feed()
+        self.set_data_feed()
         self.set_feed_attr()
-        self.set_attrs()
+        self.set_op_attrs()
 
-    def set_feed(self):
-        self.feed = {
-            "x": np.random.uniform(size=[1, 3, 10, 10]).astype('float32'),
-        }
+    @property
+    def fp16_enabled(self):
+        return True
+
+    def set_data_feed(self):
+        data = np.random.uniform(size=[1, 3, 10, 10])
+        self.feed_fp32 = {'in_0': data.astype(np.float32)}
+        self.feed_fp16 = {'in_0': data.astype(np.float16)}
 
     def set_feed_attr(self):
-        self.feed_shape = [x.shape for x in self.feed.values()]
-        self.feed_list = list(self.feed.keys())
-        self.feed_dtype = [
-            np_dtype_to_fluid_str(x.dtype) for x in self.feed.values()
-        ]
+        self.feed_shape = [x.shape for x in self.feed_fp32.values()]
+        self.feed_list = list(self.feed_fp32.keys())
+        self.feed_dtype = [x.dtype for x in self.feed_fp32.values()]
 
-    def set_attrs(self):
+    def set_op_attrs(self):
         self.attrs = {
             "pool_size": 3,
             "pool_type": 'max',
@@ -60,120 +56,126 @@ class TestBase(IPUOpTest):
             "data_format": 'NCHW',
         }
 
-    def _test_base(self, run_ipu=True):
-        scope = fluid.core.Scope()
+    def _test_base(self, exec_mode):
+        scope = paddle.static.Scope()
         main_prog = paddle.static.Program()
         startup_prog = paddle.static.Program()
-        SEED = self.SEED
-        main_prog.random_seed = SEED
-        startup_prog.random_seed = SEED
+        main_prog.random_seed = self.SEED
+        startup_prog.random_seed = self.SEED
 
-        with fluid.scope_guard(scope):
+        with paddle.static.scope_guard(scope):
             with paddle.static.program_guard(main_prog, startup_prog):
                 x = paddle.static.data(
                     name=self.feed_list[0],
                     shape=self.feed_shape[0],
-                    dtype=self.feed_dtype[0])
+                    dtype='float32')
+
                 out = paddle.fluid.layers.pool2d(x, **self.attrs)
 
-                fetch_list = [out.name]
+            fetch_list = [out.name]
 
-            if run_ipu:
-                place = paddle.IPUPlace()
-            else:
+            if exec_mode == ExecutionMode.CPU_FP32:
                 place = paddle.CPUPlace()
+            else:
+                place = paddle.IPUPlace()
+
             exe = paddle.static.Executor(place)
             exe.run(startup_prog)
 
-            if run_ipu:
+            if exec_mode != ExecutionMode.CPU_FP32:
                 feed_list = self.feed_list
                 ipu_strategy = paddle.static.IpuStrategy()
-                ipu_strategy.SetGraphConfig(is_training=self.is_training)
-                program = compiler.IPUCompiledProgram(
+                ipu_strategy.set_graph_config(is_training=self.is_training)
+                if exec_mode == ExecutionMode.IPU_POPART_FP16:
+                    ipu_strategy.set_precision_config(enable_fp16=True)
+                program = paddle.static.IpuCompiledProgram(
                     main_prog,
                     ipu_strategy=ipu_strategy).compile(feed_list, fetch_list)
             else:
                 program = main_prog
 
-            result = exe.run(program, feed=self.feed, fetch_list=fetch_list)
+            feed = self.feed_fp32
+            if exec_mode > ExecutionMode.IPU_FP32:
+                feed = self.feed_fp16
+
+            result = exe.run(program, feed=feed, fetch_list=fetch_list)
             return result[0]
 
     def test_base(self):
-        res0 = self._test_base(False)
-        res1 = self._test_base(True)
-
-        self.assertTrue(
-            np.allclose(
-                res0.flatten(), res1.flatten(), atol=self.atol))
+        output_dict = {}
+        for mode in ExecutionMode:
+            if mode > ExecutionMode.IPU_FP32 and not self.fp16_enabled:
+                break
+            output_dict[mode] = self._test_base(mode).flatten()
 
-        self.assertTrue(res0.shape == res1.shape)
+        self.check(output_dict)
 
 
 class TestCase1(TestBase):
-    def set_attrs(self):
-        super().set_attrs()
+    def set_op_attrs(self):
+        super().set_op_attrs()
         self.attrs['pool_size'] = 3
 
 
 class TestCase1_2(TestBase):
-    def set_attrs(self):
-        super().set_attrs()
+    def set_op_attrs(self):
+        super().set_op_attrs()
         self.attrs['pool_size'] = [3, 1]
 
 
 class TestCase2(TestBase):
-    def set_attrs(self):
-        super().set_attrs()
+    def set_op_attrs(self):
+        super().set_op_attrs()
         self.attrs['pool_stride'] = 2
 
 
 class TestCase2_2(TestBase):
-    def set_attrs(self):
-        super().set_attrs()
+    def set_op_attrs(self):
+        super().set_op_attrs()
         self.attrs['pool_stride'] = [2, 1]
 
 
 class TestCase3(TestBase):
-    def set_attrs(self):
-        super().set_attrs()
+    def set_op_attrs(self):
+        super().set_op_attrs()
         self.attrs['pool_padding'] = [1, 1]
 
 
 class TestCase3_2(TestBase):
-    def set_attrs(self):
-        super().set_attrs()
+    def set_op_attrs(self):
+        super().set_op_attrs()
         self.attrs['pool_padding'] = [1, 1, 2, 2]
 
 
 @unittest.skip('auto_pad is not currently supported')
 class TestCase3_3(TestBase):
-    def set_attrs(self):
-        super().set_attrs()
+    def set_op_attrs(self):
+        super().set_op_attrs()
         self.attrs['pool_padding'] = 'VALID'
 
 
 @unittest.skip('auto_pad is not currently supported')
 class TestCase3_4(TestBase):
-    def set_attrs(self):
-        super().set_attrs()
+    def set_op_attrs(self):
+        super().set_op_attrs()
         self.attrs['pool_padding'] = 'SAME'
 
 
 class TestCase4(TestBase):
-    def set_attrs(self):
-        super().set_attrs()
+    def set_op_attrs(self):
+        super().set_op_attrs()
         self.attrs['global_pooling'] = True
 
 
 class TestCase5(TestBase):
-    def set_attrs(self):
-        super().set_attrs()
+    def set_op_attrs(self):
+        super().set_op_attrs()
         self.attrs['ceil_mode'] = True
 
 
 class TestCase6(TestBase):
-    def set_attrs(self):
-        super().set_attrs()
+    def set_op_attrs(self):
+        super().set_op_attrs()
         self.attrs['exclusive'] = False
 
 

python/paddle/fluid/tests/unittests/ipu/test_pow_op_ipu.py

@@ -16,14 +16,8 @@ import unittest
 
 import numpy as np
 import paddle
-import paddle.fluid as fluid
-import paddle.fluid.compiler as compiler
-import paddle.optimizer
 import paddle.static
-from paddle.fluid.tests.unittests.ipu.op_test_ipu import (IPUOpTest,
-                                                          np_dtype_to_fluid_str)
-
-paddle.enable_static()
+from paddle.fluid.tests.unittests.ipu.op_test_ipu import IPUOpTest, ExecutionMode
 
 
 @unittest.skipIf(not paddle.is_compiled_with_ipu(),
@@ -32,124 +26,146 @@ class TestBase(IPUOpTest):
     def setUp(self):
         self.set_atol()
         self.set_training()
-        self.set_feed()
+        self.set_data_feed()
         self.set_feed_attr()
-        self.set_attrs()
+        self.set_op_attrs()
 
-    def set_feed(self):
-        self.feed = {
-            "x": np.random.uniform(size=[1, 3, 2, 2]).astype('float32'),
-        }
+    @property
+    def fp16_enabled(self):
+        return True
+
+    def set_data_feed(self):
+        data = np.random.uniform(size=[1, 3, 2, 2])
+        self.feed_fp32 = {"x": data.astype(np.float32)}
+        self.feed_fp16 = {"x": data.astype(np.float16)}
 
     def set_feed_attr(self):
-        self.feed_shape = [x.shape for x in self.feed.values()]
-        self.feed_list = list(self.feed.keys())
-        self.feed_dtype = [
-            np_dtype_to_fluid_str(x.dtype) for x in self.feed.values()
-        ]
+        self.feed_shape = [x.shape for x in self.feed_fp32.values()]
+        self.feed_list = list(self.feed_fp32.keys())
+        self.feed_dtype = [x.dtype for x in self.feed_fp32.values()]
 
-    def set_attrs(self):
+    def set_op_attrs(self):
         self.attrs = {"factor": 2.0}
 
-    def _test_base(self, run_ipu=True):
-        scope = fluid.core.Scope()
+    def _test_base(self, exec_mode):
+        scope = paddle.static.Scope()
         main_prog = paddle.static.Program()
         startup_prog = paddle.static.Program()
-        SEED = self.SEED
-        main_prog.random_seed = SEED
-        startup_prog.random_seed = SEED
+        main_prog.random_seed = self.SEED
+        startup_prog.random_seed = self.SEED
 
-        with fluid.scope_guard(scope):
+        with paddle.static.scope_guard(scope):
             with paddle.static.program_guard(main_prog, startup_prog):
                 x = paddle.static.data(
                     name=self.feed_list[0],
                     shape=self.feed_shape[0],
-                    dtype=self.feed_dtype[0])
+                    dtype='float32')
+
                 out = paddle.fluid.layers.pow(x, **self.attrs)
 
-                fetch_list = [out.name]
+            fetch_list = [out.name]
 
-            if run_ipu:
-                place = paddle.IPUPlace()
-            else:
+            if exec_mode == ExecutionMode.CPU_FP32:
                 place = paddle.CPUPlace()
+            else:
+                place = paddle.IPUPlace()
+
             exe = paddle.static.Executor(place)
             exe.run(startup_prog)
 
-            if run_ipu:
+            if exec_mode != ExecutionMode.CPU_FP32:
                 feed_list = self.feed_list
                 ipu_strategy = paddle.static.IpuStrategy()
-                ipu_strategy.SetGraphConfig(is_training=self.is_training)
-                program = compiler.IPUCompiledProgram(
+                ipu_strategy.set_graph_config(is_training=self.is_training)
+                if exec_mode == ExecutionMode.IPU_POPART_FP16:
+                    ipu_strategy.set_precision_config(enable_fp16=True)
+                program = paddle.static.IpuCompiledProgram(
                     main_prog,
                     ipu_strategy=ipu_strategy).compile(feed_list, fetch_list)
             else:
                 program = main_prog
 
-            result = exe.run(program, feed=self.feed, fetch_list=fetch_list)
+            feed = self.feed_fp32
+            if exec_mode > ExecutionMode.IPU_FP32:
+                feed = self.feed_fp16
+
+            result = exe.run(program, feed=feed, fetch_list=fetch_list)
             return result[0]
 
     def test_base(self):
-        res0 = self._test_base(False)
-        res1 = self._test_base(True)
+        output_dict = {}
+        for mode in ExecutionMode:
+            if mode > ExecutionMode.IPU_FP32 and not self.fp16_enabled:
+                break
+            output_dict[mode] = self._test_base(mode).flatten()
 
-        self.assertTrue(
-            np.allclose(
-                res0.flatten(), res1.flatten(), atol=self.atol))
-
-        self.assertTrue(res0.shape == res1.shape)
+        self.check(output_dict)
 
 
 class TestCase1(TestBase):
-    def set_feed(self):
-        self.feed = {
-            "x": np.random.uniform(size=[1, 3, 2, 2]).astype('float32'),
-            "y": np.array([2.0]).astype('float32'),
+    def set_data_feed(self):
+        data1 = np.random.uniform(size=[1, 3, 2, 2])
+        data2 = np.array([2.0])
+
+        self.feed_fp32 = {
+            "x": data1.astype(np.float32),
+            "y": data2.astype(np.float32)
+        }
+        self.feed_fp16 = {
+            "x": data1.astype(np.float16),
+            "y": data2.astype(np.float16)
         }
 
-    def set_attrs(self):
+    def set_op_attrs(self):
         self.attrs = {}
 
-    def _test_base(self, run_ipu=True):
-        scope = fluid.core.Scope()
+    def _test_base(self, exec_mode):
+        scope = paddle.static.Scope()
         main_prog = paddle.static.Program()
         startup_prog = paddle.static.Program()
-        SEED = self.SEED
-        main_prog.random_seed = SEED
-        startup_prog.random_seed = SEED
+        main_prog.random_seed = self.SEED
+        startup_prog.random_seed = self.SEED
 
-        with fluid.scope_guard(scope):
+        with paddle.static.scope_guard(scope):
             with paddle.static.program_guard(main_prog, startup_prog):
                 x = paddle.static.data(
                     name=self.feed_list[0],
                     shape=self.feed_shape[0],
-                    dtype=self.feed_dtype[0])
+                    dtype='float32')
                 factor = paddle.static.data(
                     name=self.feed_list[1],
                     shape=self.feed_shape[1],
-                    dtype=self.feed_dtype[1])
+                    dtype='float32')
+
                 out = paddle.fluid.layers.pow(x, factor=factor, **self.attrs)
 
-                fetch_list = [out.name]
+            fetch_list = [out.name]
 
-            if run_ipu:
-                place = paddle.IPUPlace()
-            else:
+            if exec_mode == ExecutionMode.CPU_FP32:
                 place = paddle.CPUPlace()
+            else:
+                place = paddle.IPUPlace()
+
             exe = paddle.static.Executor(place)
             exe.run(startup_prog)
 
-            if run_ipu:
+            if exec_mode != ExecutionMode.CPU_FP32:
                 feed_list = self.feed_list
                 ipu_strategy = paddle.static.IpuStrategy()
-                ipu_strategy.SetGraphConfig(is_training=self.is_training)
-                program = compiler.IPUCompiledProgram(
+                ipu_strategy.set_graph_config(is_training=self.is_training)
+                if exec_mode == ExecutionMode.IPU_POPART_FP16:
+                    ipu_strategy.set_precision_config(enable_fp16=True)
+                program = paddle.static.IpuCompiledProgram(
                     main_prog,
                     ipu_strategy=ipu_strategy).compile(feed_list, fetch_list)
             else:
                 program = main_prog
 
-            result = exe.run(program, feed=self.feed, fetch_list=fetch_list)
+            feed = self.feed_fp32
+            if exec_mode > ExecutionMode.IPU_FP32:
+                feed = self.feed_fp16
+
+            result = exe.run(program, feed=feed, fetch_list=fetch_list)
             return result[0]
 
 

python/paddle/fluid/tests/unittests/ipu/test_print_op_ipu.py

+#  Copyright (c) 2022 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.
+
+import unittest
+
+import numpy as np
+import paddle
+import paddle.static
+from paddle.fluid.tests.unittests.ipu.op_test_ipu import IPUOpTest
+
+
+@unittest.skipIf(not paddle.is_compiled_with_ipu(),
+                 "core is not compiled with IPU")
+class TestBase(IPUOpTest):
+    def setUp(self):
+        self.set_atol()
+        self.set_training()
+        self.set_data_feed()
+        self.set_feed_attr()
+        self.set_op_attrs()
+
+    def set_data_feed(self):
+        self.feed = {
+            "x": np.random.uniform(size=[1, 3, 3, 3]).astype('float32'),
+        }
+
+    def set_feed_attr(self):
+        self.feed_shape = [x.shape for x in self.feed.values()]
+        self.feed_list = list(self.feed.keys())
+        self.feed_dtype = [x.dtype for x in self.feed.values()]
+
+    def set_op_attrs(self):
+        self.attrs = {}
+
+    def _test_base(self, run_ipu=True):
+        scope = paddle.static.Scope()
+        main_prog = paddle.static.Program()
+        startup_prog = paddle.static.Program()
+        main_prog.random_seed = self.SEED
+        startup_prog.random_seed = self.SEED
+
+        with paddle.static.scope_guard(scope):
+            with paddle.static.program_guard(main_prog, startup_prog):
+                x = paddle.static.data(
+                    name=self.feed_list[0],
+                    shape=self.feed_shape[0],
+                    dtype=self.feed_dtype[0])
+                out = paddle.fluid.layers.conv2d(
+                    x, num_filters=3, filter_size=3)
+                out = paddle.fluid.layers.Print(out, **self.attrs)
+
+                if self.is_training:
+                    loss = paddle.mean(out)
+                    adam = paddle.optimizer.Adam(learning_rate=1e-2)
+                    adam.minimize(loss)
+                    fetch_list = [loss.name]
+                else:
+                    fetch_list = [out.name]
+
+            if run_ipu:
+                place = paddle.IPUPlace()
+            else:
+                place = paddle.CPUPlace()
+            exe = paddle.static.Executor(place)
+            exe.run(startup_prog)
+
+            if run_ipu:
+                feed_list = self.feed_list
+                ipu_strategy = paddle.static.IpuStrategy()
+                ipu_strategy.set_graph_config(is_training=self.is_training)
+                program = paddle.static.IpuCompiledProgram(
+                    main_prog,
+                    ipu_strategy=ipu_strategy).compile(feed_list, fetch_list)
+            else:
+                program = main_prog
+
+            if self.is_training:
+                result = []
+                for _ in range(self.epoch):
+                    loss_res = exe.run(program,
+                                       feed=self.feed,
+                                       fetch_list=fetch_list)
+                    result.append(loss_res[0])
+                return np.array(result)
+            else:
+                result = exe.run(program, feed=self.feed, fetch_list=fetch_list)
+                return result[0]
+
+    def test(self):
+        res0 = self._test_base(False)
+        res1 = self._test_base(True)
+
+        self.assertTrue(
+            np.allclose(
+                res0.flatten(), res1.flatten(), atol=self.atol))
+
+        self.assertTrue(res0.shape == res1.shape)
+
+
+class TestCase1(TestBase):
+    def set_op_attrs(self):
+        self.attrs = {"message": "input_data"}
+
+
+class TestTrainCase1(TestBase):
+    def set_op_attrs(self):
+        # "forward" : print forward
+        # "backward" : print forward and backward
+        # "both": print forward and backward
+        self.attrs = {"message": "input_data2", "print_phase": "both"}
+
+    def set_training(self):
+        self.is_training = True
+        self.epoch = 2
+
+
+@unittest.skip("attrs are not supported")
+class TestCase2(TestBase):
+    def set_op_attrs(self):
+        self.attrs = {
+            "first_n": 10,
+            "summarize": 10,
+            "print_tensor_name": True,
+            "print_tensor_type": True,
+            "print_tensor_shape": True,
+            "print_tensor_layout": True,
+            "print_tensor_lod": True
+        }
+
+
+if __name__ == "__main__":
+    unittest.main()

python/paddle/fluid/tests/unittests/ipu/test_reduce_x_op_ipu.py

@@ -16,14 +16,8 @@ import unittest
 
 import numpy as np
 import paddle
-import paddle.fluid as fluid
-import paddle.fluid.compiler as compiler
-import paddle.optimizer
 import paddle.static
-from paddle.fluid.tests.unittests.ipu.op_test_ipu import (IPUOpTest,
-                                                          np_dtype_to_fluid_str)
-
-paddle.enable_static()
+from paddle.fluid.tests.unittests.ipu.op_test_ipu import IPUOpTest, ExecutionMode
 
 
 @unittest.skipIf(not paddle.is_compiled_with_ipu(),
@@ -32,125 +26,137 @@ class TestMean(IPUOpTest):
     def setUp(self):
         self.set_atol()
         self.set_training()
-        self.init_op()
+        self.set_test_op()
+
+    @property
+    def fp16_enabled(self):
+        return True
 
-    def init_op(self):
+    def set_test_op(self):
         self.op = paddle.fluid.layers.reduce_mean
 
     def set_feed_attr(self):
-        self.feed_shape = [x.shape for x in self.feed.values()]
-        self.feed_list = list(self.feed.keys())
-        self.feed_dtype = [
-            np_dtype_to_fluid_str(x.dtype) for x in self.feed.values()
-        ]
-
-    def _test_base(self, run_ipu=True):
-        scope = fluid.core.Scope()
+        self.feed_shape = [x.shape for x in self.feed_fp32.values()]
+        self.feed_list = list(self.feed_fp32.keys())
+        self.feed_dtype = [x.dtype for x in self.feed_fp32.values()]
+
+    def _test_base(self, exec_mode):
+        scope = paddle.static.Scope()
         main_prog = paddle.static.Program()
         startup_prog = paddle.static.Program()
-        SEED = self.SEED
-        main_prog.random_seed = SEED
-        startup_prog.random_seed = SEED
+        main_prog.random_seed = self.SEED
+        startup_prog.random_seed = self.SEED
 
-        with fluid.scope_guard(scope):
+        with paddle.static.scope_guard(scope):
             with paddle.static.program_guard(main_prog, startup_prog):
                 x = paddle.static.data(
                     name=self.feed_list[0],
                     shape=self.feed_shape[0],
                     dtype='float32')
+
                 out = self.op(x, **self.attrs)
 
-                fetch_list = [out.name]
+            fetch_list = [out.name]
 
-            if run_ipu:
-                place = paddle.IPUPlace()
-            else:
+            if exec_mode == ExecutionMode.CPU_FP32:
                 place = paddle.CPUPlace()
+            else:
+                place = paddle.IPUPlace()
+
             exe = paddle.static.Executor(place)
             exe.run(startup_prog)
 
-            if run_ipu:
+            if exec_mode != ExecutionMode.CPU_FP32:
                 feed_list = self.feed_list
                 ipu_strategy = paddle.static.IpuStrategy()
-                ipu_strategy.SetGraphConfig(is_training=self.is_training)
-                program = compiler.IPUCompiledProgram(
+                ipu_strategy.set_graph_config(is_training=self.is_training)
+                if exec_mode == ExecutionMode.IPU_POPART_FP16:
+                    ipu_strategy.set_precision_config(enable_fp16=True)
+                program = paddle.static.IpuCompiledProgram(
                     main_prog,
                     ipu_strategy=ipu_strategy).compile(feed_list, fetch_list)
             else:
                 program = main_prog
 
-            result = exe.run(program, feed=self.feed, fetch_list=fetch_list)
+            feed = self.feed_fp32
+            if exec_mode > ExecutionMode.IPU_FP32:
+                feed = self.feed_fp16
+
+            result = exe.run(program, feed=feed, fetch_list=fetch_list)
             return result[0]
 
     def run_test_base(self):
-        res0 = self._test_base(True)
-        res1 = self._test_base(False)
-
-        self.assertTrue(
-            np.allclose(
-                res0.flatten(), res1.flatten(), atol=self.atol))
-
-    def set_feed0(self):
-        self.feed = {}
-        self.feed["in_0"] = np.random.uniform(size=[2, 4]).astype(np.float32)
+        output_dict = {}
+        for mode in ExecutionMode:
+            if mode > ExecutionMode.IPU_FP32 and not self.fp16_enabled:
+                break
+            output_dict[mode] = self._test_base(mode).flatten()
+
+        self.check(output_dict)
+
+    def set_data_feed0(self):
+        data = np.random.uniform(size=[2, 4])
+        self.feed_fp32 = {"in_0": data.astype(np.float32)}
+        self.feed_fp16 = {"in_0": data.astype(np.float16)}
         self.set_feed_attr()
 
-    def set_feed1(self):
-        self.feed = {}
-        self.feed["in_0"] = np.random.uniform(size=[2, 2, 2]).astype(np.float32)
+    def set_data_feed1(self):
+        data = np.random.uniform(size=[2, 2, 2])
+        self.feed_fp32 = {"in_0": data.astype(np.float32)}
+        self.feed_fp16 = {"in_0": data.astype(np.float16)}
         self.set_feed_attr()
 
-    def set_attr0(self):
+    def set_op_attr0(self):
         self.attrs = {}
         self.attrs['dim'] = None
         self.attrs['keep_dim'] = False
 
     def test_case0(self):
-        self.set_feed0()
-        self.set_attr0()
+        self.set_data_feed0()
+        self.set_op_attr0()
         self.run_test_base()
 
     def test_case1(self):
-        self.set_feed0()
-        self.set_attr0()
+        self.set_data_feed0()
+        self.set_op_attr0()
         self.attrs['dim'] = 0
         self.run_test_base()
 
     def test_case2(self):
-        self.set_feed0()
-        self.set_attr0()
+        self.set_data_feed0()
+        self.set_op_attr0()
         self.attrs['dim'] = -1
         self.run_test_base()
 
     def test_case3(self):
-        self.set_feed0()
-        self.set_attr0()
+        self.set_data_feed0()
+        self.set_op_attr0()
         self.attrs['dim'] = 1
         self.run_test_base()
 
     def test_case4(self):
-        self.set_feed0()
+        self.set_data_feed0()
         self.attrs = {}
         self.attrs['dim'] = 1
         self.attrs['keep_dim'] = True
         self.run_test_base()
 
     def test_case5(self):
-        self.set_feed1()
+        self.set_data_feed1()
         self.attrs = {}
         self.attrs['dim'] = [1, 2]
         self.attrs['keep_dim'] = False
         self.run_test_base()
 
     def test_case6(self):
-        self.set_feed1()
+        self.set_data_feed1()
         self.attrs = {}
         self.attrs['dim'] = [0, 1]
         self.attrs['keep_dim'] = False
         self.run_test_base()
 
     def test_case7(self):
-        self.set_feed1()
+        self.set_data_feed1()
         self.attrs = {}
         self.attrs['dim'] = [0, 1]
         self.attrs['keep_dim'] = True
@@ -158,22 +164,22 @@ class TestMean(IPUOpTest):
 
 
 class TestMax(TestMean):
-    def init_op(self):
+    def set_test_op(self):
         self.op = paddle.fluid.layers.reduce_max
 
 
 class TestMin(TestMean):
-    def init_op(self):
+    def set_test_op(self):
         self.op = paddle.fluid.layers.reduce_min
 
 
 class TestProd(TestMean):
-    def init_op(self):
+    def set_test_op(self):
         self.op = paddle.fluid.layers.reduce_prod
 
 
 class TestSum(TestMean):
-    def init_op(self):
+    def set_test_op(self):
         self.op = paddle.fluid.layers.reduce_sum
 
 

python/paddle/fluid/tests/unittests/ipu/test_reshape_inplace_op_ipu.py

@@ -16,14 +16,8 @@ import unittest
 
 import numpy as np
 import paddle
-import paddle.fluid as fluid
-import paddle.fluid.compiler as compiler
-import paddle.optimizer
 import paddle.static
-from paddle.fluid.tests.unittests.ipu.op_test_ipu import (IPUOpTest,
-                                                          np_dtype_to_fluid_str)
-
-paddle.enable_static()
+from paddle.fluid.tests.unittests.ipu.op_test_ipu import IPUOpTest, ExecutionMode
 
 
 @unittest.skipIf(not paddle.is_compiled_with_ipu(),
@@ -32,76 +26,84 @@ class TestBase(IPUOpTest):
     def setUp(self):
         self.set_atol()
         self.set_training()
-        self.set_feed()
+        self.set_data_feed()
         self.set_feed_attr()
-        self.set_attrs()
+        self.set_op_attrs()
 
-    def set_feed(self):
-        self.feed = {
-            "x": np.random.uniform(size=[1, 3, 10, 10]).astype('float32'),
-        }
+    @property
+    def fp16_enabled(self):
+        return True
+
+    def set_data_feed(self):
+        data = np.random.uniform(size=[1, 3, 10, 10])
+        self.feed_fp32 = {"x": data.astype(np.float32)}
+        self.feed_fp16 = {"x": data.astype(np.float16)}
 
     def set_feed_attr(self):
-        self.feed_shape = [x.shape for x in self.feed.values()]
-        self.feed_list = list(self.feed.keys())
-        self.feed_dtype = [
-            np_dtype_to_fluid_str(x.dtype) for x in self.feed.values()
-        ]
+        self.feed_shape = [x.shape for x in self.feed_fp32.values()]
+        self.feed_list = list(self.feed_fp32.keys())
+        self.feed_dtype = [x.dtype for x in self.feed_fp32.values()]
 
-    def set_attrs(self):
+    def set_op_attrs(self):
         self.attrs = {
             "shape": [30, 10],
             "inplace": True,
         }
 
-    def _test_base(self, run_ipu=True):
-        scope = fluid.core.Scope()
+    def _test_base(self, exec_mode):
+        scope = paddle.static.Scope()
         main_prog = paddle.static.Program()
         startup_prog = paddle.static.Program()
-        SEED = self.SEED
-        main_prog.random_seed = SEED
-        startup_prog.random_seed = SEED
+        main_prog.random_seed = self.SEED
+        startup_prog.random_seed = self.SEED
 
-        with fluid.scope_guard(scope):
+        with paddle.static.scope_guard(scope):
             with paddle.static.program_guard(main_prog, startup_prog):
                 x = paddle.static.data(
                     name=self.feed_list[0],
                     shape=self.feed_shape[0],
-                    dtype=self.feed_dtype[0])
+                    dtype='float32')
+
                 add = paddle.fluid.layers.elementwise_add(x, x)
                 out = paddle.fluid.layers.reshape(add, **self.attrs)
 
-                fetch_list = [out.name]
+            fetch_list = [out.name]
 
-            if run_ipu:
-                place = paddle.IPUPlace()
-            else:
+            if exec_mode == ExecutionMode.CPU_FP32:
                 place = paddle.CPUPlace()
+            else:
+                place = paddle.IPUPlace()
+
             exe = paddle.static.Executor(place)
             exe.run(startup_prog)
 
-            if run_ipu:
+            if exec_mode != ExecutionMode.CPU_FP32:
                 feed_list = self.feed_list
                 ipu_strategy = paddle.static.IpuStrategy()
-                ipu_strategy.SetGraphConfig(is_training=self.is_training)
-                program = compiler.IPUCompiledProgram(
+                ipu_strategy.set_graph_config(is_training=self.is_training)
+                if exec_mode == ExecutionMode.IPU_POPART_FP16:
+                    ipu_strategy.set_precision_config(enable_fp16=True)
+                program = paddle.static.IpuCompiledProgram(
                     main_prog,
                     ipu_strategy=ipu_strategy).compile(feed_list, fetch_list)
             else:
                 program = main_prog
 
-            result = exe.run(program, feed=self.feed, fetch_list=fetch_list)
+            feed = self.feed_fp32
+            if exec_mode > ExecutionMode.IPU_FP32:
+                feed = self.feed_fp16
+
+            result = exe.run(program, feed=feed, fetch_list=fetch_list)
             return result[0]
 
     def test_base(self):
-        res0 = self._test_base(True)
-        res1 = self._test_base(False)
-
-        self.assertTrue(
-            np.allclose(
-                res0.flatten(), res1.flatten(), atol=self.atol))
+        output_dict = {}
+        for mode in ExecutionMode:
+            if mode > ExecutionMode.IPU_FP32 and not self.fp16_enabled:
+                break
+            output_dict[mode] = self._test_base(mode)
 
-        self.assertTrue(res0.shape == res1.shape)
+        self.check(output_dict, check_shape=True)
 
 
 class TestCase1(TestBase):

python/paddle/fluid/tests/unittests/ipu/test_reshape_op_ipu.py

@@ -16,13 +16,8 @@ import unittest
 
 import numpy as np
 import paddle
-import paddle.fluid as fluid
-import paddle.fluid.compiler as compiler
-import paddle.optimizer
 import paddle.static
-from paddle.fluid.tests.unittests.ipu.op_test_ipu import IPUOpTest
-
-paddle.enable_static()
+from paddle.fluid.tests.unittests.ipu.op_test_ipu import IPUOpTest, ExecutionMode
 
 
 @unittest.skipIf(not paddle.is_compiled_with_ipu(),
@@ -31,82 +26,92 @@ class TestBase(IPUOpTest):
     def setUp(self):
         self.set_atol()
         self.set_training()
-        self.set_feed()
-        self.set_attrs()
+        self.set_data_feed()
+        self.set_feed_attr()
+        self.set_op_attrs()
 
-    def set_feed(self):
-        self.feed_shape = []
-        self.feed_shape.append([2, 4, 6])
+    @property
+    def fp16_enabled(self):
+        return True
 
-        self.feed = {}
-        self.feed["in_0"] = np.random.uniform(
-            size=self.feed_shape[0]).astype(np.float32)
+    def set_data_feed(self):
+        data = np.random.uniform(size=[2, 4, 6])
+        self.feed_fp32 = {"in_0": data.astype(np.float32)}
+        self.feed_fp16 = {"in_0": data.astype(np.float16)}
 
-        self.feed_list = list(self.feed.keys())
+    def set_feed_attr(self):
+        self.feed_shape = [x.shape for x in self.feed_fp32.values()]
+        self.feed_list = list(self.feed_fp32.keys())
 
-    def set_attrs(self):
+    def set_op_attrs(self):
         self.attrs = {}
         self.attrs['shape'] = [6, 8]
         self.attrs['inplace'] = False
 
-    def _test_base(self, run_ipu=True):
-        scope = fluid.core.Scope()
+    def _test_base(self, exec_mode):
+        scope = paddle.static.Scope()
         main_prog = paddle.static.Program()
         startup_prog = paddle.static.Program()
-        SEED = self.SEED
-        main_prog.random_seed = SEED
-        startup_prog.random_seed = SEED
+        main_prog.random_seed = self.SEED
+        startup_prog.random_seed = self.SEED
 
-        with fluid.scope_guard(scope):
+        with paddle.static.scope_guard(scope):
             with paddle.static.program_guard(main_prog, startup_prog):
                 x = paddle.static.data(
                     name=self.feed_list[0],
                     shape=self.feed_shape[0],
                     dtype='float32')
+
                 out = paddle.fluid.layers.reshape(x=x, **self.attrs)
 
-                fetch_list = [out.name]
+            fetch_list = [out.name]
 
-            if run_ipu:
-                place = paddle.IPUPlace()
-            else:
+            if exec_mode == ExecutionMode.CPU_FP32:
                 place = paddle.CPUPlace()
+            else:
+                place = paddle.IPUPlace()
+
             exe = paddle.static.Executor(place)
             exe.run(startup_prog)
 
-            if run_ipu:
+            if exec_mode != ExecutionMode.CPU_FP32:
                 feed_list = self.feed_list
                 ipu_strategy = paddle.static.IpuStrategy()
-                ipu_strategy.SetGraphConfig(is_training=self.is_training)
-                program = compiler.IPUCompiledProgram(
+                ipu_strategy.set_graph_config(is_training=self.is_training)
+                if exec_mode == ExecutionMode.IPU_POPART_FP16:
+                    ipu_strategy.set_precision_config(enable_fp16=True)
+                program = paddle.static.IpuCompiledProgram(
                     main_prog,
                     ipu_strategy=ipu_strategy).compile(feed_list, fetch_list)
             else:
                 program = main_prog
 
-            result = exe.run(program, feed=self.feed, fetch_list=fetch_list)
+            feed = self.feed_fp32
+            if exec_mode > ExecutionMode.IPU_FP32:
+                feed = self.feed_fp16
+
+            result = exe.run(program, feed=feed, fetch_list=fetch_list)
             return result[0]
 
     def test_base(self):
-        res0 = self._test_base(True)
-        res1 = self._test_base(False)
-
-        self.assertTrue(
-            np.allclose(
-                res0.flatten(), res1.flatten(), atol=self.atol))
+        output_dict = {}
+        for mode in ExecutionMode:
+            if mode > ExecutionMode.IPU_FP32 and not self.fp16_enabled:
+                break
+            output_dict[mode] = self._test_base(mode)
 
-        self.assertTrue(res0.shape == res1.shape)
+        self.check(output_dict, check_shape=True)
 
 
 class TestCase1(TestBase):
-    def set_attrs(self):
+    def set_op_attrs(self):
         self.attrs = {}
         self.attrs['shape'] = [2, 3, -1, 2]
         self.attrs['inplace'] = False
 
 
 class TestCase2(TestBase):
-    def set_attrs(self):
+    def set_op_attrs(self):
         self.attrs = {}
         self.attrs['shape'] = [-1, 0, 3, 2]
         self.attrs['inplace'] = False

python/paddle/fluid/tests/unittests/ipu/test_save_load.py → python/paddle/fluid/tests/unittests/ipu/test_save_load_ipu.py

@@ -12,55 +12,52 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import tempfile
 import unittest
-import shutil
 
 import numpy as np
 import paddle
-import paddle.fluid as fluid
-import paddle.fluid.compiler as compiler
-import paddle.optimizer
 import paddle.static
 from paddle.fluid.tests.unittests.ipu.op_test_ipu import IPUOpTest
 
-paddle.enable_static()
-
 
 @unittest.skipIf(not paddle.is_compiled_with_ipu(),
                  "core is not compiled with IPU")
 class TestBase(IPUOpTest):
     def setUp(self):
         self.set_atol()
-        self.set_feed()
-        self.set_attrs()
-
-    def set_feed(self):
-        self.feed_shape = []
-        self.feed_shape.append([1, 3, 10, 10])
+        self.set_data_feed()
+        self.set_feed_attr()
+        self.set_op_attrs()
 
-        self.feed = {}
-        self.feed["in_0"] = np.random.uniform(
-            size=self.feed_shape[0]).astype(np.float32)
+    def set_data_feed(self):
+        data = np.random.uniform(size=[1, 3, 10, 10])
+        self.feed_fp32 = {"in_0": data.astype(np.float32)}
+        self.feed_fp16 = {"in_0": data.astype(np.float16)}
 
-        self.feed_list = list(self.feed.keys())
+    def set_feed_attr(self):
+        self.feed_shape = [x.shape for x in self.feed_fp32.values()]
+        self.feed_list = list(self.feed_fp32.keys())
 
-    def set_attrs(self):
+    def set_op_attrs(self):
         self.attrs = {}
         self.attrs['steps'] = 100
         self.attrs['save_at_step'] = 20
         self.attrs['is_training'] = True
         self.attrs['opt_type'] = 'sgd'
+        self.attrs['enable_fp16'] = False
+        self.attrs['model_path'] = tempfile.TemporaryDirectory()
 
     def _test_base(self, save_otherwise_load):
-        scope = fluid.core.Scope()
+        scope = paddle.static.Scope()
         main_prog = paddle.static.Program()
         startup_prog = paddle.static.Program()
         main_prog.random_seed = self.SEED
         startup_prog.random_seed = self.SEED
-        generator = fluid.unique_name.UniqueNameGenerator()
+        generator = paddle.fluid.unique_name.UniqueNameGenerator()
 
-        with fluid.unique_name.guard(generator):
-            with fluid.scope_guard(scope):
+        with paddle.fluid.unique_name.guard(generator):
+            with paddle.static.scope_guard(scope):
                 with paddle.static.program_guard(main_prog, startup_prog):
                     x = paddle.static.data(
                         name=self.feed_list[0],
@@ -91,12 +88,17 @@ class TestBase(IPUOpTest):
                 exe.run(startup_prog)
 
                 if not save_otherwise_load:
-                    paddle.static.load(main_prog, "model/model")
+                    paddle.static.load(main_prog, self.attrs['model_path'].name)
 
                 ipu_strategy = paddle.static.IpuStrategy()
-                ipu_strategy.SetGraphConfig(
+                ipu_strategy.set_graph_config(
                     is_training=self.attrs['is_training'])
-                program = compiler.IPUCompiledProgram(
+                ipu_strategy.set_precision_config(
+                    enable_fp16=self.attrs['enable_fp16'])
+                ipu_strategy.set_options({
+                    'save_per_n_step': self.attrs['save_at_step']
+                })
+                program = paddle.static.IpuCompiledProgram(
                     main_prog, ipu_strategy=ipu_strategy).compile(
                         self.feed_list, fetch_list)
 
@@ -104,16 +106,17 @@ class TestBase(IPUOpTest):
                 run_steps = self.attrs['steps'] if save_otherwise_load \
                     else self.attrs['steps'] - self.attrs['save_at_step']
 
+                feed = self.feed_fp16 if self.attrs[
+                    'enable_fp16'] else self.feed_fp32
                 for i in range(run_steps):
-                    tmp = exe.run(program,
-                                  feed=self.feed,
-                                  fetch_list=fetch_list)
+                    tmp = exe.run(program, feed=feed, fetch_list=fetch_list)
 
                     # currently, we update opt state every sess.run,
                     # will optimize
                     if save_otherwise_load and \
                         i == self.attrs['save_at_step'] - 1:
-                        paddle.static.save(main_prog, "model/model")
+                        paddle.static.save(main_prog,
+                                           self.attrs['model_path'].name)
 
                     if save_otherwise_load and i >= self.attrs['save_at_step']:
                         result.append(tmp)
@@ -129,25 +132,65 @@ class TestBase(IPUOpTest):
         self.assertTrue(
             np.allclose(
                 res0.flatten(), res1.flatten(), atol=self.atol))
-        shutil.rmtree("model", True)
+        self.attrs['model_path'].cleanup()
 
 
 class TestAdam(TestBase):
-    def set_attrs(self):
+    def set_op_attrs(self):
         self.attrs = {}
         self.attrs['steps'] = 100
         self.attrs['save_at_step'] = 20
         self.attrs['is_training'] = True
         self.attrs['opt_type'] = 'adam'
+        self.attrs['enable_fp16'] = False
+        self.attrs['model_path'] = tempfile.TemporaryDirectory()
 
 
 class TestLamb(TestBase):
-    def set_attrs(self):
+    def set_op_attrs(self):
+        self.attrs = {}
+        self.attrs['steps'] = 100
+        self.attrs['save_at_step'] = 20
+        self.attrs['is_training'] = True
+        self.attrs['opt_type'] = 'lamb'
+        self.attrs['enable_fp16'] = False
+        self.attrs['model_path'] = tempfile.TemporaryDirectory()
+
+
+@unittest.skipIf(IPUOpTest.use_ipumodel(), "skip for ipumodel")
+class TestSGDFP16(TestBase):
+    def set_op_attrs(self):
+        self.attrs = {}
+        self.attrs['steps'] = 100
+        self.attrs['save_at_step'] = 20
+        self.attrs['is_training'] = True
+        self.attrs['opt_type'] = 'sgd'
+        self.attrs['enable_fp16'] = True
+        self.attrs['model_path'] = tempfile.TemporaryDirectory()
+
+
+@unittest.skipIf(IPUOpTest.use_ipumodel(), "skip for ipumodel")
+class TestAdamFP16(TestBase):
+    def set_op_attrs(self):
+        self.attrs = {}
+        self.attrs['steps'] = 100
+        self.attrs['save_at_step'] = 20
+        self.attrs['is_training'] = True
+        self.attrs['opt_type'] = 'adam'
+        self.attrs['enable_fp16'] = True
+        self.attrs['model_path'] = tempfile.TemporaryDirectory()
+
+
+@unittest.skipIf(IPUOpTest.use_ipumodel(), "skip for ipumodel")
+class TestLambFP16(TestBase):
+    def set_op_attrs(self):
         self.attrs = {}
         self.attrs['steps'] = 100
         self.attrs['save_at_step'] = 20
         self.attrs['is_training'] = True
         self.attrs['opt_type'] = 'lamb'
+        self.attrs['enable_fp16'] = True
+        self.attrs['model_path'] = tempfile.TemporaryDirectory()
 
 
 if __name__ == "__main__":

python/paddle/fluid/tests/unittests/ipu/test_scale_op_ipu.py

@@ -16,14 +16,8 @@ import unittest
 
 import numpy as np
 import paddle
-import paddle.fluid as fluid
-import paddle.fluid.compiler as compiler
-import paddle.optimizer
 import paddle.static
-from paddle.fluid.tests.unittests.ipu.op_test_ipu import (IPUOpTest,
-                                                          np_dtype_to_fluid_str)
-
-paddle.enable_static()
+from paddle.fluid.tests.unittests.ipu.op_test_ipu import IPUOpTest, ExecutionMode
 
 
 @unittest.skipIf(not paddle.is_compiled_with_ipu(),
@@ -32,80 +26,88 @@ class TestBase(IPUOpTest):
     def setUp(self):
         self.set_atol()
         self.set_training()
-        self.set_feed()
+        self.set_data_feed()
         self.set_feed_attr()
-        self.set_attrs()
+        self.set_op_attrs()
 
-    def set_feed(self):
-        self.feed = {
-            "x": np.random.uniform(size=[1, 3, 10, 10]).astype('float32'),
-        }
+    @property
+    def fp16_enabled(self):
+        return False
+
+    def set_data_feed(self):
+        data = np.random.uniform(size=[1, 3, 10, 10])
+        self.feed_fp32 = {"x": data.astype(np.float32)}
+        self.feed_fp16 = {"x": data.astype(np.float16)}
 
     def set_feed_attr(self):
-        self.feed_shape = [x.shape for x in self.feed.values()]
-        self.feed_list = list(self.feed.keys())
-        self.feed_dtype = [
-            np_dtype_to_fluid_str(x.dtype) for x in self.feed.values()
-        ]
+        self.feed_shape = [x.shape for x in self.feed_fp32.values()]
+        self.feed_list = list(self.feed_fp32.keys())
+        self.feed_dtype = [x.dtype for x in self.feed_fp32.values()]
 
-    def set_attrs(self):
+    def set_op_attrs(self):
         self.attrs = {
             "scale": 1.0,
             "bias": 0.0,
             "bias_after_scale": True,
         }
 
-    def _test_base(self, run_ipu=True):
-        scope = fluid.core.Scope()
+    def _test_base(self, exec_mode):
+        scope = paddle.static.Scope()
         main_prog = paddle.static.Program()
         startup_prog = paddle.static.Program()
-        SEED = self.SEED
-        main_prog.random_seed = SEED
-        startup_prog.random_seed = SEED
+        main_prog.random_seed = self.SEED
+        startup_prog.random_seed = self.SEED
 
-        with fluid.scope_guard(scope):
+        with paddle.static.scope_guard(scope):
             with paddle.static.program_guard(main_prog, startup_prog):
                 x = paddle.static.data(
                     name=self.feed_list[0],
                     shape=self.feed_shape[0],
-                    dtype=self.feed_dtype[0])
+                    dtype='float32')
+
                 out = paddle.fluid.layers.scale(x, **self.attrs)
 
-                fetch_list = [out.name]
+            fetch_list = [out.name]
 
-            if run_ipu:
-                place = paddle.IPUPlace()
-            else:
+            if exec_mode == ExecutionMode.CPU_FP32:
                 place = paddle.CPUPlace()
+            else:
+                place = paddle.IPUPlace()
+
             exe = paddle.static.Executor(place)
             exe.run(startup_prog)
 
-            if run_ipu:
+            if exec_mode != ExecutionMode.CPU_FP32:
                 feed_list = self.feed_list
                 ipu_strategy = paddle.static.IpuStrategy()
-                ipu_strategy.SetGraphConfig(is_training=self.is_training)
-                program = compiler.IPUCompiledProgram(
+                ipu_strategy.set_graph_config(is_training=self.is_training)
+                if exec_mode == ExecutionMode.IPU_POPART_FP16:
+                    ipu_strategy.set_precision_config(enable_fp16=True)
+                program = paddle.static.IpuCompiledProgram(
                     main_prog,
                     ipu_strategy=ipu_strategy).compile(feed_list, fetch_list)
             else:
                 program = main_prog
 
-            result = exe.run(program, feed=self.feed, fetch_list=fetch_list)
+            feed = self.feed_fp32
+            if exec_mode > ExecutionMode.IPU_FP32:
+                feed = self.feed_fp16
+
+            result = exe.run(program, feed=feed, fetch_list=fetch_list)
             return result[0]
 
     def test_base(self):
-        res0 = self._test_base(False)
-        res1 = self._test_base(True)
-
-        self.assertTrue(
-            np.allclose(
-                res0.flatten(), res1.flatten(), atol=self.atol))
+        output_dict = {}
+        for mode in ExecutionMode:
+            if mode > ExecutionMode.IPU_FP32 and not self.fp16_enabled:
+                break
+            output_dict[mode] = self._test_base(mode).flatten()
 
-        self.assertTrue(res0.shape == res1.shape)
+        self.check(output_dict)
 
 
 class TestCase1(TestBase):
-    def set_attrs(self):
+    def set_op_attrs(self):
         self.attrs = {
             "scale": 5.0,
             "bias": 0.0,
@@ -114,7 +116,7 @@ class TestCase1(TestBase):
 
 
 class TestCase2(TestBase):
-    def set_attrs(self):
+    def set_op_attrs(self):
         self.attrs = {
             "scale": 1.0,
             "bias": 0.5,
@@ -123,7 +125,16 @@ class TestCase2(TestBase):
 
 
 class TestCase3(TestBase):
-    def set_attrs(self):
+    def set_op_attrs(self):
+        self.attrs = {
+            "scale": 5.0,
+            "bias": 0.7,
+            "bias_after_scale": True,
+        }
+
+
+class TestCase4(TestBase):
+    def set_op_attrs(self):
         self.attrs = {
             "scale": 1.0,
             "bias": 0.0,
@@ -131,59 +142,66 @@ class TestCase3(TestBase):
         }
 
 
-class TestCase4(TestBase):
-    def set_feed(self):
-        self.feed = {
-            "x": np.random.uniform(size=[3, 3, 10, 10]).astype('float32'),
-            "y": np.array([3.0]).astype('float32'),
-        }
+class TestCase5(TestBase):
+    def set_data_feed(self):
+        x = np.random.uniform(size=[3, 3, 10, 10])
+        y = np.array([3.0])
+        self.feed_fp32 = {"x": x.astype(np.float32), "y": y.astype(np.float32)}
+        self.feed_fp16 = {"x": x.astype(np.float16), "y": y.astype(np.float16)}
 
-    def set_attrs(self):
+    def set_op_attrs(self):
         self.attrs = {
             "bias": 0.0,
             "bias_after_scale": True,
         }
 
-    def _test_base(self, run_ipu=True):
-        scope = fluid.core.Scope()
+    def _test_base(self, exec_mode):
+        scope = paddle.static.Scope()
         main_prog = paddle.static.Program()
         startup_prog = paddle.static.Program()
-        SEED = self.SEED
-        main_prog.random_seed = SEED
-        startup_prog.random_seed = SEED
+        main_prog.random_seed = self.SEED
+        startup_prog.random_seed = self.SEED
 
-        with fluid.scope_guard(scope):
+        with paddle.static.scope_guard(scope):
             with paddle.static.program_guard(main_prog, startup_prog):
                 x = paddle.static.data(
                     name=self.feed_list[0],
                     shape=self.feed_shape[0],
-                    dtype=self.feed_dtype[0])
+                    dtype='float32')
                 y = paddle.static.data(
                     name=self.feed_list[1],
                     shape=self.feed_shape[1],
-                    dtype=self.feed_dtype[1])
+                    dtype='float32')
+
                 out = paddle.fluid.layers.scale(x, scale=y, **self.attrs)
 
-                fetch_list = [out.name]
+            fetch_list = [out.name]
 
-            if run_ipu:
-                place = paddle.IPUPlace()
-            else:
+            if exec_mode == ExecutionMode.CPU_FP32:
                 place = paddle.CPUPlace()
+            else:
+                place = paddle.IPUPlace()
+
             exe = paddle.static.Executor(place)
             exe.run(startup_prog)
 
-            if run_ipu:
+            if exec_mode != ExecutionMode.CPU_FP32:
                 feed_list = self.feed_list
                 ipu_strategy = paddle.static.IpuStrategy()
-                ipu_strategy.SetGraphConfig(is_training=self.is_training)
-                program = compiler.IPUCompiledProgram(
+                ipu_strategy.set_graph_config(is_training=self.is_training)
+                if exec_mode == ExecutionMode.IPU_POPART_FP16:
+                    ipu_strategy.set_precision_config(enable_fp16=True)
+                program = paddle.static.IpuCompiledProgram(
                     main_prog,
                     ipu_strategy=ipu_strategy).compile(feed_list, fetch_list)
             else:
                 program = main_prog
 
-            result = exe.run(program, feed=self.feed, fetch_list=fetch_list)
+            feed = self.feed_fp32
+            if exec_mode > ExecutionMode.IPU_FP32:
+                feed = self.feed_fp16
+
+            result = exe.run(program, feed=feed, fetch_list=fetch_list)
             return result[0]