[IPU] update ipu unittests p1 (#39923)

* update ipu UTs part1

* rename ut

* sync api changes

* update uts for new api

* update use_ipumodel()

* update use_ipumodel()

* split pr

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

@@ -16,14 +16,9 @@ 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 (ExecutionMode,
+                                                          IPUOpTest)
 
 
 @unittest.skipIf(not paddle.is_compiled_with_ipu(),
@@ -32,81 +27,88 @@ 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 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())
 
-    def set_attrs(self):
+    def set_op_attrs(self):
         self.attrs = {
             "dropout_prob": 0.5,
             "is_test": True,
             "dropout_implementation": "downgrade_in_infer"
         }
 
-    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')
+
                 dropout = paddle.fluid.layers.dropout(x, **self.attrs)
                 out = paddle.fluid.layers.elementwise_add(dropout, dropout)
 
                 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)
-            return result[0]
+            feed = self.feed_fp32
+            if exec_mode > ExecutionMode.IPU_FP32:
+                feed = self.feed_fp16
 
-    def test_base(self):
-        res0 = self._test_base(True)
-        res1 = self._test_base(False)
+            result = exe.run(program, feed=feed, fetch_list=fetch_list)
+            return result[0]
 
-        self.assertTrue(
-            np.allclose(
-                res0.flatten(), res1.flatten(), atol=self.atol))
+    def test(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.assertTrue(res0.shape == res1.shape)
+        self.check(output_dict)
 
 
 class TestCase1(TestBase):
-    def set_attrs(self):
+    def set_op_attrs(self):
         self.attrs = {
             "dropout_prob": 0.5,
             "is_test": True,
@@ -115,7 +117,7 @@ class TestCase1(TestBase):
 
 
 class TestCase2(TestBase):
-    def set_attrs(self):
+    def set_op_attrs(self):
         self.attrs = {
             "dropout_prob": 0.0,
             "is_test": False,

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

@@ -16,14 +16,9 @@ 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 (ExecutionMode,
+                                                          IPUOpTest)
 
 
 @unittest.skipIf(not paddle.is_compiled_with_ipu(),
@@ -32,101 +27,136 @@ class TestMul(IPUOpTest):
     def setUp(self):
         self.set_atol()
         self.set_training()
-        self.init_op()
+        self.set_test_op()
+
+    @property
+    def fp16_enabled(self):
+        if IPUOpTest.use_ipumodel():
+            return False
+        else:
+            return True
 
-    def init_op(self):
+    def set_test_op(self):
         self.op = paddle.fluid.layers.elementwise_mul
 
     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())
+
+    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 = self.op(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 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))
+        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)
 
     def test_case0(self):
-        self.feed = {
-            "x": np.random.uniform(size=(2, 3, 4, 5)).astype('float32'),
-            "y": np.random.uniform(size=(2, 3, 4, 5)).astype('float32'),
+        data_x = np.random.uniform(size=(2, 3, 4, 5))
+        data_y = np.random.uniform(size=(2, 3, 4, 5))
+
+        self.feed_fp32 = {
+            "x": data_x.astype('float32'),
+            "y": data_y.astype('float32'),
+        }
+        self.feed_fp16 = {
+            "x": data_x.astype('float16'),
+            "y": data_y.astype('float16'),
         }
         self.attrs = {}
         self.set_feed_attr()
         self.run_test_base()
 
     def test_case1(self):
-        self.feed = {
-            "x": np.random.uniform(size=(2, 3, 4, 5)).astype('float32'),
-            "y": np.random.uniform(size=(3, 4)).astype('float32'),
+        data_x = np.random.uniform(size=(2, 3, 4, 5))
+        data_y = np.random.uniform(size=(3, 4))
+        self.feed_fp32 = {
+            "x": data_x.astype('float32'),
+            "y": data_y.astype('float32'),
+        }
+        self.feed_fp16 = {
+            "x": data_x.astype('float16'),
+            "y": data_y.astype('float16'),
         }
         self.set_feed_attr()
         self.attrs = {"axis": 1}
         self.run_test_base()
 
     def test_case2(self):
-        self.feed = {
-            "x": np.random.uniform(size=(2, 3, 4, 5)).astype('float32'),
-            "y": np.random.uniform(size=(5)).astype('float32'),
+        data_x = np.random.uniform(size=(2, 3, 4, 5))
+        data_y = np.random.uniform(size=(5))
+        self.feed_fp32 = {
+            "x": data_x.astype('float32'),
+            "y": data_y.astype('float32'),
+        }
+        self.feed_fp16 = {
+            "x": data_x.astype('float16'),
+            "y": data_y.astype('float16'),
         }
         self.set_feed_attr()
         self.attrs = {"axis": -1}
         self.run_test_base()
 
     def test_case3(self):
-        self.feed = {
-            "x": np.random.uniform(size=(2, 3, 4, 5)).astype('float32'),
-            "y": np.random.uniform(size=(2)).astype('float32'),
+        data_x = np.random.uniform(size=(2, 3, 4, 5))
+        data_y = np.random.uniform(size=(2))
+        self.feed_fp32 = {
+            "x": data_x.astype('float32'),
+            "y": data_y.astype('float32'),
+        }
+        self.feed_fp16 = {
+            "x": data_x.astype('float16'),
+            "y": data_y.astype('float16'),
         }
         self.set_feed_attr()
         self.attrs = {"axis": 0}
@@ -134,37 +164,43 @@ class TestMul(IPUOpTest):
 
 
 class TestAdd(TestMul):
-    def init_op(self):
+    def set_test_op(self):
         self.op = paddle.fluid.layers.elementwise_add
 
 
 class TestSub(TestMul):
-    def init_op(self):
+    def set_test_op(self):
         self.op = paddle.fluid.layers.elementwise_sub
 
 
 class TestDiv(TestMul):
-    def init_op(self):
+    def set_test_op(self):
         self.op = paddle.fluid.layers.elementwise_div
 
 
 class TestMin(TestMul):
-    def init_op(self):
+    def set_test_op(self):
         self.op = paddle.fluid.layers.elementwise_min
 
 
 class TestMax(TestMul):
-    def init_op(self):
+    def set_test_op(self):
         self.op = paddle.fluid.layers.elementwise_max
 
 
 class TestPow(TestMul):
-    def init_op(self):
+    def set_test_op(self):
         self.op = paddle.fluid.layers.elementwise_pow
 
 
 class TestMod(TestMul):
-    def init_op(self):
+    def set_atol(self):
+        self.atol = 1e-7
+        self.rtol = 1e-5
+        self.atol_fp16 = 1e-2
+        self.rtol_fp16 = 1e-3
+
+    def set_test_op(self):
         self.op = paddle.fluid.layers.elementwise_mod
 
 

python/paddle/fluid/tests/unittests/ipu/test_equal_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(),
@@ -32,94 +26,106 @@ 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()
-
-    def set_feed(self):
-        self.feed = {
-            "x": np.ones([1, 10]).astype('float32'),
-            "y": np.zeros([1, 10]).astype('float32'),
+        self.set_op_attrs()
+
+    @property
+    def fp16_enabled(self):
+        return True
+
+    def set_data_feed(self):
+        x = np.ones([1, 10])
+        y = np.zeros([1, 10])
+        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())
 
-    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):
-                # XX
                 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.equal(x, y, **self.attrs)
 
                 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)
-            return result[0]
+            feed = self.feed_fp32
+            if exec_mode > ExecutionMode.IPU_FP32:
+                feed = self.feed_fp16
 
-    def test_base(self):
-        res0 = self._test_base(True)
-        res1 = self._test_base(False)
+            result = exe.run(program, feed=feed, fetch_list=fetch_list)
+            return result[0]
 
-        self.assertTrue(
-            np.allclose(
-                res0.flatten(), res1.flatten(), atol=self.atol))
+    def test(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().astype(np.int32)
 
-        self.assertTrue(res0.shape == res1.shape)
+        self.check(output_dict)
 
 
 class TestCase1(TestBase):
-    def set_feed(self):
-        self.feed = {
-            "x": np.ones([1, 10]).astype('float32'),
-            "y": np.ones([1, 10]).astype('float32'),
-        }
+    def set_data_feed(self):
+        x = np.ones([1, 10])
+        y = np.ones([1, 10])
+        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 TestCase2(TestBase):
-    def set_feed(self):
-        self.feed = {
-            "x": np.ones([1, 10]).astype('float32'),
-            "y": np.arange(0, 10).reshape([1, 10]).astype('float32'),
-        }
+    def set_data_feed(self):
+        x = np.ones([1, 10])
+        y = np.arange(0, 10).reshape([1, 10])
+        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)}
 
 
 if __name__ == "__main__":

python/paddle/fluid/tests/unittests/ipu/test_expand_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 +26,142 @@ 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()
+
+    @property
+    def fp16_enabled(self):
+        return True
 
-    def set_feed(self):
-        self.feed = {"x": np.random.uniform(size=[2, 3, 1]).astype('float32')}
+    def set_data_feed(self):
+        data = np.random.uniform(size=[2, 3, 1])
+        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 = {"expand_times": [1, 2, 2]}
 
-    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.expand(x, **self.attrs)
 
                 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)
-            return result[0]
+            feed = self.feed_fp32
+            if exec_mode > ExecutionMode.IPU_FP32:
+                feed = self.feed_fp16
 
-    def test_base(self):
-        res0 = self._test_base(False)
-        res1 = self._test_base(True)
+            result = exe.run(program, feed=feed, fetch_list=fetch_list)
+            return result[0]
 
-        self.assertTrue(
-            np.allclose(
-                res0.flatten(), res1.flatten(), atol=self.atol))
+    def test(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.assertTrue(res0.shape == res1.shape)
+        self.check(output_dict)
 
 
 class TestCase1(TestBase):
-    def set_feed(self):
-        self.feed = {"x": np.random.uniform(size=[2, 2]).astype('float32')}
+    def set_data_feed(self):
+        x = np.random.uniform(size=[2, 2])
+        self.feed_fp32 = {"x": x.astype(np.float32)}
+        self.feed_fp16 = {"x": x.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 = {}
 
-    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])
-                expand_times = fluid.layers.fill_constant(
+                    dtype="float32")
+
+                expand_times = paddle.fluid.layers.fill_constant(
                     shape=[len(self.feed_shape[0])], dtype="int32", value=2)
                 out = paddle.fluid.layers.expand(
                     x, expand_times=expand_times, **self.attrs)
 
                 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_fill_any_like_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):
+        data = np.random.uniform(size=[2, 3, 1])
+        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_fp32.values()]
+        self.feed_list = list(self.feed_fp32.keys())
+
+    def set_op_attrs(self):
+        self.attrs = {'fill_value': 0.3, 'dtype': 'float32'}
+
+    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')
+
+                x_fill = paddle.full_like(x, **self.attrs)
+                out = paddle.fluid.layers.elementwise_add(x_fill, x_fill)
+
+                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(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 = {'fill_value': 3, 'dtype': 'int32'}
+
+
+if __name__ == "__main__":
+    unittest.main()

python/paddle/fluid/tests/unittests/ipu/test_fill_constant_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(),
@@ -32,21 +26,23 @@ 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()
+
+    @property
+    def fp16_enabled(self):
+        return True
 
-    def set_feed(self):
+    def set_data_feed(self):
         self.feed = {}
 
     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_dtype = [x.dtype for x in self.feed.values()]
 
-    def set_attrs(self):
+    def set_op_attrs(self):
         self.attrs = {
             'name': 'x',
             'shape': [1, 3, 3, 3],
@@ -54,33 +50,34 @@ class TestBase(IPUOpTest):
             'value': 0.3,
         }
 
-    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.fluid.layers.fill_constant(**self.attrs)
                 out = paddle.fluid.layers.elementwise_add(x, x)
-
                 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:
@@ -89,19 +86,18 @@ class TestBase(IPUOpTest):
             result = exe.run(program, feed=self.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))
+    def test(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.assertTrue(res0.shape == res1.shape)
+        self.check(output_dict)
 
 
 class TestCase1(TestBase):
-    def set_attrs(self):
+    def set_op_attrs(self):
         self.attrs = {
             'name': 'x',
             'shape': [1, 3, 3, 3],

python/paddle/fluid/tests/unittests/ipu/test_fp16_inference_io_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 shutil
+
+import numpy as np
+import paddle
+import paddle.fluid as fluid
+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_data_feed()
+        self.set_feed_attr()
+        self.set_op_attrs()
+
+    def set_atol(self):
+        self.atol = 1e-6
+        self.rtol = 1e-5
+        self.atol_fp16 = 1e-2
+        self.rtol_fp16 = 1e-3
+
+    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_fp32.values()]
+        self.feed_list = list(self.feed_fp32.keys())
+
+    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['path'] = 'model'
+        self.attrs['model_name'] = 'test'
+
+    def _test_save(self):
+        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 = paddle.fluid.unique_name.UniqueNameGenerator()
+        self.full_name = '/'.join(
+            [self.attrs['path'], self.attrs['model_name']])
+
+        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],
+                        shape=self.feed_shape[0],
+                        dtype='float32')
+
+                    scale = paddle.fluid.layers.scale(
+                        x, scale=1.0, bias=0.0, bias_after_scale=True)
+                    conv = paddle.static.nn.conv2d(
+                        scale,
+                        num_filters=3,
+                        filter_size=3,
+                        bias_attr=False,
+                        name='conv2d')
+                    loss = paddle.mean(conv)
+
+                    if self.attrs['is_training']:
+                        if self.attrs['opt_type'] == 'sgd':
+                            sgd = paddle.optimizer.SGD(learning_rate=1e-2)
+                            sgd.minimize(loss)
+                        elif self.attrs['opt_type'] == 'adam':
+                            adam = paddle.optimizer.Adam(learning_rate=1e-2)
+                            adam.minimize(loss)
+                        elif self.attrs['opt_type'] == 'lamb':
+                            lamb = paddle.optimizer.Lamb(learning_rate=1e-2)
+                            lamb.minimize(loss)
+
+                fetch_list = [loss.name]
+
+                place = paddle.IPUPlace()
+                exe = paddle.static.Executor(place)
+                exe.run(startup_prog)
+
+                ipu_strategy = paddle.static.IpuStrategy()
+                ipu_strategy.set_graph_config(is_training=True)
+                ipu_strategy.set_precision_config(enable_fp16=True)
+                program = paddle.static.IpuCompiledProgram(
+                    main_prog, ipu_strategy=ipu_strategy).compile(
+                        self.feed_list, fetch_list)
+
+                for _ in range(self.attrs['steps']):
+                    exe.run(program, feed=self.feed_fp16, fetch_list=fetch_list)
+
+                paddle.static.save_inference_model(
+                    self.full_name, x, loss, exe, program=program.org_program)
+
+    def _test_load(self, run_ipu):
+        if run_ipu:
+            place = paddle.IPUPlace()
+        else:
+            place = paddle.CPUPlace()
+        exe = paddle.static.Executor(place)
+
+        [inference_program, feed_target_names, fetch_targets] = (
+            paddle.static.load_inference_model(self.full_name, exe))
+
+        if run_ipu:
+            feed_list = feed_target_names
+            fetch_list = [fetch_targets[0].name]
+            ipu_strategy = paddle.static.IpuStrategy()
+            ipu_strategy.set_graph_config(is_training=False)
+            ipu_strategy.set_precision_config(enable_fp16=True)
+            program = paddle.static.IpuCompiledProgram(
+                inference_program,
+                ipu_strategy=ipu_strategy).compile(feed_list, fetch_list)
+        else:
+            program = inference_program
+
+        feed = self.feed_fp16 if run_ipu else self.feed_fp32
+        result = []
+        for i in range(10):
+            feed["in_0"] += np.array([1.1 * i]).astype(feed["in_0"].dtype)
+            out = exe.run(program, feed=feed, fetch_list=[fetch_targets])
+            result.append(out)
+
+        return np.array(result)
+
+    def test_base(self):
+        self._test_save()
+        cpu_res = self._test_load(False)
+        ipu_res = self._test_load(True).astype(np.float32)
+
+        self.assertTrue(
+            np.allclose(
+                cpu_res, ipu_res, rtol=self.rtol_fp16, atol=self.atol_fp16))
+
+        shutil.rmtree(self.attrs['path'], True)
+
+
+if __name__ == "__main__":
+    unittest.main()

python/paddle/fluid/tests/unittests/ipu/test_gather_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(),
@@ -32,85 +26,92 @@ 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()
+
+    @property
+    def fp16_enabled(self):
+        return True
 
-    def set_feed(self):
-        self.feed = {
-            "x": np.random.uniform(size=[10, 20]).astype('float32'),
-            "y": np.array([1, 3, 5]).astype('int32'),
-        }
+    def set_data_feed(self):
+        x = np.random.uniform(size=[10, 20])
+        y = np.array([1, 3, 5])
+        self.feed_fp32 = {"x": x.astype(np.float32), "y": y.astype(np.int32)}
+        self.feed_fp16 = {"x": x.astype(np.float16), "y": y.astype(np.int32)}
 
     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())
 
-    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')
                 y = paddle.static.data(
                     name=self.feed_list[1],
                     shape=self.feed_shape[1],
-                    dtype=self.feed_dtype[1])
+                    dtype='int32')
+
                 out = paddle.fluid.layers.gather(x, index=y, **self.attrs)
 
                 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)
-            return result[0]
+            feed = self.feed_fp32
+            if exec_mode > ExecutionMode.IPU_FP32:
+                feed = self.feed_fp16
 
-    def test_base(self):
-        res0 = self._test_base(False)
-        res1 = self._test_base(True)
+            result = exe.run(program, feed=feed, fetch_list=fetch_list)
+            return result[0]
 
-        self.assertTrue(
-            np.allclose(
-                res0.flatten(), res1.flatten(), atol=self.atol))
+    def test(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.assertTrue(res0.shape == res1.shape)
+        self.check(output_dict)
 
 
 class TestCase1(TestBase):
-    def set_feed(self):
-        self.feed = {
-            "x": np.random.uniform(size=[100]).astype('float32'),
-            "y": np.array([1, 3, 5]).astype('int32'),
-        }
+    def set_data_feed(self):
+        x = np.random.uniform(size=[100])
+        y = np.array([1, 3, 5])
+        self.feed_fp32 = {"x": x.astype(np.float32), "y": y.astype(np.int32)}
+        self.feed_fp16 = {"x": x.astype(np.float16), "y": y.astype(np.int32)}
 
 
 if __name__ == "__main__":

python/paddle/fluid/tests/unittests/ipu/test_gelu_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 +26,89 @@ 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_atol(self):
-        self.atol = 1e-3
+    @property
+    def fp16_enabled(self):
+        return True
 
-    def set_feed(self):
-        self.feed = {
-            "x": np.random.uniform(size=[1, 3, 10, 10]).astype('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)}
 
     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())
 
-    def set_attrs(self):
+    def set_op_attrs(self):
         self.attrs = {"approximate": 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=self.feed_dtype[0])
+                    dtype='float32')
+
                 out = paddle.fluid.layers.gelu(x, **self.attrs)
 
                 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)
-            return result[0]
+            feed = self.feed_fp32
+            if exec_mode > ExecutionMode.IPU_FP32:
+                feed = self.feed_fp16
 
-    def test_base(self):
-        res0 = self._test_base(False)
-        res1 = self._test_base(True)
+            result = exe.run(program, feed=feed, fetch_list=fetch_list)
+            return result[0]
 
-        self.assertTrue(
-            np.allclose(
-                res0.flatten(), res1.flatten(), atol=self.atol))
+    def test(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.assertTrue(res0.shape == res1.shape)
+        self.check(output_dict)
 
 
-@unittest.skip('approximate=True is not supported')
 class TestCase1(TestBase):
-    def set_attrs(self):
+    def set_atol(self):
+        self.atol = 1e-10
+        self.rtol = 1e-6
+        self.atol_fp16 = 2e-3
+        self.rtol_fp16 = 1e-3
+
+    def set_op_attrs(self):
         self.attrs = {"approximate": True}
 
 

python/paddle/fluid/tests/unittests/ipu/test_greater_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.randn(3, 4, 5)
+        y = np.random.randn(3, 4, 5)
+        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 = {}
+
+    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.fluid.layers.greater_than(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(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().astype(np.int32)
+
+        self.check(output_dict)
+
+
+class TestCase1(TestBase):
+    def set_data_feed(self):
+        x = np.ones([1, 10])
+        y = np.ones([10])
+        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 TestCase2(TestBase):
+    def set_data_feed(self):
+        x = np.ones([1, 10])
+        y = np.zeros([1, 10])
+        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 TestCase3(TestBase):
+    def set_data_feed(self):
+        x = np.zeros([1, 10])
+        y = np.ones([1, 10])
+        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)}
+
+
+if __name__ == "__main__":
+    unittest.main()

python/paddle/fluid/tests/unittests/ipu/test_groupnorm_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(),
@@ -32,43 +26,49 @@ 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, 8, 10, 10]).astype('float32'),
-        }
+    @property
+    def fp16_enabled(self):
+        return True
+
+    def set_atol(self):
+        self.atol = 3e-6
+        self.rtol = 1e-6
+        self.atol_fp16 = 4e-3
+        self.rtol_fp16 = 1e-3
+
+    def set_data_feed(self):
+        data = np.random.uniform(size=[1, 8, 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())
 
-    def set_attrs(self):
+    def set_op_attrs(self):
         self.attrs = {
             "groups": 8,
             "epsilon": 1e-05,
             "data_layout": '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')
 
                 if self.is_training:
                     ch = self.feed_shape[0][1]
@@ -78,62 +78,68 @@ class TestBase(IPUOpTest):
                     bias = paddle.ParamAttr(trainable=True)
                     out = paddle.fluid.layers.nn.group_norm(
                         conv1, param_attr=scale, bias_attr=bias, **self.attrs)
+                    loss = paddle.mean(out)
+                    adam = paddle.optimizer.Adam(learning_rate=1e-2)
+                    adam.minimize(loss)
                 else:
-                    scale = True
-                    bias = True
                     out = paddle.fluid.layers.nn.group_norm(
-                        x, param_attr=scale, bias_attr=bias, **self.attrs)
+                        x, param_attr=True, bias_attr=True, **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:
+            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
 
+            feed = self.feed_fp32
+            if exec_mode > ExecutionMode.IPU_FP32:
+                feed = self.feed_fp16
+
             if self.is_training:
                 result = []
                 for _ in range(self.epoch):
                     loss_res = exe.run(program,
-                                       feed=self.feed,
+                                       feed=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)
+                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
+            if mode > ExecutionMode.IPU_FP32 and self.is_training:
+                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_attrs(self):
+    def set_op_attrs(self):
         self.attrs = {
             "groups": 4,
             "epsilon": 1e-05,
@@ -147,11 +153,15 @@ class TestTrainCase1(TestBase):
         self.epoch = 10
 
 
+@unittest.skipIf(IPUOpTest.use_ipumodel(), "skip for ipumodel")
 class TestTrainCase2(TestBase):
     def set_atol(self):
-        self.atol = 1e-3
+        self.atol = 7e-4
+        self.rtol = 1e-6
+        self.atol_fp16 = 4e-3
+        self.rtol_fp16 = 1e-3
 
-    def set_attrs(self):
+    def set_op_attrs(self):
         self.attrs = {
             "groups": 4,
             "epsilon": 1e-05,
@@ -163,7 +173,5 @@ class TestTrainCase2(TestBase):
         self.epoch = 10
 
 
-# not support `group_norm(x, param_attr=False, bias_attr=False, **self.attrs)`
-
 if __name__ == "__main__":
     unittest.main()

python/paddle/fluid/tests/unittests/ipu/test_ipu_inference_model_io.py → python/paddle/fluid/tests/unittests/ipu/test_inference_model_io_ipu.py

@@ -12,59 +12,59 @@
 # 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.feed = {}
-        self.feed["in_0"] = np.random.uniform(
-            size=self.feed_shape[0]).astype(np.float32)
-
+        self.set_data_feed()
+        self.set_feed_attr()
+        self.set_op_attrs()
+
+    def set_atol(self):
+        self.atol = 1e-6
+        self.rtol = 1e-5
+        self.atol_fp16 = 1e-2
+        self.rtol_fp16 = 1e-3
+
+    def set_data_feed(self):
+        data = np.random.uniform(size=[1, 3, 10, 10])
+        self.feed = {"in_0": data.astype(np.float32)}
+
+    def set_feed_attr(self):
+        self.feed_shape = [x.shape for x in self.feed.values()]
         self.feed_list = list(self.feed.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['path'] = 'model'
+        self.attrs['path'] = tempfile.TemporaryDirectory()
         self.attrs['model_name'] = 'test'
 
     def _test_save(self):
-        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()
         self.full_name = '/'.join(
-            [self.attrs['path'], self.attrs['model_name']])
+            [self.attrs['path'].name, self.attrs['model_name']])
 
-        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],
@@ -88,16 +88,16 @@ class TestBase(IPUOpTest):
                         elif self.attrs['opt_type'] == 'lamb':
                             lamb = paddle.optimizer.Lamb(learning_rate=1e-2)
                             lamb.minimize(loss)
-                    fetch_list = [loss.name]
+                fetch_list = [loss.name]
 
                 place = paddle.IPUPlace()
                 exe = paddle.static.Executor(place)
                 exe.run(startup_prog)
 
                 ipu_strategy = paddle.static.IpuStrategy()
-                ipu_strategy.SetGraphConfig(
+                ipu_strategy.set_graph_config(
                     is_training=self.attrs['is_training'])
-                program = compiler.IPUCompiledProgram(
+                program = paddle.static.IpuCompiledProgram(
                     main_prog, ipu_strategy=ipu_strategy).compile(
                         self.feed_list, fetch_list)
 
@@ -125,8 +125,8 @@ class TestBase(IPUOpTest):
             feed_list = feed_target_names
             fetch_list = [fetch_targets[0].name]
             ipu_strategy = paddle.static.IpuStrategy()
-            ipu_strategy.SetGraphConfig(is_training=False)
-            program = compiler.IPUCompiledProgram(
+            ipu_strategy.set_graph_config(is_training=False)
+            program = paddle.static.IpuCompiledProgram(
                 inference_program,
                 ipu_strategy=ipu_strategy).compile(feed_list, fetch_list)
         else:
@@ -134,7 +134,7 @@ class TestBase(IPUOpTest):
 
         tmp = exe.run(program, feed=self.feed, fetch_list=[fetch_targets])
 
-        return tmp
+        return np.array(tmp)
 
     def test_base(self):
         self._test_save()
@@ -142,27 +142,26 @@ class TestBase(IPUOpTest):
         ipu_res = self._test_load(True)
 
         self.assertTrue(np.allclose(cpu_res, ipu_res, atol=self.atol))
-
-        shutil.rmtree(self.attrs['path'], True)
+        self.attrs['path'].cleanup()
 
 
 class TestAdam(TestBase):
-    def set_attrs(self):
+    def set_op_attrs(self):
         self.attrs = {}
         self.attrs['steps'] = 100
         self.attrs['is_training'] = True
         self.attrs['opt_type'] = 'adam'
-        self.attrs['path'] = 'model'
+        self.attrs['path'] = tempfile.TemporaryDirectory()
         self.attrs['model_name'] = 'test'
 
 
 class TestLamb(TestBase):
-    def set_attrs(self):
+    def set_op_attrs(self):
         self.attrs = {}
         self.attrs['steps'] = 100
         self.attrs['is_training'] = True
         self.attrs['opt_type'] = 'lamb'
-        self.attrs['path'] = 'model'
+        self.attrs['path'] = tempfile.TemporaryDirectory()
         self.attrs['model_name'] = 'test'
 
 

python/paddle/fluid/tests/unittests/ipu/test_instancenorm_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(),
@@ -32,39 +26,45 @@ 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()
+
+    @property
+    def fp16_enabled(self):
+        return True
+
+    def set_atol(self):
+        self.atol = 1e-6
+        self.rtol = 1e-5
+        self.atol_fp16 = 1e-2
+        self.rtol_fp16 = 1e-3
 
-    def set_feed(self):
-        self.feed = {
-            "x": np.random.uniform(size=[1, 3, 10, 10]).astype('float32'),
-        }
+    def set_data_feed(self):
+        x = np.random.uniform(size=[1, 3, 10, 10])
+        self.feed_fp32 = {"x": x.astype(np.float32)}
+        self.feed_fp16 = {"x": x.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())
 
-    def set_attrs(self):
+    def set_op_attrs(self):
         self.attrs = {"epsilon": 1e-05}
 
-    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')
 
                 if self.is_training:
                     ch = self.feed_shape[0][1]
@@ -74,58 +74,64 @@ class TestBase(IPUOpTest):
                     bias = paddle.ParamAttr(trainable=True)
                     out = paddle.fluid.layers.nn.instance_norm(
                         conv1, param_attr=scale, bias_attr=bias, **self.attrs)
+                    loss = paddle.mean(out)
+                    adam = paddle.optimizer.Adam(learning_rate=1e-2)
+                    adam.minimize(loss)
                 else:
-                    scale = True
-                    bias = True
                     out = paddle.fluid.layers.nn.instance_norm(
-                        x, param_attr=scale, bias_attr=bias, **self.attrs)
+                        x, param_attr=True, bias_attr=True, **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:
+            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
 
+            feed = self.feed_fp32
+            if exec_mode > ExecutionMode.IPU_FP32:
+                feed = self.feed_fp16
+
             if self.is_training:
                 result = []
                 for _ in range(self.epoch):
                     loss_res = exe.run(program,
-                                       feed=self.feed,
+                                       feed=feed,
                                        fetch_list=fetch_list)
                     result.append(loss_res)
                 return np.array(result)
             else:
-                result = exe.run(program, feed=self.feed, fetch_list=fetch_list)
+                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)
+    def test(self):
+        output_dict = {}
+        for mode in ExecutionMode:
+            if mode > ExecutionMode.IPU_FP32 and not self.fp16_enabled:
+                break
+            if mode > ExecutionMode.IPU_FP32 and self.is_training:
+                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 TestTrainCase1(TestBase):
@@ -134,7 +140,5 @@ class TestTrainCase1(TestBase):
         self.epoch = 10
 
 
-# not support `instance_norm(x, param_attr=False, bias_attr=False, **self.attrs)`
-
 if __name__ == "__main__":
     unittest.main()