ipu_commit_tests p6 (#38090)

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

+#  Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import print_function
+
+import numpy as np
+import unittest
+import sys
+sys.path.append("..")
+import paddle
+import paddle.fluid as fluid
+
+paddle.enable_static()
+
+
+@unittest.skipIf(not paddle.is_compiled_with_ipu(),
+                 "core is not compiled with IPU")
+class TestIpuPlace(unittest.TestCase):
+    def test_ipu_place(self):
+        num_devices = fluid.core.get_ipu_device_count()
+        self.assertGreater(num_devices, 0)
+
+        for i in range(num_devices):
+            place = paddle.IPUPlace()
+            p = fluid.core.Place()
+            p.set_place(place)
+            self.assertTrue(p.is_ipu_place())
+
+    def test_ipu_set_device(self):
+        num_devices = fluid.core.get_ipu_device_count()
+        self.assertGreater(num_devices, 0)
+
+        for i in range(num_devices):
+            paddle.set_device('ipu')
+            device = paddle.get_device()
+            self.assertTrue(device == "ipus:{{0-{}}}".format(num_devices - 1))
+
+
+if __name__ == '__main__':
+    unittest.main()

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

+#  Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import print_function
+
+import numpy as np
+import unittest
+import sys
+import paddle
+import paddle.fluid as fluid
+
+paddle.enable_static()
+
+
+@unittest.skipIf(not paddle.is_compiled_with_ipu(),
+                 "core is not compiled with IPU")
+class TestIpuShard(unittest.TestCase):
+    def _test(self):
+        # build graph
+        a = paddle.static.data(name='data', shape=[None, 1], dtype='int32')
+        b = a + 2  # scale : scale * x + bias, ipu_index : no
+
+        with paddle.fluid.ipu_shard(ipu_index=1):
+            c = b + 1  # scale, ipu_index : 1
+            with paddle.fluid.ipu_shard(ipu_index=2):
+                d = c * 2  # scale, ipu_index : 2
+            with paddle.fluid.ipu_shard(ipu_index=3):
+                e = d + 3  # scale, ipu_index : 3
+                with paddle.fluid.ipu_shard(ipu_index=1):
+                    e = e + 3  # scale, ipu_index : 1
+                    with paddle.fluid.ipu_shard(ipu_index=2):
+                        e = e + 3  # scale, ipu_index : 2
+
+        with paddle.fluid.ipu_shard(ipu_index=1):
+            f = paddle.tensor.pow(e, 2.0)  # pow, ipu_index : 1
+
+        with paddle.fluid.ipu_shard(ipu_index=2):
+            g = f - 1  # scale, ipu_index : 2
+
+        h = g + 1  # scale, ipu_index : no
+
+        ipu_index_list = []
+        main_prog = paddle.static.default_main_program()
+        for op in main_prog.global_block().ops:
+            if op.desc.has_attr("ipu_index"):
+                ipu_index_list.append(op.desc.attr("ipu_index"))
+
+        return ipu_index_list
+
+    def test_ipu_shard(self):
+        ipu_index_list = self._test()
+        expected_ipu_index_list = [1, 2, 3, 1, 2, 1, 2]
+        self.assertTrue(
+            np.allclose(
+                ipu_index_list, expected_ipu_index_list, atol=0))
+
+
+if __name__ == "__main__":
+    unittest.main()

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

+#  Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import print_function
+
+import numpy as np
+import unittest
+import sys
+import paddle
+import paddle.fluid as fluid
+import paddle.fluid.compiler as compiler
+
+paddle.enable_static()
+SEED = 2021
+
+
+@unittest.skipIf(not paddle.is_compiled_with_ipu(),
+                 "core is not compiled with IPU")
+class TestConvNet(unittest.TestCase):
+    def test_training(self):
+        ipu_strategy = compiler.get_ipu_strategy()
+
+        assert ipu_strategy.num_ipus == 1, "Default num_ipus must be 1"
+        assert ipu_strategy.is_training == True, "Default is_training is True"
+        assert ipu_strategy.enable_pipelining == False, \
+            "Default enable_pipelining is False"
+        assert ipu_strategy.enable_manual_shard == False, \
+            "Default enable_manual_shard is False"
+
+        ipu_strategy.num_ipus = 2
+        assert ipu_strategy.num_ipus == 2, "Set num_ipus Failed"
+
+        ipu_strategy.is_training = False
+        assert ipu_strategy.is_training == False, "Set is_training Failed"
+
+        ipu_strategy.enable_pipelining = True
+        assert ipu_strategy.enable_pipelining == True, \
+            "Set enable_pipelining Failed"
+
+        ipu_strategy.enable_manual_shard = True
+        assert ipu_strategy.enable_manual_shard == True, \
+            "Set enable_manual_shard Failed"
+
+
+if __name__ == "__main__":
+    unittest.main()

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

+#  Copyright (c) 2021 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.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()
+
+
+@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_feed()
+        self.set_feed_attr()
+        self.set_attrs()
+
+    def set_feed(self):
+        self.feed = {
+            "x": np.random.uniform(size=[1, 3, 10, 10]).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 = [
+            np_dtype_to_fluid_str(x.dtype) for x in self.feed.values()
+        ]
+
+    def set_attrs(self):
+        self.attrs = {
+            "scale": True,
+            "shift": True,
+            "begin_norm_axis": 1,
+            "epsilon": 1e-05,
+        }
+
+    def _test_base(self, run_ipu=True):
+        scope = fluid.core.Scope()
+        main_prog = paddle.static.Program()
+        startup_prog = paddle.static.Program()
+        SEED = self.SEED
+        main_prog.random_seed = SEED
+        startup_prog.random_seed = SEED
+
+        with fluid.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])
+
+                if self.is_training:
+                    ch = self.feed_shape[0][1]
+                    conv1 = paddle.static.nn.conv2d(
+                        x, num_filters=ch, filter_size=3, bias_attr=False)
+                    scale = paddle.ParamAttr(trainable=True)
+                    bias = paddle.ParamAttr(trainable=True)
+                    out = paddle.fluid.layers.nn.layer_norm(
+                        conv1, param_attr=scale, bias_attr=bias, **self.attrs)
+                else:
+                    # scale = True
+                    # bias = True
+                    scale = self.attrs['scale']
+                    bias = self.attrs['shift']
+                    out = paddle.fluid.layers.nn.layer_norm(
+                        x, param_attr=scale, bias_attr=bias, **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 = compiler.get_ipu_strategy()
+                ipu_strategy.is_training = self.is_training
+                program = compiler.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_base(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)
+
+
+@unittest.skip('raise error')
+class TestCase1(TestBase):
+    def set_attrs(self):
+        self.attrs = {
+            "scale": False,
+            "shift": True,
+            "begin_norm_axis": 1,
+            "epsilon": 1e-05,
+        }
+
+
+@unittest.skip('raise error')
+class TestCase2(TestBase):
+    def set_attrs(self):
+        self.attrs = {
+            "scale": True,
+            "shift": False,
+            "begin_norm_axis": 1,
+            "epsilon": 1e-05,
+        }
+
+
+class TestCase3(TestBase):
+    def set_attrs(self):
+        self.attrs = {
+            "scale": True,
+            "shift": True,
+            "begin_norm_axis": 2,
+            "epsilon": 1e-05,
+        }
+
+
+class TestTrainCase1(TestBase):
+    def set_atol(self):
+        self.atol = 1e-3
+
+    def set_training(self):
+        self.is_training = True
+        self.epoch = 10
+
+
+class TestTrainCase2(TestBase):
+    def set_atol(self):
+        self.atol = 1e-3
+
+    def set_attrs(self):
+        self.attrs = {
+            "scale": True,
+            "shift": True,
+            "begin_norm_axis": 2,
+            "epsilon": 1e-05,
+        }
+
+    def set_training(self):
+        self.is_training = True
+        self.epoch = 10
+
+
+# not support `layer_norm(x, param_attr=False, bias_attr=False, **self.attrs)`
+
+if __name__ == "__main__":
+    unittest.main()

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

+#  Copyright (c) 2021 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.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)
+import paddle.nn.functional as F
+
+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_training()
+        self.set_feed()
+        self.set_feed_attr()
+        self.set_attrs()
+
+    def set_feed(self):
+        self.feed = {
+            "x": np.random.uniform(size=[1, 3, 10, 10]).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 = [
+            np_dtype_to_fluid_str(x.dtype) for x in self.feed.values()
+        ]
+
+    def set_attrs(self):
+        self.attrs = {"axis": -1}
+
+    def _test_base(self, run_ipu=True):
+        scope = fluid.core.Scope()
+        main_prog = paddle.static.Program()
+        startup_prog = paddle.static.Program()
+        SEED = self.SEED
+        main_prog.random_seed = SEED
+        startup_prog.random_seed = SEED
+
+        with fluid.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 = F.log_softmax(x, **self.attrs)
+
+                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 = compiler.get_ipu_strategy()
+                ipu_strategy.is_training = self.is_training
+                program = compiler.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]
+
+    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))
+
+        self.assertTrue(res0.shape == res1.shape)
+
+
+class TestCase1(TestBase):
+    def set_attrs(self):
+        self.attrs = {"axis": 1}
+
+
+if __name__ == "__main__":
+    unittest.main()

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

+#  Copyright (c) 2021 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.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()
+
+
+@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_attrs()
+
+    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 set_attrs(self):
+        self.attrs = {
+            "size": [128, 16],
+            "is_sparse": False,
+            "is_distributed": False,
+            "padding_idx": -1,
+            "dtype": 'float32'
+        }
+
+    def _test_base(self, run_ipu=True):
+        scope = fluid.core.Scope()
+        main_prog = paddle.static.Program()
+        startup_prog = paddle.static.Program()
+        SEED = self.SEED
+        main_prog.random_seed = SEED
+        startup_prog.random_seed = SEED
+
+        if run_ipu:
+            self.feed = {
+                "x": np.array(
+                    [[[1], [3]], [[2], [4]], [[4], [127]]]).astype(np.int32)
+            }
+        else:
+            self.feed = {
+                "x": np.array(
+                    [[[1], [3]], [[2], [4]], [[4], [127]]]).astype(np.int64)
+            }
+
+        self.set_feed_attr()
+
+        with fluid.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.embedding(x, **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 = compiler.get_ipu_strategy()
+                ipu_strategy.is_training = self.is_training
+                program = compiler.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_base(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 TestTrainCase1(TestBase):
+    def set_training(self):
+        self.is_training = True
+        self.epoch = 10
+
+
+if __name__ == "__main__":
+    unittest.main()

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

+#  Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import print_function
+
+import numpy as np
+import unittest
+import sys
+import paddle
+import paddle.fluid as fluid
+import paddle.fluid.compiler as compiler
+from paddle.optimizer.lr import LRScheduler
+
+paddle.enable_static()
+SEED = 2021
+
+
+class LR_New(LRScheduler):
+    def __init__(self, learning_rate=1.0, last_epoch=-1, verbose=False):
+        super(LR_New, self).__init__(learning_rate, last_epoch, verbose)
+
+    def get_lr(self):
+        self.base_lr = self.base_lr + 1
+        self.last_epoch = self.last_epoch + 1
+        return self.base_lr
+
+
+@unittest.skipIf(not paddle.is_compiled_with_ipu(),
+                 "core is not compiled with IPU")
+class TestConvNet(unittest.TestCase):
+    def _test(self, run_ipu=True):
+        scope = fluid.core.Scope()
+        main_prog = paddle.static.Program()
+        startup_prog = paddle.static.Program()
+        main_prog.random_seed = SEED
+        startup_prog.random_seed = SEED
+        np.random.seed(SEED)
+
+        np_image = np.random.rand(1, 3, 10, 10).astype(np.float32)
+
+        with fluid.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')
+                conv1 = paddle.static.nn.conv2d(
+                    image, num_filters=3, filter_size=3, bias_attr=False)
+                loss = paddle.mean(conv1)
+
+                sgd = paddle.optimizer.SGD(learning_rate=LR_New())
+                sgd.minimize(loss)
+
+            if run_ipu:
+                place = paddle.IPUPlace()
+            else:
+                place = paddle.CPUPlace()
+            exe = paddle.static.Executor(place)
+            exe.run(startup_prog)
+
+            if run_ipu:
+                feed_list = [image.name]
+                fetch_list = [loss.name]
+                ipu_strategy = compiler.get_ipu_strategy()
+                ipu_strategy.is_training = True
+                program = compiler.IPUCompiledProgram(
+                    main_prog, ipu_strategy=ipu_strategy).compile(feed_list,
+                                                                  fetch_list)
+            else:
+                program = main_prog
+
+            result = []
+            for epoch in range(100):
+                if hasattr(program, "lr_sheduler"):
+                    program.lr_sheduler.step()
+                loss_res = exe.run(program,
+                                   feed={image.name: np_image},
+                                   fetch_list=[loss])
+                result.append(loss_res)
+
+            return np.array(result)
+
+    def test_training(self):
+        # cpu and ipu dimenstion mismatch, cpu:(100, 1, 1), ipu:(100, 1)
+        ipu_loss = self._test(True).flatten()
+        cpu_loss = self._test(False).flatten()
+
+        self.assertTrue(np.allclose(ipu_loss, cpu_loss, atol=1e-4))
+
+
+if __name__ == "__main__":
+    unittest.main()

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

+#  Copyright (c) 2021 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.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()
+
+
+@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_feed()
+        self.set_feed_attr()
+        self.set_attrs()
+
+    def set_feed(self):
+        self.feed = {
+            "x": np.random.uniform(size=[2, 3]).astype('float32'),
+            "y": np.random.uniform(size=[3, 2]).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 = [
+            np_dtype_to_fluid_str(x.dtype) for x in self.feed.values()
+        ]
+
+    def set_attrs(self):
+        self.attrs = {
+            "transpose_x": False,
+            "transpose_y": False,
+            "alpha": 1.0,
+        }
+
+    def _test_base(self, run_ipu=True):
+        scope = fluid.core.Scope()
+        main_prog = paddle.static.Program()
+        startup_prog = paddle.static.Program()
+        SEED = self.SEED
+        main_prog.random_seed = SEED
+        startup_prog.random_seed = SEED
+
+        with fluid.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])
+                y = paddle.static.data(
+                    name=self.feed_list[1],
+                    shape=self.feed_shape[1],
+                    dtype=self.feed_dtype[1])
+                out = paddle.fluid.layers.matmul(x, y, **self.attrs)
+
+                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 = compiler.get_ipu_strategy()
+                ipu_strategy.is_training = self.is_training
+                program = compiler.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]
+
+    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))
+
+        self.assertTrue(res0.shape == res1.shape)
+
+
+class TestCase1(TestBase):
+    def set_attrs(self):
+        self.attrs = {
+            "transpose_x": True,
+            "transpose_y": True,
+            "alpha": 1.0,
+        }
+
+
+class TestCase2(TestBase):
+    def set_attrs(self):
+        self.attrs = {
+            "transpose_x": True,
+            "transpose_y": True,
+            "alpha": 3.14,
+        }
+
+
+class TestCase3(TestBase):
+    def set_feed(self):
+        self.feed = {
+            "x": np.random.uniform(size=[5, 4, 2, 3]).astype('float32'),
+            "y": np.random.uniform(size=[5, 4, 3, 2]).astype('float32'),
+        }
+
+
+class TestCase4(TestBase):
+    def set_feed(self):
+        self.feed = {
+            "x": np.random.uniform(size=[4, 2, 3]).astype('float32'),
+            "y": np.random.uniform(size=[4, 3, 2]).astype('float32'),
+        }
+
+
+class TestCase5(TestBase):
+    def set_feed(self):
+        self.feed = {
+            "x": np.random.uniform(size=[4, 2, 3]).astype('float32'),
+            "y": np.random.uniform(size=[3, 2]).astype('float32'),
+        }
+
+
+class TestCase6(TestBase):
+    def set_feed(self):
+        self.feed = {
+            "x": np.random.uniform(size=[3]).astype('float32'),
+            "y": np.random.uniform(size=[3]).astype('float32'),
+        }
+
+
+@unittest.skip("not supported")
+class TestCase6_2(TestCase6):
+    def set_feed(self):
+        self.feed = {
+            "x": np.random.uniform(size=[3]).astype('float32'),
+            "y": np.random.uniform(size=[3]).astype('float32'),
+        }
+
+    def set_attrs(self):
+        self.attrs = {
+            "transpose_x": True,
+            "transpose_y": True,
+            "alpha": 1.0,
+        }
+
+
+class TestCase7(TestBase):
+    def set_feed(self):
+        self.feed = {
+            "x": np.random.uniform(size=[3, 1]).astype('float32'),
+            "y": np.random.uniform(size=[1, 2]).astype('float32'),
+        }
+
+
+@unittest.skip("not supported")
+class TestCase7_2(TestBase):
+    def set_feed(self):
+        self.feed = {
+            "x": np.random.uniform(size=[3]).astype('float32'),
+            "y": np.random.uniform(size=[2]).astype('float32'),
+        }
+        # equal to
+        # self.feed = {
+        #     "x": np.random.uniform(size=[3, 1]).astype('float32'),
+        #     "y": np.random.uniform(size=[1, 2]).astype('float32'),
+        # }
+
+    def set_attrs(self):
+        self.attrs = {
+            "transpose_x": True,
+            "transpose_y": True,
+            "alpha": 1.0,
+        }
+
+
+@unittest.skip("dim > 4 is not supported")
+class TestCase8(TestBase):
+    def set_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()