ipu_commit_tests p8 (#38088)

python/paddle/fluid/tests/unittests/ipu/test_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)
+
+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, 2, 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 = {"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 = paddle.fluid.layers.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": 2}
+
+
+if __name__ == "__main__":
+    unittest.main()

python/paddle/fluid/tests/unittests/ipu/test_squeeze_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, 1, 5]).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 = {"axes": [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])
+                out = paddle.fluid.layers.squeeze(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 = {"axes": []}
+
+
+class TestCase2(TestBase):
+    def set_attrs(self):
+        self.attrs = {"axes": [-2]}
+
+
+if __name__ == "__main__":
+    unittest.main()

python/paddle/fluid/tests/unittests/ipu/test_stack_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, 2]).astype('float32'),
+            "y": np.random.uniform(size=[1, 2]).astype('float32'),
+            "z": np.random.uniform(size=[1, 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 = {"axis": 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])
+                z = paddle.static.data(
+                    name=self.feed_list[2],
+                    shape=self.feed_shape[2],
+                    dtype=self.feed_dtype[2])
+                out = paddle.fluid.layers.stack([x, y, z], **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": -2}
+
+
+if __name__ == "__main__":
+    unittest.main()

python/paddle/fluid/tests/unittests/ipu/test_sum_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, 2, 2]).astype('float32'),
+            "y": np.random.uniform(size=[1, 3, 2, 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 = {}
+
+    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.sum([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)
+
+
+@unittest.skip('')
+class TestCase1(TestBase):
+    def set_feed(self):
+        self.feed = {
+            "x": np.random.uniform(size=[1, 3, 2, 2]).astype('float32'),
+            "y": np.random.uniform(size=[1, 3, 2, 2]).astype('float32'),
+            "z": np.random.uniform(size=[1, 3, 2, 2]).astype('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
+
+        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])
+                z = paddle.static.data(
+                    name=self.feed_list[2],
+                    shape=self.feed_shape[2],
+                    dtype=self.feed_dtype[2])
+                out = paddle.fluid.layers.sum([x, y, z], **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]
+
+
+if __name__ == "__main__":
+    unittest.main()

python/paddle/fluid/tests/unittests/ipu/test_topk_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
+
+paddle.enable_static()
+
+
+@unittest.skipIf(not paddle.is_compiled_with_ipu(),
+                 "core is not compiled with IPU")
+class TestTopKOp(IPUOpTest):
+    def setUp(self):
+        self.set_ops()
+        self.set_atol()
+        self.set_training()
+        self.k = 3
+        self.use_K_as_const_variable = False
+
+        self.set_feed()
+        self.set_attrs()
+
+    def set_ops(self):
+        self.ops = [
+            paddle.fluid.layers.topk,
+            paddle.topk  # use top_k_v2 implementation
+        ]
+
+    def set_feed(self):
+        self.feed_shape = []
+        self.feed_shape.append([3, 5])
+
+        self.feed = {}
+        self.feed_list = []
+        self.feed["in_0"] = np.random.uniform(
+            size=self.feed_shape[0]).astype(np.float32)
+        self.feed_list.append("in_0")
+        if self.use_K_as_const_variable:
+            # self.feed["in_1"] = np.array([self.k]).astype("int32")
+            # self.feed_list.append("in_1")
+            pass
+        print("[TestTopKop] feed data:\n%s" % self.feed["in_0"])
+
+    def set_attrs(self):
+        self.attrs = {
+            # "axis": -1,
+            # "sorted": True
+        }
+        if not self.use_K_as_const_variable:
+            self.attrs["k"] = self.k
+
+    def _test_base(self, run_ipu=True, op=None, data_feed=None):
+        assert (op is not None)
+        assert (data_feed is not None)
+        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='float32')
+                if not self.use_K_as_const_variable:
+                    topk_values, topk_indices = op(x, **self.attrs)
+                else:
+                    # !important, popart cannot accept non const tensor
+                    # K_t = paddle.static.data(name="in_1", shape=[1], dtype='int32')
+                    K_t = fluid.layers.fill_constant(
+                        shape=[1], dtype='int32', value=self.k, name="in_2")
+                    topk_values, topk_indices = op(x, K_t, **self.attrs)
+                fetch_list = [topk_values.name, topk_indices.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
+
+            print("Running inference ...")
+            result = exe.run(program, feed=data_feed, fetch_list=fetch_list)
+            print("Complete running infrence.")
+            return result
+
+    def test_base(self):
+        for op in self.ops:
+            res0_topk_values, res0_topk_indices = self._test_base(
+                True, op=op, data_feed=self.feed)
+            res1_topk_values, res1_topk_indices = self._test_base(
+                False, op=paddle.fluid.layers.topk, data_feed=self.feed)
+
+            print("[TestTopKop] IPU res0 values:\n%s\n" % res0_topk_values)
+            print("[TestTopKop] CPU res1 values:\n%s\n" % res1_topk_values)
+            view_type = np.uint32
+            print("[TestTopKop] IPU res0 indices:\n%s\n" %
+                  res0_topk_indices.astype(view_type))
+            print("[TestTopKop] CPU res1 indices:\n%s\n" % res1_topk_indices)
+
+            self.assertTrue(
+                np.allclose(
+                    res0_topk_values.flatten(),
+                    res1_topk_values.flatten(),
+                    atol=self.atol))
+
+            self.assertTrue(
+                np.allclose(
+                    res0_topk_indices.astype(view_type).flatten(),
+                    res1_topk_indices.flatten(),
+                    atol=self.atol))
+
+
+if __name__ == "__main__":
+    unittest.main()

python/paddle/fluid/tests/unittests/ipu/test_transpose_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 = {"perm": [0, 2, 3, 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 = paddle.fluid.layers.transpose(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 = {"perm": [0, 1, 2, 3]}
+
+
+class TestCase2(TestBase):
+    def set_feed(self):
+        self.feed = {
+            "x": np.random.uniform(size=[1, 2, 3, 4, 5]).astype('float32'),
+        }
+
+    def set_attrs(self):
+        self.attrs = {"perm": [4, 0, 2, 3, 1]}
+
+
+if __name__ == "__main__":
+    unittest.main()

python/paddle/fluid/tests/unittests/ipu/test_unsqueeze_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, 2, 3]).astype('float32')}
+
+    def set_feed_attr(self):
+        self.feed_shape = [x.shape for x in self.feed.values()]
+        self.feed_list = list(self.feed.keys())
+        self.feed_dtype = [
+            np_dtype_to_fluid_str(x.dtype) for x in self.feed.values()
+        ]
+
+    def set_attrs(self):
+        self.attrs = {"axes": 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])
+                out = paddle.fluid.layers.unsqueeze(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 = {"axes": -1}
+
+
+class TestCase2(TestBase):
+    def set_attrs(self):
+        self.attrs = {"axes": [1, 2]}
+
+
+if __name__ == "__main__":
+    unittest.main()

python/paddle/fluid/tests/unittests/ipu/test_varname_inplace.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
+from paddle.fluid.executor import global_scope
+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 = {
+            "shape": [30, 10],
+            "inplace": True,
+        }
+
+    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])
+                add1 = paddle.fluid.layers.elementwise_add(x, x)
+                reshape = paddle.fluid.layers.reshape(add1, **self.attrs)
+                add2 = paddle.fluid.layers.elementwise_add(reshape, reshape)
+                scale1 = paddle.fluid.layers.scale(add2)
+                scale2 = paddle.fluid.layers.scale(scale1, scale=1.3, bias=0.5)
+                scale3 = paddle.fluid.layers.scale(scale2, scale=2, bias=0.7)
+
+                fetch_list = [scale3.name]
+
+            if run_ipu:
+                place = paddle.IPUPlace()
+            else:
+                place = paddle.CPUPlace()
+            exe = paddle.static.Executor(place)
+            exe.run(startup_prog)
+            scale1_out = main_prog.global_block().ops[4].output("Out")[0]
+            main_prog.global_block().ops[4]._rename_output(scale1_out,
+                                                           add2.name)
+            main_prog.global_block().ops[5]._rename_input(scale1_out, add2.name)
+
+            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(True)
+        res1 = self._test_base(False)
+
+        self.assertTrue(
+            np.allclose(
+                res0.flatten(), res1.flatten(), atol=self.atol))
+
+        self.assertTrue(res0.shape == res1.shape)
+
+
+if __name__ == "__main__":
+    unittest.main()