[IPU] add more UTs 1/N (#44207)

* add authors
Co-authored-by: NAllen Guo <alleng@graphcore.ai>
Co-authored-by: NZhixin Yao <zhixiny@graphcore.ai>
Co-authored-by: NZhaorui Chen <zhaoruic@graphcore.ai>

* squash py changes 1/N
Co-authored-by: NZhixin Yao <zhixiny@graphcore.ai>
Co-authored-by: NZhaorui Chen <zhaoruic@graphcore.ai>

python/paddle/fluid/tests/unittests/ipu/test_dy2static_fp16_ipu.py

@@ -14,16 +14,12 @@
 
 from __future__ import print_function
 
-import numpy as np
+import tempfile
 import unittest
-import sys
-import os
+
+import numpy as np
 import paddle
-import paddle.fluid as fluid
-from paddle.jit import to_static
-from paddle.utils.cpp_extension import load
-from paddle.optimizer.lr import LRScheduler
-import tempfile
+from paddle.fluid.tests.unittests.ipu.op_test_ipu import IPUOpTest
 
 SEED = 2022
 
@@ -52,7 +48,9 @@ class SimpleLayer(paddle.nn.Layer):
         return x
 
 
-class TestBase(unittest.TestCase):
+@unittest.skipIf(not paddle.is_compiled_with_ipu(),
+                 "core is not compiled with IPU")
+class TestBase(IPUOpTest):
 
     @classmethod
     def setUpClass(cls):
@@ -124,6 +122,9 @@ class TestBase(unittest.TestCase):
 
             result.append(loss)
 
+        if use_ipu:
+            ipu_strategy.release_patch()
+
         return np.array(result)
 
     def test_training(self):

python/paddle/fluid/tests/unittests/ipu/test_dy2static_ipu.py

@@ -14,59 +14,84 @@
 
 from __future__ import print_function
 
-import numpy as np
+import tempfile
 import unittest
-import sys
+
+import numpy as np
 import paddle
-import paddle.fluid as fluid
+from paddle.fluid.dygraph.dygraph_to_static.program_translator import ProgramCache
+from paddle.fluid.tests.unittests.ipu.op_test_ipu import IPUOpTest
 from paddle.jit import to_static
-from paddle.utils.cpp_extension import load
 from paddle.optimizer.lr import LRScheduler
-from paddle.fluid.dygraph.dygraph_to_static.program_translator import ProgramCache
-import tempfile
+from functools import partial
 
 SEED = 2022
 
 
 class SimpleLayer(paddle.nn.Layer):
 
-    def __init__(self, use_ipu=False):
+    def __init__(self,
+                 loss_op=None,
+                 use_softmax=True,
+                 use_reduction=True,
+                 use_identity_loss=True):
         super(SimpleLayer, self).__init__()
-        self.use_ipu = use_ipu
+        self.loss_op = loss_op
         self.conv = paddle.nn.Conv2D(in_channels=3,
                                      out_channels=1,
                                      kernel_size=2,
                                      stride=1)
+        self.use_softmax = use_softmax
+        self.use_reduction = use_reduction
+        self.use_identity_loss = use_identity_loss
 
     @to_static()
     def forward(self, x, target=None):
         x = self.conv(x)
         x = paddle.fluid.layers.flatten(x, axis=1)
         if target is not None:
+            if self.use_softmax:
                 x = paddle.fluid.layers.softmax(x)
-            loss = paddle.fluid.layers.cross_entropy(x, target)
-            if self.use_ipu:
-                loss = paddle.incubate.identity_loss(loss, 1)
+            if self.loss_op:
+                loss = self.loss_op(x, target)
             else:
+                loss = paddle.fluid.layers.cross_entropy(x, target)
+            if self.use_reduction:
                 loss = paddle.mean(loss)
+            if self.use_identity_loss:
+                loss = paddle.incubate.identity_loss(loss, 1)
             return x, loss
         return x
 
 
-class TestBase(unittest.TestCase):
+@unittest.skipIf(not paddle.is_compiled_with_ipu(),
+                 "core is not compiled with IPU")
+class TestBase(IPUOpTest):
 
-    @classmethod
-    def setUpClass(cls):
+    def setUp(self):
         paddle.disable_static()
+        self.set_op_attrs()
+        self.set_data_feed()
+
+    def set_op_attrs(self):
+        self.loss_op = paddle.fluid.layers.cross_entropy
+
+    def set_data_feed(self):
+        self.data = paddle.uniform((32, 3, 10, 10), dtype='float32')
+        self.label = paddle.randint(0, 10, shape=[32], dtype='int64')
+
+    def create_model(self, use_ipu=False):
+        return SimpleLayer(loss_op=self.loss_op,
+                           use_softmax=True,
+                           use_reduction=not use_ipu,
+                           use_identity_loss=use_ipu)
 
     def _test(self, use_ipu=False):
         paddle.seed(SEED)
         np.random.seed(SEED)
-        model = SimpleLayer(use_ipu)
+        model = self.create_model(use_ipu)
         optim = paddle.optimizer.Adam(learning_rate=0.01,
                                       parameters=model.parameters())
-        data = paddle.uniform((32, 3, 10, 10), dtype='float32')
-        label = paddle.randint(0, 10, shape=[32], dtype='int64')
 
         if use_ipu:
             device = paddle.set_device('ipu')
@@ -80,7 +105,7 @@ class TestBase(unittest.TestCase):
         result = []
         for epoch in range(100):
             # ipu only needs call model() to do forward/backward/grad_update
-            pred, loss = model(data, label)
+            pred, loss = model(self.data, self.label)
             if not use_ipu:
                 loss.backward()
                 optim.step()
@@ -104,7 +129,6 @@ class TestSaveLoad(TestBase):
 
     @classmethod
     def setUpClass(cls):
-        paddle.disable_static()
         cls.save_path = tempfile.TemporaryDirectory()
 
     @classmethod
@@ -114,11 +138,9 @@ class TestSaveLoad(TestBase):
     def _test(self, use_ipu=False):
         paddle.seed(SEED)
         np.random.seed(SEED)
-        model = SimpleLayer(use_ipu)
+        model = self.create_model(use_ipu)
         optim = paddle.optimizer.Adam(learning_rate=0.01,
                                       parameters=model.parameters())
-        data = paddle.uniform((32, 3, 10, 10), dtype='float32')
-        label = paddle.randint(0, 10, shape=[32], dtype='int64')
         model_path = '{}/model_state_dict_{}.pdparams'.format(
             self.save_path, 'ipu' if use_ipu else 'cpu')
         optim_path = '{}/optim_state_dict_{}.pdopt'.format(
@@ -136,7 +158,7 @@ class TestSaveLoad(TestBase):
         result = []
         for epoch in range(100):
             # ipu only needs call model() to do forward/backward/grad_update
-            pred, loss = model(data, label)
+            pred, loss = model(self.data, self.label)
             if not use_ipu:
                 loss.backward()
                 optim.step()
@@ -155,7 +177,7 @@ class TestSaveLoad(TestBase):
 
         for epoch in range(100):
             # ipu only needs call model() to do forward/backward/grad_update
-            pred, loss = model(data, label)
+            pred, loss = model(self.data, self.label)
             if not use_ipu:
                 loss.backward()
                 optim.step()
@@ -169,10 +191,11 @@ class TestSaveLoad(TestBase):
         return np.array(result)
 
 
-class TestPatch(unittest.TestCase):
+@unittest.skipIf(not paddle.is_compiled_with_ipu(),
+                 "core is not compiled with IPU")
+class TestPatch(IPUOpTest):
 
-    @classmethod
-    def setUpClass(cls):
+    def setUp(cls):
         paddle.disable_static()
 
     def test(self, use_ipu=False):
@@ -189,5 +212,46 @@ class TestPatch(unittest.TestCase):
         self.assertTrue(reset_step is old_step)
 
 
+class TestWithoutIdentityLoss1(TestBase):
+
+    def create_model(self, use_ipu=False):
+        return SimpleLayer(loss_op=self.loss_op,
+                           use_softmax=True,
+                           use_reduction=True,
+                           use_identity_loss=False)
+
+
+class TestWithoutIdentityLoss2(TestBase):
+
+    def set_op_attrs(self):
+        self.loss_op = paddle.fluid.layers.softmax_with_cross_entropy
+
+    def set_data_feed(self):
+        self.data = paddle.uniform((32, 3, 10, 10), dtype='float32')
+        self.label = paddle.randint(0, 10, shape=[32, 1], dtype='int64')
+
+    def create_model(self, use_ipu=False):
+        return SimpleLayer(loss_op=self.loss_op,
+                           use_softmax=False,
+                           use_reduction=True,
+                           use_identity_loss=False)
+
+
+class TestWithoutIdentityLoss3(TestBase):
+
+    def set_op_attrs(self):
+        self.loss_op = partial(paddle.fluid.layers.kldiv_loss, reduction="none")
+
+    def set_data_feed(self):
+        self.data = paddle.uniform((32, 3, 10, 10), dtype='float32')
+        self.label = paddle.rand(shape=[32, 81], dtype='float32')
+
+    def create_model(self, use_ipu=False):
+        return SimpleLayer(loss_op=self.loss_op,
+                           use_softmax=True,
+                           use_reduction=True,
+                           use_identity_loss=False)
+
+
 if __name__ == "__main__":
     unittest.main()

python/paddle/fluid/tests/unittests/ipu/test_expand_as_v2_op_ipu.py

+#  Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+
+import numpy as np
+import paddle
+import paddle.static
+from paddle.fluid.tests.unittests.ipu.op_test_ipu import IPUOpTest
+
+
+@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()
+
+    def set_data_feed(self):
+        data_x = np.random.uniform(size=[1, 3])
+        data_y = np.random.uniform(size=[2, 2, 3])
+        self.feed_fp32 = {
+            'x': data_x.astype(np.float32),
+            'y': data_y.astype(np.float32)
+        }
+        self.feed_fp16 = {
+            'x': data_x.astype(np.float16),
+            'y': data_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())
+        self.feed_dtype = [x.dtype for x in self.feed_fp32.values()]
+
+    @IPUOpTest.static_graph
+    def build_model(self):
+        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.expand_as(x, y)
+        self.fetch_list = [out.name]
+
+    def run_model(self, exec_mode):
+        self.run_op_test(exec_mode)
+
+    def test(self):
+        for m in IPUOpTest.ExecutionMode:
+            if not self.skip_mode(m):
+                self.build_model()
+                self.run_model(m)
+        self.check()
+
+
+class TestCase1(TestBase):
+
+    def set_data_feed(self):
+        data_x = np.random.uniform(size=[2, 3])
+        data_y = np.random.uniform(size=[2, 4, 2, 3])
+        self.feed_fp32 = {
+            'x': data_x.astype(np.float32),
+            'y': data_y.astype(np.float32)
+        }
+        self.feed_fp16 = {
+            'x': data_x.astype(np.float16),
+            'y': data_y.astype(np.float16)
+        }
+
+
+@unittest.skip("corresponding dimensions must have the same value.")
+class TestCase2(TestBase):
+
+    def set_data_feed(self):
+        data_x = np.random.uniform(size=[2, 3])
+        data_y = np.random.uniform(size=[2, 4, 3, 3])
+        self.feed_fp32 = {
+            'x': data_x.astype(np.float32),
+            'y': data_y.astype(np.float32)
+        }
+        self.feed_fp16 = {
+            'x': data_x.astype(np.float16),
+            'y': data_y.astype(np.float16)
+        }
+
+
+if __name__ == "__main__":
+    unittest.main()

python/paddle/fluid/tests/unittests/ipu/test_expand_v2_op_ipu.py

+#  Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+
+import numpy as np
+import paddle
+import paddle.static
+from paddle.fluid.tests.unittests.ipu.op_test_ipu import IPUOpTest
+
+
+@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_attrs()
+
+    def set_data_feed(self):
+        data = np.random.uniform(size=[2, 3])
+        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_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):
+        self.attrs = {"shape": [2, 2, 3]}
+
+    @IPUOpTest.static_graph
+    def build_model(self):
+        x = paddle.static.data(name=self.feed_list[0],
+                               shape=self.feed_shape[0],
+                               dtype="float32")
+        out = paddle.expand(x, **self.attrs)
+        self.fetch_list = [out.name]
+
+    def run_model(self, exec_mode):
+        self.run_op_test(exec_mode)
+
+    def test(self):
+        for m in IPUOpTest.ExecutionMode:
+            if not self.skip_mode(m):
+                self.build_model()
+                self.run_model(m)
+        self.check()
+
+
+class TestCase1(TestBase):
+
+    def set_attrs(self):
+        self.attrs = {"shape": [5, 2, 2, 3]}
+
+
+class TestCase2(TestBase):
+
+    def set_data_feed(self):
+        data = np.random.uniform(size=[2, 1, 3])
+        self.feed_fp32 = {'x': data.astype(np.float32)}
+        self.feed_fp16 = {'x': data.astype(np.float16)}
+
+    def set_attrs(self):
+        self.attrs = {"shape": [5, 2, 2, 3]}
+
+
+@unittest.skip("corresponding dimensions must have the same value.")
+class TestCase3(TestBase):
+
+    def set_attrs(self):
+        self.attrs = {"shape": [5, 2, 4, 3]}
+
+
+@unittest.skip("Do not support `shape` = Tensors.")
+class TestCase4(TestBase):
+
+    def set_data_feed(self):
+        data = np.random.uniform(size=[3, 3])
+        self.feed_fp32 = {'x': data.astype(np.float32)}
+        self.feed_fp16 = {'x': data.astype(np.float16)}
+
+    @IPUOpTest.static_graph
+    def build_model(self):
+        x = paddle.static.data(name=self.feed_list[0],
+                               shape=self.feed_shape[0],
+                               dtype="float32")
+        self.attrs = {
+            'name': 'y',
+            'shape': [3],
+            'dtype': 'int32',
+            'value': 3,
+        }
+        y = paddle.fluid.layers.fill_constant(**self.attrs)
+        out = paddle.expand(x, shape=y)
+        self.fetch_list = [out.name]
+
+
+if __name__ == "__main__":
+    unittest.main()

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

@@ -69,5 +69,24 @@ class TestCase1(TestBase):
         self.attrs = {'fill_value': 3, 'dtype': 'int32'}
 
 
+class TestError(TestBase):
+
+    @IPUOpTest.static_graph
+    def build_model(self):
+        x = paddle.fluid.data('x', [-1, 3, 13], 'float32')
+        x_fill = paddle.full_like(x, **self.attrs)
+        out = paddle.fluid.layers.elementwise_add(x_fill, x_fill)
+        self.fetch_list = [out.name]
+
+    def test(self):
+        self.build_model()
+
+        def test_error():
+            self.run_op_test(IPUOpTest.ExecutionMode.IPU_FP32)
+
+        self.assertRaisesRegex(Exception, "Please check tensor shape setting",
+                               test_error)
+
+
 if __name__ == "__main__":
     unittest.main()

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

+#  Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+
+import numpy as np
+import paddle
+import paddle.static
+from paddle.fluid.tests.unittests.ipu.op_test_ipu import IPUOpTest
+
+
+@unittest.skipIf(not paddle.is_compiled_with_ipu(),
+                 "core is not compiled with IPU")
+class TestBase(IPUOpTest):
+
+    def setUp(self):
+        self.set_atol()
+        self.set_training()
+        self.set_data_feed()
+        self.set_feed_attr()
+        self.set_op_attrs()
+
+    def set_data_feed(self):
+        data = np.random.uniform(size=[2, 2, 4, 6])
+        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_fp32.values()]
+        self.feed_list = list(self.feed_fp32.keys())
+
+    def set_op_attrs(self):
+        self.attrs = {}
+        self.attrs['start_axis'] = 0
+        self.attrs['stop_axis'] = -1
+
+    @IPUOpTest.static_graph
+    def build_model(self):
+        x = paddle.static.data(name=self.feed_list[0],
+                               shape=self.feed_shape[0],
+                               dtype='float32')
+        out = paddle.flatten(x=x, **self.attrs)
+        self.fetch_list = [out.name]
+
+    def run_model(self, exec_mode):
+        self.run_op_test(exec_mode)
+
+    def test(self):
+        for m in IPUOpTest.ExecutionMode:
+            if not self.skip_mode(m):
+                self.build_model()
+                self.run_model(m)
+        self.check()
+
+
+class TestCase1(TestBase):
+
+    def set_op_attrs(self):
+        self.attrs = {}
+        self.attrs['start_axis'] = 0
+        self.attrs['stop_axis'] = 2
+
+
+class TestCase2(TestBase):
+
+    def set_op_attrs(self):
+        self.attrs = {}
+        self.attrs['start_axis'] = 1
+        self.attrs['stop_axis'] = -1
+
+
+class TestCase3(TestBase):
+
+    def set_op_attrs(self):
+        self.attrs = {}
+        self.attrs['start_axis'] = 1
+        self.attrs['stop_axis'] = 2
+
+
+if __name__ == "__main__":
+    unittest.main()

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

+#  Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+
+import numpy as np
+import paddle
+import paddle.static
+from paddle.fluid.tests.unittests.ipu.op_test_ipu import IPUOpTest
+
+
+@unittest.skipIf(not paddle.is_compiled_with_ipu(),
+                 "core is not compiled with IPU")
+class TestBase(IPUOpTest):
+
+    def setUp(self):
+        self.set_atol()
+        self.set_training()
+        self.set_feed()
+        self.set_feed_attr()
+        self.set_op_attrs()
+
+    def set_atol(self):
+        self.atol = 1e-6
+        self.rtol = 1e-6
+        self.atol_fp16 = 1e-3
+        self.rtol_fp16 = 1e-3
+
+    def set_feed(self):
+        data = np.random.uniform(size=[3, 2, 2])
+        self.feed_fp32 = {'x': data.astype(np.float32)}
+        self.feed_fp16 = {'x': data.astype(np.float16)}
+
+    def set_feed_attr(self):
+        self.feed_shape = [x.shape for x in self.feed_fp32.values()]
+        self.feed_list = list(self.feed_fp32.keys())
+        self.feed_dtype = [x.dtype for x in self.feed_fp32.values()]
+
+    def set_op_attrs(self):
+        self.attrs = {}
+        self.attrs['axis'] = [0, 1]
+
+    @IPUOpTest.static_graph
+    def build_model(self):
+        x = paddle.static.data(name=self.feed_list[0],
+                               shape=self.feed_shape[0],
+                               dtype=self.feed_dtype[0])
+        x = paddle.flip(x, **self.attrs)
+        self.fetch_list = [x.name]
+
+    def run_model(self, exec_mode):
+        self.run_op_test(exec_mode)
+
+    def test(self):
+        for m in IPUOpTest.ExecutionMode:
+            if not self.skip_mode(m):
+                self.build_model()
+                self.run_model(m)
+        self.check()
+
+
+class TestCase1(TestBase):
+
+    def set_feed(self):
+        data = np.random.randint(0, 10, size=[3, 2, 2])
+        self.feed_fp32 = {'x': data.astype(np.int32)}
+        self.feed_fp16 = {'x': data.astype(np.int32)}
+
+
+class TestCase2(TestBase):
+
+    def set_feed(self):
+        data = np.random.randint(0, 2, size=[4, 3, 2, 2])
+        self.feed_fp32 = {'x': data.astype(np.bool)}
+        self.feed_fp16 = {'x': data.astype(np.bool)}
+
+
+if __name__ == "__main__":
+    unittest.main()

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

@@ -127,5 +127,17 @@ class TestEqual(TestGreaterThan):
         self.op = paddle.fluid.layers.equal
 
 
+class TestGreaterEqual(TestGreaterThan):
+
+    def set_test_op(self):
+        self.op = paddle.fluid.layers.greater_equal
+
+
+class TestLessEqual(TestGreaterThan):
+
+    def set_test_op(self):
+        self.op = paddle.fluid.layers.less_equal
+
+
 if __name__ == "__main__":
     unittest.main()

python/paddle/fluid/tests/unittests/ipu/test_huber_loss_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
+import paddle.nn.functional as F
+
+
+@unittest.skipIf(not paddle.is_compiled_with_ipu(),
+                 "core is not compiled with IPU")
+class TestBase(IPUOpTest):
+
+    def setUp(self):
+        self.set_atol()
+        self.set_training()
+        self.set_data_feed()
+        self.set_feed_attr()
+        self.set_op_attrs()
+
+    def set_data_feed(self):
+        x = np.random.uniform(size=[3, 4, 2, 2])
+        target = np.random.uniform(size=[3, 4, 2, 2])
+        self.feed_fp32 = {
+            "x": x.astype(np.float32),
+            "target": target.astype(np.float32)
+        }
+        self.feed_fp16 = {
+            "x": x.astype(np.float16),
+            "target": target.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 = {
+            'delta': 1.0,
+        }
+
+    @IPUOpTest.static_graph
+    def build_model(self, on_ipu):
+        x = paddle.static.data(name=self.feed_list[0],
+                               shape=self.feed_shape[0],
+                               dtype="float32")
+        target = paddle.static.data(name=self.feed_list[1],
+                                    shape=self.feed_shape[1],
+                                    dtype='float32')
+        out = paddle.fluid.layers.huber_loss(x, target, **self.attrs)
+        self.fetch_list = [out.name]
+
+    def run_model(self, exec_mode):
+        self.run_op_test(exec_mode)
+
+    def test(self):
+        for m in IPUOpTest.ExecutionMode:
+            if not self.skip_mode(m):
+                self.build_model(self.is_ipu_mode(m))
+                self.run_model(m)
+        self.check()
+
+
+class TestCase1(TestBase):
+
+    def set_op_attrs(self):
+        self.attrs = {
+            'delta': 0.5,
+        }
+
+
+class TestCase2(TestBase):
+
+    def set_op_attrs(self):
+        self.attrs = {
+            'delta': 0.0,
+        }
+
+
+if __name__ == "__main__":
+    unittest.main()

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

+#  Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+
+import numpy as np
+import paddle
+import paddle.static
+from paddle.fluid.tests.unittests.ipu.op_test_ipu import IPUOpTest
+
+
+@unittest.skipIf(not paddle.is_compiled_with_ipu(),
+                 "core is not compiled with IPU")
+class TestBase(IPUOpTest):
+
+    def setUp(self):
+        self.set_atol()
+        self.set_training()
+        self.set_data_feed()
+        self.set_feed_attr()
+        self.set_op_attrs()
+
+    def set_data_feed(self):
+        x = np.random.uniform(size=[2, 3, 6, 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_fp32.values()]
+        self.feed_list = list(self.feed_fp32.keys())
+        self.feed_dtype = [x.dtype for x in self.feed_fp32.values()]
+
+    def set_op_attrs(self):
+        self.attrs = {}
+        self.attrs["size"] = [12, 12]
+
+    @IPUOpTest.static_graph
+    def build_model(self):
+        x = paddle.static.data(name=self.feed_list[0],
+                               shape=self.feed_shape[0],
+                               dtype="float32")
+        out = paddle.nn.functional.interpolate(x, **self.attrs)
+        self.fetch_list = [out.name]
+
+    def run_model(self, exec_mode):
+        self.run_op_test(exec_mode)
+
+    def test(self):
+        for m in IPUOpTest.ExecutionMode:
+            if not self.skip_mode(m):
+                self.build_model()
+                self.run_model(m)
+        self.check()
+
+
+class TestCase0(TestBase):
+
+    def set_op_attrs(self):
+        self.attrs = {}
+        self.attrs["size"] = [3, 4]
+
+
+class TestCase1(TestBase):
+
+    def set_op_attrs(self):
+        self.attrs = {}
+        self.attrs["scale_factor"] = [2, 1]
+
+
+@unittest.skip("Only one of size or scale_factor should be defined")
+class TestCase2(TestBase):
+
+    def set_op_attrs(self):
+        self.attrs = {"size": [12, 12], "scale_factor": [2, 1]}
+
+
+class TestCase3(TestBase):
+
+    def set_op_attrs(self):
+        self.attrs = {"scale_factor": 2.5}
+
+
+class TestBilinear(TestBase):
+
+    @property
+    def fp16_enabled(self):
+        return False
+
+    def set_atol(self):
+        self.atol = 1e-6
+        self.rtol = 1e-6
+        self.atol_fp16 = 1e-3
+        self.rtol_fp16 = 1e-3
+
+    def set_op_attrs(self):
+        self.attrs = {"size": [12, 12], "mode": "bilinear"}
+
+
+# Take long time
+class TestBicubic(TestBase):
+
+    @property
+    def fp16_enabled(self):
+        return False
+
+    def set_atol(self):
+        self.atol = 1e-6
+        self.rtol = 1e-6
+        self.atol_fp16 = 1e-3
+        self.rtol_fp16 = 1e-3
+
+    def set_op_attrs(self):
+        self.attrs = {"size": [12, 12], "mode": "bicubic"}
+
+
+# Trilinear requires 5-D input
+class TestTrilinear(TestBase):
+
+    @property
+    def fp16_enabled(self):
+        return False
+
+    def set_atol(self):
+        self.atol = 1e-6
+        self.rtol = 1e-6
+        self.atol_fp16 = 1e-3
+        self.rtol_fp16 = 1e-3
+
+    def set_data_feed(self):
+        x = np.random.uniform(size=[2, 3, 3, 6, 10])
+        self.feed_fp32 = {"x": x.astype(np.float32)}
+        self.feed_fp16 = {"x": x.astype(np.float16)}
+
+    def set_op_attrs(self):
+        self.attrs = {
+            "size": [12, 12, 12],
+            "mode": "trilinear",
+            "data_format": "NCDHW"
+        }
+
+
+# Linear requires 3-D input
+class TestLinear(TestBase):
+
+    @property
+    def fp16_enabled(self):
+        return False
+
+    def set_atol(self):
+        self.atol = 1e-6
+        self.rtol = 1e-6
+        self.atol_fp16 = 1e-3
+        self.rtol_fp16 = 1e-3
+
+    def set_data_feed(self):
+        x = np.random.uniform(size=[3, 6, 10])
+        self.feed_fp32 = {"x": x.astype(np.float32)}
+        self.feed_fp16 = {"x": x.astype(np.float16)}
+
+    def set_op_attrs(self):
+        self.attrs = {"size": [12], "mode": "linear", "data_format": "NCW"}
+
+
+@unittest.skip(
+    "Transfer to Pool Op with 2-D ksize, now we only support 1-D ksize.")
+class TestArea(TestBase):
+
+    def set_data_feed(self):
+        x = np.random.uniform(size=[2, 3, 6, 6])
+        self.feed_fp32 = {"x": x.astype(np.float32)}
+        self.feed_fp16 = {"x": x.astype(np.float16)}
+
+    def set_op_attrs(self):
+        self.attrs = {"size": 12, "mode": "area"}
+
+
+# align_corners option can only be set with the interpolating modes: linear | bilinear | bicubic | trilinear
+class TestAlignCorners(TestBase):
+
+    @property
+    def fp16_enabled(self):
+        return False
+
+    def set_op_attrs(self):
+        self.attrs = {
+            "size": [12, 12],
+            "align_corners": True,
+            "mode": "bilinear"
+        }
+
+
+#
+class TestAlignMode(TestBase):
+
+    def set_op_attrs(self):
+        self.attrs = {"size": [12, 12], "align_mode": 1}
+
+
+if __name__ == "__main__":
+    unittest.main()